def combine_particles(self,particle1,particle2): #get phase space of new particle #delete collided particles in state and mass #add new particle in state and mass #nparticles -= 1 mass1 = self.masses[particle1] mass2 = self.masses[particle2] xd1 = self.state[particle1,1,0] yd1 = self.state[particle1,1,1] xd2 = self.state[particle2,1,0] yd2 = self.state[particle2,1,1] totalmass = mass1+mass2 newxd = ((mass1*xd1) + (m2*xd2))/totalmass newyd = ((mass1*yd1) + (m2*yd2))/totalmass self.masses = np.delete(self.masses,[particle1,particle2],axis=0) new_phase_vector = [self.state[particle1,0,0],self.state[particle1,0,1],[newxd,newyd]] self.masses = np.append(self.masses,totalmass,axis=0) self.Nparticles -= 1 return(new_phase_vector)
def UpdateStateVectorRK4(self,t): self.event_horizon = self.get_event_horizon(t) new_state = np.ndarray((self.Nparticles,2,2)) for particle in xrange(self.Nparticles): kstate = np.copy(self.state) #do RK4 shit k1 = self.dt * self.SingleParticleDerivativeVector(kstate,particle, t+self.dt) kstate[particle] = np.copy(self.state[particle]) + k1/2 k2 = self.dt * self.SingleParticleDerivativeVector(kstate,particle,t+(self.dt/2)) kstate[particle] = np.copy(self.state[particle]) + k2/2 k3 = self.dt * self.SingleParticleDerivativeVector(kstate,particle,t+(self.dt/2)) kstate[particle] = np.copy(self.state[particle]) + k3 k4 = self.dt * self.SingleParticleDerivativeVector(kstate,particle,t+self.dt) new_state[particle] = np.copy(self.state[particle]) + (1/3)*(k1/2 + k2 + k3 + k4/2) #Get rid of gobbled or ejected particles if self.cleanup != []: for particle in self.cleanup: print("\n***particle {} shit the bed at step {}***".format(particle,int(t/self.dt))) self.remove_particle(particle) new_state = np.delete(new_state,particle,axis=0) print("***particle {} removed***".format(particle)) self.cleanup.remove(particle) if self.cleanup == []: print("***cleanup completed***") self.cleanup = [] #Cleanup collided particles if self.collisions != {}: print("*****") for key in self.collisions: p1 = self.collisions[key][1][0] p2 = self.collisions[key][1][1] new_particle = self.combine_particles() print("***{} and {} collided at step{}***".format(p1,p2,int(t/self.dt))) new_state = np.delete(new_state,[self.collisions[key][0],self.collisions[1]],axis=0) new_state = np.append(new_state,new_particle,axis = 0) self.collisions = {} return(new_state)
def remove_particle(self,particle): self.state = np.delete(self.state,particle,axis=0) self.Nparticles -= 1
def remove_particle(self,particle): self.state = np.delete(self.state,self.cleanup,axis=0) for key in self.collision_dict: if particle in self.collision_dict[key][1]: self.collision_dict[key][1].remove(particle) self.Nparticles = len(self.state)
def UpdateStateVectorRK4(self,t): self.event_horizon = self.get_event_horizon(t) new_state = np.ndarray((self.Nparticles,2,3)) for particle in xrange(self.Nparticles): kstate = np.copy(self.state) k1 = self.dt * self.SingleParticleDerivativeVector(kstate,particle, t) kstate[particle] = np.copy(self.state[particle]) + k1/2 k2 = self.dt * self.SingleParticleDerivativeVector(kstate,particle,t+(self.dt/2)) kstate[particle] = np.copy(self.state[particle]) + k2/2 k3 = self.dt * self.SingleParticleDerivativeVector(kstate,particle,t+(self.dt/2)) kstate[particle] = np.copy(self.state[particle]) + k3 k4 = self.dt * self.SingleParticleDerivativeVector(kstate,particle,t+self.dt) new_state[particle] = np.copy(self.state[particle]) + (1/3)*(k1/2 + k2 + k3 + k4/2) #Get rid of gobbled or ejected particles if self.cleanup != []: new_state = np.delete(new_state,self.cleanup,axis=0) self.remove_particle(particle) for particle in self.cleanup: print("\n***particle {} shit the bed at step {}***".format(particle,int(t/self.dt))) print("***particle {} removed***".format(particle)) self.cleanup.remove(particle) if self.cleanup == []: print("***cleanup completed***") self.cleanup = [] big_particle_list = [] big_vector_list = [] new_masses_list = [] #Cleanup collided particles if self.collisions == "inelastic": if self.collision_dict != {}: for key in self.collision_dict: particles = self.collision_dict[key][1] m = 0 x = self.collision_dict[key][0][0] y = self.collision_dict[key][0][1] mvx = 0 mvy = 0 for particle in particles: m += self.masses[particle] for particle in particles: mvx += (self.state[particle][1][0]*self.masses[particle]) mvy += (self.state[particle][1][1]*self.masses[particle]) big_particle_list.append(particle) new_masses_list.append(m) new_particle = [[x,y],[mvx/m,mvy/m]] big_vector_list.append(new_particle) self.masses = np.delete(self.masses,big_particle_list,axis=0) self.masses = np.append(self.masses,np.array(new_masses_list),axis = 0) new_state = np.delete(new_state,big_particle_list,axis=0) new_state = np.append(new_state,np.array(big_vector_list),axis = 0) self.Nparticles = len(new_state) self.collision_dict = {} self.collided_particles = np.array([]) elif self.collisions == 'elastic': distances = squareform(pdist(self.state[:,0,:])) ind1, ind2 = np.where(distances < 2 * self.collision_radius) unique = (ind1 < ind2) ind1 = ind1[unique] ind2 = ind2[unique] new_collisions = zip(ind1,ind2) for i in new_collisions: if i not in self.old_collisions: i1 = i[0] i2 = i[1] m1 = self.masses[i1] m2 = self.masses[i2] x1 = new_state[i1,0,0] y1 = new_state[i1,0,1] z1 = new_state[i2,0,3] x2 = new_state[i2,0,0] y2 = new_state[i2,0,1] z2 = new_state[i2,0,2] x1d = new_state[i1,1,0] y1d = new_state[i1,1,1] z1d = new_state[i1,1,2] x2d = new_state[i2,1,0] y2d = new_state[i2,1,1] z2d = new_state[i2,1,2] new_state[i1, 1] = [(x1d*(m1-m2) + 2*m2*x2d)/(m1+m2), (y1d*(m1-m2) + 2*m2*y2d)/(m1+m2), (z1d*(m1-m2) + 2*m2*z2d)/(m1+m2)] new_state[i2, 1] = [(x2d*(m2-m1) + 2*m1*x1d)/(m1+m2), (y2d*(m2-m1) + 2*m1*y1d)/(m1+m2), (z2d*(m2-m1) + 2*m1*z1d)/(m1+m2)] self.old_collisions = new_collisions return(new_state)
def SingleParticleNewtonianForce(self, i, soi_radius, collision_radius): forces = np.zeros([self.Nparticles,2]) x1 = self.state[i,0,0] y1 = self.state[i,0,1] m1 = self.masses[i] collided1 = 0 if self.interaction=='ClassicalNBody': xmin = x1-soi_radius xmax = x1+soi_radius ymin = y1-soi_radius ymax = y1+soi_radius sliced_indices_x = np.where(np.logical_and(self.state[:,0,0]>xmin,self.state[:,0,0]<xmax)) sliced_indices_y = np.where(np.logical_and(self.state[:,0,1]>ymin,self.state[:,0,1]<ymax)) sliced_indices = np.intersect1d(sliced_indices_x,sliced_indices_y) sliced_arr = self.state[sliced_indices] no_i_indices = np.where(np.logical_and(sliced_arr[:,0,0] == x1,sliced_arr[:,0,1]==y1)) sliced_arr = np.delete(sliced_arr,no_i_indices,axis=0) sliced_masses = self.masses[sliced_indices] sliced_masses = np.delete(sliced_masses,no_i_indices,axis=0) distances2 = np.array(np.transpose(np.matrix((sliced_arr[:,0,0]-x1)**2+(sliced_arr[:,0,1]-y1)**2))) jforce = np.array(np.transpose(np.matrix(sliced_masses)))/distances2 jforcedir = np.array(np.transpose(np.matrix([sliced_arr[:,0,0]-x1,sliced_arr[:,0,1]-y1])))/np.sqrt(distances2) forces = jforce*jforcedir for particle_num in xrange(self.Nparticles): if particle_num != i: collided2 = 0 m2 = self.masses[particle_num] x2 = self.state[particle_num,0,0] y2 = self.state[particle_num,0,1] distance2 = ((x2-x1)**2+(y2-y1)**2) """ if the collision dict is empty, put in the two particles being evaluated. if it's not empty, first search to see if the main particle is in there if the main particle is in there, move on to the secondary if the secondary particle is not in there, put it in the entry with the main particle """ if self.Nparticles == 2: if distance2>500000: print('\n',distance2) print(self.state) raise ValueError("it f****d the duck") elif self.Nparticles == 1: raise ValueError("it shit the bed") if self.collisions != False and i not in self.cleanup and particle_num not in self.cleanup: if distance2 < collision_radius: if self.collision_dict == {}: self.collision_dict[0] = [(x1,y1),[i,particle_num]] else: i_val = farts.dict_check(self.collision_dict, i) keynum = max(self.collision_dict)+1 if i_val == -1: self.collision_dict[keynum] = [(x1,y1),[i]] i_val = keynum elif farts.dict_check(self.collision_dict, particle_num) == -1: self.collision_dict[i_val][1].append(particle_num) return(np.sum(forces,axis=0))