fixed some refs to sections in decay and scaling books

Author: sveinlin

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

@@ -87,8 +87,8 @@ in electrical circuits.
 label{vib:ode1:fdm}
 
 To formulate a finite difference method for the model
-problem  (ref{vib:ode1}) we follow the ref[four steps explained in Section
-ref{decay:schemes:FE}][ in cite{Langtangen_decay}]["four steps": "${doc_notes}/sphinx-decay/main_decay.html#the-forward-euler-scheme" explained in cite{Langtangen_decay}].
+problem  (ref{vib:ode1}), we follow the four steps explained in Section 1.1.2
+in cite{Langtangen_decay}.
 
 idx{mesh!finite differences} idx{mesh function} idx{discretization of domain}
 
@@ -246,8 +246,7 @@ We may write the scheme using a compact difference notation
 % if BOOK == "book":
 listed in Appendix ref{sec:form:fdop}
 % endif
-(see also ref[Section ref{decay:fd:op}][ in cite{Langtangen_decay}][
-"examples": "${doc_notes}/sphinx-decay/main_decay.html#compact-operator-notation-for-finite-differences" in cite{Langtangen_decay}]).
+(see also Section 1.1.8 in cite{Langtangen_decay}).
 The difference (ref{vib:ode1:step3}) has the operator
 notation $[D_tD_t u]^n$ such that we can write:
 
@@ -309,10 +308,8 @@ computing community and a good programming habit (since we explicitly
 see where the different functions come from).  An alternative is to do
 `from numpy import *` and a similar ``import all'' for Matplotlib to
 avoid the `np` and `plt` prefixes and make the code as close as
-possible to MATLAB. (See ref[Section ref{softeng1:basic:modprefix}][ in
-cite{Langtangen_decay}][the section
-"Prefixing imported functions by the module name": "http://hplgit.github.io/decay-book/doc/pub/book/html/._decay-book009.html#softeng1:basic:modprefix" in the book
-"Finite Difference Computing with Exponential Decay Models": "http://tinyurl.com/nclmcng/web" cite{Langtangen_decay}] for a discussion of the two
+possible to MATLAB. (See Section 5.1.4 in
+cite{Langtangen_decay} for a discussion of the two
 types of import in Python.)
 
 A function for plotting the numerical and the exact solution is also
@@ -348,9 +345,8 @@ parser.add_argument('--num_periods', type=int, default=5)
 a = parser.parse_args()
 I, w, dt, num_periods = a.I, a.w, a.dt, a.num_periods
 !ec
-Such parsing of the command line is explained in more detail in
-ref[Section ref{softeng1:basic:UI:options_cml}][ in cite{Langtangen_decay}][ the
-section "Option-value pairs on the command line": "${decay_book_url}/html/._decay-book009.html#softeng1:basic:UI:options_cml" in "Finite Difference Computing with Exponential Decay Models": "http://tinyurl.com/nclmcng/web" cite{Langtangen_decay}].
+Such parsing of the command line is explained in more detail in Section 5.2.3 in 
+cite{Langtangen_decay}.
 
 A typical execution goes like
 
@@ -394,12 +390,7 @@ v[-1] = (u[-1] - u[-2])/dt
 !ec
 Since the loop is slow for large $N_t$, we can get rid of the loop by
 vectorizing the central difference. The above code segment goes as
-follows in its vectorized version (see ref[Problem
-ref{decay:exer:dudt}][ in cite{Langtangen_decay}][the problem
-"Differentiate a function":
-"${decay_book_url}/html/._decay-book005.html#decay:exer:dudt" in "Finite
-Difference Computing with Exponential Decay Models":
-"http://tinyurl.com/nclmcng/web" cite{Langtangen_decay}] for
+follows in its vectorized version (see Problem 1.2 in cite{Langtangen_decay} for
 explanation of details):
 
 !bc pycod
@@ -443,9 +434,7 @@ framework for Python code, because
 We shall in this book implement all software verification via such
 proper test functions, also known as unit testing.
 % endif
-See ref[Section ref{softeng1:verify:pytest}][ in cite{Langtangen_decay}][the
-section "Unit tests and test functions": "${decay_book_url}/html/._decay-book009.html#softeng1:verify:pytest" in
-"Finite Difference Computing with Exponential Decay Models": "http://tinyurl.com/nclmcng/web" cite{Langtangen_decay}]
+See Section 5.3.2 in cite{Langtangen_decay}
 for more details on how to construct test functions and utilize nose
 or pytest for automatic execution of tests. Our recommendation is to
 use pytest. With this choice, you can
@@ -490,12 +479,7 @@ idx{error norm}
 
