def test_write_trajectory(self):
traj_fname = os.path.join(self.dir, "traj.h5")
simulation = Simulation()
simulation.set_kernel("SingleCPU")
simulation.box_size = common.Vec(5,5,5)
simulation.register_particle_type("A", 0.0)
simulation.register_reaction_conversion("A->A", "A", "A", 1.)
def callback(_):
simulation.add_particle("A", common.Vec(0, 0, 0))
simulation.register_observable_n_particles(1, ["A"], callback)
traj_handle = simulation.register_observable_trajectory(0)
with closing(io.File.create(traj_fname, io.FileFlag.OVERWRITE)) as f:
traj_handle.enable_write_to_file(f, u"", 3)
simulation.run_scheme_readdy(True).write_config_to_file(f).configure(1).run(20)
r = TrajectoryReader(traj_fname)
trajectory_items = r[:]
for idx, items in enumerate(trajectory_items):
np.testing.assert_equal(len(items), idx+1)
for item in items:
np.testing.assert_equal(item.t, idx)
np.testing.assert_equal(item.position, np.array([.0, .0, .0]))
with h5py.File(traj_fname) as f:
np.testing.assert_equal("A", f["readdy/config/particle_types"][0]["name"])
def test_particle_positions_observable(self):
fname = os.path.join(self.dir, "test_observables_particle_positions.h5")
sim = Simulation("SingleCPU")
sim.context.box_size = [13., 13., 13.]
sim.context.particle_types.add("A", .1)
sim.add_particle("A", common.Vec(0, 0, 0))
# every time step, add one particle
sim.register_observable_n_particles(1, ["A"], lambda n: sim.add_particle("A", common.Vec(1.5, 2.5, 3.5)))
handle = sim.register_observable_particle_positions(1, [])
n_timesteps = 19
with closing(io.File.create(fname)) as f:
handle.enable_write_to_file(f, u"particle_positions", int(3))
sim.run(n_timesteps, 0)
handle.flush()
with h5py.File(fname, "r") as f2:
data = f2["readdy/observables/particle_positions/data"][:]
np.testing.assert_equal(len(data), n_timesteps + 1)
for t, positions in enumerate(data):
# we begin with two particles
np.testing.assert_equal(len(positions), t + 2)
np.testing.assert_equal(positions[0]["x"], 0)
np.testing.assert_equal(positions[0]["y"], 0)
np.testing.assert_equal(positions[0]["z"], 0)
for i in range(1, len(positions)):
np.testing.assert_equal(positions[i]["x"], 1.5)
np.testing.assert_equal(positions[i]["y"], 2.5)
np.testing.assert_equal(positions[i]["z"], 3.5)
def test_forces_observable(self):
fname = os.path.join(self.dir, "test_observables_particle_forces.h5")
sim = Simulation()
sim.set_kernel("CPU")
sim.box_size = common.Vec(13, 13, 13)
sim.register_particle_type("A", .1)
sim.add_particle("A", common.Vec(0, 0, 0))
# every time step, add one particle
sim.register_observable_n_particles(1, ["A"], lambda n: sim.add_particle("A", common.Vec(1.5, 2.5, 3.5)))
handle = sim.register_observable_forces(1, [])
n_timesteps = 19
with closing(io.File.create(fname)) as f:
handle.enable_write_to_file(f, u"forces", int(3))
sim.run_scheme_readdy(True).configure(1).run(n_timesteps)
handle.flush()
with h5py.File(fname, "r") as f2:
data = f2["readdy/observables/forces/data"][:]
time_series = f2["readdy/observables/forces/time"]
np.testing.assert_equal(len(data), n_timesteps + 1)
np.testing.assert_equal(time_series, np.array(range(0, n_timesteps+1)))
for t, forces in enumerate(data):
# we begin with two particles
np.testing.assert_equal(len(forces), t + 2)
np.testing.assert_equal(forces[0]["x"], 0)
np.testing.assert_equal(forces[0]["y"], 0)
np.testing.assert_equal(forces[0]["z"], 0)
for i in range(1, len(forces)):
np.testing.assert_equal(forces[i]["x"], 0)
np.testing.assert_equal(forces[i]["y"], 0)
np.testing.assert_equal(forces[i]["z"], 0)
def test_write_trajectory(self):
traj_fname = os.path.join(self.dir, "traj.h5")
simulation = Simulation()
simulation.set_kernel("SingleCPU")
simulation.box_size = common.Vec(5, 5, 5)
simulation.register_particle_type("A", 0.0)
simulation.register_reaction_conversion("A->A", "A", "A", 1.)
