def __init__(self):
KernelProvider.get().load_from_dir(platform_utils.get_readdy_plugin_dir())
self.simulation = Simulation()
self.simulation.set_kernel("CPU")
self.fig = plt.figure()
self.ax = self.fig.add_subplot(111, projection='3d')
plt.ioff()
self.fig.show()
self.prev_pos = {}
self.current_plot = None
self.T = 4000000
def __init__(self):
KernelProvider.get().load_from_dir(
platform_utils.get_readdy_plugin_dir())
self.simulation = Simulation()
self.simulation.set_kernel("CPU")
self.fig = plt.figure()
self.ax = self.fig.add_subplot(111, projection='3d')
plt.ioff()
self.fig.show()
self.prev_pos = {}
self.current_plot = None
self.T = 4000000
def __init__(self,
kernel,
context,
unit_config,
output_file="",
integrator="EulerBDIntegrator",
reaction_handler="Gillespie",
evaluate_topology_reactions=True,
evaluate_forces=True,
evaluate_observables=True,
skin=0):
"""
Creates a new simulation object
:param kernel: the kernel to use
:param context: the parent low level context object
:param unit_config: the unit configuration
:param output_file: the output file
:param integrator: the integrator
:param reaction_handler: the reaction handler
:param evaluate_topology_reactions: whether to evaluate topology reactions
:param evaluate_forces: whether to evaluate forces
:param evaluate_observables: whether to evaluate observables
:param skin: the skin size for neighbor lists
"""
available_kernels = _KernelProvider.get().available_kernels()
if kernel not in available_kernels:
raise ValueError(
"The selected kernel was \"{}\" but only {} "
"are available.".format(
kernel,
", ".join(['"{}"'.format(x) for x in available_kernels])))
self._unit_conf = unit_config
self._kernel = kernel
self._simulation = _Simulation()
self._simulation.set_kernel(kernel)
self._simulation.context = context
self.output_file = output_file
self._observables = _Observables(self)
self.integrator = integrator
self.reaction_handler = reaction_handler
self.evaluate_topology_reactions = evaluate_topology_reactions
self.evaluate_forces = evaluate_forces
self.evaluate_observables = evaluate_observables
self.skin = skin
self.simulation_scheme = "ReaDDyScheme"
self.show_progress = True
self._progress = None
if kernel == "CPU":
self._kernel_configuration = _CPUKernelConfiguration()
else:
self._kernel_configuration = _NOOPKernelConfiguration()
def __init__(self, kernel, context, unit_config, output_file="", integrator="EulerBDIntegrator",
reaction_handler="Gillespie", evaluate_topology_reactions=True, evaluate_forces=True,
evaluate_observables=True, skin=0):
"""
Creates a new simulation object
:param kernel: the kernel to use
:param context: the parent low level context object
:param unit_config: the unit configuration
:param output_file: the output file
:param integrator: the integrator
:param reaction_handler: the reaction handler
:param evaluate_topology_reactions: whether to evaluate topology reactions
:param evaluate_forces: whether to evaluate forces
:param evaluate_observables: whether to evaluate observables
:param skin: the skin size for neighbor lists
"""
available_kernels = _KernelProvider.get().available_kernels()
if kernel not in available_kernels:
raise ValueError("The selected kernel was \"{}\" but only {} "
"are available.".format(kernel, ", ".join(['"{}"'.format(x) for x in available_kernels])))
self._unit_conf = unit_config
self._kernel = kernel
