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save fit parameter and agr; more doc

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dominik
2022-03-24 17:35:10 +01:00
parent ef81030213
commit 73e4a2b4d9
19 changed files with 209 additions and 284 deletions
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4
doc/Makefile
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@@ -5,8 +5,8 @@
# from the environment for the first two.
SPHINXOPTS ?=
SPHINXBUILD ?= /autohome/dominik/miniconda3/bin/sphinx-build
SOURCEDIR = /autohome/dominik/nmreval/docs/source
BUILDDIR = /autohome/dominik/nmreval/docs/build
SOURCEDIR = /autohome/dominik/nmreval/doc/source
BUILDDIR = /autohome/dominik/nmreval/doc/_build
# Put it first so that "make" without argument is like "make help".
help:

70
doc/examples/nmr/plot_RelaxationEvaluation.py
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@@ -1,12 +1,19 @@
"""
=======================
Spin-lattice relaxation
=======================
==========
T1 minimum
==========
Example for
``RelaxationEvaluation`` is used to get width parameter from a T1 minimum.
As a subclass of ``Relaxation`` it can also be used to calculate Relaxation times.
The basic steps are:
* Determine a T1 minimum with `nmreval.nmr.RelaxationEvaluation.calculate_t1_min`
* Calculate width parameter of a spectral density/coupling constants/... with
``RelaxationEvaluation.get_increase``
* Calculate correlation times from these values with ``RelaxationEvaluation.correlation_from_t1``
"""
import numpy as np
from matplotlib import pyplot as plt
import matplotlib.pyplot as plt
from nmreval.distributions import ColeDavidson
from nmreval.nmr import Relaxation, RelaxationEvaluation
@@ -20,7 +27,7 @@ temperature = 1000/inv_temp
# spectral density parameter
ea = 0.45
tau = 1e-21 * np.exp(ea / kB / temperature)
gamma_cd = 0.1
gamma_cd = 0.4
# interaction parameter
omega = 2*np.pi*46e6
@@ -28,40 +35,57 @@ delta = 120e3
eta = 0
r = Relaxation()
r.set_distribution(ColeDavidson) # the only parameter that has to be set beforehand
r.set_distribution(ColeDavidson) # the only parameter that set beforehand
t1_values = r.t1(omega, tau, gamma_cd, mode='bpp',
prefactor=Quadrupolar.relax(delta, eta))
# add noise
rng = np.random.default_rng(123456789)
rng = np.random.default_rng()
noisy = (rng.random(t1_values.size)-0.5) * 0.5 * t1_values + t1_values
# set parameter and data
ax_t1 = plt.figure().add_subplot()
ax_t1.semilogy(inv_temp, t1_values, label='Calculated T1')
ax_t1.semilogy(inv_temp, noisy, 'o', label='Noise')
ax_t1.legend()
plt.show()
# Actual evaluation starts here
# setting necessary parameter
r_eval = RelaxationEvaluation()
r_eval.set_distribution(ColeDavidson)
r_eval.set_coupling(Quadrupolar, (delta, eta))
r_eval.data(temperature, noisy)
r_eval.set_data(temperature, noisy)
r_eval.omega = omega
# Find a T1 minumum
t1_min_data, _ = r_eval.calculate_t1_min() # second argument is None
t1_min_inter, line = r_eval.calculate_t1_min(interpolate=1, trange=(160, 195), use_log=True)
fig, ax = plt.subplots()
ax.semilogy(1000/t1_min_data[0], t1_min_data[1], 'rx', label='Data minimum')
ax.semilogy(1000/t1_min_inter[0], t1_min_inter[1], 'r+', label='Parabola')
ax.semilogy(1000/line[0], line[1])
ax_min = plt.figure().add_subplot()
ax_min.semilogy(inv_temp, noisy, 'o', label='Data')
ax_min.semilogy(1000/line[0], line[1], '--')
ax_min.semilogy(1000/t1_min_data[0], t1_min_data[1], 'C2X',label='Data minimum')
ax_min.semilogy(1000/t1_min_inter[0], t1_min_inter[1], 'C3P',label='Parabola')
ax_min.set_xlim(4.5, 7)
ax_min.set_ylim(1e-3, 1e-1)
ax_min.legend()
# Vary the first (and for Cole-Davidson, only) parameter of the spectral density
found_gamma, found_height = r_eval.get_increase(t1_min_inter[1], idx=0, mode='distribution')
print(found_gamma)
plt.axhline(found_height)
print(f'Minimum at {found_height} for {found_gamma}; input is {gamma_cd}')
plt.show()
#%%
# Now we found temperature and height of the minimum we can calculate the correlation time
##################################################################################
# Calculation of correlation times uses previously parameter for spectral density
# and prefactor
plt.semilogy(1000/temperature, tau)
tau_from_t1, opts = r_eval.correlation_from_t1()
print(opts)
plt.semilogy(1000/tau_from_t1[:, 0], tau_from_t1[:, 1], 'o')
tau_from_t1, opts = r_eval.correlation_from_t1(mode='mean')
print(f'Used options: {opts}')
ax_tau = plt.figure().add_subplot()
ax_tau.semilogy(inv_temp, tau*gamma_cd, label='Original input')
ax_tau.semilogy(1000/tau_from_t1[:, 0], tau_from_t1[:, 1], 'o', label='Calculated')
ax_tau.legend()
plt.show()

