def calculateDiffusion(simulations,
getDistanceFromOrigin=getBallDistancesFromOrigin):
squaredDistances = [
squareList(getDistanceFromOrigin(simulation))
for simulation in simulations
]
maxTimes = max(
[len(squaredDistance) for squaredDistance in squaredDistances])
minTimes = min(
[len(squaredDistance) for squaredDistance in squaredDistances])
# Since time limit differs between multiple simulations, we repeat the last element for those shorter.
# normalizedLists = []
# for squaredDistance in squaredDistances:
# if len(squaredDistance) <= maxTimes:
# normalizedLists.append(squaredDistance + [squaredDistance[-1]]*(maxTimes - len(squaredDistance)))
# Since time limit differs between multiple simulations, we keep their shortest length
print(minTimes)
normalizedLists = []
for squaredDistance in squaredDistances:
normalizedLists.append(squaredDistance[:minTimes])
# Since time limit differs between multiple simulations, we average those that exist for a specific step.
# normalizedLists = []
# for squaredDistance in squaredDistances:
# if len(squaredDistance) <= maxTimes:
# normalizedLists.append(squaredDistance + [numpy.nan]*(maxTimes - len(squaredDistance)))
averageSquaredDistances = averageLists(normalizedLists)[(minTimes) // 2:]
deviations = stdevLists(normalizedLists)[(minTimes) // 2:]
diffusion, b = linearRegression(averageSquaredDistances)
return diffusion, b, averageSquaredDistances, deviations
def tp5_e1b(simulations):
kineticEnergies = [calculateKineticEnergy(simulation) for simulation in simulations]
kineticEnergy = averageLists(kineticEnergies)
kineticErrs = stdevLists(kineticEnergies)
dt = 0.005 # seconds
fig, ax = plt.subplots()
# ax.set_yscale('log')
ax.set_ylabel('Energía cinética [J]')
ax.set_xlabel('Tiempo [s]')
fig.tight_layout()
ax.plot([x * dt for x in range(len(kineticEnergy))], kineticEnergy, 'o-', markersize=3)
saveFig(fig, '1_1b')
def calculateExitsValues(qs):
lastThird = qs[-len(qs) // 3:]
avg = averageLists(lastThird)
err = stdevLists(lastThird)
return avg, err