def __init__(self,
cell=None,
nballs=10,
step=0,
gc=None,
input=None,
logfile=sys.stdout,
velfile=None,
velint=0,
kT=None,
hist=False):
self.cell = cell
if input is not None:
self.load(input)
else:
self.balls = lattice(nballs, cell)
if kT is not None:
# ra.seed(1)
self.thermalize(kT)
self.balls.interact(cell)
self.step = step
self.gc = gc
self.logfile = logfile
self.velfile = velfile
self.velinterval = velint
self.kT = kT
self.hist = hist
self.histx = Hist(-5, +5, 0.05)
self.histy = Hist(-5, +5, 0.05)
def __init__(self,
cell=None,
nballs=10,
step=0,
gc=None,
input=None,
logfile=sys.stdout,
velfile=None,
kT=None,
hist=False):
if hist:
self.colorscheme = AbsoluteVelocity(max=5 / 2)
self.histx = Hist(-5, +5, 0.05, self.colorscheme)
self.histy = Hist(-5, +5, 0.05, self.colorscheme)
self.hist = True
else:
self.colorscheme = None
self.hist = False
if input is not None:
self.load(input)
else:
self.lattice(cell, nballs)
if kT is not None:
ra.seed(1)
self.thermalize(kT)
self.last1 = self.last2 = None
# initialize force
self.step = step
self.gc = gc
self.logfile = logfile
self.velfile = velfile
self.kT = kT
self.hist = hist
def n_dimensional_poisson_hists(rho, Us, statemap, trials, mu=None, **kwargs):
"""Multidimensional measurement outcomes. Only for 2 qubits right now"""
Us = np.array(Us)
rho = np.array(rho)
statemap = np.array(statemap)
assert Us.shape[0] == rho.shape[0]
num_sub = statemap.shape[1] # number of subspaces
numU = Us.shape[0] # number of histograms
trials = trials * np.ones(numU)
if mu is None:
mu = generateExpectedCounts(num_sub, 2, 20)
mu = np.tile(mu, (2, 1))
hists = []
for k in range(numU):
measrho = Us[k].dot(rho[k]).dot(Us[k].conj().T)
hists.append(Hist())
diag = np.real(np.diag(measrho))
pops = np.sum(statemap.T * diag, axis=1)
hists[-1].simulate_multi(nspecies=2,
trials=trials[k],
statepops=pops,
mu=[[2, 20], [2, 20]])
return hists
def one_dimensional_poisson_hists(rho, Us, P_j, trials, mu=None, **kwargs):
Us = np.array(Us)
rho = np.array(rho)
dim = rho.shape[-1]
P_j = np.array(P_j)
assert Us.shape[0] == rho.shape[0]
num_sub = P_j.shape[0] # number of subspaces
numU = Us.shape[0] # number of histograms
P_ij = np.zeros((numU, num_sub, dim, dim)) + 0j
for i in range(numU):
for j in range(num_sub):
P_ij[i, j, :, :] = Us[i].dot(P_j[j]).dot(Us[i].conj().T)
pops = np.real(
np.trace(np.einsum('iak,ijkb->ijab', rho, P_ij), axis1=-1, axis2=-2))
trials = trials * np.ones(numU)
if mu is None:
mu = generateExpectedCounts(num_sub, 2, 35)
hists = []
for k in range(numU):
#measrho = Us[k].dot(rho[k]).dot(Us[k].conj().T)
hists.append(Hist())
#diag = np.real(np.diag(measrho))
#pops = np.sum(statemap.T*diag, axis=1)
hists[-1].simPoisson(trials=trials[k], pops=pops[k], mu=mu)
return hists
def nDimPoissonHists(rho, input_state, Us, P_j, trials, train_frac, mu=None,
**simkwargs):
"""
Generates n-dimensional histograms for simulated examples.
