dev #285
@ -31,7 +31,7 @@ class QT1Widget(QtWidgets.QDialog, Ui_t1dialog):
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self.t1calculator = RelaxationEvaluation()
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self.sd_parameter = []
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self.sdmodels = [Debye, ColeCole, ColeDavidson, KWW, HavriliakNegami, LogGaussian]
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self.sdmodels = [Debye, ColeCole, ColeDavidson, KWW, HavriliakNegami, LogGaussian, GGAlpha]
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for i in self.sdmodels:
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self.specdens_combobox.addItem(i.name)
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self.specdens_combobox.currentIndexChanged.connect(self.update_specdens)
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@ -51,8 +51,14 @@ class QT1Widget(QtWidgets.QDialog, Ui_t1dialog):
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self.conv_y = QT1Widget.time_conversion[self.t1_combobox.currentIndex()]
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self.minimum = (1, np.inf)
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self.min_pos = PlotItem(x=np.array([]), y=np.array([]),
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symbol='+', symbolBrush=mkBrush(color='r'), symbolPen=mkPen(color='r'), symbolSize=14)
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self.min_pos = PlotItem(
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x=np.array([]),
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y=np.array([]),
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symbol='+',
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symbolBrush=mkBrush(color='r'),
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symbolPen=mkPen(color='r'),
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symbolSize=14,
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)
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self.parabola = PlotItem(x=np.array([]), y=np.array([]))
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self.lineEdit_2.setValidator(QtGui.QDoubleValidator())
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@ -83,10 +89,10 @@ class QT1Widget(QtWidgets.QDialog, Ui_t1dialog):
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right_b = min(np.argmin(y)+3, len(x)-1)
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self.lineEdit_2.blockSignals(True)
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self.lineEdit_2.setText('{:.2f}'.format(x[left_b]))
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self.lineEdit_2.setText(f'{x[left_b]:.2f}')
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self.lineEdit_2.blockSignals(False)
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self.lineEdit_3.blockSignals(True)
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self.lineEdit_3.setText('{:.2f}'.format(x[right_b]))
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self.lineEdit_3.setText(f'{x[right_b]:.2f}')
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self.lineEdit_3.blockSignals(False)
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self.t1calculator.set_data(x, y)
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@ -110,6 +116,7 @@ class QT1Widget(QtWidgets.QDialog, Ui_t1dialog):
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if self.sdmodels[idx].parameter is not None:
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for name in self.sdmodels[idx].parameter:
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print(name)
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_temp = FormWidget(parent=self, name=name, fixable=True)
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_temp.value = 1
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_temp.setChecked(True)
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@ -133,7 +140,7 @@ class QT1Widget(QtWidgets.QDialog, Ui_t1dialog):
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try:
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for i, v, in enumerate(values):
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self.sd_parameter[i].blockSignals(True)
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self.sd_parameter[i].value = '{:.3g}'.format(round(v, 3))
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self.sd_parameter[i].value = f'{v:.3g}'
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self.sd_parameter[i].blockSignals(False)
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except IndexError:
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pass
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@ -219,7 +226,7 @@ class QT1Widget(QtWidgets.QDialog, Ui_t1dialog):
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self.update_model()
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@QtCore.pyqtSlot(int, name='on_interpol_combobox_currentIndexChanged')
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def determine_minimum(self, idx):
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def determine_minimum(self, idx: int):
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if idx == 0:
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self.checkBox_interpol.setChecked(False)
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self.checkBox_interpol.hide()
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@ -229,9 +236,10 @@ class QT1Widget(QtWidgets.QDialog, Ui_t1dialog):
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self.checkBox_interpol.show()
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self.frame.show()
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try:
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m, i_func = self.t1calculator.calculate_t1_min(interpolate=idx,
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trange=(float(self.lineEdit_2.text()),
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float(self.lineEdit_3.text())))
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m, i_func = self.t1calculator.calculate_t1_min(
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interpolate=idx,
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trange=(float(self.lineEdit_2.text()), float(self.lineEdit_3.text())),
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)
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except ValueError:
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m, i_func = self.t1calculator.calculate_t1_min(interpolate=None)
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@ -273,11 +281,13 @@ class QT1Widget(QtWidgets.QDialog, Ui_t1dialog):
