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code refactor
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majsylw
@@ -1,7 +1,7 @@
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# Author: Iraklis Giannakis
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# Author: Iraklis Giannakis, Sylwia Majchrowska
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# E-mail: i.giannakis@ed.ac.uk
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#
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# Copyright (c) 2017 Iraklis Giannakis
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# Copyright (c) 2021 gprMax
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms are permitted
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@@ -21,190 +21,8 @@ import os
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from matplotlib import pylab as plt
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import sys
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import scipy.interpolate
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from tqdm import tqdm
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class Optimizer(object):
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def __init__(self, seed=None):
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"""
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Create particle swarm optimisation object.
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Args:
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seed (int): Seed for RandomState.
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Must be convertible to 32 bit unsigned integers.
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"""
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self.seed = seed
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def fit(self):
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"""
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Call the optimization function that tries to find an optimal set
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of relaxation times that minimise the error
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between the actual and the approximated electric permittivity.
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"""
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raise NotImplementedError()
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@staticmethod
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def plot(x, y):
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"""
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Dynamically plot the error as the optimisation takes place.
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Args:
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x (array): The number of current iterations.
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y (array): The objective value at for all x points.
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"""
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plt.rcParams["axes.facecolor"] = "black"
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plt.plot(x, y, "b-", linewidth=3.0)
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plt.ylim(min(y) - 0.1 * min(y),
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max(y) + 0.1 * max(y))
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plt.xlim(min(x), max(x))
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plt.grid(b=True, which="major", color="w",
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linewidth=0.2, linestyle="--")
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plt.suptitle("Debye fitting process")
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plt.xlabel("Iteration")
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plt.ylabel("Average Error")
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plt.pause(0.0001)
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class Particle_swarm(Optimizer):
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def __init__(self, swarmsize=40, maxiter=50,
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omega=0.9, phip=0.9, phig=0.9,
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minstep=1e-8, pflag=False, seed=None):
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"""
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Create particle swarm optimisation object with predefined parameters.
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Args:
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swarmsize (int): The number of particles in the swarm (Default: 40).
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maxiter (int): The maximum number of iterations for the swarm
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to search (Default: 50).
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omega (float): Particle velocity scaling factor (Default: 0.9).
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phip (float): Scaling factor to search away from the particle's
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best known position (Default: 0.9).
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phig (float): Scaling factor to search away from the swarm's
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best known position (Default: 0.9).
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minstep (float): The minimum stepsize of swarm's best position
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before the search terminates (Default: 1e-8).
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pflag (bool): if True will plot the actual and the approximated
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value during optimization process (Default: False).
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"""
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super(Particle_swarm, self).__init__(seed)
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self.swarmsize = swarmsize
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self.maxiter = maxiter
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self.omega = omega
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self.phip = phip
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self.phig = phig
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self.minstep = minstep
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self.pflag = pflag
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def fit(self, func, lb, ub, funckwargs={}):
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"""
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A particle swarm optimisation that tries to find an optimal set
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of relaxation times that minimise the error
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between the actual and the approximated electric permittivity.
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The current class is a modified edition of the pyswarm package
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which can be found at https://pythonhosted.org/pyswarm/
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Args:
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func (function): The function to be minimized
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lb (array): The lower bounds of the design variable(s)
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ub (array): The upper bounds of the design variable(s)
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funckwargs (dict): Additional keyword arguments passed to
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objective and constraint function
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(Default: empty dict)
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Returns:
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g (array): The swarm's best known position (optimal design).
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fg (float): The objective value at ``g``.
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"""
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np.random.seed(self.seed)
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# check input parameters
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assert len(lb) == len(ub), 'Lower- and upper-bounds must be the same length'
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assert hasattr(func, '__call__'), 'Invalid function handle'
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lb = np.array(lb)
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ub = np.array(ub)
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assert np.all(ub > lb), 'All upper-bound values must be greater than lower-bound values'
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vhigh = np.abs(ub - lb)
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vlow = -vhigh
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# Initialize objective function
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obj = lambda x: func(x=x, **funckwargs)
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# Initialize the particle swarm
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d = len(lb) # the number of dimensions each particle has
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x = np.random.rand(self.swarmsize, d) # particle positions
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v = np.zeros_like(x) # particle velocities
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p = np.zeros_like(x) # best particle positions
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fp = np.zeros(self.swarmsize) # best particle function values
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g = [] # best swarm position
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fg = np.inf # artificial best swarm position starting value
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for i in range(self.swarmsize):
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# Initialize the particle's position
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x[i, :] = lb + x[i, :] * (ub - lb)
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# Initialize the particle's best known position
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p[i, :] = x[i, :]
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# Calculate the objective's value at the current particle's
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fp[i] = obj(p[i, :])
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# At the start, there may not be any feasible starting point,
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# so just
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# give it a temporary "best" point since it's likely to change
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if i == 0:
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g = p[0, :].copy()
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# If the current particle's position is better than the swarm's,
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# update the best swarm position
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if fp[i] < fg:
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fg = fp[i]
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g = p[i, :].copy()
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# Initialize the particle's velocity
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v[i, :] = vlow + np.random.rand(d) * (vhigh - vlow)
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# Iterate until termination criterion met
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for it in tqdm(range(self.maxiter), desc='Debye fitting'):
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rp = np.random.uniform(size=(self.swarmsize, d))
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rg = np.random.uniform(size=(self.swarmsize, d))
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for i in range(self.swarmsize):
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# Update the particle's velocity
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v[i, :] = self.omega * v[i, :] + self.phip * rp[i, :] * \
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(p[i, :] - x[i, :]) + \
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self.phig * rg[i, :] * (g - x[i, :])
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# Update the particle's position,
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# correcting lower and upper bound
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# violations, then update the objective function value
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x[i, :] = x[i, :] + v[i, :]
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mark1 = x[i, :] < lb
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mark2 = x[i, :] > ub
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x[i, mark1] = lb[mark1]
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x[i, mark2] = ub[mark2]
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fx = obj(x[i, :])
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# Compare particle's best position
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# (if constraints are satisfied)
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if fx < fp[i]:
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p[i, :] = x[i, :].copy()
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fp[i] = fx
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# Compare swarm's best position to current
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# particle's position
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# (Can only get here if constraints are satisfied)
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if fx < fg:
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tmp = x[i, :].copy()
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stepsize = np.sqrt(np.sum((g - tmp) ** 2))
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if stepsize <= self.minstep:
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print(f'Stopping search: Swarm best position change less than {self.minstep}')
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return tmp, fx
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else:
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g = tmp.copy()
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fg = fx
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# Dynamically plot the error as the optimisation takes place
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if self.pflag:
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if it == 0:
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xpp = [it]
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ypp = [fg]
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else:
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xpp.append(it)
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ypp.append(fg)
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Particle_swarm.plot(xpp, ypp)
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return g, fg
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from optimization import *
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class Relaxation(object):
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@@ -213,14 +31,7 @@ class Relaxation(object):
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sigma, mu, mu_sigma,
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material_name, plot=True, save=True,
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optimizer=Particle_swarm,
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optimizer_options={'seed': 111,
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'pflag': True,
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'swarmsize': 40,
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'maxiter': 50,
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'omega': 0.9,
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'phip': 0.9,
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'phig': 0.9,
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'minstep': 1e-8}):
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optimizer_options={}):
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"""
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Create Relaxation function object for complex material.
