def __init__(self, name="", parent_app=None):
"""**Constructor**"""
super().__init__(name, parent_app)
self.parameters["xmin"] = Parameter(
name="xmin",
value=-np.Infinity,
description="Minimum x",
type=ParameterType.real,
)
self.parameters["xmax"] = Parameter(
name="xmax",
value=np.Infinity,
description="Maximum x",
type=ParameterType.real,
)
self.parameters["ymin"] = Parameter(
name="ymin",
value=-np.Infinity,
description="Minimum y",
type=ParameterType.real,
)
self.parameters["ymax"] = Parameter(
name="ymax",
value=np.Infinity,
description="Maximum y",
type=ParameterType.real,
)
def __init__(self, name="", parent_app=None):
"""**Constructor**"""
super().__init__(name, parent_app)
# self.function = self.findpeaks # main Tool function
self.parameters["threshold"] = Parameter(
name="threshold",
value=0.3,
description="threshold for peak detection",
type=ParameterType.real,
)
self.parameters["minimum_distance"] = Parameter(
name="minimum_distance",
value=5,
description="minimum distance (in datapoints) between peaks",
type=ParameterType.integer,
)
self.parameters["minpeaks"] = Parameter(
name="minpeaks",
value=False,
description="Find minimum peaks",
type=ParameterType.boolean,
display_flag=False,
)
self.parameters["parabola"] = Parameter(
name="parabola",
value=False,
description="Fit Parabola to peak",
type=ParameterType.boolean,
display_flag=False,
)
self.seriesarray = []
self.axarray = []
def __init__(self, name="", parent_dataset=None, axarr=None):
"""**Constructor**"""
super().__init__(name, parent_dataset, axarr)
self.function = self.calculate # main theory function
self.has_modes = False # True if the theory has modes
self.parameters["G0"] = Parameter(
"G0",
1e6,
"Modulus c*kB*T/N",
ParameterType.real,
opt_type=OptType.opt,
min_value=0,
)
self.parameters["tau0"] = Parameter(
"tau0",
1e-3,
"segment relaxation time",
ParameterType.real,
opt_type=OptType.opt,
min_value=0,
)
self.parameters["M0"] = Parameter(
"M0",
0.2,
"segment molar mass",
ParameterType.real,
opt_type=OptType.opt,
min_value=0.01,
)
def __init__(self, name="", parent_dataset=None, ax=None):
"""**Constructor**
Keyword Arguments:
- name {str} -- Name of the theory (default: {""})
- parent_dataset {DataSet} -- DataSet that contains this theory (default: {None})
- ax {Axes array} -- Matplotlib axes where the theory will plot the data (default: {None})
"""
super().__init__(name, parent_dataset, ax)
self.MAX_DEGREE = 10
self.function = self.polynomial
self.parameters["n"] = Parameter(
name="n",
value=1,
description="Degree of Polynomial",
type=ParameterType.integer,
opt_type=OptType.const,
display_flag=False)
for i in range(self.parameters["n"].value + 1):
self.parameters["A%d" % i] = Parameter(
"A%d" % i,
1.0,
"Coefficient order %d" % i,
ParameterType.real,
opt_type=OptType.opt)
self.Qprint("%s: A0 + A1*x + A2*x^2 + ..." % self.thname)
def __init__(self, name="", parent_dataset=None, ax=None):
"""**Constructor**"""
super().__init__(name, parent_dataset, ax)
self.function = self.TheoryTTSShiftAutomatic
self.parameters["T"] = Parameter(
name="T",
value=25,
description="Temperature to shift to, in °C",
type=ParameterType.real,
opt_type=OptType.const,
display_flag=False,
)
self.parameters["vert"] = Parameter(
name="vert",
value=True,
description="Shift vertically",
type=ParameterType.boolean,
opt_type=OptType.const,
display_flag=False,
)
self.Mwset, self.Mw, self.Tdict = self.get_cases()
self.current_master_curve = None
self.current_table = None
self.current_file_min = None
self.shiftParameters = {}
self.aT_vs_T = {}
for k in self.tables.keys():
self.shiftParameters[k] = (0.0, 0.0) # log10 of horizontal, then vertical
def __init__(self, name="", parent_dataset=None, ax=None):
"""**Constructor**"""