Args:
rho: array of fiducial state for reference experiment and
target states for probing experiments
input_state: array of fiducial state for reference experiment and 1 for
probing experiment
Us: knonw unitaries
P_j: underlying POVM elements
trials: number of trials per experiment
train_frac: fraction of reference histograms used for trainind data
mu: average number of photon counts for each underlying POVM
outcome
**simkwargs: keyword arguements for properties of state prep (fid)
Returns:
hists: histograms for reference and probing experiments
"""
Us = np.array(Us)
rho = np.array(rho)
assert Us.shape[0] == rho.shape[0]
dim = P_j.shape[2]
num_sub = P_j.shape[1] # number of subspaces
num_total = Us.shape[0] # number of histograms
# rotated projectors
P_ij = np.zeros((num_total, num_sub, dim, dim))+0j
for i in range(num_total):
for j in range(num_sub):
P_ij[i,j,:,:] = (Us[i].conj().T).dot(P_j[j]).dot(Us[i])
pops = np.real(np.trace(np.einsum('iak,ijkb->ijab', rho, P_ij),
axis1=-1, axis2=-2))
# Reduce number of trials by 10% of trials for binnning
trials = trials*np.ones(num_total) # old code
ref_ind = np.where(list(np.array(input_state[i]).size > 1
for i in range(num_total)))
trials[ref_ind] = (1/(1-train_frac))*trials[ref_ind]
if mu is None:
mu = generateExpectedCounts(num_sub, 2, 20)
mu = np.tile(mu, (2, 1))
hists = []
for k in range(num_total):
hists.append(Hist())
hists[-1].simulateMulti(dim_hist=2, trials=trials[k],
state_pops=pops[k], mu=[[2, 20],[2, 20]])
return hists
def oneDimPoissonHists(rho, input_state, Us, P_j, trials, train_frac, mu=None,
**simkwargs):
"""
Generates 1D histograms for simulated examples.
Args:
rho: array of fiducial state for reference experiment and
target states for probing experiments
input_state: array of fiducial state for reference experiment and 1 for
probing experiment
Us: knonw unitaries
P_j: underlying POVM elements
trials: number of trials per experiment
train_frac: fraction of reference histograms used for trainind data
mu: average number of photon counts for each underlying POVM
outcome
**simkwargs: keyword arguements for properties of state prep (fid)
Returns:
hists: histograms for reference and probing experiments
"""
Us = np.array(Us)
rho = np.array(rho)
dim = rho.shape[-1]
P_j = np.array(P_j)
assert Us.shape[0] == rho.shape[0]
num_sub = P_j.shape[0] # number of subspaces
num_total = Us.shape[0] # total number of experiments
# engineered POVM elements
P_ij = np.zeros((num_total, num_sub, dim, dim))+0j
for i in range(num_total):
for j in range(num_sub):
P_ij[i,j,:,:] = (Us[i].conj().T).dot(P_j[j]).dot(Us[i])
pops = np.real(np.trace(np.einsum('iak,ijkb->ijab', rho, P_ij),
axis1=-1, axis2=-2))
# Reduce number of trials by 10% of trials for binnning
trials = trials*np.ones(num_total)
ref_ind = np.where(list(np.array(input_state[i]).size > 1
for i in range(num_total)))
trials[ref_ind] = (1/(1-train_frac))*trials[ref_ind]
if mu is None:
# in High-fidelity universal gate-set paper it says 30 photons
mu = generateExpectedCounts(num_sub, 2, 20)
hists = []
for k in range(num_total):
hists.append(Hist())
hists[-1].simPoisson(trials=trials[k], pops=pops[k], mu=mu)
return hists
def responsePlots(num_trials=5000, mu=[2, 20, 40]):
"""Plots histograms of states contained in POVM elements."""
fontsi = 12
dark_pops = [1, 0, 0] # only 2 dark state
bright_pops = [0, 0, 1] # only 2 bright state
mid_pops = [0, 1, 0] # only 1 bright state
# 2 dark histogram
dark_hist = Hist()
dark_counts = dark_hist.simPoisson(num_trials, pops=dark_pops, mu=mu)
plt.bar(dark_hist.bin_bounds[0][0:-1],
dark_hist.hist / dark_hist.trials,
width=1,
color=(29 / 255, 111 / 255, 169 / 255),
edgecolor='black',
align='edge')
# 1 bright 1 dark histogram
mid_hist = Hist()
mid_counts = mid_hist.simPoisson(num_trials, pops=mid_pops, mu=mu)
plt.bar(mid_hist.bin_bounds[0][0:-1],
mid_hist.hist / mid_hist.trials,
width=1,
color=(241 / 255, 157 / 255, 25 / 255),
edgecolor='black',
align='edge')
# 2 bright histogram
bright_hist = Hist()
bright_counts = bright_hist.simPoisson(num_trials, pops=bright_pops, mu=mu)
plt.bar(bright_hist.bin_bounds[0][0:-1],
bright_hist.hist / bright_hist.trials,
width=1,
color=(183 / 255, 73 / 255, 25 / 255),
edgecolor='black',
align='edge')
# plot everything
a = plt.gca()
mpl.rc('font', family='Times New Roman')
plt.axis([0, 60, 0, np.max(dark_hist.hist / dark_hist.trials) * 1.05])
plt.ylabel('Relative frequency', fontsize=fontsi)
plt.xlabel('Counts', fontsize=fontsi)
plt.legend(['0 bright', '1 bright', '2 bright'], loc=5, fontsize=fontsi)
def main():
username = input('Введите логин VK: ')
client_get_id = ClientGetID(username)
user_id = client_get_id.execute()
if client_get_id.is_success():
print("ID: ", user_id)
else:
print('Не существует пользователя с таким ID.')
