diff --git a/LICENSE b/LICENSE index 3fc0407..93aecde 100644 --- a/LICENSE +++ b/LICENSE @@ -2,6 +2,7 @@ MIT License Copyright (c) 2023 A. Batchelor Copyright (c) 2023 M. Kalsbeek +Copyright (c) 2024 J.A.W. Poland Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/Simulations/Gao_et_al.py b/Simulations/Gao_et_al.py index 0ea6ca4..4da0f0b 100644 --- a/Simulations/Gao_et_al.py +++ b/Simulations/Gao_et_al.py @@ -1,5 +1,5 @@ # -*- coding: utf-8 -*- -#%% Setup +# %% Setup import time import logging @@ -13,8 +13,12 @@ import Particle_System_Simulator.Mesh.mesh_functions as MF import Particle_System_Simulator.ExternalForces.optical_interpolators.interpolators as interp from Particle_System_Simulator.ExternalForces.LaserBeam import LaserBeam -from Particle_System_Simulator.ExternalForces.OpticalForceCalculator import OpticalForceCalculator -from Particle_System_Simulator.ExternalForces.OpticalForceCalculator import ParticleOpticalPropertyType +from Particle_System_Simulator.ExternalForces.OpticalForceCalculator import ( + OpticalForceCalculator, +) +from Particle_System_Simulator.ExternalForces.OpticalForceCalculator import ( + ParticleOpticalPropertyType, +) global_start_time = time.time() @@ -22,78 +26,77 @@ params = { # model parameters "c": 1, # [N s/m] damping coefficient - "m_segment": 1, # [kg] mass of each node - "thickness":100e-9, # [m] thickness of PhC - + "m_segment": 1, # [kg] mass of each node + "thickness": 100e-9, # [m] thickness of PhC # simulation settings "dt": 0.1, # [s] simulation timestep - 'adaptive_timestepping':1e-2, # [m] max distance traversed per timestep + "adaptive_timestepping": 1e-2, # [m] max distance traversed per timestep "t_steps": 1e6, # [-] max number of simulated time steps "abs_tol": 1e-10, # [m/s] absolute error tolerance iterative solver "rel_tol": 1e-5, # [-] relative error tolerance iterative solver - "max_iter": int(1e2), # [-] maximum number of iterations for the bicgstab solver - + "max_iter": int( + 1e2 + ), # [-] maximum number of iterations for the bicgstab solver # Simulation Steps "convergence_threshold": 1e-6, - "min_iterations":30, - + "min_iterations": 30, # Mesh_dependent_settings "midstrip_width": 1, - "boundary_margin": 0.175 - } + "boundary_margin": 0.175, +} -params['E'] = 470e9 -params['G'] = 0 -params['E_x'] = params['E']*7/100 -params['E_y'] = params['E']*18/100 +params["E"] = 470e9 +params["G"] = 0 +params["E_x"] = params["E"] * 7 / 100 +params["E_y"] = params["E"] * 18 / 100 # Setup mesh -n_segments = 15 # make sure this is uneven so there are no particles on the centerline +n_segments = 15 # make sure this is uneven so there are no particles on the centerline length = 1 -mesh = MF.mesh_phc_square_cross(length, - mesh_edge_length=length/n_segments, - params = params, - noncompressive=True) +mesh = MF.mesh_phc_square_cross( + length, mesh_edge_length=length / n_segments, params=params, noncompressive=True +) # We have to add some particles to act as a support structure. -stiffness_support = 1e9+1 # [n/m*m] line stiffness +stiffness_support = 1e9 + 1 # [n/m*m] line stiffness k_support = stiffness_support / (length / n_segments) -l_support = length/n_segments/50 +l_support = length / n_segments / 50 simulate_3D = False -for i in range((n_segments+1)**2): +for i in range((n_segments + 1) ** 2): # calculate coordinates xyz = mesh[1][i][0].copy() - if xyz[1] ==0 and simulate_3D: - xyz[1]-=l_support + if xyz[1] == 0 and simulate_3D: + xyz[1] -= l_support elif xyz[1] == length and simulate_3D: - xyz[1]+=l_support + xyz[1] += l_support elif xyz[0] == 0: - xyz[0]-=l_support + xyz[0] -= l_support elif xyz[0] == length: - xyz[0]+=l_support - + xyz[0] += l_support if np.any(xyz != mesh[1][i][0]): - particle = [xyz, np.zeros(3), params['m_segment'], True] + particle = [xyz, np.zeros(3), params["m_segment"], True] link = [i, len(mesh[1]), k_support, 1] mesh[1].append(particle) mesh[0].append(link) xyz = mesh[1][i][0].copy() - if (np.all(xyz == [0,0,0]) - or np.all(xyz == [0,length,0]) - or np.all(xyz == [length,0,0]) - or np.all(xyz == [length,length,0])) and simulate_3D: - - if xyz[0] ==0: - xyz[0]-=l_support + if ( + np.all(xyz == [0, 0, 0]) + or np.all(xyz == [0, length, 0]) + or np.all(xyz == [length, 0, 0]) + or np.all(xyz == [length, length, 0]) + ) and simulate_3D: + + if xyz[0] == 0: + xyz[0] -= l_support elif xyz[0] == length: - xyz[0]+=l_support + xyz[0] += l_support if np.any(xyz != mesh[1][i][0]): - particle = [xyz, np.zeros(3), params['m_segment'], True] + particle = [xyz, np.zeros(3), params["m_segment"], True] link = [i, len(mesh[1]), k_support, 1] mesh[1].append(particle) mesh[0].append(link) @@ -102,22 +105,29 @@ PS = ParticleSystem(*mesh, params, clean_particles=False) starting_postions = PS.x_v_current_3D[0] # Setup the optical sytem -I_0 = 100e9 /(10*10) +I_0 = 100e9 / (10 * 10) mu_x = 0.5 mu_y = 0.5 -sigma = 1/2 -w=2*length +sigma = 1 / 2 +w = 2 * length if simulate_3D: - LB = LaserBeam(lambda x, y: I_0 * np.exp(-1/2 *((x-mu_x)/sigma)**2 # gaussian laser - -1/2 *((y-mu_y)/sigma)**2), - lambda x,y: np.outer(np.ones(x.shape),[0,1])) + LB = LaserBeam( + lambda x, y: I_0 + * np.exp( + -1 / 2 * ((x - mu_x) / sigma) ** 2 # gaussian laser + - 1 / 2 * ((y - mu_y) / sigma) ** 2 + ), + lambda x, y: np.outer(np.ones(x.shape), [0, 1]), + ) else: - LB = LaserBeam(lambda x, y: I_0 * np.exp(-2*((x-mu_x)/w)**2), # gaussian laser - lambda x,y: np.outer(np.ones(x.shape),[0,1])) + LB = LaserBeam( + lambda x, y: I_0 * np.exp(-2 * ((x - mu_x) / w) ** 2), # gaussian laser + lambda x, y: np.outer(np.ones(x.shape), [0, 1]), + ) # Import the crystal -fname = interp.PhC_library['Gao'] -#fname = interp.PhC_library['dummy'] -interp_right_side = interp.create_interpolator(fname,np.pi) +fname = interp.PhC_library["Gao"] +# fname = interp.PhC_library['dummy'] +interp_right_side = interp.create_interpolator(fname, np.pi) interp_left_side = interp.create_interpolator(fname, 0) @@ -125,29 +135,34 @@ for p in PS.particles: if simulate_3D: if p.x[1] == 0 or p.x[1] == length: - p.set_fixed(True, [0,0,1], 'plane') + p.set_fixed(True, [0, 0, 1], "plane") if p.x[0] == 0 or p.x[0] == length: - p.set_fixed(True, [0,0,1], 'plane') + p.set_fixed(True, [0, 0, 1], "plane") p.optical_type = ParticleOpticalPropertyType.ARBITRARY_PHC - if p.x[0]>length/2: + if p.x[0] > length / 2: p.optical_interpolator = interp_right_side else: p.optical_interpolator = interp_left_side OFC = OpticalForceCalculator(PS, LB) -SIM = Simulate_Lightsail(PS,OFC,params) +SIM = Simulate_Lightsail(PS, OFC, params) -#%% Plot displaced PS with distributed and net forces +# %% Plot displaced PS with distributed and net forces plot_check = True deform = True if plot_check: - PS.displace([0,0,0,0,3,0]) + PS.displace([0, 0, 0, 0, 3, 0]) if deform: - SIM.run_simulation(plotframes=0, printframes=50, simulation_function='kinetic_damping',file_id='_check_') + SIM.run_simulation( + plotframes=0, + printframes=50, + simulation_function="kinetic_damping", + file_id="_check_", + ) fig_convergence = plt.figure() ax_kin = fig_convergence.add_subplot(211) @@ -159,27 +174,43 @@ forces = OFC.force_value() net_force, net_moments = OFC.calculate_restoring_forces() - fig = plt.figure(figsize = (10,6)) + fig = plt.figure(figsize=(10, 6)) - ax = fig.add_subplot(projection='3d') + ax = fig.add_subplot(projection="3d") ax = PS.plot(ax) COM = PS.calculate_center_of_mass() - x,_ = PS.x_v_current_3D - - a_u = forces[:,0] - a_v = forces[:,1] - a_w = forces[:,2] - - x,y,z = x[:,0], x[:,1], x[:,2] - - ax.quiver(x,y,z,a_u,a_v,a_w, length = 5) - ax.quiver(COM[0],COM[1],COM[2], - net_force[0],net_force[1],net_force[2], - length = 1, label ='Net Force', color='r') - ax.quiver(COM[0],COM[1],COM[2], - net_moments[0],net_moments[1],net_moments[2], - length = 2, label ='Net Moment', color='magenta') + x, _ = PS.x_v_current_3D + + a_u = forces[:, 0] + a_v = forces[:, 1] + a_w = forces[:, 2] + + x, y, z = x[:, 0], x[:, 1], x[:, 2] + + ax.quiver(x, y, z, a_u, a_v, a_w, length=5) + ax.quiver( + COM[0], + COM[1], + COM[2], + net_force[0], + net_force[1], + net_force[2], + length=1, + label="Net Force", + color="r", + ) + ax.quiver( + COM[0], + COM[1], + COM[2], + net_moments[0], + net_moments[1], + net_moments[2], + length=2, + label="Net Moment", + color="magenta", + ) fig.tight_layout() plt.show() @@ -188,152 +219,214 @@ rot_and_trans = False if rot_and_trans: - #%% Getting translation and rotation data - print('Starting rotations and translations') - translations = np.linspace(-length,length,17) - rotations = np.linspace(-10,10,17) + # %% Getting translation and rotation data + print("Starting rotations and translations") + translations = np.linspace(-length, length, 17) + rotations = np.linspace(-10, 10, 17) translation_plot = [] trans_plot = False if trans_plot: - fig0 = plt.figure(figsize = [20,16]) + fig0 = plt.figure(figsize=[20, 16]) # Deform the PS for each step? resimulate_on_displacement = True print("\n\nCalculating rotations") for i, t in enumerate(translations): - print(f'\nTranslation {t=}') - PS.displace([t,0,0,0,0,0]) + print(f"\nTranslation {t=}") + PS.displace([t, 0, 0, 0, 0, 0]) if resimulate_on_displacement: - SIM.run_simulation(plotframes=0, printframes=50, simulation_function='kinetic_damping',file_id=f'_{t}_') + SIM.run_simulation( + plotframes=0, + printframes=50, + simulation_function="kinetic_damping", + file_id=f"_{t}_", + ) # The force data is a little sensitive to random fluctuation, so instead I'm pulling the last # 50 entries from a ring buffer I added to the history. - net_force = np.array([np.sum(forces,axis=0) for forces in PS.history['forces_ringbuffer']]) - net_force = np.sum(net_force, axis=0)/len(PS.history['forces_ringbuffer']) + net_force = np.array( + [np.sum(forces, axis=0) for forces in PS.history["forces_ringbuffer"]] + ) + net_force = np.sum(net_force, axis=0) / len(PS.history["forces_ringbuffer"]) _, net_moments = OFC.calculate_restoring_forces() else: net_force, net_moments = OFC.calculate_restoring_forces() if trans_plot: fig0.clear() - ax0 = fig0.add_subplot(projection='3d') - ax0.set_xlim([-0.5,1.5]) - ax0.set_ylim([0,1]) - ax0.set_zlim([0,0.5]) - ax0.set_aspect('equal') - ax0 = PS.plot_forces(OFC.force_value(),ax0) - ax0.set_title(f'{t:.1f}') + ax0 = fig0.add_subplot(projection="3d") + ax0.set_xlim([-0.5, 1.5]) + ax0.set_ylim([0, 1]) + ax0.set_zlim([0, 0.5]) + ax0.set_aspect("equal") + ax0 = PS.plot_forces(OFC.force_value(), ax0) + ax0.set_title(f"{t:.1f}") COM = PS.calculate_center_of_mass() - ax0.quiver(COM[0],COM[1],COM[2], - net_force[0],net_force[1],net_force[2], - length = 1/2, label ='Net Force', color='r') - ax0.quiver(COM[0],COM[1],COM[2], - net_moments[0],net_moments[1],net_moments[2], - length = 2, label ='Net Moment', color='magenta') - ax0.quiver(COM[0],COM[1],COM[2], - net_moments[0],net_moments[1],net_moments[2], - length = 1.4, color='magenta') + ax0.quiver( + COM[0], + COM[1], + COM[2], + net_force[0], + net_force[1], + net_force[2], + length=1 / 2, + label="Net Force", + color="r", + ) + ax0.quiver( + COM[0], + COM[1], + COM[2], + net_moments[0], + net_moments[1], + net_moments[2], + length=2, + label="Net Moment", + color="magenta", + ) + ax0.quiver( + COM[0], + COM[1], + COM[2], + net_moments[0], + net_moments[1], + net_moments[2], + length=1.4, + color="magenta", + ) fig0.tight_layout() - fig0.savefig(f'temp/translation-{i}-{t:.2f}.jpg', dpi = 200, format = 'jpg') + fig0.savefig(f"temp/translation-{i}-{t:.2f}.jpg", dpi=200, format="jpg") PS.un_displace() translation_plot.append([t, *net_force, *net_moments]) - rotation_plot=[] + rotation_plot = [] rot_plot = False if rot_plot: - fig0 = plt.figure(figsize = [20,16]) + fig0 = plt.figure(figsize=[20, 16]) print("\n\nCalculating rotations") for i, r in enumerate(rotations): - print(f'\nRotation {r=}') - PS.displace([0,0,0,0,r,0]) + print(f"\nRotation {r=}") + PS.displace([0, 0, 0, 0, r, 0]) if resimulate_on_displacement: - SIM.run_simulation(plotframes=0, printframes=50, simulation_function='kinetic_damping', file_id=f'_{r}_') + SIM.run_simulation( + plotframes=0, + printframes=50, + simulation_function="kinetic_damping", + file_id=f"_{r}_", + ) # The force data is a little sensitive to random fluctuation, so instead I'm pulling the last # 30 entries from a ring buffer I added to the history. - net_force = np.array([np.sum(forces,axis=0) for forces in PS.history['forces_ringbuffer']]) - net_force = np.sum(net_force, axis=0)/len(PS.history['forces_ringbuffer']) + net_force = np.array( + [np.sum(forces, axis=0) for forces in PS.history["forces_ringbuffer"]] + ) + net_force = np.sum(net_force, axis=0) / len(PS.history["forces_ringbuffer"]) _, net_moments = OFC.calculate_restoring_forces() else: net_force, net_moments = OFC.calculate_restoring_forces() if rot_plot: fig0.clear() - ax0 = fig0.add_subplot(projection='3d') - ax0 = PS.plot_forces(OFC.force_value(),ax0) - ax0.set_title(f'{r}') - ax0.quiver(COM[0],COM[1],COM[2], - net_force[0],net_force[1],net_force[2], - length = 0.5, label ='Net Force', color='r') - ax0.quiver(COM[0],COM[1],COM[2], - net_moments[0],net_moments[1],net_moments[2], - length = 5, label ='Net Moment', color='magenta') - ax0.quiver(COM[0],COM[1],COM[2], - net_moments[0],net_moments[1],net_moments[2], - length = 3.5, color='magenta') + ax0 = fig0.add_subplot(projection="3d") + ax0 = PS.plot_forces(OFC.force_value(), ax0) + ax0.set_title(f"{r}") + ax0.quiver( + COM[0], + COM[1], + COM[2], + net_force[0], + net_force[1], + net_force[2], + length=0.5, + label="Net Force", + color="r", + ) + ax0.quiver( + COM[0], + COM[1], + COM[2], + net_moments[0], + net_moments[1], + net_moments[2], + length=5, + label="Net Moment", + color="magenta", + ) + ax0.quiver( + COM[0], + COM[1], + COM[2], + net_moments[0], + net_moments[1], + net_moments[2], + length=3.5, + color="magenta", + ) ax0.legend() - ax0.set_xlim([0,1]) - ax0.set_ylim([0,1]) - ax0.set_zlim([-0.1,0.1]) - ax0.set_aspect('equal') + ax0.set_xlim([0, 1]) + ax0.set_ylim([0, 1]) + ax0.set_zlim([-0.1, 0.1]) + ax0.set_aspect("equal") fig0.tight_layout() - fig0.savefig(f'temp/rotation-{i}-{r:.1f}.jpg', dpi = 200, format = 'jpg') + fig0.savefig(f"temp/rotation-{i}-{r:.1f}.jpg", dpi=200, format="jpg") PS.un_displace() rotation_plot.append([r, *net_force, *net_moments]) - - translation_plot= np.array(translation_plot) + translation_plot = np.array(translation_plot) rotation_plot = np.array(rotation_plot) - header = ['displacement', 'Fx', 'Fy', 'Fz', 'Rx', 'Ry', 'Rz'] - pd.DataFrame(translation_plot,columns=header).to_csv(f"temp/translation_{stiffness_support=}.csv", header=True, index = False) - pd.DataFrame(rotation_plot,columns=header).to_csv(f"temp/rotation_{stiffness_support=}.csv", header=True, index = False) - + header = ["displacement", "Fx", "Fy", "Fz", "Rx", "Ry", "Rz"] + pd.DataFrame(translation_plot, columns=header).to_csv( + f"temp/translation_{stiffness_support=}.csv", header=True, index=False + ) + pd.DataFrame(rotation_plot, columns=header).to_csv( + f"temp/rotation_{stiffness_support=}.csv", header=True, index=False + ) -#%% Reproducing Fig. 4 from Gao et al 2022 +# %% Reproducing Fig. 4 from Gao et al 2022 gao_et_al_figure_four = False if gao_et_al_figure_four: - fig = plt.figure(figsize=(10,6)) + fig = plt.figure(figsize=(10, 6)) ax1 = fig.add_subplot(221) - ax1.plot(rotation_plot[:,0], rotation_plot[:,3]/(I_0/c)) - ax1.set_title('Tilt angle versus vertical force') - ax1.set_xlabel('Tilt angle [deg]') + ax1.plot(rotation_plot[:, 0], rotation_plot[:, 3] / (I_0 / c)) + ax1.set_title("Tilt angle versus vertical force") + ax1.set_xlabel("Tilt angle [deg]") ax1.set_ylabel("$F_z [I_0D/c]$") - ax1.set_ylim([0,rotation_plot[:,3].max()/(I_0/c)*1.2]) - ax1.set_xlim([-10,10]) + ax1.set_ylim([0, rotation_plot[:, 3].max() / (I_0 / c) * 1.2]) + ax1.set_xlim([-10, 10]) ax1.grid() ax2 = fig.add_subplot(222) - ax2.plot(rotation_plot[:,0], rotation_plot[:,5]/(I_0/c)) - ax2.set_title('Tilt angle versus torque') - ax2.set_xlabel('Tilt angle [deg]') + ax2.plot(rotation_plot[:, 0], rotation_plot[:, 5] / (I_0 / c)) + ax2.set_title("Tilt angle versus torque") + ax2.set_xlabel("Tilt angle [deg]") ax2.set_ylabel("$\tau_y [I_0D^2/c]$") - ax2.set_xlim([-10,10]) + ax2.set_xlim([-10, 10]) ax2.grid() ax3 = fig.add_subplot(223) - ax3.plot(rotation_plot[:,0], -rotation_plot[:,1]/(I_0/c)) - ax3.set_title('Tilt angle versus lateral force') - ax3.set_xlabel('Tilt angle [deg]') + ax3.plot(rotation_plot[:, 0], -rotation_plot[:, 1] / (I_0 / c)) + ax3.set_title("Tilt angle versus lateral force") + ax3.set_xlabel("Tilt angle [deg]") ax3.set_ylabel("$F_x [I_0D/c]$") - ax3.set_xlim([-10,10]) + ax3.set_xlim([-10, 10]) ax3.grid() ax4 = fig.add_subplot(224) - ax4.plot(translation_plot[:,0], translation_plot[:,1]/(I_0/c)) - ax4.set_title('Translation versus lateral force') - ax4.set_xlabel('Translation [D]') + ax4.plot(translation_plot[:, 0], translation_plot[:, 1] / (I_0 / c)) + ax4.set_title("Translation versus lateral force") + ax4.set_xlabel("Translation [D]") ax4.set_ylabel("$F_x [I_0D/c]$") - ax4.set_xlim([-1,1]) + ax4.set_xlim([-1, 1]) ax4.grid() fig.tight_layout() @@ -342,10 +435,15 @@ # %% Let's run some simulations! simulate = False if simulate: - SIM.run_simulation(plotframes=0, printframes=10, simulation_function='kinetic_damping', plot_forces=True) + SIM.run_simulation( + plotframes=0, + printframes=10, + simulation_function="kinetic_damping", + plot_forces=True, + ) PS.plot() # %% print time it took -delta_time = time.time()-global_start_time -print(f'All in all that took {delta_time//60:.0f}m {delta_time%60:.2f}s') +delta_time = time.time() - global_start_time +print(f"All in all that took {delta_time//60:.0f}m {delta_time%60:.2f}s") diff --git a/Simulations/Simple_config_round.py b/Simulations/Simple_config_round.py index fa7ad84..47a3db0 100644 --- a/Simulations/Simple_config_round.py +++ b/Simulations/Simple_config_round.py @@ -24,125 +24,131 @@ import Particle_System_Simulator.Mesh.mesh_functions as MF import Particle_System_Simulator.ExternalForces.optical_interpolators.interpolators as interp from Particle_System_Simulator.ExternalForces.LaserBeam import LaserBeam -from Particle_System_Simulator.ExternalForces.OpticalForceCalculator import OpticalForceCalculator -from Particle_System_Simulator.ExternalForces.OpticalForceCalculator import ParticleOpticalPropertyType +from Particle_System_Simulator.ExternalForces.OpticalForceCalculator import ( + OpticalForceCalculator, +) +from Particle_System_Simulator.ExternalForces.OpticalForceCalculator import ( + ParticleOpticalPropertyType, +) - -class OpticalForceCalculatorCross(): +class OpticalForceCalculatorCross: def __init__(self, PS, LB1, LB2): self.ParticleSystem = ParticleSystem - self.PS = self.ParticleSystem #alias for convenience + self.PS = self.ParticleSystem # alias for convenience self.LaserBeam1 = LB1 self.LaserBeam2 = LB2 self.OFC1 = OpticalForceCalculator(PS, LB1) self.OFC2 = OpticalForceCalculator(PS, LB2) def force_value(self): - return self.OFC1.force_value()+self.OFC2.force_value() + return self.OFC1.force_value() + self.OFC2.force_value() def calculate_restoring_forces(self, **kwargs): f1, m1 = self.OFC1.calculate_restoring_forces(**kwargs) f2, m2 = self.OFC2.calculate_restoring_forces(**kwargs) - return f1+f2, m1+m2 + return f1 + f2, m1 + m2 def calculate_stability_coefficients(self, **kwargs): - return self.OFC1.calculate_stability_coefficients(**kwargs) + self.OFC2.calculate_stability_coefficients(**kwargs) + return self.OFC1.calculate_stability_coefficients( + **kwargs + ) + self.OFC2.calculate_stability_coefficients(**kwargs) - def plot(self,ax, **kwargs): + def plot(self, ax, **kwargs): LB1.plot(ax, **kwargs) LB2.plot(ax, **kwargs) return ax + def override_constraints(PS: ParticleSystem): for p in PS.particles: if p.fixed: - if p.constraint_type == 'plane': + if p.constraint_type == "plane": p.set_fixed(False) - global_start_time = time.time() # Setup parameters params = { # model parameters "c": 1, # [N s/m] damping coefficient - "m_segment": 1, # [kg] mass of each node - "thickness":200e-9, # [m] thickness of PhC - "rho":3184, # [kg/m3] - + "m_segment": 1, # [kg] mass of each node + "thickness": 200e-9, # [m] thickness of PhC + "rho": 3184, # [kg/m3] # simulation settings "dt": 2e-3, # [s] simulation timestep - 'adaptive_timestepping':2.5e-4, # [m] max distance traversed per timestep + "adaptive_timestepping": 2.5e-4, # [m] max distance traversed per timestep "t_steps": 1e3, # [-] max number of simulated time steps "abs_tol": 1e-20, # [m/s] absolute error tolerance iterative solver "rel_tol": 1e-5, # [-] relative error tolerance iterative solver - "max_iter": int(1e2), # [-] maximum number of iterations for the bicgstab solver - + "max_iter": int( + 1e2 + ), # [-] maximum number of iterations for the bicgstab solver # Simulation Steps - "convergence_threshold": 5e-8, # Metric depends on size of timestep. Have to update them together. - "min_iterations":30, # Should exceed the size of the force ringbuffer in the simulation loop - + "convergence_threshold": 5e-8, # Metric depends on size of timestep. Have to update them together. + "min_iterations": 30, # Should exceed the size of the force ringbuffer in the simulation loop # Mesh_dependent_settings "midstrip_width": 1, - "boundary_margin": 0.175 - } + "boundary_margin": 0.175, +} -params['E'] = 470e9 -params['G'] = 0 -params['E_x'] = params['E']*(259+351)/1991 -params['E_y'] = params['E']*5/100 -E_av = (params['E_x'] + params['E_y'])/2 +params["E"] = 470e9 +params["G"] = 0 +params["E_x"] = params["E"] * (259 + 351) / 1991 +params["E_y"] = params["E"] * 5 / 100 +E_av = (params["E_x"] + params["E_y"]) / 2 fill_factor = 0.43 -params['rho'] *= fill_factor -density_ring = 2330 # [kg/m3] Support frame +params["rho"] *= fill_factor +density_ring = 2330 # [kg/m3] Support frame # Setup mesh -n_segments = 25 # make sure this is uneven so there are no particles on the centerline -radius = 0.5e-3 #[m] -length = 2*radius -fixed_edge_width = radius/n_segments*1.975 - -mesh = MF.mesh_round_phc_square_cross(radius, - mesh_edge_length=length/n_segments, - params = params, - noncompressive=True, - fix_outer=True, - edge = fixed_edge_width) +n_segments = 25 # make sure this is uneven so there are no particles on the centerline +radius = 0.5e-3 # [m] +length = 2 * radius +fixed_edge_width = radius / n_segments * 1.975 + +mesh = MF.mesh_round_phc_square_cross( + radius, + mesh_edge_length=length / n_segments, + params=params, + noncompressive=True, + fix_outer=True, + edge=fixed_edge_width, +) # Method for detecting edge particles isn't great, so we're overiding to add the ones it missed link_counting = defaultdict(int) for link in mesh[0]: - link_counting[link[0]] +=1 - link_counting[link[1]] +=1 + link_counting[link[0]] += 1 + link_counting[link[1]] += 1 for particle_i in link_counting.keys(): - if link_counting[particle_i] <4: + if link_counting[particle_i] < 4: mesh[1][particle_i][3] = True # We have to add some particles to act as a support structure. -stiffness_support = 5.93E+07 # [n/m*m] line stiffness +stiffness_support = 5.93e07 # [n/m*m] line stiffness n_fixed = sum([i[3] for i in mesh[1]]) circumference = 2 * np.pi * radius k_support = stiffness_support * (circumference / n_fixed) -l_support = length/n_segments/10 +l_support = length / n_segments / 10 -multiplier = (radius+l_support)/radius +multiplier = (radius + l_support) / radius new_particles = [] for i, node in enumerate(mesh[1]): xyz = node[0].copy() if node[3]: - node.append([0,0,1]) - node.append('plane') - particle = [xyz*multiplier, np.zeros(3), params['m_segment'], True] - link = [i, len(mesh[1])+len(new_particles), k_support, 1] + node.append([0, 0, 1]) + node.append("plane") + particle = [xyz * multiplier, np.zeros(3), params["m_segment"], True] + link = [i, len(mesh[1]) + len(new_particles), k_support, 1] new_particles.append(particle) mesh[0].append(link) @@ -151,83 +157,151 @@ def override_constraints(PS: ParticleSystem): # init particle system PS = ParticleSystem(*mesh, params, clean_particles=True) -PS.calculate_correct_masses(params['thickness'], params['rho']) +PS.calculate_correct_masses(params["thickness"], params["rho"]) starting_postions = PS.x_v_current_3D[0] -#Rotate PS around z so there aren't any particles on region boundaries -PS.displace([0,0,0,0,0,45]) +# Rotate PS around z so there aren't any particles on region boundaries +PS.displace([0, 0, 0, 0, 0, 45]) # and deal with a slight meshing assymetry -pos_offset = [-0.00493598*length, 0.00474392*length] -#PS.displace([*pos_offset, 0, 0, 0, 0], suppress_warnings= True) #!!! restore me +pos_offset = [-0.00493598 * length, 0.00474392 * length] +# PS.displace([*pos_offset, 0, 0, 0, 0], suppress_warnings= True) #!!! restore me PS.current_displacement = None # %% Modifying PS # Adding pre_stress -pre_stress = 300e6 # [Pa] -pre_strain = pre_stress / max(params['E_x'],params['E_y']) -shrink_factor = 1/(1+pre_strain) +pre_stress = 300e6 # [Pa] +pre_strain = pre_stress / max(params["E_x"], params["E_y"]) +shrink_factor = 1 / (1 + pre_strain) PS.stress_self(shrink_factor) -#Adding dummy mass values for support:! -width_support = 17e-6 # [m] -mean_circ_ring = (radius - width_support/2)*np.pi*2 -m_support = width_support**2 * mean_circ_ring*density_ring +# Adding dummy mass values for support:! +width_support = 17e-6 # [m] +mean_circ_ring = (radius - width_support / 2) * np.pi * 2 +m_support = width_support**2 * mean_circ_ring * density_ring m = sum([p.m for p in PS.particles]) n_fixed = sum([1 if p.fixed else 0 for p in PS.particles]) -m_fixed = m_support/n_fixed +m_fixed = m_support / n_fixed for p in PS.particles: if p.fixed: p.set_m(m_fixed) m_new = sum([p.m for p in PS.particles]) -#%% Setting up optical system +# %% Setting up optical system # import the photonic crystal(s) -dummy = interp.PhC_library['dummy'] -gao = interp.PhC_library['Gao'] -mark_4 = interp.PhC_library['Mark_4'] # likes offset 0 -mark_4_1 = interp.PhC_library['Mark_4.1'] # likes offset 0 -mark_5 = interp.PhC_library['Mark_5'] -mark_6 = interp.PhC_library['Mark_6'] # likes offset pi -mark_7 = interp.PhC_library['Mark_7'] # likes offset 0 -mark_8 = interp.PhC_library['Mark_8'] # likes offset pi -mark_9 = interp.PhC_library['Mark_9'] # likes offset pi +dummy = interp.PhC_library["dummy"] +gao = interp.PhC_library["Gao"] +mark_4 = interp.PhC_library["Mark_4"] # likes offset 0 +mark_4_1 = interp.PhC_library["Mark_4.1"] # likes offset 0 +mark_5 = interp.PhC_library["Mark_5"] +mark_6 = interp.PhC_library["Mark_6"] # likes offset pi +mark_7 = interp.PhC_library["Mark_7"] # likes offset 0 +mark_8 = interp.PhC_library["Mark_8"] # likes offset pi +mark_9 = interp.PhC_library["Mark_9"] # likes offset pi inner_phc = mark_6 inner_offset = np.pi outer_phc = mark_4 -outer_offset = np.pi*0 +outer_offset = np.pi * 0 -twist_compensation = 0/180*np.pi +twist_compensation = 0 / 180 * np.pi -r_transition = radius*0/5 +r_transition = radius * 0 / 5 # phi_start, phi_stop, r_start, r_stop, midline, PhC, offset -regions1 = [[-np.pi/4, np.pi/4, 0, r_transition, 0, inner_phc, inner_offset], - [np.pi/4, np.pi*3/4, 0, r_transition, np.pi*2/4, inner_phc, inner_offset], - [np.pi*3/4, np.pi*4/4, 0, r_transition, np.pi*4/4, inner_phc, inner_offset], - [-np.pi*3/4, -np.pi*1/4, 0, r_transition, -np.pi*2/4, inner_phc, inner_offset], - [-np.pi*4/4, -np.pi*3/4, 0, r_transition, -np.pi*4/4, inner_phc, inner_offset], - [-np.pi/4, np.pi/4, r_transition, length, 0, outer_phc, outer_offset-twist_compensation], - [np.pi/4, np.pi*3/4, r_transition, length, np.pi*2/4, outer_phc, outer_offset+twist_compensation], - [np.pi*3/4, np.pi*4/4, r_transition, length, np.pi*4/4, outer_phc, outer_offset-twist_compensation], - [-np.pi*3/4, -np.pi*1/4, r_transition, length, -np.pi*2/4, outer_phc, outer_offset+twist_compensation], - [-np.pi*4/4, -np.pi*3/4, r_transition, length, -np.pi*4/4, outer_phc, outer_offset-twist_compensation]] - -offset = np.pi*0 +regions1 = [ + [-np.pi / 4, np.pi / 4, 0, r_transition, 0, inner_phc, inner_offset], + [np.pi / 4, np.pi * 3 / 4, 0, r_transition, np.pi * 2 / 4, inner_phc, inner_offset], + [ + np.pi * 3 / 4, + np.pi * 4 / 4, + 0, + r_transition, + np.pi * 4 / 4, + inner_phc, + inner_offset, + ], + [ + -np.pi * 3 / 4, + -np.pi * 1 / 4, + 0, + r_transition, + -np.pi * 2 / 4, + inner_phc, + inner_offset, + ], + [ + -np.pi * 4 / 4, + -np.pi * 3 / 4, + 0, + r_transition, + -np.pi * 4 / 4, + inner_phc, + inner_offset, + ], + [ + -np.pi / 4, + np.pi / 4, + r_transition, + length, + 0, + outer_phc, + outer_offset - twist_compensation, + ], + [ + np.pi / 4, + np.pi * 3 / 4, + r_transition, + length, + np.pi * 2 / 4, + outer_phc, + outer_offset + twist_compensation, + ], + [ + np.pi * 3 / 4, + np.pi * 4 / 4, + r_transition, + length, + np.pi * 4 / 4, + outer_phc, + outer_offset - twist_compensation, + ], + [ + -np.pi * 3 / 4, + -np.pi * 1 / 4, + r_transition, + length, + -np.pi * 2 / 4, + outer_phc, + outer_offset + twist_compensation, + ], + [ + -np.pi * 4 / 4, + -np.pi * 3 / 4, + r_transition, + length, + -np.pi * 4 / 4, + outer_phc, + outer_offset - twist_compensation, + ], +] + +offset = np.pi * 0 inner_phc = mark_6 # phi_start, phi_stop, r_start, r_stop, midline -regions0 = [[0, np.pi/2, 0, length, np.pi*1/4, inner_phc, offset], - [np.pi/2, np.pi, 0, length, np.pi*3/4, inner_phc, offset], - [np.pi, np.pi*3/2, 0, length, np.pi*5/4, inner_phc, offset], - [np.pi*3/2, np.pi*2, 0, length, np.pi*7/4, inner_phc, offset]] +regions0 = [ + [0, np.pi / 2, 0, length, np.pi * 1 / 4, inner_phc, offset], + [np.pi / 2, np.pi, 0, length, np.pi * 3 / 4, inner_phc, offset], + [np.pi, np.pi * 3 / 2, 0, length, np.pi * 5 / 4, inner_phc, offset], + [np.pi * 3 / 2, np.pi * 2, 0, length, np.pi * 7 / 4, inner_phc, offset], +] regions = regions1 for reg in regions: - if type(reg[5])==str: - reg[5] = interp.create_interpolator(reg[5], reg[4]+reg[6]) + if type(reg[5]) == str: + reg[5] = interp.create_interpolator(reg[5], reg[4] + reg[6]) templog = [] for p in PS.particles: - x,y,z = p.x - phi = np.arctan2(y,x) + x, y, z = p.x + phi = np.arctan2(y, x) r = np.linalg.norm(p.x) templog.append([phi]) for reg in regions: @@ -250,92 +324,117 @@ def override_constraints(PS: ParticleSystem): # if p.constraint_type == 'point': # p.optical_interpolator = dummy -templog= np.array(templog) +templog = np.array(templog) # init optical system -P = 400/2# [W] 400 divided by two because superposition of two orthogonally polarised beams +P = ( + 400 / 2 +) # [W] 400 divided by two because superposition of two orthogonally polarised beams # if you want to check, set it all to specular and set sigma to radius/3 # net force should be P_original/c*2*2 (*2 for reflection, *2 for the second laser) mu_x = 0 mu_y = 0 -sigma = radius*5.5 -I_0 = 2*P / (np.pi* sigma**2) +sigma = radius * 5.5 +I_0 = 2 * P / (np.pi * sigma**2) # Setting up two beams for cross-polarisation purposes -LB1 = LaserBeam(lambda x, y: I_0 * np.exp(-1/2 *((x-mu_x)/sigma)**2 # gaussian laser - -1/2 *((y-mu_y)/sigma)**2), - lambda x,y: np.outer(np.ones(x.shape),[0,1])) -LB2 = LaserBeam(lambda x, y: I_0 * np.exp(-1/2 *((x-mu_x)/sigma)**2 # gaussian laser - -1/2 *((y-mu_y)/sigma)**2), - lambda x,y: np.outer(np.ones(x.shape),[1,0])) +LB1 = LaserBeam( + lambda x, y: I_0 + * np.exp( + -1 / 2 * ((x - mu_x) / sigma) ** 2 # gaussian laser + - 1 / 2 * ((y - mu_y) / sigma) ** 2 + ), + lambda x, y: np.outer(np.ones(x.shape), [0, 1]), +) +LB2 = LaserBeam( + lambda x, y: I_0 + * np.exp( + -1 / 2 * ((x - mu_x) / sigma) ** 2 # gaussian laser + - 1 / 2 * ((y - mu_y) / sigma) ** 2 + ), + lambda x, y: np.outer(np.ones(x.shape), [1, 0]), +) OFC = OpticalForceCalculatorCross(PS, LB1, LB2) # pick the desired simulation -SIM = Simulate_Lightsail(PS,OFC,params) +SIM = Simulate_Lightsail(PS, OFC, params) -#%% Start of using and plotting -#%%% Run a cheeky little simulation? +# %% Start of using and plotting +# %%% Run a cheeky little simulation? cheeky = False if cheeky: - PS.params['convergence_threshold'] = 1e-20 - - SIM.run_simulation(plotframes=0, printframes=10, simulation_function='kinetic_damping',file_id='_simple_') + PS.params["convergence_threshold"] = 1e-20 + + SIM.run_simulation( + plotframes=0, + printframes=10, + simulation_function="kinetic_damping", + file_id="_simple_", + ) fig_convergence = plt.figure() ax_kin = fig_convergence.add_subplot(211) ax_kin.semilogy(PS.history["E_kin"]) - ax_kin.set_ylabel('E_kin') + ax_kin.set_ylabel("E_kin") ax_f = fig_convergence.add_subplot(212) ax_f.semilogy(PS.history["net_force"]) - ax_f.set_ylabel('net_force') + ax_f.set_ylabel("net_force") f = OFC.force_value() f_net = np.sum(f, axis=0) f_abs = np.linalg.norm(f_net) - zmax = PS.x_v_current_3D[0][:,2].max() - ax = PS.plot_forces(f, length = zmax*2e9) - ax.set_aspect('auto') - zmax/=1e6 - ax.set_zlim([-zmax/2,zmax]) + zmax = PS.x_v_current_3D[0][:, 2].max() + ax = PS.plot_forces(f, length=zmax * 2e9) + ax.set_aspect("auto") + zmax /= 1e6 + ax.set_zlim([-zmax / 2, zmax]) f_react = PS.find_reaction_forces() - f_r_net = np.linalg.norm(f_react[:,:2],axis=1) #eliminate z force for now - f_circ = np.sum(f_r_net)/circumference # [N/m] - print(f'Pre-stress {pre_stress/1e6} [MPa], Edge stiffness {stiffness_support/1e3:.4g} [kN/m], Boundary Load {f_circ:.4f} [N/m]') + f_r_net = np.linalg.norm(f_react[:, :2], axis=1) # eliminate z force for now + f_circ = np.sum(f_r_net) / circumference # [N/m] + print( + f"Pre-stress {pre_stress/1e6} [MPa], Edge stiffness {stiffness_support/1e3:.4g} [kN/m], Boundary Load {f_circ:.4f} [N/m]" + ) - f_lift = m_new*9.80665 + f_lift = m_new * 9.80665 f_z = np.sum(f, axis=0)[-1] - print(f'req. {f_lift}, avail. {f_z}') + print(f"req. {f_lift}, avail. {f_z}") -#%%% Vis laser and sail +# %%% Vis laser and sail beam_plot = False if beam_plot: - fig1 = plt.figure(figsize =(12,6)) - ax = fig1.add_subplot(projection='3d') - ax = OFC.plot(ax,x_range = (-3*sigma,3*sigma), y_range = (-3*sigma,3*sigma), - z_scale = I_0/radius, arrow_length = radius/15, number_of_points = 25**2) - PS.displace([radius,0,1e-3,0,0,0]) - PS.plot_forces(OFC.force_value(), ax, length = 1e7) + fig1 = plt.figure(figsize=(12, 6)) + ax = fig1.add_subplot(projection="3d") + ax = OFC.plot( + ax, + x_range=(-3 * sigma, 3 * sigma), + y_range=(-3 * sigma, 3 * sigma), + z_scale=I_0 / radius, + arrow_length=radius / 15, + number_of_points=25**2, + ) + PS.displace([radius, 0, 1e-3, 0, 0, 0]) + PS.plot_forces(OFC.force_value(), ax, length=1e7) ax.azim = 0 ax.elev = 0 fig1.tight_layout() - ax.set_proj_type('ortho') + ax.set_proj_type("ortho") -#%%% Jacobian printing +# %%% Jacobian printing stability_check = False if stability_check: - J = OFC.calculate_stability_coefficients(displacement_range = [0.1*radius, 0.5]) - J[:,2]=0 - J[:,-1]=0 - J[2,:]=0 - J[-1,:]=0 - J_no_z = J[J!