save fit parameter and agr; more doc
This commit is contained in:
dominik
parent
ef81030213
commit
73e4a2b4d9
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@ -5,8 +5,8 @@
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# from the environment for the first two.
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SPHINXOPTS ?=
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SPHINXBUILD ?= /autohome/dominik/miniconda3/bin/sphinx-build
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SOURCEDIR = /autohome/dominik/nmreval/docs/source
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BUILDDIR = /autohome/dominik/nmreval/docs/build
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SOURCEDIR = /autohome/dominik/nmreval/doc/source
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BUILDDIR = /autohome/dominik/nmreval/doc/_build
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# Put it first so that "make" without argument is like "make help".
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help:
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@ -1,12 +1,19 @@
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"""
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=======================
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Spin-lattice relaxation
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=======================
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==========
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T1 minimum
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==========
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Example for
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``RelaxationEvaluation`` is used to get width parameter from a T1 minimum.
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As a subclass of ``Relaxation`` it can also be used to calculate Relaxation times.
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The basic steps are:
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* Determine a T1 minimum with `nmreval.nmr.RelaxationEvaluation.calculate_t1_min`
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* Calculate width parameter of a spectral density/coupling constants/... with
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``RelaxationEvaluation.get_increase``
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* Calculate correlation times from these values with ``RelaxationEvaluation.correlation_from_t1``
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"""
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import numpy as np
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from matplotlib import pyplot as plt
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import matplotlib.pyplot as plt
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from nmreval.distributions import ColeDavidson
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from nmreval.nmr import Relaxation, RelaxationEvaluation
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@ -20,7 +27,7 @@ temperature = 1000/inv_temp
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# spectral density parameter
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ea = 0.45
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tau = 1e-21 * np.exp(ea / kB / temperature)
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gamma_cd = 0.1
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gamma_cd = 0.4
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# interaction parameter
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omega = 2*np.pi*46e6
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@ -28,40 +35,57 @@ delta = 120e3
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eta = 0
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r = Relaxation()
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r.set_distribution(ColeDavidson) # the only parameter that has to be set beforehand
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r.set_distribution(ColeDavidson) # the only parameter that set beforehand
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t1_values = r.t1(omega, tau, gamma_cd, mode='bpp',
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prefactor=Quadrupolar.relax(delta, eta))
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# add noise
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rng = np.random.default_rng(123456789)
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rng = np.random.default_rng()
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noisy = (rng.random(t1_values.size)-0.5) * 0.5 * t1_values + t1_values
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# set parameter and data
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ax_t1 = plt.figure().add_subplot()
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ax_t1.semilogy(inv_temp, t1_values, label='Calculated T1')
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ax_t1.semilogy(inv_temp, noisy, 'o', label='Noise')
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ax_t1.legend()
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plt.show()
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# Actual evaluation starts here
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# setting necessary parameter
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r_eval = RelaxationEvaluation()
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r_eval.set_distribution(ColeDavidson)
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r_eval.set_coupling(Quadrupolar, (delta, eta))
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r_eval.data(temperature, noisy)
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r_eval.set_data(temperature, noisy)
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r_eval.omega = omega
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# Find a T1 minumum
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t1_min_data, _ = r_eval.calculate_t1_min() # second argument is None
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t1_min_inter, line = r_eval.calculate_t1_min(interpolate=1, trange=(160, 195), use_log=True)
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fig, ax = plt.subplots()
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ax.semilogy(1000/t1_min_data[0], t1_min_data[1], 'rx', label='Data minimum')
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ax.semilogy(1000/t1_min_inter[0], t1_min_inter[1], 'r+', label='Parabola')
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ax.semilogy(1000/line[0], line[1])
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ax_min = plt.figure().add_subplot()
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ax_min.semilogy(inv_temp, noisy, 'o', label='Data')
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ax_min.semilogy(1000/line[0], line[1], '--')
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ax_min.semilogy(1000/t1_min_data[0], t1_min_data[1], 'C2X',label='Data minimum')
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ax_min.semilogy(1000/t1_min_inter[0], t1_min_inter[1], 'C3P',label='Parabola')
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ax_min.set_xlim(4.5, 7)
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ax_min.set_ylim(1e-3, 1e-1)
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ax_min.legend()
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# Vary the first (and for Cole-Davidson, only) parameter of the spectral density
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found_gamma, found_height = r_eval.get_increase(t1_min_inter[1], idx=0, mode='distribution')
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print(found_gamma)
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plt.axhline(found_height)
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print(f'Minimum at {found_height} for {found_gamma}; input is {gamma_cd}')
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plt.show()
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#%%
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# Now we found temperature and height of the minimum we can calculate the correlation time
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##################################################################################
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# Calculation of correlation times uses previously parameter for spectral density
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# and prefactor
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plt.semilogy(1000/temperature, tau)
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tau_from_t1, opts = r_eval.correlation_from_t1()
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print(opts)
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plt.semilogy(1000/tau_from_t1[:, 0], tau_from_t1[:, 1], 'o')
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tau_from_t1, opts = r_eval.correlation_from_t1(mode='mean')
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print(f'Used options: {opts}')
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ax_tau = plt.figure().add_subplot()
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ax_tau.semilogy(inv_temp, tau*gamma_cd, label='Original input')
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ax_tau.semilogy(1000/tau_from_t1[:, 0], tau_from_t1[:, 1], 'o', label='Calculated')
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ax_tau.legend()
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plt.show()
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@ -1,179 +0,0 @@
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:orphan:
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.. _sphx_glr_gallery:
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.. examples-index:
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.. _gallery:
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========
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Examples
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========
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This page contains example plots. Click on any image to see the full image and source code.
