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2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
3 % Copyright (c) 2003-2018 by The University of Queensland
4 % http://www.uq.edu.au
5 %
6 % Primary Business: Queensland, Australia
7 % Licensed under the Apache License, version 2.0
8 % http://www.apache.org/licenses/LICENSE-2.0
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10 % Development until 2012 by Earth Systems Science Computational Center (ESSCC)
11 % Development 2012-2013 by School of Earth Sciences
12 % Development from 2014 by Centre for Geoscience Computing (GeoComp)
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14 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
16 \section{The First Steps}\label{FirstSteps}
17 This chapter is an introduction on how to use \escript to solve
18 a partial differential equation\index{partial differential equation} (PDE\index{partial differential equation!PDE}).
19 We assume you are at least a little familiar with \PYTHON.
20 The knowledge presented in the \PYTHON tutorial at \url{https://docs.python.org/2/tutorial/} is more than sufficient.
22 The PDE\index{partial differential equation} we wish to solve is the Poisson equation\index{Poisson equation}
23 \begin{equation}
24 -\Delta u=f
25 \label{eq:FirstSteps.1}
26 \end{equation}
27 for the solution $u$. The function $f$ is the given right hand side. The domain of interest, denoted by $\Omega$,
28 is the unit square
29 \begin{equation}
30 \Omega=[0,1]^2=\{ (x_0;x_1) | 0\le x_{0} \le 1 \mbox{ and } 0\le x_{1} \le 1 \}
31 \label{eq:FirstSteps.1b}
32 \end{equation}
33 The domain is shown in \fig{fig:FirstSteps.1}.
34 \begin{figure}[ht]
35 \centerline{\includegraphics{FirstStepDomain}}
36 \caption{Domain $\Omega=[0,1]^2$ with outer normal field $n$.}
37 \label{fig:FirstSteps.1}
38 \end{figure}
40 $\Delta$ denotes the Laplace operator\index{Laplace operator}, which is defined by
41 \begin{equation}
42 \Delta u = (u_{,0})_{,0}+(u_{,1})_{,1}
43 \label{eq:FirstSteps.1.1}
44 \end{equation}
45 where, for any function $u$ and any direction $i$, $u_{,i}$
46 denotes the partial derivative \index{partial derivative} of $u$ with respect
47 to $i$.\footnote{You may be more familiar with the Laplace
48 operator\index{Laplace operator} being written as $\nabla^2$, and written in
49 the form
50 \begin{equation*}
51 \nabla^2 u = \nabla^t \cdot \nabla u = \frac{\partial^2 u}{\partial x_0^2}
52 + \frac{\partial^2 u}{\partial x_1^2}
53 \end{equation*}
54 and \eqn{eq:FirstSteps.1} as
55 \begin{equation*}
56 -\nabla^2 u = f
57 \end{equation*}
58 }
59 Basically, in the subindex of a function, any index to the right of the comma denotes a spatial derivative with respect
60 to the index. To get a more compact form we will write $u_{,ij}=(u_{,i})_{,j}$
61 which leads to
62 \begin{equation}
63 \Delta u = u_{,00}+u_{,11}=\sum_{i=0}^2 u_{,ii}
64 \label{eq:FirstSteps.1.1b}
65 \end{equation}
66 We often find that use
67 of nested $\sum$ symbols makes formulas cumbersome, and we use the more
68 compact Einstein summation convention\index{summation convention}. This
69 drops the $\sum$ sign and assumes that a summation is performed over any repeated index.
70 For instance,
71 \begin{eqnarray}
72 x_{i}y_{i}=\sum_{i=0}^2 x_{i}y_{i} \\
73 x_{i}u_{,i}=\sum_{i=0}^2 x_{i}u_{,i} \\
74 u_{,ii}=\sum_{i=0}^2 u_{,ii} \\
75 x_{ij}u_{i,j}=\sum_{j=0}^2\sum_{i=0}^2 x_{ij}u_{i,j} \\
76 \label{eq:FirstSteps.1.1c}
77 \end{eqnarray}
78 With the summation convention we can write the Poisson equation \index{Poisson equation} as
79 \begin{equation}
80 - u_{,ii} =1
81 \label{eq:FirstSteps.1.sum}
82 \end{equation}
83 where $f=1$ in this example.