=0].reshape([4,4]) + J = OFC.calculate_stability_coefficients(displacement_range=[0.1 * radius, 0.5]) + J[:, 2] = 0 + J[:, -1] = 0 + J[2, :] = 0 + J[-1, :] = 0 + J_no_z = J[J != 0].reshape([4, 4]) print(J) print(np.linalg.det(J_no_z)) -#%%% Force vector plots with displacements +# %%% Force vector plots with displacements force_plot = False force_check = False if force_check: @@ -344,53 +443,66 @@ def override_constraints(PS: ParticleSystem): PS.simulate(f.ravel()) f = OFC.force_value() - f_lift = m_new*9.80665 + f_lift = m_new * 9.80665 f_z = np.sum(f, axis=0)[-1] - print(f'req. {f_lift}, avail. {f_z}') - - disp_list = [[0,0,0,0,0,0], - [0,0,0,3,0,0], - [0,0,0,0,3,0], - [radius/10,0,0,0,0,0], - [0,radius/10,0,0,0,0]]#, - #[0,radius/3,0,3,0,0]]#, - #[0,0,0,0,0,5], - #[0,0,0,0,0,-5]] + print(f"req. {f_lift}, avail. {f_z}") + + disp_list = [ + [0, 0, 0, 0, 0, 0], + [0, 0, 0, 3, 0, 0], + [0, 0, 0, 0, 3, 0], + [radius / 10, 0, 0, 0, 0, 0], + [0, radius / 10, 0, 0, 0, 0], + ] # , + # [0,radius/3,0,3,0,0]]#, + # [0,0,0,0,0,5], + # [0,0,0,0,0,-5]] for disp in disp_list: - PS.displace(disp,suppress_warnings=True) + PS.displace(disp, suppress_warnings=True) f = OFC.force_value() - #f = np.nan_to_num(f) + # f = np.nan_to_num(f) f_net = np.sum(f, axis=0) f_abs = np.linalg.norm(f_net) if force_plot: - ax = PS.plot_forces(f, length = 1/f_abs/6) + ax = PS.plot_forces(f, length=1 / f_abs / 6) ax.figure.tight_layout() - f_res,m_res =OFC.calculate_restoring_forces() + f_res, m_res = OFC.calculate_restoring_forces() padding = 25 - len(str(disp)) - print(disp,' '*padding, 'f_res', *[f'\t{i:.3g}' for i in f_res], '\tm_res', *[f'\t{i:.3g}' for i in m_res]) + print( + disp, + " " * padding, + "f_res", + *[f"\t{i:.3g}" for i in f_res], + "\tm_res", + *[f"\t{i:.3g}" for i in m_res], + ) PS.un_displace() -#%%% Trajectory plots -params["t_steps"]= 1e5 -PS.COM_offset = np.array([0,0,-width_support/2]) +# %%% Trajectory plots +params["t_steps"] = 1e5 +PS.COM_offset = np.array([0, 0, -width_support / 2]) # Pressure Damping coefficient -drag_data_1 = { 101325 : 8.1652e-06, - 80000 : 6.4288e-06, - 60000 : 4.8406e-06, - 40000 : 3.2501e-06, - 20000 : 1.6571e-06, - 10000 : 8.5704e-07, - 1000 : 1.2375e-07} - -drag_data_2 = {101325: 2.0916e-06, - 80000: 1.6505e-06, - 60000: 1.2477e-06, - 40000: 8.429e-07, - 20000: 4.314e-07, - 10000: 2.326e-07, - 1000: 4.174e-08} +drag_data_1 = { + 101325: 8.1652e-06, + 80000: 6.4288e-06, + 60000: 4.8406e-06, + 40000: 3.2501e-06, + 20000: 1.6571e-06, + 10000: 8.5704e-07, + 1000: 1.2375e-07, +} + +drag_data_2 = { + 101325: 2.0916e-06, + 80000: 1.6505e-06, + 60000: 1.2477e-06, + 40000: 8.429e-07, + 20000: 4.314e-07, + 10000: 2.326e-07, + 1000: 4.174e-08, +} # Define damping coeffs, make sure they're negative! damping_pressure = 10000 @@ -401,92 +513,105 @@ def override_constraints(PS: ParticleSystem): trajectory = False if trajectory: f = OFC.force_value() - f_lift = m_new*9.80665 + f_lift = m_new * 9.80665 f_z = np.sum(f, axis=0)[-1] - print(f'req. {f_lift}, avail. {f_z}') + print(f"req. {f_lift}, avail. {f_z}") - #PS.displace([0,radius/10,0,0,0,0],suppress_warnings=True) - initial_conditions = np.array([[radius*0.02,0,0,0,0,0], - [0,0,0,0,0,0]],dtype =np.float64) + # PS.displace([0,radius/10,0,0,0,0],suppress_warnings=True) + initial_conditions = np.array( + [[radius * 0.02, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]], dtype=np.float64 + ) override_constraints(PS) - SIM.simulate_trajectory(plotframes=0, - printframes=100, - plot_forces=False, - plot_net_force = True, - plot_angles = [10,0], - file_id = '_M9_damping_', - deform = True, - gravity = True, - #initial_conditions=initial_conditions, - damping = damping, - spin = False) - z = (np.cumsum(PS.history['position'],axis=0)[-1]/length)[2] - print(f'Final altitude is {z}') - SIM.plot_flight_hist(pos_offset = pos_offset) - - -#%%% Trajectory-sweep from initial conditions plot + SIM.simulate_trajectory( + plotframes=0, + printframes=100, + plot_forces=False, + plot_net_force=True, + plot_angles=[10, 0], + file_id="_M9_damping_", + deform=True, + gravity=True, + # initial_conditions=initial_conditions, + damping=damping, + spin=False, + ) + z = (np.cumsum(PS.history["position"], axis=0)[-1] / length)[2] + print(f"Final altitude is {z}") + SIM.plot_flight_hist(pos_offset=pos_offset) + + +# %%% Trajectory-sweep from initial conditions plot def trajectory_from_initial_conditions(x0=0, y0=0, theta_x0=0, theta_y0=0): override_constraints(PS) # Reset PS PS.update_pos_unsafe(PS.initial_positions) - v = np.zeros(PS.n*3) + v = np.zeros(PS.n * 3) PS.update_vel_unsafe(v) PS.reset_history() # Perform initial displacement - PS.displace([x0, y0, 0, theta_x0, theta_y0, 0],suppress_warnings=True) - PS.params['convergence_threshold'] = radius*2e-2 - - stable =SIM.simulate_trajectory(plotframes=0, - printframes=100, - plot_forces=False, - plot_net_force = True, - plot_angles = [10,0], - file_id = '_M4_damping_', - deform = False, - spin = False, - gravity = True, - damping = damping) - - pos = (np.sum(PS.history['position'],axis=0)/length) - print(f'Final altitude is {pos[2]}') + PS.displace([x0, y0, 0, theta_x0, theta_y0, 0], suppress_warnings=True) + PS.params["convergence_threshold"] = radius * 2e-2 + + stable = SIM.simulate_trajectory( + plotframes=0, + printframes=100, + plot_forces=False, + plot_net_force=True, + plot_angles=[10, 0], + file_id="_M4_damping_", + deform=False, + spin=False, + gravity=True, + damping=damping, + ) + + pos = np.sum(PS.history["position"], axis=0) / length + print(f"Final altitude is {pos[2]}") return stable, pos + trajectory_sweep = False if trajectory_sweep: PS.initial_positions, _ = PS.x_v_current start = time.time() - intro= "Running trajectory sweep" - buffer = int((80-len(intro)-1)/2) - print(80*"="+"\n"+buffer*' '+intro+"\n"+80*"-") - x0 = np.linspace(0, 1, 31)**3 /8 * radius - y0 = np.linspace(0, 1, 31)**3 /8 * radius - x0,y0 = np.meshgrid(x0,y0) + intro = "Running trajectory sweep" + buffer = int((80 - len(intro) - 1) / 2) + print(80 * "=" + "\n" + buffer * " " + intro + "\n" + 80 * "-") + x0 = np.linspace(0, 1, 31) ** 3 / 8 * radius + y0 = np.linspace(0, 1, 31) ** 3 / 8 * radius + x0, y0 = np.meshgrid(x0, y0) results = [] space = 62 for i, initials in enumerate(zip(x0.ravel(), y0.ravel())): - progress = round(i/len(x0.ravel()),3) - equals = round(progress*space) - if progress<0.1: + progress = round(i / len(x0.ravel()), 3) + equals = round(progress * space) + if progress < 0.1: offset = 1 else: offset = 0 - print(f"Progress: {progress*100:.1f}% "+" "*offset+"["+"="*equals+" "*(space-equals)+"]") + print( + f"Progress: {progress*100:.1f}% " + + " " * offset + + "[" + + "=" * equals + + " " * (space - equals) + + "]" + ) print(f"Initial conditions: {initials=}") stable, pos = trajectory_from_initial_conditions(*initials) - r = np.sqrt(pos[0]**2 + pos[1]**2) - converged = r<1 - results.append((stable, pos))#, PS.history)) - print(80*"-") + r = np.sqrt(pos[0] ** 2 + pos[1] ** 2) + converged = r < 1 + results.append((stable, pos)) # , PS.history)) + print(80 * "-") current = time.time() - elapsed = current-start - el_m = round(elapsed/60) - el_s = elapsed%60 + elapsed = current - start + el_m = round(elapsed / 60) + el_s = elapsed % 60 print(f"Done! that took {el_m}m {el_s}s") - print(80*"=") + print(80 * "=") stable = np.array([i[0] for i in results]) altitude = np.array([i[1][2] for i in results]) @@ -495,32 +620,37 @@ def trajectory_from_initial_conditions(x0=0, y0=0, theta_x0=0, theta_y0=0): altitude = altitude.reshape(x0.shape) marking = round(time.time()) - np.savetxt(f'temp/initial_pos_sweep/sweep_trajectories_stable_{marking}.csv', stable) - np.savetxt(f'temp/initial_pos_sweep/sweep_trajectories_x_{marking}.csv', x0) - np.savetxt(f'temp/initial_pos_sweep/sweep_trajectories_y_{marking}.csv', y0) + np.savetxt( + f"temp/initial_pos_sweep/sweep_trajectories_stable_{marking}.csv", stable + ) + np.savetxt(f"temp/initial_pos_sweep/sweep_trajectories_x_{marking}.csv", x0) + np.savetxt(f"temp/initial_pos_sweep/sweep_trajectories_y_{marking}.csv", y0) - altitude[stable]=0 + altitude[stable] = 0 - stability_contours = plt.figure(figsize=[16,6]) + stability_contours = plt.figure(figsize=[16, 6]) ax1 = stability_contours.add_subplot(122) - contour = ax1.contourf(x0/length,y0/length,altitude,cmap= 'inferno')#, levels = 3) + contour = ax1.contourf( + x0 / length, y0 / length, altitude, cmap="inferno" + ) # , levels = 3) cbar = stability_contours.colorbar(contour) - cbar.set_label('Altitude [D]') + cbar.set_label("Altitude [D]") plt.xlabel("$x_0$ [D]") plt.ylabel("$y_0$ [D]") - ax1.set_aspect('equal') + ax1.set_aspect("equal") ax2 = stability_contours.add_subplot(121) - stab_region = ax2.contourf(x0/length,y0/length,stable, cmap = 'binary', levels = 1) + stab_region = ax2.contourf( + x0 / length, y0 / length, stable, cmap="binary", levels=1 + ) cbar = stability_contours.colorbar(stab_region) - cbar.set_label('Stability (1 = stable)') + cbar.set_label("Stability (1 = stable)") plt.xlabel("$x_0$ [D]") plt.ylabel("$y_0$ [D]") - ax2.set_aspect('equal') + ax2.set_aspect("equal") stability_contours.tight_layout() - stable_rot90 = np.rot90(stable, k=3) stable_rot180 = np.rot90(stable, k=2) stable_rot270 = np.rot90(stable, k=1) @@ -530,32 +660,45 @@ def trajectory_from_initial_conditions(x0=0, y0=0, theta_x0=0, theta_y0=0): bottom = np.concatenate((stable_rot90, stable), axis=1) full_domain = np.concatenate((top, bottom), axis=0) - extent = [-np.max(x0)/length, np.max(x0)/length, -np.max(y0)/length, np.max(y0)/length] + extent = [ + -np.max(x0) / length, + np.max(x0) / length, + -np.max(y0) / length, + np.max(y0) / length, + ] fig2 = plt.figure() ax_x = fig2.add_subplot() - im = ax_x.imshow(full_domain, cmap = 'binary', origin = 'lower', extent = extent) + im = ax_x.imshow(full_domain, cmap="binary", origin="lower", extent=extent) cbar2 = fig2.colorbar(im) - cbar2.set_label('Stability (1 = stable)') + cbar2.set_label("Stability (1 = stable)") ax_x.set_xlabel("$x_0$ [D]") ax_x.set_ylabel("$y_0$ [D]") -#%%% Displacement Reaction plots + +# %%% Displacement Reaction plots def collect_reaction_data(PS, OFC, disp_range, disp_type): reactions = [] base_range = np.linspace(-1, 1, 101) - modified_range = abs(base_range)**(1/2)*np.sign(base_range) # concentrates values around 0 + modified_range = abs(base_range) ** (1 / 2) * np.sign( + base_range + ) # concentrates values around 0 displacements = modified_range * disp_range - rotational = True if 'rot' in disp_type else False + rotational = True if "rot" in disp_type else False for disp in displacements: - if disp_type in ['trans_x', 'trans_y', 'trans_z']: - displacement = [disp if disp_type == f'trans_{axis}' else 0 for axis in ['x', 'y', 'z']] + if disp_type in ["trans_x", "trans_y", "trans_z"]: + displacement = [ + disp if disp_type == f"trans_{axis}" else 0 for axis in ["x", "y", "z"] + ] displacement += [0, 0, 0] - elif disp_type in ['rot_x', 'rot_y', 'rot_z']: + elif disp_type in ["rot_x", "rot_y", "rot_z"]: displacement = [0, 0, 0] if rotational: - displacement += [disp if disp_type == f'rot_{axis}' else 0 for axis in ['x', 'y', 'z']] + displacement += [ + disp if disp_type == f"rot_{axis}" else 0 + for axis in ["x", "y", "z"] + ] else: displacement += [0, 0, 0] else: @@ -566,68 +709,71 @@ def collect_reaction_data(PS, OFC, disp_range, disp_type): f_res, m_res = OFC.calculate_restoring_forces(forces=f) reaction = np.concatenate((f_res, m_res)) reactions.append(reaction) - #PS.plot_forces(f, length = 1e6) + # PS.plot_forces(f, length = 1e6) PS.un_displace() return displacements, np.array(reactions) + def plot_displacement_vs_reaction(PS, OFC, disp_range_trans, disp_range_rot): fig, axs = plt.subplots(2, 3, figsize=(18, 10)) results = [] # Translation vs. Vertical Force - displacements, reactions = collect_reaction_data(PS, OFC, disp_range_trans, 'trans_x') - results.append((displacements,reactions)) - axs[0, 0].plot(displacements/length, reactions[:, 2], label='Vertical Force') - axs[0, 0].set_xlabel('Displacement [D]') - axs[0, 0].set_ylabel('Vertical Force [N]') - axs[0, 0].set_title('Translation vs. Vertical Force') + displacements, reactions = collect_reaction_data( + PS, OFC, disp_range_trans, "trans_x" + ) + results.append((displacements, reactions)) + axs[0, 0].plot(displacements / length, reactions[:, 2], label="Vertical Force") + axs[0, 0].set_xlabel("Displacement [D]") + axs[0, 0].set_ylabel("Vertical Force [N]") + axs[0, 0].set_title("Translation vs. Vertical Force") axs[0, 0].legend() axs[0, 0].grid(True) # Translation vs. Lateral Force - axs[0, 1].plot(displacements/length, reactions[:, 0], label='Lateral Force X') - axs[0, 1].plot(displacements/length, reactions[:, 1], label='Lateral Force Y') - axs[0, 1].set_xlabel('Displacement [D]') - axs[0, 1].set_ylabel('Lateral Force [N]') - axs[0, 1].set_title('Translation vs. Lateral Force') + axs[0, 1].plot(displacements / length, reactions[:, 0], label="Lateral Force X") + axs[0, 1].plot(displacements / length, reactions[:, 1], label="Lateral Force Y") + axs[0, 1].set_xlabel("Displacement [D]") + axs[0, 1].set_ylabel("Lateral Force [N]") + axs[0, 1].set_title("Translation vs. Lateral Force") axs[0, 1].legend() axs[0, 1].grid(True) # Translation vs. Tipping Moment - axs[0, 2].plot(displacements/length, reactions[:, 3], label='Tipping Moment X') - axs[0, 2].plot(displacements/length, reactions[:, 4], label='Tipping Moment Y') - axs[0, 2].set_xlabel('Displacement [D]') - axs[0, 2].set_ylabel('Tipping Moment [N·m]') - axs[0, 2].set_title('Translation vs. Tipping Moment') + axs[0, 2].plot(displacements / length, reactions[:, 3], label="Tipping Moment X") + axs[0, 2].plot(displacements / length, reactions[:, 4], label="Tipping Moment Y") + axs[0, 2].set_xlabel("Displacement [D]") + axs[0, 2].set_ylabel("Tipping Moment [N·m]") + axs[0, 2].set_title("Translation vs. Tipping Moment") axs[0, 2].legend() axs[0, 2].grid(True) # Rotation vs. Vertical Force - displacements, reactions = collect_reaction_data(PS, OFC, disp_range_rot, 'rot_y') - results.append((displacements,reactions)) - axs[1, 0].plot(displacements, reactions[:, 2], label='Vertical Force') - axs[1, 0].set_xlabel('Displacement [deg]') - axs[1, 0].set_ylabel('Vertical Force [N]') - axs[1, 0].set_title('Rotation vs. Vertical Force') + displacements, reactions = collect_reaction_data(PS, OFC, disp_range_rot, "rot_y") + results.append((displacements, reactions)) + axs[1, 0].plot(displacements, reactions[:, 2], label="Vertical Force") + axs[1, 0].set_xlabel("Displacement [deg]") + axs[1, 0].set_ylabel("Vertical Force [N]") + axs[1, 0].set_title("Rotation vs. Vertical Force") axs[1, 0].legend() axs[1, 0].grid(True) # Rotation vs. Lateral Force - axs[1, 1].plot(displacements, reactions[:, 0], label='Lateral Force X') - axs[1, 1].plot(displacements, reactions[:, 1], label='Lateral Force Y') - axs[1, 1].set_xlabel('Displacement [deg]') - axs[1, 1].set_ylabel('Lateral Force [N]') - axs[1, 1].set_title('Rotation vs. Lateral Force') + axs[1, 1].plot(displacements, reactions[:, 0], label="Lateral Force X") + axs[1, 1].plot(displacements, reactions[:, 1], label="Lateral Force Y") + axs[1, 1].set_xlabel("Displacement [deg]") + axs[1, 1].set_ylabel("Lateral Force [N]") + axs[1, 1].set_title("Rotation vs. Lateral Force") axs[1, 1].legend() axs[1, 1].grid(True) # Rotation vs. Tipping Moment - axs[1, 2].plot(displacements, reactions[:, 3], label='Tipping Moment X') - axs[1, 2].plot(displacements, reactions[:, 4], label='Tipping Moment Y') - axs[1, 2].set_xlabel('Displacement [deg]') - axs[1, 2].set_ylabel('Tipping Moment [N·m]') - axs[1, 2].set_title('Rotation vs. Tipping Moment') + axs[1, 2].plot(displacements, reactions[:, 3], label="Tipping Moment X") + axs[1, 2].plot(displacements, reactions[:, 4], label="Tipping Moment Y") + axs[1, 2].set_xlabel("Displacement [deg]") + axs[1, 2].set_ylabel("Tipping Moment [N·m]") + axs[1, 2].set_title("Rotation vs. Tipping Moment") axs[1, 2].legend() axs[1, 2].grid(True) @@ -646,21 +792,25 @@ def plot_displacement_vs_reaction(PS, OFC, disp_range_trans, disp_range_rot): # Generate the plots results = plot_displacement_vs_reaction(PS, OFC, disp_range_trans, disp_range_rot) -#%%% Stability regions with recursive bisection +# %%% Stability regions with recursive bisection -def binary_search_stability(theta: float, r_inner: float, r_outer: float, tolerance: float, logger) -> Tuple[float, float]: + +def binary_search_stability( + theta: float, r_inner: float, r_outer: float, tolerance: float, logger +) -> Tuple[float, float]: low, high = r_inner, r_outer stable, unstable = low, high count = 0 while high - low > tolerance: - count+=1 + count += 1 mid = (high + low) / 2 x0 = mid * np.cos(np.radians(theta)) y0 = mid * np.sin(np.radians(theta)) - - logger.info(f"Theta: {theta:.2f}, Low: {low/length:.4f} [D], High: {high/length:.4f} [D], Mid: {mid/length:.4f} [D]") + logger.info( + f"Theta: {theta:.2f}, Low: {low/length:.4f} [D], High: {high/length:.4f} [D], Mid: {mid/length:.4f} [D]" + ) stable_result, pos = trajectory_from_initial_conditions(x0=x0, y0=y0) if stable_result: stable = mid @@ -671,72 +821,100 @@ def binary_search_stability(theta: float, r_inner: float, r_outer: float, tolera high = mid direction = "inwards" - logger.info(f"Theta: {theta:.2f}, Mid: {mid/length :.4f} [D], Stable: {stable_result}, Direction: {direction}\n") + logger.info( + f"Theta: {theta:.2f}, Mid: {mid/length :.4f} [D], Stable: {stable_result}, Direction: {direction}\n" + ) if stable is None: stable = low if unstable is None: unstable = high - logger.info(f"Solution found after {count+2} iterations. Stable: {stable/length:.4f} [D], Unstable: {unstable/length:.4f} [D]") + logger.info( + f"Solution found after {count+2} iterations. Stable: {stable/length:.4f} [D], Unstable: {unstable/length:.4f} [D]" + ) return stable, unstable -def sweep_polar_coordinates(radius: float, d_theta: float, tolerance: float, logger, theta_max= 90) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + +def sweep_polar_coordinates( + radius: float, d_theta: float, tolerance: float, logger, theta_max=90 +) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: start = time.time() angles = np.arange(0, theta_max + d_theta, d_theta) stable_radii = [] unstable_radii = [] positions = [] - for theta in angles: logger.info(f"\n\nStarting binary search for theta: {theta:.2f} degrees") if len(stable_radii) == 0: - r_inner, r_outer = 0, radius*0.5 + r_inner, r_outer = 0, radius * 0.5 else: r_inner, r_outer = stable_radii[-1] / 1.2, unstable_radii[-1] * 1.2 # Check if both inner and outer are stable or unstable - inner_stable, _ = trajectory_from_initial_conditions(x0=r_inner * np.cos(np.radians(theta)), - y0=r_inner * np.sin(np.radians(theta))) - outer_stable, _ = trajectory_from_initial_conditions(x0=r_outer * np.cos(np.radians(theta)), - y0=r_outer * np.sin(np.radians(theta))) + inner_stable, _ = trajectory_from_initial_conditions( + x0=r_inner * np.cos(np.radians(theta)), + y0=r_inner * np.sin(np.radians(theta)), + ) + outer_stable, _ = trajectory_from_initial_conditions( + x0=r_outer * np.cos(np.radians(theta)), + y0=r_outer * np.sin(np.radians(theta)), + ) if inner_stable == outer_stable: - logger.info(f"Theta: {theta:.2f} degrees, pre-check caught: {r_inner/length=:.2f} [D], {r_outer/length=:.2f} [D]") + logger.info( + f"Theta: {theta:.2f} degrees, pre-check caught: {r_inner/length=:.2f} [D], {r_outer/length=:.2f} [D]" + ) r_inner, r_outer = 0.00, radius - stable, unstable = binary_search_stability(theta, r_inner, r_outer, tolerance, logger) + stable, unstable = binary_search_stability( + theta, r_inner, r_outer, tolerance, logger + ) stable_radii.append(stable) unstable_radii.append(unstable) positions.append((theta, stable, unstable)) end = time.time() - dt = end-start + dt = end - start logger.info(f"All in all that took {dt/60:.0f}m and {dt%60}s") return np.array(stable_radii), np.array(unstable_radii), np.array(positions) -def store_results(stable: np.ndarray, positions: np.ndarray, theta: np.ndarray, marking: int) -> None: - np.savetxt(f'temp/initial_pos_sweep/sweep_trajectories_stable_{marking}_sigma{sigma:.2g}_damping{damping_pressure:.2g}.csv', stable) - np.savetxt(f'temp/initial_pos_sweep/sweep_trajectories_positions_{marking}_sigma{sigma:.2g}_damping{damping_pressure:.2g}.csv', positions) - np.savetxt(f'temp/initial_pos_sweep/sweep_trajectories_theta_{marking}_sigma{sigma:.2g}_damping{damping_pressure:.2g}.csv', theta) +def store_results( + stable: np.ndarray, positions: np.ndarray, theta: np.ndarray, marking: int +) -> None: + np.savetxt( + f"temp/initial_pos_sweep/sweep_trajectories_stable_{marking}_sigma{sigma:.2g}_damping{damping_pressure:.2g}.csv", + stable, + ) + np.savetxt( + f"temp/initial_pos_sweep/sweep_trajectories_positions_{marking}_sigma{sigma:.2g}_damping{damping_pressure:.2g}.csv", + positions, + ) + np.savetxt( + f"temp/initial_pos_sweep/sweep_trajectories_theta_{marking}_sigma{sigma:.2g}_damping{damping_pressure:.2g}.csv", + theta, + ) + def plot_results(stable: np.ndarray, theta: np.ndarray, radius: float) -> None: - length = 2*radius - stable_interp = interp1d(theta,stable,bounds_error=False) + length = 2 * radius + stable_interp = interp1d(theta, stable, bounds_error=False) # Define Cartesian grid - max_radius = stable.max()*1.25 + max_radius = stable.max() * 1.25 x_cartesian = np.linspace(-max_radius, max_radius, 500) y_cartesian = np.linspace(-max_radius, max_radius, 500) x_grid, y_grid = np.meshgrid(x_cartesian, y_cartesian) r_grid = np.sqrt(x_grid**2 + y_grid**2) - theta_grid = np.rad2deg(np.arctan2(y_grid, x_grid))%360 - r_cutoff = stable_interp(theta_grid, ) - mask = r_grid None: stability_contours = plt.figure(figsize=[8, 6]) ax2 = stability_contours.add_subplot(111) - stab_region = ax2.contourf(x_grid/length, y_grid/length, stable_matrix, cmap='binary') + stab_region = ax2.contourf( + x_grid / length, y_grid / length, stable_matrix, cmap="binary" + ) plt.xlabel("x [D]") plt.ylabel("y [D]") - ax2.set_aspect('equal') + ax2.set_aspect("equal") - stable_patch = mpatches.Patch(color='black', label='stable region') - unstable_patch = mpatches.Patch(color='grey', label='unstable') + stable_patch = mpatches.Patch(color="black", label="stable region") + unstable_patch = mpatches.Patch(color="grey", label="unstable") plt.legend(handles=[stable_patch, unstable_patch]) stability_contours.tight_layout() @@ -760,7 +940,7 @@ def plot_results(stable: np.ndarray, theta: np.ndarray, radius: float) -> None: return None # Create a grid in polar coordinatesm - max_radius = stable.max()*1.25 + max_radius = stable.max() * 1.25 radii = set(stable) radii.add(0) radii.add(max_radius) @@ -787,19 +967,25 @@ def plot_results(stable: np.ndarray, theta: np.ndarray, radius: float) -> None: x_grid, y_grid = np.meshgrid(x_cartesian, y_cartesian) # Interpolate the stability and altitude data onto the Cartesian grid - stable_cartesian = griddata((x_polar.flatten(), y_polar.flatten()), - stable_matrix.flatten(), (x_grid, y_grid), method='nearest') + stable_cartesian = griddata( + (x_polar.flatten(), y_polar.flatten()), + stable_matrix.flatten(), + (x_grid, y_grid), + method="nearest", + ) # Plot the stability regions stability_contours = plt.figure(figsize=[8, 6]) ax2 = stability_contours.add_subplot(111) - stab_region = ax2.contourf(x_grid/length, y_grid/length, stable_cartesian, cmap='binary', levels=1) + stab_region = ax2.contourf( + x_grid / length, y_grid / length, stable_cartesian, cmap="binary", levels=1 + ) cbar = stability_contours.colorbar(stab_region) - cbar.set_label('Stability (1 = stable)'); + cbar.set_label("Stability (1 = stable)") plt.xlabel("x [D]") plt.ylabel("y [D]") - ax2.set_aspect('equal') + ax2.set_aspect("equal") stability_contours.tight_layout() plt.show() @@ -811,8 +997,10 @@ def plot_results(stable: np.ndarray, theta: np.ndarray, radius: float) -> None: PS.initial_positions, _ = PS.x_v_current # Setting up the logger - logging.basicConfig(level=logging.DEBUG, # Set to DEBUG to capture all messages - format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') + logging.basicConfig( + level=logging.DEBUG, # Set to DEBUG to capture all messages + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + ) logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) # Set the logger level to INFO @@ -823,16 +1011,19 @@ def plot_results(stable: np.ndarray, theta: np.ndarray, radius: float) -> None: # Running the sweep marking = int(time.time()) - stable_radii, unstable_radii, positions = sweep_polar_coordinates(radius, d_theta, tolerance, logger, theta_max) + stable_radii, unstable_radii, positions = sweep_polar_coordinates( + radius, d_theta, tolerance, logger, theta_max + ) # Storing the results - store_results(stable_radii, positions, np.arange(0, theta_max + d_theta, d_theta), marking) + store_results( + stable_radii, positions, np.arange(0, theta_max + d_theta, d_theta), marking + ) # Preparing altitude data for plotting (assuming altitude is derived from positions in some way) altitude = np.array([pos[2] for pos in positions]) # Plotting the results - stable = (positions[:,1]+ positions[:,2])/2 - theta = positions[:,0] + stable = (positions[:, 1] + positions[:, 2]) / 2 + theta = positions[:, 0] plot_results(stable, theta, radius) - diff --git a/Tutourial/Poisson's Ratio.ipynb b/Tutourial/Poisson's Ratio.ipynb index 228d412..5bcfa9a 100644 --- a/Tutourial/Poisson's Ratio.ipynb +++ b/Tutourial/Poisson's Ratio.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "db865830", "metadata": {}, "outputs": [], @@ -25,29 +25,28 @@ "import time\n", "\n", "import sys, os\n", - "sys.path.append(os.path.abspath('../.'))\n", - "sys.path.append(os.path.abspath('../..'))\n", - "from src.particleSystem.ParticleSystem import ParticleSystem \n", "\n", - "matplotlib.rcParams['figure.figsize'] = [10, 5]\n", - "#%matplotlib qt\n", + "sys.path.append(os.path.abspath(\"../.\"))\n", + "sys.path.append(os.path.abspath(\"../..\"))\n", + "from Particle_System_Simulator.particleSystem.ParticleSystem import ParticleSystem\n", + "\n", + "matplotlib.rcParams[\"figure.figsize\"] = [10, 5]\n", + "# %matplotlib qt\n", "\n", "# dictionary of required parameters\n", "params = {\n", " # model parameters\n", " \"k\": 1, # [N/m] spring stiffness\n", " \"c\": 1, # [N s/m] damping coefficient\n", - " \"m_segment\": 1, # [kg] mass of each node\n", - "\n", + " \"m_segment\": 1, # [kg] mass of each node\n", " # simulation settings\n", " \"dt\": 0.1, # [s] simulation timestep\n", " \"t_steps\": 1000, # [-] number of simulated time steps\n", " \"abs_tol\": 1e-50, # [m/s] absolute error tolerance iterative solver\n", " \"rel_tol\": 1e-5, # [-] relative error tolerance iterative solver\n", " \"max_iter\": 1e5, # [-] maximum number of iterations\n", - "\n", " # physical parameters\n", - " \"g\": 9.807 # [m/s^2] gravitational acceleration\n", + " \"g\": 9.807, # [m/s^2] gravitational acceleration\n", "}" ] }, @@ -62,51 +61,60 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "ff126088", "metadata": {}, "outputs": [], "source": [ "# grid discretization\n", - "# We will use a rectanular grid of 20 x 20, which is 21 x 21 nodes spaced 1 unit apart \n", + "# We will use a rectanular grid of 20 x 20, which is 21 x 21 nodes spaced 1 unit apart\n", "grid_width = 10\n", "grid_length = 10\n", "\n", "params[\"l0\"] = 1\n", - "params[\"n\"] = (grid_width+1) * (grid_length+1)\n", + "params[\"n\"] = (grid_width + 1) * (grid_length + 1)\n", "\n", "# Setting up the coordinates for the nodes\n", - "mesh = np.meshgrid(np.linspace(0,grid_length,grid_length+1),np.linspace(0,grid_width,grid_width+1)) \n", + "mesh = np.meshgrid(\n", + " np.linspace(0, grid_length, grid_length + 1),\n", + " np.linspace(0, grid_width, grid_width + 1),\n", + ")\n", "\n", "\n", "# Fitting it into the required format and setting boundary conditions\n", "# A the core of it this section converts the coordinate grids into a list of nodes\n", "initial_conditions = []\n", - "xy_coordinates = np.column_stack(list(zip(mesh[0],mesh[1]))).T\n", - "xyz_coordinates = np.column_stack((xy_coordinates,np.zeros(len(xy_coordinates)).T))\n", + "xy_coordinates = np.column_stack(list(zip(mesh[0], mesh[1]))).T\n", + "xyz_coordinates = np.column_stack((xy_coordinates, np.zeros(len(xy_coordinates)).T))\n", "\n", "for xyz in xyz_coordinates:\n", " fixed = False\n", - " if xyz[0] == 0 or xyz[0] == grid_length: #For fixing the other boundary use \"xyz[1] == 0 or xyz[1] == grid_width\"\n", + " if (\n", + " xyz[0] == 0 or xyz[0] == grid_length\n", + " ): # For fixing the other boundary use \"xyz[1] == 0 or xyz[1] == grid_width\"\n", " fixed = True\n", - " initial_conditions.append([xyz, np.zeros(3),params[\"m_segment\"],fixed])\n", + " initial_conditions.append([xyz, np.zeros(3), params[\"m_segment\"], fixed])\n", "\n", "# Setting up the connectivity matrix\n", - "connectivity_matrix = np.zeros((params[\"n\"],params[\"n\"]))\n", + "connectivity_matrix = np.zeros((params[\"n\"], params[\"n\"]))\n", "connections = []\n", "\n", - "#We know that all the nodes are connected to those of the next row, which is grid_length+1 units further\n", - "for i, node in enumerate(initial_conditions[:-grid_length-1]): # adding connextions in y-axis\n", - " connections.append([i, i+grid_length+1, params['k'], params['c']])\n", - " \n", - " if (i+1)%(grid_length+1):\n", - " connections.append([i, i+grid_length+2, params['k'], params['c']])\n", - " connections.append([i+1, i+grid_length+1, params['k'], params['c']])\n", - " \n", + "# We know that all the nodes are connected to those of the next row, which is grid_length+1 units further\n", + "for i, node in enumerate(\n", + " initial_conditions[: -grid_length - 1]\n", + "): # adding connextions in y-axis\n", + " connections.append([i, i + grid_length + 1, params[\"k\"], params[\"c\"]])\n", + "\n", + " if (i + 1) % (grid_length + 1):\n", + " connections.append([i, i + grid_length + 2, params[\"k\"], params[\"c\"]])\n", + " connections.append([i + 1, i + grid_length + 1, params[\"k\"], params[\"c\"]])\n", + "\n", "# We can do the same for the connections between the columns\n", - "for i, node in enumerate(initial_conditions): # adding connections in x-axis\n", - " if (i+1)%(grid_length+1): # Using modulus operator to exclude the nodes at the end of a row\n", - " connections.append([i, i+1, params['k'], params['c']])\n" + "for i, node in enumerate(initial_conditions): # adding connections in x-axis\n", + " if (i + 1) % (\n", + " grid_length + 1\n", + " ): # Using modulus operator to exclude the nodes at the end of a row\n", + " connections.append([i, i + 1, params[\"k\"], params[\"c\"]])" ] }, { @@ -120,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "d037c6dc", "metadata": {}, "outputs": [ @@ -130,13 +138,13 @@ "Text(0.5, 0.92, 'Initial state')" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -148,22 +156,23 @@ "source": [ "# Plotting mesh\n", "fig = plt.figure()\n", - "ax = fig.add_subplot(projection='3d')\n", + "ax = fig.add_subplot(projection=\"3d\")\n", "\n", "for i, node in enumerate(initial_conditions):\n", - " if node[3]: \n", - " ax.scatter(node[0][0],node[0][1],node[0][2], color = 'red', marker = 'o')\n", + " if node[3]:\n", + " ax.scatter(node[0][0], node[0][1], node[0][2], color=\"red\", marker=\"o\")\n", " else:\n", - " ax.scatter(node[0][0],node[0][1],node[0][2], color = 'blue', marker = 'o', s =5)\n", + " ax.scatter(node[0][0], node[0][1], node[0][2], color=\"blue\", marker=\"o\", s=5)\n", + "\n", + "for connection in connections:\n", + " line = np.column_stack(\n", + " [initial_conditions[connection[0]][0], initial_conditions[connection[1]][0]]\n", + " )\n", "\n", - "for connection in connections: \n", - " line = np.column_stack([initial_conditions[connection[0]][0],\n", - " initial_conditions[connection[1]][0]])\n", - " \n", - " ax.plot(line[0],line[1],line[2],color='black')\n", + " ax.plot(line[0], line[1], line[2], color=\"black\")\n", "\n", - "ax.set_box_aspect((grid_length,grid_width,1))\n", - "ax.set_zlim(-1,1)\n", + "ax.set_box_aspect((grid_length, grid_width, 1))\n", + "ax.set_zlim(-1, 1)\n", "plt.title(\"Initial state\")" ] }, @@ -178,24 +187,26 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "be38e1d1", "metadata": {}, "outputs": [], "source": [ "# Fist we have to setup the particle system and simulation\n", - "PS = ParticleSystem(connections,initial_conditions, params)\n", - "f_ext = np.array([[0, 0, 0] for i in range(params['n'])]).flatten()\n", + "PS = ParticleSystem(connections, initial_conditions, params)\n", + "f_ext = np.array([[0, 0, 0] for i in range(params[\"n\"])]).flatten()\n", "\n", - "t_vector = np.linspace(params[\"dt\"], params[\"t_steps\"] * params[\"dt\"], params[\"t_steps\"])\n", + "t_vector = np.linspace(\n", + " params[\"dt\"], params[\"t_steps\"] * params[\"dt\"], params[\"t_steps\"]\n", + ")\n", "final_step = 0\n", - "E_kin = []\n", + "E_kin = []\n", "f_int = []" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "1fef2f84", "metadata": {}, "outputs": [ @@ -205,13 +216,13 @@ "Text(0.5, 0.92, 'Initial state')" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -228,31 +239,30 @@ "stressed_state = []\n", "for node in PS.particles:\n", " if node.x[0] == grid_width:\n", - " node.x[0] *= 1+strain\n", - " \n", - " stressed_state.append([node.x, node.fixed])\n", - " \n", - "# Plotting the strained mesh \n", - "fig = plt.figure()\n", - "ax = fig.add_subplot(projection='3d')\n", + " node.x[0] *= 1 + strain\n", "\n", + " stressed_state.append([node.x, node.fixed])\n", "\n", + "# Plotting the strained mesh\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(projection=\"3d\")\n", "\n", "\n", "for i, node in enumerate(stressed_state):\n", - " if node[1]: \n", - " ax.scatter(node[0][0],node[0][1],node[0][2], color = 'red', marker = 'o')\n", + " if node[1]:\n", + " ax.scatter(node[0][0], node[0][1], node[0][2], color=\"red\", marker=\"o\")\n", " else:\n", - " ax.scatter(node[0][0],node[0][1],node[0][2], color = 'blue', marker = 'o', s =5)\n", + " ax.scatter(node[0][0], node[0][1], node[0][2], color=\"blue\", marker=\"o\", s=5)\n", + "\n", + "for connection in connections:\n", + " line = np.column_stack(\n", + " [stressed_state[connection[0]][0], stressed_state[connection[1]][0]]\n", + " )\n", "\n", - "for connection in connections: \n", - " line = np.column_stack([stressed_state[connection[0]][0],\n", - " stressed_state[connection[1]][0]])\n", - " \n", - " ax.plot(line[0],line[1],line[2],color='black')\n", + " ax.plot(line[0], line[1], line[2], color=\"black\")\n", "\n", - "ax.set_box_aspect((grid_length,grid_width,1))\n", - "ax.set_zlim(-1,1)\n", + "ax.set_box_aspect((grid_length, grid_width, 1))\n", + "ax.set_zlim(-1, 1)\n", "plt.title(\"Initial state\")" ] }, @@ -267,7 +277,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "5a3e4150", "metadata": {}, "outputs": [ @@ -277,7 +287,7 @@ "text": [ "Step: 53.7, Kinetic Energy 5.62e-19\n", "Kinetic damping PS converged 53.7\n", - "PS: 14.2248 s\n" + "PS: 10.1791 s\n" ] } ], @@ -287,24 +297,27 @@ "for step in t_vector:\n", " PS.kin_damp_sim(f_ext)\n", " final_step = step\n", - " x,v, = PS.x_v_current\n", + " (\n", + " x,\n", + " v,\n", + " ) = PS.x_v_current\n", "\n", " E_kin.append(np.matmul(v, v))\n", " f_int.append(np.linalg.norm(PS.f_int))\n", - " \n", + "\n", " clear_output(wait=True)\n", " print(\"Step: {:.1F}, Kinetic Energy {:.2e}\".format(step, E_kin[-1]))\n", - " \n", + "\n", " converged = False\n", - " if step>20*params[\"dt\"]:\n", - " if np.max(E_kin[-10:-1]) <= 1e-20*params['n']:\n", + " if step > 20 * params[\"dt\"]:\n", + " if np.max(E_kin[-10:-1]) <= 1e-20 * params[\"n\"]:\n", " converged = True\n", - " if converged and step>1:\n", + " if converged and step > 1:\n", " print(\"Kinetic damping PS converged\", step)\n", " break\n", - " \n", + "\n", "stop_time = time.time()\n", - "print(f'PS: {(stop_time - start_time):.4f} s')" + "print(f\"PS: {(stop_time - start_time):.4f} s\")" ] }, { @@ -317,7 +330,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "f0ccc10f", "metadata": {}, "outputs": [ @@ -327,13 +340,13 @@ "(0.1, 53.7)" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -347,20 +360,29 @@ "plotstop = len(E_kin)\n", "plotstart = 0\n", "\n", - "plt.plot(t_vector[:plotstop],E_kin[:plotstop], label = 'Kinetic Energy [J]')\n", - "plt.plot(t_vector[plotstart:plotstop],f_int[plotstart:plotstop], label = 'Internal Forces [N]')\n", - "\n", - "# Filtering out the peaks to mark where the kinetic damping algorithm kicked in. \n", - "df = pd.DataFrame(E_kin, index = t_vector[0:plotstop])\n", - "peaksonly=df[(df[0].shift(1) < df[0]) & (df[0].shift(-1) < df[0])]\n", - "plt.scatter(peaksonly.index,peaksonly[0], c='r',linewidths=1, marker='+', label = 'Kinetic Damping Enacted')\n", + "plt.plot(t_vector[:plotstop], E_kin[:plotstop], label=\"Kinetic Energy [J]\")\n", + "plt.plot(\n", + " t_vector[plotstart:plotstop], f_int[plotstart:plotstop], label=\"Internal Forces [N]\"\n", + ")\n", + "\n", + "# Filtering out the peaks to mark where the kinetic damping algorithm kicked in.\n", + "df = pd.DataFrame(E_kin, index=t_vector[0:plotstop])\n", + "peaksonly = df[(df[0].shift(1) < df[0]) & (df[0].shift(-1) < df[0])]\n", + "plt.scatter(\n", + " peaksonly.index,\n", + " peaksonly[0],\n", + " c=\"r\",\n", + " linewidths=1,\n", + " marker=\"+\",\n", + " label=\"Kinetic Damping Enacted\",\n", + ")\n", "\n", "plt.legend()\n", - "plt.yscale('log')\n", + "plt.yscale(\"log\")\n", "plt.title(\"Convergence Plot\")\n", - "plt.xlabel('Time [s]')\n", - "plt.ylabel('Quantity of interest')\n", - "plt.xlim(t_vector[0], t_vector[plotstop-1])" + "plt.xlabel(\"Time [s]\")\n", + "plt.ylabel(\"Quantity of interest\")\n", + "plt.xlim(t_vector[0], t_vector[plotstop - 1])" ] }, { @@ -375,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "id": "9f376722", "metadata": {}, "outputs": [ @@ -385,21 +407,62 @@ "Text(0.5, 0.92, 'Final state')" ] }, - "execution_count": 8, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\mpl_toolkits\\mplot3d\\proj3d.py:50: RuntimeWarning: divide by zero encountered in double_scalars\n", - " dz /= az\n" + "Error in callback (for post_execute), with arguments args (),kwargs {}:\n" + ] + }, + { + "ename": "LinAlgError", + "evalue": "Singular matrix", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mLinAlgError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/venv/lib64/python3.11/site-packages/matplotlib/pyplot.py:268\u001b[0m, in \u001b[0;36m_draw_all_if_interactive\u001b[0;34m()\u001b[0m\n\u001b[1;32m 266\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_draw_all_if_interactive\u001b[39m() \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 267\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m matplotlib\u001b[38;5;241m.