def test_integrate_adaptive(method, forgiveness):
use_jac = method in requires_jac
k = k0, k1, k2 = 2.0, 3.0, 4.0
y0 = [0.7, 0.3, 0.5]
f, j = _get_f_j(k)
if not use_jac:
j = None
atol, rtol = 1e-8, 1e-8
kwargs = dict(x0=0, xend=3, dx0=1e-10, atol=atol, rtol=rtol,
method=method)
# Run twice to catch possible side-effects:
xout, yout, info = integrate_adaptive(f, j, y0, **kwargs)
xout, yout, info = integrate_adaptive(f, j, y0, **kwargs)
yref = decay_get_Cref(k, y0, xout)
assert np.allclose(yout, yref,
rtol=forgiveness*rtol,
atol=forgiveness*atol)
assert info['nfev'] > 0
if method in requires_jac:
assert info['njev'] > 0
with pytest.raises(RuntimeError) as excinfo:
integrate_adaptive(f, j, y0, nsteps=7, **kwargs)
assert 'steps' in str(excinfo.value).lower()
assert '7' in str(excinfo.value).lower()
def test_integrate_adaptive(method, forgiveness):
use_jac = method in requires_jac
k = k0, k1, k2 = 2.0, 3.0, 4.0
y0 = [0.7, 0.3, 0.5]
f, j = _get_f_j(k)
if not use_jac:
j = None
atol, rtol = 1e-8, 1e-8
kwargs = dict(x0=0, xend=3, dx0=1e-10, atol=atol, rtol=rtol, method=method)
# Run twice to catch possible side-effects:
xout, yout, info = integrate_adaptive(f, j, y0, **kwargs)
xout, yout, info = integrate_adaptive(f, j, y0, **kwargs)
yref = decay_get_Cref(k, y0, xout)
assert info['success']
assert info['atol'] == atol and info['rtol'] == rtol
assert np.allclose(yout,
yref,
rtol=forgiveness * rtol,
atol=forgiveness * atol)
assert info['nfev'] > 0
if method in requires_jac:
assert info['njev'] > 0
with pytest.raises(RuntimeError) as excinfo:
integrate_adaptive(f, j, y0, nsteps=7, **kwargs)
assert 'steps' in str(excinfo.value).lower()
assert '7' in str(excinfo.value).lower()
def test_adaptive_return_on_error():
k = k0, k1, k2 = 2.0, 3.0, 4.0
y0 = [0.7, 0.3, 0.5]
atol, rtol = 1e-8, 1e-8
kwargs = dict(x0=0,
xend=3,
dx0=1e-10,
atol=atol,
rtol=rtol,
method='bsimp')
f, j = _get_f_j(k)
xout, yout, info = integrate_adaptive(f,
j,
y0,
nsteps=7,
return_on_error=True,
**kwargs)
yref = decay_get_Cref(k, y0, xout)
assert np.allclose(yout, yref, rtol=10 * rtol, atol=10 * atol)
assert xout.size == 8
assert xout[-1] > 1e-6
assert yout.shape[0] == xout.size
assert info['nfev'] > 0
assert info['njev'] > 0
assert info['success'] is False
assert xout[-1] < kwargs['xend'] # obviously not strict
def test_dx0cb():
k = 1e23, 3.0, 4.0
y0 = [.7, .0, .0]
x0, xend = 0, 5
kwargs = dict(atol=1e-8, rtol=1e-8, method='bsimp', dx0cb=lambda x, y: y[0]*1e-30)
f, j = _get_f_j(k)
xout, yout, info = integrate_adaptive(f, j, y0, x0, xend, **kwargs)
yref = decay_get_Cref(k, y0, xout)
assert np.allclose(yout, yref, atol=40*kwargs['atol'], rtol=40*kwargs['rtol'])
assert info['nfev'] > 0
assert info['njev'] > 0
assert info['success'] is True
assert xout[-1] == xend
def test_adaptive_return_on_error():
k = k0, k1, k2 = 2.0, 3.0, 4.0
y0 = [0.7, 0.3, 0.5]
atol, rtol = 1e-8, 1e-8
kwargs = dict(x0=0, xend=3, dx0=1e-10, atol=atol, rtol=rtol,
method='bsimp')
f, j = _get_f_j(k)
xout, yout, info = integrate_adaptive(f, j, y0, nsteps=7, return_on_error=True, **kwargs)
yref = decay_get_Cref(k, y0, xout)
assert np.allclose(yout, yref, rtol=10*rtol, atol=10*atol)
assert xout.size == 8
assert xout[-1] > 1e-6
assert yout.shape[0] == xout.size
assert info['nfev'] > 0
assert info['njev'] > 0
assert info['success'] is False
assert xout[-1] < kwargs['xend'] # obviously not strict
def integrate_ivp(u0=1.0,
v0=0.0,
mu=1.0,
tend=10.0,
dt0=1e-8,
nt=0,
t0=0.0,
atol=1e-8,
rtol=1e-8,
plot=False,
savefig='None',
method='bsimp',
dpi=100,
verbose=False):
f, j = get_f_and_j(mu)
if nt > 1:
tout = np.linspace(t0, tend, nt)
yout, info = integrate_predefined(f,
j, [u0, v0],
tout,
dt0,
atol,
rtol,
check_indexing=False,
method=method)
else:
tout, yout, info = integrate_adaptive(
f,
j, [u0, v0],
t0,
tend,
dt0,
atol,
rtol,
check_indexing=False,
method=method) # dfdt[:] also for len == 1
if verbose:
print(info)
if plot:
import matplotlib.pyplot as plt
plt.plot(tout, yout)
if savefig == 'None':
plt.show()
else:
plt.savefig(savefig, dpi=dpi)
def test_dx0cb():
k = 1e23, 3.0, 4.0
y0 = [.7, .0, .0]
x0, xend = 0, 5
kwargs = dict(atol=1e-8,
rtol=1e-8,
method='bsimp',
dx0cb=lambda x, y: y[0] * 1e-30)
f, j = _get_f_j(k)
xout, yout, info = integrate_adaptive(f, j, y0, x0, xend, **kwargs)
yref = decay_get_Cref(k, y0, xout)
assert np.allclose(yout,
yref,
atol=40 * kwargs['atol'],
rtol=40 * kwargs['rtol'])
assert info['nfev'] > 0
assert info['njev'] > 0
assert info['success'] is True
assert xout[-1] == xend
def integrate_ivp(u0=1.0, v0=0.0, mu=1.0, tend=10.0, dt0=1e-8, nt=0,
t0=0.0, atol=1e-8, rtol=1e-8, plot=False, savefig='None',
method='bsimp', dpi=100, verbose=False):
f, j = get_f_and_j(mu)
if nt > 1:
tout = np.linspace(t0, tend, nt)
yout, info = integrate_predefined(
f, j, [u0, v0], tout, dt0, atol, rtol,
check_indexing=False, method=method)
else:
tout, yout, info = integrate_adaptive(
f, j, [u0, v0], t0, tend, dt0, atol, rtol,
check_indexing=False, method=method) # dfdt[:] also for len == 1
if verbose:
print(info)
if plot:
import matplotlib.pyplot as plt
plt.plot(tout, yout)
if savefig == 'None':
plt.show()
else:
plt.savefig(savefig, dpi=dpi)