85 On the boundary of the domain $\Omega$ the normal derivative $n_{i} u_{,i}$
86 of the solution $u$ shall be zero, i.e. $u$ shall fulfill
87 the homogeneous Neumann boundary condition\index{Neumann
88 boundary condition!homogeneous}
89 \begin{equation}
90 n_{i} u_{,i}= 0 \;.
91 \label{eq:FirstSteps.2}
92 \end{equation}
93 $n=(n_{i})$ denotes the outer normal field
94 of the domain, see \fig{fig:FirstSteps.1}. Remember that we
95 apply the Einstein summation convention \index{summation convention}, i.e. $n_{i} u_{,i}= n_{0} u_{,0} +%
96 n_{1} u_{,1}$.\footnote{Some readers may be more familiar with the
97 notation $\frac{\partial u}{\partial n} = n_{i} u_{,i}=\mathbf{n}\cdot \nabla u$.}
98 The Neumann boundary condition of \eqn{eq:FirstSteps.2} should be fulfilled on the
99 set $\Gamma^N$, the top and right edge of the domain:
100 \begin{equation}
101 \Gamma^N=\{(x_0;x_1) \in \Omega | x_{0}=1 \mbox{ or } x_{1}=1 \}
102 \label{eq:FirstSteps.2b}
103 \end{equation}
104 On the bottom and the left edge of the domain, defined
105 as
106 \begin{equation}
107 \Gamma^D=\{(x_0;x_1) \in \Omega | x_{0}=0 \mbox{ or } x_{1}=0 \}
108 \label{eq:FirstSteps.2c}
109 \end{equation}
110 the solution shall be identical to zero:
111 \begin{equation}
112 u=0 \; .
113 \label{eq:FirstSteps.2d}
114 \end{equation}
115 A homogeneous Dirichlet boundary
116 condition\index{Dirichlet boundary condition!homogeneous}.
117 The partial differential equation in \eqn{eq:FirstSteps.1.sum} together
118 with Neumann \eqn{eq:FirstSteps.2} and
119 Dirichlet boundary conditions in \eqn{eq:FirstSteps.2d} form a so-called
120 boundary value
121 problem\index{boundary value problem} (BVP\index{boundary value problem!BVP})
122 for the unknown function~$u$.
124 \begin{figure}[ht]
125 \centerline{\includegraphics{FirstStepMesh}}
126 \caption{Mesh of $4 \times 4$ elements on a rectangular domain. Here
127 each element is a quadrilateral and described by four nodes, namely
128 the corner points. The solution is interpolated by a bi-linear
129 polynomial.}
130 \label{fig:FirstSteps.2}
131 \end{figure}
133 In general the BVP\index{boundary value problem!BVP} cannot be solved
134 analytically and numerical methods are used to construct an
135 approximation of the solution $u$.
136 Here we will use the finite element method\index{finite element method}
137 (FEM\index{finite element method!FEM}).
138 The basic idea is to fill the domain with a set of points called nodes.
139 The solution is approximated by its values on the nodes\index{finite element method!nodes}.
140 Moreover, the domain is subdivided into smaller sub-domains called
141 elements\index{finite element method!element}.
142 On each element the solution is represented by a polynomial of a certain
143 degree through its values at the nodes located in the element.
144 The nodes and their connection through elements is called a
145 mesh\index{finite element method!mesh}. \fig{fig:FirstSteps.2} shows an
146 example of a FEM mesh with four elements in the $x_0$ and four elements
147 in the $x_1$ direction over the unit square.
148 For more details we refer the reader to the literature, for instance \Refe{Zienc,NumHand}.
150 The \escript solver we want to use to solve this problem is embedded into the \PYTHON interpreter language.
151 So you can solve the problem interactively but you will learn quickly that it
152 is more efficient to use scripts which can be edited with your favorite editor.