def callback(_):
simulation.add_particle("A", common.Vec(0, 0, 0))
simulation.register_observable_n_particles(1, ["A"], callback)
traj_handle = simulation.register_observable_trajectory(0)
with closing(io.File.create(traj_fname, io.FileFlag.OVERWRITE)) as f:
traj_handle.enable_write_to_file(f, u"", 3)
simulation.run_scheme_readdy(True).write_config_to_file(
f).configure(1).run(20)
r = TrajectoryReader(traj_fname)
trajectory_items = r[:]
for idx, items in enumerate(trajectory_items):
np.testing.assert_equal(len(items), idx + 1)
for item in items:
np.testing.assert_equal(item.t, idx)
np.testing.assert_equal(item.position, np.array([.0, .0, .0]))
with h5py.File(traj_fname) as f:
np.testing.assert_equal(
"A", f["readdy/config/particle_types"][0]["name"])
def test_forces_observable(self):
fname = os.path.join(self.dir, "test_observables_particle_forces.h5")
sim = Simulation()
sim.set_kernel("CPU")
sim.box_size = common.Vec(13, 13, 13)
sim.register_particle_type("A", .1)
sim.add_particle("A", common.Vec(0, 0, 0))
# every time step, add one particle
sim.register_observable_n_particles(
1, ["A"],
lambda n: sim.add_particle("A", common.Vec(1.5, 2.5, 3.5)))
handle = sim.register_observable_forces(1, [])
n_timesteps = 19
with closing(io.File.create(fname)) as f:
handle.enable_write_to_file(f, u"forces", int(3))
sim.run_scheme_readdy(True).configure(1).run(n_timesteps)
handle.flush()
with h5py.File(fname, "r") as f2:
data = f2["readdy/observables/forces/data"][:]
time_series = f2["readdy/observables/forces/time"]
np.testing.assert_equal(len(data), n_timesteps + 1)
np.testing.assert_equal(time_series,
np.array(range(0, n_timesteps + 1)))
for t, forces in enumerate(data):
# we begin with two particles
np.testing.assert_equal(len(forces), t + 2)
np.testing.assert_equal(forces[0]["x"], 0)
np.testing.assert_equal(forces[0]["y"], 0)
np.testing.assert_equal(forces[0]["z"], 0)
for i in range(1, len(forces)):
np.testing.assert_equal(forces[i]["x"], 0)
np.testing.assert_equal(forces[i]["y"], 0)
np.testing.assert_equal(forces[i]["z"], 0)
def test_write_flat_trajectory(self):
common.set_logging_level("error")
traj_fname = os.path.join(self.dir, "flat_traj.h5")
simulation = Simulation()
simulation.set_kernel("SingleCPU")
simulation.box_size = common.Vec(5, 5, 5)
simulation.register_particle_type("A", 0.0, 0.0)
def callback(_):
simulation.add_particle("A", common.Vec(0, 0, 0))
simulation.register_observable_n_particles(1, ["A"], callback)
traj_handle = simulation.register_observable_flat_trajectory(1)
with closing(
io.File(traj_fname, io.FileAction.CREATE,
io.FileFlag.OVERWRITE)) as f:
traj_handle.enable_write_to_file(f, u"", int(3))
simulation.run_scheme_readdy(True).configure(1).run(20)
r = TrajectoryReader(traj_fname)
trajectory_items = r[:]
for idx, items in enumerate(trajectory_items):
np.testing.assert_equal(len(items), idx + 1)
for item in items:
np.testing.assert_equal(item.t, idx)
np.testing.assert_equal(item.position, np.array([.0, .0, .0]))
common.set_logging_level("debug")
def test_write_trajectory(self):
traj_fname = os.path.join(self.dir, "traj.h5")
context = Context()
context.box_size = [5., 5., 5.]
context.particle_types.add("A", 0.0)
context.reactions.add_conversion("A->A", "A", "A", 1.)
simulation = Simulation("SingleCPU", context)
def callback(_):
simulation.add_particle("A", common.Vec(0, 0, 0))
simulation.register_observable_n_particles(1, ["A"], callback)
traj_handle = simulation.register_observable_trajectory(0)
with closing(io.File.create(traj_fname, io.FileFlag.OVERWRITE)) as f:
traj_handle.enable_write_to_file(f, u"", 3)
loop = simulation.create_loop(1.)
loop.write_config_to_file(f)
loop.run(20)
r = TrajectoryReader(traj_fname)
trajectory_items = r[:]
for idx, items in enumerate(trajectory_items):
np.testing.assert_equal(len(items), idx + 1)
for item in items:
np.testing.assert_equal(item.t, idx)
np.testing.assert_equal(item.position, np.array([.0, .0, .0]))
with h5py.File(traj_fname, 'r') as f:
np.testing.assert_equal(
"A", f["readdy/config/particle_types"][0]["name"])
def test_particle_positions_observable(self):
fname = os.path.join(self.dir,
"test_observables_particle_positions.h5")
sim = Simulation()
sim.set_kernel("SingleCPU")
sim.box_size = common.Vec(13, 13, 13)
sim.register_particle_type("A", .1, .1)
sim.add_particle("A", common.Vec(0, 0, 0))
# every time step, add one particle
sim.register_observable_n_particles(
1, ["A"],
lambda n: sim.add_particle("A", common.Vec(1.5, 2.5, 3.5)))
handle = sim.register_observable_particle_positions(1, [])
n_timesteps = 19
with closing(
io.File(fname, io.FileAction.CREATE,
io.FileFlag.OVERWRITE)) as f:
handle.enable_write_to_file(f, u"particle_positions", int(3))
sim.run_scheme_readdy(True).configure(0).run(n_timesteps)
handle.flush()
with h5py.File(fname, "r") as f2:
data = f2["readdy/observables/particle_positions/data"][:]
np.testing.assert_equal(len(data), n_timesteps + 1)
for t, positions in enumerate(data):
# we begin with two particles
np.testing.assert_equal(len(positions), t + 2)
np.testing.assert_equal(positions[0]["x"], 0)
np.testing.assert_equal(positions[0]["y"], 0)
np.testing.assert_equal(positions[0]["z"], 0)
for i in range(1, len(positions)):
np.testing.assert_equal(positions[i]["x"], 1.5)
np.testing.assert_equal(positions[i]["y"], 2.5)
np.testing.assert_equal(positions[i]["z"], 3.5)
common.set_logging_level("warn")
def test_n_particles_observable(self):
common.set_logging_level("warn")
fname = os.path.join(self.dir, "test_observables_n_particles.h5")
simulation = Simulation()
simulation.set_kernel("SingleCPU")
box_size = common.Vec(10, 10, 10)
simulation.kbt = 2
simulation.periodic_boundary = [True, True, True]
simulation.box_size = box_size
simulation.register_particle_type("A", .2, 1.)
simulation.register_particle_type("B", .2, 1.)
