chore: 分离了sweep和vf部分,vf部分准备写为包
This commit is contained in:
mayge
3
.gitignore
vendored
3
.gitignore
vendored
@@ -5,4 +5,5 @@ __pycache__/
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__pypackages__/
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env/
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.venv/
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.vscode/
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.vscode/
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outputs/
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12170
capa_results.json
12170
capa_results.json
File diff suppressed because it is too large
Load Diff
@@ -1,626 +0,0 @@
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import numpy as np
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from core.sk_iter import generate_starting_poles
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from scipy.linalg import block_diag
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import skrf as rf
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from skrf import VectorFitting
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from core.freqency import auto_select_multple_ports
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import matplotlib.pyplot as plt
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import random as rnd
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def cond_row_inf(A, use_pinv=True):
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"""行条件数 κ∞(A) = ||A||∞ * ||A^{-1}||∞；矩形阵用广义逆。"""
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A = np.asarray(A)
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Ainv = np.linalg.pinv(A) if (use_pinv or A.shape[0] != A.shape[1]) else np.linalg.inv(A)
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return np.linalg.norm(A, ord=np.inf) * np.linalg.norm(Ainv, ord=np.inf)
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def cond_col_one(A, use_pinv=True):
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"""列条件数 κ1(A) = ||A||1 * ||A^{-1}||1；矩形阵用广义逆。"""
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A = np.asarray(A)
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Ainv = np.linalg.pinv(A) if (use_pinv or A.shape[0] != A.shape[1]) else np.linalg.inv(A)
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return np.linalg.norm(A, ord=1) * np.linalg.norm(Ainv, ord=1)
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class MultiPortOrthonormalBasis:
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def __init__(self,H,freqs,poles,weights=None,passivity=True,dc_enforce=True,fit_constant=True,fit_proportional=False):
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self.least_squares_condition = None
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self.least_squares_row_condition = None
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self.least_squares_col_condition = None
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self.least_squares_rms_error = None
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self.eigenval_condition = None
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self.eigenval_row_condition = None
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self.eigenval_col_condition = None
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self.eigenval_rms_error = None
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self.Cr = None
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self.dc_tol = 1e-18
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self.dc_enforce = dc_enforce
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self.fit_constant = fit_constant
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self.fit_proportional = fit_proportional
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self.freqs = freqs
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self.H = H
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self.ports = H.shape[1]
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self.s = self.freqs * 2j * np.pi
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self.P = len(poles)
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self.poles = poles
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self.Phi = self.generate_basis(self.s, self.poles)
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self.A = self.matrix_A(self.poles)
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self.B = self.vector_B(self.poles)
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self.C,self.w0,self.e = self.fit_denominator(self.H, weights=weights)
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self.D = self.w0
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self.residuals = self.C / np.sqrt(2 * np.real(-np.array(self.poles)))
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z = np.linalg.eigvals(self.A - self.B @ self.C)
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if passivity:
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self.next_poles = self.passivity_enforce(z)
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else:
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self.next_poles = z
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self.eigenval_condition,\
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self.eigenval_row_condition,\
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self.eigenval_col_condition,\
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self.eigenval_rms_error = self.eigen_metric()
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self.Dt = self.eval_Dt_state_space()
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self.Dt_Dt_1 = np.linalg.norm(self.Dt) / np.linalg.norm(weights) if weights is not None else np.linalg.norm(self.Dt)
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pass
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def eigen_metric(self):
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"""Return condition number and RMS error of eigenvalues of A-BC."""
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z = np.linalg.eigvals(self.A - self.B @ self.C)
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cond = np.linalg.cond(self.A - self.B @ self.C)
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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))
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row_cond = cond_row_inf(self.A - self.B @ self.C)
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col_cond = cond_col_one(self.A - self.B @ self.C)
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return cond,row_cond,col_cond,rms
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def least_squares_metric(self,A,b):
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"""Return condition number and RMS error of least-squares matrix A and rhs b."""
