From ba87d6d784269d2fd9675231ad2a86e8138faaab Mon Sep 17 00:00:00 2001 From: mayge Date: Wed, 24 Sep 2025 22:18:53 -0400 Subject: [PATCH] =?UTF-8?q?chore:=20=E5=88=86=E7=A6=BB=E4=BA=86sweep?= =?UTF-8?q?=E5=92=8Cvf=E9=83=A8=E5=88=86,vf=E9=83=A8=E5=88=86=E5=87=86?= =?UTF-8?q?=E5=A4=87=E5=86=99=E4=B8=BA=E5=8C=85?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 3 +- capa_results.json | 12170 ---------------- core/MultiPortOrthonormalBasis.py | 626 - core/VFManager.py | 176 + core/basis/MultiPortOrthonormalBasis.py | 362 + core/{ => basis}/MultiplePortQR.py | 4 +- core/{robust.py => basis/__init__.py} | 0 core/{ => basis}/basis.py | 4 +- core/{ => basis}/basisQR.py | 4 +- core/{ => basis}/relaxed_basisQR.py | 4 +- core/{freqency.py => sample.py} | 0 core/util.py | 909 -- core/{sk_iter.py => utils.py} | 12 + core/vectorFitting.py | 2617 ---- main.py | 558 +- models/basic.py | 155 - models/capa.py | 45 - models/mlin.py | 45 - outputs/capa_parametric.s2p | 309 - outputs/pvf_test_sample.s2p | 309 - schemas/paramer.py | 49 - sweep.py | 13 - .../test_vector_fitting.py | 0 utils.py | 55 - 24 files changed, 684 insertions(+), 17745 deletions(-) delete mode 100644 capa_results.json delete mode 100644 core/MultiPortOrthonormalBasis.py create mode 100644 core/VFManager.py create mode 100644 core/basis/MultiPortOrthonormalBasis.py rename core/{ => basis}/MultiplePortQR.py (99%) rename core/{robust.py => basis/__init__.py} (100%) rename core/{ => basis}/basis.py (99%) rename core/{ => basis}/basisQR.py (99%) rename core/{ => basis}/relaxed_basisQR.py (99%) rename core/{freqency.py => sample.py} (100%) delete mode 100644 core/util.py rename core/{sk_iter.py => utils.py} (56%) delete mode 100644 core/vectorFitting.py delete mode 100644 models/basic.py delete mode 100644 models/capa.py delete mode 100644 models/mlin.py delete mode 100644 outputs/capa_parametric.s2p delete mode 100644 outputs/pvf_test_sample.s2p delete mode 100644 schemas/paramer.py delete mode 100644 sweep.py rename vector_fitting.py => test/test_vector_fitting.py (100%) delete mode 100644 utils.py diff --git a/.gitignore b/.gitignore index 2be33ae..b25ab98 100644 --- a/.gitignore +++ b/.gitignore @@ -5,4 +5,5 @@ __pycache__/ __pypackages__/ env/ .venv/ -.vscode/ \ No newline at end of file +.vscode/ +outputs/ \ No newline at end of file diff --git a/capa_results.json b/capa_results.json deleted file mode 100644 index 7c73e82..0000000 --- a/capa_results.json +++ /dev/null @@ -1,12170 +0,0 @@ -[ - { - "parameters": { - "W": 15.52, - "L": 15.52 - }, - "id": 2626, - "result_dir": "/tmp/paramer/simulation/2626" - }, - { - "parameters": { - "W": 15.52, - "L": 16.3 - }, - "id": 2627, - "result_dir": "/tmp/paramer/simulation/2627" - }, - { - "parameters": { - "W": 15.52, - "L": 17.12 - }, - "id": 2628, - "result_dir": "/tmp/paramer/simulation/2628" - }, - { - "parameters": { - "W": 15.52, - "L": 17.98 - }, - "id": 2629, - "result_dir": 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99.09 - }, - "id": 4488, - "result_dir": "/tmp/paramer/simulation/4488" - } -] \ No newline at end of file diff --git a/core/MultiPortOrthonormalBasis.py b/core/MultiPortOrthonormalBasis.py deleted file mode 100644 index ff162d4..0000000 --- a/core/MultiPortOrthonormalBasis.py +++ /dev/null @@ -1,626 +0,0 @@ -import numpy as np -from core.sk_iter import generate_starting_poles -from scipy.linalg import block_diag -import skrf as rf -from skrf import VectorFitting -from core.freqency import auto_select_multple_ports -import matplotlib.pyplot as plt -import random as rnd - -def cond_row_inf(A, use_pinv=True): - """行条件数 κ∞(A) = ||A||∞ * ||A^{-1}||∞；矩形阵用广义逆。""" - A = np.asarray(A) - Ainv = np.linalg.pinv(A) if (use_pinv or A.shape[0] != A.shape[1]) else np.linalg.inv(A) - return np.linalg.norm(A, ord=np.inf) * np.linalg.norm(Ainv, ord=np.inf) - -def cond_col_one(A, use_pinv=True): - """列条件数 κ1(A) = ||A||1 * ||A^{-1}||1；矩形阵用广义逆。""" - A = np.asarray(A) - Ainv = np.linalg.pinv(A) if (use_pinv or A.shape[0] != A.shape[1]) else np.linalg.inv(A) - return np.linalg.norm(A, ord=1) * np.linalg.norm(Ainv, ord=1) - -class MultiPortOrthonormalBasis: - def __init__(self,H,freqs,poles,weights=None,passivity=True,dc_enforce=True,fit_constant=True,fit_proportional=False): - self.least_squares_condition = None - self.least_squares_row_condition = None - self.least_squares_col_condition = None - self.least_squares_rms_error = None - self.eigenval_condition = None - self.eigenval_row_condition = None - self.eigenval_col_condition = None - self.eigenval_rms_error = None - self.Cr = None - - self.dc_tol = 1e-18 - - self.dc_enforce = dc_enforce - self.fit_constant = fit_constant - self.fit_proportional = fit_proportional - - self.freqs = freqs - self.H = H - self.ports = H.shape[1] - self.s = self.freqs * 2j * np.pi - self.P = len(poles) - self.poles = poles - - self.Phi = self.generate_basis(self.s, self.poles) - self.A = self.matrix_A(self.poles) - self.B = self.vector_B(self.poles) - self.C,self.w0,self.e = self.fit_denominator(self.H, weights=weights) - self.D = self.w0 - - self.residuals = self.C / np.sqrt(2 * np.real(-np.array(self.poles))) - - z = np.linalg.eigvals(self.A - self.B @ self.C) - - if passivity: - self.next_poles = self.passivity_enforce(z) - else: - self.next_poles = z - - self.eigenval_condition,\ - self.eigenval_row_condition,\ - self.eigenval_col_condition,\ - self.eigenval_rms_error = self.eigen_metric() - - self.Dt = self.eval_Dt_state_space() - self.Dt_Dt_1 = np.linalg.norm(self.Dt) / np.linalg.norm(weights) if weights is not None else np.linalg.norm(self.Dt) - pass - - def eigen_metric(self): - - """Return condition number and RMS error of eigenvalues of A-BC.""" - z = np.linalg.eigvals(self.A - self.B @ self.C) - cond = np.linalg.cond(self.A - self.B @ self.C) - rms = np.sqrt(np.mean(np.abs(np.real(z) - np.real(self.poles))**2 + np.abs(np.imag(z) - np.imag(self.poles))**2)) - - row_cond = cond_row_inf(self.A - self.B @ self.C) - col_cond = cond_col_one(self.A - self.B @ self.C) - - return cond,row_cond,col_cond,rms - - def least_squares_metric(self,A,b): - """Return condition number and RMS error of least-squares matrix A and rhs b.""" - cond = np.linalg.cond(A) - rms = np.sqrt(np.mean((A @ np.linalg.pinv(A) @ b - b)**2)) - - row_cond = cond_row_inf(A) - col_cond = cond_col_one(A) - - return cond,row_cond,col_cond,rms - - - def passivity_enforce(self,poles): - """enforce poles' real parts to be negative""" - enforced_poles = [] - for pole in poles: - if pole.real > 0: - pole = -np.conj(pole) - enforced_poles.append(pole) - return enforced_poles - - def eval_Dt_state_space(self): - """Return D(s_k)=C(s_k I - A)^(-1)B + D for all k (complex 1D array).""" - s = 1j * 2*np.pi * np.asarray(self.freqs, float).ravel() - A = np.asarray(self.A, float); n = A.shape[0] - B = np.asarray(self.B, float).reshape(n, 1) - C = np.asarray(self.C, float).reshape(1, n) - D = self.D - I = np.eye(n, dtype=float) - out = np.empty_like(s, dtype=float) - for k, sk in enumerate(s): - DS = D + (C @ np.linalg.inv(sk*I - A) @ B) - out[k] = DS[0, 0] - return out - - - def generate_basis(self,s, poles): - """Real basis of (15)-(16); returns Φ(s) and a layout for packing C.""" - def all_pass(s,ap_list): - res = 1.0 +0.0j - for ap in ap_list: - res *= (s - np.conj(ap)) / (s + ap) - return res - - cols = [] - ap_list = [] - i = 0 - while i < len(poles): - ap = -poles[i] - if ap.real < 0: - raise ValueError("poles must be in the LHP") - if i+1 < len(poles) and np.isclose(poles[i+1], np.conj(-ap)): - ap1 = - poles[i+1] - phi1 = np.sqrt(2 * ap.real) * all_pass(s, ap_list) * ((s - np.abs(ap))/((s + ap)*(s + ap1))) - phi2 = np.sqrt(2 * ap.real) * all_pass(s, ap_list) * ((s + np.abs(ap))/((s + ap)*(s + ap1))) - cols += [phi1, phi2] - i += 2 - ap_list.append(ap) - ap_list.append(ap1) - else: - basis = np.sqrt(2 * ap.real) * all_pass(s, ap_list) * (1/(s + ap)) - cols.append(basis) - i += 1 - ap_list.append(ap) - Phi = np.column_stack(cols).astype(np.complex128) - return Phi - - def matrix_A(self, poles): - def A_col(p:np.complex128,index:int): - ap = -p - if abs(ap.imag) < 1e-14: - col = [] - for i in range(index): - col.append(0.0) - col.append(-ap.real) - for i in range(len(poles)-index-1): - col.append(2*(-ap).real) - return np.array([col], float) - else: - col1 = [] - col2 = [] - for i in range(index): - col1.append(0.0) - col2.append(0.0) - col1.append(-ap.real); col2.append(-ap.real - np.abs(ap)) - col1.append(-ap.real + np.abs(ap)); col2.append(-ap.real) - for i in range(len(poles)-index-2): - col1.append(2*(-ap).real) - col2.append(2*(-ap).real) - return np.array([col1, col2], float) - - i = 0 - cols = [] - while i < len(poles): - p = poles[i] - cols.extend(A_col(p,i)) - if i+1 < len(poles) and np.isclose(poles[i+1], np.conj(p)): i += 2 - else: i += 1 - A = np.column_stack(cols).astype(float) - return A - - def vector_B(self, poles): - return np.ones((len(poles), 1), float) - - def fit_denominator(self, H, weights=None, d0 = 1.0): - """ - Solve formula (70) on the real basis Φ to obtain: - - d (real) → packs into C for this state's block structure - - gamma (complex) - Optional 'weights' (K,) apply row scaling: SK weighting if 1/|D_prev|. - """ - K, N = self.Phi.shape - one = np.ones((K, 1), np.complex128) - Phi = self.Phi - dc_tol = 1e-18 - has_dc = self.dc_enforce and self.freqs[0] < dc_tol - keep = np.ones(K, dtype=bool) - - # SK weighting (applied only to the (73) rows we keep in LS) - - if has_dc: - # Enforce DC response exactly: - k0 = int(np.argmin(np.abs(self.freqs))) - keep[k0] = False - - if self.fit_constant: - Phi_w = np.hstack([one, Phi]) - index = 0 - M_kp = None - for i in range(self.ports): - for j in range(self.ports): - M0 = np.zeros((K,N*self.ports**2),dtype=complex) - M0[:,index*N:(index+1)*N] = Phi - M0 = np.hstack([M0, -(H[:,i,j].reshape(-1,1) * Phi_w)]).reshape((K, -1))[keep,:] # (K, 2N), complex - index+=1 - M_kp = M0 if M_kp is None else np.vstack([M_kp, M0]) - assert M_kp is not None - else: - index = 0 - M_kp = None - for i in range(self.ports): - for j in range(self.ports): - M0 = np.zeros((K,N*self.ports**2),dtype=complex) - M0[:,index*N:(index+1)*N] = Phi - M0 = np.hstack([M0, -(H[:,i,j].reshape(-1,1) * Phi)]).reshape((K, -1))[keep,:] # (K, 2N), complex - index+=1 - M_kp = M0 if M_kp is None else np.vstack([M_kp, M0]) - assert M_kp is not None - - if weights is None: - weights_kp = np.diag(np.ones(len(self.freqs[keep]) * self.ports**2, np.complex128)) - else: - weights_kp0 = weights[keep] - weights0 = [] - for i in range(self.ports **2 ): - for res in weights_kp0: - weights0.append(1/res) - weights_kp = np.diag(np.array(weights0)) - - - if has_dc: - M_w_kp = weights_kp @ M_kp - A_re = np.real(M_w_kp) - A_im = np.imag(M_w_kp) - mask = np.ones(K, dtype=bool); mask[k0] = False - # exact (unweighted) DC rows: - # A_dc_re = np.real(M_kp).reshape(1, -1) - # A_dc_im = np.imag(M_kp).reshape(1, -1) - else: - M_w_kp = weights_kp @ M_kp - A_re = np.real(M_w_kp) - A_im = np.imag(M_w_kp) - # A_dc_re = A_dc_im = None - - A_blocks = [A_re, A_im] - - if self.fit_constant: - Hk_sum = [] - for i in range(self.ports): - Hk_sum.append([]) - for j in range(self.ports): - Hk_kp0 = H[:,i,j][keep] - Hk_sum[i].append(np.sum(np.abs(Hk_kp0)**2)) - # Hk_kp = Hk_kp0 if Hk_kp is None else np.hstack([Hk_kp, Hk_kp0]) - K_keep = int(np.count_nonzero(keep)) - A_w0 = [] - b_w0 = [] - # Hk_sum = np.sum(np.abs(Hk_kp)**2) - for i in range(self.ports): - for j in range(self.ports): - beta_ij = float(np.sqrt(Hk_sum[i][j])) - mean_row = (beta_ij / K_keep) * np.sum(Phi_w[keep, :], axis=0) - A_w0.append(np.concatenate([np.zeros(N*self.ports**2, float), - np.real(mean_row).astype(float)] - ).reshape(1, -1)) - b_w0.append(np.array([beta_ij], float)) - b_w0 = np.asarray(b_w0).ravel() - - A_blocks += A_w0 - m = A_re.shape[0] + A_im.shape[0] - b = np.zeros(m, float) - b = np.concatenate([b, b_w0]) - else: - H_kp = None - for i in range(self.ports): - for j in range(self.ports): - H_0 = H[:,i,j][keep] - H_kp = H_0 if H_kp is None else np.hstack([H_kp, H_0]) - assert H_kp is not None - H_kp = weights_kp @ H_kp.reshape(-1,1) - - b_re = np.real(d0 * H_kp) - b_im = np.imag(d0 * H_kp) - b = np.concatenate([b_re.ravel(), b_im.ravel()]).astype(float) - - # ---- build final stacked-real system ---- - - - # if A_dc_re is not None: - # A_blocks += [A_dc_re, A_dc_im] - # b = np.concatenate([b, np.zeros(2, float)]) # DC rows → 0 - - # ---- QR solve for x = [c_H (N); c_w (N+1)] ---- - A = np.vstack(A_blocks).astype(float) - Q, R = np.linalg.qr(A, mode="reduced") - - if self.fit_constant: - Q2 = Q[:,Phi.shape[1] * self.ports**2:] - R22 = R[Phi.shape[1] * self.ports**2:,Phi.shape[1] * self.ports**2:] - else: - Q2 = Q[:,Phi.shape[1] * self.ports**2:] - R22 = R[Phi.shape[1] * self.ports**2:,Phi.shape[1] * self.ports**2:] - - x = np.linalg.solve(R22, Q2.T @ b) - - # diagnostics - resid = Q2 @ R22 @ x - b - # self.least_squares_rms_error = float(np.sqrt(np.mean(resid**2))) - # self.least_squares_condition = float(np.linalg.cond(R)) - self.least_squares_condition,\ - self.least_squares_row_condition,\ - self.least_squares_col_condition,\ - self.least_squares_rms_error = self.least_squares_metric(A, b) - - return self.extract_C_d_e(x,N,d0) - - def extract_C_d_e(self,C,N,d0=1.0): - a = np.sqrt(2 * np.real(-np.array(self.poles))) - if self.fit_proportional and self.fit_constant: - d = C[1] - e = C[0] - C = a * C[2:] - return C.reshape(1, -1), d, e - elif self.fit_proportional and not self.fit_constant: - d = 0.0 - e = C[0] - C = a * C[1:] - return C.reshape(1, -1), d, e - elif not self.fit_proportional and self.fit_constant: - d = C[0] - e = 0.0 - C = a * C[1:] - return C.reshape(1, -1), d, e - else: - C = a * C - return C.reshape(1, -1), d0, 0.0 - - - def non_bias_Cr(self,w0): - A = np.asarray(self.Phi) - den = np.diag((w0 + self.Phi @ self.residuals.T).ravel()) - Cr = [] - for i in range(self.ports): - Cr.append([]) - for j in range(self.ports): - b = np.asarray(den) @ self.H[:,i,j].reshape(-1,1) - Cr_ij, residuals, rank, s = np.linalg.lstsq(A, b, rcond=None) - Cr[i].append(Cr_ij) - return Cr - - def get_model_responses(self,freqs): - H = np.zeros((len(freqs),self.ports,self.ports),dtype=complex) - s = 1j * 2*np.pi * np.asarray(freqs, float).ravel() - phi = self.generate_basis(s, self.poles) - den = self.w0 + phi @ self.residuals.T - if self.Cr is None: - self.Cr = self.non_bias_Cr(w0=self.w0) - for i in range(self.ports): - for j in range(self.ports): - num = phi @ self.Cr[i][j] - H[:,i,j] = (num / den).reshape(1,-1) - return H - -class VFUtils(): - def __init__(self,npoles_cplx,freqs,H,model=MultiPortOrthonormalBasis,iterations:int=5): - poles = generate_starting_poles(npoles_cplx,beta_min=1e4,beta_max=freqs[-1]*1.1) - self.model=model(H=H,freqs=freqs,poles=poles) - self.freqs=freqs - self.H=H - self.iterations=iterations - - self.nports = H.shape[1] - - self.least_squares_condition = [] - self.least_squares_row_condition = [] - self.least_squares_col_condition = [] - self.least_squares_rms_error = [] - self.eigenval_condition = [] - self.eigenval_row_condition = [] - self.eigenval_col_condition = [] - self.eigenval_rms_error = [] - - self.model_responses_freqs = None - self.model_responses_H = None - - def fit(self): - for i in range(self.iterations): - print(f"Iteration {i+1}/{self.iterations}") - poles = self.model.next_poles - weights = self.model.Dt - self.model = self.model.__class__(H=self.H,freqs=self.freqs,poles=poles,weights=weights) - print("A:",self.model.A) - print("B:",self.model.B) - print("C:",self.model.C) - print("D:",self.model.D) - print("next_pozles:",self.model.next_poles) - print("Dt:",self.model.Dt) - print("Dt/Dt_1:",np.linalg.norm(self.model.Dt_Dt_1)) - self.least_squares_condition.append(self.model.least_squares_condition) - self.least_squares_row_condition.append(self.model.least_squares_row_condition) - self.least_squares_col_condition.append(self.model.least_squares_col_condition) - self.least_squares_rms_error.append(self.model.least_squares_rms_error) - self.eigenval_condition.append(self.model.eigenval_condition) - self.eigenval_row_condition.append(self.model.eigenval_row_condition) - self.eigenval_col_condition.append(self.model.eigenval_col_condition) - self.eigenval_rms_error.append(self.model.eigenval_rms_error) - return self.model - - def plot_metrics(self): - plt.figure(figsize=(16, 12)) - plt.subplot(4, 2, 1) - plt.plot(self.least_squares_condition, label='Least Squares Condition') - plt.legend() - plt.subplot(4, 2, 2) - plt.plot(self.least_squares_row_condition, label='Least Squares Row Condition') - plt.legend() - plt.subplot(4, 2, 3) - plt.plot(self.least_squares_col_condition, label='Least Squares Col Condition') - plt.legend() - plt.subplot(4, 2, 4) - plt.plot(self.least_squares_rms_error, label='Least Squares RMS Error') - plt.legend() - plt.subplot(4, 2, 5) - plt.plot(self.eigenval_condition, label='Eigenvalue Condition') - plt.legend() - plt.subplot(4, 2, 6) - plt.plot(self.eigenval_row_condition, label='Eigenvalue Row Condition') - plt.legend() - plt.subplot(4, 2, 7) - plt.plot(self.eigenval_col_condition, label='Eigenvalue Col Condition') - plt.legend() - plt.subplot(4, 2, 8) - plt.plot(self.eigenval_rms_error, label='Eigenvalue RMS Error') - plt.legend() - plt.savefig("fit_metrics.png") - - def plot_model_responses(self): - assert self.model_responses_freqs is not None and self.model_responses_H is not None, "Please run get_model_responses() first." - for i in range(self.nports): - for j in range(self.nports): - plt.figure(figsize=(12, 6)) - plt.subplot(2, 2, 1) - plt.plot(self.freqs, np.abs(self.H[:,i,j]), 'o', ms=4, color='red', label='Input Samples') - plt.plot(self.model_responses_freqs, np.abs(self.model_responses_H[:,i,j]), '-', lw=2, color='k', label='Fit') - plt.title(f"Response i={i+1}, j={j+1}") - plt.ylabel("Magnitude") - plt.legend(loc="best") - plt.subplot(2, 2, 2) - plt.plot(self.freqs, np.angle(self.H[:,i,j],deg=True), 'o', ms=4, color='red', label='Input Samples') - plt.plot(self.model_responses_freqs, np.angle(self.model_responses_H[:,i,j],deg=True), '-', lw=2, color='k', label='Fit') - plt.title(f"Response i={i+1}, j={j+1}") - plt.ylabel("Phase (deg)") - plt.legend(loc="best") - plt.tight_layout() - plt.subplot(2, 2, 3) - plt.plot(self.freqs, np.real(self.H[:,i,j]), 'o', ms=4, color='red', label='Input Samples') - plt.plot(self.model_responses_freqs, np.real(self.model_responses_H[:,i,j]), '-', lw=2, color='k', label='Fit') - plt.title(f"Response i={i+1}, j={j+1}") - plt.ylabel("Real Part") - plt.legend(loc="best") - plt.subplot(2, 2, 4) - plt.plot(self.freqs, np.imag(self.H[:,i,j]), 'o', ms=4, color='red', label='Input Samples') - plt.plot(self.model_responses_freqs, np.imag(self.model_responses_H[:,i,j]), '-', lw=2, color='k', label='Fit') - plt.title(f"Response i={i+1}, j={j+1}") - plt.ylabel("Imag Part") - plt.legend(loc="best") - plt.tight_layout() - plt.savefig(f"model_response_{i+1}{j+1}.png") - print(f"Saved model_response_{i+1}{j+1}.png") - - def get_model_responses(self,freqs): - self.model_responses_freqs = freqs - self.model_responses_H = self.model.get_model_responses(freqs) - return self.model_responses_H - - -def noise(n:complex,coeff:float=0.05): - noise_r = rnd.gauss(-coeff * n.real, coeff * n.real) - noise_i = rnd.gauss(-coeff * n.imag, coeff * n.imag) - return complex(n.real + noise_r, n.imag + noise_i) - -if __name__ == "__main__": - start_point = 0 - id = 3000 - network = rf.Network(f"/tmp/paramer/simulation/{id}/{id}.s2p") - # network = rf.data.ring_slot - ports = network.nports - K = 100 - - full_freqences = network.f[start_point:] - noised_sampled_points = network.y[start_point:,:,:].reshape(-1,ports,ports) - sampled_points = network.y[start_point:,:,:].reshape(-1,ports,ports) - - # noised_sampled_points = network.y[start_point:,0,0].reshape(-1,1,1) - # sampled_points = network.y[start_point:,0,0].reshape(-1,1,1) - - H,freqs = auto_select_multple_ports(noised_sampled_points,full_freqences,max_points=20) - poles = generate_starting_poles(2,beta_min=1e4,beta_max=freqs[-1]*1.1) - - vf = VFUtils(npoles_cplx=2,freqs=freqs,H=H,model=MultiPortOrthonormalBasis,iterations=K) - model = vf.fit() - vf.plot_metrics() - model_responses = vf.get_model_responses(full_freqences) - vf.plot_model_responses() - - # # Original plot functions - - # Dt_1 = np.ones((len(freqs),1),np.complex128) - # # Levi step (no weighting): - # basis = MultiPortOrthonormalBasis(H,freqs,poles=poles) - # Dt = basis.Dt - # poles = basis.next_poles - - # print("Levi step (no weighting):") - # print("A:",basis.A) - # print("B:",basis.B) - # print("C:",basis.C) - # print("D:",basis.D) - # print("next_pozles:",basis.next_poles) - # print("Dt:",Dt, "norm:",np.linalg.norm(Dt)) - - # # SK weighting (optional, after first pass): - # least_squares_condition = [] - # least_squares_rms_error = [] - # eigenval_condition = [] - # eigenval_rms_error = [] - # for i in range(K): - # basis = MultiPortOrthonormalBasis(H,freqs,poles=poles,weights=Dt) - # Dt_1 = Dt - # Dt = basis.Dt - # poles = basis.next_poles - # print(f"SK Iteration {i+1}/{K}") - # print("A:",basis.A) - # print("B:",basis.B) - # print("C:",basis.C) - # print("D:",basis.D) - # print("z:",basis.next_poles) - # print("Dt:",Dt) - # print("Dt/Dt-1",np.linalg.norm(Dt) / np.linalg.norm(Dt_1)) - # least_squares_condition.append(basis.least_squares_condition) - # least_squares_rms_error.append(basis.least_squares_rms_error) - # eigenval_condition.append(basis.eigenval_condition) - # eigenval_rms_error.append(basis.eigenval_rms_error) - - # # H11_evaluated = basis.evaluate_pole_residue(network.f[1:],poles,basis.C[0]) - # H_evaluated = basis.get_model_responses(full_freqences) - # fitted_points = H_evaluated - # sliced_freqences = freqs - - # input_points = H - # for i in range(ports): - # for j in range(ports): - # fig, axes = plt.subplots(3, 2, figsize=(15, 16), sharex=False) - # ax00 = axes[0][0] - # ax00.plot(full_freqences, np.abs(sampled_points[:,i,j]), 'o', ms=4, color='red', label='Samples') - # ax00.plot(full_freqences, np.abs(fitted_points[:,i,j]), '-', lw=2, color='k', label='Fit') - # ax00.plot(sliced_freqences, np.abs(input_points[:,i,j]), 'x', ms=4, color='blue', label='Input Samples') - # ax00.set_title(f"Response i={i+1}, j={j+1}") - # ax00.set_ylabel("Magnitude") - # ax00.legend(loc="best") - - # ax01 = axes[0][1] - # ax01.set_title(f"Response i={i+1}, j={j+1}") - # ax01.set_ylabel("Phase (deg)") - # ax01.plot(full_freqences, np.angle(sampled_points[:,i,j],deg=True), 'o', ms=4, color='red', label='Samples') - # ax01.plot(full_freqences, np.angle(fitted_points[:,i,j],deg=True), '-', lw=2, color='k', label='Fit') - # ax01.plot(sliced_freqences, np.angle(input_points[:,i,j],deg=True), 'x', ms=4, color='blue', label='Input Samples') - # ax01.legend(loc="best") - - # # ax00 = axes[0][0] - # # ax00.plot(full_freqences, np.real(sampled_points[:,i,j]), 'o', ms=4, color='red', label='Samples') - # # ax00.plot(full_freqences, np.real(fitted_points[:,i,j]), '-', lw=2, color='k', label='Fit') - # # ax00.plot(sliced_freqences, np.real(input_points[:,i,j]), 'x', ms=4, color='blue', label='Input Samples') - # # ax00.set_title(f"Response i={i+1}, j={j+1}") - # # ax00.set_ylabel("Real Part") - # # ax00.legend(loc="best") - - # # ax01 = axes[0][1] - # # ax01.set_title(f"Response i={i+1}, j={j+1}") - # # ax01.set_ylabel("Imag Part") - # # ax01.plot(full_freqences, np.imag(sampled_points[:,i,j]), 'o', ms=4, color='red', label='Samples') - # # ax01.plot(full_freqences, np.imag(fitted_points[:,i,j]), '-', lw=2, color='k', label='Fit') - # # ax01.plot(sliced_freqences, np.imag(input_points[:,i,j]), 'x', ms=4, color='blue', label='Input Samples') - # # ax01.legend(loc="best") - - # ax10 = axes[1][0] - # ax10.plot(least_squares_condition, label='Least Squares Condition') - # ax10.set_title("least_squares_condition") - # ax10.set_ylabel("Magnitude") - # ax10.legend(loc="best") - - # ax11 = axes[1][1] - # ax11.plot(least_squares_rms_error, label='Least Squares RMS Error') - # ax11.set_title("least_squares_rms_error") - # ax11.set_ylabel("Magnitude") - # ax11.legend(loc="best") - - # ax20 = axes[2][0] - # ax20.plot(eigenval_condition, label='Eigenvalue Condition') - # ax20.set_title("eigenval_condition") - # ax20.set_ylabel("Magnitude") - # ax20.legend(loc="best") - - # ax21 = axes[2][1] - # ax21.plot(eigenval_rms_error, label='Eigenvalue RMS Error') - # ax21.set_title("eigenval_rms_error") - # ax21.set_ylabel("Magnitude") - # ax21.legend(loc="best") - # fig.tight_layout() - # plt.savefig(f"MultiplePortQR_port_{i+1}{j+1}.png") - # print(f"Saved MultiplePortQR_port_{i+1}{j+1}.png") - - - - diff --git a/core/VFManager.py b/core/VFManager.py new file mode 100644 index 0000000..d45c22b --- /dev/null +++ b/core/VFManager.py @@ -0,0 +1,176 @@ +import matplotlib.pyplot as plt +import numpy as np +from .basis.MultiPortOrthonormalBasis import MultiPortOrthonormalBasis +from .utils import generate_starting_poles + +class VFManager(): + def __init__( + self, + npoles_cplx, + freqs, + H, + model=MultiPortOrthonormalBasis, + iterations:int=5, + fit_constant:bool=True, + fit_proportional:bool=False, + dc_enforce:bool=False, + passivity_enforce:bool=True, + verbose:bool=True + ): + + + self.freqs=freqs + self.H=H + self.iterations=iterations + self.fit_constant=fit_constant + self.fit_proportional=fit_proportional + self.dc_enforce=dc_enforce + self.passivity_enforce=passivity_enforce + self.verbose=verbose + + self.nports = H.shape[1] + self.npoles_cplx = npoles_cplx + + self.least_squares_condition = [] + self.least_squares_row_condition = [] + self.least_squares_col_condition = [] + self.least_squares_rms_error = [] + self.eigenval_condition = [] + self.eigenval_row_condition = [] + self.eigenval_col_condition = [] + self.eigenval_rms_error = [] + + self.model_instance = None + self.model_responses_freqs = None + self.model_responses_H = None + + self.model=model + + def fit(self): + self.levi() + self.model_instance = self.sk_iteration() + return self.model + + def levi(self): + self.poles = generate_starting_poles(self.npoles_cplx,beta_min=1e4,beta_max=self.freqs[-1]*1.1) + self.model_instance=self.model( + H=self.H, + freqs=self.freqs, + poles=self.poles, + fit_constant=self.fit_constant, + fit_proportional=self.fit_proportional, + dc_enforce=self.dc_enforce, + passivity_enforce=self.passivity_enforce + ) + return self.model_instance + + def sk_iteration(self): + for i in range(self.iterations): + assert self.model_instance is not None ,"Please run levi() first." + self.poles = self.model_instance.next_poles + self.weights = self.model_instance.Dt + self.model_instance = self.model( + H=self.H, + freqs=self.freqs, + poles=self.poles, + weights=self.weights, + fit_constant=self.fit_constant, + fit_proportional=self.fit_proportional, + dc_enforce=self.dc_enforce, + passivity_enforce=self.passivity_enforce + ) + if self.verbose: + print(f"Iteration {i+1}/{self.iterations}") + print("A:",self.model_instance.A) + print("B:",self.model_instance.B) + print("C:",self.model_instance.C) + print("D:",self.model_instance.D) + print("next_pozles:",self.model_instance.next_poles) + print("Dt:",self.model_instance.Dt) + print("Dt/Dt_1:",np.linalg.norm(self.model_instance.Dt_Dt_1)) + self.least_squares_condition.append(self.model_instance.least_squares_condition) + self.least_squares_row_condition.append(self.model_instance.least_squares_row_condition) + self.least_squares_col_condition.append(self.model_instance.least_squares_col_condition) + self.least_squares_rms_error.append(self.model_instance.least_squares_rms_error) + self.eigenval_condition.append(self.model_instance.eigenval_condition) + self.eigenval_row_condition.append(self.model_instance.eigenval_row_condition) + self.eigenval_col_condition.append(self.model_instance.eigenval_col_condition) + self.eigenval_rms_error.append(self.model_instance.eigenval_rms_error) + return self.model_instance + + def plot_metrics(self,show:bool=True,save_path=None): + plt.figure(figsize=(16, 12)) + plt.subplot(4, 2, 1) + plt.plot(self.least_squares_condition, label='Least Squares Condition') + plt.legend() + plt.subplot(4, 2, 2) + plt.plot(self.least_squares_row_condition, label='Least Squares Row Condition') + plt.legend() + plt.subplot(4, 2, 3) + plt.plot(self.least_squares_col_condition, label='Least Squares Col Condition') + plt.legend() + plt.subplot(4, 2, 4) + plt.plot(self.least_squares_rms_error, label='Least Squares RMS Error') + plt.legend() + plt.subplot(4, 2, 5) + plt.plot(self.eigenval_condition, label='Eigenvalue Condition') + plt.legend() + plt.subplot(4, 2, 6) + plt.plot(self.eigenval_row_condition, label='Eigenvalue Row Condition') + plt.legend() + plt.subplot(4, 2, 7) + plt.plot(self.eigenval_col_condition, label='Eigenvalue Col Condition') + plt.legend() + plt.subplot(4, 2, 8) + plt.plot(self.eigenval_rms_error, label='Eigenvalue RMS Error') + plt.legend() + if show: + plt.show() + if save_path is not None: + if self.verbose: + print(f"Saving metrics plot to {save_path}/fitting_metrics.png") + plt.savefig(f"{save_path}/fitting_metrics.png") + + def plot_model_responses(self,show:bool=True,save_path=None): + assert self.model_responses_freqs is not None and self.model_responses_H is not None, "Please run get_model_responses() first." + for i in range(self.nports): + for j in range(self.nports): + plt.figure(figsize=(12, 6)) + plt.subplot(2, 2, 1) + plt.plot(self.freqs, np.abs(self.H[:,i,j]), 'o', ms=4, color='red', label='Input Samples') + plt.plot(self.model_responses_freqs, np.abs(self.model_responses_H[:,i,j]), '-', lw=2, color='k', label='Fit') + plt.title(f"Response i={i+1}, j={j+1}") + plt.ylabel("Magnitude") + plt.legend(loc="best") + plt.subplot(2, 2, 2) + plt.plot(self.freqs, np.angle(self.H[:,i,j],deg=True), 'o', ms=4, color='red', label='Input Samples') + plt.plot(self.model_responses_freqs, np.angle(self.model_responses_H[:,i,j],deg=True), '-', lw=2, color='k', label='Fit') + plt.title(f"Response i={i+1}, j={j+1}") + plt.ylabel("Phase (deg)") + plt.legend(loc="best") + plt.tight_layout() + plt.subplot(2, 2, 3) + plt.plot(self.freqs, np.real(self.H[:,i,j]), 'o', ms=4, color='red', label='Input Samples') + plt.plot(self.model_responses_freqs, np.real(self.model_responses_H[:,i,j]), '-', lw=2, color='k', label='Fit') + plt.title(f"Response i={i+1}, j={j+1}") + plt.ylabel("Real Part") + plt.legend(loc="best") + plt.subplot(2, 2, 4) + plt.plot(self.freqs, np.imag(self.H[:,i,j]), 'o', ms=4, color='red', label='Input Samples') + plt.plot(self.model_responses_freqs, np.imag(self.model_responses_H[:,i,j]), '-', lw=2, color='k', label='Fit') + plt.title(f"Response i={i+1}, j={j+1}") + plt.ylabel("Imag Part") + plt.legend(loc="best") + plt.tight_layout() + if show: + plt.show() + if save_path is not None: + if self.verbose: + print(f"Saving response plot for port {i+1},{j+1} to {save_path}/response_{i+1}_{j+1}.png") + plt.savefig(f"{save_path}/response_{i+1}_{j+1}.png") + + def get_model_responses(self,freqs): + assert self.model_instance is not None ,"Please run levi() and sk_iteration() first." + self.model_responses_freqs = freqs + self.model_responses_H = self.model_instance.get_model_responses(freqs) + return self.model_responses_H \ No newline at end of file diff --git a/core/basis/MultiPortOrthonormalBasis.py b/core/basis/MultiPortOrthonormalBasis.py new file mode 100644 index 0000000..e35144a --- /dev/null +++ b/core/basis/MultiPortOrthonormalBasis.py @@ -0,0 +1,362 @@ +import numpy as np +import skrf as rf +from ..utils import cond_row_inf, cond_col_one, generate_starting_poles + + +class MultiPortOrthonormalBasis: + def __init__(self,H,freqs,poles,weights=None,passivity_enforce=True,dc_enforce=True,fit_constant=True,fit_proportional=False): + self.least_squares_condition = None + self.least_squares_row_condition = None + self.least_squares_col_condition = None + self.least_squares_rms_error = None + self.eigenval_condition = None + self.eigenval_row_condition = None + self.eigenval_col_condition = None + self.eigenval_rms_error = None + self.Cr = None + + self.dc_tol = 1e-18 + + self.dc_enforce = dc_enforce + self.fit_constant = fit_constant + self.fit_proportional = fit_proportional + + self.freqs = freqs + self.H = H + self.ports = H.shape[1] + self.s = self.freqs * 2j * np.pi + self.P = len(poles) + self.poles = poles + + self.Phi = self.generate_basis(self.s, self.poles) + self.A = self.matrix_A(self.poles) + self.B = self.vector_B(self.poles) + self.C,self.w0,self.e = self.fit_denominator(self.H, weights=weights) + self.D = self.w0 + + self.residuals = self.C / np.sqrt(2 * np.real(-np.array(self.poles))) + + z = np.linalg.eigvals(self.A - self.B @ self.C) + + if passivity_enforce: + self.next_poles = self.passivity_enforce(z) + else: + self.next_poles = z + + self.eigenval_condition,\ + self.eigenval_row_condition,\ + self.eigenval_col_condition,\ + self.eigenval_rms_error = self.eigen_metric() + + self.Dt = self.eval_Dt_state_space() + self.Dt_Dt_1 = np.linalg.norm(self.Dt) / np.linalg.norm(weights) if weights is not None else np.linalg.norm(self.Dt) + pass + + def eigen_metric(self): + + """Return condition number and RMS error of eigenvalues of A-BC.""" + z = np.linalg.eigvals(self.A - self.B @ self.C) + cond = np.linalg.cond(self.A - self.B @ self.C) + rms = np.sqrt(np.mean(np.abs(np.real(z) - np.real(self.poles))**2 + np.abs(np.imag(z) - np.imag(self.poles))**2)) + + row_cond = cond_row_inf(self.A - self.B @ self.C) + col_cond = cond_col_one(self.A - self.B @ self.C) + + return cond,row_cond,col_cond,rms + + def least_squares_metric(self,A,b): + """Return condition number and RMS error of least-squares matrix A and rhs b.""" + cond = np.linalg.cond(A) + rms = np.sqrt(np.mean((A @ np.linalg.pinv(A) @ b - b)**2)) + + row_cond = cond_row_inf(A) + col_cond = cond_col_one(A) + + return cond,row_cond,col_cond,rms + + + def passivity_enforce(self,poles): + """enforce poles' real parts to be negative""" + enforced_poles = [] + for pole in poles: + if pole.real > 0: + pole = -np.conj(pole) + enforced_poles.append(pole) + return enforced_poles + + def eval_Dt_state_space(self): + """Return D(s_k)=C(s_k I - A)^(-1)B + D for all k (complex 1D array).""" + s = 1j * 2*np.pi * np.asarray(self.freqs, float).ravel() + A = np.asarray(self.A, float); n = A.shape[0] + B = np.asarray(self.B, float).reshape(n, 1) + C = np.asarray(self.C, float).reshape(1, n) + D = self.D + I = np.eye(n, dtype=float) + out = np.empty_like(s, dtype=np.complex128) + for k, sk in enumerate(s): + DS = D + (C @ np.linalg.inv(sk*I - A) @ B) + out[k] = DS[0, 0] + return out + + + def generate_basis(self,s, poles): + """Real basis of (15)-(16); returns Φ(s) and a layout for packing C.""" + def all_pass(s,ap_list): + res = 1.0 +0.0j + for ap in ap_list: + res *= (s - np.conj(ap)) / (s + ap) + return res + + cols = [] + ap_list = [] + i = 0 + while i < len(poles): + ap = -poles[i] + if ap.real < 0: + raise ValueError("poles must be in the LHP") + if i+1 < len(poles) and np.isclose(poles[i+1], np.conj(-ap)): + ap1 = - poles[i+1] + phi1 = np.sqrt(2 * ap.real) * all_pass(s, ap_list) * ((s - np.abs(ap))/((s + ap)*(s + ap1))) + phi2 = np.sqrt(2 * ap.real) * all_pass(s, ap_list) * ((s + np.abs(ap))/((s + ap)*(s + ap1))) + cols += [phi1, phi2] + i += 2 + ap_list.append(ap) + ap_list.append(ap1) + else: + basis = np.sqrt(2 * ap.real) * all_pass(s, ap_list) * (1/(s + ap)) + cols.append(basis) + i += 1 + ap_list.append(ap) + Phi = np.column_stack(cols).astype(np.complex128) + return Phi + + def matrix_A(self, poles): + def A_col(p:np.complex128,index:int): + ap = -p + if abs(ap.imag) < 1e-14: + col = [] + for i in range(index): + col.append(0.0) + col.append(-ap.real) + for i in range(len(poles)-index-1): + col.append(2*(-ap).real) + return np.array([col], float) + else: + col1 = [] + col2 = [] + for i in range(index): + col1.append(0.0) + col2.append(0.0) + col1.append(-ap.real); col2.append(-ap.real - np.abs(ap)) + col1.append(-ap.real + np.abs(ap)); col2.append(-ap.real) + for i in range(len(poles)-index-2): + col1.append(2*(-ap).real) + col2.append(2*(-ap).real) + return np.array([col1, col2], float) + + i = 0 + cols = [] + while i < len(poles): + p = poles[i] + cols.extend(A_col(p,i)) + if i+1 < len(poles) and np.isclose(poles[i+1], np.conj(p)): i += 2 + else: i += 1 + A = np.column_stack(cols).astype(float) + return A + + def vector_B(self, poles): + return np.ones((len(poles), 1), float) + + def fit_denominator(self, H, weights=None, d0 = 1.0): + """ + Solve formula (70) on the real basis Φ to obtain: + - d (real) → packs into C for this state's block structure + - gamma (complex) + Optional 'weights' (K,) apply row scaling: SK weighting if 1/|D_prev|. + """ + K, N = self.Phi.shape + one = np.ones((K, 1), np.complex128) + Phi = self.Phi + dc_tol = 1e-18 + has_dc = self.dc_enforce and self.freqs[0] < dc_tol + keep = np.ones(K, dtype=bool) + + # SK weighting (applied only to the (73) rows we keep in LS) + + if has_dc: + # Enforce DC response exactly: + k0 = int(np.argmin(np.abs(self.freqs))) + keep[k0] = False + + if self.fit_constant: + Phi_w = np.hstack([one, Phi]) + index = 0 + M_kp = None + for i in range(self.ports): + for j in range(self.ports): + M0 = np.zeros((K,N*self.ports**2),dtype=complex) + M0[:,index*N:(index+1)*N] = Phi + M0 = np.hstack([M0, -(H[:,i,j].reshape(-1,1) * Phi_w)]).reshape((K, -1))[keep,:] # (K, 2N), complex + index+=1 + M_kp = M0 if M_kp is None else np.vstack([M_kp, M0]) + assert M_kp is not None + else: + index = 0 + M_kp = None + for i in range(self.ports): + for j in range(self.ports): + M0 = np.zeros((K,N*self.ports**2),dtype=complex) + M0[:,index*N:(index+1)*N] = Phi + M0 = np.hstack([M0, -(H[:,i,j].reshape(-1,1) * Phi)]).reshape((K, -1))[keep,:] # (K, 2N), complex + index+=1 + M_kp = M0 if M_kp is None else np.vstack([M_kp, M0]) + assert M_kp is not None + + if weights is None: + weights_kp = np.diag(np.ones(len(self.freqs[keep]) * self.ports**2, np.complex128)) + else: + weights_kp = np.diag(np.ones(len(self.freqs[keep]) * self.ports**2, np.complex128)) + # weights_kp0 = weights[keep] + # weights0 = [] + # for i in range(self.ports **2 ): + # for res in weights_kp0: + # weights0.append(1/res) + # weights_kp = np.diag(np.array(weights0)) + + + if has_dc: + M_w_kp = weights_kp @ M_kp + A_re = np.real(M_w_kp) + A_im = np.imag(M_w_kp) + mask = np.ones(K, dtype=bool); mask[k0] = False + # exact (unweighted) DC rows: + # A_dc_re = np.real(M_kp).reshape(1, -1) + # A_dc_im = np.imag(M_kp).reshape(1, -1) + else: + M_w_kp = weights_kp @ M_kp + A_re = np.real(M_w_kp) + A_im = np.imag(M_w_kp) + # A_dc_re = A_dc_im = None + + A_blocks = [A_re, A_im] + + if self.fit_constant: + Hk_sum = [] + for i in range(self.ports): + Hk_sum.append([]) + for j in range(self.ports): + Hk_kp0 = H[:,i,j][keep] + Hk_sum[i].append(np.sum(np.abs(Hk_kp0)**2)) + # Hk_kp = Hk_kp0 if Hk_kp is None else np.hstack([Hk_kp, Hk_kp0]) + K_keep = int(np.count_nonzero(keep)) + A_w0 = [] + b_w0 = [] + # Hk_sum = np.sum(np.abs(Hk_kp)**2) + for i in range(self.ports): + for j in range(self.ports): + beta_ij = float(np.sqrt(Hk_sum[i][j])) + mean_row = (beta_ij / K_keep) * np.sum(Phi_w[keep, :], axis=0) + A_w0.append(np.concatenate([np.zeros(N*self.ports**2, float), + np.real(mean_row).astype(float)] + ).reshape(1, -1)) + b_w0.append(np.array([beta_ij], float)) + b_w0 = np.asarray(b_w0).ravel() + + A_blocks += A_w0 + m = A_re.shape[0] + A_im.shape[0] + b = np.zeros(m, float) + b = np.concatenate([b, b_w0]) + else: + H_kp = None + for i in range(self.ports): + for j in range(self.ports): + H_0 = H[:,i,j][keep] + H_kp = H_0 if H_kp is None else np.hstack([H_kp, H_0]) + assert H_kp is not None + H_kp = weights_kp @ H_kp.reshape(-1,1) + + b_re = np.real(d0 * H_kp) + b_im = np.imag(d0 * H_kp) + b = np.concatenate([b_re.ravel(), b_im.ravel()]).astype(float) + + # ---- build final stacked-real system ---- + + + # if A_dc_re is not None: + # A_blocks += [A_dc_re, A_dc_im] + # b = np.concatenate([b, np.zeros(2, float)]) # DC rows → 0 + + # ---- QR solve for x = [c_H (N); c_w (N+1)] ---- + A = np.vstack(A_blocks).astype(float) + Q, R = np.linalg.qr(A, mode="reduced") + + if self.fit_constant: + Q2 = Q[:,Phi.shape[1] * self.ports**2:] + R22 = R[Phi.shape[1] * self.ports**2:,Phi.shape[1] * self.ports**2:] + else: + Q2 = Q[:,Phi.shape[1] * self.ports**2:] + R22 = R[Phi.shape[1] * self.ports**2:,Phi.shape[1] * self.ports**2:] + + x = np.linalg.solve(R22, Q2.T @ b) + + # diagnostics + resid = Q2 @ R22 @ x - b + # self.least_squares_rms_error = float(np.sqrt(np.mean(resid**2))) + # self.least_squares_condition = float(np.linalg.cond(R)) + self.least_squares_condition,\ + self.least_squares_row_condition,\ + self.least_squares_col_condition,\ + self.least_squares_rms_error = self.least_squares_metric(A, b) + + return self.extract_C_d_e(x,N,d0) + + def extract_C_d_e(self,C,N,d0=1.0): + a = np.sqrt(2 * np.real(-np.array(self.poles))) + if self.fit_proportional and self.fit_constant: + d = C[1] + e = C[0] + C = a * C[2:] + return C.reshape(1, -1), d, e + elif self.fit_proportional and not self.fit_constant: + d = 0.0 + e = C[0] + C = a * C[1:] + return C.reshape(1, -1), d, e + elif not self.fit_proportional and self.fit_constant: + d = C[0] + e = 0.0 + C = a * C[1:] + return C.reshape(1, -1), d, e + else: + C = a * C + return C.reshape(1, -1), d0, 0.0 + + + def non_bias_Cr(self,w0): + A = np.asarray(self.Phi) + den = np.diag((w0 + self.Phi @ self.residuals.T).ravel()) + Cr = [] + for i in range(self.ports): + Cr.append([]) + for j in range(self.ports): + b = np.asarray(den) @ self.H[:,i,j].reshape(-1,1) + Cr_ij, residuals, rank, s = np.linalg.lstsq(A, b, rcond=None) + Cr[i].append(Cr_ij) + return Cr + + def get_model_responses(self,freqs): + H = np.zeros((len(freqs),self.ports,self.ports),dtype=complex) + s = 1j * 2*np.pi * np.asarray(freqs, float).ravel() + phi = self.generate_basis(s, self.poles) + den = self.w0 + phi @ self.residuals.T + if self.Cr is None: + self.Cr = self.non_bias_Cr(w0=self.w0) + for i in range(self.ports): + for j in range(self.ports): + num = phi @ self.Cr[i][j] + H[:,i,j] = (num / den).reshape(1,-1) + return H + + + + diff --git a/core/MultiplePortQR.py b/core/basis/MultiplePortQR.py similarity index 99% rename from core/MultiplePortQR.py rename to core/basis/MultiplePortQR.py index f080085..2ea21c8 100644 --- a/core/MultiplePortQR.py +++ b/core/basis/MultiplePortQR.py @@ -1,9 +1,9 @@ import numpy as np -from core.sk_iter import generate_starting_poles +from core.utils import generate_starting_poles from scipy.linalg import block_diag import skrf as rf from skrf import VectorFitting -from core.freqency import auto_select_multple_ports +from core.sample import auto_select_multple_ports import matplotlib.pyplot as plt import random as rnd diff --git a/core/robust.py b/core/basis/__init__.py similarity index 100% rename from core/robust.py rename to core/basis/__init__.py diff --git a/core/basis.py b/core/basis/basis.py similarity index 99% rename from core/basis.py rename to core/basis/basis.py index 2f16635..70bf970 100644 --- a/core/basis.py +++ b/core/basis/basis.py @@ -1,9 +1,9 @@ import numpy as np -from core.sk_iter import generate_starting_poles +from core.utils import generate_starting_poles from scipy.linalg import block_diag import skrf as rf from skrf import VectorFitting -from core.freqency import auto_select +from core.sample import auto_select class BasicBasis: def __init__(self,H,freqs,poles,weights=None): diff --git a/core/basisQR.py b/core/basis/basisQR.py similarity index 99% rename from core/basisQR.py rename to core/basis/basisQR.py index 6ddbfe7..6a00b1e 100644 --- a/core/basisQR.py +++ b/core/basis/basisQR.py @@ -1,9 +1,9 @@ import numpy as np -from core.sk_iter import generate_starting_poles +from core.utils import generate_starting_poles from scipy.linalg import block_diag import skrf as rf from skrf import VectorFitting -from core.freqency import auto_select +from core.sample import auto_select class BasicBasisQR: def __init__(self,H,freqs,poles,weights=None): diff --git a/core/relaxed_basisQR.py b/core/basis/relaxed_basisQR.py similarity index 99% rename from core/relaxed_basisQR.py rename to core/basis/relaxed_basisQR.py index dd65277..bf957ae 100644 --- a/core/relaxed_basisQR.py +++ b/core/basis/relaxed_basisQR.py @@ -1,9 +1,9 @@ import numpy as np -from core.sk_iter import generate_starting_poles +from core.utils import generate_starting_poles from scipy.linalg import block_diag import skrf as rf from skrf import VectorFitting -from core.freqency import auto_select +from core.sample import auto_select import random as rnd class RelaxedBasicBasisQR: diff --git a/core/freqency.py b/core/sample.py similarity index 100% rename from core/freqency.py rename to core/sample.py diff --git a/core/util.py b/core/util.py deleted file mode 100644 index 53659ef..0000000 --- a/core/util.py +++ /dev/null @@ -1,909 +0,0 @@ -""" - -.. currentmodule:: skrf.util -======================================== -util (:mod:`skrf.util`) -======================================== - -Holds utilities that are general conveniences. - - -Time-related utilities ----------------------- -.. autosummary:: - :toctree: generated/ - - now_string - now_string_2_dt - - ProgressBar - -Array-related functions ------------------------ -.. autosummary:: - :toctree: generated/ - - find_nearest - find_nearest_index - has_duplicate_value - smooth - -File-related functions ----------------------- -.. autosummary:: - :toctree: generated/ - - get_fid - get_extn - basename_noext - git_version - unique_name - findReplace - dict_2_recarray - -General Purpose Objects ------------------------ -.. autosummary:: - :toctree: generated/ - - HomoList - HomoDict - -""" -from __future__ import annotations - -import collections -import contextlib -import fnmatch -import os -import pprint -import re -import sys -import warnings -from datetime import datetime -from functools import wraps -from pathlib import Path -from subprocess import PIPE, Popen -from typing import Any, Callable, Iterable, TypeVar - -import numpy as np - -from skrf.constants import Number - -try: - import matplotlib.pyplot as plt - from matplotlib.axes import Axes - from matplotlib.figure import Figure -except ImportError: - Figure = TypeVar("Figure") - Axes = TypeVar("Axes") - pass - -def plotting_available() -> bool: - return "matplotlib" in sys.modules - -def partial_with_docs(func, *args1, **kwargs1): - @wraps(func) - def method(self, *args2, **kwargs2): - return func(self, *args1, *args2, **kwargs1, **kwargs2) - return method - -def axes_kwarg(func): - """ - This decorator checks if a :class:`matplotlib.axes.Axes` object is passed, - if not the current axis will be gathered through :func:`plt.gca`. - - Raises - ------ - RuntimeError - When trying to run the decorated function without matplotlib - """ - - @wraps(func) - def wrapper(*args, **kwargs): - ax = kwargs.pop('ax', None) - try: - if ax is None: - ax = plt.gca() - except NameError as err: - raise RuntimeError("Plotting is not available") from err - func(*args, ax=ax, **kwargs) - - return wrapper - -def copy_doc(copy_func: Callable) -> Callable: - """Use Example: copy_doc(self.copy_func)(self.func) or used as deco""" - def wrapper(func: Callable) -> Callable: - func.__doc__ = copy_func.__doc__ - return func - return wrapper - -def figure(*args, **kwargs) -> Figure: - """ - Wraps the matplotlib figure call and raises if not available. - - Raises - ------ - RuntimeError - When trying to get subplots without matplotlib installed. - """ - - try: - return plt.figure(*args, **kwargs) - except NameError as err: - raise RuntimeError("Plotting is not available") from err - -def subplots(*args, **kwargs) -> tuple[Figure, np.ndarray]: - """ - Wraps the matplotlib subplots call and raises if not available. - - Raises - ------ - RuntimeError - When trying to get subplots without matplotlib installed. - """ - - try: - return plt.subplots(*args, **kwargs) - except NameError as err: - raise RuntimeError("Plotting is not available") from err - -def now_string() -> str: - """ - Return a unique sortable string, representing the current time. - - Nice for generating date-time stamps to be used in file-names, - the companion function :func:`now_string_2_dt` can be used - to read these string back into datetime objects. - - Returns - ------- - now : string - curent date-time stamps. - - See Also - -------- - now_string_2_dt - - """ - return datetime.now().