 Empirical computation of convergence rates yields a good method for
 verification. The method and its computational details are explained
-in detail ref[in Section ref{decay:convergence:rate}][ in
-cite{Langtangen_decay}][for a simple ODE model in the section
-"Computing convergence rates":
-"${decay_book_url}/html/._decay-book007.html#decay:convergence:rate"
-in "Finite Difference Computing with Exponential Decay Models":
-"http://tinyurl.com/nclmcng/web" cite{Langtangen_decay}].  Readers not
+in detail in Section 3.1.6 in cite{Langtangen_decay}.  Readers not
 familiar with the concept should look up this reference before
 proceeding.
 
@@ -585,13 +569,8 @@ It is advantageous to use dimensionless variables in simulations,
 because fewer parameters need to be set. The present problem is made
 dimensionless by introducing dimensionless variables $\bar t = t/t_c$
 and $\bar u = u/u_c$, where $t_c$ and $u_c$ are characteristic scales
-for $t$ and $u$, respectively. We refer to ref[Section
-ref{sec:scale:vib:undamped}][ in cite{Langtangen_scaling}][the section
-"Undamped vibrations without forcing":
-"http://hplgit.github.io/scaling-book/doc/pub/book/html/._scaling-book006.html#sec:scale:vib:undamped"
-in the book "Scaling of differential equations":
-"http://tinyurl.com/qfjgxmf/web" cite{Langtangen_scaling}] for all
-details about this scaling.
+for $t$ and $u$, respectively. We refer to Section 2.2.1 in 
+cite{Langtangen_scaling} for all details about this scaling.
 
 The scaled ODE problem reads
 
@@ -3296,10 +3275,8 @@ file=vib_undamped_adaptive
 Adaptive methods for solving ODEs aim at adjusting $\Delta t$ such
 that the error is within a user-prescribed tolerance. Implement the
 equation $u^{\prime\prime}+u=0$ in the "Odespy": "https://github.com/hplgit/odespy"
-software. Use the example ref[from Section
-ref{decay:fd2:adaptiveRK}][ in cite{Langtangen_decay}]["on adaptive
-schemes": "${decay_book}/._book006.html#example-adaptive-runge-kutta-methods"
-in cite{Langtangen_decay}].  Run the scheme with a very low
+software. Use the example from Section 3.2.11 in cite{Langtangen_decay}.  
+Run the scheme with a very low
 tolerance (say $10^{-14}$) and for a long time, check the number of
 time points in the solver's mesh (`len(solver.t_all)`), and compare
 the phase error with that produced by the simple finite difference

doc/Trash/vib/html/._vib-sol000.html

@@ -662,7 +662,7 @@
 <center>[3] <b>Department of Process, Energy and Environmental Technology, University College of Southeast Norway</b></center>
 <br>
 <p>
-<center><h4>Dec 23, 2016</h4></center> <!-- date -->
+<center><h4>Jan 21, 2017</h4></center> <!-- date -->
 <br>
 <p>
 <!-- Externaldocuments: ../../../../../decay-book/doc/.src/book/book -->
@@ -700,7 +700,7 @@
 
 
 <center style="font-size:80%">
-<!-- copyright --> &copy; 2016, Hans Petter Langtangen, Svein Linge. Released under CC Attribution 4.0 license
+<!-- copyright --> &copy; 2017, Hans Petter Langtangen, Svein Linge. Released under CC Attribution 4.0 license
 </center>
 
 

doc/Trash/vib/html/._vib-sol002.html

@@ -722,7 +722,8 @@ <h2 id="vib:ode1:fdm">A centered finite difference scheme</h2>
 
 <p>
 To formulate a finite difference method for the model
-problem  <a href="#mjx-eqn-1">(1)</a> we follow the <a href="http://tinyurl.com/pu5uyfn/pub/sphinx-decay/main_decay.html#the-forward-euler-scheme" target="_self">four steps</a> explained in <a href="._vib-sol003.html#Langtangen_decay">[1]</a>.
+problem  <a href="#mjx-eqn-1">(1)</a>, we follow the four steps explained in Section 1.1.2
+in <a href="._vib-sol003.html#Langtangen_decay">[1]</a>.
 