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cond = np.linalg.cond(A)
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rms = np.sqrt(np.mean((A @ np.linalg.pinv(A) @ b - b)**2))
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row_cond = cond_row_inf(A)
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col_cond = cond_col_one(A)
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return cond,row_cond,col_cond,rms
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def passivity_enforce(self,poles):
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"""enforce poles' real parts to be negative"""
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enforced_poles = []
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for pole in poles:
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if pole.real > 0:
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pole = -np.conj(pole)
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enforced_poles.append(pole)
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return enforced_poles
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def eval_Dt_state_space(self):
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"""Return D(s_k)=C(s_k I - A)^(-1)B + D for all k (complex 1D array)."""
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s = 1j * 2*np.pi * np.asarray(self.freqs, float).ravel()
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A = np.asarray(self.A, float); n = A.shape[0]
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B = np.asarray(self.B, float).reshape(n, 1)
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C = np.asarray(self.C, float).reshape(1, n)
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D = self.D
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I = np.eye(n, dtype=float)
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out = np.empty_like(s, dtype=float)
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for k, sk in enumerate(s):
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DS = D + (C @ np.linalg.inv(sk*I - A) @ B)
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out[k] = DS[0, 0]
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return out
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def generate_basis(self,s, poles):
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"""Real basis of (15)-(16); returns Φ(s) and a layout for packing C."""
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def all_pass(s,ap_list):
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res = 1.0 +0.0j
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for ap in ap_list:
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res *= (s - np.conj(ap)) / (s + ap)
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return res
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cols = []
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ap_list = []
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i = 0
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while i < len(poles):
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ap = -poles[i]
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if ap.real < 0:
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raise ValueError("poles must be in the LHP")
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if i+1 < len(poles) and np.isclose(poles[i+1], np.conj(-ap)):
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ap1 = - poles[i+1]
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phi1 = np.sqrt(2 * ap.real) * all_pass(s, ap_list) * ((s - np.abs(ap))/((s + ap)*(s + ap1)))
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phi2 = np.sqrt(2 * ap.real) * all_pass(s, ap_list) * ((s + np.abs(ap))/((s + ap)*(s + ap1)))
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cols += [phi1, phi2]
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i += 2
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ap_list.append(ap)
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ap_list.append(ap1)
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else:
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basis = np.sqrt(2 * ap.real) * all_pass(s, ap_list) * (1/(s + ap))
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cols.append(basis)
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i += 1
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ap_list.append(ap)
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Phi = np.column_stack(cols).astype(np.complex128)
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return Phi
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def matrix_A(self, poles):
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def A_col(p:np.complex128,index:int):
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ap = -p
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if abs(ap.imag) < 1e-14:
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col = []
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for i in range(index):
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col.append(0.0)
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col.append(-ap.real)
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for i in range(len(poles)-index-1):
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col.append(2*(-ap).real)
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return np.array([col], float)
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else:
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col1 = []
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col2 = []
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for i in range(index):
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col1.append(0.0)
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col2.append(0.0)
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col1.append(-ap.real); col2.append(-ap.real - np.abs(ap))
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col1.append(-ap.real + np.abs(ap)); col2.append(-ap.real)
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for i in range(len(poles)-index-2):
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col1.append(2*(-ap).real)
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col2.append(2*(-ap).real)
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return np.array([col1, col2], float)
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i = 0
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cols = []
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while i < len(poles):
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p = poles[i]
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cols.extend(A_col(p,i))
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if i+1 < len(poles) and np.isclose(poles[i+1], np.conj(p)): i += 2
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else: i += 1
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A = np.column_stack(cols).astype(float)
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return A
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def vector_B(self, poles):
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return np.ones((len(poles), 1), float)
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def fit_denominator(self, H, weights=None, d0 = 1.0):
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"""
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Solve formula (70) on the real basis Φ to obtain:
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- d (real) → packs into C for this state's block structure
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- gamma (complex)
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Optional 'weights' (K,) apply row scaling: SK weighting if 1/|D_prev|.