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@@ -239,9 +50,9 @@ class Relaxation(object):
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save (bool): if True will save approximated material parameters
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The argument is optional and if neglected save=False.
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optimizer (Optimizer class): chosen optimization method:
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Particle Swarm, Genetic or Simmulated Annealing.
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Particle Swarm, Genetic or Dual Annealing.
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optimizer_options (dict): Additional keyword arguments passed to
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optimizer class.
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optimizer class (Default: empty dict).
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"""
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self.number_of_debye_poles = number_of_debye_poles
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self.sigma = sigma
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@@ -268,8 +79,36 @@ class Relaxation(object):
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# Set the real and the imaginary part of the relaxation function
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self.rl, self.im = q.real, q.imag
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# Calling the main optimisation module
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self.optimize()
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# if one of the weights is negative increase the stabiliser
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# and repeat the optimisation
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xmp, mx, ee, rp, ip = self.optimize()
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# Print the results in gprMax format style
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properties = self.print_output(xmp, mx, ee)
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if self.save:
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self.save_result(properties)
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# Plot the actual and the approximate dielectric properties
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if self.plot:
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self.plot_result(rp + ee, ip)
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def set_freq(self, f_min, f_max, n=50):
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"""
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Interpolate frequency vector using n
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equally logarithmicaly spaced frequencies.
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Args:
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f_min (float): First bound of the frequency range
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used to approximate the given function (Hz).
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f_max (float): Second bound of the frequency range
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used to approximate the given function (Hz).
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n (int): Number of frequency points in frequency grid.
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Note:
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f_min and f_max must satisfied f_min < f_max
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"""
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# diff_freq = np.log10(f_max) - np.log10(f_min)
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self.freq = np.logspace(np.log10(f_min) + 0.00001,
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np.log10(f_max) - 0.00001,
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int(n))
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# int(n * diff_freq))
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def check_inputs(self):
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"""
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Check the validity of the inputs.
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@@ -301,6 +140,16 @@ class Relaxation(object):
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def optimize(self):
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"""
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Calling the main optimisation module.
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Returns:
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xpm (array): The best known position form optimization module
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(optimal design).
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mx (array): Resulting optimised weights for the given relaxation times
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ee (float): Average error between the actual and the approximated real part
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rp (matrix): The real part of the permittivity for the optimised relaxation
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times and weights for the frequnecies included in freq
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ip (matrix): The imaginary part of the permittivity for the optimised
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relaxation times and weights for the frequnecies included in freq
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"""
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# Define the lower and upper boundaries of search
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lb = np.full(self.number_of_debye_poles,
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@@ -315,23 +164,23 @@ class Relaxation(object):
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'freq_g': self.freq}
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)
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_, _, mx, ee, rp, ip = linear(self.rl, self.im, xmp, self.freq)
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# if one of the weights is negative increase the stabiliser
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# and repeat the optimisation
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# Print the results in gprMax format style
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properties = self.print_output(xmp, mx, ee)
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if self.save:
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self.save_result(properties)
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# Plot the actual and the approximate dielectric properties
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if self.plot:
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self.plot_result(rp + ee, ip)
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return xmp, mx, ee, rp, ip
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def print_output(self, xmp, mx, ee):
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"""Print out the resulting Debye parameters in a gprMax format"""
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"""
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Print out the resulting Debye parameters in a gprMax format.
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Args:
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xpm (array): The best known position form optimization module
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(optimal design).
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mx (): Resulting optimised weights for the given relaxation times.
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ee (): Average error between the actual and the approximated real part.
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"""
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print("Debye expansion parameters: ")
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print(f" |{'e_inf':^14s}|{'De':^14s}|{'log(t0)':^25s}|")
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print("_" * 65)
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for i in range(0, len(xmp)):
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print("Debye {0:}:|{1:^14.5f}|{2:^14.5f}|{3:^25.5f}| "
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print("Debye {0:}:|{1:^14.5f}|{2:^14.5f}|{3:^25.5f}|"
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.format(i + 1, ee/len(xmp), mx[i],
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xmp[i]))
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print("_" * 65)
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@@ -353,31 +202,16 @@ class Relaxation(object):
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material_prop.append(dispersion_prop + '\n')
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return material_prop
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@staticmethod
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def save_result(output, fdir="materials"):
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"""Save the resulting Debye parameters in a gprMax format"""
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if fdir != "materials" and os.path.isdir(fdir):
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file_path = os.path.join(fdir, "my_materials.txt")
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elif os.path.isdir("materials"):
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file_path = os.path.join("materials",
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"my_materials.txt")
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elif os.path.isdir("user_libs/materials"):
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file_path = os.path.join("user_libs", "materials",
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"my_materials.txt")
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else:
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sys.exit("Cannot save material properties "
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f"in {os.path.join(fdir, 'my_materials.txt')}!")
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fileH = open(file_path, "a")
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fileH.write(f"## {output[0].split(' ')[-1]}")
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fileH.writelines(output)
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fileH.write("\n")
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fileH.close()
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print(f"Material properties save at: {file_path}")
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def plot_result(self, rl_exp, im_exp):
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"""
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Plot the actual and the approximated electric permittivity
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using a semilogarithm X axes
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using a semilogarithm X axes.
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Args:
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rl_exp (array): Real parts of optimised Debye expansion
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for given frequency points (plus average error).
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im_exp (array): Imaginary parts of optimised Debye expansion
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for given frequency points.
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"""
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plt.close("all")
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plt.rcParams["axes.facecolor"] = "black"
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@@ -401,22 +235,46 @@ class Relaxation(object):
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plt.ylabel("Relative permittivity")
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plt.show()
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@staticmethod
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def save_result(output, fdir="../materials"):
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"""
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Save the resulting Debye parameters in a gprMax format
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Args:
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output (str): Material and resulting Debye parameters
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in a gprMax format.
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fdir (str): Path to saving directory.
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"""
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if fdir != "../materials" and os.path.isdir(fdir):
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file_path = os.path.join(fdir, "my_materials.txt")
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elif os.path.isdir("../materials"):
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file_path = os.path.join("../materials",
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"my_materials.txt")
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elif os.path.isdir("materials"):
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file_path = os.path.join("materials",
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"my_materials.txt")
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elif os.path.isdir("user_libs/materials"):
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file_path = os.path.join("user_libs", "materials",
|
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"my_materials.txt")
|
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else:
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sys.exit("Cannot save material properties "
|
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f"in {os.path.join(fdir, 'my_materials.txt')}!")
