chore: 分离了sweep和vf部分,vf部分准备写为包
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mayge
176
core/VFManager.py
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176
core/VFManager.py
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import matplotlib.pyplot as plt
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import numpy as np
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from .basis.MultiPortOrthonormalBasis import MultiPortOrthonormalBasis
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from .utils import generate_starting_poles
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class VFManager():
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def __init__(
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self,
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npoles_cplx,
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freqs,
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H,
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model=MultiPortOrthonormalBasis,
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iterations:int=5,
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fit_constant:bool=True,
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fit_proportional:bool=False,
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dc_enforce:bool=False,
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passivity_enforce:bool=True,
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verbose:bool=True
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):
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self.freqs=freqs
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self.H=H
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self.iterations=iterations
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self.fit_constant=fit_constant
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self.fit_proportional=fit_proportional
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self.dc_enforce=dc_enforce
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self.passivity_enforce=passivity_enforce
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self.verbose=verbose
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self.nports = H.shape[1]
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self.npoles_cplx = npoles_cplx
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self.least_squares_condition = []
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self.least_squares_row_condition = []
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self.least_squares_col_condition = []
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self.least_squares_rms_error = []
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self.eigenval_condition = []
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self.eigenval_row_condition = []
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self.eigenval_col_condition = []
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self.eigenval_rms_error = []
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self.model_instance = None
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self.model_responses_freqs = None
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self.model_responses_H = None
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self.model=model
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def fit(self):
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self.levi()
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self.model_instance = self.sk_iteration()
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return self.model
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def levi(self):
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self.poles = generate_starting_poles(self.npoles_cplx,beta_min=1e4,beta_max=self.freqs[-1]*1.1)
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self.model_instance=self.model(
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H=self.H,
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freqs=self.freqs,
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poles=self.poles,
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fit_constant=self.fit_constant,
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fit_proportional=self.fit_proportional,
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dc_enforce=self.dc_enforce,
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passivity_enforce=self.passivity_enforce
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)
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return self.model_instance
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def sk_iteration(self):
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for i in range(self.iterations):
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assert self.model_instance is not None ,"Please run levi() first."
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self.poles = self.model_instance.next_poles
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self.weights = self.model_instance.Dt
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self.model_instance = self.model(
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H=self.H,
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freqs=self.freqs,
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poles=self.poles,
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weights=self.weights,
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fit_constant=self.fit_constant,
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fit_proportional=self.fit_proportional,
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dc_enforce=self.dc_enforce,
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passivity_enforce=self.passivity_enforce
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)
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if self.verbose:
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print(f"Iteration {i+1}/{self.iterations}")
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print("A:",self.model_instance.A)
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print("B:",self.model_instance.B)
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print("C:",self.model_instance.C)
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print("D:",self.model_instance.D)
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print("next_pozles:",self.model_instance.next_poles)
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print("Dt:",self.model_instance.Dt)
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print("Dt/Dt_1:",np.linalg.norm(self.model_instance.Dt_Dt_1))
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self.least_squares_condition.append(self.model_instance.least_squares_condition)
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self.least_squares_row_condition.append(self.model_instance.least_squares_row_condition)
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self.least_squares_col_condition.append(self.model_instance.least_squares_col_condition)
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self.least_squares_rms_error.append(self.model_instance.least_squares_rms_error)
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self.eigenval_condition.append(self.model_instance.eigenval_condition)
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self.eigenval_row_condition.append(self.model_instance.eigenval_row_condition)
