# RepTate: Rheology of Entangled Polymers: Toolkit for the Analysis of Theory and Experiments
# --------------------------------------------------------------------------------------------------------
#
# Authors:
# Jorge Ramirez, jorge.ramirez@upm.es
# Victor Boudara, victor.boudara@gmail.com
#
# Useful links:
# http://blogs.upm.es/compsoftmatter/software/reptate/
# https://github.com/jorge-ramirez-upm/RepTate
# http://reptate.readthedocs.io
#
# --------------------------------------------------------------------------------------------------------
#
# Copyright (2017-2023): Jorge Ramirez, Victor Boudara, Universidad Politécnica de Madrid, University of Leeds
#
# This file is part of RepTate.
#
# RepTate is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# RepTate is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with RepTate. If not, see <http://www.gnu.org/licenses/>.
#
# --------------------------------------------------------------------------------------------------------
"""Module TheoryDebyeModes
Module that defines theories related to Debye modes, in the frequency and time domains.
"""
import numpy as np
from RepTate.core.DataTable import DataTable
from RepTate.core.Parameter import Parameter, ParameterType, OptType
from RepTate.gui.QTheory import QTheory
from PySide6.QtWidgets import QToolBar, QSpinBox
from PySide6.QtCore import QSize
from PySide6.QtGui import QIcon
from RepTate.core.DraggableArtists import DragType, DraggableModesSeries
[docs]
class TheoryDebyeModesFrequency(QTheory):
"""Fit a generalized Debye model to a frequency dependent relaxation function.
* **Function**
.. math::
\\begin{eqnarray}
\\epsilon'(\\omega) & = & \\epsilon_\\infty + \\sum_{1}^{n_{modes}} \\Delta\\epsilon_i \\frac{1}{1+(\\omega\\tau_i)^2} \\\\
\\epsilon''(\\omega) & = & \\sum_{1}^{n_{modes}} \\Delta\\epsilon_i \\frac{\\omega\\tau_i}{1+(\\omega\\tau_i)^2}
\\end{eqnarray}
* **Parameters**
- einf = :math:`\\epsilon_{\\infty}`: Unrelaxed permitivity
- :math:`n_{modes}`: number of Debye modes equally distributed in logarithmic scale between :math:`\\omega_{min}` and :math:`\\omega_{max}`.
- logwmin = :math:`\\log(\\omega_{min})`: decimal logarithm of the minimum frequency.
- logwmax = :math:`\\log(\\omega_{max})`: decimal logarithm of the maximum frequency.
- logDei = :math:`\\log(\\Delta\\epsilon_{i})`, where :math:`\\Delta\\epsilon_{i}=\\epsilon_{s,i}-\\epsilon_\\infty`: decimal logarithm of the relaxation strength of Debye mode :math:`i`, where :math:`\\epsilon_{s,i}` is the static permitivity of mode :math:`i`.