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fileH = open(file_path, "a")
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fileH.write(f"## {output[0].split(' ')[-1]}")
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fileH.writelines(output)
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fileH.write("\n")
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fileH.close()
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print(f"Material properties save at: {file_path}")
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class HavriliakNegami(Relaxation):
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def __init__(self, number_of_debye_poles,
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freq1, freq2, alfa, bita, einf, de, t0,
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f_min, f_max, alfa, bita, einf, de, t0,
|
||||
sigma, mu, mu_sigma,
|
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material_name, plot=False, save=True,
|
||||
optimizer=Particle_swarm,
|
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optimizer_options={'seed': 111,
|
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'pflag': True,
|
||||
'swarmsize': 40,
|
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'maxiter': 50,
|
||||
'omega': 0.9,
|
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'phip': 0.9,
|
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'phig': 0.9,
|
||||
'minstep': 1e-8}):
|
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optimizer_options={}):
|
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"""
|
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Approximate a given Havriliak-Negami function
|
||||
Havriliak-Negami function = einf + de / (1 + (1j * 2 * pi * f *t0)**alfa )**bita,
|
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@@ -426,12 +284,12 @@ class HavriliakNegami(Relaxation):
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number_of_debye_poles (int): Number of Debye functions used to
|
||||
approximate the given electric
|
||||
permittivity.
|
||||
freq1 (float): Define the first bound of the frequency range
|
||||
f_min (float): Define the first bound of the frequency range
|
||||
used to approximate the given function (Hz).
|
||||
freq2 (float): Define the second bound of the frequency range
|
||||
f_max (float): Define the second bound of the frequency range
|
||||
used to approximate the given function (Hz).
|
||||
freq1 and freq2 can be either freq1 > freq2
|
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or freq1 < freq2 but not freq1 = freq2.
|
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Note: f_min and f_max can be either f_min > f_max
|
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or f_min < f_max but not f_min = f_max.
|
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einf (float): The real relative permittivity at infinity frequency
|
||||
alfa (float): Havriliak-Negami parameter. Real positive float number
|
||||
which varies 0 < alfa < 1. For alfa = 1 and bita !=0 & bita !=1
|
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@@ -455,22 +313,22 @@ class HavriliakNegami(Relaxation):
|
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save (bool): if True will save approximated material parameters
|
||||
The argument is optional and if neglected save=False.
|
||||
optimizer (Optimizer class): chosen optimization method:
|
||||
Particle Swarm, Genetic or Simmulated Annealing.
|
||||
Particle Swarm, Genetic or Dual Annealing.
|
||||
(Default: Partocle_swarm)
|
||||
optimizer_options (dict): Additional keyword arguments passed to
|
||||
optimizer class.
|
||||
optimizer class (Default: empty dict).
|
||||
"""
|
||||
super(HavriliakNegami, self).__init__(number_of_debye_poles,
|
||||
sigma, mu, mu_sigma,
|
||||
material_name, plot, save,
|
||||
optimizer, optimizer_options)
|
||||
# Place the lower frequency bound at fr1 and the upper frequency bound at fr2
|
||||
if freq1 > freq2:
|
||||
self.freq1, self.freq2 = freq2, freq1
|
||||
# Place the lower frequency bound at f_min and the upper frequency bound at f_max
|
||||
if f_min > f_max:
|
||||
self.f_min, self.f_max = f_max, f_min
|
||||
else:
|
||||
self.freq1, self.freq2 = freq1, freq2
|
||||
# Choosing 50 frequencies logarithmicaly equally spaced between the bounds given
|
||||
self.freq = np.logspace(np.log10(freq1), np.log10(freq2), 50)
|
||||
self.f_min, self.f_max = f_min, f_max
|
||||
# Choosing n frequencies logarithmicaly equally spaced between the bounds given
|
||||
self.set_freq(self.f_min, self.f_max)
|
||||
self.einf, self.alfa, self.bita, self.de, self.t0 = einf, alfa, bita, de, t0
|
||||
|
||||
def check_inputs(self):
|
||||
@@ -480,7 +338,7 @@ class HavriliakNegami(Relaxation):
|
||||
super(HavriliakNegami, self).check_inputs()
|
||||
try:
|
||||
d = [float(i) for i in
|
||||
[self.freq1, self.freq2, self.alfa,
|
||||
[self.f_min, self.f_max, self.alfa,
|
||||
self.bita, self.einf, self.de, self.t0]]
|
||||
except ValueError:
|
||||
sys.exit("The inputs should be numeric.")
|
||||
@@ -490,7 +348,7 @@ class HavriliakNegami(Relaxation):
|
||||
sys.exit("Alfa value must range between 0-1 (0 <= Alfa <= 1)")
|
||||
if self.bita > 1:
|
||||
sys.exit("Beta value must range between 0-1 (0 <= Beta <= 1)")
|
||||
if self.freq1 == self.freq2:
|
||||
if self.f_min == self.f_max:
|
||||
sys.exit("Null frequency range")
|
||||
|
||||
def print_info(self):
|
||||
@@ -512,18 +370,11 @@ class HavriliakNegami(Relaxation):
|
||||
|
||||
class Jonscher(Relaxation):
|
||||
def __init__(self, number_of_debye_poles,
|
||||
freq1, freq2, einf, ap, omegap, n_p,
|
||||
f_min, f_max, einf, ap, omegap, n_p,
|
||||
sigma, mu, mu_sigma,
|
||||
material_name, plot=False, save=True,
|
||||
optimizer=Particle_swarm,
|
||||
optimizer_options={'seed': 111,
|
||||
'pflag': True,
|
||||
'swarmsize': 40,
|
||||
'maxiter': 50,
|
||||
'omega': 0.9,
|
||||
'phip': 0.9,
|
||||
'phig': 0.9,
|
||||
'minstep': 1e-8}):
|
||||
optimizer_options={}):
|
||||
"""
|
||||
Approximate a given Johnsher function
|
||||
Jonscher function = einf - ap * ( -1j * 2 * pi * f / omegap)**n_p,
|
||||
@@ -533,12 +384,12 @@ class Jonscher(Relaxation):
|
||||
number_of_debye_poles (int): Number of Debye functions used to
|
||||
approximate the given electric
|
||||
permittivity.
|
||||
freq1 (float): Define the first bound of the frequency range
|
||||
f_min (float): Define the first bound of the frequency range
|
||||
used to approximate the given function (Hz).
|
||||
freq2 (float): Define the second bound of the frequency range
|
||||
f_max (float): Define the second bound of the frequency range
|
||||
used to approximate the given function (Hz).
