def test_fatality_prob_curves(self):
"""
Recreate the figure in Dalamagkidis et al.
"""
alpha = 1e6
beta = 34
p_s = np.linspace(0, 1, 11)
ke = np.logspace(0, 15)
p_f = [prob_fatality(ke, alpha, beta, p) for p in p_s]
fig, ax = mpl.subplots(1, 1, figsize=(8, 5))
ax.set_xlim(1, 1e14)
ax.set_ylim(0, 1.1)
for idx, p in enumerate(p_f):
ax.plot(ke, p, label=f'$p_s={p_s[idx]:1g}$')
x_pos = np.logspace(1.6, 11.6, 11)
labelLines(ax.get_lines(), xvals=x_pos)
ax.set_xlabel('Impact Kinetic Energy [J]')
ax.set_ylabel('Probability of Fatality')
ax.set_xscale('symlog')
ax.set_title(
f'Probability of Fatality - Dalamagkidis Model\n $\\alpha={alpha:3g}$, $\\beta={beta:3g}$'
)
fig.show()
def test_ranges(self):
"""
Ensure model range is always :math: \\in [0,1]
"""
alpha = 1e6
beta = 34
p_s = np.linspace(0, 1, 11)
ke = np.logspace(0, 15)
p_f = np.array([prob_fatality(ke, alpha, beta, p) for p in p_s])
self.assertLessEqual(p_f.max(), 1)
self.assertGreaterEqual(p_f.min(), 0)
def test_curve_means(self):
"""
Test curves approx follow correct shape by testing mean values of each
"""
alpha = 1e6
beta = 34
p_s = np.linspace(0, 1, 11)
ke = np.logspace(0, 15)
p_f = [prob_fatality(ke, alpha, beta, p) for p in p_s]
means = [pf.mean() for pf in p_f]
self.assertEqual(len(p_f), 11)
np.testing.assert_almost_equal(means, [
0.88, 0.8315, 0.7731, 0.7147, 0.6563, 0.5979, 0.5395, 0.4812,
0.4235, 0.3670, 0.3132
],
decimal=4)