Python Trajectory.f_get_run_names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_get_run_names [as 别名]
    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))

# 需要导入模块: from pypet.trajectory import Trajectory [as 别名]
# 或者: from pypet.trajectory.Trajectory import f_get_run_names [as 别名]
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()