153 To enter the escript environment, use the \program{run-escript}
154 command\footnote{\program{run-escript} is not available under Windows.
155 If you run under Windows you can just use the \program{python} command and the
156 \env{OMP_NUM_THREADS} environment variable to control the number of threads.}:
157 \begin{verbatim}
158 run-escript
159 \end{verbatim}
160 which will pass you on to the \PYTHON prompt
161 \begin{verbatim}
162 Python 2.7.6 (default, Mar 22 2014, 15:40:47)
163 [GCC 4.8.2] on linux2
164 Type "help", "copyright", "credits" or "license" for more information.
165 >>>
166 \end{verbatim}
167 Here you can use all available \PYTHON commands and language features\footnote{Throughout our examples, we use the python 3 form of
168 print. That is, print(1) instead of print 1.}, for instance
169 \begin{python}
170 >>> x=2+3
171 >>> print("2+3=",x)
172 2+3= 5
173 \end{python}
174 We refer to the \PYTHON user's guide if you are not familiar with \PYTHON.
176 \escript provides the class \Poisson to define a Poisson equation\index{Poisson equation}.
177 (We will discuss a more general form of a PDE\index{partial differential equation!PDE}
178 that can be defined through the \LinearPDE class later.)
179 The instantiation of a \Poisson class object requires the specification of the domain $\Omega$.
180 In \escript \Domain class objects are used to describe the geometry of a
181 domain but it also contains information about the discretization methods and
182 the solver used to solve the PDE.
183 Here we use the FEM\index{finite element method} library \finley.
184 The following statements create the \Domain object \var{mydomain} from the
185 \finley function \method{Rectangle}:
186 \begin{python}
187 from esys.finley import Rectangle
188 mydomain = Rectangle(l0=1.,l1=1.,n0=40, n1=20)
189 \end{python}
190 In this case the domain is a rectangle with the lower left corner at point $(0,0)$
191 and the upper right corner at $(\var{l0},\var{l1})=(1,1)$.
192 The arguments \var{n0} and \var{n1} define the number of elements in $x_{0}$ and
193 $x_{1}$-direction respectively. For more details on \method{Rectangle} and
194 other \Domain generators see \Chap{chap:finley}, \Chap{chap:ripley}, and
195 \Chap{chap:speckley}.
197 The following statements define the \Poisson class object \var{mypde} with domain \var{mydomain} and
198 the right hand side $f$ of the PDE to constant $1$:
199 \begin{python}
200 from esys.escript.linearPDEs import Poisson
201 mypde = Poisson(mydomain)
202 mypde.setValue(f=1)
203 \end{python}
204 We have not specified any boundary condition but the \Poisson class implicitly
205 assumes homogeneous Neuman boundary conditions\index{Neumann boundary condition!homogeneous} defined by \eqn{eq:FirstSteps.2}.
206 With this boundary condition the BVP\index{boundary value problem!BVP} we have
207 defined has no unique solution.
208 In fact, with any solution $u$ and any constant $C$ the function $u+C$ becomes
209 a solution as well.
210 We have to add a Dirichlet boundary condition\index{Dirichlet boundary condition}.
211 This is done by defining a characteristic function\index{characteristic function}
212 which has positive values at locations $x=(x_{0},x_{1})$
213 where Dirichlet boundary condition is set and $0$ elsewhere.
214 In our case of $\Gamma^D$ defined by \eqn{eq:FirstSteps.2c}, we need to
215 construct a function \var{gammaD} which is positive for the cases $x_{0}=0$ or $x_{1}=0$.
216 To get an object \var{x} which contains the coordinates of the nodes in the domain use
217 \begin{python}
218 x=mydomain.getX()
219 \end{python}
220 The method \method{getX} of the \Domain \var{mydomain} gives access to locations
221 in the domain defined by \var{mydomain}.
222 The object \var{x} is actually a \Data object which will be discussed in
223 \Chap{ESCRIPT CHAP} in more detail.