 <h3 id="___sec3">Step 1: Discretizing the domain </h3>
 
@@ -880,8 +881,7 @@ <h3 id="___sec9">Operator notation </h3>
 
 <p>
 We may write the scheme using a compact difference notation
-(see also 
-<a href="http://tinyurl.com/pu5uyfn/pub/sphinx-decay/main_decay.html#compact-operator-notation-for-finite-differences" target="_self">examples</a> in <a href="._vib-sol003.html#Langtangen_decay">[1]</a>).
+(see also Section 1.1.8 in <a href="._vib-sol003.html#Langtangen_decay">[1]</a>).
 The difference <a href="#mjx-eqn-4">(4)</a> has the operator
 notation \( [D_tD_t u]^n \) such that we can write:
 
@@ -963,9 +963,8 @@ <h2 id="vib:impl1:solver">Making a solver function</h2>
 see where the different functions come from).  An alternative is to do
 <code>from numpy import *</code> and a similar &quot;import all&quot; for Matplotlib to
 avoid the <code>np</code> and <code>plt</code> prefixes and make the code as close as
-possible to MATLAB. (See the section
-<a href="http://hplgit.github.io/decay-book/doc/pub/book/html/._decay-book009.html#softeng1:basic:modprefix" target="_self">Prefixing imported functions by the module name</a> in the book
-<a href="http://tinyurl.com/nclmcng/web" target="_self">Finite Difference Computing with Exponential Decay Models</a> <a href="._vib-sol003.html#Langtangen_decay">[1]</a> for a discussion of the two
+possible to MATLAB. (See Section 5.1.4 in
+<a href="._vib-sol003.html#Langtangen_decay">[1]</a> for a discussion of the two
 types of import in Python.)
 
 <p>
@@ -1028,9 +1027,8 @@ <h2 id="vib:impl1:solver">Making a solver function</h2>
 I, w, dt, num_periods <span style="color: #666666">=</span> a<span style="color: #666666">.</span>I, a<span style="color: #666666">.</span>w, a<span style="color: #666666">.</span>dt, a<span style="color: #666666">.</span>num_periods
 </pre></div>
 <p>
-Such parsing of the command line is explained in more detail in
- the
-section <a href="http://hplgit.github.io/decay-book/doc/pub/book/html/._decay-book009.html#softeng1:basic:UI:options_cml" target="_self">Option-value pairs on the command line</a> in <a href="http://tinyurl.com/nclmcng/web" target="_self">Finite Difference Computing with Exponential Decay Models</a> <a href="._vib-sol003.html#Langtangen_decay">[1]</a>.
+Such parsing of the command line is explained in more detail in Section 5.2.3 in 
+<a href="._vib-sol003.html#Langtangen_decay">[1]</a>.
 
 <p>
 A typical execution goes like
@@ -1080,9 +1078,7 @@ <h3 id="___sec12">Computing \( u^{\prime} \) </h3>
 <p>
 Since the loop is slow for large \( N_t \), we can get rid of the loop by
 vectorizing the central difference. The above code segment goes as
-follows in its vectorized version (see the problem
-<a href="http://hplgit.github.io/decay-book/doc/pub/book/html/._decay-book005.html#decay:exer:dudt" target="_self">Differentiate a function</a> in <a href="http://tinyurl.com/nclmcng/web" target="_self">Finite
-Difference Computing with Exponential Decay Models</a> <a href="._vib-sol003.html#Langtangen_decay">[1]</a> for
+follows in its vectorized version (see Problem 1.2 in <a href="._vib-sol003.html#Langtangen_decay">[1]</a> for
 explanation of details):
 
 <p>
@@ -1132,9 +1128,7 @@ <h3 id="___sec14">Manual calculation </h3>
  <li> the test is formulated as a boolean condition and executed by <code>assert</code></li>
 </ul>
 
