funsim

{
  "one": "Singular sensation",
  "two": "Beady little eyes",
  "three": "Little birds pitch by my doorstep"
}

funsim

<introduction>
    Security is all about attaining an appropriate level of security in an infinite
    sea of vulnerability. Every computer system and network is compromizable
    given enough time, resources, and motivation, however such resources are
    never unlimited, so security becomes about finding the right balance. What
    you are reaching for is to raise the bar of your defensive measures at least
    high enough that it is not worthwhile for your adversaries to commit the
    resources required to breach your security. Therefore this report is about two
    things. Firstly determining the optimal place for you to raise your security
    capability to such that you put your online activities effectively out of reach

funsim

<report>
    <introduction>
        <description>INTRODUCTION_DESCRIPTION</description>
    </introduction>
    <advisaries>
        <description>ADVISARIES_DESCRIPTION</description>
        <general_advisaries>
            <subtitle>GENERAL_ADVISARIES_SUBTITLE</subtitle>
            <description>GENERAL_ADVISARIES_DESCRIPTION</description>
        </general_advisaries>

funsim

{
     "firstName": "John",
     "lastName" : "Smith",
     "age"      : 25,
     "address"  : "..."
}

funsim

from dolfin import *
from dolfin_adjoint import *

mesh = RectangleMesh(-1, -1, 1, 1, 20, 20)
V = FunctionSpace(mesh, "CG", 1)
u = Function(V, name="State")
m = Function(V, name="Control")
v = TestFunction(V)

F = (inner(grad(u), grad(v)) - m*v)*dx

funsim

""" Solves the mother problem in optimal control of PDEs """

from dolfin import *
from dolfin_adjoint import *

dolfin.set_log_level(ERROR)
dolfin.parameters["optimization"]["test_gradient"] = False

# Define the domain [-1, 1] x [-1, 1]
n = 200

funsim

''' Example for solving a nonlinear optimisation problem with time-dependent control.

It solves:

min_{u,m} 1/2 ||u(t=T)-u_d(t=T)||^2 

subject to:

\grad u/\grad t - \nabla^2 u + u^3 = m,

funsim

running build_ext
building '_instant_module_81cc3b239b71f9e22c25de888db576b6854f0f33' extension
creating build
creating build/temp.linux-x86_64-2.7
mpicc -fno-strict-aliasing -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -fPIC -I/usr/local/lib/python2.7/dist-packages/petsc/include -I/usr/lib/python2.7/dist-packages/numpy/core/include -I/usr/include/python2.7 -c mat_utils.cxx -o build/temp.linux-x86_64-2.7/mat_utils.o
cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++ [enabled by default]
mpicc -fno-strict-aliasing -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -fPIC -I/usr/local/lib/python2.7/dist-packages/petsc/include -I/usr/lib/python2.7/dist-packages/numpy/core/include -I/usr/include/python2.7 -c instant_module_81cc3b239b71f9e22c25de888db576b6854f0f33_wrap.cxx -o build/temp.linux-x86_64-2.7/instant_module_81cc3b239b71f9e22c25de888db576b6854f0f33_wrap.o
cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++ [enable

funsim

from opentidalfarm import *

class ADDolfinExpression(object):
    def element_order(self, V):
        ''' Returns the element polynomial order '''
        return TestFunction(V).element().degree()

    def __init__(self, f):
        self.f = f
        order = self.element_order(f.function_space())

funsim

from dolfin import *

mesh = UnitSquareMesh(2, 2)

V = FunctionSpace(mesh, "CG", 2)

f = project(Expression("sin(x[1]) * cos(x[0])"), V)

File("f.xyz") << f