simulation.add_particle("A", common.Vec(-2.5, 0, 0))
simulation.add_particle("B", common.Vec(0, 0, 0))
n_time_steps = 50
callback_n_particles_a_b = []
callback_n_particles_all = []
def callback_ab(value):
callback_n_particles_a_b.append(value)
simulation.add_particle("A", common.Vec(-1, -1, -1))
def callback_all(hist):
callback_n_particles_all.append(hist)
simulation.add_particle("A", common.Vec(-1, -1, -1))
simulation.add_particle("B", common.Vec(-1, -1, -1))
handle_a_b_particles = simulation.register_observable_n_particles(
1, ["A", "B"], callback_ab)
handle_all = simulation.register_observable_n_particles(
1, [], callback_all)
with closing(
io.File(fname, io.FileAction.CREATE,
io.FileFlag.OVERWRITE)) as f:
handle_a_b_particles.enable_write_to_file(f, u"n_a_b_particles",
int(3))
handle_all.enable_write_to_file(f, u"n_particles", int(5))
simulation.run(n_time_steps, 0.02)
handle_all.flush()
handle_a_b_particles.flush()
with h5py.File(fname, "r") as f2:
n_a_b_particles = f2["readdy/observables/n_a_b_particles/data"][:]
n_particles = f2["readdy/observables/n_particles/data"][:]
time_series = f2["readdy/observables/n_a_b_particles/time"]
np.testing.assert_equal(time_series,
np.array(range(0, n_time_steps + 1)))
for t in range(n_time_steps):
np.testing.assert_equal(n_a_b_particles[t][0],
callback_n_particles_a_b[t][0])
np.testing.assert_equal(n_a_b_particles[t][1],
callback_n_particles_a_b[t][1])
np.testing.assert_equal(n_particles[t][0],
callback_n_particles_all[t][0])
def test_n_particles_observable(self):
fname = os.path.join(self.dir, "test_observables_n_particles.h5")
context = Context()
box_size = [10., 10., 10.]
context.kbt = 2
context.pbc = [True, True, True]
context.box_size = box_size
context.particle_types.add("A", .2)
context.particle_types.add("B", .2)
simulation = Simulation("SingleCPU", context)
simulation.add_particle("A", common.Vec(-2.5, 0, 0))
simulation.add_particle("B", common.Vec(0, 0, 0))
n_time_steps = 50
callback_n_particles_a_b = []
callback_n_particles_all = []
def callback_ab(value):
callback_n_particles_a_b.append(value)
simulation.add_particle("A", common.Vec(-1, -1, -1))
def callback_all(hist):
callback_n_particles_all.append(hist)
simulation.add_particle("A", common.Vec(-1, -1, -1))
simulation.add_particle("B", common.Vec(-1, -1, -1))
handle_a_b_particles = simulation.register_observable_n_particles(
1, ["A", "B"], callback_ab)
handle_all = simulation.register_observable_n_particles(
1, [], callback_all)
with closing(io.File.create(fname)) as f:
handle_a_b_particles.enable_write_to_file(f, u"n_a_b_particles",
int(3))
handle_all.enable_write_to_file(f, u"n_particles", int(5))
simulation.run(n_time_steps, 0.02)
handle_all.flush()
handle_a_b_particles.flush()
with h5py.File(fname, "r") as f2:
n_a_b_particles = f2["readdy/observables/n_a_b_particles/data"][:]
n_particles = f2["readdy/observables/n_particles/data"][:]
time_series = f2["readdy/observables/n_a_b_particles/time"]
np.testing.assert_equal(time_series,
np.array(range(0, n_time_steps + 1)))
for t in range(n_time_steps):
np.testing.assert_equal(n_a_b_particles[t][0],
callback_n_particles_a_b[t][0])
np.testing.assert_equal(n_a_b_particles[t][1],
callback_n_particles_a_b[t][1])
np.testing.assert_equal(n_particles[t][0],
callback_n_particles_all[t][0])
def test_interrupt_simple(self):
sim = Simulation("SingleCPU")
sim.context.particle_types.add("A", 0.1)
# Define counter as list. This is a workaround because nosetest will complain otherwise.
counter = [0]
def increment(result):
counter[0] += 1
sim.register_observable_n_particles(1, ["A"], increment)
do_continue = lambda t: t < 5
sim.create_loop(.1).run_with_criterion(do_continue)
np.testing.assert_equal(counter[0], 6)
def test_particles_observable(self):
fname = os.path.join(self.dir, "test_observables_particles.h5")
context = Context()
context.box_size = [13., 13., 13.]
context.particle_types.add("A", .1)
context.particle_types.add("B", .1)
sim = Simulation("SingleCPU", context)
sim.add_particle("A", common.Vec(0, 0, 0))
sim.add_particle("B", common.Vec(0, 0, 0))
# every time step, add one particle
sim.register_observable_n_particles(
1, ["A"],
lambda n: sim.add_particle("A", common.Vec(1.5, 2.5, 3.5)))
handle = sim.register_observable_particles(1)
n_timesteps = 19
with closing(io.File.create(fname)) as f:
handle.enable_write_to_file(f, u"particles", int(3))
loop = sim.create_loop(0)
loop.write_config_to_file(f)
loop.run(n_timesteps)
handle.flush()
from readdy.util.io_utils import get_particle_types
particle_types = get_particle_types(fname)
with h5py.File(fname, "r") as f2:
types = f2["readdy/observables/particles/types"][:]
ids = f2["readdy/observables/particles/ids"][:]
positions = f2["readdy/observables/particles/positions"][:]
for t in range(n_timesteps):
np.testing.assert_equal(len(types[t]), t + 3)
np.testing.assert_equal(len(ids[t]), t + 3)
np.testing.assert_equal(len(positions[t]), t + 3)
np.testing.assert_equal(types[t][0],
particle_types["A"]["type_id"])
np.testing.assert_equal(positions[t][0][0], 0)
np.testing.assert_equal(positions[t][0][1], 0)
np.testing.assert_equal(positions[t][0][2], 0)
np.testing.assert_equal(positions[t][1][0], 0)
np.testing.assert_equal(positions[t][1][1], 0)
np.testing.assert_equal(positions[t][1][2], 0)
np.testing.assert_equal(types[t][1],
particle_types["B"]["type_id"])
for others in range(2, len(types[t])):
np.testing.assert_equal(types[t][others],
particle_types["A"]["type_id"])
np.testing.assert_equal(positions[t][others][0], 1.5)
np.testing.assert_equal(positions[t][others][1], 2.5)
np.testing.assert_equal(positions[t][others][2], 3.5)
def test_interrupt_simple(self):
sim = Simulation()
sim.set_kernel("SingleCPU")
sim.register_particle_type("A", 0.1)