super().__init__(name, parent_dataset, ax)
self.MAX_DEGREE = 10
self.function = self.algebraicexpression
self.parameters["n"] = Parameter(
name="n",
value=2,
description="Number of Parameters",
type=ParameterType.integer,
opt_type=OptType.const,
display_flag=False)
self.parameters["expression"] = Parameter(
name="expression",
value="A0+A1*x",
description="Algebraic Expression",
type=ParameterType.string,
opt_type=OptType.const,
display_flag=False)
for i in range(self.parameters["n"].value):
self.parameters["A%d" % i] = Parameter(
"A%d" % i,
1.0,
"Parameter %d" % i,
ParameterType.real,
opt_type=OptType.opt)
safe_list = ['sin', 'cos', 'tan', 'arccos', 'arcsin', 'arctan', 'arctan2', 'deg2rad', 'rad2deg', 'sinh', 'cosh', 'tanh', 'arcsinh', 'arccosh', 'arctanh', 'around', 'round_', 'rint', 'floor', 'ceil','trunc', 'exp', 'log', 'log10', 'fabs', 'mod', 'e', 'pi', 'power', 'sqrt']
self.safe_dict = {}
for k in safe_list:
self.safe_dict[k] = globals().get(k, None)
def __init__(self, name="", parent_dataset=None, axarr=None):
"""**Constructor**"""
super().__init__(name, parent_dataset, axarr)
self.function = self.GEX # main theory function
self.has_modes = False # True if the theory has modes
self.parameters["logW0"] = Parameter(
name="logW0",
value=5,
description="Log Normalization constant",
type=ParameterType.real,
opt_type=OptType.opt,
)
self.parameters["logM0"] = Parameter(
name="logM0",
value=5,
description="Log molecular weight",
type=ParameterType.real,
opt_type=OptType.opt,
)
self.parameters["a"] = Parameter(
name="a",
value=1,
description="Exponent parameter",
type=ParameterType.real,
opt_type=OptType.opt,
min_value=0,
)
self.parameters["b"] = Parameter(
name="b",
value=0.5,
description="Exponent parameter",
type=ParameterType.real,
opt_type=OptType.opt,
min_value=0,
)
def set_param_value(self, name, value):
"""Set the value of a theory parameter"""
if name == "nmodes":
oldn = self.parameters["nmodes"].value
message, success = super().set_param_value(name, value)
if not success:
return message, success
if name == "nmodes":
for i in range(self.parameters["nmodes"].value):
self.parameters["phi%02d" % i] = Parameter(
name="phi%02d" % i,
value=0.0,
description="Volume fraction of mode %02d" % i,
type=ParameterType.real,
opt_type=OptType.nopt,
display_flag=False,
min_value=0,
)
self.parameters["tauD%02d" % i] = Parameter(
name="tauD%02d" % i,
value=100.0,
description="Terminal time of mode %02d" % i,
type=ParameterType.real,
opt_type=OptType.nopt,
display_flag=False,
min_value=0,
)
if oldn > self.parameters["nmodes"].value:
for i in range(self.parameters["nmodes"].value, oldn):
del self.parameters["phi%02d" % i]
del self.parameters["tauD%02d" % i]
return "", True
def __init__(self, name="", parent_dataset=None, ax=None):
"""**Constructor**"""
super().__init__(name, parent_dataset, ax)
self.function = self.MaxwellModesFrequency
self.has_modes = True
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["logwmin"] = Parameter(
"logwmin",
np.log10(wmin),
"Log of frequency range minimum",
ParameterType.real,
opt_type=OptType.opt,
min_value=-10,
max_value=10)
self.parameters["logwmax"] = Parameter(
"logwmax",
np.log10(wmax),
"Log of frequency range maximum",
ParameterType.real,
opt_type=OptType.opt,
min_value=-10,
max_value=10)
self.parameters["nmodes"] = Parameter(
name="nmodes",
value=nmodes,
description="Number of Maxwell modes",
type=ParameterType.integer,
opt_type=OptType.const,
display_flag=False,
min_value=1,
max_value=self.MAX_MODES)
# Interpolate modes from data
if nmodes > 1:
w = np.logspace(np.log10(wmin), np.log10(wmax), nmodes)
else:
w = np.logspace(np.log10(wmin), np.log10(wmin), nmodes)
self.parameters["logwmax"].opt_type = OptType.const
