def main():
stds = list()
gov, insts = generate_agents(parameters.num_hei)
gov, insts, stds = evolve(gov, insts, stds)
output.produce_output(gov, insts, stds)
files = [f for f in os.listdir('results') if 'csv' in f]
for f in files:
plotter.plotting('results/' + f)
def perform(theta_sphere, phi_sphere, F):
experimentData = ExperimentData(F)
F = experimentData.F
sigma = {}
N = 200
t_sum = 0
epsilon = 10**(-3)
prev_sigma = experimentData.F_max - experimentData.F_min
flag = False
prev_step = 0
#
print(experimentData.probability_distribution) # initial
print(experimentData.fields_number)
for step in range(N):
bayesians_learning.renew_probalities(
qubit.randbin3(experimentData, F, theta_sphere, phi_sphere),
experimentData, theta_sphere, phi_sphere)
#bayesians_learning.renew_probalities(qubit.randbin(experimentData, F), experimentData)
#bayesians_learning.renew_probalities(ramsey_qubit.output(experimentData.t), experimentData)
t_sum += experimentData.t
x_peak, y_peak = find_peak(experimentData)
current_sigma = find_sigma(x_peak, y_peak, experimentData)
if current_sigma != 0:
sigma[t_sum] = current_sigma
if step <= 50 and prev_sigma == experimentData.F_max - experimentData.F_min and current_sigma != 0:
flag = True
if flag and \
step - prev_step >= 2 and \
prev_sigma + experimentData.delta_F > 2 * current_sigma and\
experimentData.const * F * experimentData.F_degree * experimentData.t <= math.pi:
prev_sigma = current_sigma
prev_step = step
experimentData.t *= experimentData.time_const
print(step)
if flag and prev_sigma < current_sigma:
prev_sigma = current_sigma
if (step) % 3 == 0:
plt.plot([
experimentData.F_min + i * experimentData.delta_F
for i in range(experimentData.fields_number)
], experimentData.probability_distribution) # distr each 50 steps
if (step + 1) % 1 == 0:
print(sum(experimentData.probability_distribution), x_peak, y_peak,
step, current_sigma, prev_sigma, t_sum,
experimentData.const * F * experimentData.t *
experimentData.F_degree, flag) # checking ~ 1
if y_peak >= 1.0 - epsilon:
break
x_peak, y_peak = find_peak(experimentData)
optimal_angles.F_model.append(x_peak)
optimal_angles.y_model = y_peak
optimal_angles.F_real.append(F)
optimal_angles.theta_angles.append(theta_sphere)
plt.plot([
experimentData.F_min + i * experimentData.delta_F
for i in range(experimentData.fields_number)
], experimentData.probability_distribution) # final distr
plt.show()
#fig.savefig('distr_' + '.png', dpi=500)
plt.close()
print(list(sigma.keys())[-1], list(sigma.values())[-1])
x_peak, y_peak = find_peak(experimentData)
print(x_peak)
#plotter.plotting(sigma)[1]
x_peak, y_peak = find_peak(experimentData)
return x_peak, plotter.plotting(sigma)[1][1], list(sigma.keys())[-1], list(
sigma.values())[-1]
def perform():
experimentData = ExperimentData()
sigma = {}
a_from_t_sum = {} #sensitivity
a_from_step = {} #sensitivity
N = 45
t_sum = 0
epsilon = 10 ** (-3)
prev_sigma = experimentData.F_max - experimentData.F_min
flag = False
prev_step = 0
prev_entropy_step = -1
#
fig, ax = plt.subplots()
ax.minorticks_on()
print(experimentData.probability_distribution) # initial
print(experimentData.fields_number)
plt.plot([experimentData.F_min + i * experimentData.delta_F for i in range(experimentData.fields_number)],
[each for each in experimentData.probability_distribution])
for step in range(N):
bayesians_learning.renew_probalities(experimentData)
#bayesians_learning.renew_probalities(qubit.randbin3(experimentData, F), experimentData)
#bayesians_learning.renew_probalities(qubit.randbin2(experimentData, F), experimentData)
