def test_f_is_completed(self):
traj = Trajectory()
traj.f_add_parameter('test', 42)
traj.f_explore({'test':[1,2,3,4]})
self.assertFalse(traj.f_is_completed())
for run_name in traj.f_get_run_names():
self.assertFalse(traj.f_is_completed(run_name))
traj._run_information[traj.f_idx_to_run(1)]['completed']=1
self.assertFalse(traj.f_is_completed())
self.assertTrue(traj.f_is_completed(1))
for run_name in traj.f_get_run_names():
traj._run_information[run_name]['completed']=1
self.assertTrue(traj.f_is_completed())
for run_name in traj.f_get_run_names():
self.assertTrue(traj.f_is_completed(run_name))
def main():
env = Environment(trajectory='Example_05_Euler_Integration',
filename='experiments/example_05/HDF5/example_05.hdf5',
file_title='Example_05_Euler_Integration',
log_folder='experiments/example_05/LOGS/',
comment = 'Go for Euler!')
traj = env.v_trajectory
trajectory_name = traj.v_name
# 1st a) phase parameter addition
add_parameters(traj)
# 1st b) phase preparation
# We will add the differential equation (well, its source code only) as a derived parameter
traj.f_add_derived_parameter(FunctionParameter,'diff_eq', diff_lorenz,
comment='Source code of our equation!')
# We want to explore some initial conditions
traj.f_explore({'initial_conditions' : [
np.array([0.01,0.01,0.01]),
np.array([2.02,0.02,0.02]),
np.array([42.0,4.2,0.42])
]})
# 3 different conditions are enough for an illustrative example
# 2nd phase let's run the experiment
# We pass `euler_scheme` as our top-level simulation function and
# the Lorenz equation 'diff_lorenz' as an additional argument
env.f_run(euler_scheme, diff_lorenz)
# We don't have a 3rd phase of post-processing here
# 4th phase analysis.
# I would recommend to do post-processing completely independent from the simulation,
# but for simplicity let's do it here.
# Let's assume that we start all over again and load the entire trajectory new.
# Yet, there is an error within this approach, do you spot it?
del traj
traj = Trajectory(filename='experiments/example_05/HDF5/example_05.hdf5')
# We will only fully load parameters and derived parameters.
# Results will be loaded manually later on.
try:
# However, this will fail because our trajectory does not know how to
# build the FunctionParameter. You have seen this coming, right?
traj.f_load(name=trajectory_name,load_parameters=2,
load_derived_parameters=2,load_results=1)
except ImportError as e:
print 'That did\'nt work, I am sorry. %s ' % e.message
# Ok, let's try again but this time with adding our parameter to the imports
traj = Trajectory(filename='experiments/example_05/HDF5/example_05.hdf5',
dynamically_imported_classes=FunctionParameter)
# Now it works:
traj.f_load(name=trajectory_name,load_parameters=2,
load_derived_parameters=2,load_results=1)
#For the fun of it, let's print the source code
print '\n ---------- The source code of your function ---------- \n %s' % traj.diff_eq
# Let's get the exploration array:
initial_conditions_exploration_array = traj.f_get('initial_conditions').f_get_range()
# Now let's plot our simulated equations for the different initial conditions:
# We will iterate through the run names
for idx, run_name in enumerate(traj.f_get_run_names()):
#Get the result of run idx from the trajectory
euler_result = traj.results.f_get(run_name).euler_evolution
# Now we manually need to load the result. Actually the results are not so large and we
# could load them all at once. But for demonstration we do as if they were huge:
traj.f_load_item(euler_result)
euler_data = euler_result.data
#Plot fancy 3d plot
fig = plt.figure(idx)
ax = fig.gca(projection='3d')
x = euler_data[:,0]
y = euler_data[:,1]
z = euler_data[:,2]
ax.plot(x, y, z, label='Initial Conditions: %s' % str(initial_conditions_exploration_array[idx]))
plt.legend()
plt.show()
# Now we free the data again (because we assume its huuuuuuge):
del euler_data
euler_result.f_empty()