224 What we need to know here is that \var{x} has \Rank (number of dimensions) and
225 a \Shape (list of dimensions) which can be viewed by calling the \method{getRank} and \method{getShape} methods:
226 \begin{python}
227 print("rank ",x.getRank(),", shape ",x.getShape())
228 \end{python}
229 This will print something like
230 \begin{python}
231 rank 1, shape (2,)
232 \end{python}
233 The \Data object also maintains type information which is represented by the
234 \FunctionSpace of the object. For instance
235 \begin{python}
236 print(x.getFunctionSpace())
237 \end{python}
238 will print
239 \begin{python}
240 Finley_Nodes [ContinuousFunction(domain)] on FinleyMesh
241 \end{python}
242 which tells us that the coordinates are stored on the nodes of (rather than on
243 points in the interior of) a Finley mesh.
244 To get the $x_{0}$ coordinates of the locations we use the statement
245 \begin{python}
246 x0=x[0]
247 \end{python}
248 Object \var{x0} is again a \Data object now with \Rank $0$ and \Shape $()$.
249 It inherits the \FunctionSpace from \var{x}:
250 \begin{python}
251 print(x0.getRank(), x0.getShape(), x0.getFunctionSpace())
252 \end{python}
253 will print
254 \begin{python}
255 0 () Finley_Nodes [ContinuousFunction(domain)] on FinleyMesh
256 \end{python}
257 We can now construct a function \var{gammaD} which is only non-zero on the
258 bottom and left edges of the domain with
259 \begin{python}
260 from esys.escript import whereZero
261 gammaD=whereZero(x[0])+whereZero(x[1])
262 \end{python}
264 \code{whereZero(x[0])} creates a function which equals $1$ where \code{x[0]} is (almost) equal to zero and $0$ elsewhere.
265 Similarly, \code{whereZero(x[1])} creates a function which equals $1$ where \code{x[1]} is equal to zero and $0$ elsewhere.
266 The sum of the results of \code{whereZero(x[0])} and \code{whereZero(x[1])}
267 gives a function on the domain \var{mydomain} which is strictly positive where $x_{0}$ or $x_{1}$ is equal to zero.
268 Note that \var{gammaD} has the same \Rank, \Shape and \FunctionSpace as \var{x0} used to define it.
269 So from
270 \begin{python}
271 print(gammaD.getRank(), gammaD.getShape(), gammaD.getFunctionSpace())
272 \end{python}
273 one gets
274 \begin{python}
275 0 () Finley_Nodes [ContinuousFunction(domain)] on FinleyMesh
276 \end{python}
277 An additional parameter \var{q} of the \code{setValue} method of the \Poisson
278 class defines the characteristic function\index{characteristic function} of
279 the locations of the domain where the homogeneous Dirichlet boundary condition\index{Dirichlet boundary condition!homogeneous} is set.
280 The complete definition of our example is now:
281 \begin{python}
282 from esys.escript.linearPDEs import Poisson
283 x = mydomain.getX()
284 gammaD = whereZero(x[0])+whereZero(x[1])
285 mypde = Poisson(domain=mydomain)
286 mypde.setValue(f=1,q=gammaD)
287 \end{python}
288 The first statement imports the \Poisson class definition from the \linearPDEs module.
289 To get the solution of the Poisson equation defined by \var{mypde} we just have to call its \method{getSolution} method.
291 Now we can write the script to solve our Poisson problem
292 \begin{python}
293 from esys.escript import *
294 from esys.escript.linearPDEs import Poisson
295 from esys.finley import Rectangle
296 # generate domain:
297 mydomain = Rectangle(l0=1.,l1=1.,n0=40, n1=20)
298 # define characteristic function of Gamma^D
299 x = mydomain.getX()
300 gammaD = whereZero(x[0])+whereZero(x[1])
301 # define PDE and get its solution u
302 mypde = Poisson(domain=mydomain)
303 mypde.setValue(f=1, q=gammaD)
304 u = mypde.getSolution()
305 \end{python}
306 The question is what we do with the calculated solution \var{u}.
307 Besides postprocessing, e.g. calculating the gradient or the average value,
308 which will be discussed later, plotting the solution is one of the things you
309 might want to do.
