init: 验证robust算法
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mayge
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.gitignore
vendored
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.gitignore
vendored
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__pycache__/
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*.pyc
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*.pyo
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*.pyd
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__pypackages__/
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env/
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.venv/
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12170
capa_results.json
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12170
capa_results.json
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core/__init__.py
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core/__init__.py
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191
core/robust.py
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core/robust.py
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from models.basic import ModelBasic
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from typing import List,Literal,Dict
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import numpy as np
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import random
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from pydantic import BaseModel
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from skrf import Network
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from models.basic import ModelBasicDatasetUnit
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class RobustParametricConfig(BaseModel):
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n_poles: int
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max_iter: int = 10
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parameter_type: Literal["s","y","z"]
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class RobustParametricModel:
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def __init__(self,model:ModelBasic,config:RobustParametricConfig):
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self.model = model
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self.config = config
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# 区分训练集和测试集
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def _train_test_split(self,train_ratio:float=0.8,random_state:int=42):
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random.seed(random_state)
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dataset = self.model.results
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random.shuffle(dataset)
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train_size = int(len(dataset)*train_ratio)
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self.train_set = dataset[:train_size]
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self.test_set = dataset[train_size:]
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return self.train_set, self.test_set
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def first_iteration_step(self, datasets: List[ModelBasicDatasetUnit],
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param_degree: int = 1):
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'''
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SK 迭代的第一步 (t=0) – 对应论文公式 (3).
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目标:
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最小化 sum_{样本 n, 频点 f} | N^{(0)}(s_f, g_n) - H(s_f, g_n) |^2
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在 t=0 阶段我们令 D^{(0)}(s,g)=1, 仅拟合分子:
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N^{(0)}(s,g) = Σ_{p∈P} Σ_{v∈V} c_{p,v} * φ_freq_p(s) * φ_param_v(g)
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因此是一个纯线性最小二乘:
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H ≈ Σ_{p,v} c_{p,v} F[:,p] ⊗ G[n,v]
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记:
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F: (Nf, P) 频率正交(或原始)基列
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G: (Ns, V) 参数多项式（含常数）基列
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设计矩阵 A 大小 (Ns*Nf, P*V), A[(n*Nf + f), (p*V + v)] = F[f,p] * G[n,v]
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输出:
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self.first_iter_coeffs[(i,j)] = C_{p,v} (P × V) 每个端口对一套系数
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self.freq_basis_F = F
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self.param_basis_G = G
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self.poles 初始极点 (供后续构造有理正交基 / SK 加权使用)
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参数:
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datasets: 训练用样本列表
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param_degree: 参数多项式最大总次数 (默认 1 → 常数 + 线性)
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注意:
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这里使用简单频率基 [1, 1/(s-a_k)]，未做 QR 正交化;
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后续 SK 迭代 / 正交化可在第二步再进行。
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'''
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assert len(datasets) > 0, "空数据集"
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# ---------------- 收集频率与端口信息 ----------------
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Ns = len(datasets)
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freqs = datasets[0].freqs # 频率需要对齐
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Nf = freqs.shape[0]
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nports = self.model.info.nports
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# ---------------- 构造初始极点 (对数分布 + 阻尼) ----------------
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def _init_poles(n_poles: int):
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fmin, fmax = freqs[0], freqs[-1]
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if n_poles <= 0:
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return np.array([], dtype=complex)
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# 避免 0 Hz，用第二个点或微小偏移
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start_f = max(fmin if fmin > 0 else freqs[1] if Nf > 1 else 1.0e-3, 1e-12)
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f_samples = np.logspace(np.log10(start_f), np.log10(fmax), n_poles)
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sigma = 0.02 * 2 * np.pi * fmax # 固定阻尼，可放入 config
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return -sigma + 1j * 2 * np.pi * f_samples
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self.poles = _init_poles(self.config.n_poles)
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s_vec = 1j * 2 * np.pi * freqs # (Nf,)
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# ---------------- 频率基 F ----------------
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# 列0: 常数 1, 后续列: 1/(s - a_k)
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P = 1 + len(self.poles)
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F = np.zeros((Nf, P), dtype=complex)
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F[:, 0] = 1.0
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for k, a in enumerate(self.poles, start=1):
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F[:, k] = 1.0 / (s_vec - a)
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self.freq_basis_F = F # (Nf, P)
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# ---------------- 参数基 G ----------------
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# 取出参数向量 g = [param1, param2, ...]
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# 假设 datasets[i].parameters 为 dict
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param_keys = list(datasets[0].parameters.keys())
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d = len(param_keys)
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# 构造参数矩阵 (Ns,d)
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Pmat = np.zeros((Ns, d), dtype=float)
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for i, unit in enumerate(datasets):
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for j, k in enumerate(param_keys):
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Pmat[i, j] = float(unit.parameters[k])
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# 标准化
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mean = Pmat.mean(axis=0)
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std = Pmat.std(axis=0) + 1e-15
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Xn = (Pmat - mean) / std
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# 生成多项式指数 (总次数 <= param_degree)
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def _gen_param_exps(dim, D):
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exps = []
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def rec(cur, idx, rem):
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if idx == dim:
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exps.append(tuple(cur)); return
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for t in range(rem + 1):
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cur.append(t); rec(cur, idx + 1, rem - t); cur.pop()
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rec([], 0, D)
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return exps
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exps = _gen_param_exps(d, param_degree) # V_exps
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V = len(exps)
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G = np.zeros((Ns, V), dtype=float)
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for vidx, e in enumerate(exps):
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val = np.ones(Ns)
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for j, p in enumerate(e):
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if p:
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val *= Xn[:, j] ** p
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G[:, vidx] = val
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self.param_basis_G = G # (Ns, V)
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self.param_basis_meta = {
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"keys": param_keys,
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"mean": mean.tolist(),
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"std": std.tolist(),
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"exponents": exps,
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"degree": param_degree
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}
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# ---------------- 构造设计矩阵 A (Kronecker) ----------------
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# A shape: (Ns*Nf, P*V)
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# 利用广播：A_block[n,f,p,v] = F[f,p] * G[n,v]
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A4 = np.einsum('fp,nv->nfpv', F, G) # (Ns, Nf, P, V)
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A = A4.reshape(Ns * Nf, P * V)
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# ---------------- 采集目标 H 数据 ----------------
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# 对每个端口对 (i,j) 分别解一个向量 c_{p,v}
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# 选择参数类型
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param_type = self.config.parameter_type.lower()
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valid_types = {"s", "y", "z"}
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if param_type not in valid_types:
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raise ValueError(f"parameter_type 必须在 {valid_types}")
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# 预先载入全部 Network (避免重复 IO)
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# H_data[(i,j)] -> (Ns,Nf) 复数
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H_data = {}
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for i_port in range(nports):
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for j_port in range(nports):
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H_mat = np.zeros((Ns, Nf), dtype=complex)
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for n, unit in enumerate(datasets):
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net: Network = unit.network
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if param_type == "s":
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sij = net.s[:, i_port, j_port]
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elif param_type == "y":