# Define counter as list. This is a workaround because nosetest will complain otherwise.
counter = [0]
def increment(result):
counter[0] += 1
sim.register_observable_n_particles(1, ["A"], increment)
scheme = sim.run_scheme_readdy(True).configure(0.1)
do_continue = lambda t: t < 5
scheme.run_with_criterion(do_continue)
np.testing.assert_equal(counter[0], 6)
def test_n_particles_observable(self):
fname = os.path.join(self.dir, "test_observables_n_particles.h5")
simulation = Simulation()
simulation.set_kernel("SingleCPU")
box_size = common.Vec(10, 10, 10)
simulation.kbt = 2
simulation.periodic_boundary = [True, True, True]
simulation.box_size = box_size
simulation.register_particle_type("A", .2)
simulation.register_particle_type("B", .2)
simulation.add_particle("A", common.Vec(-2.5, 0, 0))
simulation.add_particle("B", common.Vec(0, 0, 0))
n_time_steps = 50
callback_n_particles_a_b = []
callback_n_particles_all = []
def callback_ab(value):
callback_n_particles_a_b.append(value)
simulation.add_particle("A", common.Vec(-1, -1, -1))
def callback_all(hist):
callback_n_particles_all.append(hist)
simulation.add_particle("A", common.Vec(-1, -1, -1))
simulation.add_particle("B", common.Vec(-1, -1, -1))
handle_a_b_particles = simulation.register_observable_n_particles(1, ["A", "B"], callback_ab)
handle_all = simulation.register_observable_n_particles(1, [], callback_all)
with closing(io.File.create(fname)) as f:
handle_a_b_particles.enable_write_to_file(f, u"n_a_b_particles", int(3))
handle_all.enable_write_to_file(f, u"n_particles", int(5))
simulation.run(n_time_steps, 0.02)
handle_all.flush()
handle_a_b_particles.flush()
with h5py.File(fname, "r") as f2:
n_a_b_particles = f2["readdy/observables/n_a_b_particles/data"][:]
n_particles = f2["readdy/observables/n_particles/data"][:]
time_series = f2["readdy/observables/n_a_b_particles/time"]
np.testing.assert_equal(time_series, np.array(range(0, n_time_steps+1)))
for t in range(n_time_steps):
np.testing.assert_equal(n_a_b_particles[t][0], callback_n_particles_a_b[t][0])
np.testing.assert_equal(n_a_b_particles[t][1], callback_n_particles_a_b[t][1])
np.testing.assert_equal(n_particles[t][0], callback_n_particles_all[t][0])
def test_particles_observable(self):
fname = os.path.join(self.dir, "test_observables_particles.h5")
sim = Simulation("SingleCPU")
sim.context.box_size = [13.,13.,13.]
sim.context.particle_types.add("A", .1)
sim.context.particle_types.add("B", .1)
sim.add_particle("A", common.Vec(0, 0, 0))
sim.add_particle("B", common.Vec(0, 0, 0))
# every time step, add one particle
sim.register_observable_n_particles(1, ["A"], lambda n: sim.add_particle("A", common.Vec(1.5, 2.5, 3.5)))
handle = sim.register_observable_particles(1)
n_timesteps = 19
with closing(io.File.create(fname)) as f:
handle.enable_write_to_file(f, u"particles", int(3))
loop = sim.create_loop(0)
loop.write_config_to_file(f)
loop.run(n_timesteps)
handle.flush()
from readdy.util.io_utils import get_particle_types
particle_types = get_particle_types(fname)
with h5py.File(fname, "r") as f2:
types = f2["readdy/observables/particles/types"][:]
ids = f2["readdy/observables/particles/ids"][:]
positions = f2["readdy/observables/particles/positions"][:]
for t in range(n_timesteps):
np.testing.assert_equal(len(types[t]), t + 3)
np.testing.assert_equal(len(ids[t]), t + 3)
np.testing.assert_equal(len(positions[t]), t + 3)
np.testing.assert_equal(types[t][0], particle_types["A"]["type_id"])
np.testing.assert_equal(positions[t][0][0], 0)
np.testing.assert_equal(positions[t][0][1], 0)
np.testing.assert_equal(positions[t][0][2], 0)
np.testing.assert_equal(positions[t][1][0], 0)
np.testing.assert_equal(positions[t][1][1], 0)
np.testing.assert_equal(positions[t][1][2], 0)
np.testing.assert_equal(types[t][1], particle_types["B"]["type_id"])
for others in range(2, len(types[t])):
np.testing.assert_equal(types[t][others], particle_types["A"]["type_id"])
np.testing.assert_equal(positions[t][others][0], 1.5)
np.testing.assert_equal(positions[t][others][1], 2.5)
np.testing.assert_equal(positions[t][others][2], 3.5)
def test_interrupt_maxparticles(self):
sim = Simulation()
sim.set_kernel("SingleCPU")
sim.register_particle_type("A", 0.1)
sim.add_particle("A", Vec(0, 0, 0))
sim.register_reaction_fission("bla", "A", "A", "A", 1000., 0., 0.5, 0.5)
counter = [0]
shall_stop = [False]
def increment(result):
counter[0] += 1
if result[0] >= 8:
shall_stop[0] = True
sim.register_observable_n_particles(1, ["A"], increment)
scheme = sim.run_scheme_readdy(True).configure(1.)
