diff --git a/Changelog b/Changelog
new file mode 100644
index 0000000..9e363cf
--- /dev/null
+++ b/Changelog
@@ -0,0 +1,25 @@
+0.4:
+
+  * feature: compute the 2-point correlation function in easyeq.
+
+  * feature: compute the Fourier transform of the 2-point correlation function
+             in anyeq and easyeq.
+
+  * feature: compute the local maximum of the 2-point correlation function and
+             its Fourier transform.
+
+  * feature: compute the compressibility for anyeq.
+
+  * feature: allow for linear spacing of rho's.
+
+  * feature: print the scattering length.
+
+  * change: ux and uk now return real numbers.
+
+  * fix: error in the computation of the momentum distribution: wrong
+         definition of delta functions.
+
+  * fix: various minor bugs.
+
+  * optimization: assign explicit types to variables.
+
diff --git a/NOTICE b/NOTICE
index 216093f..ea2be90 100644
--- a/NOTICE
+++ b/NOTICE
@@ -1,4 +1,4 @@
 simplesolv:
 Licensed under the Apache 2.0 License (see LICENSE for details)
-Copyright Ian Jauslin 2021
+Copyright Ian Jauslin 2021-2023
 
diff --git a/README b/README
index ffc52d5..5a22d6a 100644
--- a/README
+++ b/README
@@ -29,7 +29,9 @@ published in the following papers:
 
   * E.A. Carlen, I. Jauslin, E.H. Lieb - Analysis of a simple equation for the
     ground state energy of the Bose gas II: Monotonicity, Convexity and
-    Condensate Fraction, to appear in the SIAM Journal on Mathematical Analysis
+    Condensate Fraction, SIAM Journal on Mathematical Analysis, volume 53,
+    number 5, pages 5322-5360, 2021
+    https://doi.org/10.1137/20M13768
     https://arxiv.org/abs/2010.13882
     http://ian.jauslin.org/publications/20cjl
 
@@ -40,6 +42,13 @@ published in the following papers:
     https://arxiv.org/abs/2011.10869
     http://ian.jauslin.org/publications/20chjl
   
+  * I. Jauslin - The Simplified approach to the Bose gas without translation
+    invariance, 2023
+    http://ian.jauslin.org/publications/23j
+
+  * I. Jauslin - Evidence of a liquid phase in repulsive Bosonic systems using
+    the Simplified approach to the Bose gas, 2023
+    http://ian.jauslin.org/publications/23j_b
 
 
 Dependencies:
diff --git a/doc/Makefile b/doc/Makefile
index 4b58b3c..d0c4c64 100644
--- a/doc/Makefile
+++ b/doc/Makefile
@@ -1,4 +1,4 @@
-## Copyright 2021 Ian Jauslin
+## Copyright 2021-2023 Ian Jauslin
 ## 
 ## Licensed under the Apache License, Version 2.0 (the "License");
 ## you may not use this file except in compliance with the License.
diff --git a/doc/bibliography/bibliography.tex b/doc/bibliography/bibliography.tex
index 53454da..e8893ca 100644
--- a/doc/bibliography/bibliography.tex
+++ b/doc/bibliography/bibliography.tex
@@ -4,10 +4,13 @@ doi:{\tt\color{blue}\href{http://dx.doi.org/10.1103/PhysRevA.103.053309}{10.1103
 \bibitem[CJL20]{CJL20}E.A. Carlen, I. Jauslin, E.H. Lieb - {\it Analysis of a simple equation for the ground state energy of the Bose gas}, Pure and Applied Analysis, volume~\-2, issue~\-3, pages~\-659-684, 2020,\par\penalty10000
 doi:{\tt\color{blue}\href{http://dx.doi.org/10.2140/paa.2020.2.659}{10.2140/paa.2020.2.659}}, arxiv:{\tt\color{blue}\href{http://arxiv.org/abs/1912.04987}{1912.04987}}.\par\medskip
  
-\bibitem[CJL20b]{CJL20b}E.A. Carlen, I. Jauslin, E.H. Lieb - {\it Analysis of a simple equation for the ground state of the Bose gas II: Monotonicity, Convexity and Condensate Fraction}, 2020, to appear in the SIAM journal of Mathematical Analysis,\par\penalty10000
-arxiv:{\tt\color{blue}\href{http://arxiv.org/abs/2010.13882}{2010.13882}}.\par\medskip
+\bibitem[CJL21]{CJL21}E.A. Carlen, I. Jauslin, E.H. Lieb - {\it Analysis of a Simple Equation for the Ground State of the Bose Gas II: Monotonicity, Convexity, and Condensate Fraction}, SIAM Journal on Mathematical Analysis, volume~\-53, number~\-5, pages~\-5322-5360, 2021,\par\penalty10000
+doi:{\tt\color{blue}\href{http://dx.doi.org/10.1137/20M1376820}{10.1137/20M1376820}}, arxiv:{\tt\color{blue}\href{http://arxiv.org/abs/2010.13882}{2010.13882}}.\par\medskip
  
 \bibitem[DLMF]{DLMF1.1.3}F.W.J. Olver, A.B. Olde Daalhuis, D.W. Lozier, B.I. Schneider, R.F. Boisvert, C.W. Clark, B.R. Miller, B.V. Saunders, H.S. Cohl, M.A. McClain (editors) - {\it NIST Digital Library of Mathematical Functions}, Release~\-1.1.3 of~\-2021-09-15, 2021.\par\medskip
  
+\bibitem[Ja23]{Ja23}I. Jauslin - {\it The Simplified approach to the Bose gas without translation invariance}, 2023\par\penalty10000
+{\tt\color{blue}\href{http://ian.jauslin.org/publications/23j/}{http://ian.jauslin.org/publications/23j/}}.\par\medskip
+ 
 \bibitem[Ta87]{Ta87}Y. Taguchi - {\it Fourier coefficients of periodic functions of Gevrey classes and ultradistributions}, Yokohama Mathematical Journal, volume~\-35, pages~\-51-60, 1987.\par\medskip
  
diff --git a/doc/libs/code.sty b/doc/libs/code.sty
index d4f4070..e067dfe 100644
--- a/doc/libs/code.sty
+++ b/doc/libs/code.sty
@@ -1,4 +1,4 @@
-%% Copyright 2021 Ian Jauslin
+%% Copyright 2021-2023 Ian Jauslin
 %% 
 %% Licensed under the Apache License, Version 2.0 (the "License");
 %% you may not use this file except in compliance with the License.
diff --git a/doc/libs/dlmf.sty b/doc/libs/dlmf.sty
index f435e96..f66157b 100644
--- a/doc/libs/dlmf.sty
+++ b/doc/libs/dlmf.sty
@@ -1,4 +1,4 @@
-%% Copyright 2021 Ian Jauslin
+%% Copyright 2021-2023 Ian Jauslin
 %% 
 %% Licensed under the Apache License, Version 2.0 (the "License");
 %% you may not use this file except in compliance with the License.
diff --git a/doc/libs/ian.cls b/doc/libs/ian.cls
index f29e6bd..762f679 100644
--- a/doc/libs/ian.cls
+++ b/doc/libs/ian.cls
@@ -219,6 +219,7 @@
   % counters
   \stepcounter{subsectioncount}
   \setcounter{subsubsectioncount}{0}
+  \setcounter{subsubsubsectioncount}{0}
   \ifsubsectionsineq
     \setcounter{seqcount}0
   \fi
@@ -268,6 +269,7 @@
 \def\subsubsection#1{
   % counters
   \stepcounter{subsubsectioncount}
+  \setcounter{subsubsubsectioncount}{0}
 
   % space before subsubsection (if not first)
   \ifnum\thesubsubsectioncount>1
@@ -302,6 +304,49 @@
   \medskip\penalty10000
 }
 
+%% subsubsubsection
+% counter
+\newcounter{subsubsubsectioncount}
+% space before subsubsubsection
+\newlength\subsubsubsecskip
+\setlength\subsubsubsecskip{20pt}
+\def\subsubsubsection#1{
+  % counters
+  \stepcounter{subsubsubsectioncount}
+
+  % space before subsubsubsection (if not first)
+  \ifnum\thesubsubsubsectioncount>1
+    \vskip\subsubsubsecskip
+    \penalty-500
+  \fi
+
+  % hyperref anchor
+  \hrefanchor
+  % define tag (for \label)
+  \xdef\tag{\sectionprefix\thesectioncount.\thesubsectioncount.\thesubsubsectioncount.\thesubsubsubsectioncount}
+
+  % get widths
+  \def\@secnum{{\bf\hskip1.5cm\sectionprefix\thesectioncount.\thesubsectioncount.\thesubsubsectioncount.\thesubsubsubsectioncount.\hskip5pt}}
+  \settowidth\secnumwidth{\@secnum}
+  \setlength\sectitlewidth\textwidth
+  \addtolength\sectitlewidth{-\secnumwidth}
+  % print name
+  \parbox{\textwidth}{
+  \@secnum
+  \parbox[t]{\sectitlewidth}{\bf #1}}
+
+  % write to table of contents
+  \iftoc
+    % save lncount in aux variable which is written to toc
+    \immediate\write\tocoutput{\noexpand\expandafter\noexpand\edef\noexpand\csname toc@subsubsubsec.\thesectioncount.\thesubsectioncount.\thesubsubsectioncount.\thesubsubsubsectioncount\endcsname{\thelncount}}
+    \write\tocoutput{\noexpand\tocsubsubsubsection{#1}{\thepage}}
+  \fi
+
+  % space
+  \par\penalty10000
+  \medskip\penalty10000
+}
+
 %% itemize
 \newlength\itemizeskip
 % left margin for items
@@ -638,6 +683,7 @@
   \stepcounter{tocsectioncount}
   \setcounter{tocsubsectioncount}{0}
   \setcounter{tocsubsubsectioncount}{0}
+  \setcounter{tocsubsubsubsectioncount}{0}
   % write
   \smallskip\hyperlink{ln.\csname toc@sec.\thetocsectioncount\endcsname}{{\bf \tocsectionprefix\thetocsectioncount}.\hskip5pt {\color{blue}#1}\leaderfill#2}\par
 }
@@ -645,6 +691,7 @@
 \def\tocsubsection #1#2{
   \stepcounter{tocsubsectioncount}
   \setcounter{tocsubsubsectioncount}{0}
+  \setcounter{tocsubsubsubsectioncount}{0}
   % write
   {\hskip10pt\hyperlink{ln.\csname toc@subsec.\thetocsectioncount.\thetocsubsectioncount\endcsname}{{\bf \thetocsectioncount.\thetocsubsectioncount}.\hskip5pt {\color{blue}\small #1}\leaderfill#2}}\par
 }
@@ -654,6 +701,12 @@
   % write
   {\hskip20pt\hyperlink{ln.\csname toc@subsubsec.\thetocsectioncount.\thetocsubsectioncount.\thetocsubsubsectioncount\endcsname}{{\bf \thetocsectioncount.\thetocsubsectioncount.\thetocsubsubsectioncount}.\hskip5pt {\color{blue}\small #1}\leaderfill#2}}\par
 }
+\newcounter{tocsubsubsubsectioncount}
+\def\tocsubsubsubsection #1#2{
+  \stepcounter{tocsubsubsubsectioncount}
+  % write
+  {\hskip30pt\hyperlink{ln.\csname toc@subsubsubsec.\thetocsectioncount.\thetocsubsectioncount.\thetocsubsubsectioncount.\thetocsubsubsubsectioncount\endcsname}{{\bf \thetocsectioncount.\thetocsubsectioncount.\thetocsubsubsectioncount.\thetocsubsubsubsectioncount}.\hskip5pt {\color{blue}\small #1}\leaderfill#2}}\par
+}
 \def\tocappendices{
   \medskip
   \setcounter{tocsectioncount}0
diff --git a/doc/libs/iantheo.sty b/doc/libs/iantheo.sty
index 1945a5f..31842a4 100644
--- a/doc/libs/iantheo.sty
+++ b/doc/libs/iantheo.sty
@@ -1,4 +1,4 @@
-%% Copyright 2021 Ian Jauslin
+%% Copyright 2021-2023 Ian Jauslin
 %% 
 %% Licensed under the Apache License, Version 2.0 (the "License");
 %% you may not use this file except in compliance with the License.
diff --git a/doc/libs/largearray.sty b/doc/libs/largearray.sty
index cf9075f..3a8762e 100644
--- a/doc/libs/largearray.sty
+++ b/doc/libs/largearray.sty
@@ -1,4 +1,4 @@
-%% Copyright 2021 Ian Jauslin
+%% Copyright 2021-2023 Ian Jauslin
 %% 
 %% Licensed under the Apache License, Version 2.0 (the "License");
 %% you may not use this file except in compliance with the License.
diff --git a/doc/libs/point.sty b/doc/libs/point.sty
index a396d1c..796e0d2 100644
--- a/doc/libs/point.sty
+++ b/doc/libs/point.sty
@@ -1,4 +1,4 @@
-%% Copyright 2021 Ian Jauslin
+%% Copyright 2021-2023 Ian Jauslin
 %% 
 %% Licensed under the Apache License, Version 2.0 (the "License");
 %% you may not use this file except in compliance with the License.
@@ -33,12 +33,15 @@
 \newif\ifresetatsubsubsection
 \DeclareOption{reset_at_subsubsection}{\resetatsubsubsectiontrue}
 \DeclareOption{no_reset_at_subsubsection}{\resetatsubsubsectionfalse}
+\newif\ifresetatsubsubsubsection
+\DeclareOption{reset_at_subsubsubsection}{\resetatsubsubsubsectiontrue}
+\DeclareOption{no_reset_at_subsubsubsection}{\resetatsubsubsubsectionfalse}
 \newif\ifresetattheo
 \DeclareOption{reset_at_theo}{\resetattheotrue}
 \DeclareOption{no_reset_at_theo}{\resetattheofalse}
 
 \def\point@defaultoptions{
-  \ExecuteOptions{reset_at_section, reset_at_subsection, reset_at_subsubsection, no_reset_at_theo}
+  \ExecuteOptions{reset_at_section, reset_at_subsection, reset_at_subsubsection, reset_at_subsubsubsection, no_reset_at_theo}
   \ProcessOptions
 
   %% reset at every new section
@@ -56,6 +59,11 @@
     \let\point@oldsubsubsection\subsubsection
     \gdef\subsubsection{\resetpointcounter\point@oldsubsubsection}
   \fi
+  %% reset at every new subsubsubsection
+  \ifresetatsubsubsubsection
+    \let\point@oldsubsubsubsection\subsubsubsection
+    \gdef\subsubsubsection{\resetpointcounter\point@oldsubsubsubsection}
+  \fi
 
   %% reset at every new theorem
   \ifresetattheo
diff --git a/doc/simplesolv-doc.tex b/doc/simplesolv-doc.tex
index 722282a..23c7247 100644
--- a/doc/simplesolv-doc.tex
+++ b/doc/simplesolv-doc.tex
@@ -1,4 +1,4 @@
-%% Copyright 2021 Ian Jauslin
+%% Copyright 2021-2023 Ian Jauslin
 %% 
 %% Licensed under the Apache License, Version 2.0 (the "License");
 %% you may not use this file except in compliance with the License.
@@ -27,7 +27,7 @@
 \hfil{\bf\Large
 {\tt simplesolv}\par
 \medskip
-\hfil \large v0.3
+\hfil \large v0.4
 }
 
 \vfill
@@ -41,7 +41,7 @@
 \pagestyle{plain}
 
 \indent
-{\tt simplesolv} is a tool to compute the solution of the equations of the ``Simplified approach'' to the repulsive Bose gas introduced in\-~\cite{CJL20,CJL20b,CHe21}.
+{\tt simplesolv} is a tool to compute the solution of the equations of the ``Simplified approach'' to the repulsive Bose gas introduced in\-~\cite{CJL20,CJL21,CHe21}.
 This approach provides an approximation to various observables of the ground state of the Hamiltonian
 \begin{equation}
   H_N=-\frac12\sum_{i=1}^N\Delta_i+\sum_{1\leqslant i\leqslant j\leqslant N}v(|x_i-x_j|)
@@ -85,6 +85,10 @@ The available methods are (see section\-~\ref{sec:methods} for further details)
   \item \refname{sec:simpleq-iteration}{{\tt simpleq-iteration}} for the Simple equation with a soft potential using the iteration defined in\-~\cite{CJL20}.
 \end{itemize}
 Each method is described in detail below, along with the list of commands ({\tt command}) and parameters ({\tt params}) compatible with them.
+In addition, the scattering length can be displayed using the {\tt scattering\_length} command:
+\begin{code}
+  julia \$SIMPLESOLV/src/main.jl scattering\_length
+\end{code}
 \bigskip
 
 \indent
@@ -165,10 +169,10 @@ Unless otherwise noted, this method takes the following parameters (specified vi
   {\tt order} ({\tt Int64}, default: 100): order used for all Gauss quadratures (denoted by $N$ below).
 
   \item\makelink{param:easyeq_minlrho_init}{}
-  {\tt minlrho\_init} ({\tt Float64}, default: {\tt rho}): to initialize the Newton algorithm, we first compute the solution for a smaller $\rho$, {\tt minlrho} is the minimal value for $\log_{10}\rho$ to start this initialization process.
+  {\tt minlrho\_init} ({\tt Float64}, default: $-6$): to initialize the Newton algorithm, we first compute the solution for a smaller $\rho$, {\tt minlrho} is the minimal value for $\log_{10}\rho$ to start this initialization process.
 
   \item\makelink{param:easyeq_nlrho_init}{}
-  {\tt nlrho\_init} ({\tt Int64}, default: 1): number of steps in the initialization process described above. Set to 1 to disable the incremental initialization process.
+  {\tt nlrho\_init} ({\tt Int64}, default: 0): number of steps in the initialization process described above. Set to 0 to disable the incremental initialization process.
 
   \item\makelink{param:easyeq_bK}{}
   {\tt bK}, {\tt bL} ({\tt Float64}, default: 1, 1): the values of $\beta_K$ and $\beta_L$.
@@ -188,7 +192,7 @@ The available {\tt commands} are the following.
   \item {\tt energy\_rho}\makelink{command:easyeq_energy_rho}{}:
   compute the energy $e$ as a function of $\rho$.
   The Newton algorithm is initialized with the hardcore scattering solution (\ref{easyeq_init}) for the lowest $\rho$, and with the previously computed $\rho$ for the larger densities.\par
-  \underline{Disabled parameters}: \refname{param:easyeq_rho}{{\tt rho}}, \refname{param:easyeq_minlrho_init}{{\tt minlrho\_init}} and \refname{param:easyeq_nlrho_init}{{\tt nlrho\_init}}.\par
+  \underline{Disabled parameters}: \refname{param:easyeq_rho}{{\tt rho}}.\par
   \underline{Extra parameters}:
   \begin{itemize}
     \item\makelink{param:easyeq_minlrho}{}
@@ -200,8 +204,18 @@ The available {\tt commands} are the following.
     \item\makelink{param:easyeq_nlrho}{}
     {\tt nlrho} ({\tt Int64}, default: $100$): number of values for $\rho$ (spaced logarithmically).
 
+    \item\makelink{param:easyeq_minrho}{}
+    {\tt minrho} ({\tt Float64}, default: $10^{-6}$): minimal value for $\rho$.
+
+    \item\makelink{param:easyeq_maxrho}{}
+    {\tt maxrho} ({\tt Float64}, default: $10^{2}$): maximal value for $\rho$.
+
+    \item\makelink{param:easyeq_nrho}{}
+    {\tt nrho} ({\tt Int64}, default: $0$): number of values for $\rho$ (spaced linearly). If {\tt nrho} is $\neq0$, then the linear spacing will be used, and \refname{param:easyeq_minlrho}{{\tt minlrho}}, \refname{param:easyeq_maxlrho}{{\tt maxlrho}}, \refname{param:easyeq_nlrho}{{\tt nlrho}} will be ignored. Otherwise, the logarithmic spacing will be used and \refname{param:easyeq_minrho}{{\tt minrho}}, \refname{param:easyeq_maxrho}{{\tt maxrho}} will be ignored.
+
     \item\makelink{param:easyeq_rhos}{}
-    {\tt rhos} ({\tt Array\{Float64\}}, default: $(10^{{\tt minlrho}+\frac{{\tt maxlrho}-{\tt minlrho}}{{\tt nlrho}}n})_n$: list of values for $\rho$, specified as a `{\tt,}' separated list.
+    {\tt rhos} ({\tt Array\{Float64\}}): list of values for $\rho$, specified as a `{\tt,}' separated list.
+    This parameter takes precedence over \refname{param:easyeq_minlrho}{{\tt minlrho}}, \refname{param:easyeq_maxlrho}{{\tt maxlrho}}, \refname{param:easyeq_nlrho}{{\tt nlrho}}, \refname{param:easyeq_minrho}{{\tt minrho}}, \refname{param:easyeq_maxrho}{{\tt maxrho}}, \refname{param:easyeq_nrho}{{\tt nrho}}.
   \end{itemize}
   \underline{Output} (one line for each value of $\rho$): [$\rho$] [$e$] [Newton error $\epsilon$].
 
@@ -241,15 +255,100 @@ The available {\tt commands} are the following.
   \underline{Extra parameters}: Same as \refname{command:easyeq_ux}{{\tt ux}}.\par
   \underline{Output} (one line for each value of $x$): [$|x|$] [$2u(|x|)-\rho u\ast u(|x|)$]
 
+  \item\makelink{command:easyeq_2pt}{}
+  {\tt 2pt}: compute the spherically averaged two-point correlation function $C_2(|x|)$ at a given $\rho$.\par
+  \underline{Extra parameters}: same as \refname{command:easyeq_ux}{{\tt ux}}, plus\par
+  \begin{itemize}
+    \item\makelink{param:easyeq_window_L}{}
+    {\tt window\_L} ({\tt Float64}, default: $10^3$): size of the Hann window used to numerically invert the Fourier transform in the computation of the two-point correlation function, see\-~(\ref{hann}).
+  \end{itemize}
+  \underline{Output} (one line for each value of $|x|$): [$|x|$] [$C_2(|x|)$]
+
+  \item\makelink{command:easyeq_2pt_max}{}
+  {\tt 2pt\_max}: compute the maximum of the spherically averaged two-point correlation function $C_2(|x|)$ at a given $\rho$.\par
+  \underline{Extra parameters}: \refname{param:easyeq_window_L}{{\tt window\_L}} plus
+  \begin{itemize}
+    \item\makelink{param:easyeq_dx}{}
+    {\tt dx} ({\tt Float64}, default: $10^{-7}$): step used to numerically approximate derivatives.
+
+    \item\makelink{param:easyeq_x0}{}
+    {\tt x0} ({\tt Float64}, default: $1$): initial guess for the maximum is $\rho^{-1/3}\mathrm{\tt x0}$.
+
+    \item\makelink{param:easyeq_maxstep}{}
+    {\tt maxstep} ({\tt Float64}, default: $\infty$): maximal size of single step in maximization algorithm.
+
+    \item\makelink{param:easyeq_tolerance_max}{}
+    {\tt tolerance\_max} ({\tt Float64}, default: \refname{param:easyeq_tolerance}{{\tt tolerance}}): same as \refname{param:easyeq_tolerance}{{\tt tolerance}}, used for the Newton algorithm underlying the maximization algorithm.
+  \end{itemize}
+  \underline{Output}: [$|x_{\mathrm{max}}|$] [$C_2(|x_{\mathrm{max}}|)$]
+
+  \item\makelink{command:easyeq_2pt_max_rho}{}
+  {\tt 2pt\_max\_rho}: compute the maximum of the spherically averaged two-point correlation function $C_2(|x|)$ for a range of $\rho$.\par
+  \underline{Extra parameters}: same as \refname{command:easyeq_2pt_max}{{\tt easyeq\_2pt\_max}} plus those of \refname{command:easyeq_energy_rho}{{\tt energy\_rho}}.\par
+  \underline{Output} (one line for each value of $\rho$): [$\rho$] [$|x_{\mathrm{max}}|$] [$C_2(|x_{\mathrm{max}}|)$]\par
+  \underline{Multithread support}: yes, different values of $\rho$ split up among workers.
+
+  \item\makelink{command:easyeq_2pt_fourier}{}
+  {\tt 2pt\_fourier}: compute the spherically averaged Fourier transform of the two-point correlation function $\hat C_2(|k|)$ at a given $\rho$.\par
+  \underline{Extra parameters}:
+  \begin{itemize}
+    \item\makelink{param:easyeq_kmin}{}
+    {\tt kmin} ({\tt Float64}, default: 0): minimum of the range of $|k|$ to be printed.
+
+    \item\makelink{param:easyeq_kmax}{}
+    {\tt kmax} ({\tt Float64}, default: 10): maximum of the range of $|k|$ to be printed.
+
+    \item\makelink{param:easyeq_nk}{}
+    {\tt nk} ({\tt Int64}, default: 100): number of $|k|$'s to be printed.
+
+    \item\makelink{param:easyeq_2pt_fourier_window_L}{}
+    {\tt window\_L} ({\tt Float64}, default: 1000): what is actually computed is $\hat C_2$ convolved with a Gaussian of variance $1/\sqrt L$ with $L=\mathrm{\tt window\_L}$, see\-~(\ref{easyeq_gaussian}).
+  \end{itemize}
+  \underline{Output} (one line for each value of $|k|$): [$|k|$] [$\hat C_2(|k|)$].\par
+  \underline{Multithread support}: yes, different values of $k$ are split up among workers.
+
+  \item\makelink{command:easyeq_2pt_fourier_max}{}
+  {\tt 2pt\_fourier\_max}: compute the maximum of the spherically averaged Fourier transformed two-point correlation function $\hat C_2(|k|)$.\par
+  \underline{Extra parameters}: \refname{param:easyeq_2pt_fourier_window_L}{{\tt window\_L}}, \refname{param:easyeq_maxstep}{{\tt maxstep}} plus
+  \begin{itemize}
+    \item\makelink{param:easyeq_dk}{}
+    {\tt dk} ({\tt Float64}, default: $10^{-7}$): step used to numerically approximate derivatives.
+
+    \item\makelink{param:easyeq_k0}{}
+    {\tt k0} ({\tt Float64}, default: $1$): initial guess for the maximum is $\rho^{1/3}\mathrm{\tt k0}$.
+  \end{itemize}
+  \underline{Output}: [$|k_{\mathrm{max}}|$] [$\hat C_2(|k_{\mathrm{max}}|)$]\par
+
+  \item\makelink{command:easyeq_2pt_fourier_max_rho}{}
+  {\tt 2pt\_fourier\_max\_rho}: compute the maximum of the spherically averaged Fourier transformed two-point correlation function $\hat C_2(|k|)$ for a range of $\rho$.\par
+  \underline{Extra parameters}: same as those of \refname{command:easyeq_2pt_fourier_max}{{\tt 2pt\_fourier\_max}} plus those of \refname{command:easyeq_energy_rho}{{\tt energy\_rho}}.\par
+  \underline{Output} (one line for each value of $\rho$): [$\rho$] [$|k_{\mathrm{max}}|$] [$\hat C_2(|k_{\mathrm{max}}|)$]\par
+  \underline{Multithread support}: yes, different values of $\rho$ split up among workers.
+
+  \item\makelink{command:easyeq_momentum_distribution}{}
+  {\tt momentum\_distribution}: compute the momentum distribution $\mathcal M(|k|)$ at a given $\rho$. The momentum distribution is computed for $k=k_{l,j}$ (see\-~(\ref{klj})).\par
+  \underline{Extra parameters}:
+  \begin{itemize}
+    \item\makelink{param:easyeq_momentum_kmin}{}
+    {\tt kmin} ({\tt Float64}, default: 0): minimum of the range of $|k|$ to be printed.
+
+    \item\makelink{param:easyeq_momentum_kmax}{}
+    {\tt kmax} ({\tt Float64}, default: 10): maximum of the range of $|k|$ to be printed.
+
+    \item\makelink{param:easyeq_momentum_window_L}{}
+    {\tt window\_L} ({\tt Float64}, default: 1000): what is actually computed is $\mathfrak M_k$ convolved with a Gaussian of variance $1/\sqrt L$ where $L=\sqrt{\mathrm{\tt window\_L}}/k^2$, see\-~(\ref{easyeq_gaussian_momt}).
+  \end{itemize}
+  \underline{Output} (one line for each value of $|k|$): [$|k|$] [$\mathcal M(|k|)$]
+
 \end{itemize}
 
 \subsubsection{Description}
-\point{\bf Fourier space formulation.}
+\subsubsubsection{Fourier space formulation}
 The computation is carried out in Fourier space.
 We take the convention that the Fourier transform of a function $f(|x|)$ is
 \begin{equation}
   \hat f(|k|)=\int_{\mathbb R^3} dx\ e^{ikx}f(|x|)
-  =\frac{4\pi}{|k|}\int_0^\infty dr\ \frac{\sin(|k|r)}rf(r)
+  =\frac{4\pi}{|k|}\int_0^\infty dr\ r\sin(|k|r)f(r)
   .
 \end{equation}
 In Fourier space, (\ref{easyeq}) becomes
@@ -268,6 +367,7 @@ with
   ,\quad
   \hat f\hat\ast\hat g(|k|):=\int_{\mathbb R^3}\frac{dp}{(2\pi)^3}\ \hat f(|k-p|)\hat g(|p|)
   .
+  \label{hatS_easyeq}
 \end{equation}
 We write this as a quadratic equation for $\hat u$, and solve it, keeping the solution that decays as $|k|\to\infty$:
 \begin{equation}
@@ -287,6 +387,15 @@ with
   \Phi(x):=\frac{2(1-\sqrt{1-x})}x
   .
 \end{equation}
+We can write this as a root finding problem:
+\begin{equation}
+  \Omega(u):=
+  \rho\hat u(|k|)-
+  \frac{\hat S(|k|)}{2A_K(|k|)(\xi(|k|)+1)}\Phi\left({\textstyle\frac{A_L(|k|)}{A_K^2(|k|)}\frac{\hat S(|k|)}{(\xi(|k|)+1)^2}}\right)
+  =0
+  .
+  \label{Omega_easyeq}
+\end{equation}
 Furthermore, using bipolar coordinates (see lemma\-~\ref{lemma:bipolar}), we write $\hat S$ as
 \begin{equation}
   \hat S(|k|)=\hat v(|k|)-\frac1{8\pi^3}\int_0^\infty dt\ t\hat u(t)\Eta(|k|,t)
@@ -305,7 +414,7 @@ By a simple change of variables,
 \end{equation}
 \bigskip
 
-\point{\bf Evaluating integrals.}
+\subsubsubsection{Evaluating integrals}
 To compute these integrals numerically, we will use Gauss-Legendre quadratures:
 \begin{equation}
   \int_0^1 dt\ f(t)\approx\frac12\sum_{i=1}^N w_if\left({\textstyle\frac{r_i+1}2}\right)
@@ -353,6 +462,7 @@ with
   \mathbb S_i:=\hat v_i-\frac1{16\pi^3\rho}\sum_{j=1}^N w_j\frac{(1-y_j)\mathbb U_j\Eta(k_i,k_j)}{y_j^3}
   ,\quad
   \mathbb E:=\hat v(0)-\frac1{16\pi^3\rho}\sum_{j=1}^N w_j\frac{(1-y_j)\mathbb U_j\Eta(0,k_j)}{y_j^3}
+  \label{bbSE_easyeq}
 \end{equation}
 \begin{equation}
   \mathbb X_i:=\frac{k_i^2}{2\rho\mathbb A_{K,i}}
@@ -361,12 +471,12 @@ with
 This is a discrete equation for the vector $(\mathbb U_i)_{i=1}^N$.
 \bigskip
 
-\point{\bf Main algorithm to compute $\mathbb U$.}\par\penalty10000
+\subsubsubsection{Main algorithm to compute $\mathbb U$}\par\penalty10000
 \medskip\penalty10000
-\subpoint
+\point
 We rewrite\-~(\ref{bbU_easyeq}) as a root finding problem:
 \begin{equation}
-  \Xi_i(\mathbb U):=\mathbb U_i-\frac{\mathbb T_i}{2(\mathbb X_i+1)}\Phi\left({\textstyle B_i\frac{\mathbb T_i}{(\mathbb X_i+1)^2}}\right)
+  \Xi_i(\mathbb U):=\mathbb U_i-\frac{\mathbb T_i}{2(\mathbb X_i+1)}\Phi\left({\textstyle\mathbb B_i\frac{\mathbb T_i}{(\mathbb X_i+1)^2}}\right)
   =0
   \label{root_easyeq}
 \end{equation}
@@ -381,7 +491,7 @@ where $D\Xi$ is the Jacobian of $\Xi$:
 \end{equation}
 \bigskip
 
-\subpoint
+\point
 For small values of $\rho$, we initialize the algorithm with the hardcore scattering solution
 \begin{equation}
   \hat u_0(k)=\frac{4\pi a_0}{k^2}
@@ -397,7 +507,7 @@ This is a good approximation for small $\rho$.
 For larger $\rho$, we choose $\mathbb U^{(0)}$ as the solution of {\tt easyeq} for a slightly smaller $\rho$, and proceed inductively (using the parameters \refname{param:easyeq_minlrho_init}{{\tt minlrho\_init}} and \refname{param:easyeq_nlrho_init}{{\tt nlrho\_init}}).
 \bigskip
 
-\subpoint
+\point
 We are left with computing the Jacobian of $\Xi$:
 \begin{equation}
   \begin{largearray}
@@ -436,7 +546,7 @@ with
 \end{equation}
 \bigskip
 
-\subpoint
+\point
 We iterate the Newton algorithm until the Newton relative error $\epsilon$ becomes smaller than the \refname{param:easyeq_tolerance}{{\tt tolerance}} parameter.
 The Newton error is defined as
 \begin{equation}
@@ -466,8 +576,8 @@ and the solution $u$ in real space is obtained by inverting the Fourier transfor
 To compute $2u-\rho u\ast u$, we replace $\hat u$ with $2\hat u-\rho\hat u^2$ in the previous equation.
 \bigskip
 
-\point{\bf Condensate fraction.}
-Finally, to compute the uncondensed fraction, we solve the modified {\tt easyeq} (see\-~\cite{CJL20b})
+\subsubsubsection{Condensate fraction}
+Finally, to compute the uncondensed fraction, we solve the modified {\tt easyeq} (see\-~\cite{CJL21})
 \begin{equation}
   (-\Delta+2\mu)u_\mu=v(1-u_\mu)-2\rho K+\rho^2L
   \label{easyeq_mu}
@@ -518,6 +628,351 @@ We then compute, using\-~(\ref{jacobian_easyeq}),
   .
 \end{equation}
 