G = 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["logG%02d" % i] = Parameter(
"logG%02d" % i,
np.log10(G[i]),
"Log of Mode %d amplitude" % i,
ParameterType.real,
opt_type=OptType.opt,
min_value=-10,
max_value=10)
# GRAPHIC MODES
self.graphicmodes = []
self.artistmodes = []
self.setup_graphic_modes()
def __init__(self, name="", parent_dataset=None, ax=None):
"""**Constructor**"""
super().__init__(name, parent_dataset, ax)
self.function = self.powerlaw
self.parameters["a"] = Parameter(
"a", 1.0, "Prefactor", ParameterType.real, opt_type=OptType.opt)
self.parameters["b"] = Parameter(
"b", 1.0, "Exponent", ParameterType.real, opt_type=OptType.opt)
self.Qprint("%s: a*x^b" % self.thname)
def __init__(self, name="", parent_dataset=None, axarr=None):
"""**Constructor**"""
super().__init__(name, parent_dataset, axarr)
self.function = self.calculate_PomPom
self.has_modes = True
self.parameters["nmodes"] = Parameter(
name="nmodes",
value=2,
description="Number of modes",
type=ParameterType.integer,
opt_type=OptType.const,
display_flag=False,
)
for i in range(self.parameters["nmodes"].value):
self.parameters["G%02d" % i] = Parameter(
name="G%02d" % i,
value=1000.0,
description="Modulus of mode %02d" % i,
type=ParameterType.real,
opt_type=OptType.nopt,
display_flag=False,
min_value=0,
)
self.parameters["tauB%02d" % i] = Parameter(
name="tauB%02d" % i,
value=10.0,
description="Backbone relaxation time of mode %02d" % i,
type=ParameterType.real,
opt_type=OptType.nopt,
display_flag=False,
min_value=0,
)
self.parameters["q%02d" % i] = Parameter(
name="q%02d" % i,
value=1,
description="Number of dangling arms of mode %02d" % i,
type=ParameterType.integer,
opt_type=OptType.opt,
min_value=1,
max_value=100,
)
self.parameters["ratio%02d" % i] = Parameter(
name="ratio%02d" % i,
value=2,
description=
"Ratio of orientation to stretch relaxation times of mode %02d"
% i,
type=ParameterType.real,
opt_type=OptType.const,
min_value=1,
max_value=5,
)
self.MAX_MODES = 40
self.init_flow_mode()
def set_param_value(self, name, value):
"""Set the value of theory parameters"""
if name == "nmodes":
oldn = self.parameters["nmodes"].value
message, success = super(BaseTheoryPomPom,
self).set_param_value(name, value)
if not success:
return message, success
if name == "nmodes":
for i in range(self.parameters["nmodes"].value):
self.parameters["G%02d" % i] = Parameter(
name="G%02d" % i,
value=1000.0,
description="Modulus of mode %02d" % i,
type=ParameterType.real,
opt_type=OptType.nopt,
display_flag=False,
min_value=0,
)
self.parameters["tauB%02d" % i] = Parameter(
name="tauB%02d" % i,
value=10.0,
description="Backbone relaxation time of mode %02d" % i,
type=ParameterType.real,
opt_type=OptType.nopt,
display_flag=False,
min_value=0,
)
self.parameters["q%02d" % i] = Parameter(
name="q%02d" % i,
value=1,
description="Number of dangling arms of mode %02d" % i,
type=ParameterType.integer,
opt_type=OptType.opt,
min_value=1,
max_value=100,
)
self.parameters["ratio%02d" % i] = Parameter(
name="ratio%02d" % i,
value=2,
description=
"Ratio of orientation to stretch relaxation times of mode %02d"
% i,
type=ParameterType.real,
opt_type=OptType.const,
min_value=1,
max_value=5,
)
if oldn > self.parameters["nmodes"].value:
for i in range(self.parameters["nmodes"].value, oldn):
del self.parameters["G%02d" % i]
del self.parameters["tauB%02d" % i]
del self.parameters["ratio%02d" % i]
del self.parameters["q%02d" % i]
return "", True
def __init__(self, name="", parent_dataset=None, axarr=None):
"""**Constructor**"""
super().__init__(name, parent_dataset, axarr)
self.reactname = "MultiMetCSTR %d" % (
rch.MMCSTR_global.mulmetCSTRnumber)
rch.MMCSTR_global.mulmetCSTRnumber += 1
self.function = self.Calc
self.simexists = False
