Installation

Primer

# Tutorial¶

SfePy can be used in two basic ways:
1. a black-box partial differential equation (PDE) solver,
2. a Python package to build custom applications involving solving PDEs by the finite element (FE) method.

This tutorial focuses on the first way and introduces the basic concepts and nomenclature used in the following parts of the documentation. Check also the Primer which focuses on a particular problem in detail.

## Notes on solving PDEs by the Finite Element Method¶

The Finite Element Method (FEM) is the numerical method for solving Partial Differential Equations (PDEs). FEM was developed in the middle of XX. century and now it is widely used in different areas of science and engineering, including mechanical and structural design, biomedicine, electrical and power design, fluid dynamics and other. FEM is based on a very elegant mathematical theory of weak solution of PDEs. In this section we will briefly discuss basic ideas underlying FEM.

### Strong form of Poisson’s equation and its integration¶

Let us start our discussion about FEM with the strong form of Poisson’s equation

(1)

(2)

(3)

where is the solution domain with the boundary , is the part of the boundary where Dirichlet boundary conditions are given, is the part of the boundary where Neumann boundary conditions are given, is the unknown function to be found, are known functions.

FEM is based on a weak formulation. The weak form of the equation (1) is

where is a test function. Integrating this equation by parts

and applying Gauss theorem we obtain:

or

The surface integral term can be split into two integrals, one over the Dirichlet part of the surface and second over the Neumann part

(4)

The equation (4) is the initial weak form of the Poisson’s problem (1)(3). But we can not work with it without applying the boundary conditions. So it is time to talk about the boundary conditions.

#### Dirichlet Boundary Conditions¶

On the Dirichlet part of the surface we have two restrictions. One is the Dirichlet boundary conditions as they are, and the second is the integral term over in equation (4). To be consistent we have to use only the Dirichlet conditions and avoid the integral term. To implement this we can take the function and the test function , where

In other words the unknown function must be continuous together with its gradient in the domain. In contrast the test function must be also continuous together with its gradient in the domain but it should be zero on the surface .

With this requirement the integral term over Dirichlet part of the surface is vanishing and the weak form of the Poisson equation for and becomes

That is why Dirichlet conditions in FEM terminology are called Essential Boundary Conditions. These conditions are not a part of the weak form and they are used as they are.

#### Neumann Boundary Conditions¶

The Neumann boundary conditions correspond to the known flux . The integral term over the Neumann surface in the equation (4) contains exactly the same flux. So we can use the known function in the integral term:

where test function also belongs to the space .

That is why Neumann conditions in FEM terminology are called Natural Boundary Conditions. These conditions are a part of weak form terms.

### The weak form of the Poisson’s equation¶

Now we can write the resulting weak form for the Poisson’s problem (1)(3). For any test function find such that

(5)

### Discussion of discretization and meshing¶

It is planned to have an example of the discretization based on the Poisson’s equation weak form (5). For now, please refer to the wikipedia page Finite Element Method for a basic description of the disretization and meshing.

### Numerical solution of the problem¶

To solve numerically given problem based on the weak form (5) we have to go through 5 steps:

1. Define geometry of the domain and surfaces and .
2. Define the known functions , and .
3. Define the unknown function and the test functions .
4. Define essential boundary conditions (Dirichlet conditions)
5. Define equation and natural boundary conditions (Neumann conditions) as the set of all integral terms , , .

In the next section we will discuss how to define all these things in SfePy.

## Basic notions¶

The simplest way of using SfePy is to solve a system of PDEs defined in a problem description file, also referred to as input file. In such a file, the problem is described using several keywords that allow one to define the equations, variables, finite element approximations, solvers, solution domain and subdomains etc., see Problem description file for a full list of those keywords.

The syntax of the problem description file is very simple yet powerful, as the file itself is just a regular Python module that can be normally imported - no special parsing is necessary. The keywords mentioned above are regular Python variables (usually of the dict type) with special names. Historically, the keywords exist in two flavors:

• long syntax is the original one - it is longer to type, but the individual fields are named, so it might be easier/understand to read for newcomers.
• short syntax was added later to offer brevity for “expert” use.

Below we show:

1. how to solve a problem given by a problem description file, and
2. explain the elements of the file on several examples.