+\subsubsubsection{Correlation function (spherical average)}
+The two-point correlation function is
+\begin{equation}
+  c_2(x):=
+  2\rho\frac{\delta e}{\delta v(x)}
+\end{equation}
+and its spherical average is
+\begin{equation}
+  C_2(|x|):=\frac1{4\pi|x|^2}\int dy\ \delta(|x|-|y|)c_2(y)
+  .
+\end{equation}
+In Fourier space,
+\begin{equation}
+  c_2(x)=
+  2\rho
+  \int dk\ e^{ikx}\frac{\delta e}{\delta\hat v(k)}
+\end{equation}
+so
+\begin{equation}
+  C_2(|x|)=
+  2\rho\int dk\ \left(\frac1{4\pi|x|^2}\int dy\ \delta(|x|-|y|)e^{iky}\right)\frac{\delta e}{\delta\hat v(k)}
+  =2\rho\int dk\ \frac{\sin(|k||x|)}{|k||x|}\frac{\delta e}{\delta\hat v(k)}
+  .
+  \label{easyeq_C2fourier}
+\end{equation}
+\bigskip
+
+\point
+We can compute this quantity by considering a modified {\tt easyeq} in Fourier space, by formally replacing $\hat v$ with
+\begin{equation}
+  \hat v+\lambda g(|k|)
+  ,\quad
+  g(|k|):=\frac{\sin(|k||x|)}{|k||x|}
+  .
+  \label{2pt_addv}
+\end{equation}
+Indeed, if $e_\lambda$ denotes the energy of this modified equation,
+\begin{equation}
+  \partial_\lambda e_\lambda|_{\lambda=0}
+  =\int dk\ \frac{\delta e}{\delta\hat v(k)}\partial_\lambda({\textstyle \hat v(k)+\lambda g(|k|)})
+  =\int dk\ g(|k|)\frac{\delta e}{\delta\hat v(k)}
+  .
+\end{equation}
+So, denoting the solution of the modified equation by $u_\lambda$,
+\begin{equation}
+  C_2(x)=2\rho\partial_\lambda e_\lambda|_{\lambda=0}
+  =\rho^2g(0)-\rho^2\int\frac{dk}{(2\pi)^3}\ (g(k)\hat u(k)+\hat v(k)\partial_\lambda\hat u_\lambda(k)|_{\lambda=0})
+  .
+\end{equation}
+We compute $\partial_\lambda u_\lambda|_{\lambda=0}$ in the same way as the uncondensed fraction: we define $\Xi(\mathbb U,\lambda)$ by formally adding $\lambda g(|k|)$ to $\hat v$, solve $\Xi(\mathbb U,\lambda)=0$, and differentiate:
+\begin{equation}
+  \partial_\lambda\mathbb U|_{\lambda=0}=-(D\Xi)^{-1}\partial_\lambda\Xi|_{\lambda=0}
+  .
+\end{equation}
+\bigskip
+
+\point
+We compute $\partial_\lambda\Xi|_{\lambda=0}$:
+\begin{equation}
+  \begin{largearray}
+    \partial_\lambda\Xi_i
+    =
+    -\frac1{2(\mathbb X_i+1)}\left(
+      \partial_\lambda\mathbb T_i-\frac{\mathbb T_i\partial_\lambda\mathbb X_i}{\mathbb X_i+1}
+    \right)\Phi\left({\textstyle\mathbb B_i\frac{\mathbb T_i}{(\mathbb X_i+1)^2}}\right)
+    \\[0.5cm]\hfill
+    -\frac{\mathbb T_i}{2(\mathbb X_i+1)^3}\left(
+      \mathbb B_i\partial_\lambda\mathbb T_i+\mathbb T_i\partial_\lambda\mathbb B_i-2\frac{\mathbb B_i\mathbb T_i\partial_\lambda\mathbb X_i}{\mathbb X_i+1}
+    \right)\partial\Phi\left({\textstyle\mathbb B_i\frac{\mathbb T_i}{(\mathbb X_i+1)^2}}\right)
+  \end{largearray}
+  \label{dXi_2pt_easyeq}
+\end{equation}
+with
+\begin{equation}
+  \partial_\lambda\mathbb B_i=
+  (\beta_L(1-\beta_K)-\beta_K(1-\beta_L))
+  \frac{\mathbb E\partial_\lambda\mathbb S_i-\mathbb S_i\partial_\lambda\mathbb E}{(\beta_K\mathbb S_i+(1-\beta_K)\mathbb E)^2}
+\end{equation}
+\begin{equation}
+  \partial_\lambda\mathbb T_i=
+  (1-\beta_K)\frac{\mathbb E\partial_\lambda\mathbb S_i-\mathbb S_i\partial_\lambda\mathbb E}{(\beta_K\mathbb S_i+(1-\beta_K)\mathbb E)^2}
+  ,\quad
+  \partial_\lambda\mathbb A_{K,i}=\beta_K\partial_\lambda\mathbb S_i+(1-\beta_K)\partial_\lambda\mathbb E
+\end{equation}
+\begin{equation}
+  \partial_\lambda\mathbb S_i:=g(k_i)
+  -\frac1{16\pi^3\rho}\sum_{j=1}^N w_j\frac{(1-y_j)\mathbb U_j\partial_\lambda\Eta(k_i,k_j)}{y_j^3}
+\end{equation}
+\begin{equation}
+  \partial_\lambda \mathbb E:=g(0)-\frac1{16\pi^3\rho}\sum_{j=1}^N w_j\frac{(1-y_j)\mathbb U_j\partial_\lambda\Eta(0,k_j)}{y_j^3}
+\end{equation}
+\begin{equation}
+  \partial_\lambda\mathbb X_i:=-\frac{k_i^2}{2\rho\mathbb A_{K,i}^2}\partial_\lambda\mathbb A_{K,i}
+\end{equation}
+where $\partial\lambda\Eta$ is computed similarly to $\Eta$\-~(\ref{Eta}) but with $\hat v$ replaced by $g$:
+\begin{equation}
+  \partial_\lambda\Eta(y,t)=
+  4\pi\left(\mathds 1_{y>t}\frac{t}y+\mathds 1_{y\leqslant t}\right)
+  \int_0^1 ds\ ((y+t)s+|y-t|(1-s))g((y+t)s+|y-t|(1-s))
+  .
+  \label{GG}
+\end{equation}
+\bigskip
+
+\point
+In order to invert the Fourier transform in\-~(\ref{easyeq_C2fourier}) numerically, we will use a Hann window (see appendix\-~\ref{appendix:hann})
+\begin{equation}
+  H_L(k):=\mathds 1_{|k|<\frac L2}\cos^2({\textstyle\frac{\pi|k|}{L}})
+  .
+  \label{hann}
+\end{equation}
+The parameter $L$ is set using \refname{param:easyeq_window_L}{{\tt window\_L}}.
+The computation is changed only in that $g$ is changed to $H_L(k)\frac{\sin(|k||x|)}{|k||x|}$.
+\bigskip
+
+\point
+To compute the maximum of $C_2$, we use a modified Newton algorithm.
+The initial guess for the maximum is $|x_0|=\rho^{-\frac13}$\refname{param:easyeq_x0}{{\tt x0}}.
+The modified Newton algorithm is an iteration:
+\begin{equation}
+  x_{n+1}=x_n+\frac{\partial C_2(|x_n|)}{|\partial^2C_2(|x_n|)|}
+  \label{easyeq_newton_2pt}
+\end{equation}
+in which the derivatives are approximated using finite differences:
+\begin{equation}
+  \partial C_2(x)\approx \frac{C_2(|x|+dx)-C_2(|x|)}{dx}
+  ,\quad
+  \partial^2 C_2(x)\approx \frac{C_2(|x|+dx)+C_2(|x|-dx)-2C_2(|x|)}{dx^2}
+  .
+  \label{easyeq_dx_2pt}
+\end{equation}
+This is a modification of the usual Newton iteration $x_n+\partial C_2/\partial^2C_2$ which is designed to follow the direction of the gradient, and thus to move toward a local maximum.
+In addition, if $|\partial C_2|/|\partial^2 C_2|$ is larger than \refname{param:easyeq_maxstep}{{\tt maxstep}}, then the step is replaced with $\pm$\refname{param:easyeq_maxstep}{{\tt maxstep}}.
+This prevents the algorithm from stepping over a maximum and land on another, further away.
+This is useful if one has a good idea of where the global maximum is, and does not want to get trapped in a smaller local maximum.
+\bigskip
+
+\indent
+The algorithm is run for a maximum of \refname{param:easyeq_maxiter}{{\tt maxiter}} iterations, or until $|x_{n+1}-x_n|$ is smaller than \refname{param:easyeq_tolerance}{{\tt tolerance}}.
+If the maximal number of iterations is reached, or if the solution found is not a local maximum, then the algorithm fails, and returns $+\infty$.
+The point thus computed is therefore a local maximum, but it is not guaranteed to be the global maximum.
+
+\subsubsubsection{Fourier transform of two-point correlation (spherical average)}
+The Fourier transform of the two-point correlation function is
+\begin{equation}
+  \hat c_2(q):=
+  2\rho\frac{\delta e}{\delta v(q)}
+\end{equation}
+and its spherical average is
+\begin{equation}
+  \hat C_2(|q|):=\frac1{4\pi|q|^2}\int dk\ \delta(|q|-|k|)c_2(k)
+  =
+  \frac\rho{2\pi|q|^2}\int dk\ \delta(|q|-|k|)\frac{\delta e}{\delta\hat v(k)}
+  .
+\end{equation}
+\bigskip
+
+\point
+To compute $\frac{\delta e}{\delta\hat v(q)}$, one idea would be to proceed in the same way as for the two-point correlation function, by replacing $\hat v$ with
+\begin{equation}
+  \hat v+\lambda g(|k|)
+  ,\quad
+  g(|k|):=\frac1{4\pi|q|^2}\delta(|q|-|k|)
+\end{equation}
+where $\delta$ is the Dirac-delta function distribution (compare this with\-~(\ref{2pt_addv})).
+However, the $\delta$ function causes all sorts of problems with the quadratures.
+\bigskip
+
+\point
+Instead, we approximate $\hat C_2$ by convolving it with a normalized Gaussian: let
+\begin{equation}
+  \Gamma_L(|k|):=\left(\frac{L}{2\pi}\right)^{\frac32}e^{-\frac L2k^2}
+  \label{easyeq_gaussian}
+\end{equation}
+\begin{equation}
+  \hat{\mathfrak C}_2(|q|)
+  :=
+  \int dp\ \hat C_2(|q-p|)\Gamma_L(|p|)
+  =\int dk\ \int dp\ \frac\rho{2\pi|q-p|^2}\delta(|q-p|-|k|)\frac{\delta e}{\delta\hat v(k)}\Gamma_L(|p|)
+\end{equation}
+which by lemma\-~\ref{lemma:bipolar} is
+\begin{equation}
+  \hat{\mathfrak C}_2(|q|)
+  =\int dk\ 
+  \frac\rho{|q|}
+  \frac{\delta e}{\delta\hat v(k)}
+  \int_0^\infty dt\ \int_{||q|-t|}^{|q|+t}ds\ s\frac{\delta(t-|k|)}t\Gamma_L(s)
+\end{equation}
+that is
+\begin{equation}
+  \hat{\mathfrak C}_2(|q|)
+  =\int dk\ 
+  \frac{\delta e}{\delta\hat v(k)}
+  \frac{\rho}{|q||k|}
+  \int_{||q|-|k||}^{|q|+|k|}ds\ s\Gamma_L(s)
+\end{equation}
+which is the directional derivative of $e$ with respect to $\hat v$ in the direction of $2\rho g$ with
+\begin{equation}
+  g(|k|):=\frac1{2|q||k|}\int_{||q|-|k||}^{|q|+|k|}ds\ s\Gamma_L(s)
+  =
+  \frac1{2|k|rL}(\Gamma_L(|k|-r)-\Gamma_L(|k|+r))
+  .
+  \label{easyeq_2pt_fourier_g}
+\end{equation}
+Note that
+\begin{equation}
+  g(0):=\Gamma_L(|q|)
+  .
+\end{equation}
+To compute this derivative, we replace $\hat v$ with
+\begin{equation}
+  \hat v+\lambda g(|k|)
+\end{equation}
+so, denoting the solution of the modified equation by $u_\lambda$, for $q\neq 0$,
+\begin{equation}
+  \hat{\mathfrak C}_2(|q|)=2\rho\partial_\lambda e_\lambda|_{\lambda=0}
+  =\rho^2\left(
+    -\int\frac{dk}{(2\pi)^3}\ g(|k|)\hat u(|k|)
+    -\int\frac{dk}{(2\pi)^3}\ \hat v(|k|)\partial_\lambda\hat u_\lambda(|k|)|_{\lambda=0}
+  \right)
+  .
+\end{equation}
+To compute $\partial_\lambda\hat u_\lambda|_{\lambda=0}$, we differentiate $\Xi(\mathbb U,\lambda)=0$:
+\begin{equation}
+  \partial_\lambda\mathbb U|_{\lambda=0}=-(D\Xi)^{-1}\partial_\lambda\Xi|_{\lambda=0}
+  .
+\end{equation}
+The computation of $\partial_\lambda\Xi|_{\lambda=0}$ is identical to\-~(\ref{dXi_2pt_easyeq}), but with the $g$ defined in\-~(\ref{easyeq_2pt_fourier_g}).
+\bigskip
+
+\point
+To compute the maximum of $\hat C_2$, we proceed as for $C_2$, see\-~(\ref{easyeq_newton_2pt})-(\ref{easyeq_dx_2pt}).
+The only difference is that the algorithm is initialized with $|k_0|=\rho^{\frac13}$\refname{param:easyeq_k0}{{\tt k0}}.
+
+\subsubsubsection{Momentum distribution}
+To compute the momentum distribution (see\-~\cite{CHe21}), we add a parameter $\lambda$ to {\tt easyeq}:
+\begin{equation}
+  -\Delta u_\lambda(|x|)
+  =
+  (1-u_\lambda(|x|))v(|x|)-2\rho K(|x|)+\rho^2 L(|x|)
+  -2\lambda \hat u_0(q)\cos(q\cdot x)
+\end{equation}
+($\hat u_0\equiv\hat u_\lambda|_{\lambda=0}$).
+The momentum distribution is then
+\begin{equation}
+  \mathcal M(q)=\partial_\lambda e|_{\lambda=0}
+  =-\frac\rho2\int\frac{dk}{(2\pi)^3}\ \hat v(k)\partial_\lambda\hat u_\lambda(k)|_{\lambda=0}
+  .
+\end{equation}
+\bigskip
+
+\point
+Note that the Fourier transform of $2\lambda\hat u_0(q)\cos(q\cdot x)$ is
+\begin{equation}
+  -(2\pi)^3\lambda\hat u_0(q)(\delta(q+k)+\delta(q-k))
+  .
+  \label{fouriermomentum}
+\end{equation}
+The presence of delta functions does not play well with the quadratures.
+To get around this, we instead compute a regularization of $\mathcal M(q)$ by convolving it with a peaked spherically symmetric function.
+Let $\Gamma_L$ denote the Gaussian with variance $1/\sqrt L$
+\begin{equation}
+  \Gamma_L(|k|):=\left(\frac{L}{2\pi}\right)^{\frac32}e^{-\frac L2k^2}
+  .
+  \label{easyeq_gaussian_momt}
+\end{equation}
+In fact, we will scale $L$ with $k$, and set $L$ to
+\begin{equation}
+  L=\sqrt{\mathrm{\refname{param:easyeq_momentum_window_L}{{\tt window\_L}}}}/k^2
+  .
+\end{equation}
+To compute
+\begin{equation}
+  \mathfrak M(q):=\mathcal M\ast\Gamma_L(q)
+\end{equation}
+we solve the equation
+\begin{equation}
+  -\Delta u_\lambda(|x|)
+  =
+  (1-u_\lambda(|x|))v(|x|)-2\rho K(|x|)+\rho^2 L(|x|)
+  -2\lambda\int dk\ \hat u_0(k)\cos(k\cdot x)\Gamma_L(q-k)
+  .
+\end{equation}
+Note that the Fourier transform of
+\begin{equation}
+  -2\lambda\int dk\ \hat u_0(k)\cos(k\cdot x)\Gamma_L(q-k)
+\end{equation}
+is
+\begin{equation}
+  -(2\pi)^3\lambda\hat u_0(q)(\Gamma_L(k+q)+\Gamma_L(k-q))
+  .
+\end{equation}
+Since the ground state is unique, $\mathcal M$ is spherically symmetric.
+The term $\Gamma_L(k\pm q)$ is not, so we take its spherical average (which will not change the final result): by lemma\-~\ref{lemma:bipolar},
+\begin{equation}
+  -\frac1{4\pi r^2}\int dq\ \delta(|q|-r)(2\pi)^3\lambda\hat u_0(q)(\Gamma_L(k+q)+\Gamma_L(k-q))
+  =
+  -\frac{(2\pi)^3}{|k|r}\lambda\hat u_0(r)\int_{||k|-r|}^{|k|+r} ds\ s\Gamma_L(s)
+  .
+\end{equation}
+In this setup, the approximation of the delta function is thus
+\begin{equation}
+  \tilde\delta(|k|,r):=
+  \frac1{2|k|r}\int_{||k|-r|}^{|k|+r} ds\ s\Gamma_L(s)
+  =
+  \frac{1}{2|k|rL}(\Gamma_L(|k|-r)-\Gamma_L(|k|+r))
+  .
+\end{equation}
+\bigskip
+
+\point
+To compute the momentum distribution at $q$, we define $\Xi(\mathbb U,\lambda)$ by replacing $\mathbb T$ with
+\begin{equation}
+  \mathbb T_{i}=
+  \frac1{\mathbb A_{K,i}}
+  \left(
+    \mathbb S_i
+    -2(2\pi)^3\lambda \hat u(|q|)\tilde\delta(k_i,|q|)
+  \right)
+  .
+\end{equation}
+Then we solve $\Xi(\mathbb U,\lambda)=0$, and differentiate:
+\begin{equation}
+  \partial_\lambda\mathbb U|_{\lambda=0}=-(D\Xi)^{-1}\partial_\lambda\Xi|_{\lambda=0}
+  .
+\end{equation}
+Finally,
+\begin{equation}
+  \partial_\lambda\Xi_{i}|_{\lambda=0}
+  =
+  -\partial_\lambda\mathbb T_{i}|_{\lambda=0}\left(
+    \frac1{2(\mathbb X_{i}+1)}
+    \Phi\left(\mathbb B_i{\textstyle\frac{\mathbb T_{i}}{(\mathbb X_{i}+1)^2}}\right)
+    +\frac{\mathbb B_i\mathbb T_{i}}{2(\mathbb X_{i}+1)^3}
+    \partial\Phi\left(\mathbb B_i{\textstyle\frac{\mathbb T_{i}}{(\mathbb X_{l,j}+1)^2}}\right)
+  \right)
+\end{equation}
+with
+\begin{equation}
+  \partial_\lambda\mathbb T_{i}|_{\lambda=0}=
+  -\frac{2(2\pi)^3}{\mathbb A_{K,i}} \hat u(|q|)\tilde\delta(k_{i},|q|)
+  .
+\end{equation}
+\bigskip
+
 \subsection{\tt anyeq}\label{sec:anyeq}
 \indent
 This method is used to solve any of the equations in the Simplified approach.
@@ -598,11 +1053,11 @@ Unless otherwise noted, this method takes the following parameters (specified vi
   \item\makelink{param:anyeq_J}{}
   {\tt J} ({\tt Int64}, default: 10): number of splines (denoted by $J$ below).
 
-  \item\makelink{param:anyeq_minlrho_init}{}
-  {\tt minlrho\_init} ({\tt Float64}, default: \refname{param:anyeq_rho}{{\tt rho}}): we initialize the Newton algorithm using the solution of \refname{eq:easyeq_medeq}{{\tt medeq}}, computed using the methods in \refname{sec:easyeq}{{\tt easyeq}}. This option is passed to the underlying \refname{sec:easyeq}{{\tt easyeq}} routine.
-
   \item\makelink{param:anyeq_nlrho_init}{}
-  {\tt nlrho\_init} ({\tt Int64}, default: 1): this option is passed to the underlying \refname{sec:easyeq}{{\tt easyeq}} routine to initialize the Newton algorithm.
+  {\tt nlrho\_init} ({\tt Int64}, default: 0): we initialize the Newton algorithm using the solution of \refname{eq:easyeq_medeq}{{\tt medeq}}, computed using the methods in \refname{sec:easyeq}{{\tt easyeq}}. If {\tt nlrho\_init} is $\neq 0$, then the solution of \refname{eq:easyeq_medeq}{{\tt medeq}} is first computed for {\tt nlrho} smaller values of $\rho$ starting from $10^{\mathrm{\refname{param:anyeq_minlrho_init}{{\tt minlrho\_init}}}}$. This is useful when $\rho$ is too large for the solution of \refname{eq:easyeq_medeq}{{\tt medeq}} to be computed directly.
+
+  \item\makelink{param:anyeq_minlrho_init}{}
+  {\tt minlrho\_init} ({\tt Float64}, default: $-6$): see \refname{param:anyeq_nlrho_init}{{\tt nlrho\_init}}.
 
   \item\makelink{param:anyeq_aK}{}
   {\tt aK}, {\tt bK}, {\tt gK}, {\tt aL1}, {\tt bL1}, {\tt aL2}, {\tt bL2}, {\tt gL2}, {\tt aL3}, {\tt bL3}, {\tt gL3} ({\tt Float64}, default: 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0): the values of $\alpha_K$, $\beta_K$, $\gamma_K$, $\alpha_{L,1}$, $\beta_{L,1}$, $\alpha_{L,2}$, $\beta_{L,2}$, $\gamma_{L,2}$, $\alpha_{L,3}$, $\beta_{L,3}$, $\gamma_{L,3}$.
@@ -623,7 +1078,7 @@ The available {\tt commands} are the following.
   \item\makelink{command:anyeq_energy_rho}{}
   {\tt energy\_rho}: compute the energy $e$ as a function of $\rho$.
   The Newton algorithm is initialized with the solution of \refname{eq:easyeq_medeq}{{\tt medeq}}.\par
-  \underline{Disabled parameters}: \refname{param:anyeq_rho}{{\tt rho}}, \refname{param:anyeq_minlrho_init}{{\tt minlrho\_init}} and \refname{param:anyeq_nlrho_init}{{\tt nlrho\_init}}.\par
+  \underline{Disabled parameters}: \refname{param:anyeq_rho}{{\tt rho}}.\par
   \underline{Extra parameters}:
   \begin{itemize}
     \item\makelink{param:anyeq_minlrho}{}
@@ -635,8 +1090,18 @@ The available {\tt commands} are the following.
     \item\makelink{param:anyeq_nlrho}{}
     {\tt nlrho} ({\tt Int64}, default: $100$): number of values for $\rho$ (spaced logarithmically).
 
+    \item\makelink{param:anyeq_minrho}{}
+    {\tt minrho} ({\tt Float64}, default: $10^{-6}$): minimal value for $\rho$.
+
+    \item\makelink{param:anyeq_maxrho}{}
+    {\tt maxrho} ({\tt Float64}, default: $10^{2}$): maximal value for $\rho$.
+
+    \item\makelink{param:anyeq_nrho}{}
+    {\tt nrho} ({\tt Int64}, default: $0$): number of values for $\rho$ (spaced linearly). If {\tt nrho} is $\neq0$, then the linear spacing will be used, and \refname{param:anyeq_minlrho}{{\tt minlrho}}, \refname{param:anyeq_maxlrho}{{\tt maxlrho}}, \refname{param:anyeq_nlrho}{{\tt nlrho}} will be ignored. Otherwise, the logarithmic spacing will be used and \refname{param:anyeq_minrho}{{\tt minrho}}, \refname{param:anyeq_maxrho}{{\tt maxrho}} will be ignored.
+
     \item\makelink{param:anyeq_rhos}{}
-    {\tt rhos} ({\tt Array\{Float64\}}, default: $(10^{{\tt minlrho}+\frac{{\tt maxlrho}-{\tt minlrho}}{{\tt nlrho}}n})_n$: list of values for $\rho$, specified as a `{\tt,}' separated list.
+    {\tt rhos} ({\tt Array\{Float64\}}): list of values for $\rho$, specified as a `{\tt,}' separated list.
+    This parameter takes precedence over \refname{param:anyeq_minlrho}{{\tt minlrho}}, \refname{param:anyeq_maxlrho}{{\tt maxlrho}}, \refname{param:anyeq_nlrho}{{\tt nlrho}}, \refname{param:anyeq_minrho}{{\tt minrho}}, \refname{param:anyeq_maxrho}{{\tt maxrho}}, \refname{param:anyeq_nrho}{{\tt nrho}}.
   \end{itemize}
   \underline{Output} (one line for each value of $\rho$): [$\rho$] [$e$] [Newton error $\epsilon$].\par
   \underline{Multithread support}: yes, different values of $\rho$ split up among workers.
@@ -686,14 +1151,10 @@ The available {\tt commands} are the following.
     {\tt nx} ({\tt Int64}, default: 100): number of points to print (linearly spaced).
 
   \end{itemize}
-  \underline{Output} (one line for each value of $x$): [$|x|$] [$\mathcal Re(u(|x|))$] [$\mathcal Im(u(|x|))$]
+  \underline{Output} (one line for each value of $x$): [$|x|$] [$u(|x|)$]
 
-  \item\makelink{command:anyeq_momentum_distribution}{}
-  {\tt momentum\_distribution}: compute the momentum distribution $\mathfrak M(|k|)$ at a given $\rho$.\par
-  \underline{Output} (one line for each value of $|k|$): [$|k|$] [$\mathfrak M(|k|)$]
-
-  \item\makelink{command_anyeq_2pt}{}
-  {\tt 2pt}: compute the two-point correlation function $C_2(|x|)$ at a given $\rho$.\par
+  \item\makelink{command:anyeq_2pt}{}
+  {\tt 2pt}: compute the spherically averaged two-point correlation function $C_2(|x|)$ at a given $\rho$.\par
   \underline{Extra parameters}: same as \refname{command:anyeq_ux}{{\tt ux}}, plus\par
   \begin{itemize}
     \item\makelink{param:anyeq_window_L}{}
@@ -701,16 +1162,99 @@ The available {\tt commands} are the following.
   \end{itemize}
   \underline{Output} (one line for each value of $|x|$): [$|x|$] [$C_2(|x|)$]
 
+  \item\makelink{command:anyeq_2pt_max}{}
+  {\tt 2pt\_max}: compute the maximum of the spherically averaged two-point correlation function $C_2(|x|)$ at a given $\rho$.\par
+  \underline{Extra parameters}: \refname{param:anyeq_window_L}{{\tt window\_L}} plus
+  \begin{itemize}
+    \item\makelink{param:anyeq_dx}{}
+    {\tt dx} ({\tt Float64}, default: $10^{-7}$): step used to numerically approximate derivatives.
+
+    \item\makelink{param:anyeq_x0}{}
+    {\tt x0} ({\tt Float64}, default: $1$): initial guess for the maximum is $\rho^{-1/3}\mathrm{\tt x0}$.
+
+    \item\makelink{param:anyeq_maxstep}{}
+    {\tt maxstep} ({\tt Float64}, default: $\infty$): maximal size of single step in maximization algorithm.
+
+    \item\makelink{param:easyeq_tolerance_max}{}
+    {\tt tolerance\_max} ({\tt Float64}, default: \refname{param:easyeq_tolerance}{{\tt tolerance}}): same as \refname{param:easyeq_tolerance}{{\tt tolerance}}, used for the Newton algorithm underlying the maximization algorithm.
+  \end{itemize}
+  \underline{Output}: [$|x_{\mathrm{max}}|$] [$C_2(|x_{\mathrm{max}}|)$]
+
+  \item\makelink{command:anyeq_2pt_max_rho}{}
+  {\tt 2pt\_max\_rho}: compute the maximum of the spherically averaged two-point correlation function $C_2(|x|)$ for a range of $\rho$.\par
+  \underline{Extra parameters}: same as \refname{command:anyeq_2pt_max}{{\tt anyeq\_2pt\_max}} plus those of \refname{command:anyeq_energy_rho}{{\tt energy\_rho}}.\par
+  \underline{Output} (one line for each value of $\rho$): [$\rho$] [$|x_{\mathrm{max}}|$] [$C_2(|x_{\mathrm{max}}|)$]\par
+  \underline{Multithread support}: yes, different values of $\rho$ split up among workers.
+
+  \item\makelink{command:anyeq_2pt_fourier}{}
+  {\tt 2pt\_fourier}: compute the spherically averaged Fourier transform of the two-point correlation function $\hat C_2(|k|)$ at a given $\rho$.\par
+  \underline{Extra parameters}:
+  \begin{itemize}
+    \item\makelink{param:anyeq_kmin}{}
+    {\tt kmin} ({\tt Float64}, default: 0): minimum of the range of $|k|$ to be printed.
+
+    \item\makelink{param:anyeq_kmax}{}
+    {\tt kmax} ({\tt Float64}, default: 10): maximum of the range of $|k|$ to be printed.
+
+    \item\makelink{param:anyeq_nk}{}
+    {\tt nk} ({\tt Int64}, default: 100): number of $|k|$'s to be printed.
+
+    \item\makelink{param:anyeq_2pt_fourier_window_L}{}
+    {\tt window\_L} ({\tt Float64}, default: 1000): what is actually computed is $\hat C_2$ convolved with a Gaussian of variance $1/\sqrt L$ where $L=\sqrt{\mathrm{\tt window\_L}}$, see\-~(\ref{anyeq_gaussian}).
+  \end{itemize}
+  \underline{Output} (one line for each value of $|k|$): [$|k|$] [$\hat C_2(|k|)$].\par
+  \underline{Multithread support}: yes, different values of $k$ are split up among workers.
+
+  \item\makelink{command:anyeq_2pt_fourier_max}{}
+  {\tt 2pt\_fourier\_max}: compute the maximum of the spherically averaged Fourier transformed two-point correlation function $\hat C_2(|k|)$.\par
+  \underline{Extra parameters}: \refname{param:anyeq_2pt_fourier_window_L}{{\tt window\_L}}, \refname{param:anyeq_maxstep}{{\tt maxstep}} plus
+  \begin{itemize}
+    \item\makelink{param:anyeq_dk}{}
+    {\tt dk} ({\tt Float64}, default: $10^{-7}$): step used to numerically approximate derivatives.
+
+    \item\makelink{param:anyeq_k0}{}
+    {\tt k0} ({\tt Float64}, default: $1$): initial guess for the maximum is $\rho^{1/3}\mathrm{\tt k0}$.
+  \end{itemize}
+  \underline{Output}: [$|k_{\mathrm{max}}|$] [$\hat C_2(|k_{\mathrm{max}}|)$]\par
+
+  \item\makelink{command:anyeq_2pt_fourier_max_rho}{}
+  {\tt 2pt\_fourier\_max\_rho}: compute the maximum of the spherically averaged Fourier transformed two-point correlation function $\hat C_2(|k|)$ for a range of $\rho$.\par
+  \underline{Extra parameters}: same as those of \refname{command:anyeq_2pt_fourier_max}{{\tt 2pt\_fourier\_max}} plus those of \refname{command:anyeq_energy_rho}{{\tt energy\_rho}}.\par
+  \underline{Output} (one line for each value of $\rho$): [$\rho$] [$|k_{\mathrm{max}}|$] [$\hat C_2(|k_{\mathrm{max}}|)$]\par
+  \underline{Multithread support}: yes, different values of $\rho$ split up among workers.
+
+  \item\makelink{command:anyeq_momentum_distribution}{}
+  {\tt momentum\_distribution}: compute the momentum distribution $\mathcal M(|k|)$ at a given $\rho$. The momentum distribution is computed for $k=k_{l,j}$ (see\-~(\ref{klj})).\par
+  \underline{Extra parameters}:
+  \begin{itemize}
+    \item\makelink{param:anyeq_momentum_kmin}{}
+    {\tt kmin} ({\tt Float64}, default: 0): minimum of the range of $|k|$ to be printed.
+
+    \item\makelink{param:anyeq_momentum_kmax}{}
+    {\tt kmax} ({\tt Float64}, default: 10): maximum of the range of $|k|$ to be printed.
+
+    \item\makelink{param:anyeq_momentum_window_L}{}
+    {\tt window\_L} ({\tt Float64}, default: 1000): what is actually computed is $\mathfrak M_k$ convolved with a Gaussian of variance $1/\sqrt L$ where $L=\sqrt{\mathrm{\tt window\_L}}/k^2$, see\-~(\ref{anyeq_gaussian_momt}).
+  \end{itemize}
+  \underline{Output} (one line for each value of $|k|$): [$|k|$] [$\mathcal M(|k|)$]
+
+  \item\makelink{command:anyeq_compressibility_rho}{}
+  {\tt compressibility\_rho}: compute the compressibility $\chi$ as a function of $\rho$.
+  The Newton algorithm is initialized with the solution of \refname{eq:easyeq_medeq}{{\tt medeq}}.
+  \underline{Disabled parameters}: same as \refname{command:anyeq_energy_rho}{{\tt energy\_rho}}.\par
+  \underline{Extra parameters}: same as \refname{command:anyeq_energy_rho}{{\tt energy\_rho}}.\par
+  \underline{Output} (one line for each value of $\rho$): [$\rho$] [$\chi$].
+
   \item \makelink{command:anyeq_save_Abar}{}
   {\tt save\_Abar}: compute the matrix $\bar A$. This matrix is used to compute observables for \refname{eq:anyeq_compleq}{{\tt compleq}}. This command is useful to output the value of $\bar A$ to a file once and for all, and use this file to run commands without recomputing $\bar A$.\par
-  \underline{Disabled parameters}: \refname{param:anyeq_rho}{{\tt rho}}, \refname{param:anyeq_tolerance}{{\tt tolerance}}, \refname{param:anyeq_maxiter}{{\tt maxiter}}, \refname{param:anyeq_minlrho_init}{{\tt minlrho\_init}}, \refname{param:anyeq_nlrho}{{\tt nlrho}}.\par
+  \underline{Disabled parameters}: \refname{param:anyeq_rho}{{\tt rho}}, \refname{param:anyeq_tolerance}{{\tt tolerance}}, \refname{param:anyeq_maxiter}{{\tt maxiter}}, \refname{param:anyeq_minlrho_init}{{\tt minlrho\_init}}, \refname{param:anyeq_nlrho_init}{{\tt nlrho\_init}}.\par
   \underline{Output}: [$\bar A$] (the output is not designed to be human-readable; it is obtained through nested {\tt for} loops; for details, see the code).\par
   \underline{Multithread support}: yes, the first indices are split up among workers, which produces $NJ$ jobs.
 
 \end{itemize}
 
 \subsubsection{Description}
-\point{\bf Fourier space formulation.}
+\subsubsubsection{Fourier space formulation}
 The computation is carried out in Fourier space.
 We take the convention that the Fourier transform of a function $f(|x|)$ is
 \begin{equation}
@@ -770,11 +1314,15 @@ with
 \end{equation}
 Therefore,
 \begin{equation}
+  \Omega(u)=0
+\end{equation}
+with
+\begin{equation}
+  \Omega(u):=
   \rho^2\hat u(|k|)^2\sigma_{L,1}(|k|)
   -\left(\frac{k^2}\rho+2\sigma_K(|k|)+2f_1(|k|)\right)\rho\hat u(|k|)
   +\left(\hat S(|k|)+\frac12f_2(|k|)+G(|k|)\right)
-  =
-  0
+  \label{Omega}
 \end{equation}
 with
 \begin{equation}
@@ -831,7 +1379,7 @@ with
 \end{equation}
 \bigskip
 
-\point{\bf Evaluating integrals.}
+\subsubsubsection{Evaluating integrals}
 To evaluate integrals numerically, we will split integration intervals over splines and use Gauss-Legendre quadratures.
 More specifically, to compute integrals of the form
 \begin{equation}
@@ -861,7 +1409,7 @@ In these terms,
   \int \frac{dk}{(2\pi)^3}\ f(U(k),k)
   =
   \sum_{l=0}^{J-1}
-  \int_{\tau_l}^{\tau_{l+1}} d\tau\ \frac{(1-\tau)^2}{(1+\tau^4}g(U({\textstyle\frac{1-\tau}{1+\tau},\frac{1-\tau}{1+\tau}})
+  \int_{\tau_l}^{\tau_{l+1}} d\tau\ \frac{(1-\tau)^2}{(1+\tau)^4}g(U({\textstyle\frac{1-\tau}{1+\tau},\frac{1-\tau}{1+\tau}})
   .
 \end{equation}
 We then change variables to $r$:
@@ -879,6 +1427,7 @@ and find
   \sum_{l=0}^{J-1}
   \frac{\tau_{l+1}-\tau_l}{16\pi}\int_{-1}^1 dr\ \cos({\textstyle\frac{\pi r}2})(1+\vartheta_l(r))^2\vartheta_l^2(r)f(U(\vartheta_l(r)),\vartheta_l(r))
   .
+  \label{anyeq_preintegration}
 \end{equation}
 We then approximate the integral using a Gauss-Legendre quadrature of order $N$ (see appendix\-~\ref{appGL}):
 \begin{equation}
@@ -901,14 +1450,14 @@ The choice of the change of variables\-~(\ref{chebyshevvars}) is made so that $U
 In this way, we can compute arbitrary integrals of functions of $U$ by just computing the values of $\mathbb U_{l,j}$.
 \bigskip
 
-\point{\bf Chebyshev polynomial expansion.}
+\subsubsubsection{Chebyshev polynomial expansion}
 In the case of \refname{sec:easyeq}{{\tt easyeq}}, we saw that using Gauss quadratures reduced the computation to evaluating $U$ at a fixed set of discrete momenta.
 This is not the case here, due to the presence of more complicated convolutions of $U$.
 Instead, we will approximate $U$ using polynomial approximations.
 We will now discuss this approximation for an arbitrary function $a(|k|)$, as we will need it for other functions than simply $U$.
 \bigskip
 
-\subpoint
+\point
 As in the previous paragraph, we compactify $[0,\infty)$ to $(-1,1]$ via the change of variables $r\mapsto\frac{1-r}{1+r}$, and split the interval into splines.
 Inside each spline, we use a Chebyshev polynomial expansion.
 However, we need to be careful in the first spline, which encapsulates the behavior at $|k|\to\infty$: $U$ decays to 0 as $|k|\to\infty$, and this behavior cannot be reproduced by a polynomial expansion.
@@ -934,7 +1483,7 @@ in which $\mathds 1_{\tau_l<\tau\leqslant\tau_{l+1}}\in\{0,1\}$ is equal to 1 if
 (see appendix\-~\ref{appChebyshev} for a discussion of the Chebyshev polynomial expansion and the error of its truncations).
 \bigskip
 
-\subpoint
+\point
 In order to compute an approximation for $a$ using\-~(\ref{a_chebyshev_spline}), we will truncate the sum over $n$ to a finite value $P$ (given by the parameter \refname{param:anyeq_P}{{\tt P}}).
 In addition, to compute the integral in\-~(\ref{bfF}), we will use a Gauss-Legendre quadrature of order $N$ (given by the parameter \refname{param:anyeq_N}{{\tt N}}), see appendix\-~\ref{appGL}:
 \begin{equation}
@@ -952,7 +1501,7 @@ with
 and $(x_j,w_j)$ are the abcissa and weights for Gauss-Legendre quadratures.
 \bigskip
 
-\subpoint
+\point
 All in all, we approximate
 \begin{equation}
   a({\textstyle\frac{1-\tau}{1+\tau}})
@@ -964,14 +1513,15 @@ Furthermore, using the Chebyshev polynomial expansion and Gauss-Legendre quadrat
 With this in mind, we will represent the function $U$ as a vector of dimension $NJ$ whose components are $\mathbb U_{l,j}$.
 \bigskip
 
-\point{\bf Convolutions.}
+\subsubsubsection{Convolutions}
 Using the Chebyshev polynomial approximation, we can compute convolutions as follows.
 \medskip
 
-\subpoint
+\point
 First, we rewrite the convolution as a two-dimensional integral, using bipolar coordinates (see lemma\-~\ref{lemma:bipolar}):
 \begin{equation}
   (a\hat\ast b)(|k|)=\frac1{4\pi^2|k|}\int_0^\infty dt\ ta(t)\int_{||k|-t|}^{|k|+t}ds\ sb(s)
+  \label{conv_approx1}
 \end{equation}
 We change variables to compactify the integrals $\tau=\frac{1-t}{1+t}$, $\sigma=\frac{1-s}{1+s}$:
 \begin{equation}
@@ -990,30 +1540,31 @@ Therefore, using the approximation\-~(\ref{approxchebyshev}), if $\mathfrak a_{l
 \begin{equation}
   (a\hat\ast b)(|k_{l,j}|)\approx
   (\mathfrak a\odot \mathfrak b)_{l,j}:=
-  \frac1{4\pi^2|k_{l,j}|}
   \sum_{n,m=0}^P
-  \sum_{l,l'=0}^{J-1}
-  \mathfrak F_{l,n}^{(\nu_a)}(\mathfrak a)\mathfrak F_{l',m}^{(\nu_b)}(\mathfrak b)
-  A_{l,n;l',m}^{(\nu_a,\nu_b)}(|k_{l,j}|)
+  \sum_{l',l''=0}^{J-1}
+  \mathfrak F_{l',n}^{(\nu_a)}(\mathfrak a)\mathfrak F_{l'',m}^{(\nu_b)}(\mathfrak b)
+  A_{l',n;l'',m}^{(\nu_a,\nu_b)}(|k_{l,j}|)
   \label{odot}
 \end{equation}
 with
 \begin{equation}
   \begin{largearray}
-    A_{l,n;l',m}^{(\nu_a,\nu_b)}(|k|):=
-    \int_{\tau_l}^{\tau_{l+1}} d\tau\ \frac{2(1-\tau)}{(1+\tau)^{3-\nu_a}}T_n({\textstyle\frac{2\tau-(\tau_l+\tau_{l+1})}{\tau_{l+1}-\tau_l}})
+    A_{l',n;l'',m}^{(\nu_a,\nu_b)}(|k|):=
+    \frac1{4\pi^2|k_{l,j}|}
+    \int_{\tau_{l'}}^{\tau_{l'+1}} d\tau\ \frac{2(1-\tau)}{(1+\tau)^{3-\nu_a}}T_n({\textstyle\frac{2\tau-(\tau_{l'}+\tau_{l'+1})}{\tau_{l'+1}-\tau_{l'}}})
     \cdot\\[0.5cm]\hfill\cdot
-    \mathds 1_{\alpha_-(|\tilde k|,\tau)<\tau_{\zeta'+1}}
-    \mathds 1_{\alpha_+(|\tilde k|,\tau)>\tau_{\zeta'}}
-    \int_{\max(\tau_{l'},\alpha_-(|k|,\tau))}^{\min(\tau_{l'+1},\alpha_+(|k|,\tau))}d\sigma\ \frac{2(1-\sigma)}{(1+\sigma)^{3-\nu_b}}T_m({\textstyle\frac{2\sigma-(\tau_{l'}+\tau_{l'+1})}{\tau_{l'+1}-\tau_{l'}}})
+    \mathds 1_{\alpha_-(|k|,\tau)<\tau_{l''+1}}
+    \mathds 1_{\alpha_+(|k|,\tau)>\tau_{l''}}
+    \int_{\max(\tau_{l''},\alpha_-(|k|,\tau))}^{\min(\tau_{l''+1},\alpha_+(|k|,\tau))}d\sigma\ \frac{2(1-\sigma)}{(1+\sigma)^{3-\nu_b}}T_m({\textstyle\frac{2\sigma-(\tau_{l''}+\tau_{l''+1})}{\tau_{l''+1}-\tau_{l''}}})
   \end{largearray}
+  \label{conv_approx2}
 \end{equation}
 (we need the indicator functions to ensure that the bounds of the integral are correct).
 Note that $A$ is independent of $a$ and $b$, and can be computed once and for all at the beginning of execution for all values of $k_{l,j}$ (\ref{klj}).
 We then compute the integrals using Gauss-Legendre quadratures (as is proved in the next paragraph, the integrand is non singular provided $\nu_a,\nu_b\geqslant 2$).
 \bigskip
 
-\subpoint
+\point
 Note that these integrals are not singular as long as $\nu_a,\nu_b\geqslant 2$: indeed (since the only possible problems occur at $-1$, it suffices to consider the case with only one spline), $\alpha_-,\alpha_+>-1$ for $\tau>-1$, and
 \begin{equation}
   \alpha_\pm(k,\tau)=-1+(1+\tau)\pm\frac k2(1+\tau)^2+O(1+\tau)^3
@@ -1047,7 +1598,7 @@ where $c_{2}:=1$ and $c_{\nu_b}:=\nu_b-2$ if $\nu_b>2$.
 The integrand is, therefore, not singular as long as $\nu_a,\nu_b\geqslant 2$.
 \bigskip
 
-\subpoint
+\point
 Evaluating convolutions at $k=0$ is not immediate, as the formula for $A(0)$ involves a bit of a computation.
 To compute $A(0)$, we expand
 \begin{equation}
@@ -1077,11 +1628,11 @@ We can then evaluate convolutions at $k=0$:
 \bigskip
 
 
-\subpoint
+\point
 Let us now compute some choices of $\mathfrak a,\mathfrak b$ more explicitly.
 \medskip
 
-\subsubpoint
+\subpoint
 Let us start with $\mathds 1_{l,n}\hat\ast a$ where $\mathds 1_{l,n}$ is the vector which has 0's everywhere except at position $(l,n)$.
 We have
 \begin{equation}
@@ -1095,7 +1646,7 @@ so
 \end{equation}
 \bigskip
 