return 0
# find age list
friends_ages_list = ClientGetFriendsAges(user_id).execute()
if not friends_ages_list:
print('Друзей не найдено')
return 0
else:
print("Ages: ", friends_ages_list)
# write gist
username_friend_gist = Hist(friends_ages_list)
username_friend_gist.printGist()
# show gist
title = "Гистограмма"
title_x = "Возраст"
title_y = "Количество друзей"
username_friend_gist.showBar(title, title_x, title_y)
class System:
def __init__(self,
cell=None,
nballs=10,
step=0,
gc=None,
input=None,
logfile=sys.stdout,
velfile=None,
velint=0,
kT=None,
hist=False):
self.cell = cell
if input is not None:
self.load(input)
else:
self.balls = lattice(nballs, cell)
if kT is not None:
# ra.seed(1)
self.thermalize(kT)
self.balls.interact(cell)
self.step = step
self.gc = gc
self.logfile = logfile
self.velfile = velfile
self.velinterval = velint
self.kT = kT
self.hist = hist
self.histx = Hist(-5, +5, 0.05)
self.histy = Hist(-5, +5, 0.05)
def thermalize(self, kT):
self.balls.randomize(kT)
# Remove total translation
velsum = np.sum(self.balls.vel, axis=0)
velsum /= self.balls.N
self.balls.vel -= velsum
def OneStep(self, dt):
# Progress Momenta (half)
self.balls.accel(dt / 2.0)
# Progress Position
self.balls.forward(dt)
self.balls.resetforce()
# Force
self.pot, virsum = self.balls.interact(self.cell)
# Progress Momenta (half)
self.balls.accel(dt / 2.0)
# temperature scaling
# if self.kT is not None:
if False:
kin = self.balls.kinetic()
dof = self.balls.N * self.cell.shape[0]
factor = (self.kT * dof / 2.0) / kin
factor = 1.0 - (1.0 - factor) * 0.001
self.balls.rescale(factor)
# Data output
if self.logfile is not None:
dof = self.balls.dim * self.balls.N
kin = self.balls.kinetic()
kT = 2.0 * kin / dof
z = 1.0 - virsum / (dof * kT)
self.logfile.write(
"%s %s %s %s %s %s\n" %
(self.step, kT, z, kin + self.pot, kin, self.pot))
if self.velfile is not None and self.step % self.velinterval == 0:
for i in range(0, N):
self.velfile.write("%s\n" %
sqrt(2.0 * self.balls[i].kinetic()))
# histogram
for v in self.balls.vel:
self.histx.accum(v[0], 1.0)
if len(v.shape) > 1:
self.histy.accum(v[1], 1.0)
self.step += 1
def draw(self):
if self.gc is not None:
dim = self.cell.shape[0]
if 2 < dim:
avgvel = 1.0
if self.kT is not None:
avgvel = sqrt(dim * self.kT)
self.balls.draw(self.cell, self.gc, avgvel)
else:
self.balls.draw(self.cell, self.gc, 0.0)
if dim > 1:
canvasx = self.cell[0] * self.gc.zoom
canvasy = self.cell[1] * self.gc.zoom
if self.hist:
self.histx.draw(0, canvasy, canvasx, canvasy / 2)
self.histx.draw(canvasx,
canvasy,
canvasx / 2,
canvasy,
vertical=True)
else:
canvasx = self.cell[0] * self.gc.zoom
canvasy = self.gc.zoom
if self.hist:
self.histx.draw(0, canvasy, canvasx, canvasy / 2)
# self.histx.draw(canvasx,canvasy,canvasx/2,canvasy,vertical=True)
def load(self, file):
self.balls = []
s = file.readline()
self.cell = unserialize(s)
s = file.readline()
x = unserialize(s)
x = int(x[0])
for i in range(0, x):
self.balls.append(Particle(file=file))
s = file.readline()
# serialize
def __str__(self):
s = serialize(self.cell)
s += "%s\n" % len(self.balls)
for i in range(0, len(self.balls)):