do_continue = lambda t: not shall_stop[0]
scheme.run_with_criterion(do_continue)
np.testing.assert_equal(counter[0], 4)
def test_interrupt_maxparticles(self):
sim = Simulation("SingleCPU")
sim.context.particle_types.add("A", 0.1)
sim.add_particle("A", Vec(0, 0, 0))
sim.context.reactions.add_fission("bla", "A", "A", "A", 1000., 0., 0.5,
0.5)
counter = [0]
shall_stop = [False]
def increment(result):
counter[0] += 1
if result[0] >= 8:
shall_stop[0] = True
sim.register_observable_n_particles(1, ["A"], increment)
loop = sim.create_loop(1.)
do_continue = lambda t: not shall_stop[0]
loop.run_with_criterion(do_continue)
np.testing.assert_equal(counter[0], 4)
def test_particles_observable(self):
fname = os.path.join(self.dir, "test_observables_particles.h5")
sim = Simulation()
sim.set_kernel("SingleCPU")
sim.box_size = common.Vec(13, 13, 13)
typeid_A = sim.register_particle_type("A", .1, .1)
typeid_B = sim.register_particle_type("B", .1, .1)
sim.add_particle("A", common.Vec(0, 0, 0))
sim.add_particle("B", common.Vec(0, 0, 0))
# every time step, add one particle
sim.register_observable_n_particles(
1, ["A"],
lambda n: sim.add_particle("A", common.Vec(1.5, 2.5, 3.5)))
handle = sim.register_observable_particles(1)
n_timesteps = 19
with closing(
io.File(fname, io.FileAction.CREATE,
io.FileFlag.OVERWRITE)) as f:
handle.enable_write_to_file(f, u"particles", int(3))
sim.run_scheme_readdy(True).configure(0).run(n_timesteps)
handle.flush()
with h5py.File(fname, "r") as f2:
types = f2["readdy/observables/particles/types"][:]
ids = f2["readdy/observables/particles/ids"][:]
positions = f2["readdy/observables/particles/positions"][:]
for t in range(n_timesteps):
np.testing.assert_equal(len(types[t]), t + 3)
np.testing.assert_equal(len(ids[t]), t + 3)
np.testing.assert_equal(len(positions[t]), t + 3)
np.testing.assert_equal(types[t][0], typeid_A)
np.testing.assert_equal(positions[t][0][0], 0)
np.testing.assert_equal(positions[t][0][1], 0)
np.testing.assert_equal(positions[t][0][2], 0)
np.testing.assert_equal(positions[t][1][0], 0)
np.testing.assert_equal(positions[t][1][1], 0)
np.testing.assert_equal(positions[t][1][2], 0)
np.testing.assert_equal(types[t][1], typeid_B)
for others in range(2, len(types[t])):
np.testing.assert_equal(types[t][others], typeid_A)
np.testing.assert_equal(positions[t][others][0], 1.5)
np.testing.assert_equal(positions[t][others][1], 2.5)
np.testing.assert_equal(positions[t][others][2], 3.5)
def test_particles_observable(self):
fname = os.path.join(self.dir, "test_observables_particles.h5")
sim = Simulation()
sim.set_kernel("SingleCPU")
sim.box_size = common.Vec(13, 13, 13)
typeid_A = sim.register_particle_type("A", .1)
typeid_B = sim.register_particle_type("B", .1)
sim.add_particle("A", common.Vec(0, 0, 0))
sim.add_particle("B", common.Vec(0, 0, 0))
# every time step, add one particle
sim.register_observable_n_particles(1, ["A"], lambda n: sim.add_particle("A", common.Vec(1.5, 2.5, 3.5)))
handle = sim.register_observable_particles(1)
n_timesteps = 19
with closing(io.File.create(fname)) as f:
handle.enable_write_to_file(f, u"particles", int(3))
sim.run_scheme_readdy(True).configure(0).run(n_timesteps)
handle.flush()
with h5py.File(fname, "r") as f2:
types = f2["readdy/observables/particles/types"][:]
ids = f2["readdy/observables/particles/ids"][:]
positions = f2["readdy/observables/particles/positions"][:]
for t in range(n_timesteps):
np.testing.assert_equal(len(types[t]), t + 3)
np.testing.assert_equal(len(ids[t]), t + 3)
np.testing.assert_equal(len(positions[t]), t + 3)
np.testing.assert_equal(types[t][0], typeid_A)
np.testing.assert_equal(positions[t][0][0], 0)
np.testing.assert_equal(positions[t][0][1], 0)
np.testing.assert_equal(positions[t][0][2], 0)
np.testing.assert_equal(positions[t][1][0], 0)
np.testing.assert_equal(positions[t][1][1], 0)
np.testing.assert_equal(positions[t][1][2], 0)
np.testing.assert_equal(types[t][1], typeid_B)
for others in range(2, len(types[t])):
np.testing.assert_equal(types[t][others], typeid_A)
np.testing.assert_equal(positions[t][others][0], 1.5)
np.testing.assert_equal(positions[t][others][1], 2.5)
np.testing.assert_equal(positions[t][others][2], 3.5)
def test_write_trajectory_as_observable(self):
traj_fname = os.path.join(self.dir, "traj_as_obs.h5")
simulation = Simulation("SingleCPU")
simulation.context.box_size = [5., 5., 5.]