-\subsubpoint
+\subpoint
 We now turn to $a\hat\ast\hat v$.
 Let
 \begin{equation}
@@ -1132,7 +1683,7 @@ The integral in\-~(\ref{Upsilon}) is computed using Gauss-Legendre quadratures,
 To maintain a high precision, we set the order of the integration to \refname{param:anyeq_J}{{\tt J}}$\times$\refname{param:anyeq_N}{{\tt N}}.
 \bigskip
 
-\subsubpoint
+\subpoint
 Finally,
 \begin{equation}
   (\mathds 1_{l',n}\odot\mathfrak v)_{l,i}=
@@ -1146,7 +1697,7 @@ and
 \end{equation}
 \bigskip
 
-\point{\bf Evaluating $\hat l_3$.}
+\subsubsubsection{Evaluating $\hat l_3$}
 The only term in\-~(\ref{anyeq}) that does not involve just convolutions (whose computation was described above) is $\hat l_3$ (\ref{l3}).
 To evaluate it, we first change variables, using a generalization of bipolar coordinates (see lemma\-~\ref{lemma:bipolar2}):
 \begin{equation}
@@ -1230,12 +1781,12 @@ Since the tensor $\bar A$ is quite large (it contains $(NJ)^5$ entries), and its
 Instead, one can use the \refname{command:anyeq_save_Abar}{{\tt save\_Abar}} method to compute $\bar A$ and save it to a file, which can then be recalled via the {\tt -s} option on the command line.
 \bigskip
 
-\point{\bf Main algorithm to compute $U$.}
+\subsubsubsection{Main algorithm to compute $U$}
 We are now ready to detail how $U\equiv\rho\hat u$ is computed.
 All of the observables we are interested in are approximated from the values $\mathbb U_{l,j}:=U(k_{l,j})$ (\ref{klj}).
 \bigskip
 
-\subpoint
+\point
 The equation for $(\mathbb U_{l,j})_{l\in\{0,\cdots,J-1\},j\in\{1,\cdots,N\}}$ is obtained by approximating\-~(\ref{anyeq_hatu}) according to the prescriptions detailed above:
 \begin{equation}
   \mathbb U_{l,j}:=
@@ -1283,7 +1834,7 @@ The equation for $(\mathbb U_{l,j})_{l\in\{0,\cdots,J-1\},j\in\{1,\cdots,N\}}$ i
 (see\-~(\ref{odot}), (\ref{otimes}) and\-~(\ref{avg}) for the definitions of $\odot$, $\otimes$ and $\left<\cdot\right>$).
 \bigskip
 
-\subpoint
+\point
 We rewrite\-~(\ref{bbU}) as a root finding problem:
 \begin{equation}
   \Xi_{l,j}(\mathbb U):=
@@ -1303,14 +1854,14 @@ where $D\Xi$ is the Jacobian of $\Xi$:
 \end{equation}
 \bigskip
 
-\subpoint
+\point
 We initialize the algorithm with the solution of the \refname{eq:easyeq_medeq}{Medium equation}, which is computed using the \refname{sec:easyeq}{{\tt easyeq}} method.
 However, \refname{sec:easyeq}{{\tt easyeq}} only computes $\hat u$ at the momenta given by the Gauss-Legendre quadratures\-~(\ref{easyeq_ki}).
 To obtain a value for $\hat u$ at $k_{l,j}$, we use a linear interpolation (code is provided for a polynomial interpolation, which has not performed as well).
-The parameters \refname{param:anyeq_tolerance}{{\tt tolerance}}, \refname{param:anyeq_maxiter}{{\tt maxiter}}, \refname{param:anyeq_minlrho_init}{{\tt minlrho\_init}}, \refname{param:anyeq_nlrho_init}{{\tt nlrho\_init}} are passed on to \refname{sec:easyeq}{easyeq} as is, and the order of the Gauss-Legendre quadratures is set to \refname{param:anyeq_J}{{\tt J}}$\times$\refname{param:anyeq_N}{{\tt N}}.
+The parameters \refname{param:anyeq_tolerance}{{\tt tolerance}}, \refname{param:anyeq_maxiter}{{\tt maxiter}} are passed on to \refname{sec:easyeq}{{\tt easyeq}} as is, and the order of the Gauss-Legendre quadratures is set to \refname{param:anyeq_J}{{\tt J}}$\times$\refname{param:anyeq_N}{{\tt N}}.
 \bigskip
 
-\subpoint
+\point
 We are left with computing the Jacobian of $\Xi$:
 \begin{equation}
   \begin{largearray}
@@ -1444,7 +1995,7 @@ with
 \end{equation}
 \bigskip
 
-\subpoint
+\point
 We iterate the Newton algorithm until the Newton relative error $\epsilon$ becomes smaller than the \refname{param:easyeq_tolerance}{{\tt tolerance}} parameter.
 The Newton error is defined as
 \begin{equation}
@@ -1471,8 +2022,8 @@ where $k_{l,j}$ was defined in\-~(\ref{klj}), and the solution $u$ in real space
 \end{equation}
 \bigskip
 
-\point{\bf Condensate fraction.}
-To compute the condensate fraction, we solve the modified {\tt anyeq} (see\-~\cite{CJL20b}):
+\subsubsubsection{Condensate fraction}
+To compute the condensate fraction, we solve the modified {\tt anyeq} (see\-~\cite{CJL21}):
 \begin{equation}
   (-\Delta+2\mu)u_\mu
   =
@@ -1519,41 +2070,53 @@ We then approximate
 (see\-~(\ref{avg})).
 \bigskip
 
-\point{\bf Correlation function.}
+\subsubsubsection{Correlation function (spherical average)}
 The two-point correlation function is
 \begin{equation}
-  C_2(x):=
+  c_2(x):=
   2\rho\frac{\delta e}{\delta v(x)}
+\end{equation}
+and its spherical average is
+\begin{equation}
+  C_2(|x|):=\frac1{4\pi|x|^2}\int dy\ \delta(|x|-|y|)c_2(y)
   .
 \end{equation}
 In Fourier space,
 \begin{equation}
-  C_2(x)=
-  2\rho\int dk\ e^{ikx}\frac{\delta e}{\delta\hat v(k)}
+  c_2(x)=
+  2\rho
+  \int dk\ e^{ikx}\frac{\delta e}{\delta\hat v(k)}
+\end{equation}
+so
+\begin{equation}
+  C_2(|x|)=
+  2\rho\int dk\ \left(\frac1{4\pi|x|^2}\int dy\ \delta(|x|-|y|)e^{iky}\right)\frac{\delta e}{\delta\hat v(k)}
   =2\rho\int dk\ \frac{\sin(|k||x|)}{|k||x|}\frac{\delta e}{\delta\hat v(k)}
   .
   \label{C2fourier}
 \end{equation}
 \bigskip
 
-\subpoint
+\point
 We can compute this quantity by considering a modified {\tt anyeq} in Fourier space, by formally replacing $\hat v$ with
 \begin{equation}
   \hat v+\lambda g(|k|)
   ,\quad
   g(|k|):=\frac{\sin(|k||x|)}{|k||x|}
   .
+  \label{2pt_addv}
 \end{equation}
 Indeed, if $e_\lambda$ denotes the energy of this modified equation,
 \begin{equation}
   \partial_\lambda e_\lambda|_{\lambda=0}
-  =\int dk\ \frac{\delta e}{\hat v(k)}\partial_\lambda({\textstyle \hat v(k)+\lambda g(|k|)})
-  =\int dk\ g(|k|)\frac{\delta e}{\hat v(k)}
+  =\int dk\ \frac{\delta e}{\delta\hat v(k)}\partial_\lambda({\textstyle \hat v(k)+\lambda g(|k|)})
+  =\int dk\ g(|k|)\frac{\delta e}{\delta\hat v(k)}
+  .
 \end{equation}
-so, denoting the solution of the modified equation by $u_\lambda$,
+So, denoting the solution of the modified equation by $u_\lambda$,
 \begin{equation}
   C_2(x)=2\rho\partial_\lambda e_\lambda|_{\lambda=0}
-  =-\rho^2\int dx\ (g(x)u_0(x)+v(x)\partial_\lambda u_\lambda(x)|_{\lambda=0})
+  =\rho^2g(0)-\rho^2\int\frac{dk}{(2\pi)^3}\ (g(k)\hat u(k)+\hat v(k)\partial_\lambda\hat u_\lambda(k)|_{\lambda=0})
   .
 \end{equation}
 We compute $\partial_\lambda u_\lambda|_{\lambda=0}$ in the same way as the uncondensed fraction: we define $\Xi(\mathbb U,\lambda)$ by formally adding $\lambda g(|k|)$ to $\hat v$, solve $\Xi(\mathbb U,\lambda)=0$, and differentiate:
@@ -1563,7 +2126,7 @@ We compute $\partial_\lambda u_\lambda|_{\lambda=0}$ in the same way as the unco
 \end{equation}
 \bigskip
 
-\subpoint
+\point
 We compute $\partial_\lambda\Xi|_{\lambda=0}$:
 \begin{equation}
   \begin{largearray}
@@ -1581,6 +2144,7 @@ We compute $\partial_\lambda\Xi|_{\lambda=0}$:
       -\partial_\lambda\mathbb Y_{l,j}
     \right)\ \partial\Phi\left({\textstyle\frac{1+\mathbb Y_{l,j}}{(\mathbb X_{l,j}+1)^2}}\right)
   \end{largearray}
+  \label{2pt_dXi}
 \end{equation}
 with
 \begin{equation}
@@ -1610,7 +2174,7 @@ with
 \begin{equation}
   \partial_\lambda\mathbb I_{l,j}=
   \frac1{\rho}\gamma_{L,2}\left(
-    \beta_{L,2}(\mathbb U\odot(\mathbb U\partial_\lambda\mathbb S)_{l,j}
+    \beta_{L,2}(\mathbb U\odot(\mathbb U\partial_\lambda\mathbb S))_{l,j}
     +
     (1-\beta_{L,2})\partial_\lambda\mathbb E(\mathbb U\odot\mathbb U)_{l,j}
   \right)
@@ -1623,7 +2187,7 @@ with
 \begin{equation}
   \begin{largearray}
     \partial_\lambda\mathbb G_{l,j}=
-    \frac2{\rho}\gamma_K\alpha_K\beta_K\left(\partial_\lambda\mathbb S\mathbb U\right)_{l,j}
+    \frac2{\rho}\gamma_K\alpha_K\beta_K\left(\mathbb U\odot\left(\partial_\lambda\mathbb S\mathbb U\right)\right)_{l,j}
     +\frac2{\rho}\gamma_K\alpha_K(1-\beta_K)\partial_\lambda\mathbb E(\mathbb U\odot\mathbb U)_{l,j}
     -
     \frac1{\rho}\alpha_{L,1}\beta_{L,1}\left(\mathbb U\odot\partial_\lambda\mathbb S\mathbb U^2\right)_{l,j}
@@ -1641,7 +2205,7 @@ To evaluate $(\mathbb U\odot\mathfrak g)$ and $\left<\mathbb U\mathfrak g\right>
 To do so, we replace $\hat v$ with $g$ in the computation of $\Upsilon$.
 \bigskip
 
-\subpoint
+\point
 In order to invert the Fourier transform in\-~(\ref{C2fourier}) numerically, we will use a Hann window (see appendix\-~\ref{appendix:hann})
 \begin{equation}
   H_L(k):=\mathds 1_{|k|<\frac L2}\cos^2({\textstyle\frac{\pi|k|}{L}})
@@ -1652,7 +2216,203 @@ The parameter $L$ is set using \refname{param:anyeq_window_L}{{\tt window\_L}}.
 The computation is changed only in that $g$ is changed to $H_L(k)\frac{\sin(|k||x|)}{|k||x|}$.
 \bigskip
 
-\point {\bf Momentum distribution.}
+\point
+To compute the maximum of $C_2$, we use a modified Newton algorithm.
+The initial guess for the maximum is $|x_0|=\rho^{-\frac13}$\refname{param:anyeq_x0}{{\tt x0}}.
+The modified Newton algorithm is an iteration:
+\begin{equation}
+  x_{n+1}=x_n+\frac{\partial C_2(|x_n|)}{|\partial^2C_2(|x_n|)|}
+  \label{anyeq_newton_2pt}
+\end{equation}
+in which the derivatives are approximated using finite differences:
+\begin{equation}
+  \partial C_2(x)\approx \frac{C_2(|x|+dx)-C_2(|x|)}{dx}
+  ,\quad
+  \partial^2 C_2(x)\approx \frac{C_2(|x|+dx)+C_2(|x|-dx)-2C_2(|x|)}{dx^2}
+  .
+  \label{anyeq_dx_2pt}
+\end{equation}
+This is a modification of the usual Newton iteration $x_n+\partial C_2/\partial^2C_2$ which is designed to follow the direction of the gradient, and thus to move toward a local maximum.
+In addition, if $|\partial C_2|/|\partial^2 C_2|$ is larger than \refname{param:anyeq_maxstep}{{\tt maxstep}}, then the step is replaced with $\pm$\refname{param:anyeq_maxstep}{{\tt maxstep}}.
+This prevents the algorithm from stepping over a maximum and land on another, further away.
+This is useful if one has a good idea of where the global maximum is, and does not want to get trapped in a smaller local maximum.
+\bigskip
+
+\indent
+The algorithm is run for a maximum of \refname{param:anyeq_maxiter}{{\tt maxiter}} iterations, or until $|x_{n+1}-x_n|$ is smaller than \refname{param:anyeq_tolerance}{{\tt tolerance}}.
+If the maximal number of iterations is reached, or if the solution found is not a local maximum, then the algorithm fails, and returns $+\infty$.
+The point thus computed is therefore a local maximum, but it is not guaranteed to be the global maximum.
+
+\subsubsubsection{Fourier transform of two-point correlation (spherical average)}
+The Fourier transform of the two-point correlation function is
+\begin{equation}
+  \hat c_2(q):=
+  2\rho\frac{\delta e}{\delta v(q)}
+\end{equation}
+and its spherical average is
+\begin{equation}
+  \hat C_2(|q|):=\frac1{4\pi|q|^2}\int dk\ \delta(|q|-|k|)c_2(k)
+  =
+  \frac\rho{2\pi|q|^2}\int dk\ \delta(|q|-|k|)\frac{\delta e}{\delta\hat v(k)}
+  .
+\end{equation}
+\bigskip
+
+\point
+To compute $\frac{\delta e}{\delta\hat v(q)}$, one idea would be to proceed in the same way as for the two-point correlation function, by replacing $\hat v$ with
+\begin{equation}
+  \hat v+\lambda g(|k|)
+  ,\quad
+  g(|k|):=\frac1{4\pi|q|^2}\delta(|q|-|k|)
+\end{equation}
+where $\delta$ is the Dirac-delta function distribution (compare this with\-~(\ref{2pt_addv})).
+However, the $\delta$ function causes all sorts of problems with the Chebyshev polynomial exansion and the quadratures.
+\bigskip
+
+\point
+Instead, we approximate $\hat C_2$ by convolving it with a normalized Gaussian: let
+\begin{equation}
+  \Gamma_L(|k|):=\left(\frac{L}{2\pi}\right)^{\frac32}e^{-\frac L2k^2}
+  \label{anyeq_gaussian}
+\end{equation}
+\begin{equation}
+  \hat{\mathfrak C}_2(|q|)
+  :=
+  \int dp\ \hat C_2(|q-p|)\Gamma_L(|p|)
+  =\int dk\ \int dp\ \frac\rho{2\pi|q-p|^2}\delta(|q-p|-|k|)\frac{\delta e}{\delta\hat v(k)}\Gamma_L(|p|)
+\end{equation}
+which by lemma\-~\ref{lemma:bipolar} is
+\begin{equation}
+  \hat{\mathfrak C}_2(|q|)
+  =\int dk\ 
+  \frac\rho{|q|}
+  \frac{\delta e}{\delta\hat v(k)}
+  \int_0^\infty dt\ \int_{||q|-t|}^{|q|+t}ds\ s\frac{\delta(t-|k|)}t\Gamma_L(s)
+\end{equation}
+that is
+\begin{equation}
+  \hat{\mathfrak C}_2(|q|)
+  =\int dk\ 
+  \frac{\delta e}{\delta\hat v(k)}
+  \frac{\rho}{|q||k|}
+  \int_{||q|-|k||}^{|q|+|k|}ds\ s\Gamma_L(s)
+\end{equation}
+which is the directional derivative of $e$ with respect to $\hat v$ in the direction of $2\rho g$ with
+\begin{equation}
+  g(|k|):=\frac1{2|q||k|}\int_{||q|-|k||}^{|q|+|k|}ds\ s\Gamma_L(s)
+  =
+  \frac1{2|k|rL}(\Gamma_L(|k|-r)-\Gamma_L(|k|+r))
+  .
+  \label{anyeq_2pt_fourier_g}
+\end{equation}
+Note that
+\begin{equation}
+  g(0):=\Gamma_L(|q|)
+  .
+\end{equation}
+To compute this derivative, we replace $\hat v$ with
+\begin{equation}
+  \hat v+\lambda g(|k|)
+\end{equation}
+so, denoting the solution of the modified equation by $u_\lambda$, for $q\neq 0$,
+\begin{equation}
+  \hat{\mathfrak C}_2(|q|)=2\rho\partial_\lambda e_\lambda|_{\lambda=0}
+  =\rho^2\left(
+    -\int\frac{dk}{(2\pi)^3}\ g(|k|)\hat u(|k|)
+    -\int\frac{dk}{(2\pi)^3}\ \hat v(|k|)\partial_\lambda\hat u_\lambda(|k|)|_{\lambda=0}
+  \right)
+  .
+\end{equation}
+To compute $\partial_\lambda\hat u_\lambda|_{\lambda=0}$, we differentiate $\Xi(\mathbb U,\lambda)=0$:
+\begin{equation}
+  \partial_\lambda\mathbb U|_{\lambda=0}=-(D\Xi)^{-1}\partial_\lambda\Xi|_{\lambda=0}
+  .
+\end{equation}
+The computation of $\partial_\lambda\Xi|_{\lambda=0}$ is identical to\-~(\ref{2pt_dXi}), but taking
+\begin{equation}
+  \mathfrak g_{l,j}=g(|k_{l,j}|)
+  .
+\end{equation}
+with $g$ defined in\-~(\ref{anyeq_2pt_fourier_g}).
+\bigskip
+
+\point
+To compute the maximum of $\hat C_2$, we proceed as for $C_2$, see\-~(\ref{anyeq_newton_2pt})-(\ref{anyeq_dx_2pt}).
+The only difference is that the algorithm is initialized with $|k_0|=\rho^{\frac13}$\refname{param:anyeq_k0}{{\tt k0}}.
+
+\subsubsubsection{Correlation function of uncondensed particles (spherical average)}
+To compute the correlation function among uncondensed particles, denoted by $\gamma_2(|\xi|)$, we solve the modified {\tt anyeq} (see\-~\cite{Ja23}):
+\begin{equation}
+  -\Delta u_\mu
+  =
+  (1-u_\mu)v-2\rho K+\rho^2 L
+  -\frac{\rho\mu}{2\pi|\xi|^2} u(|\xi|)\delta(|\xi|-|x|)
+\end{equation}
+where $K$ and $L$ are defined as in\-~(\ref{anyeq_K})-(\ref{anyeq_e}) in which $u$ is replaced with $u_\mu$.
+In Fourier space,
+\begin{equation}
+  k^2 \hat u_\mu
+  =
+  \hat S-2\rho\hat K+\rho^2\hat L
+  -2\rho\mu u(|\xi|)\frac{\sin(|k||\xi|)}{|k||\xi|}
+  .
+\end{equation}
+The uncondensed correlation function is then
+\begin{equation}
+  \gamma_2(|\xi|)=\partial_\mu e|_{\mu=0}
+  =-\frac\rho2\int dx\ v(x)\partial_\mu u_\mu(x)|_{\mu=0}
+  .
+\end{equation}
+To compute the energy in the presence of the parameter $\mu$, we proceed in the same way as for $\mu=0$, the only difference being that the term
+\begin{equation}
+  \mu g(|k|):=
+  -\mu 2\rho u(|\xi|)\frac{\sin(|k||\xi|)}{|k||\xi|}
+\end{equation}
+should formally be added to the right side of\-~(\ref{Omega}).
+In other words, we consider $\mathbb U_{j,l}=u_\mu(|k_{j,l}|)$ and define $\Xi(\mathbb U,\mu)$ in the same way as in\-~(\ref{root_anyeq}), except that $\mathbb Y_{l,j}$ should be replaced by
+\begin{equation}
+  \mathbb Y_{l,j}=
+  \frac1{\mathbb L_{l,j}}\left(\mathbb S_{l,j}-\mathbb L_{l,j}+\frac12\mathbb J_{l,j}+\mathbb G_{l,j}+\mu g(k_{l,j})\right)
+  .
+\end{equation}
+We then solve
+\begin{equation}
+  \Xi(\mathbb U_\mu,\mu)=0
+\end{equation}
+By differentiating this identity with respect to $\mu$, we find $\partial_\mu u_\mu$:
+\begin{equation}
+  \partial_\mu \mathbb U|_{\mu=0}=-(D\Xi)^{-1}\partial_\mu\Xi|_{\mu=0}
+\end{equation}
+and
+\begin{equation}
+  \partial_\mu\Xi|_{\mu=0}=
+  -\frac{\partial_\mu\mathbb Y_{l,j}}{2(\mathbb X_{l,j}+1)}
+  \Phi\left({\textstyle\frac{1+\mathbb Y_{l,j}}{(\mathbb X_{l,j}+1)^2}}\right)
+  -\frac{(1+\mathbb Y_{l,j})\partial_\mu\mathbb Y_{l,j}}{2(\mathbb X_{l,j}+1)^3}
+  \partial\Phi\left({\textstyle\frac{1+\mathbb Y_{l,j}}{(\mathbb X_{l,j}+1)^2}}\right)
+  ,\quad
+  \partial_\mu\mathbb Y_{l,j}=\frac{g(k_{l,j})}{\mathbb L_{l,j}}
+  .
+\end{equation}
+We then approximate
+\begin{equation}
+  \gamma_2(\xi)\approx
+  -\frac12\left<\mathfrak v\partial_\mu\mathbb U\right>
+\end{equation}
+(see\-~(\ref{avg})).
+\bigskip
+
+\indent
+In order to avoid numerical oscillations due to the $\sin$ function, we will use a Hann window (see appendix\-~\ref{appendix:hann})
+\begin{equation}
+  H_L(k):=\mathds 1_{|k|<\frac L2}\cos^2({\textstyle\frac{\pi|k|}{L}})
+  .
+  \label{hann}
+\end{equation}
+The parameter $L$ is set using \refname{param:anyeq_window_L}{{\tt window\_L}}.
+The computation is changed only in that $g$ is changed to $H_L(k)g(k)$.
+
+\subsubsubsection{Momentum distribution}
 To compute the momentum distribution (see\-~\cite{CHe21}), we add a parameter $\lambda$ to {\tt anyeq}:
 \begin{equation}
   -\Delta u_\lambda(|x|)
@@ -1664,7 +2424,7 @@ To compute the momentum distribution (see\-~\cite{CHe21}), we add a parameter $\
 The momentum distribution is then
 \begin{equation}
   \mathcal M(q)=\partial_\lambda e|_{\lambda=0}
-  =-\frac\rho2\int dx\ v(x)\partial_\lambda u_\lambda(x)|_{\lambda=0}
+  =-\frac\rho2\int\frac{dk}{(2\pi)^3}\ \hat v(k)\partial_\lambda\hat u_\lambda(k)|_{\lambda=0}
   .
 \end{equation}
 Note that the Fourier transform of $2\lambda\hat u_0(q)\cos(q\cdot x)$ is
@@ -1673,52 +2433,118 @@ Note that the Fourier transform of $2\lambda\hat u_0(q)\cos(q\cdot x)$ is
   .
   \label{fouriermomentum}
 \end{equation}
-We compute $\partial_\lambda u_\lambda|_{\lambda=0}$ in the same way as the uncondensed fraction.
+The presence of delta functions does not play well with the Chebyshev polynomial expansion and the quadratures.
+\bigskip
+
+\point
+We will consider two different ways of getting around this.
 \bigskip
 
 \subpoint
-In order to do so we will discretize momentum space, see\-~(\ref{klj}), and so it is necessary to construct a discrete analog of the delta-functions in\-~(\ref{fouriermomentum}).
-The starting point we take is
+One idea is to compute a regularization of $\mathcal M(q)$ by convolving it with a peaked spherically symmetric function.
+Let $\Gamma_L$ denote the Gaussian with variance $1/\sqrt L$:
 \begin{equation}
-  \int dk f(k)\delta(k-q)
-  =f(q)
+  \Gamma_L(|k|):=\left(\frac{L}{2\pi}\right)^{\frac32}e^{-\frac L2k^2}
+  .
+  \label{anyeq_gaussian_momt}
+\end{equation}
+In fact, we will scale $L$ with $k$, and set $L$ to
+\begin{equation}
+  L=\sqrt{\mathrm{\refname{param:anyeq_momentum_window_L}{{\tt window\_L}}}}/k^2
+  .
+\end{equation}
+To compute
+\begin{equation}
+  \mathfrak M(q):=\mathcal M\ast\Gamma_L(q)
+\end{equation}
+we solve the equation
+\begin{equation}
+  -\Delta u_\lambda(|x|)
+  =
+  (1-u_\lambda(|x|))v(|x|)-2\rho K(|x|)+\rho^2 L(|x|)
+  -2\lambda\int dk\ \hat u_0(k)\cos(k\cdot x)\Gamma_L(q-k)
+  .
+\end{equation}
+Note that the Fourier transform of
+\begin{equation}
+  -2\lambda\int dk\ \hat u_0(k)\cos(k\cdot x)\Gamma_L(q-k)
+\end{equation}
+is
+\begin{equation}
+  -(2\pi)^3\lambda\hat u_0(q)(\Gamma_L(k+q)+\Gamma_L(k-q))
+  .
+\end{equation}
+Since the ground state is unique, $\mathcal M$ is spherically symmetric.
+The term $\Gamma_L(k\pm q)$ is not, so we take its spherical average (which will not change the final result): by lemma\-~\ref{lemma:bipolar},
+\begin{equation}
+  -\frac1{4\pi r^2}\int dq\ \delta(|q|-r)(2\pi)^3\lambda\hat u_0(q)(\Gamma_L(k+q)+\Gamma_L(k-q))
+  =
+  -\frac{(2\pi)^3}{|k|r}\lambda\hat u_0(r)\int_{||k|-r|}^{|k|+r} ds\ s\Gamma_L(s)
+  .
+\end{equation}
+In this setup, the approximation of the delta function is thus
+\begin{equation}
+  \tilde\delta(|k|,r):=
+  \frac1{2|k|r}\int_{||k|-r|}^{|k|+r} ds\ s\Gamma_L(s)
+  =
+  \frac{1}{2|k|rL}(\Gamma_L(|k|-r)-\Gamma_L(|k|+r))
+  .
+\end{equation}
+To choose this method, set \refname{param:anyeq_window_L}{{\tt window\_L}} to a finite value.
+\bigskip
+
+\subpoint
+Another approach is to contruct a discrete analog of the delta-functions in\-~(\ref{fouriermomentum}).
+The starting point we take is, for $q\neq0$,
+\begin{equation}
+  \int dk\ f(|k|)\delta(k-q)
+  \equiv\frac1{4\pi|q|^2}\int dk\ f(|k|)\delta(|k|-|q|)
+  =f(|q|)
 \end{equation}
 so, when approximating the integral according to\-~(\ref{anyeq_integration}), we find
 \begin{equation}
-  \frac{\pi^2}2\sum_{l=0}^{J-1}
+  \frac{\pi}{8|q|^2}\sum_{l=0}^{J-1}
   (\tau_{l+1}-\tau_l)
   \sum_{j=1}^Nw_j
-  \cos({\textstyle\frac{\pi x_j}2})(1+\vartheta_l(x_j))^2\vartheta_l^2(x_j)f(\vartheta_l(x_j))\tilde\delta(\vartheta_l(x_j)-k_{l',i})
-  \approx
-  f(k_{l',i})
+  \cos({\textstyle\frac{\pi x_j}2})(1+\vartheta_l(x_j))^2\vartheta_l^2(x_j)f(\vartheta_l(x_j))\tilde\delta(\vartheta_l(x_j),|q|)
+  =
+  f(|q|)
 \end{equation}
 where $\tilde\delta$ is the approximation of the delta-function.
 Since
 \begin{equation}
   \vartheta_l(x_j)\equiv k_{l,j}
 \end{equation}
-(see\-~(\ref{chebyshevvars})), we find that the definition of $\tilde\delta$ must be
+(see\-~(\ref{chebyshevvars})), we define the approximation of the delta function as
 \begin{equation}
-  \tilde\delta_{l,j;l',i}
+  \tilde\delta(k_{l,j},k_{l',i})
   :=
   \delta_{l,l'}\delta_{j,i}
-  \frac2{\pi^2}
+  \frac{8}{\pi}
   \left(
     (\tau_{l+1}-\tau_l)
     w_j
     \cos({\textstyle\frac{\pi x_j}2})
-    (1+k_{l,j})^2k_{l,j}^2
+    (1+k_{l,j})^2
   \right)^{-1}
   .
 \end{equation}
-Note that, due to the invariance under rotation, the approximation for $\delta(q+k)$ is equal to that for $\delta(q-k)$.
+To choose this method, set \refname{param:anyeq_window_L}{{\tt window\_L}} to {\tt Inf}.
+{\color{red}This method seems to yield some fairly poor results!}
 \bigskip
 
-\subpoint
-To compute the momentum distribution at $q=k_{l',i}$, we define $\Xi(\mathbb U,\lambda)$ by formally adding $-2(2\pi)^3\lambda \hat u_0(k_{l',i})\tilde\delta_{l,j;l',i}$ to $\mathbb G_{l,j}$, which corresponds to replacing $\mathbb Y$ with
+\point
+To compute the momentum distribution at $q$, we define $\Xi(\mathbb U,\lambda)$ by formally adding
+\begin{equation}
+  -2(2\pi)^3\lambda \hat u_0(|q|)\tilde\delta(k_{l,j},|q|)
+\end{equation}
+to $\mathbb G_{l,j}$, which corresponds to replacing $\mathbb Y$ with
 \begin{equation}
   \mathbb Y_{l,j}=
-  \frac1{\mathbb L_{l,j}}\left(\mathbb S_{l,j}-\mathbb L_{l,j}+\frac12\mathbb J_{l,j}+\mathbb G_{l,j}-2(2\pi)^3\lambda\frac{\mathbb U_{l',i}}\rho\tilde\delta_{l,j;l',i}\right)
+  \frac1{\mathbb L_{l,j}}\left(\mathbb S_{l,j}-\mathbb L_{l,j}+\frac12\mathbb J_{l,j}+\mathbb G_{l,j}
+  -2(2\pi)^3\lambda \hat u(|q|)\tilde\delta(k_{l,j},|q|)
+  \right)
+  .
 \end{equation}
 Then we solve $\Xi(\mathbb U,\lambda)=0$, and differentiate:
 \begin{equation}
@@ -1739,7 +2565,33 @@ Finally,
 with
 \begin{equation}
   \partial_\lambda\mathbb Y_{l,j}|_{\lambda=0}=
-  \frac{2(2\pi)^3\mathbb U_{l',i}\tilde\delta_{l,j;l',i}}{\rho\mathbb L_{l,j}}
+  -\frac{2(2\pi)^3}{\mathbb L_{l,j}} \hat u(|q|)\tilde\delta(k_{l,j},|q|)
+  .
+\end{equation}
+\bigskip
+
+\subsubsubsection{Compressibility}
+The compressibility is defined as
+\begin{equation}
+  \chi:=\frac1{\rho^2\partial_\rho^2(\rho e)}
+  =\frac1{\partial_{\log\rho}^2(\rho e)-\partial_{\log\rho}(\rho e)}
+  .
+\end{equation}
+We approximate these derivatives by finite differences:
+\begin{equation}
+  \partial_{\log\rho}^2 f(\rho)
+  \approx
+  \frac{f(\rho_{j+1})+f(\rho_{j-1})-2f(\rho_j)}{(\log\rho_{j+2}-\log\rho_{j+1})(\log\rho_{j+1}-\log\rho_j)}
+\end{equation}
+and
+\begin{equation}
+  \partial_{\log\rho} f(\rho)
+  \approx
+  \frac12\left(
+    \frac{f(\rho_{j+1})-f(\rho_j)}{\log\rho_{j+1}-\log\rho_{j}}
+    +
+    \frac{f(\rho_{j})-f(\rho_{j-1})}{\log\rho_{j}-\log\rho_{j-1}}
+  \right)
   .
 \end{equation}
 
@@ -1755,7 +2607,7 @@ The method is used to compute observables for the simple equation
   .
   \label{simpleq}
 \end{equation}
-One can show\-~\cite[Theorem\-~1.6]{CJL20b} that the condensate fraction is
+One can show\-~\cite[Theorem\-~1.6]{CJL21} that the condensate fraction is
 \begin{equation}
   \eta=\frac{\rho\int v(x)\mathfrak Ku(x)\ dx}{1-\rho\int v(x)\mathfrak K(2u(x)-\rho u\ast u(x))\ dx}
   \label{simpleq-Kv_eta}
@@ -1824,8 +2676,18 @@ The available {\tt commands} are the following.
     \item\makelink{param:simpleq-Kv_nlrho}{}
     {\tt nlrho} ({\tt Int64}, default: $100$): number of values for $\rho$ (spaced logarithmically).
 
+    \item\makelink{param:simpleq-Kv_minrho}{}
+    {\tt minrho} ({\tt Float64}, default: $10^{-6}$): minimal value for $\rho$.
+
+    \item\makelink{param:simpleq-Kv_maxrho}{}
+    {\tt maxrho} ({\tt Float64}, default: $10^{2}$): maximal value for $\rho$.
+
+    \item\makelink{param:simpleq-Kv_nrho}{}
+    {\tt nrho} ({\tt Int64}, default: $0$): number of values for $\rho$ (spaced linearly). If {\tt nrho} is $\neq0$, then the linear spacing will be used, and \refname{param:simpleq-Kv_minlrho}{{\tt minlrho}}, \refname{param:simpleq-Kv_maxlrho}{{\tt maxlrho}}, \refname{param:simpleq-Kv_nlrho}{{\tt nlrho}} will be ignored. Otherwise, the logarithmic spacing will be used and \refname{param:simpleq-Kv_minrho}{{\tt minrho}}, \refname{param:simpleq-Kv_maxrho}{{\tt maxrho}} will be ignored.
+
     \item\makelink{param:simpleq-Kv_rhos}{}
-    {\tt rhos} ({\tt Array\{Float64\}}, default: $(10^{{\tt minlrho}+\frac{{\tt maxlrho}-{\tt minlrho}}{{\tt nlrho}}n})_n$: list of values for $\rho$, specified as a `{\tt,}' separated list.
+    {\tt rhos} ({\tt Array\{Float64\}}): list of values for $\rho$, specified as a `{\tt,}' separated list.
+    This parameter takes precedence over \refname{param:simpleq-Kv_minlrho}{{\tt minlrho}}, \refname{param:simpleq-Kv_maxlrho}{{\tt maxlrho}}, \refname{param:simpleq-Kv_nlrho}{{\tt nlrho}}, \refname{param:simpleq-Kv_minrho}{{\tt minrho}}, \refname{param:simpleq-Kv_maxrho}{{\tt maxrho}}, \refname{param:simpleq-Kv_nrho}{{\tt nrho}}.
   \end{itemize}
   \underline{Output} (one line for each value of $\rho$): [$\rho$] [$\eta$]
 
@@ -1980,8 +2842,18 @@ The available {\tt commands} are the following.
     \item\makelink{param:simpleq-hardcore_nlrho}{}
     {\tt nlrho} ({\tt Int64}, default: $100$): number of values for $\rho$ (spaced logarithmically).
 