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"""
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K, N = self.Phi.shape
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one = np.ones((K, 1), np.complex128)
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Phi = self.Phi
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dc_tol = 1e-18
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has_dc = self.dc_enforce and self.freqs[0] < dc_tol
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keep = np.ones(K, dtype=bool)
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# SK weighting (applied only to the (73) rows we keep in LS)
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if has_dc:
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# Enforce DC response exactly:
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k0 = int(np.argmin(np.abs(self.freqs)))
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keep[k0] = False
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if self.fit_constant:
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Phi_w = np.hstack([one, Phi])
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index = 0
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M_kp = None
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for i in range(self.ports):
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for j in range(self.ports):
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M0 = np.zeros((K,N*self.ports**2),dtype=complex)
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M0[:,index*N:(index+1)*N] = Phi
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M0 = np.hstack([M0, -(H[:,i,j].reshape(-1,1) * Phi_w)]).reshape((K, -1))[keep,:] # (K, 2N), complex
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index+=1
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M_kp = M0 if M_kp is None else np.vstack([M_kp, M0])
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assert M_kp is not None
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else:
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index = 0
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M_kp = None
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for i in range(self.ports):
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for j in range(self.ports):
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M0 = np.zeros((K,N*self.ports**2),dtype=complex)
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M0[:,index*N:(index+1)*N] = Phi
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M0 = np.hstack([M0, -(H[:,i,j].reshape(-1,1) * Phi)]).reshape((K, -1))[keep,:] # (K, 2N), complex
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index+=1
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M_kp = M0 if M_kp is None else np.vstack([M_kp, M0])
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assert M_kp is not None
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if weights is None:
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weights_kp = np.diag(np.ones(len(self.freqs[keep]) * self.ports**2, np.complex128))
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else:
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weights_kp0 = weights[keep]
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weights0 = []
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for i in range(self.ports **2 ):
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for res in weights_kp0:
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weights0.append(1/res)
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weights_kp = np.diag(np.array(weights0))
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if has_dc:
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M_w_kp = weights_kp @ M_kp
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A_re = np.real(M_w_kp)
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A_im = np.imag(M_w_kp)
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mask = np.ones(K, dtype=bool); mask[k0] = False
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# exact (unweighted) DC rows:
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# A_dc_re = np.real(M_kp).reshape(1, -1)
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# A_dc_im = np.imag(M_kp).reshape(1, -1)
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else:
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M_w_kp = weights_kp @ M_kp
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A_re = np.real(M_w_kp)
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A_im = np.imag(M_w_kp)
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# A_dc_re = A_dc_im = None
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A_blocks = [A_re, A_im]
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if self.fit_constant:
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Hk_sum = []
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for i in range(self.ports):
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Hk_sum.append([])
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for j in range(self.ports):
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Hk_kp0 = H[:,i,j][keep]
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Hk_sum[i].append(np.sum(np.abs(Hk_kp0)**2))
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# Hk_kp = Hk_kp0 if Hk_kp is None else np.hstack([Hk_kp, Hk_kp0])
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K_keep = int(np.count_nonzero(keep))
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A_w0 = []
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b_w0 = []
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# Hk_sum = np.sum(np.abs(Hk_kp)**2)
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for i in range(self.ports):
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for j in range(self.ports):
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beta_ij = float(np.sqrt(Hk_sum[i][j]))
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mean_row = (beta_ij / K_keep) * np.sum(Phi_w[keep, :], axis=0)
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A_w0.append(np.concatenate([np.zeros(N*self.ports**2, float),
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np.real(mean_row).astype(float)]
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).reshape(1, -1))
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b_w0.append(np.array([beta_ij], float))
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b_w0 = np.asarray(b_w0).ravel()
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A_blocks += A_w0
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m = A_re.shape[0] + A_im.shape[0]
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b = np.zeros(m, float)
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b = np.concatenate([b, b_w0])
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else:
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H_kp = None
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for i in range(self.ports):
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for j in range(self.ports):
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H_0 = H[:,i,j][keep]
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H_kp = H_0 if H_kp is None else np.hstack([H_kp, H_0])
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assert H_kp is not None
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H_kp = weights_kp @ H_kp.reshape(-1,1)
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b_re = np.real(d0 * H_kp)
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b_im = np.imag(d0 * H_kp)
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b = np.concatenate([b_re.ravel(), b_im.ravel()]).astype(float)
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# ---- build final stacked-real system ----