simulation.context.particle_types.add("A", 0.0)
def callback(_):
simulation.add_particle("A", common.Vec(0, 0, 0))
simulation.register_observable_n_particles(1, ["A"], callback)
traj_handle = simulation.register_observable_trajectory(1)
with closing(io.File.create(traj_fname, io.FileFlag.OVERWRITE)) as f:
traj_handle.enable_write_to_file(f, u"", int(3))
simulation.run(20, 1)
r = TrajectoryReader(traj_fname)
trajectory_items = r[:]
for idx, items in enumerate(trajectory_items):
np.testing.assert_equal(len(items), idx+1)
for item in items:
np.testing.assert_equal(item.t, idx)
np.testing.assert_equal(item.position, np.array([.0, .0, .0]))
def execute(self):
###################################
#
# Units:
# - [x] = µm
# - [t] = s
# - [E] = kJ/mol
#
###################################
kernel_provider = KernelProvider.get()
kernel_provider.load_from_dir(platform_utils.get_readdy_plugin_dir())
simulation = Simulation()
simulation.set_kernel("CPU")
###################################
#
# set up simulation box
#
###################################
box_size = Vec(2, 7, 12)
simulation.box_size = box_size
simulation.kbt = 2.437 # room temperature
simulation.periodic_boundary = [False, False, False]
###################################
#
# register particle types
#
###################################
# particle size, see: http://bmccellbiol.biomedcentral.com/articles/10.1186/1471-2121-5-29
# "The size of the V-ATPase complex is about 15 nm (diameter) x 25 nm (length from lumen side to tip of head)"
membrane_particle_size = .05
diffusion_factor = .5
simulation.register_particle_type("D", 2.5 * diffusion_factor,
.01) # MinD-ADP (without phosphor)
simulation.register_particle_type("D_P", 2.5 * diffusion_factor,
.01) # MinD-ATP (with phosphor)
simulation.register_particle_type("E", 2.5 * diffusion_factor,
.01) # MinE
simulation.register_particle_type("D_PB", .01 * diffusion_factor,
.01) # MinD-ATP bound
simulation.register_particle_type("DE", .01 * diffusion_factor,
.01) # MinDE
###################################
#
# register reaction types
#
###################################
reaction_radius = 4 * (
0.01 + 0.01
) # = sum of the particle radii * 5 (5 - magic number such that k_fusion makes sense, sort of) 5 *
# k_fusion = brentq(lambda x: self.erban_chapman(.093, 2.5 + .01, reaction_radius, x), 1, 5000000)
k_fusion = 1.0
print("k_fusion=%s" % k_fusion)
simulation.register_reaction_conversion("Phosphorylation", "D", "D_P",
.5)
simulation.register_reaction_fusion("bound MinD+MinE->MinDE", "D_PB",
"E", "DE", k_fusion,
reaction_radius * 3.5, .5, .5)
simulation.register_reaction_fission("MinDE to MinD and MinE, detach",
"DE", "D", "E", .25,
reaction_radius, .5, .5)
###################################
#
# register potentials
#
###################################
membrane_size = Vec(.5, 5, 10)
layer = Vec(.08, .08, .08)
extent = membrane_size + 2 * layer
origin = -.5 * membrane_size - layer
simulation.register_potential_box(
"D", 10., origin, extent,
False) # (force constant, origin, extent, considerParticleRadius)
simulation.register_potential_box(
"D_P", 10., origin, extent,
False) # (force constant, origin, extent, considerParticleRadius)
simulation.register_potential_box(
"D_PB", 10., origin, extent,
False) # (force constant, origin, extent, considerParticleRadius)
simulation.register_potential_box(
"E", 10., origin, extent,
False) # (force constant, origin, extent, considerParticleRadius)
simulation.register_potential_box(
"DE", 10., origin, extent,
False) # (force constant, origin, extent, considerParticleRadius)
# simulation.register_potential_piecewise_weak_interaction("D_P", "D_PB", 3, .02, 2, .05) # (force constant, desired dist, depth, no interaction dist)
###################################
#
# membrane particles
#
###################################
using_membrane_particles = False
if using_membrane_particles:
simulation.register_particle_type(
"M", 0, membrane_particle_size) # membrane particle
simulation.register_reaction_enzymatic(
"Attach to membrane", "M", "D_P", "D_PB", .5,
.01 + membrane_particle_size) # .01 + .025 # todo: rate?
