def test_1():
bg = 0 # background
val = 2 # values
space = 10
thick = 1
NWorldFine = np.array([128, 128])
CoefClass = buildcoef2d.Coefficient2d(NWorldFine,
bg=bg,
val=val,
length=1,
thick=thick,
space=space,
probfactor=1,
right=1,
down=0,
diagr1=0,
diagr2=0,
diagl1=0,
diagl2=0,
LenSwitch=None,
thickSwitch=None,
equidistant=True,
ChannelHorizontal=None,
ChannelVertical=True,
BoundarySpace=True)
A = CoefClass.BuildCoefficient().flatten()
plt.figure("Coefficient_test_1")
drawCoefficient(NWorldFine, A)
def test_2():
bg = 0 # background
val = 2 # values
space = 10
thick = 1
fine = 64
NWorldFine = np.array([fine, fine])
ChoosingShapes = np.array([
# shape , len, thick, space
[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1],
[4, fine - 2, 1, 5],
[4, fine - 2, 1, 5],
[4, fine - 2, 1, 5]
])
CoefClass = buildcoef2d.Coefficient2d(NWorldFine,
bg=bg,
val=val,
right=1,
LenSwitch=None,
thickSwitch=None,
equidistant=True,
ChannelHorizontal=None,
ChannelVertical=None,
BoundarySpace=True,
probfactor=1,
ChoosingShapes=ChoosingShapes)
A = CoefClass.BuildCoefficient().flatten()
Correct = CoefClass.SpecificVanish(Number=[10, 11])
plt.figure("Coefficient_test_1")
drawCoefficient(NWorldFine, Correct.flatten())
Number = [10, 11]
random.seed(32)
lis = np.zeros(80)
lis[0] = 1
for i in range(np.shape(CoefClass.ShapeRemember)[0]):
Number.append(i * random.sample(list(lis), 1)[0])
plt.figure("Perturbation")
Perturbed = CoefClass.SpecificVanish(Number=Number,
Original=True).flatten()
drawCoefficient(NWorldFine, Perturbed)
diagr1 = 0,
diagr2 = 0,
diagl1 = 0,
diagl2 = 0,
LenSwitch = None,
thickSwitch = None,
equidistant = True,
ChannelHorizontal = None,
ChannelVertical = None,
BoundarySpace = True)
A = CoefClass.BuildCoefficient()
ABase = A.flatten()
plt.figure("OriginalCoefficient")
drawCoefficient(NWorldFine, ABase)
#potentially updated
fig = plt.figure("Correcting")
ax = fig.add_subplot(2,3,1)
ax.set_title('Original', fontsize=7)
drawCoefficientwg(NWorldFine, ABase,fig,ax,Greys=True)
numbers = [2,70,97,153,205]
D = CoefClass.SpecificVanish( Number = numbers,
probfactor = 1,
PartlyVanish = None,
Original = True)
value2 = 50
writer.writerow([val])
# fine-fem
f_fine = np.ones(NpFine)
uFineFem, AFine, MFine = femsolver.solveFine(world, ABase, f_fine, None,
boundaryConditions)
# fine solution
with open("%s/finescale.txt" % ROOT, 'w') as csvfile:
writer = csv.writer(csvfile)
for val in uFineFem:
writer.writerow([val])
plt.figure("Original")
drawCoefficient(NWorldFine, ABase, greys=True)
plt.title("Original coefficient")
# random seed
random.seed(20)
# decision
valc = np.shape(CoefClass.ShapeRemember)[0]
numbers = []
decision = np.zeros(100)
decision[0] = 1
for i in range(0, valc):
a = random.sample(list(decision), 1)[0]
if a == 1:
numbers.append(i)
E = CoefClass4.BuildCoefficient()
F = CoefClass5.BuildCoefficient()
# modifications
A = CoefClass.SpecificVanish(Number=[0, 2])
B = CoefClass1.SpecificVanish(Number=[0, 2])
A1 = A.flatten()
A4 = B.flatten()
A2 = C.flatten()
A3 = D.flatten()
A5 = E.flatten()
A6 = F.flatten()
plt.figure("Shape 1")
drawCoefficient(NWorldFine, A1)
plt.figure("Shape 2")
drawCoefficient(NWorldFine, A2)
plt.figure("Shape 3")
drawCoefficient(NWorldFine, A3)
plt.figure("Shape 4")
drawCoefficient(NWorldFine, A4)
plt.figure("Shape 5")
drawCoefficient(NWorldFine, A5)
plt.figure("Shape 6")
drawCoefficient(NWorldFine, A6)
plt.figure("All shapes")
AllshapesSixdrawCoefficient(NWorldFine, A1, A2, A3, A4, A5, A6)
plt.show()
diagr2 = 0,
diagl1 = 1,
diagl2 = 0,
LenSwitch = [4,5,6,7,8],
thickSwitch = None,
equidistant = None,
ChannelHorizontal = None,
ChannelVertical = None,
BoundarySpace = None)
#Build Coefficients
A1 = CoefClass1.BuildCoefficient()
A2 = CoefClass2.BuildCoefficient()
A3 = CoefClass3.BuildCoefficient()
A4 = CoefClass4.BuildCoefficient()
A1 = A1.flatten()
A2 = A2.flatten()
A3 = A3.flatten()
A4 = A4.flatten()
plt.figure("Coefficient 1")
drawCoefficient(NWorldFine, A1, greys=True)
plt.figure("Coefficient 2")
drawCoefficient(NWorldFine, A2, greys=True)
plt.figure("Coefficient 3")
drawCoefficient(NWorldFine, A3, greys=True)
plt.figure("Coefficient 4")
drawCoefficient(NWorldFine, A4, greys=True)
plt.show()
NWorldFine = world.NWorldCoarse * world.NCoarseElement
# fine grid elements and nodes
xt = util.tCoordinates(NFine).flatten()
xp = util.pCoordinates(NFine).flatten()
# time step parameters
tau = 0.1
numTimeSteps = 150
n = 2
# ms coefficient B
B = np.kron(np.random.rand(fine / n, fine / n) * 0.9 + 0.1, np.ones((n, n)))
bFine = B.flatten()
plt.figure("OriginalCoefficient")
drawCoefficient(NWorldFine, bFine)
plt.title('$B(x,y)$', fontsize=24)
plt.show()
# ms coefficient A
A = np.kron(np.random.rand(fine / n, fine / n) * 0.9 + 0.1, np.ones((n, n)))
aFine = A.flatten()
plt.figure("OriginalCoefficient")
drawCoefficient(NWorldFine, aFine)
plt.title('$A(x,y)$', fontsize=24)
plt.show()
# localization and mesh width parameters
N = 16
k = log(N, 2)
lList = [
randomstep=None,
randomDirection=None,
Right=1,
BottomRight=1,
Bottom=1,
BottomLeft=1,
Left=1,
TopLeft=1,
Top=1,
TopRight=1,
Original=True)
A = A.flatten()
B = B.flatten()
C = C.flatten()
D = D.flatten()
plt.figure("original")
drawCoefficient(NWorldFine, A)
plt.figure("1")
drawCoefficient(NWorldFine, B)
plt.figure("2")
drawCoefficient(NWorldFine, C)
plt.figure("3")
drawCoefficient(NWorldFine, D)
plt.figure('all')
ExtradrawCoefficient(NWorldFine, A, B, C, D)
plt.show()