179
doc/source/gallery/index.rst
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@@ -1,179 +0,0 @@
:orphan:
.. _sphx_glr_gallery:
.. examples-index:
.. _gallery:
========
Examples
========
This page contains example plots. Click on any image to see the full image and source code.
.. raw:: html
<div class="sphx-glr-clear"></div>
.. _sphx_glr_gallery_distribution:
.. _distribution_examples:
.. _distribution-examples-index:
Distribution of correlation times
=================================
.. raw:: html
<div class="sphx-glr-thumbcontainer" tooltip="Example for KWW distributions">
.. only:: html
.. figure:: /gallery/distribution/images/thumb/sphx_glr_plot_KWW_thumb.png
:alt: Kohlrausch-Williams-Watts
:ref:`sphx_glr_gallery_distribution_plot_KWW.py`
.. raw:: html
</div>
.. toctree::
:hidden:
/gallery/distribution/plot_KWW
.. raw:: html
<div class="sphx-glr-thumbcontainer" tooltip="Example for Cole-Cole distributions">
.. only:: html
.. figure:: /gallery/distribution/images/thumb/sphx_glr_plot_ColeCole_thumb.png
:alt: Cole-Cole
:ref:`sphx_glr_gallery_distribution_plot_ColeCole.py`
.. raw:: html
</div>
.. toctree::
:hidden:
/gallery/distribution/plot_ColeCole
.. raw:: html
<div class="sphx-glr-thumbcontainer" tooltip="Example for Log-Gaussian distributions">
.. only:: html
.. figure:: /gallery/distribution/images/thumb/sphx_glr_plot_LogGaussian_thumb.png
:alt: Log-Gaussian
:ref:`sphx_glr_gallery_distribution_plot_LogGaussian.py`
.. raw:: html
</div>
.. toctree::
:hidden:
/gallery/distribution/plot_LogGaussian
.. raw:: html
<div class="sphx-glr-thumbcontainer" tooltip="Example for Cole-Davidson distributions">
.. only:: html
.. figure:: /gallery/distribution/images/thumb/sphx_glr_plot_ColeDavidson_thumb.png
:alt: Cole-Davidson
:ref:`sphx_glr_gallery_distribution_plot_ColeDavidson.py`
.. raw:: html
</div>
.. toctree::
:hidden:
/gallery/distribution/plot_ColeDavidson
.. raw:: html
<div class="sphx-glr-thumbcontainer" tooltip="Example for Havriliak-Negami distributions">
.. only:: html
.. figure:: /gallery/distribution/images/thumb/sphx_glr_plot_HavriliakNegami_thumb.png
:alt: Havriliak-Negami
:ref:`sphx_glr_gallery_distribution_plot_HavriliakNegami.py`
.. raw:: html
</div>
.. toctree::
:hidden:
/gallery/distribution/plot_HavriliakNegami
.. raw:: html
<div class="sphx-glr-clear"></div>
.. _sphx_glr_gallery_nmr:
.. _nmr_examples:
.. _nmr-examples-index:
NMR specifics
=============
.. raw:: html
<div class="sphx-glr-thumbcontainer" tooltip="Example for">
.. only:: html
.. figure:: /gallery/nmr/images/thumb/sphx_glr_plot_RelaxationEvaluation_thumb.png
:alt: Spin-lattice relaxation
:ref:`sphx_glr_gallery_nmr_plot_RelaxationEvaluation.py`
.. raw:: html
</div>
.. toctree::
:hidden:
/gallery/nmr/plot_RelaxationEvaluation
.. raw:: html
<div class="sphx-glr-clear"></div>

3
doc/source/gallery/searchindex.bak
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@@ -1,3 +0,0 @@
'/autohome/dominik/nmreval/doc/_build/html/index.html', (0, 6969)
'/autohome/dominik/nmreval/doc/_build/html/_static/documentation_options.js', (7168, 364)
'/autohome/dominik/nmreval/doc/_build/html/searchindex.js', (7680, 29280)

BIN
doc/source/gallery/searchindex.dat
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Binary file not shown.