#bayesians_learning.renew_probalities(ramsey_qubit.output(experimentData.t), experimentData)
t_sum += experimentData.t * experimentData.num_of_repetitions
x_peak, y_peak = find_peak(experimentData)
num_of_peaks = find_num_of_peaks(experimentData, y_peak)
pseudo_entropy = pseudo_entropy_count(experimentData, y_peak)
current_sigma = find_sigma(x_peak, y_peak, experimentData) / experimentData.gained_degree
#a_from_t_sum[t_sum] = current_sigma * (t_sum) ** 0.5
#a_from_step[step] = current_sigma * (t_sum) ** 0.5
a_from_t_sum[experimentData.t] = abs(experimentData.F - x_peak) * (t_sum) ** 0.5
if current_sigma != 0:
sigma[t_sum] = current_sigma
if step <= 50 and prev_sigma == experimentData.F_max - experimentData.F_min and current_sigma != 0:
flag = True
if flag and \
step - prev_step >= 1: # and \
#prev_sigma + experimentData.delta_F/experimentData.gained_degree > 2 * current_sigma:# and \
#experimentData.const * F * experimentData.F_degree * experimentData.t <= 3.14:
prev_sigma = current_sigma
prev_step = step
experimentData.t *= experimentData.time_const
print(step)
if flag and prev_sigma < current_sigma:
prev_sigma = current_sigma
if (step) % 1 == 0:
plt.plot([experimentData.F_min + i*experimentData.delta_F for i in range(experimentData.fields_number)], [each for each in experimentData.probability_distribution]) # distr each _ steps
if (step + 1) % 1 == 0:
print(bayesians_learning.integrate_distribution(experimentData), num_of_peaks, pseudo_entropy, x_peak, y_peak, step, current_sigma, prev_sigma, experimentData.t, experimentData.const * experimentData.F * experimentData.t*experimentData.F_degree, flag) # checking ~ 1
if pseudo_entropy == 1 or num_of_peaks == 1:
experimentData.num_of_repetitions = 51
if pseudo_entropy > 1 and step - prev_entropy_step > 1 and num_of_peaks == 1:
experimentData.num_of_repetitions = 101
experimentData.t /= experimentData.time_const ** (0)
prev_entropy_step = step
if num_of_peaks > 1:
experimentData.t /= experimentData.time_const ** (0)
experimentData.num_of_repetitions = 101
if pseudo_entropy > 2 or num_of_peaks > 2:
experimentData.t /= experimentData.time_const ** (1)
if pseudo_entropy > 3 or num_of_peaks > 3:
experimentData.t /= experimentData.time_const ** (1)
if flag and current_sigma*experimentData.gained_degree <= 10*experimentData.delta_F or num_of_peaks > 1:
plt.plot([experimentData.F_min + i * experimentData.delta_F for i in range(experimentData.fields_number)],
[each for each in experimentData.probability_distribution])
plt.show()
plt.close()
fig, ax = plt.subplots()
ax.minorticks_on()
expand_2(x_peak, y_peak, current_sigma, experimentData)
plt.plot([experimentData.F_min + i * experimentData.delta_F for i in range(experimentData.fields_number)],
[each for each in experimentData.probability_distribution])
print(experimentData.probability_distribution)
if experimentData.t >= 200*10**(-6):
break
#plt.plot([experimentData.F_min + i*experimentData.delta_F for i in range(experimentData.fields_number)], experimentData.probability_distribution) # final distr
plt.show()
#fig.savefig('distr_' + '.png', dpi=500)
plt.close()
print(list(sigma.keys())[-1], list(sigma.values())[-1])
'''try:
plotter.plotting_sensitivity(a_from_step, r'$N$')
except Exception:
pass
try:
plotter.plotting_sensitivity(a_from_t_sum, r'$t_{sum}$')
except Exception:
pass'''
#try:
# plotter.plotting_sensitivity(a_from_t_sum, r'$t_{coherense\_max}, \, \mu s$')
#except Exception:
# pass
#print("final sensitivity: ", a_from_t_sum[t_sum]*10**(-9))
x_peak, y_peak = find_peak(experimentData)
plotter.plotting(sigma)
return a_from_t_sum
def perform():