But let us begin with a slight detour...

### Sneak peek: what is going on under the hood¶

1. A top-level script (usually simple.py, as in this tutorial) reads in an input file.
2. Following the contents of the input file, a ProblemDefinition instance is created - this is the input file coming to life. Let us call the instance problem.
• The problem sets up its domain, regions (various sub-domains), fields (the FE approximations), the equations and the solvers. The equations determine the materials and variables in use - only those are fully instantiated, so the input file can safely contain definitions of items that are not used actually.
3. Prior to solution, problem.time_update() function has to be called to setup boundary conditions, material parameters and other potentially time-dependent data. This holds also for stationary problems with a single “time step”.
4. The solution is then obtained by calling problem.solve() function.
5. Finally, the solution can be stored using problem.save_state()

The above last three steps are essentially repeated for each time step. So that is it - using the code a black-box PDE solver shields the user from having to create the ProblemDefinition instance by hand. But note that this is possible, and often necessary when the flexibility of the default solvers is not enough. At the end of the tutorial an example demonstrating the interactive creation of the problem is shown, see Interactive Example: Linear Elasticity.

Now let us continue with running a simulation.

## Running a simulation¶

The following commands should be run in the top-level directory of the SfePy source tree after compiling the C extension files. See Installation for full installation instructions.

### Invoking SfePy from the command line¶

This section introduces the basics of running SfePy on the command line. The $indicates the command prompt of your terminal. • The script simple.py is the most basic starting point in SfePy. It is invoked as follows: $ ./simple.py examples/diffusion/poisson.py
• examples/diffusion/poisson.py is the SfePy problem description file, which defines the problem to be solved in terms SfePy can understand
• Running the above command creates the output file cylinder.vtk in the SfePy top-level directory
• SfePy can also be invoked interactively with the isfepy script:

$./isfepy • Follow the help information printed on startup to solve the Poisson’s equation example above ### Postprocessing the results¶ • The postproc.py script can be used for quick postprocessing and visualization of the SfePy output files. It requires mayavi2 installed on your system. • As a simple example, try: $ ./postproc.py cylinder.vtk
• The following interactive 3D window should display:

• The left mouse button by itself orbits the 3D view
• Holding shift and the left mouse button pans the view
• Holding control and the left mouse button rotates about the screen normal axis
• The right mouse button controls the zoom

## Example problem description file¶

Here we discuss the contents of the examples/diffusion/poisson.py problem description file. For additional examples, see the problem description files in the examples/ directory of SfePy.

The problem at hand is the following:

(6)

where is a subset of the domain boundary. For simplicity, we set , but we still work with the material constant even though it has no influence on the solution in this case. We also assume zero fluxes over , i.e. there. The particular boundary conditions used below are on the left side of the cylindrical domain depicted in the previous section and on the right side.

The first step to do is to write (6) in weak formulation (5). The , . So only one term in weak form (5) remains:

(7)

Comparing the above integral term with the long table in Term Overview, we can see that SfePy contains this term under name dw_laplace. We are now ready to proceed to the actual problem definition.

### Long syntax of keywords¶

The example uses long syntax of the keywords. In next subsection, we show the same example written in short syntax.

Open the examples/diffusion/poisson.py file in your favorite text editor. Note that the file is a regular python source code.

from sfepy import data_dir

filename_mesh = data_dir + '/meshes/3d/cylinder.mesh'


The filename_mesh variable points to the file containing the mesh for the particular problem. SfePy supports a variety of mesh formats.

material_2 = {
'name' : 'coef',
'values' : {'val' : 1.0},
}


Here we define just a constant coefficient of the Poisson equation, using the 'values' attribute. Other possible attribute is 'function', for material coefficients computed/obtained at runtime.