+    \item\makelink{param:simpleq-hardcore_minrho}{}
+    {\tt minrho} ({\tt Float64}, default: $10^{-6}$): minimal value for $\rho$.
+
+    \item\makelink{param:simpleq-hardcore_maxrho}{}
+    {\tt maxrho} ({\tt Float64}, default: $10^{2}$): maximal value for $\rho$.
+
+    \item\makelink{param:simpleq-hardcore_nrho}{}
+    {\tt nrho} ({\tt Int64}, default: $0$): number of values for $\rho$ (spaced linearly). If {\tt nrho} is $\neq0$, then the linear spacing will be used, and \refname{param:simpleq-hardcore_minlrho}{{\tt minlrho}}, \refname{param:simpleq-hardcore_maxlrho}{{\tt maxlrho}}, \refname{param:simpleq-hardcore_nlrho}{{\tt nlrho}} will be ignored. Otherwise, the logarithmic spacing will be used and \refname{param:simpleq-hardcore_minrho}{{\tt minrho}}, \refname{param:simpleq-hardcore_maxrho}{{\tt maxrho}} will be ignored.
+
     \item\makelink{param:simpleq-hardcore_rhos}{}
-    {\tt rhos} ({\tt Array\{Float64\}}, default: $(10^{{\tt minlrho}+\frac{{\tt maxlrho}-{\tt minlrho}}{{\tt nlrho}}n})_n$: list of values for $\rho$, specified as a `{\tt,}' separated list.
+    {\tt rhos} ({\tt Array\{Float64\}}): list of values for $\rho$, specified as a `{\tt,}' separated list.
+    This parameter takes precedence over \refname{param:simpleq-hardcore_minlrho}{{\tt minlrho}}, \refname{param:simpleq-hardcore_maxlrho}{{\tt maxlrho}}, \refname{param:simpleq-hardcore_nlrho}{{\tt nlrho}}, \refname{param:simpleq-hardcore_minrho}{{\tt minrho}}, \refname{param:simpleq-hardcore_maxrho}{{\tt maxrho}}, \refname{param:simpleq-hardcore_nrho}{{\tt nrho}}.
   \end{itemize}
   \underline{Output} (one line for each value of $\rho$): [$\rho$] [$e$] [Newton error $\epsilon$].\par
   \underline{Multithread support}: yes, different values of $\rho$ split up among workers.
@@ -2023,7 +2895,7 @@ In order to carry out the computation of the solution of\-~(\ref{linearhc}) and
 for $|x|>1$.
 \bigskip
 
-\point{\bf Energy.}
+\subsubsubsection{Energy}
 To compute the energy $e$ of this equation, with the extra parameter $\mu$, we consider the limit of the soft sphere potential $\lambda\mathds 1_{|x|<1}$ (see\-~(\ref{easyeq}) with $\beta_K=\beta_L=0$):
 \begin{equation}
   (-\Delta+2\mu+4e)u(x)=s_\lambda(x)+2e\rho u\ast u
@@ -2056,7 +2928,7 @@ Therefore,
 \end{equation}
 \bigskip
 
-\point{\bf Integral equation.}
+\subsubsubsection{Integral equation}
 We turn the differential equation in\-~(\ref{hardcore_simple}) into an integral equation.
 Let
 \begin{equation}
@@ -2115,7 +2987,7 @@ We change variables in the last integral:
 \end{equation}
 \bigskip
 
-\point{\bf The auto-convolution term.} We split
+\subsubsubsection{The auto-convolution term} We split
 \begin{equation}
   u(r)=\mathds 1_{r>1}u_+(r-1)+\mathds 1_{r\leqslant 1}
   .
@@ -2253,7 +3125,7 @@ where
 is the same as is defined for \refname{sec:anyeq}{{\tt anyeq}} (\ref{anyeq_alpha}).
 \bigskip
 
-\point{\bf Chebyshev polynomial expansion.} We use the same interpolation as we used in \refname{sec:anyeq}{{\tt anyeq}}: (\ref{a_chebyshev_spline})
+\subsubsubsection{Chebyshev polynomial expansion} We use the same interpolation as we used in \refname{sec:anyeq}{{\tt anyeq}}: (\ref{a_chebyshev_spline})
 \begin{equation}
   \frac1{(1+\tau)^{\nu_u}}u_+({\textstyle\frac{1-\tau}{1+\tau}})=\sum_{l=0}^{J-1}\mathds 1_{\tau_l\leqslant\tau<\tau_{l+1}}\sum_{n=0}^\infty \mathbf F_{l,n}^{(\nu_u)}(u_+) T_n({\textstyle\frac{2\tau-(\tau_l+\tau_{l+1})}{\tau_{l+1}-\tau_l}})
   \label{a_chebyshev_spline}
@@ -2343,10 +3215,10 @@ and
 \end{equation}
 \bigskip
 
-\point{\bf Energy.} We now compute the approximation for the energy, using\-~(\ref{hardcore_energy}).
+\subsubsubsection{Energy} We now compute the approximation for the energy, using\-~(\ref{hardcore_energy}).
 \bigskip
 
-\subpoint We start with $\partial u|_{|x|\searrow 1}$.
+\point We start with $\partial u|_{|x|\searrow 1}$.
 By\-~(\ref{uinteq}),
 \begin{equation}
   \partial u|_{|x|\searrow 1}
@@ -2386,7 +3258,7 @@ and
   .
 \end{equation}
 
-\subpoint Let us now turn to $\int_{|x|<1}dx\ u\ast u(x)$.
+\point Let us now turn to $\int_{|x|<1}dx\ u\ast u(x)$.
 We have
 \begin{equation}
   \int_{|x|<1}dx\ u\ast u(x)
@@ -2421,7 +3293,7 @@ and
 \end{equation}
 \bigskip
 
-\subpoint Thus,
+\point Thus,
 \begin{equation}
   \begin{largearray}
     e=2\pi\rho
@@ -2452,11 +3324,11 @@ and
 \end{equation}
 \bigskip
 
-\point{\bf Newton algorithm.}
+\subsubsubsection{Newton algorithm}
 In this paragraph, we set $\epsilon=e$, that is, $\mu=0$.
 \bigskip
 
-\subpoint
+\point
 As we did for \refname{sec:anyeq}{{\tt anyeq}}, we discretize the integral in\-~(\ref{bfF}) by using a Gauss-Legendre quadrature, and truncate the sum over Chebyshev polynomials to order \refname{param:simpleq-hardcore_P}{{\tt P}}.
 We then reduce the computation to a finite system of equations, whose variables are
 \begin{equation}
@@ -2519,7 +3391,7 @@ Gauss-Legendre and Gauss-Laguerre quadratures and their errors are discussed in
 The orders of the quadratures are given by the variable \refname{param:simpleq-hardcore_N}{{\tt N}}.
 \bigskip
 
-\subpoint
+\point
 We then solve $\Xi=0$ using the Newton algorithm, that is, we define a sequence of $\mathbb U$'s:
 \begin{equation}
   \mathbb U^{(n+1)}=\mathbb U^{(n)}-(D\Xi(\mathbb U^{(n)}))^{-1}\Xi(\mathbb U^{(n)})
@@ -2538,7 +3410,7 @@ We initialize the algorithm with
 \end{equation}
 \bigskip
 
-\subpoint
+\point
 We are left with computing the Jacobian of $\Xi$:
 \begin{equation}
   \begin{largearray}
@@ -2709,7 +3581,7 @@ and
 Finally, to get from $D$ to $\mathbb D$ and $\gamma$ to $\mathfrak g$, we approximate the integrate using Gauss-Legendre and Gauss-Laguerre quadratures (see appendix\-~\ref{appGL}), as described above.
 \bigskip
 
-\subpoint
+\point
 We iterate the Newton algorithm until the Newton relative error $\epsilon$ becomes smaller than the \refname{param:easyeq_tolerance}{{\tt tolerance}} parameter.
 The Newton error is defined as
 \begin{equation}
@@ -2723,7 +3595,7 @@ The energy thus obtained is
 \end{equation}
 \bigskip
 
-\point{\bf Condensate fraction.}
+\subsubsubsection{Condensate fraction}
 To compute the condensate fraction, we use the parameter $\mu$ in\-~(\ref{hardcore_simple}).
 The uncondensed fraction is
 \begin{equation}
@@ -3213,7 +4085,7 @@ In $x$-space
   \frac{12c( x^6b^6(2e-b^2) +b^4x^4(9e-7b^2) +4b^2x^2(3e-2b^2) +(5e+16b^2))}{(1+b^2x^2)^2(4+b^2x^2)^2((1+b^2x^2)^2-c)}
 \end{equation}
 The constants $a,b,c,e$ can be set using the parameters {\tt v\_a}, {\tt v\_b}, {\tt v\_c}, {\tt v\_e}, and $a,e\in\mathbb R$, $b\neq0$, $c>0$, $c\neq9$.
-Note that $v\geqslant 0$ if and only if\-~\cite{CJL20b}
+Note that $v\geqslant 0$ if and only if\-~\cite{CJL21}
 \begin{equation}
   b>0
   ,\quad
diff --git a/src/anyeq.jl b/src/anyeq.jl
index 8459cb0..fee77d8 100644
--- a/src/anyeq.jl
+++ b/src/anyeq.jl
@@ -1,4 +1,4 @@
-## Copyright 2021 Ian Jauslin
+## Copyright 2021-2023 Ian Jauslin
 ## 
 ## Licensed under the Apache License, Version 2.0 (the "License");
 ## you may not use this file except in compliance with the License.
@@ -28,24 +28,26 @@
 end
 
 # compute energy for a given rho
-# use minlrho, nlrho to incrementally compute the solution to medeq (to initialize the Newton algorithm)
-function anyeq_energy(rho,minlrho,nlrho,taus,P,N,J,a0,v,maxiter,tolerance,approx,savefile)
+function anyeq_energy(
+  rho::Float64,
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  approx::Anyeq_approx,
+  savefile::String
+)
   # initialize vectors
-  (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v)
-  # init Abar
-  if savefile!=""
-    Abar=anyeq_read_Abar(savefile,P,N,J)
-  else
-    Abar=anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx)
-  end
+  (weights,T,k,V,V0,A,Abar,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v,approx,savefile)
 
   # compute initial guess from medeq
-  rhos=Array{Float64}(undef,nlrho)
-  for j in 0:nlrho-1
-    rhos[j+1]=(nlrho==1 ? rho : 10^(minlrho+(log10(rho)-minlrho)/(nlrho-1)*j))
-  end
-  u0s=anyeq_init_medeq(rhos,N,J,k,a0,v,maxiter,tolerance)
-  u0=u0s[nlrho]
+  u0=anyeq_init_medeq([rho],minlrho_init,nlrho_init,N,J,k,a0,v,maxiter,tolerance)
 
   (u,E,error)=anyeq_hatu(u0,P,N,J,rho,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx)
 
@@ -53,72 +55,60 @@ function anyeq_energy(rho,minlrho,nlrho,taus,P,N,J,a0,v,maxiter,tolerance,approx
 end
 
 # compute energy as a function of rho
-function anyeq_energy_rho(rhos,taus,P,N,J,a0,v,maxiter,tolerance,approx,savefile)
+function anyeq_energy_rho(
+  rhos::Array{Float64,1},
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  approx::Anyeq_approx,
+  savefile::String
+)
   # initialize vectors
-  (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v)
-
-  # init Abar
-  if savefile!=""
-    Abar=anyeq_read_Abar(savefile,P,N,J)
-  else
-    Abar=anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx)
-  end
+  (weights,T,k,V,V0,A,Abar,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v,approx,savefile)
 
   # compute initial guess from medeq
-  u0s=anyeq_init_medeq(rhos,N,J,k,a0,v,maxiter,tolerance)
+  u0s=anyeq_init_medeq(rhos,minlrho_init,nlrho_init,N,J,k,a0,v,maxiter,tolerance)
 
-  # save result from each task
-  es=Array{Float64,1}(undef,length(rhos))
-  err=Array{Float64,1}(undef,length(rhos))
+  # spawn workers
+  work=spawn_workers(length(rhos))
 
-  ## spawn workers
-  # number of workers
-  nw=nworkers()
-  # split jobs among workers
-  work=Array{Array{Int64,1},1}(undef,nw)
-  # init empty arrays
-  for p in 1:nw
-    work[p]=zeros(0)
-  end
-  for j in 1:length(rhos)
-    append!(work[(j-1)%nw+1],j)
-  end
-
-  count=0
-  # for each worker
-  @sync for p in 1:nw
-    # for each task
-    @async for j in work[p]
-      count=count+1
-      if count>=nw
-        progress(count,length(rhos),10000)
-      end
-      # run the task
-      (u,es[j],err[j])=remotecall_fetch(anyeq_hatu,workers()[p],u0s[j],P,N,J,rhos[j],a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx)
-    end
-  end
+  # compute u
+  (us,es,errs)=anyeq_hatu_rho_multithread(u0s,P,N,J,rhos,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx,work)
 
   for j in 1:length(rhos)
-    @printf("% .15e % .15e % .15e\n",rhos[j],es[j],err[j])
+    @printf("% .15e % .15e % .15e\n",rhos[j],es[j],errs[j])
   end
 end
 
 # compute energy as a function of rho
 # initialize with previous rho
-function anyeq_energy_rho_init_prevrho(rhos,taus,P,N,J,a0,v,maxiter,tolerance,approx,savefile)
+function anyeq_energy_rho_init_prevrho(
+  rhos::Array{Float64,1},
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  approx::Anyeq_approx,
+  savefile::String
+)
   # initialize vectors
-  (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v)
-
-  # init Abar
-  if savefile!=""
-    Abar=anyeq_read_Abar(savefile,P,N,J)
-  else
-    Abar=anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx)
-  end
+  (weights,T,k,V,V0,A,Abar,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v,approx,savefile)
 
   # compute initial guess from medeq
-  u0s=anyeq_init_medeq([rhos[1]],N,J,k,a0,v,maxiter,tolerance)
-  u=u0s[1]
+  u=anyeq_init_medeq([rhos[1]],minlrho_init,nlrho_init,N,J,k,a0,v,maxiter,tolerance)
 
   for j in 1:length(rhos)
     progress(j,length(rhos),10000)
@@ -134,20 +124,26 @@ function anyeq_energy_rho_init_prevrho(rhos,taus,P,N,J,a0,v,maxiter,tolerance,ap
 end
 # compute energy as a function of rho
 # initialize with next rho
-function anyeq_energy_rho_init_nextrho(rhos,taus,P,N,J,a0,v,maxiter,tolerance,approx,savefile)
+function anyeq_energy_rho_init_nextrho(
+  rhos::Array{Float64,1},
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  approx::Anyeq_approx,
+  savefile::String
+)
   # initialize vectors
-  (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v)
-
-  # init Abar
-  if savefile!=""
-    Abar=anyeq_read_Abar(savefile,P,N,J)
-  else
-    Abar=anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx)
-  end
+  (weights,T,k,V,V0,A,Abar,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v,approx,savefile)
 
   # compute initial guess from medeq
-  u0s=anyeq_init_medeq([rhos[length(rhos)]],N,J,k,a0,v,maxiter,tolerance)
-  u=u0s[1]
+  u=anyeq_init_medeq([rhos[length(rhos)]],minlrho_init,nlrho_init,N,J,k,a0,v,maxiter,tolerance)
 
   for j in 1:length(rhos)
     progress(j,length(rhos),10000)
@@ -163,23 +159,26 @@ function anyeq_energy_rho_init_nextrho(rhos,taus,P,N,J,a0,v,maxiter,tolerance,ap
 end
 
 # compute u(k)
-# use minlrho, nlrho to incrementally compute the solution to medeq (to initialize the Newton algorithm)
-function anyeq_uk(minlrho,nlrho,taus,P,N,J,rho,a0,v,maxiter,tolerance,approx,savefile)
+function anyeq_uk(
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  rho::Float64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  approx::Anyeq_approx,
+  savefile::String
+)
   # init vectors
-  (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v)
-  # init Abar
-  if savefile!=""
-    Abar=anyeq_read_Abar(savefile,P,N,J)
-  else
-    Abar=anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx)
-  end
+  (weights,T,k,V,V0,A,Abar,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v,approx,savefile)
+
   # compute initial guess from medeq
-  rhos=Array{Float64}(undef,nlrho)
-  for j in 0:nlrho-1
-    rhos[j+1]=(nlrho==1 ? rho : 10^(minlrho+(log10(rho)-minlrho)/(nlrho-1)*j))
-  end
-  u0s=anyeq_init_medeq(rhos,N,J,k,a0,v,maxiter,tolerance)
-  u0=u0s[nlrho]
+  u0=anyeq_init_medeq([rho],minlrho_init,nlrho_init,N,J,k,a0,v,maxiter,tolerance)
 
   (u,E,error)=anyeq_hatu(u0,P,N,J,rho,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx)
 
@@ -192,58 +191,60 @@ function anyeq_uk(minlrho,nlrho,taus,P,N,J,rho,a0,v,maxiter,tolerance,approx,sav
 end
 
 # compute u(x)
-# use minlrho, nlrho to incrementally compute the solution to medeq (to initialize the Newton algorithm)
-function anyeq_ux(minlrho,nlrho,taus,P,N,J,rho,a0,v,maxiter,tolerance,xmin,xmax,nx,approx,savefile)
+function anyeq_ux(
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  rho::Float64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  xmin::Float64,
+  xmax::Float64,
+  nx::Int64,
+  approx::Anyeq_approx,
+  savefile::String
+)
   # init vectors
-  (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v)
-  # init Abar
-  if savefile!=""
-    Abar=anyeq_read_Abar(savefile,P,N,J)
-  else
-    Abar=anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx)
-  end
+  (weights,T,k,V,V0,A,Abar,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v,approx,savefile)
 
   # compute initial guess from medeq
-  rhos=Array{Float64}(undef,nlrho)
-  for j in 0:nlrho-1
-    rhos[j+1]=(nlrho==1 ? rho : 10^(minlrho+(log10(rho)-minlrho)/(nlrho-1)*j))
-  end
-  u0s=anyeq_init_medeq(rhos,N,J,k,a0,v,maxiter,tolerance)
-  u0=u0s[nlrho]
+  u0=anyeq_init_medeq([rho],minlrho_init,nlrho_init,N,J,k,a0,v,maxiter,tolerance)
 
   (u,E,error)=anyeq_hatu(u0,P,N,J,rho,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx)
 
   for i in 1:nx
     x=xmin+(xmax-xmin)*i/nx
-    ux=0.
-    for zeta in 0:J-1
-      for j in 1:N
-	ux+=(taus[zeta+2]-taus[zeta+1])/(16*pi*x)*weights[2][j]*cos(pi*weights[1][j]/2)*(1+k[zeta*N+j])^2*k[zeta*N+j]*u[zeta*N+j]*sin(k[zeta*N+j]*x)
-      end
-    end
-    @printf("% .15e % .15e % .15e\n",x,real(ux),imag(ux))
+    ux=anyeq_u_x(x,u,k,N,J,taus,weights)
+    @printf("% .15e % .15e % .15e\n",x,ux,)
   end
 end
 
 # compute condensate fraction for a given rho
-# use minlrho, nlrho to incrementally compute the solution to medeq (to initialize the Newton algorithm)
-function anyeq_condensate_fraction(rho,minlrho,nlrho,taus,P,N,J,a0,v,maxiter,tolerance,approx,savefile)
+function anyeq_condensate_fraction(
+  rho::Float64,
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  approx::Anyeq_approx,
+  savefile::String
+)
   # initialize vectors
-  (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v)
-  # init Abar
-  if savefile!=""
-    Abar=anyeq_read_Abar(savefile,P,N,J)
-  else
-    Abar=anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx)
-  end
+  (weights,T,k,V,V0,A,Abar,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v,approx,savefile)
 
   # compute initial guess from medeq
-  rhos=Array{Float64}(undef,nlrho)
-  for j in 0:nlrho-1
-    rhos[j+1]=(nlrho==1 ? rho : 10^(minlrho+(log10(rho)-minlrho)/(nlrho-1)*j))
-  end
-  u0s=anyeq_init_medeq(rhos,N,J,k,a0,v,maxiter,tolerance)
-  u0=u0s[nlrho]
+  u0=anyeq_init_medeq([rho],minlrho_init,nlrho_init,N,J,k,a0,v,maxiter,tolerance)
 
   (u,E,error)=anyeq_hatu(u0,P,N,J,rho,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx)
 
@@ -254,62 +255,46 @@ function anyeq_condensate_fraction(rho,minlrho,nlrho,taus,P,N,J,a0,v,maxiter,tol
 end
 
 # condensate fraction as a function of rho
-function anyeq_condensate_fraction_rho(rhos,taus,P,N,J,a0,v,maxiter,tolerance,approx,savefile)
-  ## spawn workers
-  # number of workers
-  nw=nworkers()
-  # split jobs among workers
-  work=Array{Array{Int64,1},1}(undef,nw)
-  # init empty arrays
-  for p in 1:nw
-    work[p]=zeros(0)
-  end
-  for j in 1:length(rhos)
-    append!(work[(j-1)%nw+1],j)
-  end
+function anyeq_condensate_fraction_rho(
+  rhos::Array{Float64,1},
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  approx::Anyeq_approx,
+  savefile::String
+)
+  # spawn workers
+  work=spawn_workers(length(rhos))
 
   # initialize vectors
-  (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v)
-  # init Abar
-  if savefile!=""
-    Abar=anyeq_read_Abar(savefile,P,N,J)
-  else
-    Abar=anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx)
-  end
+  (weights,T,k,V,V0,A,Abar,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v,approx,savefile)
 
   # compute initial guess from medeq
-  u0s=anyeq_init_medeq(rhos,N,J,k,a0,v,maxiter,tolerance)
+  u0s=anyeq_init_medeq(rhos,minlrho_init,nlrho_init,N,J,k,a0,v,maxiter,tolerance)
 
   # compute u
-  us=Array{Array{Float64,1}}(undef,length(rhos))
-  errs=Array{Float64,1}(undef,length(rhos))
-  count=0
-  # for each worker
-  @sync for p in 1:nw
-    # for each task
-    @async for j in work[p]
-      count=count+1
-      if count>=nw
-	progress(count,length(rhos),10000)
-      end
-      # run the task
-      (us[j],E,errs[j])=remotecall_fetch(anyeq_hatu,workers()[p],u0s[j],P,N,J,rhos[j],a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx)
-    end
-  end
+  (us,es,errs)=anyeq_hatu_rho_multithread(u0s,P,N,J,rhos,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx,work)
 
   # compute eta
-  etas=Array{Float64}(undef,length(rhos))
+  etas=Array{Float64,1}(undef,length(rhos))
   count=0
   # for each worker
-  @sync for p in 1:nw
+  @sync for p in 1:length(work)
     # for each task
     @async for j in work[p]
       count=count+1
-      if count>=nw
+      if count>=length(work)
 	progress(count,length(rhos),10000)
       end
       # run the task
-      etas[j]=anyeq_eta(us[j],P,N,J,rhos[j],weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx)
+      etas[j]=remotecall_fetch(anyeq_eta,workers()[p],us[j],P,N,J,rhos[j],weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx)
     end
   end
 
@@ -319,56 +304,78 @@ function anyeq_condensate_fraction_rho(rhos,taus,P,N,J,a0,v,maxiter,tolerance,ap
 end
 
 # compute the momentum distribution for a given rho
-# use minlrho, nlrho to incrementally compute the solution to medeq (to initialize the Newton algorithm)
-function anyeq_momentum_distribution(rho,minlrho,nlrho,taus,P,N,J,a0,v,maxiter,tolerance,approx,savefile)
+function anyeq_momentum_distribution(
+  kmin::Float64,
+  kmax::Float64,
+  rho::Float64,
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  windowL::Float64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  approx::Anyeq_approx,
+  savefile::String
+)
   # initialize vectors
-  (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v)
-  # init Abar
-  if savefile!=""
-    Abar=anyeq_read_Abar(savefile,P,N,J)
-  else
-    Abar=anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx)
-  end
+  (weights,T,k,V,V0,A,Abar,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v,approx,savefile)
 
   # compute initial guess from medeq
-  rhos=Array{Float64}(undef,nlrho)
-  for j in 0:nlrho-1
-    rhos[j+1]=(nlrho==1 ? rho : 10^(minlrho+(log10(rho)-minlrho)/(nlrho-1)*j))
-  end
-  u0s=anyeq_init_medeq(rhos,N,J,k,a0,v,maxiter,tolerance)
-  u0=u0s[nlrho]
+  u0=anyeq_init_medeq([rho],minlrho_init,nlrho_init,N,J,k,a0,v,maxiter,tolerance)
 
   (u,E,error)=anyeq_hatu(u0,P,N,J,rho,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx)
 
   # compute M
-  M=anyeq_momentum(u,P,N,J,rho,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx)
+  if windowL==Inf
+    # use discrete approximation of delta function
+    M=anyeq_momentum_discrete_delta(kmin,kmax,u,P,N,J,rho,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx)
+  else
+    M=anyeq_momentum_window(kmin,kmax,u,P,N,J,windowL,rho,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx)
+  end
 
+  # order k's in increasing order
   for zeta in 0:J-1
     for j in 1:N
-      # order k's in increasing order
-      @printf("% .15e % .15e\n",k[(J-1-zeta)*N+j],M[(J-1-zeta)*N+j])
+      q=k[(J-1-zeta)*N+j]
+      # drop if not in k interval
+      if q<kmin || q>kmax
+	continue
+      end
+      @printf("% .15e % .15e\n",q,M[(J-1-zeta)*N+j])
     end
   end
 end
 
 # 2 point correlation function
-function anyeq_2pt_correlation(minlrho,nlrho,taus,P,N,J,windowL,rho,a0,v,maxiter,tolerance,xmin,xmax,nx,approx,savefile)
+function anyeq_2pt_correlation(
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  windowL::Float64,
+  rho::Float64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  xmin::Float64,
+  xmax::Float64,
+  nx::Int64,
+  approx::Anyeq_approx,
+  savefile::String
+)
   # init vectors
-  (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v)
-  # init Abar
-  if savefile!=""
-    Abar=anyeq_read_Abar(savefile,P,N,J)
-  else
-    Abar=anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx)
-  end
+  (weights,T,k,V,V0,A,Abar,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v,approx,savefile)
 
   # compute initial guess from medeq
-  rhos=Array{Float64}(undef,nlrho)
-  for j in 0:nlrho-1
-    rhos[j+1]=(nlrho==1 ? rho : 10^(minlrho+(log10(rho)-minlrho)/(nlrho-1)*j))
-  end
-  u0s=anyeq_init_medeq(rhos,N,J,k,a0,v,maxiter,tolerance)
-  u0=u0s[nlrho]
+  u0=anyeq_init_medeq([rho],minlrho_init,nlrho_init,N,J,k,a0,v,maxiter,tolerance)
 
   # compute u and some useful integrals
   (u,E,error)=anyeq_hatu(u0,P,N,J,rho,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx)
@@ -376,39 +383,443 @@ function anyeq_2pt_correlation(minlrho,nlrho,taus,P,N,J,windowL,rho,a0,v,maxiter
 
   for i in 1:nx
     x=xmin+(xmax-xmin)*i/nx
-    C2=anyeq_2pt(x,u,P,N,J,windowL,rho,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx,S,E,II,JJ,X,Y,sL1,sK,G)
+    C2=anyeq_2pt(x,u,P,N,J,windowL,rho,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx,S,E,II,JJ,X,Y,sL1,sK)
     @printf("% .15e % .15e\n",x,C2)
   end
 end
 
+# maximum of 2 point correlation function
+function anyeq_2pt_correlation_max(
+  rho::Float64,
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  dx::Float64,
+  x0::Float64, # initial guess is x0/rho^(1/3)
+  maxstep::Float64,
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  windowL::Float64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  tolerance_max::Float64,
+  approx::Anyeq_approx,
+  savefile::String
+)
+  # init vectors
+  (weights,T,k,V,V0,A,Abar,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v,approx,savefile)
+
+  # compute initial guess from medeq
+  u0=anyeq_init_medeq([rho],minlrho_init,nlrho_init,N,J,k,a0,v,maxiter,tolerance)
+
+  # initial guess
+  x0=1/rho^(1/3)
+
+  (u,E,error)=anyeq_hatu(u0,P,N,J,rho,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx)
+  (x,f)=anyeq_2pt_max(u,P,N,J,x0/rho^(1/3),dx,maxstep,maxiter,tolerance_max,windowL,rho,weights,T,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx)
+
+  if(x==Inf)
+    @printf(stderr,"max search failed for rho=%e\n",rho)
+  else
+    @printf("% .15e % .15e\n",x,f)
+  end
+end
+
+# maximum of 2 point correlation function as a function of rho
+function anyeq_2pt_correlation_max_rho(
+  rhos::Array{Float64,1},
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  dx::Float64,
+  x0::Float64, # initial guess is x0/rho^(1/3)
+  maxstep::Float64,
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  windowL::Float64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  tolerance_max::Float64,
+  approx::Anyeq_approx,
+  savefile::String
+)
+  # init vectors
+  (weights,T,k,V,V0,A,Abar,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v,approx,savefile)
+
+  # compute initial guess from medeq
+  u0s=anyeq_init_medeq(rhos,minlrho_init,nlrho_init,N,J,k,a0,v,maxiter,tolerance)
+
+  # save result from each task
+  xs=Array{Float64,1}(undef,length(rhos))
+  fs=Array{Float64,1}(undef,length(rhos))
+
+  # spawn workers
+  work=spawn_workers(length(rhos))
+
+  # compute u
+  (us,es,errs)=anyeq_hatu_rho_multithread(u0s,P,N,J,rhos,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx,work)
+
+  count=0
+  # for each worker
+  @sync for p in 1:length(work)
+    # for each task
+    @async for j in work[p]
+      count=count+1
+      if count>=length(work)
+        progress(count,length(rhos),10000)
+      end
+      # run the task
+      (xs[j],fs[j])=remotecall_fetch(anyeq_2pt_max,workers()[p],us[j],P,N,J,x0/rhos[j]^(1/3),dx,maxstep,maxiter,tolerance_max,windowL,rhos[j],weights,T,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx)
+    end
+  end
+
+  for j in 1:length(rhos)
+    if(xs[j]==Inf)
+      @printf(stderr,"max search failed for rho=%e\n",rhos[j])
+    else
+      @printf("% .15e % .15e % .15e\n",rhos[j],xs[j],fs[j])
+    end
+  end
+end
+
+# Correlation function in Fourier space
+function anyeq_2pt_correlation_fourier(
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  windowL::Float64,
+  rho::Float64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  kmin::Float64,
+  kmax::Float64,
+  nk::Int64,
+  approx::Anyeq_approx,
+  savefile::String
+)
+  # init vectors
+  (weights,T,k,V,V0,A,Abar,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v,approx,savefile)
+
+  # compute initial guess from medeq
+  u0=anyeq_init_medeq([rho],minlrho_init,nlrho_init,N,J,k,a0,v,maxiter,tolerance)
+
+  # compute u and some useful integrals
+  (u,E,error)=anyeq_hatu(u0,P,N,J,rho,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx)
+  (S,E,II,JJ,X,Y,sL1,sK,G)=anyeq_SEIJGXY(rho*u,rho,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx)
+
+  # save result from each task
+  C2s=Array{Float64,1}(undef,nk)
+
+  # spawn workers
+  work=spawn_workers(nk)
+
+  count=0
+  # for each worker
+  @sync for p in 1:length(work)
+    # for each task
+    @async for j in work[p]
+      count=count+1
+      if count>=length(work)
+        progress(count,nk,10000)
+      end
+      # run the task
+      C2s[j]=remotecall_fetch(anyeq_2pt_fourier,workers()[p],kmin+(kmax-kmin)*j/nk,u,P,N,J,windowL,rho,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx,S,E,II,JJ,X,Y,sL1,sK)
+    end
+  end
+
+  for j in 1:nk
+    @printf("% .15e % .15e\n",kmin+(kmax-kmin)*j/nk,C2s[j])
+  end
+end
+
+# maximum of Fourier transform of 2 point correlation function
+function anyeq_2pt_correlation_fourier_max(
+  rho::Float64,
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  dk::Float64,
+  k0::Float64, # initial guess is k0*rho^(1/3)
+  maxstep::Float64,
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  windowL::Float64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  tolerance_max::Float64,
+  approx::Anyeq_approx,
+  savefile::String
+)
+  # init vectors
+  (weights,T,k,V,V0,A,Abar,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v,approx,savefile)
+
+  # compute initial guess from medeq
+  u0=anyeq_init_medeq([rho],minlrho_init,nlrho_init,N,J,k,a0,v,maxiter,tolerance)
+
+  # compute u
+  (u,E,error)=anyeq_hatu(u0,P,N,J,rho,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx)
+  (ko,f)=anyeq_2pt_fourier_max(u,P,N,J,k0*rho^(1/3),dk,maxstep,maxiter,tolerance_max,windowL,rho,weights,T,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx)
+
+  if(ko==Inf)
+    @printf(stderr,"max search failed for rho=%e\n",rho)
+  else
+    @printf("% .15e % .15e\n",ko,f)
+  end
+end
+
+# maximum of fourier transform of 2 point correlation function as a function of rho
+function anyeq_2pt_correlation_fourier_max_rho(
+  rhos::Array{Float64,1},
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  dk::Float64,
+  k0::Float64, # initial guess is k0*rho^(1/3)
+  maxstep::Float64,
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  windowL::Float64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  tolerance_max::Float64,
+  approx::Anyeq_approx,
+  savefile::String
+)
+  # init vectors
+  (weights,T,k,V,V0,A,Abar,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v,approx,savefile)
+
+  # compute initial guess from medeq
+  u0s=anyeq_init_medeq(rhos,minlrho_init,nlrho_init,N,J,k,a0,v,maxiter,tolerance)
+
+  # spawn workers
+  work=spawn_workers(length(rhos))
+
+  # compute u
+  (us,es,errs)=anyeq_hatu_rho_multithread(u0s,P,N,J,rhos,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx,work)
+
+  # save result from each task
+  ks=Array{Float64,1}(undef,length(rhos))
+  fs=Array{Float64,1}(undef,length(rhos))
+
+  count=0
+  # for each worker
+  @sync for p in 1:length(work)
+    # for each task
+    @async for j in work[p]
+      count=count+1
+      if count>=length(work)
+        progress(count,length(rhos),10000)
+      end
+      # run the task
+      (ks[j],fs[j])=remotecall_fetch(anyeq_2pt_fourier_max,workers()[p],us[j],P,N,J,k0*rhos[j]^(1/3),dk,maxstep,maxiter,tolerance_max,windowL,rhos[j],weights,T,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx)
+    end
+  end
+
+  for j in 1:length(rhos)
+    if(ks[j]==Inf)
+      @printf(stderr,"max search failed for rho=%e\n",rhos[j])
+    else
+      @printf("% .15e % .15e % .15e\n",rhos[j],ks[j],fs[j])
+    end
+  end
+end
+
+# uncondensed 2 point correlation function
+function anyeq_uncondensed_2pt_correlation(
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  windowL::Float64,
+  rho::Float64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  xmin::Float64,
+  xmax::Float64,
+  nx::Int64,
+  approx::Anyeq_approx,
+  savefile::String
+)
+  # init vectors
+  (weights,T,k,V,V0,A,Abar,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v,approx,savefile)
+
+  # compute initial guess from medeq
+  u0=anyeq_init_medeq([rho],minlrho_init,nlrho_init,N,J,k,a0,v,maxiter,tolerance)
+
+  # compute u and some useful integrals
+  (u,E,error)=anyeq_hatu(u0,P,N,J,rho,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx)
+  (S,E,II,JJ,X,Y,sL1,sK,G)=anyeq_SEIJGXY(rho*u,rho,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx)
+
+  # compute u(xi)
+  uxis=Array{Float64,1}(undef,nx)
+  for i in 1:nx
+    uxis[i]=anyeq_u_x(xmin+(xmax-xmin)*i/nx,u,k,N,J,taus,weights)
+  end
+    
+
+  # spawn workers
+  work=spawn_workers(nx)
+  gamma2s=Array{Float64,1}(undef,nx)
+  count=0
+  # for each worker
+  @sync for p in 1:length(work)
+    # for each task
+    @async for j in work[p]
+      count=count+1
+      if count>=length(work)
+        progress(count,nx,10000)
+      end
+      # run the task
+      gamma2s[j]=remotecall_fetch(anyeq_uncondensed_2pt,workers()[p],xmin+(xmax-xmin)*j/nx,uxis[j],u,P,N,J,windowL,rho,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx,S,E,II,JJ,X,Y,sL1,sK)
+    end
+  end
+
+  for i in 1:nx
+    @printf("% .15e % .15e\n",xmin+(xmax-xmin)*i/nx,gamma2s[i])
+  end
+end
+
+# compute the compressibility
+function anyeq_compressibility_rho(
+  rhos::Array{Float64,1},
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  approx::Anyeq_approx,
+  savefile::String
+)
+  # initialize vectors
+  (weights,T,k,V,V0,A,Abar,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v,approx,savefile)
+
+  # compute initial guess from medeq
+  u0s=anyeq_init_medeq(rhos,minlrho_init,nlrho_init,N,J,k,a0,v,maxiter,tolerance)
+
+  # save result from each task
+  es=Array{Float64,1}(undef,length(rhos))
+  err=Array{Float64,1}(undef,length(rhos))
+
+  # spawn workers
+  work=spawn_workers(length(rhos))
+
+  # compute u
+  (us,es,errs)=anyeq_hatu_rho_multithread(u0s,P,N,J,rhos,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx,work)
+
+  for j in 1:length(rhos)-2
+    # compute \rho^2\partial^2(\rho e)=\partial^2_{\log\rho}(\rho e)-\partial_{\log\rho}(\rho e) as a function of rho
+    # \partial^2_{\log\rho}(\rho e)
+    p2=(rhos[j+2]*es[j+2]-2*rhos[j+1]*es[j+1]+rhos[j]*es[j])/(log(rhos[j+2])-log(rhos[j+1]))/(log(rhos[j+1])-log(rhos[j]))
+    # \partial_{\log\rho}(\rho e) (take average of front and back)
+    p11=(rhos[j+2]*es[j+2]-rhos[j+1]*es[j+1])/(log(rhos[j+2])-log(rhos[j+1]))
+    p12=(rhos[j+1]*es[j+1]-rhos[j]*es[j])/(log(rhos[j+1])-log(rhos[j]))
+    # compressibility is 1/this
+    @printf("% .15e % .15e\n",rhos[j+1],1/(p2-(p11+p12)/2))
+  end
+end
+
+# Fourier transform 2 point correlation function test: compute by transforming anyeq_2pt
+function anyeq_2pt_correlation_fourier_test(
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  windowL::Float64,
+  rho::Float64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  xmax::Float64,
+  kmin::Float64,
+  kmax::Float64,
+  nk::Int64,
+  approx::Anyeq_approx,
+  savefile::String
+)
+  # init vectors
+  (weights,T,k,V,V0,A,Abar,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v,approx,savefile)
+
+  # compute initial guess from medeq
+  u0=anyeq_init_medeq([rho],minlrho_init,nlrho_init,N,J,k,a0,v,maxiter,tolerance)
+
+  # compute u and some useful integrals
+  (u,E,error)=anyeq_hatu(u0,P,N,J,rho,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx)
+  (S,E,II,JJ,X,Y,sL1,sK,G)=anyeq_SEIJGXY(rho*u,rho,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx)
+
+  # compute C2 in real space
+  C2=Array{Float64,1}(undef,N*J)
+  for i in 1:N*J
+    if k[i]<xmax
+      C2[i]=anyeq_2pt(k[i],u,P,N,J,windowL,rho,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx,S,E,II,JJ,X,Y,sL1,sK)-rho^2
+    else
+      C2[i]=0.
+    end
+  end
+  # invert Fourier transform
+  for i in 1:nk
+    k0=kmin+(kmax-kmin)*i/nk
+    hatC2=inverse_fourier_chebyshev(C2,k0,k,taus,weights,N,J)
+    @printf("% .15e % .15e\n",k0,hatC2)
+  end
+end
+
 # compute Abar
-function anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx)
+function anyeq_Abar_multithread(
+  k::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  approx::Anyeq_approx
+)
   if approx.bL3==0.
-    return []
+    # empty array
+    return Array{Array{Float64,5}}(undef,0)
   end
 
   out=Array{Array{Float64,5}}(undef,J*N)
 
-  ## spawn workers
-  # number of workers
-  nw=nworkers()
-  # split jobs among workers
-  work=Array{Array{Int64,1},1}(undef,nw)
-  # init empty arrays
-  for p in 1:nw
-    work[p]=zeros(0)
-  end
-  for j in 1:N*J
-    append!(work[(j-1)%nw+1],j)
-  end
+  # spawn workers
+  work=spawn_workers(N*J)
 
   count=0
   # for each worker
-  @sync for p in 1:nw
+  @sync for p in 1:length(work)
     # for each task
     @async for j in work[p]
       count=count+1
-      if count>=nw
+      if count>=length(work)
         progress(count,N*J,10000)
       end
       # run the task
@@ -419,14 +830,23 @@ function anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx)
   return out
 end
 
+
 # initialize computation
-@everywhere function anyeq_init(taus,P,N,J,v)
+@everywhere function anyeq_init(
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  v::Function,
+  approx::Anyeq_approx,
+  savefile::String
+)
   # Gauss-Legendre weights
   weights=gausslegendre(N)
 
   # initialize vectors V,k
-  V=Array{Float64}(undef,J*N)
-  k=Array{Float64}(undef,J*N)
+  V=Array{Float64,1}(undef,J*N)
+  k=Array{Float64,1}(undef,J*N)
   for zeta in 0:J-1
     for j in 1:N
       xj=weights[1][j]
@@ -437,7 +857,7 @@ end
     end
   end
   # potential at 0
-  V0=v(0)
+  V0=v(0.)
 
   # initialize matrix A
   T=chebyshev_polynomials(P)
@@ -449,39 +869,62 @@ end
   Upsilon=Upsilonmat(k,v,weights_plus)
   Upsilon0=Upsilon0mat(k,v,weights_plus)
 
-  return(weights,T,k,V,V0,A,Upsilon,Upsilon0)
+  # init Abar
+  if savefile!=""
+    Abar=anyeq_read_Abar(savefile,P,N,J)
+  else
+    Abar=anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx)
+  end
+
+  return(weights,T,k,V,V0,A,Abar,Upsilon,Upsilon0)
 end
 
 # compute initial guess from medeq
-@everywhere function anyeq_init_medeq(rhos,N,J,k,a0,v,maxiter,tolerance)
-  us_medeq=Array{Array{Float64,1}}(undef,length(rhos))
-  u0s=Array{Array{Float64,1}}(undef,length(rhos))
-
+@everywhere function anyeq_init_medeq(
+  rhos::Array{Float64,1},
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  N::Int64,
+  J::Int64,
+  k::Array{Float64,1},
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64
+)
   weights_medeq=gausslegendre(N*J)
+  (u0s_medeq,es,errs)=easyeq_compute_u_prevrho(rhos,minlrho_init,nlrho_init,a0,N*J,v,maxiter,tolerance,weights_medeq,Easyeq_approx(1.,1.))
 
