diff --git a/core/orthonormal_basis.py b/core/orthonormal_basis.py
new file mode 100644
index 0000000..096dea2
--- /dev/null
+++ b/core/orthonormal_basis.py
@@ -0,0 +1,174 @@
+import numpy as np
+
+# ------------------------------------------------------------
+# 按论文公式 (9)(10)(11) 生成 Muntz–Laguerre 正交有理基 (解析形式)：
+#
+# 给定稳定极点集合 {p_k} (Re(p_k)<0)。论文记法中使用 -a_k，其中 Re(a_k)>0。
+# 对应关系：p_k = -a_k  ⇒  a_k = -p_k,  Re(a_k)= -Re(p_k) >0
+#
+# 连续内积意义下（沿 jω 轴积分）这些 φ_k 解析正交。离散频率采样后数值上
+# 可能偏离，可再用加权 QR 做数值再正交（可选）。
+#
+# 公式在“稳定极点 p 表达”下的改写：
+#   (原) 实极点:  φ_p(s) = sqrt(2 Re(a_p)) / (s + a_p) * Π (s - a_i^*)/(s + a_i)
+#   变换 a_p = -p ⇒ Re(a_p)= -Re(p) = σ >0 且 (s + a_p) = (s - p)
+#   且乘积 (s - a_i^*)/(s + a_i) = (s + p_i^*)/(s - p_i)
+#   ⇒ φ_p(s) = sqrt(-2 Re(p)) / (s - p) * Π_{i<p} (s + p_i^*)/(s - p_i)
+#
+# 复对 (p, p*)，取 imag(p)>0 的 p 作为首：
+#   (原) φ_p   = sqrt(2 Re(a_p)) (s - |a_p|)/[(s + a_p)(s + a_p^*)] * Π(...)
+#   (原) φ_{p+1}= sqrt(2 Re(a_p)) (s + |a_p|)/[(s + a_p)(s + a_p^*)] * Π(...)
+#   代入 a_p=-p：
+#       Re(a_p)= -Re(p)=σ>0,  (s + a_p) = (s - p), (s + a_p^*)=(s - p^*)
+#       |a_p| = |p|
+#       乘积同上 ⇒ Π_{i<p} (s + p_i^*)/(s - p_i)
+#
+#   ⇒ φ_p(s)   = sqrt(-2 Re(p)) (s - |p|)/[(s - p)(s - p^*)] * Π_{i<p} (s + p_i^*)/(s - p_i)
+#      φ_{p^*}(s)= sqrt(-2 Re(p)) (s + |p|)/[(s - p)(s - p^*)] * Π_{i<p} (s + p_i^*)/(s - p_i)
+#
+# product 递推维护：prod_k = Π_{i≤k} (s + p_i^*)/(s - p_i)
+# 对复对要顺序乘两次（p 与 p*）。
+# ------------------------------------------------------------
+
+
+def generate_laguerre_basis(poles:list|np.ndarray, s: np.ndarray):
+    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")
+            pc = poles[i + 1]
+            if not np.isclose(pc, np.conj(poles[i])):
+                raise ValueError("复极点未按 (p, p*) 顺序排列 (正虚部在前)")
+            sigma = -np.real(poles[i])           # >0
+            scale = np.sqrt(2 * sigma)
+            r = np.abs(poles[i])
+            denom = (s - poles[i]) * (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 - poles[i])
+            product = product * (s + poles[i])  / (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(poles[i]) and np.imag(poles[i]) < 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(poles[i])
+        if sigma <= 0:
+            raise ValueError("实极点实部应为负 (稳定)。")
+        scale = np.sqrt(2 * sigma)
+        phi = scale / (s - poles[i]) * product
+        # 更新乘积
+        product = product * (s + poles[i]) / (s - poles[i])
+        i += 1
+        basis[i + 1] = phi
+    return basis
+
+
+
+
+
+# class MuntzLaguerreIterator:
+#     def __init__(self, s: np.ndarray, stable_poles: list | np.ndarray):
+#         """
+#         s: 复频率数组 (Nf,), s = j 2π f
+#         stable_poles: 稳定极点列表 (Re<0). 复共轭对要求正虚部在前 (p, p*).
+#         """
+#         self.s = np.asarray(s, dtype=complex)
+#         self.poles = list(stable_poles)
+#         self.N = len(self.poles)
+#         self.k = 0
+#         # 初始化乘积 Π_{i<p} (s + p_i^*)/(s - p_i)
+#         self.product = np.ones_like(self.s, dtype=complex)
+
+#     def __iter__(self):
+#         return self
+
+#     def __next__(self):
+#         if self.k >= self.N:
+#             raise StopIteration
+
+#         p = self.poles[self.k]
+#         if np.real(p) >= 0:
+#             raise ValueError(f"极点必须在左半平面: {p}")
+
+#         # 复对首 (正虚部)
+#         if np.iscomplex(p) and np.imag(p) > 0:
+#             if self.k + 1 >= self.N:
+#                 raise ValueError("复极点缺少共轭")
+#             pc = self.poles[self.k + 1]
+#             if not np.isclose(pc, np.conj(p)):
+#                 raise ValueError("复极点未按 (p, p*) 顺序排列 (正虚部在前)")
+#             sigma = -np.real(p)           # >0
+#             scale = np.sqrt(2 * sigma)
+#             r = np.abs(p)
+#             denom = (self.s - p) * (self.s - pc)
+
+#             # 两个基函数
+#             phi_p  = scale * (self.s - r) / denom * self.product
+#             phi_pc = scale * (self.s + r) / denom * self.product
+
+#             # product 先乘 (s + p^*)/(s - p)，再乘 (s + p)/(s - p^*)
+#             self.product = self.product * (self.s + pc) / (self.s - p)
+#             self.product = self.product * (self.s + p)  / (self.s - pc)
+
+#             self.k += 2
+#             return [phi_p, phi_pc]
+
+#         # 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(p) and np.imag(p) < 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(p)
+#         if sigma <= 0:
+#             raise ValueError("实极点实部应为负 (稳定)。")
+#         scale = np.sqrt(2 * sigma)
+#         phi = scale / (self.s - p) * self.product
+#         # 更新乘积
+#         self.product = self.product * (self.s + p) / (self.s - p)
+
+#         self.k += 1
+#         return [phi]
+    
+# def generate_muntz_laguerre_basis(s: np.ndarray, init_poles: list | np.ndarray):
+#     """
+#     生成完整基函数列表: [φ_0=1, φ_1, φ_2, ...]