Many finite element problems require the definition of material parameters. These can be handled in SfePy with material variables which associate the material parameters with the corresponding region of the mesh.

region_1000 = {
'name' : 'Omega',
'select' : 'elements of group 6',
}

region_03 = {
'name' : 'Gamma_Left',
'select' : 'nodes in (x < 0.00001)',
}

region_4 = {
'name' : 'Gamma_Right',
'select' : 'nodes in (x > 0.099999)',
}


Regions assign names to various parts of the finite element mesh. The region names can later be referred to, for example when specifying portions of the mesh to apply boundary conditions to. Regions can be specified in a variety of ways, including by element or by node. Here, Omega is the elemental domain over which the PDE is solved and Gamma_Left and Gamma_Right define surfaces upon which the boundary conditions will be applied.

field_1 = {
'name' : 'temperature',
'dtype' : 'real',
'shape' : (1,),
'region' : 'Omega',
'approx_order' : 1,
}


A field is used mainly to define the approximation on a (sub)domain, i.e. to define the discrete spaces , where we seek the solution.

The Poisson equation can be used to compute e.g. a temperature distribution, so let us call our field 'temperature'. On the region 'Omega' it will be approximated using linear finite elements.

A field in a given region defines the finite element approximation. Several variables can use the same field, see below.

variable_1 = {
'name' : 't',
'kind' : 'unknown field',
'field' : 'temperature',
'order' : 0, # order in the global vector of unknowns
}

variable_2 = {
'name' : 's',
'kind' : 'test field',
'field' : 'temperature',
'dual' : 't',
}


One field can be used to generate discrete degrees of freedom (DOFs) of several variables. Here the unknown variable (the temperature) is called 't', it’s associated DOF name is 't.0' — this will be referred to in the Dirichlet boundary section (ebc). The corresponding test variable of the weak formulation is called 's'. Notice that the 'dual' item of a test variable must specify the unknown it corresponds to.

For each unknown (or state) variable there has to be a test (or virtual) variable defined, as usual in weak formulation of PDEs.

ebc_1 = {
'name' : 't1',
'region' : 'Gamma_Left',
'dofs' : {'t.0' : 2.0},
}

ebc_2 = {
'name' : 't2',
'region' : 'Gamma_Right',
'dofs' : {'t.0' : -2.0},
}


Essential (Dirichlet) boundary conditions can be specified as above.

Boundary conditions place restrictions on the finite element formulation and create a unique solution to the problem. Here, we specify that a temperature of +2 is applied to the left surface of the mesh and a temperature of -2 is applied to the right surface.

integral_1 = {
'name' : 'i1',
'kind' : 'v',
'order' : 2,
}


Integrals specify which numerical scheme to use. Here we are using a 2nd order quadrature over a 3 dimensional space.

equations = {
'Temperature' : """dw_laplace.i1.Omega( coef.val, s, t ) = 0"""
}


The equation above directly corresponds to the discrete version of (7), namely: Find , such that

where .

The equations block is the heart of the SfePy problem definition file. Here, we are specifying that the Laplacian of the temperature (in the weak formulation) is 0, where coef.val is a material constant. We are using the i1 integral defined previously, over the domain specified by the region Omega.

The above syntax is useful for defining custom integrals with user-defined quadrature points and weights, see Integrals. The above uniform integration can be more easily achieved by:

equations = {
'Temperature' : """dw_laplace.2.Omega( coef.val, s, t ) = 0"""
}


The integration order is specified directly in place of the integral name. The integral definition is superfluous in this case.

solver_0 = {
'name' : 'ls',
'kind' : 'ls.scipy_direct',
'method' : 'auto',
}


Here, we specify which kind of solver to use for linear equations.

solver_1 = {
'name' : 'newton',
'kind' : 'nls.newton',

'i_max'      : 1,
'eps_a'      : 1e-10,
'eps_r'      : 1.0,
'macheps'   : 1e-16,
'lin_red'    : 1e-2, # Linear system error < (eps_a * lin_red).
'ls_red'     : 0.1,
'ls_red_warp' : 0.001,
'ls_on'      : 1.1,
'ls_min'     : 1e-5,
'check'     : 0,
'delta'     : 1e-6,
'is_plot'    : False,
'problem'   : 'nonlinear', # 'nonlinear' or 'linear' (ignore i_max)
}


Here, we specify the nonlinear solver kind and options. The convergence parameters can be adjusted if necessary, otherwise leave the default.

Even linear problems are solved by a nonlinear solver (KISS rule) - only one iteration is needed and the final rezidual is obtained for free.

options = {
'nls' : 'newton',
'ls' : 'ls',
}


The solvers to use are specified in the options block. We can define multiple solvers with different convergence parameters if necessary.

