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Updates to all files scripts to support MPI testing proceedure. Updates to cookbook, new section on functino spaces/domains (needs work). Finalising first 3 chapters for editing.
1 ahallam 2401
2     %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
3     %
4     % Copyright (c) 2003-2009 by University of Queensland
5     % Earth Systems Science Computational Center (ESSCC)
6     % http://www.uq.edu.au/esscc
7     %
8     % Primary Business: Queensland, Australia
9     % Licensed under the Open Software License version 3.0
10     % http://www.opensource.org/licenses/osl-3.0.php
11     %
12     %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
13    
14     \section{One Dimensional Heat Diffusion in an Iron Rod}
15 ahallam 2658 \sslist{onedheatdiff001.py and cblib.py}
16 ahallam 2401 %\label{Sec:1DHDv0}
17 ahallam 2495 We will start by examining a simple one dimensional heat diffusion example. This problem will provide a good launch pad to build our knowledge of \ESCRIPT and how to solve simple partial differential equations (PDEs)\footnote{Wikipedia provides an excellent and comprehensive introduction to \textit{Partial Differential Equations} \url{http://en.wikipedia.org/wiki/Partial_differential_equation}, however their relevance to \ESCRIPT and implementation should become a clearer as we develop our understanding further into the cookbook.}
18 ahallam 2401
19 ahallam 2494 \begin{figure}[h!]
20     \centerline{\includegraphics[width=4.in]{figures/onedheatdiff}}
21     \caption{One dimensional model of an Iron bar.}
22     \label{fig:onedhdmodel}
23     \end{figure}
24 ahallam 2495 The first model consists of a simple cold iron bar at a constant temperature of zero \reffig{fig:onedhdmodel}. The bar is perfectly insulated on all sides with a heating element at one end. Intuition tells us that as heat is applied; energy will disperse along the bar via conduction. With time the bar will reach a constant temperature equivalent to the heat source.
25 ahallam 2401
26 ahallam 2495 \subsection{1D Heat Diffusion Equation}
27     We can model the heat distribution of this problem over time using the one dimensional heat diffusion equation\footnote{A detailed discussion on how the heat diffusion equation is derived can be found at \url{http://online.redwoods.edu/instruct/darnold/DEProj/sp02/AbeRichards/paper.pdf}};
28 ahallam 2494 which is defined as:
29 ahallam 2401 \begin{equation}
30     \rho c\hackscore p \frac{\partial T}{\partial t} - \kappa \frac{\partial^{2} T}{\partial x^{2}} = q\hackscore H
31     \label{eqn:hd}
32     \end{equation}
33 ahallam 2495 where $\rho$ is the material density, $c\hackscore p$ is the specific heat and $\kappa$ is the thermal conductivity constant for a given material\footnote{A list of some common thermal conductivities is available from Wikipedia \url{http://en.wikipedia.org/wiki/List_of_thermal_conductivities}}.
34     The heat source is defined by the right hand side of \ref{eqn:hd} as $q\hackscore{H}$; this can take the form of a constant or a function of time and space. For example $q\hackscore{H} = Te^{-\gamma t}$ where we have the output of our heat source decaying with time. There are also two partial derivatives in \ref{eqn:hd}; $\frac{\partial T}{\partial t}$ describes the change in temperature with time while $\frac{\partial ^2 T}{\partial x^2}$ is the spatial change of temperature. As there is only a single spatial dimension to our problem, our temperature solution $T$ is only dependent on the time $t$ and our position along the iron bar $x$.
35 ahallam 2401
36 ahallam 2495 \subsection{Escript, PDEs and The General Form}
37 ahallam 2606 Potentially, it is now possible to solve \ref{eqn:hd} analytically and this would produce an exact solution to our problem. However, it is not always possible or practical to solve a problem this way. Alternatively, computers can be used to solve these kinds of problems when a large number of sums or a more complex visualisation is required. To do this, a numerical approach is required - \ESCRIPT can help us here - and it becomes necessary to discretize the equation so that we are left with a finite number of equations for a finite number of spatial and time steps in the model. While discretization introduces approximations and a degree of error, we find that a sufficiently sampled model is generally accurate enough for the requirements of the modeller.