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.. raw:: html
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<div class="sphx-glr-clear"></div>
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.. _sphx_glr_gallery_distribution:
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.. _distribution_examples:
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.. _distribution-examples-index:
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Distribution of correlation times
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=================================
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.. raw:: html
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<div class="sphx-glr-thumbcontainer" tooltip="Example for KWW distributions">
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.. only:: html
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.. figure:: /gallery/distribution/images/thumb/sphx_glr_plot_KWW_thumb.png
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:alt: Kohlrausch-Williams-Watts
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:ref:`sphx_glr_gallery_distribution_plot_KWW.py`
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.. raw:: html
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</div>
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.. toctree::
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:hidden:
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/gallery/distribution/plot_KWW
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.. raw:: html
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<div class="sphx-glr-thumbcontainer" tooltip="Example for Cole-Cole distributions">
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.. only:: html
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.. figure:: /gallery/distribution/images/thumb/sphx_glr_plot_ColeCole_thumb.png
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:alt: Cole-Cole
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:ref:`sphx_glr_gallery_distribution_plot_ColeCole.py`
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.. raw:: html
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</div>
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.. toctree::
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:hidden:
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/gallery/distribution/plot_ColeCole
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.. raw:: html
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<div class="sphx-glr-thumbcontainer" tooltip="Example for Log-Gaussian distributions">
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.. only:: html
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.. figure:: /gallery/distribution/images/thumb/sphx_glr_plot_LogGaussian_thumb.png
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:alt: Log-Gaussian
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:ref:`sphx_glr_gallery_distribution_plot_LogGaussian.py`
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.. raw:: html
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</div>
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.. toctree::
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:hidden:
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/gallery/distribution/plot_LogGaussian
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.. raw:: html
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<div class="sphx-glr-thumbcontainer" tooltip="Example for Cole-Davidson distributions">
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.. only:: html
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.. figure:: /gallery/distribution/images/thumb/sphx_glr_plot_ColeDavidson_thumb.png
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:alt: Cole-Davidson
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:ref:`sphx_glr_gallery_distribution_plot_ColeDavidson.py`
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.. raw:: html
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</div>
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.. toctree::
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:hidden:
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/gallery/distribution/plot_ColeDavidson
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.. raw:: html
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<div class="sphx-glr-thumbcontainer" tooltip="Example for Havriliak-Negami distributions">
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.. only:: html
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.. figure:: /gallery/distribution/images/thumb/sphx_glr_plot_HavriliakNegami_thumb.png
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:alt: Havriliak-Negami
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:ref:`sphx_glr_gallery_distribution_plot_HavriliakNegami.py`
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.. raw:: html
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</div>
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.. toctree::
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:hidden:
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/gallery/distribution/plot_HavriliakNegami
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.. raw:: html
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<div class="sphx-glr-clear"></div>
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.. _sphx_glr_gallery_nmr:
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.. _nmr_examples:
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.. _nmr-examples-index:
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NMR specifics
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=============
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.. raw:: html
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<div class="sphx-glr-thumbcontainer" tooltip="Example for">
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.. only:: html
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.. figure:: /gallery/nmr/images/thumb/sphx_glr_plot_RelaxationEvaluation_thumb.png
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:alt: Spin-lattice relaxation
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:ref:`sphx_glr_gallery_nmr_plot_RelaxationEvaluation.py`
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.. raw:: html
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</div>
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.. toctree::
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:hidden:
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/gallery/nmr/plot_RelaxationEvaluation
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.. raw:: html
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<div class="sphx-glr-clear"></div>
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@ -1,3 +0,0 @@
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'/autohome/dominik/nmreval/doc/_build/html/index.html', (0, 6969)
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'/autohome/dominik/nmreval/doc/_build/html/_static/documentation_options.js', (7168, 364)
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'/autohome/dominik/nmreval/doc/_build/html/searchindex.js', (7680, 29280)
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Binary file not shown.
