def test_integrate_predefined(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
xout = np.linspace(0, 3, 31)
kwargs = dict(atol=1e-8, rtol=1e-8, dx0=1e-10, method=method)
atol, rtol = 1e-8, 1e-8
# Run twice to catch possible side-effects:
yout, info = integrate_predefined(f, j, y0, xout, **kwargs)
yout, info = integrate_predefined(f, j, y0, xout, **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
if os.name == 'posix':
assert info['time_cpu'] >= 0
assert info['time_wall'] >= 0
def test_bad_j():
k0, k1, k2 = decay_default_k
def f(t, y, fout):
fout[0] = -k0*y[0]
fout[1] = k0*y[0] - k1*y[1]
fout[2] = k1*y[1] - k2*y[2]
def j(t, y, jmat_out, dfdx_out):
y[0] = -1 # read-only! should raise ValueError
jmat_out[0, 0] = -k0
jmat_out[0, 1] = 0
jmat_out[0, 2] = 0
jmat_out[1, 0] = k0
jmat_out[1, 1] = -k1
jmat_out[1, 2] = 0
jmat_out[2, 0] = 0
jmat_out[2, 1] = k1
jmat_out[2, 2] = -k2
dfdx_out[0] = 0
dfdx_out[1] = 0
dfdx_out[2] = 0
with pytest.raises(ValueError):
yout, info = integrate_predefined(f, j, method='bsimp',
check_callable=False,
check_indexing=False,
**decay_defaults)
assert yout # silence pyflakes
def test_bad_j():
k0, k1, k2 = decay_default_k
def f(t, y, fout):
fout[0] = -k0 * y[0]
fout[1] = k0 * y[0] - k1 * y[1]
fout[2] = k1 * y[1] - k2 * y[2]
def j(t, y, jmat_out, dfdx_out):
y[0] = -1 # read-only! should raise ValueError
jmat_out[0, 0] = -k0
jmat_out[0, 1] = 0
jmat_out[0, 2] = 0
jmat_out[1, 0] = k0
jmat_out[1, 1] = -k1
jmat_out[1, 2] = 0
jmat_out[2, 0] = 0
jmat_out[2, 1] = k1
jmat_out[2, 2] = -k2
dfdx_out[0] = 0
dfdx_out[1] = 0
dfdx_out[2] = 0
with pytest.raises(ValueError):
yout, info = integrate_predefined(f,
j,
method='bsimp',
check_callable=False,
check_indexing=False,
**decay_defaults)
assert yout # silence pyflakes
def test_bad_f():
k0, k1, k2 = decay_default_k
def f(t, y, fout):
y[0] = -1 # read-only! should raise ValueError
fout[0] = -k0*y[0]
fout[1] = k0*y[0] - k1*y[1]
fout[2] = k1*y[1] - k2*y[2]
with pytest.raises(ValueError):
yout, info = integrate_predefined(f, None, method='rkck',
check_callable=False,
check_indexing=False,
**decay_defaults)
assert yout # silence pyflakes
def test_integrate_predefined(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
xout = np.linspace(0, 3, 31)
kwargs = dict(atol=1e-8, rtol=1e-8, dx0=1e-10, method=method)
atol, rtol = 1e-8, 1e-8
# Run twice to catch possible side-effects:
yout, info = integrate_predefined(f, j, y0, xout, **kwargs)
yout, info = integrate_predefined(f, j, y0, xout, **kwargs)
yref = decay_get_Cref(k, y0, xout)
print(yout)
print(yref)
assert np.allclose(yout, yref,
rtol=forgiveness*rtol,
atol=forgiveness*atol)
assert info['nfev'] > 0
assert info['time_cpu'] > 1e-9
assert info['time_wall'] > 1e-9
if method in requires_jac:
assert info['njev'] > 0
def test_predefined_return_on_error():