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sij = net.y[:, i_port, j_port]
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else:
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sij = net.z[:, i_port, j_port]
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# 插值或对齐假设已完成
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H_mat[n, :] = sij
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H_data[(i_port, j_port)] = H_mat
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# ---------------- 最小二乘求系数 ----------------
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self.first_iter_coeffs = {}
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# 可选: 预计算 A^+ (伪逆) 如果数据不巨大
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# pinv = np.linalg.pinv(A)
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for key, Hmat in H_data.items():
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b = Hmat.reshape(Ns * Nf) # (Ns*Nf,)
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# 解 x (长度 P*V)
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x, *_ = np.linalg.lstsq(A, b, rcond=None)
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C = x.reshape(P, V)
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self.first_iter_coeffs[key] = C
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# ---------------- 存储便于后续 SK 迭代使用 ----------------
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self.meta_first_iter = {
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"A_shape": A.shape,
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"num_samples": Ns,
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"num_freqs": Nf,
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"freq_basis_dim": P,
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"param_basis_dim": V
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}
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if getattr(self.config, "verbose", True):
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print(f"[t=0] 线性最小二乘完成: A={A.shape}, 频率基P={P}, 参数基V={V}, 端口对={nports*nports}")
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return self.first_iter_coeffs
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core/sk_iter.py
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core/sk_iter.py
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import numpy as np
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from dataclasses import dataclass
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from typing import List, Sequence, Tuple, Optional
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@dataclass
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class OPVFConfig:
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n_poles: int
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max_iter: int = 5
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tol: float = 1e-3
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add_constraint: bool = True
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real_split: bool = True
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lambda_reg: float = 0.0
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verbose: bool = True
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# ---------- 参数正交基 ----------
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class ParamOrthoBasis:
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def __init__(self, params: np.ndarray, total_degree: int):
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# params: (Ns, d)
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self.mean = params.mean(0)
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self.std = params.std(0) + 1e-15
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self.exps = self._gen_exps(params.shape[1], total_degree)
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M = self._build_monomial_matrix(params) # (Ns,M)
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Q,R = np.linalg.qr(M)
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self.Q = Q
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self.R = R
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def _gen_exps(self, dim, D):
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exps=[]
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def rec(cur, i, rem):
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if i==dim:
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exps.append(tuple(cur)); return
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for k in range(rem+1):
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cur.append(k); rec(cur, i+1, rem-k); cur.pop()
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rec([],0,D)
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return exps
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def _build_monomial_matrix(self, params):
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X = (params - self.mean)/self.std
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Ns = X.shape[0]
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M = np.zeros((Ns, len(self.exps)))
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for idx,e in enumerate(self.exps):
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v = np.ones(Ns)
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for j,p in enumerate(e):
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if p: v *= X[:,j]**p
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M[:,idx]=v
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return M
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def eval(self, g: np.ndarray) -> np.ndarray:
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x = (g - self.mean)/self.std
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mono=[]
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for e in self.exps:
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val=1.0
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for j,p in enumerate(e):
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if p: val*= x[j]**p
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mono.append(val)
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mono=np.array(mono)
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# φ = mono @ R^{-1}
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return mono @ np.linalg.inv(self.R)
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# ---------- 频率正交(有理)基 ----------
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class RationalFreqBasis:
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def __init__(self, freqs: np.ndarray, poles: np.ndarray):
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# 构造原始矩阵 G: 列0 常数 1, 后面 1/(s-a_k)
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s = 1j*2*np.pi*freqs
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cols=[np.ones_like(s)]
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for a in poles:
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cols.append(1.0/(s - a))
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G = np.vstack(cols).T # (Nf, K+1)
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# 加权可加 w_f，这里统一 1
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Q, R = np.linalg.qr(G)
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self.Q = Q # Φ(f) (Nf, K+1) 频率正交基取样
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self.R = R # 变换: G = Q R
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self.freqs = freqs
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def eval(self) -> np.ndarray:
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return self.Q # 直接返回已正交基取样 (Nf, K+1)
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# ---------- 主模型 ----------
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class OrthonormalParametricVF:
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"""
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H(s, μ) = N(s,μ)/D(s,μ)
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N(s,μ)= Σ_k c_k(μ) φ_k(s); D(s,μ)= Σ_k ĉ_k(μ) φ_k(s)
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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:
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c_k(μ)= Σ_q C[k,q] ψ_q(μ); ĉ_k(μ)= Σ_q Ctilde[k,q] ψ_q(μ)
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"""
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def __init__(self, cfg: OPVFConfig, freq_basis: RationalFreqBasis, param_basis: ParamOrthoBasis):
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self.cfg = cfg
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self.fb = freq_basis
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self.pb = param_basis
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Kp1 = self.fb.Q.shape[1]
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Qp = self.pb.Q.shape[1]
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self.C = np.zeros((Kp1, Qp), dtype=complex) # 分子
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self.Ct = np.zeros((Kp1, Qp), dtype=complex) # 分母
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# 初始化分母：ĉ_0 ≈ 1，其余 0
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self.Ct[0,0] = 1.0
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def _assemble_phi_param(self, params: np.ndarray) -> np.ndarray:
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# 返回 (Ns, Qp)
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return self.pb.Q
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def fit(self, H_samples: List[np.ndarray], params: np.ndarray):
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"""
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H_samples: list 长度 Ns, 每个 (Nf,) 复
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params: (Ns,d)
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"""
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Φf = self.fb.eval() # (Nf, K+1)
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Ns = len(H_samples)
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Nf, Kp1 = Φf.shape
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Φμ = self._assemble_phi_param(params) # (Ns, Qp)
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Qp = Φμ.shape[1]
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# t=0: 只解 C (式3, D=1)
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self._solve_t0(H_samples, Φf, Φμ)
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D_prev = self._eval_D(Φf, Φμ) # (Nf, Ns)
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for it in range(1, self.cfg.max_iter+1):
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A, b = self._build_iter_system(H_samples, Φf, Φμ, D_prev)
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if self.cfg.lambda_reg>0:
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lam = self.cfg.lambda_reg
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A = np.vstack([A, lam*np.eye(A.shape[1])])
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b = np.concatenate([b, np.zeros(A.shape[1])])
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x, *_ = np.linalg.lstsq(A, b, rcond=None)
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self._unpack_iter_solution(x, Kp1, Qp)
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D_new = self._eval_D(Φf, Φμ)
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rel = np.max(np.abs(D_new-D_prev)/(np.abs(D_prev)+1e-12))
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if self.cfg.verbose:
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print(f"[OPVF-Iter {it}] rel_change={rel:.3e}")
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D_prev = D_new
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if rel < self.cfg.tol:
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if self.cfg.verbose:
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print(f"[OPVF] converged at {it}")
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break
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return self
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def _solve_t0(self, H_samples, Φf, Φμ):
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Ns = len(H_samples); Nf,Kp1 = Φf.shape; Qp = Φμ.shape[1]
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# 设计矩阵行数 Ns*Nf；未知数 Kp1*Qp
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cols = Kp1*Qp
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A = np.zeros((Ns*Nf, cols), dtype=complex)
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b = np.zeros(Ns*Nf, dtype=complex)
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r = 0
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for n,Hn in enumerate(H_samples):