dx = np.linspace(
origin[0] + layer[0],
-1 * origin[0] - layer[0],
int(float(membrane_size[0]) / membrane_particle_size),
endpoint=True)
dy = np.linspace(
origin[1] + layer[1],
-1 * origin[1] - layer[1],
int(float(membrane_size[1]) / membrane_particle_size),
endpoint=True)
dz = np.linspace(
origin[2] + layer[2],
-1 * origin[2] - layer[2],
int(float(membrane_size[2]) / membrane_particle_size),
endpoint=True)
for y in dy:
for z in dz:
simulation.add_particle(
"M", Vec(-1 * origin[0] - layer[0], y, z))
print("done adding membrane particles")
else:
simulation.register_reaction_conversion("Phosphorylation", "D_P",
"D_PB", .5)
simulation.register_reaction_enzymatic(
"Enzymatic DP+DPB->DPB + DPB", "D_PB", "D_P", "D_PB", .5, .02)
using_uniform_distribution = True
n_minE_particles = 3120
n_minD_particles = n_minE_particles * 4
mine_x = np.random.uniform(origin[0] + layer[0],
-1 * origin[0] - layer[0], n_minE_particles)
mine_y = np.random.uniform(origin[1] + layer[1],
-1 * origin[1] - layer[1], n_minE_particles)
if using_uniform_distribution:
mine_z = np.random.uniform(origin[2] + layer[2],
-1 * origin[2] - layer[2],
n_minE_particles)
else:
mine_z = np.random.uniform(origin[2] + layer[2],
.5 * (-1 * origin[2] - layer[2]),
n_minE_particles)
mind_x = np.random.uniform(origin[0] + layer[0],
-1 * origin[0] - layer[0], n_minD_particles)
mind_y = np.random.uniform(origin[1] + layer[1],
-1 * origin[1] - layer[1], n_minD_particles)
if using_uniform_distribution:
mind_z = np.random.uniform(origin[2] + layer[2],
-1 * origin[2] - layer[2],
n_minD_particles)
else:
mind_z = np.random.uniform(.5 * (-1 * origin[2] - layer[2]),
-1 * origin[2] - layer[2],
n_minD_particles)
for i in range(n_minE_particles):
simulation.add_particle("E", Vec(mine_x[i], mine_y[i], mine_z[i]))
for i in range(int(.5 * n_minD_particles)):
simulation.add_particle("D", Vec(mind_x[i], mind_y[i], mind_z[i]))
for i in range(int(.5 * n_minD_particles), n_minD_particles):
simulation.add_particle("D_P", Vec(mind_x[i], mind_y[i],
mind_z[i]))
self.timestep = simulation.get_recommended_time_step(2)
###################################
#
# register observables
#
###################################
# simulation.register_observable_center_of_mass(1, self.com_callback_mind, ["D", "D_P", "D_PB"])
# simulation.register_observable_center_of_mass(1, self.com_callback_mine, ["E"])
# simulation.register_observable_center_of_mass(1, self.com_callback_minde, ["DE", "D_PB"])
print("histogram start")
# simulation.register_observable_histogram_along_axis(100, self.histrogram_callback_minD, np.arange(-3, 3, .1), ["D", "D_P", "D_PB"], 2)
# simulation.register_observable_histogram_along_axis(100, self.histrogram_callback_minE, np.arange(-3, 3, .1), ["D_PB", "DE"], 2)
stride = int(.01 / self.timestep)
self.stride = stride
print("using stride=%s" % stride)
bins = np.linspace(-7, 7, 80)
simulation.register_observable_histogram_along_axis(
stride, bins, 2, ["D"], self.histogram_callback_minD)
simulation.register_observable_histogram_along_axis(
stride, bins, 2, ["D_P"], self.histogram_callback_minDP)
simulation.register_observable_histogram_along_axis(
stride, bins, 2, ["D_PB"], self.histogram_callback_minDPB)
simulation.register_observable_histogram_along_axis(
stride, bins, 2, ["E"], self.histogram_callback_minE)
simulation.register_observable_histogram_along_axis(
stride, bins, 2, ["DE"], self.histogram_callback_minDE)
simulation.register_observable_histogram_along_axis(
stride, bins, 2, ["D", "D_P", "D_PB", "DE"],
self.histogram_callback_M)
simulation.register_observable_n_particles(
stride, ["D", "D_P", "D_PB", "E", "DE"], self.n_particles_callback)
print("histogram end")
self.n_timesteps = int(1200. / self.timestep)
print("starting simulation for effectively %s sec" %
(self.timestep * self.n_timesteps))
simulation.run_scheme_readdy(True).with_reaction_scheduler(
"GillespieParallel").configure(self.timestep).run(self.n_timesteps)
if self._result_fname is not None:
with open(self._result_fname, 'w') as f:
np.save(f, np.array(self._hist_data))
def execute(self):
###################################
#
# Units:
# - [x] = µm
# - [t] = s
# - [E] = kJ/mol
#
###################################
kernel_provider = KernelProvider.get()
kernel_provider.load_from_dir(platform_utils.get_readdy_plugin_dir())
simulation = Simulation()
simulation.set_kernel("CPU")
###################################
#
# set up simulation box
#
###################################
box_size = Vec(2, 7, 12)
simulation.box_size = box_size
simulation.kbt = 2.437 # room temperature
simulation.periodic_boundary = [False, False, False]
###################################
#
# register particle types
#
###################################
# particle size, see: http://bmccellbiol.biomedcentral.com/articles/10.1186/1471-2121-5-29
# "The size of the V-ATPase complex is about 15 nm (diameter) x 25 nm (length from lumen side to tip of head)"
membrane_particle_size = .05
diffusion_factor = .5
simulation.register_particle_type("D", 2.5 * diffusion_factor, .01) # MinD-ADP (without phosphor)
simulation.register_particle_type("D_P", 2.5 * diffusion_factor, .01) # MinD-ATP (with phosphor)
simulation.register_particle_type("E", 2.5 * diffusion_factor, .01) # MinE
simulation.register_particle_type("D_PB", .01 * diffusion_factor, .01) # MinD-ATP bound
simulation.register_particle_type("DE", .01 * diffusion_factor, .01) # MinDE
###################################
#
# register reaction types
#
###################################
reaction_radius = 4*(0.01 + 0.01) # = sum of the particle radii * 5 (5 - magic number such that k_fusion makes sense, sort of) 5 *
# k_fusion = brentq(lambda x: self.erban_chapman(.093, 2.5 + .01, reaction_radius, x), 1, 5000000)
k_fusion = 1.0
print("k_fusion=%s" % k_fusion)
simulation.register_reaction_conversion("Phosphorylation", "D", "D_P", .5)
simulation.register_reaction_fusion("bound MinD+MinE->MinDE", "D_PB", "E", "DE", k_fusion, reaction_radius*3.5, .5, .5)
simulation.register_reaction_fission("MinDE to MinD and MinE, detach", "DE", "D", "E", .25, reaction_radius, .5, .5)
###################################
#
# register potentials
#
###################################
membrane_size = Vec(.5, 5, 10)
layer = Vec(.08, .08, .08)
extent = membrane_size + 2 * layer
origin = -.5 * membrane_size - layer
simulation.register_potential_box("D", 10., origin, extent, False) # (force constant, origin, extent, considerParticleRadius)
simulation.register_potential_box("D_P", 10., origin, extent, False) # (force constant, origin, extent, considerParticleRadius)
simulation.register_potential_box("D_PB", 10., origin, extent, False) # (force constant, origin, extent, considerParticleRadius)
simulation.register_potential_box("E", 10., origin, extent, False) # (force constant, origin, extent, considerParticleRadius)
simulation.register_potential_box("DE", 10., origin, extent, False) # (force constant, origin, extent, considerParticleRadius)
# simulation.register_potential_piecewise_weak_interaction("D_P", "D_PB", 3, .02, 2, .05) # (force constant, desired dist, depth, no interaction dist)
###################################
#
# membrane particles
#
###################################
using_membrane_particles = False
if using_membrane_particles:
simulation.register_particle_type("M", 0, membrane_particle_size) # membrane particle
simulation.register_reaction_enzymatic("Attach to membrane", "M", "D_P", "D_PB", .5, .01 + membrane_particle_size) # .01 + .025 # todo: rate?
