# 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 TheoryRetardationModes
Module that defines theories related to Retardation 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 TheoryRetardationModesTime(QTheory):
"""Fit a discrete Retardation spectrum to time dependent creep data
* **Function**
.. math::
\\gamma(t) = \\sigma_0 \\left( J_0 + \\sum_{1}^{n_{modes}} J_i \\left[ 1 - \\exp\\left(\\frac{-t}{\\tau_i}\\right) \\right] + \\frac{t}{\\eta_0} \\right)
where:
- :math:`\\sigma_0`: constant stress applied during the creep experiment.
* **Parameters**
- :math:`J_0`: Instantaneous compliance (``logJini``, in logarithmic scale).
- :math:`\\eta_0`: Terminal viscosity (``logeta0``, in logarithmic scale).
- :math:`n_{modes}`: number of Retardation modes equally distributed in logarithmic scale between :math:`t_{min}` and :math:`t_{max}`.
- logtmin = :math:`\\log(t_{min})`: decimal logarithm of the minimum time range for the modes.
- logtmax = :math:`\\log(t_{max})`: decimal logarithm of the maximum time.
- logJi = :math:`\\log(J_{i})`: decimal logarithm of the compliance of Retardation mode :math:`i`.
"""
thname = "Retardation Modes"
description = "Fit Retardation modes to time dependent creep data"
citations = []
doi = []
html_help_file = "http://reptate.readthedocs.io/manual/Applications/Creep/Theory/theory.html#retardation-modes"
single_file = False
def __init__(self, name="", parent_dataset=None, ax=None):
"""**Constructor**"""
super().__init__(name, parent_dataset, ax)
self.function = self.RetardationModesTime
self.has_modes = True
self.MAX_MODES = 40
self.view_modes = True
tmin = self.parent_dataset.minpositivecol(0)
tmax = self.parent_dataset.maxcol(0)
nmodes = int(np.round(np.log10(tmax / tmin)))
self.parameters["logJini"] = Parameter(
"logJini",
-4.0,
"Log of Instantaneous Compliance",
ParameterType.real,
opt_type=OptType.opt,
)
self.parameters["logeta0"] = Parameter(
"logeta0",
0.0,
"Log of Terminal Viscosity",
ParameterType.real,
opt_type=OptType.opt,
)
self.parameters["logtmin"] = Parameter(
"logtmin",
np.log10(tmin),
"Log of time range minimum",
ParameterType.real,
opt_type=OptType.opt,
)
self.parameters["logtmax"] = Parameter(
"logtmax",
np.log10(tmax),
"Log of time range maximum",
ParameterType.real,
opt_type=OptType.opt,
)
self.parameters["nmodes"] = Parameter(
name="nmodes",
value=nmodes,
description="Number of Retardation modes",
type=ParameterType.integer,
opt_type=OptType.const,
display_flag=False,
)
# Interpolate modes from data
try:
sigma = float(self.parent_dataset.files[0].file_parameters["stress"])
except (ValueError, KeyError):
self.Qprint("Invalid stress value")
return
tau = np.logspace(np.log10(tmin), np.log10(tmax), nmodes)
J = (
np.abs(
np.interp(
tau,
self.parent_dataset.files[0].data_table.data[:, 0],
self.parent_dataset.files[0].data_table.data[:, 1],
)
)
/ sigma
)
for i in range(self.parameters["nmodes"].value):
self.parameters["logJ%02d" % i] = Parameter(
"logJ%02d" % i,
np.log10(J[i]),
"Log of Mode %d amplitude" % i,
ParameterType.real,
opt_type=OptType.opt,
)
# GRAPHIC MODES
self.graphicmodes = None
self.artistmodes = None
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)
# if self.view_modes:
# self.artistmodes.connect()
# else:
# self.artistmodes.disconnect()
# self.do_calculate("")
[docs]
def handle_spinboxValueChanged(self, value):
"""Handle a change of the parameter 'nmode'"""
nmodesold = self.parameters["nmodes"].value
tminold = self.parameters["logtmin"].value
tmaxold = self.parameters["logtmax"].value
tauold = np.logspace(tminold, tmaxold, nmodesold)
Gold = np.zeros(nmodesold)
for i in range(nmodesold):
Gold[i] = self.parameters["logJ%02d" % i].value
del self.parameters["logJ%02d" % i]
nmodesnew = value
self.set_param_value("nmodes", nmodesnew)
taunew = np.logspace(tminold, tmaxold, nmodesnew)
Gnew = np.interp(taunew, tauold, Gold)
for i in range(nmodesnew):
self.parameters["logJ%02d" % i] = Parameter(
"logJ%02d" % i,
Gnew[i],
"Log of Mode %d compliance" % 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):
"""Drag modes around"""
nmodes = self.parameters["nmodes"].value
self.set_param_value("logtmin", dx[0])
self.set_param_value("logtmax", dx[nmodes - 1])
for i in range(nmodes):
self.set_param_value("logJ%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 helpers"""
nmodes = self.parameters["nmodes"].value
tau = np.logspace(
self.parameters["logtmin"].value, self.parameters["logtmax"].value, nmodes
)
J = np.zeros(nmodes)
for i in range(nmodes):
J[i] = np.power(10, self.parameters["logJ%02d" % i].value)
self.graphicmodes = self.ax.plot(tau, J)[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.ax.lines.remove(self.graphicmodes)
self.graphicmodes.remove()
[docs]
def graphicmodes_visible(self, state):
"""Change visibility of 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
tau = np.logspace(
self.parameters["logtmin"].value, self.parameters["logtmax"].value, nmodes
)
J = np.zeros(nmodes)
for i in range(nmodes):
J[i] = 1.0 / np.power(10, self.parameters["logJ%02d" % i].value)
return tau, J, True
[docs]
def RetardationModesTime(self, f=None):
"""Calculate the theory"""
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]
try:
stress = float(f.file_parameters["stress"])
except (ValueError, KeyError):
self.Qprint("Invalid stress value")
return
nmodes = self.parameters["nmodes"].value
J0 = np.power(10, self.parameters["logJini"].value)
eta0 = np.power(10, self.parameters["logeta0"].value)
tau = np.logspace(
self.parameters["logtmin"].value, self.parameters["logtmax"].value, nmodes
)
try:
rec = int(f.file_parameters["rec"])
except (ValueError, KeyError):
rec = 0
for i in range(nmodes):
if self.stop_theory_flag:
break
expT_tau = 1.0 - np.exp(-tt.data[:, 0] / tau[i])
J = np.power(10, self.parameters["logJ%02d" % i].value)
tt.data[:, 1] += stress * J * expT_tau
if rec == 1:
tt.data[:, 1] += stress * J0
else:
tt.data[:, 1] += stress * (J0 + tt.data[:, 0] / eta0)
[docs]
def plot_theory_stuff(self):
"""Plot theory helpers"""
if not self.view_modes:
return
data_table_tmp = DataTable(self.axarr)
data_table_tmp.num_columns = 2
nmodes = self.parameters["nmodes"].value
data_table_tmp.num_rows = nmodes
data_table_tmp.data = np.zeros((nmodes, 2))
tau = np.logspace(
self.parameters["logtmin"].value, self.parameters["logtmax"].value, nmodes
)
data_table_tmp.data[:, 0] = tau
for i in range(nmodes):
if self.stop_theory_flag:
break
data_table_tmp.data[i, 1] = np.power(
10, self.parameters["logJ%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()