experimentData = ExperimentData()
F = experimentData.F
sigma = {}
a_from_t_sum = {} #sensitivity
a_from_step = {} #sensitivity
N = 90
t_sum = 0
epsilon = 10**(-3)
prev_sigma = experimentData.F_max - experimentData.F_min
flag = False
prev_step = 0
#
print(experimentData.probability_distribution) # initial
print(experimentData.fields_number)
for step in range(N):
bayesians_learning.renew_probalities(qubit.randbin(experimentData, F),
experimentData)
#bayesians_learning.renew_probalities(qubit.randbin3(experimentData, F), experimentData)
#bayesians_learning.renew_probalities(qubit.randbin2(experimentData, F), experimentData)
#bayesians_learning.renew_probalities(ramsey_qubit.output(experimentData.t), experimentData)
t_sum += experimentData.t
x_peak, y_peak = find_peak(experimentData)
current_sigma = find_sigma_2(x_peak, y_peak, experimentData)
a_from_t_sum[t_sum] = current_sigma * (t_sum)**0.5
a_from_step[step] = current_sigma * (t_sum)**0.5
if current_sigma != 0:
sigma[t_sum] = current_sigma
if step <= 50 and prev_sigma == experimentData.F_max - experimentData.F_min and current_sigma != 0:
flag = True
if flag and \
step - prev_step >= 2 and \
prev_sigma + experimentData.delta_F > 2 * current_sigma:# and \
#experimentData.const * F * experimentData.F_degree * experimentData.t <= 3.14:
prev_sigma = current_sigma
prev_step = step
experimentData.t *= experimentData.time_const
print(step)
if flag and prev_sigma < current_sigma:
prev_sigma = current_sigma
if (step) % 5 == 0:
plt.plot([
experimentData.F_min + i * experimentData.delta_F
for i in range(experimentData.fields_number)
], experimentData.probability_distribution) # distr each 50 steps
if (step + 1) % 1 == 0:
print(sum(experimentData.probability_distribution), x_peak, y_peak,
step, current_sigma, prev_sigma, t_sum,
experimentData.const * F * experimentData.t *
experimentData.F_degree, flag) # checking ~ 1
if y_peak >= 1.0 - epsilon or t_sum >= 8 * 10**(-6):
break
plt.plot([
experimentData.F_min + i * experimentData.delta_F
for i in range(experimentData.fields_number)
], experimentData.probability_distribution) # final distr
plt.show()
#fig.savefig('distr_' + '.png', dpi=500)
plt.close()
print(list(sigma.keys())[-1], list(sigma.values())[-1])
plotter.plotting_sensitivity(a_from_step, r'$N$')
plotter.plotting_sensitivity(a_from_t_sum, r'$t_{sum}$')
x_peak, y_peak = find_peak(experimentData)
plotter.plotting(sigma)
def perform():
experimentData = ExperimentData()
sigma = {}
a_from_t_sum = {} #sensitivity
a_from_step = {} #sensitivity
N = 40
t_sum = 0
epsilon = 10**(-5)
for step in range(N):
outcome = int(
round(
sum([
qubit.randbin(experimentData, experimentData.F)
for i in range(experimentData.num_of_repetitions)
]) / experimentData.num_of_repetitions))
if bayesians_learning.P_qubit_state_on_F_i(outcome, experimentData.F_min, experimentData)\
> bayesians_learning.P_qubit_state_on_F_i(outcome, experimentData.F_max, experimentData):
experimentData.F_max = (experimentData.F_max +
experimentData.F_min) / 2
else:
experimentData.F_min = (experimentData.F_max +
experimentData.F_min) / 2
t_sum += experimentData.t * experimentData.num_of_repetitions
current_sigma = (experimentData.F_max - experimentData.F_min) / 2
sigma[t_sum] = current_sigma
center = (experimentData.F_max + experimentData.F_min) / 2
a_from_t_sum[experimentData.t] = max(abs(center - experimentData.F),
current_sigma) * (t_sum)**0.5
#a_from_step[step] = current_sigma * (t_sum) ** 0.5
experimentData.t *= experimentData.time_const
print(step, center, current_sigma, experimentData.t * 10**6, t_sum)
#x = np.arange(experimentData.F_min, experimentData.F_max, 0.01)