+#     """
+#     basis = [np.ones_like(s, dtype=complex)]
+#     for block in MuntzLaguerreIterator(s, init_poles):
+#         basis.extend(block)
+#     return basis
+
+# if __name__ == "__main__":
+#     # 示例稳定极点 (复对正虚部在前)
+#     stable_poles = [
+#         -0.8e9,
+#         -1.0e9 + 2.5e9j,
+#         -1.0e9 - 2.5e9j,
+#         -2.2e9
+#     ]
+#     freqs = np.linspace(1e8, 8e9, 400)
+#     s = 1j * 2 * np.pi * freqs
+
+#     basis = generate_muntz_laguerre_basis(s, stable_poles)
+#     print(f"生成 {len(basis)} 个基函数,{basis}")
\ No newline at end of file
diff --git a/core/sk_iter.py b/core/sk_iter.py
index 0cda0f1..f230398 100644
--- a/core/sk_iter.py
+++ b/core/sk_iter.py
@@ -1,250 +1,326 @@
 import numpy as np
 from dataclasses import dataclass
-from typing import List, Sequence, Tuple, Optional
+from typing import List, Dict, Tuple
 
-@dataclass
-class OPVFConfig:
-    n_poles: int
-    max_iter: int = 5
-    tol: float = 1e-3
-    add_constraint: bool = True
-    real_split: bool = True
-    lambda_reg: float = 0.0
-    verbose: bool = True
-
-# ---------- 参数正交基 ----------
-class ParamOrthoBasis:
-    def __init__(self, params: np.ndarray, total_degree: int):
-        # params: (Ns, d)
-        self.mean = params.mean(0)
-        self.std = params.std(0) + 1e-15
-        self.exps = self._gen_exps(params.shape[1], total_degree)
-        M = self._build_monomial_matrix(params)  # (Ns,M)
-        Q,R = np.linalg.qr(M)
-        self.Q = Q
-        self.R = R
-    def _gen_exps(self, dim, D):
-        exps=[]
-        def rec(cur, i, rem):
-            if i==dim:
-                exps.append(tuple(cur)); return
-            for k in range(rem+1):
-                cur.append(k); rec(cur, i+1, rem-k); cur.pop()
-        rec([],0,D)
-        return exps
-    def _build_monomial_matrix(self, params):
-        X = (params - self.mean)/self.std
-        Ns = X.shape[0]
-        M = np.zeros((Ns, len(self.exps)))
-        for idx,e in enumerate(self.exps):
-            v = np.ones(Ns)
-            for j,p in enumerate(e):
-                if p: v *= X[:,j]**p
-            M[:,idx]=v
-        return M
-    def eval(self, g: np.ndarray) -> np.ndarray:
-        x = (g - self.mean)/self.std
-        mono=[]
-        for e in self.exps:
-            val=1.0
-            for j,p in enumerate(e):
-                if p: val*= x[j]**p
-            mono.append(val)
-        mono=np.array(mono)
-        # φ = mono @ R^{-1}
-        return mono @ np.linalg.inv(self.R)
-
-# ---------- 频率正交(有理)基 ----------
-class RationalFreqBasis:
-    def __init__(self, freqs: np.ndarray, poles: np.ndarray):
-        # 构造原始矩阵 G: 列0 常数 1, 后面 1/(s-a_k)
-        s = 1j*2*np.pi*freqs
-        cols=[np.ones_like(s)]
-        for a in poles:
-            cols.append(1.0/(s - a))
-        G = np.vstack(cols).T  # (Nf, K+1)
-        # 加权可加 w_f，这里统一 1
-        Q, R = np.linalg.qr(G)
-        self.Q = Q            # Φ(f)  (Nf, K+1) 频率正交基取样
-        self.R = R            # 变换: G = Q R
-        self.freqs = freqs
-    def eval(self) -> np.ndarray:
-        return self.Q   # 直接返回已正交基取样 (Nf, K+1)
-
-# ---------- 主模型 ----------
-class OrthonormalParametricVF:
+# ---------- 1. 生成初始极点（按论文  Re(-a_p) 小、Im 线性分布） ----------
+def generate_starting_poles(n_pairs: int, beta_min: float, beta_max: float, alpha_scale: float = 0.01):
     """
-    H(s, μ) = N(s,μ)/D(s,μ)
-    N(s,μ)= Σ_k c_k(μ) φ_k(s);  D(s,μ)= Σ_k ĉ_k(μ) φ_k(s)
-    其中 φ_k(s) 是频率正交有理基; c_k(μ), ĉ_k(μ) 在参数正交基上展开:
-    c_k(μ)= Σ_q C[k,q] ψ_q(μ);  ĉ_k(μ)= Σ_q Ctilde[k,q] ψ_q(μ)
+    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)
     """
-    def __init__(self, cfg: OPVFConfig, freq_basis: RationalFreqBasis, param_basis: ParamOrthoBasis):
-        self.cfg = cfg
-        self.fb = freq_basis
-        self.pb = param_basis
-        Kp1 = self.fb.Q.shape[1]
-        Qp = self.pb.Q.shape[1]
-        self.C = np.zeros((Kp1, Qp), dtype=complex)        # 分子
-        self.Ct = np.zeros((Kp1, Qp), dtype=complex)       # 分母
-        # 初始化分母：ĉ_0 ≈ 1，其余 0
-        self.Ct[0,0] = 1.0
+    betas = 2*np.pi*np.linspace(beta_min, beta_max, n_pairs)
+    poles = []
+    for b in betas:
+        alpha = alpha_scale * b
+        p = -alpha + 1j * b
+        poles += [p, np.conj(p)]
+    print(f"生成 {len(poles)} 个初始极点 (复对) {poles}]")
+    return poles
 
-    def _assemble_phi_param(self, params: np.ndarray) -> np.ndarray:
-        # 返回 (Ns, Qp)
-        return self.pb.Q
+# ---------- 2. Muntz–Laguerre 频率基 (与你现有 orthonormal_basis 中一致的核心) ----------
+def muntz_laguerre_basis(freqs_hz: np.ndarray, stable_poles: List[complex]) -> np.ndarray:
+    """
+    返回 Φ_ml  (Nf, P)  列顺序: φ_0=1, 后续依次 (单极点 / 复对两列)
+    """
+    s = 1j * 2 * np.pi * freqs_hz
+    basis = [np.ones_like(s, dtype=complex)]
+    product = np.ones_like(s, dtype=complex)
+    k = 0
+    N = len(stable_poles)
+    while k < N:
+        p = stable_poles[k]
+        if np.real(p) >= 0:
+            raise ValueError("极点需在左半平面")
+        if np.imag(p) > 0:  # 复对首
+            if k + 1 >= N or not np.isclose(stable_poles[k+1], np.conj(p)):
+                raise ValueError("复对未配对")
+            pc = stable_poles[k+1]
+            sigma = -np.real(p)
+            scale = np.sqrt(2 * sigma)