dx = np.linspace(origin[0] + layer[0], -1 * origin[0] - layer[0], int(float(membrane_size[0]) / membrane_particle_size), endpoint=True)
dy = np.linspace(origin[1] + layer[1], -1 * origin[1] - layer[1], int(float(membrane_size[1]) / membrane_particle_size), endpoint=True)
dz = np.linspace(origin[2] + layer[2], -1 * origin[2] - layer[2], int(float(membrane_size[2]) / membrane_particle_size), endpoint=True)
for y in dy:
for z in dz:
simulation.add_particle("M", Vec(-1 * origin[0] - layer[0], y, z))
print("done adding membrane particles")
else:
simulation.register_reaction_conversion("Phosphorylation", "D_P", "D_PB", .5)
simulation.register_reaction_enzymatic("Enzymatic DP+DPB->DPB + DPB", "D_PB", "D_P", "D_PB", .5, .02)
using_uniform_distribution = True
n_minE_particles = 3120
n_minD_particles = n_minE_particles * 4
mine_x = np.random.uniform(origin[0] + layer[0], -1 * origin[0] - layer[0], n_minE_particles)
mine_y = np.random.uniform(origin[1] + layer[1], -1 * origin[1] - layer[1], n_minE_particles)
if using_uniform_distribution:
mine_z = np.random.uniform(origin[2] + layer[2], -1 * origin[2] - layer[2], n_minE_particles)
else:
mine_z = np.random.uniform(origin[2] + layer[2], .5 * (-1 * origin[2] - layer[2]), n_minE_particles)
mind_x = np.random.uniform(origin[0] + layer[0], -1 * origin[0] - layer[0], n_minD_particles)
mind_y = np.random.uniform(origin[1] + layer[1], -1 * origin[1] - layer[1], n_minD_particles)
if using_uniform_distribution:
mind_z = np.random.uniform(origin[2] + layer[2], -1 * origin[2] - layer[2], n_minD_particles)
else:
mind_z = np.random.uniform(.5 * (-1 * origin[2] - layer[2]), -1 * origin[2] - layer[2], n_minD_particles)
for i in range(n_minE_particles):
simulation.add_particle("E", Vec(mine_x[i], mine_y[i], mine_z[i]))
for i in range(int(.5 * n_minD_particles)):
simulation.add_particle("D", Vec(mind_x[i], mind_y[i], mind_z[i]))
for i in range(int(.5 * n_minD_particles), n_minD_particles):
simulation.add_particle("D_P", Vec(mind_x[i], mind_y[i], mind_z[i]))
self.timestep = simulation.get_recommended_time_step(2)
###################################
#
# register observables
#
###################################
# simulation.register_observable_center_of_mass(1, self.com_callback_mind, ["D", "D_P", "D_PB"])
# simulation.register_observable_center_of_mass(1, self.com_callback_mine, ["E"])
# simulation.register_observable_center_of_mass(1, self.com_callback_minde, ["DE", "D_PB"])
print("histogram start")
# simulation.register_observable_histogram_along_axis(100, self.histrogram_callback_minD, np.arange(-3, 3, .1), ["D", "D_P", "D_PB"], 2)
# simulation.register_observable_histogram_along_axis(100, self.histrogram_callback_minE, np.arange(-3, 3, .1), ["D_PB", "DE"], 2)
stride = int(.01/self.timestep)
self.stride = stride
print("using stride=%s" % stride)
bins = np.linspace(-7, 7, 80)
simulation.register_observable_histogram_along_axis(stride, bins, 2, ["D"], self.histogram_callback_minD)
simulation.register_observable_histogram_along_axis(stride, bins, 2, ["D_P"], self.histogram_callback_minDP)
simulation.register_observable_histogram_along_axis(stride, bins, 2, ["D_PB"], self.histogram_callback_minDPB)
simulation.register_observable_histogram_along_axis(stride, bins, 2, ["E"], self.histogram_callback_minE)
simulation.register_observable_histogram_along_axis(stride, bins, 2, ["DE"], self.histogram_callback_minDE)
simulation.register_observable_histogram_along_axis(stride, bins, 2, ["D", "D_P", "D_PB", "DE"], self.histogram_callback_M)
simulation.register_observable_n_particles(stride, ["D", "D_P", "D_PB", "E", "DE"], self.n_particles_callback)
print("histogram end")
self.n_timesteps = int(1200./self.timestep)
print("starting simulation for effectively %s sec" % (self.timestep * self.n_timesteps))
simulation.run_scheme_readdy(True).with_reaction_scheduler("GillespieParallel").configure(self.timestep).run(self.n_timesteps)
if self._result_fname is not None:
with open(self._result_fname, 'w') as f:
np.save(f, np.array(self._hist_data))
