76 lines
2.7 KiB
Python
76 lines
2.7 KiB
Python
import scipy.optimize
|
|
import numpy as np
|
|
|
|
from SpectralParameters import SpectralParams
|
|
import f_models
|
|
from f_norms import FNorm
|
|
|
|
class OptAlgorithm:
|
|
"""Base class for optimization algorithms.
|
|
|
|
"""
|
|
|
|
def __init__(self, initial_model: SpectralParams, freq_bins, amplitude_spectrum, weights, travel_time, config,
|
|
logger):
|
|
self.initial_model = initial_model
|
|
self.config = config
|
|
self.travel_time = travel_time
|
|
|
|
self.f_model = getattr(f_models, config.get("sp_fit_model"))(freq_bins=freq_bins,
|
|
spectral_parameters=initial_model)
|
|
self.f_norm = FNorm(norm=self.config.get("norm"), spectral_params=initial_model, freq_bins=freq_bins,
|
|
amplitude_spectrum=amplitude_spectrum, weights=weights, travel_time=travel_time,
|
|
source_model=self.f_model, logger=logger)
|
|
self.solution = None
|
|
self.error = None
|
|
self.name = self.__class__.__name__
|
|
|
|
|
|
def run(self):
|
|
"""Run the optimization algorithm and return SpectralParams results
|
|
|
|
:return:
|
|
"""
|
|
return self.error, self.solution
|
|
|
|
def __repr__(self):
|
|
if self.solution:
|
|
output = f"{self.name} results:\n"
|
|
output += f" {self.solution.__str__()} \n"
|
|
output += f" Error: {self.error:.4f}"
|
|
return output
|
|
else:
|
|
return f"{self.name}: no solution"
|
|
|
|
|
|
class OptNelderMead(OptAlgorithm):
|
|
"""
|
|
Minimize a function using the downhill simplex algorithm from scipy.optimize.
|
|
"""
|
|
|
|
def __init__(self, initial_model: SpectralParams, freq_bins, amplitude_spectrum, weights, travel_time, config,
|
|
logger):
|
|
super().__init__(initial_model, freq_bins, amplitude_spectrum, weights, travel_time, config, logger)
|
|
self.initial_q = (self.config.get("q_min")+self.config.get("q_max"))/2
|
|
|
|
def run(self):
|
|
def prepare_fun(x):
|
|
self.f_norm.spectral_par = SpectralParams(mo=x[0], fo=x[1], q=x[2], )
|
|
return self.f_norm.calculate().misfit
|
|
|
|
# Initial model parameters
|
|
x0 = [self.initial_model.mo, self.initial_model.fo, self.initial_q]
|
|
# Optimization bounds
|
|
bounds = [(None, None), (1 / self.config.get("window_len"), self.config.get("freq_max")),
|
|
(self.config.get("q_min"), self.config.get("q_max"))]
|
|
|
|
|
|
# Perform optimization
|
|
xopt = scipy.optimize.minimize(method='Nelder-Mead', fun=prepare_fun, x0=np.array(x0), bounds=bounds)
|
|
|
|
# Store the results
|
|
self.solution = SpectralParams(mo=xopt.x[0], fo=xopt.x[1], q=xopt.x[2])
|
|
self.error = xopt.fun
|
|
|
|
return self.error, self.solution
|