38 gross 2477
39 ahallam 2606 \ESCRIPT interfaces with any given PDE via a general form. In this example we will illustrate a simpler version of the full linear PDE general form which is available in the \ESCRIPT user's guide. A simplified form that suits our heat diffusion problem\footnote{In the form of the \ESCRIPT users guide which using the Einstein convention is written as
40 gross 2477 $-(A\hackscore{jl} u\hackscore{,l})\hackscore{,j}+D u =Y$}
41 ahallam 2606 is described by;
42 gross 2477 \begin{equation}\label{eqn:commonform nabla}
43 jfenwick 2657 -\nabla\cdot(A\cdot\nabla u) + Du = f
44 ahallam 2411 \end{equation}
45 ahallam 2494 where $A$, $D$ and $f$ are known values. The symbol $\nabla$ which is called the \textit{Nabla operator} or \textit{del operator} represents
46 ahallam 2495 the spatial derivative of its subject - in this case $u$. Lets assume for a moment that we deal with a one-dimensional problem then ;
47 gross 2477 \begin{equation}
48     \nabla = \frac{\partial}{\partial x}
49     \end{equation}
50 ahallam 2495 and we can write \ref{eqn:commonform nabla} as;
51 ahallam 2411 \begin{equation}\label{eqn:commonform}
52 gross 2477 -A\frac{\partial^{2}u}{\partial x^{2}} + Du = f
53 ahallam 2411 \end{equation}
54 ahallam 2645 if $A$ is constant then \ref{eqn:commonform} is consistent with our heat diffusion problem in \ref{eqn:hd} with the exception of $u$. Thus when comparing equations \ref{eqn:hd} and \ref{eqn:commonform} we see that;
55 ahallam 2411 \begin{equation}
56 ahallam 2494 A = \kappa; D = \rho c \hackscore{p}; f = q \hackscore{H}
57 ahallam 2411 \end{equation}
58 gross 2477
59 ahallam 2495 We can write the partial $\frac{\partial T}{\partial t}$ in terms of $u$ by discretising the time of our solution. The method we will use is the Backwards Euler approximation, which states;
60 ahallam 2494 \begin{equation}
61 ahallam 2645 \frac{\partial f(x)}{\partial x} \approx \frac{f(x+h)-f(x)}{h}
62 ahallam 2494 \label{eqn:beuler}
63     \end{equation}
64     where h is the the discrete step size $\Delta x$.
65 ahallam 2645 Now if the temperature $T(t)$ is substituted in by letting $f(x) = T(t)$ then from \ref{eqn:beuler} we see that;
66 ahallam 2494 \begin{equation}
67     T'(t) \approx \frac{T(t+h) - T(t)}{h}
68     \end{equation}
69     which can also be written as;
70     \begin{equation}
71 ahallam 2645 \frac{\partial T^{(n)}}{\partial t} \approx \frac{T^{(n)} - T^{(n-1)}}{h}
72 ahallam 2494 \label{eqn:Tbeuler}
73     \end{equation}
74 ahallam 2495 where $n$ denotes the n\textsuperscript{th} time step. Substituting \ref{eqn:Tbeuler} into \ref{eqn:hd} we get;
75 ahallam 2494 \begin{equation}
76     \frac{\rho c\hackscore p}{h} (T^{(n)} - T^{(n-1)}) - \kappa \frac{\partial^{2} T}{\partial x^{2}} = q\hackscore H
77     \label{eqn:hddisc}
78     \end{equation}
79 ahallam 2606 To fit our simplified general form we can rearrange \ref{eqn:hddisc};
80 ahallam 2494 \begin{equation}
81 ahallam 2645 \frac{\rho c\hackscore p}{h} T^{(n)} - \kappa \frac{\partial^2 T^{(n)}}{\partial x^2} = q\hackscore H + \frac{\rho c\hackscore p}{h} T^{(n-1)}
82 ahallam 2494 \label{eqn:hdgenf}
83     \end{equation}
84 jfenwick 2657 The PDE is now in a form that satisfies \refEq{eqn:commonform nabla} which is required for \ESCRIPT to solve our PDE. This can be done by generating a solution for successive increments in the time nodes $t^{(n)}$ where
85 ahallam 2495 $t^{(0)}=0$ and $t^{(n)}=t^{(n-1)}+h$ where $h>0$ is the step size and assumed to be constant.