|
||||
freq1 and freq2 can be either freq1 > freq2
|
||||
or freq1 < freq2 but not freq1 = freq2.
|
||||
f_min and f_max can be either f_min > f_max
|
||||
or f_min < f_max but not f_min = f_max.
|
||||
einf (float): The real relative permittivity at infinity frequency
|
||||
ap (float): Jonscher parameter. Real positive float number.
|
||||
omegap (float): Jonscher parameter. Real positive float number.
|
||||
@@ -555,21 +406,21 @@ class Jonscher(Relaxation):
|
||||
save (bool): if True will save approximated material parameters
|
||||
The argument is optional and if neglected save=False.
|
||||
optimizer (Optimizer class): chosen optimization method:
|
||||
Particle Swarm, Genetic or Simmulated Annealing.
|
||||
Particle Swarm, Genetic or Dual Annealing.
|
||||
optimizer_options (dict): Additional keyword arguments passed to
|
||||
optimizer class.
|
||||
optimizer class (Default: empty dict).
|
||||
"""
|
||||
super(Jonscher, self).__init__(number_of_debye_poles,
|
||||
sigma, mu, mu_sigma,
|
||||
material_name, plot, save,
|
||||
optimizer, optimizer_options)
|
||||
# Place the lower frequency bound at fr1 and the upper frequency bound at fr2
|
||||
if freq1 > freq2:
|
||||
self.freq1, self.freq2 = freq2, freq1
|
||||
# Place the lower frequency bound at f_min and the upper frequency bound at f_max
|
||||
if f_min > f_max:
|
||||
self.f_min, self.f_max = f_max, f_min
|
||||
else:
|
||||
self.freq1, self.freq2 = freq1, freq2
|
||||
# Choosing 50 frequencies logarithmicaly equally spaced between the bounds given
|
||||
self.freq = np.logspace(np.log10(freq1), np.log10(freq2), 50)
|
||||
self.f_min, self.f_max = f_min, f_max
|
||||
# Choosing n frequencies logarithmicaly equally spaced between the bounds given
|
||||
self.set_freq(self.f_min, self.f_max)
|
||||
self.einf, self.ap, self.omegap, self.n_p = einf, ap, omegap, n_p
|
||||
|
||||
def check_inputs(self):
|
||||
@@ -579,7 +430,7 @@ class Jonscher(Relaxation):
|
||||
super(Jonscher, self).check_inputs()
|
||||
try:
|
||||
d = [float(i) for i in
|
||||
[self.freq1, self.freq2, self.n_p,
|
||||
[self.f_min, self.f_max, self.n_p,
|
||||
self.einf, self.omegap, self.ap]]
|
||||
except ValueError:
|
||||
sys.exit("The inputs should be numeric.")
|
||||
@@ -587,7 +438,7 @@ class Jonscher(Relaxation):
|
||||
sys.exit("The inputs should be positive.")
|
||||
if self.n_p > 1:
|
||||
sys.exit("n_p value must range between 0-1 (0 <= n_p <= 1)")
|
||||
if self.freq1 == self.freq2:
|
||||
if self.f_min == self.f_max:
|
||||
sys.exit("Error: Null frequency range")
|
||||
|
||||
def print_info(self):
|
||||
@@ -613,17 +464,10 @@ class Jonscher(Relaxation):
|
||||
class Crim(Relaxation):
|
||||
|
||||
def __init__(self, number_of_debye_poles,
|
||||
freq1, freq2, a, f1, e1, sigma,
|
||||
f_min, f_max, a, f1, e1, sigma,
|
||||
mu, mu_sigma, material_name, plot=False, save=True,
|
||||
optimizer=Particle_swarm,
|
||||
optimizer_options={'seed': 111,
|
||||
'pflag': True,
|
||||
'swarmsize': 40,
|
||||
'maxiter': 50,
|
||||
'omega': 0.9,
|
||||
'phip': 0.9,
|
||||
'phig': 0.9,
|
||||
'minstep': 1e-8}):
|
||||
optimizer_options={}):
|
||||
"""
|
||||
Approximate a given CRIM function
|
||||
CRIM = (sum([volumetric_fraction[i]*(material[i][0] + material[i][1] /
|
||||
@@ -634,12 +478,12 @@ class Crim(Relaxation):
|
||||
number_of_debye_poles (int): Number of Debye functions used to
|
||||
approximate the given electric
|
||||
permittivity.
|
||||
freq1 (float): Define the first bound of the frequency range
|
||||
f_min (float): Define the first bound of the frequency range
|
||||
used to approximate the given function (Hz).
|
||||
freq2 (float): Define the second bound of the frequency range
|
||||
f_max (float): Define the second bound of the frequency range
|
||||
used to approximate the given function (Hz).
|
||||
freq1 and freq2 can be either freq1 > freq2
|
||||
or freq1 < freq2 but not freq1 = freq2.
|
||||
f_min and f_max can be either f_min > f_max
|
||||
or f_min < f_max but not f_min = f_max.
|
||||
a (float): shape factor
|
||||
f1 (list): volumetric fraction
|
||||
e1 (list): materials
|
||||
@@ -654,21 +498,21 @@ class Crim(Relaxation):
|
||||
save (bool): if True will save approximated material parameters
|
||||
The argument is optional and if neglected save=False.
|
||||
optimizer (Optimizer class): chosen optimization method:
|
||||
Particle Swarm, Genetic or Simmulated Annealing.
|
||||
Particle Swarm, Genetic or Dual Annealing.
|
||||
optimizer_options (dict): Additional keyword arguments passed to
|
||||
optimizer class.
|
||||
optimizer class (Default: empty dict).
|
||||
"""
|
||||
super(Crim, self).__init__(number_of_debye_poles,
|
||||
sigma, mu, mu_sigma,
|
||||
material_name, plot, save,
|
||||
optimizer, optimizer_options)
|
||||
# Place the lower frequency bound at fr1 and the upper frequency bound at fr2
|
||||
if freq1 > freq2:
|
||||
self.freq1, self.freq2 = freq2, freq1
|
||||
# Place the lower frequency bound at f_min and the upper frequency bound at f_max
|
||||
if f_min > f_max:
|
||||
self.f_min, self.f_max = f_max, f_min
|
||||
else:
|
||||
self.freq1, self.freq2 = freq1, freq2
|
||||
# Choosing 50 frequencies logarithmicaly equally spaced between the bounds given
|
||||
self.freq = np.logspace(np.log10(freq1), np.log10(freq2), 50)
|
||||
self.f_min, self.f_max = f_min, f_max
|
||||
# Choosing n frequencies logarithmicaly equally spaced between the bounds given
|
||||
self.set_freq(self.f_min, self.f_max)
|
||||
self.a, self.f1, self.e1 = a, f1, e1
|
||||
|
||||
def check_inputs(self):
|
||||
@@ -678,7 +522,7 @@ class Crim(Relaxation):
|
||||
super(Crim, self).check_inputs()
|
||||
try:
|
||||
d = [float(i) for i in
|
||||
[self.freq1, self.freq2, self.a]]
|
||||
[self.f_min, self.f_max, self.a]]
|
||||
except ValueError:
|
||||
sys.exit("The inputs should be numeric.")