That’s it! Now it is possible to proceed as described in Invoking SfePy from the command line.

### Short syntax of keywords¶

The same diffusion equation example as above in short syntax reads, see examples/diffusion/poisson_short_syntax.py, as follows:

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 r""" Laplace equation. The same example as poisson.py, but using the short syntax of keywords. Find :math:t such that: .. math:: \int_{\Omega} c \nabla s \cdot \nabla t = 0 \;, \quad \forall s \;. """ from sfepy import data_dir filename_mesh = data_dir + '/meshes/3d/cylinder.mesh' materials = { 'coef' : ({'val' : 1.0},), } regions = { 'Omega' : ('all', {}), # or 'elements of group 6' 'Gamma_Left' : ('nodes in (x < 0.00001)', {}), 'Gamma_Right' : ('nodes in (x > 0.099999)', {}), } fields = { 'temperature' : ('real', 1, 'Omega', 1), } variables = { 't' : ('unknown field', 'temperature', 0), 's' : ('test field', 'temperature', 't'), } ebcs = { 't1' : ('Gamma_Left', {'t.0' : 2.0}), 't2' : ('Gamma_Right', {'t.0' : -2.0}), } integrals = { 'i1' : ('v', 2), } equations = { 'Temperature' : """dw_laplace.i1.Omega( coef.val, s, t ) = 0""" } solvers = { 'ls' : ('ls.scipy_direct', {}), 'newton' : ('nls.newton', {'i_max' : 1, 'eps_a' : 1e-10, }), } options = { 'nls' : 'newton', 'ls' : 'ls', } 

## Interactive Example: Linear Elasticity¶

This example shows how to use SfePy interactively, but also how to make a custom simulation script. We will use isfepy for the explanation, but regular Python shell, or IPython would do as well, provided the proper modules are imported (see the help information printed on startup of isfepy).

We wish to solve the following linear elasticity problem:

(8)

where the stress is defined as , , are the Lamé’s constants, the strain is and are volume forces. This can be written in general form as , where in our case .

In the weak form the equation (8) is

(9)

where is the test function, and both , belong to a suitable function space.

Hint: Whenever you create a new object (e.g. a Mesh instance, see below), try to print it using the print statement - it will give you insight about the object internals.

The whole example summarized in a script is below in Complete Example as a Script.

Run the isfepy script:

\$ ./isfepy

The output should look like this:

Python 2.6.5 console for SfePy 2010.2-git-11cfd34 (8c4664610ed4b85851966326aaa7ce36e560ce7a)

These commands were executed:
>>> from sfepy.base.base import *
>>> from sfepy.fem import *
>>> from sfepy.applications import solve_pde
>>> from sfepy.postprocess import Viewer

When in SfePy source directory, try:
>>> pb, vec, data = solve_pde('examples/diffusion/poisson.py')
>>> view = Viewer(pb.get_output_name())
>>> view()

When in another directory (and SfePy is installed), try:
>>> from sfepy import data_dir
>>> pb, vec, data = solve_pde(data_dir + '/examples/diffusion/poisson.py')
>>> view = Viewer(pb.get_output_name())
>>> view()

Documentation can be found at http://sfepy.org

In [1]:


Read a finite element mesh, that defines the domain .

In [1]: mesh = Mesh.from_file('meshes/2d/rectangle_tri.mesh')


Create a domain. The domain allows defining regions or subdomains.

In [2]: domain = Domain('domain', mesh)
sfepy: setting up domain edges...
sfepy: ...done in 0.01 s


Define the regions - the whole domain , where the solution is sought, and , , where the boundary conditions will be applied. As the domain is rectangular, we first get a bounding box to get correct bounds for selecting the boundary edges.

In [3]: min_x, max_x = domain.get_mesh_bounding_box()[:,0]
In [4]: eps = 1e-8 * (max_x - min_x)
In [5]: omega = domain.create_region('Omega', 'all')
In [6]: gamma1 = domain.create_region('Gamma1',
...:                               'nodes in x < %.10f' % (min_x + eps))
In [7]: gamma2 = domain.create_region('Gamma2',
...:                               'nodes in x > %.10f' % (max_x - eps))


Next we define the actual finite element approximation using the Field class.