self._simulation = _Simulation()
self._simulation.set_kernel(kernel)
self._simulation.context = context
self._evaluate_topology_reactions = True
self._evaluate_forces = True
self._evaluate_observables = True
self._skin = 0
self._integrator = "EulerBDIntegrator"
self._reaction_handler = "Gillespie"
self._simulation_scheme = "ReaDDyScheme"
self._output_file = ""
self.output_file = output_file
self._observables = _Observables(self)
self.integrator = integrator
self.reaction_handler = reaction_handler
self.evaluate_topology_reactions = evaluate_topology_reactions
self.evaluate_forces = evaluate_forces
self.evaluate_observables = evaluate_observables
self.skin = skin
self._show_progress = True
self._progress_output_stride = 10
self._progress = None
if kernel == "CPU":
self._kernel_configuration = _CPUKernelConfiguration()
else:
self._kernel_configuration = _NOOPKernelConfiguration()
def setUpClass(cls):
cls.kernel_provider = KernelProvider.get()
cls.kernel_provider.load_from_dir(
platform_utils.get_readdy_plugin_dir())
cls.simulation = Simulation()
cls.simulation.set_kernel("SingleCPU")
cls.fig = plt.figure()
cls.ax = cls.fig.add_subplot(111, projection='3d')
cls.prev_pos = {}
cls.fig.show()
cls.ax.set_xlim([-3, 3])
cls.ax.set_ylim([-3, 3])
cls.ax.set_zlim([-3, 3])
cls.current_plot = None
plt.ioff()
def setUpClass(cls):
cls.kernel_provider = KernelProvider.get()
cls.kernel_provider.load_from_dir(
platform_utils.get_readdy_plugin_dir())
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 rdf_callback(pair):
global rdf, centers, n_calls, T
if centers is None:
centers = pair[0]
if rdf is None:
rdf = pair[1]
else:
rdf += pair[1]
n_calls += 1
if n_calls % 10000 == 0:
print("%s" % (10. * float(n_calls) / float(T)))
if __name__ == '__main__':
KernelProvider.get().load_from_dir(platform_utils.get_readdy_plugin_dir())
simulation = Simulation()
simulation.set_kernel("CPU")
box_size = 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.register_potential_harmonic_repulsion("A", "B", 10)
simulation.add_particle("A", Vec(-2.5, 0, 0))
simulation.add_particle("B", Vec(0, 0, 0))
simulation.register_observable_radial_distribution(
10, np.arange(0, 5, .01), ["A"], ["B"],
def setUpClass(cls):
cls.kernel_provider = KernelProvider.get()
cls.kernel_provider.load_from_dir(platform_utils.get_readdy_plugin_dir())
def __init__(self, kernel, time_step):
self.kernel_provider = KernelProvider.get()
self.kernel_provider.load_from_dir(
platform_utils.get_readdy_plugin_dir())
self.kernel = kernel
self.time_step = time_step
def setUp(self):
super().setUp()
self.kernel_provider = KernelProvider.get()
self.kernel_provider.load_from_dir(platform_utils.get_readdy_plugin_dir())
self.simulation = Simulation()
def setUpClass(cls):
cls.kernel_provider = KernelProvider.get()
cls.kernel_provider.load_from_dir(platform_utils.get_readdy_plugin_dir())
cls.dir = tempfile.mkdtemp("test-observables-io")
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 run(self):
###################################
#
# Units:
# - [t] = sec
#
###################################
tau = 1e-3
kernel_provider = KernelProvider.get()
kernel_provider.load_from_dir(platform_utils.get_readdy_plugin_dir())
simulation = Simulation()
simulation.set_kernel("CPU")
simulation.kbt = 1.0
simulation.box_size = Vec(3, 3, 3)
simulation.periodic_boundary = [True, True, True]
D = 1.0