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# if A_dc_re is not None:
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# A_blocks += [A_dc_re, A_dc_im]
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# b = np.concatenate([b, np.zeros(2, float)]) # DC rows → 0
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# ---- QR solve for x = [c_H (N); c_w (N+1)] ----
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A = np.vstack(A_blocks).astype(float)
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Q, R = np.linalg.qr(A, mode="reduced")
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if self.fit_constant:
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Q2 = Q[:,Phi.shape[1] * self.ports**2:]
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R22 = R[Phi.shape[1] * self.ports**2:,Phi.shape[1] * self.ports**2:]
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else:
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Q2 = Q[:,Phi.shape[1] * self.ports**2:]
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R22 = R[Phi.shape[1] * self.ports**2:,Phi.shape[1] * self.ports**2:]
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x = np.linalg.solve(R22, Q2.T @ b)
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# diagnostics
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resid = Q2 @ R22 @ x - b
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# self.least_squares_rms_error = float(np.sqrt(np.mean(resid**2)))
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# self.least_squares_condition = float(np.linalg.cond(R))
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self.least_squares_condition,\
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self.least_squares_row_condition,\
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self.least_squares_col_condition,\
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self.least_squares_rms_error = self.least_squares_metric(A, b)
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return self.extract_C_d_e(x,N,d0)
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def extract_C_d_e(self,C,N,d0=1.0):
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a = np.sqrt(2 * np.real(-np.array(self.poles)))
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if self.fit_proportional and self.fit_constant:
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d = C[1]
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e = C[0]
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C = a * C[2:]
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return C.reshape(1, -1), d, e
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elif self.fit_proportional and not self.fit_constant:
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d = 0.0
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e = C[0]
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C = a * C[1:]
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return C.reshape(1, -1), d, e
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elif not self.fit_proportional and self.fit_constant:
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d = C[0]
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e = 0.0
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C = a * C[1:]
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return C.reshape(1, -1), d, e
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else:
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C = a * C
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return C.reshape(1, -1), d0, 0.0
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def non_bias_Cr(self,w0):
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A = np.asarray(self.Phi)
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den = np.diag((w0 + self.Phi @ self.residuals.T).ravel())
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Cr = []
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for i in range(self.ports):
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Cr.append([])
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for j in range(self.ports):
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b = np.asarray(den) @ self.H[:,i,j].reshape(-1,1)
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Cr_ij, residuals, rank, s = np.linalg.lstsq(A, b, rcond=None)
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Cr[i].append(Cr_ij)
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return Cr
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def get_model_responses(self,freqs):
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H = np.zeros((len(freqs),self.ports,self.ports),dtype=complex)
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s = 1j * 2*np.pi * np.asarray(freqs, float).ravel()
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phi = self.generate_basis(s, self.poles)
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den = self.w0 + phi @ self.residuals.T
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if self.Cr is None:
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self.Cr = self.non_bias_Cr(w0=self.w0)
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for i in range(self.ports):
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for j in range(self.ports):
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num = phi @ self.Cr[i][j]
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H[:,i,j] = (num / den).reshape(1,-1)
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return H
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class VFUtils():
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def __init__(self,npoles_cplx,freqs,H,model=MultiPortOrthonormalBasis,iterations:int=5):
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poles = generate_starting_poles(npoles_cplx,beta_min=1e4,beta_max=freqs[-1]*1.1)
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self.model=model(H=H,freqs=freqs,poles=poles)
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self.freqs=freqs
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self.H=H
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self.iterations=iterations
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self.nports = H.shape[1]
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self.least_squares_condition = []
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self.least_squares_row_condition = []
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self.least_squares_col_condition = []
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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")
|
||||
|
||||
|
||||
|
||||
|
||||
176
core/VFManager.py
Normal file
176
core/VFManager.py
Normal file
@@ -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
|
||||
362
core/basis/MultiPortOrthonormalBasis.py
Normal file
362
core/basis/MultiPortOrthonormalBasis.py
Normal file
@@ -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
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -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):
|
||||
@@ -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):
|
||||
@@ -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:
|
||||
909
core/util.py
909
core/util.py
@@ -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
|
||||
@@ -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*。
|
||||
File diff suppressed because it is too large
Load Diff
558
main.py
558
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")
|
||||
155
models/basic.py
155
models/basic.py
@@ -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
|
||||
@@ -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)
|
||||
]
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -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)
|
||||
]
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -1,309 +0,0 @@
|
||||
! Created with skrf 1.8.0 (http://scikit-rf.org).
