def test_reaction_counts_observable(self):
fname = os.path.join(self.dir,
"test_observables_particle_reaction_counts.h5")
context = Context()
context.box_size = [10., 10., 10.]
context.particle_types.add("A", .0)
context.particle_types.add("B", .0)
context.particle_types.add("C", .0)
context.reactions.add_conversion("mylabel", "A", "B", .00001)
context.reactions.add_conversion("A->B", "A", "B", 1e16)
context.reactions.add_fusion("B+C->A", "B", "C", "A", 1e16, 1.0, .5,
.5)
sim = Simulation("CPU", context)
sim.add_particle("A", common.Vec(0, 0, 0))
sim.add_particle("B", common.Vec(1.0, 1.0, 1.0))
sim.add_particle("C", common.Vec(1.1, 1.0, 1.0))
n_timesteps = 1
handle = sim.register_observable_reaction_counts(1)
with closing(io.File.create(fname)) as f:
handle.enable_write_to_file(f, u"reactions", int(3))
loop = sim.create_loop(1)
loop.use_reaction_scheduler("Gillespie")
loop.write_config_to_file(f)
loop.run(1)
import readdy.util.io_utils as io_utils
reactions = io_utils.get_reactions(fname)
with h5py.File(fname, "r") as f2:
data = f2["readdy/observables/reactions"]
time_series = f2["readdy/observables/reactions/time"]
np.testing.assert_equal(time_series,
np.array(range(0, n_timesteps + 1)))
def get_item(name, collection):
return next(x for x in collection if x["name"] == name)
mylabel_id = get_item("mylabel", reactions.values())["id"]
atob_id = get_item("A->B", reactions.values())["id"]
fusion_id = get_item("B+C->A", reactions.values())["id"]
# counts of first time step, time is first index
np.testing.assert_equal(data["counts/" + str(mylabel_id)][0],
np.array([0]))
np.testing.assert_equal(data["counts/" + str(atob_id)][0],
np.array([0]))
np.testing.assert_equal(data["counts/" + str(fusion_id)][0],
np.array([0]))
# counts of second time step
np.testing.assert_equal(data["counts/" + str(mylabel_id)][1],
np.array([0]))
np.testing.assert_equal(data["counts/" + str(atob_id)][1],
np.array([1]))
np.testing.assert_equal(data["counts/" + str(fusion_id)][1],
np.array([1]))
def test_reaction_counts_observable(self):
fname = os.path.join(self.dir, "test_observables_particle_reaction_counts.h5")
sim = Simulation("CPU")
sim.context.box_size = [10., 10., 10.]
sim.context.particle_types.add("A", .0)
sim.context.particle_types.add("B", .0)
sim.context.particle_types.add("C", .0)
sim.context.reactions.add_conversion("mylabel", "A", "B", .00001)
sim.context.reactions.add_conversion("A->B", "A", "B", 1e16)
sim.context.reactions.add_fusion("B+C->A", "B", "C", "A", 1e16, 1.0, .5, .5)
sim.add_particle("A", common.Vec(0, 0, 0))
sim.add_particle("B", common.Vec(1.0, 1.0, 1.0))
sim.add_particle("C", common.Vec(1.1, 1.0, 1.0))
n_timesteps = 1
handle = sim.register_observable_reaction_counts(1)
with closing(io.File.create(fname)) as f:
handle.enable_write_to_file(f, u"reactions", int(3))
loop = sim.create_loop(1)
loop.use_reaction_scheduler("Gillespie")
loop.write_config_to_file(f)
loop.run(1)
import readdy.util.io_utils as io_utils
reactions = io_utils.get_reactions(fname)
with h5py.File(fname, "r") as f2:
data = f2["readdy/observables/reactions"]
time_series = f2["readdy/observables/reactions/time"]
np.testing.assert_equal(time_series, np.array(range(0, n_timesteps+1)))
def get_item(name, collection):
return next(x for x in collection if x["name"] == name)
mylabel_id = get_item("mylabel", reactions.values())["id"]
atob_id = get_item("A->B", reactions.values())["id"]
fusion_id = get_item("B+C->A", reactions.values())["id"]
# counts of first time step, time is first index
np.testing.assert_equal(data["counts/"+str(mylabel_id)][0], np.array([0]))
np.testing.assert_equal(data["counts/"+str(atob_id)][0], np.array([0]))
np.testing.assert_equal(data["counts/"+str(fusion_id)][0], np.array([0]))
# counts of second time step
np.testing.assert_equal(data["counts/"+str(mylabel_id)][1], np.array([0]))
np.testing.assert_equal(data["counts/"+str(atob_id)][1], np.array([1]))
np.testing.assert_equal(data["counts/"+str(fusion_id)][1], np.array([1]))
def test_reaction_counts_observable(self):
common.set_logging_level("warn")
fname = os.path.join(self.dir,
"test_observables_particle_reaction_counts.h5")
sim = Simulation()
sim.set_kernel("CPU")
sim.box_size = common.Vec(10, 10, 10)
sim.register_particle_type("A", .0, 5.0)
sim.register_particle_type("B", .0, 6.0)
sim.register_particle_type("C", .0, 6.0)
sim.register_reaction_conversion("mylabel", "A", "B", .00001)
sim.register_reaction_conversion("A->B", "A", "B", 1.)
sim.register_reaction_fusion("B+C->A", "B", "C", "A", 1.0, 1.0, .5, .5)
sim.add_particle("A", common.Vec(0, 0, 0))
sim.add_particle("B", common.Vec(1.0, 1.0, 1.0))
sim.add_particle("C", common.Vec(1.1, 1.0, 1.0))
n_timesteps = 1
handle = sim.register_observable_reaction_counts(1)
with closing(
io.File(fname, io.FileAction.CREATE,
io.FileFlag.OVERWRITE)) as f:
handle.enable_write_to_file(f, u"reactions", int(3))
sim.run_scheme_readdy(True).write_config_to_file(
f).with_reaction_scheduler(
"GillespieParallel").configure_and_run(n_timesteps, 1)
with h5py.File(fname, "r") as f2:
data = f2["readdy/observables/reactions"]
time_series = f2["readdy/observables/reactions/time"]
np.testing.assert_equal(time_series,
np.array(range(0, n_timesteps + 1)))
def get_item(name, collection):
return next(x for x in collection if x["name"] == name)
order_1_reactions = data["registered_reactions/order1_reactions"]
order_2_reactions = data["registered_reactions/order2_reactions"]
mylabel_reaction = get_item("mylabel", order_1_reactions)
reaction_idx_mylabel = mylabel_reaction["index"]
atob_reaction = get_item("A->B", order_1_reactions)
reaction_idx_atob = atob_reaction["index"]
# counts of first time step, time is first index
np.testing.assert_equal(
data["counts/order1/A[id=0]"][0, reaction_idx_mylabel],
np.array([0]))
np.testing.assert_equal(
data["counts/order1/A[id=0]"][0, reaction_idx_atob],
np.array([0]))
np.testing.assert_equal(
data["counts/order2/B[id=1] + C[id=2]"][0, 0], np.array([0]))
# counts of second time step
np.testing.assert_equal(
data["counts/order1/A[id=0]"][1, reaction_idx_mylabel],
np.array([0]))
np.testing.assert_equal(
data["counts/order1/A[id=0]"][1, reaction_idx_atob],
np.array([1]))
np.testing.assert_equal(
data["counts/order2/B[id=1] + C[id=2]"][1, 0], np.array([1]))
common.set_logging_level("warn")
