def test_do_fit(self):
guess = dict(A0=0.25, Freq=0.55, Sigma=0.25, Phi=0.0)
target = dict(A0=0.2, Freq=0.5, Sigma=0.2, Phi=0.0)
do_a_fit(np.arange(0.1, 16, 0.2),
'GaussBessel',
guess,
target,
atol=0.01)
def test_do_fit(self):
guess = dict(A0=0.25, Radius=10.5, LambdaTrans=0.25)
target = dict(A0=0.2, Radius=10, LambdaTrans=0.2)
do_a_fit(np.arange(0.1, 16, 0.2),
'ZFelectronDipole',
guess,
target,
atol=0.01)
def test_do_fit(self):
guess = dict(A0=0.25, FreqA=0.35, FreqD=0.25, FCut=1.05, Phi=0.0)
target = dict(A0=0.2, FreqA=0.3, FreqD=0.2, FCut=1.0, Phi=0.0)
do_a_fit(np.arange(0.1, 16, 0.2),
'ZFMuonium',
guess,
target,
atol=0.01)
def test_do_fit(self):
guess = dict(A0=0.25, KTdelta=0.15, B0=11.0, Gauss=0.25)
target = dict(A0=0.2, KTdelta=0.1, B0=10.0, Gauss=0.2)
do_a_fit(np.arange(0.1, 16, 0.2),
'CompositePCRmagnet',
guess,
target,
atol=0.01)
def test_do_fit(self):
guess = dict(A=1.1, Lambda=0.15, N0=1.1, Tau=5.0, Phi=0.3, Nu=0.2)
target = dict(A=1.0, Lambda=0.1, N0=1.0, Tau=5.0, Phi=0.3, Nu=0.2)
do_a_fit(np.arange(0.1, 16, 0.2),
'MuMinusExpTF',
guess,
target,
atol=0.01)
def test_do_fit(self):
guess = dict(A0=0.25, Field=0.1, A=1.5, Phi=0.0)
target = dict(A0=0.2, Field=0.1, A=1.5, Phi=0.0)
do_a_fit(np.arange(0.1, 16, 0.2),
'LowTFMuonium',
guess,
target,
atol=0.01)
def test_do_fit(self):
guess = dict(A0=0.25, SigmaOverW=0.15, H0=3.0, Toff=0.15)
target = dict(A0=0.2, SigmaOverW=0.1, H0=3.0, Toff=0.1)
do_a_fit(np.arange(0.1, 16, 0.2),
'PCRmagnetfnorm',
guess,
target,
atol=0.01)
def test_do_fit(self):
guess = dict(A0=0.25, Delta=0.15, H0=10.0, Toff=0.15)
target = dict(A0=0.2, Delta=0.1, H0=10.0, Toff=0.1)
do_a_fit(np.arange(0.1, 16, 0.2),
'PCRmagnetZFKT',
guess,
target,
atol=0.01)
def test_do_fit(self):
do_a_fit(np.arange(0.1, 16, 0.2),
'DampedBessel',
guess=dict(A0=0.25,
Field=10.5,
Phi=0.05,
LambdaL=0.15,
LambdaT=0.15,
FractionL=0.15),
target=dict(A0=0.2,
Field=10,
Phi=0.0,
LambdaL=0.1,
LambdaT=0.1,
FractionL=0.1),
atol=0.01)
def test_do_fit(self):
do_a_fit(np.arange(0.1, 16, 0.2),
'PEARLTransVoigt',
guess=dict(Position=1096,
LorentzianFWHM=45.5,
GaussianFWHM=25.4,
Bg0=25.2,
Bg1=0.017,
Bg2=0.0),
target=dict(Position=1096.3,
LorentzianFWHM=45.8,
GaussianFWHM=25.227,
Bg0=25.0,
Bg1=0.015,
Bg2=0.0),
atol=0.01)
def test_do_fit(self):
do_a_fit(np.arange(0.1, 16, 0.2),
'StandardSC',
guess=dict(A0=0.5,
FieldSC=10,
FieldBG=11,
Phi=0.05,
Sigma=0.2,
Abg=0.1),
target=dict(A0=0.5,
FieldSC=10,
FieldBG=11,
Phi=0.0,
Sigma=0.2,
Abg=0.1),
atol=0.01)
def test_do_fit(self):
