#! /usr/bin/env python
import openturns as ot
ref_values = [[1.0, 0.0], [0.0, 0.5]]
mats = [ot.Matrix(ref_values),
ot.SquareMatrix(ref_values),
ot.TriangularMatrix(ref_values),
ot.SymmetricMatrix(ref_values),
ot.CovarianceMatrix(ref_values),
ot.CorrelationMatrix(ref_values)]
mats.extend([
ot.ComplexMatrix(ref_values),
ot.HermitianMatrix(ref_values),
ot.TriangularComplexMatrix(ref_values),
ot.SquareComplexMatrix(ref_values)])
for a in mats:
# try conversion
ref = ot.Matrix([[1.0, 0.0], [0.0, 0.5]])
iname = a.__class__.__name__
print('a=', a)
# try scalar mul
try:
s = 5.
ats = a * s
print('a*s=', ats)
sta = s * a
# %%
# Define the spectral function:
def s(f):
if(f <= 5.0):
return 1.0
else:
x = f - 5.0
return m.exp(-2.0 * x * x)
# %%
# Create the collection of HermitianMatrix:
coll = ot.HermitianMatrixCollection()
for k in range(N):
frequency = fgrid.getValue(k)
matrix = ot.HermitianMatrix(1)
matrix[0, 0] = s(frequency)
coll.add(matrix)
# %%
# Create the spectral model:
spectralModel = ot.UserDefinedSpectralModel(fgrid, coll)
# Get the spectral density function computed at first frequency values
firstFrequency = fgrid.getStart()
frequencyStep = fgrid.getStep()
firstHermitian = spectralModel(firstFrequency)
# Get the spectral function at frequency + 0.3 * frequencyStep
spectralModel(frequency + 0.3 * frequencyStep)
print("with transpose, array", a0.transpose(), "=> ComplexMatrix", m0)
a0 = np.array(((1. + 3j, 2. - 5j), (3. + 7j, 4. - 9j)))
m0 = ot.SquareComplexMatrix(a0)
print("array", a0, "=> SquareComplexMatrix", m0)
a1 = np.array(m0)
print("SquareComplexMatrix", m0, "=> array", a1)
a0 = np.array(((1. + 3j, 0.), (3. + 7j, 4. - 9j)))
m0 = ot.TriangularComplexMatrix(a0)
print("array", a0, "=> TriangularComplexMatrix", m0)
a1 = np.array(m0)
print("TriangularComplexMatrix", m0, "=> array", a1)
a0 = np.array(((1. + 3j, 2. - 5j), (2. + 5j, 4. - 9j)))
m0 = ot.HermitianMatrix(a0)
print("array", a0, "=> HermitianMatrix", m0)
m0[1, 0] = 3.0 - 5j
a1 = np.array(m0)
print("HermitianMatrix", m0, "=> array", a1)
# check np.matrix / Matrix conversion
a0 = np.matrix(((1., 2.), (3., 4.), (5., 6.)))
m0 = ot.Matrix(a0)
print("matrix", a0, "=> Matrix", m0)
a1 = np.array(m0)
print("Matrix", m0, "=> matrix", a1)
m0 = ot.Matrix(a0.transpose())
print("with transpose, matrix", a0.transpose(), "=> Matrix", m0)
a0 = np.matrix(((1. + 3j, 2. - 5j, 3. + 7j), (4. - 9j, 5. + 11j, 6. - 13j)))