R = .01
simulation.register_particle_type("A", D, R)
simulation.register_particle_type("2A", D, R)
simulation.register_particle_type("3A", D, R)
simulation.register_particle_type("B", D, R)
simulation.register_particle_type("GA", D, R)
simulation.register_particle_type("GB", D, R)
reaction_radius = .2
k1 = 4 * 1e-5
k2 = 50
k3 = 10
k4 = 250
V = simulation.box_size * simulation.box_size
N_GA = 100.0
N_GB = 1000.0
k_enzymatic = brentq(lambda x: erban_chapman(k1, 2, reaction_radius, x), 1e-10, 5000000000000)
k_enzymatic = 10. # k_enzymatic for reaction_radius = .1
print("k_enzymatic=%s" % k_enzymatic)
print("2 * k3 - k3 * k3 * tau = %s" % (2.0 * k3 - k3 * k3 * tau))
print("k3 * k3 * tau = %s" % (k3 * k3 * tau))
print("k_birthA = %s" % (k2 * V / N_GA))
print("k_birthB = %s" % (k4 * V / N_GB))
print("sqrt(R*R/D) = %s" % (np.sqrt(reaction_radius * reaction_radius / D)))
simulation.register_reaction_conversion("2A -> A", "2A", "A", 2.0 * k3 - k3 * k3 * tau)
simulation.register_reaction_decay("2A -> 0", "2A", k3 * k3 * tau)
simulation.register_reaction_fusion("A + A -> 2A", "A", "A", "2A", 1.0 / tau, reaction_radius, .5, .5)
simulation.register_reaction_enzymatic("2A + B -> 2A + A", "2A", "B", "A", k_enzymatic, reaction_radius)
simulation.register_reaction_fission("GA -> GA + A", "GA", "GA", "A", k2 * V / N_GA, reaction_radius, .5, .5)
simulation.register_reaction_decay("A -> 0", "A", k3)
simulation.register_reaction_fission("GB -> GB + B", "GB", "GB", "B", k4 * V / N_GB, reaction_radius, .5, .5)
simulation.add_particle("A", Vec(0, 0, 0))
ga_x = np.random.uniform(-0.5 * simulation.box_size[0], 0.5 * simulation.box_size[0], int(N_GA))
ga_y = np.random.uniform(-0.5 * simulation.box_size[1], 0.5 * simulation.box_size[1], int(N_GA))
ga_z = np.random.uniform(-0.5 * simulation.box_size[2], 0.5 * simulation.box_size[2], int(N_GA))
gb_x = np.random.uniform(-0.5 * simulation.box_size[0], 0.5 * simulation.box_size[0], int(N_GB))
gb_y = np.random.uniform(-0.5 * simulation.box_size[1], 0.5 * simulation.box_size[1], int(N_GB))
gb_z = np.random.uniform(-0.5 * simulation.box_size[2], 0.5 * simulation.box_size[2], int(N_GB))
for i in range(int(N_GA)):
simulation.add_particle("GA", Vec(ga_x[i], ga_y[i], ga_z[i]))
for i in range(int(N_GB)):
simulation.add_particle("GB", Vec(gb_x[i], gb_y[i], gb_z[i]))
N_B = 30000
boxsize = np.array([simulation.box_size[0], simulation.box_size[1], simulation.box_size[2]])
for i in range(N_B):
pos = np.random.random(3) * boxsize - 0.5 * boxsize
simulation.add_particle("B", Vec(pos[0], pos[1], pos[2]))
def callback(result):
n_a, n_b = result[0] + 2 * result[1] + 3 * result[2], result[3]
self._data[self._t, 0] = n_a
self._data[self._t, 1] = n_b
print("(%s,%s,a=%s,a2=%s,a3=%s)"%(n_a, n_b,result[0], result[1], result[2]))
self.lines.set_xdata(self._data[:self._t, 0])
self.lines.set_ydata(self._data[:self._t, 1])
self.ax.relim()
self.ax.autoscale_view()
#We need to draw *and* flush
self.fig.canvas.draw()
self.fig.canvas.flush_events()
plt.pause(.1)
self._t += 1
simulation.register_observable_n_particles_types(1, ["A", "2A", "3A", "B"], callback)
print("start run")
simulation.run(self._timesteps, tau)
def __init__(self, kernel, time_step):
self.kernel_provider = KernelProvider.get()
self.kernel_provider.load_from_dir(platform_utils.get_readdy_plugin_dir())
self.kernel = kernel
self.time_step = time_step