\u001b[39mis_interactive():\n\u001b[0;32m--> 268\u001b[0m \u001b[43mdraw_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/venv/lib64/python3.11/site-packages/matplotlib/_pylab_helpers.py:131\u001b[0m, in \u001b[0;36mGcf.draw_all\u001b[0;34m(cls, force)\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m manager \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mget_all_fig_managers():\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m force \u001b[38;5;129;01mor\u001b[39;00m manager\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mstale:\n\u001b[0;32m--> 131\u001b[0m \u001b[43mmanager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw_idle\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/venv/lib64/python3.11/site-packages/matplotlib/backend_bases.py:1905\u001b[0m, in \u001b[0;36mFigureCanvasBase.draw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1903\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_idle_drawing:\n\u001b[1;32m 1904\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_idle_draw_cntx():\n\u001b[0;32m-> 1905\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/venv/lib64/python3.11/site-packages/matplotlib/backends/backend_agg.py:387\u001b[0m, in \u001b[0;36mFigureCanvasAgg.draw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 384\u001b[0m \u001b[38;5;66;03m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[1;32m 385\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\u001b[38;5;241m.\u001b[39m_wait_cursor_for_draw_cm() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\n\u001b[1;32m 386\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m nullcontext()):\n\u001b[0;32m--> 387\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 388\u001b[0m \u001b[38;5;66;03m# A GUI class may be need to update a window using this draw, so\u001b[39;00m\n\u001b[1;32m 389\u001b[0m \u001b[38;5;66;03m# don't forget to call the superclass.\u001b[39;00m\n\u001b[1;32m 390\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mdraw()\n", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/venv/lib64/python3.11/site-packages/matplotlib/artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization..draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 95\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m 97\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/venv/lib64/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 70\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/venv/lib64/python3.11/site-packages/matplotlib/figure.py:3162\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 3159\u001b[0m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m 3161\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3162\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3163\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3165\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfigure\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 3166\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/venv/lib64/python3.11/site-packages/matplotlib/image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m 131\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 132\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 134\u001b[0m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m 135\u001b[0m image_group \u001b[38;5;241m=\u001b[39m []\n", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/venv/lib64/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 70\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/venv/lib64/python3.11/site-packages/mpl_toolkits/mplot3d/axes3d.py:428\u001b[0m, in \u001b[0;36mAxes3D.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 426\u001b[0m \u001b[38;5;66;03m# add the projection matrix to the renderer\u001b[39;00m\n\u001b[1;32m 427\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mM \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_proj()\n\u001b[0;32m--> 428\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minvM \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinalg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mM\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 430\u001b[0m collections_and_patches \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 431\u001b[0m artist \u001b[38;5;28;01mfor\u001b[39;00m artist \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_children\n\u001b[1;32m 432\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(artist, (mcoll\u001b[38;5;241m.\u001b[39mCollection, mpatches\u001b[38;5;241m.\u001b[39mPatch))\n\u001b[1;32m 433\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_visible())\n\u001b[1;32m 434\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcomputed_zorder:\n\u001b[1;32m 435\u001b[0m \u001b[38;5;66;03m# Calculate projection of collections and patches and zorder\u001b[39;00m\n\u001b[1;32m 436\u001b[0m \u001b[38;5;66;03m# them. Make sure they are drawn above the grids.\u001b[39;00m\n", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/venv/lib64/python3.11/site-packages/numpy/linalg/_linalg.py:608\u001b[0m, in \u001b[0;36minv\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m 605\u001b[0m signature \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mD->D\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m isComplexType(t) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124md->d\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 606\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m errstate(call\u001b[38;5;241m=\u001b[39m_raise_linalgerror_singular, invalid\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcall\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 607\u001b[0m over\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m'\u001b[39m, divide\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m'\u001b[39m, under\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 608\u001b[0m ainv \u001b[38;5;241m=\u001b[39m \u001b[43m_umath_linalg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minv\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 609\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m wrap(ainv\u001b[38;5;241m.\u001b[39mastype(result_t, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m))\n", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/venv/lib64/python3.11/site-packages/numpy/linalg/_linalg.py:104\u001b[0m, in \u001b[0;36m_raise_linalgerror_singular\u001b[0;34m(err, flag)\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_raise_linalgerror_singular\u001b[39m(err, flag):\n\u001b[0;32m--> 104\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m LinAlgError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSingular matrix\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mLinAlgError\u001b[0m: Singular matrix" + ] + }, + { + "ename": "LinAlgError", + "evalue": "Singular matrix", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mLinAlgError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/venv/lib64/python3.11/site-packages/IPython/core/formatters.py:343\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 341\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m 342\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 343\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 344\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m 345\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/venv/lib64/python3.11/site-packages/IPython/core/pylabtools.py:170\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m 167\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m 168\u001b[0m FigureCanvasBase(fig)\n\u001b[0;32m--> 170\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 171\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m 172\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/venv/lib64/python3.11/site-packages/matplotlib/backend_bases.py:2175\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m 2172\u001b[0m \u001b[38;5;66;03m# we do this instead of `self.figure.draw_without_rendering`\u001b[39;00m\n\u001b[1;32m 2173\u001b[0m \u001b[38;5;66;03m# so that we can inject the orientation\u001b[39;00m\n\u001b[1;32m 2174\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_draw_disabled\u001b[39m\u001b[38;5;124m\"\u001b[39m, nullcontext)():\n\u001b[0;32m-> 2175\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2176\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[1;32m 2177\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/venv/lib64/python3.11/site-packages/matplotlib/artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization..draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 95\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m 97\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/venv/lib64/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 70\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/venv/lib64/python3.11/site-packages/matplotlib/figure.py:3162\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 3159\u001b[0m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m 3161\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3162\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3163\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3165\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfigure\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 3166\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/venv/lib64/python3.11/site-packages/matplotlib/image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m 131\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 132\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 134\u001b[0m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m 135\u001b[0m image_group \u001b[38;5;241m=\u001b[39m []\n", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/venv/lib64/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 70\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/venv/lib64/python3.11/site-packages/mpl_toolkits/mplot3d/axes3d.py:428\u001b[0m, in \u001b[0;36mAxes3D.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 426\u001b[0m \u001b[38;5;66;03m# add the projection matrix to the renderer\u001b[39;00m\n\u001b[1;32m 427\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mM \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_proj()\n\u001b[0;32m--> 428\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minvM \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinalg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mM\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 430\u001b[0m collections_and_patches \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 431\u001b[0m artist \u001b[38;5;28;01mfor\u001b[39;00m artist \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_children\n\u001b[1;32m 432\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(artist, (mcoll\u001b[38;5;241m.\u001b[39mCollection, mpatches\u001b[38;5;241m.\u001b[39mPatch))\n\u001b[1;32m 433\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_visible())\n\u001b[1;32m 434\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcomputed_zorder:\n\u001b[1;32m 435\u001b[0m \u001b[38;5;66;03m# Calculate projection of collections and patches and zorder\u001b[39;00m\n\u001b[1;32m 436\u001b[0m \u001b[38;5;66;03m# them. Make sure they are drawn above the grids.\u001b[39;00m\n", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/venv/lib64/python3.11/site-packages/numpy/linalg/_linalg.py:608\u001b[0m, in \u001b[0;36minv\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m 605\u001b[0m signature \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mD->D\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m isComplexType(t) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124md->d\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 606\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m errstate(call\u001b[38;5;241m=\u001b[39m_raise_linalgerror_singular, invalid\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcall\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 607\u001b[0m over\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m'\u001b[39m, divide\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m'\u001b[39m, under\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 608\u001b[0m ainv \u001b[38;5;241m=\u001b[39m \u001b[43m_umath_linalg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minv\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 609\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m wrap(ainv\u001b[38;5;241m.\u001b[39mastype(result_t, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m))\n", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/venv/lib64/python3.11/site-packages/numpy/linalg/_linalg.py:104\u001b[0m, in \u001b[0;36m_raise_linalgerror_singular\u001b[0;34m(err, flag)\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_raise_linalgerror_singular\u001b[39m(err, flag):\n\u001b[0;32m--> 104\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m LinAlgError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSingular matrix\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mLinAlgError\u001b[0m: Singular matrix" ] }, { "data": { - "image/png": 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\n", 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" ] @@ -409,25 +472,28 @@ } ], "source": [ - "final_positions = [[particle.x, particle.v, particle.m, particle.fixed] for particle in PS.particles]\n", + "final_positions = [\n", + " [particle.x, particle.v, particle.m, particle.fixed] for particle in PS.particles\n", + "]\n", "\n", "# Plotting final results\n", "fig = plt.figure()\n", - "ax = fig.add_subplot(projection='3d')\n", + "ax = fig.add_subplot(projection=\"3d\")\n", "\n", "for i, node in enumerate(final_positions):\n", - " if node[3]: \n", - " ax.scatter(node[0][0],node[0][1],node[0][2], color = 'red', marker = 'o')\n", + " if node[3]:\n", + " ax.scatter(node[0][0], node[0][1], node[0][2], color=\"red\", marker=\"o\")\n", " else:\n", - " ax.scatter(node[0][0],node[0][1],node[0][2], color = 'blue', marker = 'o', s=5)\n", + " ax.scatter(node[0][0], node[0][1], node[0][2], color=\"blue\", marker=\"o\", s=5)\n", + "\n", + "for connection in connections:\n", + " line = np.column_stack(\n", + " [final_positions[connection[0]][0], final_positions[connection[1]][0]]\n", + " )\n", "\n", - "for connection in connections: \n", - " line = np.column_stack([final_positions[connection[0]][0],\n", - " final_positions[connection[1]][0]])\n", - " \n", - " ax.plot(line[0],line[1],line[2],color='black')\n", + " ax.plot(line[0], line[1], line[2], color=\"black\")\n", "\n", - "ax.legend(['Fixed nodes', 'Free nodes'])\n", + "ax.legend([\"Fixed nodes\", \"Free nodes\"])\n", "\n", "# Finding bounding box and setting aspect ratio\n", "xyz = np.array([particle.x for particle in PS.particles])\n", @@ -447,7 +513,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "00aaac19", "metadata": {}, "outputs": [ @@ -460,7 +526,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -471,8 +537,8 @@ ], "source": [ "# Selecting central nodes\n", - "centre_set = final_positions[int(grid_width/2)::grid_length+1]\n", - "centre_set_initial = initial_conditions[int(grid_width/2)::grid_length+1]\n", + "centre_set = final_positions[int(grid_width / 2) :: grid_length + 1]\n", + "centre_set_initial = initial_conditions[int(grid_width / 2) :: grid_length + 1]\n", "\n", "xyz_coordinates_final = []\n", "\n", @@ -480,19 +546,33 @@ "fig3 = plt.figure()\n", "ax3 = fig3.add_subplot()\n", "for i, node in enumerate(centre_set):\n", - " ax3.scatter(node[0][0],node[0][1], color = 'b', marker = 'o')\n", - " ax3.scatter(centre_set_initial[i][0][0],centre_set_initial[i][0][1], color = 'r', marker = 'o')\n", + " ax3.scatter(node[0][0], node[0][1], color=\"b\", marker=\"o\")\n", + " ax3.scatter(\n", + " centre_set_initial[i][0][0], centre_set_initial[i][0][1], color=\"r\", marker=\"o\"\n", + " )\n", " xyz_coordinates_final.append(node[0])\n", "\n", "xyz_coordinates_final = np.array(xyz_coordinates_final)\n", - "new_width = np.ptp(xyz_coordinates_final[:,1])\n", + "new_width = np.ptp(xyz_coordinates_final[:, 1])\n", "\n", "transverse_strain = (new_width - grid_width) / grid_width\n", "\n", - "poissons_ratio = - transverse_strain/ strain\n", + "poissons_ratio = -transverse_strain / strain\n", "\n", - "print(\"Longitudinal strain {:.3F}, Transverse strain: {:.3F}, Poisson's ratio: {:.2F}\".format(strain, transverse_strain, poissons_ratio))\n" + "print(\n", + " \"Longitudinal strain {:.3F}, Transverse strain: {:.3F}, Poisson's ratio: {:.2F}\".format(\n", + " strain, transverse_strain, poissons_ratio\n", + " )\n", + ")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6930aa4c", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -511,7 +591,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.11.6" } }, "nbformat": 4, diff --git a/Tutourial/Tutorial 1.ipynb b/Tutourial/Tutorial 1.ipynb index 630ed90..74fc55f 100644 --- a/Tutourial/Tutorial 1.ipynb +++ b/Tutourial/Tutorial 1.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 31, "id": "db865830", "metadata": {}, "outputs": [], @@ -21,25 +21,24 @@ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import sys, os\n", - "sys.path.append(os.path.abspath('..'))\n", - "from src.particleSystem.ParticleSystem import ParticleSystem \n", + "\n", + "sys.path.append(os.path.abspath(\"..\"))\n", + "from Particle_System_Simulator.particleSystem.ParticleSystem import ParticleSystem\n", "\n", "# dictionary of required parameters\n", "params = {\n", " # model parameters\n", " \"k\": 2, # [N/m] spring stiffness\n", " \"c\": 1, # [N s/m] damping coefficient\n", - " \"m_segment\": 1, # [kg] mass of each node\n", - "\n", + " \"m_segment\": 1, # [kg] mass of each node\n", " # simulation settings\n", " \"dt\": 0.1, # [s] simulation timestep\n", " \"t_steps\": 1000, # [-] number of simulated time steps\n", " \"abs_tol\": 1e-50, # [m/s] absolute error tolerance iterative solver\n", " \"rel_tol\": 1e-5, # [-] relative error tolerance iterative solver\n", " \"max_iter\": 1e5, # [-] maximum number of iterations\n", - "\n", " # physical parameters\n", - " \"g\": 9.807 # [m/s^2] gravitational acceleration\n", + " \"g\": 9.807, # [m/s^2] gravitational acceleration\n", "}" ] }, @@ -53,18 +52,22 @@ "\n", "It is important to format the data such that we can feed it to the simulation library the way it expects it. We will have to supply three sturctures:\n", "\n", - "- **The initial conditions**: This represents the starting locations, velocities, masses and the boundary conditions of the nodes. Becuase they're point masses the only supported boundary conditions are fixed and free. The structure is a list with an entry for each node. It has the following form:\n", + "- **The initial conditions**: This represents the starting locations, velocities, masses and the boundary conditions of the nodes. Becuase they're point masses the only supported boundary conditions are fixed and free, provided by a boolean `True` or `False`. The structure is a list with an entry for each node. It has the following form:\n", "```python\n", - "initial_conditions = [[[x_1, y_1, z_1], [u_1, v_1, w_1], m_1, Fixed_1],\n", - " ...,\n", - " [[x_n, y_n, z_n], [u_n, v_n, w_n], m_n, Fixed_n]]\n", + " initial_conditions = [\n", + " [np.array([x_1, y_1, z_1]), np.array[u_1, v_1, w_1]), m_1, Fixed_1],\n", + " ...,\n", + " [np.array[x_n, y_n, z_n]), np.array[u_n, v_n, w_n]), m_n, Fixed_n]\n", + " ]\n", "```\n", - "- **The connectivity matrix**: This is an n by n array where n is the total number of nodes. It represents the connections between nodes, where the indices of a cell in the array represents which cells are connected. A connection is marked by a non-zero entry. Four nodes connected in a line will have the following connectivity matrix:\n", + "- **The connectivity matrix**: This is an n by 4 array where n is the total number of nodes, and the 4 column positions are [index of point a, index of point b, k, c, linktype=\"default\"]. Point a is linked to point b, k is the stiffness and c is the damping.\n", + "Example:\n", "```python\n", - "array([[0., 1., 0., 0.],\n", - " [1., 0., 1., 0.],\n", - " [0., 1., 0., 1.],\n", - " [0., 0., 1., 0.]])\n", + " array([[4, 1, 3.3, 1.,\"default\"],\n", + " [4, 0, 2.5, 1.,\"pulley\"],\n", + " [1, 2, 3.3, 1.,\"noncompressive\"],\n", + " [3, 0, 2.5, 1.,\"nontensile\"],\n", + " ...])\n", "```\n", "- **The external forces**: The external forces are passed to the simulation at every timestep. They are represented by F_x, F_y, F_z components for each node, but flattened into a 1D list. This allows the forces to be recalculated each timestep to take into account geometric non-linearities. The list has the form of:\n", "```python\n", @@ -74,50 +77,77 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 33, "id": "ff126088", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "type of connections: , shape: (45, 4)\n", + "connections: [[0, 7, 2, 1], [1, 8, 2, 1], [2, 9, 2, 1], [3, 10, 2, 1], [4, 11, 2, 1], [5, 12, 2, 1], [6, 13, 2, 1], [7, 14, 2, 1], [8, 15, 2, 1], [9, 16, 2, 1], [10, 17, 2, 1], [11, 18, 2, 1], [12, 19, 2, 1], [13, 20, 2, 1], [14, 21, 2, 1], [15, 22, 2, 1], [16, 23, 2, 1], [17, 24, 2, 1], [18, 25, 2, 1], [19, 26, 2, 1], [20, 27, 2, 1], [0, 1, 2, 1], [1, 2, 2, 1], [2, 3, 2, 1], [3, 4, 2, 1], [4, 5, 2, 1], [5, 6, 2, 1], [7, 8, 2, 1], [8, 9, 2, 1], [9, 10, 2, 1], [10, 11, 2, 1], [11, 12, 2, 1], [12, 13, 2, 1], [14, 15, 2, 1], [15, 16, 2, 1], [16, 17, 2, 1], [17, 18, 2, 1], [18, 19, 2, 1], [19, 20, 2, 1], [21, 22, 2, 1], [22, 23, 2, 1], [23, 24, 2, 1], [24, 25, 2, 1], [25, 26, 2, 1], [26, 27, 2, 1]]\n", + "type of initial_conditions: , len: 28\n", + "initial_conditions: [[array([0., 0., 0.]), array([0., 0., 0.]), 1, True], [array([1., 0., 0.]), array([0., 0., 0.]), 1, False], [array([2., 0., 0.]), array([0., 0., 0.]), 1, False], [array([3., 0., 0.]), array([0., 0., 0.]), 1, False], [array([4., 0., 0.]), array([0., 0., 0.]), 1, False], [array([5., 0., 0.]), array([0., 0., 0.]), 1, False], [array([6., 0., 0.]), array([0., 0., 0.]), 1, True], [array([0., 1., 0.]), array([0., 0., 0.]), 1, True], [array([1., 1., 0.]), array([0., 0., 0.]), 1, False], [array([2., 1., 0.]), array([0., 0., 0.]), 1, False], [array([3., 1., 0.]), array([0., 0., 0.]), 1, False], [array([4., 1., 0.]), array([0., 0., 0.]), 1, False], [array([5., 1., 0.]), array([0., 0., 0.]), 1, False], [array([6., 1., 0.]), array([0., 0., 0.]), 1, True], [array([0., 2., 0.]), array([0., 0., 0.]), 1, True], [array([1., 2., 0.]), array([0., 0., 0.]), 1, False], [array([2., 2., 0.]), array([0., 0., 0.]), 1, False], [array([3., 2., 0.]), array([0., 0., 0.]), 1, False], [array([4., 2., 0.]), array([0., 0., 0.]), 1, False], [array([5., 2., 0.]), array([0., 0., 0.]), 1, False], [array([6., 2., 0.]), array([0., 0., 0.]), 1, True], [array([0., 3., 0.]), array([0., 0., 0.]), 1, True], [array([1., 3., 0.]), array([0., 0., 0.]), 1, False], [array([2., 3., 0.]), array([0., 0., 0.]), 1, False], [array([3., 3., 0.]), array([0., 0., 0.]), 1, False], [array([4., 3., 0.]), array([0., 0., 0.]), 1, False], [array([5., 3., 0.]), array([0., 0., 0.]), 1, False], [array([6., 3., 0.]), array([0., 0., 0.]), 1, True]]\n", + "type of params: , len of params: 11\n", + "params: {'k': 2, 'c': 1, 'm_segment': 1, 'dt': 0.1, 't_steps': 1000, 'abs_tol': 1e-50, 'rel_tol': 1e-05, 'max_iter': 100000.0, 'g': 9.807, 'l0': 1, 'n': 28}\n" + ] + } + ], "source": [ "# grid discretization\n", - "# We will use a rectanular grid of 6 x 3, which is 6 x 4 nodes spaced 1 unit apart \n", + "# We will use a rectanular grid of 6 x 3, which is 6 x 4 nodes spaced 1 unit apart\n", "grid_width = 3\n", "grid_length = 6\n", "\n", "params[\"l0\"] = 1\n", - "params[\"n\"] = (grid_width+1) * (grid_length+1)\n", + "params[\"n\"] = (grid_width + 1) * (grid_length + 1)\n", "\n", "# Setting up the coordinates for the nodes\n", - "mesh = np.meshgrid(np.linspace(0,grid_length,grid_length+1),np.linspace(0,grid_width,grid_width+1)) \n", + "mesh = np.meshgrid(\n", + " np.linspace(0, grid_length, grid_length + 1),\n", + " np.linspace(0, grid_width, grid_width + 1),\n", + ")\n", "\n", "\n", "# Fitting it into the required format and setting boundary conditions\n", "# A the core of it this section converts the coordinate grids into a list of nodes\n", "initial_conditions = []\n", - "xy_coordinates = np.column_stack(list(zip(mesh[0],mesh[1]))).T\n", - "xyz_coordinates = np.column_stack((xy_coordinates,np.zeros(len(xy_coordinates)).T))\n", + "xy_coordinates = np.column_stack(list(zip(mesh[0], mesh[1]))).T\n", + "xyz_coordinates = np.column_stack((xy_coordinates, np.zeros(len(xy_coordinates)).T))\n", "\n", "for xyz in xyz_coordinates:\n", " fixed = False\n", - " if xyz[0] == 0 or xyz[0] == grid_length: #For fixing the other boundary use \"xyz[1] == 0 or xyz[1] == grid_width\"\n", + " if (\n", + " xyz[0] == 0 or xyz[0] == grid_length\n", + " ): # For fixing the other boundary use \"xyz[1] == 0 or xyz[1] == grid_width\"\n", " fixed = True\n", - " initial_conditions.append([xyz, np.zeros(3),params[\"m_segment\"],fixed])\n", + " initial_conditions.append([xyz, np.zeros(3), params[\"m_segment\"], fixed])\n", "\n", "# Setting up the connectivity matrix\n", "connections = []\n", "\n", - "#We know that all the nodes are connected to those of the next row, which is grid_length+1 units further\n", - "for i, node in enumerate(initial_conditions[:-grid_length-1]): # adding connextions in y-axis\n", - " connections.append([i, i+grid_length+1, params['k'], params['c']])\n", - " \n", - "# We can do the same for the connections between the columns\n", - "for i, node in enumerate(initial_conditions): # adding connections in x-axis\n", - " if (i+1)%(grid_length+1): # Using modulus operator to exclude the nodes at the end of a row\n", - " connections.append([i, i+1, params['k'], params['c']])\n", + "# We know that all the nodes are connected to those of the next row, which is grid_length+1 units further\n", + "for i, node in enumerate(\n", + " initial_conditions[: -grid_length - 1]\n", + "): # adding connextions in y-axis\n", + " connections.append([i, i + grid_length + 1, params[\"k\"], params[\"c\"]])\n", "\n", - "# For convenience \n", - "#connections = np.nonzero(np.triu(connectivity_matrix)) # Getting indices of cells with a connection\n", - "#connections = np.column_stack((connections[0], connections[1]))" + "# We can do the same for the connections between the columns\n", + "for i, node in enumerate(initial_conditions): # adding connections in x-axis\n", + " if (i + 1) % (\n", + " grid_length + 1\n", + " ): # Using modulus operator to exclude the nodes at the end of a row\n", + " connections.append([i, i + 1, params[\"k\"], params[\"c\"]])\n", + "\n", + "print(f\"type of connections: {type(connections)}, shape: {np.shape(connections)}\")\n", + "print(f\"connections: {connections}\")\n", + "print(\n", + " f\"type of initial_conditions: {type(initial_conditions)}, len: {len(initial_conditions)}\"\n", + ")\n", + "print(f\"initial_conditions: {initial_conditions}\")\n", + "print(f\"type of params: {type(params)}, len of params: {len(params)}\")\n", + "print(f\"params: {params}\")" ] }, { @@ -131,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 24, "id": "d037c6dc", "metadata": {}, "outputs": [ @@ -141,13 +171,13 @@ "Text(0.5, 0.92, 'Initial state')" ] }, - "execution_count": 15, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -159,29 +189,32 @@ "source": [ "# Applying external forces\n", "# Just using a simple load in z for this example\n", - "f_ext = np.array([[0, 0, 1] for i in range(params['n'])]).flatten()\n", + "f_ext = np.array([[0, 0, 1] for i in range(params[\"n\"])]).flatten()\n", "\n", "\n", "# Plotting mesh with external forces\n", "fig = plt.figure()\n", - "ax = fig.add_subplot(projection='3d')\n", + "ax = fig.add_subplot(projection=\"3d\")\n", "\n", "for i, node in enumerate(initial_conditions):\n", - " if node[3]: \n", - " ax.scatter(node[0][0],node[0][1],node[0][2], color = 'red', marker = 'o')\n", + " if node[3]:\n", + " ax.scatter(node[0][0], node[0][1], node[0][2], color=\"red\", marker=\"o\")\n", " else:\n", - " ax.scatter(node[0][0],node[0][1],node[0][2], color = 'blue', marker = 'o')\n", + " ax.scatter(node[0][0], node[0][1], node[0][2], color=\"blue\", marker=\"o\")\n", "\n", - " ax.quiver(*node[0].tolist(), f_ext[3*i], f_ext[3*i+1], f_ext[3*i+2], length = 1)\n", + " ax.quiver(\n", + " *node[0].tolist(), f_ext[3 * i], f_ext[3 * i + 1], f_ext[3 * i + 2], length=1\n", + " )\n", "\n", - "for connection in connections: \n", - " line = np.column_stack([initial_conditions[connection[0]][0],\n", - " initial_conditions[connection[1]][0]])\n", - " \n", - " ax.plot(line[0],line[1],line[2],color='black')\n", + "for connection in connections:\n", + " line = np.column_stack(\n", + " [initial_conditions[connection[0]][0], initial_conditions[connection[1]][0]]\n", + " )\n", "\n", - "ax.set_box_aspect((grid_length,grid_width,1))\n", - "ax.set_zlim(-1,1)\n", + " ax.plot(line[0], line[1], line[2], color=\"black\")\n", + "\n", + "ax.set_box_aspect((grid_length, grid_width, 1))\n", + "ax.set_zlim(-1, 1)\n", "plt.title(\"Initial state\")" ] }, @@ -196,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 25, "id": "5a3e4150", "metadata": {}, "outputs": [ @@ -204,7 +237,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Kinetic damping PS converged 77.0\n" + "Kinetic damping PS converged 54.6\n" ] } ], @@ -212,25 +245,30 @@ "# Now we can setup the particle system and simulation\n", "PS = ParticleSystem(connections, initial_conditions, params)\n", "\n", - "t_vector = np.linspace(params[\"dt\"], params[\"t_steps\"] * params[\"dt\"], params[\"t_steps\"])\n", + "t_vector = np.linspace(\n", + " params[\"dt\"], params[\"t_steps\"] * params[\"dt\"], params[\"t_steps\"]\n", + ")\n", "final_step = 0\n", - "E_kin = []\n", + "E_kin = []\n", "f_int = []\n", "\n", "# And run the simulation\n", "for step in t_vector:\n", " PS.kin_damp_sim(f_ext)\n", - " \n", + "\n", " final_step = step\n", - " x,v, = PS.x_v_current\n", - " E_kin.append(np.linalg.norm(v*v))\n", + " (\n", + " x,\n", + " v,\n", + " ) = PS.x_v_current\n", + " E_kin.append(np.linalg.norm(v * v))\n", " f_int.append(np.linalg.norm(PS.f_int))\n", - " \n", + "\n", " converged = False\n", - " if step>10:\n", + " if step > 10:\n", " if np.max(E_kin[-10:-1]) <= 1e-29:\n", " converged = True\n", - " if converged and step>1:\n", + " if converged and step > 1:\n", " print(\"Kinetic damping PS converged\", step)\n", " break" ] @@ -249,23 +287,23 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "id": "f0ccc10f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.1, 77.1)" + "(0.1, 54.7)" ] }, - "execution_count": 17, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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+GFXxv5j+sfGfko9VNfjYjPuVfFhRO0TVVPJ5EkKICmXnmlRMAqQ6cPFqLvet2EtSphZPJzUfTQqnb6BH2UJZSRD1ChxdbwyKALB1ALe2nMxUczHXlo7tAgjycoa8a4ZXVgJkXAZdAWReMbwu/Vm/DyiEEEI0VXpHk4pJgGRhSZn5PPTJHyRlauno7cSaaX3xd3coW+jQp/DzS6AtXsUW0B9CH4CgweARDCoVb649wK8nk3kztCdBfQPKXq/XQ06KIVDKMAzJkZ0MRflQmHfdv1rQF4Gir+ClFFemmPYxFL9XrjtU0fGqPjbjfiUfVtQOUTVFPk9CCFEpnR1Q/S4VEiBZkLZIx/S1B7iUlke7Vg58/mh/vF2uy4ZdpIUfn4dD6wzvW/eG0W9CQF/zbmRlBc4+hpd/uOUeQAghhGjuMjPhRddqi0mAZEFv/3yKv+Iz8XBU899HKgiOvnwIzv4CqGD4yzDwGUOwI4QQQohGRQIkC9lzNpWVv8cB8MY9PQnwuG5YTVcIG6YZgiNbB7j/U+h4WwO1VAghhBDVkQDJAgqK9Px783EA/tG/Lbd18ylb4OeX4NQWsLGDB/8HwUPqv5FCCCGEMJmM71jAlwcuEpeag6eTmn+N6Vr25PFNsH+F4eN710hwJIQQQjQBEiDVUpFOz4rfzgHw9PCOOGmu65S7dh6+e9rw8aBI6DLG8g1ISDBsYJtQ/Yx8IYQQQphGAqRa+uXvJK6k59HKUc39fW5Yjr91LhRkQ9sBMPSlumlAQgIsXCgBkhBCCGFBMgepljbHGLYReeimdmWzZJ/aCqd+NGSyHvseWFv+U30+NYfo87nYdx7ISL2CdfWXCCGEEMIEEiDV0h/n0sDWnvG92pQeLCqArS8aPh7wJHh1tug9E05f4N8/nSEqQWs4MG4uq/f8zTCr4u0l/PwMLyGEEELUiAyx1ZJOr9DT35Ugz+tSlx/faJh/5OQDt75g0fvFpmRz9+pDRCVosdbrjMevrloH4eGG14oVFr2nEEII0dK06ABp/PjxuLu7c++999aqnuFdrlvWr9fDnvcNH980EzROtar7eln5hTyy9gDJqOnsasPPY3wZ4pBvODltGkRHG14zZljsnkIIIURL1KIDpNmzZ/Ppp5/Wup6bgq/biPbMNkg5AWpn6DO91nVfb8n2M5y/mksbN3s+f2oIHYb0A5fidOnt2kHv3oaXDK8JIYQQtdKiA6QhQ4bg7OxcqzrUNlaEBriVHtj7geHfPtPArvq9Xkx1KS2XtXvPA/DahB54OmlMv1hSAQghhBBmabQB0q5duxg7diytW7dGpVKxefPmcmWWLVtGYGAgdnZ29O/fn/3799d7O3u0di1dvXY1Fi7sBpWVYXjNgv775wV0eoWBHVoxuJNX6QlNcaDk5lb5xZIKQAghhDBLow2QcnJyCA0NZdmyZRWeX79+PZGRkcyfP59Dhw4RGhrKyJEjSU5ONpYJCwsjJCSk3Cs+Pt5i7Qzyum5y9pEvDf+2HwYurS12j4IiPRsOXgZgyoDAsiftigMkV8v1VgkhhBAtXaNd5j969GhGjx5d6fnFixcTERHBtGnTAFi+fDlbtmxh9erVzJkzB4CYmBiLtUer1aLVao3vMzMzAQhsVbwpraIYVq8BhD5osfsCHL54jbScAlo5qhnWxdu0ixISKLwSz/b4fC6eusi99i60OnSo9LykAhBCCCEq1Wh7kKpSUFBAdHQ0I0aMMB6zsrJixIgR7Nu3r07uuWjRIlxdXY2vgABD1uy2rYp7kFJOQto5sNZAp5EWvfeuMykADOroiY21aV+yuI/WcsdHfzJzbzqLrrrw315jICJCUgEIIYQQJmi0PUhVSU1NRafT4ePjU+a4j48PJ0+eNLmeESNGcOTIEXJycvD392fDhg0MGDCgwrJz584lMjLS+D4zM5OAgADatbI3HDj5g+Hf4CGgqd3E7xv9fiYVgFs6elVT0iAxI5+JtuEkexUaj2VpHGHlSsMqN5DeIyGEEKIKTTJAspTt27ebXFaj0aDRlF855u9ePMR25hfDvxbekFZbpOP4lQwAbm7fqtryiqLwr2+OkZxTSCcfJ3q0cWPTIcP8JWMaACGEEEJUqUkOsXl6emJtbU1SUlKZ40lJSfj6+tZrWzQ21qDNhivRhgPBQy1a//mruegVcNbY4OdqV235vbFX+fVkMmprK5b9ozeezmqLtkcIIYRoCZpkgKRWqwkPDycqKsp4TK/XExUVVekQWZ26+Afoi8CtHbi3s2jV51KyAQj2ckSlUlVb/pPdcQBM7BtAR5/rhvoGDJBhNSGEEMJEjTZAys7OJiYmxrgSLS4ujpiYGC5evAhAZGQkK1euZN26dZw4cYKZM2eSk5NjXNVWr87vMvwbdIvFq45LzQEg2Kv6LUuSMvP59aQhzcH0QUFlT1YXIFWVTFISTQohhGhhGu0cpIMHDzJ0aOlwVckE6SlTprB27VomTpxISkoK8+bNIzExkbCwMLZu3Vpu4na9uFw8vNbW8r1X56/mAhB8/Wa4lfjttGG1W2iAW9nNc01RkkzyrrvKB1JVnRNCCCGaoUYbIA0ZMgRFUaosM2vWLGbNmlVPLaqEXg8JRwwft+5VZ7cpk5CyEr+dMgRIZTJt11J+oY4LGYW0tdFgb7FahRBCiMat0QZITca1OCjIAht78OxcZ7cxrparhALsPmtIB2BqgKTEx7M9+jy/xOeTkJiOZsK/cf4pFudDGeRl53E0S+FsLugU6HffAr6SRJNCCCFaCAmQaquk98ivJ1jX3afTxqrqCdpJGflk5BVibaWiR5vqtx1Jzy1g5tLf2Kd3KT6igY43QRaQlVuufJxHG4iYXHpg/nzDvCQhhBCiGZIAqbaS/jb869uzQZtxrngydzsPB9Q2Vc+9zy/U8fAn+zmmd8HeWsU/2jvQLf0KBZ9+RtZDU8jybo1VXg4hzips3VyZ+vs1w4WSaFIIIUQLIQFSbaUZltXjVXfDa6a4Ph1Add7dfppjVzLwcFTzv4ib6OzrDIcOwbM/w+rXyiSTPJGQCb//bngjiSaFEEK0EI12mX+TkXbO8G+r9g3ajNgUQw9S+2rSAcSn5/HJ74ag7o17ehqCIyGEEEKUIQFSbaWfN/zr0bABUra2CKi+B2ndvvMU6RX6B3lwW7frUiL4+RnmFVU2dObkJMNqQgghWgwZYqstfSFY24Grf0O3BIAgz8p7kHR6hQ0HDfuyPXpLcNmTfn5VT7p2dpYASQghRIshPUiW4BEMVtYN3QoAHDWVt+Po5XTScgpwtrNhaGfL5UqqkmThFkII0QRJgGQJHsHVl2kEdhYnkhzUwRMb63r60pdk4ZYASQghRBMiQ2yW4NI0hp5KEkkOqYfeo7wCHanZWjKuFeLu7EWbOr+jEEIIYTkSIFmCUwPs/2YmBTidmAVAWIC7xetPzy3gl7+T2H4iieMXr3Elq6D05BNr+H7vUXqUvJcs3EIIIRo5CZAswcm7oVtQrdRsLVnaIqxU0K5V1duWmKOgSM/SHWdZuesceYW6Muc0hVq0thoA4j5cQ4+ndhlOSBZuIYQQjZwESJbQBHqQzhXnSfJ3d8DO1jITyq/lFDBt7QFiLqUD0MnHiTt6tOYmN4VOhZm4qVU89MMF9uZpICICBr9ruFB6j4QQQjRyEiBZQh30IFW985r5Yoszbbc3IdO2KfILdUxZs5+jlzNwtbflP+NDuKOHHyrVDS3/LQ3yCiAoSLJwCyGEaDIkQLIEx8Y/xJZbYBj+Cq4m07apFv14gqOXM3B3sOWrGQPo6CMZuYUQQjQfsszfEprAHKQSQZ6170H6Kz6DT/+4AMB7D/SqOjhSG+Yg4eZW6/sKIYQQ9UUCpNrSuIKNpqFbYTIHde3nHy3ZfgZFgTt6+nFrp2pSBmjUhn/dLb9yTgghhKgrEiDVVhMYXrOk+PQ8ok4kAfDMiI4N3BohhBCibkiAVFv2bg3dgnq1MfoyegVuCvagg7fMOxJCCNE8SYBUW6qW9SnccSoZgPG96jk3tuzpJoQQoh61rN/uolYy8go5Upzz6JaO9bTZbQnZ000IIUQ9kgBJmGxfbCp6xZBLqbWbfb3dV1EU8nVKvd1PCCGEkDxIwmRHLmcA0D+4VZ3eJ79Qx1/xmfxx5Dx/nrvKX9cKuabV8UbIcO47dKi0oOzpJoQQoo5IgCRMFle8XUkHCyWbBDh+JYMNBy9x9EoGaTkFpGUXkKUtqqCkimj/btwXEVF6SPZ0E0IIUUckQBImO5dq2K4k2ALblRQU6fnPlr9Zt+9ChefdHWzp7+dIf2c9JzMKWR+XZzixcmXpliXSeySEEKKOSIAkTKLXK5y/mgtA+1r2IOn1Cs9vPMK3MfEAjA1tze3dfPBztcPdUY2nkwYXOxvjvm7LdpyFuFOGi3v3lj3dhBBC1DkJkIRJruYUAKC2sar1BO3P91/k25h4bKxULHuoNyO7+1qiiUIIIYTFtOgAKTAwEBcXF6ysrHB3d2fHjh0N3SSzqer5foGtHLC2qvldr2ZrefOnkwC8dEdX84Kj3r1lWE0IIUS9aNEBEsDevXtxcrLcpOPmzsfFrlbXf/HnRbK0RXTzc2HygEDzLpYASQghRD2RPEjCLFaqmvceFen0/PdPw6Tsx24NrlVPlEkk+7YQQogaarQB0q5duxg7diytW7dGpVKxefPmcmWWLVtGYGAgdnZ29O/fn/3795t1D5VKxeDBg+nbty+ff/65hVouKhN94RpJmVrcHWwZ06MeeoIk+7YQQogaarRDbDk5OYSGhjJ9+nQmTJhQ7vz69euJjIxk+fLl9O/fnyVLljBy5EhOnTqFt7c3AGFhYRQVlc+ps23bNlq3bs3u3btp06YNCQkJjBgxgh49etCzZ886f7aWateZFABu7eSF2qbuYvMcbRGnk7LISMgnv9MAOmQW0aHO7iaEEKI5arQB0ujRoxk9enSl5xcvXkxERATTpk0DYPny5WzZsoXVq1czZ84cAGJiYqq8R5s2hg1X/fz8GDNmDIcOHao0QNJqtWi1WuP7zMxMcx5HALtOpwJwax3s45ZfqOOrg5fY9Eccx5NzMe5MMv4l1FuTOWB3EFe1lWTfFkIIYZJGO8RWlYKCAqKjoxkxYoTxmJWVFSNGjGDfvn0m1ZGTk0NWVhYA2dnZ/Prrr3Tv3r3S8osWLcLV1dX4CggIqN1DtDB5BUUcu2LYquSWjp4WrfvY5QxGLdnFvG//4kiSITjyzrpKt6RYAApQcW30XRAeDitWWPTeQgghmqdG24NUldTUVHQ6HT4+PmWO+/j4cPLkSZPqSEpKYvz48QDodDoiIiLo27dvpeXnzp1LZGSk8X1mZmaTCJJU9Z4IoGLnUg3blLRyVONdy5Vw19t7NpVpaw+gLdLj7azhyXAvbnMqoLWDHxw6RMhfuWRrHGDzZnC2kd4jIYQQJmmSAZIlBAcHc+TIEZPLazQaNBpNHbaoeTtXvI+bJbYpKRGbkk3EpwfRFum5tZMXHzzYC1d727KF/jL0ItG9O3ha7t5CCCGatyYZIHl6emJtbU1SUlKZ40lJSfj6Slbmxig2pXgfN0/L5JzS6RVmf3mYnAId/YM8WDk5HI2NtUXqFkIIIZrkHCS1Wk14eDhRUVHGY3q9nqioKAYMGNCALROVsXQP0qZDlzl+JRMXOxvee6BXxcGRnx9Ir58QQogaaLQ9SNnZ2Zw9e9b4Pi4ujpiYGDw8PGjbti2RkZFMmTKFPn360K9fP5YsWUJOTo5xVZtonIJrudEtGBJOvrf9DABPDeuIr2slc5pKAiRt+VQPQgghRFUabYB08OBBhg4danxfMkF6ypQprF27lokTJ5KSksK8efNITEwkLCyMrVu3lpu4LRqXAI/abXQL8NvpFK6k5+HuYMvDA9pZoFXFEhIMq9xmzJDJ3EII0cI12gBpyJAhKIpSZZlZs2Yxa9asemqRsAQbq9qP6n554BIA9/T2x87WgvOOSjJv33WXBEhCCNHCNck5SKLlyi/U8XtxRu57wv0tWnd1AbkQQoiWo9H2IAlRkUMXr5FfaMh51MXXudb1JZy+wLq9cexI0HIuo5D+E1/ls+hDpdmjJPO2EEK0SNKDJJqUPWcN25UM6uCJSlW7JJgbDl5i+OojLD+Zw6mMIgpRsTuwF/lPzDJk3bZk5u2EBFiwQDbOFUKIJkICJNGkHIi7BsCA9q1qVc+n+87z/Maj5GJN71a2vNnXtfTksqUQHW14zZhRq/sYlcxvkgBJCCGaBBliE01KScLJrn4uNa5jf1waC7//G4DHB7fnhZGdyS/S8cKBnw0FwnpB795m16soCum5hSRk5JOYmUdaTiE52iKytUUUXs5irEcb2te41UIIIeqTBEiiycjIK+RqTgEAQTXcNqRIp+ffm4+h0yuM79WGF0d1rtVQ3aW0XLafSGLX6RSOXckkNVtbadnjQ6az6tCh0gMyv0kIIRotCZBEkxFXvOGtj4sGR03NvnW/PnyF00nZuDvYsmBs94qDIxO2q9kXe5WPd8Wy41RKuXOeTmp8XOxolXgJ57+Pkurgxp9te5ClcYCIiNKC8+cb5iUJIYRodCRAElWq5TxoizpXHCDVZj+3z/ZdAGDG4Pa4OthWXKiKACkhI49Xvv+bn44nAobPT79AD27r5kN4O3e6+Lpgry7OzZSQAAld2HIpjz/3pRuOrVxZOnwnvUdCCNFoSYAkmoyCIj0AQTXcz+34lQyOXclAbWPF/X0CzL5+f1waj312kPTcQqytVDzYL4BHBwUTWNlwX8kQmk0C7CseWuvdu0bzm4QQQtQvCZBEk9POw6FG120/kQTA8C7eeDiqzbr2h6PxRK4/QoFOT482rrx5b89aTRQXQgjRuEmAJJoc4xCWmX4/Y8ihNLiTl1nX7Y1N5Z9fxlCkVxgd4svi+8Nq1oZ27WRYTQghmggJkJoZFY1o0lAjkpVfRMyldAAGdfQ0+bq0nAJmFwdHY0Nb897EMKysavg5DgyUAEkIIZoISRQpWoSjV9LR6RXaejjg7276EN3rP50gJUtLB28n3rq3Z82DIyGEEE2KBEiiRTiXYlgB18nH9BVwZ5Ky2Bh9GYA37umJnW3NhvaEEEI0PRIgiRbhXHEG7mAv0wOklb+fQ6/A7cVL+IUQQrQcEiCJOtVY8ijFGXMomZYiICu/kO+PGPZNi7g1uM7aJYQQonGSAKmRaiyBRXNx/mouYPoWJVuPJ5JXqCPYy5E+0nskhBAtjgRIokUxdYhtV3FKgDt6+NVqrzazJSQYth9JSKi/ewohhChHAiTRYqhtrPB0qj5BpF5R2HvWECAN6mB6SgCLSEiAhQslQBJCiAYmeZBamJY8dGdjpTKpN+hUUhZXcwpwUFvTq239DK8V6vTsPptK7KlsbnPzpV293FUIIURlJEAS4gbHLmcA0KutG2qbuu9k/XHX3yzccYGkPMNec/uHPsLHhw6VFijZ000IIUS9kSG22mrBPTLNVUlKgA5mpASoqfejzvDEj3HG4AggU+MIEREQHm54rVhR5+0QQghRlvQgCXGDc8UpAUxd8VZTm6Ivs/iX0wDM6OxIR1cbnttv6L1i5Uro3dvwcW16jxISDAHWjBnSCyWEEGaQHiQhblCSdducpJLmSs7MZ963xwF4algH5k4bgqb9dfmWevcufdU2QJJJ30IIYTYJkIS4wZX0PKBue5De+vkUOQU6wgLceGZEpzq7j06v1FndQgjRnMkQmxAVUNtY0drNvk7qTs3WsjnmCgAv39mt/Aa4QYE17jXSFun4Zc8pvo6J56/0Qq7m6Xiu3z08LpO+hRDCLBIgCVEBHxcN1jcGLhay/sAlCnUKoQFuFe/xFhhodgBzOimL9Qcu8fWhy1zLLbzujIrdQWE8HhFRemj+fEMySiGEEJWSAEmICljXYcKon44b5gP9o19Areq5lJbLL38n8f3ReA5fTDce93Wy5b4ADQqw9IRhRZ7FJn0LIUQLYXaAtGvXLm6++WZsbMpeWlRUxN69e7n11lst1ri69vbbb7NmzRpUKhVz5sxh0qRJDd0ks9XrNhii1q5mazl+JROAoV28zb4+OSufzYev8PWhK5xMzDIet7FSMbyrNw/0bcutnbywtlKx+fAVOBFjKFAy4VsIIYRJzA6Qhg4dSkJCAt7eZX+4Z2RkMHToUHQ6ncUaV5eOHTvGF198QXR0NIqiMHToUO68807c3NwaummiGdtdvIVJVz8XvJ3tTL5Or1f4cOdZ3v/1LAVFhpxJ1lYq+ga6c1s3X8aG+plVnxBCiKqZHSApilJhr8XVq1dxdKzbvDGWdOLECQYMGICdneGXSmhoKFu3buWBBx5o4JY1btJhVTt/nEsD4JaOpu/xptMrzP7yMD8cNQzNhQa48UDfAEZ198Xdsfq95QgOlmE1IYQwk8kB0oQJEwDDkM7UqVPRaDTGczqdjqNHj3LzzTdbrGG7du3irbfeIjo6moSEBL755hvGjRtXpsyyZct46623SExMJDQ0lA8++IB+/fqZVH9ISAgLFy4kPT0dRVHYuXMnnTrV3XJrIQBikw1zgrq3djH5mjd/PskPRxNQW1vxf+NDuC/c37yh1eD2EiAJIYSZTA6QXF1dAUMPkrOzM/b2pUug1Wo1N910ExHXr5SppZycHEJDQ5k+fboxOLve+vXriYyMZPny5fTv358lS5YwcuRITp06ZRz+CwsLo6ioqNy127Zto1u3bjz99NMMGzYMV1dXbrrpJqytrS3WfiEqci7VECAFe5qWhPLo5XQ+3nUOgLfvD+Wu0NZ10zDJuC2EEGWYHCCtWbMGgMDAQJ577rk6H04bPXo0o0ePrvT84sWLiYiIYNq0aQAsX76cLVu2sHr1aubMmQNATExMlfeYMWMGM2bMAODRRx+lY8eOlZbVarVotVrj+8zMTFMfRTSgxjQimJlfRGp2AQCBng4mXfN/W06gKDAurHXdBUdQmnH7rrskQBJCCGqQSfuFF14o071/4cIFlixZwrZt2yzasKoUFBQQHR3NiBEjjMesrKwYMWIE+/btM7me5ORkAE6dOsX+/fsZOXJkpWUXLVqEq6ur8RUQULsl2vVF5gw1HiWb4Ho7a3C2s622/F/xGeyPS8PGSsWLo7vUdfOEEEJcx+xJ2nfffTcTJkzg8ccfJz09nX79+qFWq0lNTWXx4sXMnDmzLtpZRmpqKjqdDh8fnzLHfXx8OHnypMn13H333WRkZODo6MiaNWvKpS643ty5c4mMjDS+z8zMbDJBkmgctMWrz4K9TOt9/fzPiwCMDPHFz9XyWb318fH8eug8OxK0pCddZVqbrvSRjNtCCAHUIEA6dOgQ7777LgAbN27E19eXw4cPs2nTJubNm1cvAZKlmNPbpNFoykxMF6KmAltVHyApikLUiSQA7u9j+UD8SnoeT37wOzFKyVwoe/Jvuo9PJOO2EEIANQiQcnNzcXZ2BgyTnSdMmICVlRU33XQTFy5csHgDK+Lp6Ym1tTVJSUlljiclJeHr61svbRCipuxsq18McDopm6RMLRobK/oHeVj0/okZ+dzz4V4SFSecbFTYWEF6gUKBtY1k3BZCiGJmz0Hq0KEDmzdv5tKlS/z888/cfvvtgGE+j4uL6UuXa0OtVhMeHk5UVJTxmF6vJyoqigEDBtRLG4SoS7tOpwDQP7iVSQGVqXR6hZmfR5OYmU+wlyNbnx3C/PE9SwuUZNzu3VsCJCFEi2Z2gDRv3jyee+45AgMD6devnzEg2bZtG7169bJYw7Kzs4mJiTGuRIuLiyMmJoaLFw3zMiIjI1m5ciXr1q3jxIkTzJw5k5ycHOOqNiGassOXrgFwc/tWFq33q4OXOHwxHWeNDWun9sPf3bTVdEII0dKYPcR27733MmjQIBISEggNDTUeHz58OOPHj7dYww4ePMjQoUON70smSE+ZMoW1a9cyceJEUlJSmDdvHomJiYSFhbF169ZyE7eFaIrOpeQA0MnHtHxJpijS6Xk/6gwAs0d0pG2rG4Kj9pJQUgghSpgdIAH4+vqSnZ3NL7/8wq233oq9vT19+/a16MapQ4YMQVGUKsvMmjWLWbNmWeyeQjQGegXiUg0BkqkJJU2x/UQSCRn5tHJU8/CAduULSIAkhBBGZg+xXb16leHDh9OpUyfGjBlDQoJhf6hHHnmEZ5991uINFKKliU/PQ1ukx9Zahb+75Zb3f3XwMgAT+wagsZGs8UIIURWzA6RnnnkGW1tbLl68iINDaRf9xIkT2bp1q0UbJ0RLdPlaHgDtWjliY232f9EKaYt07I1NBWBsTTNyJyQYlv0X/1EkhBDNmdk/fbdt28Ybb7yBv79/meMdO3ast2X+QrQEwZ6W284n+vw18gv1eDlr6OLrXLNKSrYjkQBJCNECmD0HKScnp0zPUYm0tDRJpCiEBbV2s9zw2u9nDb1Ht3T0rNFcwcvXcolL1OLQpgthegUZoBNCNHdmB0i33HILn376Ka+++ioAKpUKvV7Pm2++WWbVmRAVkb3hTGdlwU/WX/GGzZX7BpqXdPLsX+f499ZY/kgxbLLLpLeZ+kssC6yK2ybbkQghmimzA6Q333yT4cOHc/DgQQoKCnjhhRf466+/SEtLY8+ePXXRRiFELZVslNvey/RVcYcvXuPhz46TjTVWeh16K0O/UdzhE/CviYZCsh2JEKKZMnsOUkhICKdPn2bQoEHcfffd5OTkMGHCBA4fPkz79u3roo1CiFrIL9RxJd0w8dvUjXKv5RQQ8elBsrGmn6ea38f68Y53uuFkSAhERxteM2bUUauFEKJhmdWDVFhYyKhRo1i+fDkvvfRSXbVJiHqnovmO/V1Iy0VRwNnOhlaOapOuWfTTCVKzC+jo7cSaJwfiqLGBFC0kZ4CLa+l+bUII0UyZ1YNka2vL0aNH66otQog6kJKlBSDYy8mkCdoJGXlsOnQFgEUTehiCIyGEaGHMHmKbNGkSn3zySV20RVRBJjeL2mpvYtqAz/ZdQKdX6B/kQZ/rJ3W7uRn+rWy1quRJEkI0I2b/aVhUVMTq1avZvn074eHhODqW/aG7ePFiizVOCGE5rZxMG17bcswQ4EweEFj2hKsbcKHqAGnhQrjrLlnZJoRo8swOkI4fP07v4vkHp0+fLnPOknuxicahOc/NqS+N5b+FKf8/z6fmcOFqLrbWKgZ39qqHVgkhRONkdoC0Y8eOumiHEKIR+O10CgB92nngZMrco4QESEjgWFohi/deJnB4BPMPHSo9b26epIQEWLHCsDpOeqGEEA2oxhs9nT17lp9//pm8PMPyYUVRLNYoIarSSDpkmqX9cWkADOroadoFK1bwy8SZjN2eyo5cO9b0uRsiIiA83PBascK8Bsh2JkKIRsLsHqSrV69y//33s2PHDlQqFWfOnCE4OJhHHnkEd3d33nnnnbpopzBRYxnOEU1TbHFCya5+pu3XFnv/FJ4q7Au66w6uXFmaBsCMXqD03AJOpWhx9GlPiMlXCSFE3TA7QHrmmWewtbXl4sWLdO3a1Xh84sSJREZGSoAkRBOlKBCXmgNAsKdpGbdf2p1Ivg46eDtxNjkbG12RITgyMU9SfqGOTTv+YuORRA5fLTQcnPoev/15hHYlhWQ7EyFEAzB7iG3btm288cYb+Pv7lznesWNHLly4YLGGCSHqV3x6HtoiPWprK/zdq98o99DFa/xxLg1baxVv3tvTrHspisI3hy8z9O2dvPTrJWNwpFL0ACS+9k7Nh+mEEMICzO5BysnJwcHBodzxtLQ0NJUt/xUtlqyCazrOFfcetWvlgI119X87rd4dB8C4sDa0cSsOqKytqu3tSc7M58VNR9lxyjAhvLWTLdPa23F3W3se3BpPbKEVvPACDCweaJPeIyFEAzA7QLrlllv49NNPefXVVwHD0mG9Xs+bb77J0KFDLd7AlksCC9EwTNmvraBIz87iAOehm9qVnrCyrjKg2XI0gZc2HyM9txC1jRWzh3fkkUFB2NkaNsJl51Yo1EGnjrKdiRCiQZkdIL355psMHz6cgwcPUlBQwAsvvMBff/1FWloae/bsqYs2CiHqkb97+R7iG0VfuEa2tghPJzU927iSkq2tsnx+oY6XvjnOpkOXAeje2oV3J4bRyce0yeBCCFHfzA6QQkJCOH36NEuXLsXZ2Zns7GwmTJjAk08+iZ90hTd6LXnIq+U+uXlsrKv/TO08nQzArZ28sLKquvzVbC2PfRZN9IVrWKngyaEdeGpYR9Q2FQzj2dgAOmjVqiZNF0IIizE7QLp48SIBAQG89NJLFZ5r27atRRomhGi8/rqSCcBNQVUHMjnaIiZ9sp8TCZk429nw0UPhVedYsrEBtOAhAZIQomGZvYotKCiIlJSUcsevXr1KUFCQRRrVtEi/hLCMppTDqiRfUnvvqtMBvLz5OCcSMvF00vDNEzebnoBSCCEamNkBkqIoFe7plJ2djZ2dnUUaJYRovHK0RSRk5APQvooJ3XvPpvL14SuoVLB8Um86eMt8IyFE02HyEFtkZCRgWLX28ssvl1nqr9Pp+PPPPwkLC7N4A4UQjUtJMklPJzVuDupKy73/6xkAJvVvR59Aj3ppm+zlJoSwFJMDpMOHDwOGHqRjx46hVpf+YFSr1YSGhvLcc89ZvoVCiEblak4BAMFelQ+vnUjI5I9zaVhbqZg5pH19Na10L7e77pIASQhRKyYHSDt27ABg2rRpvPfee7i4uNRZo4QQjV9Vw2vfHL4CwMjuPrR2qz4rtxBCNDZmr2Jbs2ZNXbSjTo0fP56dO3cyfPhwNm7caDyenp7OiBEjKCoqoqioiNmzZxMREdGALRWNRUXz7ERZVQ2v/VacRHJUSN314hTq9ERfuMbvh+M4l5BBYp4OsnN4JrAXtx46VFpQ9nITQtRAjbYaef3114mKiiI5ORm9Xl/m/Llz5yzWOEuZPXs206dPZ926dWWOOzs7s2vXLhwcHMjJySEkJIQJEybQqgnlYJFf46IyDRXjJWbkcyopC5UKbulg+VVrBUV6NkRfYumvZ42TxUup+SJsFLde/4fO/PmwYIHF2yGEaN7MDpAeffRRfvvtNx5++GH8/PyaxF/aQ4YMYefOneWOW1tbGyeba7VaFEVBUZR6bl3T0vi/2qKh7TuXCkBPfzfcHSvvZaqJ86k5PPH5If5OMORh8nBUMzjAiVD7In5N0LIrUYtOZQUrV5ZuVSK9R0KIGjA7QPrpp5/YsmULAwcOtEgDdu3axVtvvUV0dDQJCQl88803jBs3rkyZZcuW8dZbb5GYmEhoaCgffPAB/fr1s8j909PTGTx4MGfOnOGtt97C01PytAhRG6eTDDmSerSx7DzFk4mZPLTyT67mFODuYMvTwzvyYL+2xn3c1H9eZNc3xwyFe/eWvdyEELVidh4kd3d3PDwst2Q3JyeH0NBQli1bVuH59evXExkZyfz58zl06BChoaGMHDmS5ORkY5mwsDBCQkLKveLj46u9v5ubG0eOHCEuLo4vvviCpKQkiz2bEC3RueIkksGeVSeRNEdyZj5TVx/gak4B3Vu7sPWftzJt4HWb3AohhIWZ3YP06quvMm/ePNatW1cmF1JNjR49mtGjR1d6fvHixURERDBt2jQAli9fzpYtW1i9ejVz5swBICYmptbt8PHxITQ0lN9//51777233HmtVotWW7ohZ2ZmZq3vKURzFJtiyJNUXZZtUymKwnMbj5KYmU97L0e+ePQmXB1sK7+gSxcZVhNC1JrZAdI777xDbGwsPj4+BAYGYmtb9gfVoetXj9RSQUEB0dHRzJ0713jMysqKESNGsG/fvlrXn5SUhIODA87OzmRkZLBr1y5mzpxZYdlFixaxcOHCWt+zvrX4OUMt/hNQv4r0CheuGgKkYM/K0wCY47sj8ew6nYLaxooVD/epOjgCCZCEEBZhdoB04/ygupSamopOp8PHx6fMcR8fH06ePGlyPSNGjODIkSPk5OTg7+/Phg0bGDBgABcuXOCxxx4zTs5+6qmn6NGjR4V1zJ0715hNHAw9SAEBATV7MCGasUKdgsbGijYWyH9UqNPz7i+nAXhqaAc6WKhXqkqSjVsIQQ0CpPnz59dFO+rU9u3bKzzer18/k4fnNBoNGo3Ggq0Sovlq18oBK6vad99tPZ7I+au5tHJUM31QPW2GLdm4hRDUYJJ2ffL09MTa2rrcxOmkpCR8fX0bqFVC1E4TyIxRaxoby0ye/urgJQD+0b8tjhqz/56rEa1OUn0IIUzsQfLw8OD06dN4enri7u5eZe6jtLQ0izVOrVYTHh5OVFSUcWhPr9cTFRXFrFmzLHYfIUTjk5iRz+6zhpxK94Vbfjg7OTOf6AvX+Cs+k4tXrnLpajaXsnWkavWMmPBvVkk2biFaNJMCpHfffRdnZ2cAlixZYtEGZGdnc/bsWeP7uLg4YmJi8PDwoG3btkRGRjJlyhT69OlDv379WLJkCTk5OcZVbUKI5mnX6RQUBXq1daNtq9qvmAXDirhfTyazdu95fj+TWmm5P9r2hIj7Sw9INm4hWhyTAqQpU6ZU+LElHDx4kKFDhxrfl0yEnjJlCmvXrmXixImkpKQwb948EhMTCQsLY+vWreUmbgshmpc9sYYAZpCFtitJyyngmfUx/HbasE+cSgWdfZzp1dadILWOAH0OVioVM/ZcM1wg2biFaNHqZ1C/CkOGDKl2e49Zs2bJkJoQLYgC7I29CsDN7WsfICVm5HP/in1cTMtFbWPF1JsDefimdgR4lO2ZunA1B/bsNLyRbNxCtGgNHiAJUZ2WMKlZlHUxLZeULC1qGyt6t3OrVV15BTqmrN7PxbRcAjzs+WRKXzr5OFumoUKIZqtRr2ITQrRMcamlySZruyLutR9PcCopCy9nDV88epNpwZFGU/2wWkKCYV5SQkKt2ieEaJxMCpCOHj2KXq+v67YIIa7TknvO4oq3KwmqZTbuEwmZ/PfPCwAsvj+03JBapUwNkBYulABJiGbKpACpV69epKYaJkwGBwdz9erVOm2UEKL+qBrhfiwlPUi1DZDe2XYKRYE7evpxS0cvSzQNnV5hb2wqq0/ncNynvUXqFEI0PibNQXJzcyMuLg5vb2/Onz8vvUlCiDpliQDpUlouUSeTAXj2tk4Wadfe/ad5ads54rJ1AASMm8vvki9JiGbJpADpnnvuYfDgwfj5+aFSqejTpw/W1hXPCzh37pxFGyiEaHkKdIY/woK9ah4gbYi+jKLAwA6tCPaq/R5u38ZcIfLr0+iu63FLt3eGiImlhSRfkhDNhkkB0scff8yECRM4e/YsTz/9NBEREcbEkUIIUVfaetQ8QPrxmGFu0P19ap+F+9DFazy34Qg6VNzd1o5HOzkxdnsqKIrkSxKimTJ5mf+oUaMAiI6OZvbs2RIgCSHqnJ1tzRbaJmbkczY5G5UKhnTyrlUbCnV6nt9whEKdwugQX979R28upOXC9p2GApIvSYhmyew8SGvWrDF+fPnyZQD8/f0t1yIhhKilPcV7uPVs44qrg22t6vrf/ovEpuTQylHN6/f0xMqq8U1qF0JYntl/nun1el555RVcXV1p164d7dq1w83NjVdffVUmbwshGoV954qzcNdymxK9XmHNnvMAzB7REVf7G4ItOzsZVhOimTI7QHrppZdYunQpr7/+OocPH+bw4cO89tprfPDBB7z88st10UbRjMjf3qI+nEnKAiDU37VW9ew7d5W41BycNDbc07uCnvKaBEhVJZiU5JNCNBpmD7GtW7eOVatWcddddxmP9ezZkzZt2vDEE0/wn//8x6INFEI0Dk0lcaWiwLniRJO1Xb32w1FDoHJXWGscNRbamakkweRdd5UPrqo6J4SoV2b/j09LS6NLly7ljnfp0oW0tDSLNEqI+tZUfvmL6l3N0ZKlLUKlgramZs6uxO6zKQCM6Fq7id4pWVpiLqVzNVtLdmw2un4TUE5mo2TGoqCgKKAoCqqEbEZ6+NOhVncTQliC2QFSaGgoS5cu5f333y9zfOnSpYSGhlqsYUIIURMlSSb93e2xs635Pm4XruZwKS0PW2sV/YNamX29Tq+w4eAl1u49z8nErLInh06Ho1lw9GS563aMfoqNknxSiAZndoD05ptvcscdd7B9+3YGDBgAwL59+7h06RI//vijxRsoDBrjdhBCNEbpuYUABHnWbnhtX6xhonevAHezh9fi0/OY+fkhjlxKB0CFQqeUC/hnJONYkIuNXo9K0aNCQaWAlaIn2cmDHe37kmHnBBERpZVJ8kkhGoTZAdLgwYM5ffo0y5Yt4+RJw18/EyZM4IknnqB169YWb6BoWDL0VHuN5VPYWNpRX4Ja1W547UxyNgAhbcyb6H35Wi73L99HfEY+znY2zB7ekXv9bXG7Vvzz8dAhQwBUkmAyxTCMt09xYcfO4mkKknxSiAZXo1mHrVu3lsnYQohGzc1BXavrS4bqzNnuJL9QxyNrDxKfkU97L0fWTutHQMk8qKAbMnrfmGAy9irs/KPic0KIelezNLVCNCTp1hL14FyKoQfJnADp7Z9PcSopC08nDZ8/elNpcFQXJCWAEHVKAqRmRmIH0RCa27ddgU7PpWt5AASbOJfpwtUc1u07D8Bb9/bE19Wu4oJ+foZ5RZUNnXl5mzasVpISQAIkIeqEhRJ7CCFE81FQZNgVwFFtjY+LxqRrPtwRS6FO4dZOXgztUkVaAD+/qidde3lVGyApioJWp1BJCCaEsAAJkESdkh4t0ZQFejqiMuGbOCu/kO+OxAMwa2jdZTH648BpVu27yN7kAvKK9Dx30308KSkBhKgTZg+xzZ8/nwsXLtRFW8R1JLAQLZEpwUh9clSb9jfkD0cTyCvU0d7Lkb6B7hZvh06vMP/b4zyw6Qzb47XkFikoqNjbLtSwIi483PBascLi9xaipTI7QPr2229p3749w4cP54svvkCr1dZFu4QQosn49WQyAON7tbF4kKcoCs9vPMK6fYY/TP8R7MCMztdNHF+5EqKjDa8ZMyx6byFaMrMDpJiYGA4cOED37t2ZPXs2vr6+zJw5kwMHDtRF+4SFNbI/0OtVY+udaKwkKal5inR6/ihOKnlLRy+L1//ZHxf4+tAVbKxUfPRQb157bCjdenUsLVCSEqB3bxleE8KCarSKrVevXrz//vvEx8fzySefcPnyZQYOHEjPnj157733yMjIsHQ7hRCiUTp6JYMsbREudjZmJ5WsTkqWljd+MiTknTumK6N7SAAkRH2p1TJ/RVEoLCykoKAARVFwd3dn6dKlBAQEsH79eku1sXGTXglhIdJz0zQdunANgP7BrbC2suzX8INfz5BToCPU35VpNweWLxAcJL1GQtSRGgVI0dHRzJo1Cz8/P5555hl69erFiRMn+O233zhz5gz/+c9/ePrppy3dViGEaHRiUwwZt7v4Olu03mxtEZuiLwPwwqguWFUUfAUHS4AkRB0xO0Dq0aMHN910E3FxcXzyySdcunSJ119/nQ4dSpe2Pvjgg6QU7y/UGIwfPx53d3fuvffeMsdPnTpFWFiY8WVvb8/mzZsbppFCiCYpLtX8jNum+C4mnpwCHcFejtzcvlXNK5KM20LUiNkB0v3338/58+fZsmUL48aNw9raulwZT09P9Hq9RRpoCbNnz+bTTz8td7xz587ExMQQExPD7t27cXR05LbbbmuAForGRga7hKnOFfcgBZmYcdtUPx03BDQT+wTUboGBZNwWokbMDpBK5hrdKC8vj1deecUijbK0IUOG4Oxcdff3d999x/Dhw3F0tOxfgXVNpkCJhiLfepCjLSI5y5DqJMjTcj878gt17I9LA6g6K7cQos6YHSAtXLiQ7Ozscsdzc3NZuHCh2Q3YtWsXY8eOpXXr1qhUqgqHuJYtW0ZgYCB2dnb079+f/fv3m32f6nz11VdMnDjR4vU2N7JUXjRWDfGtqVcM/3o6qXG1t7VYvdEXrqEt0uPjoqGjdw16phISOLr9D/69cgcPfX+eHcF94NCh0pf0JglRLbO3GlEUpcJfkkeOHMHDw8PsBuTk5BAaGsr06dOZMGFCufPr168nMjKS5cuX079/f5YsWcLIkSM5deoU3t6Gv6zCwsIoKioqd+22bdto3bp1tW3IzMxk7969fPnll2a3XwghAjwcLFrfn+cMeZUGtvc0+4+SgiI9C5Zu5QtdSc+TBpvwOxkaEVFaaP78qveDE0KYHiC5u7ujUqlQqVR06tSpzH9anU5HdnY2jz/+uNkNGD16NKNHj670/OLFi4mIiGDatGkALF++nC1btrB69WrmzJkDGJJX1sa3337L7bffjp1d5Vs/arXaMlnDMzMza3VPIUTzobauVcaUcs4kG3rpu5uZV6lIp+fx/0bzq67ssJxeZWXIuN27t+GArHwTolomB0hLlixBURSmT5/OwoULcXUt/Y+rVqsJDAxkwIABFm1cQUEB0dHRzJ0713jMysqKESNGsG/fPovd56uvvuKxxx6rssyiRYtqNITY0Fr6iFgLf3zRRMWlGiZ+m7sybvEvp/n1ZDJ2tlZ8NCmc9NwCnll/xHCyJNu2EMIkJgdIU6ZMASAoKIibb74ZW1vLjbdXJjU1FZ1Oh4+PT5njPj4+nDx50uR6RowYwZEjR8jJycHf358NGzYYg7mMjAz279/Ppk2bqqxj7ty5REZGGt9nZmYSEBBgxtMIIUT19IrCxdRcAILNmPh9OimL5b/FAvDWvaEM7ezNN4cv10kbhWgJTAqQMjMzcXFxAQzbjOTl5ZGXl1dh2ZJyjcn27dsrPefq6kpSUlK1dWg0GjQajSWbJVooyZgtqpKQkY+2SI+ttQp/d9PnNr259SR6BUZ292Fs6A1zL9u3l2E1IcxkUoDk7u5OQkIC3t7euLm5VThpsGTytk6ns1jjPD09sba2LhfAJCUl4evra7H7CCEsq6UP7dZGboHhZ2i7Vo4mb11yKS2XqJPJALw4qkv5AhIgCWE2kwKkX3/91bhCbceOHXXaoOup1WrCw8OJiopi3LhxAOj1eqKiopg1a1a9tUMIIepbYCvTh9f+t/8iigK3dPQk2KuGCSsTEmDFCpgxQ4IpITAxQBo8eLDx46CgIAICymd2VRSFS5cumd2A7Oxszp49a3wfFxdHTEwMHh4etG3blsjISKZMmUKfPn3o168fS5YsIScnx7iqTQghmiNHTfldCirz81+JANzfpxbzIksybt91lwRIQlCDPEhBQUHG4bbrpaWlERQUZPYQ28GDBxk6dKjxfclE6ClTprB27VomTpxISkoK8+bNIzExkbCwMLZu3Vpu4rYQQrRE8el5xKbkYKWCWzt5NXRzhGg2LJYoMjs7u8o8QpUZMmQIiqJUWWbWrFkypCaEEBXYfSYVgJ7+buZn805IIP9yPDsS8jl+4jKqWybxjz9jME7x9vOT3iTRYpkcIJX07KhUKl5++WUcHEpXV+h0Ov7880/CwsIs3kAhWiqZ5yxM8Wfxnm2DOniafe33H25gYboHqY7ugBPc/ACZX//AK088YiggGbdFC2ZygHT48GHA0IN07Ngx1Gq18ZxarSY0NJTnnnvO8i0UQghRqdgUQ9btbq3NS7GybMdZ3ipsD47Q2sGK+Fw9ANlDhsEbxduSSO+RaMFMDpBKVq9NmzaN9957r1HmOxJCiJZEUeBccYBkTtbt74/E89bPpwCYMTiYZ2/rzJqvdrPoaBZ4tJKM20JQgzlIa9asqYt2CCGEMFNaTgGZ+UWoVKanBbiareVf3xwD4PHB7ZkzuoK8SUII8wOknJwcXn/9daKiokhOTkav15c5f+7cOYs1TgghROXOFe/Z1trVHjtb09ICvBd1hqz8Irq3duH5kZ1LT7i4AFngYF8HLRWi6TE7QHr00Uf57bffePjhh/Hz86twRZsQQoi6V1Bk+APV1OG1jNxC1h8w5Kt76Y6uZTN1Ozsb/rWXAEkIqEGA9NNPP7FlyxYGDhxYF+0RQghhpnatTNuzbdOhy2iL9HTxdWZAcKs6bpUQTZuVuRe4u7sbtx0RQojmpKluJGxv4vDad0fiAfhH/7a16/1PSDAs/09IqHkdQjRyZgdIr776KvPmzSM3N7cu2iNEOU31l5YQjUlGbiFHL6cDcFu3Wu5EULItiQRIohkze4jtnXfeITY2Fh8fHwIDA7G1LZu59dChQxZrnBBCCMvYG5uKXoH2Xo74uco8IyGqY3aANG7cuDpohhCiocg6i5Zhb+xVAG7pWLP92nRX4vl+fxxbLuVxNS2LF/270//6P4hlWxLRzJgdIM2fP78u2iGEEKIOnUrKAiAswM3sa+PT83h86W6OKiWr5dRs6jGc/hERpYVkWxLRzJgdIAkhRFNQlz1jTbHTLa44Z5I5GbcBkjPzufejvcQrjrjYqijQK+TrQK9SwcqVpVm3pfdINDNmB0g6nY53332Xr776iosXL1JQUFDmfFpamsUaJ4QQovay8otIydICEORpeoCk1ys8+cUh4jPyCfZ05LNH+/NdTDxvbD1pKNC7t2xLIpots1exLVy4kMWLFzNx4kQyMjKIjIxkwoQJWFlZsUC6V4UQFiSJaC2jpPfIy1mDs51tNaVLbTx0mQPnr+GkseGTqX1p4yaTu0XLYXaA9Pnnn7Ny5UqeffZZbGxsePDBB1m1ahXz5s3jjz/+qIs2CiGEqIWMvELAvN6jQp2exdtOA/D08A7lrw0Lk2E10ayZHSAlJibSo0cPAJycnMjIyADgzjvvZMuWLZZtnWh2pEdAiIYTbEaAFHUiicTMfDyd1Ey5ObB8gbBe1QdIklBSNGFmB0j+/v4kFH+zt2/fnm3btgFw4MABNBqNZVsnhGg0JLZt+lztTR9e+2K/Yc+2+/sEoLExLVN3OZJQUjRhZgdI48ePJyoqCoCnnnqKl19+mY4dOzJ58mSmT59u8QYKIYSoX7kFReyLTQXgnnD/Bm6NEA3D7FVsr7/+uvHjiRMn0rZtW/bt20fHjh0ZO3asRRsnSslf70KI+vJnXBqFOoU2bvZmDcsB5F68zMZ9cexJ1pKRlknA6NlE7o/BOBgnCSVFE1HrPEgDBgxgwIABlmiLaIQkLqu9RhPcNpZ2iAo1mu8TYPcZQ+/RLR09zZo3ePB8Gk8sP0Ay6uIjGv7oeRv+n/2X2TMfMRyShJKiiTA7QPr000+rPD958uQaN0YIUzSi3yNCNEuHLl4DYED7ViZfE33hGv9Y9ScFqGnraM1D7R1Y+Vc6qTprCu4aBx88bygovUeiiTA7QJo9e3aZ94WFheTm5qJWq3FwcJAAqYHJzveiQci3XfOhwLkUQ96kjt7OJl2SkVfI4/+NpqBIz5DOXnz4UG8c1DYkfrKDNWdywddHEkqKJsfsSdrXrl0r88rOzubUqVMMGjSI//3vf3XRRiGEEPXkWm6B2XmT3v3lNClZWoK9HFn2D0NwJERTZ3aAVJGOHTvy+uuvl+tdEjUnf5CLlqjRfd83ugbVvXPFWbfbuNljr65+eX9qtpYv/rwIwCt3heCouS44ciwOsJycLN5OIeqaRQIkABsbG+Lj4y1VnRCigTSmycKi/l24mguY3nu0/sAlCnR6QgPcGNTRs+xJCZBEE2Z2P+h3331X5r2iKCQkJLB06VIGDhxosYYJYWnye18I0wV7mRYgbYq+DMDDN7Wry+YIUe/MDpDGjRtX5r1KpcLLy4thw4bxzjvvWKpdQrQ40nMjGhNTNqa9lJbLudQcrK1UjOzuU/ObJSTAihUwY4aschONhtlDbHq9vsxLp9ORmJjIF198gV8j/Ma+dOkSQ4YMoVu3bvTs2ZMNGzaUOT9+/Hjc3d259957G6iFQgjR+FhbVR+x7zqTAkDvtm4425m+jUk5siWJaIRqPAcpNTWVzMxMS7alTtjY2LBkyRL+/vtvtm3bxj//+U9ycnKM52fPnl1tbichhBDl7TlbklDSq8Z16PQK0akFbG/fj3ydYqmmCVFrZgVI6enpPPnkk3h6euLj44O7uzu+vr7MnTuX3Nzcumpjrfj5+REWFgaAr68vnp6epKWlGc8PGTIEZ2fTcn00TjIuUyeawafVnAzINau/TqsXTcDJhCwAerd1N/taJT6eLzbs5uZXt3LPr1d59N55bNh1Cg4dMrykN0k0MJMDpLS0NPr378+6deu45557eOedd3jnnXe46667+OCDD7j11lvJz89n//79vP/++yY3YNeuXYwdO5bWrVujUqnYvHlzuTLLli0jMDAQOzs7+vfvz/79+02u/3rR0dHodDoCAgJqdL1oFnGDaKbke7N+Fen0XEwz/GFs6oTuEgVFemYtjeJf0Rkk5emNx5O//QnCww2vFSss2l4hzGXyJO1XXnkFtVpNbGwsPj4+5c7dfvvtPPzww2zbts2sACknJ4fQ0FCmT5/OhAkTyp1fv349kZGRLF++nP79+7NkyRJGjhzJqVOn8Pb2BiAsLIyioqJy127bto3WrVsDhgBv8uTJrFy50uS2CSFEU1aXmfUvpuVSpFewt7XG18XO5OsUReHfm4+xRe+BrRW82MOZc3GJfJHpCHfcAUueMRSs6ZxWmfAtLMTkAGnz5s2sWLGiXHAEhqGrN998kzFjxjB//nymTJlicgNGjx7N6NGjKz2/ePFiIiIimDZtGgDLly9ny5YtrF69mjlz5gAQExNT5T20Wi3jxo1jzpw53HzzzSa37cY6tFqt8X1TmH8FsvVIXQ8zCdFSlSSUDPJ0xMqECd0ltv2dxFcHL2OlghWT+zCsiw8LVu2AzFzw9av9liQlE77vuksCJFErJg+xJSQk0L1790rPh4SEYGVlxfz58y3SMICCggKio6MZMWKE8ZiVlRUjRoxg3759JtWhKApTp05l2LBhPPzwwzVuy6JFi3B1dTW+ZJhOCNGSpecatiMxZ3itoEjPK9//DcDMIe0Z1sXM1AAJCbBggcxPEvXC5ADJ09OT8+fPV3o+Li7OOORlKampqeh0unK9Vj4+PiQmJppUx549e1i/fj2bN28mLCyMsLAwjh07Zjw/YsQI7rvvPn788Uf8/f0rDbzmzp1LRkaG8XXp0qWaP5ho0VpCp1YLeERRLNjL9CzZPx1P4Ep6Hl7OGmYN7Vh6oiTjtnM1dVWQDkCnV9j1xykiP/qV2177mUFfX+LWx1Yy9Yc4LuzaLxO+RY2ZPMQ2cuRIXnrpJX755RfUanWZc1qtlpdffplRo0ZZvIG1NWjQIPR6faXnt2/fblI9Go0GjUZjqWYJIUSz4GZvev6jz/ZdAOCh/m3L7vPm6AikVLoliaIonE3OJiVJi7pNV+yvFaK9eI2oE0l8fegKCRn515W2AXc/LubCd3MW89S+9TB/vqHnSQgzmDVJu0+fPnTs2JEnn3ySLl26oCgKJ06c4MMPP0Sr1Vo8n5CnpyfW1tYkJSWVOZ6UlISvr69F7yWEEI1Jc+uFS8nScvDCNQAe7NfW5Ot+3PU3i367yKUcneHApLfgl1TDq5irxpq7AzQM89PgFneGOYeyOOkdRNHjM2HpCzIXSdSIyQFSyfDTE088wdy5c1EUQ0IvlUrFbbfdxtKlS2nb1vRvelOo1WrCw8OJiooybnGi1+uJiopi1qxZFr2XEEKIuvN7cdbt7q1d8DFx1duK32JZ9FMcAPYF+bTJTKbIyoYctR0qRSE04TTjunsx4uVZaGyKe6QO2dHnu1Wc9A4yBEa9O9XJ84jmz6y92IKCgvjpp5+4du0aZ86cAaBDhw54eHjUuAHZ2dmcPXvW+D4uLo6YmBg8PDxo27YtkZGRTJkyhT59+tCvXz+WLFlCTk6OcVWbEM1VS5irJFqOXacNAdKtnUzLuv37mRQW/XQSgIhOjkSG+GJ/9BpERMDKlcWr3UIMQZCNddWVCVEDZm9WC+Du7k6/fv0s0oCDBw8ydOhQ4/vIyEgApkyZwtq1a5k4cSIpKSnMmzePxMREwsLC2Lp1a4XpBoQQQtRMXQfk0RcNw2uDOnhWW7agSM+cTYbFNJNuastL43oYTtgUN7J378rTAfj5Qd++oKt1k0ULV6MAyZKGDBliHK6rzKxZs2RITQghmqj8Qj2Xr+UB0Nm3+q2dfjgaz5X0PLydNfxrTFfzbubnB337wB8Xa9JUIYxqvFmtEEIIYYoLV3NQFHCxs6GVo7ra8mv2nAdgys2BOKiv+zvez8+wIs1Sk64lr5KoggRIQggh6lRsiiHrdrCXU7XZ7a+k53HsSgZWKnig7w0Jef38DAGNJQOkG/IqCVFCAiQhhGjiGvuWOnHF25K0NyGpZMlk7rAAN1o5Se450XAafA6SEEKIlsGUbUl+O2UIkIZ0tuzODEYJCejj49mVqGXfsUtMcvEm4NCh0vN+fpI3SQASIAkhRK018g6cRsPf3b7aMoeKV7sN7NCqTtpw+qN1PHdBw1G/ToATuvCx/DsiorSAZN0WxSRAEkIIUS9srKqe1ZGVX0RylhaAjj7Vr3Yz15/nrjJVH0qeX+n2U1ob2+vyKiG9R8JIAiQhRLOkanabdTQOddlbdq54rpKnkwYXO9P3eDPF5Wu5PLruIHlFegYEtyLIy5Ev/ixOBVBVXiXRYskkbSGEEI3CuZRsANqbMFfJHIqi8NI3x8nSFtGrrRtrpvXFSyaAi2pIgCSEEKJRKEkmGWzCajdzHLp4jd9Op6C2tuLt+0Kxs71ua5K+fWVYTVRIAiQhhBCNiqV7kD7Zbdjwdlyv1uVTDfTtV7MASZJMNnsSIAkhhGhUfF3tLFZXVn4h2/5KAmDawCCL1StJJps/maQthBCiUbGy4EzwPWevUqRXCPJ0pKufS63ry8gtZP/5NC6fziG0dWdkanfzJQGSEEKIZuu34szcgzt51biOIp2eP+PSWL/rNFvPXqOgOEuA8/2vcDj6UOkvUkky2axIgCSEEKLZ2hebCsCtnTxNvkZRFGIupbP7TCr7z6cRfeEauQU643mv7DRSnDzI0jiim/kQNroiwwlJMtmsSIAkhBCiWdIW6bmYlgtASBtXk65JyMhj9v9i2H8+rcxxV3tbxnZw5X4fhXZOPoRuNsxr4qOPIFySTDZHEiAJIUQx2TKkeblwNQe9As4aG5PyHqVma5nw4V4SMvLR2FgxvKs3/YNa0S/Ig84+zlhZGb5BMvMLYfM2w0VhvSTJZDMlAZJo9OSXlhCiJs6lGDJzB3s7oTLhB8nzG46QkJFPkKcjn07vR4CHQ103UTRissxfCCFEs3QutTgzt2f1eZUOnE9jxylDMskVD4ebHhz5+tamiaIRkwBJCCHMJL2aTUOhTgGgvXf1mblX/BYLwD3hbehkzka5fpUESJJIssmTAEkI0WhJHCIsoW01vUGZ+YXsPGVIB/DIIAslk5REkk2eBEhCCCGaNbVN1b/qdp9JpUivEOzpSAdvM3qPRLMmk7RFvZIegaZLJV890Uz9ejIZgKFdvGtXUUICJ09c5O3jWexLyKPN9GV8e/AQ9iXnJZFkkyI9SEIgc0pECyHf5xWKuZQOwKCOpieTrMjeD79gwg+X2B6vJUex4rRXO87MfwPCww2vFSss0FpRX6QHqYloqF/gEjhYQuP4JEoPUOMmX52GUaTXc+GqIR1AZ3MmZ98gNVvL0zYh5KqLuNlbzd7kAsOJl/4NN/cwfCy9R02K9CCJJkeCNiGahqYQlF9My6VQp+CgtsbXxa7G9bz7y2lS84ro7OPM6qeG0dqh+Ndrl86GRJK9e0uA1MRIgNTMSPAgGkJT+EUoREVik4uTSXo5GjNlmyslS8uG6MsALLy7O3a21hZrn2g4EiA1Us0l0DEle60QJRrbt0sja46R/L+ynMTMfADae1WfK6ky3xy+TEGRntAAN/oHeRgOWhUHSZ61m9ckGk6zD5AuXbrEkCFD6NatGz179mTDhg1lzgcGBtKzZ0/CwsIYOnRoA7Wy/rToH6wt+NHNIZ8m0RLVJkAqWQU3Pqx16c9YawsESJJsskE1+0naNjY2LFmyhLCwMBITEwkPD2fMmDE4Opamnt+7dy9OTjX8z9GSAw5hUfKdJETD8TRhM9uKZOcXcfD8NQCGdDYzTUBCgmFl24wZFc9PKkk2edddMn+pATT7HiQ/Pz/CwsIA8PX1xdPTk7S0tIZtlBBCNHItrbe5po/7Z1waRXqFIE9HAk3Y860MybbdqDV4gLRr1y7Gjh1L69aGrsnNmzeXK7Ns2TICAwOxs7Ojf//+7N+/v0b3io6ORqfTERAQYDymUqkYPHgwffv25fPPP6/pYzSYFvYzrN40h09rXT+DTMwWAk4lZgEQ6u9q1nVXs7XsSdKyt21PLmYXUajTA5B/6Qp/bN3Hks9+47mfYjnv5geHDpW+JJiqNw0+xJaTk0NoaCjTp09nwoQJ5c6vX7+eyMhIli9fTv/+/VmyZAkjR47k1KlTeHsbujPDwsIoKioqd+22bdto3bo1AGlpaUyePJmVK1eWKbN7927atGlDQkICI0aMoEePHvTs2bMOnrR5kIBMNFbyvSkawrlUwyo4U+cwXY29yMKfz/D9xXwUgAdfgx9TsP7pJ3ztrUnJKaBAVbIKzgHP0NuZExFRWsH8+YZ5SaLONXiANHr0aEaPHl3p+cWLFxMREcG0adMAWL58OVu2bGH16tXMmTMHgJiYmCrvodVqGTduHHPmzOHmm28uc65NmzaAYShuzJgxHDp0qMIASavVotVqje8zMzNNer6G1tJ/abT05xctm3z/173Y5GwA2ntXHyAlZ+XzwKr9nFMMm48EpV1BpShcdvWmwEbNlVwdqKzxtLPCzlrF5Rwdhda2sHKlIY8SyFyketTgQ2xVKSgoIDo6mhEjRhiPWVlZMWLECPbt22dSHYqiMHXqVIYNG8bDDz9c5lxOTg5ZWYbu0ezsbH799Ve6d+9eYT2LFi3C1dXV+Lp+mE4Ic8jvLCGaj9gUQ4AU7FX1/CNFUXh+w1HOKfa0drDih9s82dHPil9XPc7Jzlf5405vNg5rRdTkbhyYP4o7+wSWXlySaFKSTdarBu9Bqkpqaio6nQ4fH58yx318fDh58qRJdezZs4f169fTs2dP4/ymzz77jB49epCUlMT48eMB0Ol0RERE0Ldv3wrrmTt3LpGRkcb3mZmZEiQJURnpumgxWvqXWlukR6WCwFZVB0g/HU/kt9MpqG2sWDdjEB19nOGQLQBW4b3x7d0b3/posDBZow6QLGHQoEHo9foKzwUHB3PkyBGT6tFoNGg0NVsGKoQQ5mppq8gsqb4XELR2ta8ye7aiKKz4LRaAx28NNgRHprrpJuk1aiCNeojN09MTa2trkpKSyhxPSkrC11dibdG8yS9IIZoGO9uqf5UevZzBkcsZaGysmHxzYOkJPz/DpOuqAqABAyRAaiCNOkBSq9WEh4cTFRVlPKbX64mKimLAgAEN2DIhhGheJG1D3Yk6Yfgjf0RXn7IJKf38DCvS6iIAkizctdbgQ2zZ2dmcPXvW+D4uLo6YmBg8PDxo27YtkZGRTJkyhT59+tCvXz+WLFlCTk6OcVWbEEII0ZjtPJ0CwODOXvV3U8nCXWsNHiAdPHiwzB5oJROhp0yZwtq1a5k4cSIpKSnMmzePxMREwsLC2Lp1a7mJ242VTqejsLDQ7Otc1dDGuXRMW19YQH5+frlybjeU091QzsFKX+a8jVJUYT2V0RUWlLneTY1Z17trbmyflvx883a6tlPpytShxvRn8HW0wrF4TyRHjXWl1xUUlb2Hl4OVWc9ZFWt9YZm6Ha31FdZtrZhWzlQ2SlGZ+hwqqc8S5QoLyn6ftLJT1ajtXvYqcq6rR6vNx1ox7ceULYb26RW4ll/xvEMh6tvVnAKOXs4AYEinegyQRK01eIA0ZMgQFEWpssysWbOYNWtWPbXIMhRFITExkfT09Bpdf1ewDbcFlO7rU5SRRFx2Srlyd7e34fa2peUK0xOJyyodOe3tUUiHoaXnPVSZxMXlmNyOQp2eBdddb29rRVxcnMnX39tJzZ1BpdfnXk0gLt28kd1uzoVl2uCuyTW5DS/c7IGu+NvLSkWl1ymKUuYettYqs56zKq0UXZm6ne30FdbtWa6crlZt8FbdWF9RhfX5WhWZVM7Pumw5F7tCYzmdvuznT21j3vdJiYhezhTqSvPJJFy+iJWJc7Ha2pa0T6FQp5Cn0qIoSouYy9X8n7DpOplgSCUT2MoBbxe7OrtPclY+MUfiiL2QQnyujoykqxTdPYe7dv3NqJJCfn7Sm2SGBg+QmquS4Mjb2xsHBwezf0irr+WSrS3NDt62lWOFqyRuLNeulSOa68qlZGmxyylNcOnnaoeLvdrkdmgLdShXSwMqJ40NbdwdTL7e9louOTe0T13Fao+KXM3RoskqfQZvZzvcHU17hqLkLPR6Q4RkZaUiyLvi1SN6RaEwKcv4Xm1tTVA1eU1MlZVfiHV6nvG9u4O6wh+UWXkFWGeU9rq4O6rxdq75D9TMvAJsTKjvxnIejmq8KiiXUa6cBi9nw3yKQp0eXXE+GAA7W2vaVbPsuUKpORQU6YxvA72dsbYy7f9Oem4Btpn5oCgoRQUU5qSTmJiIn/xCEA3oXPH/iw4mJJI0V462iC8PXOKrA5c4dd3PLwN76DKII6eSGfVMuOGQZOE2iwRIdUCn0xmDo1atWtWoDhu1DpWutKdFY2dXYYBkbVt1OVstqLSlPXRqjR12dqYHSFjrUNmUDhFa29piZ2f6L+2K2qcxM0CyLQSVzfXPoMHOzrSUC1Y2WpTrAqTK2q5XFFQ22uuuszbrOauiVaxR2ZT+0rdRayqsW6u3QmVTOjRkW0k5U+WbWJ+p5fKqKGet06OyKTCes7a1qVHbrW0LUVH6ubKzszM5QFLrVMb2qWw1tLK3JT39Gt7e3lhbm/c9JyonvVXmKU0kadkA6bsj8cz/9jjXcg0/n1Uq6NzKns4OCgGONiRcTmZTlgNaj1YQHW24SP5YMIsESHWgZM6Rg4PpPS1CCMtSa+zQZhn+P0qAJBpK6V5tlumRBng/6gyLfzkNGIbuIm4NZkyIX5me9RO//smmbalga1u6TYkwiwRIdaglzH0QotGS/3+iEUgv7uExdTPb6nx14JIxOHpiSHsib+uEjXWjztjTZMlnVQghRKPSHENbSwyxXUrL5eVvjwMwe3hHXhjVpfLgyNPT8K+V/JqvKfnMCbM8cPco/vnPf9b5fc6fP49KpSImJqbO79VU7Ny5E5VKhUqlYty4cZWWmzp1KiqVCndHDb9u3VJ/DRRCVMrFrvYDNq/9eAJtkZ4Bwa2YPbxj1YW9ilMKWMnwck1JgCSMpk6dWu4X79ebNtG3gy/rViwFYPnaL3j11Vfr/L4BAQEkJCQQEhJSozpLAqyQNm6EBrgTGuBOSBs3VCoVf/zxhwVa3XBOnTrF2rVrje9v/Py99957JEj2XCHqTEP0cMWmZPPT8URUKlhwV3esTFy8IGpO5iCJSq1atYonn3ySl15bzLiJDwHg5u6Bs7PlJhtWxtra+rr99oqqLFuVVV9+S1DHzoY6rVR09nWp8cpCUyiKgk6nw8am7v5reXt74+bmVul5V1dXXF1d6+z+Qoj698WfFwEY1tmbzr5mbHYrakx6kOqBoijkFhSZ9cor0JFfWPqqabnqknBW5s033+Spp57i0/9+YQyOoPwQW2BgIK+99hrTp0/H2dmZtm3b8vHHH5epKzH+Ms/PnMag7u3w8/Hi7rvv5vz58wAsWLCAdevW8e233xqHj3bu3FnhENvZUyeYNXUiga29cHZ25pZbbiE2NrbK53Bzd8fT28f48vX1xdbW1njvsLAwPvvsM4KDghjYrS0vPDGdnOzSfCJ6vZ5FixYRFBSEvb09oaGhbNy40Xi+ZNjrp59+Ijw8HI1Gw+7du8nKyuKhhx7C0dGRzkFt+Wzlhzxy3528uWAuAK+88kqFvWP3j7yFpW/9p+ovjhCiRdHrFTYfvgLApJva1c9NZS836UGqD3mFOrrN+7lB7r3j2SFmX/Piiy/y4Ycf8sMPPzDw1iGcLpeArKx33nmHV199lX/9619s3LiRmTNnMnjwYDp37kxhYSEzJ91Lz959WbPxRzr4uPLmG4sYNWoUR48e5bnnnuPEiRNkZmayZs0aADw8PIiPjy9zj6SEeKbfewd9Bgzimx+20tbXkz179lBUVPPeJYDY2Fg2b97Md99/z6HTl3j+iemsXraE515aAMCiRYv473//y/Lly+nYsSO7du1i0qRJeHl5MXjwYGM9c+bM4e233yY4OBh3d3ciIyPZs2cP3333HQ6uHvz75XmcOH6Uzt17ADB9+nQWLlzIgQMH6Nu3LwBHYmI4feIvFn/8Wa2eSQjRvPwVn8nVnAKcNDYM6uhZPzeVvdwkQBJl/fTTT3z77bdERUUxbNgw8gt11V4zZswYnnjiCcAQXL377rvs2LGDzp07s2XzJvR6PQveeh+VSkVnH2fWrFmDm5sbO3fu5Pbbb8fe3h6tVnvdkFp569etwsnFhTeWfUKglwutnDR06tSp2rZNunskqutWcVipDBskl9Dr9axduxZHJydwD+DOCffz555dAGi1Wl577TW2b9/OgAEDAAgODmb37t2sWLGiTID0yiuvcNtttwGQlZXFunXr+OKLLxg+fDgZeYW88s5SRvTpZizv7+/PyJEjWbNmjTFA+vy/6wi/aSD+7QKrfS4hRMux64xhm6kB7VthWw9L+rVFOjLzdWjUDrjU+d0aLwmQ6oG9rTV/vzLSrGsuXs0lM780g3V7byfsK8hAfSE1lyxtabkO3k5lMmln5BZy3S4d1erZsyepqanMnz+ffv36YaOxN+maEiqVCl9fX5KTkwE48dcxLp0/x4AuAYAhQAHDhrfVDY9d79Tfx+jdb4BxeMxUb3+0mnbtDYGUtZWKjj5lx+4DAwNxdnZGXzwU6entS9pVww+js2fPkpubawx8ShQUFNCrV68yx/r06WP8+Ny5cxQWFtKvXz/jMWcXVwLbdyhzTUREBNOnT2fx4sVYWVmx8av1RL78f2Y9nxCi+dtzNhWAW+uo9+hcSja/nkxm719XOJWcw5Vcwx/G6qc+5/t9R+lcUrCF7eUmAVI9UKlUOKjN+1Tbq60p0JVu6+CgtqkwQLJXW1OoL1vu+gApM8+8Iag2bdqwceNGhg4dyqhRo/jmux+qvebGoEWlUqEvblNOTjZde4Sx6H3DvKRgz9K92Ly8TN/ZWmNXfaBWEd/WbWgbFAwYAqQOrctOXq6o7Upx20t6mrZs2UKbNm3KtkdTdqsTR0fzJ66PHTsWjUbDN998g1qtpqiwkNvG3G12PUKIxseSiYJPJRqmOfRq626xOgHOJmex+JfT/HgsscLzBTa2nFy2hs6zDL3qLW0vNwmQRDnt2rXjt99+Y+jQodx15xgWr16Po1PNVk2E9Azjh2824eHpiZOzC+19nMvtxaZWq9Hpqh7K69S1O99t/F/xNi41C5bM1a1bNzQaDRcvXiwznFad4OBgbG1tOXDgAG3btgUgKzODC+diCe9/s7GcjY0NU6ZMYc2aNajVaibcex929vXzbEKIpiEjr5CrOYZ9DoM8LbeC+Is/L7Lgu78o0OlRqWBQB08G+2oItckn2NmaWb9cYl+eBiIiYPC7hotaUO8RSIAkKhEQEMDOnTsZMnQoMyfdw4efbcTJ2fzR6LvvmcjyD5Yw+5GHePLZuah7dCIx/jJff/01L7zwAv7+/gQGBvLzzz9z6tQpWrVqVeES9QemRvC/NR/z4pOPMGfOHNr5efHHH3/Qr18/OnfuXMGdDdKvXSM1OQkw9CAlWuXh5uZm0kaqzs7OPPfcczzzzDPo9XoGDRpERkYGe/bswcXFhSlTplR63ZQpU3j++efx8PDA3sWdBfPmY2VlheqGDCqPPvooXbt2BeDnqJ3VtkkI0bKcK97s1tfFDkeNZX5lr9x1jv/8eAKAwZ28+NeYruVSB1j9kQ55BRAU1GL3cpNl/qJS/v7+/PxLFNfS0pg56V6yszLNrsPewYE1G7fg18afyMcmE9YzhEceeYT8/HxcXAwBV0REBJ07d6ZPnz54eXmxZ8+ecvW4uXuwcv235ObkcPfo2wgPD2flypXVzkl69IG7GR7eheHhXRjSqzN+fn5s3rzZ5Pa/+uqrvPzyyyxatIiuXbsyatQotmzZQlBQUJXXLV68mAEDBnDnnXcy7o7RhPXpT1CHTqjtyg7NdezYkZtvvpkuXbrQp2+/Smqrml6vr9O8S0KIhlOy2W2whTa7/f1MCq/9ZAiOnh7ekbXT+kpepUrIT1VhdH125hJt2rTh+10Hje+//HYrgdd185bkM7rejduDeHr78H/vfgRA5wqG2Ly8vNi2bVu5ekpyOCVn5QPQqWsIyz/fRBs3e1o5acqVv15gYCCKovBXfAY6vaEeaysV3a+bg7RgwQIW3DCePunRmUx6dKbxvUqlYvbs2cyePbvC+wwZMqTCXFPOzs58/vnngKGL/MSlZFYseYN7Hirb66QoCvHx8cZVgDWRnJxMhw4dqi8ohGhySja7tUSAlFeg48WNR1EUeLBfWyJvq2IlsEYDFEAVSWmbO+lBEqIOHD58mP/973/ExsYSc/gw/3rqMQCG3j7GWCYlJYWlS5eSmJjItGnTTK7b39+fBx98kGvXrvHDDz+wc+dORowYYTz/+OOP4+RkmZ3DhRCNQ5Bn7f9Pr/r9HPEZ+bRxs2fend2qLqxRG/5twQGS9CAJUUfefvttTp06ha1aTZeQUNZs/BF3j9JtTry9vfH09OTjjz/G3d2djNyCKuvr378/Z86cAcDJyYnp06dz4MABnn32We6+u3T12yuvvMJzzz1HZl4BBRrZcqQ+3Di3TAhLc7M3L8XJjbRFOtbtOw/AC6M6Y6+uh01sExJgxQqYMaNJTvCWAEmIOtCrVy+io6MBwxDbhas55cqYuw2Mvb19maG0b775psJy3t7eeHt7k55bwMW0XLPuIURdsuDK91prTG2pD1uPJ5KaXYCvix1jetRTsNLEs3FLgCSEEEI0cz8V5zq6v49/nWbjTs3WcvxKBmeTs8k9n8V4Vx8C6uxudUsCJCGEEKIZK9Lp2RNryMY9rKuPxevPLSji60NX+DbmCgfPX+P6vvHYWx7mvUOHSg80oWzcEiAJIYQQzdiRy+lk5Rfh5mBLjzaWm5eoLdKxctc5Pt51jsz80l0b2l+9RGwrQ79RtsbBkGyyRFXZuBvZnCUJkIQQQohm7M+4NABubt8KayvLTL46cimdZ9bHGPM0tWvlwMM3tWOMjzWts/346lwuLxzMMBReubI02WRVgU9t5yxZOMCSAEk0ei1sLqUQQlhUbLIhiOnqa/5uCBX57XQKj316EG2RHk8nDf++oytjQ1tfF3y1A90lOHjU8LZ37/rJxm3hSeESIAkhhJlkWb9oSs6lGrYrCfaqfS6l2JRsnvz8ENoiPcO6ePPuxDBca5OCICEBEhKIyypi0x/n0N86mZkHD2PM7W3inCW9XuHI1QICHFzxrHlrypBEkaJZOX/+PCqVqlw27+ZmyJAhqFSqGj/r9dcfO3LE8g0UohFp2eGswrkUy2xXoigKL28+Tra2iP5BHiyfFF59cNSpU5UBTtHyFbz/1Fvc/kM8S6858+GA+/lp6ZcQHm54rVhRZfUZcZdY9b9dDHvtZ8ZHXWXG+Jfg0KHSV0JCTR4VaAEB0qVLlxgyZAjdunWjZ8+ebNiwwXju7bffpnv37oSEhPDf//63AVvZOEydOpVx48aZdY1KpTJrb7PG8JPq+uCg5GVtZUVRUVH1FzciERERJCQkEBISApQGh97e3mRlZZUpe//IW3h70f8Z33/99dfs37/frPs1gi+dqCXp+Wp50nIKyMgrRKWCIM/aBUjbTySzN/YqGhsr3r4vFLWNCSFEFQFSQkYe97gNZvEtkyi0Lg208idPg+how2vGjAqvPZuczYsbj9J/xWH+70gW57N1AKQ4uhsmhZsYYFWl2Q+x2djYsGTJEsLCwkhMTCQ8PJwxY8Zw7tw5vvjiC6Kjo1EUhaFDh3LnnXfi1kjTqjf3H2uFhYXVbjxrSREREbzyyivG93pFIVVXs/8OBQUFqNVqSzXNZA4ODvj6+pY7npWVxdtvv80zL75U6bUeHh5kZpq/+bCoO6pmmLmwGT5Sk1O8FSWtXe2xs6159mxFUVi64ywA0wYGEeDhUKt2JWbkc+9H+7iSnoeLnQ2v3B3Ctj0n+fFyPgQEVDpnSa9X+HDnWZZsP0ORXgGs6eJqw01eataeLU6Ma+qk8Go0+x4kPz8/wsLCAPD19cXT05O0tDROnDjBgAEDsLOzw97entDQULZu3Vo3jVAUKMgx66UqzC3zqnE5M7M1X2/IkCFEPvNP3v3PPG4JCWJY784sefM/xvOBgYEAjB8/HpVKZXwP8O2339K7d2+6+LdizMAwlr/7RpkeGpVKxUcffcRdd92Fo6Mj//nPf1iwYAFhYWF89tlnBAYG4urqygMPPED2db0he3Zs547bhuLm5karVq248847iY2NNfvZSoKL618ltv/4HXcO7odGoyEwMJB33nmnzLWBgYG8+uqrTJ48GRcXFx57zLDP2p49exgyZAgODg64u7szcuRIrl27BoBer+eTpYsZfXMo7f08CA0NZePGjcY6r127RsS0KQwJ7UC/Dn6MvSWcL//7qdnPBfDUU0+xePFiUpKTa3S9KNVYf8E30maJRqxtLQOaQxfTOXIpHY2NFY8MCqpVXfmFOmZ8dpAr6XkEeTqy5elbGNerTbXf1zq9wnMbj/D2ttMU6RWGdfFm4+MD+GnO7Yy9Lay0YMmk8N69axUgNXgP0q5du3jrrbeIjo4mISGBb775ptwwz7Jly3jrrbdITEwkNDSUDz74gH79+pl9r+joaHQ6HQEBAWRlZbFw4ULS09NRFIWdO3fSqVMVOxtXyoQfVYW58Fprs2pta2K5dtWcV806B9T8r4bPP/uUhx59gv9+v52j0Qd4OfIJ7rhtKLfddhsHDhzA29ubNWvWMGrUKKytDff5/fffmTx5Mu+//z6B3cM5efoMr8z5JwDvvlE6zLNgwQJef/11lixZgo2NDatXryY2NpbNmzfzww8/cO3aNe6//37ef/dtps2eC0BeXi4zZ81mYP9wsrOzmTdvHuPHjycmJgYrq9rH+38fjeH5mdOY9excnnx0Mnv37uWJJ56gVatWTJ061Vju7bffZt68ecyfPx+AmJgYhg8fzvTp03nvvfewsbFhx44d6HQ6rIBPli5myzcb+PdriwkL6cqpmP1MmjQJLy8vBg8ezMsvv8zJkydY9ukG3Dxacen8OTSqmg35Pfjgg/zyyy+8+fp/eOrfi2r9ORFCNH22pgyHVeHrQ5cBuKOHH17OmlrV9fLm4xy5nIGbgy3rpvUr7Y2ytwPywbV8rqYinZ7nNhxhc0w81lYq/jMuhAf6mfqbsmYaPEDKyckhNDSU6dOnM2HChHLn169fT2RkJMuXL6d///4sWbKEkSNHcurUKby9vQEICwurcP7Itm3baN3aEJikpaUxefJkVq5cCUC3bt14+umnGTZsGK6urtx0003GX/CiVEiPHjz+zIsAtAtqz1efriIqKorbbrsNLy8vANzc3Mr0wCxcuJA5c+YwZcoU4lJzcPf158nn/sWS/ywoEyD94x//KLeLvV6vZ+3atTg7G9YwPPzww/y6Y4cxQBox5i7auNnTysnwH3T16tV4eXnx999/G+fimOLDDz9k1apVxvePPfYYUyPn8dnKZfQbOJgnIl+kk68znTp14u+//+att94qEyANGzaMZ599tsyz9OnThw8//NB4rHv37gAkp2ezaum7fPy/bwgN74enk4YBYd3YvXs3K1asYPDgwVy8eJGeoWF0D+0FQJuAtjX+IaRSqXj99dcZO3Ys4yY9RkBg7f7aEy1TS+ulamnPa46CIj0/HDVMdp7Q279Wde04mcyG6MtYqWDpg71p2+q6ni07eyAdXMunI3j9p5NsjonHxkrFBw/2YnRl+8l5uFssyWSDB0ijR49m9OjRlZ5fvHgxERERxl+ky5cvZ8uWLaxevZo5c+YAVLuKR6vVMm7cOObMmcPNN99sPD5jxgxmFE8Ae/TRR+nYsWOVdWi1WuN7s+Zv2DrAv+JNLw9cTMslI6/Q+L6jt1OF48cXruaSmV95OSXXCtCWu85UPXr0LPPe28eX5GqGbo4cOcKePXv4z3/+g6KAAuh1OrTafHJzc9G4GIKfPn36lLs2MDDQGByBYYg0JTXF+P5CXCwL3n+DmOiDpKamotfrAbh48WKFAVJlP/QeeughXnqpdI6Oi6sriVo4d/Y0Q28fU6bswIEDWbJkCTqdzhhE39j2mJgY7rvvvgrvdS72LPl5ucz4h+EPAJXK0K6CggJ69TIERDNnzuSee+7hYHQ0A24dytCRd3Db0FsraX31Ro4cyU0DBrLs7f/w+tJV1V/QLMivuKaqsQ5lCoPDF6+RkVdIK0c1A9q3qnE9RTo9C7//C4BHBgUxqKNpC/K3/53Eqt1xALw7Mazy4AjA3aP5BEhVKSgoIDo6mrlz5xqPWVlZMWLECPbt22dSHYqiMHXqVIYNG8bDDz9c5lxycjLe3t6cOnWK/fv3s3z58krrWbRoEQsXLqzZg6hUoDZv9YBiC0pRaeCD2hEqCJAUW1B0VZTLyze3tWXY3DBxWqVSGYOSymRnZ7Nw4UImTJjApbRccrSlvXt2dnbGjx0dy39ObpyorVKpUK6739PTHiQosB0rV66kdevW6PV6QkJCKCgoMOu5XF1d6dChg/G9XlFIvJJh8vU3tt3e3r7SsjnZhiW2S9eux9vXDw9HNd4uhs+DRmPoJRo9ejTHTp7hf5u+Y9/vO3jsgXFMjZjB8g+WmNymG81/9f+4feitTHn86RrXIUR1JLapG40paNx37ioAA2qZifvbmHjOX82llaOa2SNMm9KSlV/IS5uPAfDooCDGhpo3XaU2GvUk7dTUVHQ6HT4+ZTfX8/HxITEx0aQ69uzZw/r169m8eTNhYWGEhYVx7Jjhk3333XfTrVs3Jk2axJo1a7CxqTxenDt3LhkZGcbXpUuXav5gzYitrS06na7Msd69e3Pq1Ck6dOhAYHB72gYFG1+1mSeUfi2N87FniHx+DsOHD6dr167GSdCWEtyhE4cP/Fnm2J49e+jUqVOVQ7A9e/YkKiqqwnOdu3ZFrdGQEH+JtkHBBAW3p0OHDnTo0IGAgNJ9rj29vLjrvgdZ9P7HPL/gNT5ft7pWzxLepy/DR4/lvUU1DOxFg2pEvx+bnPoOLprjCsTr7YstDZBqSlEUVuwyLKh59JZgnDSm9c+s3n2epEwt7Vo58NzIzjW+f0006h4kSxg0aFClPR6m9kKB4S/9kr/2RanAwECioqIYOHAgGo0Gd3d35s2bx5133knbtm25afgY8gr1nPr7OLGnTvDB4jdqfC8XVzfc3D34dO0ndGnfjosXLxqHWS1l8mOz+Medw/hw8Rs8+ehk9u3bx9KlS8vMLarI3Llz6dGjB0888QSPP/44arWaHTt2cN999+Hs7MqUx2bx9sKXUPQKw4fcSoKiZc+ePbi4uDBlyhTmzZtHl5CeuPgFU1CgZVfUz3TsVPsfBrNe+Df3DB+AtXWz/68uaqmZ/44XNVSkUzh8MR2AAcE1D5COXs7gdFI2GhsrHrrJtMnVWfmFrPr9HADP3d65VmkKaqJR9yB5enpibW1NUlJSmeNJSUkV5n8R9e+dd97hl19+ISAgwDifZuTIkfzwww9s27aNcbcP5uG7b+O/qz7Czz+gmtqqZmVlxRvLPuHI4cOEhITwzDPP8NZbb1niMYy69gjlrY/W8OO3mwgJCWHevHm88sorZSZoV6RTp05s27aNI0eO0K9fPwYMGMC3335r7JV88vmXeGz283yy7F0G9+/FqFGj2LJlC0FBhgnUarWaV+a/zH23D2L6vXdgbWXNR5/UbJn/9QKDO3D3xIfQams31CoaOQluRB25dC2XAp0ee1vrWiWaLFkFN7K7Ly52puW8++5IPFnaItp7OXJHVfOOzLB6dxy7TqdUX5BG3oOkVqsJDw8nKirKuPRfr9cTFRXFrFmzGrZxzdDatWvLvN+5cyf5hTpOJ5XmIfr40y8JvO4/ydixYxk7dmy5ukaOHMnIkSOJS80h67pJ5CWUCvIzLViwgAULFpQ59s9//pOHHnmchAzDL/ibbhnC3oMxxlVsN9YVGBhYYd03PldVRoy5izvuGk9nX+cKz58/f77C44MHD2bPnj3ljhuy2Kp46JHHeeiRx/Fy0uDnVnbO0r///W+eevYFLlzNNR4zdxVbZc8+7/UlzHt9Sa2X5goB0tPUVFjq6xSXaphDGeTpWOOhREVR2H7CsLhnXC/T5xCtP2CYyvJgv7ZY1WLuU4nlv8Xy+k8nsdaZ9gdjg/cgZWdnExMTY1yJFhcXR0xMDBcvXgQgMjKSlStXsm7dOk6cOMHMmTPJyckptzxciJbmww8/xMnJyTinzhyjR482piEQQojKXB8g1dTFtFyupOdha63iJhOH6S5fy+Xo5QysrVSM79Wmxvcu8f2ReF7/6SQAjw40LfVJg/cgHTx4kKFDhxrfR0ZGAjBlyhTWrl3LxIkTSUlJYd68eSQmJhIWFsbWrVvLTdwWoiX5/PPPycvLA6BtW/OTpa1atcp4vYunL4nZTWsfOtG8NcdeqqY6kTsupfYB0t7iSd69AtxxUJsWduw5mwpAWIBbmRGDmjh+JYPnNxo25X7s1mCeHNSGf5lwXYMHSEOGDKl2SGTWrFkypCbEddq0qd1fVNdfn55bAEiAJISpmmqwUxNZxWlaahMg7Y9LA+AmM1bB/X7GECAN7GBarqTK5BfqePKLQ+QX6hncyYsXR3UhJzur+gtpBENsQgghhGjcAmsRIJ1NzgYgpHX5DNmV+aM499KgWgZIy3ac5cLVXHxd7Hj/wV5m5XGSAEkIIYQQVXKxq9mAk0LpPKZgL9OCrLScAlKzDcl/Q9qYHlTdKDVby6rfDRm4F9zVDVd701bPlZAASQghhBB1IiVLS7a2CCsVpZvSVqMkoGrtamfynKWKrPo9jrxCHaH+rozsbn5qIAmQhBBCCFEnSoKdAA8HNDamJXo8l1LS4+RU4/sW6vRsOGhIE/DE0A41mjcmAZIQQggh6kR2DSZ5n0sxzFkydUiuIjtOJnM1pwAvZw3Du3jXqA4JkIRZHrh7FP/85z/r/D7nz59HpVIZ82M1VwsWLCAsLKyhm9EkBQYGsmTJkoZuhhDCBIGtTA92cgoM+3sG12Ji+JZjCQCM79UGG+uahToSIAmjqVOnGjOWl/h60yb6dvBl3YqlACxf+wWvvvpqnd83ICCAhIQEQkJCalRnSYDVvY0boQHuDOgSwN1Db+LJJ5/kzJkzFmi1ZTz33HOVbnJrSR8tfp3Wbg6oVKoyry5dutT5va8nQY0QLZOpm9Ner6Yr5xRFYc9Zwyq4YTXsPYJGkAdJNF6rVq3iySef5KXXFjNu4kMAuLl74Oxc86jeVNbW1tftt1fzHD2ffPktgR07k5+XR+zpv/nms1WEhoby/fffM3z4cMs0thacnJxwcqr5OLs5Onftxs5fywZjJXvFCSFEY+NYg6AK4HRSNqnZWuxsrejV1q3G95ceJFGhN998k6eeeopP//uFMTiC8kNsgYGBvPbaa0yfPh1nZ2fatm3Lxx9/XKauxPjLPD9zGoO6t8PP24u7777buJ/ZggULWLduHd9++62xV2Pnzp0VDrGdPXWCWVMnEtjaC2dnZ2655RZiY2OrfA5Xd3c8vX3wbxfIsJF3sH37dvr3788jjzyCTmfoxo2NjWXcuHEM7dWJmzr78487hrF3144y9QQGBvJ///d/TJ48GScnJ9q1a8d3331HSkoKd999N05OTvTs2ZODBw8ar1m7di1ubm788N23jL0lnL4dfHn8oXu4cvmyscyNQ2wvP/ME/3zkIdYt/4CwzkG0atWKJ598ksLC0v3sEhISuOOOO7C3tycoKIgvvvjCpJ6ZkqDz+penZ2mOkdEDerLqg3d45skZlX4t331tPmNv7UP/jq3pH9qVl19+uUzbAHb+8hMTRg7Gzs4OT09Pxo8fDxiSwl64cIFnnnnG+LUusXv3bm655RZ6Bnpze7/uvD7vRXJzc4znk5OTGTt2rPGZP//88yqfVQjRcu2LNSSZ7BvoYfLE8IpIgNTI2SQl4rP4dUhIqLd7vvjii7z66qv88MMP3H3D0FdF3nnnHfr06cPhw4d54oknmDlzJqdOnQKgsLCQmZPuxcHRiTUbf2THb7twcnJi1KhRFBQU8Nxzz3H//fczatQoEhISSEhI4Oabby53j6SEeKbfewdqtYZvfthKdHQ006dPp6jIvN4lKysrZs+ezYULF4iOjgYM+wGOHj2aj/+3mfVbf+PmIcOZOWWicT/AEu+++y4DBw7k8OHD3HHHHTz88MNMnjyZSZMmcejQIdq3b8/kyZPLZIbPzc3lnTdf5/+WfMS6r7eSlZnBzEcmV9nGA/t+59KFODZ8v5V169axdu3aMhsJT548mfj4eHbu3MmmTZv4+OOPSU5ONuvzUJlPP15GaK/eFX4tARwdnXl18TK+/vUPXn39bVauXMm7775rPL8r6mciIx5m8PDbOXz4MFFRUfTr1w+Ar7/+Gn9/f1555RXj1xoMAeqoUaO45557+O7Xvbz54WoOH/iDRf9+wVjv1KlTuXTpEjt27GDjxo18+OGHFntmIUTzcqp4g/VeAW61qkf61xs52+QkfN59A+0D90Bb/zq/308//cS3335LVFQUw4YNI79QV+01Y8aM4YknngAMwdW7777Ljh076Ny5M1s2b0Kv17PgrfdRqVR09nVmzZo1uLm5sXPnTm6//Xbs7e3RarXXDamVt37dKpxcXHhj2ScEernQyklDp06davSMJfNuzp8/T79+/QgNDaVHz54cv5IBwKznX2Lnz1v47rvvymxxM2bMGGbMmAHAvHnz+Oijj+jbty/33Xef8dkHDBhAUlKS8VkKCwt5a/F7eLU3bAz7f+9+yLih/dm/f78xcLiRi6sbc//vLXzdHPDrG8Ydd9xBVFQUERERnDx5ku3bt3PgwAH69OkDGIZCO3bsWO1zn/z7r3LDeZMmTeL1xe8b3w8adhtTH52Br6t9ua8lwGOznzOW7dWtE8mX4/jyyy955lnD8VUfvMPIuyYw+4WX6OBtuFdoaCgAHh4eWFtb4+zsXOZrvWjRIh566CH++c9/cjopC9+AIF5c+DqP3Hcn+fmruHL5Ej/99BP79++nb9++AHzyySd07dq12mcWQrQ8sRZIEwASIIkb9OzZk9TUVObPn0+/fv2w0dibdE0JlUqFr6+v8a/7E38d49L5cwzoEgBASZb3/Pz8aofHrnfq72P07jcAW1vzMqFWpKSHp2SIJzs7m/nz5/PNdz+QmpxIUZEObX5euR6k65+zZLPkHj16lDuWnJxsDABsbGzo3acPl64ZNoYN6tAJV1c3Tpw4UWmA1L5TF6ytS7uF/fz8OHbsGACnTp0y1Nm7t/F8hw4dcHd3r/a523fsxI8/fF/mmItL2Sy1nbp2N35849cSYOt3X/O/NSu4dOE8ebk56IqKytRx6q/jTHhwSrVtud6RI0c4evQon3/+OYpiyLyrKAp6vZ64uDhiz57BxsaG8PBw4zVdunTBzc3NrPsIIVqG0jxKtZsvKwFSY5SQgPrEOey1RdgfN+xAbHX4MJSMpfr5GV51oE2bNmzcuJGhQ4cyatQovvnuh2qvuTFoUalU6PV6AHJysunaI4xF7xvmsgR7OaIufg4vLy+T26Wxqz5QM9WJEycACAoKAgwryX755ReemrOQtoFBaOzseX7mVAoKCspcd/1zlgRXFR0refaasrGp/PNZG7a2tnTo0KHcccNmtdXfe/+ff/Cvpx9jZuQcbh48nHZ+XkRt+YZ33nnHWF5jZ2d2u7Kzs5kxYwZPP/00canZaAtLn7V9+/bEnm08qw6FEI1bVn4h13IN8yJrs8EuyBykxmnFCvyGD6LjmCH4vzAbANvHZ0B4uOG1YkWd3r5du3b89ttvJCYmctedY0ze+bgiIT3DuBgXi4enJ22DgmnfoQMdil+urq4AqNVq44TpynTq2p1D+/eVmxBsLr1ez/vvv09QUBC9evUCYM+ePUyZMoXho++kY9fueHp7c+XSxWpqMk1RURGHi+c6AZyPPUNGRnqNh4c6d+5sqPPwYeOxs2fPcu3atVq3tTr7/9iHX5sAIp5+ju6hvQhu34ELFy6UKdOxa3f+3PNbpXVU9LXu3bs3f//9Nx06dKBdUHvaBgUbX+r/b+/Oo6K48j2Af5t96aZBxaYRBB0RjBHiNopJNEHi8hLHJYvjGEeMQePg0vry4jgTwfgyhknemzl6JtE8k9Exz2gmcdAXn+tTxNEYUQxBEwfRkKgRwQ16gaa3en8gBb0gDXTT0PP9nNPnUFW3bt36UbQ/q27dGxCApKQkmEwmsc8Y0HAnrbq62qXnR0TdX2NyFCkLhCyoY08cmCB1RQsXouLICZTtO4brb68HABg3vQ8UFTV87veDcafY2FgcO3YMt27dwqIXn4VWo25XPVOfnYnwHj2xbP5snDv9BcrLy3Hs2DEsXboU1++/zRUfH4+SkhKUlpbi9u3bDpOgn2dkQqfRYGXWfHx1rghlZWX46KOPrDoQO1Jz7x5uV1Xi+g/fI//QPqSnp6OwsBAffvih+BgrISEBeXl5+Mc351H67Xn8enGmS+7YAA13bV77VxVKvjqLb0uKsXpFFoaP/GmLj9dak5SUhPT0dCxYsACFhYX46quvsGDBAgQHB7c6lL7ZbMbNmzetPpWVlU4fu/+AAbh54zr279mFa9+X44NN7yIvL8+qzCvLV+LAnl1Y//bvcPHiRZw/fx6///3vxe3x8fE4fvw4fvzxR9y+3fCmycqVK/HFF19g8eLFuHihBD+UX0H+wX1Y9/q/AWhICidNmoSFCxfi9OnTKCoqwssvv4zgYNfdVSQi79LRu0cAE6SuSamEIeUR1A1JQd3DDR1cLUOHAsOGNXzc9HjNVkxMDA4ePoJ7d+9i0YvPtStJCg4JwZbP/hfKPjFYseCXeGTIw5g/fz70er3YdyUzMxOJiYkYMWIEIiMjcfLkSbt6wiN6YPMne1Cr02Hq5KcwfPhwbN68udU+SfN/PhXjhyfh2acexR/XvYFBgwahpKQETz75pFjmD3/4AyIiIjB32kQsnTcLY8al4aEhKW0+V0dCQkKwbMWrWLU4E3NnTEJIaCg2fvhRh+rctm0bFAoFxo4di+nTpyMzMxMymQxBrTzeKr34LZRKpdUnLi7O6eP+y9NT8OLLi5C7+jW8MGkszhZ+idWrV1uVGZn6GN7ZtBVHDu7DI488grS0NBQWForb165di++//x4/+clPxEesycnJKCgowKVLl/CLqZMwc9I4vPef69Bb0dSRe8uWLYiOjsa4ceMwY8YMLFiwAL17t38AOCLybhEhHe+vyj5IJGr+KnmjPn364PPjTWP77NxzwGp008bxjJqznR6kV28F3vzjRgBAYpTMblyKyMhIHDp0yK6exs7UtzR6AMDAQQ9j0/Zd6BMejJ7SwAeeS3x8PARBwLc3amCyNNTj6yPB4Gi5w7L/d+SI+BYbAMx9+RUkRskeeJ7NX+dvfkxbP5s2HSmPTxCXI5u1fc2aNVizZo24/O9/fM9uf9vxjZRKJfbt2ycuX79+HVVVVQ77FzVatOLXyM7JgVJuf9elsQ/S/lMldttsf5fLf7sWy3+7FgDQWxaEKHkQVCoVjOamO27pk6fgZ1Oni2+xNTd69Gh8/fXXdutHjhyJQ4cO4VKlxuGbk1FRUdi717o/3Jw5cxycKRERIEHbJ6e1xQSpizP2VqBy+UrIozrnrhF1fUePHoVWq8WQIUNQUVGB1157DfHx8Rg7dqynm0ZE5DWYIHVxJkUUKlf8GnKFrPXC9E/BaDTiN7/5Db777jvIZDKMGTMG27dvd8kQCERE1IAJEpGbZGRkICMjAzV1HXvzztbEiRMxceJEl9ZJRETW2EmbiIiIyAYTJCIiIiIbTJDcyNEbTUTUSfj3R0QdwATJDRo7y9bW1nq4JUT/vAz1DcNDsPM6EbUHO2m7ga+vL8LDw8VJPkNCQlod5diWyVAPwWQSl+v1esDsa1fObHxwOaOhHoKpaa4tQ70P9D7OjxJdbzRb7W82WqDX27ejJY7aJ/g5vz/Qwjn4OXd3wGIyQLg/DpLFRwK9Xu+4nCBYHcMi+LZYtq0M9Uaruo0GQK+3vx4M9Qanyjl/XOfqa385CRpDZDRbrK8TiRl6fdu/XsxGAwRT0zhIer0evj7OxUBs3/3f5R1dNWIUvawm/iUichYTJDdpnM29+UzobXFHa0BdswHzJNpA+Pva3/C7o61HXbPJPW3LqeuMUOubEhRTTQBCApz/B8NotqBKXS8ua/x9YKh+8CCNzd3W1kPfrH0+2kD4OTiPB9HojaipazoHY4g/qgOdu3Srqutgvp9L+UgAP53j6SkEQUBVdVNC5O8rAbRtn3jVkTqjGXe0TclDXZAfdMH2dzXqDGbc0bVezunjOllfrcGMuzbltA7LmXBX1/RGnr5ZObNFQFVNU/wC/HxgUTt/nTSqVOthNDclv361QfBx8j8XunrT/XmYBBjNAvSSIIxIiWp1PyIiR5gguYlEIoFSqUTv3r3bNcHq1j3f4MTlW+LyB78ciX6R9nPLbNl9ASev3BaXP5w70mqk6y0nyvHfp2+Iy68//RCe7Of8FA0/3NFhzZ4z4nJq/154c3qS0/u/v6sEZ76/Ky5ve+mn6BMR4vT+APBJ4VX819+/E5eXpw/EM0nRTu274t0TYoIYGuiH/1n8mMNyBpMFmXnHxeW+PUKwZV77JpS1daLsFtbkfyMuPz88Bq880c+u3N8vVWFN/rfi8gsjYrFwnH05Z+WXVuHNZvX9/Kd9kfm4fX1H/1GF3zUr94tRfTH/MftyRy5WYl3+RXH5xVFxmHe/3D2dAQt3fyFuS4oKw7uznb9OGv1u6xmU39GJy3uXPIbgAOe+pvafr8B/5JfCIgD39BZkPZnQ5ju3RESNmCC5ma+vb7tu8dcYgB81TXeQfPwDHM61VW1TztemXJ3Fx2q7SeLX6pxdzfn6m6z2rzagTfvfq7dun19AYJv2BwC94GtVhwHOn8NNnQX3ahv2DTNKWtzPx2SxOkZwsNDmdrbE7ONvVbfO7OOwbttytS2Uc/q4Ej+n6jO1t5ylqZy/SWK1LTK8ffG7VSdY1RMYFIQgJxMkI6zbR0TUEeykTURERGSDCRIRERGRDSZIRERERDbYB6mdGgeBVNeZALXa5fUb6rSw1DeNo6TVqKEOsX+13WG54KZyep319lqtBuo2tFejsd7fUKdt0/627dNo1FD7t62fSJ1OY30OOufPwazXwVLf0EnbLPFtcT+DyWJ1DJNe0qbzfBCd1rr9+lrHMXS2nLNqbevTOa7PFeU02nqrbcY6/3a13aTXWdWjVqthcrIPkm376lo4jwfRqGttziPQYR0ajW25oA5fL1qN2qrO+gf8/u1+Fy2UdbYcYP93VtfC35mz5RqPZ//9Y/+yiW05XQvlnFXvsD7rl0M68t2orTe1+n3RcC03vdmpVqudHrKio+0D7K+ntn53G+t0D/y3pTXOfq80Z6h18O+Z2rm//7acb+P61gZzlggc7rldrl+/jtjYWE83g4iIiNrh2rVriImJaXE7E6R2slgsuHHjBmQyGV8ldiO1Wo3Y2Fhcu3YNYWFhnm6OV2OsOwfj3HkY687TnWItCAI0Gg2io6Ph49NyTyM+YmsnHx+fB2ae5FphYWFd/o/OWzDWnYNx7jyMdefpLrGWy+WtlmEnbSIiIiIbTJCIiIiIbDBBoi4tMDAQOTk5CAxs+7xe1DaMdedgnDsPY915vDHW7KRNREREZIN3kIiIiIhsMEEiIiIissEEiYiIiMgGEyQiIiIiG0yQyOOOHz+OKVOmIDo6GhKJBLt377baLggCsrOzoVQqERwcjPT0dJSVlXmmsd3cW2+9hZEjR0Imk6F3796YNm0aSktLrcro9XpkZWWhZ8+ekEqlePbZZ1FZWemhFndfGzduRHJysjhwXmpqKvbv3y9uZ5zdIzc3FxKJBCqVSlzHWLvGmjVrIJFIrD5JSUnidm+LMxMk8jidToeUlBS8++67Dre//fbb2LBhAzZt2oTTp08jNDQUEydOhF6vd1ieWlZQUICsrCx8+eWXOHz4MIxGIyZMmACdTieWWb58OT7//HN8+umnKCgowI0bNzBjxgwPtrp7iomJQW5uLoqKinD27FmkpaVh6tSp+OabbwAwzu5w5swZvP/++0hOTrZaz1i7zuDBg1FRUSF+Tpw4IW7zujgLRF0IACEvL09ctlgsQlRUlPDOO++I66qrq4XAwEBhx44dHmihd6mqqhIACAUFBYIgNMTW399f+PTTT8UyFy9eFAAIp06d8lQzvUZERITwwQcfMM5uoNFohISEBOHw4cPCuHHjhGXLlgmCwGvalXJycoSUlBSH27wxzryDRF1aeXk5bt68ifT0dHGdXC7HqFGjcOrUKQ+2zDvU1NQAAHr06AEAKCoqgtFotIp3UlIS+vbty3h3gNlsxs6dO6HT6ZCamso4u0FWVhaefvppq5gCvKZdraysDNHR0ejfvz9mz56Nq1evAvDOOHOyWurSbt68CQBQKBRW6xUKhbiN2sdisUClUuHRRx/Fww8/DKAh3gEBAQgPD7cqy3i3z/nz55Gamgq9Xg+pVIq8vDw89NBDKC4uZpxdaOfOnTh37hzOnDljt43XtOuMGjUKW7duRWJiIioqKvDGG2/g8ccfx4ULF7wyzkyQiP5JZWVl4cKFC1Z9CMi1EhMTUVxcjJqaGnz22WeYO3cuCgoKPN0sr3Lt2jUsW7YMhw8fRlBQkKeb49UmT54s/pycnIxRo0YhLi4Of/3rXxEcHOzBlrkHH7FRlxYVFQUAdm9CVFZWituo7RYvXoy9e/ciPz8fMTEx4vqoqCgYDAZUV1dblWe82ycgIAADBgzA8OHD8dZbbyElJQXr169nnF2oqKgIVVVVGDZsGPz8/ODn54eCggJs2LABfn5+UCgUjLWbhIeHY+DAgbh8+bJXXtNMkKhL69evH6KionDkyBFxnVqtxunTp5GamurBlnVPgiBg8eLFyMvLw9GjR9GvXz+r7cOHD4e/v79VvEtLS3H16lXG2wUsFgvq6+sZZxcaP348zp8/j+LiYvEzYsQIzJ49W/yZsXYPrVaLK1euQKlUeuU1zUds5HFarRaXL18Wl8vLy1FcXIwePXqgb9++UKlUePPNN5GQkIB+/fph9erViI6OxrRp0zzX6G4qKysLH3/8Mfbs2QOZTCb2DZDL5QgODoZcLsf8+fOxYsUK9OjRA2FhYViyZAlSU1MxevRoD7e+e1m1ahUmT56Mvn37QqPR4OOPP8axY8dw8OBBxtmFZDKZ2IeuUWhoKHr27CmuZ6xd49VXX8WUKVMQFxeHGzduICcnB76+vpg1a5Z3XtOefo2OKD8/XwBg95k7d64gCA2v+q9evVpQKBRCYGCgMH78eKG0tNSzje6mHMUZgLBlyxaxTF1dnfCrX/1KiIiIEEJCQoTp06cLFRUVnmt0N/XSSy8JcXFxQkBAgBAZGSmMHz9eOHTokLidcXaf5q/5CwJj7SozZ84UlEqlEBAQIPTp00eYOXOmcPnyZXG7t8VZIgiC4KHcjIiIiKhLYh8kIiIiIhtMkIiIiIhsMEEiIiIissEEiYiIiMgGEyQiIiIiG0yQiIiIiGwwQSIiIiKywQSJiLxGRkaGR0ZY37p1KyQSCSQSCVQqlVP7ZGRkiPvs3r3bre0jorbjVCNE1C1IJJIHbs/JycH69evhqbFvw8LCUFpaitDQUKfKr1+/Hrm5uVAqlW5uGRG1BxMkIuoWKioqxJ8/+eQTZGdno7S0VFwnlUohlUo90TQADQlcW2Ytl8vlkMvlbmwREXUEH7ERUbcQFRUlfuRyuZiQNH6kUqndI7YnnngCS5YsgUqlQkREBBQKBTZv3gydTod58+ZBJpNhwIAB2L9/v9WxLly4gMmTJ0MqlUKhUGDOnDm4fft2m9v83nvvISEhAUFBQVAoFHjuuec6GgYi6iRMkIjIq/3lL39Br169UFhYiCVLlmDRokV4/vnnMWbMGJw7dw4TJkzAnDlzUFtbCwCorq5GWloahg4dirNnz+LAgQOorKzECy+80Kbjnj17FkuXLsXatWtRWlqKAwcOYOzYse44RSJyAz5iIyKvlpKSgtdffx0AsGrVKuTm5qJXr17IzMwEAGRnZ2Pjxo0oKSnB6NGj8ac//QlDhw7FunXrxDr+/Oc/IzY2FpcuXcLAgQOdOu7Vq1cRGhqKZ555BjKZDHFxcRg6dKjrT5CI3IJ3kIjIqyUnJ4s/+/r6omfPnhgyZIi4TqFQAACqqqoAAF9//TXy8/PFPk1SqRRJSUkAgCtXrjh93KeeegpxcXHo378/5syZg+3bt4t3qYio62OCRERezd/f32pZIpFYrWt8O85isQAAtFotpkyZguLiYqtPWVlZmx6RyWQynDt3Djt27IBSqUR2djZSUlJQXV3d8ZMiIrfjIzYiomaGDRuGXbt2IT4+Hn5+HfuK9PPzQ3p6OtLT05GTk4Pw8HAcPXoUM2bMcFFrichdeAeJiKiZrKws3L17F7NmzcKZM2dw5coVHDx4EPPmzYPZbHa6nr1792LDhg0oLi7GDz/8gG3btsFisSAxMdGNrSciV2GCRETUTHR0NE6ePAmz2YwJEyZgyJAhUKlUCA8Ph4+P81+Z4eHh+Nvf/oa0tDQMGjQImzZtwo4dOzB48GA3tp6IXEUieGrYWSIiL7F161aoVKp29S+SSCTIy8vzyBQpRNQy3kEiInKBmpoaSKVSrFy50qnyr7zyikdH/iaiB+MdJCKiDtJoNKisrATQ8GitV69ere5TVVUFtVoNAFAqlU7P4UZEnYMJEhEREZENPmIjIiIissEEiYiIiMgGEyQiIiIiG0yQiIiIiGwwQSIiIiKywQSJiIiIyAYTJCIiIiIbTJCIiIiIbDBBIiIiIrLx/0Fdr/VsN8UmAAAAAElFTkSuQmCC", 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" ] @@ -281,19 +319,28 @@ "plotstop = len(E_kin)\n", "plotstart = 0\n", "\n", - "plt.plot(t_vector[:plotstop],E_kin[:plotstop], label = 'Kinetic Energy [J]')\n", - "plt.plot(t_vector[plotstart:plotstop],f_int[plotstart:plotstop], label = 'Internal Forces [N]')\n", - "\n", - "# Filtering out the peaks to mark where the kinetic damping algorithm kicked in. \n", - "df = pd.DataFrame(E_kin, index = t_vector[0:plotstop])\n", - "peaksonly=df[(df[0].shift(1) < df[0]) & (df[0].shift(-1) < df[0])]\n", - "plt.scatter(peaksonly.index,peaksonly[0], c='r',linewidths=1, marker='+', label = 'Kinetic Damping Enacted')\n", + "plt.plot(t_vector[:plotstop], E_kin[:plotstop], label=\"Kinetic Energy [J]\")\n", + "plt.plot(\n", + " t_vector[plotstart:plotstop], f_int[plotstart:plotstop], label=\"Internal Forces [N]\"\n", + ")\n", + "\n", + "# Filtering out the peaks to mark where the kinetic damping algorithm kicked in.\n", + "df = pd.DataFrame(E_kin, index=t_vector[0:plotstop])\n", + "peaksonly = df[(df[0].shift(1) < df[0]) & (df[0].shift(-1) < df[0])]\n", + "plt.scatter(\n", + " peaksonly.index,\n", + " peaksonly[0],\n", + " c=\"r\",\n", + " linewidths=1,\n", + " marker=\"+\",\n", + " label=\"Kinetic Damping Enacted\",\n", + ")\n", "\n", "plt.legend()\n", - "plt.yscale('log')\n", + "plt.yscale(\"log\")\n", "plt.title(\"Convergence Plot\")\n", - "plt.xlabel('Time [s]')\n", - "plt.ylabel('Quantity of interest')\n", + "plt.xlabel(\"Time [s]\")\n", + "plt.ylabel(\"Quantity of interest\")\n", "plt.xlim(t_vector[0], t_vector[plotstop])" ] }, @@ -309,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 14, "id": "9f376722", "metadata": {}, "outputs": [ @@ -319,13 +366,13 @@ "Text(0.5, 0.92, 'Final state')" ] }, - "execution_count": 18, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -335,27 +382,32 @@ } ], "source": [ - "final_positions = [[particle.x, particle.v, particle.m, particle.fixed] for particle in PS.particles]\n", + "final_positions = [\n", + " [particle.x, particle.v, particle.m, particle.fixed] for particle in PS.particles\n", + "]\n", "\n", "# Plotting final results\n", "fig = plt.figure()\n", - "ax = fig.add_subplot(projection='3d')\n", + "ax = fig.add_subplot(projection=\"3d\")\n", "\n", "for i, node in enumerate(final_positions):\n", - " if node[3]: \n", - " ax.scatter(node[0][0],node[0][1],node[0][2], color = 'red', marker = 'o')\n", + " if node[3]:\n", + " ax.scatter(node[0][0], node[0][1], node[0][2], color=\"red\", marker=\"o\")\n", " else:\n", - " ax.scatter(node[0][0],node[0][1],node[0][2], color = 'blue', marker = 'o')\n", + " ax.scatter(node[0][0], node[0][1], node[0][2], color=\"blue\", marker=\"o\")\n", + "\n", + " ax.quiver(\n", + " *node[0].tolist(), f_ext[3 * i], f_ext[3 * i + 1], f_ext[3 * i + 2]\n", + " ) # , length = 0.3)\n", "\n", - " ax.quiver(*node[0].tolist(), f_ext[3*i], f_ext[3*i+1], f_ext[3*i+2])#, length = 0.3)\n", + "for connection in connections:\n", + " line = np.column_stack(\n", + " [final_positions[connection[0]][0], final_positions[connection[1]][0]]\n", + " )\n", "\n", - "for connection in connections: \n", - " line = np.column_stack([final_positions[connection[0]][0],\n", - " final_positions[connection[1]][0]])\n", - " \n", - " ax.plot(line[0],line[1],line[2],color='black')\n", + " ax.plot(line[0], line[1], line[2], color=\"black\")\n", "\n", - "ax.legend(['Fixed nodes', 'Forces', 'Free nodes'])\n", + "ax.legend([\"Fixed nodes\", \"Forces\", \"Free nodes\"])\n", "\n", "# Finding bounding box and setting aspect ratio\n", "xyz = np.array([particle.x for particle in PS.particles])\n", @@ -364,6 +416,14 @@ "\n", "plt.title(\"Final state\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "296b9be9", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -382,7 +442,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.11.6" } }, "nbformat": 4, diff --git a/Tutourial/Tutourial2.ipynb b/Tutourial/Tutourial2.ipynb index 9c8bbfb..4b61604 100644 --- a/Tutourial/Tutourial2.ipynb +++ b/Tutourial/Tutourial2.ipynb @@ -5,26 +5,26 @@ "id": "c218106a-606c-402c-bd8c-954b424f75b2", "metadata": {}, "source": [ - "# Tutorial 1: Basic Setup and Simulation\r\n", - "\r\n", - "## Introduction to the Project\r\n", - "\r\n", + "# Tutorial 1: Basic Setup and Simulation\n", + "\n", + "## Introduction to the Project\n", + "\n", "In this tutorial, we will set up, mesh, and simulate a basic membrane using the particle model. The aim is to provide an introductionLightSailSim. \n", "\n", - "Objectives:\r\n", - "1. Generate and modify meshes for particle systems.\r\n", - "2. Initialize and configure the particle system.\r\n", - "3. Set up optical forces and laser beams.\r\n", + "Objectives:\n", + "1. Generate and modify meshes for particle systems.\n", + "2. Initialize and configure the particle system.\n", + "3. Set up optical forces and laser beams.\n", "4. Run simulations and analyze resu\n", - "tions.sers.\r\n", - "\r\n", - "Let's get started by setting up the necessary modules and parameters for otions.\r\n", - "ur simulation.\r\n" + "tions.sers.\n", + "\n", + "Let's get started by setting up the necessary modules and parameters for otions.\n", + "ur simulation.\n" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 2, "id": "9e50042d-c4d7-4562-ade2-cbdd38114b79", "metadata": {}, "outputs": [], @@ -48,16 +48,20 @@ "from scipy.interpolate import interp1d\n", "\n", "# Add the src directory to the system path for imports\n", - "sys.path.append(os.path.abspath('..'))\n", + "sys.path.append(os.path.abspath(\"..\"))\n", "\n", "# LLS specific imports\n", - "from src.particleSystem.ParticleSystem import ParticleSystem\n", - "from src.Sim.simulations import Simulate_Lightsail\n", - "import src.Mesh.mesh_functions as MF\n", - "import src.ExternalForces.optical_interpolators.interpolators as interp\n", - "from src.ExternalForces.LaserBeam import LaserBeam\n", - "from src.ExternalForces.OpticalForceCalculator import OpticalForceCalculator\n", - "from src.ExternalForces.OpticalForceCalculator import ParticleOpticalPropertyType" + "from Particle_System_Simulator.particleSystem.ParticleSystem import ParticleSystem\n", + "from Particle_System_Simulator.Sim.simulations import Simulate_Lightsail\n", + "import Particle_System_Simulator.Mesh.mesh_functions as MF\n", + "import Particle_System_Simulator.ExternalForces.optical_interpolators.interpolators as interp\n", + "from Particle_System_Simulator.ExternalForces.LaserBeam import LaserBeam\n", + "from Particle_System_Simulator.ExternalForces.OpticalForceCalculator import (\n", + " OpticalForceCalculator,\n", + ")\n", + "from Particle_System_Simulator.ExternalForces.OpticalForceCalculator import (\n", + " ParticleOpticalPropertyType,\n", + ")" ] }, { @@ -77,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 3, "id": "ce46965f-660e-4a53-8a05-aa82413ac46d", "metadata": {}, "outputs": [], @@ -90,15 +94,13 @@ " \"m_segment\": 1, # [kg] mass of each node\n", " \"thickness\": 200e-9, # [m] thickness of PhC\n", " \"rho\": 3184, # [kg/m^3] Density of SiN\n", - " \n", " # Simulation settings\n", " \"dt\": 2e-3, # [s] simulation timestep\n", - " 'adaptive_timestepping': 2.5e-4, # [m] max distance traversed per timestep\n", + " \"adaptive_timestepping\": 2.5e-4, # [m] max distance traversed per timestep\n", " \"t_steps\": 1e3, # [-] max number of simulated time steps\n", " \"abs_tol\": 1e-20, # [m/s] absolute error tolerance iterative solver\n", " \"rel_tol\": 1e-5, # [-] relative error tolerance iterative solver\n", " \"max_iter\": int(1e2), # [-] maximum number of iterations for the bicgstab solver\n", - " \n", " # Simulation Steps\n", " \"convergence_threshold\": 5e-8, # Metric depends on size of timestep. Have to update them together.\n", " \"min_iterations\": 30, # Should exceed the size of the force ring buffer in the simulation loop\n", @@ -110,27 +112,27 @@ "id": "dac5e22b-c8c0-4ed6-a764-f12df983c2cd", "metadata": {}, "source": [ - "## Meshing\r\n", - "\r\n", + "## Meshing\n", + "\n", "For this tutorial, we will simulate a rectangular grid for the membrane. We will use the `mesh_square` function from `mesh_functions` to create the mesh.\n", - "First we have to add some physical constants to the params file, this will then allow the mesher to set the springs to the correct. We can immediately adjust them to reflect that the PhC is mostly empty space as well. nits.\r\n", - "h.\r\n" + "First we have to add some physical constants to the params file, this will then allow the mesher to set the springs to the correct. We can immediately adjust them to reflect that the PhC is mostly empty space as well. nits.\n", + "h.\n" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 4, "id": "09c28cd5-6da5-45a9-83fb-dbcc3c57eb4a", "metadata": {}, "outputs": [], "source": [ - "params['E'] = 470e9\n", - "params['G'] = 0\n", - "params['E_x'] = params['E'] * (259 + 351) / 1991 # stiff direction\n", - "params['E_y'] = params['E'] * 5 / 100 # not-so-stiff direction\n", + "params[\"E\"] = 470e9\n", + "params[\"G\"] = 0\n", + "params[\"E_x\"] = params[\"E\"] * (259 + 351) / 1991 # stiff direction\n", + "params[\"E_y\"] = params[\"E\"] * 5 / 100 # not-so-stiff direction\n", "\n", "fill_factor = 0.43\n", - "params['rho'] *= fill_factor" + "params[\"rho\"] *= fill_factor" ] }, { @@ -138,20 +140,20 @@ "id": "a75997fa-35a2-427c-bba3-e7b9a8986de7", "metadata": {}, "source": [ - "### Mesh Generation\r\n", - "\r\n", - "In this section, we generate the mesh for the particle system. The mesh defines the spatial configuration of particles and their connections. Here’s what we do:\r\n", - "1. **Generate initial mesh**: Using the `mesh_round_phc_square_cross` function to create a round photonic crystal mesh with a square cross pattern.\r\n", - "2. **Fix edge particles**: Ensure that edge particles are correctly identified and fixed if necessary.\r\n", - "3. **Add support structure**: Introduce additional particles and links to act as a support structure, ensuring stability and accuracy in simulations.\r\n", - "\r\n", - "The mesh is a critical component that defines the structure and connectivity of the particles in the system.\r\n", - ".\r\n" + "### Mesh Generation\n", + "\n", + "In this section, we generate the mesh for the particle system. The mesh defines the spatial configuration of particles and their connections. Here’s what we do:\n", + "1. **Generate initial mesh**: Using the `mesh_round_phc_square_cross` function to create a round photonic crystal mesh with a square cross pattern.\n", + "2. **Fix edge particles**: Ensure that edge particles are correctly identified and fixed if necessary.\n", + "3. **Add support structure**: Introduce additional particles and links to act as a support structure, ensuring stability and accuracy in simulations.\n", + "\n", + "The mesh is a critical component that defines the structure and connectivity of the particles in the system.\n", + ".\n" ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 5, "id": "f35b38f9-b4d1-4d8b-91a4-3284f17bd389", "metadata": {}, "outputs": [], @@ -160,13 +162,17 @@ "n_segments = 25 # Make sure this is uneven so there are no particles on the centerline\n", "radius = 0.5 # [m]\n", "length = 2 * radius\n", - "fixed_edge_width = radius / n_segments * 2 # thickness of the region from the outside in to fix\n", + "fixed_edge_width = (\n", + " radius / n_segments * 2\n", + ") # thickness of the region from the outside in to fix\n", "\n", - "mesh = MF.mesh_phc_square_cross(length,\n", - " mesh_edge_length=length / n_segments,\n", - " params=params,\n", - " noncompressive=True,\n", - " fix_outer=True)" + "mesh = MF.mesh_phc_square_cross(\n", + " length,\n", + " mesh_edge_length=length / n_segments,\n", + " params=params,\n", + " noncompressive=True,\n", + " fix_outer=True,\n", + ")" ] }, { @@ -181,18 +187,18 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 6, "id": "5a9fa770-a61e-4109-af76-b56ae5511484", "metadata": {}, "outputs": [], "source": [ "# Add some particles to act as a support structure\n", - "stiffness_support = 5.93E+07 # [N/m*m] line stiffness\n", + "stiffness_support = 5.93e07 # [N/m*m] line stiffness\n", "\n", "n_fixed = sum([i[3] for i in mesh[1]])\n", "circumference = 2 * np.pi * radius\n", "k_support = stiffness_support * (circumference / n_fixed)\n", - "l_support = length / n_segments \n", + "l_support = length / n_segments\n", "multiplier = (radius + l_support) / radius" ] }, @@ -206,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 7, "id": "8b209188-f6b5-4b69-bd0e-73a8a2bbd3ef", "metadata": {}, "outputs": [], @@ -215,9 +221,9 @@ "for i, node in enumerate(mesh[1]):\n", " xyz = node[0].copy()\n", " if node[3]:\n", - " node.append([0, 0, 1]) # Define orientation of constraint\n", - " node.append('plane') # Define type of constraint\n", - " particle = [xyz * multiplier, np.zeros(3), params['m_segment'], True]\n", + " node.append([0, 0, 1]) # Define orientation of constraint\n", + " node.append(\"plane\") # Define type of constraint\n", + " particle = [xyz * multiplier, np.zeros(3), params[\"m_segment\"], True]\n", " link = [i, len(mesh[1]) + len(new_particles), k_support, 1]\n", " new_particles.append(particle)\n", " mesh[0].append(link)\n", @@ -237,13 +243,13 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 8, "id": "f18c9479-cdda-4d14-a62c-89c8e1d90b41", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -253,9 +259,11 @@ } ], "source": [ - "PS = ParticleSystem(*mesh, params) # the * operator unpacks the mesh into the two sublists\n", - "fig = plt.figure(figsize = [12,8])\n", - "ax = fig.add_subplot(projection = '3d') \n", + "PS = ParticleSystem(\n", + " *mesh, params\n", + ") # the * operator unpacks the mesh into the two sublists\n", + "fig = plt.figure(figsize=[12, 8])\n", + "ax = fig.add_subplot(projection=\"3d\")\n", "ax = PS.plot(ax)\n", "ax.set_zlim(-1, 1)\n", "ax.set_title(\"Initial state\")\n", @@ -272,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 9, "id": "ae896668-04e9-4f16-9dc9-32d0a66a6db4", "metadata": {}, "outputs": [ @@ -285,7 +293,7 @@ } ], "source": [ - "PS.calculate_correct_masses(params['thickness'], params['rho'])\n", + "PS.calculate_correct_masses(params[\"thickness\"], params[\"rho\"])\n", "print(f\"Total mass: {sum([p.m for p in PS.particles])*1e3:.3g} [g]\")" ] }, @@ -299,15 +307,15 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 10, "id": "1dc2414c-50aa-40b4-9bdb-5457f91179d3", "metadata": {}, "outputs": [], "source": [ "# Adding pre_stress\n", - "pre_stress = 30e6 # [Pa]\n", - "pre_strain = pre_stress / max(params['E_x'],params['E_y'])\n", - "shrink_factor = 1/(1+pre_strain)\n", + "pre_stress = 30e6 # [Pa]\n", + "pre_strain = pre_stress / max(params[\"E_x\"], params[\"E_y\"])\n", + "shrink_factor = 1 / (1 + pre_strain)\n", "PS.stress_self(shrink_factor)" ] }, @@ -323,32 +331,34 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 15, "id": "d7e7549d-7470-445a-836e-9325577eec9a", "metadata": {}, "outputs": [], "source": [ "# import the photonic crystal(s)\n", - "gao = interp.PhC_library['Gao']\n", - "# modify the path because the current working directory is not root:\n", - "gao = '../'+ gao\n", + "gao = interp.PhC_library[\"Gao\"]\n", "specular = ParticleOpticalPropertyType.SPECULAR\n", "offset = np.pi\n", "# phi_start, phi_stop, r_start, r_stop, midline, PhC, rotational offset\n", - "regions = [[0, np.pi*2, 0, length/3, np.pi*1/4, specular, offset],\n", - " [0, np.pi/2, length/3, length*2, np.pi*1/4, gao, offset],\n", - " [np.pi/2, np.pi, length/3, length*2, np.pi*3/4, gao, offset],\n", - " [np.pi, np.pi*3/2, length/3, length*2, np.pi*5/4, gao, offset],\n", - " [np.pi*3/2, np.pi*2, length/3, length*2, np.pi*7/4, gao, offset]]\n", + "regions = [\n", + " [0, np.pi * 2, 0, length / 3, np.pi * 1 / 4, specular, offset],\n", + " [0, np.pi / 2, length / 3, length * 2, np.pi * 1 / 4, gao, offset],\n", + " [np.pi / 2, np.pi, length / 3, length * 2, np.pi * 3 / 4, gao, offset],\n", + " [np.pi, np.pi * 3 / 2, length / 3, length * 2, np.pi * 5 / 4, gao, offset],\n", + " [np.pi * 3 / 2, np.pi * 2, length / 3, length * 2, np.pi * 7 / 4, gao, offset],\n", + "]\n", "\n", "for reg in regions:\n", - " if type(reg[5])==str:\n", - " reg[5] = interp.create_interpolator(reg[5], reg[4]+reg[6]) # Set interpolator with the right rotation\n", + " if type(reg[5]) == str:\n", + " reg[5] = interp.create_interpolator(\n", + " reg[5], reg[4] + reg[6]\n", + " ) # Set interpolator with the right rotation\n", "\n", "# Now we apply them to the PS\n", "for p in PS.particles:\n", - " x,y,z = p.x\n", - " phi = np.arctan2(y,x)%(2*np.pi)\n", + " x, y, z = p.x\n", + " phi = np.arctan2(y, x) % (2 * np.pi)\n", " r = np.linalg.norm(p.x)\n", " for reg in regions:\n", " if r >= reg[2] and r <= reg[3] and phi <= reg[1] and phi >= reg[0]:\n", @@ -356,7 +366,7 @@ " p.optical_type = ParticleOpticalPropertyType.SPECULAR\n", " else:\n", " p.optical_type = ParticleOpticalPropertyType.ARBITRARY_PHC\n", - " p.optical_interpolator = reg[5]\n" + " p.optical_interpolator = reg[5]" ] }, { @@ -369,24 +379,31 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 16, "id": "1317d109-9733-4e1f-8da5-346861dc1bf9", "metadata": {}, "outputs": [], "source": [ "# init optical system\n", - "P = 400/2# [W] 400 divided by two because superposition of two orthogonally polarised beams\n", + "P = (\n", + " 400 / 2\n", + ") # [W] 400 divided by two because superposition of two orthogonally polarised beams\n", "\n", "# if you want to check, set it all to specular and set sigma to radius/3\n", "# net force should be P_original/c*2*2 (*2 for reflection, *2 for the second laser)\n", "mu_x = 0\n", "mu_y = 0\n", - "sigma = radius*1\n", - "I_0 = 2*P / (np.pi* sigma**2)\n", + "sigma = radius * 1\n", + "I_0 = 2 * P / (np.pi * sigma**2)\n", "\n", - "LB = LaserBeam(lambda x, y: I_0 * np.exp(-1/2 *((x-mu_x)/sigma)**2 # gaussian laser\n", - " -1/2 *((y-mu_y)/sigma)**2),\n", - " lambda x,y: np.outer(np.ones(x.shape),[0,1]))\n", + "LB = LaserBeam(\n", + " lambda x, y: I_0\n", + " * np.exp(\n", + " -1 / 2 * ((x - mu_x) / sigma) ** 2 # gaussian laser\n", + " - 1 / 2 * ((y - mu_y) / sigma) ** 2\n", + " ),\n", + " lambda x, y: np.outer(np.ones(x.shape), [0, 1]),\n", + ")\n", "OFC = OpticalForceCalculator(PS, LB)" ] }, @@ -400,29 +417,29 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 18, "id": "fd768e85-7628-4738-b62e-cc310a5cb46f", "metadata": {}, "outputs": [ { "ename": "IndexError", - "evalue": "boolean index did not match indexed array along dimension 0; dimension is 560 but corresponding boolean dimension is 776", + "evalue": "boolean index did not match indexed array along axis 0; size of axis is 560 but size of corresponding boolean axis is 776", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[120], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m forces \u001b[38;5;241m=\u001b[39m \u001b[43mOFC\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforce_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2\u001b[0m fig \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mfigure(figsize \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m16\u001b[39m,\u001b[38;5;241m20\u001b[39m])\n\u001b[0;32m 3\u001b[0m ax \u001b[38;5;241m=\u001b[39m fig\u001b[38;5;241m.\u001b[39madd_subplot(projection \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m3d\u001b[39m\u001b[38;5;124m'\u001b[39m) \n", - "File \u001b[1;32mD:\\Documents\\OBSIDIAN\\Projects\\Lightsails\\Thesis Code\\LightSailSim\\src\\ExternalForces\\OpticalForceCalculator.py:88\u001b[0m, in \u001b[0;36mOpticalForceCalculator.force_value\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 84\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124moptical_interpolators\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m 85\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcreate_phc_map(mask)\n\u001b[1;32m---> 88\u001b[0m forces[mask] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcalculate_arbitrary_phc_force\u001b[49m\u001b[43m(\u001b[49m\u001b[43marea_vectors\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmask\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 89\u001b[0m \u001b[43m \u001b[49m\u001b[43mintensity_vectors\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmask\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 90\u001b[0m \u001b[43m \u001b[49m\u001b[43mpolarisation_vectors\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmask\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 91\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptical_interpolators\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 92\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forces\n", - "File \u001b[1;32mD:\\Documents\\OBSIDIAN\\Projects\\Lightsails\\Thesis Code\\LightSailSim\\src\\ExternalForces\\OpticalForceCalculator.py:243\u001b[0m, in \u001b[0;36mOpticalForceCalculator.calculate_arbitrary_phc_force\u001b[1;34m(self, area_vectors, intensity_vectors, polarisation_vectors, optical_interpolators)\u001b[0m\n\u001b[0;32m 241\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m phc \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mphc_dict:\n\u001b[0;32m 242\u001b[0m submask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mphc_dict[phc]\n\u001b[1;32m--> 243\u001b[0m reflected_ray[submask] \u001b[38;5;241m=\u001b[39m phc(\u001b[43mincoming_ray\u001b[49m\u001b[43m[\u001b[49m\u001b[43msubmask\u001b[49m\u001b[43m]\u001b[49m)\n\u001b[0;32m 246\u001b[0m \u001b[38;5;66;03m# reflected_ray = [interp(incoming_ray[i])\u001b[39;00m\n\u001b[0;32m 247\u001b[0m \u001b[38;5;66;03m# reflected_ray = [interp(tuple(incoming_ray[i]))\u001b[39;00m\n\u001b[0;32m 248\u001b[0m \u001b[38;5;66;03m# for i, interp\u001b[39;00m\n\u001b[0;32m 249\u001b[0m \u001b[38;5;66;03m# in enumerate(optical_interpolators)] # [polar_out, azimuth_out, magnitude]\u001b[39;00m\n\u001b[0;32m 250\u001b[0m polar_angles_out, azimuth_angles_out, magnitudes \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(reflected_ray)\u001b[38;5;241m.\u001b[39mT\n", - "\u001b[1;31mIndexError\u001b[0m: boolean index did not match indexed array along dimension 0; dimension is 560 but corresponding boolean dimension is 776" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m forces \u001b[38;5;241m=\u001b[39m \u001b[43mOFC\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforce_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m fig \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m16\u001b[39m, \u001b[38;5;241m20\u001b[39m])\n\u001b[1;32m 3\u001b[0m ax \u001b[38;5;241m=\u001b[39m fig\u001b[38;5;241m.\u001b[39madd_subplot(projection\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m3d\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/src/Particle_System_Simulator/ExternalForces/OpticalForceCalculator.py:88\u001b[0m, in \u001b[0;36mOpticalForceCalculator.force_value\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124moptical_interpolators\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 85\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcreate_phc_map(mask)\n\u001b[0;32m---> 88\u001b[0m forces[mask] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcalculate_arbitrary_phc_force\u001b[49m\u001b[43m(\u001b[49m\u001b[43marea_vectors\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmask\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 89\u001b[0m \u001b[43m \u001b[49m\u001b[43mintensity_vectors\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmask\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 90\u001b[0m \u001b[43m \u001b[49m\u001b[43mpolarisation_vectors\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmask\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 91\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptical_interpolators\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 92\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forces\n", + "File \u001b[0;32m~/ownCloud/phd/code/Particle_System_Simulator/src/Particle_System_Simulator/ExternalForces/OpticalForceCalculator.py:243\u001b[0m, in \u001b[0;36mOpticalForceCalculator.calculate_arbitrary_phc_force\u001b[0;34m(self, area_vectors, intensity_vectors, polarisation_vectors, optical_interpolators)\u001b[0m\n\u001b[1;32m 241\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m phc \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mphc_dict:\n\u001b[1;32m 242\u001b[0m submask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mphc_dict[phc]\n\u001b[0;32m--> 243\u001b[0m reflected_ray[submask] \u001b[38;5;241m=\u001b[39m phc(\u001b[43mincoming_ray\u001b[49m\u001b[43m[\u001b[49m\u001b[43msubmask\u001b[49m\u001b[43m]\u001b[49m)\n\u001b[1;32m 246\u001b[0m \u001b[38;5;66;03m# reflected_ray = [interp(incoming_ray[i])\u001b[39;00m\n\u001b[1;32m 247\u001b[0m \u001b[38;5;66;03m# reflected_ray = [interp(tuple(incoming_ray[i]))\u001b[39;00m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;66;03m# for i, interp\u001b[39;00m\n\u001b[1;32m 249\u001b[0m \u001b[38;5;66;03m# in enumerate(optical_interpolators)] # [polar_out, azimuth_out, magnitude]\u001b[39;00m\n\u001b[1;32m 250\u001b[0m polar_angles_out, azimuth_angles_out, magnitudes \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(reflected_ray)\u001b[38;5;241m.\u001b[39mT\n", + "\u001b[0;31mIndexError\u001b[0m: boolean index did not match indexed array along axis 0; size of axis is 560 but size of corresponding boolean axis is 776" ] } ], "source": [ "forces = OFC.force_value()\n", - "fig = plt.figure(figsize = [16,20])\n", - "ax = fig.add_subplot(projection = '3d') \n", - "PS.plot_forces(forces, ax=ax, length = 1e8)" + "fig = plt.figure(figsize=[16, 20])\n", + "ax = fig.add_subplot(projection=\"3d\")\n", + "PS.plot_forces(forces, ax=ax, length=1e8)" ] }, { @@ -638,8 +655,8 @@ "metadata": {}, "outputs": [], "source": [ - "fig = plt.figure(figsize = [16,20])\n", - "ax = fig.add_subplot(projection = '3d') " + "fig = plt.figure(figsize=[16, 20])\n", + "ax = fig.add_subplot(projection=\"3d\")" ] }, { @@ -647,13 +664,13 @@ "id": "52f7c92e-16ae-48df-bb86-2839bfc14c11", "metadata": {}, "source": [ - "## Importing and Assigning a Photonic Crystal (PhC)\r\n", - "\r\n", - "In this section, we will import a Photonic Crystal (PhC) and assign it to the particles in our mesh. This will involve using the `interpolators` module from the `optical_interpolators` subpackage.\r\n", - "\r\n", - "### Step 1: Import the Photonic Crystal Data\r\n", - "\r\n", - "First, we need to import the necessary modules and load the PhC data.\r\n" + "## Importing and Assigning a Photonic Crystal (PhC)\n", + "\n", + "In this section, we will import a Photonic Crystal (PhC) and assign it to the particles in our mesh. This will involve using the `interpolators` module from the `optical_interpolators` subpackage.\n", + "\n", + "### Step 1: Import the Photonic Crystal Data\n", + "\n", + "First, we need to import the necessary modules and load the PhC data.\n" ] }, { @@ -664,10 +681,14 @@ "outputs": [], "source": [ "# Importing necessary modules for optical interpolators\n", - "from src.ExternalForces.optical_interpolators import interpolators as interp\n", + "from Particle_System_Simulator.ExternalForces.optical_interpolators import (\n", + " interpolators as interp,\n", + ")\n", "\n", "# Load the Photonic Crystal (PhC) data\n", - "phc_data = interp.PhC_library['Gao'] # Replace 'Gao' with the appropriate key for your PhC data" + "phc_data = interp.PhC_library[\n", + " \"Gao\"\n", + "] # Replace 'Gao' with the appropriate key for your PhC data" ] }, { @@ -675,9 +696,9 @@ "id": "3d2a9ddf-c93f-4ba6-9e87-48a30e49a17e", "metadata": {}, "source": [ - "### Step 2: Assign the PhC to Particles\r\n", - "\r\n", - "Next, we will assign the PhC properties to the particles in our mesh. We will iterate over the particles and set their optical properties using the loaded PhC dat.\r\n" + "### Step 2: Assign the PhC to Particles\n", + "\n", + "Next, we will assign the PhC properties to the particles in our mesh. We will iterate over the particles and set their optical properties using the loaded PhC dat.