|
||||
# Hz S RI R 50.0
|
||||
!freq ReS11 ImS11 ReS21 ImS21 ReS12 ImS12 ReS22 ImS22
|
||||
0.0 0.5169316987261682 -0.14305278198989352 0.4988694955145437 0.12530267192181432 0.4988694955145437 0.12530267192181432 0.5263854174928203 -0.14417829506357768
|
||||
1000.0 0.5169316947040424 -0.14305278151191225 0.4988694984093659 0.12530266799393616 0.4988694984093659 0.12530266799393616 0.5263854133767084 -0.14417829453782283
|
||||
10000.0 0.5169316585049104 -0.14305277721008075 0.4988695244627657 0.12530263264303276 0.4988695244627657 0.12530263264303276 0.5263853763317018 -0.14417828980602918
|
||||
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@@ -1,309 +0,0 @@
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! Created with skrf 1.8.0 (http://scikit-rf.org).
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# Hz S RI R 50.0
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146400000000.0 -0.2216026014350534 -0.033269816857179724 0.8272503265293534 -0.6990279093186933 0.8272503265293534 -0.6990279093186933 -0.2196880807812216 0.019593573630156097
|
||||
147200000000.0 -0.22580093800793388 -0.03254336548639425 0.8292516859700565 -0.703684156691762 0.8292516859700565 -0.703684156691762 -0.22394510521148314 0.020602828016014235
|
||||
148000000000.0 -0.22999927458081426 -0.0318169141156088 0.8312530454107596 -0.7083404040648307 0.8312530454107596 -0.7083404040648307 -0.22820212964174458 0.021612082401872346
|
||||
148800000000.0 -0.23419761115369475 -0.031090462744823327 0.8332544048514626 -0.7129966514378994 0.8332544048514626 -0.7129966514378994 -0.23245915407200612 0.022621336787730456
|
||||
149600000000.0 -0.23839594772657513 -0.03036401137403788 0.8352557642921656 -0.717652898810968 0.8352557642921656 -0.717652898810968 -0.23671617850226767 0.023630591173588594
|
||||
150400000000.0 -0.24259428429945573 -0.029637560003252406 0.8372571237328688 -0.7223091461840367 0.8372571237328688 -0.7223091461840367 -0.24097320293252933 0.024639845559446732
|
||||
151200000000.0 -0.2467926208723361 -0.02891110863246693 0.8392584831735719 -0.7269653935571054 0.8392584831735719 -0.7269653935571054 -0.24523022736279076 0.025649099945304843
|
||||
152000000000.0 -0.2509909574452166 -0.028184657261681484 0.841259842614275 -0.7316216409301741 0.841259842614275 -0.7316216409301741 -0.2494872517930523 0.02665835433116298
|
||||
152800000000.0 -0.2551892940180971 -0.02745820589089601 0.8432612020549781 -0.7362778883032428 0.8432612020549781 -0.7362778883032428 -0.25374427622331386 0.02766760871702109
|
||||
153600000000.0 -0.25938763059097747 -0.026731754520110562 0.8452625614956811 -0.7409341356763115 0.8452625614956811 -0.7409341356763115 -0.2580013006535753 0.02867686310287923
|
||||
154400000000.0 -0.26358596716385796 -0.026005303149325087 0.8472639209363841 -0.7455903830493802 0.8472639209363841 -0.7455903830493802 -0.26225832508383684 0.02968611748873734
|
||||
155200000000.0 -0.26778430373673834 -0.02527885177853964 0.8492652803770873 -0.7502466304224489 0.8492652803770873 -0.7502466304224489 -0.2665153495140984 0.03069537187459545
|
||||
156000000000.0 -0.27198264030961894 -0.024552400407754138 0.8512666398177904 -0.7549028777955176 0.8512666398177904 -0.7549028777955176 -0.27077237394436005 0.03170462626045362
|
||||
156800000000.0 -0.2761809768824993 -0.02382594903696869 0.8532679992584935 -0.7595591251685863 0.8532679992584935 -0.7595591251685863 -0.2750293983746215 0.03271388064631173