In [8]: field = Field('fu', nm.float64, 'vector', omega,
...:               space='H1', poly_space_base='lagrange', approx_order=2)


Using the field fu, we can define both the unknown variable and the test variable .

In [9]: u = FieldVariable('u', 'unknown', field, mesh.dim)
In [10]: v = FieldVariable('v', 'test', field, mesh.dim,
....:                   primary_var_name='u')


Before we can define the terms to build the equation of linear elasticity, we have to create also the materials, i.e. define the (constitutive) parameters. The linear elastic material m will be defined using the two Lamé constants , . The volume forces will be defined also as a material, as a constant (column) vector .

In [11]: m = Material('m', lam=1.0, mu=1.0)
In [12]: f = Material('f', val=[[0.02], [0.01]])


One more thing needs to be defined - the numerical quadrature that will be used to integrate each term over its domain.

In [14]: integral = Integral('i', order=3)


Now we are ready to define the two terms and build the equations.

In [15]: from sfepy.terms import Term
In [16]: t1 = Term.new('dw_lin_elastic_iso(m.lam, m.mu, v, u)',
integral, omega, m=m, v=v, u=u)
In [17]: t2 = Term.new('dw_volume_lvf(f.val, v)', integral, omega, f=f, v=v)
In [18]: eq = Equation('balance', t1 + t2)
In [19]: eqs = Equations([eq])
sfepy: setting up dof connectivities...
sfepy: ...done in 0.00 s
sfepy: describing geometries...
sfepy: ...done in 0.00 s


The equations have to be completed by boundary conditions. Let us clamp the left edge , and shift the right edge in the direction a bit, depending on the coordinate.

In [20]: from sfepy.fem.conditions import Conditions, EssentialBC
In [21]: fix_u = EssentialBC('fix_u', gamma1, {'u.all' : 0.0})
In [22]: def shift_u_fun(ts, coors, bc=None, shift=0.0):
....:     val = shift * coors[:,1]**2
....:     return val
....:
In [23]: bc_fun = Function('shift_u_fun', shift_u_fun,
....:                   extra_args={'shift' : 0.01})
In [24]: shift_u = EssentialBC('shift_u', gamma2, {'u.0' : bc_fun})


The last thing to define before building the problem are the solvers. Here we just use a sparse direct SciPy solver and the SfePy Newton solver with default parameters. We also wish to store the convergence statistics of the Newton solver. As the problem is linear, it should converge in one iteration.

In [25]: from sfepy.solvers.ls import ScipyDirect
In [26]: from sfepy.solvers.nls import Newton
In [27]: ls = ScipyDirect({})
In [28]: nls_status = IndexedStruct()
In [29]: nls = Newton({}, lin_solver=ls, status=nls_status)


Now we are ready to create a ProblemDefinition instance. Note that the step above is not really necessary - the above solvers are constructed by default. We did them to get the nls_status.

In [30]: pb = ProblemDefinition('elasticity', equations=eqs, nls=nls, ls=ls)


The ProblemDefinition has several handy methods for debugging. Let us try saving the regions into a VTK file.

In [31]: pb.save_regions_as_groups('regions')
sfepy: saving regions as groups...
sfepy:   Omega
sfepy:   Gamma1
sfepy:   Gamma2
sfepy:   Gamma1
sfepy: ...done


And view them.

In [32]: view = Viewer('regions.vtk')
In [33]: view()
sfepy: point scalars Gamma1 [ 0.  0.  0.]
sfepy: point scalars Gamma2 [ 11.   0.   0.]
sfepy: point scalars Omega [ 22.   0.   0.]
Out[33]: <sfepy.postprocess.viewer.ViewerGUI object at 0x93ea5f0>


You should see this:

Finally, we apply the boundary conditions, solve the problem, save and view the results.