target = dict(A=1.0, R=3.5, L=1.7)
# we need only one parameter when fitting only one histogram,
# else we incur in overfitting
guesses = (dict(A=1.0, R=3.5, L=3.0), dict(A=1.0, R=0.5, L=1.7))
fixes = (['A', 'R'], ['A', 'L'])
for (guess, fix) in zip(guesses, fixes):
status, fit = do_a_fit(np.arange(0.1, 2.2, 0.2),
'EISFDiffCylinder', guess=guess, fixes=fix,
target=target, atol=0.01)
if not status:
msg_p = 'param {} target value was {}, obtained = {}'
msg = '\n'.join([msg_p.format(*[p, target[p], fit.Function[p]])
for p in target])
self.fail('\n'+msg)
def test_do_fit(self):
target = dict(A=1.0, R=3.5, L=1.7)
# we need only one parameter when fitting only one histogram,
# else we incur in overfitting
guesses = (dict(A=1.0, R=3.5, L=3.0), dict(A=1.0, R=0.5, L=1.7))
fixes = (['A', 'R'], ['A', 'L'])
for (guess, fix) in zip(guesses, fixes):
status, fit = do_a_fit(np.arange(0.1, 2.2, 0.2),
'EISFDiffCylinder',
guess=guess,
fixes=fix,
target=target,
atol=0.01)
if not status:
msg_p = 'param {} target value was {}, obtained = {}'
msg = '\n'.join([
msg_p.format(*[p, target[p], fit.Function[p]])
for p in target
])
self.fail('\n' + msg)
def test_do_fit(self):
do_a_fit(np.arange(0.1, 16, 0.2),
'RFresonance',
guess=dict(A0=0.25, Boffset=10.5, B1=10.5, B1GauWidth=0.25),
target=dict(A0=0.2, Boffset=10, B1=10, B1GauWidth=0.2),
atol=0.01)
def test_do_fit(self):
do_a_fit(np.arange(0.1, 16, 0.2),
'TriangleOsc',
guess=dict(A0=0.5, Freq=1, Phi=0.0),
target=dict(A0=0.5, Freq=1, Phi=0.0),
atol=0.01)
def test_do_fit(self):
guess = dict(A0=0.25, Freq=1.15, ModFreq=0.15, Phi=0.0)
target = dict(A0=0.2, Freq=1.0, ModFreq=0.1, Phi=0.0)
do_a_fit(np.arange(0.1, 16, 0.2), 'ModOsc', guess, target, atol=0.01)
def test_do_fit(self):
do_a_fit(np.arange(0.1, 2.2, 0.2),
'EISFDiffSphere',
guess=dict(A=2.0, R=1.0),
target=dict(A=1.0, R=3.5),
atol=0.01)
def test_do_fit(self):
do_a_fit(np.arange(0.1, 16, 0.2),
'StretchedKT',
guess=dict(A0=0.55, Beta=1.5, Sigma=0.35),
target=dict(A0=0.5, Beta=2, Sigma=0.3),
atol=0.01)
def test_do_fit(self):
do_a_fit(np.arange(0.1, 2.2, 0.2), 'EISFDiffSphere',
guess=dict(A=2.0, R=1.0),
target=dict(A=1.0, R=3.5), atol=0.01)
def test_do_fit(self):
guess = dict(A0=0.25, Delta=0.25, Nu=0.15)
target = dict(A0=0.2, Delta=0.2, Nu=0.1)
do_a_fit(np.arange(0.1, 16, 0.2), 'PCRmagRedfield', guess, target, atol=0.01)
def test_do_fit(self):
do_a_fit(np.arange(0.1, 16, 0.2),
'TFMuonium',
guess=dict(A0=0.55, Field=5.5, A=605, Phi=0.0),
target=dict(A0=0.5, Field=5, A=600, Phi=0.0),
atol=0.01)
def test_do_fit(self):