310 \escript offers two ways to do this, both based on external modules or packages.
311 The first option uses the \MATPLOTLIB module which allows plotting of 2D
312 results relatively quickly from within the \PYTHON script, see~\cite{matplotlib}.
313 However, there are limitations when using this tool, especially for large
314 problems and when solving three-dimensional problems.
315 Therefore, \escript provides functionality to export data as files which can
316 subsequently be read by third-party software packages such as
317 \mayavi\cite{mayavi} or \VisIt~\cite{VisIt}.
319 \subsection{Plotting Using \MATPLOTLIB}
320 The \MATPLOTLIB module provides a simple and easy-to-use way to visualize PDE
321 solutions (or other \Data objects).
322 To hand over data from \escript to \MATPLOTLIB the values need to be mapped onto
323 a rectangular grid. We will make use of the \numpy module for this.
325 First we need to create a rectangular grid which is accomplished by the following statements:
326 \begin{python}
327 import numpy
328 x_grid = numpy.linspace(0., 1., 50)
329 y_grid = numpy.linspace(0., 1., 50)
330 \end{python}
331 \var{x_grid} is an array defining the x coordinates of the grid while
332 \var{y_grid} defines the y coordinates of the grid.
333 In this case we use $50$ points over the interval $[0,1]$ in both directions.
335 Now the values created by \escript need to be interpolated to this grid.
336 We will use the \MATPLOTLIB \function{mlab.griddata} function to do this.
337 Spatial coordinates are easily extracted as a \var{list} by
338 \begin{python}
339 x=mydomain.getX()[0].toListOfTuples()
340 y=mydomain.getX()[1].toListOfTuples()
341 \end{python}
342 In principle we can apply the same \member{toListOfTuples} method to extract the values from the PDE solution \var{u}.
343 However, we have to make sure that the \Data object we extract the values from
344 uses the same \FunctionSpace as we have used when extracting \var{x} and \var{y}.
345 We apply the \function{interpolation} to \var{u} before extraction to achieve this:
346 \begin{python}
347 z=interpolate(u, mydomain.getX().getFunctionSpace())
348 \end{python}
349 The values in \var{z} are the values at the points with the coordinates given by \var{x} and \var{y}.
350 These values are interpolated to the grid defined by \var{x_grid} and \var{y_grid} by using
351 \begin{python}
352 import matplotlib
353 z_grid = matplotlib.mlab.griddata(x, y, z, xi=x_grid, yi=y_grid)
354 \end{python}
355 Now \var{z_grid} gives the values of the PDE solution \var{u} at the grid which can be plotted using \function{contourf}:
356 \begin{python}
357 matplotlib.pyplot.contourf(x_grid, y_grid, z_grid, 5)
358 matplotlib.pyplot.savefig("u.png")
359 \end{python}
360 Here we use 5 contours. The last statement writes the plot to the file \file{u.png} in the PNG format.
361 Alternatively, one can use
362 \begin{python}
363 matplotlib.pyplot.contourf(x_grid, y_grid, z_grid, 5)
364 matplotlib.pyplot.show()
365 \end{python}
366 which gives an interactive browser window.