|
||||
if (np.array(d) < 0).sum() != 0:
|
||||
@@ -689,7 +533,7 @@ class Crim(Relaxation):
|
||||
if len(self.f1) < 2:
|
||||
sys.exit("The materials should be at least 2")
|
||||
# Check if the frequency range is null
|
||||
if self.freq1 == self.freq2:
|
||||
if self.f_min == self.f_max:
|
||||
sys.exit("Null frequency range")
|
||||
# Check if the inputs are positive
|
||||
f = [i for i in self.f1 if i < 0]
|
||||
@@ -736,16 +580,9 @@ class Rawdata(Relaxation):
|
||||
sigma, mu, mu_sigma,
|
||||
material_name, plot=False, save=True,
|
||||
optimizer=Particle_swarm,
|
||||
optimizer_options={'seed': 111,
|
||||
'pflag': True,
|
||||
'swarmsize': 40,
|
||||
'maxiter': 50,
|
||||
'omega': 0.9,
|
||||
'phip': 0.9,
|
||||
'phig': 0.9,
|
||||
'minstep': 1e-8}):
|
||||
optimizer_options={}):
|
||||
"""
|
||||
Interpolate data given from a text file
|
||||
Interpolate data given from a text file.
|
||||
|
||||
Args:
|
||||
number_of_debye_poles (int): Number of Debye functions used to
|
||||
@@ -764,9 +601,9 @@ class Rawdata(Relaxation):
|
||||
save (bool): if True will save approximated material parameters
|
||||
The argument is optional and if neglected save=False.
|
||||
optimizer (Optimizer class): chosen optimization method:
|
||||
Particle Swarm, Genetic or Simmulated Annealing.
|
||||
Particle Swarm, Genetic or Dual Annealing.
|
||||
optimizer_options (dict): Additional keyword arguments passed to
|
||||
optimizer class.
|
||||
optimizer class (Default: empty dict).
|
||||
"""
|
||||
super(Rawdata, self).__init__(number_of_debye_poles,
|
||||
sigma, mu, mu_sigma,
|
||||
@@ -791,9 +628,10 @@ class Rawdata(Relaxation):
|
||||
f" using {self.number_of_debye_poles} Debye poles")
|
||||
|
||||
def calculation(self):
|
||||
"""Interpolate real and imaginary part from data.
|
||||
Column framework of the input file three columns comma-separated
|
||||
Frequency(Hz),Real,Imaginary
|
||||
"""
|
||||
Interpolate real and imaginary part from data.
|
||||
Column framework of the input file three columns comma-separated
|
||||
Frequency(Hz),Real,Imaginary
|
||||
"""
|
||||
# Read the file
|
||||
with open(self.filename) as f:
|
||||
@@ -804,95 +642,33 @@ class Rawdata(Relaxation):
|
||||
except ValueError:
|
||||
sys.exit("Error: The inputs should be numeric")
|
||||
|
||||
# Interpolate using 50 equally logarithmicaly spaced frequencies
|
||||
self.freq = np.logspace(np.log10(min(array[:, 0])) + 0.00001,
|
||||
np.log10(max(array[:, 0])) - 0.00001,
|
||||
50)
|
||||
self.set_freq(min(array[:, 0]), max(array[:, 0]))
|
||||
|
||||
rl_interp = scipy.interpolate.interp1d(array[:, 0], array[:, 1])
|
||||
im_interp = scipy.interpolate.interp1d(array[:, 0], array[:, 2])
|
||||
return rl_interp(self.freq) - 1j * im_interp(self.freq)
|
||||
|
||||
|
||||
def cost_function(x, rl_g, im_g, freq_g):
|
||||
"""
|
||||
The cost function is the average error between
|
||||
the actual and the approximated electric permittivity.
|
||||
|
||||
Returns:
|
||||
cost: The final error
|
||||
"""
|
||||
cost, cost2, _, _, _, _ = linear(rl_g, im_g, x, freq_g)
|
||||
cost = cost + cost2
|
||||
return cost
|
||||
|
||||
|
||||
def linear(rl, im, logt, freq):
|
||||
"""
|
||||
Returns:
|
||||
x: Resulting optimised weights for the given relaxation times
|
||||
cost: The final error
|
||||
ee: Average error between the actual and the approximated real part
|
||||
rp: The real part of the permittivity for the optimised relaxation
|
||||
times and weights for the frequnecies included in freq
|
||||
ip: The imaginary part of the permittivity for the optimised
|
||||
relaxation times and weights for the frequnecies included in freq
|
||||
"""
|
||||
# The relaxation time of the Debyes are given at as logarithms
|
||||
# logt=log10(t0) for efficiency during the optimisation
|
||||
# Here they are transformed back t0=10**logt
|
||||
tt = [10**logt[i] for i in range(0, len(logt))]
|
||||
# y = Ax , here the A matrix for the real and the imaginary part is builded
|
||||
d_r = np.array(
|
||||
[[calc([1, 1, 0, 1, tt[i]], [freq[j]])[0]
|
||||
for i in range(0, len(tt))] for j in
|
||||
range(0, len(freq))])
|
||||
d = np.array(
|
||||
[[calc([1, 1, 0, 1, tt[i]], [freq[j]])[1]
|
||||
for i in range(0, len(tt))] for j in
|
||||
range(0, len(freq))])
|
||||
|
||||
# Adding dumping (Marquart least squares)
|
||||
# Solving the overdetermined system y=Ax
|
||||
x = np.abs(np.linalg.lstsq(d, im)[0])
|
||||
mx, my, my2 = np.matrix(x), np.matrix(d), np.matrix(d_r)
|
||||
rp, ip = my2 * np.transpose(mx), my * np.transpose(mx)
|
||||
cost = np.sum([np.abs(ip[i]-im[i]) for i in range(0, len(im))])/len(im)
|
||||
ee = (np.mean(rl - rp))
|
||||
if ee < 1:
|
||||
ee = 1
|
||||
cost2 = np.sum([np.abs(rp[i] - rl[i] + ee)
|
||||
for i in range(0, len(im))])/len(im)
|
||||
return cost, cost2, x, ee, rp, ip
|
||||
|
||||
|
||||
def calc(cal_inputs, freq):
|
||||
# Calculates the Havriliak-Negami function for the given cal_inputs
|
||||
q = [cal_inputs[2] + cal_inputs[3] / (np.array(1 + np.array(
|
||||
1j * 2 * np.pi * f * cal_inputs[4]) ** cal_inputs[0]
|
||||
) ** cal_inputs[1]) for f in freq]
|
||||
# Return the real and the imaginary part of the relaxation function
|
||||
if len(q) > 1:
|
||||
rl = [q[i].real for i in range(0, len(q))]
|
||||
im = [q[i].imag for i in range(0, len(q))]
|
||||
else:
|
||||
