def calc_S_mat():
time_points, data_conc = data()
constants, ode_info, data_info = model_info(time_points)
vald_modell, num_coefficient, num_tidserier, num_tidsteg, h = constants
results = read_results(constants)
sol_k, krashade = calc_sol_k()
suc2, mig1, mig1_phos, X = sol_k
t_span, t_eval, y0 = ode_info
best_coefficients, min_cost_funk, min_viktad_cost_funk = get_min_cost(results)
Kinetic_constants = best_coefficients
s_suc2 = np.zeros((num_tidsteg, num_coefficient))
s_mig1 = np.zeros((num_tidsteg, num_coefficient))
s_mig1_phos = np.zeros((num_tidsteg, num_coefficient))
s_X = np.zeros((num_tidsteg, num_coefficient))
dy = np.zeros([num_tidserier, num_tidsteg, 1])
for i in range(num_coefficient):
d_Kinetic_constants = Kinetic_constants.copy()
d_Kinetic_constants[i] = d_Kinetic_constants[i] + h
d_solv = integrate.solve_ivp(fun=lambda t, y: model(vald_modell, t, y, d_Kinetic_constants), t_span=t_span, y0=y0,
method="RK45", t_eval=t_eval)
d_solv_k = np.zeros([num_tidserier, num_tidsteg, 1])
d_solv_k[:, :, 0] = d_solv.y
dy_new = dy.copy()
dy_new[:] = d_solv_k
s_suc2[:, i] = np.transpose((dy_new[0] - suc2) / h)
s_mig1[:, i] = np.transpose((dy_new[1] - mig1) / h)
s_mig1_phos[:, i] = np.transpose((dy_new[2] - mig1_phos) / h)
s_X[:, i] = np.transpose((dy_new[3] - X) / h)
S = np.array([s_suc2, s_mig1, s_mig1_phos, s_X])
return S
def solve_ODE(kinetic_constants_0, constants, ode_info):
vald_modell, num_coefficient, num_tidserier, num_tidsteg, h = constants
t_span, t_eval, y0 = ode_info
sol = integrate.solve_ivp(fun=lambda t, y: model(vald_modell, t, y, kinetic_constants_0), t_span=t_span, y0=y0,
method="RK45",
t_eval=t_eval)
sol_k = np.empty([num_tidserier, len(t_eval), 1])
sol_k[:, :, 0] = sol.y
return sol_k
def calc_sol_k():
time_points, data_conc = data()
constants, ode_info, data_info = model_info(time_points)
vald_modell, num_coefficient, num_tidserier, num_tidsteg, h = constants
results = read_results(constants)
best_coefficients, min_cost_funk, min_viktad_cost_funk = get_min_cost(results)
t_span, t_eval, y0 = ode_info
sol = integrate.solve_ivp(fun=lambda t, y: model(vald_modell, t, y, best_coefficients), t_span=t_span, y0=y0, method="RK45",
t_eval=t_eval)
sol_k = np.empty([num_tidserier, num_tidsteg, 1])
krashade = False
try:
sol_k[:, :, 0] = sol.y
except ValueError:
krashade = True
return sol_k, krashade
def calc_sol_k_step(kinetic_constants_0, constants, ode_info):
# ODE-lösare för att ta fram modellerade värden för en förskjutning h av respektive parameter.
# Steg 3 i optimeringen.
vald_modell, num_coefficient, num_tidserier, num_tidsteg, h = constants
t_span, t_eval, y0 = ode_info
sol_k_step = np.empty([num_tidserier, num_tidsteg, num_coefficient])
for k in range(num_coefficient):
kinetic_constants_step = kinetic_constants_0.copy()
kinetic_constants_step[k] = kinetic_constants_step[k] + h
sol = integrate.solve_ivp(
fun=lambda t, y: model(vald_modell, t, y, kinetic_constants_step),
t_span=t_span,
y0=y0,
method="RK45",
t_eval=t_eval)
sol_k_step[:, :, k] = sol.y
return sol_k_step
def calc_sol_k(kinetic_constants_0, constants, ode_info):
# ODE-lösare som beräknar de modellerade värderna givet p stycken koefficienter.
# Steg 2 i optimeringen.
vald_modell, num_coefficient, num_tidserier, num_tidsteg, h = constants
t_span, t_eval, y0 = ode_info
sol = integrate.solve_ivp(
fun=lambda t, y: model(vald_modell, t, y, kinetic_constants_0),
t_span=t_span,
y0=y0,
method="RK45",
t_eval=t_eval)
sol_k = np.empty([num_tidserier, num_tidsteg, 1])
krashade = False
try:
sol_k[:, :, 0] = sol.y
except ValueError:
krashade = True
return sol_k, krashade