+            r = np.abs(p)
+            denom = (s - p) * (s - pc)
+            phi_p  = scale * (s - r) / denom * product
+            phi_pc = scale * (s + r) / denom * product
+            basis.extend([phi_p, phi_pc])
+            # update product
+            product = product * (s + pc)/(s - p) * (s + p)/(s - pc)
+            k += 2
+        elif np.imag(p) < 0:
+            raise ValueError("负虚部复极点必须跟在正虚部之后")
+        else:  # 实极点
+            sigma = -np.real(p)
+            scale = np.sqrt(2 * sigma)
+            phi = scale / (s - p) * product
+            basis.append(phi)
+            product = product * (s + p)/(s - p)
+            k += 1
+    return np.column_stack(basis)  # (Nf, P)
 
-    def fit(self, H_samples: List[np.ndarray], params: np.ndarray):
-        """
-        H_samples: list 长度 Ns, 每个 (Nf,) 复
-        params: (Ns,d)
-        """
-        Φf = self.fb.eval()           # (Nf, K+1)
-        Ns = len(H_samples)
-        Nf, Kp1 = Φf.shape
-        Φμ = self._assemble_phi_param(params)  # (Ns, Qp)
-        Qp = Φμ.shape[1]
+# ---------- 3. 原始部分分式基 ----------
+def raw_partial_fraction_basis(freqs_hz: np.ndarray, stable_poles: List[complex]) -> np.ndarray:
+    s = 1j * 2 * np.pi * freqs_hz
+    cols = [np.ones_like(s, dtype=complex)]
+    for p in stable_poles:
+        cols.append(1.0 / (s - p))
+    return np.column_stack(cols)  # (Nf, P)
 
-        # t=0: 只解 C (式3, D=1)
-        self._solve_t0(H_samples, Φf, Φμ)
+# ---------- 4. 变换矩阵 Φ_ml T = G_raw ----------
+def compute_transform_matrix(Phi_ml: np.ndarray, G_raw: np.ndarray, weights: np.ndarray | None = None) -> np.ndarray:
+    # 加权最小二乘 T = (Φ^H W Φ)^{-1} Φ^H W G
+    if weights is None:
+        WPhi = Phi_ml
+        WG = G_raw
+        M = Phi_ml.conj().T @ WPhi
+        RHS = Phi_ml.conj().T @ WG
+    else:
+        w = weights[:, None]
+        M = Phi_ml.conj().T @ (w * Phi_ml)
+        RHS = Phi_ml.conj().T @ (w * G_raw)
+    T = np.linalg.solve(M, RHS)
+    return T  # (P,P),  Φ T = G
 
-        D_prev = self._eval_D(Φf, Φμ)  # (Nf, Ns)
+# ---------- 5. 参数多项式正交基 ----------
+def generate_param_basis(param_matrix: np.ndarray, total_degree: int):
+    """
+    param_matrix: (Ns, d)
+    返回:
+        Qμ: (Ns, V) 正交列
+        Rμ: (V, V) 上三角
+        exps: 多指数列表
+    """
+    X = param_matrix
+    Ns, d = X.shape
+    # 生成多指数
+    exps=[]
+    def rec(cur,i,rem):
+        if i==d:
+            exps.append(tuple(cur)); return
+        for k in range(rem+1):
+            cur.append(k); rec(cur,i+1,rem-k); cur.pop()
+    rec([],0,total_degree)
+    V = len(exps)
+    M = np.zeros((Ns, V))
+    for idx,e in enumerate(exps):
+        v = np.ones(Ns)
+        for j,p in enumerate(e):
+            if p: v *= X[:,j]**p
+        M[:,idx]=v
+    Q,R = np.linalg.qr(M)
+    return Q,R,exps
 
-        for it in range(1, self.cfg.max_iter+1):
-            A, b = self._build_iter_system(H_samples, Φf, Φμ, D_prev)
-            if self.cfg.lambda_reg>0:
-                lam = self.cfg.lambda_reg
-                A = np.vstack([A, lam*np.eye(A.shape[1])])
-                b = np.concatenate([b, np.zeros(A.shape[1])])
-            x, *_ = np.linalg.lstsq(A, b, rcond=None)
-            self._unpack_iter_solution(x, Kp1, Qp)
-            D_new = self._eval_D(Φf, Φμ)
-            rel = np.max(np.abs(D_new-D_prev)/(np.abs(D_prev)+1e-12))
-            if self.cfg.verbose:
-                print(f"[OPVF-Iter {it}] rel_change={rel:.3e}")
-            D_prev = D_new
-            if rel < self.cfg.tol:
-                if self.cfg.verbose:
-                    print(f"[OPVF] converged at {it}")
-                break
-        return self
+# ---------- 6. t=0 构建设计矩阵并解 C ----------
+def fit_t0(H_list: List[np.ndarray], Phi_f: np.ndarray, Qμ: np.ndarray) -> np.ndarray:
+    """
+    H_list: 长度 Ns, 每项 (Nf,) 复数
+    Phi_f: (Nf, P)
+    Qμ:    (Ns, V)
+    返回: C (P,V)
+    """
+    Ns = len(H_list); Nf,P = Phi_f.shape; V = Qμ.shape[1]
+    cols = P*V
+    A = np.zeros((Ns*Nf, cols), dtype=complex)
+    b = np.zeros(Ns*Nf, dtype=complex)
+    r=0
+    for n,Hn in enumerate(H_list):
+        blk = np.einsum('fp,v->fpv', Phi_f, Qμ[n]).reshape(Nf, cols)
+        A[r:r+Nf,:]=blk
+        b[r:r+Nf]=Hn
+        r+=Nf
+    x, *_ = np.linalg.lstsq(A, b, rcond=None)
+    C = x.reshape(P, V)
+    return C
 
-    def _solve_t0(self, H_samples, Φf, Φμ):
-        Ns = len(H_samples); Nf,Kp1 = Φf.shape; Qp = Φμ.shape[1]
-        # 设计矩阵行数 Ns*Nf；未知数 Kp1*Qp
-        cols = Kp1*Qp
-        A = np.zeros((Ns*Nf, cols), dtype=complex)
-        b = np.zeros(Ns*Nf, dtype=complex)
-        r = 0
-        for n,Hn in enumerate(H_samples):
-            phiμ = Φμ[n]  # (Qp,)
-            # Φf (Nf,Kp1) 外积 phiμ -> (Nf, Kp1*Qp)
-            blk = np.einsum('fk,q->fkq', Φf, phiμ).reshape(Nf, cols)
-            A[r:r+Nf,:] = blk
-            b[r:r+Nf] = Hn
-            r += Nf
-        x, *_ = np.linalg.lstsq(A, b, rcond=None)
-        self.C = x.reshape(Kp1, Qp)
-        # 分母保持初始 (ĉ_0=1)
-
-    def _build_iter_system(self, H_samples, Φf, Φμ, D_prev):
-        Ns = len(H_samples); Nf,Kp1 = Φf.shape; Qp=Φμ.shape[1]
-        # 未知：C (Kp1*Qp) + Ct (Kp1*Qp) 但固定 Ct[0,0]=1，可去掉该变量
-        mask_fix = np.zeros((Kp1,Qp), dtype=bool)
-        mask_fix[0,0]=True