__str__().replace('-','.').replace(':','.').replace(' ','.') - - -def now_string_2_dt(s: str) -> datetime: - """ - Converts the output of :func:`now_string` to a datetime object. - - Parameters - ---------- - s : str - date-time stamps string as generated by :func:`now_string` - - Returns - ------- - dt : datetime - date-time stamps - - See Also - -------- - now_string - - """ - return datetime(*[int(k) for k in s.split('.')]) - - -def find_nearest(array: np.ndarray, value: Number) -> Number: - """ - Find the nearest value in array. - - Parameters - ---------- - array : np.ndarray - array we are searching for a value in - value : element of the array - value to search for - - Returns - -------- - found_value : an element of the array - the value that is numerically closest to `value` - - """ - idx = find_nearest_index(array, value) - return array[idx] - - -def find_nearest_index(array: np.ndarray, value: Number) -> int: - """ - Find the nearest index for a value in array. - - Parameters - ---------- - array : np.ndarray - array we are searching for a value in - value : element of the array - value to search for - - Returns - -------- - found_index : int - the index at which the numerically closest element to `value` - was found at - - References - ---------- - taken from http://stackoverflow.com/questions/2566412/find-nearest-value-in-numpy-array - - """ - return (np.abs(array-value)).argmin() - - -def slice_domain(x: np.ndarray, domain: tuple): - """ - Returns a slice object closest to the `domain` of `x` - - domain = x[slice_domain(x, (start, stop))] - - Parameters - ---------- - vector : np.ndarray - an array of values - domain : tuple - tuple of (start,stop) values defining the domain over - which to slice - - Examples - -------- - >>> x = linspace(0,10,101) - >>> idx = slice_domain(x, (2,6)) - >>> x[idx] - - """ - start = find_nearest_index(x, domain[0]) - stop = find_nearest_index(x, domain[1]) - return slice(start, stop+1) - -# file IO - - -def get_fid(file, *args, **kwargs): - r""" - Return a file object, given a filename or file object. - - Useful when you want to allow the arguments of a function to - be either files or filenames - - Parameters - ---------- - file : str/unicode, Path, or file-object - file to open - \*args, \*\*kwargs : arguments and keyword arguments to `open()` - - Returns - ------- - fid : file object - - """ - if isinstance(file, (str, Path)): - return open(file, *args, **kwargs) - else: - return file - - -def get_extn(filename: str | Path) -> str: - """ - Get the extension from a filename. - - The extension is defined as everything passed the last '.'. - Returns None if it ain't got one - - Parameters - ---------- - filename : string or Path - the filename - - Returns - ------- - ext : string, None - either the extension (not including '.') or None if there - isn't one - - """ - - if isinstance(filename, Path): - return filename.suffix.strip('.') or None - - ext = os.path.splitext(filename)[-1] - if len(ext) == 0: - return None - else: - return ext[1:] - - -def basename_noext(filename: str) -> str: - """ - Get the basename and strips extension. - - Parameters - ---------- - filename : string - the filename - - Returns - ------- - basename : str - file basename (ie. without extension) - - """ - return os.path.splitext(os.path.basename(filename))[0] - - -# git -def git_version(modname: str) -> str: - """ - Return output 'git describe', executed in a module's root directory. - - Parameters - ---------- - modname : str - module name - - Returns - ------- - out : str - output of 'git describe' - - """ - mod = __import__(modname) - mod_dir = os.path.split(mod.__file__)[0] - p = Popen(['git', 'describe'], stdout=PIPE, stderr=PIPE, cwd=mod_dir) - - try: - out, er = p.communicate() - except(OSError): - return None - - out = out.strip('\n') - if out == '': - return None - return out - - -def dict_2_recarray(d: dict, delim: str, dtype: list[tuple]) -> np.ndarray: - """ - Turn a dictionary of structured keys to a record array of objects. - - This is useful if you save data-base like meta-data in the form - or file-naming conventions, aka 'the poor-mans database' - - Parameters - ---------- - d : dict - dictionnary of structured keys - delim : str - delimiter string - dtype : list of tuple - list of type, where a type is tuple like ('type_name', type) - - Returns - ------- - ra : numpy.array - - Examples - -------- - Given a directory of networks like: - - >>> ls - a1,0.0,0.0.s1p a1,3.0,3.0.s1p a2,3.0,-3.0.s1p b1,-3.0,3.0.s1p - ... - - you can sort based on the values or each field, after defining their - type with `dtype`. The `values` field accesses the objects. - - >>> d = rf.read_all_networks('/tmp/') - >>> delim = ',' - >>> dtype = [('name', object), ('voltage', float), ('current', float)] - >>> ra = dict_2_recarray(d=rf.ran(dir), delim=delim, dtype =dtype) - - then you can sift like you do with numpy arrays - - >>> ra[ra['voltage'] < 3]['values'] - array([1-Port Network: 'a2,0.0,-3.0', 450-800 GHz, 101 pts, z0=[ 50.+0.j], - 1-Port Network: 'b1,0.0,3.0', 450-800 GHz, 101 pts, z0=[ 50.+0.j], - 1-Port Network: 'a1,0.0,-3.0', 450-800 GHz, 101 pts, z0=[ 50.+0.j], - """ - - split_keys = [tuple(k.split(delim)+[d[k]]) for k in d.keys()] - x = np.array(split_keys, dtype=dtype+[('values',object)]) - return x - - -def findReplace(directory: str, find: str, replace: str, file_pattern: str): - r""" - Find/replace some txt in all files in a directory, recursively. - - This was found in [1]_ . - - Parameters - ---------- - directory : str - path of a directory - find : str - pattern to search for - replace : str - string to replace with - file_pattern : str - file pattern for filtering. Ex: '\*.txt'. - - Examples - -------- - >>> rf.findReplace('some_dir', 'find this', 'replace with this', '*.txt') - - References - ---------- - .. [1] http://stackoverflow.com/questions/4205854/python-way-to-recursively-find-and-replace-string-in-text-files - """ - for path, _dirs, files in os.walk(os.path.abspath(directory)): - for filename in fnmatch.filter(files, file_pattern): - filepath = os.path.join(path, filename) - with open(filepath) as f: - s = f.read() - s = s.replace(find, replace) - with open(filepath, "w") as f: - f.write(s) - - -# general purpose objects - -class HomoList(collections.abc.Sequence): - """ - A Homogeneous Sequence. - - Provides a class for a list-like object which contains - homogeneous values. Attributes of the values can be accessed through - the attributes of HomoList. Searching is done like numpy arrays. - - Initialized from a list of all the same type - - >>> h = HomoDict([Foo(...), Foo(...)]) - - The individual values of `h` can be access in identical fashion to - Lists. - - >>> h[0] - - Assuming that `Foo` has property `prop` and function `func` ... - - Access elements' properties: - - >>> h.prop - - Access elements' functions: - - >>> h.func() - - Searching: - - >>> h[h.prop == value] - >>> h[h.prop < value] - - Multiple search: - - >>> h[set(h.prop==value1) & set( h.prop2==value2)] - - Combos: - - >>> h[h.prop==value].func() - """ - - - def __init__(self, list_): - self.store = list(list_) - - def __eq__(self, value): - return [k for k in range(len(self)) if self.store[k] == value ] - - def __ne__(self, value): - return [k for k in range(len(self)) if self.store[k] != value ] - - def __gt__(self, value): - return [k for k in range(len(self)) if self.store[k] > value ] - - def __ge__(self, value): - return [k for k in range(len(self)) if self.store[k] >= value ] - - def __lt__(self, value): - return [k for k in range(len(self)) if self.store[k] < value ] - - def __le__(self, value): - return [k for k in range(len(self)) if self.store[k] <= value ] - - def __getattr__(self, name): - return self.__class__( - [k.__getattribute__(name) for k in self.store]) - - def __getitem__(self, idx): - try: - return self.store[idx] - except(TypeError): - return self.__class__([self.store[k] for k in idx]) - - - def __call__(self, *args, **kwargs): - return self.__class__( - [k(*args,**kwargs) for k in self.store]) - - def __setitem__(self, idx, value): - self.store[idx] = value - - def __delitem__(self, idx): - del self.store[idx] - - def __iter__(self): - return iter(self.store) - - def __len__(self): - return len(self.store) - - def __str__(self): - return pprint.pformat(self.store) - - def __repr__(self): - return pprint.pformat(self.store) - - -class HomoDict(collections.abc.MutableMapping): - """ - A Homogeneous Mutable Mapping. - - Provides a class for a dictionary-like object which contains - homogeneous values. Attributes of the values can be accessed through - the attributes of HomoDict. Searching is done like numpy arrays. - - Initialized from a dictionary containing values of all the same type - - >>> h = HomoDict({'a':Foo(...),'b': Foo(...), 'c':Foo(..)}) - - The individual values of `h` can be access in identical fashion to - Dictionaries. - - >>> h['key'] - - Assuming that `Foo` has property `prop` and function `func` ... - - Access elements' properties: - - >>> h.prop - - Access elements' functions: - - >>> h.func() - - Searching: - - >>> h[h.prop == value] - >>> h[h.prop < value] - - Multiple search: - - >>> h[set(h.prop==value1) & set( h.prop2==value2)] - - Combos: - - >>> h[h.prop==value].func() - """ - def __init__(self, dict_): - self.store = dict(dict_) - - def __eq__(self, value): - return [k for k in self.store if self.store[k] == value ] - - def __ne__(self, value): - return [k for k in self.store if self.store[k] != value ] - - def __gt__(self, value): - return [k for k in self.store if self.store[k] > value ] - - def __ge__(self, value): - return [k for k in self.store if self.store[k] >= value ] - - def __lt__(self, value): - return [k for k in self.store if self.store[k] < value ] - - def __le__(self, value): - return [k for k in self.store if self.store[k] <= value ] - - def __getattr__(self, name): - return self.__class__( - {k: getattr(self.store[k],name) for k in self.store}) - - def __getitem__(self, key): - if isinstance(key, str): - return self.store[key] - else: - c = self.__class__({k:self.store[k] for k in key}) - return c - #if len(c) == 1: - # return c.store.values()[0] - #else: - # return c - - def __call__(self, *args, **kwargs): - return self.__class__( - {k: self.store[k](*args, **kwargs) for k in self.store}) - - def __setitem__(self, key, value): - self.store[key] = value - - def __delitem__(self, key): - del self.store[key] - - def __iter__(self): - return iter(self.store) - - def __len__(self): - return len(self.store) - - def __str__(self): - return pprint.pformat(self.store) - - def __repr__(self): - return pprint.pformat(self.store) - - - def copy(self): - return HomoDict(self.store) - - - def filter_nones(self): - self.store = {k:self.store[k] for k in self.store \ - if self.store[k] is not None} - - def filter(self, **kwargs): - """ - Filter self based on kwargs - - This is equivalent to: - - >>> h = HomoDict(...) - >>> for k in kwargs: - >>> h = h[k ==kwargs[k]] - >>> return h - - prefixing the kwarg value with a '!' causes a not equal test (!=) - - Examples - ---------- - >>> h = HomoDict(...) - >>> h.filter(name='jean', age = '18', gender ='!female') - - """ - a = self - for k in kwargs: - if kwargs[k][0] == '!': - a = a[a.__getattr__(k) != kwargs[k][1:]] - else: - a = a[a.__getattr__(k) == kwargs[k]] - return a - - -def has_duplicate_value(value: Any, values: Iterable, index: int) -> bool | int: - """ - Check if there is another value of the current index in the list. - - Parameters - ---------- - value : Any - any value in a list - values : Iterable - the iterable containing the values - index : int - the index of the current item we are checking for. - - Returns - ------- - index : bool or int - returns None if no duplicate found, or the index of the first found duplicate - - Examples - -------- - >>> rf.has_duplicate_value(0, [1, 2, 0, 3, 0], -1) # -> 2 - >>> rf.has_duplicate_value(0, [1, 2, 0, 3, 0], 2) # -> 4 - >>> rf.has_duplicate_value(3, [1, 2, 0, 3, 0], 0) # -> 3 - >>> rf.has_duplicate_value(3, [1, 2, 0, 3, 0], 3) # -> False - """ - - for i, val in enumerate(values): - if i == index: - continue - if value == val: - return i - return False - - -def unique_name(name: str, names: list, exclude: int = -1) -> str: - """ - Pass in a name and a list of names, and increment with _## as necessary to ensure a unique name. - - Parameters - ---------- - name : str - the chosen name, to be modified if necessary - names : list - list of names (str) - exclude : int, optional - the index of an item to be excluded from the search. Default is -1. - - Returns - ------- - unique_name : str - - """ - if not has_duplicate_value(name, names, exclude): - return name - else: - if re.match(r"_\d\d", name[-3:]): - name_base = name[:-3] - suffix = int(name[-2:]) - else: - name_base = name - suffix = 1 - - for num in range(suffix, 100, 1): - name = f"{name_base:s}_{num:02d}" - if not has_duplicate_value(name, names, exclude): - break - return name - - -def smooth(x: np.ndarray, window_len: int = 11, window: str = 'flat') -> np.ndarray: - """ - Smooth the data using a window with requested size. - - Based on the function from the scipy cookbook [#]_ - - This method is based on the convolution of a scaled window with the signal. - The signal is prepared by introducing reflected copies of the signal - (with the window size) in both ends so that transient parts are minimized - in the beginning and end part of the output signal. - - Parameters - ---------- - x : numpy.array - the input signal - window_len : int, optional - the dimension of the smoothing window; should be an odd integer. - Default is 11. - window : str, optional - the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman' - flat window will produce a moving average smoothing. Default is 'flat' - - Returns - ------- - y : numpy.array - The smoothed signal - - Examples - -------- - >>> t = linspace(-2, 2, 0.1) - >>> x = sin(t) + randn(len(t))*0.1 - >>> y = smooth(x) - - See Also - -------- - numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve - scipy.signal.lfilter - - Note - ---- - `length(output) != length(input)`. - To correct this: `return y[(window_len/2-1):-(window_len/2)]` instead of just `y`. - - References - ---------- - .. [#] http://scipy-cookbook.readthedocs.io/items/SignalSmooth.html - - """ - - if x.ndim != 1: - raise ValueError("smooth only accepts 1 dimension arrays.") - - if x.size < window_len: - raise ValueError("Input vector needs to be bigger than window size.") - - if window_len < 3: - return x - - if window not in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']: - raise ValueError("Window is one of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'") - - s = np.r_[x[window_len - 1:0:-1], x, x[-2:-window_len - 1:-1]] - if window == 'flat': # moving average - w = np.ones(window_len, 'd') - else: - w = eval('np.' + window + '(window_len)') - y = np.convolve(w / w.sum(), s, mode='same') - return y[window_len-1:-(window_len-1)] - - -class ProgressBar: - """ - A progress bar based off of the notebook/ipython progress bar from PyMC. - - Useful when waiting for long operations such as taking a large number - of VNA measurements that may take a few minutes. - - Examples - -------- - >>> from time import sleep - >>> pb = rf.ProgressBar(10) - >>> for idx in range(10): - >>> sleep(1) - >>> pb.animate(idx) - - """ - def __init__(self, iterations: int, label: str = "iterations"): - """ - Progress bar constructor. - - Parameters - ---------- - iterations : int - Number of expected iterations - label : str, optional - Progress bar label, by default "iterations" - """ - self.iterations = iterations - self.label = label - self.prog_bar = '[]' - self.fill_char = '*' - self.width = 50 - self.__update_amount(0) - - def animate(self, iteration: int): - """ - Animate the progress bar. - - Parameters - ---------- - iteration : int - current iteration - """ - print('\r', self, end='') - sys.stdout.flush() - self.update_iteration(iteration + 1) - - def update_iteration(self, elapsed_iter: int): - self.__update_amount((elapsed_iter / float(self.iterations)) * 100.0) - self.prog_bar += ' %d of %s %s complete' % (elapsed_iter, self.iterations, self.label) - - def __update_amount(self, new_amount: int): - percent_done = int(round((new_amount / 100.0) * 100.0)) - all_full = self.width - 2 - num_hashes = int(round((percent_done / 100.0) * all_full)) - self.prog_bar = '[' + self.fill_char * num_hashes + ' ' * (all_full - num_hashes) + ']' - pct_place = (len(self.prog_bar) // 2) - len(str(percent_done)) - pct_string = '%d%%' % percent_done - self.prog_bar = self.prog_bar[0:pct_place] + \ - (pct_string + self.prog_bar[pct_place + len(pct_string):]) - - def __str__(self): - return str(self.prog_bar) - - -@contextlib.contextmanager -def suppress_numpy_warnings(**kw): - olderr = np.seterr(**kw) - yield - np.seterr(**olderr) - - -def suppress_warning_decorator(msg): - def suppress_warnings_decorated(func): - @wraps(func) - def suppressed_func(*k, **kw): - with warnings.catch_warnings(): - warnings.filterwarnings("ignore", message=f"{msg}.*") - res = func(*k, **kw) - return res - return suppressed_func - return suppress_warnings_decorated diff --git a/core/sk_iter.py b/core/utils.py similarity index 56% rename from core/sk_iter.py rename to core/utils.py index 47681f6..dbeaabe 100644 --- a/core/sk_iter.py +++ b/core/utils.py @@ -2,6 +2,18 @@ import numpy as np from dataclasses import dataclass from typing import List, Dict, Tuple +def cond_row_inf(A, use_pinv=True): + """行条件数 κ∞(A) = ||A||∞ * ||A^{-1}||∞；矩形阵用广义逆。""" + A = np.asarray(A) + Ainv = np.linalg.pinv(A) if (use_pinv or A.shape[0] != A.shape[1]) else np.linalg.inv(A) + return np.linalg.norm(A, ord=np.inf) * np.linalg.norm(Ainv, ord=np.inf) + +def cond_col_one(A, use_pinv=True): + """列条件数 κ1(A) = ||A||1 * ||A^{-1}||1；矩形阵用广义逆。""" + A = np.asarray(A) + Ainv = np.linalg.pinv(A) if (use_pinv or A.shape[0] != A.shape[1]) else np.linalg.inv(A) + return np.linalg.norm(A, ord=1) * np.linalg.norm(Ainv, ord=1) + def generate_starting_poles(n_pairs: int, beta_min: float, beta_max: float, alpha_scale: float = 0.01): """ 仅生成复共轭对: p = -alpha + j beta, p*。 diff --git a/core/vectorFitting.py b/core/vectorFitting.py deleted file mode 100644 index 3317aff..0000000 --- a/core/vectorFitting.py +++ /dev/null @@ -1,2617 +0,0 @@ -from __future__ import annotations - -import logging -import os -import warnings -from timeit import default_timer as timer -from typing import TYPE_CHECKING, Any - -import numpy as np -from scipy.integrate import trapezoid - -from .util import Axes, axes_kwarg - -# imports for type hinting -if TYPE_CHECKING: - from skrf import Network - - -logger = logging.getLogger(__name__) - - -class VectorFitting: - """ - This class provides a Python implementation of the Vector Fitting algorithm and various functions for the fit - analysis, passivity evaluation and enforcement, and export of SPICE equivalent circuits. - - Parameters - ---------- - network : :class:`skrf.network.Network` - Network instance of the :math:`N`-port holding the frequency responses to be fitted, for example a - scattering, impedance or admittance matrix. - - Examples - -------- - Load the `Network`, create a `VectorFitting` instance, perform the fit with a given number of real and - complex-conjugate starting poles: - - >>> nw_3port = skrf.Network('my3port.s3p') - >>> vf = skrf.VectorFitting(nw_3port) - >>> vf.vector_fit(n_poles_real=1, n_poles_cmplx=4) - - Notes - ----- - The fitting code is based on the original algorithm [#Gustavsen_vectfit]_ and on two improvements for relaxed pole - relocation [#Gustavsen_relaxed]_ and efficient (fast) solving [#Deschrijver_fast]_. See also the Vector Fitting - website [#vectfit_website]_ for further information and download of the papers listed below. A Matlab implementation - is also available there for reference. - - References - ---------- - .. [#Gustavsen_vectfit] B. Gustavsen, A. Semlyen, "Rational Approximation of Frequency Domain Responses by Vector - Fitting", IEEE Transactions on Power Delivery, vol. 14, no. 3, pp. 1052-1061, July 1999, - DOI: https://doi.org/10.1109/61.772353 - - .. [#Gustavsen_relaxed] B. Gustavsen, "Improving the Pole Relocating Properties of Vector Fitting", IEEE - Transactions on Power Delivery, vol. 21, no. 3, pp. 1587-1592, July 2006, - DOI: https://doi.org/10.1109/TPWRD.2005.860281 - - .. [#Deschrijver_fast] D. Deschrijver, M. Mrozowski, T. Dhaene, D. De Zutter, "Marcomodeling of Multiport Systems - Using a Fast Implementation of the Vector Fitting Method", IEEE Microwave and Wireless Components Letters, - vol. 18, no. 6, pp. 383-385, June 2008, DOI: https://doi.org/10.1109/LMWC.2008.922585 - - .. [#vectfit_website] Vector Fitting website: https://www.sintef.no/projectweb/vectorfitting/ - """ - - def __init__(self, network: Network): - self.network = network - """ Instance variable holding the Network to be fitted. This is the Network passed during initialization, - which may be changed or set to *None*. """ - - self.poles = None - """ Instance variable holding the list of fitted poles. Will be initialized by :func:`vector_fit`. """ - - self.residues = None - """ Instance variable holding the list of fitted residues. Will be initialized by :func:`vector_fit`. """ - - self.proportional_coeff = None - """ Instance variable holding the list of fitted proportional coefficients. Will be initialized by - :func:`vector_fit`. """ - - self.constant_coeff = None - """ Instance variable holding the list of fitted constants. Will be initialized by :func:`vector_fit`. """ - - self.max_iterations = 100 - """ Instance variable specifying the maximum number of iterations for the fitting process and for the passivity - enforcement. To be changed by the user before calling :func:`vector_fit` and/or :func:`passivity_enforce`. """ - - self.max_tol = 1e-6 - """ Instance variable specifying the convergence criterion in terms of relative tolerance. To be changed by the - user before calling :func:`vector_fit`. """ - - self.wall_clock_time = 0 - """ Instance variable holding the wall-clock time (in seconds) consumed by the most recent fitting process with - :func:`vector_fit`. Subsequent calls of :func:`vector_fit` will overwrite this value. """ - - self.d_res_history = [] - self.delta_max_history = [] - self.history_max_sigma = [] - self.history_cond_A = [] - self.history_rank_deficiency = [] - - @staticmethod - def get_spurious(poles: np.ndarray, residues: np.ndarray, n_freqs: int = 101, gamma: float = 0.03) -> np.ndarray: - """ - Classifies fitted pole-residue pairs as spurious or not spurious. The implementation is based on the evaluation - of band-limited energy norms (p=2) of the resonance curves of individual pole-residue pairs, as proposed in - [#Grivet-Talocia]_. - - Parameters - ---------- - poles : ndarray, shape (N) - Array of fitted poles - - residues : ndarray, shape (M, N) - Array of fitted residues - - n_freqs : int, optional - Number of frequencies for the evaluation. The frequency range is chosen automatically and the default - 101 frequencies should be appropriate in most cases. - - gamma : float, optional - Sensitivity threshold for the classification. Typical values range from 0.01 to 0.05. - - Returns - ------- - ndarray, bool, shape (M) - Boolean array having the same shape as :attr:`poles`. `True` marks the respective pole as spurious. - - References - ---------- - .. [#Grivet-Talocia] S. Grivet-Talocia and M. Bandinu, "Improving the convergence of vector fitting for - equivalent circuit extraction from noisy frequency responses," in IEEE Transactions on Electromagnetic - Compatibility, vol. 48, no. 1, pp. 104-120, Feb. 2006, DOI: https://doi.org/10.1109/TEMC.2006.870814 - """ - - omega_eval = np.linspace(np.min(poles.imag) / 3, np.max(poles.imag) * 3, n_freqs) - h = (residues[:, None, :] / (1j * omega_eval[:, None] - poles) - + np.conj(residues[:, None, :]) / (1j * omega_eval[:, None] - np.conj(poles))) - norm2 = np.sqrt(trapezoid(h.real ** 2 + h.imag ** 2, omega_eval, axis=1)) - spurious = np.all(norm2 / np.mean(norm2) < gamma, axis=0) - return spurious - - @staticmethod - def get_model_order(poles: np.ndarray) -> int: - """ - Returns the model order calculated with :math:`N_{real} + 2 N_{complex}` for a given set of poles. - - Parameters - ---------- - poles: ndarray - The poles of the model as a list or NumPy array. - - Returns - ------- - order: int - """ - # poles.imag != 0 is True(1) for complex poles, False (0) for real poles. - # Adding one to each element gives 2 columns for complex and 1 column for real poles. - return np.sum((poles.imag != 0) + 1) - - def vector_fit(self, n_poles_real: int = 2, n_poles_cmplx: int = 2, init_pole_spacing: str = 'lin', - parameter_type: str = 's', fit_constant: bool = True, fit_proportional: bool = False) -> None: - """ - Main work routine performing the vector fit. The results will be stored in the class variables - :attr:`poles`, :attr:`residues`, :attr:`proportional_coeff` and :attr:`constant_coeff`. - - Parameters - ---------- - n_poles_real : int, optional - Number of initial real poles. See notes. - - n_poles_cmplx : int, optional - Number of initial complex conjugate poles. See notes. - - init_pole_spacing : str, optional - Type of initial pole spacing across the frequency interval of the S-matrix. Either *linear* (`'lin'`), - *logarithmic* (`'log'`), or `custom`. In case of `custom`, the initial poles must be stored in :attr:`poles` - as a NumPy array before calling this method. They will be overwritten by the final poles. The - initialization parameters `n_poles_real` and `n_poles_cmplx` will be ignored in case of `'custom'`. - - parameter_type : str, optional - Representation type of the frequency responses to be fitted. Either *scattering* (`'s'` or `'S'`), - *impedance* (`'z'` or `'Z'`) or *admittance* (`'y'` or `'Y'`). It's recommended to perform the fit on the - original S parameters. Otherwise, scikit-rf will convert the responses from S to Z or Y, which might work - for the fit but can cause other issues. - - fit_constant : bool, optional - Include a constant term **d** in the fit. - - fit_proportional : bool, optional - Include a proportional term **e** in the fit. - - Returns - ------- - None - No return value. - - Notes - ----- - The required number of real or complex conjugate starting poles depends on the behaviour of the frequency - responses. To fit a smooth response such as a low-pass characteristic, 1-3 real poles and no complex conjugate - poles is usually sufficient. If resonances or other types of peaks are present in some or all of the responses, - a similar number of complex conjugate poles is required. Be careful not to use too many poles, as excessive - poles will not only increase the computation workload during the fitting and the subsequent use of the model, - but they can also introduce unwanted resonances at frequencies well outside the fit interval. - - See Also - -------- - auto_fit : Automatic vector fitting routine with pole adding and skimming. - """ - - timer_start = timer() - - # use normalized frequencies during the iterations (seems to be more stable during least-squares fit) - norm = np.average(self.network.f) - # norm = np.exp(np.mean(np.log(self.network.f))) - freqs_norm = np.array(self.network.f) / norm - - # get initial poles - poles = self._init_poles(freqs_norm, n_poles_real, n_poles_cmplx, init_pole_spacing) - - # check and normalize custom poles - if poles is None: - if self.poles is not None and len(self.poles) > 0: - poles = self.poles / norm - else: - raise ValueError('Initial poles must be provided in `self.poles` when calling with ' - '`init_pole_spacing == \'custom\'`.') - - # save initial poles (un-normalize first) - initial_poles = poles * norm - max_singular = 1 - - logger.info('### Starting pole relocation process.\n') - - # select network representation type - if parameter_type.lower() == 's': - nw_responses = self.network.s - elif parameter_type.lower() == 'z': - nw_responses = self.network.z - elif parameter_type.lower() == 'y': - nw_responses = self.network.y - else: - warnings.warn('Invalid choice of matrix parameter type (S, Z, or Y); proceeding with scattering ' - 'representation.', UserWarning, stacklevel=2) - nw_responses = self.network.s - - # stack frequency responses as a single vector - # stacking order (row-major): - # s11, s12, s13, ..., s21, s22, s23, ... - freq_responses = [] - for i in range(self.network.nports): - for j in range(self.network.nports): - freq_responses.append(nw_responses[:, i, j]) - freq_responses = np.array(freq_responses) - - # responses will be weighted according to their norm; - # alternative: equal weights with weight_response = 1.0 - # or anti-proportional weights with weight_response = 1 / np.linalg.norm(freq_response) - weights_responses = np.linalg.norm(freq_responses, axis=1) - #weights_responses = np.ones(self.network.nports ** 2) - #weights_responses = 10 / np.exp(np.mean(np.log(np.abs(freq_responses)), axis=1)) - - # ITERATIVE FITTING OF POLES to the provided frequency responses - # initial set of poles will be replaced with new poles after every iteration - iterations = self.max_iterations - self.d_res_history = [] - self.delta_max_history = [] - self.history_cond_A = [] - self.history_rank_deficiency = [] - converged = False - - # POLE RELOCATION LOOP - while iterations > 0: - logger.info(f'Iteration {self.max_iterations - iterations + 1}') - - poles, d_res, cond, rank_deficiency, residuals, singular_vals = self._pole_relocation( - poles, freqs_norm, freq_responses, weights_responses, fit_constant, fit_proportional) - - logger.info(f'Condition number of coefficient matrix is {int(cond)}') - self.history_cond_A.append(cond) - - self.history_rank_deficiency.append(rank_deficiency) - logger.info(f'Rank deficiency is {rank_deficiency}.') - - self.d_res_history.append(d_res) - logger.info(f'd_res = {d_res}') - - # calculate relative changes in the singular values; stop iteration loop once poles have converged - new_max_singular = np.amax(singular_vals) - delta_max = np.abs(1 - new_max_singular / max_singular) - self.delta_max_history.append(delta_max) - logger.info(f'Max. relative change in residues = {delta_max}\n') - max_singular = new_max_singular - - stop = False - if delta_max < self.max_tol: - if converged: - # is really converged, finish - logger.info(f'Pole relocation process converged after {self.max_iterations - iterations + 1} ' - 'iterations.') - stop = True - else: - # might be converged, but do one last run to be sure - converged = True - else: - if converged: - # is not really converged, continue - converged = False - - iterations -= 1 - - if iterations == 0: - # loop ran into iterations limit; trying to assess the issue - max_cond = np.amax(self.history_cond_A) - max_deficiency = np.amax(self.history_rank_deficiency) - if max_cond > 1e10: - hint_illcond = ('\nHint: the linear system was ill-conditioned (max. condition number was ' - f'{max_cond}).') - else: - hint_illcond = '' - if max_deficiency < 0: - hint_rank = ('\nHint: the coefficient matrix was rank-deficient (max. rank deficiency was ' - f'{max_deficiency}).') - else: - hint_rank = '' - if converged and stop is False: - warnings.warn('Vector Fitting: The pole relocation process barely converged to tolerance. ' - f'It took the max. number of iterations (N_max = {self.max_iterations}). ' - 'The results might not have converged properly.' - + hint_illcond + hint_rank, RuntimeWarning, stacklevel=2) - else: - warnings.warn('Vector Fitting: The pole relocation process stopped after reaching the ' - f'maximum number of iterations (N_max = {self.max_iterations}). ' - 'The results did not converge properly.' - + hint_illcond + hint_rank, RuntimeWarning, stacklevel=2) - - if stop: - iterations = 0 - - # ITERATIONS DONE - logger.info('Initial poles before relocation:') - logger.info(initial_poles) - - logger.info('Final poles:') - logger.info(poles * norm) - - logger.info('\n### Starting residues calculation process.\n') - - # finally, solve for the residues with the previously calculated poles - residues, constant_coeff, proportional_coeff, residuals, rank, singular_vals = self._fit_residues( - poles, freqs_norm, freq_responses, fit_constant, fit_proportional) - - # save poles, residues, d, e in actual frequencies (un-normalized) - self.poles = poles * norm - self.residues = np.array(residues) * norm - self.constant_coeff = np.array(constant_coeff) - self.proportional_coeff = np.array(proportional_coeff) / norm - - timer_stop = timer() - self.wall_clock_time = timer_stop - timer_start - - logger.info(f'\n### Vector fitting finished in {self.wall_clock_time} seconds.\n') - - # raise a warning if the fitted Network is passive but the fit is not (only without proportional_coeff): - if self.network.is_passive() and not fit_proportional: - if not self.is_passive(): - warnings.warn('The fitted network is passive, but the vector fit is not passive. Consider running ' - '`passivity_enforce()` to enforce passivity before using this model.', - UserWarning, stacklevel=2) - - def auto_fit(self, n_poles_init_real: int = 3, n_poles_init_cmplx: int = 3, n_poles_add: int = 3, - model_order_max: int = 100, iters_start: int = 3, iters_inter: int = 3, iters_final: int = 5, - target_error: float = 1e-2, alpha: float = 0.03, gamma: float = 0.03, nu_samples: float = 1.0, - parameter_type: str = 's') -> (np.ndarray, np.ndarray): - """ - Automatic fitting routine implementing the "vector fitting with adding and skimming" algorithm as proposed in - [#Grivet-Talocia]_. This algorithm is able to provide high quality macromodels with automatic model order - optimization, while improving both the rate of convergence and the fit quality in case of noisy data. - The resulting model parameters will be stored in the class variables :attr:`poles`, :attr:`residues`, - :attr:`proportional_coeff` and :attr:`constant_coeff`. - - Parameters - ---------- - n_poles_init_real: int, optional - Number of real poles in the initial model. - - n_poles_init_cmplx: int, optional - Number of complex conjugate poles in the initial model. - - n_poles_add: int, optional - Number of new poles allowed to be added in each refinement iteration, if possible. This controls how fast - the model order is allowed to grow. Unnecessary poles will have to be skimmed and removed later. This - parameter has a strong effect on the convergence. - - model_order_max: int, optional - Maximum model order as calculated with :math:`N_{real} + 2 N_{complex}`. This parameter provides a stopping - criterion in case the refinement process is not converging. - - iters_start: int, optional - Number of initial iterations for pole relocations as in regular vector fitting. - - iters_inter: int, optional - Number of intermediate iterations for pole relocations during each iteration of the refinement process. - - iters_final: int, optional - Number of final iterations for pole relocations after the refinement process. - - target_error: float, optional - Target for the model error to be reached during the refinement process. The actual achievable error is - bound by the noise in the data. If specified with a number greater than the noise floor, this parameter - provides another stopping criterion for the refinement process. It therefore affects both the convergence, - the final error, and the final model order (number of poles used in the model). - - alpha: float, optional - Threshold for the error decay to stop the refinement loop in case of error stagnation. This parameter - provides another stopping criterion for cases where the model already has enough poles but the target error - still cannot be reached because of excess noise (target error too small for noise level in the data). - - gamma: float, optional - Threshold for the detection of spurious poles. - - nu_samples: float, optional - Required and enforced (relative) spacing in terms of frequency samples between existing poles and - relocated or added poles. The number can be a float, it does not have to be an integer. - - parameter_type: str, optional - Representation type of the frequency responses to be fitted. Either *scattering* (`'s'` or `'S'`), - *impedance* (`'z'` or `'Z'`) or *admittance* (`'y'` or `'Y'`). It's recommended to perform the fit on the - original S parameters. Otherwise, scikit-rf will convert the responses from S to Z or Y, which might work - for the fit but can cause other issues. - - Returns - ------- - None - No return value. - - See Also - -------- - vector_fit : Regular vector fitting routine. - - References - ---------- - .. [#Grivet-Talocia] S. Grivet-Talocia and M. Bandinu, "Improving the convergence of vector fitting for - equivalent circuit extraction from noisy frequency responses," in IEEE Transactions on Electromagnetic - Compatibility, vol. 48, no. 1, pp. 104-120, Feb. 2006, DOI: https://doi.org/10.1109/TEMC.2006.870814 - """ - - self.d_res_history = [] - self.delta_max_history = [] - self.history_cond_A = [] - self.history_rank_deficiency = [] - max_singular = 1 - error_peak_history = [] - model_order_history = [] - - timer_start = timer() - - # use normalized frequencies during the iterations (seems to be more stable during least-squares fit) - norm = np.average(self.network.f) - # norm = np.exp(np.mean(np.log(self.network.f))) - freqs_norm = np.array(self.network.f) / norm - omega_norm = 2 * np.pi * freqs_norm - nu = (omega_norm[1] - omega_norm[0]) * nu_samples - - # get initial poles - poles = self._init_poles(freqs_norm, n_poles_init_real, n_poles_init_cmplx, 'lin') - - logger.info('### Starting pole relocation process.\n') - - # select network representation type - if parameter_type.lower() == 's': - nw_responses = self.network.s - fit_constant = True - fit_proportional = False - elif parameter_type.lower() == 'z': - nw_responses = self.network.z - fit_constant = True - fit_proportional = True - elif parameter_type.lower() == 'y': - nw_responses = self.network.y - fit_constant = True - fit_proportional = True - else: - warnings.warn('Invalid choice of matrix parameter type (S, Z, or Y); proceeding with scattering ' - 'representation.', UserWarning, stacklevel=2) - nw_responses = self.network.s - fit_constant = True - fit_proportional = False - - # stack frequency responses as a single vector - # stacking order (row-major): - # s11, s12, s13, ..., s21, s22, s23, ... - freq_responses = [] - for i in range(self.network.nports): - for j in range(self.network.nports): - freq_responses.append(nw_responses[:, i, j]) - freq_responses = np.array(freq_responses) - - # responses will be weighted according to their norm; - # alternative: equal weights with weight_response = 1.0 - # or anti-proportional weights with weight_response = 1 / np.linalg.norm(freq_response) - weights_responses = np.linalg.norm(freq_responses, axis=1) - # weights_responses = np.ones(self.network.nports ** 2) - # weights_responses = 10 / np.exp(np.mean(np.log(np.abs(freq_responses)), axis=1)) - - # INITIAL POLE RELOCATION FOR i_start ITERATIONS - for _ in range(iters_start): - poles, d_res, cond, rank_deficiency, residuals, singular_vals = self._pole_relocation( - poles, freqs_norm, freq_responses, weights_responses, fit_constant, fit_proportional) - - self.d_res_history.append(d_res) - - logger.info(f'Condition number of coefficient matrix is {int(cond)}') - self.history_cond_A.append(cond) - - self.history_rank_deficiency.append(rank_deficiency) - logger.info(f'Rank deficiency is {rank_deficiency}.') - - new_max_singular = np.amax(singular_vals) - delta_max = np.abs(1 - new_max_singular / max_singular) - self.delta_max_history.append(delta_max) - logger.info(f'Max. relative change in residues = {delta_max}\n') - max_singular = new_max_singular - - # RESIDUE FITTING FOR ERROR COMPUTATION - residues, constant_coeff, proportional_coeff, residuals, rank, singular_vals = self._fit_residues( - poles, freqs_norm, freq_responses, fit_constant, fit_proportional, enforce_dc=False) - delta = self._get_delta(poles, residues, constant_coeff, proportional_coeff, freqs_norm, freq_responses, - weights_responses) - error_peak = np.max(delta) - error_peak_history.append(error_peak) - - model_order = self.get_model_order(poles) - model_order_history.append(model_order) - - delta_eps = 10 * alpha - - # POLE SKIMMING AND ADDING LOOP - while error_peak > target_error and model_order < model_order_max and delta_eps > alpha: - - # SKIMMING OF SPURIOUS POLES - spurious = self.get_spurious(poles, residues, gamma=gamma) - n_skim = np.sum(spurious) - poles = poles[~spurious] - - # REPLACING SPURIOUS POLE AND ADDING NEW POLES - idx_freqs_start, idx_freqs_stop, idx_freqs_max, delta_mean_bands = self._find_error_bands(freqs_norm, delta) - - n_bands = len(idx_freqs_max) - if n_bands < n_skim: - n_add = n_bands - elif n_bands < n_skim + n_poles_add: - n_add = n_bands - else: - n_add = n_skim + n_poles_add - - for i in range(n_add): - omega_add = omega_norm[idx_freqs_max[i]] - pole_add = (-0.01 + 1j) * omega_add - - # compute distance to neighbouring poles - abs_poles_existing = np.abs(poles) - pole_add.imag # (equation 16) - #abs_poles_existing = np.abs(poles - pole_add) # (equation 17) - - # avoid forbidden bands (too close to neighbour) - if np.min(abs_poles_existing) < nu or pole_add.imag < nu: - # decide shift direction (towards higher or lower frequencies) - if idx_freqs_max[i] > 0: - delta_below = delta[idx_freqs_max[i] - 1] - else: - delta_below = 0 - if idx_freqs_max[i] < len(omega_norm) - 1: - delta_above = delta[idx_freqs_max[i] + 1] - else: - delta_above = 0 - - if delta_above > delta_below: - # shift to higher frequencies - pole_add += 1j * nu - else: - # shift to lower frequencies - pole_add -= 1j * nu - - poles = np.append(poles, [pole_add]) - - # INTERMEDIATE POLE RELOCATION FOR i_inter ITERATIONS - for _ in range(iters_inter): - poles, d_res, cond, rank_deficiency, residuals, singular_vals = self._pole_relocation( - poles, freqs_norm, freq_responses, weights_responses, fit_constant, fit_proportional) - - self.d_res_history.append(d_res) - - logger.info(f'Condition number of coefficient matrix is {int(cond)}') - self.history_cond_A.append(cond) - - self.history_rank_deficiency.append(rank_deficiency) - logger.info(f'Rank deficiency is {rank_deficiency}.') - - new_max_singular = np.amax(singular_vals) - delta_max = np.abs(1 - new_max_singular / max_singular) - self.delta_max_history.append(delta_max) - logger.info(f'Max. relative change in residues = {delta_max}\n') - max_singular = new_max_singular - - # RESIDUE FITTING FOR ERROR COMPUTATION - residues, constant_coeff, proportional_coeff, residuals, rank, singular_vals = self._fit_residues( - poles, freqs_norm, freq_responses, fit_constant, fit_proportional, enforce_dc=False) - delta = self._get_delta(poles, residues, constant_coeff, proportional_coeff, freqs_norm, freq_responses, - weights_responses) - error_peak_history.append(np.max(delta)) - - m = 3 - if len(error_peak_history) > m: - delta_eps = np.mean(np.abs(np.diff(error_peak_history[-1-m:-1]))) - else: - delta_eps = 1 - - model_order = self.get_model_order(poles) - model_order_history.append(model_order) - - # SKIMMING OF SPURIOUS POLES - spurious = self.get_spurious(poles, residues, gamma=gamma) - poles = poles[~spurious] - - # FINAL POLE RELOCATION FOR i_final ITERATIONS - for _ in range(iters_final): - poles, d_res, cond, rank_deficiency, residuals, singular_vals = self._pole_relocation( - poles, freqs_norm, freq_responses, weights_responses, fit_constant, fit_proportional) - - self.d_res_history.append(d_res) - - logger.info(f'Condition number of coefficient matrix is {int(cond)}') - self.history_cond_A.append(cond) - - self.history_rank_deficiency.append(rank_deficiency) - logger.info(f'Rank deficiency is {rank_deficiency}.') - - new_max_singular = np.amax(singular_vals) - delta_max = np.abs(1 - new_max_singular / max_singular) - self.delta_max_history.append(delta_max) - logger.info(f'Max. relative change in residues = {delta_max}\n') - max_singular = new_max_singular - - # FINAL RESIDUE FITTING - residues, constant_coeff, proportional_coeff, residuals, rank, singular_vals = self._fit_residues( - poles, freqs_norm, freq_responses, fit_constant, fit_proportional, enforce_dc=True) - - # save poles, residues, d, e in actual frequencies (un-normalized) - self.poles = poles * norm - self.residues = np.array(residues) * norm - self.constant_coeff = np.array(constant_coeff) - self.proportional_coeff = np.array(proportional_coeff) / norm - - timer_stop = timer() - self.wall_clock_time = timer_stop - timer_start - - @staticmethod - def _init_poles(freqs: list, n_poles_real: int, n_poles_cmplx: int, init_pole_spacing: str): - # create initial poles and space them across the frequencies in the provided Touchstone file - - fmin = np.amin(freqs) - fmax = np.amax(freqs) - - # poles cannot be at f=0; hence, f_min for starting pole must be greater than 0 - if fmin == 0.0: - # random choice: use 1/1000 of first non-zero frequency - fmin = freqs[1] / 1000 - - init_pole_spacing = init_pole_spacing.lower() - if init_pole_spacing == 'log': - pole_freqs_real = np.geomspace(fmin, fmax, n_poles_real) - pole_freqs_cmplx = np.geomspace(fmin, fmax, n_poles_cmplx) - elif init_pole_spacing == 'lin': - pole_freqs_real = np.linspace(fmin, fmax, n_poles_real) - pole_freqs_cmplx = np.linspace(fmin, fmax, n_poles_cmplx) - elif init_pole_spacing == 'custom': - pole_freqs_real = None - pole_freqs_cmplx = None - else: - warnings.warn('Invalid choice of initial pole spacing; proceeding with linear spacing.', - UserWarning, stacklevel=2) - pole_freqs_real = np.linspace(fmin, fmax, n_poles_real) - pole_freqs_cmplx = np.linspace(fmin, fmax, n_poles_cmplx) - - if pole_freqs_real is not None and pole_freqs_cmplx is not None: - # init poles array of correct length - poles = np.zeros(n_poles_real + n_poles_cmplx, dtype=complex) - - # add real poles - for i, f in enumerate(pole_freqs_real): - omega = 2 * np.pi * f - poles[i] = -1 * omega - - # add complex-conjugate poles (store only positive imaginary parts) - i_offset = len(pole_freqs_real) - for i, f in enumerate(pole_freqs_cmplx): - omega = 2 * np.pi * f - poles[i_offset + i] = (-0.01 + 1j) * omega - - return poles - - else: - return None - - @staticmethod - def _pole_relocation(poles, freqs, freq_responses, weights_responses, fit_constant, fit_proportional): - n_responses, n_freqs = np.shape(freq_responses) - n_samples = n_responses * n_freqs - omega = 2 * np.pi * freqs - s = 1j * omega - - # weight of extra equation to avoid trivial solution - weight_extra = np.linalg.norm(weights_responses[:, None] * freq_responses) / n_samples - - # weights w are applied directly to the samples, which get squared during least-squares fitting; hence sqrt(w) - weights_responses = np.sqrt(weights_responses) - weight_extra = np.sqrt(weight_extra) - - # count number of rows and columns in final coefficient matrix to solve for (c_res, d_res) - # (ratio #real/#complex poles might change during iterations) - - # We need two columns for complex poles and one column for real poles in A matrix. - # This number equals the model order. - n_cols_unused = VectorFitting.get_model_order(poles) - - n_cols_used = n_cols_unused - n_cols_used += 1 - idx_constant = [] - idx_proportional = [] - if fit_constant: - idx_constant = [n_cols_unused] - n_cols_unused += 1 - if fit_proportional: - idx_proportional = [n_cols_unused] - n_cols_unused += 1 - - real_mask = poles.imag == 0 - # list of indices in 'poles' with real values - idx_poles_real = np.nonzero(real_mask)[0] - # list of indices in 'poles' with complex values - idx_poles_complex = np.nonzero(~real_mask)[0] - - # positions (columns) of coefficients for real and complex-conjugate terms in the rows of A determine the - # respective positions of the calculated residues in the results vector. - # to have them ordered properly for the subsequent assembly of the test matrix H for eigenvalue extraction, - # place real poles first, then complex-conjugate poles with their respective real and imaginary parts: - # [r1', r2', ..., (r3', r3''), (r4', r4''), ...] - n_real = len(idx_poles_real) - n_cmplx = len(idx_poles_complex) - idx_res_real = np.arange(n_real) - idx_res_complex_re = n_real + 2 * np.arange(n_cmplx) - idx_res_complex_im = idx_res_complex_re + 1 - - # complex coefficient matrix of shape [N_responses, N_freqs, n_cols_unused + n_cols_used] - # layout of each row: - # [pole1, pole2, ..., (constant), (proportional), pole1, pole2, ..., constant] - A = np.empty((n_responses, n_freqs, n_cols_unused + n_cols_used), dtype=complex) - - # calculate coefficients for real and complex residues in the solution vector - # - # real pole-residue term (r = r', p = p'): - # fractional term is r' / (s - p') - # coefficient for r' is 1 / (s - p') - coeff_real = 1 / (s[:, None] - poles[None, idx_poles_real]) - - # complex-conjugate pole-residue pair (r = r' + j r'', p = p' + j p''): - # fractional term is r / (s - p) + conj(r) / (s - conj(p)) - # = [1 / (s - p) + 1 / (s - conj(p))] * r' + [1j / (s - p) - 1j / (s - conj(p))] * r'' - # coefficient for r' is 1 / (s - p) + 1 / (s - conj(p)) - # coefficient for r'' is 1j / (s - p) - 1j / (s - conj(p)) - coeff_complex_re = (1 / (s[:, None] - poles[None, idx_poles_complex]) + - 1 / (s[:, None] - np.conj(poles[None, idx_poles_complex]))) - coeff_complex_im = (1j / (s[:, None] - poles[None, idx_poles_complex]) - - 1j / (s[:, None] - np.conj(poles[None, idx_poles_complex]))) - - # part 1: first sum of rational functions (variable c) - A[:, :, idx_res_real] = coeff_real - A[:, :, idx_res_complex_re] = coeff_complex_re - A[:, :, idx_res_complex_im] = coeff_complex_im - - # part 2: constant (variable d) and proportional term (variable e) - A[:, :, idx_constant] = 1 - A[:, :, idx_proportional] = s[:, None] - - # part 3: second sum of rational functions multiplied with frequency response (variable c_res) - A[:, :, n_cols_unused + idx_res_real] = -1 * freq_responses[:, :, None] * coeff_real - A[:, :, n_cols_unused + idx_res_complex_re] = -1 * freq_responses[:, :, None] * coeff_complex_re - A[:, :, n_cols_unused + idx_res_complex_im] = -1 * freq_responses[:, :, None] * coeff_complex_im - - # part 4: constant (variable d_res) - A[:, :, -1] = -1 * freq_responses - - A_ri = np.hstack((A.real, A.imag)) - - # calculation of matrix sizes after QR decomposition: - # stacked coefficient matrix (A.real, A.imag) has shape (L, M, N) - # with - # L = n_responses = n_ports ** 2 - # M = 2 * n_freqs (because of hstack with 2x n_freqs) - # N = n_cols_unused + n_cols_used - # then - # R has shape (L, K, N) with K = min(M, N) - dim_m = 2 * n_freqs - dim_n = n_cols_unused + n_cols_used - dim_k = min(dim_m, dim_n) - - # QR decomposition - # R = np.linalg.qr(A_ri, 'r') - - # direct QR of stacked matrices for linalg.qr() only works with numpy>=1.22.0 - # workaround for old numpy: - R = np.empty((n_responses, dim_k, dim_n)) - for i in range(n_responses): - R[i] = np.linalg.qr(A_ri[i], mode='r') - - # only R22 is required to solve for c_res and d_res - # R12 and R22 can have a different number of rows, depending on K - if dim_k == dim_m: - # K = M - n_rows_r12 = n_freqs - n_rows_r22 = n_freqs - else: - # K = N - n_rows_r12 = n_cols_unused - n_rows_r22 = n_cols_used - R22 = R[:, n_rows_r12:, n_cols_unused:] - - # weighting - R22 = weights_responses[:, None, None] * R22 - - # assemble compressed coefficient matrix A_fast by row-stacking individual upper triangular matrices R22 - dim0 = n_responses * n_rows_r22 + 1 - - A_fast = np.empty((dim0, n_cols_used)) - A_fast[:-1, :] = R22.reshape((dim0 - 1, n_cols_used)) - - # extra equation to avoid trivial solution - A_fast[-1, idx_res_real] = np.sum(coeff_real.real, axis=0) - A_fast[-1, idx_res_complex_re] = np.sum(coeff_complex_re.real, axis=0) - A_fast[-1, idx_res_complex_im] = np.sum(coeff_complex_im.real, axis=0) - A_fast[-1, -1] = n_freqs - - # weighting - A_fast[-1, :] = weight_extra * A_fast[-1, :] - - scaling = 1 / np.linalg.norm(A_fast, axis=0) - A_fast = scaling * A_fast - - # right hand side vector (weighted) - b = np.zeros(dim0) - b[-1] = weight_extra * n_samples - - # check condition of the linear system - cond = np.linalg.cond(A_fast) - full_rank = np.min(A_fast.shape) - - # solve least squares for real parts - x, residuals, rank, singular_vals = np.linalg.lstsq(A_fast, b, rcond=None) - - x = scaling * x - - # rank deficiency - rank_deficiency = full_rank - rank - - # assemble individual result vectors from single LS result x - c_res = x[:-1] - d_res = x[-1] - - # check if d_res is suited for zeros calculation - tol_res = 1e-8 - if np.abs(d_res) < tol_res: - # d_res is too small, discard solution and proceed the |d_res| = tol_res - logger.info(f'Replacing d_res solution as it was too small ({d_res}).') - d_res = tol_res * (d_res / np.abs(d_res)) - - # build test matrix H, which will hold the new poles as eigenvalues - H = np.zeros((len(c_res), len(c_res))) - - poles_real = poles[np.nonzero(real_mask)] - poles_cplx = poles[np.nonzero(~real_mask)] - - H[idx_res_real, idx_res_real] = poles_real.real - H[idx_res_real] -= c_res / d_res - - H[idx_res_complex_re, idx_res_complex_re] = poles_cplx.real - H[idx_res_complex_re, idx_res_complex_im] = poles_cplx.imag - H[idx_res_complex_im, idx_res_complex_re] = -1 * poles_cplx.imag - H[idx_res_complex_im, idx_res_complex_im] = poles_cplx.real - H[idx_res_complex_re] -= 2 * c_res / d_res - - poles_new = np.linalg.eigvals(H) - - # replace poles for next iteration - # complex poles need to come in complex conjugate pairs; append only the positive part - poles = poles_new[np.nonzero(poles_new.imag >= 0)] - - # flip real part of unstable poles (real part needs to be negative for stability) - poles.real = -1 * np.abs(poles.real) - - return poles, d_res, cond, rank_deficiency, residuals, singular_vals - - @staticmethod - def _fit_residues(poles, freqs, freq_responses, fit_constant, fit_proportional, enforce_dc=True): - n_responses, n_freqs = np.shape(freq_responses) - omega = 2 * np.pi * freqs - s = 1j * omega - - # We need two columns for complex poles and one column for real poles in A matrix. - # This number equals the model order. - n_cols = VectorFitting.get_model_order(poles) - - idx_constant = [] - idx_proportional = [] - if fit_constant: - idx_constant = [n_cols] - n_cols += 1 - if fit_proportional: - idx_proportional = [n_cols] - n_cols += 1 - - # list of indices in 'poles' with real and with complex values - real_mask = poles.imag == 0 - idx_poles_real = np.nonzero(real_mask)[0] - idx_poles_complex = np.nonzero(~real_mask)[0] - - # find and save indices of real and complex poles in the poles list - i = 0 - idx_res_real = [] - idx_res_complex_re = [] - idx_res_complex_im = [] - for pole in poles: - if pole.imag == 0: - idx_res_real.append(i) - i += 1 - else: - idx_res_complex_re.append(i) - idx_res_complex_im.append(i + 1) - i += 2 - - # complex coefficient matrix of shape [N_freqs, n_cols] - # layout of each row: - # [pole1, pole2, ..., (constant), (proportional)] - A = np.empty((n_freqs, n_cols), dtype=complex) - - # calculate coefficients for real and complex residues in the solution vector - # - # real pole-residue term (r = r', p = p'): - # fractional term is r' / (s - p') - # coefficient for r' is 1 / (s - p') - coeff_real = 1 / (s[:, None] - poles[None, idx_poles_real]) - - # complex-conjugate pole-residue pair (r = r' + j r'', p = p' + j p''): - # fractional term is r / (s - p) + conj(r) / (s - conj(p)) - # = [1 / (s - p) + 1 / (s - conj(p))] * r' + [1j / (s - p) - 1j / (s - conj(p))] * r'' - # coefficient for r' is 1 / (s - p) + 1 / (s - conj(p)) - # coefficient for r'' is 1j / (s - p) - 1j / (s - conj(p)) - coeff_complex_re = (1 / (s[:, None] - poles[None, idx_poles_complex]) + - 1 / (s[:, None] - np.conj(poles[None, idx_poles_complex]))) - coeff_complex_im = (1j / (s[:, None] - poles[None, idx_poles_complex]) - - 1j / (s[:, None] - np.conj(poles[None, idx_poles_complex]))) - - # part 1: first sum of rational functions (variable c) - A[:, idx_res_real] = coeff_real - A[:, idx_res_complex_re] = coeff_complex_re - A[:, idx_res_complex_im] = coeff_complex_im - - # part 2: constant (variable d) and proportional term (variable e) - A[:, idx_constant] = 1 - A[:, idx_proportional] = s[:, None] - - scaling = 1 / np.linalg.norm(A, axis=0) - A = scaling * A - - # DC POINT ENFORCEMENT - if enforce_dc and freqs[0] == 0.0: - # data contains the dc point; enforce dc point via linear equality constraint: - # 1: remove one variable from the solution vector (constant term, if possible). - # 2: solve remaining linear system (without data at dc) with regular least-squares, as usual. the size of - # the solution vector, the coefficient matrix, and the right-hand side are reduced by 1 - # 3: calculate the removed variable (constant term) with the data from the dc point - # - # linear system: A * x = b - # solution vector x contains the unknown residues - # right-hand side b contains the frequency response to be fitted, sorted by ascending frequency (dc first) - # coefficient matrix A and vector b are split: A = [[A11, A12], [A21, A22]], b = [[b1], [b2]] - # [A11, A12] is the first row used later for dc enforcement - # A21 is a column vector, which is not required anymore - # A22 is the rest of the matrix for usual least-squares fitting - - # indexing mask of constrained variable in the columns of matrix A - mask_idx_constrained = np.zeros(n_cols, dtype=bool) - if fit_constant: - # use constant term for constrained - mask_idx_constrained[idx_constant] = True - else: - # constant term not present; arbitrarily use first residue instead - mask_idx_constrained[0] = True - - A22 = A[1:, ~mask_idx_constrained] - b2 = freq_responses[:, 1:] - - A22_ri = np.vstack((A22.real, A22.imag)) - b22_ri = np.hstack((b2.real, b2.imag)) - - logger.info(f'Condition number of coefficient matrix = {int(np.linalg.cond(A22_ri))}') - - # solve least-squares and obtain results as stack of real part vector and imaginary part vector - x2, residuals, rank, singular_vals = np.linalg.lstsq(A22_ri, b22_ri.T, rcond=None) - - # solve for x1 using the first row (the dc row): - b1 = freq_responses[:, 0] - A11 = A[0, mask_idx_constrained] - A12 = A[0, ~mask_idx_constrained] - x1 = np.real(1 / A11 * (b1 - np.dot(A12, x2))) - - # reassemble x from x1 and x2 - x = np.empty((n_cols, n_responses)) - x[mask_idx_constrained, :] = x1 - x[~mask_idx_constrained, :] = x2 - else: - # dc point not included; use and solve the entire linear system with least-squares - A_ri = np.vstack((A.real, A.imag)) - b_ri = np.hstack((freq_responses.real, freq_responses.imag)) - - logger.info(f'Condition number of coefficient matrix = {int(np.linalg.cond(A_ri))}') - - # solve least-squares and obtain results as stack of real part vector and imaginary part vector - x, residuals, rank, singular_vals = np.linalg.lstsq(A_ri, b_ri.T, rcond=None) - - x = scaling[:, None] * x - - # extract residues from solution vector and align them with poles to get matching pole-residue pairs - residues = np.empty((len(freq_responses), len(poles)), dtype=complex) - residues[:, idx_poles_real] = np.transpose(x[idx_res_real]) - residues[:, idx_poles_complex] = np.transpose(x[idx_res_complex_re] + 1j * x[idx_res_complex_im]) - - # extract constant and proportional coefficient, if available - if fit_constant: - constant_coeff = x[idx_constant][0] - else: - constant_coeff = np.zeros(n_responses) - - if fit_proportional: - proportional_coeff = x[idx_proportional][0] - else: - proportional_coeff = np.zeros(n_responses) - - return residues, constant_coeff, proportional_coeff, residuals, rank, singular_vals - - @staticmethod - def _get_delta(poles, residues, constant_coeff, proportional_coeff, freqs, freq_responses, weights_responses): - s = 2j * np.pi * freqs - model = proportional_coeff[:, None] * s + constant_coeff[:, None] - for i, pole in enumerate(poles): - if np.imag(pole) == 0.0: - # real pole - model += residues[:, i, None] / (s - pole) - else: - # complex conjugate pole - model += (residues[:, i, None] / (s - pole) + - np.conjugate(residues[:, i, None]) / (s - np.conjugate(pole))) - - # compute weighted error and return global maximum at each frequency across all individual responses - delta = np.abs(model - freq_responses) * weights_responses[:, None] - - return np.max(delta, axis=0) - - @staticmethod - def _find_error_bands(freqs, delta): - # compute error bands (maximal fit deviation) - delta_mean = np.mean(delta) - error = delta - delta_mean - - # find limits of error bands - idx_limits = np.nonzero(np.diff(error > 0))[0] - idx_limits_filtered = idx_limits[np.diff(idx_limits, prepend=0) > 2] - - freqs_bands = np.split(freqs, idx_limits_filtered) - error_bands = np.split(error, idx_limits_filtered) - n_bands = len(freqs_bands) - - idx_freqs_start = [] - idx_freqs_stop = [] - idx_freqs_max = [] - delta_mean_bands = [] - for i_band in range(n_bands): - band_error_mean = np.mean(error_bands[i_band]) - if band_error_mean > 0: - # band with excess error; - # find frequency index of error maximum inside this band - i_band_max_error = np.argmax(error_bands[i_band]) - i_start = np.nonzero(freqs == freqs_bands[i_band][0])[0][0] - i_stop = np.nonzero(freqs == freqs_bands[i_band][-1])[0][0] - i_max = np.nonzero(freqs == freqs_bands[i_band][i_band_max_error])[0][0] - idx_freqs_start.append(i_start) - idx_freqs_stop.append(i_stop) - idx_freqs_max.append(i_max) - delta_mean_bands.append(np.mean(delta[i_start:i_stop])) - - idx_freqs_start = np.array(idx_freqs_start) - idx_freqs_stop = np.array(idx_freqs_stop) - idx_freqs_max = np.array(idx_freqs_max) - delta_mean_bands = np.array(delta_mean_bands) - - i_sort = np.flip(np.argsort(delta_mean_bands)) - - return idx_freqs_start[i_sort], idx_freqs_stop[i_sort], idx_freqs_max[i_sort], delta_mean_bands[i_sort] - - def get_rms_error(self, i=-1, j=-1, parameter_type: str = 's'): - r""" - Returns the root-mean-square (rms) error magnitude of the fit, i.e. - :math:`\sqrt{ \mathrm{mean}(|S - S_\mathrm{fit} |^2) }`, - either for an individual response :math:`S_{i+1,j+1}` or for larger slices of the network. - - Parameters - ---------- - i : int, optional - Row indices of the responses to be evaluated. Either a single row selected by an integer - :math:`i \in [0, N_\mathrm{ports}-1]`, or multiple rows selected by a list of integers, or all rows - selected by :math:`i = -1` (*default*). - - j : int, optional - Column indices of the responses to be evaluated. Either a single column selected by an integer - :math:`j \in [0, N_\mathrm{ports}-1]`, or multiple columns selected by a list of integers, or all columns - selected by :math:`j = -1` (*default*). - - parameter_type: str, optional - Representation type of the fitted frequency responses. Either *scattering* (:attr:`s` or :attr:`S`), - *impedance* (:attr:`z` or :attr:`Z`) or *admittance* (:attr:`y` or :attr:`Y`). - - Returns - ------- - rms_error : ndarray - The rms error magnitude between the vector fitted model and the original network data. - - Raises - ------ - ValueError - If the specified parameter representation type is not :attr:`s`, :attr:`z`, nor :attr:`y`. - """ - - if i == -1: - list_i = range(self.network.nports) - elif isinstance(i, int): - list_i = [i] - else: - list_i = i - - if j == -1: - list_j = range(self.network.nports) - elif isinstance(j, int): - list_j = [j] - else: - list_j = j - - if parameter_type.lower() == 's': - nw_responses = self.network.s - elif parameter_type.lower() == 'z': - nw_responses = self.network.z - elif parameter_type.lower() == 'y': - nw_responses = self.network.y - else: - raise ValueError(f'Invalid parameter type `{parameter_type}`. Valid options: `s`, `z`, or `y`') - - error_mean_squared = 0 - for i in list_i: - for j in list_j: - nw_ij = nw_responses[:, i, j] - fit_ij = self.get_model_response(i, j, self.network.f) - error_mean_squared += np.mean(np.square(np.abs(nw_ij - fit_ij))) - - return np.sqrt(error_mean_squared) - - def _get_ABCDE(self) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: - """ - Private method. - Returns the real-valued system matrices of the state-space representation of the current rational model, as - defined in [#]_. - - Returns - ------- - A : ndarray - State-space matrix A holding the poles on the diagonal as real values with imaginary parts on the sub- - diagonal - B : ndarray - State-space matrix B holding coefficients (1, 2, or 0), depending on the respective type of pole in A - C : ndarray - State-space matrix C holding the residues - D : ndarray - State-space matrix D holding the constants - E : ndarray - State-space matrix E holding the proportional coefficients (usually 0 in case of fitted S-parameters) - - Raises - ------ - ValueError - If the model parameters have not been initialized (by running :func:`vector_fit()` or :func:`read_npz()`). - - References - ---------- - .. [#] B. Gustavsen and A. Semlyen, "Fast Passivity Assessment for S-Parameter Rational Models Via a Half-Size - Test Matrix," in IEEE Transactions on Microwave Theory and Techniques, vol. 56, no. 12, pp. 2701-2708, - Dec. 2008, DOI: 10.1109/TMTT.2008.2007319. - """ - - # initial checks - if self.poles is None: - raise ValueError('self.poles = None; nothing to do. You need to run vector_fit() first.') - if self.residues is None: - raise ValueError('self.residues = None; nothing to do. You need to run vector_fit() first.') - if self.proportional_coeff is None: - raise ValueError('self.proportional_coeff = None; nothing to do. You need to run vector_fit() first.') - if self.constant_coeff is None: - raise ValueError('self.constant_coeff = None; nothing to do. You need to run vector_fit() first.') - - # assemble real-valued state-space matrices A, B, C, D, E from fitted complex-valued pole-residue model - - # determine size of the matrix system - n_ports = int(np.sqrt(len(self.constant_coeff))) - n_poles_real = 0 - n_poles_cplx = 0 - for pole in self.poles: - if np.imag(pole) == 0.0: - n_poles_real += 1 - else: - n_poles_cplx += 1 - n_matrix = (n_poles_real + 2 * n_poles_cplx) * n_ports - - # state-space matrix A holds the poles on the diagonal as real values with imaginary parts on the sub-diagonal - # state-space matrix B holds coefficients (1, 2, or 0), depending on the respective type of pole in A - # assemble A = [[poles_real, 0, 0], - # [0, real(poles_cplx), imag(poles_cplx], - # [0, -imag(poles_cplx), real(poles_cplx]] - A = np.identity(n_matrix) - B = np.zeros(shape=(n_matrix, n_ports)) - i_A = 0 # index on diagonal of A - for j in range(n_ports): - for pole in self.poles: - if np.imag(pole) == 0.0: - # adding a real pole - A[i_A, i_A] = np.real(pole) - B[i_A, j] = 1 - i_A += 1 - else: - # adding a complex-conjugate pole - A[i_A, i_A] = np.real(pole) - A[i_A, i_A + 1] = np.imag(pole) - A[i_A + 1, i_A] = -1 * np.imag(pole) - A[i_A + 1, i_A + 1] = np.real(pole) - B[i_A, j] = 2 - i_A += 2 - - # state-space matrix C holds the residues - # assemble C = [[R1.11, R1.12, R1.13, ...], [R2.11, R2.12, R2.13, ...], ...] - C = np.zeros(shape=(n_ports, n_matrix)) - for i in range(n_ports): - for j in range(n_ports): - # i: row index - # j: column index - i_response = i * n_ports + j - - j_residues = 0 - for zero in self.residues[i_response]: - if np.imag(zero) == 0.0: - C[i, j * (n_poles_real + 2 * n_poles_cplx) + j_residues] = np.real(zero) - j_residues += 1 - else: - C[i, j * (n_poles_real + 2 * n_poles_cplx) + j_residues] = np.real(zero) - C[i, j * (n_poles_real + 2 * n_poles_cplx) + j_residues + 1] = np.imag(zero) - j_residues += 2 - - # state-space matrix D holds the constants - # assemble D = [[d11, d12, ...], [d21, d22, ...], ...] - D = np.zeros(shape=(n_ports, n_ports)) - for i in range(n_ports): - for j in range(n_ports): - # i: row index - # j: column index - i_response = i * n_ports + j - D[i, j] = self.constant_coeff[i_response] - - # state-space matrix E holds the proportional coefficients (usually 0 in case of fitted S-parameters) - # assemble E = [[e11, e12, ...], [e21, e22, ...], ...] - E = np.zeros(shape=(n_ports, n_ports)) - for i in range(n_ports): - for j in range(n_ports): - # i: row index - # j: column index - i_response = i * n_ports + j - E[i, j] = self.proportional_coeff[i_response] - - return A, B, C, D, E - - @staticmethod - def _get_s_from_ABCDE(freqs: np.ndarray, - A: np.ndarray, B: np.ndarray, C: np.ndarray, D: np.ndarray, E: np.ndarray) -> np.ndarray: - """ - Private method. - Returns the S-matrix of the vector fitted model calculated from the real-valued system matrices of the state- - space representation, as provided by `_get_ABCDE()`. - - Parameters - ---------- - freqs : ndarray - Frequencies (in Hz) at which to calculate the S-matrices. - A : ndarray - B : ndarray - C : ndarray - D : ndarray - E : ndarray - - Returns - ------- - ndarray - Complex-valued S-matrices (fxNxN) calculated at frequencies `freqs`. - """ - - dim_A = np.shape(A)[0] - stsp_poles = np.linalg.inv(2j * np.pi * freqs[:, None, None] * np.identity(dim_A)[None, :, :] - A[None, :, :]) - stsp_S = np.matmul(np.matmul(C, stsp_poles), B) - stsp_S += D + 2j * np.pi * freqs[:, None, None] * E - return stsp_S - - def passivity_test(self, parameter_type: str = 's') -> np.ndarray: - """ - Evaluates the passivity of reciprocal vector fitted models by means of a half-size test matrix [#]_. Any - existing frequency bands of passivity violations will be returned as a sorted list. - - Parameters - ---------- - parameter_type: str, optional - Representation type of the fitted frequency responses. Either *scattering* (:attr:`s` or :attr:`S`), - *impedance* (:attr:`z` or :attr:`Z`) or *admittance* (:attr:`y` or :attr:`Y`). Currently, only scattering - parameters are supported for passivity evaluation. - - Raises - ------ - NotImplementedError - If the function is called for `parameter_type` different than `S` (scattering). - - ValueError - If the function is used with a model containing nonzero proportional coefficients. - - Returns - ------- - violation_bands : ndarray - NumPy array with frequency bands of passivity violation: - `[[f_start_1, f_stop_1], [f_start_2, f_stop_2], ...]