3
doc/source/gallery/searchindex.dir
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@@ -1,3 +0,0 @@
'/autohome/dominik/nmreval/doc/_build/html/index.html', (0, 6969)
'/autohome/dominik/nmreval/doc/_build/html/_static/documentation_options.js', (7168, 364)
'/autohome/dominik/nmreval/doc/_build/html/searchindex.js', (7680, 29280)

2
nmreval/gui_qt/data/container.py
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@@ -485,7 +485,7 @@ class PointContainer(ExperimentContainer):
}
if sym_kwargs['symbol'] is None and line_kwargs['style'] is None:
if len(self._data) > 1000:
if len(self._data) > 500:
line_kwargs['style'] = LineStyle.Solid
sym_kwargs['symbol'] = SymbolStyle.No
else:

2
nmreval/gui_qt/data/conversion.py
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@@ -138,8 +138,6 @@ class ConversionDialog(QtWidgets.QDialog, Ui_Dialog):
src_sets.append((set_id_real, set_id_imag, graph_id, type_idx))
print(src_sets)
self.convertSets.emit(src_sets)
return src_sets

4
nmreval/gui_qt/fit/fit_parameter.py
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@@ -124,7 +124,7 @@ class QFitParameterWidget(QtWidgets.QWidget, Ui_FormFit):
self.data_parameter[idx].blockSignals(False)
@QtCore.pyqtSlot(str, object)
def change_global_choice(self, argname, value):
def change_global_choice(self, _, value):
idx = self.global_parameter.index(self.sender())
self.glob_values[idx] = value
if self.data_values[self.comboBox.currentData()][idx] is None:
@@ -242,6 +242,8 @@ class QFitParameterWidget(QtWidgets.QWidget, Ui_FormFit):
else:
if p_i is None:
kw_p.update(g.value)
elif isinstance(p_i, dict):
kw_p.update(p_i)
else:
kw_p[g.argname] = p_i

10
nmreval/gui_qt/graphs/graphwindow.py
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@@ -64,7 +64,7 @@ class QGraphWindow(QtWidgets.QGraphicsView, Ui_GraphWindow):
# reconnect "Export..." in context menu to our function
self.scene.contextMenu[0].disconnect()
self.scene.contextMenu[0].triggered.connect(self.export)
self.scene.contextMenu[0].triggered.connect(self.export_dialog)
def _init_gui(self):
self.setWindowTitle('Graph ' + str(next(QGraphWindow.counter)))
@@ -515,7 +515,7 @@ class QGraphWindow(QtWidgets.QGraphicsView, Ui_GraphWindow):
(item in self.graphic.items() or other_item in self.graphic.items()):
self.legend.addItem(item, convert(item.opts.get('name', ''), old='tex', new='html'))
def export(self):
def export_dialog(self):
filters = 'All files (*.*);;AGR (*.agr);;SVG (*.svg);;PDF (*.pdf)'
for imgformat in QtGui.QImageWriter.supportedImageFormats():
str_format = imgformat.data().decode('utf-8')
@@ -524,6 +524,9 @@ class QGraphWindow(QtWidgets.QGraphicsView, Ui_GraphWindow):
outfile, _ = QtWidgets.QFileDialog.getSaveFileName(self, caption='Export graphic', filter=filters,
options=QtWidgets.QFileDialog.DontConfirmOverwrite)
if outfile:
self.export(outfile)
def export(self, outfile: str):
_, suffix = os.path.splitext(outfile)
if suffix == '':
QtWidgets.QMessageBox.warning(self, 'No file extension',
@@ -566,7 +569,6 @@ class QGraphWindow(QtWidgets.QGraphicsView, Ui_GraphWindow):
from ..io.exporters import PDFPrintExporter
PDFPrintExporter(self.graphic).export(outfile)
elif suffix == '.svg':
from pyqtgraph.exporters import SVGExporter
SVGExporter(self.scene).export(outfile)
@@ -591,8 +593,6 @@ class QGraphWindow(QtWidgets.QGraphicsView, Ui_GraphWindow):
if item_dic:
dic['items'].append(item_dic)
print(dic)
return dic
def get_state(self) -> dict:

