feat: OVF formula 67

main.py

@@ -1,15 +1,117 @@
-from models.capa import Capa
-W = []
-L = []
-i = 15.52
-
-while i <= 100:
-    W.append(i)
-    L.append(i)
-    i = int(i*1.05*100 +0.5) / 100.0
-
-capa = Capa()
-capa.sweep(W,L)
-print(capa.network(result_dir=capa.results[0].result_dir,id=capa.results[0].id))
+from core.orthonormal_basis import generate_laguerre_basis
+from core.sk_iter import generate_starting_poles
+import numpy as np
+import skrf as rf
+import matplotlib.pyplot as plt
+import sympy as sp
+from scipy.linalg import null_space
+
+
+network = rf.Network("/tmp/paramer/simulation/4000/4000.s2p")
+freqs = network.f
+s = freqs * 2j * np.pi
+vf = rf.VectorFitting(network)
+vf.vector_fit(n_poles_real=2, n_poles_cmplx=1,parameter_type="y")
+
+poles = vf.poles
+residues = vf.residues
+y = vf.network.y
+
+fig, ax = plt.subplots(2, 2)
+fig.set_size_inches(12, 8)
+vf.plot("mag",0,0,freqs,ax=ax[0][0],parameter="y")
+rms_error11 = vf.get_rms_error(0,0,"y")
+print("rms_error11",rms_error11)
+vf.plot("mag",1,0,freqs,ax=ax[1][0],parameter="y")
+rms_error21 = vf.get_rms_error(1,0,"y")
+print("rms_error21",rms_error21)
+vf.plot("mag",0,1,freqs,ax=ax[0][1],parameter="y")
+rms_error12 = vf.get_rms_error(0,1,"y")
+print("rms_error12",rms_error12)
+vf.plot("mag",1,1,freqs,ax=ax[1][1],parameter="y")
+rms_error22 = vf.get_rms_error(1,1,"y")
+print("rms_error22",rms_error22)
+fig.tight_layout()
+plt.show()
+plt.savefig(f"img.png")
+
+diag_values = [y[i][0][0] for i in range(len(y))]
+H = np.diag(diag_values)
+P = 4
+start_poles = generate_starting_poles(P,beta_min=1e8,beta_max=freqs[-1])
+basis = generate_laguerre_basis(start_poles,s).T
+print("start_poles",start_poles)
+print("basis",basis)
+
+# first step iteration
+# A*x = b
+
+A11 = np.real(basis)
+print("A11 shape:",A11.shape)
+A12 = np.real(- H @ basis[:,1:])
+print("A12 shape:",A12.shape)
+# print("A11",A11)
+A21 = np.imag(basis)
+print("A21 shape:",A21.shape)
+A22 = np.imag(- H @ basis[:,1:])
+print("A22 shape:",A22.shape)
+A1 = np.hstack([A11,A12])
+A2 = np.hstack([A21,A22])
+A = np.vstack([A1,A2])
+print ("A shape:",A.shape)
+
+b1 = np.real(H @ basis[:,0])
+b2 = np.imag(H @ basis[:,0])
+b = np.hstack([b1,b2])
+Q, R = np.linalg.qr(A, mode='reduced')
+print("Q :",Q)
+print("R :",R)
+print("b shape:",b.shape)
+# x = np.linalg.solve(R, Q.T @ b)
+x_star, residuals, rank, s = np.linalg.lstsq(A, b, rcond=None)
+
+# print("x_qr",x_star)
+print("residuals",residuals)
+# print("rank",rank)
+# print("s",s)
+
+x_ne = np.linalg.inv(A.T @ A) @ A.T @ b
+print("x_ne",x_ne)
+
+# sk iteration
+target_vec = np.array([1+0j] + list(x_ne[len(x_ne)//2+1:]))
+D_t = basis @ target_vec
+print("D_t",D_t)
+K = 25
+
+for i in range(K):
+    print(f"Iteration {i+1}/{K}")
+    A11 = np.real(basis / D_t[:, np.newaxis])
+    A12 = np.real(- H @ basis[:,1:] / D_t[:, np.newaxis])
+    A21 = np.imag(basis / D_t[:, np.newaxis])
+    A22 = np.imag(- H @ basis[:,1:] / D_t[:, np.newaxis])
+    A1 = np.hstack([A11,A12])
+    A2 = np.hstack([A21,A22])
+    A = np.vstack([A1,A2])
+    b1 = np.real(H @ basis[:,0])
+    b2 = np.imag(H @ basis[:,0])
+    b = np.hstack([b1,b2])
+    x_star, residuals, rank, s = np.linalg.lstsq(A, b, rcond=None)
+    # print("x_lstsq",x_star)
+    # print("residuals",residuals)
+    # print("rank",rank)
+    # print("s",s)
+    x_ne = np.linalg.inv(A.T @ A) @ A.T @ b
+    # print("x_ne",x_ne)
+    target_vec = np.array([1+0j] + list(x_ne[len(x_ne)//2+1:]))
+    D_t_pre = D_t
+    D_t = basis @ target_vec
+    print("D_t/D_t_pre",np.linalg.norm(D_t)/np.linalg.norm(D_t_pre))
+    # print("D_t",D_t)
+    n_target_vec = np.array(list(x_ne[:len(x_ne)//2+1]))
+    N_t = basis @ n_target_vec
+    # print("H = N_t / D_t",np.linalg.norm(H - N_t / D_t)/np.linalg.norm(H))
+
+