def test_XYLike_chi2():
# Get fake data with Gaussian noise
yerr = np.array(gauss_sigma)
y = np.array(gauss_signal)
# Fit
xy = XYLike("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 = XYLike("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_XYLike_chi2():
# Get fake data with Gaussian noise
yerr = np.array(gauss_sigma)
y = np.array(gauss_signal)
# Fit
xy = XYLike("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 = XYLike("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_goodness_of_fit():
# Let's generate some data with y = Powerlaw(x)
gen_function = Powerlaw()
# Generate a dataset using the power law, and a
# constant 30% error
x = np.logspace(0, 2, 50)
xyl_generator = XYLike.from_function(
"sim_data", function=gen_function, x=x, yerr=0.3 * gen_function(x)
)
y = xyl_generator.y
y_err = xyl_generator.yerr
fit_function = Powerlaw()
xyl = XYLike("data", x, y, y_err)
parameters, like_values = xyl.fit(fit_function)
gof, all_results, all_like_values = xyl.goodness_of_fit()
# Compute the number of degrees of freedom
n_dof = len(xyl.x) - len(fit_function.free_parameters)
# Get the observed value for chi2
obs_chi2 = 2 * like_values["-log(likelihood)"]["data"]
theoretical_gof = scipy.stats.chi2(n_dof).sf(obs_chi2)
assert np.isclose(theoretical_gof, gof["total"], rtol=0.1)
def test_xy_plot():
# Get fake data with Gaussian noise
yerr = np.array(gauss_sigma)
y = np.array(gauss_signal)
# Fit
xy = XYLike("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_xy_plot():
# Get fake data with Gaussian noise
yerr = np.array(gauss_sigma)
y = np.array(gauss_signal)
# Fit
xy = XYLike("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_XYLike_poisson():
# Now Poisson case
y = np.array(poiss_sig)
xy = XYLike("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_XYLike_poisson():
# Now Poisson case
y = np.array(poiss_sig)
xy = XYLike("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_goodness_of_fit():
# Let's generate some data with y = Powerlaw(x)
gen_function = Powerlaw()
# Generate a dataset using the power law, and a
# constant 30% error
x = np.logspace(0, 2, 50)
xyl_generator = XYLike.from_function("sim_data", function=gen_function,
x=x,
yerr=0.3 * gen_function(x))
y = xyl_generator.y
y_err = xyl_generator.yerr
fit_function = Powerlaw()
xyl = XYLike("data", x, y, y_err)
parameters, like_values = xyl.fit(fit_function)
gof, all_results, all_like_values = xyl.goodness_of_fit()
# Compute the number of degrees of freedom
n_dof = len(xyl.x) - len(fit_function.free_parameters)
# Get the observed value for chi2
obs_chi2 = 2 * like_values['-log(likelihood)']['data']
theoretical_gof = scipy.stats.chi2(n_dof).sf(obs_chi2)
assert np.isclose(theoretical_gof, gof['total'], rtol=0.1)