42
nmreval/gui_qt/io/filedialog.py
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@@ -1,3 +1,7 @@
from __future__ import annotations
import pathlib
from ..Qt import QtWidgets, QtCore
@@ -74,15 +78,45 @@ class SaveDirectoryDialog(_FileDialog):
self.setOption(QtWidgets.QFileDialog.DontConfirmOverwrite, False)
self.setAcceptMode(QtWidgets.QFileDialog.AcceptSave)
lay = self.layout()
self.label = QtWidgets.QLabel(self)
self.label.setTextFormat(QtCore.Qt.RichText)
self.label.setText('Use <b>&lt;label&gt;</b> as placeholder in filename. (e.g. <i>t1_&lt;label&gt;.dat</i>)')
self.layout().addWidget(self.label, self.layout().rowCount(), 0, 1, self.layout().columnCount())
lay.addWidget(self.label, lay.rowCount(), 0, 1, lay.columnCount())
line = QtWidgets.QFrame(self)
line.setFrameShape(line.HLine)
line.setFrameShadow(line.Sunken)
lay.addWidget(line, lay.rowCount(), 0, 1, lay.columnCount())
h_layout = QtWidgets.QHBoxLayout()
h_layout.setContentsMargins(0, 0, 0, 0)
h_layout.setSpacing(3)
self.checkBox = QtWidgets.QCheckBox(self)
self.checkBox.setChecked(True)
self.checkBox.setText('Replace spaces with underscore')
self.layout().addWidget(self.checkBox, self.layout().rowCount(), 0, 1, self.layout().columnCount())
self.checkBox.setText('Replace spaces with _')
h_layout.addWidget(self.checkBox)
self.agr_cb = QtWidgets.QCheckBox(self)
self.agr_cb.setChecked(True)
self.agr_cb.setText('Save graph as Grace file')
h_layout.addWidget(self.agr_cb)
self.fit_cb = QtWidgets.QCheckBox(self)
self.fit_cb.setChecked(True)
self.fit_cb.setText('Save fit parameter')
h_layout.addWidget(self.fit_cb)
lay.addLayout(h_layout, lay.rowCount(), 0, 1, lay.columnCount())
self.setWindowTitle('Save')
self.setNameFilters(['All files (*.*)', 'Session file (*.nmr)', 'Text file (*.dat)', 'HDF file (*.h5)', 'Grace files (*.agr)'])
self.setNameFilters(['All files (*.*)', 'Session file (*.nmr)', 'Text file (*.dat)',
'HDF file (*.h5)', 'Grace files (*.agr)'])
def save_file(self) -> pathlib.Path | None:
outfile = self.selectedFiles()
if outfile:
return pathlib.Path(outfile[0])
return

2
nmreval/gui_qt/lib/forms.py
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@@ -193,6 +193,8 @@ class SelectionWidget(QtWidgets.QWidget):
@value.setter
def value(self, val):
if isinstance(val, dict):
val = list(val.values())[0]
key = [k for k, v in self.options.items() if v == val][0]
self.comboBox.setCurrentIndex(self.comboBox.findText(key))

11
nmreval/gui_qt/lib/pg_objects.py
View File

@@ -341,7 +341,7 @@ class PlotItem(PlotDataItem):
if opts['symbol'] is None:
item_dic['symbol'] = SymbolStyle.No
item_dic['symbolcolor'] = Colors.Black
item_dic['symbolcolor'] = None
else:
item_dic['symbol'] = SymbolStyle.from_str(opts['symbol'])
item_dic['symbolcolor'] = opts['symbolcolor']
@@ -354,9 +354,16 @@ class PlotItem(PlotDataItem):
item_dic['linewidth'] = pen.widthF()
else:
item_dic['linestyle'] = LineStyle.No
item_dic['linecolor'] = item_dic['symbolcolor']
item_dic['linecolor'] = None
item_dic['linewidth'] = 0.0
if item_dic['linecolor'] is None and item_dic['symbolcolor'] is None:
item_dic['symbolcolor'] = Colors.Black.rgb()
elif item_dic['linecolor'] is None:
item_dic['linecolor'] = item_dic['symbolcolor']
elif item_dic['symbolcolor'] is None:
item_dic['symbolcolor'] = item_dic['linecolor']
return item_dic