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return
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with busy_cursor():
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calc_stretching, mini = self.t1calculator.get_increase(height=self.minimum[1],
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idx=var_idx, mode=notfix,
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omega=2*np.pi*self.frequency,
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dist_parameter=sd_args, prefactor=cp_args,
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coupling_kwargs=cp_kwargs)
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calc_stretching, mini = self.t1calculator.get_increase(
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height=self.minimum[1],
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idx=var_idx, mode=notfix,
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omega=2*np.pi*self.frequency,
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dist_parameter=sd_args, prefactor=cp_args,
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coupling_kwargs=cp_kwargs
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)
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self.label_t1min.setText(f'{mini:.4g} s')
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@ -292,9 +302,13 @@ class QT1Widget(QtWidgets.QDialog, Ui_t1dialog):
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sd_args, _ = self.get_sd_values()
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cp_args, cp_kwargs, _ = self.get_cp_values()
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tau_mode = ['fit', 'peak', 'mean', 'logmean'][self.tau_combox.currentIndex()]
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corr, opts = self.t1calculator.correlation_from_t1(omega=2*np.pi*self.frequency, dist_parameter=sd_args,
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coupling_param=cp_args, coupling_kwargs=cp_kwargs,
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mode=tau_mode, interpolate=self.checkBox_interpol.isChecked())
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corr, opts = self.t1calculator.correlation_from_t1(
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omega=2*np.pi*self.frequency,
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dist_parameter=sd_args,
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coupling_param=cp_args, coupling_kwargs=cp_kwargs,
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mode=tau_mode,
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interpolate=self.checkBox_interpol.isChecked()
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)
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name = self.name + '-' + str(self.t1calculator) + '('
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name += ','.join([f'{a:.3g}' for a in sd_args])
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@ -332,4 +346,4 @@ class QT1Widget(QtWidgets.QDialog, Ui_t1dialog):
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@QtCore.pyqtSlot(int, name='on_graph_checkbox_stateChanged')
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def changed_state(self, checked):
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self.graph_combobox.setEnabled(checked != QtCore.Qt.Checked)
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self.graph_combobox.setEnabled(checked != QtCore.Qt.CheckState.Checked)
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@ -26,3 +26,4 @@ from .coledavidson import ColeDavidson
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from .debye import Debye
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from .kww import KWW
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from .loggaussian import LogGaussian
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from .gengamma import GGAlpha
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@ -20,10 +20,10 @@ class AbstractGG(Distribution, ABC):
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@classmethod
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def correlation(cls, t, tau0, *args):
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tt = np.asanyarray(t)
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tt = np.atleast_1d(t)
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taus, ln_tau = AbstractGG._prepare_integration(tau0)
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g_tau = cls.distribution(taus, tau0, *args)
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ret_val = np.array([simpson(np.exp(-t_i/taus) * g_tau, ln_tau) for t_i in tt])
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ret_val = np.array([simpson(np.exp(-t_i/taus) * g_tau, ln_tau) for t_i in tt]).squeeze()
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return ret_val
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@ -32,11 +32,11 @@ class AbstractGG(Distribution, ABC):
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r"""
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Calculate spectral density \int G(ln(tau) tau/(1+(w*tau)^2) dln(tau)
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"""
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w = np.asanyarray(omega)
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w = np.atleast_1d(omega)
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taus, ln_tau = AbstractGG._prepare_integration(tau0)
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g_tau = cls.distribution(taus, tau0, *args)
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ret_val = np.array([simpson(g_tau / (1 - 1j*w_i*taus), ln_tau) for w_i in w])
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ret_val = np.array([simpson(g_tau / (1 - 1j*w_i*taus), ln_tau) for w_i in w]).squeeze()
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return ret_val
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@ -45,17 +45,23 @@ class AbstractGG(Distribution, ABC):
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r"""
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Calculate spectral density \int G(ln(tau) tau/(1+(w*tau)^2) dln(tau)
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"""
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w = np.asanyarray(omega)
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taus, ln_tau = AbstractGG._prepare_integration(tau0)
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g_tau = cls.distribution(taus, tau0, *args)
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w = np.atleast_1d(omega)