|
||||
157600000000.0 -0.2803793134553798 -0.023099497666183216 0.8552693586991966 -0.764215372541655 0.8552693586991966 -0.764215372541655 -0.279286422804883 0.03372313503216984
|
||||
158400000000.0 -0.2845776500282602 -0.02237304629539777 0.8572707181398996 -0.7688716199147236 0.8572707181398996 -0.7688716199147236 -0.2835434472351446 0.034732389418027976
|
||||
159200000000.0 -0.2887759866011407 -0.021646594924612295 0.8592720775806026 -0.7735278672877923 0.8592720775806026 -0.7735278672877923 -0.287800471665406 0.035741643803886086
|
||||
160000000000.0 -0.29297432317402117 -0.020920143553826848 0.8612734370213058 -0.778184114660861 0.8612734370213058 -0.778184114660861 -0.29205749609566756 0.036750898189744224
|
||||
160800000000.0 -0.29717265974690155 -0.020193692183041373 0.8632747964620089 -0.7828403620339297 0.8632747964620089 -0.7828403620339297 -0.2963145205259291 0.037760152575602335
|
||||
161600000000.0 -0.30137099631978215 -0.019467240812255898 0.865276155902712 -0.7874966094069984 0.865276155902712 -0.7874966094069984 -0.30057154495619076 0.03876940696146047
|
||||
162400000000.0 -0.3055693328926625 -0.01874078944147045 0.8672775153434151 -0.7921528567800671 0.8672775153434151 -0.7921528567800671 -0.3048285693864522 0.03977866134731861
|
||||
163200000000.0 -0.309767669465543 -0.018014338070684977 0.8692788747841181 -0.7968091041531358 0.8692788747841181 -0.7968091041531358 -0.30908559381671374 0.04078791573317672
|
||||
164000000000.0 -0.3139660060384234 -0.01728788669989953 0.8712802342248211 -0.8014653515262045 0.8712802342248211 -0.8014653515262045 -0.3133426182469753 0.04179717011903483
|
||||
164800000000.0 -0.3181643426113039 -0.016561435329114055 0.8732815936655243 -0.8061215988992732 0.8732815936655243 -0.8061215988992732 -0.31759964267723684 0.04280642450489297
|
||||
165600000000.0 -0.32236267918418426 -0.015834983958328608 0.8752829531062274 -0.8107778462723418 0.8752829531062274 -0.8107778462723418 -0.3218566671074983 0.04381567889075108
|
||||
166400000000.0 -0.32656101575706475 -0.015108532587543133 0.8772843125469305 -0.8154340936454103 0.8772843125469305 -0.8154340936454103 -0.3261136915377598 0.04482493327660922
|
||||
167200000000.0 -0.33075935232994524 -0.014382081216757686 0.8792856719876335 -0.820090341018479 0.8792856719876335 -0.820090341018479 -0.33037071596802137 0.04583418766246733
|
||||
168000000000.0 -0.33495768890282573 -0.013655629845972211 0.8812870314283366 -0.8247465883915479 0.8812870314283366 -0.8247465883915479 -0.3346277403982829 0.04684344204832547
|
||||
168800000000.0 -0.3391560254757062 -0.012929178475186737 0.8832883908690397 -0.8294028357646166 0.8832883908690397 -0.8294028357646166 -0.33888476482854446 0.047852696434183606
|
||||
169600000000.0 -0.3433543620485866 -0.01220272710440129 0.8852897503097428 -0.8340590831376853 0.8852897503097428 -0.8340590831376853 -0.343141789258806 0.048861950820041716
|
||||
170400000000.0 -0.3475526986214671 -0.011476275733615815 0.8872911097504459 -0.8387153305107538 0.8872911097504459 -0.8387153305107538 -0.34739881368906755 0.04987120520589983
|
||||
171200000000.0 -0.35175103519434747 -0.010749824362830368 0.889292469191149 -0.8433715778838224 0.889292469191149 -0.8433715778838224 -0.351655838119329 0.050880459591757965
|
||||