23
nmreval/gui_qt/main/mainwindow.py
View File

@@ -1,4 +1,5 @@
import pathlib
import re
from pathlib import Path
from typing import List, Tuple
@@ -249,11 +250,20 @@ class NMRMainWindow(QtWidgets.QMainWindow, Ui_BaseWindow):
mode = save_dialog.exec()
if mode == QtWidgets.QDialog.Accepted:
path = save_dialog.selectedFiles()
savefile = save_dialog.save_file()
selected_filter = save_dialog.selectedNameFilter()
if path:
self.management.save(path[0], selected_filter)
if savefile is not None:
use_underscore = save_dialog.checkBox.isChecked()
self.management.save(savefile, selected_filter, strip_spaces=use_underscore)
param_outfile = re.sub('[_\s-]?<label>[_\s-]?', '', savefile.stem)
if save_dialog.agr_cb.isChecked():
self.current_graph_widget.export(savefile.with_name(param_outfile + '.agr'))
if save_dialog.fit_cb.isChecked():
self.management.save_fit_parameter(savefile.with_name(param_outfile + '.dat'))
@QtCore.pyqtSlot()
@QtCore.pyqtSlot(list)
@@ -266,7 +276,7 @@ class NMRMainWindow(QtWidgets.QMainWindow, Ui_BaseWindow):
@QtCore.pyqtSlot(name='on_actionExportGraphic_triggered')
def export_graphic(self):
self.current_graph_widget.export()
self.current_graph_widget.export_dialog()
@QtCore.pyqtSlot(name='on_actionNew_window_triggered')
def new_graph(self):
@@ -304,7 +314,6 @@ class NMRMainWindow(QtWidgets.QMainWindow, Ui_BaseWindow):
@QtCore.pyqtSlot(str)
def remove_graph(self, gid: str):
print(gid, self.current_graph_widget)
self.datawidget.remove_item(gid)
w = None
for w in self.area.subWindowList():
@@ -655,7 +664,6 @@ class NMRMainWindow(QtWidgets.QMainWindow, Ui_BaseWindow):
@QtCore.pyqtSlot(str)
def delete_data(self, sid):
print('remove', sid)
if self.valuewidget.shown_set == sid:
self.tabWidget.setCurrentIndex(0)
@@ -765,7 +773,8 @@ class NMRMainWindow(QtWidgets.QMainWindow, Ui_BaseWindow):
def _select_fitwidget(self, onoff: bool, block_window: bool):
if self.current_graph_widget is not None:
print('select', self.current_graph_widget.id)
pass
if onoff:
if self.management.active_sets:
self.fit_dialog.connected_figure = self.management.current_graph

12
nmreval/gui_qt/main/management.py
View File

@@ -1,3 +1,5 @@
from __future__ import annotations
import pathlib
import re
import uuid
@@ -260,7 +262,6 @@ class UpperManagement(QtCore.QObject):
@QtCore.pyqtSlot(str)
def delete_sets(self, rm_sets: list = None):
rm_graphs = []
print(rm_sets)
if rm_sets is None:
rm_sets = self.graphs[self.current_graph].sets + [self.current_graph]
@@ -489,9 +490,6 @@ class UpperManagement(QtCore.QObject):
f_id = self.add(fit, color=color, src=k)
if subplots:
print('subplots')
f_id_list.append(f_id)
data_k.set_fits(f_id)
gid = data_k.graph
@@ -516,7 +514,7 @@ class UpperManagement(QtCore.QObject):
self.newData.emit(p_id_list, graph_id)
def save_fit_parameter(self, fname: str, fit_sets: List[str] = None):
def save_fit_parameter(self, fname: str | pathlib.Path, fit_sets: List[str] = None):
if fit_sets is None:
fit_sets = [s for (s, _) in self.active_sets]
@@ -1009,7 +1007,7 @@ class UpperManagement(QtCore.QObject):
def append(self, idx: str):
self.data[idx].add([0.0, 0.0, 0.0])
def save(self, outpath: str, extension: str, strip_spaces=False):
def save(self, outpath: str | pathlib.Path, extension: str, strip_spaces=False):
path = pathlib.Path(outpath)
suffix = path.suffix
@@ -1030,7 +1028,7 @@ class UpperManagement(QtCore.QObject):
real_outnames = []
for set_id, set_name in self.active_sets:
full_name = path.stem
if '<label>' in outpath:
if '<label>' in full_name:
full_name = full_name.replace('<label>', convert(set_name, old='tex', new='str'))
data_i = self.data[set_id]

3
nmreval/gui_qt/nmr/t1widget.py
View File

@@ -87,7 +87,7 @@ class QT1Widget(QtWidgets.QDialog, Ui_t1dialog):
self.lineEdit_3.setText('{:.2f}'.format(x[right_b]))
self.lineEdit_3.blockSignals(False)
self.t1calculator.data(x, y)
self.t1calculator.set_data(x, y)
self.determine_minimum(self.interpol_combobox.currentIndex())
self.name = name
@@ -159,7 +159,6 @@ class QT1Widget(QtWidgets.QDialog, Ui_t1dialog):
_temp.valueChanged.connect(self.update_coupling_parameter)
_temp.stateChanged.connect(self.update_coupling_parameter)
self.cp_parameter.append(_temp)
print(self.cp_parameter)
if self.coupling[idx].choice is not None:
for (name, kw_name, opts) in self.coupling[idx].choice:

