def test_histogram_along_axis_observable(self): fname = os.path.join(self.dir, "test_observables_hist_along_axis.h5") simulation = Simulation("SingleCPU") box_size = [10.,10.,10.] simulation.context.kbt = 2 simulation.context.pbc = [True, True, True] simulation.context.box_size = box_size simulation.context.particle_types.add("A", .2) simulation.context.particle_types.add("B", .2) simulation.context.potentials.add_harmonic_repulsion("A", "B", 10, 2.) simulation.add_particle("A", common.Vec(-2.5, 0, 0)) simulation.add_particle("B", common.Vec(0, 0, 0)) bin_borders = np.arange(0, 5, .01) n_time_steps = 50 callback_hist = [] def hist_callback(hist): callback_hist.append(hist) handle = simulation.register_observable_histogram_along_axis(2, bin_borders, 0, ["A", "B"], hist_callback) with closing(io.File.create(fname)) as f: handle.enable_write_to_file(f, u"hist_along_x_axis", int(3)) simulation.run(n_time_steps, 0.02) handle.flush() with h5py.File(fname, "r") as f2: histogram = f2["readdy/observables/hist_along_x_axis/data"][:] time_series = f2["readdy/observables/hist_along_x_axis/time"] np.testing.assert_equal(time_series, np.array(range(0, n_time_steps+1))[::2]) for t in range(n_time_steps // 2): np.testing.assert_equal(histogram[t], np.array(callback_hist[t]))
def test_histogram_along_axis_observable(self): common.set_logging_level("warn") fname = os.path.join(self.dir, "test_observables_hist_along_axis.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.register_potential_harmonic_repulsion("A", "B", 10) simulation.add_particle("A", common.Vec(-2.5, 0, 0)) simulation.add_particle("B", common.Vec(0, 0, 0)) bin_borders = np.arange(0, 5, .01) n_time_steps = 50 callback_hist = [] def hist_callback(hist): callback_hist.append(hist) handle = simulation.register_observable_histogram_along_axis( 2, bin_borders, 0, ["A", "B"], hist_callback) with closing( io.File(fname, io.FileAction.CREATE, io.FileFlag.OVERWRITE)) as f: handle.enable_write_to_file(f, u"hist_along_x_axis", int(3)) simulation.run(n_time_steps, 0.02) handle.flush() with h5py.File(fname, "r") as f2: histogram = f2["readdy/observables/hist_along_x_axis/data"][:] time_series = f2["readdy/observables/hist_along_x_axis/time"] np.testing.assert_equal(time_series, np.array(range(0, n_time_steps + 1))[::2]) for t in range(n_time_steps // 2): np.testing.assert_equal(histogram[t], np.array(callback_hist[t]))
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))