fixed refs to sections in decay and scaling books

Author: sveinlin

doc/.src/chapters/diffu/diffu_fd1.do.txt

@@ -72,9 +72,8 @@ The exposition below assumes that the reader is familiar with the
 basic ideas of discretization and implementation of wave
 equations from Chapter ref{ch:wave}. Readers not familiar with the
 Forward Euler, Backward Euler, and Crank-Nicolson (or centered or
-midpoint) discretization methods in time should consult, e.g.,
-ref[Section ref{decay:basics}][ in cite{Langtangen_decay}][the
-section "Finite difference methods": "http://hplgit.github.io/decay-book/doc/pub/book/sphinx/._book002.html#finite-difference-methods" in cite{Langtangen_decay}].
+midpoint) discretization methods in time should consult, e.g., Section 1.1 
+in cite{Langtangen_decay}.
 % endif
 
 ===== The initial-boundary value problem for 1D diffusion =====
@@ -513,10 +512,8 @@ solutions.
 
 We target a scaled diffusion problem where $x/L$ is a new spatial
 coordinate and $\dfc t/L^2$ is a new time coordinate. The source term
-$f$ is omitted, and $u$ is scaled by $\max_{x\in [0,L]}|I(x)|$ (see
-ref[Section ref{sec:scale:diffu}][ in
-cite{Langtangen_scaling}]["Scaling of the diffusion equation":
-"http://hplgit.github.io/scaling-book/doc/pub/book/html/._scaling-book008.html#sec:scale:diffu" cite{Langtangen_scaling}] for details).
+$f$ is omitted, and $u$ is scaled by $\max_{x\in [0,L]}|I(x)|$ (see Section 3.2 in
+ cite{Langtangen_scaling} for details).
 The governing PDE is then
 
 !bt

doc/.src/chapters/softeng2/softeng2.do.txt

@@ -198,8 +198,8 @@ magic. The idea is that `joblib` looks at the `name` parameter
 and saves the return value `data` to disk if the `name` parameter
 has not been used in a previous call. Otherwise, if `name` is
 already registered, `joblib` fetches the `data` object from
-file and returns it (this is example of a memoize function,
-see ref[Section ref{sec:scale:decay:prog}][in cite{Langtangen_scaling}][the document "Scaling of differential equations": "http://tinyurl.com/k3sdbuv/pub/scale" cite{Langtangen_scaling} for a brief explanation]).
+file and returns it (this is an example of a memoize function,
+see Section 2.1.4 in cite{Langtangen_scaling} for a brief explanation]).
 
 ===== Using a hash to create a file or directory name =====
 label{softeng2:wave1D:filestorage:hash}

doc/.src/chapters/vib/vib_gen.do.txt

@@ -657,11 +657,8 @@ numerical parameters and compute the solution, and a class
 `Visualizer` to display the solution.
 
 !bhint
-Use the ideas and examples ref[from Section ref{decay:prog:se:class}
-and ref{decay:prog:se:class2}][ in cite{Langtangen_decay}][for an "ODE
-model":
-"${decay_book}/._book009.html#classes-for-problem-and-solution-method"
-in cite{Langtangen_decay}].  More specifically, make a superclass
+Use the ideas and examples from Sections 5.5.1 and 5.5.2 
+in cite{Langtangen_decay}.  More specifically, make a superclass
 `Problem` for holding the scalar physical parameters of a problem and
 let subclasses implement the $s(u)$ and $F(t)$ functions as methods.
 Try to call up as much existing functionality in `vib.py` as possible.

doc/.src/chapters/wave/wave1D_fd1.do.txt

@@ -533,9 +533,8 @@ empirical convergence rate of the method.
 
 !enotice
 
-An introduction to the computing of convergence rates is given in
-ref[Section ref{decay:convergence:rate}][ in cite{Langtangen_decay}][the section
-on "convergence rates": "${decay_book}/._book007.html#computing-convergence-rates" in cite{Langtangen_decay}].
+An introduction to the computing of convergence rates is given in Section 3.1.6
+ in cite{Langtangen_decay}.
 % if BOOK == "book":
 There is also a detailed example on computing convergence rates in
 Section ref{vib:ode1:verify}.

doc/.src/chapters/wave/wave1D_prog.do.txt

@@ -390,12 +390,7 @@ the mathematical problem we can often reduce the need to estimate
 physical parameters dramatically. The scaling technique consists of
 introducing new independent and dependent variables, with the aim that
 the absolute values of these lie in $[0,1]$. We introduce the
-dimensionless variables (details are found in ref[Section
-ref{sec:scale:wave:bc_u0}][ in cite{Langtangen_scaling}][the section
-"Homogeneous Dirichlet conditions in 1D":
-"http://hplgit.github.io/scaling-book/doc/pub/book/html/._scaling-book007.html#sec:scale:wave:bc_u0"
-in the book "Scaling of differential equations":
-"http://tinyurl.com/qfjgxmf/web" cite{Langtangen_scaling}]
+dimensionless variables (details are found in Section 3.1.1 in cite{Langtangen_scaling})
 
 !bt
 \[ \bar x = \frac{x}{L},\quad \bar t = \frac{c}{L}t,\quad

doc/Trash/vib/html/._vib-sol003.html

@@ -1385,8 +1385,7 @@ <h2 id="vib:exer:gen:class">Exercise 19: Implement the solver via classes</h2>
 <p><div class="collapse" id="exer_19_1">
 
 <p>
-Use the ideas and examples for an <a href="http://hplgit.github.io/decay-book/doc/pub/book/sphinx/._book009.html#classes-for-problem-and-solution-method" target="_self">ODE
-model</a>
+Use the ideas and examples from Sections 5.5.1 and 5.5.2 
 in <a href="#Langtangen_decay">[1]</a>.  More specifically, make a superclass
 <code>Problem</code> for holding the scalar physical parameters of a problem and
 let subclasses implement the \( s(u) \) and \( F(t) \) functions as methods.

doc/Trash/vib/html/._vib-solarized003.html

@@ -1247,8 +1247,7 @@ <h2 id="vib:exer:gen:class">Exercise 19: Implement the solver via classes</h2>
 
 <p>
 <b>Hint.</b>
-Use the ideas and examples for an <a href="http://hplgit.github.io/decay-book/doc/pub/book/sphinx/._book009.html#classes-for-problem-and-solution-method" target="_self">ODE
-model</a>
+Use the ideas and examples from Sections 5.5.1 and 5.5.2 
 in <a href="#Langtangen_decay">[1]</a>.  More specifically, make a superclass
 <code>Problem</code> for holding the scalar physical parameters of a problem and
 let subclasses implement the \( s(u) \) and \( F(t) \) functions as methods.