"""
thname = "Debye modes"
description = "Fit Debye modes"
citations = []
doi = []
html_help_file = "http://reptate.readthedocs.io/manual/Applications/Dielectric/Theory/theory.html#debye-modes"
single_file = True
def __init__(self, name="", parent_dataset=None, ax=None):
"""**Constructor**"""
super().__init__(name, parent_dataset, ax)
self.function = self.DebyeModesFrequency
self.has_modes = False
self.MAX_MODES = 40
self.view_modes = True
wmin = self.parent_dataset.minpositivecol(0)
wmax = self.parent_dataset.maxcol(0)
nmodes = int(np.round(np.log10(wmax / wmin)))
self.parameters["einf"] = Parameter(
"einf",
0.0,
"Unrelaxed permittivity",
ParameterType.real,
opt_type=OptType.opt,
min_value=0,
)
self.parameters["logwmin"] = Parameter(
"logwmin",
np.log10(wmin),
"Log of frequency range minimum",
ParameterType.real,
opt_type=OptType.opt,
)
self.parameters["logwmax"] = Parameter(
"logwmax",
np.log10(wmax),
"Log of frequency range maximum",
ParameterType.real,
opt_type=OptType.opt,
)
self.parameters["nmodes"] = Parameter(
name="nmodes",
value=nmodes,
description="Number of Debye modes",
type=ParameterType.integer,
opt_type=OptType.const,
display_flag=False,
)
# Interpolate modes from data
w = np.logspace(np.log10(wmin), np.log10(wmax), nmodes)
eps = np.abs(
np.interp(
w,
self.parent_dataset.files[0].data_table.data[:, 0],
self.parent_dataset.files[0].data_table.data[:, 1],
)
)
for i in range(self.parameters["nmodes"].value):
self.parameters["logDe%02d" % i] = Parameter(
"logDe%02d" % i,
np.log10(eps[i]),
"Log of Mode %d amplitude" % i,
ParameterType.real,
opt_type=OptType.opt,
)
# GRAPHIC MODES
self.graphicmodes = []
self.artistmodes = []
self.setup_graphic_modes()
# add widgets specific to the theory
tb = QToolBar()
tb.setIconSize(QSize(24, 24))
self.spinbox = QSpinBox()
self.spinbox.setRange(1, self.MAX_MODES) # min and max number of modes
self.spinbox.setSuffix(" modes")
self.spinbox.setValue(self.parameters["nmodes"].value) # initial value
tb.addWidget(self.spinbox)
self.modesaction = tb.addAction(
QIcon(":/Icon8/Images/new_icons/icons8-visible.png"), "View modes"
)
self.modesaction.setCheckable(True)
self.modesaction.setChecked(True)
self.thToolsLayout.insertWidget(0, tb)
connection_id = self.spinbox.valueChanged.connect(
self.handle_spinboxValueChanged
)
connection_id = self.modesaction.triggered.connect(self.modesaction_change)
[docs]
def modesaction_change(self, checked):
"""Change visibility of modes"""
self.graphicmodes_visible(checked)
# self.view_modes = self.modesaction.isChecked()
# self.graphicmodes.set_visible(self.view_modes)
# self.do_calculate("")
[docs]
def handle_spinboxValueChanged(self, value):
"""Handle a change of the parameter 'nmode'"""
nmodesold = self.parameters["nmodes"].value
wminold = self.parameters["logwmin"].value
wmaxold = self.parameters["logwmax"].value
wold = np.logspace(wminold, wmaxold, nmodesold)
Gold = np.zeros(nmodesold)
for i in range(nmodesold):
Gold[i] = self.parameters["logDe%02d" % i].value
del self.parameters["logDe%02d" % i]
nmodesnew = value
self.set_param_value("nmodes", nmodesnew)
wnew = np.logspace(wminold, wmaxold, nmodesnew)
Gnew = np.interp(wnew, wold, Gold)
for i in range(nmodesnew):
self.parameters["logDe%02d" % i] = Parameter(
"logDe%02d" % i,
Gnew[i],
"Log of Mode %d amplitude" % i,
ParameterType.real,
opt_type=OptType.opt,
)
if self.autocalculate:
self.parent_dataset.handle_actionCalculate_Theory()
self.update_parameter_table()
[docs]
def drag_mode(self, dx, dy):
"""Move around modes"""
nmodes = self.parameters["nmodes"].value
if self.parent_dataset.parent_application.current_view.log_x:
self.set_param_value("logwmin", np.log10(dx[0]))
self.set_param_value("logwmax", np.log10(dx[nmodes - 1]))
else:
self.set_param_value("logwmin", dx[0])