#plt.plot(x, 1 / (current_sigma * np.sqrt(2 * np.pi)) *
# np.exp(- (x - center) ** 2 / (2 * current_sigma ** 2)),
# linewidth=2, color='r')
if current_sigma <= epsilon or experimentData.t >= 200 * 10**(-6):
break
#plt.show()
#plt.close()
print(list(sigma.keys())[-1], list(sigma.values())[-1])
#try:
# plotter.plotting_sensitivity(a_from_step, r'$N$')
#except Exception:
# pass
try:
plotter.plotting_sensitivity(a_from_t_sum,
r'$t_{coherense\_max}, \, \mu s$')
except Exception:
pass
plotter.plotting(sigma)
return a_from_t_sum
def perform(p_err, a_err, F, n_rep):
experimentData = ExperimentData()
experimentData.F = F
experimentData.F_min = 0 # min field Tesla
experimentData.F_max = 50 # max field Tesla
experimentData.F_degree = 10**(-9)
experimentData.amp_err = p_err
experimentData.phase_err = a_err
experimentData.gained_degree = 1
experimentData.delta_F = 1 # accuracy of F defining
experimentData.fields_number = round(
(experimentData.F_max - experimentData.F_min + experimentData.delta_F)
/ experimentData.delta_F) # amount of discrete F meanings
experimentData.time_const = 2
experimentData.mu = 10**(5) * 927 * 10**(-26
) # magnetic moment of the qubit
experimentData.h = 6.62 * 10**(-34) # plank's constant
experimentData.const = experimentData.mu / experimentData.h # mu/h
experimentData.t = math.pi / (experimentData.const *
experimentData.F_degree *
experimentData.F_max / 2) * 2**(-1)
experimentData.t_init = experimentData.t # time of interaction in seconds
experimentData.num_of_repetitions = n_rep # repetitions for one experiment
experimentData.probability_distribution = [
gaussian(15, 0, 1, 25, i) for i in range(experimentData.fields_number)
]
experimentData.phase_err = p_err
experimentData.amp_err = a_err
sigma = {}
a_from_t_sum = {} #sensitivity
a_from_step = {} #sensitivity
N = 13
t_sum = 0
epsilon = 20 * 10**(-3) # 20 pT
prev_sigma = experimentData.F_max - experimentData.F_min
flag = False
prev_step = 0
prev_entropy_step = -1
fig, ax = plt.subplots()
font = {'fontname': 'Times New Roman'}
ax.set_title(r'b)')
ax.minorticks_on()
ax.grid(which='major', axis='both')
ax.grid(which='minor', axis='both', linestyle=':')
# Подписи:
ax.set_xlabel("Field segment, $nT$", **font)
ax.set_ylabel(r'$P(F_k)$', **font)
print(experimentData.probability_distribution) # initial
print(experimentData.fields_number)
ax.plot([
experimentData.F_min + i * experimentData.delta_F
for i in range(experimentData.fields_number)
], [each for each in experimentData.probability_distribution],
label='k=0')
#answers = qubit.randbin2(experimentData, experimentData.F)
#answers = real_experiment.output(experimentData)
for step in range(N):
#bayesians_learning.renew_probalities(answers[experimentData.t], experimentData)
#bayesians_learning.renew_probalities(1, experimentData)
#bayesians_learning.renew_probalities(qubit.randbin2(experimentData, F), experimentData)
#bayesians_learning.renew_probalities(ramsey_qubit.output(experimentData.t), experimentData)
t_sum += experimentData.t * experimentData.num_of_repetitions
x_peak, y_peak = find_peak(experimentData)
num_of_peaks = find_num_of_peaks(experimentData, y_peak)
pseudo_entropy = pseudo_entropy_count(experimentData, y_peak)
current_sigma = find_sigma(
x_peak, y_peak, experimentData) / experimentData.gained_degree
#a_from_t_sum[t_sum] = current_sigma * (t_sum) ** 0.5
#a_from_step[step] = current_sigma * (t_sum) ** 0.5
a_from_t_sum[experimentData.t] = current_sigma * (
t_sum
)**0.5 #max(abs(experimentData.F - x_peak), current_sigma) * (t_sum) ** 0.5
