def test_partial_loading(self):
traj = Trajectory(name='TestPartial', filename=make_temp_file('testpartially.hdf5'))
res = traj.f_add_result('mytest.test', a='b', c='d')
traj.f_store()
traj.f_remove_child('results', recursive=True)
traj.f_update_skeleton()
traj.f_load_item(traj.test, load_only=['a', 'x'])
self.assertTrue('a' in traj.test)
self.assertTrue('c' not in traj.test)
traj.f_remove_child('results', recursive=True)
traj.f_update_skeleton()
load_except= ['c', 'd']
traj.f_load_item(traj.test, load_except=load_except)
self.assertTrue(len(load_except)==2)
self.assertTrue('a' in traj.test)
self.assertTrue('c' not in traj.test)
with self.assertRaises(ValueError):
traj.f_load_item(traj.test, load_except=['x'], load_only=['y'])
# trajectory name on creation (if you do not want this, just say `add_time=False`).
# Thus, the name is not `example_09_huge_data`, but `example_09_huge_data_XXXX_XX_XX_XXhXXmXXs`:
old_traj_name = traj.v_name
del traj
traj = Trajectory(filename=filename)
# We only want to load the skeleton but not the data:
traj.f_load(name=old_traj_name, load_results=pypetconstants.LOAD_SKELETON)
# Check if we only loaded the skeleton, that means the `huge_matrices` result must be empty:
if traj.huge_matrices.f_is_empty():
print('Told you!')
else:
print('Unbelievable, this sucks!')
# Now let's only load `monty` and `mat1`.
# We can do this by passing the keyword argument `load_only` to the load item function:
traj.f_load_item('huge_matrices', load_only=['monty','mat1'])
# Check if this worked:
if ('monty' in traj.huge_matrices and
'mat1' in traj.huge_matrices and
not 'mat2' in traj.huge_matrices ):
val_mat1 = traj.huge_matrices.mat1[10,10,10]
print('mat1 contains %f at position [10,10,10]' % val_mat1)
print('And do not forget: %s' % traj.huge_matrices.monty)
else:
print('That\'s it, I quit! I cannot work like this!')
# Thanks for your attention!
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()
traj.f_add_result('starwars.gross_income_of_film', amount=10.1 ** 11, currency='$$$',
comment='George Lucas is rich, dude!')
# This is a large number, we better store it and than free the memory:
traj.f_store_item('gross_income_of_film')
traj.gross_income_of_film.f_empty()
# Now lets reload the trajectory
del traj
traj = Trajectory(filename='experiments/example_02/HDF5/example_02.hdf5')
# We want to load the last trajectory in the file, therefore index = -1
# We want to load the parameters, therefore load_parameters=2
# We only want to load the skeleton of the results, so load_results=1
traj.f_load(index=-1,load_parameters=2,load_results=1)
# Let's check if our result is really empty
if traj.gross_income_of_film.f_is_empty():
print 'Nothing there!'
else:
print 'I found something!'
# Ok, let's manually reload the result
traj.f_load_item('gross_income_of_film')
if traj.gross_income_of_film.f_is_empty():
print 'Still empty :-('
else:
print 'George Lucas earned %s%s!' %(str(traj.gross_income_of_film.amount),
traj.gross_income_of_film.currency)
# And that's how it works! If you wish, you can inspect the
# experiments/example_02/HDF5/example_02.hdf5 file to take a look at the tree structure
# trajectory. Let's keep the old trajectory name in mind. The current time is added to the
# trajectory name on creation (if you do not want this, just say `add_time=False`).
# Thus, the name is not `example_09_huge_data`, but `example_09_huge_data_XXXX_XX_XX_XXhXXmXXs`:
old_traj_name = traj.v_name
del traj
traj = Trajectory(filename=filename)
# We only want to load the skeleton but not the data:
traj.f_load(name=old_traj_name, load_results=pypetconstants.LOAD_SKELETON)
# Check if we only loaded the skeleton, that means the `huge_matrices` result must be empty:
if traj.huge_matrices.f_is_empty():
print('Told you!')
else:
print('Unbelievable, this sucks!')
# Now let's only load `monty` and `mat1`.
# We can do this by passing the keyword argument `load_only` to the load item function:
traj.f_load_item('huge_matrices', load_only=['monty', 'mat1'])
# Check if this worked:
if ('monty' in traj.huge_matrices and 'mat1' in traj.huge_matrices
and not 'mat2' in traj.huge_matrices):
val_mat1 = traj.huge_matrices.mat1[10, 10, 10]
print('mat1 contains %f at position [10,10,10]' % val_mat1)
print('And do not forget: %s' % traj.huge_matrices.monty)
else:
print('That\'s it, I quit! I cannot work like this!')
# Thanks for your attention!