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phiμ = Φμ[n] # (Qp,)
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# Φf (Nf,Kp1) 外积 phiμ -> (Nf, Kp1*Qp)
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blk = np.einsum('fk,q->fkq', Φf, phiμ).reshape(Nf, cols)
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A[r:r+Nf,:] = blk
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b[r:r+Nf] = Hn
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r += Nf
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x, *_ = np.linalg.lstsq(A, b, rcond=None)
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self.C = x.reshape(Kp1, Qp)
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# 分母保持初始 (ĉ_0=1)
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def _build_iter_system(self, H_samples, Φf, Φμ, D_prev):
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Ns = len(H_samples); Nf,Kp1 = Φf.shape; Qp=Φμ.shape[1]
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# 未知：C (Kp1*Qp) + Ct (Kp1*Qp) 但固定 Ct[0,0]=1，可去掉该变量
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mask_fix = np.zeros((Kp1,Qp), dtype=bool)
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mask_fix[0,0]=True
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idx_map={}
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col=0
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for i in range(Kp1):
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for j in range(Qp):
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idx_map[('C',i,j)] = col; col+=1
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for i in range(Kp1):
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for j in range(Qp):
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if mask_fix[i,j]: continue
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idx_map[('Ct',i,j)] = col; col+=1
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cols = col
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rows = Ns*Nf
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A = np.zeros((rows, cols), dtype=complex)
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b = np.zeros(rows, dtype=complex)
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r=0
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for n,Hn in enumerate(H_samples):
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phiμ = Φμ[n]
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Dp = D_prev[:,n] # (Nf,)
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invDp = 1.0/(Dp + 1e-12)
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# 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
|
||||
# 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):
|
||||
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):
|
||||
if mask_fix[i,j]: continue
|
||||
Ct_new[i,j]=x[pos]; pos+=1
|
||||
self.Ct = Ct_new
|
||||
|
||||
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
|
||||
|
||||
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)
|
||||
15
main.py
Normal file
15
main.py
Normal file
@@ -0,0 +1,15 @@
|
||||
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))
|
||||
|
||||
|
||||
149
models/basic.py
Normal file
149
models/basic.py
Normal file
@@ -0,0 +1,149 @@
|
||||
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 = "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:
|
||||
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(0.01)
|
||||
|
||||
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(0.01) # Wait for 2 seconds before checking the status again
|
||||
|
||||
status = uuid_model.status
|
||||
while status != "completed" and status != "failed":
|
||||
time.sleep(0.01) # 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."
|
||||
uuid_model = UuidResponseUnit(**response)
|
||||
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
|
||||
45
models/capa.py
Normal file
45
models/capa.py
Normal file
@@ -0,0 +1,45 @@
|
||||
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)
|
||||
]
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
309
outputs/capa_parametric.s2p
Normal file
309
outputs/capa_parametric.s2p
Normal file
@@ -0,0 +1,309 @@
|
||||
! 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
|
||||
12589.0 0.5169316480916267 -0.14305277597258723 0.4988695319574604 0.12530262247375623 0.4988695319574604 0.12530262247375623 0.5263853656750881 -0.14417828844484987
|
||||
15849.0 0.5169316349794967 -0.14305277441436826 0.4988695413945808 0.12530260966887344 0.4988695413945808 0.12530260966887344 0.5263853522565635 -0.14417828673088906
|
||||
19953.0 0.5169316184726925 -0.1430527724527331 0.4988695532749311 0.12530259354886147 0.4988695532749311 0.12530259354886147 0.5263853353640404 -0.14417828457319115
|
||||
25119.0 0.5169315976943908 -0.14305276998348182 0.4988695682295826 0.12530257325744293 0.4988695682295826 0.12530257325744293 0.5263853141002066 -0.14417828185714157
|
||||
31623.0 0.5169315715344848 -0.1430527668746916 0.4988695870575062 0.1253025477105234 0.4988695870575062 0.1253025477105234 0.526385287329015 -0.14417827843763203
|
||||
39811.0 0.5169315386013189 -0.14305276296098088 0.4988696107603104 0.12530251554905705 0.4988696107603104 0.12530251554905705 0.5263852536262912 -0.1441782741327513
|
||||
50119.0 0.5169314971412463 -0.14305275803394987 0.49886964060013766 0.12530247506048903 0.49886964060013766 0.12530247506048903 0.5263852111974101 -0.1441782687132703
|
||||
63096.0 0.5169314449461201 -0.14305275183118685 0.4988696781662454 0.1253024240884142 0.4988696781662454 0.1253024240884142 0.5263851577826266 -0.1441782618905496
|
||||
79433.0 0.5169313792366513 -0.14305274402240672 0.49886972545895575 0.12530235991866878 0.49886972545895575 0.12530235991866878 0.5263850905377072 -0.1441782533012926
|
||||
100000.0 0.5169312965135905 -0.14305273419176578 0.498869784996764 0.12530227913399875 0.498869784996764 0.12530227913399875 0.5263850058816346 -0.14417824248809258
|
||||
125893.0 0.5169311923686877 -0.14305272181539658 0.4988698599523953 0.12530217742944966 0.4988698599523953 0.12530217742944966 0.5263848993031504 -0.1441782288747222
|
||||
158489.0 0.5169310612634759 -0.14305270623511884 0.4988699543120198 0.12530204939633327 0.4988699543120198 0.12530204939633327 0.5263847651343683 -0.1441782117372171
|
||||
199526.0 0.5169308962075003 -0.14305268662020118 0.49887007310683856 0.1253018882079974 0.49887007310683856 0.1253018882079974 0.5263845962214859 -0.14417819016181527
|
||||
251189.0 0.5169306884124163 -0.14305266192625446 0.49887022266203807 0.12530168528202823 0.49887022266203807 0.12530168528202823 0.5263843835707991 -0.1441781629997424
|
||||
316228.0 0.5169304268173779 -0.14305263083883016 0.4988704109383793 0.12530142981676085 0.4988704109383793 0.12530142981676085 0.526384115863 -0.14417812880517264
|
||||
398107.0 0.5169300974897413 -0.14305259170220114 0.4988706479635265 0.12530110820602533 0.4988706479635265 0.12530110820602533 0.5263837788398773 -0.14417808575689117
|
||||
501187.0 0.5169296828890162 -0.14305254243189108 0.4988709463617992 0.125300703320345 0.4988709463617992 0.125300703320345 0.5263833545510671 -0.14417803156208114
|
||||
630957.0 0.5169291609377541 -0.14305248040426072 0.4988713220228765 0.12530019359959674 0.4988713220228765 0.12530019359959674 0.5263828204032315 -0.1441779633348741
|
||||
794328.0 0.5169285038390437 -0.14305240231598146 0.4988717949528746 0.12529955189821454 0.4988717949528746 0.12529955189821454 0.5263821479499213 -0.14417787744177832
|
||||
1000000.0 0.5169276766003909 -0.14305230400861618 0.49887239033674674 0.12529874404365846 0.49887239033674674 0.12529874404365846 0.5263813013809635 -0.14417776930872658
|
||||
1258925.0 0.5169266351714739 -0.14305218024731395 0.49887313987858567 0.12529772701780695 0.49887313987858567 0.12529772701780695 0.526380235616701 -0.14417763317765178
|
||||
1584893.0 0.5169253240871785 -0.14305202444071294 0.49887408349798956 0.12529644665522024 0.49887408349798956 0.12529644665522024 0.5263788938959513 -0.14417746179839447
|
||||
1995262.0 0.5169236735314454 -0.14305182829201415 0.4988752714432822 0.12529483477578932 0.4988752714432822 0.12529483477578932 0.5263772047712447 -0.144177246044902
|
||||
2511886.0 0.5169215956047378 -0.14305158135541474 0.49887676697790806 0.1252928055396649 0.49887676697790806 0.1252928055396649 0.5263750782890728 -0.1441769744273278
|
||||
3162278.0 0.5169189796463092 -0.14305127048021582 0.4988786497471103 0.12529025087913537 0.4988786497471103 0.12529025087913537 0.5263724012028501 -0.14417663248057871
|
||||
3981072.0 0.5169156863538554 -0.1430508791120138 0.49888102001016127 0.12528703475606862 0.49888102001016127 0.12528703475606862 0.5263690309551585 -0.14417620199566117
|
||||
5011872.0 0.5169115403466041 -0.14305038640891315 0.4988840039928882 0.12528298589926556 0.4988840039928882 0.12528298589926556 0.5263647880670566 -0.14417566004756066
|
||||
6309573.0 0.5169063208299607 -0.14304976613213158 0.49888776060655593 0.12527788868785483 0.49888776060655593 0.12527788868785483 0.5263594465845839 -0.14417497777496466
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||||
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196000000000.0 -0.27140495363703976 -0.04936845163339515 1.0662546473147576 -0.6445614466265462 1.0662546473147576 -0.6445614466265462 -0.28037250643289346 -0.04113034425076027
|
||||
196800000000.0 -0.2746226542589304 -0.0489860666115319 1.0685705050772074 -0.6477037491512334 1.0685705050772074 -0.6477037491512334 -0.28366539591830453 -0.040709740369891625
|
||||
197600000000.0 -0.27784035488082104 -0.04860368158966864 1.0708863628396572 -0.6508460516759206 1.0708863628396572 -0.6508460516759206 -0.2869582854037156 -0.04028913648902299
|
||||
198400000000.0 -0.2810580555027117 -0.04822129656780538 1.073202220602107 -0.6539883542006077 1.073202220602107 -0.6539883542006077 -0.29025117488912666 -0.03986853260815436
|
||||
199200000000.0 -0.2842757561246023 -0.04783891154594214 1.0755180783645568 -0.6571306567252949 1.0755180783645568 -0.6571306567252949 -0.2935440643745377 -0.03944792872728571
|
||||
200000000000.0 -0.28749345674649296 -0.047456526524078876 1.0778339361270066 -0.6602729592499821 1.0778339361270066 -0.6602729592499821 -0.2968369538599488 -0.03902732484641708
|
||||
309
outputs/pvf_test_sample.s2p
Normal file
309
outputs/pvf_test_sample.s2p
Normal file
@@ -0,0 +1,309 @@
|
||||
! 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 0.54669299140207 -0.1662104177109195 0.46100154888068945 0.1530653599528759 0.46100154888068945 0.1530653599528759 0.5593473899566386 -0.16509997898188067
|
||||
1000.0 0.5466929861541492 -0.16621041680285528 0.46100155138238874 0.1530653541325667 0.46100155138238874 0.1530653541325667 0.559347384635358 -0.16509997772031268
|
||||
10000.0 0.5466929389228627 -0.16621040863027736 0.4610015738976825 0.15306530174978375 0.4610015738976825 0.15306530174978375 0.5593473367438332 -0.16509996636620083
|
||||
12589.0 0.546692925335996 -0.16621040627929912 0.46100158037458194 0.15306528668100317 0.46100158037458194 0.15306528668100317 0.5593473229670379 -0.16509996310000133
|
||||
15849.0 0.5466929082277745 -0.16621040331900977 0.4610015885301217 0.15306526770679513 0.4610015885301217 0.15306526770679513 0.5593473056196633 -0.1650999589872897
|
||||
19953.0 0.5466928866903079 -0.16621039959231423 0.4610015987970956 0.1530652438202461 0.4610015987970956 0.1530652438202461 0.559347283781128 -0.1650999538098147
|
||||
25119.0 0.5466928595795495 -0.1662103949012545 0.46100161172087417 0.15306521375252868 0.46100161172087417 0.15306521375252868 0.5593472562913927 -0.16509994729255453
|
||||
31623.0 0.5466928254470731 -0.16621038899520488 0.46100162799192645 0.15306517589723756 0.46100162799192645 0.15306517589723756 0.5593472216817841 -0.16509993908731635
|
||||
39811.0 0.5466927824770983 -0.16621038155997508 0.4610016484758403 0.1530651282405457 0.4610016484758403 0.1530651282405457 0.5593471781111391 -0.1650999287575977
|
||||
50119.0 0.5466927283815316 -0.16621037219964918 0.4610016742633567 0.1530650682447983 0.4610016742633567 0.1530650682447983 0.5593471232593793 -0.16509991575335498
|
||||
63096.0 0.5466926602792644 -0.16621036041569986 0.4610017067279085 0.15306499271464558 0.4610017067279085 0.15306499271464558 0.5593470542051218 -0.16509989938198727
|
||||
79433.0 0.5466925745439837 -0.1662103455806548 0.46100174759817003 0.15306489762825393 0.46100174759817003 0.15306489762825393 0.5593469672713616 -0.16509987877175114
|
||||
100000.0 0.5466924666099984 -0.16621032690449813 0.46100179905061955 0.15306477792195428 0.46100179905061955 0.15306477792195428 0.5593468578285848 -0.16509985282508244
|
||||
125893.0 0.5466923307255872 -0.16621030339199147 0.46100186382711955 0.15306462721668773 0.46100186382711955 0.15306462721668773 0.5593467200446678 -0.16509982015930266