-        idx_map={}
+# ---------- 7. t=1 (首轮 SK) 解 C, Ct ----------
+def fit_t1_SK(H_list: List[np.ndarray], Phi_f: np.ndarray, Qμ: np.ndarray,
+              C_prev: np.ndarray, max_iter: int =1, tol: float =1e-3):
+    """
+    只做一轮 (或少量) SK，初始 D=1。
+    返回: C_new, Ct_new
+    """
+    Ns = len(H_list); Nf,P = Phi_f.shape; V = Qμ.shape[1]
+    # 初始化分母系数 Ct:  Ct[0,0]=1
+    Ct = np.zeros((P,V), dtype=complex)
+    Ct[0,0]=1.0
+    C = C_prev.copy()
+    for it in range(max_iter):
+        # 构建线性系统: (N - D*H)=0
+        # 未知顺序: C(:), Ct(:) 去掉固定 Ct[0,0]
+        mask_fix = np.zeros((P,V), dtype=bool); mask_fix[0,0]=True
+        col_map={}
         col=0
-        for i in range(Kp1):
-            for j in range(Qp):
-                idx_map[('C',i,j)] = col; col+=1
-        for i in range(Kp1):
-            for j in range(Qp):
+        for i in range(P):
+            for j in range(V):
+                col_map[('C',i,j)]=col; col+=1
+        for i in range(P):
+            for j in range(V):
                 if mask_fix[i,j]: continue
-                idx_map[('Ct',i,j)] = col; col+=1
-        cols = col
-        rows = Ns*Nf
-        A = np.zeros((rows, cols), dtype=complex)
-        b = np.zeros(rows, dtype=complex)
+                col_map[('Ct',i,j)]=col; col+=1
+        A = np.zeros((Ns*Nf, col), dtype=complex)
+        b = np.zeros(Ns*Nf, dtype=complex)
         r=0
-        for n,Hn in enumerate(H_samples):
-            phiμ = Φμ[n]
-            Dp = D_prev[:,n]  # (Nf,)
-            invDp = 1.0/(Dp + 1e-12)
-            # 分子块: (Φf φμ^T) / D_prev
-            Num_blk = np.einsum('fk,q->fkq', Φf, phiμ).reshape(Nf, Kp1*Qp)
-            Num_blk = (Num_blk.T * invDp).T
+        # 评价当前 D_prev= Σ Ct φ ψ
+        for n,Hn in enumerate(H_list):
+            # 分子块 (Φ_f ⊗ ψ_n)
+            blk = np.einsum('fp,v->fpv', Phi_f, Qμ[n]).reshape(Nf, P*V)
             # 填 C 部分
-            for i in range(Kp1):
-                for j in range(Qp):
-                    A[r:r+Nf, idx_map[('C',i,j)]] = Num_blk[:, i*Qp + j]
-            # 分母块: -(H * Φf φμ^T)/D_prev
-            Den_blk = (Num_blk.T * Hn).T * (-1.0)
-            for i in range(Kp1):
-                for j in range(Qp):
+            for i in range(P):
+                for j in range(V):
+                    A[r:r+Nf, col_map[('C',i,j)]] = blk[:, i*V + j]
+            # 分母块: - H * blk
+            for i in range(P):
+                for j in range(V):
                     if mask_fix[i,j]: continue
-                    A[r:r+Nf, idx_map[('Ct',i,j)]] = Den_blk[:, i*Qp + j]
-            # 右端 0
-            r += Nf
-
-        # 约束行 (式5)(8) 可选：Re( Σ_k D^{(t)}/D^{(t-1)} ) = K+1
-        if self.cfg.add_constraint:
-            row_c = np.zeros(cols, dtype=complex)
-            # D^{(t)} ≈ Σ Ct_k φ_k(s) ; 用代表样本 n=0, 对所有频率平均
-            n0=0
-            phiμ0=Φμ[n0]
-            mean_phiμ = phiμ0  # 可换平均
-            # φ_k(s) 平均
-            mean_phif = Φf.mean(0)  # (Kp1,)
-            for i in range(Kp1):
-                for j in range(Qp):
-                    if mask_fix[i,j]: continue
-                    row_c[idx_map[('Ct',i,j)]] = mean_phif[i]*mean_phiμ[j]
-            rhs = (Kp1)  # K+1
-            A = np.vstack([A, np.real(row_c) if self.cfg.real_split else row_c])
-            b = np.concatenate([b, [rhs]])
-
-        # 拆实虚
-        if self.cfg.real_split:
-            A_real = np.vstack([np.real(A), np.imag(A)])
-            b_real = np.concatenate([np.real(b), np.imag(b)])
-        else:
-            A_real, b_real = A, b
-        return A_real, b_real
-
-    def _unpack_iter_solution(self, x, Kp1, Qp):
-        # 重新填回 C, Ct (保持 Ct[0,0]=1)
-        # 构建与 _build_iter_system 相同的 idx_map
-        mask_fix = np.zeros((Kp1,Qp), dtype=bool); mask_fix[0,0]=True
-        idx_C = Kp1*Qp
-        # 注意：我们当时 C 索引从 0..(Kp1*Qp-1)
-        self.C = x[:idx_C].reshape(Kp1, Qp)
-        Ct_new = self.Ct.copy()
-        pos = idx_C
-        for i in range(Kp1):
-            for j in range(Qp):
+                    A[r:r+Nf, col_map[('Ct',i,j)]] = - Hn * blk[:, i*V + j]
+            r+=Nf
+        # 求解
+        x, *_ = np.linalg.lstsq(A, b, rcond=None)
+        # 拆回
+        idx=0
+        for i in range(P):
+            for j in range(V):
+                C[i,j]=x[idx]; idx+=1
+        Ct_new = Ct.copy()
+        for i in range(P):
+            for j in range(V):
                 if mask_fix[i,j]: continue
-                Ct_new[i,j]=x[pos]; pos+=1
-        self.Ct = Ct_new
+                Ct_new[i,j]=x[idx]; idx+=1
+        # 收敛性简单检查：分母变化
+        diff = np.max(np.abs(Ct_new - Ct))
+        Ct = Ct_new
+        if diff < tol:
+            break
+    return C, Ct
 
-    def _eval_C_mu(self, phiμ):
-        # 返回 c_k(μ) (Kp1,)
-        return self.C @ phiμ
-    def _eval_Ct_mu(self, phiμ):
-        return self.Ct @ phiμ
-    def _eval_D(self, Φf, Φμ):
-        # D(f, sample)= Σ_k ĉ_k(μ_n) φ_k(f)
-        Kp1 = Φf.shape[1]
-        Ns = Φμ.shape[0]
-        D = np.zeros((Φf.shape[0], Ns), dtype=complex)
-        for n in range(Ns):
-            ct_mu = self._eval_Ct_mu(Φμ[n])
-            D[:,n] = Φf @ ct_mu
-        return D
+# ---------- 8. 假极点检测 (在 raw 基上) ----------
+def detect_spurious_poles(C_ml: np.ndarray, Ct_ml: np.ndarray, T: np.ndarray,
+                          Qμ: np.ndarray,
+                          tau_cancel=1e-2, tau_small=1e-3, eps=1e-14):
+    """
+    C_ml, Ct_ml: (P,V)  (Muntz-Laguerre 基)
+    T:  Φ_ml T = G_raw
+    Qμ: (Ns,V)
+    """
+    # raw 系数: c_raw = T^{-1} c_ml
+    Tinv = np.linalg.inv(T)
+    C_raw  = Tinv @ C_ml
+    Ct_raw = Tinv @ Ct_ml
+    P,V = C_ml.shape
+    Ns = Qμ.shape[0]
+    r_vals = C_raw @ Qμ.T
+    q_vals = Ct_raw @ Qμ.T
+    metrics={}
+    scales=[]
+    for k in range(1,P):  # 跳过常数列
+        rv = r_vals[k]; qv = q_vals[k]
+        diff = np.max(np.abs(rv - qv))
+        scale = np.max([np.max(np.abs(rv)), np.max(np.abs(qv)), eps])
+        eta = diff / scale
+        metrics[k] = {"diff": diff, "scale": scale, "eta": eta}
+        scales.append(scale)
+    S_max = max(scales) if scales else 1.0
+    cancel_spurious = [k for k in range(1,P)
+                       if metrics[k]["eta"] < tau_cancel and metrics[k]["scale"] > tau_small * S_max]
+    small_spurious = [k for k in range(1,P)
+                      if metrics[k]["scale"] <= tau_small * S_max]
+    return {
+        "cancel_spurious": cancel_spurious,
+        "small_spurious": small_spurious,
+        "metrics": metrics,
+        "S_max": S_max,
+        "C_raw": C_raw,
+        "Ct_raw": Ct_raw
+    }
 
-    def evaluate(self, freqs: np.ndarray, g: np.ndarray):
-        assert np.allclose(freqs, self.fb.freqs), "频率需在训练网格上 (示例简化)"
-        Φf = self.fb.Q            # (Nf,K+1)
-        phiμ = self.pb.eval(g)    # (Qp,)
-        num = Φf @ (self.C @ phiμ)
-        den = Φf @ (self.Ct @ phiμ)
-        return num / (den + 1e-15)
\ No newline at end of file
+# ---------- 9. 统一入口 ----------
+@dataclass
+class OPVFResult:
+    C_ml: np.ndarray
+    Ct_ml: np.ndarray
+    C_raw: np.ndarray
+    Ct_raw: np.ndarray
+    T: np.ndarray
+    Qμ: np.ndarray
+    Rμ: np.ndarray
+    exps: List[Tuple[int]]
+    poles: List[complex]
+    spurious: Dict
+
+def opvf_from_H(H_list: List[np.ndarray],
+                freqs_hz: np.ndarray,
+                param_matrix: np.ndarray,
+                total_degree: int,
+                poles: List[complex],
+                do_t1: bool = True) -> OPVFResult:
+    """
+    H_list: 长度 Ns; 每项 shape (Nf,)
+    freqs_hz: (Nf,)
+    param_matrix: (Ns,d) 归一化前参数值 (内部不自动标准化以保持可控)
+    poles: 初始稳定极点列表
+    """
+    # 频率基
+    Phi_ml = muntz_laguerre_basis(freqs_hz, poles)  # (Nf,P)
+    G_raw  = raw_partial_fraction_basis(freqs_hz, poles)  # (Nf,P)
+    # 简单权 (ω 权): w = Δf (因 (1/2π)∫ φφ* dω => ∑ Δf)
+    w = _trap_weights(freqs_hz)
+    T = compute_transform_matrix(Phi_ml, G_raw, w)
+
+    # 参数基
+    Qμ, Rμ, exps = generate_param_basis(param_matrix, total_degree)
+
+    # t=0
+    C_ml = fit_t0(H_list, Phi_ml, Qμ)
+
+    if do_t1:
+        C_ml, Ct_ml = fit_t1_SK(H_list, Phi_ml, Qμ, C_ml, max_iter=1)
+    else:
+        # D=1
+        P,V = C_ml.shape
+        Ct_ml = np.zeros((P,V), dtype=complex)
+        Ct_ml[0,0]=1.0
+
+    # 假极点检测
+    spurious = detect_spurious_poles(C_ml, Ct_ml, T, Qμ)
+
+    return OPVFResult(
+        C_ml=C_ml,
+        Ct_ml=Ct_ml,
+        C_raw=spurious["C_raw"],
+        Ct_raw=spurious["Ct_raw"],
+        T=T,
+        Qμ=Qμ,
+        Rμ=Rμ,
+        exps=exps,
+        poles=poles,
+        spurious=spurious
+    )
+
+# ---------- 辅助: 梯形权 ----------
+def _trap_weights(f: np.ndarray):
+    if len(f)==1: return np.ones(1)
+    df = np.diff(f)
+    w = np.zeros_like(f)
+    w[0]=0.5*df[0]; w[-1]=0.5*df[-1]
+    if len(f)>2:
+        w[1:-1]=0.5*(df[:-1]+df[1:])
+    return w
+
+# ---------- 简单测试占位 ----------
+if __name__ == "__main__":
+    # 虚构数据: 2 个参数样本 (Ns=2), 频率 200 点
+    freqs = np.linspace(1e8, 5e9, 200)
+    # 真正模型 (示例): H = Σ_k R_k/(s - p_k)
+    true_poles = [-0.5e3 + 1.2e9j, -0.5e3 - 1.2e9j]
+    s = 1j*2*np.pi*freqs
+    def synth(Rs):
+        return Rs[0]/(s - true_poles[0]) + Rs[1]/(s - true_poles[1])
+    H_list = [synth([0.8+0.1j, 1.2-0.2j]), synth([0.9+0.05j, 1.1+0.1j])]
+    params = np.array([[0.0],[1.0]])  # 1 维参数
+    # 给一个冗余极点集合 (含真实 + 额外)
+    start_poles = generate_starting_poles(n_pairs=10, beta_min=5e8, beta_max=2.0e9, alpha_scale=0.000001)
+    res = opvf_from_H(H_list, freqs, params, total_degree=1, poles=start_poles)
+    print("C_ml shape:", res.C_ml.shape)
+    print("假极点(cancel):", res.spurious["cancel_spurious"])
+    print("假极点(small):",  res.spurious["small_spurious"])
\ No newline at end of file
diff --git a/docs/内积与基函数正交归一化的定义.md b/docs/内积与基函数正交归一化的定义.md
index afd179f..9de7e0c 100644
--- a/docs/内积与基函数正交归一化的定义.md
+++ b/docs/内积与基函数正交归一化的定义.md
@@ -1,3 +1,5 @@
+## 留数定理
+
 ### 1. 正交归一化条件
 
 首先给出了一组基函数 \(\phi_m(s)\)，要求它们满足正交归一化条件：
diff --git a/docs/有理函数正交化.md b/docs/有理函数正交化.md
new file mode 100644
index 0000000..49da11c
--- /dev/null
+++ b/docs/有理函数正交化.md
@@ -0,0 +1,13 @@
+$$
+\begin{align}
+&N(s) = \sum^N_{i=0}n_is^i=\xi_1\prod^N_{i=0}(s-p_{ni})\\
+&D(s) = \sum^M_{i=0}d_is^i=\xi_2\prod^M_{i=0}(s-p_{mi})\\
+&H(s) = \xi\frac{\prod^N_{i=0}(s-p_{ni})}{\prod^M_{i=0}(s-p_{mi})}\\
+&let \space \phi_x=\frac{1}{s-p_x}\\
+&H(s) = \frac{\sum_{i=0}^Nc_{ni}\phi_i(s)}{\sum_{j=0}^Mc_{mj}\phi_j(s)}\\
+&当M=N时，通分之后得到\\
