def run_transition_search(eps, cold_ic, sd):
# L96 EBM Parameters
K = 50
S = 10
a0 = 0.5
a1 = 0.4
sigma = 1 / 180**4
F = 0.5
Tref = 270.
delT = 60.
alpha = 2.
beta = 1.
delta = 1.
p = np.array([K, S, a0, a1, sigma, F, Tref, delT, alpha, beta, eps, delta])
if cold_ic:
ic = cold_attractor
transition_cb = de.DiscreteCallback(near_hot_attractor, de.terminate_b)
else:
ic = hot_attractor
transition_cb = de.DiscreteCallback(near_cold_attractor,
de.terminate_b)
transition_search = l96_ebm_transition_search(p,
ic,
transition_cb,
sd,
transition_limit=100,
dt=1,
number_of_blocks=10000,
block_length=1000)
transition_search.run()
# Setting up SDEProblem Object
prob = de.SDEProblem(numba_f, numba_g, ic, tspan, p, saveat=time_points)
# Callbacks determine when we end integrations
def entered_hot_condition(u, t, integrator):
return np.any(np.array(u).flatten() > 295)
def entered_cold_condition(u, t, integrator):
return np.any(np.array(u)[K].flatten() < 258)
if start_cold:
entry_cb = de.DiscreteCallback(entered_hot_condition, de.terminate_b)
else:
entry_cb = de.DiscreteCallback(entered_cold_condition, de.terminate_b)
# Creating Save directory
if not os.path.exists(sd):
os.makedirs(sd)
print(f'Made directory at {sd}')
# Looker object setup
looker = SolutionObserver(prob.p)
def get_max_end_number(sd):
max_end_number = 0
for f in os.listdir(sd):
sigma = 1 / 180**4
F = 8.
Tref = 270.
delT = 60.
alpha = 2.
beta = 1.
delta = 1.
p = np.array([K, S, a0, a1, sigma, F, Tref, delT, alpha, beta, eps, delta])
# Set Initial Condition and callback
from experiment_utilities import get_random_hot_point, get_random_cold_point, get_entered_hot_condition, get_entered_cold_condition
if start_cold:
ic = get_random_cold_point(S=S)
cb_condition = get_entered_hot_condition(S, K)
cb = de.DiscreteCallback(cb_condition, de.terminate_b)
else:
ic = get_random_hot_point(S=S)
cb_condition = get_entered_cold_condition(S, K)
cb = de.DiscreteCallback(cb_condition, de.terminate_b)
# Setting up SDEProblem Object
prob = de.SDEProblem(numba_f,
numba_additive_g,
ic,
tspan,
p,
saveat=time_points)
# Creating Save directory
if not os.path.exists(sd):
def run_experiment(S=10, epsilon=5, delta=1, cold=True):
"""
S, float - reduced solar constant.
epsilon, float - noise strength.
cold, boolean - whether we start on cold attractor or not.
"""
####################################
### Fixed Experiment Settings
####################################
# Time Set Up
end_time = 1000. # Length of integration where we look for a transitions
dt = 1.0 # How often output is saved, since we're not saving integrations it can be coarse
number_of_searches = int(1.e4) # How many integration runs we do
tspan = (0., end_time)
max_iters = 1.e20
# Fixed L96 EBM Parameters
K = 50
a0 = 0.5
a1 = 0.4
sigma = 1 / 180**4
F = 8.
Tref = 270.
delT = 60.
alpha = 2.
beta = 1.
p = np.array(
[K, S, a0, a1, sigma, F, Tref, delT, alpha, beta, epsilon, delta])
# Initialising Dictionary to Store Experiment Results
global experiment_results
global cpu_time
global integration_time
experiment_results = {
'epsilon': epsilon,
'delta': delta,
'S': S,
'cpu_times(s)': [],
'integration_times': [],
'number_of_transitions': 0,
'parameters': p
}
# Setting IC & Callbacks, File locations will need updating for cluster run
if cold:
ic = get_random_cold_point(S=S)
cb_condition = get_entered_hot_condition(S, K)
cb = de.DiscreteCallback(cb_condition, de.terminate_b)
else:
ic = get_random_hot_point(S=S)
cb_condition = get_entered_cold_condition(S, K)
cb = de.DiscreteCallback(cb_condition, de.terminate_b)
# Setting up diffeqpy solver
prob = de.SDEProblem(numba_f, numba_multiplicative_g, ic, tspan, p)
####################################
### Looped Search for Transitions
####################################
experiment_header(epsilon, delta, S, cold)
start = tm.time() # CPU Clock begins
last_successful_block = -1
for i in range(number_of_searches):
print()
print(
f'Search number {i + 1}/{number_of_searches}. Solving for {prob.tspan}.\n'
)
# Integrate only saving T variable
sol = de.solve(prob,
de.SOSRI(),
saveat=dt,
callback=cb,
maxiters=max_iters)
# If we find a transition, save the result & reset experiment
if sol.retcode == 'Terminated':
print(
f'Found transition in search number {i + 1}. Saving Results.\n'
)
# CPU Time Since Last Success
end = tm.time()
cpu_time = end - start
# Integration Time Since Last Success
completed_blocks_since_last_success = i - (last_successful_block +
1)
block_length = end_time
integration_time_in_this_block = sol.t[-1]
integration_time = completed_blocks_since_last_success * block_length + integration_time_in_this_block
update_results()
save_results(pd=experiment_results_pd,
S=S,
epsilon=epsilon,
cold=cold,
delta=delta)
# Check if we have found enough samples and can stop
if experiment_results['number_of_transitions'] == 100:
print(f'Found 100 transitions - will quit.')
quit()
# Reset Experiment
start = tm.time()
last_successful_block = i
if cold:
ic = get_random_cold_point(S=S)
else:
ic = get_random_hot_point(S=S)
prob = de.remake(prob, u0=ic, tspan=tspan)
# If we don't find a transition, remake the problem
else:
print(f'Exited due to {sol.retcode}\n')
prob = de.remake(prob, u0=sol.u[-1], tspan=(0, end_time))
return
numba_g = numba.jit(g)
########################################################################
### Callback test for detecting transition
########################################################################
def hot_callback_condition(u, t, integrator):
return np.any(np.array(u).flatten() > 270)
def affect_b(integrator):
return de.terminate_b(integrator)
cb = de.DiscreteCallback(hot_callback_condition, de.terminate_b)
########################################################################
### Functions that save/look at results
########################################################################
def save_directory():
"Returns different if we're on cluster or not"
if str(Path.home()) == '/Users/cfn18':
return '/Users/cfn18/Documents/PhD-Work/Third-Year/Instanton-Work/L96-EBM-Instanton/Stochastic-Model/Transitions/Transition-Time-Test/Results-Corrected-S-EM-TEST/'
else:
return '/rds/general/user/cfn18/home/Instantons/L96-EBM-Instanton/Stochastic-Model/Transitions/Transition-Time-Test/Long-Timing-Results/'
def experiment_header(p):
eps, delta = p