s += "%s\n" % self.balls[i]
return s
def save(self, file):
file.write("%s\n" % self)
class System:
def __init__(self,
cell=None,
nballs=10,
step=0,
gc=None,
input=None,
logfile=sys.stdout,
velfile=None,
kT=None,
hist=False):
if hist:
self.colorscheme = AbsoluteVelocity(max=5 / 2)
self.histx = Hist(-5, +5, 0.05, self.colorscheme)
self.histy = Hist(-5, +5, 0.05, self.colorscheme)
self.hist = True
else:
self.colorscheme = None
self.hist = False
if input is not None:
self.load(input)
else:
self.lattice(cell, nballs)
if kT is not None:
ra.seed(1)
self.thermalize(kT)
self.last1 = self.last2 = None
# initialize force
self.step = step
self.gc = gc
self.logfile = logfile
self.velfile = velfile
self.kT = kT
self.hist = hist
def lattice(self, cell, nballs):
self.balls = []
self.cell = cell
dim = len(cell)
if dim == 1:
self.lattice1d(nballs)
self.walls = [Wall(coeff=[1.0, 0.0]), Wall(coeff=[1.0, cell[0]])]
self.area = 2.0 * 1.0
self.volume = cell[0]
elif dim == 2:
self.lattice2d(nballs)
# d次元の壁はd+1個の係数で指示する。
# 最後の要素以外は単位ベクトルでなければいけない。
self.walls = [
Wall(coeff=[1.0, 0.0, 0.0]),
Wall(coeff=[1.0, 0.0, cell[0]]),
Wall(coeff=[0.0, 1.0, 0.0]),
Wall(coeff=[0.0, 1.0, cell[1]])
]
self.area = 2.0 * (cell[0] + cell[1])
self.volume = cell[0] * cell[1]
elif dim == 3:
self.lattice3d(nballs)
self.walls = [
Wall(coeff=[1.0, 0.0, 0.0, 0.0]),
Wall(coeff=[1.0, 0.0, 0.0, cell[0]]),
Wall(coeff=[0.0, 1.0, 0.0, 0.0]),
Wall(coeff=[0.0, 1.0, 0.0, cell[1]]),
Wall(coeff=[0.0, 0.0, 1.0, 0.0]),
Wall(coeff=[0.0, 0.0, 1.0, cell[1]])
]
self.area = 2.0 * (cell[0] * cell[1] + cell[1] * cell[2] +
cell[0] * cell[2])
self.volume = cell[0] * cell[1] * cell[2]
def thermalize(self, kT):
dim = len(self.cell)
N = len(self.balls)
# 1粒子だけにエネルギーを与える。
self.balls[0].randomize(sqrt(dim * kT * float(N)))
def rescale(self, factor):
for b in self.balls:
b.rescale(factor)
def lattice1d(self, nballs):
n = nballs
x = 0.1
while 0 < n:
self.balls += [HardDisk([x], [0.0])]
n -= 1
x += 1.12
def lattice2d(self, nballs):
n = nballs
x = 0.1
y = 0.1
while 0 < n:
self.balls += [
HardDisk([x, y], [0.0, 0.0], colorscheme=self.colorscheme)
]
n -= 1
x += 1.12
if self.cell[0] < x:
x -= self.cell[0] - (1.12 / 2.0)
y += 1.12 * sqrt(3.0) / 2.0
def lattice3d(self, nballs):
n = nballs
x = 0.1
y = 0.1
z = 0.1
while 0 < n:
self.balls += [HardDisk([x, y, z], [0.0, 0.0, 0.0])]
n -= 1
x += 1.12
if self.cell[0] < x:
x -= self.cell[0] - (1.12 / 2.0)
y += 1.12 * sqrt(3.0) / 2.0
if self.cell[0] < y:
y = 0.1
z += 1.12 * sqrt(3.0) / 2.0
def OneCollision(self, deltat):
dtmin = deltat
object1 = 0
object2 = 0
# 粒子のいずれかが壁にぶつかるまでの最短時間を調べる。
for b in self.balls:
for w in self.walls:
if b != self.last1 or w != self.last2:
dt = b.collideWall(w)
if 0 < dt < dtmin:
dtmin = dt
object1 = b
object2 = w
# 粒子同士が衝突するまでの最短時間を調べる。
N = len(self.balls)
for i in range(0, N):
for j in range(i + 1, N):
if self.balls[i] != self.last1 or self.balls[j] != self.last2:
dt = self.balls[i].collide(self.balls[j])
if 0 < dt < dtmin:
dtmin = dt
object1 = self.balls[i]
object2 = self.balls[j]
# 粒子をdtminだけ進める。
for b in self.balls:
b.forward(dtmin)
impulse = 0.0
