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GPE_phase_exp.py
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GPE_phase_exp.py
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'''
Copyright <2019> <Andrei E. Tarkhov, Skolkovo Institute of Science and Technology, https://github.com/TarkhovAndrei/DGPE>
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following 2 conditions:
1) If any part of the present source code is used for any purposes with subsequent publication of obtained results,
the GitHub repository shall be cited in all publications, according to the citation rule:
"Andrei E. Tarkhov, Skolkovo Institute of Science and Technology,
source code from the GitHub repository https://github.com/TarkhovAndrei/DGPE, 2019."
2) The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
'''
import numpy as np
from GPElib.dynamics_generator import DynamicsGenerator
from GPElib.visualisation import Visualisation
import matplotlib
print(matplotlib.matplotlib_fname())
import matplotlib.pyplot as plt
import sys
sys.stderr = sys.stdout
def init_instability(inst, traj_seed=42, E=1., from_backup=False, init_conds=None):
if from_backup == False:
inst.generate_init(traj_seed, E, kind='random_population_and_phase')
inst.set_init_XY(inst.X[:,:,:,0], inst.Y[:,:,:,0])
err = 0
else:
inst.set_init_XY(init_conds[0], init_conds[1])
err = 0
return err
print(len(sys.argv), sys.argv)
if len(sys.argv) > 4:
seed_from = int(sys.argv[1])
seed_to = int(sys.argv[2])
my_id = int(sys.argv[3])
unique_id = sys.argv[4]
else:
seed_from = 0
seed_to = 1
needed_trajs = np.arange(seed_from, seed_to)
my_id = 0
unique_id = 'ID_not_stated139'
needed_trajs = np.arange(seed_from, seed_to)
perturb_seeds = np.arange(123,124)#(2381,2382)#(100, 110)#(106,108)#(97,98)#(97, 100)#(15, 18) #[53, 12, 20, 87]
# time = 30 * 40 * 80.
time = 51.# * 40 * 80.
time_backup = 51.
N_backups = int(np.ceil(time / time_backup)) + 1
time = N_backups * time_backup
step_large = 0.5
step = 0.05
step_small = 0.01
step_LE = 1.
N_wells = 10.
W = 0.
gamma_tmp = 1.
lyap = DynamicsGenerator(N_part_per_well=1.,
W=W, disorder_seed=53,
N_wells=(30,30,30), dimensionality=3, anisotropy=1.0,
threshold_XY_to_polar=0.25,
beta=10., #beta_disorder_amplitude=2.,
local_disorder_amplitude=0.00,
FloatPrecision=np.float64,
integration_method='RK45',
rtol=1e-8, atol=1e-8,
smooth_quench=True,
reset_steps_duration=5,
calculation_type='lyap_save_all',
integrator='scipy',
time=time_backup, step=step, gamma=gamma_tmp)
lyap_dynamic = DynamicsGenerator(N_part_per_well=1.,
W=W, disorder_seed=53,
N_wells=(30,30,30), dimensionality=3, anisotropy=1.0,
threshold_XY_to_polar=0.25,
beta=10.,
local_disorder_amplitude=0.00,
#beta_disorder_amplitude=2.,
FloatPrecision=np.float64,
integration_method='RK45',
rtol=1e-8, atol=1e-8,
reset_steps_duration=5,
calculation_type='lyap_save_all',
integrator='scipy',
time=time_backup, step=step, gamma=1.)
