dt=7e-5,
epsilon=1e-9,
**params
)
flipped_init_state, mu_flip = imag_time_gpe1D(
init_wavefunction=flipped_init_state,
g=g,
v=flipped_initial_trap,
dt=1e-6,
epsilon=1e-11,
**params
)
gpe_qsys = SplitOpGPE1D(
v=v,
g=g,
dt=propagation_dt,
**params
)
figTrap = plt.figure(fignum, figsize=(8,6))
fignum+=1
plt.title('Trapping Potential')
x = gpe_qsys.x * L_xmum
plt.plot(x, initial_trap(x) * muK_conv)
plt.xlabel('$x$ ($\mu$m) ')
plt.ylabel('$V(x)$ ($\mu$K)')
plt.xlim([-80 * L_xmum, 80 * L_xmum])
plt.savefig('Trapping Potential' + '.png')
########################################################################################################################
x_amplitude=12.,
v=v,
k=k,
diff_v=diff_v,
diff_k=diff_k,
g=1000,
# epsilon=1e-2,
)
##################################################################################################
# create the harmonic oscillator with time-independent hamiltonian
harmonic_osc = SplitOpGPE1D(dt=0.001, **harmonic_osc_params)
# set the initial condition
harmonic_osc.set_wavefunction(
#lambda x: np.exp(-1 * (x - 0.2) ** 2)
imag_time_gpe1D(dt=1e-5, **harmonic_osc_params)
)
# get time duration of 6 periods
T = 3 * 2. * np.pi / omega
# propagate till time T and for each time step save a probability density
wavefunctions = [harmonic_osc.propagate(t).copy() for t in np.arange(0, T, harmonic_osc.dt)]
fig1 = plt.figure(1)
plt.title(
"Test 1: Time evolution of harmonic oscillator with $\\omega$ = {:.1f} (a.u.)".format(omega)
def run_single_case(params):
"""
Find the initial state and then propagate
:param params: dict with parameters for propagation
:return: dict containing results
"""
# get the initial state
init_state, mu = imag_time_gpe1D(v=params['initial_trap'],
g=g,
dt=5e-5,
epsilon=1e-9,
**params)
init_state, mu = imag_time_gpe1D(v=params['initial_trap'],
g=g,
init_wavefunction=init_state,
dt=1e-6,
epsilon=1e-11,
**params)
####################################################################################################################
# Propagate GPE equation
####################################################################################################################
print("\nPropagate GPE equation")
gpe_propagator = SplitOpGPE1D(v=v, g=g, dt=propagation_dt, **params)
gpe_propagator.set_wavefunction(
init_state *
np.exp(1j * params['init_momentum_kick'] * gpe_propagator.x))
# propagate till time T and for each time step save a probability density
gpe_wavefunctions = [
gpe_propagator.propagate(t).copy() for t in params['times']
]
####################################################################################################################
# Propagate Schrodinger equation
####################################################################################################################
print("\nPropagate Schrodinger equation")
schrodinger_propagator = SplitOpGPE1D(v=v,
g=0.,
dt=propagation_dt,
**params)
schrodinger_propagator.set_wavefunction(
init_state *
np.exp(1j * params['init_momentum_kick'] * schrodinger_propagator.x))
# Propagate till time T and for each time step save a probability density
schrodinger_wavefunctions = [
schrodinger_propagator.propagate(t).copy() for t in params['times']
]
# bundle results into a dictionary
return {
'init_state': init_state,
'mu': mu,
# bundle separately GPE data
'gpe': {
'wavefunctions':
gpe_wavefunctions,
'extent': [
gpe_propagator.x.min(),
gpe_propagator.x.max(), 0.,
max(gpe_propagator.times)
],
'times':
gpe_propagator.times,
'x_average':
gpe_propagator.x_average,
'x_average_rhs':
gpe_propagator.x_average_rhs,
'p_average':
gpe_propagator.p_average,
'p_average_rhs':
gpe_propagator.p_average_rhs,
'hamiltonian_average':
gpe_propagator.hamiltonian_average,
'time_increments':
gpe_propagator.time_increments,
'dx':
gpe_propagator.dx,
'x':
gpe_propagator.x,
},
# bundle separately Schrodinger data
'schrodinger': {
'wavefunctions':
schrodinger_wavefunctions,
'extent': [
schrodinger_propagator.x.min(),
schrodinger_propagator.x.max(), 0.,
max(schrodinger_propagator.times)
],
'times':
schrodinger_propagator.times,
'x_average':
schrodinger_propagator.x_average,
'x_average_rhs':
schrodinger_propagator.x_average_rhs,
'p_average':
schrodinger_propagator.p_average,
'p_average_rhs':
schrodinger_propagator.p_average_rhs,
'hamiltonian_average':
schrodinger_propagator.hamiltonian_average,
'time_increments':
schrodinger_propagator.time_increments,
},
# collect parameters for export
'parameters': params
}
def run_single_case(params):
"""
This function that will be run on different processors.