|
||||
158489.0 0.5466921596643636 -0.16621027379273035 0.46100194537250994 0.1530644374978885 0.46100194537250994 0.1530644374978885 0.5593465465922074 -0.16509977903723272
|
||||
199526.0 0.5466919443054411 -0.16621023652849923 0.46100204803474415 0.1530641986498592 0.46100204803474415 0.1530641986498592 0.5593463282228179 -0.16509972726626743
|
||||
251189.0 0.5466916731821132 -0.16621018961517778 0.4610021772800351 0.15306389795522415 0.4610021772800351 0.15306389795522415 0.5593460533095016 -0.16509966208988075
|
||||
316228.0 0.5466913318625978 -0.1662101305555894 0.46100233998805595 0.15306351940813304 0.46100233998805595 0.15306351940813304 0.5593457072187367 -0.16509958003876077
|
||||
398107.0 0.5466909021680975 -0.16621005620419965 0.46100254482469305 0.1530630428470347 0.46100254482469305 0.1530630428470347 0.5593452715176075 -0.16509947674283593
|
||||
501187.0 0.54669036121243 -0.16620996260094054 0.46100280269985694 0.1530624428895607 0.46100280269985694 0.1530624428895607 0.5593447230000097 -0.16509934670040832
|
||||
630957.0 0.5466896801897587 -0.16620984476144754 0.46100312734537524 0.15306168758803368 0.46100312734537524 0.15306168758803368 0.5593440324574342 -0.16509918298673124
|
||||
794328.0 0.5466888228317034 -0.16620969641008893 0.4610035360504917 0.1530607367182967 0.4610035360504917 0.1530607367182967 0.5593431631145115 -0.1650989768831084
|
||||
1000000.0 0.5466877434813538 -0.166209509646706 0.46100405057999033 0.15305953964365956 0.46100405057999033 0.15305953964365956 0.5593420686761007 -0.16509871741389834
|
||||
1258925.0 0.5466863846634824 -0.16620927452617953 0.4610046983324818 0.15305803262009574 0.4610046983324818 0.15305803262009574 0.5593406908635374 -0.16509839076240854
|
||||
1584893.0 0.5466846740092625 -0.16620897852630398 0.46100551380639954 0.1530561353855411 0.46100551380639954 0.1530561353855411 0.5593389562963631 -0.16509797953161645
|
||||
1995262.0 0.5466825204252861 -0.16620860588490077 0.4610065404262399 0.1530537469110683 0.4610065404262399 0.1530537469110683 0.5593367726077901 -0.16509746182322513
|
||||
2511886.0 0.546679809223494 -0.16620813675713456 0.46100783286413954 0.15305073999963972 0.46100783286413954 0.15305073999963972 0.5593340235065535 -0.16509681006692783
|
||||
3162278.0 0.5466763960178437 -0.16620754615943462 0.4610094599493512 0.15304695451708789 0.4610094599493512 0.15304695451708789 0.559330562588262 -0.16509598955320468
|
||||
3981072.0 0.5466720990518489 -0.166206802641905 0.4610115083257286 0.1530421888828234 0.4610115083257286 0.1530421888828234 0.5593262055556852 -0.16509495658891016
|
||||
5011872.0 0.5466666894951747 -0.16620586660931375 0.46101408707736796 0.15303618930808321 0.46101408707736796 0.15303618930808321 0.5593207203797069 -0.16509365616463398
|
||||
6309573.0 0.5466598792632135 -0.16620468821347584 0.4610173335350524 0.15302863628699287 0.4610173335350524 0.15302863628699287 0.5593138149486316 -0.16509201902660173
|
||||
7943282.0 0.5466513056879083 -0.1662032047007977 0.46102142058371554 0.15301912759544334 0.46102142058371554 0.15301912759544334 0.5593051215247256 -0.1650899579916349
|
||||
10000000.0 0.546640512194909 -0.16620133706878468 0.4610265658736982 0.15300715686071253 0.4610265658736982 0.15300715686071253 0.5592941771512603 -0.16508736330205745
|
||||
12589254.0 0.5466269239952031 -0.16619898585988765 0.46103304340861984 0.15299208660179292 0.46103304340861984 0.15299208660179292 0.5592803990043426 -0.16508409678211294
|
||||
15848932.0 0.5466098174634991 -0.16619602586294838 0.46104119814279354 0.15297311426788723 0.46104119814279354 0.15297311426788723 0.5592630533432417 -0.16507998447671546
|
||||
19952623.0 0.5465882816184877 -0.1661922994480081 0.46105146434369926 0.1529492295173389 0.46105146434369926 0.1529492295173389 0.55924121645219 -0.1650748073915405
|
||||
25118864.0 0.5465611695953194 -0.16618760816943778 0.4610643887251971 0.1529191603972328 0.4610643887251971 0.1529191603972328 0.5592137254345031 -0.16506828982730595
|
||||
31622777.0 0.546527037575551 -0.16618170219879488 0.4610806595598022 0.15288130561245666 0.4610806595598022 0.15288130561245666 0.5591791162888364 -0.16506008469890535
|
||||
39810717.0 0.5464840679156028 -0.16617426702349875 0.4611011433235758 0.15283364926981186 0.4611011433235758 0.15283364926981186 0.5591355459630696 -0.16504975505596017
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||||
50118723.0 0.5464299723173738 -0.1661649066921378 0.46112693085497947 0.152773653487488 0.46112693085497947 0.152773653487488 0.559080694171358 -0.16503675080562896
|
||||
63095734.0 0.5463618699925138 -0.1661531227328507 0.4611593954343257 0.15269812327076424 0.4611593954343257 0.15269812327076424 0.5590116398552845 -0.16502037942404513
|
||||
79432823.0 0.5462761342447099 -0.16613828760697735 0.46120026591845537 0.15260303636108943 0.46120026591845537 0.15260303636108943 0.5589247056215441 -0.16499976907563838
|
||||
100000000.0 0.5461681993304599 -0.1661196112895713 0.4612517188107773 0.15248332903124232 0.4612517188107773 0.15248332903124232 0.5588152619028559 -0.1649738221836484
|
||||
125892541.0 0.5460323173281535 -0.1660960991996931 0.46131649416249504 0.15233262643622567 0.46131649416249504 0.15233262643622567 0.5586774804283577 -0.16494115698294182
|
||||
158489319.0 0.5458612520216092 -0.16606649923211647 0.46139804149922853 0.1521429031088094 0.46139804149922853 0.1521429031088094 0.5585040238279905 -0.16490003393149014
|
||||
199526231.0 0.5456458935609996 -0.16602923508089748 0.46150070351328915 0.15190405559168585 0.46150070351328915 0.15190405559168585 0.5582856549068324 -0.16484826307721756
|
||||
251188643.0 0.545374773318821 -0.16598232229337812 0.46162994733327123 0.1516033643789841 0.46162994733327123 0.1516033643789841 0.5580107447193196 -0.1647830874323488
|
||||
316227766.0 0.5450334531578723 -0.1659232625933056 0.4617926556618101 0.1512248165719648 0.4617926556618101 0.1512248165719648 0.5576646532999023 -0.16470103615717366
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||||
398107171.0 0.5446037565321509 -0.1658489108358039 0.46199749331205503 0.1507482531164152 0.46199749331205503 0.1507482531164152 0.5572289500156269 -0.164597739721414
|
||||
501187234.0 0.5440628005341162 -0.16575530751947015 0.4622553686335967 0.15014829527571583 0.4622553686335967 0.15014829527571583 0.556680432082547 -0.16446769721431742
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||||
630957344.0 0.5433817772855165 -0.16563746792659956 0.46258001442705865 0.1493929931084779 0.46258001442705865 0.1493929931084779 0.5559898889218124 -0.16430398339847893
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||||
794328235.0 0.5425244198022299 -0.16548911666695781 0.4629887192708573 0.14844212400590961 0.4629887192708573 0.14844212400590961 0.5551205465790867 -0.1640978799131498
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||||
800000996.0 0.5424946496022605 -0.16548396543570207 0.46300291081308503 0.14840910678277924 0.46300291081308503 0.14840910678277924 0.5550903602263816 -0.16409072333950084
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1000000000.0 0.5414450706859694 -0.16530235349743766 0.4635032481815683 0.14724505073654004 0.4635032481815683 0.14724505073654004 0.5540261094188117 -0.163838410999558
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||||
1600000990.0 0.5382963130608676 -0.16475751407036499 0.46500427023877794 0.1437528594446324 0.46500427023877794 0.1437528594446324 0.5508333358280478 -0.16308146896121212
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2400000990.0 0.5340979764879871 -0.16403106269957954 0.467005629679481 0.13909661207156374 0.467005629679481 0.13909661207156374 0.5465763113977863 -0.16207221457535398
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||||
4800000980.0 0.521502966821825 -0.16185170859630377 0.47300970797657327 0.12512787001056075 0.47300970797657327 0.12512787001056075 0.5338052381602144 -0.1590444514303953
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||||
5600000970.0 0.5173046303014237 -0.16112525723459895 0.47501106739225935 0.12047162269569517 0.47501106739225935 0.12047162269569517 0.5295482137831657 -0.15803519705715285
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||||
6400000970.0 0.5131062937285432 -0.1603988058638135 0.4770124268329624 0.11581537532262648 0.4770124268329624 0.11581537532262648 0.5252911893529042 -0.15702594267129472
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7200000960.0 0.508907957208142 -0.15967235450210868 0.47901378624864854 0.1111591280077609 0.47901378624864854 0.1111591280077609 0.5210341649758555 -0.1560166882980523
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8800000960.0 0.5005112840623811 -0.15821945176053775 0.4830165051300547 0.10184663326162352 0.4830165051300547 0.10184663326162352 0.5125201161153324 -0.15399817952633604
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9600000950.0 0.4963129475419798 -0.15749300039883293 0.48501786454574075 0.09719038594675794 0.48501786454574075 0.09719038594675794 0.5082630917382837 -0.1529889251530936
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10400000900.0 0.49211461123149536 -0.15676654907345067 0.4870192238613589 0.09253413886470471 0.4870192238613589 0.09253413886470471 0.5040060675740862 -0.15197967083031386
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||||
12800000900.0 0.479519601512854 -0.15458719496109427 0.4930233021834681 0.07856539674549864 0.4930233021834681 0.07856539674549864 0.4912349942833016 -0.1489519076727395
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13600000900.0 0.47532126493997356 -0.15386074359030882 0.4950246616241712 0.07390914937242997 0.4950246616241712 0.07390914937242997 0.4869779698530401 -0.14794265328688136
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14400000900.0 0.47112292836709313 -0.15313429221952335 0.4970260210648743 0.06925290199936128 0.4970260210648743 0.06925290199936128 0.48272094542277855 -0.14693339890102325
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||||
15200000900.0 0.46692459179421264 -0.1524078408487379 0.4990273805055774 0.0645966546262926 0.4990273805055774 0.0645966546262926 0.478463920992517 -0.1459241445151651
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16000000900.0 0.4627262552213322 -0.15168138947795243 0.5010287399462805 0.059940407253223915 0.5010287399462805 0.059940407253223915 0.47420689656225545 -0.144914890129307
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16800000900.0 0.45852791864845177 -0.15095493810716698 0.5030300993869835 0.05528415988015524 0.5030300993869835 0.05528415988015524 0.4699498721319939 -0.14390563574344886
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17600000900.0 0.4543295820755713 -0.1502284867363815 0.5050314588276866 0.05062791250708655 0.5050314588276866 0.05062791250708655 0.4656928477017324 -0.14289638135759075
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20000000900.0 0.4417345723569299 -0.1480491326240251 0.5110355371497959 0.036659170387880496 0.5110355371497959 0.036659170387880496 0.45292177441094783 -0.13986861820001636
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20800000900.0 0.4375362357840495 -0.14732268125323966 0.5130368965904989 0.03200292301481182 0.5130368965904989 0.03200292301481182 0.4486647499806863 -0.13885936381415825
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21600000900.0 0.433337899211169 -0.1465962298824542 0.515038256031202 0.027346675641743118 0.515038256031202 0.027346675641743118 0.44440772555042474 -0.13785010942830012
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22400000900.0 0.42913956263828856 -0.1458697785116687 0.5170396154719051 0.02269042826867443 0.5170396154719051 0.02269042826867443 0.4401507011201632 -0.136840855042442
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23200000900.0 0.4249412260654081 -0.14514332714088327 0.5190409749126081 0.018034180895605767 0.5190409749126081 0.018034180895605767 0.4358936766899017 -0.13583160065658387
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24800000900.0 0.4165445529196472 -0.14369042439931234 0.5230436937940144 0.008721686149468444 0.5230436937940144 0.008721686149468444 0.42737962782937866 -0.13381309188486762
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25600000900.0 0.4123462163467667 -0.14296397302852687 0.5250450532347174 0.004065438776399727 0.5250450532347174 0.004065438776399727 0.4231226033991171 -0.13280383749900948
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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
|
||||
583
param_vf.py
Normal file
583
param_vf.py
Normal file
@@ -0,0 +1,583 @@
|
||||
"""
|
||||
Parametric (multivariate) orthonormal vector fitting for geometry + frequency.