3
nmreval/io/bds_reader.py
View File

@@ -112,8 +112,7 @@ class BDSReader:
warnings.warn('Number of set temperatures does not match number of data points')
_y = np.array(_y).reshape((actual_temps_length, freq_values_length, 9))
print(_y.shape)
print(f.tell())
# last 3 entries are zero, save only 6
# Z.imag*omega), Z.real, meas.time, meas. temp., ac voltage, dc voltage
self.y = np.transpose(_y[:, :, :6], (2, 0, 1))

8
nmreval/io/graceeditor.py
View File

@@ -450,7 +450,6 @@ class GraceGraph(GraceProperties):
self.idx = idx
def set_limits(self, x=None, y=None):
print(x, y)
for i, line in enumerate(self):
m = self._RE_ENTRY.match(line)
if m and m.group('key') == 'world':
@@ -702,10 +701,3 @@ def _convert_to_str(value):
return ', '.join(map(str, value))
else:
return str(value)
if __name__ == '__main__':
agr = GraceEditor('/autohome/dominik/nmreval/testdata/02_relax_2.agr')
import pprint
pprint.pprint(agr.graphs)
agr.graphs[0].set_property(title='"asdasdasd"')

112
nmreval/nmr/relaxation.py
View File

@@ -4,9 +4,10 @@ Relaxation
Classes to calculate spin-lattice and spin-spin relaxation, as well as to evaluate T1 data and calculate correlation times
"""
from __future__ import annotations
from pathlib import Path
from typing import Any, Optional, Tuple, Type, Union
from typing import Any, Tuple, Type
from warnings import warn
import numpy as np
@@ -42,8 +43,8 @@ class Relaxation:
else:
return super().__repr__()
def set_coupling(self, coupling: Union[float, Type[Coupling]],
parameter: Union[tuple, list] = None, keywords: dict = None):
def set_coupling(self, coupling: float | Type[Coupling],
parameter: tuple | list = None, keywords: dict = None):
if parameter is not None:
self.coup_parameter = parameter
@@ -61,7 +62,7 @@ class Relaxation:
else:
raise ValueError(f'`coupling` is not number or of type `Coupling`, found {coupling!r}')
def set_distribution(self, dist: Type[Distribution], parameter: Union[tuple, list] = None, keywords: dict = None):
def set_distribution(self, dist: Type[Distribution], parameter: tuple | list = None, keywords: dict = None):
self.distribution = dist
if parameter is not None:
@@ -71,7 +72,7 @@ class Relaxation:
self._dist_kw = keywords
def t1(self, omega: ArrayLike, tau: ArrayLike, *specdens_args: Any,
mode: str = 'bpp', **kwargs) -> Union[np.ndarray, float]:
mode: str = 'bpp', **kwargs) -> np.ndarray | float:
r"""
Convenience function
@@ -109,7 +110,7 @@ class Relaxation:
def t1_dipolar(self, omega: ArrayLike, tau: ArrayLike, *specdens_args: Any, inverse: bool = True,
prefactor: float = None, omega_coup: ArrayLike = None,
gamma_coup: str = None, gamma_obs: str = None) -> Union[np.ndarray, float]:
gamma_coup: str = None, gamma_obs: str = None) -> np.ndarray | float:
r"""Calculate T1 under heteronuclear dipolar coupling.
.. math::
@@ -162,7 +163,7 @@ class Relaxation:
return rate
def t1_bpp(self, omega: ArrayLike, tau: ArrayLike, *specdens_args: Any,
inverse: bool = True, prefactor: float = None) -> Union[np.ndarray, float]:
inverse: bool = True, prefactor: float = None) -> np.ndarray | float:
r"""Calculate T1 under homonuclear dipolar coupling or quadrupolar coupling.
.. math::
@@ -197,7 +198,7 @@ class Relaxation:
return rate
def t1_csa(self, omega: ArrayLike, tau: ArrayLike, *specdens_args: Any,
inverse: bool = True, prefactor: float = None) -> Union[np.ndarray, float]:
inverse: bool = True, prefactor: float = None) -> np.ndarray | float:
r"""Calculate T1 under chemical shift anisotropy.
.. math::
@@ -234,7 +235,7 @@ class Relaxation:
return rate
def t1q(self, omega: ArrayLike, tau: ArrayLike, *specdens_args: Any,
inverse: bool = True, prefactor: float = None) -> Union[np.ndarray, float]:
inverse: bool = True, prefactor: float = None) -> np.ndarray | float:
r"""Calculate T1q for homonuclear dipolar coupling or quadrupolar coupling (I=1).
.. math::
@@ -305,7 +306,7 @@ class Relaxation:
return rate
def t2(self, omega: ArrayLike, tau: ArrayLike, *specdens_args: Any,
mode: str = 'bpp', **kwargs) -> Union[np.ndarray, float]:
mode: str = 'bpp', **kwargs) -> np.ndarray | float:
r"""
Convenience function
@@ -342,7 +343,7 @@ class Relaxation:
return self.t2_csa(omega, tau, *specdens_args, **kwargs)
def t2_bpp(self, omega: ArrayLike, tau: ArrayLike, *specdens_args: Any,
inverse: bool = True, prefactor: float = None) -> Union[np.ndarray, float]:
inverse: bool = True, prefactor: float = None) -> np.ndarray | float:
r"""Calculate T2 under homonuclear dipolar coupling or quadrupolar coupling.
.. math::
@@ -379,7 +380,7 @@ class Relaxation:
def t2_dipolar(self, omega: ArrayLike, tau: ArrayLike, *specdens_args: Any,
inverse: bool = True, prefactor: float = None, omega_coup: ArrayLike = None,
gamma_coup: str = None, gamma_obs: str = None) -> Union[np.ndarray, float]:
gamma_coup: str = None, gamma_obs: str = None) -> np.ndarray | float:
r"""Calculate T2 under heteronuclear dipolar coupling.
.. math::
@@ -435,7 +436,7 @@ class Relaxation:
return rate
def t2_csa(self, omega: ArrayLike, tau: ArrayLike, *specdens_args: Any,
inverse: bool = True, prefactor: float = None) -> Union[np.ndarray, float]:
inverse: bool = True, prefactor: float = None) -> np.ndarray | float:
r"""Calculate T1 under chemical shift anisotropy.
.. math::
@@ -471,7 +472,7 @@ class Relaxation:
class RelaxationEvaluation(Relaxation):
def __init__(self, distribution=None):
def __init__(self, distribution: Type[Distribution] = None):
super().__init__(distribution=distribution)
self.t1min = (np.nan, np.nan)
self._interpolate = None
@@ -481,7 +482,15 @@ class RelaxationEvaluation(Relaxation):
self.x = None
self.y = None
def data(self, temp, t1):
def set_data(self, temp: ArrayLike, t1: ArrayLike):
"""
Set data for evaluation.
Args:
temp (array-like): Temperature values
t1 (array-like): T1 values
"""
temp = np.asanyarray(temp)
t1 = np.asanyarray(t1)
sortidx = temp.argsort()
@@ -490,10 +499,11 @@ class RelaxationEvaluation(Relaxation):
self.calculate_t1_min()
def get_increase(self, height: float = None, idx: int = 0, mode: str = None, omega: float = None,
dist_parameter: Union[tuple, list] = None, prefactor: Union[tuple, list, float] = None,
dist_parameter: tuple | list = None, prefactor: tuple | list | float = None,
coupling_kwargs: dict = None):
"""
Determine a single parameter from a T1 minimum
Determine a single parameter from a T1 minimum.
It replaces the previously set value.
Args:
height (float, optional): Height of T1 minimum
@@ -501,14 +511,16 @@ class RelaxationEvaluation(Relaxation):
idx (int): Default is 0.
omega (float, optional): Larmor frequency (in 1/s)
dist_parameter (tuple, optional):
prefactor (tuple, float, optional):
coupling_kwargs (dict, optional):
prefactor (tuple, float, optional): Prefactor for
coupling_kwargs (dict, optional): Keyword arguments for coupling, replacing old values
Returns:
A tuple of the value of varied parameter, or nan if nothing was varied
and the minimum height calculated for given parameters.
"""
stretching = mini = np.nan
stretching = minimon = np.nan
if height is None:
height = self.t1min[1]
@@ -605,6 +617,7 @@ class RelaxationEvaluation(Relaxation):
else:
stretching = t1_no_coup / height
prefactor = stretching
else:
raise ValueError('Use `distribution` or `coupling` to set parameter')
@@ -617,13 +630,30 @@ class RelaxationEvaluation(Relaxation):
self.prefactor = self.coupling.relax(*self.coup_parameter, **self.coup_kw)
else:
self.prefactor = prefactor
mini = np.min(self.t1(omega, np.logspace(*tau_lims, num=1001), *self.dist_parameter,
prefactor=self.prefactor))
minimon = np.min(self.t1(omega, np.logspace(*tau_lims, num=1001), *self.dist_parameter,
prefactor=self.prefactor))
return stretching, mini
return stretching, minimon
def calculate_t1_min(self, interpolate: int = None, trange: Tuple[float, float] = None, use_log: bool = False) -> \
Tuple[Tuple[float, float], Optional[Tuple[np.ndarray, np.ndarray]]]:
Tuple[Tuple[float, float], Tuple[np.ndarray, np.ndarray] | None]:
"""
Determine a minimum position for given T1 data
Args:
interpolate (int, optional):