-  (us_medeq[1],E,err)=easyeq_hatu(easyeq_init_u(a0,J*N,weights_medeq),J*N,rhos[1],v,maxiter,tolerance,weights_medeq,Easyeq_approx(1.,1.))
-  u0s[1]=easyeq_to_anyeq(us_medeq[1],weights_medeq,k,N,J)
-  if err>tolerance
-    print(stderr,"warning: computation of initial Ansatz failed for rho=",rhos[1],"\n")
-  end
-
-  for j in 2:length(rhos)
-    (us_medeq[j],E,err)=easyeq_hatu(us_medeq[j-1],J*N,rhos[j],v,maxiter,tolerance,weights_medeq,Easyeq_approx(1.,1.))
-    u0s[j]=easyeq_to_anyeq(us_medeq[j],weights_medeq,k,N,J)
-
-    if err>tolerance
+  # check errs
+  for j in 1:length(errs)
+    if errs[j]>tolerance
       print(stderr,"warning: computation of initial Ansatz failed for rho=",rhos[j],"\n")
     end
   end
 
-  return u0s
+  # return a single vector if there is a single rho
+  if length(rhos)>1
+    u0s=Array{Array{Float64,1}}(undef,length(rhos))
+    for j in 1:length(u0s_medeq)
+      u0s[j]=easyeq_to_anyeq(u0s_medeq[j],weights_medeq,k,N,J)
+    end
+    return u0s
+  else
+    return easyeq_to_anyeq(u0s_medeq,weights_medeq,k,N,J)
+  end
 end
 
 # interpolate the solution of medeq to an input for anyeq
-@everywhere function easyeq_to_anyeq(u_simple,weights,k,N,J)
+@everywhere function easyeq_to_anyeq(
+  u_simple::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  k::Array{Float64,1},
+  N::Int64,
+  J::Int64
+)
   # reorder u_simple, which is evaluated at (1-x_j)/(1+x_j) with x_j\in[-1,1]
   u_s=zeros(Float64,length(u_simple))
-  k_s=Array{Float64}(undef,length(u_simple))
+  k_s=Array{Float64,1}(undef,length(u_simple))
   for j in 1:length(u_simple)
     xj=weights[1][j]
     k_s[length(u_simple)-j+1]=(1-xj)/(1+xj)
@@ -501,7 +944,27 @@ end
 
 
 # compute u using chebyshev expansions
-@everywhere function anyeq_hatu(u0,P,N,J,rho,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx)
+@everywhere function anyeq_hatu(
+  u0::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  rho::Float64,
+  a0::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  k::Array{Float64,1},
+  taus::Array{Float64,1},
+  V::Array{Float64,1},
+  V0::Float64,
+  A::Array{Array{Float64,2},1},
+  Abar::Array{Array{Float64,5},1},
+  Upsilon::Array{Array{Float64,1},1},
+  Upsilon0::Array{Float64,1},
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  approx::Anyeq_approx
+)
   # init
   # rescale by rho (that's how u is defined)
   u=rho*u0
@@ -512,7 +975,7 @@ end
   # run Newton algorithm
   for i in 1:maxiter-1
     (S,E,II,JJ,X,Y,sL1,sK,G)=anyeq_SEIJGXY(u,rho,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx)
-    new=u-inv(anyeq_DXi(u,rho,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx,S,E,II,JJ,X,Y,sL1,sK))*anyeq_Xi(u,X,Y)
+    new=u-inv(anyeq_DXi(u,rho,k,taus,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx,S,E,II,JJ,X,Y,sL1,sK))*anyeq_Xi(u,X,Y)
 
     error=norm(new-u)/norm(u)
     if(error<tolerance)
@@ -528,8 +991,60 @@ end
 end
 
 
+# compute u for various rho
+function anyeq_hatu_rho_multithread(
+  u0s::Array{Array{Float64,1},1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  rhos::Array{Float64,1},
+  a0::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  k::Array{Float64,1},
+  taus::Array{Float64,1},
+  V::Array{Float64,1},
+  V0::Float64,
+  A::Array{Array{Float64,2},1},
+  Abar::Array{Array{Float64,5},1},
+  Upsilon::Array{Array{Float64,1},1},
+  Upsilon0::Array{Float64,1},
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  approx::Anyeq_approx,
+  work::Array{Array{Int64,1},1}
+)
+  # compute u
+  us=Array{Array{Float64,1}}(undef,length(rhos))
+  es=Array{Float64,1}(undef,length(rhos))
+  errs=Array{Float64,1}(undef,length(rhos))
+  count=0
+  # for each worker
+  @sync for p in 1:length(work)
+    # for each task
+    @async for j in work[p]
+      count=count+1
+      if count>=length(work)
+	progress(count,length(rhos),10000)
+      end
+      # run the task
+      (us[j],es[j],errs[j])=remotecall_fetch(anyeq_hatu,workers()[p],u0s[j],P,N,J,rhos[j],a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx)
+    end
+  end
+
+  return (us,es,errs)
+end
+
+
 # save Abar
-function anyeq_save_Abar(taus,P,N,J,v,approx)
+function anyeq_save_Abar(
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  v::Function,
+  approx::Anyeq_approx
+)
   # initialize vectors
   (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v)
 
@@ -555,7 +1070,12 @@ function anyeq_save_Abar(taus,P,N,J,v,approx)
 end
 
 # read Abar
-function anyeq_read_Abar(savefile,P,N,J)
+function anyeq_read_Abar(
+  savefile::String,
+  P::Int64,
+  N::Int64,
+  J::Int64
+)
    # open file
   ff=open(savefile,"r")
   # read all lines
@@ -623,13 +1143,39 @@ end
 
 # Xi
 # takes the vector of kj's and xn's as input
-@everywhere function anyeq_Xi(U,X,Y)
+@everywhere function anyeq_Xi(
+  U::Array{Float64,1},
+  X::Array{Float64,1},
+  Y::Array{Float64,1}
+)
   return U-(Y.+1)./(2*(X.+1)).*dotPhi((Y.+1)./((X.+1).^2))
 end
 
 # DXi
 # takes the vector of kj's as input
-@everywhere function anyeq_DXi(U,rho,k,taus,v,v0,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx,S,E,II,JJ,X,Y,sL1,sK)
+@everywhere function anyeq_DXi(
+  U::Array{Float64,1},
+  rho::Float64,
+  k::Array{Float64,1},
+  taus::Array{Float64,1},
+  A::Array{Array{Float64,2},1},
+  Abar::Array{Array{Float64,5},1},
+  Upsilon::Array{Array{Float64,1},1},
+  Upsilon0::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  approx::Anyeq_approx,
+  S::Array{Float64,1},
+  E::Float64,
+  II::Array{Float64,1},
+  JJ::Array{Float64,1},
+  X::Array{Float64,1},
+  Y::Array{Float64,1},
+  sL1::Array{Float64,1},
+  sK::Array{Float64,1}
+)
   out=Array{Float64,2}(undef,N*J,N*J)
   for zetapp in 0:J-1
     for n in 1:N
@@ -720,7 +1266,23 @@ end
 end
 
 # compute S,E,I,J,X and Y
-@everywhere function anyeq_SEIJGXY(U,rho,k,taus,v,v0,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx)
+@everywhere function anyeq_SEIJGXY(
+  U::Array{Float64,1},
+  rho::Float64,
+  k::Array{Float64,1},
+  taus::Array{Float64,1},
+  v::Array{Float64,1},
+  v0::Float64,
+  A::Array{Array{Float64,2},1},
+  Abar::Array{Array{Float64,5},1},
+  Upsilon::Array{Array{Float64,1},1},
+  Upsilon0::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  approx::Anyeq_approx
+)
   # Chebyshev expansion of U
   FU=chebyshev(U,taus,weights,P,N,J,2)
 
@@ -798,79 +1360,207 @@ end
   return(S,E,II,JJ,X,Y,sL1,sK,G)
 end
 
+
+# u(x)
+@everywhere function anyeq_u_x(
+  x::Float64,
+  u::Array{Float64,1},
+  k::Array{Float64,1},
+  N::Int64,
+  J::Int64,
+  taus::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}}
+)
+  ux=0.
+  for zeta in 0:J-1
+    for j in 1:N
+      ux+=(taus[zeta+2]-taus[zeta+1])/(16*pi*x)*weights[2][j]*cos(pi*weights[1][j]/2)*(1+k[zeta*N+j])^2*k[zeta*N+j]*u[zeta*N+j]*sin(k[zeta*N+j]*x)
+    end
+  end
+  return real(ux)
+end
+
 # condensate fraction
-@everywhere function anyeq_eta(u,P,N,J,rho,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx)
+@everywhere function anyeq_eta(
+  u::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  rho::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  k::Array{Float64,1},
+  taus::Array{Float64,1},
+  V::Array{Float64,1},
+  V0::Float64,
+  A::Array{Array{Float64,2},1},
+  Abar::Array{Array{Float64,5},1},
+  Upsilon::Array{Array{Float64,1},1},
+  Upsilon0::Array{Float64,1},
+  approx::Anyeq_approx
+)
   # compute dXi/dmu
   (S,E,II,JJ,X,Y,sL1,sK,G)=anyeq_SEIJGXY(rho*u,rho,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx)
   dXidmu=(Y.+1)./(rho*sL1)./(2*(X.+1).^2).*dotPhi((Y.+1)./((X.+1).^2))+(Y.+1).^2 ./((X.+1).^4)./(rho*sL1).*dotdPhi((Y.+1)./(X.+1).^2)
 
   # compute eta
-  du=-inv(anyeq_DXi(rho*u,rho,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx,S,E,II,JJ,X,Y,sL1,sK))*dXidmu
+  du=-inv(anyeq_DXi(rho*u,rho,k,taus,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx,S,E,II,JJ,X,Y,sL1,sK))*dXidmu
   eta=-avg_v_chebyshev(du,Upsilon0,k,taus,weights,N,J)/2
 
   return eta
 end
 
-# momentum distribution
-@everywhere function anyeq_momentum(u,P,N,J,rho,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx)
-  # compute dXi/dlambda (without delta functions)
-  (S,E,II,JJ,X,Y,sL1,sK,G)=anyeq_SEIJGXY(rho*u,rho,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx)
-  dXidlambda=-(2*pi)^3*2*u./sL1.*(dotPhi((Y.+1)./((X.+1).^2))./(2*(X.+1))+(Y.+1)./(2*(X.+1).^3).*dotdPhi((Y.+1)./(X.+1).^2))
-
-  # approximation for delta function (without Kronecker deltas)
-  delta=Array{Float64}(undef,J*N)
-  for zeta in 0:J-1
-    for n in 1:N
-      delta[zeta*N+n]=2/pi^2/((taus[zeta+2]-taus[zeta+1])*weights[2][n]*cos(pi*weights[1][n]/2)*(1+k[zeta*N+n])^2*k[zeta*N+n]^2)
-    end
-  end
-  
-  # compute dXidu
-  dXidu=inv(anyeq_DXi(rho*u,rho,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx,S,E,II,JJ,X,Y,sL1,sK))
-
-  M=Array{Float64}(undef,J*N)
-  for i in 1:J*N
-    # du/dlambda
-    du=dXidu[:,i]*dXidlambda[i]*delta[i]
-
-    # compute M
-    M[i]=-avg_v_chebyshev(du,Upsilon0,k,taus,weights,N,J)/2
-  end
-
-  return M
-end
-
 
 # correlation function
-@everywhere function anyeq_2pt(x,u,P,N,J,windowL,rho,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx,S,E,II,JJ,X,Y,sL1,sK,G)
-  # initialize dV
-  dV=Array{Float64}(undef,J*N)
-  for i in 1:J*N
-    if x>0
-      dV[i]=sin(k[i]*x)/(k[i]*x)*hann(k[i],windowL)
-    else
-      dV[i]=hann(k[i],windowL)
-    end
+@everywhere function anyeq_2pt(
+  x::Float64,
+  u::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  windowL::Float64,
+  rho::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  k::Array{Float64,1},
+  taus::Array{Float64,1},
+  V::Array{Float64,1},
+  V0::Float64,
+  A::Array{Array{Float64,2},1},
+  Abar::Array{Array{Float64,5},1},
+  Upsilon::Array{Array{Float64,1},1},
+  Upsilon0::Array{Float64,1},
+  approx::Anyeq_approx,
+  S::Array{Float64,1},
+  E::Float64,
+  II::Array{Float64,1},
+  JJ::Array{Float64,1},
+  X::Array{Float64,1},
+  Y::Array{Float64,1},
+  sL1::Array{Float64,1},
+  sK::Array{Float64,1}
+)
+  g=(r,x)->sinc(r*x)*hann(r,windowL)
+  du=anyeq_dudv(g, x, u, P, N, J, rho, weights, k, taus, V, V0, A, Abar, Upsilon, Upsilon0, approx, S, E, II, JJ, X, Y, sL1, sK)
+
+  C2=rho^2*(1-integrate_f_chebyshev(s->g(s,x),u,k,taus,weights,N,J)-integrate_f_chebyshev(s->1.,V.*du,k,taus,weights,N,J))
+end
+
+# uncondensed correlation function
+@everywhere function anyeq_uncondensed_2pt(
+  xi::Float64,
+  uxi::Float64,
+  u::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  windowL::Float64,
+  rho::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  k::Array{Float64,1},
+  taus::Array{Float64,1},
+  V::Array{Float64,1},
+  V0::Float64,
+  A::Array{Array{Float64,2},1},
+  Abar::Array{Array{Float64,5},1},
+  Upsilon::Array{Array{Float64,1},1},
+  Upsilon0::Array{Float64,1},
+  approx::Anyeq_approx,
+  S::Array{Float64,1},
+  E::Float64,
+  II::Array{Float64,1},
+  JJ::Array{Float64,1},
+  X::Array{Float64,1},
+  Y::Array{Float64,1},
+  sL1::Array{Float64,1},
+  sK::Array{Float64,1}
+)
+  # compute dXi/dmu
+  g=Array{Float64,1}(undef,length(k))
+  for i in 1:length(k)
+    g[i]=-2*rho*uxi*sinc(k[i]*xi)*hann(k[i],windowL)
   end
-  dV0=1.
+  dXidmu=-g./sL1./(2*(X.+1)).*dotPhi((Y.+1)./((X.+1).^2))-(Y.+1).*g./sL1./(2(X.+1).^3).*dotdPhi((Y.+1)./(X.+1).^2)
+
+  # compute gamma2
+  du=-inv(anyeq_DXi(rho*u,rho,k,taus,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx,S,E,II,JJ,X,Y,sL1,sK))*dXidmu
+  gamma2=-avg_v_chebyshev(du,Upsilon0,k,taus,weights,N,J)/2
+
+  return gamma2
+end
+
+# compute the directional derivative of u with respect to v in direction g
+@everywhere function anyeq_dudv(
+  g::Function,# should be of the form g(k,x) where x is a parameter
+  x::Float64,
+  u::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  rho::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  k::Array{Float64,1},
+  taus::Array{Float64,1},
+  V::Array{Float64,1},
+  V0::Float64,
+  A::Array{Array{Float64,2},1},
+  Abar::Array{Array{Float64,5},1},
+  Upsilon::Array{Array{Float64,1},1},
+  Upsilon0::Array{Float64,1},
+  approx::Anyeq_approx,
+  S::Array{Float64,1},
+  E::Float64,
+  II::Array{Float64,1},
+  JJ::Array{Float64,1},
+  X::Array{Float64,1},
+  Y::Array{Float64,1},
+  sL1::Array{Float64,1},
+  sK::Array{Float64,1}
+)
+  # initialize dV
+  dV=Array{Float64,1}(undef,J*N)
+  for i in 1:J*N
+    dV[i]=g(k[i],x)
+  end
+  dV0=g(0.,x)
 
   # compute dUpsilon
   # Upsilonmat does not use splines, so increase precision
   weights_plus=gausslegendre(N*J)
-  dUpsilon=Upsilonmat(k,r->sin(r*x)/(r*x)*hann(r,windowL),weights_plus)
-  dUpsilon0=Upsilon0mat(k,r->sin(r*x)/(r*x)*hann(r,windowL),weights_plus)
+  dUpsilon=Upsilonmat(k,s->g(s,x),weights_plus)
+  dUpsilon0=Upsilon0mat(k,s->g(s,x),weights_plus)
 
-  du=-inv(anyeq_DXi(rho*u,rho,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx,S,E,II,JJ,X,Y,sL1,sK))*anyeq_dXidv(x,rho*u,rho,k,taus,dV,dV0,A,Abar,dUpsilon,dUpsilon0,weights,P,N,J,approx,S,E,II,JJ,X,Y,sL1,sK)
+  du=-inv(anyeq_DXi(rho*u,rho,k,taus,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx,S,E,II,JJ,X,Y,sL1,sK))*anyeq_dXidv(rho*u,rho,k,taus,dV,dV0,A,Abar,dUpsilon,dUpsilon0,weights,P,N,J,approx,S,E,II,JJ,X,Y,sL1,sK)
   # rescale rho
   du=du/rho
 
-  C2=rho^2*(1-integrate_f_chebyshev(s->1.,u.*dV+V.*du,k,taus,weights,N,J))
-
-  return C2
+  return du
 end
 
-# derivative of Xi with respect to v in the direction sin(kx)/kx
-@everywhere function anyeq_dXidv(x,U,rho,k,taus,dv,dv0,A,Abar,dUpsilon,dUpsilon0,weights,P,N,J,approx,S,E,II,JJ,X,Y,sL1,sK)
+# derivative of Xi with respect to v in the specified by dUpsilon and dUpsilon0
+@everywhere function anyeq_dXidv(
+  U::Array{Float64,1},
+  rho::Float64,
+  k::Array{Float64,1},
+  taus::Array{Float64,1},
+  dv::Array{Float64,1},
+  dv0::Float64,
+  A::Array{Array{Float64,2},1},
+  Abar::Array{Array{Float64,5},1},
+  dUpsilon::Array{Array{Float64,1},1},
+  dUpsilon0::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  approx::Anyeq_approx,
+  S::Array{Float64,1},
+  E::Float64,
+  II::Array{Float64,1},
+  JJ::Array{Float64,1},
+  X::Array{Float64,1},
+  Y::Array{Float64,1},
+  sL1::Array{Float64,1},
+  sK::Array{Float64,1}
+)
   # Chebyshev expansion of U
   FU=chebyshev(U,taus,weights,P,N,J,2)
 
@@ -896,7 +1586,7 @@ end
       dJJ+=approx.gL3*approx.bL3*double_conv_S_chebyshev(FU,FU,FU,FU,dFS,Abar)
     end
     if approx.bL3!=1.
-      dJJ=approx.gL3*(1-approx.bL3)*dE*(UU/rho).^2
+      dJJ+=approx.gL3*(1-approx.bL3)*dE*(UU/rho).^2
     end
   end
 
@@ -947,3 +1637,220 @@ end
   out=((Y.+1).*dX./(X.+1)-dY)./(2*(X.+1)).*dotPhi((Y.+1)./((X.+1).^2))+(Y.+1)./(2*(X.+1).^3).*(2*(Y.+1)./(X.+1).*dX-dY).*dotdPhi((Y.+1)./(X.+1).^2)
   return out
 end
+
+# maximum of 2 point correlation function
+@everywhere function anyeq_2pt_max(
+  u::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  x0::Float64,
+  dx::Float64,
+  maxstep::Float64,
+  maxiter::Int64,
+  tolerance::Float64,
+  windowL::Float64,
+  rho::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  T::Array{Polynomial,1},
+  k::Array{Float64,1},
+  taus::Array{Float64,1},
+  V::Array{Float64,1},
+  V0::Float64,
+  A::Array{Array{Float64,2},1},
+  Abar::Array{Array{Float64,5},1},
+  Upsilon::Array{Array{Float64,1},1},
+  Upsilon0::Array{Float64,1},
+  approx::Anyeq_approx
+)
+  # compute some useful integrals
+  (S,E,II,JJ,X,Y,sL1,sK,G)=anyeq_SEIJGXY(rho*u,rho,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx)
+
+  (x,f)=newton_maximum(y->anyeq_2pt(y,u,P,N,J,windowL,rho,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx,S,E,II,JJ,X,Y,sL1,sK),x0,dx,maxiter,tolerance,maxstep)
+
+  return(x,f)
+end
+
+
+# Fourier transform of 2pt correlation function at q
+@everywhere function anyeq_2pt_fourier(
+  q::Float64,
+  u::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  windowL::Float64,
+  rho::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  k::Array{Float64,1},
+  taus::Array{Float64,1},
+  V::Array{Float64,1},
+  V0::Float64,
+  A::Array{Array{Float64,2},1},
+  Abar::Array{Array{Float64,5},1},
+  Upsilon::Array{Array{Float64,1},1},
+  Upsilon0::Array{Float64,1},
+  approx::Anyeq_approx,
+  S::Array{Float64,1},
+  E::Float64,
+  II::Array{Float64,1},
+  JJ::Array{Float64,1},
+  X::Array{Float64,1},
+  Y::Array{Float64,1},
+  sL1::Array{Float64,1},
+  sK::Array{Float64,1}
+)
+  # direction in which to differentiate u
+  weights_plus=gausslegendre(N*J)
+  #g=(r,x)->(r>0. ? (1.)/(2*x*r)*integrate_legendre(s->s*gaussian(s,(1.)/windowL),abs(x-r),x+r,weights_plus) : gaussian(x,(1.)/windowL))
+  g=(r,x)->(r>0. ? (1.)/(2*x*r*windowL)*(gaussian(x-r,(1.)/windowL)-gaussian(x+r,(1.)/windowL)) : gaussian(x,(1.)/windowL))
+
+  du=anyeq_dudv(g,q,u,P,N,J,rho,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx,S,E,II,JJ,X,Y,sL1,sK)
+
+  C2=rho^2*(-integrate_f_chebyshev(s->g(s,q),u,k,taus,weights,N,J)-integrate_f_chebyshev(s->1.,V.*du,k,taus,weights,N,J))
+
+  return C2
+end
+
+# maximum of Fourier transform of 2 point correlation function
+@everywhere function anyeq_2pt_fourier_max(
+  u::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  k0::Float64,
+  dk::Float64,
+  maxstep::Float64,
+  maxiter::Int64,
+  tolerance::Float64,
+  windowL::Float64,
+  rho::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  T::Array{Polynomial,1},
+  k::Array{Float64,1},
+  taus::Array{Float64,1},
+  V::Array{Float64,1},
+  V0::Float64,
+  A::Array{Array{Float64,2},1},
+  Abar::Array{Array{Float64,5},1},
+  Upsilon::Array{Array{Float64,1},1},
+  Upsilon0::Array{Float64,1},
+  approx::Anyeq_approx
+)
+  # compute some useful integrals
+  (S,E,II,JJ,X,Y,sL1,sK,G)=anyeq_SEIJGXY(rho*u,rho,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx)
+
+  (ko,f)=newton_maximum(y->anyeq_2pt_fourier(y,u,P,N,J,windowL,rho,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx,S,E,II,JJ,X,Y,sL1,sK),k0,dk,maxiter,tolerance,maxstep)
+
+  return(ko,f)
+end
+
+# momentum distribution, computed using a Gaussian window
+@everywhere function anyeq_momentum_window(
+  kmin::Float64,
+  kmax::Float64,
+  u::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  windowL::Float64, # L is windowL/k^2
+  rho::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  k::Array{Float64,1},
+  taus::Array{Float64,1},
+  V::Array{Float64,1},
+  V0::Float64,
+  A::Array{Array{Float64,2},1},
+  Abar::Array{Array{Float64,5},1},
+  Upsilon::Array{Array{Float64,1},1},
+  Upsilon0::Array{Float64,1},
+  approx::Anyeq_approx
+)
+
+  # compute dXi/dlambda without the delta function of u(q)
+  (S,E,II,JJ,X,Y,sL1,sK,G)=anyeq_SEIJGXY(rho*u,rho,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx)
+  dXidlambda=(16*pi^3)*(dotPhi((Y.+1)./((X.+1).^2))./(2*(X.+1))+(Y.+1)./(2*(X.+1).^3).*dotdPhi((Y.+1)./(X.+1).^2))./sL1
+  
+  # compute dXidu
+  dXidu=inv(anyeq_DXi(rho*u,rho,k,taus,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx,S,E,II,JJ,X,Y,sL1,sK))
+
+  M=Array{Float64,1}(undef,N*J)
+  for j in 1:J*N
+    # the Chebyshev polynomial expansion is often not good enough to compute M away from k[i]
+    q=k[j]
+
+    # drop the computation if not in k interval
+    if q<kmin || q>kmax
+      continue
+    end
+
+    # delta function
+    delta=Array{Float64,1}(undef,J*N)
+    L=windowL/q^2
+    for i in 1:J*N
+      delta[i]=(1.)/(2*k[i]*q*L)*(gaussian(k[i]-q,(1.)/L)-gaussian(k[i]+q,(1.)/L))
+    end
+
+    # du/dlambda
+    du=-dXidu*(dXidlambda.*delta*u[j])
+    # rescale u
+    du=du/rho
+
+    # compute M
+    M[j]=-avg_v_chebyshev(du,Upsilon0,k,taus,weights,N,J)*rho/2
+  end
+
+  return M
+end
+
+# momentum distribution, computed using a discrete approximation of the delta function
+@everywhere function anyeq_momentum_discrete_delta(
+  kmin::Float64,
+  kmax::Float64,
+  u::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  rho::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  k::Array{Float64,1},
+  taus::Array{Float64,1},
+  V::Array{Float64,1},
+  V0::Float64,
+  A::Array{Array{Float64,2},1},
+  Abar::Array{Array{Float64,5},1},
+  Upsilon::Array{Array{Float64,1},1},
+  Upsilon0::Array{Float64,1},
+  approx::Anyeq_approx
+)
+  # compute dXi/dlambda (without delta functions)
+  (S,E,II,JJ,X,Y,sL1,sK,G)=anyeq_SEIJGXY(rho*u,rho,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx)
+  dXidlambda=(2*pi)^3*2*u./sL1.*(dotPhi((Y.+1)./((X.+1).^2))./(2*(X.+1))+(Y.+1)./(2*(X.+1).^3).*dotdPhi((Y.+1)./(X.+1).^2))
+
+  # approximation for delta function (without Kronecker deltas)
+  delta=Array{Float64,1}(undef,J*N)
+  for zeta in 0:J-1
+    for n in 1:N
+      delta[zeta*N+n]=8/pi/((taus[zeta+2]-taus[zeta+1])*weights[2][n]*cos(pi*weights[1][n]/2)*(1+k[zeta*N+n])^2)
+    end
+  end
+  
+  # compute dXidu
+  dXidu=inv(anyeq_DXi(rho*u,rho,k,taus,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx,S,E,II,JJ,X,Y,sL1,sK))
+
+  M=Array{Float64,1}(undef,J*N)
+  for i in 1:J*N
+    # drop the computation if not in k interval
+    if k[i]<kmin || k[i]>kmax
+      continue
+    end
+
+    # du/dlambda
+    du=-dXidu[:,i]*dXidlambda[i]*delta[i]
+
+    # compute M
+    M[i]=-avg_v_chebyshev(du,Upsilon0,k,taus,weights,N,J)/2
+  end
+
+  return M
+end
diff --git a/src/chebyshev.jl b/src/chebyshev.jl
index 28c8f1f..af4be40 100644
--- a/src/chebyshev.jl
+++ b/src/chebyshev.jl
@@ -1,4 +1,4 @@
-## Copyright 2021 Ian Jauslin
+## Copyright 2021-2023 Ian Jauslin
 ## 
 ## Licensed under the Apache License, Version 2.0 (the "License");
 ## you may not use this file except in compliance with the License.
@@ -13,7 +13,15 @@
 ## limitations under the License.
 
 # Chebyshev expansion
-@everywhere function chebyshev(a,taus,weights,P,N,J,nu)
+@everywhere function chebyshev(
+  a::Array{Float64,1},
+  taus::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  nu::Int64
+)
   out=zeros(Float64,J*(P+1))
   for zeta in 0:J-1
     for n in 0:P
@@ -27,7 +35,15 @@
 end
 
 # evaluate function from Chebyshev expansion
-@everywhere function chebyshev_eval(Fa,x,taus,chebyshev,P,J,nu)
+@everywhere function chebyshev_eval(
+  Fa::Array{Float64,1},
+  x::Float64,
+  taus::Array{Float64,1},
+  chebyshev::Array{Polynomial,1},
+  P::Int64,
+  J::Int64,
+  nu::Int64
+)
   # change variable
   tau=(1-x)/(1+x)
 
@@ -48,7 +64,11 @@ end
 
 # convolution 
 # input the Chebyshev expansion of a and b, as well as the A matrix
-@everywhere function conv_chebyshev(Fa,Fb,A)
+@everywhere function conv_chebyshev(
+  Fa::Array{Float64,1},
+  Fb::Array{Float64,1},
+  A::Array{Array{Float64,2},1}
+)
   out=zeros(Float64,length(A))
   for i in 1:length(A)
     out[i]=dot(Fa,A[i]*Fb)
@@ -57,12 +77,27 @@ end
 end
 
 # <ab>
-@everywhere function avg_chebyshev(Fa,Fb,A0)
+@everywhere function avg_chebyshev(
+  Fa::Array{Float64,1},
+  Fb::Array{Float64,1},
+  A0::Float64
+)
   return dot(Fa,A0*Fb)
 end
 
 # 1_n * a
-@everywhere function conv_one_chebyshev(n,zetapp,Fa,A,taus,weights,P,N,J,nu1)
+@everywhere function conv_one_chebyshev(
+  n::Int64,
+  zetapp::Int64,
+  Fa::Array{Float64,1},
+  A::Array{Array{Float64,2},1},
+  taus::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  nu1::Int64
+)
   out=zeros(Float64,N*J)
   for m in 1:N*J
     for l in 0:P
@@ -74,7 +109,15 @@ end
   return out
 end
 # a * v
-@everywhere function conv_v_chebyshev(a,Upsilon,k,taus,weights,N,J)
+@everywhere function conv_v_chebyshev(
+  a::Array{Float64,1},
+  Upsilon::Array{Array{Float64,1},1},
+  k::Array{Float64,1},
+  taus::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  N::Int64,
+  J::Int64
+)
   out=zeros(Float64,J*N)
   for i in 1:J*N
     for zetap in 0:J-1
@@ -86,7 +129,16 @@ end
   end
   return out
 end
-@everywhere function conv_one_v_chebyshev(n,zetap,Upsilon,k,taus,weights,N,J)
+@everywhere function conv_one_v_chebyshev(
+  n::Int64,
+  zetap::Int64,
+  Upsilon::Array{Array{Float64,1},1},
+  k::Array{Float64,1},
+  taus::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  N::Int64,
+  J::Int64
+)
   out=zeros(Float64,J*N)
   xj=weights[1][n]
   for i in 1:J*N
@@ -96,7 +148,14 @@ end
 end
 
 # <av>
-@everywhere function avg_v_chebyshev(a,Upsilon0,k,taus,weights,N,J)
+@everywhere function avg_v_chebyshev(a,
+  Upsilon0::Array{Float64,1},
+  k::Array{Float64,1},
+  taus::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  N::Int64,
+  J::Int64
+)
   out=0.
   for zetap in 0:J-1
     for j in 1:N
@@ -107,13 +166,28 @@ end
   return out
 end
 # <1_nv>
-@everywhere function avg_one_v_chebyshev(n,zetap,Upsilon0,k,taus,weights,N)
+@everywhere function avg_one_v_chebyshev(
+  n::Int64,
+  zetap::Int64,
+  Upsilon0::Array{Float64,1},
+  k::Array{Float64,1},
+  taus::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  N::Int64
+)
   xj=weights[1][n]
   return (taus[zetap+2]-taus[zetap+1])/(32*pi)*weights[2][n]*cos(pi*xj/2)*(1+k[zetap*N+n])^2*k[zetap*N+n]*Upsilon0[zetap*N+n]
 end
 
 # compute \int dq dxi u1(k-xi)u2(q)u3(xi)u4(k-q)u5(xi-q)
-@everywhere function double_conv_S_chebyshev(FU1,FU2,FU3,FU4,FU5,Abar)
+@everywhere function double_conv_S_chebyshev(
+  FU1::Array{Float64,1},
+  FU2::Array{Float64,1},
+  FU3::Array{Float64,1},
+  FU4::Array{Float64,1},
+  FU5::Array{Float64,1},
+  Abar::Array{Float64,5}
+)
   out=zeros(Float64,length(Abar))
   for i in 1:length(Abar)
     for j1 in 1:length(FU1)
@@ -133,7 +207,17 @@ end
 
 
 # compute A
-@everywhere function Amat(k,weights,taus,T,P,N,J,nua,nub)
+@everywhere function Amat(
+  k::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  nua::Int64,
+  nub::Int64
+)
   out=Array{Array{Float64,2},1}(undef,J*N)
   for i in 1:J*N
     out[i]=zeros(Float64,J*(P+1),J*(P+1))
@@ -152,7 +236,11 @@ end
 end
 
 # compute Upsilon
-@everywhere function Upsilonmat(k,v,weights)
+@everywhere function Upsilonmat(
+  k::Array{Float64,1},
+  v::Function,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}}
+)
   out=Array{Array{Float64,1},1}(undef,length(k))
   for i in 1:length(k)
     out[i]=Array{Float64,1}(undef,length(k))
@@ -162,7 +250,11 @@ end
   end
   return out
 end
-@everywhere function Upsilon0mat(k,v,weights)
+@everywhere function Upsilon0mat(
+  k::Array{Float64,1},
+  v::Function,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}}
+)
   out=Array{Float64,1}(undef,length(k))
   for j in 1:length(k)
     out[j]=2*k[j]*v(k[j])
@@ -171,17 +263,36 @@ end
 end
 
 # alpha_-
-@everywhere function alpham(k,t)
+@everywhere function alpham(
+  k::Float64,
+  t::Float64
+)
   return (1-k-(1-t)/(1+t))/(1+k+(1-t)/(1+t))
 end
 # alpha_+
-@everywhere function alphap(k,t)
+@everywhere function alphap(
+  k::Float64,
+  t::Float64
+)
   return (1-abs(k-(1-t)/(1+t)))/(1+abs(k-(1-t)/(1+t)))
 end
 
 
 # compute \bar A
-@everywhere function barAmat(k,weights,taus,T,P,N,J,nu1,nu2,nu3,nu4,nu5)
+@everywhere function barAmat(
+  k::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  nu1::Int64,
+  nu2::Int64,
+  nu3::Int64,
+  nu4::Int64,
+  nu5::Int64
+)
   out=zeros(Float64,J*(P+1),J*(P+1),J*(P+1),J*(P+1),J*(P+1))
   for zeta1 in 0:J-1
     for n1 in 0:P
@@ -211,27 +322,107 @@ end
 
   return out
 end
-@everywhere function barAmat_int1(tau,k,taus,T,weights,nu1,nu2,nu3,nu4,nu5,zeta1,zeta2,zeta3,zeta4,zeta5,n1,n2,n3,n4,n5)
+@everywhere function barAmat_int1(tau,
+  k::Float64,
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  nu1::Int64,
+  nu2::Int64,
+  nu3::Int64,
+  nu4::Int64,
+  nu5::Int64,
+  zeta1::Int64,
+  zeta2::Int64,
+  zeta3::Int64,
+  zeta4::Int64,
+  zeta5::Int64,
+  n1::Int64,
+  n2::Int64,
+  n3::Int64,
+  n4::Int64,
+  n5::Int64
+)
   if(alpham(k,tau)<taus[zeta2+2] && alphap(k,tau)>taus[zeta2+1])
     return 2*(1-tau)/(1+tau)^(3-nu1)*T[n1+1]((2*tau-(taus[zeta1+1]+taus[zeta1+2]))/(taus[zeta1+2]-taus[zeta1+1]))*integrate_legendre(sigma->barAmat_int2(tau,sigma,k,taus,T,weights,nu2,nu3,nu4,nu5,zeta2,zeta3,zeta4,zeta5,n2,n3,n4,n5),max(taus[zeta2+1],alpham(k,tau)),min(taus[zeta2+2],alphap(k,tau)),weights)
   else
     return 0.
   end
 end
-@everywhere function barAmat_int2(tau,sigma,k,taus,T,weights,nu2,nu3,nu4,nu5,zeta2,zeta3,zeta4,zeta5,n2,n3,n4,n5)
+@everywhere function barAmat_int2(tau,
+  sigma::Float64,
+  k::Float64,
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  nu2::Int64,
+  nu3::Int64,
+  nu4::Int64,
+  nu5::Int64,
+  zeta2::Int64,
+  zeta3::Int64,
+  zeta4::Int64,
+  zeta5::Int64,
+  n2::Int64,
+  n3::Int64,
+  n4::Int64,
+  n5::Int64
+)
   return 2*(1-sigma)/(1+sigma)^(3-nu2)*T[n2+1]((2*sigma-(taus[zeta2+1]+taus[zeta2+2]))/(taus[zeta2+2]-taus[zeta2+1]))*integrate_legendre(taup->barAmat_int3(tau,sigma,taup,k,taus,T,weights,nu3,nu4,nu5,zeta3,zeta4,zeta5,n3,n4,n5),taus[zeta3+1],taus[zeta3+2],weights)
 end
-@everywhere function barAmat_int3(tau,sigma,taup,k,taus,T,weights,nu3,nu4,nu5,zeta3,zeta4,zeta5,n3,n4,n5)
+@everywhere function barAmat_int3(tau,
+  sigma::Float64,
+  taup::Float64,
+  k::Float64,
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  nu3::Int64,
+  nu4::Int64,
+  nu5::Int64,
+  zeta3::Int64,
+  zeta4::Int64,
+  zeta5::Int64,
+  n3::Int64,
+  n4::Int64,
+  n5::Int64
+)
   if(alpham(k,taup)<taus[zeta4+2] && alphap(k,taup)>taus[zeta4+1])
     return 2*(1-taup)/(1+taup)^(3-nu3)*T[n3+1]((2*taup-(taus[zeta3+1]+taus[zeta3+2]))/(taus[zeta3+2]-taus[zeta3+1]))*integrate_legendre(sigmap->barAmat_int4(tau,sigma,taup,sigmap,k,taus,T,weights,nu4,nu5,zeta4,zeta5,n4,n5),max(taus[zeta4+1],alpham(k,taup)),min(taus[zeta4+2],alphap(k,taup)),weights)
   else
     return 0.
   end
 end
-@everywhere function barAmat_int4(tau,sigma,taup,sigmap,k,taus,T,weights,nu4,nu5,zeta4,zeta5,n4,n5)
+@everywhere function barAmat_int4(tau,
+  sigma::Float64,
+  taup::Float64,
+  sigmap::Float64,
+  k::Float64,
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  nu4::Int64,
+  nu5::Int64,
+  zeta4::Int64,
+  zeta5::Int64,
+  n4::Int64,
+  n5::Int64
+)
   return 2*(1-sigmap)/(1+sigmap)^(3-nu4)*T[n4+1]((2*sigma-(taus[zeta4+1]+taus[zeta4+2]))/(taus[zeta4+2]-taus[zeta4+1]))*integrate_legendre(theta->barAmat_int5(tau,sigma,taup,sigmap,theta,k,taus,T,weights,nu5,zeta5,n5),0.,2*pi,weights)
 end
-@everywhere function barAmat_int5(tau,sigma,taup,sigmap,theta,k,taus,T,weights,nu5,zeta5,n5)
+@everywhere function barAmat_int5(tau,
+  sigma::Float64,
+  taup::Float64,
+  sigmap::Float64,
+  theta::Float64,
+  k::Float64,
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  nu5::Int64,
+  zeta5::Int64,
+  n5::Int64
+)
   R=barAmat_R((1-sigma)/(1+sigma),(1-tau)/(1+tau),(1-sigmap)/(1+sigmap),(1-taup)/(1+taup),theta,k)
   if((1-R)/(1+R)<taus[zeta5+2] && (1-R)/(1+R)>taus[zeta5+1])
     return (2/(2+R))^nu5*T[n5+1]((2*(1-R)/(1+R)-(taus[zeta5+1]+taus[zeta5+2]))/(taus[zeta5+2]-taus[zeta5+1]))
@@ -240,13 +431,22 @@ end
   end
 end
 # R(s,t,s',t,theta,k)
-@everywhere function barAmat_R(s,t,sp,tp,theta,k)
-  return sqrt(k^2*(s^2+t^2+sp^2+tp^2)-k^4-(s^2-t^2)*(sp^2-tp^2)-sqrt((4*k^2*s^2-(k^2+s^2-t^2)^2)*(4*k^2*sp^2-(k^2+sp^2-tp^2)^2))*cos(theta))/(sqrt(2)*k)
+@everywhere function barAmat_R(
+  s::Float64,
+  t::Float64,
+  sp::Float64,
+  tp::Float64,
+  theta::Float64,
+  k::Float64
+)
+  return sqrt(k^2*(s^2+t^2+sp^2+tp^2)-k^4-(s^2-t^2)*(sp^2-tp^2)-sqrt((4*k^2*s^2-(k^2+s^2-t^2)^2)*(4*k^2*sp^2-(k^2+sp^2-tp^2)^2))*cos(theta))/(sqrt(2.)*k)
 end
 
 # compute Chebyshev polynomials
-@everywhere function chebyshev_polynomials(P)
-  T=Array{Polynomial}(undef,P+1)
+@everywhere function chebyshev_polynomials(
+  P::Int64
+)
+  T=Array{Polynomial,1}(undef,P+1)
   T[1]=Polynomial([1])
   T[2]=Polynomial([0,1])
   for n in 1:P-1
@@ -258,7 +458,15 @@ end
 end
 