# 衝突相手が粒子なら
if isinstance(object2, HardDisk):
(v1, t1, v2, t2) = object1.reflect(object2)
if self.velfile is not None:
self.velfile.write("%s %s\n" % (v1, t1))
self.velfile.write("%s %s\n" % (v2, t2))
self.last1 = object1
self.last2 = object2
# 壁に衝突する場合は、壁への力積から圧力が出せる。
elif isinstance(object1, HardDisk):
impulse = object1.reflectWall(object2)
self.last1 = object1
self.last2 = object2
# 最後に衝突した物体と、消費した時間を返す。
return (dtmin, impulse)
def OneStep(self, dt):
bunbo = dt * self.area
sumpulse = 0.0
ncollision = 0
while 0.0 < dt:
# 次の衝突またはdtまで粒子を進める。
(progress, impulse) = self.OneCollision(dt)
dt -= progress
sumpulse += impulse
if impulse == 0.0:
ncollision += 1
if self.hist:
for b in self.balls:
self.histx.accum(b.vel[0], progress)
if len(b.vel) > 1:
self.histy.accum(b.vel[1], progress)
# Data output
if self.logfile is not None:
dim = len(self.cell)
N = len(self.balls)
kin = 0.0
for b in self.balls:
kin += b.kinetic()
kT = 2.0 * kin / (dim * N)
#z = 1.0 + virsum / (dim * N * kT )
pressure = sumpulse / bunbo
self.logfile.write("%s %s %s %s %s\n" %
(self.step, kT, pressure * self.volume /
(N * kT), kin, ncollision))
# histogram
if self.hist:
for b in self.balls:
self.histx.accum(b.vel[0], 1.0)
if len(b.vel) > 1:
self.histy.accum(b.vel[1], 1.0)
self.step += 1
def draw(self):
if self.gc is not None:
dim = len(self.cell)
if 2 < dim:
avgvel = 0.0
if self.kT > 0.0:
avgvel = sqrt(dim * self.kT)
tmp = list(self.balls)
tmp.sort(key=lambda x: -x.pos[2])
for b in tmp:
b.draw(self.cell, self.gc, avgvel)
else:
for b in self.balls:
b.draw(self.cell, self.gc, 0.0)
if dim > 1:
canvasx = self.cell[0] * self.gc.zoom
canvasy = self.cell[1] * self.gc.zoom
if self.hist:
self.histx.draw(0, canvasy, canvasx, canvasy / 2)
self.histx.draw(canvasx,
canvasy,
canvasx / 2,
canvasy,
vertical=True)
else:
canvasx = self.cell[0] * self.gc.zoom
canvasy = self.gc.zoom
if self.hist:
self.histx.draw(0, canvasy, canvasx, canvasy / 2)
# self.histx.draw(canvasx,canvasy,canvasx/2,canvasy,vertical=True)
def load(self, file):
s = file.readline()
self.cell = unserialize(s)
self.balls = []
s = file.readline()
x = unserialize(s)
x = int(x[0])
for i in range(0, x):
self.balls.append(HardDisk(file=file))
self.walls = []
s = file.readline()
x = unserialize(s)
x = int(x[0])
for i in range(0, x):
self.walls.append(Wall(file=file))
s = file.readline()
x = unserialize(s)
self.area = float(x[0])
s = file.readline()
x = unserialize(s)
self.volume = float(x[0])
s = file.readline()
# serialize
def __str__(self):
s = serialize(self.cell)
s += "%s\n" % len(self.balls)
for i in range(0, len(self.balls)):
s += "%s\n" % self.balls[i]
s += "%s\n" % len(self.walls)
for i in range(0, len(self.walls)):
s += "%s\n" % self.walls[i]
s += "%s\n" % self.area
s += "%s\n" % self.volume
return s
def save(self, file):
file.write("%s\n" % self)
from librip.gens import gen_random from histogram import Hist a = [a for a in gen_random(1, 50, 300)] username_friend_gist = Hist(a) username_friend_gist.printGist() # show gist title = "Гистограмма" title_x = "Числа" title_y = "Количество чисел" username_friend_gist.showBar(title, title_x, title_y)