In [34]: pb.time_update(ebcs=Conditions([fix_u, shift_u]))
sfepy: updating materials...
sfepy:     m
sfepy:     f
sfepy: ...done in 0.01 s
sfepy: updating variables...
sfepy: ...done
sfepy: matrix shape: (1815, 1815)
sfepy: assembling matrix graph...
sfepy: ...done in 0.00 s
sfepy: matrix structural nonzeros: 39145 (1.19e-02% fill)
In [35]: vec = pb.solve()
sfepy: nls: iter: 0, residual: 1.343114e+01 (rel: 1.000000e+00)
sfepy:   rezidual:    0.00 [s]
sfepy:      solve:    0.01 [s]
sfepy:     matrix:    0.00 [s]
sfepy: nls: iter: 1, residual: 2.567997e-14 (rel: 1.911972e-15)
In [36]: print nls_status
-------> print(nls_status)
IndexedStruct
condition:
0
err:
2.56799662867e-14
err0:
13.4311385972
time_stats:
{'rezidual': 0.0, 'solve': 0.010000000000001563, 'matrix': 0.0}
In [37]: pb.save_state('linear_elasticity.vtk', vec)
In [38]: view = Viewer('linear_elasticity.vtk')
In [39]: view()
sfepy: point vectors u [ 0.  0.  0.]


This is the resulting image:

The default view is not very fancy. Let us show the displacements by shifting the mesh. Close the previous window and do:

In [56]: view(vector_mode='warp_norm', rel_scaling=2,
....:      is_scalar_bar=True, is_wireframe=True)
sfepy: point vectors u [ 0.  0.  0.]

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 #!/usr/bin/env python from optparse import OptionParser import numpy as nm import sys sys.path.append('.') from sfepy.base.base import IndexedStruct from sfepy.fem import (Mesh, Domain, H1NodalVolumeField, FieldVariable, Material, Integral, Function, Equation, Equations, ProblemDefinition) from sfepy.terms import Term from sfepy.fem.conditions import Conditions, EssentialBC from sfepy.solvers.ls import ScipyDirect from sfepy.solvers.nls import Newton from sfepy.postprocess import Viewer def shift_u_fun(ts, coors, bc=None, problem=None, shift=0.0): """ Define a displacement depending on the y coordinate. """ val = shift * coors[:,1]**2 return val usage = """%prog [options]""" help = { 'show' : 'show the results figure', } def main(): from sfepy import data_dir parser = OptionParser(usage=usage, version='%prog') parser.add_option('-s', '--show', action="store_true", dest='show', default=False, help=help['show']) options, args = parser.parse_args() mesh = Mesh.from_file(data_dir + '/meshes/2d/rectangle_tri.mesh') domain = Domain('domain', mesh) min_x, max_x = domain.get_mesh_bounding_box()[:,0] eps = 1e-8 * (max_x - min_x) omega = domain.create_region('Omega', 'all') gamma1 = domain.create_region('Gamma1', 'nodes in x < %.10f' % (min_x + eps)) gamma2 = domain.create_region('Gamma2', 'nodes in x > %.10f' % (max_x - eps)) field = H1NodalVolumeField('fu', nm.float64, 'vector', omega, approx_order=2) u = FieldVariable('u', 'unknown', field, mesh.dim) v = FieldVariable('v', 'test', field, mesh.dim, primary_var_name='u') m = Material('m', lam=1.0, mu=1.0) f = Material('f', val=[[0.02], [0.01]]) integral = Integral('i', order=3) t1 = Term.new('dw_lin_elastic_iso(m.lam, m.mu, v, u)', integral, omega, m=m, v=v, u=u) t2 = Term.new('dw_volume_lvf(f.val, v)', integral, omega, f=f, v=v) eq = Equation('balance', t1 + t2) eqs = Equations([eq]) fix_u = EssentialBC('fix_u', gamma1, {'u.all' : 0.0}) bc_fun = Function('shift_u_fun', shift_u_fun, extra_args={'shift' : 0.01}) shift_u = EssentialBC('shift_u', gamma2, {'u.0' : bc_fun}) ls = ScipyDirect({}) nls_status = IndexedStruct() nls = Newton({}, lin_solver=ls, status=nls_status) pb = ProblemDefinition('elasticity', equations=eqs, nls=nls, ls=ls) pb.save_regions_as_groups('regions') pb.time_update(ebcs=Conditions([fix_u, shift_u])) vec = pb.solve() print nls_status pb.save_state('linear_elasticity.vtk', vec) if options.show: view = Viewer('linear_elasticity.vtk') view(vector_mode='warp_norm', rel_scaling=2, is_scalar_bar=True, is_wireframe=True) if __name__ == '__main__': main()