self.dist_exists = False
self.ndist = 0
self.has_modes = False # True if the theory has modes
self.autocalculate = False
self.do_priority_seniority = False
self.parameters["num_to_make"] = Parameter(
name="num_to_make",
value=1000,
description="Number of molecules made in the simulation",
type=ParameterType.real,
opt_type=OptType.const,
)
self.parameters["mon_mass"] = Parameter(
name="mon_mass",
value=28,
description=
"Mass, in a.m.u., of a monomer (usually set to 28 for PE)",
type=ParameterType.real,
opt_type=OptType.const,
)
self.parameters["Me"] = Parameter(
name="Me",
value=1000,
description="Entanglement molecular weight",
type=ParameterType.real,
opt_type=OptType.const,
)
self.parameters["nbin"] = Parameter(
name="nbin",
value=50,
description="Number of molecular weight bins",
type=ParameterType.real,
opt_type=OptType.const,
)
self.NUMCAT_MAX = 30
# default parameters value
self.init_param_values()
self.signal_request_dist.connect(rgt.request_more_dist)
self.signal_request_polymer.connect(rgt.request_more_polymer)
self.signal_request_arm.connect(rgt.request_more_arm)
self.signal_mulmet_dialog.connect(rgt.launch_mulmet_dialog)
self.do_xrange("", visible=self.xrange.get_visible())
def __init__(self, name="", parent_dataset=None, ax=None):
"""**Constructor**"""
super().__init__(name, parent_dataset, ax)
self.function = self.exponential
self.parameters["a"] = Parameter(
"a", 1.0, "Prefactor", ParameterType.real, opt_type=OptType.opt)
self.parameters["T"] = Parameter(
"T",
1.0,
"Exponential time constant",
ParameterType.real,
opt_type=OptType.opt)
self.Qprint("%s: a*exp(-x/T)" % self.thname)
def __init__(self, name="", parent_app=None):
"""**Constructor**"""
super().__init__(name, parent_app)
self.parameters["x"] = Parameter(
name="x",
value="x",
description="Expression for abscissa",
type=ParameterType.string,
)
self.parameters["y"] = Parameter(
name="y",
value="y",
description="Expression for ordinate",
type=ParameterType.string,
)
safe_list = [
"sin",
"cos",
"tan",
"arccos",
"arcsin",
"arctan",
"arctan2",
"deg2rad",
"rad2deg",
"sinh",
"cosh",
"tanh",
"arcsinh",
"arccosh",
"arctanh",
"around",
"round_",
"rint",
"floor",
"ceil",
"trunc",
"exp",
"log",
"log10",
"fabs",
"mod",
"e",
"pi",
"power",
"sqrt",
]
self.safe_dict = {}
for k in safe_list:
self.safe_dict[k] = globals().get(k, None)
def __init__(self, name="", parent_dataset=None, axarr=None):
"""**Constructor**"""
super().__init__(name, parent_dataset, axarr)
self.function = self.calculate # main theory function
self.has_modes = False # True if the theory has modes
self.autocalculate = False
self.parameters["Ge"] = Parameter(
name="Ge",
value=10605.97,
description="Entanglement modulus",
type=ParameterType.real,
opt_type=OptType.opt,
min_value=1,
max_value=1e8,
)
self.parameters["tau_s"] = Parameter(
name="tau_s",
value=0.01435800,
description="sticker time",
type=ParameterType.real,
opt_type=OptType.opt,
min_value=1e-5,
max_value=1e2,
)
self.parameters["Zs"] = Parameter(
name="Zs",
value=4.022881,
description="Number of stickers per chain",
type=ParameterType.real,
opt_type=OptType.opt,
min_value=0,
max_value=100,
)
self.parameters["Ze"] = Parameter(
name="Ze",
value=10.49686,
description="Number of entanglements",
type=ParameterType.real,
opt_type=OptType.opt,
min_value=0,
max_value=100,
)
self.parameters["alpha"] = Parameter(
name="alpha",
value=10,
description="CLF parameter",
type=ParameterType.real,
opt_type=OptType.const,
)
def set_param_value(self, name, value):
"""Change a parameter value, in particular *n*
"""
if name == 'n':
nold = self.parameters["n"].value
Aold = np.zeros(nold)
for i in range(nold):
Aold[i] = self.parameters["A%d" % i].value
del self.parameters["A%d" % i]
nnew = value