grname = 'GPE_phase_' + unique_id
vis = Visualisation(is_local=0, HOMEDIR='/data/tarkhov/data/', GROUP_NAMES=grname)
vis_backup = Visualisation(is_local=0, HOMEDIR='/data/tarkhov/data/backups/', GROUP_NAMES=grname)
# vis = Visualisation(is_local=0, HOMEDIR='/data1/andrey/data/', GROUP_NAMES=grname)
# vis_backup = Visualisation(is_local=0, HOMEDIR='/data1/andrey/data/backups/', GROUP_NAMES=grname)
# vis = Visualisation(is_local=1, HOMEDIR='/Users/tarkhov/tmp/', GROUP_NAMES=grname)
# vis_backup = Visualisation(is_local=1, HOMEDIR='/Users/tarkhov/tmp/backups/', GROUP_NAMES=grname)
def try_find_backup():
try:
backup_intro = np.load(vis_backup.filename('_BACKUP_PRESENT_' + str(my_id)))
backup_id = backup_intro['backup_id']
print(backup_id)
backup = np.load(vis_backup.filename('_BACKUP_PRESENT_' + str(my_id) + '_intermediate_' + str(backup_id)))
print('Start from backup file')
backup_present = True
return backup, backup_id, backup_present
except:
print('No backup file, start from 0')
backup_id = -1
backup_present = False
backup = None
return backup, backup_id, backup_present
backup, backup_id, backup_present = try_find_backup()
print("Noise ", W)
print("Characteristic, full, step times, n_steps")
print(lyap.tau_char, lyap.time, lyap.step, lyap.n_steps)
if backup_present == False:
num_good = 0
lmbdas = []
lmbdas_no_regr = []
chosen_trajs = []
effective_nonlinearity = []
energies = []
temperatures = []
energies_true = []
order_parameters = []
distances = []
numb_of_part = []
next_traj = 0
next_seed = 0
backup_id = -1
curr_traj = -1
curr_seed = -1
else:
num_good = backup['num_good']
lmbdas = backup['lambdas']
lmbdas_no_regr = backup['lambdas_no_regr']
chosen_trajs = backup['chosen']
effective_nonlinearity = backup['eff_nonl']
energies = backup['energies']
temperatures = backup['temperatures']
energies_true = backup['energies_true']
order_parameters = backup['order_parameters']
distances = backup['distance']
numb_of_part = backup['numb_of_part']
curr_traj = backup['curr_traj']
perturb_seeds = backup['pert_seeds']
curr_seed = backup['curr_seed']
time_finished = backup['time_finished']
backup_id = backup['backup_id']
def save_backup(backup_id):
if backup_id == 0:
init_conds = [lyap.X[:,:,:,0], lyap.Y[:,:,:,0]]
else:
init_conds = [lyap.X[:,:,:,lyap.icurr], lyap.Y[:,:,:,lyap.icurr]]
np.savez_compressed(vis_backup.filename('_BACKUP_PRESENT_' + str(my_id)),
backup_id=backup_id)
np.savez_compressed(vis_backup.filename('_BACKUP_PRESENT_' + str(my_id) + '_intermediate_' + str(backup_id)),
lambdas=lmbdas, lambdas_no_regr=lmbdas_no_regr,
eff_nonl=effective_nonlinearity,
init_conds=init_conds,
numb_of_part=numb_of_part, energies=energies,
temperatures = temperatures,
energies_true = energies_true,
order_parameters = order_parameters,
pert_seeds=perturb_seeds,
chosen=chosen_trajs, step=lyap.step, time=lyap.time, n_steps=lyap.n_steps,
my_info=[seed_from, seed_to, my_id], needed_trajs=needed_trajs,
checksum=lyap.consistency_checksum, error_code=lyap.error_code,
distance=distances,
curr_traj=i_traj, curr_seed=j_traj,
time_finished=k_traj * time_backup,
backup_id=backup_id, num_good=num_good
)
for i_traj, traj_seed in enumerate(needed_trajs):
if i_traj < curr_traj:
continue
if num_good > needed_trajs.shape[0] - 1:
print('We really have enough trajs, exit!')
break
for j_traj, pert_seed in enumerate(perturb_seeds):
if j_traj < curr_seed:
continue
if num_good > needed_trajs.shape[0] - 1:
print('We really have enough trajs, exit!')
break
for k_traj in range(N_backups):
if k_traj <= backup_id:
continue
backup, backup_id, backup_present = try_find_backup()
lyap.X = lyap.X * 0.
lyap.Y = lyap.Y * 0.
lyap.RHO = lyap.RHO * 0.
lyap.THETA = lyap.THETA * 0.
