Python Trajectory.f_store_item方法代码示例

# 需要导入模块: from pypet.trajectory import Trajectory [as 别名]
# 或者: from pypet.trajectory.Trajectory import f_store_item [as 别名]
    def test_overwrite_stuff(self):
        traj = Trajectory(name='Test', filename=make_temp_file('testowrite.hdf5'))

        res = traj.f_add_result('mytest.test', a='b', c='d')

        traj.f_store()

        res['a'] = 333
        res['c'] = 123445

        traj.f_store_item(res, overwrite='a')

        # Should emit a warning
        traj.f_store_item(res, overwrite=['a', 'b'])

        traj.f_load(load_results=3)

        res = traj.test

        self.assertTrue(res['a']==333)
        self.assertTrue(res['c']=='d')

        res['c'] = 123445

        traj.f_store_item(res, overwrite=True)
        res.f_empty()

        traj.f_load(load_results=3)

        self.assertTrue(traj.test['c']==123445)

# 需要导入模块: from pypet.trajectory import Trajectory [as 别名]
# 或者: from pypet.trajectory.Trajectory import f_store_item [as 别名]
# We free the data:
traj.huge_matrices.f_empty()

# Check if the data was deleted
if traj.huge_matrices.f_is_empty():
    print('As promised: Nothing there!')
else:
    print('What a disappointing peace of crap this software is!')

# Lucky, it worked.
# Ok we could it add some more stuff to the result object if we want to:
traj.huge_matrices.f_set(monty='Always look on the bright side of life!')

# Next we can store our new string called monty to disk. Since our trajectory was already
# stored to disk once, we can make use of the functionality to store individual items:
traj.f_store_item('huge_matrices')

# Neat, hu? Ok now let's load some of it back, for educational purposes let's start with a fresh
# 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!')

# 需要导入模块: from pypet.trajectory import Trajectory [as 别名]
# 或者: from pypet.trajectory.Trajectory import f_store_item [as 别名]
# Now let's see what fast access is:
print 'The name of the actor playing Luke is %s.' % traj.luke_skywalker

# And now what happens if you forbid it
traj.v_fast_access=False
print 'The object found for luke_skywalker is `%s`.' % str(traj.luke_skywalker)

#Let's store the trajectory:
traj.f_store()

# That was easy, let's assume we already completed a simulation and now we add a veeeery large
# result that we want to store to disk immediately and than empty it
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: