.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here ` to download the full example code or to run this example in your browser via Binder
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_fitting_2D_fitting_plot_2_Coesite_DAS.py:
17O DAS NMR of Coesite
^^^^^^^^^^^^^^^^^^^^^^
Coesite is a high-pressure (2-3 GPa) and high-temperature (700°C) polymorph of silicon
dioxide :math:`\text{SiO}_2`. Coesite has five crystallographic :math:`^{17}\text{O}`
sites. The experimental dataset used in this example is published in
Grandinetti `et. al.` [#f1]_
.. code-block:: python
import numpy as np
import csdmpy as cp
import matplotlib as mpl
import matplotlib.pyplot as plt
import mrsimulator.signal_processing as sp
import mrsimulator.signal_processing.apodization as apo
from mrsimulator import Simulator
from mrsimulator.methods import Method2D
from mrsimulator.utils import get_spectral_dimensions
from mrsimulator.utils.collection import single_site_system_generator
from mrsimulator.utils.spectral_fitting import LMFIT_min_function, make_LMFIT_params
from lmfit import Minimizer, report_fit
# global plot configuration
mpl.rcParams["figure.figsize"] = [4.5, 3.0]
Import the dataset
------------------
.. code-block:: python
filename = "https://sandbox.zenodo.org/record/687656/files/DASCoesite.csdf"
experiment = cp.load(filename)
# For spectral fitting, we only focus on the real part of the complex dataset
experiment = experiment.real
# Convert the coordinates along each dimension from Hz to ppm.
_ = [item.to("ppm", "nmr_frequency_ratio") for item in experiment.dimensions]
# Normalize the spectrum
experiment /= experiment.max()
# plot of the dataset.
levels = (np.arange(10) + 0.3) / 15 # contours are drawn at these levels.
ax = plt.subplot(projection="csdm")
cb = ax.contour(experiment, colors="k", levels=levels, alpha=0.5, linewidths=0.5)
plt.colorbar(cb)
ax.invert_xaxis()
ax.set_ylim(30, -30)
plt.tight_layout()
plt.show()
.. image:: /fitting/2D_fitting/images/sphx_glr_plot_2_Coesite_DAS_001.png
:alt: plot 2 Coesite DAS
:class: sphx-glr-single-img
Create a fitting model
----------------------
The fitting model includes the Simulator and SignalProcessor objects. First,
create the Simulator object.
.. code-block:: python
# Create the guess sites and spin systems.
# default unit of isotropic_chemical_shift is ppm and Cq is Hz.
shifts = [29, 41, 57, 53, 58] # in ppm
Cq = [6.1e6, 5.4e6, 5.5e6, 5.5e6, 5.1e6] # in Hz
eta = [0.1, 0.2, 0.1, 0.1, 0.3]
abundance = [1, 1, 2, 2, 2]
spin_systems = single_site_system_generator(
isotopes="17O",
isotropic_chemical_shifts=shifts,
quadrupolar={"Cq": Cq, "eta": eta},
abundance=abundance,
)
# Create the DAS method.
# Get the spectral dimension paramters from the experiment.
spectral_dims = get_spectral_dimensions(experiment)
.. code-block:: python
das = Method2D(
channels=["17O"],
magnetic_flux_density=11.7, # in T
spectral_dimensions=[
{
**spectral_dims[0],
"events": [
{"fraction": 0.5, "rotor_angle": 37.38 * 3.14159 / 180},
{"fraction": 0.5, "rotor_angle": 79.19 * 3.14159 / 180},
],
},
# The last spectral dimension block is the direct-dimension
{**spectral_dims[1], "events": [{"rotor_angle": 54.735 * 3.14159 / 180}]},
],
experiment=experiment, # also add the measurement to the method.
)
# Optimize the script by pre-setting the transition pathways for each spin system from
# the das method.
for sys in spin_systems:
sys.transition_pathways = das.get_transition_pathways(sys)
.. code-block:: python
# Create the Simulator object and add the method and spin system objects.
sim = Simulator()
sim.spin_systems = spin_systems # add the spin systems
sim.methods = [das] # add the method
sim.run()
.. code-block:: python
# Add Post simulation processing.
processor = sp.SignalProcessor(
operations=[
# Gaussian convolution along both dimensions.
sp.IFFT(dim_index=(0, 1)),
apo.Gaussian(FWHM="0.15 kHz", dim_index=0),
apo.Gaussian(FWHM="0.15 kHz", dim_index=1),
sp.FFT(dim_index=(0, 1)),
sp.Scale(factor=1 / 8),
]
)
# Apply post simulation operations.
processed_data = processor.apply_operations(data=sim.methods[0].simulation).real
.. code-block:: python
# The plot of the simulation after signal processing.
ax = plt.subplot(projection="csdm")
ax.contour(processed_data, colors="r", levels=levels, alpha=0.75, linewidths=0.5)
cb = ax.contour(experiment, colors="k", levels=levels, alpha=0.5, linewidths=0.5)
plt.colorbar(cb)
ax.invert_xaxis()
ax.set_ylim(30, -30)
plt.tight_layout()
plt.show()
.. image:: /fitting/2D_fitting/images/sphx_glr_plot_2_Coesite_DAS_002.png
:alt: plot 2 Coesite DAS
:class: sphx-glr-single-img
Least-squares minimization with LMFIT
-------------------------------------
First, create the fitting parameters.
Use the :func:`~mrsimulator.utils.spectral_fitting.make_LMFIT_params` for a quick
setup.
.. code-block:: python
params = make_LMFIT_params(sim, processor)
# Here, we fix the abundance parameters to their initial value.
for i in range(5):
params[f"sys_{i}_abundance"].vary = False
params.pretty_print()
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
Name Value Min Max Stderr Vary Expr Brute_Step
operation_1_Gaussian_FWHM 0.15 -inf inf None True None None
operation_2_Gaussian_FWHM 0.15 -inf inf None True None None
operation_4_Scale_factor 0.125 -inf inf None True None None
sys_0_abundance 12.5 0 100 None False None None
sys_0_site_0_isotropic_chemical_shift 29 -inf inf None True None None
sys_0_site_0_quadrupolar_Cq 6.1e+06 -inf inf None True None None
sys_0_site_0_quadrupolar_eta 0.1 0 1 None True None None
sys_1_abundance 12.5 0 100 None False None None
sys_1_site_0_isotropic_chemical_shift 41 -inf inf None True None None
sys_1_site_0_quadrupolar_Cq 5.4e+06 -inf inf None True None None
sys_1_site_0_quadrupolar_eta 0.2 0 1 None True None None
sys_2_abundance 25 0 100 None False None None
sys_2_site_0_isotropic_chemical_shift 57 -inf inf None True None None
sys_2_site_0_quadrupolar_Cq 5.5e+06 -inf inf None True None None
sys_2_site_0_quadrupolar_eta 0.1 0 1 None True None None
sys_3_abundance 25 0 100 None False None None
sys_3_site_0_isotropic_chemical_shift 53 -inf inf None True None None
sys_3_site_0_quadrupolar_Cq 5.5e+06 -inf inf None True None None
sys_3_site_0_quadrupolar_eta 0.1 0 1 None True None None
sys_4_abundance 25 0 100 None False 100-sys_0_abundance-sys_1_abundance-sys_2_abundance-sys_3_abundance None
sys_4_site_0_isotropic_chemical_shift 58 -inf inf None True None None
sys_4_site_0_quadrupolar_Cq 5.1e+06 -inf inf None True None None
sys_4_site_0_quadrupolar_eta 0.3 0 1 None True None None
Run the minimization using LMFIT
.. code-block:: python
minner = Minimizer(LMFIT_min_function, params, fcn_args=(sim, processor))