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self.eigenval_col_condition.append(self.model_instance.eigenval_col_condition)
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self.eigenval_rms_error.append(self.model_instance.eigenval_rms_error)
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return self.model_instance
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def plot_metrics(self,show:bool=True,save_path=None):
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plt.figure(figsize=(16, 12))
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plt.subplot(4, 2, 1)
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plt.plot(self.least_squares_condition, label='Least Squares Condition')
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plt.legend()
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plt.subplot(4, 2, 2)
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plt.plot(self.least_squares_row_condition, label='Least Squares Row Condition')
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plt.legend()
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plt.subplot(4, 2, 3)
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plt.plot(self.least_squares_col_condition, label='Least Squares Col Condition')
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plt.legend()
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plt.subplot(4, 2, 4)
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plt.plot(self.least_squares_rms_error, label='Least Squares RMS Error')
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plt.legend()
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plt.subplot(4, 2, 5)
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plt.plot(self.eigenval_condition, label='Eigenvalue Condition')
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plt.legend()
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plt.subplot(4, 2, 6)
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plt.plot(self.eigenval_row_condition, label='Eigenvalue Row Condition')
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plt.legend()
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plt.subplot(4, 2, 7)
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plt.plot(self.eigenval_col_condition, label='Eigenvalue Col Condition')
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plt.legend()
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plt.subplot(4, 2, 8)
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plt.plot(self.eigenval_rms_error, label='Eigenvalue RMS Error')
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plt.legend()
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if show:
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plt.show()
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if save_path is not None:
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if self.verbose:
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print(f"Saving metrics plot to {save_path}/fitting_metrics.png")
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plt.savefig(f"{save_path}/fitting_metrics.png")
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def plot_model_responses(self,show:bool=True,save_path=None):
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assert self.model_responses_freqs is not None and self.model_responses_H is not None, "Please run get_model_responses() first."
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for i in range(self.nports):
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for j in range(self.nports):
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plt.figure(figsize=(12, 6))
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plt.subplot(2, 2, 1)
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plt.plot(self.freqs, np.abs(self.H[:,i,j]), 'o', ms=4, color='red', label='Input Samples')
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plt.plot(self.model_responses_freqs, np.abs(self.model_responses_H[:,i,j]), '-', lw=2, color='k', label='Fit')
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plt.title(f"Response i={i+1}, j={j+1}")
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plt.ylabel("Magnitude")
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plt.legend(loc="best")
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plt.subplot(2, 2, 2)
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plt.plot(self.freqs, np.angle(self.H[:,i,j],deg=True), 'o', ms=4, color='red', label='Input Samples')
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plt.plot(self.model_responses_freqs, np.angle(self.model_responses_H[:,i,j],deg=True), '-', lw=2, color='k', label='Fit')
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plt.title(f"Response i={i+1}, j={j+1}")
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plt.ylabel("Phase (deg)")
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plt.legend(loc="best")
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plt.tight_layout()
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plt.subplot(2, 2, 3)
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plt.plot(self.freqs, np.real(self.H[:,i,j]), 'o', ms=4, color='red', label='Input Samples')
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plt.plot(self.model_responses_freqs, np.real(self.model_responses_H[:,i,j]), '-', lw=2, color='k', label='Fit')
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plt.title(f"Response i={i+1}, j={j+1}")
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plt.ylabel("Real Part")
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plt.legend(loc="best")
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plt.subplot(2, 2, 4)
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plt.plot(self.freqs, np.imag(self.H[:,i,j]), 'o', ms=4, color='red', label='Input Samples')
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plt.plot(self.model_responses_freqs, np.imag(self.model_responses_H[:,i,j]), '-', lw=2, color='k', label='Fit')
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plt.title(f"Response i={i+1}, j={j+1}")
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plt.ylabel("Imag Part")
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plt.legend(loc="best")
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plt.tight_layout()
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if show:
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plt.show()
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if save_path is not None:
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if self.verbose:
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print(f"Saving response plot for port {i+1},{j+1} to {save_path}/response_{i+1}_{j+1}.png")
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plt.savefig(f"{save_path}/response_{i+1}_{j+1}.png")
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def get_model_responses(self,freqs):
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assert self.model_instance is not None ,"Please run levi() and sk_iteration() first."
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self.model_responses_freqs = freqs
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self.model_responses_H = self.model_instance.get_model_responses(freqs)
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return self.model_responses_H
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