|
||||
|
||||
根据论文: Robust Parametric Macromodeling Using Multivariate Orthonormal Vector Fitting
|
||||
|
||||
核心思想 (简化实现版本):
|
||||
1. 在频域上使用一组全局极点 a_k, 通过所有参数样本共享。
|
||||
2. 对每个几何样本 s (由 (W,L) 给出) 与端口对 (i,j) 构建线性最小二乘问题, 固定极点估计该样本的残值 r_{k,s}, 以及常数/斜率项 d_s, e_s。
|
||||
3. 将随几何参数变化的残值/常数/斜率在一个离散正交 (QR) 的多元多项式基 Φ_q(W,L) 上展开: r_k(W,L) ≈ Σ_q c_{k,q} Φ_q(W,L)。
|
||||
4. 评价阶段: 先用 (W,L) 计算 Φ(W,L), 再恢复 r_k,d,e, 组合成有理函数 Σ_k r_k/(p-a_k)+d+p e。
|
||||
|
||||
该文件实现一个最小可用版本, 方便后续扩展(极点迭代 / 被动性约束 / 模型压缩等)。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
from typing import List, Tuple, Dict, Any, Optional
|
||||
import numpy as np
|
||||
from skrf import Network
|
||||
import skrf as rf
|
||||
from models.capa import Capa
|
||||
from models.basic import ModelBasicDatasetUnit
|
||||
import warnings
|
||||
|
||||
|
||||
@dataclass
|
||||
class ParametricVFConfig:
|
||||
# 有理函数项数量 (K 个极点)
|
||||
n_poles: int = 8
|
||||
# 参数多项式最大总次数 (a+b <= degree)
|
||||
param_total_degree: int = 3
|
||||
# 频率单位: Hz (Network 中 f 默认 Hz)
|
||||
damping: float = 0.02 # 极点实部相对于 2π f_max 的比例, 保证稳定
|
||||
# 最大保留频率点 (特征频率抽样); None 表示不裁剪
|
||||
max_freq_points: Optional[int] = None
|
||||
# 频率选择方法: 'curvature' 或 'uniform'
|
||||
freq_selection_method: str = 'curvature'
|
||||
# 曲率阈值(可选), 仅 method='curvature' 时生效
|
||||
curvature_threshold: Optional[float] = None
|
||||
# VF 极点迭代次数 (0 表示不迭代)
|
||||
vf_iterations: int = 0
|
||||
# 极点迭代时是否打印警告
|
||||
verbose: bool = True
|
||||
# 未来可添加: 加权, 正则, 被动性投影等
|
||||
|
||||
|
||||
def generate_monomial_exponents(dim: int, total_degree: int) -> List[Tuple[int, ...]]:
|
||||
"""生成所有满足指数和 <= total_degree 的多元单项式指数。
|
||||
对于 2 维 (W,L) 返回 (a,b) 列表。
|
||||
"""
|
||||
exps: List[Tuple[int, ...]] = []
|
||||
def rec(prefix: Tuple[int, ...], remaining_dim: int, max_sum: int):
|
||||
if remaining_dim == 1:
|
||||
for i in range(max_sum + 1):
|
||||
exps.append(prefix + (i,))
|
||||
return
|
||||
for i in range(max_sum + 1):
|
||||
rec(prefix + (i,), remaining_dim - 1, max_sum - i)
|
||||
rec(tuple(), dim, total_degree)
|
||||
return exps
|
||||
|
||||
|
||||
class OrthonormalParamBasis:
|
||||
"""离散点集上 (W,L) 的多项式基 QR 正交化封装。"""
|
||||
def __init__(self, W: np.ndarray, L: np.ndarray, total_degree: int):
|
||||
assert W.shape == L.shape
|
||||
self.W_mean = W.mean()
|
||||
self.W_std = W.std() or 1.0
|
||||
self.L_mean = L.mean()
|
||||
self.L_std = L.std() or 1.0
|
||||
self.exps = generate_monomial_exponents(2, total_degree)
|
||||
# 构建单项式矩阵 M (Ns x M)
|
||||
M = []
|
||||
Wn = (W - self.W_mean) / self.W_std
|
||||
Ln = (L - self.L_mean) / self.L_std
|
||||
for a, b in self.exps:
|
||||
M.append((Wn ** a) * (Ln ** b))
|
||||
M = np.stack(M, axis=1) # (Ns, M)
|
||||
# QR 分解获得离散正交基 Q, R 上三角
|
||||
Q, R = np.linalg.qr(M)
|
||||
self.Q = Q # (Ns, Q)
|
||||
self.R = R # (Q, Q)
|
||||
self.R_inv = np.linalg.inv(R)
|
||||
|
||||
@property
|
||||
def n_basis(self) -> int:
|
||||
return self.Q.shape[1]
|
||||
|
||||
def phi_matrix(self) -> np.ndarray:
|
||||
return self.Q # 对应样本点的基函数值
|
||||
|
||||
def eval(self, W: float, L: float) -> np.ndarray:
|
||||
Wn = (W - self.W_mean) / self.W_std
|
||||
Ln = (L - self.L_mean) / self.L_std
|
||||
monomials = []
|
||||
for a, b in self.exps:
|
||||
monomials.append((Wn ** a) * (Ln ** b))
|
||||
m = np.asarray(monomials) # (M,)
|
||||
# M = Q R => 对新点 m = phi R => phi = m R^{-1}
|
||||
phi = m @ self.R_inv
|
||||
return phi # (Q,)
|
||||
|
||||
|
||||
class ParametricVectorFitting:
|
||||
"""多参数 (W,L) 二端口/多端口 S 参数的简化 Parametric VF。
|
||||
|
||||
模型形式 (单个端口对):
|
||||
H(p; μ) ≈ Σ_{k=1..K} r_k(μ)/(p - a_k) + d(μ) + p e(μ)
|
||||
其中 μ = (W,L)。r_k, d, e 在参数域被正交基展开。
|
||||
"""
|
||||
def __init__(self, config: ParametricVFConfig):
|
||||
self.cfg = config
|
||||
self.frequencies: np.ndarray | None = None # (Nf,)
|
||||
self.poles: np.ndarray | None = None # (K,)
|
||||
self.basis: OrthonormalParamBasis | None = None
|
||||
# 残值系数: (nports, nports, K, Q)
|
||||
self.residue_coeffs: np.ndarray | None = None
|
||||
# d,e 系数: (nports, nports, Q)
|
||||
self.d_coeffs: np.ndarray | None = None
|
||||
self.e_coeffs: np.ndarray | None = None
|
||||
self.meta: Dict[str, Any] = {}
|
||||
|
||||
def _init_global_poles(self, freqs: np.ndarray) -> np.ndarray:
|
||||
f_min, f_max = freqs.min(), freqs.max()
|
||||
# 使用对数均匀分布 (排除 0) + 负实部保证稳定
|
||||
f_samples = np.logspace(np.log10(max(f_min, freqs[1] if len(freqs) > 1 else f_min*1.1)), np.log10(f_max), self.cfg.n_poles)
|
||||
# 极点: -σ + j 2π f_k
|
||||
sigma = self.cfg.damping * 2 * np.pi * f_max
|
||||
poles = -sigma + 1j * 2 * np.pi * f_samples
|
||||
return poles
|
||||
|
||||
# ----------------- 内部工具函数 (前置以便类型检查) -----------------
|
||||
def _build_frequency_matrix(self, p_vec: np.ndarray, poles: np.ndarray) -> np.ndarray:
|
||||
K = len(poles)
|
||||
F_base = np.zeros((len(p_vec), K + 2), dtype=complex)
|
||||
for k, a_k in enumerate(poles):
|
||||
F_base[:, k] = 1.0 / (p_vec - a_k)
|
||||
F_base[:, K] = 1.0
|
||||
F_base[:, K + 1] = p_vec
|
||||
return F_base
|
||||
|
||||
def _solve_residues_given_poles(self, aligned_S: List[np.ndarray], p_vec: np.ndarray):
|
||||
assert self.poles is not None, "Poles not initialized"
|
||||
nports = aligned_S[0].shape[1]
|
||||
Ns = len(aligned_S)
|
||||
K = len(self.poles)
|
||||
residues_samples = np.zeros((nports, nports, K, Ns), dtype=complex)
|
||||
d_samples = np.zeros((nports, nports, Ns), dtype=complex)
|
||||
e_samples = np.zeros((nports, nports, Ns), dtype=complex)
|
||||
F_base = self._build_frequency_matrix(p_vec, self.poles)
|
||||
for s_idx, Sdata in enumerate(aligned_S):
|
||||
for i in range(nports):
|
||||
for j in range(nports):
|
||||
y = Sdata[:, i, j]
|
||||
x, *_ = np.linalg.lstsq(F_base, y, rcond=None)
|
||||
residues_samples[i, j, :, s_idx] = x[:K]
|
||||
d_samples[i, j, s_idx] = x[K]
|
||||
e_samples[i, j, s_idx] = x[K+1]
|
||||
return residues_samples, d_samples, e_samples
|
||||
|
||||
def _select_characteristic_freqs(self, freqs: np.ndarray, S_list: List[np.ndarray], max_points: int,
|
||||
method: str = 'curvature', threshold: Optional[float] = None) -> np.ndarray:
|
||||
Nf = len(freqs)
|
||||
if max_points >= Nf:
|
||||
return np.arange(Nf)
|
||||
if method == 'uniform':
|
||||
idx = np.linspace(0, Nf-1, max_points, dtype=int)
|
||||
return np.unique(idx)
|
||||
mags = []
|
||||
for S in S_list:
|
||||
mags.append(np.mean(np.abs(S), axis=(1,2))) # (Nf,)
|
||||
agg = np.mean(np.stack(mags, axis=1), axis=1) # (Nf,)
|
||||
curv = np.zeros_like(agg)
|
||||
curv[1:-1] = np.abs(agg[2:] - 2*agg[1:-1] + agg[:-2])
|
||||
base = {0, Nf-1}
|
||||
if threshold is not None:
|
||||
cand = np.where(curv >= threshold)[0]
|
||||
else:
|
||||
order = np.argsort(-curv)
|
||||
cand = order
|
||||
selected = list(base)
|
||||
for idx in cand:
|
||||
if idx in base:
|
||||
continue
