def main():
n_vertices = 10
edges = cg.cycle_graph(n_vertices)
laplacian = gl.get_laplacian(edges,n_vertices,isDirected = False)
print("Laplacian Matrix: \n",laplacian)
eigenVals = np.linalg.eigvals(laplacian)
eigenVals = np.sort(eigenVals)
print("Two smallest eigen values are = ",eigenVals[:2])
print("Two largest eigen values are = ",eigenVals[::-1][:2])
# initialize x
x = np.zeros((len(x_0), len(time_arr)))
x[:, 0] = x_0 # copy x_0 to 1st col of x
for i in range(0, len(time_arr) - 1):
x[:, i + 1] = (-1) * (np.matmul(L, x[:, i])) * dt + x[:, i]
for j in range(0, len(x_0)):
plt.plot(time_arr, x[j, :])
plt.show()
if __name__ == '__main__':
x_0 = [20, 10, 15, 12, 30, 12, 15, 16, 25] # initial conditions
T = 20 # simulation time
# Define graph
# E = [[0,2],[1,2],[3,2],[2,4],[2,6],[5,4],[7,4],[4,6],[5,6],[7,6],[8,6]] # for Exercise 5 - 2
E = [[0, 2], [1, 2], [3, 2], [2, 6], [5, 4], [7, 4], [4, 6], [5, 6],
[8, 6]] # for Exercise 5 - 3
# E = [[0,2],[1,2],[3,2],[2,4],[2,6],[5,4],[7,4],[4,6],[7,6],[8,6],[1,3],[2,8],] # for Exercise 5 - 4
# E = [[0,2],[1,2],[3,2],[2,4],[5,4],[7,6],[8,6]] # for Exercise 5 - 5
n_vertices = 9
laplacian = gl.get_laplacian(E, n_vertices, isDirected=False)
eigenVals = np.linalg.eigvals(laplacian)
print("Eigen values are: ", np.sort(eigenVals))
simulate_consensus(x_0, T, laplacian, dt=0.001)
x[:, 0] = x_0 # copy x_0 to 1st col of x
for i in range(0, len(time_arr) - 1):
x[:, i + 1] = (-1) * (np.matmul(L, x[:, i])) * dt + x[:, i]
for j in range(0, len(x_0)):
plt.plot(time_arr, x[j, :])
plt.ylabel('Robot(s) Reading')
plt.xlabel('Time')
plt.show()
if __name__ == '__main__':
x_0 = [20, 10, 15, 12, 30, 12, 15] # initial conditions
T = 20 # simulation time
# Define graph
# E = [[0,2],[1,4],[2,1],[2,3],[2,5],[4,5],[6,3]] # for Figure 3(a)
# E = [[0,2],[1,4],[2,1],[2,3],[3,6],[5,2],[5,4]] # for Figure 3(b)
# E = [[1,4],[2,5],[4,5],[6,3]] # for Figure 4(a)
# E = [[2,1],[5,4],[3,6]] # for Figure 4(b)
E = [[0, 2], [2, 3], [5, 2]] # for Figure 4(c)
n_vertices = 7
laplacian = gl.get_laplacian(E, n_vertices, isDirected=True)
eigenVals = np.linalg.eigvals(laplacian)
print("Eigen values are: ", np.sort(eigenVals))
simulate_consensus(x_0, T, laplacian, dt=0.001)
if __name__ == '__main__':
x_0 = [20, 10, 15, 12, 30, 12, 15] # initial conditions
T = 300 # simulation time
switch_time = 2
# Define graph
# E1 = [[0,2],[1,2],[1,4],[2,0],[2,1],[2,3],[2,5],[3,2],[3,6],[4,1],[4,5],[5,2],[5,4],[6,3]] # for Figure 2(a)
# E2 = [[0,2],[1,2],[1,4],[2,0],[2,1],[2,3],[3,2],[3,6],[4,1],[4,5],[5,4],[6,3]] # for Figure 2(b)
# E3 = [[0,2],[1,2],[2,0],[2,1],[2,3],[2,5],[3,2],[3,6],[4,5],[5,2],[5,4],[6,3]] # for Figure 2(c)
# E = [E1,E2,E3]
# E1 = [[0,2],[1,4],[2,1],[2,3],[2,5],[4,5],[6,3]] # for Figure 3(a)
# E2 = [[0,2],[1,4],[2,1],[2,3],[3,6],[5,2],[5,4]] # for Figure 3(b)
# E = [E1,E2]
E1 = [[1, 4], [2, 5], [4, 5], [6, 3]] # for Figure 4(a)
E2 = [[2, 1], [5, 4], [3, 6]] # for Figure 4(b)
E3 = [[0, 2], [2, 3], [5, 2]] # for Figure 4(c)
E = [E1, E2, E3]
laplacian_list = []
n_vertices = 7
for i in range(0, len(E)):
laplacian_list.append(
gl.get_laplacian(E[i], n_vertices, isDirected=True))
simulate_consensus_switch(x_0, T, laplacian_list, switch_time, dt=0.001)