\n" ] }, { @@ -745,7 +766,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.11.6" } }, "nbformat": 4, diff --git a/code_Validation/airbag_problem/airbag_square_cross.py b/code_Validation/airbag_problem/airbag_square_cross.py index e52b7a3..a81d0cb 100644 --- a/code_Validation/airbag_problem/airbag_square_cross.py +++ b/code_Validation/airbag_problem/airbag_square_cross.py @@ -58,7 +58,7 @@ initial_conditions, connections = MF.mesh_airbag_square_cross( half_width, mesh_edge_length=edge_length, params=params, noncompressive=True ) -PS = ParticleSystem(connections, initial_conditions, params) +PS = ParticleSystem(connections, initial_conditions, params, clean_particles=True) Sim = Simulate_airbag(PS, params) diff --git a/code_Validation/hencky_problem/hencky_problem.py b/code_Validation/hencky_problem/hencky_problem.py index 8ccf226..7a9e8a3 100644 --- a/code_Validation/hencky_problem/hencky_problem.py +++ b/code_Validation/hencky_problem/hencky_problem.py @@ -1,21 +1,24 @@ """ Script for PS framework validation, benchmark case where saddle form of self stressed network is sought """ + import numpy as np import hencky_problem_input as input import matplotlib.pyplot as plt import pandas as pd import time -from Msc_Alexander_Batchelor.src.particleSystem.ParticleSystem import ParticleSystem +from Particle_System_Simulator.particleSystem.ParticleSystem import ParticleSystem def instantiate_ps(): - return ParticleSystem(input.c_matrix, input.init_cond, input.element_param, input.params) + return ParticleSystem( + input.c_matrix, input.init_cond, input.element_param, input.params + ) def force(ps: ParticleSystem): - p_t = input.p # transverse uniform pressure - e_l = input.element_list # list containing particles forming quadrilateral elements + p_t = input.p # transverse uniform pressure + e_l = input.element_list # list containing particles forming quadrilateral elements particles = ps.particles vectors = [] @@ -28,7 +31,7 @@ def force(ps: ParticleSystem): v1 = p2 - p1 v2 = p3 - p2 normal_vector = np.cross(v1, v2) - normal_vector = normal_vector/np.linalg.norm(normal_vector) + normal_vector = normal_vector / np.linalg.norm(normal_vector) x_avg = (p1[0] + p2[0] + p3[0] + p4[0]) / 4.0 y_avg = (p1[1] + p2[1] + p3[1] + p4[1]) / 4.0 z_avg = (p1[2] + p2[2] + p3[2] + p4[2]) / 4.0 @@ -39,10 +42,12 @@ def force(ps: ParticleSystem): def calc_f(ps: ParticleSystem): - p_t = input.p # transverse uniform pressure - e_l = input.element_list # list containing particles forming quadrilateral elements + p_t = input.p # transverse uniform pressure + e_l = input.element_list # list containing particles forming quadrilateral elements particles = ps.particles - force_vector = np.zeros(len(particles)*3, ) + force_vector = np.zeros( + len(particles) * 3, + ) for element in e_l: p1 = particles[element[0]].x @@ -52,35 +57,58 @@ def calc_f(ps: ParticleSystem): v1 = p2 - p1 v2 = p3 - p2 normal_vector = np.cross(v1, v2) - normal_vector = normal_vector/np.linalg.norm(normal_vector) + normal_vector = normal_vector / np.linalg.norm(normal_vector) area = np.linalg.norm(normal_vector) / 2.0 - force_value = area*p_t + force_value = area * p_t for corner_particle_index in element: - force_vector[corner_particle_index*3:(corner_particle_index+1)*3] += 0.25*force_value*normal_vector + force_vector[ + corner_particle_index * 3 : (corner_particle_index + 1) * 3 + ] += (0.25 * force_value * normal_vector) return force_vector def analytical_solution(a_n, radius, loading_param): - c = [1, 0.982, 0.912, 0.842, 0.772, 0.702, 0.632, 0.561, 0.491, 0.421, 0.351, 0.281, 0.211, 0.140, 0.07, 0.] + c = [ + 1, + 0.982, + 0.912, + 0.842, + 0.772, + 0.702, + 0.632, + 0.561, + 0.491, + 0.421, + 0.351, + 0.281, + 0.211, + 0.140, + 0.07, + 0.0, + ] w = [] for coordinate in c: series = 0 for i in range(len(a_n)): - series += a_n[i] * (1 - coordinate ** (2*i + 2)) - w.append(loading_param ** (1/3) * series) + series += a_n[i] * (1 - coordinate ** (2 * i + 2)) + w.append(loading_param ** (1 / 3) * series) # print(np.around(w, 4)) - w = np.array(w)*radius#*input.d + w = np.array(w) * radius # *input.d # print(np.around(w, 5)) # print(c) return w, c def plot(psystem: ParticleSystem, psystem2: ParticleSystem): - n = input.params['n'] - t_vector = np.linspace(input.params["dt"], input.params["t_steps"] * input.params["dt"], input.params["t_steps"]) + n = input.params["n"] + t_vector = np.linspace( + input.params["dt"], + input.params["t_steps"] * input.params["dt"], + input.params["t_steps"], + ) x = {} for i in range(n): @@ -98,42 +126,48 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem): f_ext = calc_f(ps) start_time = time.time() - for step in t_vector: # propagating the simulation for each timestep and saving results + for ( + step + ) in t_vector: # propagating the simulation for each timestep and saving results print(step) f_ext = calc_f(ps) position.loc[step], _ = psystem.simulate(f_ext) - for i, particle in enumerate(ps.particles): # need to exclude fixed particles for force-based convergence + for i, particle in enumerate( + ps.particles + ): # need to exclude fixed particles for force-based convergence if particle.fixed: - f_ext[i*3:(i+1)*3] = 0 + f_ext[i * 3 : (i + 1) * 3] = 0 final_step = step # break - if np.linalg.norm(psystem.f_int+f_ext) <= 1e-3: + if np.linalg.norm(psystem.f_int + f_ext) <= 1e-3: print("Classic PS converged") break stop_time = time.time() start_time2 = time.time() - for step in t_vector: # propagating the simulation for each timestep and saving results + for ( + step + ) in t_vector: # propagating the simulation for each timestep and saving results print(step) f_ext = calc_f(psystem2) position2.loc[step], _ = psystem2.kin_damp_sim(f_ext) for i, particle in enumerate(psystem2.particles): if particle.fixed: - f_ext[i*3:(i+1)*3] = 0 + f_ext[i * 3 : (i + 1) * 3] = 0 final_step2 = step - if np.linalg.norm(psystem2.f_int+f_ext) <= 1e-3: + if np.linalg.norm(psystem2.f_int + f_ext) <= 1e-3: print("Kinetic damping PS converged") break stop_time2 = time.time() - print(f'PS classic: {(stop_time - start_time):.4f} s') - print(f'PS kinetic: {(stop_time2 - start_time2):.4f} s') + print(f"PS classic: {(stop_time - start_time):.4f} s") + print(f"PS kinetic: {(stop_time2 - start_time2):.4f} s") # plotting & graph configuration fig = plt.figure(figsize=plt.figaspect(0.5)) ax = fig.add_subplot(1, 2, 1, projection="3d") ax2 = fig.add_subplot(1, 2, 2, projection="3d") - labels = ['Particle', 'Spring damper element', 'Analytical solution'] + labels = ["Particle", "Spring damper element", "Analytical solution"] handles = [] # Data final step PS viscous damping @@ -156,33 +190,48 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem): # Data analytical solution deflection, radial_distance = analytical_solution(input.a, input.r, input.q) - circle = np.linspace(0, 2*np.pi, 361) + circle = np.linspace(0, 2 * np.pi, 361) # Plot result PS viscous damping - nodes = ax.scatter(X, Y, Z, c='red', label=labels[0]) + nodes = ax.scatter(X, Y, Z, c="red", label=labels[0]) handles.append(nodes) for i, indices in enumerate(input.c_matrix): - line = ax.plot([X[indices[0]], X[indices[1]]], [Y[indices[0]], Y[indices[1]]], [Z[indices[0]], Z[indices[1]]], - color='black', label=labels[1]) + line = ax.plot( + [X[indices[0]], X[indices[1]]], + [Y[indices[0]], Y[indices[1]]], + [Z[indices[0]], Z[indices[1]]], + color="black", + label=labels[1], + ) if i == 0: handles.append(line[0]) # Add analytical result to plot for i, distance in enumerate(radial_distance): - x = np.cos(circle)*(distance*input.r) - y = np.sin(circle)*(distance*input.r) - z = np.ones(len(x),)*deflection[i] - line = ax.plot(x, y, z, color='green', label=labels[2]) - ax2.plot(x, y, z, color='green', label=labels[2]) + x = np.cos(circle) * (distance * input.r) + y = np.sin(circle) * (distance * input.r) + z = ( + np.ones( + len(x), + ) + * deflection[i] + ) + line = ax.plot(x, y, z, color="green", label=labels[2]) + ax2.plot(x, y, z, color="green", label=labels[2]) if i == 0: handles.append(line[0]) # Plot result PS kinetic damping - ax2.scatter(X_f, Y_f, Z_f, c='red', label=labels[0]) + ax2.scatter(X_f, Y_f, Z_f, c="red", label=labels[0]) for indices in input.c_matrix: - ax2.plot([X_f[indices[0]], X_f[indices[1]]], [Y_f[indices[0]], Y_f[indices[1]]], [Z_f[indices[0]], - Z_f[indices[1]]], color='black', label=labels[1]) + ax2.plot( + [X_f[indices[0]], X_f[indices[1]]], + [Y_f[indices[0]], Y_f[indices[1]]], + [Z_f[indices[0]], Z_f[indices[1]]], + color="black", + label=labels[1], + ) ax.set_title("PS viscous damping") ax2.set_title("PS kinetic damping") diff --git a/code_Validation/hencky_problem/hencky_problem_input.py b/code_Validation/hencky_problem/hencky_problem_input.py index ae76334..b62d584 100644 --- a/code_Validation/hencky_problem/hencky_problem_input.py +++ b/code_Validation/hencky_problem/hencky_problem_input.py @@ -1,9 +1,10 @@ """ Input file for validation of PS, Hencky problem """ + import numpy as np -filename = 'hencky_mesh.msh' # mesh file +filename = "hencky_mesh_finer_mesh.msh" # mesh file # Matlab code to calculate value of b_0, as Python's sympy library is way too slow to calculate numeric values: """ @@ -31,41 +32,41 @@ # 0.4, 1.7769 b_0 = 1.6204 -r = 0.1425 # [m] radius circular membrane -p = 100 # [kPa] uniform transverse pressure -E_t = 311488 # [N/m] Young's modules membrane material -d = 10 # [m] Membrane thickness +r = 0.1425 # [m] radius circular membrane +p = 100 # [kPa] uniform transverse pressure +E_t = 311488 # [N/m] Young's modules membrane material +d = 10 # [m] Membrane thickness q = (p * r) / E_t # for n = 10, first 11 relations of the a_2n parameter a0 = 1 / b_0 -a2 = 1 / (2 * b_0 ** 4) -a4 = 5 / (9 * b_0 ** 7) -a6 = 55 / (72 * b_0 ** 10) -a8 = 7 / (6 * b_0 ** 13) -a10 = 205 / (108 * b_0 ** 16) -a12 = 17051 / (5292 * b_0 ** 19) -a14 = 2864485 / (508032 * b_0 ** 22) -a16 = 103863265 / (10287648 * b_0 ** 25) -a18 = 27047983 / (1469664 * b_0 ** 28) -a20 = 42367613873 / (1244805408 * b_0 ** 31) +a2 = 1 / (2 * b_0**4) +a4 = 5 / (9 * b_0**7) +a6 = 55 / (72 * b_0**10) +a8 = 7 / (6 * b_0**13) +a10 = 205 / (108 * b_0**16) +a12 = 17051 / (5292 * b_0**19) +a14 = 2864485 / (508032 * b_0**22) +a16 = 103863265 / (10287648 * b_0**25) +a18 = 27047983 / (1469664 * b_0**28) +a20 = 42367613873 / (1244805408 * b_0**31) a = [a0, a2, a4, a6, a8, a10, a12, a14, a16, a18, a20] def cm_and_ic(mesh_file, m, E, c): coordinates = [] connections = [] - fixed = 20 # write automation later when I have more experience with the formatting of .msh files + fixed = 20 # write automation later when I have more experience with the formatting of .msh files n = 0 # read mesh file - with open(mesh_file, 'r') as file: + with open(mesh_file, "r") as file: lines = file.readlines() for i, line in enumerate(lines): - if line.startswith("$Nodes"): # retrieve nodal coordinates - entity_bloc, nodes_total, min_node_tag, max_node_tag = lines[i+1].split() + if line.startswith("$Nodes"): # retrieve nodal coordinates + entity_bloc, nodes_total, min_node_tag, max_node_tag = lines[i + 1].split() n = int(nodes_total) total_lines = (int(entity_bloc) + int(nodes_total)) * 2 + 1 for j in range(1, total_lines): @@ -73,60 +74,62 @@ def cm_and_ic(mesh_file, m, E, c): coordinate = lines[i + j].split() coordinates.append([float(coordinate[i]) for i in range(3)]) - if line.startswith("$Elements"): # retrieve nodal connections + if line.startswith("$Elements"): # retrieve nodal connections entity_bloc, nodes_total, min_node_tag, max_node_tag = lines[i + 1].split() total_lines = int(entity_bloc) + int(nodes_total) + 2 for j in range(1, total_lines): if len(lines[i + j].split()) != 4: connection = lines[i + j].split() - connections.append([int(connection[i]) for i in range(1, len(connection))]) + connections.append( + [int(connection[i]) for i in range(1, len(connection))] + ) - i_c = [] # construct initial conditions matrix: [x, v, m, fixed] + i_c = [] # construct initial conditions matrix: [x, v, m, fixed] for i in range(n): if i < fixed: i_c.append([coordinates[i], [0, 0, 0], m, True]) else: i_c.append([coordinates[i], [0, 0, 0], m, False]) - c_m = [] # construct connectivity matrix: [[index p1, index p2], ...] + c_m = [] # construct connectivity matrix: [[index p1, index p2], ...] for element in connections: for i in range(len(element)): if i + 1 == len(element): - c_m.append([element[i]-1, element[0]-1]) + c_m.append([element[i] - 1, element[0] - 1]) else: - c_m.append([element[i]-1, element[i+1]-1]) + c_m.append([element[i] - 1, element[i + 1] - 1]) c_m = list(set(tuple(sorted(pair)) for pair in c_m)) c_m = [list(pair) for pair in c_m if pair[0] != pair[1]] - e_p = [] # construct element parameter array: [k, l0, c] + e_p = [] # construct element parameter array: [k, l0, c] for nodes in c_m: node1, node2 = nodes[0], nodes[1] A = 0 for element in connections: - if node1+1 in element and node2+1 in element and len(element) > 2: + if node1 + 1 in element and node2 + 1 in element and len(element) > 2: # print("nodes:", node1+1, node2+1) # print("element:", element) - p1 = element.index(node1+1) - p2 = element.index(node2+1) + p1 = element.index(node1 + 1) + p2 = element.index(node2 + 1) all_indices = list(range(len(element))) all_indices.remove(p1) all_indices.remove(p2) p3, p4 = all_indices # print(p1, p2, p3, p4) - p1 = np.array(i_c[element[p1]-1][0]) - p2 = np.array(i_c[element[p2]-1][0]) - p3 = np.array(i_c[element[p3]-1][0]) - p4 = np.array(i_c[element[p4]-1][0]) + p1 = np.array(i_c[element[p1] - 1][0]) + p2 = np.array(i_c[element[p2] - 1][0]) + p3 = np.array(i_c[element[p3] - 1][0]) + p4 = np.array(i_c[element[p4] - 1][0]) # print("p1:", p1) # print("p2:", p2) # print("p3:", p3) # print("p4:", p4) # print() - v1 = np.linalg.norm(p1-p2) - v2 = np.linalg.norm(p3-p4) + v1 = np.linalg.norm(p1 - p2) + v2 = np.linalg.norm(p3 - p4) length = 0.5 * (v1 + v2) A += 0.5 * length * d # print("A", A) @@ -134,11 +137,13 @@ def cm_and_ic(mesh_file, m, E, c): l0 = np.linalg.norm(np.array(i_c[node1][0]) - np.array(i_c[node2][0])) # A = np.sqrt(A) # A = A*d - k = E*A/l0 #* (2 * r / l0) + k = E * A / l0 # * (2 * r / l0) # print(k) e_p.append([k, l0, c]) - connections = np.array([connection for connection in connections if len(connection) == 4])-1 + connections = ( + np.array([connection for connection in connections if len(connection) == 4]) - 1 + ) return c_m, i_c, e_p, n, connections @@ -151,30 +156,30 @@ def cm_and_ic(mesh_file, m, E, c): "L": 10, # [m] tether length "m_block": 100, # [kg] mass attached to end of tether "rho_tether": 0.1, # [kg/m] mass density tether - # simulation settings "dt": 1, # [s] simulation timestep "t_steps": 1000, # [-] number of simulated time steps "abs_tol": 1e-50, # [m/s] absolute error tolerance iterative solver "rel_tol": 1e-5, # [-] relative error tolerance iterative solver "max_iter": 1e5, # [-] maximum number of iterations - # physical parameters "g": 9.807, # [m/s**2] gravitational acceleration "v_w": [5, 0, 0], # [m/s] wind velocity vector - 'rho': 1.225, # [kg/ m3] air density - 'c_d_bridle': 1.05, # [-] drag-coefficient of bridles - "d_bridle": 0.02 # [m] diameter of bridle lines + "rho": 1.225, # [kg/ m3] air density + "c_d_bridle": 1.05, # [-] drag-coefficient of bridles + "d_bridle": 0.02, # [m] diameter of bridle lines } # calculated parameters -params["l0"] = 0#np.sqrt( 2 * (grid_length/(grid_size-1))**2) +params["l0"] = 0 # np.sqrt( 2 * (grid_length/(grid_size-1))**2) params["m_segment"] = 1 -params["k"] = E_t*d +params["k"] = E_t * d # instantiate connectivity matrix and initial conditions array -c_matrix, init_cond, element_param, params["n"], element_list = cm_and_ic(filename, 1, E_t, params["c"]) +c_matrix, init_cond, element_param, params["n"], element_list = cm_and_ic( + filename, 1, E_t, params["c"] +) # print(init_cond) @@ -191,15 +196,20 @@ def cm_and_ic(mesh_file, m, E, c): y.append(init_cond[i][0][1]) z.append(init_cond[i][0][2]) - fig= plt.figure() + fig = plt.figure() ax = fig.add_subplot(projection="3d") - labels = ['Mesh particle', 'Mesh spring damper element'] + labels = ["Mesh particle", "Mesh spring damper element"] handles = [] - nodes = ax.scatter(x, y, z, c='red', label=labels[0]) + nodes = ax.scatter(x, y, z, c="red", label=labels[0]) handles.append(nodes) for i, indices in enumerate(c_matrix): - line = ax.plot([x[indices[0]], x[indices[1]]], [y[indices[0]], y[indices[1]]], [z[indices[0]], z[indices[1]]], - color='black', label=labels[1]) + line = ax.plot( + [x[indices[0]], x[indices[1]]], + [y[indices[0]], y[indices[1]]], + [z[indices[0]], z[indices[1]]], + color="black", + label=labels[1], + ) if i == 0: handles.append(line[0]) diff --git a/code_Validation/inflated_SiN_membrane/inflated_membrane.py b/code_Validation/inflated_SiN_membrane/inflated_membrane.py index 83fd7ca..732bcf8 100644 --- a/code_Validation/inflated_SiN_membrane/inflated_membrane.py +++ b/code_Validation/inflated_SiN_membrane/inflated_membrane.py @@ -55,7 +55,7 @@ import numpy as np import matplotlib.pyplot as plt -from Particle_System_Simulator.particleSystem.ParticleSystem import ParticleSystem +from Particle_System_Simulator.particleSystem import ParticleSystem from Particle_System_Simulator.Sim.simulations import Simulate_airbag import Particle_System_Simulator.Mesh.mesh_functions as MF diff --git a/code_Validation/saddle_form/saddle_form.py b/code_Validation/saddle_form/saddle_form.py index e405551..d3e73bb 100644 --- a/code_Validation/saddle_form/saddle_form.py +++ b/code_Validation/saddle_form/saddle_form.py @@ -3,11 +3,11 @@ """ import numpy as np -import code_Validation.saddle_form.saddle_form_input as input +import saddle_form_input as input import matplotlib.pyplot as plt import pandas as pd import time -from Particle_System_Simulator.particleSystem.ParticleSystem import ParticleSystem +from Particle_System_Simulator.particleSystem import ParticleSystem def instantiate_ps(): diff --git a/code_Validation/tether_deflection_gravity/tether_deflection_gravity.py b/code_Validation/tether_deflection_gravity/tether_deflection_gravity.py index 57821ad..ef52a34 100644 --- a/code_Validation/tether_deflection_gravity/tether_deflection_gravity.py +++ b/code_Validation/tether_deflection_gravity/tether_deflection_gravity.py @@ -2,6 +2,7 @@ Script for PS framework validation, benchmark case where tether is fixed at both ends and is deflected by perpendicular gravity which results in a catenary line """ + import numpy as np import numpy.typing as npt import tether_deflection_gravity_input as input @@ -9,77 +10,88 @@ import pandas as pd import sys import time -from Msc_Alexander_Batchelor.src.particleSystem.ParticleSystem import ParticleSystem - +from Particle_System_Simulator.particleSystem import ParticleSystem from sympy import * def instantiate_ps(): - return ParticleSystem(input.c_matrix, input.init_cond, input.elem_params, input.params) + return ParticleSystem( + input.c_matrix, input.init_cond, input.elem_params, input.params + ) def generate_animation(pos, n: int, t: npt.ArrayLike): - from matplotlib import animation - import matplotlib - import math - matplotlib.rcParams['animation.ffmpeg_path'] = r'C:\\FFmpeg\\bin\\ffmpeg.exe' - filename = f"Gravity_deflection-{input.params['n']}Particles-{input.params['k']}stiffness-{input.params['c']}" \ - f"damping_coefficient-{input.params['dt']}timestep-{input.params['t_steps']}steps-.mov" - savelocation = r"C:\\Users\\Alexander\\Documents\\Master\\Thesis\\Figures\\GIFs\\" - - # configuration of plot - fig, ax = plt.subplots() - ax.set_xlim((-1, 11)) - ax.set_ylim((-1, 0.5)) - ax.grid(which='major') - plt.ylabel("height [m]") - plt.xlabel("x position [m]") - plt.title(f"Animation of tether deflected by gravity") - - # calculation which values for each frame - fps = 60 # 1 / input.params['dt'] - multi = round(input.params['dt']**-1 / fps) - n_frames = math.floor(len(t)/multi) - frame_indeces = [i * multi for i in range(n_frames)] - - line, = ax.plot([], [], lw=2) - - def init(): - line.set_data([], []) - return (line,) - - def animate(i): - index = frame_indeces[i] - timestep = t[index] - x = pos.loc[timestep, [f'x{j + 1}' for j in range(n)]] - y = pos.loc[timestep, [f'z{j + 1}' for j in range(n)]] - line.set_data(x, y) - return (line,) - - anim = animation.FuncAnimation(fig, animate, init_func=init, - frames=n_frames, interval=20, blit=True) # , save_count=len(self.t)) - - writervideo = animation.FFMpegWriter(fps=fps) - anim.save(savelocation + filename, writer=writervideo) - plt.cla() - return + from matplotlib import animation + import matplotlib + import math + + matplotlib.rcParams["animation.ffmpeg_path"] = r"C:\\FFmpeg\\bin\\ffmpeg.exe" + filename = ( + f"Gravity_deflection-{input.params['n']}Particles-{input.params['k']}stiffness-{input.params['c']}" + f"damping_coefficient-{input.params['dt']}timestep-{input.params['t_steps']}steps-.mov" + ) + savelocation = r"C:\\Users\\Alexander\\Documents\\Master\\Thesis\\Figures\\GIFs\\" + + # configuration of plot + fig, ax = plt.subplots() + ax.set_xlim((-1, 11)) + ax.set_ylim((-1, 0.5)) + ax.grid(which="major") + plt.ylabel("height [m]") + plt.xlabel("x position [m]") + plt.title(f"Animation of tether deflected by gravity") + + # calculation which values for each frame + fps = 60 # 1 / input.params['dt'] + multi = round(input.params["dt"] ** -1 / fps) + n_frames = math.floor(len(t) / multi) + frame_indeces = [i * multi for i in range(n_frames)] + + (line,) = ax.plot([], [], lw=2) + + def init(): + line.set_data([], []) + return (line,) + + def animate(i): + index = frame_indeces[i] + timestep = t[index] + x = pos.loc[timestep, [f"x{j + 1}" for j in range(n)]] + y = pos.loc[timestep, [f"z{j + 1}" for j in range(n)]] + line.set_data(x, y) + return (line,) + + anim = animation.FuncAnimation( + fig, animate, init_func=init, frames=n_frames, interval=20, blit=True + ) # , save_count=len(self.t)) + + writervideo = animation.FFMpegWriter(fps=fps) + anim.save(savelocation + filename, writer=writervideo) + plt.cla() + return def analytical_solution(sag: float, t_l: float): # catenary line equation analytical solution - L = input.params['L'] + L = input.params["L"] h = abs(sag) - a = (0.25 * t_l**2 - h**2)/(2*h) + a = (0.25 * t_l**2 - h**2) / (2 * h) x = np.linspace(0, L, 1000) - y = a*np.cosh((x-0.5*L)/a) # shift curve 0.5*L in direction of positive x-axis - y -= y[0] # adjust height + y = a * np.cosh( + (x - 0.5 * L) / a + ) # shift curve 0.5*L in direction of positive x-axis + y -= y[0] # adjust height return x, y def plot(psystem: ParticleSystem): - n = input.params['n'] - t_vector = np.linspace(input.params["dt"], input.params["t_steps"] * input.params["dt"], input.params["t_steps"]) + n = input.params["n"] + t_vector = np.linspace( + input.params["dt"], + input.params["t_steps"] * input.params["dt"], + input.params["t_steps"], + ) x = {} v = {} @@ -98,15 +110,19 @@ def plot(psystem: ParticleSystem): n = input.params["n"] # f_ext = np.array([[0, 0, -g] for i in range(n)]).flatten() particles = ps.particles - f_ext = np.array([[0, 0, -g*particle.m] for particle in particles]).flatten() + f_ext = np.array([[0, 0, -g * particle.m] for particle in particles]).flatten() f_check = f_ext.copy() f_check[0:3] = f_check[-2:] = 0 start_time = time.time() - for step in t_vector: # propagating the simulation for each timestep and saving results + for ( + step + ) in t_vector: # propagating the simulation for each timestep and saving results # position.loc[step], velocity.loc[step] = psystem.simulate(f_ext) # position.loc[step], velocity.loc[step] = psystem.kin_damp_sim(f_ext) - position.loc[step], velocity.loc[step] = psystem.kin_damp_sim(f_ext, q_correction=True) + position.loc[step], velocity.loc[step] = psystem.kin_damp_sim( + f_ext, q_correction=True + ) residual_f = f_check + psystem.f_int # print(np.linalg.norm(residual_f)) @@ -115,7 +131,7 @@ def plot(psystem: ParticleSystem): print("convergence criteria satisfied") break stop_time = time.time() - print(f'convergence time: {(stop_time - start_time):.4f} s') + print(f"convergence time: {(stop_time - start_time):.4f} s") # generate animation of results, requires smarter configuration to make usable on other PCs # generate_animation(position, n, t_vector) @@ -125,18 +141,18 @@ def plot(psystem: ParticleSystem): particles = ps.particles for i in range(n - 1): tether_length += np.linalg.norm(particles[i].x - particles[i + 1].x) - x_pos = position["x2"].count()-1 + x_pos = position["x2"].count() - 1 x, y = analytical_solution(min(position.iloc[x_pos]), tether_length) # plotting & graph configuration plt.figure(0) - for i in range(1, n-1): + for i in range(1, n - 1): position[f"z{i + 1}"].plot() # plt.plot(t, exact) plt.xlabel("time [s]") plt.ylabel("position [m]") plt.title("Particle deflection of PS with kinetic damping without q-correction") - plt.legend([f"displacement particle {i + 1}" for i in range(1, n-1)]) + plt.legend([f"displacement particle {i + 1}" for i in range(1, n - 1)]) plt.grid() # saving resulting figure @@ -144,11 +160,15 @@ def plot(psystem: ParticleSystem): figure.set_size_inches(8.3, 5.8) # set window to size of a3 paper # Not sure if this is the smartest way to automate saving results relative to other users directories - file_path = sys.path[1] + "/Msc_Alexander_Batchelor/code_Validation/benchmark_results/" \ - "tether_deflection_gravity/" - img_name = f"{input.params['n']}Particles-{input.params['k']}stiffness-{input.params['c']}damping_coefficient-" \ - f"{input.params['dt']}timestep-{input.params['t_steps']}steps.jpeg" - plt.savefig(file_path + img_name, dpi=300, bbox_inches='tight') + file_path = ( + sys.path[1] + "/Msc_Alexander_Batchelor/code_Validation/benchmark_results/" + "tether_deflection_gravity/" + ) + img_name = ( + f"{input.params['n']}Particles-{input.params['k']}stiffness-{input.params['c']}damping_coefficient-" + f"{input.params['dt']}timestep-{input.params['t_steps']}steps.jpeg" + ) + plt.savefig(file_path + img_name, dpi=300, bbox_inches="tight") # plot of end state simulation vs analytical solution plt.figure(1) @@ -163,7 +183,9 @@ def plot(psystem: ParticleSystem): plt.xlabel("x position [m]") plt.ylabel("y position [m]") # plt.title(f"Analytical caternary and resulting catenary of PS with viscous damping, n = {input.params['n']}") - plt.title(f"Analytical caternary and resulting catenary of PS with kinetic damping with q correction, n = {input.params['n']}") + plt.title( + f"Analytical caternary and resulting catenary of PS with kinetic damping with q correction, n = {input.params['n']}" + ) plt.grid() plt.legend(["Simulation final state particles", "Analytical catenary"]) plt.show() diff --git a/code_Validation/tether_deflection_windFlow/tether_deflection_windFlow.py b/code_Validation/tether_deflection_windFlow/tether_deflection_windFlow.py index 26cd10e..bbba31e 100644 --- a/code_Validation/tether_deflection_windFlow/tether_deflection_windFlow.py +++ b/code_Validation/tether_deflection_windFlow/tether_deflection_windFlow.py @@ -2,6 +2,7 @@ Script for PS framework validation, benchmark case where tether is fixed at both ends and is deflected by perpendicular wind flow """ + import numpy as np import numpy.typing as npt import tether_deflection_windFlow_input as input @@ -9,85 +10,103 @@ import pandas as pd import sys import time -from Msc_Alexander_Batchelor.src.particleSystem.ParticleSystem import ParticleSystem +from Particle_System_Simulator.particleSystem import ParticleSystem def instantiate_ps(): - return ParticleSystem(input.c_matrix, input.init_cond, input.elem_params, input.params) + return ParticleSystem( + input.c_matrix, input.init_cond, input.elem_params, input.params + ) def generate_animation(pos, n: int, t: npt.ArrayLike): - from matplotlib import animation - import matplotlib - import math - matplotlib.rcParams['animation.ffmpeg_path'] = r'C:\\FFmpeg\\bin\\ffmpeg.exe' - filename = f"windFlow_deflection-{input.params['n']}Particles-{input.params['k']}stiffness-{input.params['c']}"\ - f"damping_coefficient-{input.params['dt']}timestep-{input.params['t_steps']}steps-.mov" - savelocation = r"C:\\Users\\Alexander\\Documents\\Master\\Thesis\\Figures\\GIFs\\" - - # configuration of plot - fig, ax = plt.subplots() - ax.set_xlim((-1, 0.5)) - ax.set_ylim((-1, 11)) - ax.grid(which='major') - plt.ylabel("height [m]") - plt.xlabel("x position [m]") - plt.title(f"Animation of tether deflected by perpendicular wind flow") - - # calculation which values for each frame - fps = 60 # 1 / input.params['dt'] - multi = round(input.params['dt']**-1 / fps) - n_frames = math.floor(len(t)/multi) - frame_indeces = [i * multi for i in range(n_frames)] - - line, = ax.plot([], [], lw=2) - - def init(): - line.set_data([], []) - return (line,) - - def animate(i): - index = frame_indeces[i] - timestep = t[index] - x = pos.loc[timestep, [f'x{j + 1}' for j in range(n)]] - y = pos.loc[timestep, [f'z{j + 1}' for j in range(n)]] - line.set_data(x, y) - return (line,) - - anim = animation.FuncAnimation(fig, animate, init_func=init, - frames=n_frames, interval=20, blit=True) # , save_count=len(self.t)) - - writervideo = animation.FFMpegWriter(fps=fps) - anim.save(savelocation + filename, writer=writervideo) - plt.cla() - return + from matplotlib import animation + import matplotlib + import math + + matplotlib.rcParams["animation.ffmpeg_path"] = r"C:\\FFmpeg\\bin\\ffmpeg.exe" + filename = ( + f"windFlow_deflection-{input.params['n']}Particles-{input.params['k']}stiffness-{input.params['c']}" + f"damping_coefficient-{input.params['dt']}timestep-{input.params['t_steps']}steps-.mov" + ) + savelocation = r"C:\\Users\\Alexander\\Documents\\Master\\Thesis\\Figures\\GIFs\\" + + # configuration of plot + fig, ax = plt.subplots() + ax.set_xlim((-1, 0.5)) + ax.set_ylim((-1, 11)) + ax.grid(which="major") + plt.ylabel("height [m]") + plt.xlabel("x position [m]") + plt.title(f"Animation of tether deflected by perpendicular wind flow") + + # calculation which values for each frame + fps = 60 # 1 / input.params['dt'] + multi = round(input.params["dt"] ** -1 / fps) + n_frames = math.floor(len(t) / multi) + frame_indeces = [i * multi for i in range(n_frames)] + + (line,) = ax.plot([], [], lw=2) + + def init(): + line.set_data([], []) + return (line,) + + def animate(i): + index = frame_indeces[i] + timestep = t[index] + x = pos.loc[timestep, [f"x{j + 1}" for j in range(n)]] + y = pos.loc[timestep, [f"z{j + 1}" for j in range(n)]] + line.set_data(x, y) + return (line,) + + anim = animation.FuncAnimation( + fig, animate, init_func=init, frames=n_frames, interval=20, blit=True + ) # , save_count=len(self.t)) + + writervideo = animation.FFMpegWriter(fps=fps) + anim.save(savelocation + filename, writer=writervideo) + plt.cla() + return def calculate_f_a(ps: ParticleSystem): particle_list = ps.particles - f_a = np.zeros(input.params['n']*3, ) + f_a = np.zeros( + input.params["n"] * 3, + ) rho = 1.225 for i in range(len(particle_list) - 1): - V_b = 0.5 * (particle_list[i].v + particle_list[i + 1].v) # velocity of the bridle = avg vel. of the particles + V_b = 0.5 * ( + particle_list[i].v + particle_list[i + 1].v + ) # velocity of the bridle = avg vel. of the particles V_b_app = input.params["v_w"] - V_b # apparent velocity of bridle V_b_norm = np.linalg.norm(V_b_app) x = particle_list[i].x - particle_list[i + 1].x l_element = np.linalg.norm(x) # derivation of equation below, see "Bridle Particle pdf" - S_eff_bridle = input.params["d_bridle"] * np.sqrt(l_element ** 2 - (np.dot(V_b_app, x) / V_b_norm) ** 2) - F_a_drag = 0.5 * rho * V_b_app * V_b_norm * S_eff_bridle * input.params['c_d_bridle'] + S_eff_bridle = input.params["d_bridle"] * np.sqrt( + l_element**2 - (np.dot(V_b_app, x) / V_b_norm) ** 2 + ) + F_a_drag = ( + 0.5 * rho * V_b_app * V_b_norm * S_eff_bridle * input.params["c_d_bridle"] + ) # Drag force, includes the direction of the velocity - f_a[i * 3:(i + 1) * 3] += 0.5 * F_a_drag - f_a[(i + 1) * 3:(i + 2) * 3] += 0.5 * F_a_drag + f_a[i * 3 : (i + 1) * 3] += 0.5 * F_a_drag + f_a[(i + 1) * 3 : (i + 2) * 3] += 0.5 * F_a_drag return f_a def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSystem): - n = input.params['n'] - t_vector = np.linspace(input.params["dt"], input.params["t_steps"] * input.params["dt"], input.params["t_steps"]) + n = input.params["n"] + t_vector = np.linspace( + input.params["dt"], + input.params["t_steps"] * input.params["dt"], + input.params["t_steps"], + ) x = {} v = {} @@ -113,7 +132,9 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSy f_aero = calculate_f_a(psystem) start_time = time.time() - for step in t_vector: # propagating the simulation for each timestep and saving results + for ( + step + ) in t_vector: # propagating the simulation for each timestep and saving results # f_aero = calculate_f_a(psystem) position.loc[step], velocity.loc[step] = psystem.simulate(f_ext + f_aero) @@ -127,7 +148,9 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSy stop_time = time.time() start_time2 = time.time() - for step in t_vector: # propagating the simulation for each timestep and saving results + for ( + step + ) in t_vector: # propagating the simulation for each timestep and saving results f_aero = calculate_f_a(psystem2) position2.loc[step], velocity2.loc[step] = psystem2.kin_damp_sim(f_ext + f_aero) @@ -141,10 +164,14 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSy stop_time2 = time.time() start_time3 = time.time() - for step in t_vector: # propagating the simulation for each timestep and saving results + for ( + step + ) in t_vector: # propagating the simulation for each timestep and saving results f_aero = calculate_f_a(psystem3) - position3.loc[step], velocity3.loc[step] = psystem3.kin_damp_sim(f_ext + f_aero, q_correction=True) + position3.loc[step], velocity3.loc[step] = psystem3.kin_damp_sim( + f_ext + f_aero, q_correction=True + ) f_aero[0:3] = 0 f_aero[-3:] = 0 residual_f = f_aero + psystem3.f_int @@ -154,9 +181,9 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSy break stop_time3 = time.time() - print(f'PS classic: {(stop_time - start_time):.4f} s') - print(f'PS kinetic: {(stop_time2 - start_time2):.4f} s') - print(f'PS kinetic q: {(stop_time3 - start_time3):.4f} s') + print(f"PS classic: {(stop_time - start_time):.4f} s") + print(f"PS kinetic: {(stop_time2 - start_time2):.4f} s") + print(f"PS kinetic q: {(stop_time3 - start_time3):.4f} s") # generate animation of results, requires smarter configuration to make usable on other PCs # generate_animation(position, n, t_vector) @@ -172,9 +199,13 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSy # plt.plot(t, exact) plt.xlabel("time [s]") plt.ylabel("position [m]") - plt.title("Validation PS framework, deflection of particles by wind flow, with Implicit Euler scheme") - plt.legend([f"displacement particle {i + 1}" for i in range(n)] + - [f"kinetic damped particle {i + 1}" for i in range(n)]) + plt.title( + "Validation PS framework, deflection of particles by wind flow, with Implicit Euler scheme" + ) + plt.legend( + [f"displacement particle {i + 1}" for i in range(n)] + + [f"kinetic damped particle {i + 1}" for i in range(n)] + ) plt.grid() # saving resulting figure @@ -182,11 +213,15 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSy figure.set_size_inches(8.3, 5.8) # set window to size of a3 paper # Not sure if this is the smartest way to automate saving results relative to other users directories - file_path = sys.path[1] + "/Msc_Alexander_Batchelor/code_Validation/benchmark_results/" \ - "tether_deflection_windFlow/" - img_name = f"{input.params['n']}Particles-{input.params['k_t']}stiffness-{input.params['c']}damping_coefficient-" \ - f"{input.params['dt']}timestep-{input.params['t_steps']}steps.jpeg" - plt.savefig(file_path + img_name, dpi=300, bbox_inches='tight') + file_path = ( + sys.path[1] + "/Msc_Alexander_Batchelor/code_Validation/benchmark_results/" + "tether_deflection_windFlow/" + ) + img_name = ( + f"{input.params['n']}Particles-{input.params['k_t']}stiffness-{input.params['c']}damping_coefficient-" + f"{input.params['dt']}timestep-{input.params['t_steps']}steps.jpeg" + ) + plt.savefig(file_path + img_name, dpi=300, bbox_inches="tight") # plot of end state simulation vs analytical solution plt.figure(1) @@ -196,9 +231,9 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSy sim2y = [] sim3x = [] sim3y = [] - x_pos1 = position["x2"].count()-1 - x_pos2 = position2["x2"].count()-1 - x_pos3 = position3["x2"].count()-1 + x_pos1 = position["x2"].count() - 1 + x_pos2 = position2["x2"].count() - 1 + x_pos3 = position3["x2"].count() - 1 for i in range(n): sim1x.append(position[f"x{i + 1}"].iloc[x_pos1]) sim1y.append(position[f"z{i + 1}"].iloc[x_pos1]) @@ -209,14 +244,22 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSy sim3x.append(position3[f"x{i + 1}"].iloc[x_pos3]) sim3y.append(position3[f"z{i + 1}"].iloc[x_pos3]) - plt.plot(sim1x, sim1y, 'b') - plt.plot(sim2x, sim2y, 'r--', lw=2.5) - plt.plot(sim3x, sim3y, 'orange', ls='--') + plt.plot(sim1x, sim1y, "b") + plt.plot(sim2x, sim2y, "r--", lw=2.5) + plt.plot(sim3x, sim3y, "orange", ls="--") plt.xlabel("x position [m]") plt.ylabel("y position [m]") - plt.title(f"Found shapes of tether deflected by perpendicular windflow, n = {input.params['n']}") + plt.title( + f"Found shapes of tether deflected by perpendicular windflow, n = {input.params['n']}" + ) plt.grid() - plt.legend(["PS with viscous damping", "PS with kinetic damping without q", "PS with kinetic damping with q"]) + plt.legend( + [ + "PS with viscous damping", + "PS with kinetic damping without q", + "PS with kinetic damping with q", + ] + ) plt.show() plt.show() diff --git a/code_Validation/tether_longitudal_oscillations/tether_longitudal_oscillations.py b/code_Validation/tether_longitudal_oscillations/tether_longitudal_oscillations.py index 80db2e0..7b75e8c 100644 --- a/code_Validation/tether_longitudal_oscillations/tether_longitudal_oscillations.py +++ b/code_Validation/tether_longitudal_oscillations/tether_longitudal_oscillations.py @@ -2,6 +2,7 @@ Script for PS framework validation, benchmark case where tether is fixed at top end and exhibits longitudal oscillations due to a dropped mass fixed at its other end at t = 0. """ + import numpy as np import numpy.typing as npt import tether_longitudal_oscillations_input as input @@ -9,12 +10,14 @@ import pandas as pd import sys import time -from Msc_Alexander_Batchelor.src.particleSystem.ParticleSystem import ParticleSystem -from Msc_Alexander_Batchelor.src.AnalysisModules.SystemEnergy import system_energy +from Particle_System_Simulator.particleSystem import ParticleSystem +from Particle_System_Simulator.AnalysisModules import system_energy def instantiate_ps(): - return ParticleSystem(input.c_matrix, input.init_cond, input.elem_params, input.params) + return ParticleSystem( + input.c_matrix, input.init_cond, input.elem_params, input.params + ) def exact_solution(t_vector: npt.ArrayLike): @@ -31,19 +34,21 @@ def exact_solution(t_vector: npt.ArrayLike): x = {f"steady_state_p{i + 1}": np.zeros(len(t_vector)) for i in range(n - 1)} exact_x = pd.DataFrame(index=t_vector, columns=x) - steady_state_displacement = np.array([np.sum(m[i + 1:]) * -g / (k / (i + 1)) for i in range(n - 1)]) + steady_state_displacement = np.array( + [np.sum(m[i + 1 :]) * -g / (k / (i + 1)) for i in range(n - 1)] + ) for step in t_vector: exact_x.loc[step] = steady_state_displacement # Estimated (expected) decay rate of implicit Euler scheme as a function of t - dt = input.params['dt'] - decay = np.exp(-0.5 * omega ** 2 * dt * t_vector) + dt = input.params["dt"] + decay = np.exp(-0.5 * omega**2 * dt * t_vector) - zeta = c/(2 * omega) # critical damping faction + zeta = c / (2 * omega) # critical damping faction # (Estimated) system damping, based on - if zeta <1: + if zeta < 1: print("system is underdamped") elif zeta == 1: print("system is critically damped") @@ -54,8 +59,10 @@ def exact_solution(t_vector: npt.ArrayLike): def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSystem): - n = input.params['n'] - t_vector = np.linspace(0, input.params["t_steps"] * input.params["dt"], input.params["t_steps"]+1) + n = input.params["n"] + t_vector = np.linspace( + 0, input.params["t_steps"] * input.params["dt"], input.params["t_steps"] + 1 + ) x = {} v = {} @@ -69,8 +76,10 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSy position = pd.DataFrame(index=t_vector, columns=x) velocity = pd.DataFrame(index=t_vector, columns=v) - sys_energy = pd.DataFrame(index=t_vector, columns={'E': np.zeros(len(t_vector))}) - v_prev = np.zeros(n*3, ) + sys_energy = pd.DataFrame(index=t_vector, columns={"E": np.zeros(len(t_vector))}) + v_prev = np.zeros( + n * 3, + ) g = input.params["g"] n = input.params["n"] @@ -79,18 +88,22 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSy f_ext = np.array([[0, 0, -g * m[i]] for i in range(n)]).flatten() f_check = f_ext.copy() f_check[:3] = 0 - m = np.array([[input.init_cond[i][-2] for j in range(3)] for i in range(n)]).flatten() + m = np.array( + [[input.init_cond[i][-2] for j in range(3)] for i in range(n)] + ).flatten() m = np.diag(m) start_time = time.time() - for step in t_vector: # propagating the simulation for each timestep and saving results + for ( + step + ) in t_vector: # propagating the simulation for each timestep and saving results if step == 0: x, v = psystem.x_v_current position.loc[step], velocity.loc[step] = x, v sys_energy.loc[step] = np.matmul(np.matmul(v_prev, m), v_prev) f_1 = f_ext.copy() v_1 = -g * np.sqrt(0.1 / (0.5 * g)) - f_1[-3:] = [0, 0, 10 * v_1/input.params["dt"]] + f_1[-3:] = [0, 0, 10 * v_1 / input.params["dt"]] x_next, v_next = psystem.simulate(f_1) v_prev = v_next continue @@ -109,10 +122,10 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSy print("convergence criteria satisfied") break stop_time = time.time() - print(f'convergence time: {(stop_time - start_time):.4f} s') + print(f"convergence time: {(stop_time - start_time):.4f} s") # calculating system energy over time - norm_energy = sys_energy #/ sys_energy.iloc[0] + norm_energy = sys_energy # / sys_energy.iloc[0] # generating analytical solution for the same time vector exact, decay = exact_solution(t_vector) @@ -123,9 +136,11 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSy position[f"z{i + 1}"].plot() x_length = position["z2"].count() - plt.plot(t_vector[:x_length], exact.iloc[:x_length], ls='--') + plt.plot(t_vector[:x_length], exact.iloc[:x_length], ls="--") - for i in range(n - 1): # setting particle colors equivalent to their analytical solution + for i in range( + n - 1 + ): # setting particle colors equivalent to their analytical solution color = plt.gca().lines[i].get_color() plt.gca().lines[i + n - 1].set_color(color) @@ -133,8 +148,10 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSy plt.ylabel("position [m]") # plt.title("Benchmark 1, PS with viscous damping simulation of longitudal tether oscillations") plt.title("Benchmark 1, PS with kinetic damping simulation with q correction") - plt.legend([f"displacement particle {i + 2}" for i in range(n - 1)] + - [f"Analytical steady state pos. particle {i + 2}" for i in range(n - 1)]) + plt.legend( + [f"displacement particle {i + 2}" for i in range(n - 1)] + + [f"Analytical steady state pos. particle {i + 2}" for i in range(n - 1)] + ) plt.grid() # saving resulting figure @@ -142,11 +159,15 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSy figure.set_size_inches(8.3, 5.8) # set window to size of a3 paper # Not sure if this is the smartest way to automate saving results relative to other users directories - file_path = sys.path[1] + "/Msc_Alexander_Batchelor/code_Validation/benchmark_results/" \ - "tether_longitudal_oscillations/" - img_name = f"{input.params['n']}Particles-{input.params['k']:.3f}stiffness-{input.params['c']:.3f}damping_coefficient-" \ - f"{input.params['dt']}timestep-{input.params['t_steps']}steps.jpeg" - plt.savefig(file_path + img_name, dpi=300, bbox_inches='tight') + file_path = ( + sys.path[1] + "/Msc_Alexander_Batchelor/code_Validation/benchmark_results/" + "tether_longitudal_oscillations/" + ) + img_name = ( + f"{input.params['n']}Particles-{input.params['k']:.3f}stiffness-{input.params['c']:.3f}damping_coefficient-" + f"{input.params['dt']}timestep-{input.params['t_steps']}steps.jpeg" + ) + plt.savefig(file_path + img_name, dpi=300, bbox_inches="tight") # separate plot for system energy norm_energy.plot() @@ -158,19 +179,23 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSy # error between analytical and steady-state positions for i in range(1, n): - print(f"error p{i}: ", exact[f"steady_state_p{i}"].iloc[x_length-1]-position[f"z{i+1}"].iloc[x_length-1]) + print( + f"error p{i}: ", + exact[f"steady_state_p{i}"].iloc[x_length - 1] + - position[f"z{i+1}"].iloc[x_length - 1], + ) # frequency analysis - fourier = np.fft.fft(position[f"z{n}"].iloc[:x_length-1]) + fourier = np.fft.fft(position[f"z{n}"].iloc[: x_length - 1]) plt.figure(3) T = input.params["dt"] N = x_length xf = np.linspace(0.0, 1.0 / (2.0 * T), N // 2) - yf = 2.0 / N * np.abs(fourier[:N // 2]) + yf = 2.0 / N * np.abs(fourier[: N // 2]) print("main frequency peak [hz]:", xf[np.where(yf == max(yf[2:]))]) - plt.plot(xf, 2.0 / N * np.abs(fourier[:N // 2])) + plt.plot(xf, 2.0 / N * np.abs(fourier[: N // 2])) plt.xlabel("frequency [hz]") plt.ylabel("|y(f)|") plt.title("fft of benchmark 1") diff --git a/code_Verification/damping_force/damping_force.py b/code_Verification/damping_force/damping_force.py index 9d26c44..ca05d0e 100644 --- a/code_Verification/damping_force/damping_force.py +++ b/code_Verification/damping_force/damping_force.py @@ -1,6 +1,7 @@ """ Script for verification of correct implementation spring force of SpringDamper object within ParticleSystem framework """ + import numpy as np import numpy.typing as npt import input_damping_force as input @@ -9,12 +10,14 @@ import pandas as pd from scipy.integrate import solve_ivp import sys -from Msc_Alexander_Batchelor.src.particleSystem.ParticleSystem import ParticleSystem -from Msc_Alexander_Batchelor.src.AnalysisModules.SystemEnergy import system_energy +from Particle_System_Simulator.particleSystem import ParticleSystem +from Particle_System_Simulator.AnalysisModules import system_energy def instantiate_ps(): - return ParticleSystem(input.c_matrix, input.init_cond, input.elem_param, input.params) + return ParticleSystem( + input.c_matrix, input.init_cond, input.elem_param, input.params + ) def exact_solution(t_vector: npt.ArrayLike): @@ -22,11 +25,11 @@ def exact_solution(t_vector: npt.ArrayLike): c = input.params["c"] m = input.init_cond[1][-2] omega = np.sqrt(k / m) - gamma = c/m - zeta = c/(2 * omega) # critical damping faction + gamma = c / m + zeta = c / (2 * omega) # critical damping faction # Analytical solution depends on value of zeta - if zeta <1: + if zeta < 1: print("system is underdamped") elif zeta == 1: print("system is critically damped") @@ -37,7 +40,7 @@ def exact_solution(t_vector: npt.ArrayLike): y0 = np.array([1, 0]) def syst_of_diff_eq(t, y): - A = np.array([[0, 1], [-omega**2, -gamma]]) + A = np.array([[0, 1], [-(omega**2), -gamma]]) system = np.matmul(A, y) return system @@ -54,15 +57,15 @@ def syst_of_diff_eq(t, y): # + c2 * np.exp(-t_vector * np.sqrt((c/2)**2 - omega**2)))) # Estimated (expected) decay rate of implicit Euler scheme as a function of t - dt = input.params['dt'] - decay = np.exp(-0.5 * omega ** 2 * dt * t_vector) + dt = input.params["dt"] + decay = np.exp(-0.5 * omega**2 * dt * t_vector) return ivp_solution, decay def s_energy(pos, vel, t_vector): - k = input.params['k'] - c = input.params['c'] + k = input.params["k"] + c = input.params["c"] dt = t_vector[1] m = input.init_cond[1][-2] @@ -76,13 +79,13 @@ def s_energy(pos, vel, t_vector): ke = 0.5 * m * vel**2 # Energy dissipated by friction - ed = pd.DataFrame(index=t_vector, columns={'ed': np.zeros(len(t_vector))}) + ed = pd.DataFrame(index=t_vector, columns={"ed": np.zeros(len(t_vector))}) ed.iloc[0] = 0 for i in range(1, len(t_vector)): - ed.iloc[i] = c * dt * abs(vel.iloc[i]**2 - vel.iloc[i - 1]**2) + ed.iloc[i] = c * dt * abs(vel.iloc[i] ** 2 - vel.iloc[i - 1] ** 2) # Total system energy - total_energy = ep['x'] + ke['v'] - ed['ed'] + total_energy = ep["x"] + ke["v"] - ed["ed"] # print("ep:", ep) # print("ke:", ke) @@ -96,26 +99,38 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSy # visualization of simulation and analytical results # time vector for simulation loop, data storage and plotting - dt = input.params['dt'] + dt = input.params["dt"] t_steps = input.params["t_steps"] - t_vector = np.linspace(0, t_steps * dt, t_steps+1) + t_vector = np.linspace(0, t_steps * dt, t_steps + 1) # DataFrames as storage method of choice - x = {"x": np.zeros(len(t_vector), )} - v = {"v": np.zeros(len(t_vector), )} + x = { + "x": np.zeros( + len(t_vector), + ) + } + v = { + "v": np.zeros( + len(t_vector), + ) + } position = pd.DataFrame(index=t_vector, columns=x) velocity = pd.DataFrame(index=t_vector, columns=v) position2 = pd.DataFrame(index=t_vector, columns=x) velocity2 = pd.DataFrame(index=t_vector, columns=v) position3 = pd.DataFrame(index=t_vector, columns=x) velocity3 = pd.DataFrame(index=t_vector, columns=v) - sys_en = pd.DataFrame(index=t_vector, columns={'SE': np.zeros(len(t_vector))}) + sys_en = pd.DataFrame(index=t_vector, columns={"SE": np.zeros(len(t_vector))}) # addition of (constant) external forces - f_ext = np.zeros(input.params['n'] * 3, ) + f_ext = np.zeros( + input.params["n"] * 3, + ) start_time = time.time() - for step in t_vector: # propagating the simulation for each timestep and saving results + for ( + step + ) in t_vector: # propagating the simulation for each timestep and saving results if step == 0: x, v = psystem.x_v_current position.loc[step], velocity.loc[step] = x[-1], v[-1] @@ -130,7 +145,9 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSy stop_time = time.time() start_time2 = time.time() - for step in t_vector: # propagating the simulation for each timestep and saving results + for ( + step + ) in t_vector: # propagating the simulation for each timestep and saving results if step == 0: x, v = psystem2.x_v_current position2.loc[step], velocity2.loc[step] = x[-1], v[-1] @@ -145,7 +162,9 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSy stop_time2 = time.time() start_time3 = time.time() - for step in t_vector: # propagating the simulation for each timestep and saving results + for ( + step + ) in t_vector: # propagating the simulation for each timestep and saving results if step == 0: x, v = psystem3.x_v_current position3.loc[step], velocity3.loc[step] = x[-1], v[-1] @@ -159,9 +178,9 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSy break stop_time3 = time.time() - print(f'PS classic: {(stop_time - start_time):.4f} s') - print(f'PS kinetic w/o q: {(stop_time2 - start_time2):.4f} s') - print(f'PS kinetic with q: {(stop_time3 - start_time3):.4f} s') + print(f"PS classic: {(stop_time - start_time):.4f} s") + print(f"PS kinetic w/o q: {(stop_time2 - start_time2):.4f} s") + print(f"PS kinetic with q: {(stop_time3 - start_time3):.4f} s") # t_vector = np.linspace(0, t_steps * dt, t_steps) # calculating system energy over time @@ -171,10 +190,10 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSy # generating analytical solution for the same time vector start_time3 = time.time() - x_length = position['x'].count() + x_length = position["x"].count() exact, decay = exact_solution(t_vector[:x_length]) stop_time3 = time.time() - print(f'IVP solved: {(stop_time3 - start_time3):.4f} s') + print(f"IVP solved: {(stop_time3 - start_time3):.4f} s") # correcting simulation for decay rate # corrected = np.divide(np.array(position["x"]), decay) @@ -217,18 +236,26 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSy # plot including kinetic damped system plt.figure() - plt.plot(position, 'b', lw=3) - plt.plot(t_vector[:x_length], exact.y[0], 'g--') - plt.plot(t_vector, np.array(position2["x"]), 'r--') - plt.plot(t_vector, np.array(position3["x"]), 'orange', ls='--') + plt.plot(position, "b", lw=3) + plt.plot(t_vector[:x_length], exact.y[0], "g--") + plt.plot(t_vector, np.array(position2["x"]), "r--") + plt.plot(t_vector, np.array(position3["x"]), "orange", ls="--") plt.xlabel("time [s]") plt.ylabel("position [m]") - plt.title(f"Simulation of critically damped harmonic oscillator, without external loads. h = {input.params['dt']} s," - f" k = {input.params['k']} N/m, c = {input.params['c']:.1f} N s/m") + plt.title( + f"Simulation of critically damped harmonic oscillator, without external loads. h = {input.params['dt']} s," + f" k = {input.params['k']} N/m, c = {input.params['c']:.1f} N s/m" + ) plt.grid() - plt.legend(["PS simulation", "Exact solution", "Kinetic damping w/o q-correction", - "Kinetic damping with q-correction"]) + plt.legend( + [ + "PS simulation", + "Exact solution", + "Kinetic damping w/o q-correction", + "Kinetic damping with q-correction", + ] + ) plt.show() plt.show() @@ -241,4 +268,3 @@ def plot(psystem: ParticleSystem, psystem2: ParticleSystem, psystem3: ParticleSy ps3 = instantiate_ps() plot(ps, ps2, ps3) - diff --git a/code_Verification/gravitational_force/gravitational_force.py b/code_Verification/gravitational_force/gravitational_force.py index 3f2bf66..7174342 100644 --- a/code_Verification/gravitational_force/gravitational_force.py +++ b/code_Verification/gravitational_force/gravitational_force.py @@ -1,17 +1,20 @@ """ Script for verification of correct implementation external gravitational force within ParticleSystem framework """ + import numpy as np import numpy.typing as npt import input_gravitational_force as input import matplotlib.pyplot as plt import pandas as pd import sys -from Msc_Alexander_Batchelor.src.particleSystem.ParticleSystem import ParticleSystem +from Particle_System_Simulator.particleSystem import ParticleSystem def instantiate_ps(): - return ParticleSystem(input.c_matrix, input.init_cond, input.elem_params, input.params) + return ParticleSystem( + input.c_matrix, input.init_cond, input.elem_params, input.params + ) def exact_solution(t_vector: npt.ArrayLike): @@ -23,29 +26,47 @@ def exact_solution(t_vector: npt.ArrayLike): def plot(psystem: ParticleSystem, ps2: ParticleSystem, ps3: ParticleSystem): # time vectors for simulation loop, data storage and plotting - t_vector = np.linspace(input.params["dt"], input.params["t_steps"] * input.params["dt"], input.params["t_steps"]) - t2 = np.linspace(0.1, 100*0.1, 100) - t3 = np.linspace(1, 10*1, 10) + t_vector = np.linspace( + input.params["dt"], + input.params["t_steps"] * input.params["dt"], + input.params["t_steps"], + ) + t2 = np.linspace(0.1, 100 * 0.1, 100) + t3 = np.linspace(1, 10 * 1, 10) # DataFrames as storage method of choice - x = {"x": np.zeros(len(t_vector),)} + x = { + "x": np.zeros( + len(t_vector), + ) + } position = pd.DataFrame(index=t_vector, columns=x) # addition of (constant) external forces - f_ext = np.array([[0, 0, -input.params['g']] for i in range(input.params['n'])]).flatten() + f_ext = np.array( + [[0, 0, -input.params["g"]] for i in range(input.params["n"])] + ).flatten() for step in t_vector: x_next, _ = psystem.simulate(f_ext) position.loc[step] = x_next[-1] - x2 = {"x": np.zeros(len(t2),)} + x2 = { + "x": np.zeros( + len(t2), + ) + } p2 = pd.DataFrame(index=t2, columns=x2) for step in t2: x_next, _ = ps2.simulate(f_ext) - p2.loc[step]= x_next[-1] + p2.loc[step] = x_next[-1] - x3 = {"x": np.zeros(len(t3),)} + x3 = { + "x": np.zeros( + len(t3), + ) + } p3 = pd.DataFrame(index=t3, columns=x3) for step in t3: @@ -55,15 +76,21 @@ def plot(psystem: ParticleSystem, ps2: ParticleSystem, ps3: ParticleSystem): exact = exact_solution(t_vector) # graph configuration - plt.plot(position, 'k--', lw=2) + plt.plot(position, "k--", lw=2) plt.plot(p2) plt.plot(p3) plt.plot(t_vector, exact) plt.xlabel("time [s]") plt.ylabel("position [m]") plt.title("Simulation of constant external load, gravity, without internal forces") - plt.legend([f"PS simulation, dt = 0.01 s", f"PS simulation, dt = {input.params['dt']} s", f"PS simulation, dt = 1 s" - , "Exact solution"]) + plt.legend( + [ + f"PS simulation, dt = 0.01 s", + f"PS simulation, dt = {input.params['dt']} s", + f"PS simulation, dt = 1 s", + "Exact solution", + ] + ) plt.grid() # # # saving resulting figure @@ -93,20 +120,22 @@ def plot(psystem: ParticleSystem, ps2: ParticleSystem, ps3: ParticleSystem): # plot showing local error propotional to timestep squared, property of first-order scheme h = np.linspace(0, 1.2, 13) r3 = abs(p3["x"].iloc[0]) - r1 = abs(position["x"].iloc[0] - exact[t_vector.index(0.01)])/r3 - r2 = abs(p2["x"].iloc[0] - exact[t_vector.index(0.1)])/r3 - r3 = r3/r3 + r1 = abs(position["x"].iloc[0] - exact[t_vector.index(0.01)]) / r3 + r2 = abs(p2["x"].iloc[0] - exact[t_vector.index(0.1)]) / r3 + r3 = r3 / r3 print(r1, r2, r3) - plt.plot(0.01, r1, 'k', marker='.', markersize=10) - plt.plot(0.1, r2, 'b', marker='.', markersize=10) - plt.plot(1, r3, 'tab:orange', marker='.', markersize=10) + plt.plot(0.01, r1, "k", marker=".", markersize=10) + plt.plot(0.1, r2, "b", marker=".", markersize=10) + plt.plot(1, r3, "tab:orange", marker=".", markersize=10) plt.plot(h, h**2) plt.xlabel("time step value [s]") plt.ylabel("normalized error [-]") - plt.title("Normalized errors at the first simulation step and timestep squared curve") + plt.title( + "Normalized errors at the first simulation step and timestep squared curve" + ) plt.legend(["timestep 1 s", "timestep 0.1 s", "timestep 0.01 s", "quadratic of h"]) plt.grid() plt.show() @@ -115,7 +144,9 @@ def plot(psystem: ParticleSystem, ps2: ParticleSystem, ps3: ParticleSystem): if __name__ == "__main__": - ps = ParticleSystem(input.c_matrix, input.init_cond, input.elem_params, input.params) + ps = ParticleSystem( + input.c_matrix, input.init_cond, input.elem_params, input.params + ) params = input.params.copy() params2 = input.params.copy() diff --git a/code_Verification/spring_force/input_spring_force.py b/code_Verification/spring_force/input_spring_force.py deleted file mode 100644 index d5a565e..0000000 --- a/code_Verification/spring_force/input_spring_force.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -Input file for verification of correct implementation spring force, by modeling undamped harmonic oscillator -""" -import numpy as np - - -def connectivity_matrix(): - matrix = [[0, 1]] - return matrix - - -def initial_conditions(): - conditions = [[[0, 0, 0], [0, 0, 0], 1, True], [[0, 0, 1], [0, 0, 0], 1, False]] - return conditions - - -def element_parameters(k, l0, c): - e_m = [[k, l0, c]] - return e_m - - -# dictionary of required parameters -params = { - # model parameters - "n": 2, # [-] number of particles - "k": 2e4, # [N/m] spring stiffness - "c": 0, # [N s/m] damping coefficient - "L": 0, # [m] tether length - - # simulation settings - "dt": 0.001, # [s] simulation timestep - "t_steps": 1000, # [-] number of simulated time steps - "abs_tol": 1e-50, # [m/s] absolute error tolerance iterative solver - "rel_tol": 1e-5, # [-] relative error tolerance iterative solver - "max_iter": 1e5, # [-] maximum number of iterations - - # physical parameters - "g": 9.81 # [m/s^2] gravitational acceleration -} -# calculated parameters -params["l0"] = params["L"]/(params["n"]-1) - -# instantiate connectivity matrix and initial conditions array -c_matrix = connectivity_matrix() -init_cond = initial_conditions() -elem_params = element_parameters(params["k"], params["l0"], params["c"]) diff --git a/code_Verification/spring_force/spring_force.py b/code_Verification/spring_force/spring_force.py index 5e8f416..59de011 100644 --- a/code_Verification/spring_force/spring_force.py +++ b/code_Verification/spring_force/spring_force.py @@ -3,18 +3,56 @@ """ import numpy as np -import input_spring_force as input import numpy.typing as npt import matplotlib.pyplot as plt import pandas as pd import sys -from Msc_Alexander_Batchelor.src.particleSystem.ParticleSystem import ParticleSystem +from Particle_System_Simulator.particleSystem.ParticleSystem import ParticleSystem + + +def connectivity_matrix(): + matrix = [[0, 1]] + return matrix + + +def initial_conditions(): + conditions = [[[0, 0, 0], [0, 0, 0], 1, True], [[0, 0, 1], [0, 0, 0], 1, False]] + return conditions + + +def element_parameters(k, l0, c): + e_m = [[k, l0, c]] + return e_m def instantiate_ps(): - return ParticleSystem( - input.c_matrix, input.init_cond, input.elem_params, input.params - ) + + # dictionary of required parameters + params = { + # model parameters + "n": 2, # [-] number of particles + "k": 2e4, # [N/m] spring stiffness + "c": 0, # [N s/m] damping coefficient + "L": 0, # [m] tether length + # simulation settings + "dt": 0.001, # [s] simulation timestep + "t_steps": 1000, # [-] number of simulated time steps + "abs_tol": 1e-50, # [m/s] absolute error tolerance iterative solver + "rel_tol": 1e-5, # [-] relative error tolerance iterative solver + "max_iter": 1e5, # [-] maximum number of iterations + # physical parameters + "g": 9.81, # [m/s^2] gravitational acceleration + } + # calculated parameters + params["l0"] = params["L"] / (params["n"] - 1) + + # instantiate connectivity matrix and initial conditions array + c_matrix = connectivity_matrix() + init_cond = initial_conditions() + elem_params = element_parameters(params["k"], params["l0"], params["c"]) + + # instantiate ParticleSystem object + return ParticleSystem(c_matrix, init_cond, params) def exact_solution(t_vector: npt.ArrayLike): # analytical solution for this test case diff --git a/pyproject.toml b/pyproject.toml index 009b63c..3bb7e5b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,13 +14,8 @@ build-backend = "setuptools.build_meta" [project] name = "Particle_System_Simulator" version = "0.1.0" -dependencies = [ -"numpy", -"pandas", -"matplotlib", -"scipy", -] - +readme = "README.md" +license = {file = "LICENSE"} requires-python = ">=3.8" authors = [ {name = "Mark Kalsbeek, Jelle Poland, Alexander Batchelor"}, @@ -28,14 +23,31 @@ authors = [ maintainers = [ {name = "Mark Kalsbeek, Jelle Poland"} ] -description = "LightSailSim" -readme = "README.md" keywords = ["Membrane Analysis", "Particle Systems", "Lightsail", "Kite", "Tensile Structures"] # Add your desired keywords here + classifiers = [ "Development Status :: 3 - Alpha", "Programming Language :: Python", +"License :: OSI Approved :: MIT License", ] +dependencies = [ +"numpy", +"pandas", +"matplotlib", +"scipy", +"sympy", +"ipykernel", +] + +[project.optional-dependencies] +dev = [ + "pytest", + "pytest-cov", + "black", + ] + + [project.urls] Repository = "https://github.com/awegroup/Particle_System_Simulator" diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 0000000..171e48c --- /dev/null +++ b/pytest.ini @@ -0,0 +1,7 @@ +# pytest.ini +[pytest] +log_cli = true +log_cli_level = INFO +# Set the desired log level here (DEBUG, INFO, WARNING, ERROR, CRITICAL) + +# Run pytest with cmd: pytest --log-cli-level=DEBUG \ No newline at end of file diff --git a/src/Particle_System_Simulator/Mesh/mesh_functions.py b/src/Particle_System_Simulator/Mesh/mesh_functions.py index 7f963dd..7c4b727 100644 --- a/src/Particle_System_Simulator/Mesh/mesh_functions.py +++ b/src/Particle_System_Simulator/Mesh/mesh_functions.py @@ -7,8 +7,7 @@ import numpy as np from scipy.spatial.transform import Rotation as R -from ..particleSystem.SpringDamper import SpringDamperType - +from Particle_System_Simulator.particleSystem.SpringDamper import SpringDamperType params = { @@ -16,207 +15,224 @@ "k": 1, # [N/m] spring stiffness "k_d": 1, # [N/m] spring stiffness "c": 1, # [N s/m] damping coefficient - "m_segment": 1, # [kg] mass of each node - } + "m_segment": 1, # [kg] mass of each node +} -def mesh_square(length, width, mesh_edge_length, params = params): - n_wide = int(width/ mesh_edge_length + 1) - n_long = int(length/ mesh_edge_length + 1) +def mesh_square(length, width, mesh_edge_length, params=params): + n_wide = int(width / mesh_edge_length + 1) + n_long = int(length / mesh_edge_length + 1) - mesh = np.meshgrid(np.linspace(0, length, n_long), - np.linspace(0, width, n_wide)) + mesh = np.meshgrid(np.linspace(0, length, n_long), np.linspace(0, width, n_wide)) initial_conditions = [] - xy_coordinates = np.column_stack(list(zip(mesh[0],mesh[1]))).T - xyz_coordinates = np.column_stack((xy_coordinates,np.zeros(len(xy_coordinates)).T)) + xy_coordinates = np.column_stack(list(zip(mesh[0], mesh[1]))).T + xyz_coordinates = np.column_stack((xy_coordinates, np.zeros(len(xy_coordinates)).T)) for xyz in xyz_coordinates: - initial_conditions.append([xyz, np.zeros(3), params['m_segment'], False]) + initial_conditions.append([xyz, np.zeros(3), params["m_segment"], False]) connections = [] - #We know that all the nodes are connected to those of the next row, which is grid_length+1 units further - for i, node in enumerate(initial_conditions[:-n_long]): # adding connextions in y-axis - connections.append([i, i+n_long, params['k'], params['c']]) - + # We know that all the nodes are connected to those of the next row, which is grid_length+1 units further + for i, node in enumerate( + initial_conditions[:-n_long] + ): # adding connextions in y-axis + connections.append([i, i + n_long, params["k"], params["c"]]) # We can do the same for the connections between the columns - for i, node in enumerate(initial_conditions): # adding connections in x-axis - if (i+1)%(n_long): # Using modulus operator to exclude the nodes at the end of a row - connections.append([i, i+1, params['k'], params['c']]) + for i, node in enumerate(initial_conditions): # adding connections in x-axis + if (i + 1) % ( + n_long + ): # Using modulus operator to exclude the nodes at the end of a row + connections.append([i, i + 1, params["k"], params["c"]]) return connections, initial_conditions -def mesh_square_cross(length, width, mesh_edge_length, params = params): - n_wide = int(width/ mesh_edge_length + 1) - n_long = int(length/ mesh_edge_length + 1) +def mesh_square_cross(length, width, mesh_edge_length, params=params): + n_wide = int(width / mesh_edge_length + 1) + n_long = int(length / mesh_edge_length + 1) - mesh = np.meshgrid(np.linspace(0, length, n_long), - np.linspace(0, width, n_wide)) + mesh = np.meshgrid(np.linspace(0, length, n_long), np.linspace(0, width, n_wide)) initial_conditions = [] - xy_coordinates = np.column_stack(list(zip(mesh[0],mesh[1]))).T - xyz_coordinates = np.column_stack((xy_coordinates,np.zeros(len(xy_coordinates)).T)) + xy_coordinates = np.column_stack(list(zip(mesh[0], mesh[1]))).T + xyz_coordinates = np.column_stack((xy_coordinates, np.zeros(len(xy_coordinates)).T)) for xyz in xyz_coordinates: - initial_conditions.append([xyz, np.zeros(3), params['m_segment'], False]) + initial_conditions.append([xyz, np.zeros(3), params["m_segment"], False]) connections = [] - #We know that all the nodes are connected to those of the next row, which is grid_length+1 units further - for i, node in enumerate(initial_conditions[:-n_long]): # adding connextions in y-axis - connections.append([i, i+n_long, params['k'], params['c']]) + # We know that all the nodes are connected to those of the next row, which is grid_length+1 units further + for i, node in enumerate( + initial_conditions[:-n_long] + ): # adding connextions in y-axis + connections.append([i, i + n_long, params["k"], params["c"]]) - if (i+1)%(n_long): #cross connections - connections.append([i, i+n_long+1, params['k_d'], params['c']]) - connections.append([i+1, i+n_long, params['k_d'], params['c']]) + if (i + 1) % (n_long): # cross connections + connections.append([i, i + n_long + 1, params["k_d"], params["c"]]) + connections.append([i + 1, i + n_long, params["k_d"], params["c"]]) # We can do the same for the connections between the columns - for i, node in enumerate(initial_conditions): # adding connections in x-axis - if (i+1)%(n_long): # Using modulus operator to exclude the nodes at the end of a row - connections.append([i, i+1, params['k'], params['c']]) + for i, node in enumerate(initial_conditions): # adding connections in x-axis + if (i + 1) % ( + n_long + ): # Using modulus operator to exclude the nodes at the end of a row + connections.append([i, i + 1, params["k"], params["c"]]) return connections, initial_conditions -def mesh_square_cross_sparse(length, width, mesh_edge_length, params = params): - n_wide = int(width/ mesh_edge_length + 1) - n_long = int(length/ mesh_edge_length + 1) +def mesh_square_cross_sparse(length, width, mesh_edge_length, params=params): + n_wide = int(width / mesh_edge_length + 1) + n_long = int(length / mesh_edge_length + 1) - mesh = np.meshgrid(np.linspace(0, length, n_long), - np.linspace(0, width, n_wide)) + mesh = np.meshgrid(np.linspace(0, length, n_long), np.linspace(0, width, n_wide)) initial_conditions = [] - xy_coordinates = np.column_stack(list(zip(mesh[0],mesh[1]))).T - xyz_coordinates = np.column_stack((xy_coordinates,np.zeros(len(xy_coordinates)).T)) + xy_coordinates = np.column_stack(list(zip(mesh[0], mesh[1]))).T + xyz_coordinates = np.column_stack((xy_coordinates, np.zeros(len(xy_coordinates)).T)) for xyz in xyz_coordinates: - initial_conditions.append([xyz, np.zeros(3), params['m_segment'], False]) + initial_conditions.append([xyz, np.zeros(3), params["m_segment"], False]) connections = [] - #We know that all the nodes are connected to those of the next row, which is grid_length+1 units further + # We know that all the nodes are connected to those of the next row, which is grid_length+1 units further flip = True - for i, node in enumerate(initial_conditions[:-n_long]): # adding connextions in y-axis - connections.append([i, i+n_long, params['k'], params['c']]) + for i, node in enumerate( + initial_conditions[:-n_long] + ): # adding connextions in y-axis + connections.append([i, i + n_long, params["k"], params["c"]]) - if (i+1)%(n_long): #cross connections + if (i + 1) % (n_long): # cross connections if flip: - connections.append([i, i+n_long+1, params['k_d'], params['c']]) + connections.append([i, i + n_long + 1, params["k_d"], params["c"]]) flip = False else: - connections.append([i+1, i+n_long, params['k_d'], params['c']]) - flip= True + connections.append([i + 1, i + n_long, params["k_d"], params["c"]]) + flip = True else: flip = not flip # We can do the same for the connections between the columns - for i, node in enumerate(initial_conditions): # adding connections in x-axis - if (i+1)%(n_long): # Using modulus operator to exclude the nodes at the end of a row - connections.append([i, i+1, params['k'], params['c']]) + for i, node in enumerate(initial_conditions): # adding connections in x-axis + if (i + 1) % ( + n_long + ): # Using modulus operator to exclude the nodes at the end of a row + connections.append([i, i + 1, params["k"], params["c"]]) return connections, initial_conditions -def mesh_square_concentric(length, mesh_edge_length, params = params ,fix_outer = False): - n_long = int(length/ mesh_edge_length + 1) - x_space = np.linspace(-length/2, length/2, n_long) +def mesh_square_concentric(length, mesh_edge_length, params=params, fix_outer=False): + n_long = int(length / mesh_edge_length + 1) + + x_space = np.linspace(-length / 2, length / 2, n_long) y_space = x_space - mesh = np.meshgrid(x_space,y_space) + mesh = np.meshgrid(x_space, y_space) initial_conditions = [] - xy_coordinates = np.column_stack(list(zip(mesh[0],mesh[1]))).T - xyz_coordinates = np.column_stack((xy_coordinates,np.zeros(len(xy_coordinates)).T)) + xy_coordinates = np.column_stack(list(zip(mesh[0], mesh[1]))).T + xyz_coordinates = np.column_stack((xy_coordinates, np.zeros(len(xy_coordinates)).T)) for xyz in xyz_coordinates: - if (abs(xyz[0]) == length/2 and abs(xyz[1]) == length/2) and fix_outer: - initial_conditions.append([xyz, np.zeros(3), params['m_segment'], True]) + if (abs(xyz[0]) == length / 2 and abs(xyz[1]) == length / 2) and fix_outer: + initial_conditions.append([xyz, np.zeros(3), params["m_segment"], True]) else: - initial_conditions.append([xyz, np.zeros(3), params['m_segment'], False]) + initial_conditions.append([xyz, np.zeros(3), params["m_segment"], False]) connections = [] - dia_counter = [0,n_long-2] - #We know that all the nodes are connected to those of the next row, which is grid_length+1 units further - for i, node in enumerate(initial_conditions[:-n_long]): # adding connextions in y-axis - if abs(node[0][0])>abs(node[0][1]): - connections.append([i, i+n_long, params['k'], params['c']]) + dia_counter = [0, n_long - 2] + # We know that all the nodes are connected to those of the next row, which is grid_length+1 units further + for i, node in enumerate( + initial_conditions[:-n_long] + ): # adding connextions in y-axis + if abs(node[0][0]) > abs(node[0][1]): + connections.append([i, i + n_long, params["k"], params["c"]]) - if abs(node[0][0])>=abs(node[0][1]) and