86 ahallam 2645 In the following the upper index ${(n)}$ refers to a value at time $t^{(n)}$. Finally, by comparing \ref{eqn:hdgenf} with \ref{eqn:commonform} it can be seen that;
87 ahallam 2494 \begin{equation}
88     A = \kappa; D = \frac{\rho c \hackscore{p}}{h}; f = q \hackscore{H} + \frac{\rho c\hackscore p}{h} T^{(n-1)}
89     \end{equation}
90    
91 ahallam 2606 Now that the general form has been established, it can be submitted to \ESCRIPT. Note that it is necessary to establish the state of our system at time zero or $T^{(n=0)}$. This is due to the time derivative approximation we have used from \ref{eqn:Tbeuler}. Our model stipulates a starting temperature in the iron bar of 0\textcelsius. Thus the temperature distribution is simply;
92 ahallam 2495 \begin{equation}
93     T(x,0) = T\hackscore{ref} = 0
94     \end{equation}
95     for all $x$ in the domain.
96 ahallam 2494
97 ahallam 2495 \subsection{Boundary Conditions}
98 ahallam 2606 With the PDE sufficiently modified, consideration must now be given to the boundary conditions of our model. Typically there are two main types of boundary conditions known as Neumann and Dirichlet\footnote{More information on Boundary Conditions is available at Wikipedia \url{http://en.wikipedia.org/wiki/Boundary_conditions}}. In this example, we have utilised both conditions. Dirichlet is conceptually simpler and is used to prescribe a known value to the model on its boundary. This is like holding a snake by the tail; we know where the tail will be as we hold it however, we have no control over the rest of the snake. Dirichlet boundary conditions exist where we have applied our heat source. As the heat source is a constant, we can simulate its presence on that boundary. This is done by continuously resetting the temperature of the boundary, so that is is the same as the heat source.
99 ahallam 2495
100 jfenwick 2657 Neumann boundary conditions describe the radiation or flux normal to the boundary surface. This aptly describes our insulation conditions as we do not want to exert a constant temperature as with the heat source. However, we do want to prevent any loss of energy from the system. These natural boundary conditions can be described by specifying a radiation condition which prescribes the normal component of the flux $\kappa T\hackscore{,i}$ to be proportional
101 ahallam 2645 to the difference of the current temperature to the surrounding temperature $T\hackscore{ref}$; in general terms this is;
102 ahallam 2494 \begin{equation}
103 ahallam 2495 \kappa T\hackscore{,i} n\hackscore i = \eta (T\hackscore{ref}-T)
104     \label{eqn:hdbc}
105 ahallam 2494 \end{equation}
106 ahallam 2645 and simplified to our one dimensional model we have;
107     \begin{equation}
108     \kappa \frac{\partial T}{\partial dx} n\hackscore x = \eta (T\hackscore{ref}-T)
109     \end{equation}
110     where $\eta$ is a given material coefficient depending on the material of the block and the surrounding medium and $n\hackscore i$ is the $i$-th component of the outer normal field \index{outer normal field} at the surface of the domain. These two conditions form a boundary value problem that has to be solved for each time step. Due to the perfect insulation in our model we can set $\eta = 0$ which results in zero flux - no energy in or out - we do not need to worry about the Neumann terms of the general form for this example.