|
||||
selected.append(idx)
|
||||
if len(selected) >= max_points:
|
||||
break
|
||||
return np.array(sorted(selected))
|
||||
|
||||
def _vf_iterate_poles(self, aligned_S: List[np.ndarray], p_vec: np.ndarray,
|
||||
residues_samples: np.ndarray, d_samples: np.ndarray, e_samples: np.ndarray) -> np.ndarray:
|
||||
assert self.poles is not None, "Poles not initialized"
|
||||
nports = residues_samples.shape[0]
|
||||
K = len(self.poles)
|
||||
Ns = len(aligned_S)
|
||||
Nf = len(p_vec)
|
||||
n_pairs = nports * nports
|
||||
n_r = n_pairs * K
|
||||
n_c = K
|
||||
n_d = n_pairs
|
||||
n_e = n_pairs
|
||||
total_unknowns = n_r + n_c + n_d + n_e
|
||||
rows = Ns * Nf * n_pairs
|
||||
A = np.zeros((rows, total_unknowns), dtype=complex)
|
||||
b = np.zeros(rows, dtype=complex)
|
||||
def pair_index(i,j):
|
||||
return i*nports + j
|
||||
row = 0
|
||||
for s_idx, Sdata in enumerate(aligned_S):
|
||||
for f_idx, p in enumerate(p_vec):
|
||||
denom = p - self.poles
|
||||
basis_freq = 1.0/denom
|
||||
for i in range(nports):
|
||||
for j in range(nports):
|
||||
H = Sdata[f_idx, i, j]
|
||||
b[row] = H
|
||||
pair = pair_index(i,j)
|
||||
r_offset = pair*K
|
||||
A[row, r_offset:r_offset+K] = basis_freq
|
||||
A[row, n_r:n_r+K] = -H * basis_freq
|
||||
A[row, n_r + n_c + pair] = 1.0
|
||||
A[row, n_r + n_c + n_d + pair] = p
|
||||
row += 1
|
||||
x, *_ = np.linalg.lstsq(A, b, rcond=None)
|
||||
c_k = x[n_r:n_r+n_c]
|
||||
poly_total = np.poly(self.poles)
|
||||
acc = poly_total.copy()
|
||||
for k in range(K):
|
||||
others = [self.poles[m] for m in range(K) if m != k]
|
||||
term = c_k[k] * np.poly(others)
|
||||
acc = np.polyadd(acc, term)
|
||||
new_poles = np.roots(acc)
|
||||
return new_poles
|
||||
|
||||
def fit(self, dataset: List[ModelBasicDatasetUnit], network_loader=Capa.network):
|
||||
# 载入所有 Network
|
||||
networks: List[Network] = []
|
||||
W_list, L_list = [], []
|
||||
for unit in dataset:
|
||||
net = network_loader(unit.result_dir, unit.id)
|
||||
networks.append(net)
|
||||
W_list.append(unit.parameters["W"])
|
||||
L_list.append(unit.parameters["L"])
|
||||
assert len(networks) > 0, "Empty dataset"
|
||||
# 处理不同频率点数量: 取公共重叠频段并插值到统一网格
|
||||
freqs_list = [n.f for n in networks]
|
||||
f_start = max(f[0] for f in freqs_list)
|
||||
f_stop = min(f[-1] for f in freqs_list)
|
||||
if f_stop <= f_start:
|
||||
raise ValueError("No overlapping frequency range among networks")
|
||||
# 选择基准网格: 使用第一个网络在重叠区间内的频点, 若其它网络缺该点则插值
|
||||
base_freqs = networks[0].f
|
||||
mask = (base_freqs >= f_start) & (base_freqs <= f_stop)
|
||||
freqs = base_freqs[mask]
|
||||
# 如果其它网络点数差异较大, 可选: 统一稠密度 (这里只在差异>5%时使用最小长度网格)
|
||||
for f in freqs_list[1:]:
|
||||
# 判断差异比例
|
||||
if abs(len(f) - len(freqs)) / max(len(freqs),1) > 0.2:
|
||||
# 使用所有网络在重叠区间内最少点数, 构造等间距网格
|
||||
n_common = min(len(ff[(ff>=f_start)&(ff<=f_stop)]) for ff in freqs_list)
|
||||
freqs = np.linspace(f_start, f_stop, n_common)
|
||||
warnings.warn("Frequency grids differ significantly; using uniform linspace for interpolation.")
|
||||
break
|
||||
# 对每个网络插值到 freqs (实部/虚部分开线性插值)
|
||||
aligned_S = [] # List[(Nf, nports, nports)]
|
||||
for net in networks:
|
||||
f_src = net.f
|
||||
# 获取在目标范围内原始数据 (若后面改用插值网格不同于原点, 仍需完整插值)
|
||||
S_src = net.s # (Nf_src, nports, nports)
|
||||
nports = net.nports
|
||||
if not np.array_equal(f_src, freqs):
|
||||
# 线性插值 (对每个端口对实部与虚部)
|
||||
S_interp = np.zeros((len(freqs), nports, nports), dtype=complex)
|
||||
for i in range(nports):
|
||||
for j in range(nports):
|
||||
y = S_src[:, i, j]
|
||||
# numpy.interp 需要单调递增; 为类型检查器显式转为 np.ndarray
|
||||
y_arr = np.asarray(y)
|
||||
y_real = np.real(y_arr)
|
||||
y_imag = np.imag(y_arr)
|
||||
real_part = np.interp(freqs, f_src, y_real)
|
||||
imag_part = np.interp(freqs, f_src, y_imag)
|
||||
S_interp[:, i, j] = real_part + 1j * imag_part
|
||||
aligned_S.append(S_interp)
|
||||
else:
|
||||
aligned_S.append(S_src[mask] if len(S_src)==len(base_freqs) and mask.sum()!=len(base_freqs) else S_src)
|
||||
self.frequencies = freqs
|
||||
nports = networks[0].nports
|
||||
Ns = len(networks)
|
||||
# 参数正交基
|
||||
self.basis = OrthonormalParamBasis(np.array(W_list), np.array(L_list), self.cfg.param_total_degree)
|
||||
Phi = self.basis.phi_matrix() # (Ns, Q)
|
||||
Qn = Phi.shape[1]
|
||||
# (可选) 特征频率子采样
|
||||
if self.cfg.max_freq_points is not None and len(freqs) > self.cfg.max_freq_points:
|
||||
sel_idx = self._select_characteristic_freqs(freqs, aligned_S, self.cfg.max_freq_points,
|
||||
method=self.cfg.freq_selection_method,
|
||||
threshold=self.cfg.curvature_threshold)
|
||||
freqs = freqs[sel_idx]
|
||||
aligned_S = [S[sel_idx, :, :] for S in aligned_S]
|
||||
if self.cfg.verbose:
|
||||
warnings.warn(f"Frequency reduced to {len(freqs)} points (method={self.cfg.freq_selection_method}).")
|
||||
# 初始化极点
|
||||
self.poles = self._init_global_poles(freqs)
|
||||
p_vec = 1j * 2 * np.pi * freqs
|
||||
# 首次求解残值
|
||||
residues_samples, d_samples, e_samples = self._solve_residues_given_poles(aligned_S, p_vec)
|
||||
# 极点迭代 (简化共享极点 VF)
|
||||
if self.cfg.vf_iterations > 0:
|
||||
for it in range(self.cfg.vf_iterations):
|
||||
new_poles = self._vf_iterate_poles(aligned_S, p_vec, residues_samples, d_samples, e_samples)
|
||||
# 反射不稳定极点
|
||||
new_poles = np.array([p if p.real < 0 else complex(-p.real, p.imag) for p in new_poles])
|
||||
# 去重: 近似重复 (距离 < 1e-3 * |p|) 过滤
|
||||
filtered = []
|
||||
for p in new_poles:
|
||||
if all(abs(p - q) > 1e-3*max(1.0, abs(p)) for q in filtered):
|
||||
filtered.append(p)
|
||||
if self.cfg.verbose:
|
||||
print(f"[VF-Iter {it+1}] poles -> {len(filtered)} kept")
|
||||
self.poles = np.array(filtered, dtype=complex)
|
||||
p_vec = 1j * 2 * np.pi * freqs
|
||||
residues_samples, d_samples, e_samples = self._solve_residues_given_poles(aligned_S, p_vec)
|
||||
# 在参数基上再拟合: 由于 Phi 正交, 系数 = Phi^H * values
|
||||
PhiH = Phi.conj().T # (Q, Ns)
|
||||
K = self.poles.shape[0]
|
||||
self.residue_coeffs = np.zeros((nports, nports, K, Qn), dtype=complex)
|
||||
self.d_coeffs = np.zeros((nports, nports, Qn), dtype=complex)
|
||||
self.e_coeffs = np.zeros((nports, nports, Qn), dtype=complex)
|
||||
for i in range(nports):
|
||||
for j in range(nports):
|
||||
for k in range(K):
|
||||
vk = residues_samples[i, j, k, :] # (Ns,)
|
||||
self.residue_coeffs[i, j, k, :] = PhiH @ vk
|
||||
self.d_coeffs[i, j, :] = PhiH @ d_samples[i, j, :]
|
||||
self.e_coeffs[i, j, :] = PhiH @ e_samples[i, j, :]
|
||||
# 简单误差评估 (训练点重建 RMS)
|
||||
F_base = self._build_frequency_matrix(p_vec, self.poles)
|
||||
train_rms = self._compute_training_rms(residues_samples, d_samples, e_samples, F_base, aligned_S)
|
||||
self.meta['train_rms'] = train_rms
|
||||
self.meta['nports'] = nports
|
||||
self.meta['Ns'] = Ns
|
||||
self.meta['Q'] = Qn
|
||||
return self