lyap.icurr = 0
lyap.inext = 1
if backup_present:
print('Backup found: ', backup_id, k_traj)
# err = init_instability(lyap, traj_seed, from_backup=True, init_conds=backup['init_conds'])
# lyap.traj_seed = traj_seed
# lyap.pert_seed = pert_seed
#
np.random.seed()
traj_seed = np.random.randint(100000)
pert_seed = np.random.randint(100000)
lyap.traj_seed = traj_seed
lyap.pert_seed = pert_seed
err = 1
while err == 1:
traj_seed = np.random.randint(100000)
pert_seed = np.random.randint(100000)
print("SEED: ", traj_seed)
err = init_instability(lyap, traj_seed)
if err == 1:
print('Bad trajectory! ', i_traj)
print('Good trajectory found!')
else:
print('No backup found, going to find a track')
np.random.seed()
traj_seed = np.random.randint(100000)
pert_seed = np.random.randint(100000)
lyap.traj_seed = traj_seed
lyap.pert_seed = pert_seed
err = 1
while err == 1:
traj_seed = np.random.randint(100000)
print("SEED: ", traj_seed)
err = init_instability(lyap, traj_seed)
if err == 1:
print('Bad trajectory! ', i_traj)
print('Good trajectory found!')
lyap.n_steps = int(time_backup / step)
lyap.time = time_backup
save_backup(k_traj)
continue
x0 = lyap.X[:,:,:,0].copy()
y0 = lyap.Y[:,:,:,0].copy()
lyap.X *= 0
lyap.Y *= 0
lyap.RHO *= 0
lyap.THETA *= 0
lyap.icurr = 0
lyap.inext = 1
lyap.set_init_XY(x0, y0)
lyap.step = step_small
lyap.run_relaxation(E_desired=(-4. + lyap.beta_amplitude/2)*lyap.N_wells, temperature_dependent_rate=False, N_max=200)
x0 = lyap.X[:,:,:,lyap.icurr-1].copy()
y0 = lyap.Y[:,:,:,lyap.icurr-1].copy()
E0 = lyap.calc_energy_XY(x0, y0, 0)
lyap.X *= 0
lyap.Y *= 0
lyap.RHO *= 0
lyap.THETA *= 0
lyap.icurr = 0
lyap.inext = 1
lyap.set_init_XY(x0, y0)
lyap.step = step_large
lyap.n_steps = 200
lyap.run_dynamics(no_pert=False)
x0 = lyap.X[:,:,:,lyap.icurr-1].copy()
y0 = lyap.Y[:,:,:,lyap.icurr-1].copy()
E_min_all = (-2.7 + lyap.beta_amplitude/2.) * lyap.N_wells
E_max_all = (3 + lyap.beta_amplitude/2.) * lyap.N_wells
Energies = np.linspace(E_min_all, E_max_all, num=40)
gammas = np.linspace(0.1, 2., num=40)
#Efinal = (np.linspace(-4,1, num=40) + lyap.beta_amplitude/2) * lyap.N_wells
Efinal = (np.zeros(40) - 4. + lyap.beta_amplitude/2) * lyap.N_wells
#Efinal = (np.linspace(-4,1, num=40) + 10./2) * 1000
T_conservative = 40.
dt_conservative = 0.05
n_conservative = int(T_conservative/dt_conservative)
T_quench = 0.
dt_quench = 0.05
n_quench = int(T_quench/dt_quench)
T_conservative_post_quench = 0.
