def testBuildConstantFunction2(self):
f = Function.ConstantFunction(-2)
self.assertAlmostEqual(f(-1, -1), -2, 9)
self.assertAlmostEqual(f(0, 0), -2, 9)
self.assertAlmostEqual(f(1, 2), -2, 9)
self.assertEqual(str(f), "-2.0")
def testBuildConstantFunction1(self):
f = Function.ConstantFunction(1)
self.assertAlmostEqual(f(-1, -1), 1, 9)
self.assertAlmostEqual(f(0, 0), 1, 9)
self.assertAlmostEqual(f(1, 2), 1, 9)
self.assertEqual(str(f), "1.0")
def computeProductionVector(z, slopeFunctions, parameters):
#Return value
P = scipy.sparse.lil_matrix((parameters.n, 1))
#Functions
beta = Function.FunctionWrapper(parameters.productionEffciency, "B")
rho = Function.ConstantFunction(parameters.productionThreshold)
#For each element
for element in parameters.omegaThree:
#Build a sum of non-zero functions on this element
sum = Function.ConstantFunction(0)
for i in range(0, 3):
#Get the point
xi = element.points[i]
#The coefficient
zl = z[xi.index, 0]
#The shape function
slopeL = slopeFunctions[element.index][i]
zFunction = Function.ConstantFunction(zl)
slopeLWrapper = Function.FunctionWrapper(slopeL.evaluate,
str(slopeL))
sumElement = zFunction * slopeLWrapper
sum = sum + sumElement
#Build a 'B(x)(rho - sum)+' term
g = beta * Function.PositivePartFunction(rho - sum)
#Integrate
for i in range(0, 3):
xi = element.points[i]
slopeK = slopeFunctions[element.index][i]
slopeKWrapper = Function.FunctionWrapper(slopeK.evaluate,
str(slopeK))
h = g * slopeKWrapper
I = Integrator.integrate2D(h, element)
P[xi.index, 0] += I
return P.tocsc()