+&H(s) = \frac{\frac{\sum_{i=0}^Nc_i\prod_{a=0,a\not=i}(s-p_a)}{\prod^N_{i=0}(s-p_i)}}{\frac{\sum_{j=0}^Nc_j\prod_{b=0,b\not=j}(s-p_b)}{\prod^N_{j=0}(s-p_j)}}\\
+&此时由于H(s)分母的分子连乘部分存在缺项，所以p_b为假极点
+\end{align}
+$$
+
diff --git a/env b/env
new file mode 100644
index 0000000..27df2ed
--- /dev/null
+++ b/env
@@ -0,0 +1,2 @@
+export PYTHONPATH=$(pwd)/core:$PYTHONPATH
+export PYTHONPATH=$(pwd)/models:$PYTHONPATH
\ No newline at end of file
diff --git a/img.png b/img.png
new file mode 100644
index 0000000..fd59237
Binary files /dev/null and b/img.png differ
diff --git a/main.py b/main.py
index 609d240..b9dc3ee 100644
--- a/main.py
+++ b/main.py
@@ -1,15 +1,117 @@
-from models.capa import Capa
-W = []
-L = []
-i = 15.52
-
-while i <= 100:
-    W.append(i)
-    L.append(i)
-    i = int(i*1.05*100 +0.5) / 100.0
-
-capa = Capa()
-capa.sweep(W,L)
-print(capa.network(result_dir=capa.results[0].result_dir,id=capa.results[0].id))
+from core.orthonormal_basis import generate_laguerre_basis
+from core.sk_iter import generate_starting_poles
+import numpy as np
+import skrf as rf
+import matplotlib.pyplot as plt
+import sympy as sp
+from scipy.linalg import null_space
+
+
+network = rf.Network("/tmp/paramer/simulation/4000/4000.s2p")
+freqs = network.f
+s = freqs * 2j * np.pi
+vf = rf.VectorFitting(network)
+vf.vector_fit(n_poles_real=2, n_poles_cmplx=1,parameter_type="y")
+
+poles = vf.poles
+residues = vf.residues
+y = vf.network.y
+
+fig, ax = plt.subplots(2, 2)
+fig.set_size_inches(12, 8)
+vf.plot("mag",0,0,freqs,ax=ax[0][0],parameter="y")
+rms_error11 = vf.get_rms_error(0,0,"y")
+print("rms_error11",rms_error11)
+vf.plot("mag",1,0,freqs,ax=ax[1][0],parameter="y")
+rms_error21 = vf.get_rms_error(1,0,"y")
+print("rms_error21",rms_error21)
+vf.plot("mag",0,1,freqs,ax=ax[0][1],parameter="y")
+rms_error12 = vf.get_rms_error(0,1,"y")
+print("rms_error12",rms_error12)
+vf.plot("mag",1,1,freqs,ax=ax[1][1],parameter="y")
+rms_error22 = vf.get_rms_error(1,1,"y")
+print("rms_error22",rms_error22)
+fig.tight_layout()
+plt.show()
+plt.savefig(f"img.png")
+
+diag_values = [y[i][0][0] for i in range(len(y))]
+H = np.diag(diag_values)
+P = 4
+start_poles = generate_starting_poles(P,beta_min=1e8,beta_max=freqs[-1])
+basis = generate_laguerre_basis(start_poles,s).T
+print("start_poles",start_poles)
+print("basis",basis)
+
+# first step iteration
+# A*x = b
+
+A11 = np.real(basis)
+print("A11 shape:",A11.shape)
+A12 = np.real(- H @ basis[:,1:])
+print("A12 shape:",A12.shape)
+# print("A11",A11)
+A21 = np.imag(basis)
+print("A21 shape:",A21.shape)
+A22 = np.imag(- H @ basis[:,1:])
+print("A22 shape:",A22.shape)
+A1 = np.hstack([A11,A12])
+A2 = np.hstack([A21,A22])
+A = np.vstack([A1,A2])
+print ("A shape:",A.shape)
+
+b1 = np.real(H @ basis[:,0])
+b2 = np.imag(H @ basis[:,0])
+b = np.hstack([b1,b2])
+Q, R = np.linalg.qr(A, mode='reduced')
+print("Q :",Q)
+print("R :",R)
+print("b shape:",b.shape)
+# x = np.linalg.solve(R, Q.T @ b)
+x_star, residuals, rank, s = np.linalg.lstsq(A, b, rcond=None)
+
+# print("x_qr",x_star)
+print("residuals",residuals)
+# print("rank",rank)
+# print("s",s)
+
+x_ne = np.linalg.inv(A.T @ A) @ A.T @ b
+print("x_ne",x_ne)
+
+# sk iteration
+target_vec = np.array([1+0j] + list(x_ne[len(x_ne)//2+1:]))
+D_t = basis @ target_vec
+print("D_t",D_t)
+K = 25
+
+for i in range(K):
+    print(f"Iteration {i+1}/{K}")
+    A11 = np.real(basis / D_t[:, np.newaxis])
+    A12 = np.real(- H @ basis[:,1:] / D_t[:, np.newaxis])
+    A21 = np.imag(basis / D_t[:, np.newaxis])
+    A22 = np.imag(- H @ basis[:,1:] / D_t[:, np.newaxis])
+    A1 = np.hstack([A11,A12])
+    A2 = np.hstack([A21,A22])
+    A = np.vstack([A1,A2])
+    b1 = np.real(H @ basis[:,0])
+    b2 = np.imag(H @ basis[:,0])
+    b = np.hstack([b1,b2])
+    x_star, residuals, rank, s = np.linalg.lstsq(A, b, rcond=None)
+    # print("x_lstsq",x_star)
+    # print("residuals",residuals)
+    # print("rank",rank)
+    # print("s",s)
+    x_ne = np.linalg.inv(A.T @ A) @ A.T @ b
+    # print("x_ne",x_ne)
+    target_vec = np.array([1+0j] + list(x_ne[len(x_ne)//2+1:]))
+    D_t_pre = D_t
+    D_t = basis @ target_vec
+    print("D_t/D_t_pre",np.linalg.norm(D_t)/np.linalg.norm(D_t_pre))
+    # print("D_t",D_t)
+    n_target_vec = np.array(list(x_ne[:len(x_ne)//2+1]))
+    N_t = basis @ n_target_vec
+    # print("H = N_t / D_t",np.linalg.norm(H - N_t / D_t)/np.linalg.norm(H))
+
+
 
 
diff --git a/test/lazy_evaulation.py b/test/lazy_evaulation.py
deleted file mode 100644
index c5ab46f..0000000
--- a/test/lazy_evaulation.py
+++ /dev/null
@@ -1,202 +0,0 @@
-import numpy as np
-
-# ------------------------------------------------------------
-# 按论文公式 (9)(10)(11) 生成 Muntz–Laguerre 正交有理基 (解析形式)：
-#
-# 给定稳定极点集合 {p_k} (Re(p_k)<0)。论文记法中使用 -a_k，其中 Re(a_k)>0。
-# 对应关系：p_k = -a_k  ⇒  a_k = -p_k,  Re(a_k)= -Re(p_k) >0
-#
-# 连续内积意义下（沿 jω 轴积分）这些 φ_k 解析正交。离散频率采样后数值上
-# 可能偏离，可再用加权 QR 做数值再正交（可选）。
-#
-# 公式在“稳定极点 p 表达”下的改写：
-#   (原) 实极点:  φ_p(s) = sqrt(2 Re(a_p)) / (s + a_p) * Π (s - a_i^*)/(s + a_i)
-#   变换 a_p = -p ⇒ Re(a_p)= -Re(p) = σ >0 且 (s + a_p) = (s - p)
-#   且乘积 (s - a_i^*)/(s + a_i) = (s + p_i^*)/(s - p_i)
-#   ⇒ φ_p(s) = sqrt(-2 Re(p)) / (s - p) * Π_{i<p} (s + p_i^*)/(s - p_i)
-#
-# 复对 (p, p*)，取 imag(p)>0 的 p 作为首：
-#   (原) φ_p   = sqrt(2 Re(a_p)) (s - |a_p|)/[(s + a_p)(s + a_p^*)] * Π(...)