172000000000.0 -0.35594937176722796 -0.010023372992044893 0.891293828631852 -0.8480278252568911 0.891293828631852 -0.8480278252568911 -0.35591286254959054 0.051889713977616075
|
||||
172800000000.0 -0.36014770834010834 -0.009296921621259446 0.893295188072555 -0.8526840726299598 0.893295188072555 -0.8526840726299598 -0.3601698869798521 0.052898968363474214
|
||||
173600000000.0 -0.36434604491298894 -0.008570470250473944 0.8952965475132582 -0.8573403200030285 0.8952965475132582 -0.8573403200030285 -0.36442691141011363 0.05390822274933235
|
||||
174400000000.0 -0.3685443814858693 -0.007844018879688497 0.8972979069539613 -0.8619965673760972 0.8972979069539613 -0.8619965673760972 -0.3686839358403752 0.05491747713519046
|
||||
175200000000.0 -0.3727427180587498 -0.007117567508903022 0.8992992663946644 -0.8666528147491659 0.8992992663946644 -0.8666528147491659 -0.3729409602706367 0.0559267315210486
|
||||
176000000000.0 -0.3769410546316303 -0.006391116138117575 0.9013006258353674 -0.8713090621222346 0.9013006258353674 -0.8713090621222346 -0.37719798470089827 0.05693598590690671
|
||||
176800000000.0 -0.3811393912045107 -0.0056646647673321004 0.9033019852760705 -0.8759653094953033 0.9033019852760705 -0.8759653094953033 -0.3814550091311597 0.05794524029276482
|
||||
177600000000.0 -0.38533772777739117 -0.0049382133965466535 0.9053033447167735 -0.880621556868372 0.9053033447167735 -0.880621556868372 -0.38571203356142125 0.05895449467862296
|
||||
178400000000.0 -0.38953606435027155 -0.004211762025761179 0.9073047041574767 -0.8852778042414406 0.9073047041574767 -0.8852778042414406 -0.3899690579916828 0.05996374906448107
|
||||
179200000000.0 -0.39373440092315204 -0.0034853106549757318 0.9093060635981798 -0.8899340516145093 0.9093060635981798 -0.8899340516145093 -0.39422608242194435 0.06097300345033921
|
||||
180000000000.0 -0.3979327374960324 -0.002758859284190257 0.9113074230388828 -0.894590298987578 0.9113074230388828 -0.894590298987578 -0.3984831068522058 0.06198225783619732
|
||||
180800000000.0 -0.4021310740689129 -0.00203240791340481 0.9133087824795859 -0.8992465463606467 0.9133087824795859 -0.8992465463606467 -0.40274013128246733 0.06299151222205543
|
||||
181600000000.0 -0.4063294106417933 -0.0013059565426193354 0.9153101419202889 -0.9039027937337152 0.9153101419202889 -0.9039027937337152 -0.4069971557127289 0.06400076660791357
|
||||
182400000000.0 -0.410527747214674 -0.0005795051718338606 0.9173115013609922 -0.9085590411067841 0.9173115013609922 -0.9085590411067841 -0.41125418014299053 0.06501002099377173
|
||||
183200000000.0 -0.4147260837875544 0.0001469461989516141 0.9193128608016952 -0.9132152884798528 0.9193128608016952 -0.9132152884798528 -0.4155112045732521 0.06601927537962984
|
||||
184000000000.0 -0.41892442036043487 0.0008733975697370611 0.9213142202423983 -0.9178715358529215 0.9213142202423983 -0.9178715358529215 -0.4197682290035136 0.06702852976548798
|
||||
184800000000.0 -0.42312275693331536 0.0015998489405225358 0.9233155796831014 -0.9225277832259902 0.9233155796831014 -0.9225277832259902 -0.4240252534337752 0.06803778415134609
|
||||
185600000000.0 -0.42732109350619574 0.0023263003113079828 0.9253169391238044 -0.9271840305990589 0.9253169391238044 -0.9271840305990589 -0.4282822778640366 0.0690470385372042
|
||||
186400000000.0 -0.4315194300790762 0.0030527516820934575 0.9273182985645074 -0.9318402779721275 0.9273182985645074 -0.9318402779721275 -0.43253930229429816 0.07005629292306234