First it will find the initial state and then it will propagate
:param params: dict with parameters for propagation
:return: dict contaning results
"""
# get the initial state
init_state, mu = imag_time_gpe1D(v=params['initial_trap'],
g=g,
dt=1e-3,
epsilon=1e-8,
**params)
init_state, mu = imag_time_gpe1D(v=params['initial_trap'],
g=g,
init_wavefunction=init_state,
dt=1e-4,
epsilon=1e-9,
**params)
########################################################################################################################
#
# Propagate GPE equation
#
########################################################################################################################
print("\nPropagate GPE equation")
gpe_propagator = SplitOpGPE1D(v=v, g=g, dt=propagation_dt,
**params).set_wavefunction(init_state)
# propagate till time T and for each time step save a probability density
gpe_wavefunctions = [
gpe_propagator.propagate(t).copy() for t in params['times']
]
####################################################################################################################
#
# Propagate Schrodinger equation
#
####################################################################################################################
print("\nPropagate Schrodinger equation")
schrodinger_propagator = SplitOpGPE1D(
v=v, g=0., dt=propagation_dt, **params).set_wavefunction(init_state)
# propagate till time T and for each time step save a probability density
schrodinger_wavefunctions = [
schrodinger_propagator.propagate(t).copy() for t in params['times']
]
# bundle results into a dictionary
return {
'init_state': init_state,
'mu': mu,
# bundle separately GPE data
'gpe': {
'wavefunctions':
gpe_wavefunctions,
'extent': [
gpe_propagator.x.min(),
gpe_propagator.x.max(), 0.,
max(gpe_propagator.times)
],
'times':
gpe_propagator.times,
'x_average':
gpe_propagator.x_average,
'x_average_rhs':
gpe_propagator.x_average_rhs,
'p_average':
gpe_propagator.p_average,
'p_average_rhs':
gpe_propagator.p_average_rhs,
'hamiltonian_average':
gpe_propagator.hamiltonian_average,
'time_increments':
gpe_propagator.time_increments,
'dx':
gpe_propagator.dx,
'x':
gpe_propagator.x,
},
# bundle separately Schrodinger data
'schrodinger': {
'wavefunctions':
schrodinger_wavefunctions,
'extent': [
schrodinger_propagator.x.min(),
schrodinger_propagator.x.max(), 0.,
max(schrodinger_propagator.times)
],
'times':
schrodinger_propagator.times,
'x_average':
schrodinger_propagator.x_average,
'x_average_rhs':
schrodinger_propagator.x_average_rhs,
'p_average':
schrodinger_propagator.p_average,
'p_average_rhs':
schrodinger_propagator.p_average_rhs,
'hamiltonian_average':
schrodinger_propagator.hamiltonian_average,
'time_increments':
schrodinger_propagator.time_increments,
},
}