guess = dict(A0 = 0.25, Freq = 2.5, Angle = 50.5, Field = 11, Phi = 0.0)
target = dict(A0 = 0.2, Freq = 2, Angle = 50, Field = 10, Phi = 0.0)
do_a_fit(np.arange(0.1, 16, 0.2), 'AFMLF', guess, target, atol = 0.01)
def test_do_fit(self):
do_a_fit(np.arange(0.1, 2.2, 0.2), 'TeixeiraWater',
guess=dict(Tau=2.0, L=1.0),
target=dict(Tau=1.0, L=3.5), atol=0.01)
def test_do_fit(self):
do_a_fit(np.arange(0.1, 16, 0.2),
'SilverBaseline',
guess=dict(A0=0.15),
target=dict(A0=0.1),
atol=0.01)
def test_do_fit(self):
guess = dict(A0=0.25, Freq=1.2, Angle=50, Sigma=10.5, Phi=0.0)
target = dict(A0=0.2, Freq=1, Angle=50, Sigma=0.2, Phi=0.0)
do_a_fit(np.arange(0.1, 16, 0.2), 'AFMZF', guess, target, atol=0.01)
def test_do_fit(self):
do_a_fit(np.arange(0.1, 16, 0.2),
'StaticLorentzianKT',
guess=dict(A0=0.2, A=1.1, Field=0.9),
target=dict(A0=0.2, A=1, Field=1),
atol=0.01)
def test_do_fit(self): guess = dict(A0 = 0.25, R = 0.45, Delta0 = 0.25) target = dict(A0 = 0.2, R = 0.4, Delta0 = 0.2) do_a_fit(np.arange(0.1, 16, 0.2), 'GauBroadGauKT', guess, target, atol = 0.01)
def test_do_fit():
do_a_fit(np.asarray([0.01, 0.1, 1.0, 10.0]), 'ChudleyElliot',
guess=dict(Tau=1.0, L=1.0),
target=dict(Tau=1.42, L=2.42), atol=0.01)
def test_do_fit(self):
do_a_fit(np.arange(0.1, 2.2, 0.2),
'TeixeiraWater',
guess=dict(Tau=2.0, L=1.0),
target=dict(Tau=1.0, L=3.5),
atol=0.01)
def test_do_fit(self):
do_a_fit(np.arange(0.1, 16, 0.2),
'FmuF',
guess=dict(A0=0.55, FreqD=0.25, Lambda=0.15, Sigma=0.25),
target=dict(A0=0.5, FreqD=0.2, Lambda=0.1, Sigma=0.2),
atol=0.01)
def test_do_fit(self):
do_a_fit(np.arange(0.1, 16, 0.2),
'Redfield',
guess=dict(A0=0.2, Hloc=0.1, Tau=0.1),
target=dict(A0=0.2, Hloc=0.1, Tau=0.1),
atol=0.01)
def test_do_fit(self): do_a_fit(np.arange(0.1, 16, 0.2), 'Meier', guess = dict(A0 = 0.55, FreqD = 0.015, FreqQ = 0.055, Spin = 3.55, Lambda = 0.15, Sigma = 0.25), target = dict(A0 = 0.5, FreqD = 0.01, FreqQ = 0.05, Spin = 3.5, Lambda = 0.1, Sigma = 0.2), atol = 0.01)
def test_do_fit(self):
do_a_fit(np.arange(0.1, 16, 0.2),
'SpinGlass',
guess=dict(A0=0.25, Width=0.15, Nu=1.1, Q=0.15),
target=dict(A0=0.2, Width=0.1, Nu=1, Q=0.1),
atol=0.01)
def test_do_fit(self):
do_a_fit(np.arange(0.1, 16, 0.2),
'CombGaussLorentzKT',
guess=dict(A0=0.55, Lambda=0.15, Sigma=0.5),
target=dict(A0=0.5, Lambda=0.1, Sigma=0.2),
atol=0.01)
def test_do_a_fit(self):
do_a_fit(np.arange(0.1, 2.2, 0.2), 'EISFDiffSphereAlkyl',
guess=dict(A=2.0, Rmin=2.0, Rmax=3.0),
target=dict(A=1.0, Rmin=0.5, Rmax=6.3), atol=0.01)