Natural Naming, Storage and Loading

Download: example_02_trajectory_access_and_storage.py

The following code snippet shows how natural naming works, and how you can store and load a trajectory.

import os  # To allow pathnames under Windows and Linux

from pypet import NotUniqueNodeError, Trajectory

# We first generate a new Trajectory
filename = os.path.join("hdf5", "example_02.hdf5")
traj = Trajectory("Example", filename=filename, overwrite_file=True, comment="Access and Storage!")


# We add our first parameter with the data 'Harrison Ford'
traj.f_add_parameter("starwars.characters.han_solo", "Harrison Ford")

# This automatically added the groups 'starwars' and the subgroup 'characters'
# Let's get the characters subgroup
characters = traj.parameters.starwars.characters

# Since characters is unique we could also use shortcuts
characters = traj.characters

# Or the get method
characters = traj.f_get("characters")

# Or square brackets
characters = traj["characters"]

# Lets add another character
characters.f_add_parameter("luke_skywalker", "Mark Hamill", comment="May the force be with you!")

# The full name of luke skywalker is now `parameters.starwars.characters.luke_skywalker`:
print(
    "The full name of the new Skywalker Parameter is %s" % traj.f_get("luke_skywalker").v_full_name
)

# Lets see what happens if we have not unique entries:
traj.f_add_parameter_group("spaceballs.characters")

# Now our shortcuts no longer work, since we have two character groups!
try:
    traj.characters
except NotUniqueNodeError as exc:
    print("Damn it, there are two characters groups in the trajectory: %s" % repr(exc))

# But if we are more specific we have again a unique finding
characters = traj.starwars.characters

# 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()

# Moreover, if you don't like prefixes `f_` and `v_` you can also use `func` and `vars`:
traj.func.add_result("starwars.robots", c3p0="android", r2d2="beeep!", comment="Help me Obiwan!")
print(traj.results.starwars.robots.vars.comment)

# Now lets reload the trajectory
del traj
traj = Trajectory(filename=filename)
# 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