rl = q[0].real
|
||||
im = q[0].imag
|
||||
return rl, im
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
setup = Rawdata(3, "Test.txt", 0.1, 1, 0.1, "M1", plot=True)
|
||||
setup = Rawdata(3, "Test.txt", 0.1, 1, 0.1, "M1", plot=True,
|
||||
optimizer_options={'seed':111,
|
||||
'pflag':True})
|
||||
setup.run()
|
||||
setup = HavriliakNegami(6, 1e12, 1e-3, 0.5, 1, 10, 5,
|
||||
1e-6, 0.1, 1, 0, "M2", plot=True)
|
||||
1e-6, 0.1, 1, 0, "M2", plot=True,
|
||||
optimizer=Dual_annealing,
|
||||
optimizer_options={'seed':111,
|
||||
'maxiter':50})
|
||||
setup.run()
|
||||
setup = Jonscher(4, 1e6, 1e-5, 50, 1, 1e5, 0.7,
|
||||
0.1, 1, 0.1, "M3", plot=True)
|
||||
0.1, 1, 0.1, "M3", plot=True,
|
||||
optimizer_options={'seed':111})
|
||||
setup.run()
|
||||
f = [0.5, 0.5]
|
||||
material1 = [3, 25, 1e6]
|
||||
material2 = [3, 0, 1e3]
|
||||
materials = [material1, material2]
|
||||
setup = Crim(2, 1*1e-1, 1e-9, 0.5, f, materials, 0.1,
|
||||
1, 0, "M4", plot=True)
|
||||
1, 0, "M4", plot=True,
|
||||
optimizer_options={'seed':111})
|
||||
setup.run()
|
||||
385
user_libs/DebyeFit/optimization.py
普通文件
385
user_libs/DebyeFit/optimization.py
普通文件
@@ -0,0 +1,385 @@
|
||||
# Author: Iraklis Giannakis, Sylwia Majchrowska
|
||||
# E-mail: i.giannakis@ed.ac.uk
|
||||
#
|
||||
# Copyright (c) 2021 gprMax
|
||||
# All rights reserved.
|
||||
#
|
||||
# Redistribution and use in source and binary forms are permitted
|
||||
# provided that the above copyright notice and this paragraph are
|
||||
# duplicated in all such forms and that any documentation,
|
||||
# advertising materials, and other materials related to such
|
||||
# distribution and use acknowledge that the software was developed
|
||||
# as part of gprMax. The name of gprMax may not be used to
|
||||
# endorse or promote products derived from this software without
|
||||
# specific prior written permission.
|
||||
# THIS SOFTWARE IS PROVIDED ``AS IS'' AND WITHOUT ANY EXPRESS OR
|
||||
# IMPLIED WARRANTIES, INCLUDING, WITHOUT LIMITATION, THE IMPLIED
|
||||
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.
|
||||
|
||||
import numpy as np
|
||||
from matplotlib import pylab as plt
|
||||
import scipy.optimize
|
||||
from tqdm import tqdm
|
||||
|
||||
|
||||
class Optimizer(object):
|
||||
|
||||
def __init__(self, maxiter=1000, seed=None):
|
||||
"""
|
||||
Create particle swarm optimisation object.
|
||||
|
||||
Args:
|
||||
maxiter (int): The maximum number of iterations for the swarm
|
||||
to search (Default: 1000).
|
||||
seed (int): Seed for RandomState.
|
||||
Must be convertible to 32 bit unsigned integers.
|
||||
"""
|
||||
self.maxiter = maxiter
|
||||
self.seed = seed
|
||||
|
||||
def fit(self):
|
||||
"""
|
||||
Call the optimization function that tries to find an optimal set
|
||||
of relaxation times that minimise the error
|
||||
between the actual and the approximated electric permittivity.
|
||||
"""
|
||||
raise NotImplementedError()
|
||||
|
||||
@staticmethod
|
||||
def plot(x, y):
|
||||
"""
|
||||
Dynamically plot the error as the optimisation takes place.
|
||||
|
||||
Args:
|
||||
x (array): The number of current iterations.
|
||||
y (array): The objective value at for all x points.
|
||||
"""
|
||||
plt.rcParams["axes.facecolor"] = "black"
|
||||
plt.plot(x, y, "b-", linewidth=3.0)
|
||||
plt.ylim(min(y) - 0.1 * min(y),
|
||||
max(y) + 0.1 * max(y))
|
||||
plt.xlim(min(x), max(x))
|
||||
plt.grid(b=True, which="major", color="w",
|
||||
linewidth=0.2, linestyle="--")
|
||||
plt.suptitle("Debye fitting process")
|
||||
plt.xlabel("Iteration")
|
||||
plt.ylabel("Average Error")
|
||||
plt.pause(0.0001)
|
||||
|
||||
|
||||
class Particle_swarm(Optimizer):
|
||||
def __init__(self, swarmsize=40, maxiter=50,
|
||||
omega=0.9, phip=0.9, phig=0.9,
|
||||
minstep=1e-8, minfun=1e-8,
|
||||
pflag=False, seed=None):
|
||||
"""
|
||||
Create particle swarm optimisation object with predefined parameters.
|
||||
|
||||
Args:
|
||||
swarmsize (int): The number of particles in the swarm (Default: 40).
|
||||
maxiter (int): The maximum number of iterations for the swarm
|
||||
to search (Default: 50).
|
||||
omega (float): Particle velocity scaling factor (Default: 0.9).
|
||||
phip (float): Scaling factor to search away from the particle's
|
||||
best known position (Default: 0.9).
|
||||
phig (float): Scaling factor to search away from the swarm's
|
||||
best known position (Default: 0.9).
|
||||
minstep (float): The minimum stepsize of swarm's best position
|
||||
before the search terminates (Default: 1e-8).
|
||||
minfun (float): The minimum change of swarm's best objective value
|
||||
before the search terminates (Default: 1e-8)
|
||||
pflag (bool): if True will plot the actual and the approximated
|
||||
value during optimization process (Default: False).
|
||||
"""
|
||||
super(Particle_swarm, self).__init__(maxiter, seed)
|
||||
self.swarmsize = swarmsize
|
||||
self.omega = omega
|
||||
self.phip = phip
|
||||
self.phig = phig
|
||||
self.minstep = minstep
|
||||
self.minfun = minfun
|
||||
self.pflag = pflag
|
||||
|
||||
def fit(self, func, lb, ub, funckwargs={}):
|
||||
"""
|
||||
A particle swarm optimisation that tries to find an optimal set
|
||||
of relaxation times that minimise the error
|
||||
between the actual and the approximated electric permittivity.