|
||||
187200000000.0 -0.4357177666519566 0.0037792030528789045 0.9293196580052105 -0.9364965253451962 0.9293196580052105 -0.9364965253451962 -0.4367963267245597 0.07106554730892045
|
||||
188000000000.0 -0.4399161032248371 0.004505654423664379 0.9313210174459137 -0.9411527727182649 0.9313210174459137 -0.9411527727182649 -0.44105335115482114 0.07207480169477859
|
||||
188800000000.0 -0.4441144397977175 0.005232105794449826 0.9333223768866168 -0.9458090200913336 0.9333223768866168 -0.9458090200913336 -0.4453103755850827 0.0730840560806367
|
||||
189600000000.0 -0.44831277637059797 0.005958557165235301 0.9353237363273198 -0.9504652674644023 0.9353237363273198 -0.9504652674644023 -0.44956740001534423 0.07409331046649481
|
||||
190400000000.0 -0.45251111294347846 0.006685008536020748 0.9373250957680228 -0.9551215148374708 0.9373250957680228 -0.9551215148374708 -0.4538244244456058 0.07510256485235295
|
||||
191200000000.0 -0.45670944951635883 0.007411459906806223 0.9393264552087259 -0.9597777622105395 0.9393264552087259 -0.9597777622105395 -0.4580814488758673 0.07611181923821106
|
||||
192000000000.0 -0.4609077860892392 0.00813791127759167 0.941327814649429 -0.9644340095836081 0.941327814649429 -0.9644340095836081 -0.46233847330612887 0.0771210736240692
|
||||
192800000000.0 -0.4651061226621198 0.008864362648377144 0.9433291740901322 -0.9690902569566768 0.9433291740901322 -0.9690902569566768 -0.4665954977363902 0.07813032800992731
|
||||
193600000000.0 -0.4693044592350002 0.009590814019162591 0.9453305335308352 -0.9737465043297455 0.9453305335308352 -0.9737465043297455 -0.47085252216665174 0.07913958239578542
|
||||
194400000000.0 -0.4735027958078808 0.010317265389948094 0.9473318929715384 -0.9784027517028144 0.9473318929715384 -0.9784027517028144 -0.4751095465969135 0.08014883678164358
|
||||
195200000000.0 -0.4777011323807612 0.011043716760733568 0.9493332524122414 -0.9830589990758831 0.9493332524122414 -0.9830589990758831 -0.47936657102717506 0.08115809116750172
|
||||
196000000000.0 -0.4818994689536418 0.011770168131519015 0.9513346118529444 -0.9877152464489518 0.9513346118529444 -0.9877152464489518 -0.4836235954574366 0.08216734555335983
|
||||
196800000000.0 -0.48609780552652215 0.01249661950230449 0.9533359712936476 -0.9923714938220205 0.9533359712936476 -0.9923714938220205 -0.48788061988769815 0.08317659993921797
|
||||
197600000000.0 -0.49029614209940253 0.013223070873089937 0.9553373307343507 -0.9970277411950892 0.9553373307343507 -0.9970277411950892 -0.4921376443179597 0.08418585432507608
|
||||
198400000000.0 -0.49449447867228313 0.013949522243875412 0.9573386901750538 -1.0016839885681579 0.9573386901750538 -1.0016839885681579 -0.496394668748221 0.08519510871093419
|
||||
199200000000.0 -0.4986928152451635 0.014675973614660859 0.9593400496157568 -1.0063402359412263 0.9593400496157568 -1.0063402359412263 -0.5006516931784826 0.08620436309679233
|
||||
200000000000.0 -0.5028911518180439 0.015402424985446334 0.9613414090564599 -1.010996483314295 0.9613414090564599 -1.010996483314295 -0.5049087176087441 0.08721361748265047
|
||||
@@ -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
|
||||
13
sweep.py
13
sweep.py
@@ -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")
|
||||
55
utils.py
55
utils.py
@@ -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
|
||||
Reference in New Issue
Block a user