node[0][1]<0: - connections.append([i, i+n_long, params['k'], params['c']]) + if abs(node[0][0]) >= abs(node[0][1]) and node[0][1] < 0: + connections.append([i, i + n_long, params["k"], params["c"]]) - if dia_counter[0] == i: #cross connections - dia_counter[0] += n_long +1 - connections.append([i, i+n_long+1, params['k_d'], params['c']]) + if dia_counter[0] == i: # cross connections + dia_counter[0] += n_long + 1 + connections.append([i, i + n_long + 1, params["k_d"], params["c"]]) if dia_counter[1] == i: - dia_counter[1] += n_long-1 - connections.append([i+1, i+n_long, params['k_d'], params['c']]) + dia_counter[1] += n_long - 1 + connections.append([i + 1, i + n_long, params["k_d"], params["c"]]) # We can do the same for the connections between the columns - for i, node in enumerate(initial_conditions): # adding connections in x-axis - if (i+1)%(n_long) and abs(node[0][0])<=abs(node[0][1]) and node[0][0]<0: # Using modulus operator to exclude the nodes at the end of a row - connections.append([i, i+1, params['k'], params['c']]) - elif (i+1)%(n_long) and abs(node[0][0])=0: # Using modulus operator to exclude the nodes at the end of a row - connections.append([i, i+1, params['k'], params['c']]) + for i, node in enumerate(initial_conditions): # adding connections in x-axis + if ( + (i + 1) % (n_long) and abs(node[0][0]) <= abs(node[0][1]) and node[0][0] < 0 + ): # Using modulus operator to exclude the nodes at the end of a row + connections.append([i, i + 1, params["k"], params["c"]]) + elif ( + (i + 1) % (n_long) and abs(node[0][0]) < abs(node[0][1]) and node[0][0] >= 0 + ): # Using modulus operator to exclude the nodes at the end of a row + connections.append([i, i + 1, params["k"], params["c"]]) return connections, initial_conditions -def mesh_airbag_square_cross(length, width= 0, mesh_edge_length = 1/10, params = params, noncompressive = False, sparse = False): + +def mesh_airbag_square_cross( + length, + width=0, + mesh_edge_length=1 / 10, + params=params, + noncompressive=False, + sparse=False, +): if sparse: meshfunct = mesh_square_cross_sparse else: meshfunct = mesh_square_cross - if width==0: + if width == 0: width = length - initial_conditions, connections = meshfunct(length, - width, - mesh_edge_length, - params) - + initial_conditions, connections = meshfunct(length, width, mesh_edge_length, params) # We iterate over the particle and set specific constraint conditions # to match the symmetry of the airbag being cut into 8 pieces for particle in initial_conditions: # sequence of if/elif statements matters becuase some of the used # critera can override others. - + print(f"particle: {particle}, type: {type(particle)}") # Fixing s.t. center node only move in z axis - if (particle[0] == [0,0,0]).all(): + if particle[0] == [0, 0, 0]: particle[3] = True - particle.append([0,0,1]) - particle.append('line') + particle.append([0, 0, 1]) + particle.append("line") elif particle[0][0] == length and particle[0][1] == 0: particle[3] = True - particle.append([1,0,0]) - particle.append('line') + particle.append([1, 0, 0]) + particle.append("line") elif particle[0][0] == 0 and particle[0][1] == length: particle[3] = True - particle.append([0,1,0]) - particle.append('line') - - elif ( - (particle[0][0] == length and particle[0][1]>0) - or - (particle[0][1] == width and particle[0][0]>0) - ): + particle.append([0, 1, 0]) + particle.append("line") + + elif (particle[0][0] == length and particle[0][1] > 0) or ( + particle[0][1] == width and particle[0][0] > 0 + ): particle[3] = True - particle.append([0,0,1]) - particle.append('plane') + particle.append([0, 0, 1]) + particle.append("plane") - elif particle[0][0] == 0 and particle[0][1]>0 and particle[0][1] 0 and particle[0][1] < length: particle[3] = True - particle.append([1,0,0]) - particle.append('plane') + particle.append([1, 0, 0]) + particle.append("plane") - elif particle[0][1] == 0 and particle[0][0]>0 and particle[0][0] 0 and particle[0][0] < length: particle[3] = True - particle.append([0,1,0]) - particle.append('plane') + particle.append([0, 1, 0]) + particle.append("plane") if noncompressive: linktype = SpringDamperType.NONCOMPRESSIVE @@ -226,69 +242,86 @@ def mesh_airbag_square_cross(length, width= 0, mesh_edge_length = 1/10, params return connections, initial_conditions -def mesh_phc_square_cross(length, - width= 0, - mesh_edge_length = 1/10, - params = params, - noncompressive = False, - sparse = False, - fix_outer = True, - center_lightsail = True): - required = ['E_x', 'E_y', "G", "thickness"] + +def mesh_phc_square_cross( + length, + width=0, + mesh_edge_length=1 / 10, + params=params, + noncompressive=False, + sparse=False, + fix_outer=True, + center_lightsail=True, +): + required = ["E_x", "E_y", "G", "thickness"] for key in required: if not key in params.keys(): raise KeyError(f"{key} missing from params") - if width==0: + if width == 0: width = length - n_wide = int(width/ mesh_edge_length + 1) # x count - n_long = int(length/ mesh_edge_length + 1) # y count - x_length = width/n_wide - y_length = length/n_long + n_wide = int(width / mesh_edge_length + 1) # x count + n_long = int(length / mesh_edge_length + 1) # y count + x_length = width / n_wide + y_length = length / n_long # Calculate k's from the given stifnesses # First calculate the diagnonal, it is required in the calculation of the orthogonal ones - params["k_d"] = params["G"] * params["thickness"] * x_length / (y_length*n_wide/np.sqrt(2)) + params["k_d"] = ( + params["G"] * params["thickness"] * x_length / (y_length * n_wide / np.sqrt(2)) + ) params["k_x"] = params["E_x"] * params["thickness"] params["k_y"] = params["E_y"] * params["thickness"] # Now we have to reduce the influence of the orthogonal springs in order to account for the # contribution of the diagonal ones - params["k_x"]*= params["k_x"]*n_long / (params["k_x"]*n_long + params["k_d"]*np.sqrt(2)*n_long) - params["k_y"]*= params["k_y"]*n_wide / (params["k_y"]*n_wide + params["k_d"]*np.sqrt(2)*n_wide) + params["k_x"] *= ( + params["k_x"] + * n_long + / (params["k_x"] * n_long + params["k_d"] * np.sqrt(2) * n_long) + ) + params["k_y"] *= ( + params["k_y"] + * n_wide + / (params["k_y"] * n_wide + params["k_d"] * np.sqrt(2) * n_wide) + ) # Perform the meshing - mesh = np.meshgrid(np.linspace(0, length, n_long), - np.linspace(0, width, n_wide)) + mesh = np.meshgrid(np.linspace(0, length, n_long), np.linspace(0, width, n_wide)) initial_conditions = [] - xy_coordinates = np.column_stack(list(zip(mesh[0],mesh[1]))).T - xyz_coordinates = np.column_stack((xy_coordinates,np.zeros(len(xy_coordinates)).T)) + xy_coordinates = np.column_stack(list(zip(mesh[0], mesh[1]))).T + xyz_coordinates = np.column_stack((xy_coordinates, np.zeros(len(xy_coordinates)).T)) for xyz in xyz_coordinates: - if (fix_outer and (xyz[0] == 0 or xyz[0] == length or - xyz[1] == 0 or xyz[1] == width)): - initial_conditions.append([xyz, np.zeros(3), params['m_segment'], True]) + if fix_outer and ( + xyz[0] == 0 or xyz[0] == length or xyz[1] == 0 or xyz[1] == width + ): + initial_conditions.append([xyz, np.zeros(3), params["m_segment"], True]) else: - initial_conditions.append([xyz, np.zeros(3), params['m_segment'], False]) + initial_conditions.append([xyz, np.zeros(3), params["m_segment"], False]) neglect_diagonals = False if params["G"] == 0: neglect_diagonals = True connections = [] - #We know that all the nodes are connected to those of the next row, which is grid_length+1 units further - for i, node in enumerate(initial_conditions[:-n_long]): # adding connextions in y-axis - connections.append([i, i+n_long, params['k_y'], params['c']]) + # We know that all the nodes are connected to those of the next row, which is grid_length+1 units further + for i, node in enumerate( + initial_conditions[:-n_long] + ): # adding connextions in y-axis + connections.append([i, i + n_long, params["k_y"], params["c"]]) - if (i+1)%(n_long) and not neglect_diagonals: #cross connections - connections.append([i, i+n_long+1, params['k_d'], params['c']]) - connections.append([i+1, i+n_long, params['k_d'], params['c']]) + if (i + 1) % (n_long) and not neglect_diagonals: # cross connections + connections.append([i, i + n_long + 1, params["k_d"], params["c"]]) + connections.append([i + 1, i + n_long, params["k_d"], params["c"]]) # We can do the same for the connections between the columns - for i, node in enumerate(initial_conditions): # adding connections in x-axis - if (i+1)%(n_long): # Using modulus operator to exclude the nodes at the end of a row - connections.append([i, i+1, params['k_x'], params['c']]) + for i, node in enumerate(initial_conditions): # adding connections in x-axis + if (i + 1) % ( + n_long + ): # Using modulus operator to exclude the nodes at the end of a row + connections.append([i, i + 1, params["k_x"], params["c"]]) if noncompressive: linktype = SpringDamperType.NONCOMPRESSIVE @@ -297,57 +330,62 @@ def mesh_phc_square_cross(length, link.append(linktype) if center_lightsail: - offset = np.array([width/2, length/2,0]) + offset = np.array([width / 2, length / 2, 0]) for p in initial_conditions: p[0] -= offset return connections, initial_conditions -def mesh_circle_square_cross(radius, mesh_edge_length, params = params, fix_outer = False, edge = 0): - n_wide = int(radius/ mesh_edge_length + 1) +def mesh_circle_square_cross( + radius, mesh_edge_length, params=params, fix_outer=False, edge=0 +): + n_wide = int(radius / mesh_edge_length + 1) n_long = n_wide - mesh = np.meshgrid(np.linspace(-radius, radius, n_long), - np.linspace(-radius, radius, n_wide)) + mesh = np.meshgrid( + np.linspace(-radius, radius, n_long), np.linspace(-radius, radius, n_wide) + ) initial_conditions = [] - xy_coordinates = np.column_stack(list(zip(mesh[0],mesh[1]))).T - xyz_coordinates = np.column_stack((xy_coordinates,np.zeros(len(xy_coordinates)).T)) + xy_coordinates = np.column_stack(list(zip(mesh[0], mesh[1]))).T + xyz_coordinates = np.column_stack((xy_coordinates, np.zeros(len(xy_coordinates)).T)) for xyz in xyz_coordinates: - initial_conditions.append([xyz, np.zeros(3), params['m_segment'], False]) + initial_conditions.append([xyz, np.zeros(3), params["m_segment"], False]) connections = [] - #We know that all the nodes are connected to those of the next row, which is grid_length+1 units further - for i, node in enumerate(initial_conditions[:-n_long]): # adding connextions in y-axis - connections.append([i, i+n_long, params['k'], params['c']]) + # We know that all the nodes are connected to those of the next row, which is grid_length+1 units further + for i, node in enumerate( + initial_conditions[:-n_long] + ): # adding connextions in y-axis + connections.append([i, i + n_long, params["k"], params["c"]]) - if (i+1)%(n_long): #cross connections - connections.append([i, i+n_long+1, params['k_d'], params['c']]) - connections.append([i+1, i+n_long, params['k_d'], params['c']]) + if (i + 1) % (n_long): # cross connections + connections.append([i, i + n_long + 1, params["k_d"], params["c"]]) + connections.append([i + 1, i + n_long, params["k_d"], params["c"]]) # We can do the same for the connections between the columns - for i, node in enumerate(initial_conditions): # adding connections in x-axis - if (i+1)%(n_long): # Using modulus operator to exclude the nodes at the end of a row - connections.append([i, i+1, params['k'], params['c']]) + for i, node in enumerate(initial_conditions): # adding connections in x-axis + if (i + 1) % ( + n_long + ): # Using modulus operator to exclude the nodes at the end of a row + connections.append([i, i + 1, params["k"], params["c"]]) # Now to trim the excess nodes and connections - mask = xy_coordinates[:,0]**2 + xy_coordinates[:,1]**2 <= radius**2 - + mask = xy_coordinates[:, 0] ** 2 + xy_coordinates[:, 1] ** 2 <= radius**2 dumplist = [] for i, keepit in enumerate(mask): if not keepit: - initial_conditions[i][3]= True + initial_conditions[i][3] = True # print(f'Iterating point {i}') for j, link in enumerate(connections): if i in link[:2]: # print(f'dumping link {j} for being connected to {i}: {link}') dumplist.append(j) - dumplist = list(set(dumplist)) dumplist = sorted(dumplist)[::-1] # print(f'dumplist length: {len(set(dumplist))}\n{dumplist[::-1]}') @@ -355,73 +393,102 @@ def mesh_circle_square_cross(radius, mesh_edge_length, params = params, fix_oute for i in dumplist: del connections[i] - - #for i, item in enumerate(initial_conditions): + # for i, item in enumerate(initial_conditions): # if mask[i]: # del initial_conditions[i] if fix_outer: if edge == 0: edge = mesh_edge_length * 1.5 - inner = xy_coordinates[:,0]**2 + xy_coordinates[:,1]**2 <= (radius-edge)**2 + inner = ( + xy_coordinates[:, 0] ** 2 + xy_coordinates[:, 1] ** 2 + <= (radius - edge) ** 2 + ) for i, freeit in enumerate(inner): if not freeit: - initial_conditions[i][3]= True + initial_conditions[i][3] = True return connections, initial_conditions -def mesh_round_phc_square_cross(radius, mesh_edge_length=1/10, params=None, noncompressive=False, sparse=False, fix_outer=True, edge=0): + +def mesh_round_phc_square_cross( + radius, + mesh_edge_length=1 / 10, + params=None, + noncompressive=False, + sparse=False, + fix_outer=True, + edge=0, +): if params is None: params = {} - required = ['E_x', 'E_y', "G", "thickness", "m_segment", "c"] + required = ["E_x", "E_y", "G", "thickness", "m_segment", "c"] for key in required: if key not in params.keys(): raise KeyError(f"{key} missing from params") # Calculating grid dimensions - n_wide = int(2*radius / mesh_edge_length) + 1 + n_wide = int(2 * radius / mesh_edge_length) + 1 n_long = n_wide - y_length = radius/n_long + y_length = radius / n_long x_length = y_length - mesh = np.meshgrid(np.linspace(-radius, radius, n_long), np.linspace(-radius, radius, n_wide)) + mesh = np.meshgrid( + np.linspace(-radius, radius, n_long), np.linspace(-radius, radius, n_wide) + ) xy_coordinates = np.column_stack([mesh[0].flatten(), mesh[1].flatten()]) - mask = xy_coordinates[:,0]**2 + xy_coordinates[:,1]**2 <= radius**2 + mask = xy_coordinates[:, 0] ** 2 + xy_coordinates[:, 1] ** 2 <= radius**2 filtered_xy_coordinates = xy_coordinates[mask] - xyz_coordinates = np.column_stack((filtered_xy_coordinates, np.zeros(len(filtered_xy_coordinates)))) - initial_conditions = [[xyz, np.zeros(3), params['m_segment'], False] for xyz in xyz_coordinates] + xyz_coordinates = np.column_stack( + (filtered_xy_coordinates, np.zeros(len(filtered_xy_coordinates))) + ) + initial_conditions = [ + [xyz, np.zeros(3), params["m_segment"], False] for xyz in xyz_coordinates + ] # Calculate k's from the given stifnesses # First calculate the diagnonal, it is required in the calculation of the orthogonal ones - params["k_d"] = params["G"] * params["thickness"] * x_length / (y_length*n_wide/np.sqrt(2)) + params["k_d"] = ( + params["G"] * params["thickness"] * x_length / (y_length * n_wide / np.sqrt(2)) + ) params["k_x"] = params["E_x"] * params["thickness"] params["k_y"] = params["E_y"] * params["thickness"] # Now we have to reduce the influence of the orthogonal springs in order to account for the # contribution of the diagonal ones - params["k_x"]*= params["k_x"]*n_long / (params["k_x"]*n_long + params["k_d"]*np.sqrt(2)*n_long) - params["k_y"]*= params["k_y"]*n_wide / (params["k_y"]*n_wide + params["k_d"]*np.sqrt(2)*n_wide) - - + params["k_x"] *= ( + params["k_x"] + * n_long + / (params["k_x"] * n_long + params["k_d"] * np.sqrt(2) * n_long) + ) + params["k_y"] *= ( + params["k_y"] + * n_wide + / (params["k_y"] * n_wide + params["k_d"] * np.sqrt(2) * n_wide) + ) connections = [] for i, cond_i in enumerate(initial_conditions): - for j, cond_j in enumerate(initial_conditions[i+1:], start=i+1): - distance = np.linalg.norm(cond_i[0][:2] - cond_j[0][:2])/1.01 + for j, cond_j in enumerate(initial_conditions[i + 1 :], start=i + 1): + distance = np.linalg.norm(cond_i[0][:2] - cond_j[0][:2]) / 1.01 if distance <= mesh_edge_length: # Direct neighbors - k_value = params['k_x'] if cond_i[0][1] == cond_j[0][1] else params['k_y'] - connection = [i, j, k_value, params['c']] + k_value = ( + params["k_x"] if cond_i[0][1] == cond_j[0][1] else params["k_y"] + ) + connection = [i, j, k_value, params["c"]] if noncompressive: connection.append(SpringDamperType.NONCOMPRESSIVE) connections.append(connection) - elif distance <= mesh_edge_length * np.sqrt(2) and params["G"]!=0: # Diagonal connections only if G != 0 - connection = [i, j, params['k_d'], params['c']] + elif ( + distance <= mesh_edge_length * np.sqrt(2) and params["G"] != 0 + ): # Diagonal connections only if G != 0 + connection = [i, j, params["k_d"], params["c"]] if noncompressive: connection.append(SpringDamperType.NONCOMPRESSIVE) connections.append(connection) - # Optionally fix the outer nodes + # Optionally fix the outer nodes if fix_outer: if edge == 0: edge = mesh_edge_length * 1.5 @@ -436,13 +503,13 @@ def mesh_rotate_and_trim(initial_conditions, connections, angle): """ NOTE: Input mesh is expected to be square! """ - center_of_mass = np.array([0,0,0],dtype ='float64') + center_of_mass = np.array([0, 0, 0], dtype="float64") for particle in initial_conditions: - center_of_mass+=particle[0] + center_of_mass += particle[0] center_of_mass = center_of_mass / len(initial_conditions) x_cleaned = np.array([i[0] for i in initial_conditions]) - x_range, y_range, z_range = np.ptp(x_cleaned, axis = 0) + x_range, y_range, z_range = np.ptp(x_cleaned, axis=0) # rotation shrinks size of inscribed rectangle # Going for constant angle for consistent size @@ -450,7 +517,7 @@ def mesh_rotate_and_trim(initial_conditions, connections, angle): x_range *= factor y_range *= factor - rotation_matrix = R.from_euler('z', angle, degrees = True).as_matrix() + rotation_matrix = R.from_euler("z", angle, degrees=True).as_matrix() for particle in initial_conditions: particle[0] -= center_of_mass @@ -461,13 +528,13 @@ def mesh_rotate_and_trim(initial_conditions, connections, angle): for i, link in enumerate(connections): xyz_0 = initial_conditions[link[0]][0] xyz_1 = initial_conditions[link[1]][0] - if abs(xyz_0[0])> x_range/2: + if abs(xyz_0[0]) > x_range / 2: dumplist.add(i) - elif abs(xyz_0[1])> y_range/2: + elif abs(xyz_0[1]) > y_range / 2: dumplist.add(i) - elif abs(xyz_1[0])> x_range/2: + elif abs(xyz_1[0]) > x_range / 2: dumplist.add(i) - elif abs(xyz_1[1])> y_range/2: + elif abs(xyz_1[1]) > y_range / 2: dumplist.add(i) dumplist = list(dumplist) dumplist.sort() @@ -478,30 +545,30 @@ def mesh_rotate_and_trim(initial_conditions, connections, angle): return connections, initial_conditions -def ps_fix_opposite_boundaries_x(ParticleSystem, margin = 0.075): +def ps_fix_opposite_boundaries_x(ParticleSystem, margin=0.075): """ Fixes two boundaries in preparation for unidirectional pull test """ - center_of_mass = np.array([0,0,0],dtype ='float64') + center_of_mass = np.array([0, 0, 0], dtype="float64") for particle in ParticleSystem.particles: - center_of_mass+=particle.x + center_of_mass += particle.x center_of_mass = center_of_mass / len(ParticleSystem.particles) x_cleaned = np.array([particle.x[0] for particle in ParticleSystem.particles]) - x_range = np.ptp(x_cleaned, axis = 0) + x_range = np.ptp(x_cleaned, axis=0) for particle in ParticleSystem.particles: - particle.update_pos_unsafe(particle.x-center_of_mass) + particle.update_pos_unsafe(particle.x - center_of_mass) boundary_x_min = [] boundary_x_plus = [] for i, particle in enumerate(ParticleSystem.particles): - if abs(particle.x[0]) > ((x_range/2)*(1-margin)): + if abs(particle.x[0]) > ((x_range / 2) * (1 - margin)): particle.set_fixed(True) - if particle.x[0]>0: + if particle.x[0] > 0: boundary_x_plus.append(i) else: boundary_x_min.append(i) @@ -509,6 +576,7 @@ def ps_fix_opposite_boundaries_x(ParticleSystem, margin = 0.075): return ParticleSystem, boundaries + def ps_stretch_in_x(ParticleSystem, boundary, displacement): for indice in boundary: particle = ParticleSystem.particles[indice] @@ -516,79 +584,85 @@ def ps_stretch_in_x(ParticleSystem, boundary, displacement): new_pos[0] += displacement particle.update_pos(new_pos) + def ps_find_reaction_of_boundary(ParticleSystem, boundary): # !!! ATTENTION !!! DRAFT CODE! COMPLETLY UNTESTED! internal_forces = ParticleSystem._ParticleSystem__one_d_force_vector() reaction = np.array([0.0, 0.0, 0.0]) for indice in boundary: - reaction += internal_forces[indice*3: indice*3+3] + reaction += internal_forces[indice * 3 : indice * 3 + 3] return reaction -def ps_find_mid_strip_y(ParticleSystem, width= 1): + +def ps_find_mid_strip_y(ParticleSystem, width=1): center_of_mass = np.mean(ParticleSystem.x_v_current_3D[0], axis=0) for particle in ParticleSystem.particles: - particle.update_pos_unsafe(particle.x-center_of_mass) + particle.update_pos_unsafe(particle.x - center_of_mass) midstrip = [] for i, particle in enumerate(ParticleSystem.particles): pos = particle.x - if abs(pos[0]) <= width/2: + if abs(pos[0]) <= width / 2: midstrip.append(i) return midstrip + def ps_find_strip_dimentions(ParticleSystem, midstrip): positions = [] for indice in midstrip: particle = ParticleSystem.particles[indice] positions.append(particle.x) positions = np.array(positions) - point_to_point_range = np.ptp(positions, axis = 0) + point_to_point_range = np.ptp(positions, axis=0) return point_to_point_range -if __name__ == '__main__': + +if __name__ == "__main__": from Particle_System_Simulator.particleSystem.ParticleSystem import ParticleSystem import matplotlib.pyplot as plt + params = { # model parameters "k": 1, # [N/m] spring stiffness "k_d": 1, # [N/m] spring stiffness for diagonal elements "c": 1, # [N s/m] damping coefficient - "m_segment": 1, # [kg] mass of each node + "m_segment": 1, # [kg] mass of each node # simulation settings "dt": 0.1, # [s] simulation timestep "t_steps": 1000, # [-] number of simulated time steps "abs_tol": 1e-50, # [m/s] absolute error tolerance iterative solver "rel_tol": 1e-5, # [-] relative error tolerance iterative solver "max_iter": 1e5, # [-] maximum number of iterations - "E_x":100e9, # [Pa] - "E_y":100e9, # [Pa] - "G":0, # [Pa] - "thickness":100e-9 # [m] - } + "E_x": 100e9, # [Pa] + "E_y": 100e9, # [Pa] + "G": 0, # [Pa] + "thickness": 100e-9, # [m] + } # !!! Don't forget to add new meshing functions to this list! - meshing_functions = [mesh_square, - mesh_square_cross, - mesh_square_cross_sparse, - mesh_airbag_square_cross, - mesh_square_concentric, - mesh_circle_square_cross, - mesh_round_phc_square_cross] - inputs = [16,8,1, params] + meshing_functions = [ + mesh_square, + mesh_square_cross, + mesh_square_cross_sparse, + mesh_airbag_square_cross, + mesh_square_concentric, + mesh_circle_square_cross, + mesh_round_phc_square_cross, + ] + inputs = [16, 8, 1, params] nplots = len(meshing_functions) + 1 - cols = int(np.sqrt(nplots)) +1 + cols = int(np.sqrt(nplots)) + 1 rows = nplots // cols - if nplots % cols !=0: - rows +=1 - + if nplots % cols != 0: + rows += 1 fig = plt.figure() pslist = [] for i, function in enumerate(meshing_functions): - ax = fig.add_subplot(rows, cols,i+1,projection='3d') - ax.set_box_aspect([1,1,1]) - if i ==4: + ax = fig.add_subplot(rows, cols, i + 1, projection="3d") + ax.set_box_aspect([1, 1, 1]) + if i == 4: inputs = inputs[1:] initial_conditions, connections = function(*inputs) PS = ParticleSystem(connections, initial_conditions, params) @@ -597,20 +671,24 @@ def ps_find_strip_dimentions(ParticleSystem, midstrip): PS.plot(ax) ax.set_title(function.__name__) - - initial_conditions, connections = mesh_square_cross(20,20,1,params) - initial_conditions, connections = mesh_rotate_and_trim(initial_conditions, - connections, - 45/2) - PS = ParticleSystem(connections, initial_conditions,params) - PS, boundaries = ps_fix_opposite_boundaries_x(PS, margin = 0.175) + initial_conditions, connections = mesh_square_cross(20, 20, 1, params) + initial_conditions, connections = mesh_rotate_and_trim( + initial_conditions, connections, 45 / 2 + ) + PS = ParticleSystem(connections, initial_conditions, params) + PS, boundaries = ps_fix_opposite_boundaries_x(PS, margin=0.175) ps_stretch_in_x(PS, boundaries[1], 1) pslist.append(PS) - ax = fig.add_subplot(rows, cols, nplots, projection='3d') + ax = fig.add_subplot(rows, cols, nplots, projection="3d") PS.plot(ax) - ax.set_title((mesh_square_cross.__name__, mesh_rotate_and_trim.__name__, ps_fix_opposite_boundaries_x.__name__, ps_stretch_in_x.__name__)) - - + ax.set_title( + ( + mesh_square_cross.__name__, + mesh_rotate_and_trim.__name__, + ps_fix_opposite_boundaries_x.__name__, + ps_stretch_in_x.__name__, + ) + ) diff --git a/src/Particle_System_Simulator/Sim/simulations.py b/src/Particle_System_Simulator/Sim/simulations.py index 2e179b7..14e11a2 100644 --- a/src/Particle_System_Simulator/Sim/simulations.py +++ b/src/Particle_System_Simulator/Sim/simulations.py @@ -15,9 +15,8 @@ import numpy as np import matplotlib.pyplot as plt -from ..particleSystem.ParticleSystem import ParticleSystem -from ..Mesh import mesh_functions as MF - +from Particle_System_Simulator.particleSystem.ParticleSystem import ParticleSystem +from Particle_System_Simulator.Mesh import mesh_functions as MF class Simulate: diff --git a/src/Particle_System_Simulator/particleSystem/ParticleSystem.py b/src/Particle_System_Simulator/particleSystem/ParticleSystem.py index a8ec4af..c23ebee 100644 --- a/src/Particle_System_Simulator/particleSystem/ParticleSystem.py +++ b/src/Particle_System_Simulator/particleSystem/ParticleSystem.py @@ -4,7 +4,6 @@ """ import logging - import numpy as np import numpy.typing as npt from scipy.sparse.linalg import bicgstab @@ -13,6 +12,7 @@ from scipy.spatial.transform import Rotation import matplotlib.pyplot as plt from mpl_toolkits.mplot3d.art3d import Line3DCollection +import copy from .Particle import Particle from .SpringDamper import SpringDamper @@ -62,6 +62,13 @@ def __init__( self.__atol = sim_param["abs_tol"] self.__maxiter = int(sim_param["max_iter"]) + ## Dealing with the occurence of pulleys + if "pulley_other_line_pair" in sim_param: + # copy.deepcopy is used, because np.copy only works for np.arrays -this must remain an dict + self.__pulley_other_line_pair = copy.deepcopy( + sim_param["pulley_other_line_pair"] + ) + # allocate memory self.__particles = [] self.__springdampers = [] @@ -126,6 +133,7 @@ def __instantiate_springdampers(self): link = link.copy() # needed to not override the __connectivity_matrix link[0] = self.__particles[link[0]] link[1] = self.__particles[link[1]] + # SpringDamper takes in (p1, p2, k, c, linktype="default") SD = SpringDamper(*link) self.__springdampers.append(SD) link[0].connections.append(SD) @@ -377,16 +385,50 @@ def __pack_v_current(self): def __pack_x_current(self): return np.array([particle.x for particle in self.__particles]).flatten() - def __one_d_force_vector(self): - # self.__f[self.__f != 0] = 0 - self.__f = np.zeros(self.__f.shape, dtype=np.float64) + # TODO: this commented out is the old version, which can not deal with pulleys + # def __one_d_force_vector(self): + # # self.__f[self.__f != 0] = 0 + # self.__f = np.zeros(self.__f.shape, dtype=np.float64) - for n in range(len(self.__springdampers)): - f_int = self.__springdampers[n].force_value() - i, j, *_ = self.__connectivity_matrix[n] + # for n in range(len(self.__springdampers)): + # f_int = self.__springdampers[n].force_value() + # i, j, *_ = self.__connectivity_matrix[n] + + # self.__f[i * 3 : i * 3 + 3] += f_int + # self.__f[j * 3 : j * 3 + 3] -= f_int - self.__f[i * 3 : i * 3 + 3] += f_int - self.__f[j * 3 : j * 3 + 3] -= f_int + # return self.__f + + def __one_d_force_vector(self): + self.__f[self.__f != 0] = 0 + + # calling this once, instead of for each springdamper + x_current = self.__pack_x_current() + # print(f"len(x_current): {len(x_current)} ") + + for idx, SD in enumerate(self.__springdampers): + # if pulley + if SD.linktype == "pulley": + idx_p3, idx_p4, rest_length_p3p4 = self.__pulley_other_line_pair[ + str(idx) + ] + # points are flat, so we need to configure a bit + p3 = np.array([x_current[int(idx_p3) * 3 : int(idx_p3) * 3 + 3]]) + p4 = np.array([x_current[int(idx_p4) * 3 : int(idx_p4) * 3 + 3]]) + norm_p3p4 = np.linalg.norm(p3 - p4) + delta_length_pulley_other_line = norm_p3p4 - rest_length_p3p4 + f_int = SD.force_value(delta_length_pulley_other_line) + + i, j = self.__connectivity_matrix[idx] + self.__f[i * 3 : i * 3 + 3] += f_int + self.__f[j * 3 : j * 3 + 3] -= f_int + + # if regular spring-damper + else: + f_int = self.__springdampers[idx].force_value() + i, j = self.__connectivity_matrix[idx] + self.__f[i * 3 : i * 3 + 3] += f_int + self.__f[j * 3 : j * 3 + 3] -= f_int return self.__f diff --git a/src/Particle_System_Simulator/particleSystem/SpringDamper.py b/src/Particle_System_Simulator/particleSystem/SpringDamper.py index e377987..4eacd59 100644 --- a/src/Particle_System_Simulator/particleSystem/SpringDamper.py +++ b/src/Particle_System_Simulator/particleSystem/SpringDamper.py @@ -1,6 +1,7 @@ """ Child Class 'SpringDamper', for spring-damper objects to be instantiated in ParticleSystem -""" +""" + from enum import Enum import numpy as np @@ -11,7 +12,7 @@ class SpringDamperType(Enum): """ Enumeration representing the various types of SpringDamper objects. - + Attributes ---------- DEFAULT : str @@ -20,38 +21,54 @@ class SpringDamperType(Enum): Represents a SpringDamper that cannot be compressed, only stretched. NONTENSILE : str Represents a SpringDamper that cannot be stretched, only compressed. - + PULLEY : str + Represents a SpringDamper that is a pulley, NONCOMPRESSIVE and connected + ROTATIONAL: str + Represents a SpringDamper that has rotational resistance, beside its compression and tension properties + Notes ----- The SpringDamper type affects the initialization and behavior of the SpringDamper objects. Each type might have specific properties or behaviors associated with it in the SpringDamper class. """ + DEFAULT = "default" NONCOMPRESSIVE = "noncompressive" NONTENSILE = "nontensile" + PULLEY = "pulley" + ROTATIONAL = "rotational" + class SpringDamper(ImplicitForce): """ #TODO one line summary - - + + Attributes: #TODO finish this - + """ - def __init__(self, p1: Particle, p2: Particle, k: float, c: float, linktype=SpringDamperType.DEFAULT): + + def __init__( + self, + p1: Particle, + p2: Particle, + k: float, + c: float, + linktype=SpringDamperType.DEFAULT, + ): """Initializes the spring damper - + Args: - p1, p2: + p1, p2: The two Particle instances to be connected - k: + k: A float representing the stiffness of the spring in N/m. - c: + c: A float representing the damping coefficient in Ns/m linktype: A SpringDamperType enum representing the properties of the link See SpringDamperType for more information - + """ super().__init__(p1, p2) self.__k = k @@ -61,10 +78,12 @@ def __init__(self, p1: Particle, p2: Particle, k: float, c: float, linktype=Spri return def __str__(self): - return f"SpringDamper object, spring stiffness [n/m]: {self.__k}, rest length [m]: {self.l0}\n" \ - f"Damping coefficient [N s/m]: {self.__c}\n" \ - f"Assigned particles\n p1: {self.p1}\n p2: {self.p2}\n"\ - f"Link type: {self.__linktype}" + return ( + f"SpringDamper object, spring stiffness [n/m]: {self.__k}, rest length [m]: {self.l0}\n" + f"Damping coefficient [N s/m]: {self.__c}\n" + f"Assigned particles\n p1: {self.p1}\n p2: {self.p2}\n" + f"Link type: {self.__linktype}" + ) def __relative_pos(self): return np.array([self.p1.x - self.p2.x]) @@ -72,25 +91,7 @@ def __relative_pos(self): def __relative_vel(self): return np.array([self.p1.v - self.p2.v]) - def force_value(self): - if self.__linktype == SpringDamperType.DEFAULT: - return self.__calculate_f_spring() + self.__calculate_f_damping() - - elif self.__linktype == SpringDamperType.NONCOMPRESSIVE: - l = np.linalg.norm(self.__relative_pos()) - if l >=self.l0: - return self.__calculate_f_spring() + self.__calculate_f_damping() - else: - return np.array([0, 0, 0]) - - elif self.__linktype == SpringDamperType.NONTENSILE: - l = np.linalg.norm(self.__relative_pos()) - if l <=self.l0: - return self.__calculate_f_spring() + self.__calculate_f_damping() - else: - return np.array([0, 0, 0]) - - def __calculate_f_spring(self): + def __calculate_f_spring(self, delta_length_pulley_other_line: float = 0): relative_pos = self.__relative_pos() norm_pos = np.linalg.norm(relative_pos) @@ -99,7 +100,11 @@ def __calculate_f_spring(self): else: unit_vector = np.array([0, 0, 0]) - f_spring = -self.__k * (norm_pos - self.l0) * unit_vector + f_spring = ( + -self.__k + * (norm_pos - self.l0 + delta_length_pulley_other_line) + * unit_vector + ) return np.squeeze(f_spring) def __calculate_f_damping(self): @@ -115,21 +120,65 @@ def __calculate_f_damping(self): f_damping = -self.__c * np.dot(relative_vel, unit_vector) * unit_vector return np.squeeze(f_damping) + # TODO: OLD LSS Method + # def force_value(self): + # if self.__linktype == SpringDamperType.DEFAULT: + # return self.__calculate_f_spring() + self.__calculate_f_damping() + + # elif self.__linktype == SpringDamperType.NONCOMPRESSIVE: + # l = np.linalg.norm(self.__relative_pos()) + # if l >= self.l0: + # return self.__calculate_f_spring() + self.__calculate_f_damping() + # else: + # return np.array([0, 0, 0]) + + # elif self.__linktype == SpringDamperType.NONTENSILE: + # l = np.linalg.norm(self.__relative_pos()) + # if l <= self.l0: + # return self.__calculate_f_spring() + self.__calculate_f_damping() + # else: + # return np.array([0, 0, 0]) + + def force_value(self, delta_length_pulley_other_line: float = 0): + if self.__linktype == SpringDamperType.DEFAULT: + return self.__calculate_f_spring() + self.__calculate_f_damping() + + elif self.__linktype == SpringDamperType.NONCOMPRESSIVE: + l = np.linalg.norm(self.__relative_pos()) + if l >= self.l0: + return self.__calculate_f_spring() + self.__calculate_f_damping() + else: + return np.array([0, 0, 0]) + + elif self.__linktype == SpringDamperType.PULLEY: + l = np.linalg.norm(self.__relative_pos()) + delta_length_pulley_other_line + if l >= self.l0: + return ( + self.__calculate_f_spring(delta_length_pulley_other_line) + + self.__calculate_f_damping() + ) + else: + return np.array([0, 0, 0]) + + elif self.__linktype == SpringDamperType.NONTENSILE: + l = np.linalg.norm(self.__relative_pos()) + if l <= self.l0: + return self.__calculate_f_spring() + self.__calculate_f_damping() + else: + return np.array([0, 0, 0]) + + elif self.__linktype == SpringDamperType.ROTATIONAL: + raise NotImplementedError("Rotational SpringDamper not implemented yet") + def calculate_jacobian(self): relative_pos = self.__relative_pos() norm_pos = np.linalg.norm(relative_pos) # Using guard classes to return early in special cases - if ( - self.__linktype == SpringDamperType.NONCOMPRESSIVE and - norm_pos <= self.__l0 - ): + if self.__linktype == SpringDamperType.NONCOMPRESSIVE and norm_pos <= self.__l0: return np.zeros((3, 3)), np.zeros((3, 3)) - - elif ( - self.__linktype == SpringDamperType.NONTENSILE and - norm_pos >= self.__l0 - ): + + elif self.__linktype == SpringDamperType.NONTENSILE and norm_pos >= self.__l0: return np.zeros(3), np.zeros(3) if norm_pos != 0: @@ -142,34 +191,42 @@ def calculate_jacobian(self): T = np.matmul(np.transpose(unit_vector), unit_vector) jx = -self.__k * ((self.l0 / norm_pos - 1) * (T - i) + T) - jv = -self.__c*i + jv = -self.__c * i return jx, jv - + def line_segment(self): """Returns coordinate tuple of particles at either end of segment""" return (self.p1.x, self.p2.x) - + @property def l(self): return np.linalg.norm(self.p1.x - self.p2.x) - + @property def l0(self): return self.__l0 - + @l0.setter - def l0(self,value): # Exposed to enable self-stressing of mesh + def l0(self, value): # Exposed to enable self-stressing of mesh self.__l0 = value @property def c(self): return self.__c - + @c.setter - def c(self,value): # Exposed to enable resetting when using kinetic damping + def c(self, value): # Exposed to enable resetting when using kinetic damping self.__c = value + @property + def k(self): + return self.__k + + @property + def linktype(self): + return self.__linktype + if __name__ == "__main__": @@ -178,7 +235,7 @@ def c(self,value): # Exposed to enable resetting when using kinetic damping stiffness = 1e5 damping = 10 rest_length = 0 - linktype = 'noncomp' + linktype = "noncomp" springdamper = SpringDamper(particle1, particle2, stiffness, damping)