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@ -1,3 +0,0 @@
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'/autohome/dominik/nmreval/doc/_build/html/index.html', (0, 6969)
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'/autohome/dominik/nmreval/doc/_build/html/_static/documentation_options.js', (7168, 364)
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'/autohome/dominik/nmreval/doc/_build/html/searchindex.js', (7680, 29280)
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@ -485,7 +485,7 @@ class PointContainer(ExperimentContainer):
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}
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if sym_kwargs['symbol'] is None and line_kwargs['style'] is None:
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if len(self._data) > 1000:
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if len(self._data) > 500:
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line_kwargs['style'] = LineStyle.Solid
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sym_kwargs['symbol'] = SymbolStyle.No
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else:
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@ -138,8 +138,6 @@ class ConversionDialog(QtWidgets.QDialog, Ui_Dialog):
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src_sets.append((set_id_real, set_id_imag, graph_id, type_idx))
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print(src_sets)
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self.convertSets.emit(src_sets)
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return src_sets
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@ -124,7 +124,7 @@ class QFitParameterWidget(QtWidgets.QWidget, Ui_FormFit):
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self.data_parameter[idx].blockSignals(False)
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@QtCore.pyqtSlot(str, object)
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def change_global_choice(self, argname, value):
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def change_global_choice(self, _, value):
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idx = self.global_parameter.index(self.sender())
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self.glob_values[idx] = value
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if self.data_values[self.comboBox.currentData()][idx] is None:
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@ -242,6 +242,8 @@ class QFitParameterWidget(QtWidgets.QWidget, Ui_FormFit):
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else:
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if p_i is None:
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kw_p.update(g.value)
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elif isinstance(p_i, dict):
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kw_p.update(p_i)
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else:
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kw_p[g.argname] = p_i
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@ -64,7 +64,7 @@ class QGraphWindow(QtWidgets.QGraphicsView, Ui_GraphWindow):
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# reconnect "Export..." in context menu to our function
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self.scene.contextMenu[0].disconnect()
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self.scene.contextMenu[0].triggered.connect(self.export)
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self.scene.contextMenu[0].triggered.connect(self.export_dialog)
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def _init_gui(self):
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self.setWindowTitle('Graph ' + str(next(QGraphWindow.counter)))
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@ -515,7 +515,7 @@ class QGraphWindow(QtWidgets.QGraphicsView, Ui_GraphWindow):
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(item in self.graphic.items() or other_item in self.graphic.items()):
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self.legend.addItem(item, convert(item.opts.get('name', ''), old='tex', new='html'))
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def export(self):
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def export_dialog(self):
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filters = 'All files (*.*);;AGR (*.agr);;SVG (*.svg);;PDF (*.pdf)'
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for imgformat in QtGui.QImageWriter.supportedImageFormats():
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str_format = imgformat.data().decode('utf-8')
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@ -524,6 +524,9 @@ class QGraphWindow(QtWidgets.QGraphicsView, Ui_GraphWindow):
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outfile, _ = QtWidgets.QFileDialog.getSaveFileName(self, caption='Export graphic', filter=filters,
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options=QtWidgets.QFileDialog.DontConfirmOverwrite)
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if outfile:
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self.export(outfile)
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def export(self, outfile: str):
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_, suffix = os.path.splitext(outfile)
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if suffix == '':
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QtWidgets.QMessageBox.warning(self, 'No file extension',
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@ -566,7 +569,6 @@ class QGraphWindow(QtWidgets.QGraphicsView, Ui_GraphWindow):
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from ..io.exporters import PDFPrintExporter
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PDFPrintExporter(self.graphic).export(outfile)
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elif suffix == '.svg':
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from pyqtgraph.exporters import SVGExporter
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SVGExporter(self.scene).export(outfile)
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@ -591,8 +593,6 @@ class QGraphWindow(QtWidgets.QGraphicsView, Ui_GraphWindow):
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if item_dic:
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dic['items'].append(item_dic)
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print(dic)
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return dic
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def get_state(self) -> dict:
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@ -1,3 +1,7 @@
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from __future__ import annotations
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import pathlib
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from ..Qt import QtWidgets, QtCore
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@ -74,15 +78,45 @@ class SaveDirectoryDialog(_FileDialog):
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self.setOption(QtWidgets.QFileDialog.DontConfirmOverwrite, False)
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self.setAcceptMode(QtWidgets.QFileDialog.AcceptSave)
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lay = self.layout()
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self.label = QtWidgets.QLabel(self)
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self.label.setTextFormat(QtCore.Qt.RichText)
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self.label.setText('Use <b><label></b> as placeholder in filename. (e.g. <i>t1_<label>.dat</i>)')
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self.layout().addWidget(self.label, self.layout().rowCount(), 0, 1, self.layout().columnCount())
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lay.addWidget(self.label, lay.rowCount(), 0, 1, lay.columnCount())
