""" Test the properties of ODE solvers
.. autosummary::
:toctree: _generated/
:template: tests-formatting/base.rst
:nosignatures:
test_Implicit_ODE
test_Explicit_ODE
"""
import pytest
from .test_config import *
import numpy as np
import choreo
[docs]
@ParametrizeDocstrings
@pytest.mark.parametrize(("method_x", "method_v"), [(method_x, method_v) for (method_x, method_v) in SymplecticImplicitRKMethodPairs])
@pytest.mark.parametrize("nsteps", Small_orders)
@pytest.mark.parametrize("ivp", [pytest.param(define_ODE_ivp(eq_name), id=eq_name) for eq_name in all_eq_names])
@pytest.mark.parametrize("fun_type", all_fun_types)
@pytest.mark.parametrize("vector_calls", [True, False])
@pytest.mark.parametrize("DoEFT", [True, False])
def test_Implicit_ODE(float64_tols, method_x, method_v, nsteps, ivp, fun_type, vector_calls, DoEFT):
"""Tests the accuracy of implicit ODE solvers.
"""
rk_x = choreo.scipy_plus.multiprec_tables.ComputeImplicitRKTable_Gauss(nsteps, method = method_x)
rk_v = choreo.scipy_plus.multiprec_tables.ComputeImplicitRKTable_Gauss(nsteps, method = method_v)
fgun = ivp["fgun"].get((fun_type, vector_calls))
if fgun is None:
return
fun, gun = fgun
t_span = ivp["t_span"]
ex_sol_x = ivp["ex_sol_x"]
ex_sol_v = ivp["ex_sol_v"]
nint_OK = ivp["nint"](rk_x.th_cvg_rate)
if nint_OK is None:
nint = 1
else:
nint = nint_OK
x0 = ex_sol_x(t_span[0])
v0 = ex_sol_v(t_span[0])
xf, vf = choreo.scipy_plus.ODE.ImplicitSymplecticIVP(
fun ,
gun ,
t_span ,
x0 ,
v0 ,
rk_x = rk_x ,
rk_v = rk_v ,
vector_calls = vector_calls ,
nint = nint ,
DoEFT = DoEFT ,
)
xf_ex = ex_sol_x(t_span[1])
vf_ex = ex_sol_v(t_span[1])
print(np.linalg.norm(xf-xf_ex))
if nint_OK is not None:
assert np.allclose(xf, xf_ex, rtol = float64_tols.rtol, atol = float64_tols.atol)
print(np.linalg.norm(vf-vf_ex))
if nint_OK is not None:
assert np.allclose(vf, vf_ex, rtol = float64_tols.rtol, atol = float64_tols.atol)
[docs]
@ParametrizeDocstrings
@pytest.mark.parametrize("rk", [pytest.param(rk, id=name) for name, rk in Explicit_tables_dict.items()])
@pytest.mark.parametrize("ivp", [pytest.param(define_ODE_ivp(eq_name), id=eq_name) for eq_name in all_eq_names])
@pytest.mark.parametrize("fun_type", all_fun_types)
@pytest.mark.parametrize("DoEFT", [True, False])
def test_Explicit_ODE(float64_tols, rk, ivp, fun_type, DoEFT):
"""Tests the accuracy of explicit ODE solvers.
"""
fgun = ivp["fgun"].get((fun_type, False))
if fgun is None:
return
fun, gun = fgun
t_span = ivp["t_span"]
ex_sol_x = ivp["ex_sol_x"]
ex_sol_v = ivp["ex_sol_v"]
nint_OK = ivp["nint"](rk.th_cvg_rate)
if nint_OK is None:
nint = 1
else:
nint = nint_OK
x0 = ex_sol_x(t_span[0])
v0 = ex_sol_v(t_span[0])
xf, vf = choreo.scipy_plus.ODE.ExplicitSymplecticIVP(
fun ,
gun ,
t_span ,
x0 ,
v0 ,
rk = rk ,
nint = nint ,
DoEFT = DoEFT ,
)
xf_ex = ex_sol_x(t_span[1])
vf_ex = ex_sol_v(t_span[1])
print(np.linalg.norm(xf-xf_ex))
if nint_OK is not None:
assert np.allclose(xf, xf_ex, rtol = float64_tols.rtol, atol = float64_tols.atol)
print(np.linalg.norm(vf-vf_ex))
if nint_OK is not None:
assert np.allclose(vf, vf_ex, rtol = float64_tols.rtol, atol = float64_tols.atol)
