<jupyter_start><jupyter_text>Создаем модели наблюдателя и объекта:<jupyter_code>observer_x, observer_y, observer_course, observer_velocity = 0, 0, 0, 3
observer = Object('Наблюдатель', observer_x, observer_y, observer_course,
                observer_velocity, verbose=True)
target_bearing, target_distance, target_course, target_velocity = 0, 20, 45, 10
target = Object('Объект', target_bearing, target_distance, target_course,
              target_velocity, observer, mode='bdcv', verbose=True)<jupyter_output><empty_output><jupyter_text>Моделирование траекторий<jupyter_code>observer.forward_movement(3 * 60)
observer.change_course(270, 'left', omega=0.5)
observer.forward_movement(2 * 60)
observer.change_course(90, 'right', omega=0.5)
observer.forward_movement(5 * 60)

target.forward_movement(len(observer.coords[0])-1)
model = Model(observer, target=target, noise_std=np.radians(0.5), verbose=True)
%%time
dict_results = swarm(model, n=1000, seeded=True, fixed_target=False, fixed_noise=False, p0=[0., 20., 45., 10.])
df = get_df(dict_results)
df.head().round(3)
df.plot.scatter(x='t', y='Nf');
res = stats.spearmanr(df['t'], df['Nf'])
print('stat = {}, p-value = {}'.format(*res))
df.plot.scatter(x='t', y='Iter');
res = stats.spearmanr(df['t'], df['Iter'])
print('stat = {}, p-value = {}'.format(*res))<jupyter_output>stat = 0.8974668656017237, p-value = 0.0
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