class AveragesCollector: def __init__(self, env, constants): self._env = env self._constants = constants self._stats = ParticleStatistics(env, constants) self._reduce = createReduce(env, constants.scalar.dtype) self._creduce = createReduce(env, constants.complex.dtype) self.times = [] self.n1 = [] self.n2 = [] self.i = [] def __call__(self, t, cloud): self.times.append(t) ensembles = cloud.a.size / self._constants.cells get = self._env.fromDevice reduce = self._reduce creduce = self._creduce dV = self._constants.dV i = self._stats._getInteraction(cloud.a, cloud.b) n1 = self._stats.getDensity(cloud.a) n2 = self._stats.getDensity(cloud.b) self.i.append(get(creduce(i, ensembles)) * dV) self.n1.append(get(reduce(n1, ensembles)) * dV) self.n2.append(get(reduce(n2, ensembles)) * dV) def getData(self): return numpy.array(self.times), self.i, self.n1, self.n2
class AveragesCollector:
def __init__(self, env, constants):
self._env = env
self._constants = constants
self._stats = ParticleStatistics(env, constants)
self._reduce = createReduce(env, constants.scalar.dtype)
self._creduce = createReduce(env, constants.complex.dtype)
self.times = []
self.n1 = []
self.n2 = []
self.i = []
def __call__(self, t, cloud):
self.times.append(t)
ensembles = cloud.a.size / self._constants.cells
get = self._env.fromDevice
reduce = self._reduce
creduce = self._creduce
dV = self._constants.dV
i = self._stats._getInteraction(cloud.a, cloud.b)
n1 = self._stats.getDensity(cloud.a)
n2 = self._stats.getDensity(cloud.b)
self.i.append(get(creduce(i, ensembles)) * dV)
self.n1.append(get(reduce(n1, ensembles)) * dV)
self.n2.append(get(reduce(n2, ensembles)) * dV)
def getData(self):
return numpy.array(self.times), self.i, self.n1, self.n2
def __init__(self, env, constants):
self._env = env
self._constants = constants
self._stats = ParticleStatistics(env, constants)
self._reduce = createReduce(env, constants.scalar.dtype)
self._creduce = createReduce(env, constants.complex.dtype)
self.times = []
self.n1 = []
self.n2 = []
self.i = []
def testRephasing(gpu):
m = Model(N=44000, detuning=-41, nvx=8, nvy=8, nvz=64, ensembles=1, e_cut=1e6, dt_evo=1e-5)
t_max = 0.5
t_step = 0.05
constants = Constants(m, double=False if gpu else True)
env = envs.cuda() if gpu else envs.cpu()
evolution = SplitStepEvolution(env, constants)
pulse = Pulse(env, constants)
stats = ParticleStatistics(env, constants)
gs = GPEGroundState(env, constants)
times = [0.0]
vis = [1.0]
cloud = gs.createCloud()
# cloud.toWigner()
pulse.apply(cloud, 0.5 * math.pi, matrix=True)
t = t_step
while t <= t_max:
evolution.run(cloud, t_step / 2, noise=False)
print "Pre-pi step finished, t=" + str(cloud.time)
new_cloud = cloud.copy()
pulse.apply(new_cloud, math.pi, matrix=True)
evolution.run(new_cloud, t / 2, noise=False)
print "Post-pi step finished"
times.append(t)
vis.append(stats.getVisibility(new_cloud.a, new_cloud.b))
print "Visibility=" + str(vis[-1])
del new_cloud
t += t_step
times = numpy.array(times)
vis = numpy.array(vis)
XYPlot([XYData("test", times, vis, ymin=0, ymax=1, xname="Time, s", yname="Visibility")]).save(
"rephasing_" + str(env) + ".pdf"
)
env.release()
def __init__(self, env, constants): self._env = env self._constants = constants self._stats = ParticleStatistics(env, constants) self._reduce = createReduce(env, constants.scalar.dtype) self._creduce = createReduce(env, constants.complex.dtype) self.times = [] self.n1 = [] self.n2 = [] self.i = []
def testRephasing(wigner):
if wigner:
nvx = nvy = 8
nvz = 64
dt_evo = 4e-5
else:
nvx = nvy = 16
nvz = 128
dt_evo = 1e-5
m = Model(N=55000, detuning=-37,
nvx=nvx, nvy=nvy, nvz=nvz, dt_evo=dt_evo, ensembles=64, e_cut=1e6,
fx=97.0, fy=97.0 * 1.03, fz=11.69,
gamma12=1.53e-20, gamma22=7.7e-20,
a12=97.99, a22=95.57)
t_max = 8.0
t_step = 0.05
constants = Constants(m, double=True)
env = envs.cuda(device_num=0)
evolution = SplitStepEvolution(env, constants)