`. - - See Also - -------- - is_passive : Query the model passivity as a boolean value. - passivity_enforce : Enforces the passivity of the vector fitted model, if required. - - Examples - -------- - Load and fit the `Network`, then evaluate the model passivity: - - >>> nw_3port = skrf.Network('my3port.s3p') - >>> vf = skrf.VectorFitting(nw_3port) - >>> vf.vector_fit(n_poles_real=1, n_poles_cmplx=4) - >>> violations = vf.passivity_test() - - References - ---------- - .. [#] B. Gustavsen and A. Semlyen, "Fast Passivity Assessment for S-Parameter Rational Models Via a Half-Size - Test Matrix," in IEEE Transactions on Microwave Theory and Techniques, vol. 56, no. 12, pp. 2701-2708, - Dec. 2008, DOI: 10.1109/TMTT.2008.2007319. - """ - - if parameter_type.lower() != 's': - raise NotImplementedError('Passivity testing is currently only supported for scattering (S) parameters.') - if parameter_type.lower() == 's' and len(np.flatnonzero(self.proportional_coeff)) > 0: - raise ValueError('Passivity testing of scattering parameters with nonzero proportional coefficients does ' - 'not make any sense; you need to run vector_fit() with option `fit_proportional=False` ' - 'first.') - - # # the network needs to be reciprocal for this passivity test method to work: S = transpose(S) - # if not np.allclose(self.residues, np.transpose(self.residues)) or \ - # not np.allclose(self.constant_coeff, np.transpose(self.constant_coeff)) or \ - # not np.allclose(self.proportional_coeff, np.transpose(self.proportional_coeff)): - # logger.error('Passivity testing with unsymmetrical model parameters is not supported. ' - # 'The model needs to be reciprocal.') - # return - - # get state-space matrices - A, B, C, D, E = self._get_ABCDE() - n_ports = np.shape(D)[0] - - # build half-size test matrix P from state-space matrices A, B, C, D - inv_neg = np.linalg.inv(D - np.identity(n_ports)) - inv_pos = np.linalg.inv(D + np.identity(n_ports)) - prod_neg = np.matmul(np.matmul(B, inv_neg), C) - prod_pos = np.matmul(np.matmul(B, inv_pos), C) - P = np.matmul(A - prod_neg, A - prod_pos) - - # extract eigenvalues of P - P_eigs = np.linalg.eigvals(P) - - # purely imaginary square roots of eigenvalues identify frequencies (2*pi*f) of borders of passivity violations - freqs_violation = [] - for sqrt_eigenval in np.sqrt(P_eigs): - if np.real(sqrt_eigenval) == 0.0: - freqs_violation.append(np.imag(sqrt_eigenval) / 2 / np.pi) - - # include dc (0) unless it's already included - if len(np.nonzero(np.array(freqs_violation) == 0.0)[0]) == 0: - freqs_violation.append(0.0) - - # sort the output from lower to higher frequencies - freqs_violation = np.sort(freqs_violation) - - # identify frequency bands of passivity violations - - # sweep the bands between crossover frequencies and identify bands of passivity violations - violation_bands = [] - for i, freq in enumerate(freqs_violation): - if i == len(freqs_violation) - 1: - # last band stops always at infinity - f_start = freq - f_stop = np.inf - f_center = 1.1 * f_start # 1.1 is chosen arbitrarily to have any frequency for evaluation - else: - # intermediate band between this frequency and the previous one - f_start = freq - f_stop = freqs_violation[i + 1] - f_center = 0.5 * (f_start + f_stop) - - # calculate singular values at the center frequency between crossover frequencies to identify violations - s_center = self._get_s_from_ABCDE(np.array([f_center]), A, B, C, D, E) - sigma = np.linalg.svd(s_center[0], compute_uv=False) - passive = True - for singval in sigma: - if singval > 1: - # passivity violation in this band - passive = False - if not passive: - # add this band to the list of passivity violations - if violation_bands is None: - violation_bands = [[f_start, f_stop]] - else: - violation_bands.append([f_start, f_stop]) - - return np.array(violation_bands) - - def is_passive(self, parameter_type: str = 's') -> bool: - """ - Returns the passivity status of the model as a boolean value. - - Parameters - ---------- - parameter_type : str, optional - Representation type of the fitted frequency responses. Either *scattering* (:attr:`s` or :attr:`S`), - *impedance* (:attr:`z` or :attr:`Z`) or *admittance* (:attr:`y` or :attr:`Y`). Currently, only scattering - parameters are supported for passivity evaluation. - - Returns - ------- - passivity : bool - :attr:`True` if model is passive, else :attr:`False`. - - See Also - -------- - passivity_test : Verbose passivity evaluation routine. - passivity_enforce : Enforces the passivity of the vector fitted model, if required. - - Examples - -------- - Load and fit the `Network`, then check whether or not the model is passive: - - >>> nw_3port = skrf.Network('my3port.s3p') - >>> vf = skrf.VectorFitting(nw_3port) - >>> vf.vector_fit(n_poles_real=1, n_poles_cmplx=4) - >>> vf.is_passive() # returns True or False - """ - - viol_bands = self.passivity_test(parameter_type) - if len(viol_bands) == 0: - return True - else: - return False - - def passivity_enforce(self, n_samples: int = 200, f_max: float = None, parameter_type: str = 's', - preserve_dc: bool = True) -> None: - """ - Enforces the passivity of the vector fitted model, if required. This is an implementation of the methods - presented in [#]_ and [#]_ using singular value perturbation. To preserve the dc point in the model during - passivity enforcement, only the residues are perturbed, not the constant term. - - Parameters - ---------- - n_samples : int, optional - Number of linearly spaced frequency samples at which passivity will be evaluated and enforced. - (Default: 200). If there are very narrow frequency bands of passivity violations, a sufficiently large - number of frequency samples is required. - - f_max : float or None, optional - Highest frequency of interest for the passivity enforcement (in Hz, not rad/s). This limit usually - equals the highest sample frequency of the fitted Network. If None, the highest frequency in - :attr:`self.network` is used, which must not be None is this case. If `f_max` is not None, it overrides the - highest frequency in :attr:`self.network`. - - parameter_type : str, optional - Representation type of the fitted frequency responses. Either *scattering* (:attr:`s` or :attr:`S`), - *impedance* (:attr:`z` or :attr:`Z`) or *admittance* (:attr:`y` or :attr:`Y`). Currently, only scattering - parameters are supported for passivity evaluation. - - preserve_dc : bool, optional - Enables dc point preservation during passivity enforcement. This only works if the fitted model is already - passive at the dc point, which is not always the case. If it is not passive, dc point preservation is - disabled and passivity is also enforced on the dc point. - - Returns - ------- - None - - Raises - ------ - NotImplementedError - If the function is called for `parameter_type` different than `S` (scattering). - - ValueError - If the function is used with a model containing nonzero proportional coefficients. Or if both `f_max` and - :attr:`self.network` are None. - - See Also - -------- - is_passive : Returns the passivity status of the model as a boolean value. - passivity_test : Verbose passivity evaluation routine. - plot_passivation : Convergence plot for passivity enforcement iterations. - - Examples - -------- - Load and fit the `Network`, then enforce the passivity of the model: - - >>> nw_3port = skrf.Network('my3port.s3p') - >>> vf = skrf.VectorFitting(nw_3port) - >>> vf.vector_fit(n_poles_real=1, n_poles_cmplx=4) - >>> vf.passivity_enforce() # won't do anything if model is already passive - - References - ---------- - .. [#] T. Dhaene, D. Deschrijver and N. Stevens, "Efficient Algorithm for Passivity Enforcement of S-Parameter- - Based Macromodels," in IEEE Transactions on Microwave Theory and Techniques, vol. 57, no. 2, pp. 415-420, - Feb. 2009, DOI: 10.1109/TMTT.2008.2011201 - - .. [#] D. Deschrijver and T. Dhaene, "DC-Preserving Passivity Enforcement for S-Parameter Based Macromodels," - in IEEE Transactions on Microwave Theory and Techniques, vol. 58, no. 4, pp. 923-928, April 2010, - DOI: 10.1109/TMTT.2010.2042556 - """ - - if parameter_type.lower() != 's': - raise NotImplementedError('Passivity testing is currently only supported for scattering (S) parameters.') - if parameter_type.lower() == 's' and len(np.flatnonzero(self.proportional_coeff)) > 0: - raise ValueError('Passivity testing of scattering parameters with nonzero proportional coefficients does ' - 'not make any sense; you need to run vector_fit() with option `fit_proportional=False` ' - 'first.') - - # always run passivity test first; this will write 'self.violation_bands' - if self.is_passive(): - # model is already passive; do nothing and return - logger.info('Passivity enforcement: The model is already passive. Nothing to do.') - return - - # check dc passivity and find the highest relevant frequency; either - # 1) the highest frequency of passivity violation (f_viol_max) - # or - # 2) the highest fitting frequency (f_samples_max) - violation_bands = self.passivity_test() - f_viol_min = violation_bands[0, 0] - f_viol_max = violation_bands[-1, 1] - - # check passivity at the dc point; 1) in the model, 2) in the original data, if available - if preserve_dc and f_viol_min == 0.0: - # cannot preserve a non-passive dc point during passivity enforcement - preserve_dc = False - hint = '' - - if self.network is not None: - if self.network.f[0] == 0.0 and not self.network.is_passive(): - hint = '\nHint: The dc point in the original network data is already non-passive.' - - warnings.warn('Passivity enforcement: The dc point in the model is not passive. Cannot ' - f'preserve the dc point during passivity enforcement. {hint}', UserWarning, stacklevel=2) - - if f_max is None: - if self.network is None: - raise RuntimeError('Both `self.network` and parameter `f_max` are None. One of them is required to ' - 'specify the frequency band of interest for the passivity enforcement.') - else: - f_samples_max = self.network.f[-1] - else: - f_samples_max = f_max - - # deal with unbounded violation interval (f_viol_max == np.inf) - if np.isinf(f_viol_max): - f_viol_max = 1.5 * violation_bands[-1, 0] - warnings.warn( - 'Passivity enforcement: The passivity violations of this model are unbounded. ' - 'Passivity enforcement might still work, but consider re-fitting with a lower number of poles ' - 'and/or without the constants (`fit_constant=False`) if the results are not satisfactory.', - UserWarning, stacklevel=2) - - # the frequency band for the passivity evaluation is from dc to 20% above the highest relevant frequency - if f_viol_max < f_samples_max: - f_eval_max = 1.2 * f_samples_max - else: - f_eval_max = 1.2 * f_viol_max - - # let's not automatically adjust n_samples. The calculated number can - # be huge (>100k). Combined with a high number of poles in the model, this can bust the memory. - freqs_eval = np.linspace(0, f_eval_max, n_samples) - - # get model state-space matrices - A, B, C_t, D, E = self._get_ABCDE() - dim_A = np.shape(A)[0] - - # ASYMPTOTIC PASSIVITY ENFORCEMENT - - # check if constant term has been fitted (not zero) - # a model without the constant term is always asymptotically passive - if len(np.nonzero(D)[0]) != 0: - # D was fitted; - # asymptotic passivity needs to be checked and enforced, if violated. - # for dc preservation, the asymptotic passivity violations in D are compensated using C - # D is not touched, because it contains the dc point ( lim s --> {inf S(s)} = D) - u, sigma, vh = np.linalg.svd(D, compute_uv=True) - - # find and perturb singular values that cause passivity violations - # sigma_viol = sigma * upsilon - psi with - # upsilon[sigma > delta] = 1 - # upsilon[sigma <= delta] = 0 - # psi[sigma > delta] = delta - # psi[sigma <= delta] = 0 - # (implemented below in a more compact form) - delta = 1 - idx_viol = np.nonzero(sigma > delta) - sigma_viol = np.zeros_like(sigma) - sigma_viol[idx_viol] = sigma[idx_viol] - delta - - # calculate S_viol from perturbed sigma and previous U and Vh - S_viol = np.dot(u * sigma_viol, vh) - - # find new set of residues C_viol by solving underdetermined least-squares problem - # S_viol = C_viol * B - # - # mind the transpose of the system to compensate for the exchanged order of matrix multiplication: - # S_viol = C_viol * B <==> transpose(S_viol) = transpose(B) * transpose(C_viol) - C_viol, residuals, rank, singular_vals = np.linalg.lstsq(np.vstack((B.T.real, B.T.imag)), - np.vstack((S_viol.T.real, S_viol.T.imag)), - rcond=None) - C_t -= C_viol.T - - # UNIFORM PASSIVITY ENFORCEMENT - - # preparing coefficient matrix; can be reused in every iteration - # S(s_eval) = D_t + s_eval * C_t * inv(s_eval * I - A) * B - # = D_t + s_eval * C_t * A_freq * B - # with - # A_freq = inv(s_eval * I - A) - # s_eval = j * omega_eval = 2j * pi * freqs_eval - A_freq = np.linalg.inv(2j * np.pi * freqs_eval[:, None, None] * np.identity(dim_A)[None, :, :] - A[None, :, :]) - - # construct coefficient matrix for least-squares residue fitting (C_viol) - coeffs = np.matmul(A_freq, B) - - C_viol = np.empty_like(C_t) - n_ports = np.shape(C_viol)[0] - model_order = self.get_model_order(self.poles) - - # predefined tolerance parameter (users should not need to change this) - delta_threshold = 0.999 - sigma_max = 1.1 # just to enter iteration loop for the first time - - # iterative compensation of passivity violations - t = 0 - self.history_max_sigma = [] - while t < self.max_iterations and sigma_max > 1.0: - logger.info(f'Passivity enforcement; Iteration {t + 1}') - - # calculate S-matrix of the model at freqs_eval (shape fxNxN) - #S_eval = self._get_s_from_ABCDE(freqs_eval, A, B, C_t, D, E) - S_eval = D + np.matmul(C_t, coeffs) # much faster! - - # singular value decomposition, - # shape(u) = (n_samples, n_ports, n_ports) - # shape(sigma) = (n_samples, n_ports) - # shape(vh) = (n_samples, n_ports, n_ports) - u, sigma, vh = np.linalg.svd(S_eval) - - # keep track of the greatest singular value in every iteration step - sigma_max = np.amax(sigma) - self.history_max_sigma.append(sigma_max) - - if sigma_max > delta_threshold: - delta = delta_threshold - else: - delta = sigma_max - - # find and perturb singular values that cause passivity violations - # sigma_viol = sigma * upsilon - psi with - # upsilon[sigma > delta] = 1 - # upsilon[sigma <= delta] = 0 - # psi[sigma > delta] = delta - # psi[sigma <= delta] = 0 - # (implemented below in a more compact form) - idx_viol = np.nonzero(sigma > delta) - sigma_viol = np.zeros_like(sigma) - sigma_viol[idx_viol] = sigma[idx_viol] - delta - - S_viol = np.matmul(u * sigma_viol[:, None, :], vh) - - # stack frequency responses as a single vector - # stacking order (row-major): - # s11, s12, s13, ..., s21, s22, s23, ... - S_viol_stacked = [] - for i in range(n_ports): - for j in range(n_ports): - S_viol_stacked.append(S_viol[:, i, j]) - S_viol_stacked = np.array(S_viol_stacked) - - # The existing method _fit_residues() can be use here to fit the violation residues. Enabling `fit_constant` - # in combination with `enforce_dc` removes the dc rows from the linear system and enforces the dc solution - # on the constant term. In case of dc preservation during passivity enforcement, we can ignore that constant - # term entirely and only use the violation residues. - # If dc preservation is disabled, we could also perturb the constant term. This is not currently done. In - # this new method, we always only perturb the residues. Disabling `fit_constant` and `preserve_dc` in this - # case will solve for the residues without the constant term in the linear system. - C_viol_stacked, D_viol_stacked, E_viol_stacked, residuals, rank, singular_vals = self._fit_residues( - self.poles, freqs_eval, S_viol_stacked, fit_constant=preserve_dc, fit_proportional=False, - enforce_dc=preserve_dc) - - # reshape C_viol into state-space format: [[R1.11, R2.11, R3.11, ..., R1.1N, R2.1N, R3.1N, ...], - # [R1.21, R2.21, R3.21, ..., R1.2N, R3.2N, R3.2N, ...], - # ... - # [R1.N1, R2.N1, R3.N1, ..., R1.NN, R3.NN, R3.NN, ...]] - for i_port in range(n_ports): - for j_port in range(n_ports): - j_residues = 0 - for residue in C_viol_stacked[i_port * n_ports + j_port]: - if np.imag(residue) == 0.0: - C_viol[i_port, j_port * model_order + j_residues] = np.real(residue) - j_residues += 1 - else: - C_viol[i_port, j_port * model_order + j_residues] = np.real(residue) - C_viol[i_port, j_port * model_order + j_residues + 1] = np.imag(residue) - j_residues += 2 - - # perturb residues by subtracting respective row and column in C_t - C_t = C_t - C_viol - - t += 1 - - # PASSIVATION PROCESS DONE; model is either passive or max. number of iterations have been exceeded - if t == self.max_iterations: - warnings.warn('Passivity enforcement: Aborting after the max. number of iterations has been ' - 'exceeded.', RuntimeWarning, stacklevel=2) - - # save/update model parameters (perturbed residues) - self.history_max_sigma = np.array(self.history_max_sigma) - - n_ports = np.shape(D)[0] - for i in range(n_ports): - k = 0 # column index in C_t - for j in range(n_ports): - i_response = i * n_ports + j - z = 0 # column index self.residues - for pole in self.poles: - if np.imag(pole) == 0.0: - # real pole --> real residue - self.residues[i_response, z] = C_t[i, k] - k += 1 - else: - # complex-conjugate pole --> complex-conjugate residue - self.residues[i_response, z] = C_t[i, k] + 1j * C_t[i, k + 1] - k += 2 - z += 1 - - # run final passivity test to make sure passivation was successful - violation_bands = self.passivity_test() - if len(violation_bands) > 0: - # trying to determine the required number of evaluation samples based on the bandwidth and separation - # distance of the violation bands - violation_band_separation = np.diff(violation_bands.flat) - min_spacing_nonzero = np.amin(violation_band_separation[violation_band_separation != 0.0]) - - # we should need an absolute minimum of 1 sample in each violating frequency band. - # in practice, the frequency spacing should preferrably be much more dense. - # let's recommend 2 samples per violation band. - n_samples_required = int(f_eval_max / min_spacing_nonzero * 2) - - if n_samples_required > n_samples: - hint = f'Consider trying again with n_samples > {n_samples_required}.' - else: - hint = '' - - warnings.warn('Passivity enforcement was not successful.\nModel is still non-passive in these ' - f'frequency bands: {violation_bands}.\nTry running this routine again with a larger number of' - f' samples (parameter `n_samples`). This run was using n_samples = {n_samples}. {hint}', - RuntimeWarning, stacklevel=2) - - def write_npz(self, path: str) -> None: - """ - Writes the model parameters in :attr:`poles`, :attr:`residues`, - :attr:`proportional_coeff` and :attr:`constant_coeff` to a labeled NumPy .npz file. - - Parameters - ---------- - path : str - Target path without filename for the export. The filename will be added automatically based on the network - name in :attr:`network` - - Returns - ------- - None - - See Also - -------- - read_npz : Reads all model parameters from a .npz file - - Examples - -------- - Load and fit the `Network`, then export the model parameters to a .npz file: - - >>> nw_3port = skrf.Network('my3port.s3p') - >>> vf = skrf.VectorFitting(nw_3port) - >>> vf.vector_fit(n_poles_real=1, n_poles_cmplx=4) - >>> vf.write_npz('./data/') - - The filename depends on the network name stored in `nw_3port.name` and will have the prefix `coefficients_`, for - example `coefficients_my3port.npz`. The coefficients can then be read using NumPy's load() function: - - >>> coeffs = numpy.load('./data/coefficients_my3port.npz') - >>> poles = coeffs['poles'] - >>> residues = coeffs['residues'] - >>> prop_coeffs = coeffs['proportionals'] - >>> constants = coeffs['constants'] - - Alternatively, the coefficients can be read directly into a new instance of `VectorFitting`, see - :func:`read_npz`. - """ - - if self.poles is None: - warnings.warn('Nothing to export; Poles have not been fitted.', RuntimeWarning, stacklevel=2) - return - if self.residues is None: - warnings.warn('Nothing to export; Residues have not been fitted.', RuntimeWarning, stacklevel=2) - return - if self.proportional_coeff is None: - warnings.warn('Nothing to export; Proportional coefficients have not been fitted.', RuntimeWarning, - stacklevel=2) - return - if self.constant_coeff is None: - warnings.warn('Nothing to export; Constants have not been fitted.', RuntimeWarning, stacklevel=2) - return - - filename = self.network.name - - logger.info(f'Exporting results as compressed NumPy array to {path}') - np.savez_compressed(os.path.join(path, f'coefficients_{filename}'), - poles=self.poles, residues=self.residues, proportionals=self.proportional_coeff, - constants=self.constant_coeff) - - def read_npz(self, file: str) -> None: - """ - Reads all model parameters :attr:`poles`, :attr:`residues`, :attr:`proportional_coeff` and - :attr:`constant_coeff` from a labeled NumPy .npz file. - - Parameters - ---------- - file : str - NumPy .npz file containing the parameters. See notes. - - Returns - ------- - None - - Raises - ------ - ValueError - If the shapes of the coefficient arrays in the provided file are not compatible. - - Notes - ----- - The .npz file needs to include the model parameters as individual NumPy arrays (ndarray) labeled '*poles*', - '*residues*', '*proportionals*' and '*constants*'. The shapes of those arrays need to match the network - properties in :class:`network` (correct number of ports). Preferably, the .npz file was created by - :func:`write_npz`. - - See Also - -------- - write_npz : Writes all model parameters to a .npz file - - Examples - -------- - Create an empty `VectorFitting` instance (with or without the fitted `Network`) and load the model parameters: - - >>> vf = skrf.VectorFitting(None) - >>> vf.read_npz('./data/coefficients_my3port.npz') - - This can be useful to analyze or process a previous vector fit instead of fitting it again, which sometimes - takes a long time. For example, the model passivity can be evaluated and enforced: - - >>> vf.passivity_enforce() - """ - - with np.load(file) as data: - poles = data['poles'] - - # legacy support for exported residues - if 'zeros' in data: - # old .npz file from deprecated write_npz() with residues called 'zeros' - residues = data['zeros'] - else: - # new .npz file from current write_npz() - residues = data['residues'] - - proportional_coeff = data['proportionals'] - constant_coeff = data['constants'] - - n_ports = int(np.sqrt(len(constant_coeff))) - n_resp = n_ports ** 2 - if np.shape(residues)[0] == np.shape(proportional_coeff)[0] == np.shape(constant_coeff)[0] == n_resp: - self.poles = poles - self.residues = residues - self.proportional_coeff = proportional_coeff - self.constant_coeff = constant_coeff - else: - raise ValueError('The shapes of the provided parameters are not compatible. The coefficient file needs ' - 'to contain NumPy arrays labled `poles`, `residues`, `proportionals`, and ' - '`constants`. Their shapes must match the number of network ports and the number of ' - 'frequencies.') - - def get_model_response(self, i: int, j: int, freqs: Any = None) -> np.ndarray: - """ - Returns one of the frequency responses :math:`H_{i+1,j+1}` of the fitted model :math:`H`. - - Parameters - ---------- - i : int - Row index of the response in the response matrix. - - j : int - Column index of the response in the response matrix. - - freqs : list of float or ndarray or None, optional - List of frequencies for the response plot. If None, the sample frequencies of the fitted network in - :attr:`network` are used. - - Returns - ------- - response : ndarray - Model response :math:`H_{i+1,j+1}` at the frequencies specified in `freqs` (complex-valued Numpy array). - - Examples - -------- - Get fitted S11 at 101 frequencies from 0 Hz to 10 GHz: - - >>> import skrf - >>> vf = skrf.VectorFitting(skrf.data.ring_slot) - >>> vf.vector_fit(3, 0) - >>> s11_fit = vf.get_model_response(0, 0, numpy.linspace(0, 10e9, 101)) - """ - - if self.poles is None: - warnings.warn('Returning a zero-vector; Poles have not been fitted.', - RuntimeWarning, stacklevel=2) - return np.zeros_like(freqs) - if self.residues is None: - warnings.warn('Returning a zero-vector; Residues have not been fitted.', - RuntimeWarning, stacklevel=2) - return np.zeros_like(freqs) - if self.proportional_coeff is None: - warnings.warn('Returning a zero-vector; Proportional coefficients have not been fitted.', - RuntimeWarning, stacklevel=2) - return np.zeros_like(freqs) - if self.constant_coeff is None: - warnings.warn('Returning a zero-vector; Constants have not been fitted.', - RuntimeWarning, stacklevel=2) - return np.zeros_like(freqs) - if freqs is None: - freqs = np.linspace(np.amin(self.network.f), np.amax(self.network.f), 1000) - - s = 2j * np.pi * np.array(freqs) - n_ports = int(np.sqrt(len(self.constant_coeff))) - i_response = i * n_ports + j - residues = self.residues[i_response] - - resp = self.proportional_coeff[i_response] * s + self.constant_coeff[i_response] - for i, pole in enumerate(self.poles): - if np.imag(pole) == 0.0: - # real pole - resp += residues[i] / (s - pole) - else: - # complex conjugate pole - resp += residues[i] / (s - pole) + np.conjugate(residues[i]) / (s - np.conjugate(pole)) - return resp - - @axes_kwarg - def plot(self, component: str, i: int = -1, j: int = -1, freqs: Any = None, - parameter: str = 's', *, ax: Axes = None) -> Axes: - """ - Plots the specified component of the parameter :math:`H_{i+1,j+1}` in the fit, where :math:`H` is - either the scattering (:math:`S`), the impedance (:math:`Z`), or the admittance (:math:`H`) response specified - in `parameter`. - - Parameters - ---------- - component : str - The component to be plotted. Must be one of the following items: - ['db', 'mag', 'deg', 'deg_unwrap', 're', 'im']. - `db` for magnitude in decibels, - `mag` for magnitude in linear scale, - `deg` for phase in degrees (wrapped), - `deg_unwrap` for phase in degrees (unwrapped/continuous), - `re` for real part in linear scale, - `im` for imaginary part in linear scale. - - i : int, optional - Row index of the response. `-1` to plot all rows. - - j : int, optional - Column index of the response. `-1` to plot all columns. - - freqs : list of float or ndarray or None, optional - List of frequencies