111 ahallam 2494
112 ahallam 2495 \subsection{A \textit{1D} Clarification}
113 ahallam 2645 It is necessary for clarification that we revisit the general PDE from \refeq{eqn:commonform nabla} under the light of a two dimensional domain. \ESCRIPT is inherently designed to solve problems that are greater than one dimension and so \ref{eqn:commonform nabla} needs to be read as a higher dimensional problem. In the case of two spatial dimensions the \textit{Nabla operator} has in fact two components $\nabla = (\frac{\partial}{\partial x}, \frac{\partial}{\partial y})$. In full, \ref{eqn:commonform nabla} assuming a constant coefficient $A$, takes the form;
114 gross 2477 \begin{equation}\label{eqn:commonform2D}
115     -A\hackscore{00}\frac{\partial^{2}u}{\partial x^{2}}
116     -A\hackscore{01}\frac{\partial^{2}u}{\partial x\partial y}
117     -A\hackscore{10}\frac{\partial^{2}u}{\partial y\partial x}
118     -A\hackscore{11}\frac{\partial^{2}u}{\partial y^{2}}
119     + Du = f
120     \end{equation}
121 ahallam 2606 Notice that for the higher dimensional case $A$ becomes a matrix. It is also
122 ahallam 2495 important to notice that the usage of the Nabla operator creates
123     a compact formulation which is also independent from the spatial dimension.
124 gross 2477 So to make the general PDE~\ref{eqn:commonform2D} one dimensional as
125     shown in~\ref{eqn:commonform} we need to set
126 ahallam 2606 \begin{equation}
127 ahallam 2494 A\hackscore{00}=A; A\hackscore{01}=A\hackscore{10}=A\hackscore{11}=0
128 gross 2477 \end{equation}
129    
130 ahallam 2495 \subsection{Developing a PDE Solution Script}
131     To solve \ref{eqn:hd} we will write a simple python script which uses the \modescript, \modfinley and \modmpl modules. At this point we assume that you have some basic understanding of the python programming language. If not there are some pointers and links available in Section \ref{sec:escpybas} .
132 gross 2477
133 ahallam 2495 Our goal here is to develop a script for \ESCRIPT that will solve the heat equation at successive time steps for a predefined period using our general form \ref{eqn:hdgenf}. Firstly it is necessary to import all the libraries\footnote{The libraries contain predefined scripts that are required to solve certain problems, these can be simple like sin and cos functions or more complicated like those from our \ESCRIPT library.}
134     that we will require.
135 gross 2477 \begin{verbatim}
136 ahallam 2495 from esys.escript import *
137 ahallam 2606 # This defines the LinearPDE module as LinearPDE
138     from esys.escript.linearPDEs import LinearPDE
139     # This imports the rectangle domain function from finley.
140     from esys.finley import Rectangle
141     # A useful unit handling package which will make sure all our units
142     # match up in the equations under SI.
143     from esys.escript.unitsSI import *
144     import pylab as pl #Plotting package.
145     import numpy as np #Array package.
146     import os #This package is necessary to handle saving our data.
147 gross 2477 \end{verbatim}
148 ahallam 2606 It is generally a good idea to import all of the \modescript library, although if you know the packages you need you can specify them individually. The function \verb|LinearPDE| has been imported for ease of use later in the script. \verb|Rectangle| is going to be our type of domain. The package \verb unitsSI is a module of \esc that provides support for units definitions with our variables; and the \verb|os| package is needed to handle file outputs once our PDE has been solved. \verb pylab and \verb numpy are modules developed independently of \esc. They are used because they have efficient plotting and array handling capabilities.
149 gross 2477
150 ahallam 2495 Once our library dependencies have been established, defining the problem specific variables is the next step. In general the number of variables needed will vary between problems. These variables belong to two categories. They are either directly related to the PDE and can be used as inputs into the \ESCRIPT solver, or they are script variables used to control internal functions and iterations in our problem. For this PDE there are a number of constants which will need values. Firstly, the domain upon which we wish to solve our problem needs to be defined. There are many different types of domains in \modescript which we will demonstrate in later tutorials but for our iron rod, we will simply use a rectangular domain.