k = k0, k1, k2 = 2.0, 300.0, 4.0
y0 = [0.7, 0.0, 0.2]
atol, rtol = 1e-12, 1e-12
kwargs = dict(dx0=1e-11, atol=atol, rtol=rtol, method='bsimp',
return_on_error=True, nsteps=10)
f, j = _get_f_j(k)
xout = np.logspace(-10, 1, 5)
yout, info = integrate_predefined(f, j, y0, xout, **kwargs)
yref = decay_get_Cref(k, y0, xout - xout[0])
assert np.allclose(yout[:info['nreached'], :], yref[:info['nreached'], :],
rtol=10*rtol, atol=10*atol)
assert 2 < info['nreached'] < 5 # not strict
assert yout.shape[0] == xout.size
assert info['nfev'] > 0
assert info['njev'] > 0
assert info['success'] is False
def test_bad_f():
k0, k1, k2 = decay_default_k
def f(t, y, fout):
y[0] = -1 # read-only! should raise ValueError
fout[0] = -k0 * y[0]
fout[1] = k0 * y[0] - k1 * y[1]
fout[2] = k1 * y[1] - k2 * y[2]
with pytest.raises(ValueError):
yout, info = integrate_predefined(f,
None,
method='rkck',
check_callable=False,
check_indexing=False,
**decay_defaults)
assert yout # silence pyflakes
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_predefined_autorestart():
k = k0, k1, k2 = 2.0, 3.0, 4.0
y0 = [0.7, 0.3, 0.5]
atol, rtol = 1e-8, 1e-8
x0, xend = 0, 3
kwargs = dict(dx0=1e-10, atol=atol, rtol=rtol,
method='bsimp', nsteps=62,
autorestart=10)
f, j = _get_f_j(k)
xout = np.linspace(x0, xend)
yout, info = integrate_predefined(f, j, y0, xout, **kwargs)
yref = decay_get_Cref(k, y0, xout)
assert np.allclose(yout, yref,
rtol=10*rtol,
atol=10*atol)
assert xout[-1] > 1e-6
assert yout.shape[0] == xout.size
assert info['nfev'] > 0
assert info['njev'] > 0
assert info['success']
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)
def test_predefined_autorestart():
k = k0, k1, k2 = 2.0, 3.0, 4.0
y0 = [0.7, 0.3, 0.5]
atol, rtol = 1e-8, 1e-8
x0, xend = 0, 3
kwargs = dict(dx0=1e-10,
atol=atol,
rtol=rtol,
method='bsimp',
nsteps=62,
autorestart=10)
f, j = _get_f_j(k)
xout = np.linspace(x0, xend)
yout, info = integrate_predefined(f, j, y0, xout, **kwargs)
yref = decay_get_Cref(k, y0, xout)
assert np.allclose(yout, yref, rtol=10 * rtol, atol=10 * atol)
assert xout[-1] > 1e-6
assert yout.shape[0] == xout.size
assert info['nfev'] > 0
assert info['njev'] > 0
assert info['success']
assert xout[-1] == xend
def test_predefined_return_on_error():
k = k0, k1, k2 = 2.0, 300.0, 4.0
y0 = [0.7, 0.0, 0.2]
atol, rtol = 1e-12, 1e-12
kwargs = dict(dx0=1e-11,
atol=atol,
rtol=rtol,
method='bsimp',
return_on_error=True,
nsteps=10)
f, j = _get_f_j(k)
xout = np.logspace(-10, 1, 5)
yout, info = integrate_predefined(f, j, y0, xout, **kwargs)
yref = decay_get_Cref(k, y0, xout - xout[0])
assert np.allclose(yout[:info['nreached'], :],
yref[:info['nreached'], :],
rtol=10 * rtol,
atol=10 * atol)
assert 2 < info['nreached'] < 5 # not strict
assert yout.shape[0] == xout.size
assert info['nfev'] > 0
assert info['njev'] > 0
assert info['success'] is False