-#   (原) φ_{p+1}= sqrt(2 Re(a_p)) (s + |a_p|)/[(s + a_p)(s + a_p^*)] * Π(...)
-#   代入 a_p=-p：
-#       Re(a_p)= -Re(p)=σ>0,  (s + a_p) = (s - p), (s + a_p^*)=(s - p^*)
-#       |a_p| = |p|
-#       乘积同上 ⇒ Π_{i<p} (s + p_i^*)/(s - p_i)
-#
-#   ⇒ φ_p(s)   = sqrt(-2 Re(p)) (s - |p|)/[(s - p)(s - p^*)] * Π_{i<p} (s + p_i^*)/(s - p_i)
-#      φ_{p^*}(s)= sqrt(-2 Re(p)) (s + |p|)/[(s - p)(s - p^*)] * Π_{i<p} (s + p_i^*)/(s - p_i)
-#
-# product 递推维护：prod_k = Π_{i≤k} (s + p_i^*)/(s - p_i)
-# 对复对要顺序乘两次（p 与 p*）。
-# ------------------------------------------------------------
-
-class MuntzLaguerreIterator:
-    def __init__(self, s: np.ndarray, stable_poles: list | np.ndarray):
-        """
-        s: 复频率数组 (Nf,), s = j 2π f
-        stable_poles: 稳定极点列表 (Re<0). 复共轭对要求正虚部在前 (p, p*).
-        """
-        self.s = np.asarray(s, dtype=complex)
-        self.poles = list(stable_poles)
-        self.N = len(self.poles)
-        self.k = 0
-        # 初始化乘积 Π_{i<p} (s + p_i^*)/(s - p_i)
-        self.product = np.ones_like(self.s, dtype=complex)
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        if self.k >= self.N:
-            raise StopIteration
-
-        p = self.poles[self.k]
-        if np.real(p) >= 0:
-            raise ValueError(f"极点必须在左半平面: {p}")
-
-        # 复对首 (正虚部)
-        if np.iscomplex(p) and np.imag(p) > 0:
-            if self.k + 1 >= self.N:
-                raise ValueError("复极点缺少共轭")
-            pc = self.poles[self.k + 1]
-            if not np.isclose(pc, np.conj(p)):
-                raise ValueError("复极点未按 (p, p*) 顺序排列 (正虚部在前)")
-            sigma = -np.real(p)           # >0
-            scale = np.sqrt(2 * sigma)
-            r = np.abs(p)
-            denom = (self.s - p) * (self.s - pc)
-
-            # 两个基函数
-            phi_p  = scale * (self.s - r) / denom * self.product
-            phi_pc = scale * (self.s + r) / denom * self.product
-
-            # product 先乘 (s + p^*)/(s - p)，再乘 (s + p)/(s - p^*)
-            self.product = self.product * (self.s + pc) / (self.s - p)
-            self.product = self.product * (self.s + p)  / (self.s - pc)
-
-            self.k += 2
-            return [phi_p, phi_pc]
-
-        # 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(p) and np.imag(p) < 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(p)
-        if sigma <= 0:
-            raise ValueError("实极点实部应为负 (稳定)。")
-        scale = np.sqrt(2 * sigma)
-        phi = scale / (self.s - p) * self.product
-        # 更新乘积
-        self.product = self.product * (self.s + p) / (self.s - p)
-
-        self.k += 1
-        return [phi]
-
-def generate_muntz_laguerre_basis(s: np.ndarray, stable_poles: list | np.ndarray):
-    """
-    生成完整基函数列表: [φ_0=1, φ_1, φ_2, ...]
-    """
-    basis = [np.ones_like(s, dtype=complex)]
-    for block in MuntzLaguerreIterator(s, stable_poles):
-        basis.extend(block)
-    return basis
-
-# ---------- 可选：离散再正交 (加权 QR) ----------
-# def trapezoid_weights(freqs: np.ndarray):
-#     if len(freqs) == 1:
-#         return np.ones(1)
-#     df = np.diff(freqs)
-#     w = np.zeros_like(freqs, dtype=float)
-#     w[0] = 0.5 * df[0]
-#     w[-1] = 0.5 * df[-1]
-#     if len(freqs) > 2:
-#         w[1:-1] = 0.5 * (df[:-1] + df[1:])
-#     return w
-
-def weighted_qr_from_basis(basis_cols: list[np.ndarray], weights: np.ndarray | None = None):
-    A = np.column_stack(basis_cols)  # (Nf, M)
-    if weights is None:
-        sw = np.ones(A.shape[0])
-    else:
-        sw = np.sqrt(weights.real)
-    Aw = sw[:, None] * A
-    Qw, R = np.linalg.qr(Aw)
-    Phi = Qw / (sw[:, None] + 1e-30)
-    return Phi, R  # Raw = Phi R
-
-def check_discrete_orthogonality(Phi: np.ndarray, w: np.ndarray):
-    G = Phi.conj().T @ (w[:, None] * Phi)
-    off = np.max(np.abs(G - np.eye(G.shape[0])))
-    return G, off
-
-def verify_orthonormal(Phi: np.ndarray, w: np.ndarray, atol=1e-10, rtol=1e-8):
-    """
-    返回:
-        G        : Gram 矩阵 (Φ^H W Φ)
-        max_off  : 最大非对角幅值
-        diag_err : max |diag(G)-1|
-        passed   : 是否满足阈值
-    """
-    G = Phi.conj().T @ (w[:, None] * Phi)
-    I = np.eye(G.shape[0])
-    diag_err = np.max(np.abs(np.diag(G) - 1.0))
-    max_off = np.max(np.abs(G - I + np.diag(np.diag(G) - 1.0)))
-    passed = (diag_err <= atol) and (max_off <= rtol)
-    return G, max_off, diag_err, passed
-
-def omega_weights(freqs_hz: np.ndarray):
-    """
-    基于 ω=2πf 的梯形法得到 w_ω = Δω/(2π)，使得
-    (1/2π) ∫_{-∞}^{∞} → Σ w_ω,k
-    """
-    f = freqs_hz
-    if len(f) == 1:
-        return np.ones(1)
-    df = np.diff(f)
-    w_f = np.zeros_like(f)
-    w_f[0] = 0.5 * df[0]
-    w_f[-1] = 0.5 * df[-1]
-    if len(f) > 2:
-        w_f[1:-1] = 0.5 * (df[:-1] + df[1:])
-    # dω = 2π df,  (1/2π) * dω = df  ⇒ 直接 w_f 就是 w_ω
-    return w_f  # 已等价于 Δω/(2π)
-
-# ------------------ 示例 ------------------
-if __name__ == "__main__":
-    # 示例稳定极点 (复对正虚部在前)
