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 ex3_4(simulations):
diffusionSlope, diffusionB, averageSquaredDistances, deviations = calculateDiffusion(
simulations)
print(f'Coeficiente de difusion aproximado: {diffusionSlope}')
fig, ax = plt.subplots()
x_axis = [
x + len(averageSquaredDistances)
for x in range(len(averageSquaredDistances))
]
markers, caps, bars = ax.errorbar(x_axis,
averageSquaredDistances,
yerr=deviations,
capsize=5,
capthick=2,
fmt="o",
zorder=1,
markersize=2)
ax.set_xlabel('Step')
ax.set_ylabel('DCM = <z^2>')
ax.set_title(
f'Movimiento Browniano (N={len(simulations[0].steps[0].particles)}) - Ultima mitad del tiempo'
)
fig.tight_layout()
# loop through bars and caps and set the alpha value
[bar.set_alpha(0.5) for bar in bars]
# [cap.set_alpha(0.5) for cap in caps]
# Create linear regresion
x = np.linspace(min(x_axis), max(x_axis), 1000)
y = diffusionSlope * (x - len(averageSquaredDistances)) + diffusionB
ax.plot(x, y, '--', zorder=2, linewidth=2)
ax.legend(loc='upper left')
saveFig(fig, '3_4')