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line = QtWidgets.QFrame(self)
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line.setFrameShape(line.HLine)
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line.setFrameShadow(line.Sunken)
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lay.addWidget(line, lay.rowCount(), 0, 1, lay.columnCount())
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h_layout = QtWidgets.QHBoxLayout()
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h_layout.setContentsMargins(0, 0, 0, 0)
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h_layout.setSpacing(3)
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self.checkBox = QtWidgets.QCheckBox(self)
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self.checkBox.setChecked(True)
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self.checkBox.setText('Replace spaces with underscore')
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self.layout().addWidget(self.checkBox, self.layout().rowCount(), 0, 1, self.layout().columnCount())
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self.checkBox.setText('Replace spaces with _')
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h_layout.addWidget(self.checkBox)
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self.agr_cb = QtWidgets.QCheckBox(self)
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self.agr_cb.setChecked(True)
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self.agr_cb.setText('Save graph as Grace file')
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h_layout.addWidget(self.agr_cb)
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self.fit_cb = QtWidgets.QCheckBox(self)
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self.fit_cb.setChecked(True)
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self.fit_cb.setText('Save fit parameter')
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h_layout.addWidget(self.fit_cb)
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lay.addLayout(h_layout, lay.rowCount(), 0, 1, lay.columnCount())
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self.setWindowTitle('Save')
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self.setNameFilters(['All files (*.*)', 'Session file (*.nmr)', 'Text file (*.dat)', 'HDF file (*.h5)', 'Grace files (*.agr)'])
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self.setNameFilters(['All files (*.*)', 'Session file (*.nmr)', 'Text file (*.dat)',
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'HDF file (*.h5)', 'Grace files (*.agr)'])
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def save_file(self) -> pathlib.Path | None:
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outfile = self.selectedFiles()
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if outfile:
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return pathlib.Path(outfile[0])
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return
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@ -193,6 +193,8 @@ class SelectionWidget(QtWidgets.QWidget):
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@value.setter
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def value(self, val):
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if isinstance(val, dict):
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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))
|
||||
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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]
|
||||
|
|
|
|||
|
|
@ -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:
|
||||
|
|
|
|||
|
|
@ -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))
|
||||
|
|
|
|||
|
|
@ -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"')
|
||||
|
|
|
|||
|
|
@ -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):
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return t1_min, parabola
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def correlation_from_t1(self, mode: str = 'raw', interpolate: bool = False, omega: float = None,
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dist_parameter: Union[float, list, tuple] = None, prefactor: Union[float, tuple, list] = None,
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dist_parameter: list | tuple = None, prefactor: float = None,
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coupling_param: list = None, coupling_kwargs: dict = None) -> Tuple[np.ndarray, dict]:
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"""
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Calculate correlation times from set T1 data.
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Optional arguments overwrite previousliy set parameter.
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Args:
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mode (str, {`raw`, `mean`, `logmean`, `max`}): Type of correlation time. Default is `raw`.
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interpolate (bool): If ``True`` and T1 minimum was determined by nterpolation,
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T1 on interpolated line instead of measured value is used. Default is `False`.
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omega (float, optional): Larmor frequency (in 1/s)
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dist_parameter (list, optional): List of parameter of spectral density
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prefactor (float, optional): Prefactor of T1 calculation, will
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coupling_param (list, optional): Parameter for coupling constant, ignored if `prefactor`is given.
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coupling_kwargs (dict, optional): Keyword arguments for coupling constant, ignored if `prefactor`is given.
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|
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Returns:
|
||||
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"""
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if self.x is None:
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raise ValueError('Temperature is not set')
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@ -694,6 +740,7 @@ class RelaxationEvaluation(Relaxation):
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if coupling_param is None:
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prefactor = self.prefactor
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coupling_param = self.coup_parameter
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else:
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prefactor = self.coupling.relax(*coupling_param, **coupling_kwargs)
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@ -711,7 +758,7 @@ class RelaxationEvaluation(Relaxation):
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base_taus = np.logspace(-10, -7, num=1001)
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min_tau = base_taus[np.argmin(self.t1(omega, base_taus, *dist_parameter, prefactor=prefactor))]
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|
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taus = np.geomspace(min_tau, 100. * min_tau, num=501)
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taus = np.geomspace(min_tau, 100. * min_tau, num=1001)
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current_t1 = self.t1(omega, taus, *dist_parameter, prefactor=prefactor)
|
||||
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for i in range(1, len(slow_t1) + 1):
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|
@ -722,7 +769,7 @@ class RelaxationEvaluation(Relaxation):
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|||
t1_i = self._interpolate(slow_temp[-i])
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||||
|
||||
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
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user