message, success = super().set_param_value("n", nnew)
for i in range(nnew):
if i < nold:
Aval = Aold[i]
else:
Aval = 1.0
self.parameters["A%d" % i] = Parameter(
"A%d" % i,
Aval,
"Parameter %d" % i,
ParameterType.real,
opt_type=OptType.opt)
else:
message, success = super().set_param_value(name, value)
if self.autocalculate:
self.parent_dataset.handle_actionCalculate_Theory()
self.update_parameter_table()
return message, success
def set_equally_spaced_bins(self):
"""Find the first active file in the dataset and setup the bins"""
for f in self.theory_files():
ft = f.data_table.data
m_arr = ft[:, 0]
w_arr = ft[:, 1]
try:
mmin = min(m_arr[np.nonzero(w_arr)])
mmax = max(m_arr[np.nonzero(w_arr)])
except:
mmin = m_arr[0]
mmax = m_arr[-1]
self.parameters["logmmin"].value = np.log10(mmin)
self.parameters["logmmax"].value = np.log10(mmax)
nbin = self.parameters["nbin"].value
bins_edges = np.logspace(np.log10(mmin), np.log10(mmax), nbin + 1)
for i in range(nbin + 1):
self.parameters["logM%02d" % i] = Parameter(
"logM%02d" % i,
np.log10(bins_edges[i]),
"Log of molecular mass",
ParameterType.real,
opt_type=OptType.const,
display_flag=False,
)
self.current_file = f
def __init__(self, name="", parent_dataset=None, axarr=None):
"""**Constructor**"""
super().__init__(name, parent_dataset, axarr)
self.function = self.Calc
self.simexists = False
self.calc_exists = False
self.has_modes = False # True if the theory has modes
self.parameters["nbin"] = Parameter(
name="nbin",
value=100,
description="Number of molecular weight bins",
type=ParameterType.real,
opt_type=OptType.const,
)
self.reactname = "ReactMix"
self.dists = [] # index of the react_dist array used in mix
self.weights = [] # weight of the dist
self.n_inmix = 0 # number of theories in mix
self.theory_names = [] # names of theories in mix
self.theory_simnumber = [
] # 'react_dist[].simnumber' of theories in mix
self.calcexists = False
self.do_priority_seniority = False
self.signal_mix_dialog.connect(rgt.launch_mix_dialog)
self.ratios = [] # list of ratios in dialog
self.include = [] # list of (0 or 1) include in dialog
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()
def __init__(self, name="", parent_dataset=None, axarr=None):
"""**Constructor**"""
super().__init__(name, parent_dataset, axarr)
self.reactname = "CreatePolyconf"
self.function = self.calculate # main theory function
self.has_modes = False # True if the theory has modes
self.signal_param_dialog.connect(self.launch_param_dialog)
self.parameters["nbin"] = Parameter(
name="nbin",
value=50,
description="Number of molecular weight bins",
type=ParameterType.integer,
opt_type=OptType.const,
min_value=1,
)
self.polyconf_file_out = None # full path of target polyconf file
self.autocalculate = False
self.bch = BobCtypesHelper(self)
self.do_priority_seniority = False
self.inp_counter = 0 # counter for the 'virtual' input file for BoB
self.virtual_input_file = [] # 'virtual' input file for BoB
self.proto_counter = 0 # counter for the 'virtual' proto file for BoB
self.virtual_proto_file = [] # 'virtual' proto file for BoB
self.from_file_filename = [] # file names of the "from file" type
self.from_file_filename_counter = 0 # counter
self.protoname = [] # list of proto/polycode names
def __init__(self, name="", parent_dataset=None, axarr=None):
"""**Constructor**"""
super().__init__(name, parent_dataset, axarr)
self.function = self.calculate_giesekus
self.has_modes = True
self.parameters["nmodes"] = Parameter(name="nmodes",
value=2,
description="Number of modes",
type=ParameterType.integer,
opt_type=OptType.const,
display_flag=False)
self.parameters["nstretch"] = Parameter(
name="nstretch",
value=2,
description="Number of strecthing modes",
type=ParameterType.integer,