* 0 or None: No interpolation, minimum is data minimum
* 1: Interpolation with a parabola
* 2: Interpolation with a cubic spline
* 3: Interpolation with Akima spline (less wiggly than cubic)
trange (tuple): Range (left border, range border) of interpolation in K.
Interpolation without a given range uses two points left and right of minimum value.
use_log (bool): Default is `True`.
Returns:
The minimum position (`T_min`, `T1_min`)
"""
min_index = np.argmin(self.y)
t1_min = (self.x[min_index], self.y[min_index])
parabola = None
@@ -674,9 +704,25 @@ class RelaxationEvaluation(Relaxation):
return t1_min, parabola
def correlation_from_t1(self, mode: str = 'raw', interpolate: bool = False, omega: float = None,
dist_parameter: Union[float, list, tuple] = None, prefactor: Union[float, tuple, list] = None,
dist_parameter: list | tuple = None, prefactor: float = None,
coupling_param: list = None, coupling_kwargs: dict = None) -> Tuple[np.ndarray, dict]:
"""
Calculate correlation times from set T1 data.
Optional arguments overwrite previousliy set parameter.
Args:
mode (str, {`raw`, `mean`, `logmean`, `max`}): Type of correlation time. Default is `raw`.
interpolate (bool): If ``True`` and T1 minimum was determined by nterpolation,
T1 on interpolated line instead of measured value is used. Default is `False`.
omega (float, optional): Larmor frequency (in 1/s)
dist_parameter (list, optional): List of parameter of spectral density
prefactor (float, optional): Prefactor of T1 calculation, will
coupling_param (list, optional): Parameter for coupling constant, ignored if `prefactor`is given.
coupling_kwargs (dict, optional): Keyword arguments for coupling constant, ignored if `prefactor`is given.
Returns:
"""
if self.x is None:
raise ValueError('Temperature is not set')
@@ -694,6 +740,7 @@ class RelaxationEvaluation(Relaxation):
if coupling_param is None:
prefactor = self.prefactor
coupling_param = self.coup_parameter
else:
prefactor = self.coupling.relax(*coupling_param, **coupling_kwargs)
@@ -711,7 +758,7 @@ class RelaxationEvaluation(Relaxation):
base_taus = np.logspace(-10, -7, num=1001)
min_tau = base_taus[np.argmin(self.t1(omega, base_taus, *dist_parameter, prefactor=prefactor))]
taus = np.geomspace(min_tau, 100. * min_tau, num=501)
taus = np.geomspace(min_tau, 100. * min_tau, num=1001)
current_t1 = self.t1(omega, taus, *dist_parameter, prefactor=prefactor)
for i in range(1, len(slow_t1) + 1):
@@ -722,7 +769,7 @@ class RelaxationEvaluation(Relaxation):
t1_i = self._interpolate(slow_temp[-i])
if np.min(current_t1) > t1_i:
warn('Correlation time could not be calculated')
warn(f'Value {t1_i} below set minimum, wonky correlation time')
correlation_times[offset - i] = taus[0]
continue
@@ -738,7 +785,7 @@ class RelaxationEvaluation(Relaxation):
fast_t1 = self.y[fast_idx]
fast_temp = self.x[fast_idx]
taus = np.geomspace(0.01 * min_tau, min_tau, num=501)
taus = np.geomspace(0.01 * min_tau, min_tau, num=1001)
current_t1 = self.t1(omega, taus, *dist_parameter, prefactor=prefactor)
for i in range(len(fast_t1)):
@@ -750,7 +797,7 @@ class RelaxationEvaluation(Relaxation):
if current_t1[-1] > t1_i:
correlation_times[offset + i] = taus[-1]
warn(f'Correlation time for {correlation_times[offset + i]} could not be calculated')
warn(f'Value {t1_i} below set minimum, wonky correlation time')
continue
cross_idx = np.where(np.diff(np.sign(current_t1 - t1_i)))[0]
@@ -763,11 +810,10 @@ class RelaxationEvaluation(Relaxation):
correlation_times[offset + i] = (taus[cross_idx + 1] * lamb + (1 - lamb) * taus[cross_idx])[0]
opts = {'distribution': (self.distribution.name, dist_parameter),
'frequency': omega / 2 / np.pi}
'frequency': omega / 2 / np.pi,
'prefactor': self.prefactor}
if self.coupling is not None:
opts['coupling'] = (self.coupling.name, self.prefactor, coupling_param, coupling_kwargs)
else:
opts['coupling'] = (self.prefactor,)
opts['coupling'] = (self.coupling.name, coupling_param, coupling_kwargs)
return np.c_[self.x, self.distribution.mean_value(correlation_times, *dist_parameter, mode=mode)], opts