#a_from_t_sum[experimentData.t] = max(abs(experimentData.F - x_peak), current_sigma)
if current_sigma != 0:
sigma[t_sum] = current_sigma
if step <= 50 and prev_sigma == experimentData.F_max - experimentData.F_min and current_sigma != 0:
flag = True
if flag and \
step - prev_step >= 1: # and \
#prev_sigma + experimentData.delta_F/experimentData.gained_degree > 2 * current_sigma:# and \
#experimentData.const * F * experimentData.F_degree * experimentData.t <= 3.14:
prev_sigma = current_sigma
prev_step = step
experimentData.t *= experimentData.time_const
#print(step)
if flag and prev_sigma < current_sigma:
prev_sigma = current_sigma
if (step) % 10 == 0:
ax.plot([
experimentData.F_min + i * experimentData.delta_F
for i in range(experimentData.fields_number)
], [each for each in experimentData.probability_distribution],
label='k={}'.format(step + 1)) # distr each _ steps
if (step + 1) % 1 == 0:
print(num_of_peaks, pseudo_entropy, x_peak, y_peak, step,
current_sigma, prev_sigma, experimentData.t,
experimentData.const * experimentData.F * experimentData.t *
experimentData.F_degree, flag) # checking ~ 1
if pseudo_entropy == 1 or num_of_peaks == 1:
experimentData.num_of_repetitions = n_rep
if pseudo_entropy > 1 and step - prev_entropy_step > 1 and num_of_peaks == 1:
experimentData.num_of_repetitions = n_rep
experimentData.t /= experimentData.time_const**(0)
prev_entropy_step = step
if num_of_peaks > 1:
experimentData.t /= experimentData.time_const**(0)
experimentData.num_of_repetitions = n_rep
if pseudo_entropy > 2 or num_of_peaks > 2:
experimentData.t /= experimentData.time_const**(1)
if pseudo_entropy > 3 or num_of_peaks > 3:
experimentData.t /= experimentData.time_const**(1)
if flag and current_sigma * experimentData.gained_degree <= 5 * experimentData.delta_F or num_of_peaks > 1:
#plt.plot([experimentData.F_min + i * experimentData.delta_F for i in range(experimentData.fields_number)],
# [each for each in experimentData.probability_distribution], label='k={}'.format(step+1))
plt.legend(loc="best")
plt.show()
plt.close()
fig, ax = plt.subplots()
ax.minorticks_on()
ax.set_title(r'b)')
ax.grid(which='major', axis='both')
ax.grid(which='minor', axis='both', linestyle=':')
# Подписи:
ax.set_xlabel("Field segment, $nT$", **font)
ax.set_ylabel(r'$P(F_k)$', **font)
expand_2(x_peak, y_peak, current_sigma, experimentData)
plt.plot([
experimentData.F_min + i * experimentData.delta_F
for i in range(experimentData.fields_number)
], [each for each in experimentData.probability_distribution],
label='k={}'.format(step + 1))
#print(experimentData.probability_distribution)
if experimentData.t >= experimentData.T_2:
break
'''ax.plot([experimentData.F_min + i*experimentData.delta_F for i in range(experimentData.fields_number)], experimentData.probability_distribution, label='final') # final distr
plt.legend(loc="best")
plt.show()
#fig.savefig('distr_' + '.png', dpi=500)
plt.close()'''
print("t_sum: ",
list(sigma.keys())[-1], ', sigma:',
list(sigma.values())[-1])
print("t_coh_max: ",
list(a_from_t_sum.keys())[-1] * 10**6, ", sensitivity: ",
list(a_from_t_sum.values())[-1] * experimentData.F_degree)
'''try:
plotter.plotting_sensitivity(a_from_step, r'$N$')
except Exception:
pass
try:
plotter.plotting_sensitivity(a_from_t_sum, r'$t_{sum}$')
except Exception:
pass'''
try:
plotter.plotting_sensitivity(a_from_t_sum,
r'$t_{coherense\_max}, \, \mu s$')
except Exception:
pass
#print("final sensitivity: ", a_from_t_sum[t_sum]*10**(-9))
x_peak, y_peak = find_peak(experimentData)
succeeded = 0
if abs(x_peak - experimentData.F) <= epsilon:
succeeded = 1
plotter.plotting(sigma)
return succeeded