pulse = Pulse(env, constants)
stats = ParticleStatistics(env, constants)
unc = Uncertainty(env, constants)
gs = GPEGroundState(env, constants)
times = [0.0]
vis = [1.0]
phnoise = []
phis = []
ypss = []
cloud = gs.createCloud()
if wigner:
cloud.toWigner()
phnoise.append(stats.getPhaseNoise(cloud.a, cloud.b))
phi, yps = unc.getSpins(cloud.a, cloud.b)
phis.append(phi)
ypss.append(yps)
pulse.apply(cloud, 0.5 * math.pi, matrix=True)
if wigner:
name = 'rephasing_wigner'
else:
name = 'rephasing_gpe'
t = t_step
while t <= t_max:
evolution.run(cloud, t_step / 2, noise=wigner)
print "Pre-pi step finished, t=" + str(cloud.time)
new_cloud = cloud.copy()
pulse.apply(new_cloud, math.pi, matrix=True)
evolution.run(new_cloud, t / 2, noise=wigner)
print "Post-pi step finished"
times.append(t)
vis.append(stats.getVisibility(new_cloud.a, new_cloud.b))
print "Visibility=" + str(vis[-1])
if wigner:
phnoise.append(stats.getPhaseNoise(new_cloud.a, new_cloud.b))
phi, yps = unc.getSpins(new_cloud.a, new_cloud.b)
phis.append(phi)
ypss.append(yps)
XYData(name, numpy.array(times), numpy.array(vis),
ymin=0, ymax=1, xname="T, s", yname="$\\mathcal{V}$").save(name + '_vis.json')
if wigner:
XYData(name, numpy.array(times), numpy.array(phnoise), ymin=0,
xname="T, s", yname="Phase noise, rad").save(name + '_phnoise.json')
Data('spins',
['name', 'phi', 'yps', 'time', 'timename', 'phiname', 'ypsname'],
time=numpy.array(times), phi=numpy.array(phis), yps=numpy.array(ypss), timename="T (s)",
phiname="Spin, azimuth, rad", ypsname="Spin, inclination, rad",
name=name).save(name + "_phnoise_points.pickle")
XYPlot([XYData.load(name + '_vis.json')]).save(name + '_vis.pdf')
del new_cloud
t += t_step
env.release()
def testRephasing(wigner):
if wigner:
nvx = nvy = 8
nvz = 64
dt_evo = 4e-5
else:
nvx = nvy = 16
nvz = 128
dt_evo = 1e-5
m = Model(N=55000,
detuning=-37,
nvx=nvx,
nvy=nvy,
nvz=nvz,
dt_evo=dt_evo,
ensembles=64,
e_cut=1e6,
fx=97.0,
fy=97.0 * 1.03,
fz=11.69,
gamma12=1.53e-20,
gamma22=7.7e-20,
a12=97.99,
a22=95.57)
t_max = 8.0
t_step = 0.05
constants = Constants(m, double=True)
env = envs.cuda(device_num=0)
evolution = SplitStepEvolution(env, constants)
pulse = Pulse(env, constants)
stats = ParticleStatistics(env, constants)
unc = Uncertainty(env, constants)
gs = GPEGroundState(env, constants)
times = [0.0]
vis = [1.0]
phnoise = []
phis = []
ypss = []
cloud = gs.createCloud()
if wigner:
cloud.toWigner()
phnoise.append(stats.getPhaseNoise(cloud.a, cloud.b))
phi, yps = unc.getSpins(cloud.a, cloud.b)
phis.append(phi)
ypss.append(yps)
pulse.apply(cloud, 0.5 * math.pi, matrix=True)
if wigner:
name = 'rephasing_wigner'
else:
name = 'rephasing_gpe'
t = t_step
while t <= t_max:
evolution.run(cloud, t_step / 2, noise=wigner)
print "Pre-pi step finished, t=" + str(cloud.time)
new_cloud = cloud.copy()
pulse.apply(new_cloud, math.pi, matrix=True)
evolution.run(new_cloud, t / 2, noise=wigner)
print "Post-pi step finished"
times.append(t)
vis.append(stats.getVisibility(new_cloud.a, new_cloud.b))
print "Visibility=" + str(vis[-1])
if wigner:
phnoise.append(stats.getPhaseNoise(new_cloud.a, new_cloud.b))
phi, yps = unc.getSpins(new_cloud.a, new_cloud.b)
phis.append(phi)
ypss.append(yps)
XYData(name,
numpy.array(times),
numpy.array(vis),
ymin=0,
ymax=1,
xname="T, s",
yname="$\\mathcal{V}$").save(name + '_vis.json')
if wigner:
XYData(name,
numpy.array(times),
numpy.array(phnoise),
ymin=0,
xname="T, s",
yname="Phase noise, rad").save(name + '_phnoise.json')
Data('spins', [
'name', 'phi', 'yps', 'time', 'timename', 'phiname', 'ypsname'
],
time=numpy.array(times),
phi=numpy.array(phis),
yps=numpy.array(ypss),
timename="T (s)",
phiname="Spin, azimuth, rad",
ypsname="Spin, inclination, rad",
name=name).save(name + "_phnoise_points.pickle")
XYPlot([XYData.load(name + '_vis.json')]).save(name + '_vis.pdf')
del new_cloud
t += t_step
env.release()