opt_type=OptType.const,
display_flag=False)
for i in range(self.parameters["nmodes"].value):
self.parameters["G%02d" % i] = Parameter(
name="G%02d" % i,
value=1000.0,
description="Modulus of mode %02d" % i,
type=ParameterType.real,
opt_type=OptType.nopt,
display_flag=False,
min_value=0)
self.parameters["tauD%02d" % i] = Parameter(
name="tauD%02d" % i,
value=10.0,
description="Terminal time of mode %02d" % i,
type=ParameterType.real,
opt_type=OptType.nopt,
display_flag=False,
min_value=0)
self.parameters["alpha%02d" % i] = Parameter(
name="alpha%02d" % i,
value=0.5,
description="Dimensionless mobility factor of mode %02d" % i,
type=ParameterType.real,
opt_type=OptType.opt,
min_value=0,
max_value=1)
self.MAX_MODES = 40
self.init_flow_mode()
def __init__(self, name="", parent_app=None):
"""**Constructor**"""
super().__init__(name, parent_app)
self.parameters["window"] = Parameter(
name="window",
value=11,
description=
"Length of filter window. Positive odd integer, smaller than the size of y and larger than order",
type=ParameterType.integer,
)
self.parameters["order"] = Parameter(
name="order",
value=3,
description=
"Order of smoothing polynomial (must be smaller than window)",
type=ParameterType.integer,
)
def set_param_value(self, name, value):
"""Set the value of a theory parameter"""
if name == "nmodes":
oldn = self.parameters["nmodes"].value
if CmdBase.mode == CmdMode.GUI:
self.spinbox.setMaximum(int(value))
message, success = super(BaseTheoryRoliePoly,
self).set_param_value(name, value)
if not success:
return message, success
if name == "nmodes":
for i in range(self.parameters["nmodes"].value):
self.parameters["G%02d" % i] = Parameter(
name="G%02d" % i,
value=1000.0,
description="Modulus of mode %02d" % i,
type=ParameterType.real,
opt_type=OptType.nopt,
display_flag=False,
min_value=0,
)
self.parameters["tauD%02d" % i] = Parameter(
name="tauD%02d" % i,
value=10.0,
description="Terminal time of mode %02d" % i,
type=ParameterType.real,
opt_type=OptType.nopt,
display_flag=False,
min_value=0,
)
self.parameters["tauR%02d" % i] = Parameter(
name="tauR%02d" % i,
value=0.5,
description="Rouse time of mode %02d" % i,
type=ParameterType.real,
opt_type=OptType.opt,
display_flag=True,
min_value=0,
)
if oldn > self.parameters["nmodes"].value:
for i in range(self.parameters["nmodes"].value, oldn):
del self.parameters["G%02d" % i]
del self.parameters["tauD%02d" % i]
del self.parameters["tauR%02d" % i]
return "", True
def __init__(self, name="", parent_app=None):
"""**Constructor**"""
super().__init__(name, parent_app)
self.parameters["n"] = Parameter(
name="n",
value=1,
description="Power law exponent",
type=ParameterType.real,
opt_type=OptType.const,
)
def set_param_value(self, name, value):
"""Set value of a theory parameter"""
if (name == "nmodes"):
oldn = self.parameters["nmodes"].value
if CmdBase.mode == CmdMode.GUI:
self.spinbox.setMaximum(int(value))
message, success = super(BaseTheoryGiesekus,
self).set_param_value(name, value)
if not success:
return message, success
if (name == "nmodes"):
for i in range(self.parameters["nmodes"].value):
self.parameters["G%02d" % i] = Parameter(
name="G%02d" % i,
value=1000.0,
description="Modulus of mode %02d" % i,
type=ParameterType.real,
opt_type=OptType.nopt,
display_flag=False,
min_value=0)
self.parameters["tauD%02d" % i] = Parameter(
name="tauD%02d" % i,
value=10.0,
description="Terminal time of mode %02d" % i,
type=ParameterType.real,
opt_type=OptType.nopt,
display_flag=False,
min_value=0)
self.parameters["alpha%02d" % i] = Parameter(
name="alpha%02d" % i,
value=0.5,
description="Constant of proportionality of mode %02d" % i,
type=ParameterType.real,
opt_type=OptType.opt,
display_flag=True,
min_value=0,
max_value=1)
