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test_optimization.py
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from src.utils import RK4, compute_error
from scipy.integrate import solve_ivp
def test_1():
def create_fun(u, x):
valor = x
return valor
fun = create_fun
a_par = 0
b_par = 10
N_par = 1000
alpha_par = 1.5
rk4_solution = RK4(f=fun, a=a_par, b=b_par, alpha=alpha_par, N=N_par)
solver_solution = solve_ivp(fun, (a_par, b_par), [alpha_par], max_step=1 / N_par, min_step=1 / N_par)
error = compute_error(rk4_solution[1][0:len(rk4_solution[0])],
solver_solution['y'].flatten()[0:len(rk4_solution[0])])
assert (error < 1e-07)
def test_2():
def create_fun(u, x):
valor = x**2/(x+1)
return valor
fun = create_fun
a_par = 0
b_par = 10
N_par = 100
alpha_par = 10
rk4_solution = RK4(f=fun, a=a_par, b=b_par, alpha=alpha_par, N=N_par)
solver_solution = solve_ivp(fun, (a_par, b_par), [alpha_par], max_step=1 / N_par, min_step=1 / N_par)
error = compute_error(rk4_solution[1][0:len(rk4_solution[0])],
solver_solution['y'].flatten()[0:len(rk4_solution[0])])
assert (error < 1e-07)
def test_3():
def create_fun(u,x):
valor = (2*x**2+1)/x
return valor
fun = create_fun
a_par = 0
b_par = 0.35
N_par = 1000
alpha_par = 1.5
rk4_solution = RK4(f=fun, a=a_par, b=b_par, alpha=alpha_par, N=N_par)
solver_solution = solve_ivp(fun, (a_par,b_par), [alpha_par], max_step = 1/N_par, min_step = 1/N_par)
error = compute_error(rk4_solution[1][0:len(rk4_solution[0])],solver_solution['y'].flatten()[0:len(rk4_solution[0])])
assert(error < 1e-07)
def test_4():
def create_fun(u, x):
valor = (-0.1 * x ** 2 - 3) / x
return valor
fun = create_fun
a_par = 0
b_par = 0.35
N_par = 1000
alpha_par = 1.5
rk4_solution = RK4(f=fun, a=a_par, b=b_par, alpha=alpha_par, N=N_par)
solver_solution = solve_ivp(fun, (a_par, b_par), [alpha_par], max_step=1 / N_par, min_step=1 / N_par)
error = compute_error(rk4_solution[1][0:len(rk4_solution[0])],
solver_solution['y'].flatten()[0:len(rk4_solution[0])])
assert (error < 1e-07)