self.set_param_value("logwmax", dx[nmodes - 1])
if self.parent_dataset.parent_application.current_view.log_y:
for i in range(nmodes):
self.set_param_value("logDe%02d" % i, np.log10(dy[i]))
else:
for i in range(nmodes):
self.set_param_value("logDe%02d" % i, dy[i])
self.do_calculate("")
self.update_parameter_table()
[docs]
def update_modes(self):
"""Do nothing"""
pass
[docs]
def setup_graphic_modes(self):
"""Setup graphic representation of modes"""
nmodes = self.parameters["nmodes"].value
w = np.logspace(
self.parameters["logwmin"].value, self.parameters["logwmax"].value, nmodes
)
eps = np.zeros(nmodes)
for i in range(nmodes):
eps[i] = np.power(10, self.parameters["logDe%02d" % i].value)
self.graphicmodes = self.ax.plot(w, eps)[0]
self.graphicmodes.set_marker("D")
self.graphicmodes.set_linestyle("")
self.graphicmodes.set_visible(self.view_modes)
self.graphicmodes.set_markerfacecolor("yellow")
self.graphicmodes.set_markeredgecolor("black")
self.graphicmodes.set_markeredgewidth(3)
self.graphicmodes.set_markersize(8)
self.graphicmodes.set_alpha(0.5)
self.artistmodes = DraggableModesSeries(
self.graphicmodes,
DragType.special,
self.parent_dataset.parent_application,
self.drag_mode,
)
self.plot_theory_stuff()
[docs]
def destructor(self):
"""Called when the theory tab is closed"""
self.graphicmodes_visible(False)
self.graphicmodes.remove()
# self.ax.lines.remove(self.graphicmodes)
[docs]
def graphicmodes_visible(self, state):
"""Set visibility of graphic modes"""
self.view_modes = state
self.graphicmodes.set_visible(self.view_modes)
if self.view_modes:
self.artistmodes.connect()
else:
self.artistmodes.disconnect()
# self.do_calculate("")
self.parent_dataset.parent_application.update_plot()
[docs]
def get_modes(self):
"""Get the values of Maxwell Modes from this theory"""
nmodes = self.parameters["nmodes"].value
freq = np.logspace(
self.parameters["logwmin"].value, self.parameters["logwmax"].value, nmodes
)
tau = 1.0 / freq
eps = np.zeros(nmodes)
for i in range(nmodes):
eps[i] = np.power(10, self.parameters["logDe%02d" % i].value)
return tau, eps, True
[docs]
def DebyeModesFrequency(self, f=None):
"""Actual function that calculates the thoery"""
ft = f.data_table
tt = self.tables[f.file_name_short]
tt.num_columns = ft.num_columns
tt.num_rows = ft.num_rows
tt.data = np.zeros((tt.num_rows, tt.num_columns))
tt.data[:, 0] = ft.data[:, 0]
einf = self.parameters["einf"].value
nmodes = self.parameters["nmodes"].value
freq = np.logspace(
self.parameters["logwmin"].value, self.parameters["logwmax"].value, nmodes
)
tau = 1.0 / freq
tt.data[:, 1] += einf
for i in range(nmodes):
if self.stop_theory_flag:
break
wT = tt.data[:, 0] * tau[i]
wTsq = wT ** 2
eps = np.power(10, self.parameters["logDe%02d" % i].value)
tt.data[:, 1] += eps * 1 / (1 + wTsq)
tt.data[:, 2] += eps * wT / (1 + wTsq)
[docs]
def plot_theory_stuff(self):
"""Plot theory graphic modes"""
# if not self.view_modes:
# return
data_table_tmp = DataTable(self.axarr)
data_table_tmp.num_columns = 3
nmodes = self.parameters["nmodes"].value
data_table_tmp.num_rows = nmodes
data_table_tmp.data = np.zeros((nmodes, 3))
freq = np.logspace(
self.parameters["logwmin"].value, self.parameters["logwmax"].value, nmodes
)
data_table_tmp.data[:, 0] = freq
for i in range(nmodes):
if self.stop_theory_flag:
break
data_table_tmp.data[i, 1] = data_table_tmp.data[i, 2] = np.power(
10, self.parameters["logDe%02d" % i].value
)
view = self.parent_dataset.parent_application.current_view
try:
x, y, success = view.view_proc(data_table_tmp, None)
except TypeError as e:
print(e)
return
self.graphicmodes.set_data(x, y)
for i in range(data_table_tmp.MAX_NUM_SERIES):
for nx in range(len(self.axarr)):
# self.axarr[nx].lines.remove(data_table_tmp.series[nx][i])
data_table_tmp.series[nx][i].remove()