if (oldn > self.parameters["nmodes"].value):
for i in range(self.parameters["nmodes"].value, oldn):
del self.parameters["G%02d" % i]
del self.parameters["tauD%02d" % i]
del self.parameters["alpha%02d" % i]
return '', True
def __init__(self, name="", parent_dataset=None, axarr=None):
"""**Constructor**"""
super().__init__(name, parent_dataset, axarr)
self.function = self.calculate
self.has_modes = False
# self.parameters["logwmin"] = Parameter(
# "logwmin",
# -5,
# "Log of frequency range minimum",
# ParameterType.real,
# opt_type=OptType.nopt)
# self.parameters["logwmax"] = Parameter(
# "logwmax",
# 4,
# "Log of frequency range maximum",
# ParameterType.real,
# opt_type=OptType.nopt)
self.parameters["G0"] = Parameter(
"G0",
1e6,
"Modulus c*kB*T/N",
ParameterType.real,
opt_type=OptType.opt,
min_value=0,
)
self.parameters["tau0"] = Parameter(
"tau0",
1e-3,
"Segment relaxation time",
ParameterType.real,
opt_type=OptType.opt,
min_value=0,
)
self.parameters["M0"] = Parameter(
"M0",
0.2,
"Segment molar mass",
ParameterType.real,
opt_type=OptType.opt,
min_value=0,
)
def __init__(self, name="", parent_dataset=None, ax=None):
"""**Constructor**"""
super().__init__(name, parent_dataset, ax)
self.function = self.two_exponentials
self.parameters["a1"] = Parameter(
"a1", 0.9, "Prefactor 1", ParameterType.real, opt_type=OptType.opt)
self.parameters["T1"] = Parameter(
"T1",
1.0,
"Exponential time constant 1",
ParameterType.real,
opt_type=OptType.opt)
self.parameters["a2"] = Parameter(
"a2", 0.1, "Prefactor 2", ParameterType.real, opt_type=OptType.opt)
self.parameters["T2"] = Parameter(
"T2",
10.0,
"Exponential time constant 2",
ParameterType.real,
opt_type=OptType.opt)
self.Qprint("%s: a1*exp(-x/T1) + a2*exp(-x/T2)" % self.thname)
def __init__(self, name="", parent_dataset=None, axarr=None):
"""**Constructor**"""
super().__init__(name, parent_dataset, axarr)
self.function = self.calculate_UCM
self.has_modes = True
self.parameters["nmodes"] = Parameter(
name="nmodes",
value=2,
description="Number of modes",
type=ParameterType.integer,
opt_type=OptType.const,
display_flag=False,
)
for i in range(self.parameters["nmodes"].value):
self.parameters["G%02d" % i] = Parameter(
name="G%02d" % i,
value=1000.0,
description="Modulus of mode %02d" % i,
type=ParameterType.real,
opt_type=OptType.nopt,
display_flag=False,
min_value=0,
)
self.parameters["tauD%02d" % i] = Parameter(
name="tauD%02d" % i,
value=10.0,
description="Terminal time of mode %02d" % i,
type=ParameterType.real,
opt_type=OptType.nopt,
display_flag=False,
min_value=0,
)
self.MAX_MODES = 40
self.init_flow_mode()
def set_param_value(self, name, value):
"""Change other parameters when nmodes is changed, else call parent function"""
if name == 'nmodes':
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["logG%02d" % i].value
del self.parameters["logG%02d" % i]
nmodesnew = int(value)
message, success = super().set_param_value("nmodes", nmodesnew)
if nmodesnew > 1 and nmodesold == 1:
if wminold > wmaxold:
wminold, wmaxold = wmaxold, wminold
self.parameters["logwmax"].opt_type = OptType.opt
if nmodesnew > 1:
wnew = np.logspace(wminold, wmaxold, nmodesnew)
Gnew = np.interp(wnew, wold, Gold)
else:
wnew = np.logspace(wminold, wminold, nmodesnew)
Gnew = np.array([Gold[0]])
self.parameters["logwmax"].opt_type = OptType.const
for i in range(nmodesnew):
self.parameters["logG%02d" % i] = Parameter(
"logG%02d" % i,
Gnew[i],
"Log of Mode %d amplitude" % i,
ParameterType.real,
opt_type=OptType.opt,
min_value=-10,
max_value=10)
if CmdBase.mode == CmdMode.GUI:
self.spinbox.blockSignals(True)
self.spinbox.setValue(nmodesnew)
self.spinbox.blockSignals(False)
else:
message, success = super().set_param_value(name, value)
return message, success