-    stable_poles = [
-        -0.8e9,
-        -1.0e9 + 2.5e9j,
-        -1.0e9 - 2.5e9j,
-        -2.2e9
-    ]
-    freqs = np.linspace(1e8, 8e9, 400)
-    s = 1j * 2 * np.pi * freqs
-
-    basis = generate_muntz_laguerre_basis(s, stable_poles)
-    print("解析基函数数量 =", len(basis))
-    print("前5个基函数示例 (每个前10个频点):")
-    for i in range(5):
-        print(f"φ_{i}:", basis[i][:10])
-
-    w = omega_weights(freqs)
-    Phi_num, R = weighted_qr_from_basis(basis, w)
-    Gram, off = check_discrete_orthogonality(Phi_num, w)
-    print("离散 Gram 最大非对角元素 =", off)
-    print("R 形状:", R.shape)
-    # 验证 Raw ≈ Phi R
-    raw = np.column_stack(basis)
-    err = np.max(np.abs(raw - Phi_num @ R))
-    print("重构误差 ||Raw - Phi R||_∞ =", err)
-
-    # 验证正交性
-    print("离散 Gram 矩阵 (前5x5):")
-    print(Gram[:5, :5])
-
-    Gcheck, max_off, diag_err, ok = verify_orthonormal(Phi_num, w)
-    print(f"Diag 误差={diag_err:.3e}, Max off={max_off:.3e}, Orthonormal={ok}")
-    # 额外: 验证 R
-    # raw = Φ R  =>  R ≈ Φ^H W raw  (因为 Φ^H W Φ = I)
-    R_alt = Phi_num.conj().T @ (w[:,None] * raw)
-    print("R 差异 ||R - R_alt||_max =", np.max(np.abs(R - R_alt)))
diff --git a/test/test_numpy.py b/test/test_numpy.py
new file mode 100644
index 0000000..5e03142
--- /dev/null
+++ b/test/test_numpy.py
@@ -0,0 +1,9 @@
+import numpy as np
+
+# 创建一个复数矩阵
+A = np.array([[1+2j, 2-1j], [3+4j, 4+0j]])
+
+Q, R = np.linalg.qr(A)
+
+print("Q =\n", Q)
+print("R =\n", R)
\ No newline at end of file
diff --git a/test/test_orthonormal_basis.py b/test/test_orthonormal_basis.py
new file mode 100644
index 0000000..c5116b4
--- /dev/null
+++ b/test/test_orthonormal_basis.py
@@ -0,0 +1,102 @@
+import numpy as np
+from core.orthonormal_basis import generate_muntz_laguerre_basis
+
+# ---------- 可选：离散再正交 (加权 QR) ----------
+# def trapezoid_weights(freqs: np.ndarray):
+#     if len(freqs) == 1:
+#         return np.ones(1)
+#     df = np.diff(freqs)
+#     w = np.zeros_like(freqs, dtype=float)
+#     w[0] = 0.5 * df[0]
+#     w[-1] = 0.5 * df[-1]
+#     if len(freqs) > 2:
+#         w[1:-1] = 0.5 * (df[:-1] + df[1:])
+#     return w
+
+def weighted_qr_from_basis(basis_cols: list[np.ndarray], weights: np.ndarray | None = None):
+    A = np.column_stack(basis_cols)  # (Nf, M)
+    if weights is None:
+        sw = np.ones(A.shape[0])
+    else:
+        sw = np.sqrt(weights.real)
+    Aw = sw[:, None] * A
+    Qw, R = np.linalg.qr(Aw)
+    Phi = Qw / (sw[:, None] + 1e-30)
+    return Phi, R  # Raw = Phi R
+
+def check_discrete_orthogonality(Phi: np.ndarray, w: np.ndarray):
+    G = Phi.conj().T @ (w[:, None] * Phi)
+    off = np.max(np.abs(G - np.eye(G.shape[0])))
+    return G, off
+
+def verify_orthonormal(Phi: np.ndarray, w: np.ndarray, atol=1e-10, rtol=1e-8):
+    """
+    返回:
+        G        : Gram 矩阵 (Φ^H W Φ)
+        max_off  : 最大非对角幅值
+        diag_err : max |diag(G)-1|
+        passed   : 是否满足阈值
+    """
+    G = Phi.conj().T @ (w[:, None] * Phi)
+    I = np.eye(G.shape[0])
+    diag_err = np.max(np.abs(np.diag(G) - 1.0))
+    max_off = np.max(np.abs(G - I + np.diag(np.diag(G) - 1.0)))
+    passed = (diag_err <= atol) and (max_off <= rtol)
+    return G, max_off, diag_err, passed
+
+def omega_weights(freqs_hz: np.ndarray):
+    """
+    基于 ω=2πf 的梯形法得到 w_ω = Δω/(2π)，使得
+    (1/2π) ∫_{-∞}^{∞} → Σ w_ω,k
+    """
+    f = freqs_hz
+    if len(f) == 1:
+        return np.ones(1)
+    df = np.diff(f)
+    w_f = np.zeros_like(f)
+    w_f[0] = 0.5 * df[0]
+    w_f[-1] = 0.5 * df[-1]
+    if len(f) > 2:
+        w_f[1:-1] = 0.5 * (df[:-1] + df[1:])
+    # dω = 2π df,  (1/2π) * dω = df  ⇒ 直接 w_f 就是 w_ω
+    return w_f  # 已等价于 Δω/(2π)
+
+def evaluate(basis,freqs):
+    w = omega_weights(freqs)
+    Phi_num, R = weighted_qr_from_basis(basis, w)
+    Gram, off = check_discrete_orthogonality(Phi_num, w)
+    print("离散 Gram 最大非对角元素 =", off)
+    print("R 形状:", R.shape)
+    # 验证 Raw ≈ Phi R
+    raw = np.column_stack(basis)
+    err = np.max(np.abs(raw - Phi_num @ R))
+    print("重构误差 ||Raw - Phi R||_∞ =", err)
+
+    # 验证正交性
+    print("离散 Gram 矩阵 (前5x5):")
+    print(Gram[:5, :5])
+
+    Gcheck, max_off, diag_err, ok = verify_orthonormal(Phi_num, w)
+    print(f"Diag 误差={diag_err:.3e}, Max off={max_off:.3e}, Orthonormal={ok}")
+    # 额外: 验证 R
+    # raw = Φ R  =>  R ≈ Φ^H W raw  (因为 Φ^H W Φ = I)
+    R_alt = Phi_num.conj().T @ (w[:,None] * raw)
+    print("R 差异 ||R - R_alt||_max =", np.max(np.abs(R - R_alt)))
+
+# ------------------ 示例 ------------------
+if __name__ == "__main__":
+    # 示例稳定极点 (复对正虚部在前)
+    init_poles = [
+        -1.0e3 + 2.5e9j,
+        -1.0e3 - 2.5e9j,
+    ]
+    freqs = np.linspace(1e8, 8e9, 40)
+    s = 1j * 2 * np.pi * freqs
+
+    basis = generate_muntz_laguerre_basis(s, init_poles)
+    print("解析基函数数量 =", len(basis))
+    print("基函数:")
+    for i in range(len(basis)):
+        print(f"φ_{i}:", basis[i][:])
+
+    evaluate(basis, freqs)