def test_resolution_per_bin():
# For a normal distribution, the resolution at `percentile=68.27` is equal to 1 sigma
size = 1000000
loc = np.random.rand() * 100
scale = np.random.rand() * 10
x = np.linspace(0, 10, size)
y_true = loc * np.ones(size)
y_reco = np.random.normal(loc=loc, scale=scale, size=size)
for scaling in ['s0', 's1', 's2', 's3', 's4']:
bins, res = ana.resolution_per_bin(x,
y_true,
y_reco,
bins=6,
relative_scaling_method=scaling)
np.testing.assert_allclose(
res[:, 0],
scale / ana.relative_scaling(y_true, y_reco, scaling).mean(),
rtol=1e-1)
bias = 50
y_reco = np.random.normal(loc=loc + bias, scale=scale, size=size)
bins, res = ana.resolution_per_bin(x, y_true, y_reco, bias_correction=True)
np.testing.assert_allclose(res[:, 0],
scale /
ana.relative_scaling(y_true, y_reco).mean(),
rtol=1e-1)
def test_plot_resolution_difference():
from ctaplot.ana.ana import resolution_per_bin, irf_cta
size = 1000
simu = np.logspace(-2, 2, size)
reco = 2 * simu
reco2 = 3 * simu
irf = irf_cta()
bin = irf.E_bin
bins, res1 = resolution_per_bin(simu,
simu,
reco,
bins=bin,
relative_scaling_method='s1')
bins, res2 = resolution_per_bin(simu,
simu,
reco2,
bins=bin,
relative_scaling_method='s1')
plots.plot_resolution_difference(bins,
res1,
res2,
ax=None,
color='red',
alpha=0.8,
label='nice diff')
def test_plot_resolution():
x = np.linspace(0, 10, 1000)
y_true = 0.5 + np.random.rand(1000)
y_reco = np.random.normal(loc=1, size=1000)
from ctaplot.ana.ana import resolution_per_bin
bins, res = resolution_per_bin(x, y_true, y_reco)
plots.plot_resolution(bins, res, color='black')
def test_resolution_per_bin_empty():
'''
testing for empty bins
'''
# For a normal distribution, the resolution at `percentile=68.27` is equal to 1 sigma
size = 1000000
loc = np.random.rand() * 100
scale = np.random.rand() * 10
x = np.linspace(0, 10, size)
bins = np.array([1, 2, 3, 5, 11, 15])
y_true = loc * np.ones(size)
y_reco = np.random.normal(loc=loc, scale=scale, size=size)
for scaling in ['s0', 's1', 's2', 's3', 's4']:
bins, res = ana.resolution_per_bin(x,
y_true,
y_reco,
bins=bins,
relative_scaling_method=scaling,
bias_correction=True)
v = scale / ana.relative_scaling(y_true, y_reco, scaling).mean()
expected_res = np.array([v, v, v, v, 0])
np.testing.assert_allclose(res[:, 0], expected_res, rtol=1e-1)