|
||||
|
||||
def _compute_training_rms(self, residues_samples, d_samples, e_samples, F_base, original_S_list):
|
||||
"""计算每个样本的平均端口对 RMS 误差。"""
|
||||
nports = residues_samples.shape[0]
|
||||
K = residues_samples.shape[2]
|
||||
Ns = residues_samples.shape[3]
|
||||
Nf = F_base.shape[0]
|
||||
rms = []
|
||||
for s in range(Ns):
|
||||
err_sq_sum = 0.0
|
||||
count = 0
|
||||
S_ref = original_S_list[s]
|
||||
for i in range(nports):
|
||||
for j in range(nports):
|
||||
x = np.concatenate([
|
||||
residues_samples[i, j, :, s],
|
||||
[d_samples[i, j, s], e_samples[i, j, s]]
|
||||
]) # (K+2,)
|
||||
y_hat = F_base @ x # (Nf,)
|
||||
y_ref = S_ref[:, i, j]
|
||||
err = y_ref - y_hat
|
||||
err_sq_sum += np.mean(np.abs(err)**2)
|
||||
count += 1
|
||||
rms.append(np.sqrt(err_sq_sum / max(count,1)))
|
||||
return rms
|
||||
|
||||
def evaluate(self, W: float, L: float, freqs: np.ndarray | None = None) -> np.ndarray:
|
||||
"""返回插值后的 S 参数 (Nf, nports, nports)。"""
|
||||
assert self.frequencies is not None and self.poles is not None
|
||||
assert self.basis is not None
|
||||
assert self.residue_coeffs is not None and self.d_coeffs is not None and self.e_coeffs is not None, "Model not fitted"
|
||||
if freqs is None:
|
||||
freqs = self.frequencies
|
||||
# 若请求频率网格不同, 需要重建频率矩阵
|
||||
p_vec = 1j * 2 * np.pi * freqs
|
||||
K = len(self.poles)
|
||||
nports = self.residue_coeffs.shape[0]
|
||||
phi = self.basis.eval(W, L) # (Q,)
|
||||
# 预分配
|
||||
S_eval = np.zeros((len(freqs), nports, nports), dtype=complex)
|
||||
denom_cache = np.zeros((len(freqs), K), dtype=complex)
|
||||
for k, a_k in enumerate(self.poles):
|
||||
denom_cache[:, k] = 1.0 / (p_vec - a_k)
|
||||
for i in range(nports):
|
||||
for j in range(nports):
|
||||
# 恢复参数依赖的残值/常数/斜率
|
||||
r_k = (self.residue_coeffs[i, j, :, :] @ phi) # (K,)
|
||||
d = self.d_coeffs[i, j, :] @ phi
|
||||
e = self.e_coeffs[i, j, :] @ phi
|
||||
Hf = denom_cache @ r_k + d + p_vec * e # (Nf,)
|
||||
S_eval[:, i, j] = Hf
|
||||
return S_eval
|
||||
|
||||
def export_s2p(self, W: float, L: float, filepath: str, freqs: np.ndarray | None = None, z0: float | complex = 50) -> str:
|
||||
"""在给定几何参数 (W,L) 下计算 S 并写入 Touchstone (.sNp) 文件。
|
||||
|
||||
参数:
|
||||
W, L: 几何参数
|
||||
filepath: 目标文件完整路径, 可包含或不包含 .s2p/.sNp 后缀
|
||||
freqs: 可选自定义频率数组 (需在训练频率范围内); 默认使用训练频率
|
||||
z0: 端口参考阻抗 (标量或与端口数相同长度可扩展)
|
||||
|
||||
返回:
|
||||
实际写入的文件路径
|
||||
"""
|
||||
assert self.frequencies is not None, "Model not fitted"
|
||||
S_eval = self.evaluate(W, L, freqs)
|
||||
if freqs is None:
|
||||
freqs = self.frequencies
|
||||
nports = S_eval.shape[1]
|
||||
freqs_arr = np.asarray(freqs)
|
||||
# 构建 Network 对象
|
||||
ntw = rf.Network(f=freqs_arr, s=S_eval, z0=z0)
|
||||
# 解析输出路径
|
||||
import os
|
||||
base_dir = os.path.dirname(filepath) or '.'
|
||||
os.makedirs(base_dir, exist_ok=True)
|
||||
base_name = os.path.basename(filepath)
|
||||
# 去掉可能已有的扩展名, 由 skrf 自动加 .sNp
|
||||
if '.' in base_name:
|
||||
base_name = '.'.join(base_name.split('.')[:-1]) or base_name
|
||||
# 写文件
|
||||
ntw.write_touchstone(base_name, dir=base_dir)
|
||||
# 构造最终文件名 (.sNp)
|
||||
final_path = os.path.join(base_dir, f"{base_name}.s{nports}p")
|
||||
return final_path
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
'config': self.cfg.__dict__,
|
||||
'frequencies': self.frequencies.tolist() if self.frequencies is not None else None,
|
||||
'poles': self.poles.tolist() if self.poles is not None else None,
|
||||
'basis': {
|
||||
'W_mean': getattr(self.basis, 'W_mean', None),
|
||||
'W_std': getattr(self.basis, 'W_std', None),
|
||||
'L_mean': getattr(self.basis, 'L_mean', None),
|
||||
'L_std': getattr(self.basis, 'L_std', None),
|
||||
'exps': getattr(self.basis, 'exps', None),
|
||||
'R': (lambda _R: _R.tolist() if _R is not None else None)(getattr(self.basis, 'R', None)),
|
||||
},
|
||||
'residue_coeffs': self.residue_coeffs.tolist() if self.residue_coeffs is not None else None,
|
||||
'd_coeffs': self.d_coeffs.tolist() if self.d_coeffs is not None else None,
|
||||
'e_coeffs': self.e_coeffs.tolist() if self.e_coeffs is not None else None,
|
||||
'meta': self.meta,
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def from_dict(data: Dict[str, Any]) -> 'ParametricVectorFitting':
|
||||
cfg = ParametricVFConfig(**data['config'])
|
||||
obj = ParametricVectorFitting(cfg)
|
||||
obj.frequencies = np.array(data['frequencies']) if data['frequencies'] is not None else None
|
||||
obj.poles = np.array(data['poles']) if data['poles'] is not None else None
|
||||
# 重建 basis (仅用于 eval); 重新构造 R_inv
|
||||
if data.get('basis') and data['basis']['R'] is not None:
|
||||
basis_stub = OrthonormalParamBasis(np.array([0.0, 1.0]), np.array([0.0, 1.0]), 1) # 占位
|
||||
basis_stub.W_mean = data['basis']['W_mean']
|
||||
basis_stub.W_std = data['basis']['W_std']
|
||||
basis_stub.L_mean = data['basis']['L_mean']
|
||||
basis_stub.L_std = data['basis']['L_std']
|
||||
basis_stub.exps = [tuple(e) for e in data['basis']['exps']]
|
||||
R = np.array(data['basis']['R'])
|
||||
basis_stub.R = R
|
||||
basis_stub.R_inv = np.linalg.inv(R)
|
||||
# Q 不需要 (仅用于训练点), 设为 None
|
||||
basis_stub.Q = None # type: ignore
|
||||
obj.basis = basis_stub
|
||||
if data.get('residue_coeffs') is not None:
|
||||
obj.residue_coeffs = np.array(data['residue_coeffs'])
|
||||
if data.get('d_coeffs') is not None:
|
||||
obj.d_coeffs = np.array(data['d_coeffs'])
|
||||
if data.get('e_coeffs') is not None:
|
||||
obj.e_coeffs = np.array(data['e_coeffs'])
|
||||
obj.meta = data.get('meta', {})
|
||||
return obj
|
||||
|
||||
# 划分
|
||||
def split_dataset(dataset: List[ModelBasicDatasetUnit], train_ratio: float = 0.7, shuffle: bool = True, seed: int = 0) -> Tuple[List[ModelBasicDatasetUnit], List[ModelBasicDatasetUnit]]:
|
||||
"""划分数据集 70% 训练 / 30% 测试 (默认可改)。"""
|
||||
ds = list(dataset)
|
||||
if shuffle:
|
||||
rng = np.random.default_rng(seed)
|
||||
rng.shuffle(ds)
|
||||
n_train = int(len(ds) * train_ratio)
|
||||
return ds[:n_train], ds[n_train:]
|
||||
|
||||
|
||||
def _interp_s_to_freqs(S: np.ndarray, f_src: np.ndarray, f_tgt: np.ndarray) -> np.ndarray:
|
||||
"""将 S(f_src) 线性插值到 f_tgt (复数分离实虚部)。"""
|
||||
if np.array_equal(f_src, f_tgt):
|
||||
return S
|
||||
nports = S.shape[1]
|
||||
S_new = np.zeros((len(f_tgt), nports, nports), dtype=complex)
|
||||
for i in range(nports):
|
||||
for j in range(nports):
|
||||
y = S[:, i, j]
|
||||
real_part = np.interp(f_tgt, f_src, np.real(y))
|
||||
imag_part = np.interp(f_tgt, f_src, np.imag(y))
|
||||
S_new[:, i, j] = real_part + 1j * imag_part
|
||||
return S_new
|
||||
|
||||
|
||||
def evaluate_dataset(model: ParametricVectorFitting,
|
||||
subset: List[ModelBasicDatasetUnit],
|
||||
network_loader=Capa.network) -> Dict[str, Any]:
|
||||
"""对一个子集评估拟合质量，输出每样本与整体统计."""