 # compute \int f*u dk/(2*pi)^3
-@everywhere function integrate_f_chebyshev(f,u,k,taus,weights,N,J)
+@everywhere function integrate_f_chebyshev(
+  f::Function,
+  u::Array{Float64,1},
+  k::Array{Float64,1},
+  taus::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  N::Int64,
+  J::Int64
+)
   out=0.
   for zeta in 0:J-1
     for i in 1:N
@@ -268,7 +476,15 @@ end
   return out
 end
 
-@everywhere function inverse_fourier_chebyshev(u,x,k,taus,weights,N,J)
+@everywhere function inverse_fourier_chebyshev(
+  u::Array{Float64,1},
+  x::Float64,
+  k::Array{Float64,1},
+  taus::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  N::Int64,
+  J::Int64
+)
   out=0.
   for zeta in 0:J-1
     for j in 1:N
@@ -277,3 +493,54 @@ end
   end
   return out
 end
+
+# compute B (for the computation of the fourier transform of the two-point correlation)
+@everywhere function Bmat(
+  q::Float64,
+  k::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  nu::Int64
+)
+  out=Array{Array{Float64,1},1}(undef,J*N)
+  for i in 1:J*N
+    out[i]=zeros(Float64,J*(P+1))
+    for zeta in 0:J-1
+      for n in 0:P
+	out[i][zeta*(P+1)+n+1]=1/(8*pi^3*k[i]*q)*(betam(k[i],q)>taus[zeta+2] || betap(k[i],q)<taus[zeta+1] ? 0. : integrate_legendre(sigma->(1-sigma)/(1+sigma)^(3-nu)*T[n+1]((2*sigma-(taus[zeta+1]+taus[zeta+2]))/(taus[zeta+2]-taus[zeta+1])),max(taus[zeta+1],betam(k[i],q)),min(taus[zeta+2],betap(k[i],q)),weights))
+      end
+    end
+  end
+
+  return out
+end
+# beta_-
+@everywhere function betam(
+  k::Float64,
+  q::Float64
+)
+  return (1-k-q)/(1+k+q)
+end
+# beta_+
+@everywhere function betap(
+  k::Float64,
+  q::Float64
+)
+  return (1-abs(k-q))/(1+abs(k-q))
+end
+
+# mathfrak S (for the computation of the fourier transform of the two-point correlation)
+@everywhere function chebyshev_frakS(
+  Ff::Array{Float64,1},
+  B::Array{Array{Float64,1},1}
+)
+  out=zeros(Float64,length(B))
+  for i in 1:length(B)
+    out[i]=dot(Ff,B[i])
+  end
+  return out
+end
diff --git a/src/easyeq.jl b/src/easyeq.jl
index 0bde3ab..2dbbd1a 100644
--- a/src/easyeq.jl
+++ b/src/easyeq.jl
@@ -1,4 +1,4 @@
-## Copyright 2021 Ian Jauslin
+## Copyright 2021-2023 Ian Jauslin
 ## 
 ## Licensed under the Apache License, Version 2.0 (the "License");
 ## you may not use this file except in compliance with the License.
@@ -19,47 +19,65 @@
 end
 
 # compute energy
-function easyeq_energy(minlrho_init,nlrho_init,order,rho,a0,v,maxiter,tolerance,approx)
+function easyeq_energy(
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  order::Int64,
+  rho::Float64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  approx::Easyeq_approx
+)
   # compute gaussian quadrature weights
   weights=gausslegendre(order)
 
-  # compute initial guess from previous rho
-  (u,E,err)=easyeq_hatu(easyeq_init_u(a0,order,weights),order,(10.)^minlrho_init,v,maxiter,tolerance,weights,approx)
-  for j in 2:nlrho_init
-    rho_tmp=10^(minlrho_init+(log10(rho)-minlrho_init)*(j-1)/(nlrho_init-1))
-    (u,E,err)=easyeq_hatu(u,order,rho_tmp,v,maxiter,tolerance,weights,approx)
-  end
+  # compute u
+  (u,E,err)= easyeq_compute_u_prevrho([rho],minlrho_init,nlrho_init,a0,order,v,maxiter,tolerance,weights,approx)
 
   # print energy
   @printf("% .15e % .15e\n",real(E),err)
 end
 
 # compute energy as a function of rho
-function easyeq_energy_rho(rhos,order,a0,v,maxiter,tolerance,approx)
+function easyeq_energy_rho(
+  rhos::Array{Float64,1},
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  order::Int64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  approx::Easyeq_approx
+)
   # compute gaussian quadrature weights
   weights=gausslegendre(order)
-  # init u
-  u=easyeq_init_u(a0,order,weights)
+  # compute u
+  (us,es,errs)= easyeq_compute_u_prevrho(rhos,minlrho_init,nlrho_init,a0,order,v,maxiter,tolerance,weights,approx)
 
   for j in 1:length(rhos)
-    # compute u (init newton with previously computed u)
-    (u,E,err)=easyeq_hatu(u,order,rhos[j],v,maxiter,tolerance,weights,approx)
-
-    @printf("% .15e % .15e % .15e\n",rhos[j],real(E),err)
-
+    @printf("% .15e % .15e % .15e\n",rhos[j],real(es[j]),errs[j])
   end
 end
 
 # compute u(k)
-function easyeq_uk(minlrho_init,nlrho_init,order,rho,a0,v,maxiter,tolerance,approx)
+function easyeq_uk(
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  order::Int64,
+  rho::Float64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  approx::Easyeq_approx
+)
   weights=gausslegendre(order)
 
-  # compute initial guess from previous rho
-  (u,E,err)=easyeq_hatu(easyeq_init_u(a0,order,weights),order,(10.)^minlrho_init,v,maxiter,tolerance,weights,approx)
-  for j in 2:nlrho_init
-    rho_tmp=10^(minlrho_init+(log10(rho)-minlrho_init)*(j-1)/(nlrho_init-1))
-    (u,E,err)=easyeq_hatu(u,order,rho_tmp,v,maxiter,tolerance,weights,approx)
-  end
+  # compute u
+  (u,E,err)= easyeq_compute_u_prevrho_error([rho],minlrho_init,nlrho_init,a0,order,v,maxiter,tolerance,weights,approx)
 
   for i in 1:order
     k=(1-weights[1][i])/(1+weights[1][i])
@@ -68,15 +86,24 @@ function easyeq_uk(minlrho_init,nlrho_init,order,rho,a0,v,maxiter,tolerance,appr
 end
 
 # compute u(x)
-function easyeq_ux(minlrho_init,nlrho_init,order,rho,a0,v,maxiter,tolerance,xmin,xmax,nx,approx)
+function easyeq_ux(
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  order::Int64,
+  rho::Float64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  xmin::Float64,
+  xmax::Float64,
+  nx::Int64,
+  approx::Easyeq_approx
+)
   weights=gausslegendre(order)
 
-  # compute initial guess from previous rho
-  (u,E,err)=easyeq_hatu(easyeq_init_u(a0,order,weights),order,(10.)^minlrho_init,v,maxiter,tolerance,weights,approx)
-  for j in 2:nlrho_init
-    rho_tmp=10^(minlrho_init+(log10(rho)-minlrho_init)*(j-1)/(nlrho_init-1))
-    (u,E,err)=easyeq_hatu(u,order,rho_tmp,v,maxiter,tolerance,weights,approx)
-  end
+  # compute u
+  (u,E,err)= easyeq_compute_u_prevrho_error([rho],minlrho_init,nlrho_init,a0,order,v,maxiter,tolerance,weights,approx)
 
   for i in 1:nx
     x=xmin+(xmax-xmin)*i/nx
@@ -85,15 +112,24 @@ function easyeq_ux(minlrho_init,nlrho_init,order,rho,a0,v,maxiter,tolerance,xmin
 end
 
 # compute 2u(x)-rho u*u(x)
-function easyeq_uux(minlrho_init,nlrho_init,order,rho,a0,v,maxiter,tolerance,xmin,xmax,nx,approx)
+function easyeq_uux(
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  order::Int64,
+  rho::Float64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  xmin::Float64,
+  xmax::Float64,
+  nx::Int64,
+  approx::Easyeq_approx
+)
   weights=gausslegendre(order)
 
-  # compute initial guess from previous rho
-  (u,E,err)=easyeq_hatu(easyeq_init_u(a0,order,weights),order,(10.)^minlrho_init,v,maxiter,tolerance,weights,approx)
-  for j in 2:nlrho_init
-    rho_tmp=10^(minlrho_init+(log10(rho)-minlrho_init)*(j-1)/(nlrho_init-1))
-    (u,E,err)=easyeq_hatu(u,order,rho_tmp,v,maxiter,tolerance,weights,approx)
-  end
+  # compute u
+  (u,E,err)= easyeq_compute_u_prevrho_error([rho],minlrho_init,nlrho_init,a0,order,v,maxiter,tolerance,weights,approx)
 
   for i in 1:nx
     x=xmin+(xmax-xmin)*i/nx
@@ -102,16 +138,22 @@ function easyeq_uux(minlrho_init,nlrho_init,order,rho,a0,v,maxiter,tolerance,xmi
 end
 
 # condensate fraction
-function easyeq_condensate_fraction(minlrho_init,nlrho_init,order,rho,a0,v,maxiter,tolerance,approx)
+function easyeq_condensate_fraction(
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  order::Int64,
+  rho::Float64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  approx::Easyeq_approx
+)
   # compute gaussian quadrature weights
   weights=gausslegendre(order)
 
-  # compute initial guess from previous rho
-  (u,E,err)=easyeq_hatu(easyeq_init_u(a0,order,weights),order,(10.)^minlrho_init,v,maxiter,tolerance,weights,approx)
-  for j in 2:nlrho_init
-    rho_tmp=10^(minlrho_init+(log10(rho)-minlrho_init)*(j-1)/(nlrho_init-1))
-    (u,E,err)=easyeq_hatu(u,order,rho_tmp,v,maxiter,tolerance,weights,approx)
-  end
+  # compute u
+  (u,E,err)= easyeq_compute_u_prevrho([rho],minlrho_init,nlrho_init,a0,order,v,maxiter,tolerance,weights,approx)
 
   # compute eta
   eta=easyeq_eta(u,order,rho,v,maxiter,tolerance,weights,approx)
@@ -121,25 +163,350 @@ function easyeq_condensate_fraction(minlrho_init,nlrho_init,order,rho,a0,v,maxit
 end
 
 # condensate fraction as a function of rho
-function easyeq_condensate_fraction_rho(rhos,order,a0,v,maxiter,tolerance,approx)
+function easyeq_condensate_fraction_rho(
+  rhos::Array{Float64,1},
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  order::Int64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  approx::Easyeq_approx
+)
   weights=gausslegendre(order)
-  # init u
-  u=easyeq_init_u(a0,order,weights)
+  # compute u
+  (us,es,errs)= easyeq_compute_u_prevrho(rhos,minlrho_init,nlrho_init,a0,order,v,maxiter,tolerance,weights,approx)
 
   for j in 1:length(rhos)
-    # compute u (init newton with previously computed u)
-    (u,E,err)=easyeq_hatu(u,order,rhos[j],v,maxiter,tolerance,weights,approx)
-
     # compute eta
-    eta=easyeq_eta(u,order,rhos[j],v,maxiter,tolerance,weights,approx)
+    eta=easyeq_eta(us[j],order,rhos[j],v,maxiter,tolerance,weights,approx)
+    @printf("% .15e % .15e % .15e\n",rhos[j],eta,errs[j])
+  end
+end
 
-    @printf("% .15e % .15e % .15e\n",rhos[j],eta,err)
+# 2 pt correlation function
+function easyeq_2pt(
+  xmin::Float64,
+  xmax::Float64,
+  nx::Int64,
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  order::Int64,
+  windowL::Float64,
+  rho::Float64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  approx::Easyeq_approx
+)
+  # compute gaussian quadrature weights
+  weights=gausslegendre(order)
+
+  # compute u
+  (u,E,err)= easyeq_compute_u_prevrho_error([rho],minlrho_init,nlrho_init,a0,order,v,maxiter,tolerance,weights,approx)
+
+  # compute useful terms
+  (V,V0)=easyeq_init_v(weights,v)
+  (Eta,Eta0)=easyeq_init_H(weights,v)
+  (E,S,A,T,B,X)=easyeq_ESATBX(rho*u,V,V0,Eta,Eta0,weights,rho,approx)
+
+  # compute C2
+  for j in 1:nx
+    x=xmin+(xmax-xmin)/nx*j
+    C2=easyeq_C2(x,u,windowL,rho,weights,Eta,Eta0,approx,E,S,A,T,B,X)
+    @printf("% .15e % .15e\n",x,C2)
+  end
+end
+
+# maximum of 2 point correlation function
+function easyeq_2pt_max(
+  dx::Float64,
+  x0::Float64, # initial guess is x0/rho^(1/3)
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  order::Int64,
+  windowL::Float64,
+  rho::Float64,
+  a0::Float64,
+  v::Function,
+  maxstep::Float64,
+  maxiter::Int64,
+  tolerance::Float64,
+  tolerance_max::Float64,
+  approx::Easyeq_approx
+)
+  # compute gaussian quadrature weights
+  weights=gausslegendre(order)
+
+  # compute u
+  (u,E,err)= easyeq_compute_u_prevrho_error([rho],minlrho_init,nlrho_init,a0,order,v,maxiter,tolerance,weights,approx)
+
+  # compute useful terms
+  (V,V0)=easyeq_init_v(weights,v)
+  (Eta,Eta0)=easyeq_init_H(weights,v)
+
+  (x,f)=easyeq_C2_max(u,x0/rho^(1/3),dx,maxstep,maxiter,tolerance_max,windowL,rho,weights,V,V0,Eta,Eta0,approx)
+
+  if(x==Inf)
+    @printf(stderr,"max search failed for rho=%e\n",rho)
+  else
+    @printf("% .15e % .15e\n",x,f)
+  end
+end
+
+# maximum of 2 point correlation function as a function of rho
+function easyeq_2pt_max_rho(
+  rhos::Array{Float64,1},
+  dx::Float64,
+  x0::Float64, # initial guess is x0/rho^(1/3)
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  order::Int64,
+  windowL::Float64,
+  a0::Float64,
+  v::Function,
+  maxstep::Float64,
+  maxiter::Int64,
+  tolerance::Float64,
+  tolerance_max::Float64,
+  approx::Easyeq_approx
+)
+  # compute gaussian quadrature weights
+  weights=gausslegendre(order)
+
+  # compute u
+  (us,es,errs)= easyeq_compute_u_prevrho_error(rhos,minlrho_init,nlrho_init,a0,order,v,maxiter,tolerance,weights,approx)
+
+  # compute useful terms
+  (V,V0)=easyeq_init_v(weights,v)
+  (Eta,Eta0)=easyeq_init_H(weights,v)
+
+  # save result from each task
+  xs=Array{Float64,1}(undef,length(rhos))
+  fs=Array{Float64,1}(undef,length(rhos))
+
+  # spawn workers
+  work=spawn_workers(length(rhos))
+
+  count=0
+  # for each worker
+  @sync for p in 1:length(work)
+    # for each task
+    @async for j in work[p]
+      count=count+1
+      if count>=length(work)
+        progress(count,length(rhos),10000)
+      end
+      # run the task
+      (xs[j],fs[j])=remotecall_fetch(easyeq_C2_max,workers()[p],us[j],x0/rhos[j]^(1/3),dx,maxstep,maxiter,tolerance_max,windowL,rhos[j],weights,V,V0,Eta,Eta0,approx)
+    end
+  end
+
+  for j in 1:length(rhos)
+    if(xs[j]==Inf)
+      @printf(stderr,"max search failed for rho=%e\n",rhos[j])
+    else
+      @printf("% .15e % .15e % .15e\n",rhos[j],xs[j],fs[j])
+    end
   end
 end
 
 
-# initialize u
-@everywhere function easyeq_init_u(a0,order,weights)
+# Fourier transform of 2 pt correlation function
+function easyeq_2pt_fourier(
+  kmin::Float64,
+  kmax::Float64,
+  nk::Int64,
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  order::Int64,
+  windowL::Float64,
+  rho::Float64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  approx::Easyeq_approx
+)
+  # compute gaussian quadrature weights
+  weights=gausslegendre(order)
+
+  # compute u
+  (u,E,err)= easyeq_compute_u_prevrho_error([rho],minlrho_init,nlrho_init,a0,order,v,maxiter,tolerance,weights,approx)
+
+  # compute useful terms
+  (V,V0)=easyeq_init_v(weights,v)
+  (Eta,Eta0)=easyeq_init_H(weights,v)
+  (E,S,A,T,B,X)=easyeq_ESATBX(rho*u,V,V0,Eta,Eta0,weights,rho,approx)
+
+  # compute C2
+  for j in 1:nk
+    k=kmin+(kmax-kmin)/nk*j
+    C2=easyeq_C2_fourier(k,u,windowL,rho,weights,Eta,Eta0,approx,E,S,A,T,B,X)
+    @printf("% .15e % .15e\n",k,C2)
+  end
+end
+
+# maximum of Fourier transform of 2 point correlation function
+function easyeq_2pt_fourier_max(
+  dk::Float64,
+  k0::Float64, # initial guess is k0*rho^(1/3)
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  order::Int64,
+  windowL::Float64,
+  rho::Float64,
+  a0::Float64,
+  v::Function,
+  maxstep::Float64,
+  maxiter::Int64,
+  tolerance::Float64,
+  tolerance_max::Float64,
+  approx::Easyeq_approx
+)
+  # compute gaussian quadrature weights
+  weights=gausslegendre(order)
+
+  # compute u
+  (u,E,err)= easyeq_compute_u_prevrho_error([rho],minlrho_init,nlrho_init,a0,order,v,maxiter,tolerance,weights,approx)
+
+  # compute useful terms
+  (V,V0)=easyeq_init_v(weights,v)
+  (Eta,Eta0)=easyeq_init_H(weights,v)
+
+  (k,f)=easyeq_C2_fourier_max(u,k0*rho^(1/3),dk,maxstep,maxiter,tolerance_max,windowL,rho,weights,V,V0,Eta,Eta0,approx)
+
+  if(k==Inf)
+    @printf(stderr,"max search failed for rho=%e\n",rho)
+  else
+    @printf("% .15e % .15e\n",k,f)
+  end
+end
+
+# maximum of Fourier transform of 2 point correlation function as a function of rho
+function easyeq_2pt_fourier_max_rho(
+  rhos::Array{Float64,1},
+  dk::Float64,
+  k0::Float64, # initial guess is k0*rho^(1/3)
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  order::Int64,
+  windowL::Float64,
+  a0::Float64,
+  v::Function,
+  maxstep::Float64,
+  maxiter::Int64,
+  tolerance::Float64,
+  tolerance_max::Float64,
+  approx::Easyeq_approx
+)
+  # compute gaussian quadrature weights
+  weights=gausslegendre(order)
+
+  # compute u
+  (us,es,errs)= easyeq_compute_u_prevrho_error(rhos,minlrho_init,nlrho_init,a0,order,v,maxiter,tolerance,weights,approx)
+
+  # compute useful terms
+  (V,V0)=easyeq_init_v(weights,v)
+  (Eta,Eta0)=easyeq_init_H(weights,v)
+
+  # save result from each task
+  ks=Array{Float64,1}(undef,length(rhos))
+  fs=Array{Float64,1}(undef,length(rhos))
+
+  # spawn workers
+  work=spawn_workers(length(rhos))
+
+  count=0
+  # for each worker
+  @sync for p in 1:length(work)
+    # for each task
+    @async for j in work[p]
+      count=count+1
+      if count>=length(work)
+        progress(count,length(rhos),10000)
+      end
+      # run the task
+      (ks[j],fs[j])=remotecall_fetch(easyeq_C2_fourier_max,workers()[p],us[j],k0*rhos[j]^(1/3),dk,maxstep,maxiter,tolerance_max,windowL,rhos[j],weights,V,V0,Eta,Eta0,approx)
+    end
+  end
+
+  for j in 1:length(rhos)
+    if(ks[j]==Inf)
+      @printf(stderr,"max search failed for rho=%e\n",rhos[j])
+    else
+      @printf("% .15e % .15e % .15e\n",rhos[j],ks[j],fs[j])
+    end
+  end
+end
+
+# momentum distribution
+function easyeq_momentum_distribution(
+  kmin::Float64,
+  kmax::Float64,
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  order::Int64,
+  windowL::Float64, #L=windowL/k^2
+  rho::Float64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  approx::Easyeq_approx
+)
+  # compute gaussian quadrature weights
+  weights=gausslegendre(order)
+
+  # compute u
+  (u,E,err)= easyeq_compute_u_prevrho_error([rho],minlrho_init,nlrho_init,a0,order,v,maxiter,tolerance,weights,approx)
+
+  # compute useful terms
+  (V,V0)=easyeq_init_v(weights,v)
+  (Eta,Eta0)=easyeq_init_H(weights,v)
+  (E,S,A,T,B,X)=easyeq_ESATBX(rho*u,V,V0,Eta,Eta0,weights,rho,approx)
+  # dXi/dlambda without the delta function and u
+  dXidlambda=(dotPhi(B.*T./((X.+1).^2))./(2*(X.+1))+B.*T./(2*(X.+1).^3).*dotdPhi(B.*T./(X.+1).^2))./A*(16*pi^3)
+
+  dXi=inv(easyeq_dXi(rho*u,Eta,Eta0,weights,rho,approx,E,S,A,T,B,X))
+
+  # compute momentum distribution
+  for j in 1:order
+    q=(1-weights[1][j])/(1+weights[1][j])
+    # drop if not in k interval
+    if q<kmin || q>kmax
+      continue
+    end
+
+    # delta(k_i,q)
+    delta=Array{Float64,1}(undef,order)
+    L=windowL/q^2
+    for i in 1:order
+      k=(1-weights[1][i])/(1+weights[1][i])
+      delta[i]=(1.)/(2*k*q*L)*(gaussian(k-q,(1.)/L)-gaussian(k+q,(1.)/L))
+    end
+
+    # du/dlambda
+    du=-dXi*(dXidlambda.*delta*u[j])
+    # rescale u
+    du=du/rho
+
+    # compute M
+    M=-integrate_legendre_sampled(y->(1-y)/y^3,Eta0.*du,0.,1.,weights)*rho/(16*pi^3)
+
+    @printf("% .15e % .15e\n",q,M)
+  end
+end
+
+
+# initialize u from scattering solution
+@everywhere function easyeq_init_u(
+  a0::Float64,
+  order::Int64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}}
+)
   u=zeros(Float64,order)
   for j in 1:order
     # transformed k
@@ -150,8 +517,100 @@ end
   return u
 end
 
+# compute u for an array of rhos
+# use scattering solution for the first one, and the previous rho for the others
+@everywhere function easyeq_compute_u_rho(
+  rhos::Array{Float64,1},
+  a0::Float64,
+  order::Int64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  approx::Easyeq_approx
+)
+  us=Array{Array{Float64,1}}(undef,length(rhos))
+  es=Array{Float64,1}(undef,length(rhos))
+  errs=Array{Float64,1}(undef,length(rhos))
+
+  (us[1],es[1],errs[1])=easyeq_hatu(easyeq_init_u(a0,order,weights),order,rhos[1],v,maxiter,tolerance,weights,approx)
+  for j in 2:length(rhos)
+    (us[j],es[j],errs[j])=easyeq_hatu(us[j-1],order,rhos[j],v,maxiter,tolerance,weights,approx)
+  end
+
+  return (us,es,errs)
+end
+
+# compute u for an array of rhos
+# start from a smaller rho and work up to rhos[1]
+@everywhere function easyeq_compute_u_prevrho(
+  rhos::Array{Float64,1},
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  a0::Float64,
+  order::Int64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  approx::Easyeq_approx
+)
+
+  # only work up to rhos[1] if nlrho_init>0
+  if nlrho_init>0
+    rhos_init=Array{Float64,1}(undef,nlrho_init)
+    for j in 0:nlrho_init-1
+      rhos_init[j+1]=(nlrho_init==1 ? 10^minlrho_init : 10^(minlrho_init+(log10(rhos[1])-minlrho_init)/(nlrho_init-1)*j))
+    end
+    append!(rhos_init,rhos)
+  # start from rhos[1] if nlrho_init=0
+  else
+    rhos_init=rhos
+  end
+  (us,es,errs)=easyeq_compute_u_rho(rhos_init,a0,order,v,maxiter,tolerance,weights,approx)
+
+  # return a single value if there was a single input
+  if length(rhos)==1
+    return (us[nlrho_init+1],es[nlrho_init+1],errs[nlrho_init+1])
+  else
+    return (us[nlrho_init+1:length(us)],es[nlrho_init+1:length(es)],errs[nlrho_init+1:length(errs)])
+  end
+end
+# with error message if the computation failed to be accurate enough
+@everywhere function easyeq_compute_u_prevrho_error(
+  rhos::Array{Float64,1},
+  minlrho_init::Float64,
+  nlrho_init::Int64,
+  a0::Float64,
+  order::Int64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  approx::Easyeq_approx
+)
+  (us,es,errs)=easyeq_compute_u_prevrho(rhos,minlrho_init,nlrho_init,a0,order,v,maxiter,tolerance,weights,approx)
+  # check errs
+  for j in 1:length(errs)
+    if errs[j]>tolerance
+      print(stderr,"warning: computation of u failed for rho=",rhos[j],"\n")
+    end
+  end
+  return (us,es,errs)
+end
+
+
 # \hat u(k) computed using Newton
-@everywhere function easyeq_hatu(u0,order,rho,v,maxiter,tolerance,weights,approx)
+@everywhere function easyeq_hatu(
+  u0::Array{Float64,1},
+  order::Int64,
+  rho::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  approx::Easyeq_approx
+)
   # initialize V and Eta
   (V,V0)=easyeq_init_v(weights,v)
   (Eta,Eta0)=easyeq_init_H(weights,v)
@@ -162,7 +621,8 @@ end
   # iterate
   err=Inf
   for i in 1:maxiter-1
-    new=u-inv(easyeq_dXi(u,V,V0,Eta,Eta0,weights,rho,approx))*easyeq_Xi(u,V,V0,Eta,Eta0,weights,rho,approx)
+    (E,S,A,T,B,X)=easyeq_ESATBX(u,V,V0,Eta,Eta0,weights,rho,approx)
+    new=u-inv(easyeq_dXi(u,Eta,Eta0,weights,rho,approx,E,S,A,T,B,X))*easyeq_Xi(u,order,S,A,T,B,X)
 
     err=norm(new-u)/norm(u)
     if(err<tolerance)
@@ -176,15 +636,23 @@ end
 end
 
 # \Eta
-@everywhere function easyeq_H(x,t,weights,v)
-  return (x>t ? 2*t/x : 2)* integrate_legendre(y->2*pi*((x+t)*y+abs(x-t)*(1-y))*v((x+t)*y+abs(x-t)*(1-y)),0,1,weights)
+@everywhere function easyeq_H(
+  x::Float64,
+  t::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  v::Function
+)
+  return (x>t ? 2*t/x : 2)* integrate_legendre(y->2*pi*((x+t)*y+abs(x-t)*(1-y))*v((x+t)*y+abs(x-t)*(1-y)),0.,1.,weights)
 end
 
 # initialize V
-@everywhere function easyeq_init_v(weights,v)
+@everywhere function easyeq_init_v(
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  v::Function
+)
   order=length(weights[1])
-  V=Array{Float64}(undef,order)
-  V0=v(0)
+  V=Array{Float64,1}(undef,order)
+  V0=v(0.)
   for i in 1:order
     k=(1-weights[1][i])/(1+weights[1][i])
     V[i]=v(k)
@@ -193,29 +661,58 @@ end
 end
 
 # initialize Eta
-@everywhere function easyeq_init_H(weights,v)
+@everywhere function easyeq_init_H(
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  v::Function
+)
   order=length(weights[1])
-  Eta=Array{Array{Float64}}(undef,order)
-  Eta0=Array{Float64}(undef,order)
+  Eta=Array{Array{Float64,1},1}(undef,order)
+  Eta0=Array{Float64,1}(undef,order)
   for i in 1:order
     k=(1-weights[1][i])/(1+weights[1][i])
-    Eta[i]=Array{Float64}(undef,order)
+    Eta[i]=Array{Float64,1}(undef,order)
     for j in 1:order
       y=(weights[1][j]+1)/2
       Eta[i][j]=easyeq_H(k,(1-y)/y,weights,v)
     end
     y=(weights[1][i]+1)/2
-    Eta0[i]=easyeq_H(0,(1-y)/y,weights,v)
+    Eta0[i]=easyeq_H(0.,(1-y)/y,weights,v)
   end
   return(Eta,Eta0)
 end
 
 # Xi(u)
-@everywhere function easyeq_Xi(u,V,V0,Eta,Eta0,weights,rho,approx)
+@everywhere function easyeq_Xi(
+  u::Array{Float64,1},
+  order::Int64,
+  S::Array{Float64,1},
+  A::Array{Float64,1},
+  T::Array{Float64,1},
+  B::Array{Float64,1},
+  X::Array{Float64,1}
+)
+  return u.-T./(2*(X.+1)).*dotPhi(B.*T./(X.+1).^2)
+end
+
+# compute E,S,A,T,B,X
+@everywhere function easyeq_ESATBX(
+  u::Array{Float64,1},
+  V::Array{Float64,1},
+  V0::Float64,
+  Eta::Array{Array{Float64,1},1},
+  Eta0::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  rho::Float64,
+  approx::Easyeq_approx
+)
   order=length(weights[1])
 
   # init
-  out=zeros(Float64,order)
+  S=zeros(Float64,order)
+  A=zeros(Float64,order)
+  T=zeros(Float64,order)
+  B=zeros(Float64,order)
+  X=zeros(Float64,order)
 
   # compute E before running the loop
   E=easyeq_en(u,V0,Eta0,rho,weights)
@@ -224,188 +721,359 @@ end
     # k_i
     k=(1-weights[1][i])/(1+weights[1][i])
     # S_i
-    S=V[i]-1/(rho*(2*pi)^3)*integrate_legendre_sampled(y->(1-y)/y^3,Eta[i].*u,0,1,weights)
+    S[i]=V[i]-1/(rho*(2*pi)^3)*integrate_legendre_sampled(y->(1-y)/y^3,Eta[i].*u,0.,1.,weights)
 
     # A_K,i
-    A=0.
+    A[i]=0.
     if approx.bK!=0.
-      A+=approx.bK*S
+      A[i]+=approx.bK*S[i]
     end
     if approx.bK!=1.
-      A+=(1-approx.bK)*E
+      A[i]+=(1-approx.bK)*E
     end
 
-    # T
+    # T_i
     if approx.bK==1.
-      T=1.
+      T[i]=1.
     else
-      T=S/A
+      T[i]=S[i]/A[i]
     end
 
-    # B
+    # B_i
     if approx.bK==approx.bL
-      B=1.
+      B[i]=1.
     else
-      B=(approx.bL*S+(1-approx.bL*E))/(approx.bK*S+(1-approx.bK*E))
+      B[i]=(approx.bL*S[i]+(1-approx.bL*E))/(approx.bK*S[i]+(1-approx.bK*E))
     end
 
     # X_i
-    X=k^2/(2*A*rho)
-
-    # U_i
-    out[i]=u[i]-T/(2*(X+1))*Phi(B*T/(X+1)^2)
+    X[i]=k^2/(2*A[i]*rho)
   end
 
-  return out
+  return (E,S,A,T,B,X)
 end
 
 # derivative of Xi
-@everywhere function easyeq_dXi(u,V,V0,Eta,Eta0,weights,rho,approx)
+@everywhere function easyeq_dXi(
+  u::Array{Float64,1},
+  Eta::Array{Array{Float64,1},1},
+  Eta0::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  rho::Float64,
+  approx::Easyeq_approx,
+  E::Float64,
+  S::Array{Float64,1},
+  A::Array{Float64,1},
+  T::Array{Float64,1},
+  B::Array{Float64,1},
+  X::Array{Float64,1}
+)
   order=length(weights[1])
 
   # init
   out=zeros(Float64,order,order)
 
-  # compute E before the loop
-  E=easyeq_en(u,V0,Eta0,rho,weights)
-
   for i in 1:order
     # k_i
     k=(1-weights[1][i])/(1+weights[1][i])
-    # S_i
-    S=V[i]-1/(rho*(2*pi)^3)*integrate_legendre_sampled(y->(1-y)/y^3,Eta[i].*u,0,1,weights)
-
-    # A_K,i
-    A=0.
-    if approx.bK!=0.
-      A+=approx.bK*S
-    end
-    if approx.bK!=1.
-      A+=(1-approx.bK)*E
-    end
-
-    # T
-    if approx.bK==1.
-      T=1.
-    else
-      T=S/A
-    end
-
-    # B
-    if approx.bK==approx.bL
-      B=1.
-    else
-      B=(approx.bL*S+(1-approx.bL*E))/(approx.bK*S+(1-approx.bK*E))
-    end
-
-    # X_i
-    X=k^2/(2*A*rho)
 
     for j in 1:order
       y=(weights[1][j]+1)/2
       dS=-1/rho*(1-y)*Eta[i][j]/(2*(2*pi)^3*y^3)*weights[2][j]
       dE=-1/rho*(1-y)*Eta0[j]/(2*(2*pi)^3*y^3)*weights[2][j]
+      dU=(i==j ? 1. : 0.)
 
-      # dA
-      dA=0.
-      if approx.bK!=0.
-	dA+=approx.bK*dS
-      end
-      if approx.bK!=1.
-	dA+=(1-approx.bK)*dE
-      end
-
-      # dT
-      if approx.bK==1.
-	dT=0.
-      else
-	dT=(1-approx.bK)*(E*dS-S*dE)/A^2
-      end
-
-      # dB
-      if approx.bK==approx.bL
-	dB=0.
-      else
-	dB=(approx.bL*(1-approx.bK)-approx.bK*(1-approx.bL))*(E*dS-S*dE)/(approx.bK*S+(1-approx.bK*E))^2
-      end
-
-      dX=-k^2/(2*A^2*rho)*dA
-
-      out[i,j]=(i==j ? 1 : 0)-(dT-T*dX/(X+1))/(2*(X+1))*Phi(B*T/(X+1)^2)-T/(2*(X+1)^3)*(B*dT+T*dB-2*B*T*dX/(X+1))*dPhi(B*T/(X+1)^2)
+      out[i,j]=easyeq_dXi_of_dSdEdU(k,dS,dE,dU,E,S[i],A[i],T[i],B[i],X[i],rho,approx)
     end
   end
-
+  
   return out
 end
 
-# derivative of Xi with respect to mu
-@everywhere function easyeq_dXidmu(u,V,V0,Eta,Eta0,weights,rho,approx)
-  order=length(weights[1])
+# dXi given dS, dE and dU
+@everywhere function easyeq_dXi_of_dSdEdU(
+  k::Float64,
+  dS::Float64,
+  dE::Float64,
+  dU::Float64,
+  E::Float64,
+  S::Float64,
+  A::Float64,
+  T::Float64,
+  B::Float64,
+  X::Float64,
+  rho::Float64,
+  approx::Easyeq_approx
+)
+  # dA
+  dA=0.
+  if approx.bK!=0.
+    dA+=approx.bK*dS
+  end
+  if approx.bK!=1.
+    dA+=(1-approx.bK)*dE
+  end
 
+  # dT,dB
+  # nothing to do if bK=bL=1
+  if approx.bK!=1. || approx.bK!=approx.bL
+    dB=(E*dS-S*dE)/A^2
+  end
+  if approx.bK==1.
+    dT=0.
+  else
+    dT=(1-approx.bK)*dB
+  end
+  if approx.bK==approx.bL
+    dB=0.
+  else
+    dB=(approx.bL*(1-approx.bK)-approx.bK*(1-approx.bL))*dB
+  end
+
+  dX=-k^2/(2*A^2*rho)*dA
+
+  return dU-(dT-T*dX/(X+1))/(2*(X+1))*Phi(B*T/(X+1)^2)-T/(2*(X+1)^3)*(B*dT+T*dB-2*B*T*dX/(X+1))*dPhi(B*T/(X+1)^2)
+end
+
+# derivative of Xi with respect to mu
+@everywhere function easyeq_dXidmu(
+  u::Array{Float64,1},
+  order::Int64,
+  rho::Float64,
+  A::Array{Float64,1},
+  T::Array{Float64,1},
+  B::Array{Float64,1},
+  X::Array{Float64,1}
+)
   # init
   out=zeros(Float64,order)
-
-  # compute E before running the loop
-  E=easyeq_en(u,V0,Eta0,rho,weights)
-
   for i in 1:order
-    # k_i
-    k=(1-weights[1][i])/(1+weights[1][i])
-    # S_i
-    S=V[i]-1/(rho*(2*pi)^3)*integrate_legendre_sampled(y->(1-y)/y^3,Eta[i].*u,0,1,weights)
-
-    # A_K,i
-    A=0.
-    if approx.bK!=0.
-      A+=approx.bK*S
-    end
-    if approx.bK!=1.
-      A+=(1-approx.bK)*E
-    end
-
-    # T
-    if approx.bK==1.
-      T=1.
-    else
-      T=S/A
-    end
-
-    # B
-    if approx.bK==approx.bL
-      B=1.
-    else
-      B=(approx.bL*S+(1-approx.bL*E))/(approx.bK*S+(1-approx.bK*E))
-    end
-
-    # X_i
-    X=k^2/(2*A*rho)
-
-    out[i]=T/(2*rho*A*(X+1)^2)*Phi(B*T/(X+1)^2)+B*T^2/(rho*A*(X+1)^4)*dPhi(B*T/(X+1)^2)
+    out[i]=T[i]/(2*rho*A[i]*(X[i]+1)^2)*Phi(B[i]*T[i]/(X[i]+1)^2)+B[i]*T[i]^2/(rho*A[i]*(X[i]+1)^4)*dPhi(B[i]*T[i]/(X[i]+1)^2)
   end
 
   return out
 end
 
 # energy
-@everywhere function easyeq_en(u,V0,Eta0,rho,weights)
-  return V0-1/(rho*(2*pi)^3)*integrate_legendre_sampled(y->(1-y)/y^3,Eta0.*u,0,1,weights)
+@everywhere function easyeq_en(
+  u::Array{Float64,1},
+  V0::Float64,
+  Eta0::Array{Float64,1},
+  rho::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}}
+)
+  return V0-1/(rho*(2*pi)^3)*integrate_legendre_sampled(y->(1-y)/y^3,Eta0.*u,0.,1.,weights)
 end
 
 # condensate fraction
-@everywhere function easyeq_eta(u,order,rho,v,maxiter,tolerance,weights,approx)
+@everywhere function easyeq_eta(
+  u::Array{Float64,1},
+  order::Int64,
+  rho::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  approx::Easyeq_approx
+)
   (V,V0)=easyeq_init_v(weights,v)
   (Eta,Eta0)=easyeq_init_H(weights,v)
 
-  du=-inv(easyeq_dXi(rho*u,V,V0,Eta,Eta0,weights,rho,approx))*easyeq_dXidmu(rho*u,V,V0,Eta,Eta0,weights,rho,approx)
+  (E,S,A,T,B,X)=easyeq_ESATBX(rho*u,V,V0,Eta,Eta0,weights,rho,approx)
+  du=-inv(easyeq_dXi(rho*u,Eta,Eta0,weights,rho,approx,E,S,A,T,B,X))*easyeq_dXidmu(rho*u,order,rho,A,T,B,X)
 