|
||||
The current class is a modified edition of the pyswarm package
|
||||
which can be found at https://pythonhosted.org/pyswarm/
|
||||
|
||||
Args:
|
||||
func (function): The function to be minimized.
|
||||
lb (array): The lower bounds of the design variable(s).
|
||||
ub (array): The upper bounds of the design variable(s).
|
||||
funckwargs (dict): Additional keyword arguments passed to
|
||||
objective and constraint function
|
||||
(Default: empty dict).
|
||||
|
||||
Returns:
|
||||
g (array): The swarm's best known position (optimal design).
|
||||
fg (float): The objective value at ``g``.
|
||||
"""
|
||||
np.random.seed(self.seed)
|
||||
# check input parameters
|
||||
assert len(lb) == len(ub), 'Lower- and upper-bounds must be the same length'
|
||||
assert hasattr(func, '__call__'), 'Invalid function handle'
|
||||
lb = np.array(lb)
|
||||
ub = np.array(ub)
|
||||
assert np.all(ub > lb), 'All upper-bound values must be greater than lower-bound values'
|
||||
|
||||
vhigh = np.abs(ub - lb)
|
||||
vlow = -vhigh
|
||||
|
||||
# Initialize objective function
|
||||
obj = lambda x: func(x=x, **funckwargs)
|
||||
|
||||
# Initialize the particle swarm
|
||||
d = len(lb) # the number of dimensions each particle has
|
||||
x = np.random.rand(self.swarmsize, d) # particle positions
|
||||
v = np.zeros_like(x) # particle velocities
|
||||
p = np.zeros_like(x) # best particle positions
|
||||
fp = np.zeros(self.swarmsize) # best particle function values
|
||||
g = [] # best swarm position
|
||||
fg = np.inf # artificial best swarm position starting value
|
||||
|
||||
for i in range(self.swarmsize):
|
||||
# Initialize the particle's position
|
||||
x[i, :] = lb + x[i, :] * (ub - lb)
|
||||
# Initialize the particle's best known position
|
||||
p[i, :] = x[i, :]
|
||||
# Calculate the objective's value at the current particle's
|
||||
fp[i] = obj(p[i, :])
|
||||
# At the start, there may not be any feasible starting point,
|
||||
# so just
|
||||
# give it a temporary "best" point since it's likely to change
|
||||
if i == 0:
|
||||
g = p[0, :].copy()
|
||||
# If the current particle's position is better than the swarm's,
|
||||
# update the best swarm position
|
||||
if fp[i] < fg:
|
||||
fg = fp[i]
|
||||
g = p[i, :].copy()
|
||||
# Initialize the particle's velocity
|
||||
v[i, :] = vlow + np.random.rand(d) * (vhigh - vlow)
|
||||
|
||||
# Iterate until termination criterion met
|
||||
for it in tqdm(range(self.maxiter), desc='Debye fitting'):
|
||||
rp = np.random.uniform(size=(self.swarmsize, d))
|
||||
rg = np.random.uniform(size=(self.swarmsize, d))
|
||||
for i in range(self.swarmsize):
|
||||
# Update the particle's velocity
|
||||
v[i, :] = self.omega * v[i, :] + self.phip * rp[i, :] * \
|
||||
(p[i, :] - x[i, :]) + \
|
||||
self.phig * rg[i, :] * (g - x[i, :])
|
||||
# Update the particle's position,
|
||||
# correcting lower and upper bound
|
||||
# violations, then update the objective function value
|
||||
x[i, :] = x[i, :] + v[i, :]
|
||||
mark1 = x[i, :] < lb
|
||||
mark2 = x[i, :] > ub
|
||||
x[i, mark1] = lb[mark1]
|
||||
x[i, mark2] = ub[mark2]
|
||||
fx = obj(x[i, :])
|
||||
# Compare particle's best position
|
||||
if fx < fp[i]:
|
||||
p[i, :] = x[i, :].copy()
|
||||
fp[i] = fx
|
||||
# Compare swarm's best position to current
|
||||
# particle's position
|
||||
if fx < fg:
|
||||
tmp = x[i, :].copy()
|
||||
stepsize = np.sqrt(np.sum((g - tmp) ** 2))
|
||||
if np.abs(fg - fx) <= self.minfun:
|
||||
print(f'Stopping search: Swarm best objective '
|
||||
f'change less than {self.minfun}')
|
||||
return tmp, fx
|
||||
elif stepsize <= self.minstep:
|
||||
print(f'Stopping search: Swarm best position '
|
||||
f'change less than {self.minstep}')
|
||||
return tmp, fx
|
||||
else:
|
||||
g = tmp.copy()
|
||||
fg = fx
|
||||
|
||||
# Dynamically plot the error as the optimisation takes place
|
||||
if self.pflag:
|
||||
if it == 0:
|
||||
xpp = [it]
|
||||
ypp = [fg]
|
||||
else:
|
||||
xpp.append(it)
|
||||
ypp.append(fg)
|
||||
Particle_swarm.plot(xpp, ypp)
|
||||
return g, fg
|
||||
|
||||
|
||||
class Dual_annealing(Optimizer):
|
||||
def __init__(self, maxiter=100,
|
||||
local_search_options={}, initial_temp=5230.0,
|
||||
restart_temp_ratio=2e-05, visit=2.62, accept=- 5.0,
|
||||
maxfun=1e7, no_local_search=False,
|
||||
callback=None, x0=None, seed=None):
|
||||
"""
|
||||
Create dual annealing object with predefined parameters.
|
||||
|
||||
Args:
|
||||
maxiter (int): The maximum number of iterations for the swarm
|
||||
to search (Default: 100).
|
||||
local_search_options (dict): Extra keyword arguments to be passed
|
||||
to the local minimizer, reffer to
|
||||
scipy.optimize.minimize() function
|
||||
(Default: empty dict).
|
||||
initial_temp (float): The initial temperature, use higher values to
|
||||
facilitates a wider search of the energy
|
||||
landscape, allowing dual_annealing to escape
|
||||
local minima that it is trapped in.
|
||||
Range is (0.01, 5.e4] (Default: 5230).
|
||||
restart_temp_ratio (float): During the annealing process,
|
||||
temperature is decreasing, when it
|
||||
reaches initial_temp * restart_temp_ratio,
|
||||
the reannealing process is triggered.
|
||||
Range is (0, 1) (Default: 2e-5).
|
||||
visit (float): Parameter for visiting distribution. The value range is (1, 3]
|
||||
(Default: 2.62).
