def generate_f_lambda(self):
"""
Prepares a purely Python-based integrator.
"""
self.DDE = python_core.dde_integrator(self.f_sym, self.past,
self.helpers)
def test_integration(self): past = past_generator() double_past = [] for entry in past: double_past += [( entry[0], np.hstack((entry[1],entry[1])), np.hstack((entry[2],entry[2])) )] self.DDE = dde_integrator(lambda: chain(f(),f()), double_past) self.assertEqual(self.DDE.y[0], y0) self.assertEqual(self.DDE.diff[0], dy0) self.assertEqual(self.DDE.y[1], y0) self.assertEqual(self.DDE.diff[1], dy0) self.assertEqual(self.DDE.t, 0.0) self.DDE.get_next_step(1.0) self.assertEqual(self.DDE.y[0], y0) self.assertEqual(self.DDE.diff[0], dy0) self.assertEqual(self.DDE.y[1], y0) self.assertEqual(self.DDE.diff[1], dy0) self.assertEqual(self.DDE.t, 0.0) self.assertAlmostEqual(self.DDE.error[1], expected_error) self.DDE.accept_step() self.assertAlmostEqual(self.DDE.y[0], expected_y) self.assertAlmostEqual(self.DDE.y[1], expected_y) self.assertEqual(self.DDE.t, 1.0)
def test_integration(self): past = past_generator() double_past = [] for entry in past: double_past += [( entry[0], np.hstack((entry[1],entry[1])), np.hstack((entry[2],entry[2])) )] self.DDE = dde_integrator(lambda: chain(f(),f()), double_past) self.assertEqual(self.DDE.y[0], y0) self.assertEqual(self.DDE.diff[0], dy0) self.assertEqual(self.DDE.y[1], y0) self.assertEqual(self.DDE.diff[1], dy0) self.assertEqual(self.DDE.t, 0.0) self.DDE.get_next_step(1.0) self.assertEqual(self.DDE.y[0], y0) self.assertEqual(self.DDE.diff[0], dy0) self.assertEqual(self.DDE.y[1], y0) self.assertEqual(self.DDE.diff[1], dy0) self.assertEqual(self.DDE.t, 0.0) self.assertAlmostEqual(self.DDE.error[1], expected_error) self.DDE.accept_step() self.assertAlmostEqual(self.DDE.y[0], expected_y) self.assertAlmostEqual(self.DDE.y[1], expected_y) self.assertEqual(self.DDE.t, 1.0)
def test_jump(self): n = 10 τ = 10 width = 10**np.random.uniform(-7,0)*τ factor = np.random.random(n) times = sorted(np.random.uniform(0,τ,5)) past = Past(n, [ (time,np.random.random(n),0.1*np.random.random(n)) for time in times ]) jump_time = np.random.uniform(past[-2][0],past[-1][0]) jump_size = np.random.random(n) # Use a derivative for which it is clear how a jump affects it: f = lambda: [ factor[i]*y(i) + y(i,t-τ) for i in range(n) ] self.DDE = dde_integrator(f,past) state = self.DDE.get_recent_state(jump_time+width) derivative = self.DDE.eval_f(jump_time+width,state) self.DDE.apply_jump(jump_size,jump_time,width) assert self.DDE[-1][0] == jump_time+width assert_allclose( self.DDE[-1][1], state+jump_size ) assert_allclose( self.DDE[-1][2], derivative+factor*jump_size )
def generate_lambdas(self):
"""
Explicitly initiates a purely Python-based integrator.
"""
assert len(
self.past) > 1, "You need to add at least two past points first."
self.DDE = python_core.dde_integrator(self.f_sym, self.past,
self.helpers, self.control_pars,
self.n_basic)
self.compile_attempt = False
def setUp(self): self.n_basic = 3 self.n = 6*self.n_basic self.past = [] for i in range(np.random.randint(3,10)): if i==0: time = np.random.uniform(-10,10) else: time = self.past[-1][0] + 0.1 + np.random.random() state = np.random.random(self.n) diff = np.random.random(self.n) self.past.append((time, state, diff)) self.DDE = dde_integrator(lambda: [], self.past, n_basic=self.n_basic) self.DDE.n = self.n
def setUp(self): self.n_basic = 3 self.n = 6*self.n_basic self.past = [] for i in range(np.random.randint(3,10)): if i==0: time = np.random.uniform(-10,10) else: time = self.past[-1][0] + 0.1 + np.random.random() state = np.random.random(self.n) diff = np.random.random(self.n) self.past.append((time, state, diff)) self.DDE = dde_integrator(lambda: [], self.past, n_basic=self.n_basic) self.DDE.n = self.n
def setUpClass(self): self.DDE = dde_integrator(lambda: [], past[:]) self.DDE.anchor_mem = np.ones(1000, dtype=int)
def setUpClass(self): self.DDE = dde_integrator(lambda: [], past)
def setUp(self): self.DDE = dde_integrator(f_alt, past_generator(), f_alt_helpers)
def setUp(self): self.DDE = dde_integrator(f, past_generator())
def setUpClass(self): self.DDE = dde_integrator(lambda: [], past[:]) self.DDE.anchor_mem = np.ones(1000, dtype=int)
def setUp(self): self.DDE = dde_integrator(f_alt, past_generator(), f_alt_helpers)
def setUp(self): self.DDE = dde_integrator(f, past_generator())
