def testInitHarmonicModel(self):
# Define the input, and derive what the output should be
freqList = [2.1, 3.4]
freqNameList = ["f_1", "f_2"]
maxNharmonics = 2
nParameters = 2 * maxNharmonics * len(freqList)
covariateName = "t"
regressorNames = [
"sin(2*pi*1*f_1*t)",
"cos(2*pi*1*f_1*t)",
"sin(2*pi*2*f_1*t)",
"cos(2*pi*2*f_1*t)",
"sin(2*pi*1*f_2*t)",
"cos(2*pi*1*f_2*t)",
"sin(2*pi*2*f_2*t)",
"cos(2*pi*2*f_2*t)",
]
designMatrix = np.empty((self.nObservations, nParameters))
runningIndex = 0
for n in range(len(freqList)):
for k in range(1, maxNharmonics + 1):
designMatrix[:, runningIndex] = np.sin(2 * pi * k * freqList[n] * self.time)
designMatrix[:, runningIndex + 1] = np.cos(2 * pi * k * freqList[n] * self.time)
runningIndex += 2
# Assert if the output is what it should be
harmonicModel = HarmonicModel(self.time, covariateName, freqList, freqNameList, maxNharmonics)
self.assertTrue(harmonicModel.nParameters() == nParameters)
self.assertTrue(harmonicModel.regressorNames() == regressorNames)
self.assertTrue(harmonicModel.nObservations() == self.nObservations)
self.assertTrue(np.alltrue(harmonicModel.designMatrix() == designMatrix))
def testInitHarmonicModel(self):
# Defining a harmonic model with no covariance matrix should be the
# same as one with a diagonal covariance matrix with ones on the diagonal.
freqList = [2.1]
freqNameList = ["f_1"]
maxNharmonics = 1
nParameters = 2 * maxNharmonics * len(freqList)
covariateName = "t"
regressorNames = ["sin(2*pi*1*f_1*t)", "cos(2*pi*1*f_1*t)"]
covMatrixObserv = np.diag(np.ones(self.nObservations))
harmonicModel1 = HarmonicModel(self.time, covariateName, freqList, freqNameList, maxNharmonics)
harmonicModel2 = HarmonicModel(self.time, covariateName, freqList, freqNameList, maxNharmonics, covMatrixObserv)
self.assertTrue(np.alltrue(harmonicModel1.designMatrix() == harmonicModel2.designMatrix()))
def testInitHarmonicModel(self):
# Define the input, and derive what the output should be
freqList = [2.1, 3.4]
freqNameList = ["f_1", "f_2"]
maxNharmonics = 2
nParameters = 2*maxNharmonics*len(freqList)
covariateName = "t"
regressorNames = ["sin(2*pi*1*f_1*t)", "cos(2*pi*1*f_1*t)", \
"sin(2*pi*2*f_1*t)", "cos(2*pi*2*f_1*t)", \
"sin(2*pi*1*f_2*t)", "cos(2*pi*1*f_2*t)", \
"sin(2*pi*2*f_2*t)", "cos(2*pi*2*f_2*t)"]
designMatrix = np.empty((self.nObservations, nParameters))
runningIndex = 0
for n in range(len(freqList)):
for k in range(1,maxNharmonics+1):
designMatrix[:,runningIndex] = np.sin(2*pi*k*freqList[n]*self.time)
designMatrix[:,runningIndex+1] = np.cos(2*pi*k*freqList[n]*self.time)
runningIndex += 2
# Assert if the output is what it should be
harmonicModel = HarmonicModel(self.time, covariateName, freqList, freqNameList,maxNharmonics)
self.assertTrue(harmonicModel.nParameters() == nParameters)
self.assertTrue(harmonicModel.regressorNames() == regressorNames)
self.assertTrue(harmonicModel.nObservations() == self.nObservations)
self.assertTrue(np.alltrue(harmonicModel.designMatrix() == designMatrix))
def testInitHarmonicModel(self):
# Defining a harmonic model with no covariance matrix should be the
# same as one with a diagonal covariance matrix with ones on the diagonal.
freqList = [2.1]
freqNameList = ["f_1"]
maxNharmonics = 1
nParameters = 2*maxNharmonics*len(freqList)
covariateName = "t"
regressorNames = ["sin(2*pi*1*f_1*t)", "cos(2*pi*1*f_1*t)"]
covMatrixObserv = np.diag(np.ones(self.nObservations))
harmonicModel1 = HarmonicModel(self.time, covariateName, freqList, freqNameList, maxNharmonics)
harmonicModel2 = HarmonicModel(self.time, covariateName, freqList, freqNameList, maxNharmonics, covMatrixObserv)
self.assertTrue(np.alltrue(harmonicModel1.designMatrix() == harmonicModel2.designMatrix()))