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 generate_one(K):
# Let's generate some data with y = Powerlaw(x)
gen_function = Powerlaw()
gen_function.K = K
# 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
# xyl = XYLike("data", x, y, y_err)
# xyl.plot(x_scale='log', y_scale='log')
return x, y, y_err
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)