for the response plot. If None, the sample frequencies of the fitted network in - :attr:`network` are used. This only works if :attr:`network` is not `None`. - - parameter : str, optional - The network representation to be used. This is only relevant for the plot of the original sampled response - in :attr:`network` that is used for comparison with the fit. Must be one of the following items unless - :attr:`network` is `None`: ['s', 'z', 'y'] for *scattering* (default), *impedance*, or *admittance*. - - ax : :class:`matplotlib.Axes` object or None - matplotlib axes to draw on. If None, the current axes is fetched with :func:`gca()`. - - Returns - ------- - :class:`matplotlib.Axes` - matplotlib axes used for drawing. Either the passed :attr:`ax` argument or the one fetch from the current - figure. - - Raises - ------ - ValueError - If the `freqs` parameter is not specified while the Network in :attr:`network` is `None`. - Also if `component` and/or `parameter` are not valid. - """ - - components = ['db', 'mag', 'deg', 'deg_unwrap', 're', 'im'] - if component.lower() in components: - if self.residues is None or self.poles is None: - raise RuntimeError('Poles and/or residues have not been fitted. Cannot plot the model response.') - - n_ports = int(np.sqrt(np.shape(self.residues)[0])) - - if i == -1: - list_i = range(n_ports) - elif isinstance(i, int): - list_i = [i] - else: - list_i = i - - if j == -1: - list_j = range(n_ports) - elif isinstance(j, int): - list_j = [j] - else: - list_j = j - - if self.network is not None: - # plot the original network response at each sample frequency (scatter plot) - if parameter.lower() == 's': - responses = self.network.s - elif parameter.lower() == 'z': - responses = self.network.z - elif parameter.lower() == 'y': - responses = self.network.y - else: - raise ValueError('The network parameter type is not valid, must be `s`, `z`, or `y`, ' - f'got `{parameter}`.') - - i_samples = 0 - for i in list_i: - for j in list_j: - if i_samples == 0: - label = 'Samples' - else: - label = '_nolegend_' - i_samples += 1 - - y_vals = None - if component.lower() == 'db': - y_vals = 20 * np.log10(np.abs(responses[:, i, j])) - elif component.lower() == 'mag': - y_vals = np.abs(responses[:, i, j]) - elif component.lower() == 'deg': - y_vals = np.rad2deg(np.angle(responses[:, i, j])) - elif component.lower() == 'deg_unwrap': - y_vals = np.rad2deg(np.unwrap(np.angle(responses[:, i, j]))) - elif component.lower() == 're': - y_vals = np.real(responses[:, i, j]) - elif component.lower() == 'im': - y_vals = np.imag(responses[:, i, j]) - - ax.scatter(self.network.f, y_vals, color='r', label=label) - - if freqs is None: - # get frequency array from the network - freqs = self.network.f - - if freqs is None: - raise ValueError( - 'Neither `freqs` nor `self.network` is specified. Cannot plot model response without any ' - 'frequency information.') - - # plot the fitted responses - y_label = '' - i_fit = 0 - for i in list_i: - for j in list_j: - if i_fit == 0: - label = 'Fit' - else: - label = '_nolegend_' - i_fit += 1 - - y_model = self.get_model_response(i, j, freqs) - y_vals = None - if component.lower() == 'db': - y_vals = 20 * np.log10(np.abs(y_model)) - y_label = 'Magnitude (dB)' - elif component.lower() == 'mag': - y_vals = np.abs(y_model) - y_label = 'Magnitude' - elif component.lower() == 'deg': - y_vals = np.rad2deg(np.angle(y_model)) - y_label = 'Phase (Degrees)' - elif component.lower() == 'deg_unwrap': - y_vals = np.rad2deg(np.unwrap(np.angle(y_model))) - y_label = 'Phase (Degrees)' - elif component.lower() == 're': - y_vals = np.real(y_model) - y_label = 'Real Part' - elif component.lower() == 'im': - y_vals = np.imag(y_model) - y_label = 'Imaginary Part' - - ax.plot(freqs, y_vals, color='k', label=label) - - ax.set_xlabel('Frequency (Hz)') - ax.set_ylabel(y_label) - ax.legend(loc='best') - - # only print title if a single response is shown - if i_fit == 1: - ax.set_title(f'Response i={i}, j={j}') - - return ax - else: - raise ValueError(f'The specified component ("{component}") is not valid. Must be in {components}.') - - def plot_s_db(self, *args, **kwargs) -> Axes: - """ - Plots the magnitude in dB of the scattering parameter response(s) in the fit. - - Parameters - ---------- - *args : any, optional - Additonal arguments to be passed to :func:`plot`. - - **kwargs : dict, optional - Additonal keyword arguments to be passed to :func:`plot`. - - Returns - ------- - :class:`matplotlib.Axes` - matplotlib axes used for drawing. Either the passed :attr:`ax` argument or the one fetch from the current - figure. - - Notes - ----- - This simply calls ``plot('db', *args, **kwargs)``. - """ - - return self.plot('db', *args, **kwargs) - - def plot_s_mag(self, *args, **kwargs) -> Axes: - """ - Plots the magnitude in linear scale of the scattering parameter response(s) in the fit. - - Parameters - ---------- - *args : any, optional - Additonal arguments to be passed to :func:`plot`. - - **kwargs : dict, optional - Additonal keyword arguments to be passed to :func:`plot`. - - Returns - ------- - :class:`matplotlib.Axes` - matplotlib axes used for drawing. Either the passed :attr:`ax` argument or the one fetch from the current - figure. - - Notes - ----- - This simply calls ``plot('mag', *args, **kwargs)``. - """ - - return self.plot('mag', *args, **kwargs) - - def plot_s_deg(self, *args, **kwargs) -> Axes: - """ - Plots the phase in degrees of the scattering parameter response(s) in the fit. - - Parameters - ---------- - *args : any, optional - Additonal arguments to be passed to :func:`plot`. - - **kwargs : dict, optional - Additonal keyword arguments to be passed to :func:`plot`. - - Returns - ------- - :class:`matplotlib.Axes` - matplotlib axes used for drawing. Either the passed :attr:`ax` argument or the one fetch from the current - figure. - - Notes - ----- - This simply calls ``plot('deg', *args, **kwargs)``. - """ - - return self.plot('deg', *args, **kwargs) - - def plot_s_deg_unwrap(self, *args, **kwargs) -> Axes: - """ - Plots the unwrapped phase in degrees of the scattering parameter response(s) in the fit. - - Parameters - ---------- - *args : any, optional - Additonal arguments to be passed to :func:`plot`. - - **kwargs : dict, optional - Additonal keyword arguments to be passed to :func:`plot`. - - Returns - ------- - :class:`matplotlib.Axes` - matplotlib axes used for drawing. Either the passed :attr:`ax` argument or the one fetch from the current - figure. - - Notes - ----- - This simply calls ``plot('deg_unwrap', *args, **kwargs)``. - """ - - return self.plot('deg_unwrap', *args, **kwargs) - - def plot_s_re(self, *args, **kwargs) -> Axes: - """ - Plots the real part of the scattering parameter response(s) in the fit. - - Parameters - ---------- - *args : any, optional - Additonal arguments to be passed to :func:`plot`. - - **kwargs : dict, optional - Additonal keyword arguments to be passed to :func:`plot`. - - Returns - ------- - :class:`matplotlib.Axes` - matplotlib axes used for drawing. Either the passed :attr:`ax` argument or the one fetch from the current - figure. - - Notes - ----- - This simply calls ``plot('re', *args, **kwargs)``. - """ - - return self.plot('re', *args, **kwargs) - - def plot_s_im(self, *args, **kwargs) -> Axes: - """ - Plots the imaginary part of the scattering parameter response(s) in the fit. - - Parameters - ---------- - *args : any, optional - Additonal arguments to be passed to :func:`plot`. - - **kwargs : dict, optional - Additonal keyword arguments to be passed to :func:`plot`. - - Returns - ------- - :class:`matplotlib.Axes` - matplotlib axes used for drawing. Either the passed :attr:`ax` argument or the one fetch from the current - figure. - - Notes - ----- - This simply calls ``plot('im', *args, **kwargs)``. - """ - - return self.plot('im', *args, **kwargs) - - @axes_kwarg - def plot_s_singular(self, freqs: Any = None, *, ax: Axes = None) -> Axes: - """ - Plots the singular values of the vector fitted S-matrix in linear scale. - - Parameters - ---------- - freqs : list of float or ndarray or None, optional - List of frequencies for the response plot. If None, the sample frequencies of the fitted network in - :attr:`network` are used. This only works if :attr:`network` is not `None`. - - ax : :class:`matplotlib.Axes` object or None - matplotlib axes to draw on. If None, the current axes is fetched with :func:`gca()`. - - Returns - ------- - :class:`matplotlib.Axes` - matplotlib axes used for drawing. Either the passed :attr:`ax` argument or the one fetch from the current - figure. - - Raises - ------ - ValueError - If the `freqs` parameter is not specified while the Network in :attr:`network` is `None`. - """ - - if freqs is None: - if self.network is None: - raise ValueError( - 'Neither `freqs` nor `self.network` is specified. Cannot plot model response without any ' - 'frequency information.') - else: - freqs = self.network.f - - # get system matrices of state-space representation - A, B, C, D, E = self._get_ABCDE() - - n_ports = np.shape(D)[0] - - # calculate and save singular values for each frequency - u, sigma, vh = np.linalg.svd(self._get_s_from_ABCDE(freqs, A, B, C, D, E)) - - # plot the frequency response of each singular value - for n in range(n_ports): - ax.plot(freqs, sigma[:, n], label=fr'$\sigma_{n + 1}$') - ax.set_xlabel('Frequency (Hz)') - ax.set_ylabel('Magnitude') - ax.legend(loc='best') - return ax - - @axes_kwarg - def plot_convergence(self, ax: Axes = None) -> Axes: - """ - Plots the history of the model residue parameter **d_res** during the iterative pole relocation process of the - vector fitting, which should eventually converge to a fixed value. Additionally, the relative change of the - maximum singular value of the coefficient matrix **A** are plotted, which serve as a convergence indicator. - - Parameters - ---------- - ax : :class:`matplotlib.Axes` object or None - matplotlib axes to draw on. If None, the current axes is fetched with :func:`gca()`. - - Returns - ------- - :class:`matplotlib.Axes` - matplotlib axes used for drawing. Either the passed :attr:`ax` argument or the one fetch from the current - figure. - """ - - ax.semilogy(np.arange(len(self.delta_max_history)) + 1, self.delta_max_history, color='darkblue') - ax.set_xlabel('Iteration step') - ax.set_ylabel('Max. relative change', color='darkblue') - ax2 = ax.twinx() - ax2.plot(np.arange(len(self.d_res_history)) + 1, self.d_res_history, color='orangered') - ax2.set_ylabel('Residue', color='orangered') - return ax - - @axes_kwarg - def plot_passivation(self, ax: Axes = None) -> Axes: - """ - Plots the history of the greatest singular value during the iterative passivity enforcement process, which - should eventually converge to a value slightly lower than 1.0 or stop after reaching the maximum number of - iterations specified in the class variable :attr:`max_iterations`. - - Parameters - ---------- - ax : :class:`matplotlib.Axes` object or None - matplotlib axes to draw on. If None, the current axes is fetched with :func:`gca()`. - - Returns - ------- - :class:`matplotlib.Axes` - matplotlib axes used for drawing. Either the passed :attr:`ax` argument or the one fetch from the current - figure. - """ - - ax.plot(np.arange(len(self.history_max_sigma)) + 1, self.history_max_sigma) - ax.set_xlabel('Iteration step') - ax.set_ylabel('Max. singular value') - return ax - - def write_spice_subcircuit_s(self, file: str, fitted_model_name: str = "s_equivalent", - create_reference_pins: bool = False) -> None: - """ - Creates an equivalent N-port subcircuit based on its vector fitted scattering (S) parameter responses - in spice simulator netlist syntax (compatible with LTspice, ngspice, Xyce, ...). The circuit synthesis is based - on a direct implementation of the state-space representation of the vector fitted model [#vf-book]_. - - Parameters - ---------- - file : str - Path and filename including file extension (usually .sp) for the subcircuit file. - - fitted_model_name: str - Name of the resulting subcircuit, default "s_equivalent" - - create_reference_pins: bool - If set to True, the synthesized subcircuit will have N pin-pairs: - p1 p1_ref p2 p2_ref ... pN pN_ref - - If set to False, the synthesized subcircuit will have N pins - p1 p2 ... pN - In this case, the reference nodes will be internally connected - to the global ground net 0. - - The default is False - - Returns - ------- - None - - Examples - -------- - Load and fit the `Network`, then export the equivalent subcircuit: - - >>> nw_3port = skrf.Network('my3port.s3p') - >>> vf = skrf.VectorFitting(nw_3port) - >>> vf.auto_fit() - >>> vf.write_spice_subcircuit_s('/my3port_model.sp') - - References - ---------- - .. [#vf-book] S. Grivet-Talocia and B. Gustavsen, "Passive Macromodeling", Wiley, 2016, - doi: https://doi.org/10.1002/9781119140931 - - """ - - if np.any(self.proportional_coeff): - build_e = True - else: - build_e = False - - with open(file, 'w') as f: - # write title line - f.write('* EQUIVALENT CIRCUIT FOR VECTOR FITTED S-MATRIX\n') - f.write('* Created using scikit-rf vectorFitting.py\n') - f.write('*\n') - - # Create subcircuit pin string and reference nodes - if create_reference_pins: - str_input_nodes = " ".join(map(lambda x: f'p{x + 1} p{x + 1}_ref', range(self.network.nports))) - else: - str_input_nodes = " ".join(map(lambda x: f'p{x + 1}', range(self.network.nports))) - - f.write(f'.SUBCKT {fitted_model_name} {str_input_nodes}\n') - - for i in range(self.network.nports): - f.write('*\n') - f.write(f'* Port network for port {i + 1}\n') - - if create_reference_pins: - node_ref_i = f'p{i + 1}_ref' - else: - node_ref_i = '0' - - # reference impedance (real, i.e. resistance) of port i - z0_i = np.real(self.network.z0[0, i]) - - # transfer gain of the controlled current sources representing the incident power wave a_i at port i - # - # the gain values result from the definition of the incident power wave: - # a_i = 1 / 2 / sqrt(Z0_i) * (V_i + Z0_i * I_i) = 1 / 2 / sqrt(Z0_i) * V_i + sqrt(Z0_i) / 2 * I_i - gain_vccs_a_i = 1 / 2 / np.sqrt(z0_i) - gain_cccs_a_i = np.sqrt(z0_i) / 2 - - # transfer gain of the controlled current source representing the reflected power wave b_i at port i - # - # the gain values result from the definition of the reflected power wave: - # b_i = 1 / 2 / sqrt(Z0_i) * (V_i - Z0_i * I_i) - # - # depending on the circuit topology used for the equivalent port network, this can be implemented - # with either controlled current and/or controlled voltage sources. in case of the Norton current - # source used in this implementation, the reflected power wave relates to the source current as: - # b_i = sqrt(Z0_i) / 2 * I_b_i <==> I_b_i = 2 / sqrt(Z0_i) * b_i - gain_b_i = 2 / np.sqrt(z0_i) - - # dummy voltage source (v = 0) for port current sensing (I_i) - f.write(f'V{i + 1} p{i + 1} s{i + 1} 0\n') - - # adding port reference resistor Ri = Z0_i - f.write(f'R{i + 1} s{i + 1} {node_ref_i} {z0_i}\n') - - # transfer of states and inputs from port j to input/output network of port i - for j in range(self.network.nports): - if create_reference_pins: - node_ref_j = f'p{j + 1}_ref' - else: - node_ref_j = '0' - - # reference impedance (real, i.e. resistance) of port i - z0_j = np.real(self.network.z0[0, j]) - - # Stacking order in VectorFitting class variables: - # s11, s12, s13, ..., s21, s22, s23, ... - idx_S_i_j = i * self.network.nports + j - - # VCCS and CCCS adding their currents to represent the incident wave a_j - gain_vccs_a_j = 1 / 2 / np.sqrt(z0_j) - gain_cccs_a_j = np.sqrt(z0_j) / 2 - - d = self.constant_coeff[idx_S_i_j] - e = self.proportional_coeff[idx_S_i_j] - - if d != 0.0: - # avoid zero-valued coefficients (in case of fit_constant=False) - - # input a_j is scaled by constant term d_i_j and by current gain for b_i - g_ij = gain_b_i * d * gain_vccs_a_j - f_ij = gain_b_i * d * gain_cccs_a_j - f.write(f'Gd{i + 1}_{j + 1} {node_ref_i} s{i + 1} p{j + 1} {node_ref_j} {g_ij}\n') - f.write(f'Fd{i + 1}_{j + 1} {node_ref_i} s{i + 1} V{j + 1} {f_ij}\n') - - if build_e and e != 0.0: - # avoid zero-valued coefficients (in case of fit_proportional=False) - # proportional coefficients require an extra node for the differentiation using an inductor - # [Y(s) ~ s * E * U(s)] - - # differentiated input a_j is scaled by proportional term e_i_j and by current gain for b_i - g_ij = gain_b_i * e - f.write(f'Ge{i + 1}_{j + 1} {node_ref_i} s{i + 1} e{j + 1} 0 {g_ij}\n') - - # each residue rk_i_j at port i is multiplied by its respective state signal xk_j - for k in range(len(self.poles)): - pole = self.poles[k] - residue = self.residues[idx_S_i_j, k] - g_re = gain_b_i * np.real(residue) - g_im = gain_b_i * np.imag(residue) - - if np.imag(pole) == 0.0: - # Real pole/residue pair; represented by one state - xkj = f'x{k + 1}_a{j + 1}' - f.write(f'Gr{k + 1}_{i + 1}_{j + 1} {node_ref_i} s{i + 1} {xkj} 0 {g_re}\n') - else: - # Complex-conjugate pole/residue pair; represented by two states - # real part at x_{k + 1}_re_{j + 1} - # imaginary part at x_{k + 1}_im_{j + 1} - xk_re_j = f'x{k + 1}_re_a{j + 1}' - xk_im_j = f'x{k + 1}_im_a{j + 1}' - f.write(f'Gr{k + 1}_re_{i + 1}_{j + 1} {node_ref_i} s{i + 1} {xk_re_j} 0 {g_re}\n') - f.write(f'Gr{k + 1}_im_{i + 1}_{j + 1} {node_ref_i} s{i + 1} {xk_im_j} 0 {g_im}\n') - - # create state networks driven by this port i (input variable u = a_i) - f.write('*\n') - f.write(f'* State networks driven by port {i + 1}\n') - for k in range(len(self.poles)): - pole = self.poles[k] - pole_re = np.real(pole) - pole_im = np.imag(pole) - - # Transfer of input (a_i) to state networks (node xk_i) using VCCS and CCCS - if pole_im == 0.0: - # Real pole; represented by one state, input a_i is scaled by b = 1 - xki = f'x{k + 1}_a{i + 1}' - f.write(f'Cx{k + 1}_a{i + 1} {xki} 0 1.0\n') # 1F capacitor makes math easy - f.write(f'Gx{k + 1}_a{i + 1} 0 {xki} p{i + 1} {node_ref_i} {1 * gain_vccs_a_i}\n') - f.write(f'Fx{k + 1}_a{i + 1} 0 {xki} V{i + 1} {1 * gain_cccs_a_i}\n') - f.write(f'Rp{k + 1}_a{i + 1} 0 {xki} {-1 / pole_re}\n') - else: - # Complex pole of a conjugate pair; represented by two states - # real part at x_{k + 1}_re_{i + 1}, input a_i is scaled by b = 2 - xk_re_i = f'x{k + 1}_re_a{i + 1}' - xk_im_i = f'x{k + 1}_im_a{i + 1}' - f.write(f'Cx{k + 1}_re_a{i + 1} {xk_re_i} 0 1.0\n') # 1F capacitor makes math easy - f.write( - f'Gx{k + 1}_re_a{i + 1} 0 {xk_re_i} p{i + 1} {node_ref_i} {2 * gain_vccs_a_i}\n') - f.write(f'Fx{k + 1}_re_a{i + 1} 0 {xk_re_i} V{i + 1} {2 * gain_cccs_a_i}\n') - f.write(f'Rp{k + 1}_re_re_a{i + 1} 0 {xk_re_i} {-1 / pole_re}\n') - f.write(f'Gp{k + 1}_re_im_a{i + 1} 0 {xk_re_i} {xk_im_i} 0 {pole_im}\n') - - # imaginary part at x_{k + 1}_im_{i + 1}, input a_i is inactive (b = 0) - f.write(f'Cx{k + 1}_im_a{i + 1} {xk_im_i} 0 1.0\n') # 1F capacitor makes math easy - f.write(f'Gp{k + 1}_im_re_a{i + 1} 0 {xk_im_i} {xk_re_i} 0 {-1 * pole_im}\n') - f.write(f'Rp{k + 1}_im_im_a{i + 1} 0 {xk_im_i} {-1 / pole_re}\n') - - # create differentiation network for this port i (input variable u = a_i) - if build_e: - f.write('*\n') - f.write(f'* Network with derivative of input a_{i + 1} for proportional term\n') - # voltage on node 'e{i + 1}' to gnd (0) represents time-derivative of input a_i for terms e_j_i - f.write(f'Le{i + 1} e{i + 1} 0 1.0\n') # 1H inductor makes math easy - f.write(f'Ge{i + 1} 0 e{i + 1} p{i + 1} {node_ref_i} {gain_vccs_a_i}\n') - f.write(f'Fe{i + 1} 0 e{i + 1} V{i + 1} {gain_cccs_a_i}\n') - - f.write(f'.ENDS {fitted_model_name}\n') diff --git a/main.py b/main.py index 4453f7c..b8e565e 100644 --- a/main.py +++ b/main.py @@ -1,446 +1,136 @@ -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 -from plotly.subplots import make_subplots -import plotly.graph_objs -from core.freqency import auto_select -from scipy.linalg import block_diag +from core.VFManager import VFManager +from core.utils import generate_starting_poles +from core.sample import auto_select_multple_ports +from core.basis.MultiPortOrthonormalBasis import MultiPortOrthonormalBasis -network = rf.Network("/tmp/paramer/simulation/3500/3500.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") - -def formula_67(s,y): - diag_values = [y[i][0][0] for i in range(len(y))] - H = np.diag(diag_values) - P = 2 - 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("Dt", D_t) - # print("D_t/D_t_pre",D_t/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.abs(N_t / D_t)) - -class formula_70_psi: - """ - VF-(70) with final pole-residue model. - After fit(): - self.poles : (P,) complex - self.res : (P, M) complex # residues per response (columns) - self.h : (M,) complex # optional constants - self.g : (M,) complex # optional proportional terms - """ - - # -------- internals -------- - - def __init__(self, s, P, H, beta_min, beta_max ,alpha_scale=0.01, n_iter=20, d0=1.0, include_const=True, include_linear=False, verbose=True): - self.s = s - self.P = P - self.H = H - self.beta_min = beta_min - self.beta_max = beta_max - self.alpha_scale = alpha_scale - self.z0 = self._generate_starting_poles(P, beta_min=beta_min, beta_max=beta_max,alpha_scale=alpha_scale) - self.z0 = np.array(self.z0, dtype=np.complex128) - self.n_iter = n_iter - self.d0 = d0 - self.include_const = include_const - self.include_linear = include_linear - self.verbose = verbose - - self.rel_iteration = [] - self.cond_iteration = [] - self.conf_poles_recognize = [] - self.rms_error_iteration = [] - self.rms_error_poles_recongnize = [] - - @staticmethod - def _generate_starting_poles(P, beta_min:float, beta_max:float, alpha_scale=0.01): - """ - 仅生成复共轭对: p = -alpha + j beta, p*。 - n_pairs: 复对数量 (总极点数 = 2*n_pairs) - beta_min,beta_max: 想要覆盖的虚部范围 (单位: rad/s) - alpha_scale: alpha = alpha_scale * beta (文中 {α_p}=0.01{β_p}) - 返回: list[complex] (正虚部先, 后跟共轭) - """ - betas = 2*np.pi*np.linspace(beta_min, beta_max, P) - poles = [] - for b in betas: - alpha = alpha_scale * b - p = -alpha + 1j * b - poles += [p, np.conj(p)] - return poles - - def _generate_laguerre_basis(self, s: np.ndarray, z: np.ndarray): - poles = z - poles = sorted(poles, key=lambda p: np.real(p)) - basis = np.zeros((len(poles)+1,len(s)),dtype=complex) - - product = np.ones(len(s),dtype=complex) - - basis[0] = np.ones(len(s),dtype=complex) # φ_0 = 1 - i = 0 - while i < len(poles): - if np.real(poles[i]) >= 0: - raise ValueError(f"极点必须在左半平面: {poles[i]}") - - # 复对首 (正虚部) - if np.iscomplex(poles[i]) and np.imag(poles[i]) > 0: - if i + 1 >= len(poles): - raise ValueError("复极点缺少共轭") - pn = poles[i] - pc = poles[i + 1] - if not np.isclose(pc, np.conj(pn)): - pc, pn = pn,pc - if not np.isclose(pc, np.conj(pn)): - raise ValueError("复极点未按 (p, p*) 顺序排列 (正虚部在前)") - poles[i], poles[i+1] = pc, pn # swap - sigma = -np.real(pn) # >0 - scale = np.sqrt(2 * sigma) - r = np.abs(pn) - denom = (s - pn) * (s - pc) - - # 两个基函数 - phi_p = scale * (s - r) / denom * product - phi_pc = scale * (s + r) / denom * product - - # product 先乘 (s + p^*)/(s - p)，再乘 (s + p)/(s - p^*) - product = product * (s + pc) / (s - pn) - product = product * (s + pn) / (s - pc) - - basis[i + 1] = phi_p - basis[i + 2] = phi_pc - i += 2 - continue - - # 复对次 (负虚部) —— 应该被首元素处理，出现表示顺序错误 - if np.iscomplex(pn) and np.imag(pn) < 0: - raise ValueError("检测到负虚部复极点但其共轭尚未处理，请将正虚部成员放在前面。") - - # 实极点 - sigma = -np.real(pn) - if sigma <= 0: - raise ValueError("实极点实部应为负 (稳定)。") - scale = np.sqrt(2 * sigma) - phi = scale / (s - pn) * product - # 更新乘积 - product = product * (s + pn) / (s - pn) - i += 1 - basis[i + 1] = phi - return basis - - - def _orthonormal_psi(self,s,z): - s = np.asarray(s, np.complex128).reshape(-1) - z = np.asarray(z, np.complex128).reshape(-1) - return self._generate_laguerre_basis(s,z).T - - @staticmethod - def _psi(s, z): - s = np.asarray(s, np.complex128).reshape(-1) - z = np.asarray(z, np.complex128).reshape(-1) - return 1.0 / (s[:, None] + z[None, :]) - - # @staticmethod - # def _lhp(z): - # z = np.asarray(z, np.complex128).reshape(-1).copy() - # z[z.real > 0] = -np.conj(z[z.real > 0]) - # return z - - def _build_70(self, s, H_list, z_ref, d0): - Hs = [np.asarray(h, np.complex128).reshape(-1) for h in H_list] - K, P, M = len(s), len(z_ref), len(Hs) - Psi = self._psi(s, z_ref) - Z = np.zeros((K, P)) - rows, rhs = [], [] - for m, H in enumerate(Hs): - Hp = H[:, None] - L_re = np.real(Hp * Psi); L_im = np.imag(Hp * Psi) # acts on c - R_re = -np.real(Psi); R_im = -np.imag(Psi) # acts on r^(m) - rows.append(np.hstack([L_re] + [R_re if j == m else Z for j in range(M)])) - rhs.append(-np.real(d0 * H)) - rows.append(np.hstack([L_im] + [R_im if j == m else Z for j in range(M)])) - rhs.append(-np.imag(d0 * H)) - A = np.vstack(rows) - b = np.concatenate(rhs) - cond_poles_recognize = np.linalg.cond(A) - rms_error_poles_recongnize = np.sqrt(np.mean(np.abs(A @ np.zeros(A.shape[1]) - b)**2)) - return A, b, M, P, cond_poles_recognize, rms_error_poles_recongnize - - def _step_70(self, s, H_list, z_ref, d0=1.0, scale=True): - A, b, M, P, cond_poles_recognize, rms_error_poles_recongnize = self._build_70(s, H_list, z_ref, d0) - if scale: - coln = np.maximum(np.linalg.norm(A, axis=0), 1e-12) - x, *_ = np.linalg.lstsq(A / coln, b, rcond=None) - x = x / coln - cond_iteration = np.linalg.cond(A) - rms_error_iteration = np.sqrt(np.mean(np.abs(A @ x - b)**2)) - else: - x, *_ = np.linalg.lstsq(A, b, rcond=None) - cond_iteration = np.linalg.cond(A) - rms_error_iteration = np.sqrt(np.mean(np.abs(A @ x - b)**2)) - - c = x[:P] - res_ratio = np.empty((P, M), np.complex128) - off = P - for m in range(M): - res_ratio[:, m] = x[off:off+P]; off += P - # relocate poles with test matrix - Sigma = np.diag(-np.asarray(z_ref, np.complex128)) - T = Sigma - (np.ones((P, 1), np.complex128) @ (c.reshape(1, -1) / d0)) - z_new = -np.linalg.eigvals(T) - # z_new = self._lhp(z_new) - # cond = np.linalg.cond(T) - return z_new, c, cond_iteration, rms_error_iteration, cond_poles_recognize, rms_error_poles_recongnize, res_ratio - - # -------- public API -------- - def fit(self): - """ - s : (K,) complex samples (j*2π*f_sel) - H : (K,) complex or (K,M) or list of M vectors - z0: initial poles (P,) complex (LHP + conjugate pairs recommended) - """ - # normalize responses -> list - if isinstance(self.H, (list, tuple)): - H_list = [np.asarray(h, np.complex128).reshape(-1) for h in self.H] - else: - H_arr = np.asarray(self.H, np.complex128) - if H_arr.ndim == 1: - H_list = [H_arr] - elif H_arr.ndim == 2 and H_arr.shape[0] == len(self.s): - H_list = [H_arr[:, i].copy() for i in range(H_arr.shape[1])] - else: - raise ValueError("H must be (K,), list of (K,), or (K,M) with M responses.") - M = len(H_list) - - # z = self._lhp(np.asarray(self.z0, np.complex128)) - z = self.z0 - # SK/VF relocations - for it in range(self.n_iter): - z_next, c_last, cond_iteration, rms_error_iteration, cond_poles_recognize, rms_error_poles_recongnize, _ = self._step_70(self.s, H_list, z, d0=self.d0, scale=True) - rel = np.linalg.norm(z_next) / max(1.0, np.linalg.norm(z)) - if self.verbose: - print(f"[VF-70] iter {it+1:02d}/{self.n_iter:02d} Δz_rel={rel}") - self.cond_iteration.append(cond_iteration) - self.rms_error_iteration.append(rms_error_iteration) - self.conf_poles_recognize.append(cond_poles_recognize) - self.rms_error_poles_recongnize.append(rms_error_poles_recongnize) - self.rel_iteration.append(rel) - z = z_next - - # ---- Finalize: refit residues with fixed poles z (pole–residue model) ---- - Phi = self._psi(self.s, z) # K x P - # Build design matrix for extras - extras = [] - if self.include_const: extras.append(np.ones(len(self.s), np.complex128)) - if self.include_linear: extras.append(self.s.astype(np.complex128)) - if extras: - X_base = np.column_stack([Phi] + extras) # K x (P + E) - else: - X_base = Phi - - res = np.empty((len(z), M), np.complex128) - h = np.zeros(M, np.complex128) - g = np.zeros(M, np.complex128) - - for m, Hm in enumerate(H_list): - theta, *_ = np.linalg.lstsq(X_base, Hm, rcond=None) # complex LS - res[:, m] = theta[:len(z)] - e = len(theta) - len(z) - if e >= 1: h[m] = theta[len(z)] - if e >= 2: g[m] = theta[len(z)+1] - - # store the rational function - self.poles = z # (P,) - self.res = res # (P, M) - self.h = h # (M,) - self.g = g # (M,) - return self - - # Evaluate the stored **rational** model on any grid - def evaluate(self, s_eval, m=None): - if not hasattr(self, "poles"): - raise RuntimeError("Model not fitted. Call fit(...) first.") - s_eval = np.asarray(s_eval, np.complex128).reshape(-1) - Phi = self._psi(s_eval, self.poles) # K_eval x P - if m is None: - H = Phi @ self.res - if np.any(self.h): H += self.h - if np.any(self.g): H += s_eval[:, None] * self.g - return H # (K_eval, M) or (K_eval,1) - m = int(m) - H = Phi @ self.res[:, m] - H += self.h[m] - H += s_eval * self.g[m] - return H # (K_eval,) - - # def plot_rel_and_cond(self): - # fig, (ax1, ax2) = plt.subplots(2,1,figsize=(15,20)) - # ax1.plot(np.log(self.rel), 'g-', label='rel') - # ax2.plot(np.log(self.cond), 'b-', label='cond') - # ax1.set_xlabel('Iteration') - # ax1.set_ylabel('Relative Change', color='g') - # ax2.set_ylabel('Condition Number', color='b') - # ax1.tick_params(axis='y', labelcolor='g') - # ax2.tick_params(axis='y', labelcolor='b') - # fig.tight_layout() - # plt.title('Relative Change and Condition Number per Iteration') - # # plt.show() - # plt.savefig(f"img_rel_cond.png") - def plot_rel_and_cond(self): - fig = make_subplots(rows=3, cols=2) - fig.add_trace(plotly.graph_objs.Scatter(y=self.rel_iteration, mode='lines+markers', name='rel'), row=1, col=1) - fig.add_trace(plotly.graph_objs.Scatter(y=self.cond_iteration, mode='lines+markers', name='cond_iteration'), row=2, col=1) - fig.add_trace(plotly.graph_objs.Scatter(y=self.rms_error_iteration, mode='lines+markers', name='rms_error_iteration'), row=3, col=1) - fig.add_trace(plotly.graph_objs.Scatter(y=self.conf_poles_recognize, mode='lines+markers', name='cond_poles_recognize'), row=2, col=2) - fig.add_trace(plotly.graph_objs.Scatter(y=self.rms_error_poles_recongnize, mode='lines+markers', name='rms_error_poles_recognize'), row=3, col=2) - fig.update_xaxes(title_text='Iteration', row=1, col=1) - fig.update_yaxes(title_text='Relative Change', row=1, col=1) - fig.update_yaxes(title_text='Condition Number (Iteration)', row=2, col=1) - fig.update_yaxes(title_text='RMS Error (Iteration)', row=3, col=1) - fig.update_yaxes(title_text='Condition Number (Pole Recognition)', row=2, col=2) - fig.update_yaxes(title_text='RMS Error (Pole Recognition)', row=3, col=2) - fig.update_layout(height=800, width=800, title_text='Relative Change and Condition Number per Iteration') - fig.write_image("img_rel_cond.png") - # fig.show() - - def evaluate_on_freq(self, freq_eval, m=None): - return self.evaluate(1j * 2*np.pi * np.asarray(freq_eval, float), m=m) - - - if __name__ == "__main__": - # formula_67(s,y) - H11 = np.array([y[i,0,0] for i in range(len(y))]) - H11_slice,freqs_slice = auto_select(H11,freqs,max_points=20) - s_slice = freqs_slice * 2j * np.pi - P_pairs = 2 + start_point = 0 + id = 3000 + network = rf.Network(f"/tmp/paramer/simulation/{id}/{id}.s2p") + # network = rf.data.ring_slot + ports = network.nports + K = 100 - K = 10 - f70 = formula_70_psi(s_slice,P_pairs, H11_slice, beta_min=1e8, beta_max=freqs_slice[-1],alpha_scale=0.01, n_iter=K, d0=1.0, verbose=True) - model = f70.fit() - model.plot_rel_and_cond() + full_freqences = network.f[start_point:] + noised_sampled_points = network.y[start_point:,:,:].reshape(-1,ports,ports) + sampled_points = network.y[start_point:,:,:].reshape(-1,ports,ports) - Hfit_dense = model.evaluate_on_freq(freqs) + # noised_sampled_points = network.y[start_point:,0,0].reshape(-1,1,1) + # sampled_points = network.y[start_point:,0,0].reshape(-1,1,1) - fig, axes = plt.subplots(2, 1, figsize=(10, 8), sharex=False) + H,freqs = auto_select_multple_ports(noised_sampled_points,full_freqences,max_points=20) - # (1) Magnitude plot like your screenshot - ax0 = axes[0] - ax0.plot(freqs, np.abs(H11), 'o', ms=4, color='red', label='Samples') - ax0.plot(freqs, np.abs(Hfit_dense), '-', lw=2, color='k', label='Fit') - ax0.plot(freqs_slice, np.abs(H11_slice), 'x', ms=4, color='blue', label='Input Samples') - ax0.set_title("Response i=0, j=0") - ax0.set_ylabel("Magnitude") - ax0.legend(loc="best") + vf = VFManager(npoles_cplx=2,freqs=freqs,H=H,model=MultiPortOrthonormalBasis,iterations=K,verbose=False) + model = vf.fit() + vf.plot_metrics(show=False,save_path="outputs") + model_responses = vf.get_model_responses(full_freqences) + vf.plot_model_responses(show=False,save_path="outputs") - # (2) RMS error vs iteration - # ax1 = axes[1] - # its = np.arange(1, K+1) - # ax1.plot(its, hist["rms_rel"], '-o', lw=2) - # ax1.set_xlabel("Iteration") - # ax1.set_ylabel("RMS error (relative)") - # ax1.grid(True, alpha=0.3) - # ax1.set_title(f"RMS(final) = {hist['rms_rel'][-1]:.3e}") + # # Original plot functions + + # Dt_1 = np.ones((len(freqs),1),np.complex128) + # # Levi step (no weighting): + # basis = MultiPortOrthonormalBasis(H,freqs,poles=poles) + # Dt = basis.Dt + # poles = basis.next_poles - fig.tight_layout() - plt.savefig(f"img_formula_70.png") + # print("Levi step (no weighting):") + # print("A:",basis.A) + # print("B:",basis.B) + # print("C:",basis.C) + # print("D:",basis.D) + # print("next_pozles:",basis.next_poles) + # print("Dt:",Dt, "norm:",np.linalg.norm(Dt)) + # # SK weighting (optional, after first pass): + # least_squares_condition = [] + # least_squares_rms_error = [] + # eigenval_condition = [] + # eigenval_rms_error = [] + # for i in range(K): + # basis = MultiPortOrthonormalBasis(H,freqs,poles=poles,weights=Dt) + # Dt_1 = Dt + # Dt = basis.Dt + # poles = basis.next_poles + # print(f"SK Iteration {i+1}/{K}") + # print("A:",basis.A) + # print("B:",basis.B) + # print("C:",basis.C) + # print("D:",basis.D) + # print("z:",basis.next_poles) + # print("Dt:",Dt) + # print("Dt/Dt-1",np.linalg.norm(Dt) / np.linalg.norm(Dt_1)) + # least_squares_condition.append(basis.least_squares_condition) + # least_squares_rms_error.append(basis.least_squares_rms_error) + # eigenval_condition.append(basis.eigenval_condition) + # eigenval_rms_error.append(basis.eigenval_rms_error) + # # H11_evaluated = basis.evaluate_pole_residue(network.f[1:],poles,basis.C[0]) + # H_evaluated = basis.get_model_responses(full_freqences) + # fitted_points = H_evaluated + # sliced_freqences = freqs + + # input_points = H + # for i in range(ports): + # for j in range(ports): + # fig, axes = plt.subplots(3, 2, figsize=(15, 16), sharex=False) + # ax00 = axes[0][0] + # ax00.plot(full_freqences, np.abs(sampled_points[:,i,j]), 'o', ms=4, color='red', label='Samples') + # ax00.plot(full_freqences, np.abs(fitted_points[:,i,j]), '-', lw=2, color='k', label='Fit') + # ax00.plot(sliced_freqences, np.abs(input_points[:,i,j]), 'x', ms=4, color='blue', label='Input Samples') + # ax00.set_title(f"Response i={i+1}, j={j+1}") + # ax00.set_ylabel("Magnitude") + # ax00.legend(loc="best") + + # ax01 = axes[0][1] + # ax01.set_title(f"Response i={i+1}, j={j+1}") + # ax01.set_ylabel("Phase (deg)") + # ax01.plot(full_freqences, np.angle(sampled_points[:,i,j],deg=True), 'o', ms=4, color='red', label='Samples') + # ax01.plot(full_freqences, np.angle(fitted_points[:,i,j],deg=True), '-', lw=2, color='k', label='Fit') + # ax01.plot(sliced_freqences, np.angle(input_points[:,i,j],deg=True), 'x', ms=4, color='blue', label='Input Samples') + # ax01.legend(loc="best") + + # # ax00 = axes[0][0] + # # ax00.plot(full_freqences, np.real(sampled_points[:,i,j]), 'o', ms=4, color='red', label='Samples') + # # ax00.plot(full_freqences, np.real(fitted_points[:,i,j]), '-', lw=2, color='k', label='Fit') + # # ax00.plot(sliced_freqences, np.real(input_points[:,i,j]), 'x', ms=4, color='blue', label='Input Samples') + # # ax00.set_title(f"Response i={i+1}, j={j+1}") + # # ax00.set_ylabel("Real Part") + # # ax00.legend(loc="best") + + # # ax01 = axes[0][1] + # # ax01.set_title(f"Response i={i+1}, j={j+1}") + # # ax01.set_ylabel("Imag Part") + # # ax01.plot(full_freqences, np.imag(sampled_points[:,i,j]), 'o', ms=4, color='red', label='Samples') + # # ax01.plot(full_freqences, np.imag(fitted_points[:,i,j]), '-', lw=2, color='k', label='Fit') + # # ax01.plot(sliced_freqences, np.imag(input_points[:,i,j]), 'x', ms=4, color='blue', label='Input Samples') + # # ax01.legend(loc="best") + + # ax10 = axes[1][0] + # ax10.plot(least_squares_condition, label='Least Squares Condition') + # ax10.set_title("least_squares_condition") + # ax10.set_ylabel("Magnitude") + # ax10.legend(loc="best") + + # ax11 = axes[1][1] + # ax11.plot(least_squares_rms_error, label='Least Squares RMS Error') + # ax11.set_title("least_squares_rms_error") + # ax11.set_ylabel("Magnitude") + # ax11.legend(loc="best") + + # ax20 = axes[2][0] + # ax20.plot(eigenval_condition, label='Eigenvalue Condition') + # ax20.set_title("eigenval_condition") + # ax20.set_ylabel("Magnitude") + # ax20.legend(loc="best") + + # ax21 = axes[2][1] + # ax21.plot(eigenval_rms_error, label='Eigenvalue RMS Error') + # ax21.set_title("eigenval_rms_error") + # ax21.set_ylabel("Magnitude") + # ax21.legend(loc="best") + # fig.tight_layout() + # plt.savefig(f"MultiplePortQR_port_{i+1}{j+1}.png") + # print(f"Saved MultiplePortQR_port_{i+1}{j+1}.png") \ No newline at end of file diff --git a/models/basic.py b/models/basic.py deleted file mode 100644 index 722209c..0000000 --- a/models/basic.py +++ /dev/null @@ -1,155 +0,0 @@ -from abc import abstractmethod -from skrf import Network -from schemas.paramer import SimulationRequestUnit, UuidResponseUnit, SimulationResponseUnit -from typing import List, Literal, Union, Dict -import requests -import time -from utils import send_get_request -from pydantic import BaseModel, Field -import itertools -import json -import numpy as np - -class ModelBasicParametersUnit(BaseModel): - name: str - type: Literal["number","integer","string","boolean"] - range: List[Union[float,int,str,bool]] - -class ModelBasicInfo(BaseModel): - base_url: str = Field(default="http://localhost:8105/api/v1") - nports: int = Field(default=2) - cell_name: str - template_name: str - user_id: int = Field(default=0) - template_version: str = Field(default="") - -class ModelBasicDatasetUnit(BaseModel): - nports: int = Field(default=2) - parameters: Dict[str, Union[float, int, str, bool]] - id: int = Field(default=0) - result_dir: str = Field(default="") - - @property - def network(self) -> Network: - try: - network = Network(f"{self.result_dir}/{self.id}.s{self.nports}p") - return network - except Exception as e: - raise RuntimeError(f"Error loading network from {self.result_dir}: {e}") - - @property - def s_params(self) -> np.ndarray: - return self.network.s - - @property - def y_params(self) -> np.ndarray: - return self.network.y - - @property - def z_params(self) -> np.ndarray: - return self.network.z - - @property - def freqs(self) -> np.ndarray: - return self.network.f - - -class ModelBasic: - def __init__(self): - self._dataset:List[ModelBasicDatasetUnit] = [] - - @property - @abstractmethod - def info(self)->ModelBasicInfo: - pass - - @property - @abstractmethod - def parameters(self)->List[ModelBasicParametersUnit]: - pass - - @property - @abstractmethod - def settings(self)->dict: - pass - - def sweep(self): - parameters_list = [] - lst = [res.range for res in self.parameters] - parameters_name = [res.name for res in self.parameters] - result = [list(item) for item in itertools.product(*lst)] - for res in result: - parameters_list.append({parameters_name[i]:res[i] for i in range(len(res))}) - - for res in parameters_list: - print(f"Simulating with parameters: {res}") - request_unit = SimulationRequestUnit( - user_id=self.info.user_id, - template_name=self.info.template_name, - template_version=self.info.template_version, - cell_name=self.info.cell_name, - parameters=res, - settings=self.settings - ) - response = self.simulate(request_unit) - - self._dataset.append(ModelBasicDatasetUnit(nports=self.info.nports,parameters=res, id=response.id, result_dir=response.result_path)) - - @property - def results(self)->List[ModelBasicDatasetUnit]: - return self._dataset - - def export(self, path:str|None): - if path is None: - path = f"{self.info.cell_name}_dataset.json" - with open(path,"w") as f: - json.dump([dict(item) for item in self.results],f,indent=4) - - def load(self, path:str|None): - if path is None: - path = f"{self.info.cell_name}_dataset.json" - with open(path,"r") as f: - data = json.load(f) - self._dataset += [ModelBasicDatasetUnit(**item) for item in data] - - def clear(self): - self._dataset = [] - - def simulate(self,simulation_request:SimulationRequestUnit)->UuidResponseUnit: - def send_simulate_request(url, data:list)->list[dict]: - response = requests.post(url, json = data) - if response.status_code not in [200,201,202]: - raise RuntimeError(f"send_simulate_request: {response.status_code}, {response.text}") - return response.json() - - response = send_simulate_request(f"{self.info.base_url}/simulations/create", [dict(simulation_request)]) - - simulation_response_model:SimulationResponseUnit = SimulationResponseUnit(**(response[0])) - - time.sleep(0.05) - - response = send_get_request(f"{self.info.base_url}/simulations/input_hash/{simulation_response_model.input_hash}") - - assert isinstance(response, dict), "Response is not a dictionary." - uuid_model = UuidResponseUnit(**response) - - - time.sleep(0.05) # Wait for 2 seconds before checking the status again - - status = uuid_model.status - while status != "completed" and status != "failed": - time.sleep(0.05) # Wait for 2 seconds before checking again - response = send_get_request(f"{self.info.base_url}/simulations/input_hash/{simulation_response_model.input_hash}") - assert isinstance(response, dict), "Response is not a dictionary." - try: - uuid_model = UuidResponseUnit(**response) - except Exception as e: - print(f"Error parsing response: {e}") - continue - assert response is not None, "No response received from the server." - status = uuid_model.status - - if status == "failed": - raise RuntimeError(f"Simulation failed: {uuid_model.error_message}") - else: - return uuid_model \ No newline at end of file diff --git a/models/capa.py b/models/capa.py deleted file mode 100644 index 885253b..0000000 --- a/models/capa.py +++ /dev/null @@ -1,45 +0,0 @@ -from typing import List,Optional -from pydantic import BaseModel, Field -from schemas.paramer import SimulationRequestUnit -from skrf import Network -import re -from models.basic import ModelBasic, ModelBasicParametersUnit, ModelBasicInfo, ModelBasicDatasetUnit - -W = [] -L = [] -i = 15.52 -while i <= 100: - W.append(i) - L.append(i) - i = int(i*1.05*100 + 0.5) / 100.0 - -class Capa(ModelBasic): - def __init__(self): - super().__init__() - - @property - def info(self) -> ModelBasicInfo: - return ModelBasicInfo( - nports=2, - cell_name="capa", - template_name="em_interface_compound", - user_id=0, - template_version="" - ) - - @property - def settings(self) -> dict: - return {} - - @property - def parameters(self) -> List[ModelBasicParametersUnit]: - return [ - ModelBasicParametersUnit(name="W",type="number",range=W), - ModelBasicParametersUnit(name="L",type="number",range=L) - ] - - - - - - \ No newline at end of file diff --git a/models/mlin.py b/models/mlin.py deleted file mode 100644 index c080f67..0000000 --- a/models/mlin.py +++ /dev/null @@ -1,45 +0,0 @@ -from typing import List,Optional -from pydantic import BaseModel, Field -from schemas.paramer import SimulationRequestUnit -from skrf import Network -import re -from models.basic import ModelBasic, ModelBasicParametersUnit, ModelBasicInfo, ModelBasicDatasetUnit - -W = [] -L = [] -i = 15.52 -while i <= 100: - W.append(i) - L.append(i) - i = int(i*1.05*100 + 0.5) / 100.0 - -class Mlin(ModelBasic): - def __init__(self): - super().__init__() - - @property - def info(self) -> ModelBasicInfo: - return ModelBasicInfo( - nports=2, - cell_name="mlin", - template_name="em_interface_compound", - user_id=0, - template_version="" - ) - - @property - def settings(self) -> dict: - return {} - - @property - def parameters(self) -> List[ModelBasicParametersUnit]: - return [ - ModelBasicParametersUnit(name="W",type="number",range=W), - ModelBasicParametersUnit(name="L",type="number",range=L) - ] - - - - - - \ No newline at end of file diff --git a/outputs/capa_parametric.s2p b/outputs/capa_parametric.s2p deleted file mode 100644 index dccfb21..0000000 --- a/outputs/capa_parametric.s2p +++ /dev/null @@ -1,309 +0,0 @@ -! 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b/schemas/paramer.py deleted file mode 100644 index f0f4563..0000000 --- a/schemas/paramer.py +++ /dev/null @@ -1,49 +0,0 @@ -from pydantic import BaseModel, Field -from typing import List,Union,Literal,Optional - -class SimulationRequestUnit(BaseModel): - user_id:int = Field(default=0) - template_name:str = Field(default="em_interface_compound") - template_version:str = Field(default="") - cell_name:str = Field(default="capa") - parameters:dict = Field(default={"W":10,"L":10}) - settings:dict = Field(default={}) - -class UuidResponseUnit(BaseModel): - id:int - user_id:Optional[int] - template_name:str - template_version:str - cell_name:str - status:Literal["pending","running","completed","failed"] - parameters:dict - settings:dict - input_hash:str - storage_type: str - result_path:str - completed_at:Optional[str] - result_json:Optional[dict] - error_message:Optional[str] - failure_count:Optional[int] - last_failed_at:Optional[str] - cpu_usage:Optional[float] - memory_usage:Optional[float] - disk_usage:Optional[float] - gpu_usage:Optional[float] - node_name:Optional[str] - jobs:Optional[List] - results:Optional[List] - created_at:str - updated_at:str - -class SimulationResponseUnit(BaseModel): - template_name:str - template_version:str - template_id:int - cell_name:str - cell_id:int - parameters:dict - settings:dict - input_hash:str - status:str - message:str \ No newline at end of file diff --git a/sweep.py b/sweep.py deleted file mode 100644 index 7ae768d..0000000 --- a/sweep.py +++ /dev/null @@ -1,13 +0,0 @@ -from models.mlin import Mlin - -W = [] -L = [] -i = 15.52 -while i <= 100: - W.append(i) - L.append(i) - i = int(i*1.05*100 + 0.5) / 100.0 - -model = Mlin() -model.sweep() -model.export("mlin_sweep") \ No newline at end of file diff --git a/vector_fitting.py b/test/test_vector_fitting.py similarity index 100% rename from vector_fitting.py rename to test/test_vector_fitting.py diff --git a/utils.py b/utils.py deleted file mode 100644 index 36dd88a..0000000 --- a/utils.py +++ /dev/null @@ -1,55 +0,0 @@ -import requests -import time -from schemas.paramer import SimulationRequestUnit, SimulationResponseUnit, UuidResponseUnit -import skrf as rf -from typing import Literal -import matplotlib.pyplot as plt - - -def send_get_request(url): - try: - response = requests.get(url) - response.raise_for_status() # Raise an error for bad responses - return response.json() - except requests.exceptions.RequestException as e: - print(f"An error occurred: {e}") - return None - -def get_network(file_path: str) -> rf.Network: - network = rf.Network(file_path) - return network - -def get_rms_error(vf: rf.VectorFitting,parameter_type:Literal['s','y','z']): - rms_error = [] - for i in range(vf.network.nports): - rms_error_row = [] - for j in range(vf.network.nports): - rms_error = vf.get_rms_error(i, j, parameter_type) - rms_error_row.append(rms_error) - rms_error.append(rms_error_row) - return rms_error - -def show_rms_error(vf: rf.VectorFitting,parameter_type:Literal['s','y','z'],show_plot:bool=True,save_path:str|None=None): - freqs = vf.network.f - nports = vf.network.nports - fig, ax = plt.subplots(nports, nports) - fig.set_size_inches(6*nports, 4*nports) - for i in range(nports): - for j in range(nports): - vf.plot("mag",0,1,freqs,ax=ax[i][j],parameter=parameter_type) - fig.tight_layout() - if show_plot: - plt.show() - if save_path: - plt.savefig(save_path) - -def remove_simulation_with_high_rms_error(simulations:list[rf.VectorFitting],limit:float=0.015,parameter_type:Literal['s','y','z']='s'): - nports = simulations[0].network.nports - index_of_filtered_simulations = [] - for res in simulations: - for i in range(nports): - for j in range(nports): - rms_error = res.get_rms_error(i, j, parameter_type) - if rms_error > limit: - index_of_filtered_simulations.append(i*nports+j) - break