-  eta=-1/(2*(2*pi)^3)*integrate_legendre_sampled(y->(1-y)/y^3,Eta0.*du,0,1,weights)
+  eta=-1/(2*(2*pi)^3)*integrate_legendre_sampled(y->(1-y)/y^3,Eta0.*du,0.,1.,weights)
 
   return eta
 end
 
 # inverse Fourier transform
-@everywhere function easyeq_u_x(x,u,weights)
+@everywhere function easyeq_u_x(
+  x::Float64,
+  u::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}}
+)
   order=length(weights[1])
-  out=integrate_legendre_sampled(y->(1-y)/y^3*sin(x*(1-y)/y)/x/(2*pi^2),u,0,1,weights)
+  out=integrate_legendre_sampled(y->(1-y)/y^3*sin(x*(1-y)/y)/x/(2*pi^2),u,0.,1.,weights)
   return out
 end
+
+
+# correlation function
+@everywhere function easyeq_C2(
+  x::Float64,
+  u::Array{Float64,1},
+  windowL::Float64,
+  rho::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  Eta::Array{Array{Float64,1},1},
+  Eta0::Array{Float64,1},
+  approx::Easyeq_approx,
+  E::Float64,
+  S::Array{Float64,1},
+  A::Array{Float64,1},
+  T::Array{Float64,1},
+  B::Array{Float64,1},
+  X::Array{Float64,1}
+)
+  g=(r,x)->(r>0. ? sin(r*x)/(r*x)*hann(r,windowL) : hann(r,windowL))
+  (dEta,dEta0)=easyeq_init_H(weights,k->g(k,x))
+  du=easyeq_dudv(g,x,u,rho,weights,Eta,Eta0,dEta,dEta0,approx,E,S,A,T,B,X)
+
+  return rho^2*(1-integrate_legendre_sampled(y->(1-y)/y^3,dEta0.*u+Eta0.*du,0.,1.,weights)/(8*pi^3))
+end
+
+# derivative of u with respect to v in direction g
+@everywhere function easyeq_dudv(
+  g::Function,# should be of the form g(k,x) where x is a parameter
+  x::Float64,
+  u::Array{Float64,1},
+  rho::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  Eta::Array{Array{Float64,1},1},
+  Eta0::Array{Float64,1},
+  dEta::Array{Array{Float64,1},1},
+  dEta0::Array{Float64,1},
+  approx::Easyeq_approx,
+  E::Float64,
+  S::Array{Float64,1},
+  A::Array{Float64,1},
+  T::Array{Float64,1},
+  B::Array{Float64,1},
+  X::Array{Float64,1}
+)
+  # initialize dV and dEta
+  (dV,dV0)=easyeq_init_v(weights,k->g(k,x))
+
+  du=-inv(easyeq_dXi(rho*u,Eta,Eta0,weights,rho,approx,E,S,A,T,B,X))*easyeq_dXidv(rho*u,dV,dV0,dEta,dEta0,weights,rho,approx,E,S,A,T,B,X)
+  # rescale rho
+  du=du/rho
+
+  return du
+end
+
+# derivative of Xi with respect to potential
+@everywhere function easyeq_dXidv(
+  u::Array{Float64,1},
+  dv::Array{Float64,1},
+  dv0::Float64,
+  dEta::Array{Array{Float64,1},1},
+  dEta0::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  rho::Float64,
+  approx::Easyeq_approx,
+  E::Float64,
+  S::Array{Float64,1},
+  A::Array{Float64,1},
+  T::Array{Float64,1},
+  B::Array{Float64,1},
+  X::Array{Float64,1}
+)
+
+  order=length(weights[1])
+
+  # init
+  out=zeros(Float64,order)
+
+  for i in 1:order
+    # k_i
+    k=(1-weights[1][i])/(1+weights[1][i])
+
+    dS=dv[i]
+    dE=dv0
+    for j in 1:order
+      y=(weights[1][j]+1)/2
+      dS+=-1/rho*(1-y)*u[j]*dEta[i][j]/(2*(2*pi)^3*y^3)*weights[2][j]
+      dE+=-1/rho*(1-y)*u[j]*dEta0[j]/(2*(2*pi)^3*y^3)*weights[2][j]
+    end
+
+    out[i]=easyeq_dXi_of_dSdEdU(k,dS,dE,0.,E,S[i],A[i],T[i],B[i],X[i],rho,approx)
+  end
+
+  return out
+end
+
+# maximum of 2 point correlation function
+@everywhere function easyeq_C2_max(
+  u::Array{Float64,1},
+  x0::Float64,
+  dx::Float64,
+  maxstep::Float64,
+  maxiter::Int64,
+  tolerance::Float64,
+  windowL::Float64,
+  rho::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  V::Array{Float64,1},
+  V0::Float64,
+  Eta::Array{Array{Float64,1},1},
+  Eta0::Array{Float64,1},
+  approx::Easyeq_approx
+)
+  # compute some useful terms
+  (E,S,A,T,B,X)=easyeq_ESATBX(rho*u,V,V0,Eta,Eta0,weights,rho,approx)
+
+  (x,f)=newton_maximum(y->easyeq_C2(y,u,windowL,rho,weights,Eta,Eta0,approx,E,S,A,T,B,X),x0,dx,maxiter,tolerance,maxstep)
+
+  return(x,f)
+end
+
+# Fourier transform of correlation function
+@everywhere function easyeq_C2_fourier(
+  q::Float64,
+  u::Array{Float64,1},
+  windowL::Float64,
+  rho::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  Eta::Array{Array{Float64,1},1},
+  Eta0::Array{Float64,1},
+  approx::Easyeq_approx,
+  E::Float64,
+  S::Array{Float64,1},
+  A::Array{Float64,1},
+  T::Array{Float64,1},
+  B::Array{Float64,1},
+  X::Array{Float64,1}
+)
+  # direction in which to differentiate u
+  g=(r,x)->(r>0. ? (1.)/(2*x*r*windowL)*(gaussian(x-r,(1.)/windowL)-gaussian(x+r,(1.)/windowL)) : gaussian(x,(1.)/windowL))
+  (dEta,dEta0)=easyeq_init_H(weights,k->g(k,q))
+  du=easyeq_dudv(g,q,u,rho,weights,Eta,Eta0,dEta,dEta0,approx,E,S,A,T,B,X)
+
+  return rho^2*(-integrate_legendre_sampled(y->(1-y)/y^3,dEta0.*u+Eta0.*du,0.,1.,weights)/(8*pi^3))
+end
+
+
+# maximum of Fourier transform of 2 point correlation function
+@everywhere function easyeq_C2_fourier_max(
+  u::Array{Float64,1},
+  k0::Float64,
+  dk::Float64,
+  maxstep::Float64,
+  maxiter::Int64,
+  tolerance::Float64,
+  windowL::Float64,
+  rho::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  V::Array{Float64,1},
+  V0::Float64,
+  Eta::Array{Array{Float64,1},1},
+  Eta0::Array{Float64,1},
+  approx::Easyeq_approx
+)
+  # compute some useful terms
+  (E,S,A,T,B,X)=easyeq_ESATBX(rho*u,V,V0,Eta,Eta0,weights,rho,approx)
+
+  (k,f)=newton_maximum(y->easyeq_C2_fourier(y,u,windowL,rho,weights,Eta,Eta0,approx,E,S,A,T,B,X),k0,dk,maxiter,tolerance,maxstep)
+
+  return (k,f)
+end
+
+
+@everywhere function barEta(
+  q::Float64,
+  y::Float64,
+  t::Float64
+)
+  return (t>=abs(y-q) && t<=y+q ? pi/y/q : 0.)
+end
diff --git a/src/integration.jl b/src/integration.jl
index 9be4641..223d6cc 100644
--- a/src/integration.jl
+++ b/src/integration.jl
@@ -1,4 +1,4 @@
-## Copyright 2021 Ian Jauslin
+## Copyright 2021-2023 Ian Jauslin
 ## 
 ## Licensed under the Apache License, Version 2.0 (the "License");
 ## you may not use this file except in compliance with the License.
@@ -13,7 +13,12 @@
 ## limitations under the License.
 
 # approximate \int_a^b f using Gauss-Legendre quadratures
-@everywhere function integrate_legendre(f,a,b,weights)
+@everywhere function integrate_legendre(
+  f::Function,
+  a::Float64,
+  b::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}}
+)
   out=0
   for i in 1:length(weights[1])
     out+=(b-a)/2*weights[2][i]*f((b-a)/2*weights[1][i]+(b+a)/2)
@@ -21,7 +26,13 @@
   return out
 end
 # \int f*g where g is sampled at the Legendre nodes
-@everywhere function integrate_legendre_sampled(f,g,a,b,weights)
+@everywhere function integrate_legendre_sampled(
+  f::Function,
+  g::Array{Float64,1},
+  a::Float64,
+  b::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}}
+)
   out=0
   for i in 1:length(weights[1])
     out+=(b-a)/2*weights[2][i]*f((b-a)/2*weights[1][i]+(b+a)/2)*g[i]
@@ -31,7 +42,12 @@ end
 
 
 # approximate \int_a^b f/sqrt((b-x)(x-a)) using Gauss-Chebyshev quadratures
-@everywhere function integrate_chebyshev(f,a,b,N)
+@everywhere function integrate_chebyshev(
+  f::Function,
+  a::Float64,
+  b::Float64,
+  N::Int64
+)
   out=0
   for i in 1:N
     out=out+pi/N*f((b-a)/2*cos((2*i-1)/(2*N)*pi)+(b+a)/2)
@@ -40,7 +56,11 @@ end
 end
 
 # approximate \int_0^\infty dr f(r)*exp(-a*r) using Gauss-Chebyshev quadratures
-@everywhere function integrate_laguerre(f,a,weights_gL)
+@everywhere function integrate_laguerre(
+  f::Function,
+  a::Float64,
+  weights_gL::Tuple{Array{Float64,1},Array{Float64,1}}
+)
   out=0.
   for i in 1:length(weights_gL[1])
     out+=1/a*f(weights_gL[1][i]/a)*weights_gL[2][i]
@@ -49,10 +69,28 @@ end
 end
 
 # Hann window
-@everywhere function hann(x,L)
+@everywhere function hann(
+  x::Float64,
+  L::Float64
+)
   if abs(x)<L/2
     return cos(pi*x/L)^2
   else
     return 0.
   end
 end
+# Fourier transform (in 3d)
+@everywhere function hann_fourier(
+  k::Float64,
+  L::Float64
+)
+  return L^2*4*pi^3/k*(((k*L)^3-4*k*L*pi^2)*cos(k*L/2)-2*(3*(k*L)^2-4*pi^2)*sin(k*L/2))/((k*L)^3-4*k*L*pi^2)^2
+end
+
+# normalized Gaussian (in 3d)
+@everywhere function gaussian(
+  k::Float64,
+  L::Float64
+)
+  return exp(-k^2/(2*L))/sqrt(8*pi^3*L^3)
+end
diff --git a/src/interpolation.jl b/src/interpolation.jl
index fa3bcdb..066bc20 100644
--- a/src/interpolation.jl
+++ b/src/interpolation.jl
@@ -1,4 +1,4 @@
-## Copyright 2021 Ian Jauslin
+## Copyright 2021-2023 Ian Jauslin
 ## 
 ## Licensed under the Apache License, Version 2.0 (the "License");
 ## you may not use this file except in compliance with the License.
@@ -14,7 +14,11 @@
 
 # linear interpolation: given vectors x,y, compute a linear interpolation for y(x0)
 # assume x is ordered
-@everywhere function linear_interpolation(x0,x,y)
+@everywhere function linear_interpolation(
+  x0::Float64,
+  x::Array{Float64,1},
+  y::Array{Float64,1}
+)
   # if x0 is beyond all x's, then return the corresponding boundary value.
   if x0>x[length(x)]
     return y[length(y)]
@@ -28,7 +32,12 @@
   # interpolate
   return y[i]+(y[i+1]-y[i])*(x0-x[i])/(x[i+1]-x[i])
 end
-@everywhere function bracket(x0,x,a,b)
+@everywhere function bracket(
+  x0::Float64,
+  x::Array{Float64,1},
+  a::Int64,
+  b::Int64
+)
   i=floor(Int64,(a+b)/2)
   if x0<x[i]
     return bracket(x0,x,a,i)
@@ -41,15 +50,18 @@ end
 
 
 # polynomial interpolation of a family of points
-@everywhere function poly_interpolation(x,y)
+@everywhere function poly_interpolation(
+  x::Array{Float64,1},
+  y::Array{Float64,1}
+)
   # init for recursion
-  rec=Array{Polynomial{Float64}}(undef,length(x))
+  rec=Array{Polynomial{Float64},1}(undef,length(x))
   for i in 1:length(x)
     rec[i]=Polynomial([1.])
   end
 
   # compute \prod (x-x_i)
-  poly_interpolation_rec(rec,x,1,length(x))
+  poly_interpolation_rec(rec,x,1.,length(x))
 
   # sum terms together
   out=0.
@@ -60,7 +72,12 @@ end
   return out
 end
 # recursive helper function
-@everywhere function poly_interpolation_rec(out,x,a,b)
+@everywhere function poly_interpolation_rec(
+  out::Array{Float64,1},
+  x::Array{Float64,1},
+  a::Float64,
+  b::Float64
+)
   if a==b
     return
   end
@@ -91,7 +108,10 @@ end
   return
 end
 ## the following does the same, but has complexity N^2, the function above has N*log(N)
-#@everywhere function poly_interpolation(x,y)
+#@everywhere function poly_interpolation(
+#  x::Array{Float64,1},
+#  y::Array{Float64,1}
+#)
 #  out=Polynomial([0.])
 #  for i in 1:length(x)
 #    prod=Polynomial([1.])
diff --git a/src/main.jl b/src/main.jl
index 382fe6b..28fc2be 100644
--- a/src/main.jl
+++ b/src/main.jl
@@ -1,4 +1,4 @@
-## Copyright 2021 Ian Jauslin
+## Copyright 2021-2023 Ian Jauslin
 ## 
 ## Licensed under the Apache License, Version 2.0 (the "License");
 ## you may not use this file except in compliance with the License.
@@ -22,6 +22,8 @@ include("chebyshev.jl")
 include("integration.jl")
 include("interpolation.jl")
 include("tools.jl")
+include("multithread.jl")
+include("optimization.jl")
 include("potentials.jl")
 include("print.jl")
 include("easyeq.jl")
@@ -36,9 +38,6 @@ function main()
   rho=1e-6
   e=1e-4
 
-  # incrementally initialize Newton algorithm
-  nlrho_init=1
-
   # potential
   v=k->v_exp(k,1.)
   a0=a0_exp(1.)
@@ -49,19 +48,29 @@ function main()
   v_param_e=1.
 
   # plot range when plotting in rho
-  minlrho=-6
-  maxlrho=2
+  # linear
+  minrho=1e-6
+  maxrho=1e2
+  nrho=0
+  # logarithmic
+  minlrho=-6.
+  maxlrho=2.
   nlrho=100
-  rhos=Array{Float64}(undef,0)
+  # list
+  rhos=Array{Float64,1}(undef,0)
   # plot range when plotting in e
-  minle=-6
-  maxle=2
+  minle=-6.
+  maxle=2.
   nle=100
-  es=Array{Float64}(undef,0)
+  es=Array{Float64,1}(undef,0)
   # plot range when plotting in x
-  xmin=0
-  xmax=100
+  xmin=0.
+  xmax=100.
   nx=100
+  # plot range when plotting in k
+  kmin=0.
+  kmax=10.
+  nk=100
 
   # cutoffs
   tolerance=1e-11
@@ -77,8 +86,8 @@ function main()
   J=10
 
   # starting rho from which to incrementally initialize Newton algorithm
-  # default must be set after reading rho, if not set explicitly
-  minlrho_init=nothing
+  minlrho_init=-6.
+  nlrho_init=0
 
   # Hann window for Fourier transforms
   windowL=1e3
@@ -98,6 +107,18 @@ function main()
   anyeq_compleq_approx=Anyeq_approx(1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.)
   anyeq_approx=anyeq_bigeq_approx
 
+  # numerical approximations of derivatives
+  dx=1e-7
+  dk=1e-7
+
+  # initial guess for 2pt_max
+  x0=1.
+  k0=1.
+  # maximum step in 2pt_max
+  maxstep=Inf
+  # tolerance for max search
+  tolerance_max=Inf
+
   # read cli arguments
   (params,potential,method,savefile,command)=read_args(ARGS)
 
@@ -109,8 +130,8 @@ function main()
 	print(stderr,"error: could not read parameter '",param,"'.\n")
 	exit(-1)
       end
-      lhs=terms[1]
-      rhs=terms[2]
+      lhs=string(terms[1])
+      rhs=string(terms[2])
       if lhs=="rho"
 	rho=parse(Float64,rhs)
       elseif lhs=="minlrho_init"
@@ -133,6 +154,12 @@ function main()
 	maxlrho=parse(Float64,rhs)
       elseif lhs=="nlrho"
 	nlrho=parse(Int64,rhs)
+      elseif lhs=="minrho"
+	minrho=parse(Float64,rhs)
+      elseif lhs=="maxrho"
+	maxrho=parse(Float64,rhs)
+      elseif lhs=="nrho"
+	nrho=parse(Int64,rhs)
       elseif lhs=="es"
 	es=parse_list(rhs)
       elseif lhs=="minle"
@@ -147,6 +174,12 @@ function main()
 	xmax=parse(Float64,rhs)
       elseif lhs=="nx"
 	nx=parse(Int64,rhs)
+      elseif lhs=="kmin"
+	kmin=parse(Float64,rhs)
+      elseif lhs=="kmax"
+	kmax=parse(Float64,rhs)
+      elseif lhs=="nk"
+	nk=parse(Int64,rhs)
       elseif lhs=="P"
 	P=parse(Int64,rhs)
       elseif lhs=="N"
@@ -155,6 +188,18 @@ function main()
 	J=parse(Int64,rhs)
       elseif lhs=="window_L"
 	windowL=parse(Float64,rhs)
+      elseif lhs=="dx"
+	dx=parse(Float64,rhs)
+      elseif lhs=="x0"
+	x0=parse(Float64,rhs)
+      elseif lhs=="dk"
+	dk=parse(Float64,rhs)
+      elseif lhs=="k0"
+	k0=parse(Float64,rhs)
+      elseif lhs=="maxstep"
+	maxstep=parse(Float64,rhs)
+      elseif lhs=="tolerance_max"
+	tolerance_max=parse(Float64,rhs)
       elseif lhs=="aK"
 	anyeq_approx.aK=parse(Float64,rhs)
       elseif lhs=="bK"
@@ -242,37 +287,48 @@ function main()
   ## set parameters
   # rhos
   if length(rhos)==0
-    rhos=Array{Float64}(undef,nlrho)
-    for j in 0:nlrho-1
-      rhos[j+1]=(nlrho==1 ? 10^minlrho : 10^(minlrho+(maxlrho-minlrho)/(nlrho-1)*j))
+    # linear only if nrho is specified
+    if nrho>0
+      rhos=Array{Float64,1}(undef,nrho)
+      for j in 0:nrho-1
+	rhos[j+1]=(nrho==1 ? minrho : minrho+(maxrho-minrho)/(nrho-1)*j)
+      end
+    else
+      rhos=Array{Float64,1}(undef,nlrho)
+      for j in 0:nlrho-1
+	rhos[j+1]=(nlrho==1 ? 10^minlrho : 10^(minlrho+(maxlrho-minlrho)/(nlrho-1)*j))
+      end
     end
   end
+
   # es
   if length(es)==0
-    es=Array{Float64}(undef,nle)
+    es=Array{Float64,1}(undef,nle)
     for j in 0:nle-1
       es[j+1]=(nle==1 ? 10^minle : 10^(minle+(maxle-minle)/(nle-1)*j))
     end
   end
 
-  # default minlrho_init
-  if (minlrho_init==nothing)
-    minlrho_init=log10(rho)
-  end
-
   # splines
-  taus=Array{Float64}(undef,J+1)
+  taus=Array{Float64,1}(undef,J+1)
   for j in 0:J
     taus[j+1]=-1+2*j/J
   end
 
+  # tolerance_max
+  if tolerance_max==Inf
+    tolerance_max=tolerance
+  end
+
   ## run command
-  if method=="easyeq"
+  if command=="scattering_length"
+    @printf("% .15e\n",a0)
+  elseif method=="easyeq"
     if command=="energy"
       easyeq_energy(minlrho_init,nlrho_init,order,rho,a0,v,maxiter,tolerance,easyeq_approx)
     # e(rho)
     elseif command=="energy_rho"
-      easyeq_energy_rho(rhos,order,a0,v,maxiter,tolerance,easyeq_approx)
+      easyeq_energy_rho(rhos,minlrho_init,nlrho_init,order,a0,v,maxiter,tolerance,easyeq_approx)
     # u(k)
     elseif command=="uk"
       easyeq_uk(minlrho_init,nlrho_init,order,rho,a0,v,maxiter,tolerance,easyeq_approx)
@@ -285,7 +341,28 @@ function main()
     elseif command=="condensate_fraction"
       easyeq_condensate_fraction(minlrho_init,nlrho_init,order,rho,a0,v,maxiter,tolerance,easyeq_approx)
     elseif command=="condensate_fraction_rho"
-      easyeq_condensate_fraction_rho(rhos,order,a0,v,maxiter,tolerance,easyeq_approx)
+      easyeq_condensate_fraction_rho(rhos,minlrho_init,nlrho_init,order,a0,v,maxiter,tolerance,easyeq_approx)
+    # 2pt correlation
+    elseif command=="2pt"
+      easyeq_2pt(xmin,xmax,nx,minlrho_init,nlrho_init,order,windowL,rho,a0,v,maxiter,tolerance,easyeq_approx)
+    # max of 2pt correlation
+    elseif command=="2pt_max"
+      easyeq_2pt_max(dx,x0,minlrho_init,nlrho_init,order,windowL,rho,a0,v,maxstep,maxiter,tolerance,tolerance_max,easyeq_approx)
+    # max of 2pt correlation as a function of rho
+    elseif command=="2pt_max_rho"
+      easyeq_2pt_max_rho(rhos,dx,x0,minlrho_init,nlrho_init,order,windowL,a0,v,maxstep,maxiter,tolerance,tolerance_max,easyeq_approx)
+    # fourier transform of 2pt correlation
+    elseif command=="2pt_fourier"
+      easyeq_2pt_fourier(kmin,kmax,nk,minlrho_init,nlrho_init,order,windowL,rho,a0,v,maxiter,tolerance,easyeq_approx)
+    # max of fourier transform of 2pt correlation
+    elseif command=="2pt_fourier_max"
+      easyeq_2pt_fourier_max(dk,k0,minlrho_init,nlrho_init,order,windowL,rho,a0,v,maxstep,maxiter,tolerance,tolerance_max,easyeq_approx)
+    # max of 2pt correlation as a function of rho
+    elseif command=="2pt_fourier_max_rho"
+      easyeq_2pt_fourier_max_rho(rhos,dk,k0,minlrho_init,nlrho_init,order,windowL,a0,v,maxstep,maxiter,tolerance,tolerance_max,easyeq_approx)
+    # momentum distribution
+    elseif command=="momentum_distribution"
+      easyeq_momentum_distribution(kmin,kmax,minlrho_init,nlrho_init,order,windowL,rho,a0,v,maxiter,tolerance,easyeq_approx)
     else
       print(stderr,"unrecognized command '",command,"'.\n")
       exit(-1)
@@ -316,7 +393,7 @@ function main()
       anyeq_energy(rho,minlrho_init,nlrho_init,taus,P,N,J,a0,v,maxiter,tolerance,anyeq_approx,savefile)
     # e(rho)
     elseif command=="energy_rho"
-      anyeq_energy_rho(rhos,taus,P,N,J,a0,v,maxiter,tolerance,anyeq_approx,savefile)
+      anyeq_energy_rho(rhos,minlrho_init,nlrho_init,taus,P,N,J,a0,v,maxiter,tolerance,anyeq_approx,savefile)
     elseif command=="energy_rho_init_prevrho"
       anyeq_energy_rho_init_prevrho(rhos,taus,P,N,J,a0,v,maxiter,tolerance,anyeq_approx,savefile)
     elseif command=="energy_rho_init_nextrho"
@@ -331,12 +408,29 @@ function main()
     elseif command=="condensate_fraction"
       anyeq_condensate_fraction(rho,minlrho_init,nlrho_init,taus,P,N,J,a0,v,maxiter,tolerance,anyeq_approx,savefile)
     elseif command=="condensate_fraction_rho"
-      anyeq_condensate_fraction_rho(rhos,taus,P,N,J,a0,v,maxiter,tolerance,anyeq_approx,savefile)
+      anyeq_condensate_fraction_rho(rhos,minlrho_init,nlrho_init,taus,P,N,J,a0,v,maxiter,tolerance,anyeq_approx,savefile)
     # momentum distribution
     elseif command=="momentum_distribution"
-      anyeq_momentum_distribution(rho,minlrho_init,nlrho_init,taus,P,N,J,a0,v,maxiter,tolerance,anyeq_approx,savefile)
+      anyeq_momentum_distribution(kmin,kmax,rho,minlrho_init,nlrho_init,taus,P,N,J,windowL,a0,v,maxiter,tolerance,anyeq_approx,savefile)
     elseif command=="2pt"
       anyeq_2pt_correlation(minlrho_init,nlrho_init,taus,P,N,J,windowL,rho,a0,v,maxiter,tolerance,xmin,xmax,nx,anyeq_approx,savefile)
+    elseif command=="2pt_max"
+      anyeq_2pt_correlation_max(rho,minlrho_init,nlrho_init,dx,x0,maxstep,taus,P,N,J,windowL,a0,v,maxiter,tolerance,tolerance_max,anyeq_approx,savefile)
+    elseif command=="2pt_max_rho"
+      anyeq_2pt_correlation_max_rho(rhos,minlrho_init,nlrho_init,dx,x0,maxstep,taus,P,N,J,windowL,a0,v,maxiter,tolerance,tolerance_max,anyeq_approx,savefile)
+    elseif command=="2pt_fourier"
+      anyeq_2pt_correlation_fourier(minlrho_init,nlrho_init,taus,P,N,J,windowL,rho,a0,v,maxiter,tolerance,kmin,kmax,nk,anyeq_approx,savefile)
+    elseif command=="2pt_fourier_test"
+      anyeq_2pt_correlation_fourier_test(minlrho_init,nlrho_init,taus,P,N,J,windowL,rho,a0,v,maxiter,tolerance,xmax,kmin,kmax,nk,anyeq_approx,savefile)
+    elseif command=="2pt_fourier_max"
+      anyeq_2pt_correlation_fourier_max(rho,minlrho_init,nlrho_init,dk,k0,maxstep,taus,P,N,J,windowL,a0,v,maxiter,tolerance,tolerance_max,anyeq_approx,savefile)
+    elseif command=="2pt_fourier_max_rho"
+      anyeq_2pt_correlation_fourier_max_rho(rhos,minlrho_init,nlrho_init,dk,k0,maxstep,taus,P,N,J,windowL,a0,v,maxiter,tolerance,tolerance_max,anyeq_approx,savefile)
+    elseif command=="uncondensed_2pt"
+      anyeq_uncondensed_2pt_correlation(minlrho_init,nlrho_init,taus,P,N,J,windowL,rho,a0,v,maxiter,tolerance,xmin,xmax,nx,anyeq_approx,savefile)
+    # compressibility
+    elseif command=="compressibility_rho"
+      anyeq_compressibility_rho(rhos,minlrho_init,nlrho_init,taus,P,N,J,a0,v,maxiter,tolerance,anyeq_approx,savefile)
     else
       print(stderr,"unrecognized command: '",command,"'\n")
       exit(-1)
@@ -360,9 +454,11 @@ function main()
 end
 
 # parse a comma separated list as an array of Float64
-function parse_list(str)
+function parse_list(
+  str::String
+)
   elems=split(str,",")
-  out=Array{Float64}(undef,length(elems))
+  out=Array{Float64,1}(undef,length(elems))
   for i in 1:length(elems)
     out[i]=parse(Float64,elems[i])
   end
@@ -370,7 +466,9 @@ function parse_list(str)
 end
 
 # read cli arguments
-function read_args(ARGS)
+function read_args(
+  ARGS
+)
   # flag
   flag=""
 
diff --git a/src/multithread.jl b/src/multithread.jl
new file mode 100644
index 0000000..f61cdd7
--- /dev/null
+++ b/src/multithread.jl
@@ -0,0 +1,16 @@
+# split up 1...n among workers
+function spawn_workers(n::Int64)
+  # number of workers
+  nw=nworkers()
+  # split jobs among workers
+  work=Array{Array{Int64,1},1}(undef,nw)
+  # init empty arrays
+  for p in 1:nw
+    work[p]=Int64[]
+  end
+  for i in 1:n
+    append!(work[(i-1)%nw+1],[i])
+  end
+
+  return work
+end
diff --git a/src/optimization.jl b/src/optimization.jl
new file mode 100644
index 0000000..bacead7
--- /dev/null
+++ b/src/optimization.jl
@@ -0,0 +1,94 @@
+# gradient descent: find local minimum of function of one variable from initial guess
+# numerically estimate the derivative
+@everywhere function gradient_descent(
+  f::Function,
+  x0::Float64,
+  delta::Float64, # shift is delta*df
+  dx::Float64, # finite difference for numerical derivative evaluation
+  maxiter::Int64 # interrupt and fail after maxiter steps
+)
+  counter=0
+
+  # init
+  x=x0
+
+  while counter<maxiter
+    # value at x and around
+    val=f(x)
+    valm=f(x-dx)
+    valp=f(x+dx)
+    # quit if minimum
+    if(val<valm && val<valp)
+      return(x,val)
+    end
+
+    # derivative
+    df=(valp-val)/dx
+    # step
+    x=x-delta*df
+    counter+=1
+  end
+
+  # fail
+  return(Inf,Inf)
+end
+
+# Newton algorithm to compute extrema
+# numerically estimate the derivatives
+@everywhere function newton_maximum(
+  f::Function,
+  x0::Float64,
+  dx::Float64, # finite difference for numerical derivative evaluation
+  maxiter::Int64,
+  tolerance::Float64,
+  maxstep::Float64 # maximal size of step
+)
+  counter=0
+
+  # init
+  x=x0
+
+  while counter<maxiter
+    # value at x and around
+    val=f(x)
+    valm=f(x-dx)
+    valp=f(x+dx)
+
+    # derivative
+    dfp=(valp-val)/dx
+    dfm=(val-valm)/dx
+    # second derivative
+    ddf=(dfp-dfm)/dx
+
+    #@printf(stderr,"% .15e % .15e % .15e % .15e\n",x,val,dfp,ddf)
+
+    if abs(dfp/ddf)<tolerance
+      # check it is a local maximum
+      if ddf<0
+	return(x,val)
+      else
+	return(Inf,Inf)
+      end
+    end
+
+    # step
+    step=dfp/abs(ddf)
+
+    # step too large
+    if abs(step)>maxstep
+      step=maxstep*sign(step)
+    end
+
+    x=x+step
+
+    # fail if off to infinity
+    if x==Inf || x==-Inf
+      return(x,val)
+    end
+    counter+=1
+  end
+
+  # fail
+  return(Inf,Inf)
+end
+
diff --git a/src/potentials.jl b/src/potentials.jl
index 46cafc0..b480db0 100644
--- a/src/potentials.jl
+++ b/src/potentials.jl
@@ -1,4 +1,4 @@
-## Copyright 2021 Ian Jauslin
+## Copyright 2021-2023 Ian Jauslin
 ## 
 ## Licensed under the Apache License, Version 2.0 (the "License");
 ## you may not use this file except in compliance with the License.
@@ -13,10 +13,15 @@
 ## limitations under the License.
 
 # exponential potential in 3 dimensions
-@everywhere function v_exp(k,a)
+@everywhere function v_exp(
+  k::Float64,
+  a::Float64
+)
   return 8*pi/(1+k^2)^2*a
 end
-@everywhere function a0_exp(a)
+@everywhere function a0_exp(
+  a::Float64
+)
   if a>0.
     return log(a)+2*MathConstants.eulergamma+2*besselk(0,2*sqrt(a))/besseli(0,2*sqrt(a))
   elseif a<0.
@@ -27,15 +32,24 @@ end
 end
 
 # exp(-x)-a*exp(-b*x) in 3 dimensions
-@everywhere function v_expcry(k,a,b)
+@everywhere function v_expcry(
+  k::Float64,
+  a::Float64,
+  b::Float64
+)
   return 8*pi*((1+k^2)^(-2)-a*b*(b^2+k^2)^(-2))
 end
-@everywhere function a0_expcry(a,b)
+@everywhere function a0_expcry(
+  a::Float64,
+  b::Float64
+)
   return 1.21751642717932720441274114683413710125487579284827462 #ish
 end
 
 # x^2*exp(-|x|) in 3 dimensions
-@everywhere function v_npt(k)
+@everywhere function v_npt(
+  k::Float64
+)
   return 96*pi*(1-k^2)/(1+k^2)^4
 end
 @everywhere function a0_npt()
@@ -43,7 +57,9 @@ end
 end
 
 # 1/(1+x^4/4) potential in 3 dimensions
-@everywhere function v_alg(k)
+@everywhere function v_alg(
+  k::Float64
+)
   if(k==0)
     return 4*pi^2
   else
@@ -53,32 +69,50 @@ end
 a0_alg=1. #ish
 
 # (1+a x^4)/(1+x^2)^4 potential in 3 dimensions
-@everywhere function v_algwell(k)
+@everywhere function v_algwell(
+  k::Float64
+)
   a=4
   return pi^2/24*exp(-k)*(a*(k^2-9*k+15)+k^2+3*k+3)
 end
 a0_algwell=1. #ish
 
 # potential corresponding to the exact solution c/(1+b^2x^2)^2
-@everywhere function v_exact(k,b,c,e)
+@everywhere function v_exact(
+  k::Float64,
+  b::Float64,
+  c::Float64,
+  e::Float64
+)
   if k!=0
     return 48*pi^2*((18+3*sqrt(c)-(4-3*e/b^2)*c-(1-2*e/b^2)*c^1.5)/(4*(3+sqrt(c))^2*sqrt(c))*exp(-sqrt(1-sqrt(c))*k/b)+(-18+3*sqrt(c)+(4-3*e/b^2)*c-(1-2*e/b^2)*c^1.5)/(4*(3-sqrt(c))^2*sqrt(c))*exp(-sqrt(1+sqrt(c))*k/b)+(1-k/b)/2*exp(-k/b)-c*e/b^2*(3*(9-c)*k/b+8*c)/(8*(9-c)^2)*exp(-2*k/b))/k
   else
     return 48*pi^2*(-sqrt(1-sqrt(c))/b*(18+3*sqrt(c)-(4-3*e/b^2)*c-(1-2*e/b^2)*c^1.5)/(4*(3+sqrt(c))^2*sqrt(c))-sqrt(1+sqrt(c))/b*(-18+3*sqrt(c)+(4-3*e/b^2)*c-(1-2*e/b^2)*c^1.5)/(4*(3-sqrt(c))^2*sqrt(c))-1/b-c*e/b^2*(27-19*c)/(8*(9-c)^2))
   end
 end
-@everywhere function a0_exact(b,c,e)
+@everywhere function a0_exact(
+  b::Float64,
+  c::Float64,
+  e::Float64
+)
   return 1. #ish
 end
 
 # tent potential (convolution of soft sphere with itself): a*pi/12*(2*|x|/b-2)^2*(2*|x|/b+4) for |x|<b
-@everywhere function v_tent(k,a,b)
+@everywhere function v_tent(
+  k::Float64,
+  a::Float64,
+  b::Float64
+)
   if k!=0
     return (b/2)^3*a*(4*pi*(sin(k*b/2)-k*b/2*cos(k*b/2))/(k*b/2)^3)^2
   else
     return (b/2)^3*a*(4*pi/3)^2
   end
 end
-@everywhere function a0_tent(a,b)
+@everywhere function a0_tent(
+  a::Float64,
+  b::Float64
+)
   return b #ish
 end
diff --git a/src/print.jl b/src/print.jl
index bef1c4d..3587728 100644
--- a/src/print.jl
+++ b/src/print.jl
@@ -1,4 +1,4 @@
-## Copyright 2021 Ian Jauslin
+## Copyright 2021-2023 Ian Jauslin
 ## 
 ## Licensed under the Apache License, Version 2.0 (the "License");
 ## you may not use this file except in compliance with the License.
diff --git a/src/simpleq-Kv.jl b/src/simpleq-Kv.jl
index 8789656..5a6579c 100644
--- a/src/simpleq-Kv.jl
+++ b/src/simpleq-Kv.jl
@@ -1,4 +1,4 @@
-## Copyright 2021 Ian Jauslin
+## Copyright 2021-2023 Ian Jauslin
 ## 
 ## Licensed under the Apache License, Version 2.0 (the "License");
 ## you may not use this file except in compliance with the License.
@@ -13,12 +13,27 @@
 ## limitations under the License.
 
 # Compute Kv=(-\Delta+v+4e(1-\rho u*))^{-1}v
-function anyeq_Kv(minlrho,nlrho,taus,P,N,J,rho,a0,v,maxiter,tolerance,xmin,xmax,nx)
+function anyeq_Kv(
+  minlrho::Float64,
+  nlrho::Int64,
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  rho::Float64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  xmin::Float64,
+  xmax::Float64,
+  nx::Int64
+)
   # init vectors
   (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v)
 
   # compute initial guess from medeq
-  rhos=Array{Float64}(undef,nlrho)
+  rhos=Array{Float64,1}(undef,nlrho)
   for j in 0:nlrho-1
     rhos[j+1]=(nlrho==1 ? rho : 10^(minlrho+(log10(rho)-minlrho)/(nlrho-1)*j))
   end
@@ -44,7 +59,17 @@ function anyeq_Kv(minlrho,nlrho,taus,P,N,J,rho,a0,v,maxiter,tolerance,xmin,xmax,
 end
 
 # Compute the condensate fraction for simpleq using Kv
-function simpleq_Kv_condensate_fraction(rhos,taus,P,N,J,a0,v,maxiter,tolerance)
+function simpleq_Kv_condensate_fraction(
+  rhos::Array{Float64,1},
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64
+)
   # init vectors
   (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v)
 
@@ -67,19 +92,35 @@ function simpleq_Kv_condensate_fraction(rhos,taus,P,N,J,a0,v,maxiter,tolerance)
 end
 
 # Compute the two-point correlation function for simpleq using Kv
-function simpleq_Kv_2pt(minlrho,nlrho,taus,P,N,J,rho,a0,v,maxiter,tolerance,xmin,xmax,nx)
+function simpleq_Kv_2pt(
+  minlrho::Float64,
+  nlrho::Int64,
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  rho::Float64,
+  a0::Float64,
+  v::Function,
+  maxiter::Int64,
+  tolerance::Float64,
+  xmin::Float64,
+  xmax::Float64,
+  nx::Int64
+)
   # init vectors
   (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v)
 
   # compute initial guess from medeq
-  rhos=Array{Float64}(undef,nlrho)
+  rhos=Array{Float64,1}(undef,nlrho)
   for j in 0:nlrho-1
     rhos[j+1]=(nlrho==1 ? rho : 10^(minlrho+(log10(rho)-minlrho)/(nlrho-1)*j))
   end
   u0s=anyeq_init_medeq(rhos,N,J,k,a0,v,maxiter,tolerance)
   u0=u0s[nlrho]
 
-  (u,E,error)=anyeq_hatu(u0,P,N,J,rho,a0,weights,k,taus,V,V0,A,nothing,Upsilon,Upsilon0,v,maxiter,tolerance,Anyeq_approx(0.,0.,1.,0.,0.,0.,0.,0.,0.,0.,0.))
+  Abar=Array{Float64,5}(undef,0,0,0,0,0)
+  (u,E,error)=anyeq_hatu(u0,P,N,J,rho,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,Anyeq_approx(0.,0.,1.,0.,0.,0.,0.,0.,0.,0.,0.))
 
   # Kv in Fourier space
   Kvk=simpleq_Kv_Kvk(u,V,E,rho,Upsilon,k,taus,weights,N,J)
@@ -103,7 +144,18 @@ function simpleq_Kv_2pt(minlrho,nlrho,taus,P,N,J,rho,a0,v,maxiter,tolerance,xmin
 end
 
 # Kv
-function simpleq_Kv_Kvk(u,V,E,rho,Upsilon,k,taus,weights,N,J)
+function simpleq_Kv_Kvk(
+  u::Array{Float64,1},
+  V::Array{Float64,1},
+  E::Float64,
+  rho::Float64,
+  Upsilon::Array{Array{Float64,1},1},
+  k::Array{Float64,1},
+  taus::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  N::Int64,
+  J::Int64
+)
   # (-Delta+v+4e(1-\rho u*)) in Fourier space
   M=Array{Float64,2}(undef,N*J,N*J)
   for zetapp in 0:J-1
diff --git a/src/simpleq-hardcore.jl b/src/simpleq-hardcore.jl
index ca64f78..398ac06 100644
--- a/src/simpleq-hardcore.jl
+++ b/src/simpleq-hardcore.jl
@@ -1,4 +1,4 @@
-## Copyright 2021 Ian Jauslin
+## Copyright 2021-2023 Ian Jauslin
 ## 
 ## Licensed under the Apache License, Version 2.0 (the "License");
 ## you may not use this file except in compliance with the License.
@@ -13,28 +13,26 @@
 ## limitations under the License.
 
