def ex2_4(simulations):
print(f'simulations: {len(simulations)}')
for simulation in simulations:
velocities = [step.speeds() for step in simulation.getSecondHalf()]
velocities = [item for sublist in velocities
for item in sublist] #flatten
bin_size = 200
print("Plotting\n")
fig, ax = plt.subplots()
values = np.histogram(velocities, density=True, bins=bin_size)
bin_centres = (values[1][:-1] + values[1][1:]) / 2.
bin_centres_x = (values[0][:-1] + values[0][1:]) / 2.
ax.plot(bin_centres, values[0], 'o', markersize=2)
adjustment = [mb(x) for x in bin_centres]
ax.plot(bin_centres, adjustment, '-')
ax.set_xlabel('Velocidad (m/s)')
ax.set_ylabel('PDF')
fig.tight_layout()
saveFig(fig, '2_4')
# plot error
results, rang = errorFn(bin_centres, values[0])
fig, ax = plt.subplots()
ax.plot(rang, results, 'o', markersize=2)
ax.set_ylabel('Error')
ax.set_xlabel('Parámetro Libre (a)')
fig.tight_layout()
saveFig(fig, '2_4_error')
def error(simulations):
diffusionSlope, diffusionB, averageSquaredDistances, deviations = calculateDiffusion(simulations)
print(f'Coeficiente de difusion aproximado: {diffusionSlope}')
fig, ax = plt.subplots()
y_axis, x_axis = errorFn(range(len(averageSquaredDistances)),averageSquaredDistances)
ax.plot([x * 10 ** 5 for x in x_axis], y_axis)
ax.set_xlabel('C (10^-5)')
ax.set_ylabel('Error')
ax.set_title(f'Error del ajuste por función lineal')
fig.tight_layout()
saveFig(fig, 'error')
def bev():
xs = [0.15, 0.17, 0.22, 0.25] # Acá van los valores usados de D
ys = [1, 2, 3, 4] # Acá van los valores de caudales para cada D
err = [0.1, 0.1, 0.1, 0.1] #Acá van los errores del caudal para cada D
results, rang = errorFn(xs, ys)
fig, ax = plt.subplots()
ax.set_ylabel('Caudal promedio [p/s]')
ax.set_xlabel('Ancho de apertura [m]')
fig.tight_layout()
ax.plot(xs, ys, 'o-', markersize=4)
ax.plot(rang, results, 'o', markersize=2)
saveFig(fig, '1_1bev')