151 ahallam 2401
152 ahallam 2645 Using a rectangular domain simplifies our rod which would be a \textit{3D} object, into a single dimension. The iron rod will have a lengthways cross section that looks like a rectangle. As a result we do not need to model the volume of the rod because a cylinder is symmetrical about its centre. There are four arguments we must consider when we decide to create a rectangular domain, the model \textit{length}, \textit{width} and \textit{step size} in each direction. When defining the size of our problem it will help us determine appropriate values for our domain arguments. If we make our dimensions large but our step sizes very small we will to a point, increase the accuracy of our solution. Unfortunately we also increase the number of calculations that must be solved per time step. This means more computational time is required to produce a solution. In our \textit{1D} problem we will define our bar as being 1 metre long. An appropriate \verb|ndx| would be 1 to 10\% of the length. Our \verb|ndy| need only be 1, This is because our problem stipulates no partial derivatives in the $y$ direction so the temperature does not vary with $y$. Thus the domain parameters can be defined as follows; note we have used the \verb unitsSI convention to make sure all our input units are converted to SI.
153 ahallam 2495 \begin{verbatim}
154     #Domain related.
155 ahallam 2606 mx = 1*m #meters - model length
156 ahallam 2495 my = .1*m #meters - model width
157 ahallam 2606 ndx = 100 # mesh steps in x direction
158     ndy = 1 # mesh steps in y direction - one dimension means one element
159 ahallam 2495 \end{verbatim}
160     The material constants and the temperature variables must also be defined. For the iron rod in the model they are defined as:
161     \begin{verbatim}
162     #PDE related
163     q=200. * Celsius #Kelvin - our heat source temperature
164 ahallam 2606 Tref = 0. * Celsius #Kelvin - starting temp of iron bar
165 ahallam 2495 rho = 7874. *kg/m**3 #kg/m^{3} density of iron
166 ahallam 2606 cp = 449.*J/(kg*K) #j/Kg.K thermal capacity
167 ahallam 2495 rhocp = rho*cp
168 ahallam 2606 kappa = 80.*W/m/K #watts/m.Kthermal conductivity
169 ahallam 2495 \end{verbatim}
170     Finally, to control our script we will have to specify our timing controls and where we would like to save the output from the solver. This is simple enough:
171     \begin{verbatim}
172     t=0 #our start time, usually zero
173 ahallam 2606 tend=5.*minute #seconds - time to end simulation
174 ahallam 2495 outputs = 200 # number of time steps required.
175     h=(tend-t)/outputs #size of time step
176 ahallam 2606 #user warning statement
177     print "Expected Number of time outputs is: ", (tend-t)/h
178     i=0 #loop counter
179     #the folder to put our outputs in, leave blank "" for script path
180     save_path="data/onedheatdiff001"
181 ahallam 2495 \end{verbatim}
182 ahallam 2606 Now that we know our inputs we will build a domain using the \verb Rectangle() function from \verb finley . The four arguments allow us to define our domain \verb rod as:
183 ahallam 2495 \begin{verbatim}
184 ahallam 2606 #generate domain using rectangle
185     rod = Rectangle(l0=mx,l1=my,n0=ndx, n1=ndy)
186 ahallam 2495 \end{verbatim}
187 ahallam 2658 \verb rod now describes a domain in the manner of Section \ref{ss:domcon}. As we define our variables, various function spaces will be created to accomodate them. There is an easy way to extract finite points from the domain \verb|rod| using the domain property function \verb|getX()| . This function sets the vertices of each cell as finite points to solve in the solution. If we let \verb|x| be these finite points, then;
188 ahallam 2495 \begin{verbatim}
189 ahallam 2606 #extract finite points - the solution points
190     x=rod.getX()
191 ahallam 2495 \end{verbatim}
192 ahallam 2658 The data locations of specific function spaces can be returned in a similar manner by extracting the relevent function space from the domain followed by the \verb .getX() operator.