368 \begin{figure}
369 \centerline{\includegraphics[width=\figwidth]{FirstStepResultMATPLOTLIB}}
370 \caption{Visualization of the Poisson Equation Solution for $f=1$ using \MATPLOTLIB}
371 \label{fig:FirstSteps.3b}
372 \end{figure}
374 Now we can write the script to solve our Poisson problem
375 \begin{python}
376 from esys.escript import *
377 from esys.escript.linearPDEs import Poisson
378 from esys.finley import Rectangle
379 import numpy
380 import matplotlib
381 import pylab
382 # generate domain:
383 mydomain = Rectangle(l0=1.,l1=1.,n0=40, n1=20)
384 # define characteristic function of Gamma^D
385 x = mydomain.getX()
386 gammaD = whereZero(x[0])+whereZero(x[1])
387 # define PDE and get its solution u
388 mypde = Poisson(domain=mydomain)
389 mypde.setValue(f=1,q=gammaD)
390 u = mypde.getSolution()
391 # interpolate u to a matplotlib grid:
392 x_grid = numpy.linspace(0.,1.,50)
393 y_grid = numpy.linspace(0.,1.,50)
394 x=mydomain.getX()[0].toListOfTuples()
395 y=mydomain.getX()[1].toListOfTuples()
396 z=interpolate(u,mydomain.getX().getFunctionSpace()).toListOfTuples()
397 z_grid = matplotlib.mlab.griddata(x,y,z,xi=x_grid,yi=y_grid )
398 # interpolate u to a rectangular grid:
399 matplotlib.pyplot.contourf(x_grid, y_grid, z_grid, 5)
400 matplotlib.pyplot.savefig("u.png")
401 \end{python}
402 The entire code is available as \file{poisson_matplotlib.py} in the \ExampleDirectory.
403 You can run the script using the {\it escript} environment
404 \begin{verbatim}
405 run-escript poisson_matplotlib.py
406 \end{verbatim}
407 This will create a file called \file{u.png}, see \fig{fig:FirstSteps.3b}.
408 For details on the usage of the \MATPLOTLIB module we refer to the documentation~\cite{matplotlib}.
410 As pointed out, \MATPLOTLIB is restricted to the two-dimensional case and
411 should be used for small problems only.
412 It can not be used under \MPI as the \member{toListOfTuples} method is not
413 safe under \MPI\footnote{The phrase 'safe under \MPI' means that a program
414 will produce correct results when run on more than one processor under \MPI.}.
416 \begin{figure}
417 \centerline{\includegraphics[width=\figwidth]{FirstStepResult}}
418 \caption{Visualization of the Poisson Equation Solution for $f=1$}
419 \label{fig:FirstSteps.3}
420 \end{figure}
422 \subsection{Visualization using export files}
424 As an alternative to \MATPLOTLIB, {\it escript} supports exporting data to
425 \VTK and \SILO files which can be read by visualization tools such as
426 \mayavi\cite{mayavi} and \VisIt~\cite{VisIt}. This method is \MPI safe and
427 works with large 2D and 3D problems.
429 To write the solution \var{u} of the Poisson problem in the \VTK file format
430 to the file \file{u.vtu} one needs to add:
431 \begin{python}
432 from esys.weipa import saveVTK
433 saveVTK("u.vtu", sol=u)
434 \end{python}
435 This file can then be opened in a \VTK compatible visualization tool where the
436 solution is accessible by the name {\it sol}. Similarly,
437 \begin{python}
438 from esys.weipa import saveSilo
439 saveSilo("u.silo", sol=u)
440 \end{python}
441 will write \var{u} to a \SILO file if escript was compiled with support for
442 LLNL's \SILO library.
444 The Poisson problem script is now
445 \begin{python}
446 from esys.escript import *
447 from esys.escript.linearPDEs import Poisson
448 from esys.finley import Rectangle
449 from esys.weipa import saveVTK
450 # generate domain:
451 mydomain = Rectangle(l0=1.,l1=1.,n0=40, n1=20)
452 # define characteristic function of Gamma^D
453 x = mydomain.getX()
454 gammaD = whereZero(x[0])+whereZero(x[1])
455 # define PDE and get its solution u
456 mypde = Poisson(domain=mydomain)
457 mypde.setValue(f=1,q=gammaD)
458 u = mypde.getSolution()
459 # write u to an external file
460 saveVTK("u.vtu",sol=u)
461 \end{python}
462 The entire code is available as \file{poisson_vtk.py} in the \ExampleDirectory.
464 You can run the script using the {\it escript} environment and visualize the
465 solution using \mayavi:
466 \begin{verbatim}
467 run-escript poisson_vtk.py
468 mayavi2 -d u.vtu -m Surface
469 \end{verbatim}
470 The result is shown in \fig{fig:FirstSteps.3}.


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