|
||||
accept (float): Parameter for acceptance distribution. It is used to control
|
||||
the probability of acceptance. The lower the acceptance parameter,
|
||||
the smaller the probability of acceptance. The value range (-1e4, -5]
|
||||
(Default: -5.0).
|
||||
no_local_search (bool):
|
||||
maxfun (int): Soft limit for the number of objective function calls.
|
||||
(Default: 1e7).
|
||||
callback (callable): A callback function with signature callback(x, f, context),
|
||||
which will be called for all minima found.
|
||||
x and f are the coordinates and function value of
|
||||
the latest minimum found, and context has value in [0, 1, 2],
|
||||
with the following meaning:
|
||||
0: minimum detected in the annealing process.
|
||||
1: detection occurred in the local search process.
|
||||
2: detection done in the dual annealing process.
|
||||
If the callback implementation returns True,
|
||||
the algorithm will stop.
|
||||
x0 (ndarray): Coordinates of a single N-D starting point, shape(n,).
|
||||
(Default: None).
|
||||
seed (None, int): Specify seed for repeatable minimizations.
|
||||
The random numbers generated with this seed only
|
||||
affect the visiting distribution function and
|
||||
new coordinates generation (Default: None).
|
||||
pflag (bool): if True will plot the actual and the approximated
|
||||
value during optimization process (Default: False).
|
||||
"""
|
||||
super(Dual_annealing, self).__init__(maxiter, seed)
|
||||
self.local_search_options = local_search_options
|
||||
self.initial_temp = initial_temp
|
||||
self.restart_temp_ratio = restart_temp_ratio
|
||||
self.visit = visit
|
||||
self.accept = accept
|
||||
self.maxfun = maxfun
|
||||
self.no_local_search = no_local_search
|
||||
self.callback = callback
|
||||
self.x0 = x0
|
||||
#self.pflag = pflag
|
||||
|
||||
def fit(self, func, lb, ub, funckwargs={}):
|
||||
"""
|
||||
Find the global minimum of a function using Dual Annealing.
|
||||
The current class is a modified edition of the scipy.optimize
|
||||
package which can be found at:
|
||||
https://docs.scipy.org/doc/scipy/reference/generated/
|
||||
scipy.optimize.dual_annealing.html#scipy.optimize.dual_annealing
|
||||
|
||||
Args:
|
||||
func (function): The function to be minimized
|
||||
lb (array): The lower bounds of the design variable(s)
|
||||
ub (array): The upper bounds of the design variable(s)
|
||||
funckwargs (dict): Additional keyword arguments passed to
|
||||
objective and constraint function
|
||||
(Default: empty dict)
|
||||
|
||||
Returns:
|
||||
g (array): The solution array (optimal design).
|
||||
fg (float): The objective value at the solution.
|
||||
"""
|
||||
np.random.seed(self.seed)
|
||||
result = scipy.optimize.dual_annealing(func,
|
||||
bounds=list(zip(lb, ub)),
|
||||
args=funckwargs.values(),
|
||||
maxiter=self.maxiter,
|
||||
local_search_options=self.local_search_options,
|
||||
initial_temp=self.initial_temp,
|
||||
restart_temp_ratio=self.restart_temp_ratio,
|
||||
visit=self.visit,
|
||||
accept=self.accept,
|
||||
maxfun=self.maxfun,
|
||||
no_local_search=self.no_local_search,
|
||||
callback=self.callback,
|
||||
x0=self.x0)
|
||||
return result.x, result.fun
|
||||
|
||||
|
||||
def cost_function(x, rl_g, im_g, freq_g):
|
||||
"""
|
||||
The cost function is the average error between
|
||||
the actual and the approximated electric permittivity.
|
||||
|
||||
Returns:
|
||||
cost: The final error
|
||||
"""
|
||||
cost1, cost2, _, _, _, _ = linear(rl_g, im_g, x, freq_g)
|
||||
cost = cost1 + cost2
|
||||
return cost
|
||||
|
||||
|
||||
def linear(rl, im, logt, freq):
|
||||
"""
|
||||
Returns:
|
||||
cost1: Error (?)
|
||||
cost2: Error (?)
|
||||
x: Resulting optimised weights for the given relaxation times
|
||||
ee: Average error between the actual and the approximated real part
|
||||
rp: The real part of the permittivity for the optimised relaxation
|
||||
times and weights for the frequnecies included in freq
|
||||
ip: The imaginary part of the permittivity for the optimised
|
||||
relaxation times and weights for the frequnecies included in freq
|
||||
"""
|
||||
# The relaxation time of the Debyes are given at as logarithms
|
||||
# logt=log10(t0) for efficiency during the optimisation
|
||||
# Here they are transformed back t0=10**logt
|
||||
tt = [10**logt[i] for i in range(0, len(logt))]
|
||||
# y = Ax , here the A matrix for the real and the imaginary part is builded
|
||||
d_r = np.array(
|
||||
[[calc([1, 1, 0, 1, tt[i]], [freq[j]])[0]
|
||||
for i in range(0, len(tt))] for j in
|
||||
range(0, len(freq))])
|
||||
d = np.array(
|
||||
[[calc([1, 1, 0, 1, tt[i]], [freq[j]])[1]
|
||||
for i in range(0, len(tt))] for j in
|
||||
range(0, len(freq))])
|
||||
|
||||
# Adding dumping (Marquart least squares)
|
||||
# Solving the overdetermined system y=Ax
|
||||
x = np.abs(np.linalg.lstsq(d, im)[0])
|
||||
mx, my, my2 = np.matrix(x), np.matrix(d), np.matrix(d_r)
|
||||
rp, ip = my2 * np.transpose(mx), my * np.transpose(mx)
|
||||
cost1 = np.sum([np.abs(ip[i]-im[i]) for i in range(0, len(im))])/len(im)
|
||||
ee = (np.mean(rl - rp))
|
||||
if ee < 1:
|
||||
ee = 1
|
||||
cost2 = np.sum([np.abs(rp[i] - rl[i] + ee)
|
||||
for i in range(0, len(im))])/len(im)
|
||||
return cost1, cost2, x, ee, rp, ip
|
||||
|
||||
|
||||
def calc(cal_inputs, freq):
|
||||
# Calculates the Havriliak-Negami function for the given cal_inputs
|
||||
q = [cal_inputs[2] + cal_inputs[3] / (np.array(1 + np.array(
|
||||
1j * 2 * np.pi * f * cal_inputs[4]) ** cal_inputs[0]
|
||||
) ** cal_inputs[1]) for f in freq]
|
||||
# Return the real and the imaginary part of the relaxation function
|
||||
if len(q) > 1:
|
||||
rl = [q[i].real for i in range(0, len(q))]
|
||||
im = [q[i].imag for i in range(0, len(q))]
|
||||
else:
|
||||
rl = q[0].real
|
||||
im = q[0].imag
|
||||
return rl, im
|
||||
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