193    
194 ahallam 2495 With a domain and all our required variables established, it is now possible to set up our PDE so that it can be solved by \ESCRIPT. The first step is to define the type of PDE that we are trying to solve in each time step. In this example it is a single linear PDE\footnote{in comparison to a system of PDEs which will be discussed later.}. We also need to state the values of our general form variables.
195     \begin{verbatim}
196     mypde=LinearSinglePDE(rod)
197     mypde.setValue(A=kappa*kronecker(rod),D=rhocp/h)
198     \end{verbatim}
199 ahallam 2401
200 ahallam 2495 In a few special cases it may be possible to decrease the computational time of the solver if our PDE is symmetric. Symmetry of a PDE is defined by;
201     \begin{equation}\label{eqn:symm}
202     A\hackscore{jl}=A\hackscore{lj}
203     \end{equation}
204 ahallam 2606 Symmetry is only dependent on the $A$ coefficient in the general form and the others $D$ and $d$ as well as the RHS coefficients $Y$ and $y$ may take any value. From the above definition we can see that our PDE is symmetric. The \verb LinearPDE class provides the method \method{checkSymmetry} to check if the given PDE is symmetric. As our PDE is symmetrical we will enable symmetry via;
205 ahallam 2495 \begin{verbatim}
206     myPDE.setSymmetryOn()
207     \end{verbatim}
208 ahallam 2401
209 ahallam 2495 We now need to specify our boundary conditions and initial values. The initial values required to solve this PDE are temperatures for each discrete point in our domain. We will set our bar to:
210 ahallam 2401 \begin{verbatim}
211     T = Tref
212     \end{verbatim}
213 ahallam 2606 Boundary conditions are a little more difficult. Fortunately the escript solver will handle our insulated boundary conditions by default with a zero flux operator. However, we will need to apply our heat source $q_{H}$ to the end of the bar at $x=0$ . \ESCRIPT makes this easy by letting us define areas in our domain. The finite points in the domain were previously defined as \verb x and it is possible to set all of points that satisfy $x=0$ to \verb q via the \verb whereZero() function. There are a few \verb where functions available in \ESCRIPT. They will return a value \verb 1 where they are satisfied and \verb 0 where they are not. In this case our \verb qH is only applied to the far LHS of our model as required.
214 ahallam 2495 \begin{verbatim}
215     # ... set heat source: ....
216     qH=q*whereZero(x[0])
217     \end{verbatim}
218 ahallam 2401
219 ahallam 2606 Finally we will initialise an iteration loop to solve our PDE for all the time steps we specified in the variable section. As the RHS of the general form is dependent on the previous values for temperature \verb T across the bar this must be updated in the loop. Our output at each timestep is \verb T the heat distribution and \verb totT the total heat in the system.
220 ahallam 2495 \begin{verbatim}
221     while t<=tend:
222 ahallam 2606 i+=1 #increment the counter
223     t+=h #increment the current time
224     mypde.setValue(Y=qH+rhocp/h*T) #set variable PDE coefficients
225     T=mypde.getSolution() #get the PDE solution
226     totT = rhocp*T #get the total heat solution in the system
227 ahallam 2495 \end{verbatim}
228 ahallam 2401
229 ahallam 2606 \subsection{Plotting the heat solutions}
230     Visualisation of the solution can be achieved using \mpl a module contained with \pylab. We start by modifying our solution script from before. Prior to the \verb while loop we will need to extract our finite solution points to a data object that is compatible with \mpl. First it is necessary to convert \verb x to a list of tuples. These are then converted to a \numpy array and the $x$ locations extracted via an array slice to the variable \verb plx .