|
||||
assert model.frequencies is not None
|
||||
freqs = model.frequencies
|
||||
results = []
|
||||
mag_err_db_all = []
|
||||
rms_all = []
|
||||
for unit in subset:
|
||||
net = network_loader(unit.result_dir, unit.id)
|
||||
S_ref = _interp_s_to_freqs(net.s, net.f, freqs) # (Nf, nports, nports)
|
||||
S_pred = model.evaluate(unit.parameters["W"], unit.parameters["L"], freqs)
|
||||
err = S_pred - S_ref
|
||||
rms = float(np.sqrt(np.mean(np.abs(err)**2)))
|
||||
# 幅度 dB 误差
|
||||
mag_ref_db = 20*np.log10(np.clip(np.abs(S_ref), 1e-15, None))
|
||||
mag_pred_db = 20*np.log10(np.clip(np.abs(S_pred), 1e-15, None))
|
||||
mag_err_db = float(np.mean(np.abs(mag_pred_db - mag_ref_db)))
|
||||
rms_all.append(rms)
|
||||
mag_err_db_all.append(mag_err_db)
|
||||
results.append({
|
||||
'id': unit.id,
|
||||
'W': unit.parameters['W'],
|
||||
'L': unit.parameters['L'],
|
||||
'rms': rms,
|
||||
'mag_err_db': mag_err_db
|
||||
})
|
||||
summary = {
|
||||
'count': len(subset),
|
||||
'rms_mean': float(np.mean(rms_all)) if rms_all else None,
|
||||
'rms_max': float(np.max(rms_all)) if rms_all else None,
|
||||
'mag_err_db_mean': float(np.mean(mag_err_db_all)) if mag_err_db_all else None,
|
||||
'mag_err_db_max': float(np.max(mag_err_db_all)) if mag_err_db_all else None,
|
||||
'details': results
|
||||
}
|
||||
return summary
|
||||
|
||||
|
||||
# 简要使用示例 (非执行):
|
||||
if __name__ == "__main__":
|
||||
|
||||
capa = Capa()
|
||||
capa.sweep()
|
||||
print("sweep finished")
|
||||
capa.export('capa_results.json')
|
||||
capa.clear()
|
||||
capa.load('capa_results.json')
|
||||
dataset = capa.results
|
||||
|
||||
# 70% 训练 / 30% 测试
|
||||
train_set, test_set = split_dataset(dataset, train_ratio=0.7, shuffle=True, seed=42)
|
||||
print(f"Train: {len(train_set)} Test: {len(test_set)}")
|
||||
|
||||
cfg = ParametricVFConfig(n_poles=10, param_total_degree=3, vf_iterations=2,
|
||||
max_freq_points=50)
|
||||
pvf = ParametricVectorFitting(cfg).fit(train_set)
|
||||
|
||||
train_metrics = evaluate_dataset(pvf, train_set)
|
||||
test_metrics = evaluate_dataset(pvf, test_set)
|
||||
|
||||
pvf.meta['train_eval'] = train_metrics
|
||||
pvf.meta['test_eval'] = test_metrics
|
||||
|
||||
def _print_metrics(title: str, m: Dict[str, Any]):
|
||||
print(f"[{title}] samples={m['count']}"
|
||||
f" RMS_mean={m['rms_mean']:.4e} RMS_max={m['rms_max']:.4e}"
|
||||
f" |Δ|dB_mean={m['mag_err_db_mean']:.3f}dB |Δ|dB_max={m['mag_err_db_max']:.3f}dB")
|
||||
|
||||
_print_metrics("TRAIN", train_metrics)
|
||||
_print_metrics("TEST", test_metrics)
|
||||
|
||||
# 示例：导出一个测试点的拟合结果为 s2p
|
||||
if test_set:
|
||||
sample = test_set[0]
|
||||
out_path = pvf.export_s2p(sample.parameters["W"], sample.parameters["L"], filepath='outputs/pvf_test_sample')
|
||||
print("Exported:", out_path)
|
||||
50
schemas/paramer.py
Normal file
50
schemas/paramer.py
Normal file
@@ -0,0 +1,50 @@
|
||||
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
|
||||
started_at:str
|
||||
completed_at: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
|
||||
125
test/lazy_evaulation.py
Normal file
125
test/lazy_evaulation.py
Normal file
@@ -0,0 +1,125 @@
|
||||
import numpy as np
|
||||
|
||||
class PhiBasisIterator:
|
||||
"""
|
||||
逐步生成有理正交 (类 Muntz-Laguerre) 基函数 φ_p(s) / (或复对产生两列) 的迭代器。
|
||||
|
||||
给定:
|
||||
s: 标量或数组 (复频率采样点) s = j*2*pi*f
|
||||
poles: 极点序列 (允许包含复共轭对). 约定:
|
||||
- 纯实极点按自身顺序出现: a = -alpha (alpha>0)
|
||||
- 复极点以正虚部在前, 紧跟其共轭: a = -σ + jω, a* = -σ - jω
|
||||
|
||||
迭代:
|
||||
每次 __next__ 返回一个 list:
|
||||
- 实极点 -> [φ_p(s)]
|
||||
- 复成对 -> [φ_p^(1)(s), φ_p^(2)(s)]
|
||||
使用时可把返回列表中函数 append 到总基函数数组中。
|
||||
|
||||
说明:
|
||||
这里实现的公式与用户原始代码意图相同, 但修正了:
|
||||
1) 原代码 sqrt(2*Re(a)) 在 Re(a)<0 (典型稳定极点) 下产生 NaN, 改为 alpha=-Re(a) >0.
|
||||
2) 原来在 __next__ 中使用 yield (非法); 改为标准迭代协议返回 list。
|
||||
3) 复极点的乘积与后续迭代用的累计乘积 product 分开维护。
|
||||
若需严格匹配论文中“多变量正交化”可再加数值正交 (QR) 步骤。
|
||||
|
||||
注意:
|
||||
本实现假设 poles 已排序; 若不确定, 可预处理把正虚部极点放在其共轭之前。
|
||||
"""
|
||||
def __init__(self, s, poles):
|
||||
self.s = np.asarray(s, dtype=complex) # (Nf,) 或标量
|
||||
self.poles = list(poles)
|
||||
self.k = 0 # 当前处理到第 k 个极点(或复对的首元素)
|
||||
self.N = len(self.poles)
|
||||
# 累计乘积 Π_{已完成的块} ( (s - a_i*)/(s + a_i) ) (复对时使用 (s - a)(s - a*) / (s + a)(s + a*))
|
||||
self.product = np.ones_like(self.s, dtype=complex)
|
||||
|
||||
def __iter__(self):
|
||||
return self
|
||||
|
||||
def __next__(self):
|
||||
if self.k >= self.N:
|
||||
raise StopIteration
|
||||
|
||||
a = self.poles[self.k]
|
||||
|
||||
# 复对: 需要检查正虚部并且下一个是共轭
|
||||
if np.iscomplex(a) and np.imag(a) > 0:
|
||||
if self.k + 1 >= self.N or not np.isclose(self.poles[self.k + 1], np.conj(a)):
|
||||
raise ValueError(f"极点序列中复极点 {a} 未紧跟其共轭, 请排序.")
|
||||
a_conj = self.poles[self.k + 1]
|
||||
sigma = -np.real(a) # >0
|
||||
alpha = sigma
|
||||
# (s + a)(s + a*) = (s + a)(s + conj(a))
|
||||
denom = (self.s + a) * (self.s + a_conj)
|
||||
# |a| 用于生成两组分子 (参考原代码的构造方式)
|
||||
r = np.abs(a)
|
||||
scale = np.sqrt(2 * alpha)
|
||||
|
||||
numerator1 = scale * (self.s - r)
|
||||
numerator2 = scale * (self.s + r)
|
||||
|
||||
phi1 = numerator1 / denom * self.product
|
||||
phi2 = numerator2 / denom * self.product
|
||||
|
||||
# 更新累计乘积用于后续阶次：
|
||||
# 对复对的“整体”传统一致写成 ((s - a)(s - a*) / (s + a)(s + a*))
|
||||
self.product = self.product * ((self.s - a) * (self.s - a_conj)) / denom
|
||||
|
||||
self.k += 2
|
||||
return [phi1, phi2]
|
||||
|
||||
else:
|
||||
# 实极点 (允许传入 float 或实部为负的 complex(实数))
|
||||
a_real = np.real(a)
|
||||
# 稳定极点: a_real < 0, 令 alpha = -a_real > 0
|
||||
if a_real >= 0:
|
||||
raise ValueError(f"实极点需要在左半平面 (负实部), 得到 {a_real}")
|
||||
alpha = -a_real
|
||||
scale = np.sqrt(2 * alpha)
|
||||
# Laguerre 风格: (s - alpha)/(s + alpha)
|
||||
numerator = scale * (self.s - alpha)
|
||||
denom = (self.s + alpha)
|
||||
phi = numerator / denom * self.product
|
||||
# 更新累计乘积
|
||||
self.product = self.product * (self.s - alpha) / (self.s + alpha)
|
||||
self.k += 1
|
||||
return [phi]
|
||||
|
||||
# ------------------ 辅助函数: 批量生成全部基函数 ------------------
|
||||
def generate_phi_basis(s, poles):
|
||||
"""
|
||||
返回一个 list, 其中包含依次生成的所有基函数 (按 real → 1 函数, complex pair → 2 函数).
|
||||
"""
|
||||
basis = []
|
||||
it = PhiBasisIterator(s, poles)
|
||||
for block in it:
|
||||
basis.extend(block)
|
||||
return basis
|
||||
|
||||
# ------------------ 示例与自测 ------------------
|
||||
if __name__ == "__main__":
|
||||
# 示例极点: 1 个实极点 (-1), 一个复对 (-2+0.5j, -2-0.5j), 再一个复对 (-3+1.2j, -3-1.2j)
|
||||
poles = [-0.005+500j, -0.005-500j, -0.012+1200j, -0.012-1200j]
|
||||
f = np.linspace(1e9, 1e11, 200) # 频率 (Hz)
|
||||
s = 1j * 2 * np.pi * f
|
||||
|
||||
basis_funcs = generate_phi_basis(s, poles)
|
||||
print(f"生成基函数数量 = {len(basis_funcs)}") # 实极点 1 →1, 两个复对 →2+2 共 5 个
|
||||
# 简单数值检查 (避免 NaN/Inf)
|
||||
for i, bf in enumerate(basis_funcs):
|
||||
if not np.all(np.isfinite(bf)):
|
||||
print(f"第 {i} 个基函数存在非有限值")
|
||||
else:
|
||||
# print(f"phi[{i}] max|.|={np.max(np.abs(bf)):.3e}, min|.|={np.min(np.abs(bf)):.3e}")
|
||||
print(f"phi[{i}] = {bf}")
|
||||
|
||||
# 验证是否为正交基
|
||||
for i, bf in enumerate(basis_funcs):
|
||||
print(f"phi[{i}]*phi[{i}] = {np.vdot(bf, bf)}") # 自身内积
|
||||
for j in range(i+1, len(basis_funcs)):
|
||||
bf2 = basis_funcs[j]
|
||||
ip = np.vdot(bf, bf2)
|
||||
print(f"phi[{i}] 与 phi[{j}] 内积 = {ip}")
|
||||
if np.abs(ip) > 1e-6:
|
||||
print(f"phi[{i}] 与 phi[{j}] 非正交, 内积={ip}")
|
||||
55
utils.py
Normal file
55
utils.py
Normal file
@@ -0,0 +1,55 @@
|
||||
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