-See the
-section <a href="http://hplgit.github.io/decay-book/doc/pub/book/html/._decay-book009.html#softeng1:verify:pytest" target="_self">Unit tests and test functions</a> in
-<a href="http://tinyurl.com/nclmcng/web" target="_self">Finite Difference Computing with Exponential Decay Models</a> <a href="._vib-sol003.html#Langtangen_decay">[1]</a>
+See Section 5.3.2 in <a href="._vib-sol003.html#Langtangen_decay">[1]</a>
 for more details on how to construct test functions and utilize nose
 or pytest for automatic execution of tests. Our recommendation is to
 use pytest. With this choice, you can
@@ -1176,9 +1170,7 @@ <h3 id="___sec16">Checking convergence rates </h3>
 <p>
 Empirical computation of convergence rates yields a good method for
 verification. The method and its computational details are explained
-in detail for a simple ODE model in the section
-<a href="http://hplgit.github.io/decay-book/doc/pub/book/html/._decay-book007.html#decay:convergence:rate" target="_self">Computing convergence rates</a>
-in <a href="http://tinyurl.com/nclmcng/web" target="_self">Finite Difference Computing with Exponential Decay Models</a> <a href="._vib-sol003.html#Langtangen_decay">[1]</a>.  Readers not
+in detail in Section 3.1.6 in <a href="._vib-sol003.html#Langtangen_decay">[1]</a>.  Readers not
 familiar with the concept should look up this reference before
 proceeding.
 
@@ -1333,10 +1325,8 @@ <h2 id="___sec18">Scaled model </h2>
 because fewer parameters need to be set. The present problem is made
 dimensionless by introducing dimensionless variables \( \bar t = t/t_c \)
 and \( \bar u = u/u_c \), where \( t_c \) and \( u_c \) are characteristic scales
-for \( t \) and \( u \), respectively. We refer to the section
-<a href="http://hplgit.github.io/scaling-book/doc/pub/book/html/._scaling-book006.html#sec:scale:vib:undamped" target="_self">Undamped vibrations without forcing</a>
-in the book <a href="http://tinyurl.com/qfjgxmf/web" target="_self">Scaling of differential equations</a> <a href="._vib-sol003.html#Langtangen_scaling">[2]</a> for all
-details about this scaling.
+for \( t \) and \( u \), respectively. We refer to Section 2.2.1 in 
+<a href="._vib-sol003.html#Langtangen_scaling">[2]</a> for all details about this scaling.
 
 <p>
 The scaled ODE problem reads
@@ -4691,9 +4681,8 @@ <h2 id="vib:exer:undamped:adaptive">Exercise 4: See if adaptive methods improve
 Adaptive methods for solving ODEs aim at adjusting \( \Delta t \) such
 that the error is within a user-prescribed tolerance. Implement the
 equation \( u^{\prime\prime}+u=0 \) in the <a href="https://github.com/hplgit/odespy" target="_self">Odespy</a>
-software. Use the example <a href="http://hplgit.github.io/decay-book/doc/pub/book/sphinx/._book006.html#example-adaptive-runge-kutta-methods" target="_self">on adaptive
-schemes</a>
-in <a href="._vib-sol003.html#Langtangen_decay">[1]</a>.  Run the scheme with a very low
+software. Use the example from Section 3.2.11 in <a href="._vib-sol003.html#Langtangen_decay">[1]</a>.  
+Run the scheme with a very low
 tolerance (say \( 10^{-14} \)) and for a long time, check the number of
 time points in the solver's mesh (<code>len(solver.t_all)</code>), and compare
 the phase error with that produced by the simple finite difference

doc/Trash/vib/html/._vib-solarized000.html

@@ -529,7 +529,7 @@
 <center>[3] <b>Department of Process, Energy and Environmental Technology, University College of Southeast Norway</b></center>
 <br>
 <p>
-<center><h4>Dec 23, 2016</h4></center> <!-- date -->
+<center><h4>Jan 21, 2017</h4></center> <!-- date -->
 <br>
 <p>
 <!-- Externaldocuments: ../../../../../decay-book/doc/.src/book/book -->
@@ -551,7 +551,7 @@
 
 
 <center style="font-size:80%">
-<!-- copyright --> &copy; 2016, Hans Petter Langtangen, Svein Linge. Released under CC Attribution 4.0 license
+<!-- copyright --> &copy; 2017, Hans Petter Langtangen, Svein Linge. Released under CC Attribution 4.0 license
 </center>