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_t = np.atleast_1d(tau0)
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ret_val = np.zeros((w.size, _t.size))
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ret_val = np.array([simpson(g_tau * taus / (1 + (w_i*taus)**2), ln_tau) for w_i in w])
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for i, tau_i in enumerate(_t):
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taus, ln_tau = AbstractGG._prepare_integration(tau_i)
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g_tau = cls.distribution(taus, tau_i, *args)
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return ret_val
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ret_val[:, i] = np.array([simpson(g_tau * taus / (1 + (w_i*taus)**2), ln_tau) for w_i in w]).squeeze()
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return ret_val.squeeze()
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@staticmethod
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def _prepare_integration(
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tau0: float, limits: tuple[int, int] = (20, 20), num_steps: int = 4001
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tau0: float,
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limits: tuple[int, int] = (20, 20),
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num_steps: int = 4001,
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) -> tuple[np.ndarray, np.ndarray]:
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"""
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Create array of correlation times for integration over ln(tau)
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@ -77,8 +83,8 @@ class AbstractGG(Distribution, ABC):
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# noinspection PyMethodOverriding
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class GGAlpha(AbstractGG):
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name = r'General \Gamma (\alpha)'
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parameter = [r'\tau', r'\alpha', r'\beta']
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name = r'General Gamma (alpha)'
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parameter = [r'\alpha', r'\beta']
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@staticmethod
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def distribution(taus: float | np.ndarray, tau: float, alpha: float, beta: float) -> float | np.ndarray:
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@ -92,8 +98,8 @@ class GGAlpha(AbstractGG):
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# noinspection PyMethodOverriding
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class GGAlphaEW(AbstractGG):
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name = r'General \Gamma (\alpha + EW)'
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parameter = [r'\tau', r'\alpha', r'\beta', r'\sigma', r'\gamma']
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name = r'General Gamma (alpha + EW)'
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parameter = [r'\alpha', r'\beta', r'\sigma', r'\gamma']
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@staticmethod
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def distribution(tau: float | np.ndarray, tau0: float,
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@ -117,8 +123,8 @@ class GGAlphaEW(AbstractGG):
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# noinspection PyMethodOverriding
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class GGBeta(AbstractGG):
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name = r'General \Gamma (\beta)'
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parameter = [r'\tau', 'a', 'b']
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name = r'General Gamma (beta)'
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parameter = ['a', 'b']
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@staticmethod
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def distribution(tau: float | np.ndarray, tau0: float, a: float, b: float) -> float | np.ndarray:
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@ -2,6 +2,7 @@ import numpy as np
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from ..distributions import *
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from ..distributions.energy import EnergyBarriers
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from ..distributions.gengamma import GGAlpha
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from ..distributions.intermolecular import FFHS
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from ..nmr.relaxation import Relaxation
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from ..utils.constants import gamma
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@ -82,6 +83,13 @@ class FFHSFC(_AbstractFC):
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relax = Relaxation(distribution=FFHS)
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class GGAFC(_AbstractFC):
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name = 'GG(alpha)'
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params = _AbstractFC.params + [r'\alpha', r'\beta']
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bounds = _AbstractFC.bounds + [(None, None), (None, None)]
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relax = Relaxation(distribution=GGAlpha)
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class EnergyFC(_AbstractFC):
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name = 'Energy distribution'
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params = ['C', 'T'] + EnergyBarriers.parameter
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@ -525,7 +525,7 @@ class RelaxationEvaluation(Relaxation):
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dist_parameter: tuple | list = None,
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prefactor: tuple | list | float = None,
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coupling_kwargs: dict = None,
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) -> None:
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) -> tuple[float, float] :
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"""
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Determine a single parameter from a T1 minimum.
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It replaces the previously set value.
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Loading…
Reference in New Issue
Block a user