dt_conservative_post_quench = 0.05
n_conservative_post_quench = int(T_conservative_post_quench/dt_conservative_post_quench)
total_time_steps = n_conservative + n_quench + n_conservative_post_quench
energy_i = np.full((1, Energies.shape[0],) + (total_time_steps,), np.nan)
order_parameter_i = np.full((1, Energies.shape[0],) + lyap.X.shape[:-1] + (total_time_steps,), np.nan, dtype=np.complex64)
order_parameter_i_1 = np.full((1, Energies.shape[0],) + lyap.X.shape[:-1] + (total_time_steps,), np.nan, dtype=np.complex64)
temperature_i = np.full((1, Energies.shape[0],) + (total_time_steps,), np.nan)
temperature_Amp_i = np.full((1, Energies.shape[0],) + (total_time_steps,), np.nan)
temperature_Ph_i = np.full((1, Energies.shape[0],) + (total_time_steps,), np.nan)
x1 = x0.copy()
y1 = y0.copy()
idx_iterations = np.hstack(
(np.hstack((np.nonzero(Energies > E0)[0], (-1))),
np.nonzero(np.logical_not(Energies > E0))[0][::-1])
)
for j in idx_iterations:
if j == -1:
continue
x1 = x0.copy()
y1 = y0.copy()
print('Energy %f' % Energies[j])
lyap.X *= 0
lyap.Y *= 0
lyap.RHO *= 0
lyap.THETA *= 0
lyap.icurr = 0
lyap.inext = 1
lyap.set_init_XY(x1, y1)
lyap.step = dt_conservative
lyap.n_steps = n_conservative
lyap.gamma = gammas[j]
#lyap.run_dynamics(no_pert=False)
lyap.run_relaxation(no_pert=False, E_desired=-1e+9, temperature_dependent_rate=True,
N_max=lyap.n_steps)
for istep in np.arange(lyap.n_steps):
energy_i[0,j,istep] = lyap.calc_energy_XY(lyap.X[:,:,:,istep],lyap.Y[:,:,:,istep], 0)
order_parameter_i[0,j,:,:,:,istep] = lyap.X[:,:,:,istep] + 1j * lyap.Y[:,:,:,istep]
order_parameter_i_1[0,j,:,:,:,istep] = lyap.X[:,:,:,istep] + 1j * lyap.Y[:,:,:,istep]
if backup_present:
print('Backup present, go adding results')
num_good = backup['num_good']
lmbdas = backup['lambdas']
lmbdas_no_regr = backup['lambdas_no_regr']
chosen_trajs = backup['chosen']
effective_nonlinearity = backup['eff_nonl']
energies = backup['energies']
temperatures = backup['temperatures']
energies_true = backup['energies_true']
order_parameters = backup['order_parameters']
distances = backup['distance']
numb_of_part = backup['numb_of_part']
curr_traj = backup['curr_traj']
curr_seed = backup['curr_seed']
time_finished = backup['time_finished']
backup_id = backup['backup_id']
if len(energies_true) == 0:
energies_true = energy_i
temperatures = temperature_i
temperatures_Ph = temperature_Ph_i
temperatures_Amp = temperature_Amp_i
order_parameters = order_parameter_i
order_parameters_1 = order_parameter_i_1
else:
# print(energies_true.shape, energy_i.shape)
energies_true = np.concatenate((energies_true, energy_i), axis=0)
temperatures = np.concatenate((temperatures, temperature_i), axis=0)
temperatures_Amp = np.concatenate((temperatures_Amp, temperature_Amp_i), axis=0)
temperatures_Ph = np.concatenate((temperatures_Ph, temperature_Ph_i), axis=0)
# print('stack axis 0: ', order_parameters.shape, order_parameter_i.shape)
order_parameters = np.concatenate((order_parameters, order_parameter_i), axis=0)
order_parameters_1 = np.concatenate((order_parameters_1, order_parameter_i_1), axis=0)
energies = np.hstack((energies, lyap.energy))
numb_of_part = np.hstack((numb_of_part, lyap.number_of_particles))
save_backup(k_traj)
num_good += 1
print("Error code: ", lyap.error_code)
print("\n\nChecksum: ", lyap.consistency_checksum)
np.savez_compressed(vis.filename(my_id),
lambdas=lmbdas, lambdas_no_regr=lmbdas_no_regr,
eff_nonl=effective_nonlinearity,
numb_of_part=numb_of_part, energies=energies,
temperatures = temperatures,
energies_true = energies_true,
order_parameters = order_parameters,
chosen=chosen_trajs, step=lyap.step, time=lyap.time, n_steps=lyap.n_steps,
my_info=[seed_from, seed_to, my_id], needed_trajs=needed_trajs,
checksum=lyap.consistency_checksum, error_code=lyap.error_code,
distance=distances)