def test_UnresolvedExtendedXYLike_dataframe():
yerr = np.array(gauss_sigma)
y = np.array(gauss_signal)
# chi2 version
xy = UnresolvedExtendedXYLike("test", x, y, yerr)
df = xy.to_dataframe()
# read back in dataframe
new_xy = UnresolvedExtendedXYLike.from_dataframe('df', df)
assert not xy.is_poisson
# poisson version
xy = UnresolvedExtendedXYLike("test", x, y, poisson_data=True)
df = xy.to_dataframe()
# read back in dataframe
new_xy = UnresolvedExtendedXYLike.from_dataframe('df', df, poisson=True)
assert xy.is_poisson
def test_UnresolvedExtendedXYLike_chi2():
# Get fake data with Gaussian noise
yerr = np.array(gauss_sigma)
y = np.array(gauss_signal)
# Fit
xy = UnresolvedExtendedXYLike("test", x, y, yerr)
fitfun = Line() + Gaussian()
fitfun.F_2 = 60.0
fitfun.mu_2 = 4.5
res = xy.fit(fitfun)
# Verify that the fit converged where it should have
assert np.allclose(
res[0]['value'].values,
[0.82896119, 40.20269202, 62.80359114, 5.04080011, 0.27286713],
rtol=0.05)
# test not setting yerr
xy = UnresolvedExtendedXYLike("test", x, y)
assert np.all(xy.yerr == np.ones_like(y))
fitfun = Line() + Gaussian()
fitfun.F_2 = 60.0
fitfun.mu_2 = 4.5
res = xy.fit(fitfun)
def test_UnresolvedExtendedXYLike_poisson():
# Now Poisson case
y = np.array(poiss_sig)
xy = UnresolvedExtendedXYLike("test", x, y, poisson_data=True)
fitfun = Line() + Gaussian()
fitfun.F_2 = 60.0
fitfun.F_2.bounds = (0, 200.0)
fitfun.mu_2 = 5.0
fitfun.a_1.bounds = (0.1, 5.0)
fitfun.b_1.bounds = (0.1, 100.0)
res = xy.fit(fitfun)
# Verify that the fit converged where it should have
#print res[0]['value']
assert np.allclose(res[0]['value'],
[0.783748, 40.344599, 71.560055, 4.989727, 0.330570],
rtol=0.05)
def test_UnresolvedExtendedXYLike_txt():
yerr = np.array(gauss_sigma)
y = np.array(gauss_signal)
# chi2 version
xy = UnresolvedExtendedXYLike("test", x, y, yerr)
fname = 'test_txt.txt'
xy.to_txt(fname)
# read back in txt file
new_xy = UnresolvedExtendedXYLike.from_text_file('txt', fname)
assert not xy.is_poisson
# poisson version
xy = UnresolvedExtendedXYLike("test", x, y, poisson_data=True)
fname = 'test_txt_poisson.txt'
xy.to_txt(fname)
# read back in txt file
new_xy = UnresolvedExtendedXYLike.from_text_file('txt', fname)
assert new_xy.is_poisson
# Remove files
os.remove('test_txt.txt')
os.remove('test_txt_poisson.txt')
def test_UnresolvedExtendedxy_plot():
# Get fake data with Gaussian noise
yerr = np.array(gauss_sigma)
y = np.array(gauss_signal)
# Fit
xy = UnresolvedExtendedXYLike("test", x, y, yerr)
xy.plot()
fitfun = Line() + Gaussian()
fitfun.F_2 = 60.0
fitfun.mu_2 = 4.5
res = xy.fit(fitfun)
xy.plot()
def test_UnresolvedExtendedXYLike_assign_to_source():
# Get fake data with Gaussian noise
yerr = np.array(gauss_sigma)
y = np.array(gauss_signal)
# Fit
xy = UnresolvedExtendedXYLike("test", x, y, yerr)
xy.assign_to_source("exs1")
fitfun = Line() + Gaussian()
fitfun.F_2 = 60.0
fitfun.mu_2 = 4.5
fitfun2 = Line() + Gaussian()
fitfun2.F_2 = 60.0
fitfun2.mu_2 = 4.5
shape = Gaussian_on_sphere()
exs1 = ExtendedSource("exs1", spatial_shape=shape, spectral_shape=fitfun)
pts2 = PointSource("pts2", ra=2.5, dec=3.2, spectral_shape=fitfun2)
for parameter in list(fitfun2.parameters.values()):
parameter.fix = True
for parameter in list(shape.parameters.values()):
parameter.fix = True
model = Model(exs1, pts2)
data = DataList(xy)
jl = JointLikelihood(model, data)
_ = jl.fit()
predicted_parameters = np.array(
[0.82896119, 40.20269202, 62.80359114, 5.04080011, 0.27286713])
assert np.allclose([
fitfun.a_1.value, fitfun.b_1.value, fitfun.F_2.value,
fitfun.mu_2.value, fitfun.sigma_2.value
],
predicted_parameters,
rtol=0.05)
# Test that the likelihood does not change by changing the parameters of the other source
log_like_before = jl.minus_log_like_profile(*predicted_parameters)
fitfun2.F_2 = 120.0
log_like_after = jl.minus_log_like_profile(*predicted_parameters)
assert log_like_before == log_like_after
# Now test that if we do not assign a source, then the log likelihood value will change
xy.assign_to_source(None)
# Test that the likelihood this time changes by changing the parameters of the other source
log_like_before = jl.minus_log_like_profile(*predicted_parameters)
fitfun2.F_2 = 60.0
log_like_after = jl.minus_log_like_profile(*predicted_parameters)
assert log_like_before != log_like_after