 # compute energy as a function of rho
-function simpleq_hardcore_energy_rho(rhos,taus,P,N,J,maxiter,tolerance)
-  ## spawn workers
-  # number of workers
-  nw=nworkers()
-  # split jobs among workers
-  work=Array{Array{Int64,1},1}(undef,nw)
-  # init empty arrays
-  for p in 1:nw
-    work[p]=zeros(0)
-  end
-  for j in 1:length(rhos)
-    append!(work[j%nw+1],j)
-  end
+function simpleq_hardcore_energy_rho(
+  rhos::Array{Float64,1},
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  maxiter::Int64,
+  tolerance::Float64
+)
+  # spawn workers
+  work=spawn_workers(length(rhos))
 
   # initialize vectors
   (weights,weights_gL,r,T)=simpleq_hardcore_init(taus,P,N,J)
 
   # initial guess
-  u0s=Array{Array{Float64}}(undef,length(rhos))
-  e0s=Array{Float64}(undef,length(rhos))
+  u0s=Array{Array{Float64,1}}(undef,length(rhos))
+  e0s=Array{Float64,1}(undef,length(rhos))
   for j in 1:length(rhos)
-    u0s[j]=Array{Float64}(undef,N*J)
+    u0s[j]=Array{Float64,1}(undef,N*J)
     for i in 1:N*J
       u0s[j][i]=1/(1+r[i]^2)^2
     end
@@ -43,17 +41,17 @@ function simpleq_hardcore_energy_rho(rhos,taus,P,N,J,maxiter,tolerance)
 
 
   # save result from each task
-  us=Array{Array{Float64}}(undef,length(rhos))
-  es=Array{Float64}(undef,length(rhos))
-  err=Array{Float64}(undef,length(rhos))
+  us=Array{Array{Float64,1}}(undef,length(rhos))
+  es=Array{Float64,1}(undef,length(rhos))
+  err=Array{Float64,1}(undef,length(rhos))
 
   count=0
   # for each worker
-  @sync for p in 1:nw
+  @sync for p in 1:length(work)
     # for each task
     @async for j in work[p]
       count=count+1
-      if count>=nw
+      if count>=length(work)
         progress(count,length(rhos),10000)
       end
       # run the task
@@ -67,12 +65,20 @@ function simpleq_hardcore_energy_rho(rhos,taus,P,N,J,maxiter,tolerance)
 end
 
 # compute u(x)
-function simpleq_hardcore_ux(rho,taus,P,N,J,maxiter,tolerance)
+function simpleq_hardcore_ux(
+  rho::Float64,
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  maxiter::Int64,
+  tolerance::Float64
+)
   # initialize vectors
   (weights,weights_gL,r,T)=simpleq_hardcore_init(taus,P,N,J)
 
   # initial guess
-  u0=Array{Float64}(undef,N*J)
+  u0=Array{Float64,1}(undef,N*J)
   for i in 1:N*J
     u0[i]=1/(1+r[i]^2)^2
   end
@@ -86,28 +92,26 @@ function simpleq_hardcore_ux(rho,taus,P,N,J,maxiter,tolerance)
 end
 
 # compute condensate fraction as a function of rho
-function simpleq_hardcore_condensate_fraction_rho(rhos,taus,P,N,J,maxiter,tolerance)
-  ## spawn workers
-  # number of workers
-  nw=nworkers()
-  # split jobs among workers
-  work=Array{Array{Int64,1},1}(undef,nw)
-  # init empty arrays
-  for p in 1:nw
-    work[p]=zeros(0)
-  end
-  for j in 1:length(rhos)
-    append!(work[j%nw+1],j)
-  end
+function simpleq_hardcore_condensate_fraction_rho(
+  rhos::Array{Float64,1},
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  maxiter::Int64,
+  tolerance::Float64
+)
+  # spawn workers
+  work=spawn_workers(length(rhos))
 
   # initialize vectors
   (weights,weights_gL,r,T)=simpleq_hardcore_init(taus,P,N,J)
 
   # initial guess
-  u0s=Array{Array{Float64}}(undef,length(rhos))
-  e0s=Array{Float64}(undef,length(rhos))
+  u0s=Array{Array{Float64,1}}(undef,length(rhos))
+  e0s=Array{Float64,1}(undef,length(rhos))
   for j in 1:length(rhos)
-    u0s[j]=Array{Float64}(undef,N*J)
+    u0s[j]=Array{Float64,1}(undef,N*J)
     for i in 1:N*J
       u0s[j][i]=1/(1+r[i]^2)^2
     end
@@ -116,17 +120,17 @@ function simpleq_hardcore_condensate_fraction_rho(rhos,taus,P,N,J,maxiter,tolera
 
 
   # save result from each task
-  us=Array{Array{Float64}}(undef,length(rhos))
-  es=Array{Float64}(undef,length(rhos))
-  err=Array{Float64}(undef,length(rhos))
+  us=Array{Array{Float64,1}}(undef,length(rhos))
+  es=Array{Float64,1}(undef,length(rhos))
+  err=Array{Float64,1}(undef,length(rhos))
 
   count=0
   # for each worker
-  @sync for p in 1:nw
+  @sync for p in 1:length(work)
     # for each task
     @async for j in work[p]
       count=count+1
-      if count>=nw
+      if count>=length(work)
         progress(count,length(rhos),10000)
       end
       # run the task
@@ -142,13 +146,18 @@ end
 
 
 # initialize computation
-@everywhere function simpleq_hardcore_init(taus,P,N,J)
+@everywhere function simpleq_hardcore_init(
+  taus::Array{Float64,1},
+  P::Int64,
+  N::Int64,
+  J::Int64
+)
   # Gauss-Legendre weights
   weights=gausslegendre(N)
   weights_gL=gausslaguerre(N)
 
   # r
-  r=Array{Float64}(undef,J*N)
+  r=Array{Float64,1}(undef,J*N)
   for zeta in 0:J-1
     for j in 1:N
       xj=weights[1][j]
@@ -164,9 +173,23 @@ end
 end
 
 # compute u using chebyshev expansions
-@everywhere function simpleq_hardcore_hatu(u0,e0,rho,r,taus,T,weights,weights_gL,P,N,J,maxiter,tolerance)
+@everywhere function simpleq_hardcore_hatu(
+  u0::Array{Float64,1},
+  e0::Float64,
+  rho::Float64,
+  r::Array{Float64,1},
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  weights_gL::Tuple{Array{Float64,1},Array{Float64,1}},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  maxiter::Int64,
+  tolerance::Float64
+)
   # init
-  vec=Array{Float64}(undef,J*N+1)
+  vec=Array{Float64,1}(undef,J*N+1)
   for i in 1:J*N
     vec[i]=u0[i]
   end
@@ -194,8 +217,20 @@ end
 end
 
 # Xi
-@everywhere function simpleq_hardcore_Xi(u,e,rho,r,taus,T,weights,weights_gL,P,N,J)
-  out=Array{Float64}(undef,J*N+1)
+@everywhere function simpleq_hardcore_Xi(
+  u::Array{Float64,1},
+  e::Float64,
+  rho::Float64,
+  r::Array{Float64,1},
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  weights_gL::Tuple{Array{Float64,1},Array{Float64,1}},
+  P::Int64,
+  N::Int64,
+  J::Int64
+)
+  out=Array{Float64,1}(undef,J*N+1)
   FU=chebyshev(u,taus,weights,P,N,J,4)
 
   #D's
@@ -215,7 +250,19 @@ end
   return out
 end
 # DXi
-@everywhere function simpleq_hardcore_DXi(u,e,rho,r,taus,T,weights,weights_gL,P,N,J)
+@everywhere function simpleq_hardcore_DXi(
+  u::Array{Float64,1},
+  e::Float64,
+  rho::Float64,
+  r::Array{Float64,1},
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  weights_gL::Tuple{Array{Float64,1},Array{Float64,1}},
+  P::Int64,
+  N::Int64,
+  J::Int64
+)
   out=Array{Float64,2}(undef,J*N+1,J*N+1)
   FU=chebyshev(u,taus,weights,P,N,J,4)
 
@@ -232,15 +279,15 @@ end
 
   for zetapp in 0:J-1
     for n in 1:N
-      one=zeros(Int64,J*N)
-      one[zetapp*N+n]=1
+      one=zeros(Float64,J*N)
+      one[zetapp*N+n]=1.
       Fone=chebyshev(one,taus,weights,P,N,J,4)
 
       for i in 1:J*N
 	# du/du
 	out[i,zetapp*N+n]=dot(Fone,d1[i])+2*dot(FU,d2[i]*Fone)-(zetapp*N+n==i ? 1 : 0)
 	# du/de
-	out[i,J*N+1]=(dsed0[i]+dot(FU,dsed1[i])+dot(FU,dsed2[i]*FU))/(2*sqrt(abs(e)))*(e>=0 ? 1 : -1)
+	out[i,J*N+1]=(dsed0[i]+dot(FU,dsed1[i])+dot(FU,dsed2[i]*FU))/(2*sqrt(abs(e)))*(e>=0. ? 1. : -1.)
       end
       # de/du
       out[J*N+1,zetapp*N+n]=2*pi*rho*
@@ -253,14 +300,26 @@ end
   #de/de
   out[J*N+1,J*N+1]=-1+2*pi*rho*
     (2-dsedgamma0(e,rho,weights)-dot(FU,dsedgamma1(e,rho,taus,T,weights,weights_gL,P,J,4))-dot(FU,dsedgamma2(e,rho,taus,T,weights,weights_gL,P,J,4)*FU))/denom/
-    (2*sqrt(abs(e)))*(e>=0 ? 1 : -1)
+    (2*sqrt(abs(e)))*(e>=0. ? 1. : -1.)
 
   return out
 end
 
 # dXi/dmu
-@everywhere function simpleq_hardcore_dXidmu(u,e,rho,r,taus,T,weights,weights_gL,P,N,J)
-  out=Array{Float64}(undef,J*N+1)
+@everywhere function simpleq_hardcore_dXidmu(
+  u::Array{Float64,1},
+  e::Float64,
+  rho::Float64,
+  r::Array{Float64,1},
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  weights_gL::Tuple{Array{Float64,1},Array{Float64,1}},
+  P::Int64,
+  N::Int64,
+  J::Int64
+)
+  out=Array{Float64,1}(undef,J*N+1)
   FU=chebyshev(u,taus,weights,P,N,J,4)
 
   #D's
@@ -281,17 +340,37 @@ end
 end
 
 # B's
-@everywhere function B0(r)
+@everywhere function B0(
+  r::Float64
+)
   return pi/12*(r-1)^2*(r+5)
 end
-@everywhere function B1(r,zeta,n,taus,T,weights,nu)
+@everywhere function B1(
+  r::Float64,
+  zeta::Int64,
+  n::Int64,
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  nu::Int64
+)
   return (taus[zeta+1]>=(2-r)/r || taus[zeta+2]<=-r/(r+2) ? 0 :
     8*pi/(r+1)*integrate_legendre(tau->
       (1-(r-(1-tau)/(1+tau))^2)/(1+tau)^(3-nu)*T[n+1]((2*tau-(taus[zeta+1]+taus[zeta+2]))/(taus[zeta+2]-taus[zeta+1])),max(taus[zeta+1],-r/(r+2))
     ,min(taus[zeta+2],(2-r)/r),weights)
   )
 end
-@everywhere function B2(r,zeta,n,zetap,m,taus,T,weights,nu)
+@everywhere function B2(
+  r::Float64,
+  zeta::Int64,
+  n::Int64,
+  zetap::Int64,
+  m::Int64,
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  nu::Int64
+)
   return 32*pi/(r+1)*integrate_legendre(tau->
     1/(1+tau)^(3-nu)*T[n+1]((2*tau-(taus[zeta+1]+taus[zeta+2]))/(taus[zeta+2]-taus[zeta+1]))*
     (taus[zetap+1]>=alphap(abs(r-(1-tau)/(1+tau))-2*tau/(1+tau),tau) || taus[zetap+2]<=alpham(1+r,tau) ? 0 :
@@ -303,30 +382,49 @@ end
 end
 
 # D's
-@everywhere function D0(e,rho,r,weights,N,J)
-  out=Array{Float64}(undef,J*N)
+@everywhere function D0(
+  e::Float64,
+  rho::Float64,
+  r::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  N::Int64,
+  J::Int64
+)
+  out=Array{Float64,1}(undef,J*N)
   for i in 1:J*N
     out[i]=exp(-2*sqrt(abs(e))*r[i])/(r[i]+1)+
       rho*sqrt(abs(e))/(r[i]+1)*integrate_legendre(s->
 	(s+1)*B0(s)*(exp(-2*sqrt(abs(e))*(r[i]-s))-exp(-2*sqrt(abs(e))*(r[i]+s)))/2
-      ,0,min(1,r[i]),weights)+
+      ,0.,min(1.,r[i]),weights)+
       (r[i]>=1 ? 0 :
 	rho*sqrt(abs(e))/(2*(r[i]+1))*(1-exp(-4*sqrt(abs(e))*r[i]))*integrate_legendre(s->
 	  (s+r[i]+1)*B0(s+r[i])*exp(-2*sqrt(abs(e))*s)
-	,0,1-r[i],weights)
+	,0.,1. -r[i],weights)
       )
   end
   return out
 end
-@everywhere function D1(e,rho,r,taus,T,weights,weights_gL,P,N,J,nu)
-  out=Array{Array{Float64}}(undef,J*N)
+@everywhere function D1(
+  e::Float64,
+  rho::Float64,
+  r::Array{Float64,1},
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  weights_gL::Tuple{Array{Float64,1},Array{Float64,1}},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  nu::Int64
+)
+  out=Array{Array{Float64,1},1}(undef,J*N)
   for i in 1:J*N
-    out[i]=Array{Float64}(undef,(P+1)*J)
+    out[i]=Array{Float64,1}(undef,(P+1)*J)
     for zeta in 0:J-1
       for n in 0:P
 	out[i][zeta*(P+1)+n+1]=rho*sqrt(abs(e))/(r[i]+1)*integrate_legendre(s->
 	    (s+1)*B1(s,zeta,n,taus,T,weights,nu)*(exp(-2*sqrt(abs(e))*(r[i]-s))-exp(-2*sqrt(abs(e))*(r[i]+s)))/2
-	  ,0,r[i],weights)+
+	  ,0.,r[i],weights)+
 	  rho*sqrt(abs(e))/(2*(r[i]+1))*(1-exp(-4*sqrt(abs(e))*r[i]))*integrate_laguerre(s->
 	    (s+r[i]+1)*B1(s+r[i],zeta,n,taus,T,weights,nu)
 	  ,2*sqrt(abs(e)),weights_gL)
@@ -336,7 +434,19 @@ end
 
   return out
 end
-@everywhere function D2(e,rho,r,taus,T,weights,weights_gL,P,N,J,nu)
+@everywhere function D2(
+  e::Float64,
+  rho::Float64,
+  r::Array{Float64,1},
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  weights_gL::Tuple{Array{Float64,1},Array{Float64,1}},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  nu::Int64
+)
   out=Array{Array{Float64,2}}(undef,J*N)
   for i in 1:J*N
     out[i]=Array{Float64,2}(undef,(P+1)*J,(P+1)*J)
@@ -346,7 +456,7 @@ end
 	  for m in 0:P
 	    out[i][zeta*(P+1)+n+1,zetap*(P+1)+m+1]=rho*sqrt(abs(e))/(r[i]+1)*integrate_legendre(s->
 		(s+1)*B2(s,zeta,n,zetap,m,taus,T,weights,nu)*(exp(-2*sqrt(abs(e))*(r[i]-s))-exp(-2*sqrt(abs(e))*(r[i]+s)))/2
-	      ,0,r[i],weights)+
+	      ,0.,r[i],weights)+
 	      rho*sqrt(abs(e))/(2*(r[i]+1))*(1-exp(-4*sqrt(abs(e))*r[i]))*integrate_laguerre(s->
 		(s+r[i]+1)*B2(s+r[i],zeta,n,zetap,m,taus,T,weights,nu)
 	      ,2*sqrt(abs(e)),weights_gL)
@@ -359,28 +469,47 @@ end
 end
 
 # dD/d sqrt(abs(e))'s
-@everywhere function dseD0(e,rho,r,weights,N,J)
-  out=Array{Float64}(undef,J*N)
+@everywhere function dseD0(
+  e::Float64,
+  rho::Float64,
+  r::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  N::Int64,
+  J::Int64
+)
+  out=Array{Float64,1}(undef,J*N)
   for i in 1:J*N
     out[i]=-2*r[i]*exp(-2*sqrt(abs(e))*r[i])/(r[i]+1)+
       rho/(r[i]+1)*integrate_legendre(s->
 	(s+1)*B0(s)*((1-2*sqrt(abs(e))*r[i])*(exp(-2*sqrt(abs(e))*(r[i]-s))-exp(-2*sqrt(abs(e))*(r[i]+s)))/2+2*sqrt(abs(e))*s*(exp(-2*sqrt(abs(e))*(r[i]-s))+exp(-2*sqrt(abs(e))*(r[i]+s)))/2)
-      ,0,min(1,r[i]),weights)+
+      ,0.,min(1.,r[i]),weights)+
       (r[i]>=1 ? 0 :
-	rho/(2*(r[i]+1))*integrate_legendre(s->(s+r[i]+1)*B0(s+r[i])*((1-2*sqrt(abs(e))*s)*(1-exp(-4*sqrt(abs(e))*r[i]))*exp(-2*sqrt(abs(e))*s)+4*sqrt(abs(e))*r[i]*exp(-2*sqrt(abs(e))*(2*r[i]+s))),0,1-r[i],weights)
+	rho/(2*(r[i]+1))*integrate_legendre(s->(s+r[i]+1)*B0(s+r[i])*((1-2*sqrt(abs(e))*s)*(1-exp(-4*sqrt(abs(e))*r[i]))*exp(-2*sqrt(abs(e))*s)+4*sqrt(abs(e))*r[i]*exp(-2*sqrt(abs(e))*(2*r[i]+s))),0.,1. -r[i],weights)
       )
   end
   return out
 end
-@everywhere function dseD1(e,rho,r,taus,T,weights,weights_gL,P,N,J,nu)
-  out=Array{Array{Float64}}(undef,J*N)
+@everywhere function dseD1(
+  e::Float64,
+  rho::Float64,
+  r::Array{Float64,1},
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  weights_gL::Tuple{Array{Float64,1},Array{Float64,1}},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  nu::Int64
+)
+  out=Array{Array{Float64,1},1}(undef,J*N)
   for i in 1:J*N
-    out[i]=Array{Float64}(undef,(P+1)*J)
+    out[i]=Array{Float64,1}(undef,(P+1)*J)
     for zeta in 0:J-1
       for n in 0:P
 	out[i][zeta*(P+1)+n+1]=rho/(r[i]+1)*integrate_legendre(s->
 	  (s+1)*B1(s,zeta,n,taus,T,weights,nu)*((1-2*sqrt(abs(e))*r[i])*(exp(-2*sqrt(abs(e))*(r[i]-s))-exp(-2*sqrt(abs(e))*(r[i]+s)))/2+2*sqrt(abs(e))*s*(exp(-2*sqrt(abs(e))*(r[i]-s))+exp(-2*sqrt(abs(e))*(r[i]+s)))/2)
-	,0,r[i],weights)+
+	,0.,r[i],weights)+
 	rho/(2*(r[i]+1))*integrate_laguerre(s->
 	  (s+r[i]+1)*B1(s+r[i],zeta,n,taus,T,weights,nu)*((1-2*sqrt(abs(e))*s)*(1-exp(-4*sqrt(abs(e))*r[i]))+4*sqrt(abs(e))*r[i]*exp(-4*sqrt(abs(e))*r[i]))
 	,2*sqrt(abs(e)),weights_gL)
@@ -389,7 +518,19 @@ end
   end
   return out
 end
-@everywhere function dseD2(e,rho,r,taus,T,weights,weights_gL,P,N,J,nu)
+@everywhere function dseD2(
+  e::Float64,
+  rho::Float64,
+  r::Array{Float64,1},
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  weights_gL::Tuple{Array{Float64,1},Array{Float64,1}},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  nu::Int64
+)
   out=Array{Array{Float64,2}}(undef,J*N)
   for i in 1:J*N
     out[i]=Array{Float64,2}(undef,(P+1)*J,(P+1)*J)
@@ -399,7 +540,7 @@ end
 	  for m in 0:P
 	    out[i][zeta*(P+1)+n+1,zetap*(P+1)+m+1]=rho/(r[i]+1)*integrate_legendre(s->
 	      (s+1)*B2(s,zeta,n,zetap,m,taus,T,weights,nu)*((1-2*sqrt(abs(e))*r[i])*(exp(-2*sqrt(abs(e))*(r[i]-s))-exp(-2*sqrt(abs(e))*(r[i]+s)))/2+2*sqrt(abs(e))*s*(exp(-2*sqrt(abs(e))*(r[i]-s))+exp(-2*sqrt(abs(e))*(r[i]+s)))/2)
-	    ,0,r[i],weights)+
+	    ,0.,r[i],weights)+
 	    rho/(2*(r[i]+1))*integrate_laguerre(s->
 	      (s+r[i]+1)*B2(s+r[i],zeta,n,zetap,m,taus,T,weights,nu)*((1-2*sqrt(abs(e))*s)*(1-exp(-4*sqrt(abs(e))*r[i]))+4*sqrt(abs(e))*r[i]*exp(-4*sqrt(abs(e))*r[i]))
 	    ,2*sqrt(abs(e)),weights_gL)
@@ -412,28 +553,47 @@ end
 end
 
 # dD/d sqrt(abs(e+mu/2))'s
-@everywhere function dsmuD0(e,rho,r,weights,N,J)
-  out=Array{Float64}(undef,J*N)
+@everywhere function dsmuD0(
+  e::Float64,
+  rho::Float64,
+  r::Array{Float64,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  N::Int64,
+  J::Int64
+)
+  out=Array{Float64,1}(undef,J*N)
   for i in 1:J*N
     out[i]=-2*r[i]*exp(-2*sqrt(abs(e))*r[i])/(r[i]+1)+
       rho/(r[i]+1)*integrate_legendre(s->
 	(s+1)*B0(s)*((-1-2*sqrt(abs(e))*r[i])*(exp(-2*sqrt(abs(e))*(r[i]-s))-exp(-2*sqrt(abs(e))*(r[i]+s)))/2+2*sqrt(abs(e))*s*(exp(-2*sqrt(abs(e))*(r[i]-s))+exp(-2*sqrt(abs(e))*(r[i]+s)))/2)
-      ,0,min(1,r[i]),weights)+
+      ,0.,min(1.,r[i]),weights)+
       (r[i]>=1 ? 0 :
-	rho/(2*(r[i]+1))*integrate_legendre(s->(s+r[i]+1)*B0(s+r[i])*((-1-2*sqrt(abs(e))*s)*(1-exp(-4*sqrt(abs(e))*r[i]))*exp(-2*sqrt(abs(e))*s)+4*sqrt(abs(e))*r[i]*exp(-2*sqrt(abs(e))*(2*r[i]+s))),0,1-r[i],weights)
+	rho/(2*(r[i]+1))*integrate_legendre(s->(s+r[i]+1)*B0(s+r[i])*((-1-2*sqrt(abs(e))*s)*(1-exp(-4*sqrt(abs(e))*r[i]))*exp(-2*sqrt(abs(e))*s)+4*sqrt(abs(e))*r[i]*exp(-2*sqrt(abs(e))*(2*r[i]+s))),0.,1. -r[i],weights)
       )
   end
   return out
 end
-@everywhere function dsmuD1(e,rho,r,taus,T,weights,weights_gL,P,N,J,nu)
-  out=Array{Array{Float64}}(undef,J*N)
+@everywhere function dsmuD1(
+  e::Float64,
+  rho::Float64,
+  r::Array{Float64,1},
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  weights_gL::Tuple{Array{Float64,1},Array{Float64,1}},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  nu::Int64
+)
+  out=Array{Array{Float64,1},1}(undef,J*N)
   for i in 1:J*N
-    out[i]=Array{Float64}(undef,(P+1)*J)
+    out[i]=Array{Float64,1}(undef,(P+1)*J)
     for zeta in 0:J-1
       for n in 0:P
 	out[i][zeta*(P+1)+n+1]=rho/(r[i]+1)*integrate_legendre(s->
 	  (s+1)*B1(s,zeta,n,taus,T,weights,nu)*((-1-2*sqrt(abs(e))*r[i])*(exp(-2*sqrt(abs(e))*(r[i]-s))-exp(-2*sqrt(abs(e))*(r[i]+s)))/2+2*sqrt(abs(e))*s*(exp(-2*sqrt(abs(e))*(r[i]-s))+exp(-2*sqrt(abs(e))*(r[i]+s)))/2)
-	,0,r[i],weights)+
+	,0.,r[i],weights)+
 	rho/(2*(r[i]+1))*integrate_laguerre(s->
 	  (s+r[i]+1)*B1(s+r[i],zeta,n,taus,T,weights,nu)*((-1-2*sqrt(abs(e))*s)*(1-exp(-4*sqrt(abs(e))*r[i]))+4*sqrt(abs(e))*r[i]*exp(-4*sqrt(abs(e))*r[i]))
 	,2*sqrt(abs(e)),weights_gL)
@@ -442,7 +602,19 @@ end
   end
   return out
 end
-@everywhere function dsmuD2(e,rho,r,taus,T,weights,weights_gL,P,N,J,nu)
+@everywhere function dsmuD2(
+  e::Float64,
+  rho::Float64,
+  r::Array{Float64,1},
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  weights_gL::Tuple{Array{Float64,1},Array{Float64,1}},
+  P::Int64,
+  N::Int64,
+  J::Int64,
+  nu::Int64
+)
   out=Array{Array{Float64,2}}(undef,J*N)
   for i in 1:J*N
     out[i]=Array{Float64,2}(undef,(P+1)*J,(P+1)*J)
@@ -452,7 +624,7 @@ end
 	  for m in 0:P
 	    out[i][zeta*(P+1)+n+1,zetap*(P+1)+m+1]=rho/(r[i]+1)*integrate_legendre(s->
 	      (s+1)*B2(s,zeta,n,zetap,m,taus,T,weights,nu)*((-1-2*sqrt(abs(e))*r[i])*(exp(-2*sqrt(abs(e))*(r[i]-s))-exp(-2*sqrt(abs(e))*(r[i]+s)))/2+2*sqrt(abs(e))*s*(exp(-2*sqrt(abs(e))*(r[i]-s))+exp(-2*sqrt(abs(e))*(r[i]+s)))/2)
-	    ,0,r[i],weights)+
+	    ,0.,r[i],weights)+
 	    rho/(2*(r[i]+1))*integrate_laguerre(s->
 	      (s+r[i]+1)*B2(s+r[i],zeta,n,zetap,m,taus,T,weights,nu)*((-1-2*sqrt(abs(e))*s)*(1-exp(-4*sqrt(abs(e))*r[i]))+4*sqrt(abs(e))*r[i]*exp(-4*sqrt(abs(e))*r[i]))
 	    ,2*sqrt(abs(e)),weights_gL)
@@ -465,11 +637,25 @@ end
 end
 
 # gamma's
-@everywhere function gamma0(e,rho,weights)
-  return 2*rho*e*integrate_legendre(s->(s+1)*B0(s)*exp(-2*sqrt(abs(e))*s),0,1,weights)
+@everywhere function gamma0(
+  e::Float64,
+  rho::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}}
+)
+  return 2*rho*e*integrate_legendre(s->(s+1)*B0(s)*exp(-2*sqrt(abs(e))*s),0.,1.,weights)
 end
-@everywhere function gamma1(e,rho,taus,T,weights,weights_gL,P,J,nu)
-  out=Array{Float64}(undef,J*(P+1))
+@everywhere function gamma1(
+  e::Float64,
+  rho::Float64,
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  weights_gL::Tuple{Array{Float64,1},Array{Float64,1}},
+  P::Int64,
+  J::Int64,
+  nu::Int64
+)
+  out=Array{Float64,1}(undef,J*(P+1))
   for zeta in 0:J-1
     for n in 0:P
       out[zeta*(P+1)+n+1]=2*rho*e*integrate_laguerre(s->(s+1)*B1(s,zeta,n,taus,T,weights,nu),2*sqrt(abs(e)),weights_gL)
@@ -477,7 +663,17 @@ end
   end
   return out
 end
-@everywhere function gamma2(e,rho,taus,T,weights,weights_gL,P,J,nu)
+@everywhere function gamma2(
+  e::Float64,
+  rho::Float64,
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  weights_gL::Tuple{Array{Float64,1},Array{Float64,1}},
+  P::Int64,
+  J::Int64,
+  nu::Int64
+)
   out=Array{Float64,2}(undef,J*(P+1),J*(P+1))
   for zeta in 0:J-1
     for n in 0:P
@@ -492,11 +688,25 @@ end
 end
 
 # dgamma/d sqrt(abs(e))'s
-@everywhere function dsedgamma0(e,rho,weights)
-  return 4*rho*sqrt(abs(e))*integrate_legendre(s->(s+1)*B0(s)*(1-sqrt(abs(e))*s)*exp(-2*sqrt(abs(e))*s),0,1,weights)
+@everywhere function dsedgamma0(
+  e::Float64,
+  rho::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}}
+)
+  return 4*rho*sqrt(abs(e))*integrate_legendre(s->(s+1)*B0(s)*(1-sqrt(abs(e))*s)*exp(-2*sqrt(abs(e))*s),0.,1.,weights)
 end
-@everywhere function dsedgamma1(e,rho,taus,T,weights,weights_gL,P,J,nu)
-  out=Array{Float64}(undef,J*(P+1))
+@everywhere function dsedgamma1(
+  e::Float64,
+  rho::Float64,
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  weights_gL::Tuple{Array{Float64,1},Array{Float64,1}},
+  P::Int64,
+  J::Int64,
+  nu::Int64
+)
+  out=Array{Float64,1}(undef,J*(P+1))
   for zeta in 0:J-1
     for n in 0:P
       out[zeta*(P+1)+n+1]=4*rho*e*integrate_laguerre(s->(s+1)*B1(s,zeta,n,taus,T,weights,nu)*(1-sqrt(abs(e))*s),2*sqrt(abs(e)),weights_gL)
@@ -504,7 +714,17 @@ end
   end
   return out
 end
-@everywhere function dsedgamma2(e,rho,taus,T,weights,weights_gL,P,J,nu)
+@everywhere function dsedgamma2(
+  e::Float64,
+  rho::Float64,
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  weights_gL::Tuple{Array{Float64,1},Array{Float64,1}},
+  P::Int64,
+  J::Int64,
+  nu::Int64
+)
   out=Array{Float64,2}(undef,J*(P+1),J*(P+1))
   for zeta in 0:J-1
     for n in 0:P
@@ -519,11 +739,25 @@ end
 end
 
 # dgamma/d sqrt(e+mu/2)'s
-@everywhere function dsmudgamma0(e,rho,weights)
-  return -4*rho*e*integrate_legendre(s->(s+1)*s*B0(s)*exp(-2*sqrt(abs(e))*s),0,1,weights)
+@everywhere function dsmudgamma0(
+  e::Float64,
+  rho::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}}
+)
+  return -4*rho*e*integrate_legendre(s->(s+1)*s*B0(s)*exp(-2*sqrt(abs(e))*s),0.,1.,weights)
 end
-@everywhere function dsmudgamma1(e,rho,taus,T,weights,weights_gL,P,J,nu)
-  out=Array{Float64}(undef,J*(P+1))
+@everywhere function dsmudgamma1(
+  e::Float64,
+  rho::Float64,
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  weights_gL::Tuple{Array{Float64,1},Array{Float64,1}},
+  P::Int64,
+  J::Int64,
+  nu::Int64
+)
+  out=Array{Float64,1}(undef,J*(P+1))
   for zeta in 0:J-1
     for n in 0:P
       out[zeta*(P+1)+n+1]=-4*rho*e*integrate_laguerre(s->s*(s+1)*B1(s,zeta,n,taus,T,weights,nu),2*sqrt(abs(e)),weights_gL)
@@ -531,7 +765,17 @@ end
   end
   return out
 end
-@everywhere function dsmudgamma2(e,rho,taus,T,weights,weights_gL,P,J,nu)
+@everywhere function dsmudgamma2(
+  e::Float64,
+  rho::Float64,
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  weights_gL::Tuple{Array{Float64,1},Array{Float64,1}},
+  P::Int64,
+  J::Int64,
+  nu::Int64
+)
   out=Array{Float64,2}(undef,J*(P+1),J*(P+1))
   for zeta in 0:J-1
     for n in 0:P
@@ -546,25 +790,41 @@ end
 end
 
 # \bar gamma's
-@everywhere function gammabar0(weights)
-  return 4*pi*integrate_legendre(s->s^2*B0(s-1),0,1,weights)
+@everywhere function gammabar0(
+  weights::Tuple{Array{Float64,1},Array{Float64,1}}
+)
+  return 4*pi*integrate_legendre(s->s^2*B0(s-1),0.,1.,weights)
 end
-@everywhere function gammabar1(taus,T,weights,P,J,nu)
-  out=Array{Float64}(undef,J*(P+1))
+@everywhere function gammabar1(
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  P::Int64,
+  J::Int64,
+  nu::Int64
+)
+  out=Array{Float64,1}(undef,J*(P+1))
   for zeta in 0:J-1
     for n in 0:P
-      out[zeta*(P+1)+n+1]=4*pi*integrate_legendre(s->s^2*B1(s-1,zeta,n,taus,T,weights,nu),0,1,weights)
+      out[zeta*(P+1)+n+1]=4*pi*integrate_legendre(s->s^2*B1(s-1,zeta,n,taus,T,weights,nu),0.,1.,weights)
     end
   end
   return out
 end
-@everywhere function gammabar2(taus,T,weights,P,J,nu)
+@everywhere function gammabar2(
+  taus::Array{Float64,1},
+  T::Array{Polynomial,1},
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  P::Int64,
+  J::Int64,
+  nu::Int64
+)
   out=Array{Float64,2}(undef,J*(P+1),J*(P+1))
   for zeta in 0:J-1
     for n in 0:P
       for zetap in 0:J-1
 	for m in 0:P
-	  out[zeta*(P+1)+n+1,zetap*(P+1)+m+1]=4*pi*integrate_legendre(s->s^2*B2(s-1,zeta,n,zetap,m,taus,T,weights,nu),0,1,weights)
+	  out[zeta*(P+1)+n+1,zetap*(P+1)+m+1]=4*pi*integrate_legendre(s->s^2*B2(s-1,zeta,n,zetap,m,taus,T,weights,nu),0.,1.,weights)
 	end
       end
     end
diff --git a/src/simpleq-iteration.jl b/src/simpleq-iteration.jl
index 98977b8..4c4de07 100644
--- a/src/simpleq-iteration.jl
+++ b/src/simpleq-iteration.jl
@@ -1,4 +1,4 @@
-## Copyright 2021 Ian Jauslin
+## Copyright 2021-2023 Ian Jauslin
 ## 
 ## Licensed under the Apache License, Version 2.0 (the "License");
 ## you may not use this file except in compliance with the License.
@@ -13,7 +13,12 @@
 ## limitations under the License.
 
 # compute rho(e) using the iteration
-function simpleq_iteration_rho_e(es,order,v,maxiter)
+function simpleq_iteration_rho_e(
+  es::Array{Float64,1},
+  order::Int64,
+  v::Function,
+  maxiter::Int64
+)
   for j in 1:length(es)
     (u,rho)=simpleq_iteration_hatun(es[j],order,v,maxiter)
     @printf("% .15e % .15e\n",es[j],real(rho[maxiter+1]))
@@ -21,7 +26,15 @@ function simpleq_iteration_rho_e(es,order,v,maxiter)
 end
 
 # compute u(x) using the iteration and print at every step
-function simpleq_iteration_ux(order,e,v,maxiter,xmin,xmax,nx)
+function simpleq_iteration_ux(
+  order::Int64,
+  e::Float64,
+  v::Function,
+  maxiter::Int64,
+  xmin::Float64,
+  xmax::Float64,
+  nx::Int64
+)
   (u,rho)=simpleq_iteration_hatun(e,order,v,maxiter)
 
   weights=gausslegendre(order)
@@ -37,7 +50,12 @@ end
 
 
 # \hat u_n
-function simpleq_iteration_hatun(e,order,v,maxiter)
+function simpleq_iteration_hatun(
+  e::Float64,
+  order::Int64,
+  v::Function,
+  maxiter::Int64
+)
   # gauss legendre weights
   weights=gausslegendre(order)
 
@@ -46,7 +64,7 @@ function simpleq_iteration_hatun(e,order,v,maxiter)
   (Eta,Eta0)=easyeq_init_H(weights,v)
 
   # init u and rho
-  u=Array{Array{Float64}}(undef,maxiter+1)
+  u=Array{Array{Float64,1},1}(undef,maxiter+1)
   u[1]=zeros(Float64,order)
   rho=zeros(Float64,maxiter+1)
 
@@ -60,7 +78,11 @@ function simpleq_iteration_hatun(e,order,v,maxiter)
 end
 
 # A
-function simpleq_iteration_A(e,weights,Eta)
+function simpleq_iteration_A(
+  e::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  Eta::Array{Float64,1}
+)
   N=length(weights[1])
   out=zeros(Float64,N,N)
   for i in 1:N
@@ -75,7 +97,12 @@ function simpleq_iteration_A(e,weights,Eta)
 end
 
 # b
-function simpleq_iteration_b(u,e,rho,V)
+function simpleq_iteration_b(
+  u::Array{Float64,1},
+  e::Float64,
+  rho::Float64,
+  V::Array{Float64,1}
+)
   out=zeros(Float64,length(V))
   for i in 1:length(V)
     out[i]=V[i]+2*e*rho*u[i]^2
@@ -84,7 +111,13 @@ function simpleq_iteration_b(u,e,rho,V)
 end
 
 # rho_n
-function simpleq_iteration_rhon(u,e,weights,V0,Eta0)
+function simpleq_iteration_rhon(
+  u::Array{Float64,1},
+  e::Float64,
+  weights::Tuple{Array{Float64,1},Array{Float64,1}},
+  V0::Float64,
+  Eta0::Float64
+)
   S=V0
   for i in 1:length(weights[1])
     y=(weights[1][i]+1)/2
diff --git a/src/tools.jl b/src/tools.jl
index 0d3dc7f..5d1678d 100644
--- a/src/tools.jl
+++ b/src/tools.jl
@@ -1,4 +1,4 @@
-## Copyright 2021 Ian Jauslin
+## Copyright 2021-2023 Ian Jauslin
 ## 
 ## Licensed under the Apache License, Version 2.0 (the "License");
 ## you may not use this file except in compliance with the License.
@@ -13,7 +13,9 @@
 ## limitations under the License.
 
 # \Phi(x)=2*(1-sqrt(1-x))/x
-@everywhere function Phi(x)
+@everywhere function Phi(
+  x::Float64
+)
   if abs(x)>1e-5
     return 2*(1-sqrt(abs(1-x)))/x
   else
@@ -21,7 +23,9 @@
   end
 end
 # \partial\Phi
-@everywhere function dPhi(x)
+@everywhere function dPhi(
+  x::Float64
+)
   #if abs(x-1)<1e-5
   #  @printf(stderr,"warning: dPhi is singular at 1, and evaluating it at (% .8e+i% .8e)\n",real(x),imag(x))
   #end
@@ -33,17 +37,25 @@ end
 end
 
 # apply Phi to every element of a vector
-@everywhere function dotPhi(v)
+@everywhere function dotPhi(
+  v::Array{Float64,1}
+)
   out=zeros(Float64,length(v))
   for i in 1:length(v)
     out[i]=Phi(v[i])
   end
   return out
 end
-@everywhere function dotdPhi(v)
+@everywhere function dotdPhi(
+  v::Array{Float64,1}
+)
   out=zeros(Float64,length(v))
   for i in 1:length(v)
     out[i]=dPhi(v[i])
   end
   return out
 end
+
+@everywhere function sinc(x::Float64)
+  return (x == 0 ? 1 : sin(x)/x)
+end