231     \begin{verbatim}
232     #convert solution points for plotting
233     plx = x.toListOfTuples()
234     plx = np.array(plx) #convert to tuple to numpy array
235     plx = plx[:,0] #extract x locations
236     \end{verbatim}
237 jfenwick 2657 As there are two solution outputs, we will generate two plots and save each to a file for every time step in the solution. The following is appended to the end of the \verb while loop and creates two figures. The first figure is for the temperature distribution, and the second the total temperature in the bar. Both cases are similar with a few minor changes for scale and labelling. We start by converting the solution to a tuple and then plotting this against our \textit{x coordinates} \verb plx from before. The axis is then standardised and a title applied. The figure is then saved to a *.png file and cleared for the following iteration.
238 ahallam 2606 \begin{verbatim}
239     #establish figure 1 for temperature vs x plots
240     tempT = T.toListOfTuples(scalarastuple=False)
241     pl.figure(1) #current figure
242     pl.plot(plx,tempT) #plot solution
243     #define axis extents and title
244     pl.axis([0,1.0,273.14990+0.00008,0.004+273.1499])
245     pl.title("Temperature accross Rod")
246     #save figure to file
247     pl.savefig(os.path.join(save_path+"/tempT","rodpyplot%03d.png") %i)
248     pl.clf() #clear figure
249    
250     #establish figure 2 for total temperature vs x plots and repeat
251     tottempT = totT.toListOfTuples(scalarastuple=False)
252     pl.figure(2)
253     pl.plot(plx,tottempT)
254     pl.axis([0,1.0,9.657E08,12000+9.657E08])
255     pl.title("Total temperature accross Rod")
256     pl.savefig(os.path.join(save_path+"/totT","ttrodpyplot%03d.png")%i)
257     pl.clf()
258     \end{verbatim}
259 ahallam 2645 \begin{figure}
260     \begin{center}
261     \includegraphics[width=4in]{figures/ttrodpyplot150}
262     \caption{Total temperature ($T$) distribution in rod at $t=150$}
263     \label{fig:onedheatout}
264     \end{center}
265     \end{figure}
266    
267 jfenwick 2657 \subsubsection{Parallel scripts (MPI)}
268     In some of the example files for this cookbook the plot part of the script looks a little different.
269     For example,
270     \begin{verbatim}
271     pl.title("Total temperature accross Rod")
272     if getMPIRankWorld() == 0:
273     pl.savefig(os.path.join(save_path+"/totT","ttrodpyplot%03d.png")%i)
274     pl.clf()
275     \end{verbatim}
276    
277     The additional \verb if statement is not necessary for normal desktop use.
278     It becomes important for scripts run on parallel computers.
279     Its purpose is to ensure that only one copy of the file is written.
280     For more details on writing scripts for parallel computers please consult the \emph{user's guide}.
281    
282 ahallam 2606 \subsection{Make a video}
283     Our saved plots from the previous section can be cast into a video using the following command appended to the end of the script. \verb mencoder is linux only however, and other platform users will need to use an alternative video encoder.
284     \begin{verbatim}
285     # compile the *.png files to create two *.avi videos that show T change
286 jfenwick 2657 # with time. This operation uses linux mencoder. For other operating
287 ahallam 2606 # systems it is possible to use your favourite video compiler to
288     # convert image files to videos.
289 gross 2477
290 ahallam 2606 os.system("mencoder mf://"+save_path+"/tempT"+"/*.png -mf type=png:\
291     w=800:h=600:fps=25 -ovc lavc -lavcopts vcodec=mpeg4 -oac copy -o \
292     onedheatdiff001tempT.avi")
293 gross 2477
294 ahallam 2606 os.system("mencoder mf://"+save_path+"/totT"+"/*.png -mf type=png:\
295     w=800:h=600:fps=25 -ovc lavc -lavcopts vcodec=mpeg4 -oac copy -o \
296     onedheatdiff001totT.avi")
297     \end{verbatim}
298 gross 2477

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