result = minner.minimize()
report_fit(result)
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
[[Fit Statistics]]
# fitting method = leastsq
# function evals = 371
# data points = 131072
# variables = 18
chi-square = 363.301226
reduced chi-square = 0.00277215
Akaike info crit = -771751.293
Bayesian info crit = -771575.190
[[Variables]]
sys_0_site_0_isotropic_chemical_shift: 27.4394744 +/- 0.16037405 (0.58%) (init = 29)
sys_0_site_0_quadrupolar_Cq: 6044709.32 +/- 10092.8744 (0.17%) (init = 6100000)
sys_0_site_0_quadrupolar_eta: 0.09058695 +/- 0.00541829 (5.98%) (init = 0.1)
sys_0_abundance: 12.5 (fixed)
sys_1_site_0_isotropic_chemical_shift: 40.2138886 +/- 0.20780233 (0.52%) (init = 41)
sys_1_site_0_quadrupolar_Cq: 5453223.41 +/- 14094.8807 (0.26%) (init = 5400000)
sys_1_site_0_quadrupolar_eta: 0.20537807 +/- 0.00544236 (2.65%) (init = 0.2)
sys_1_abundance: 12.5 (fixed)
sys_2_site_0_isotropic_chemical_shift: 54.3501005 +/- 0.09034982 (0.17%) (init = 57)
sys_2_site_0_quadrupolar_Cq: 5394012.50 +/- 6386.20846 (0.12%) (init = 5500000)
sys_2_site_0_quadrupolar_eta: 0.17076714 +/- 0.00278735 (1.63%) (init = 0.1)
sys_2_abundance: 25 (fixed)
sys_3_site_0_isotropic_chemical_shift: 52.3588929 +/- 0.10898489 (0.21%) (init = 53)
sys_3_site_0_quadrupolar_Cq: 5497997.60 +/- 7105.04765 (0.13%) (init = 5500000)
sys_3_site_0_quadrupolar_eta: 0.21373888 +/- 0.00275690 (1.29%) (init = 0.1)
sys_3_abundance: 25 (fixed)
sys_4_site_0_isotropic_chemical_shift: 54.7343082 +/- 0.10652839 (0.19%) (init = 58)
sys_4_site_0_quadrupolar_Cq: 5042385.28 +/- 7655.24974 (0.15%) (init = 5100000)
sys_4_site_0_quadrupolar_eta: 0.29135745 +/- 0.00309323 (1.06%) (init = 0.3)
sys_4_abundance: 25.0000000 +/- 0.00000000 (0.00%) == '100-sys_0_abundance-sys_1_abundance-sys_2_abundance-sys_3_abundance'
operation_1_Gaussian_FWHM: 0.39458618 +/- 0.00896349 (2.27%) (init = 0.15)
operation_2_Gaussian_FWHM: 0.15185217 +/- 4.4454e-04 (0.29%) (init = 0.15)
operation_4_Scale_factor: 0.00977120 +/- 2.7355e-05 (0.28%) (init = 0.125)
[[Correlations]] (unreported correlations are < 0.100)
C(sys_3_site_0_isotropic_chemical_shift, sys_3_site_0_quadrupolar_Cq) = 0.810
C(sys_0_site_0_isotropic_chemical_shift, sys_0_site_0_quadrupolar_Cq) = 0.801
C(sys_1_site_0_isotropic_chemical_shift, sys_1_site_0_quadrupolar_Cq) = 0.792
C(sys_4_site_0_isotropic_chemical_shift, sys_4_site_0_quadrupolar_Cq) = 0.792
C(sys_2_site_0_isotropic_chemical_shift, sys_2_site_0_quadrupolar_Cq) = 0.789
C(operation_2_Gaussian_FWHM, operation_4_Scale_factor) = 0.467
C(sys_2_site_0_quadrupolar_eta, operation_1_Gaussian_FWHM) = -0.362
C(sys_0_site_0_quadrupolar_eta, operation_1_Gaussian_FWHM) = -0.347
C(sys_3_site_0_quadrupolar_eta, operation_1_Gaussian_FWHM) = -0.191
C(sys_0_site_0_isotropic_chemical_shift, sys_0_site_0_quadrupolar_eta) = 0.147
C(sys_4_site_0_quadrupolar_Cq, operation_4_Scale_factor) = 0.144
C(operation_1_Gaussian_FWHM, operation_4_Scale_factor) = 0.144
C(sys_2_site_0_isotropic_chemical_shift, sys_2_site_0_quadrupolar_eta) = 0.136
C(sys_0_site_0_quadrupolar_eta, sys_2_site_0_quadrupolar_eta) = 0.133
C(sys_4_site_0_isotropic_chemical_shift, operation_4_Scale_factor) = 0.126
C(sys_4_site_0_quadrupolar_eta, operation_1_Gaussian_FWHM) = -0.126
C(sys_3_site_0_quadrupolar_Cq, operation_4_Scale_factor) = 0.119
C(sys_1_site_0_quadrupolar_eta, operation_1_Gaussian_FWHM) = -0.115
C(sys_2_site_0_quadrupolar_Cq, operation_4_Scale_factor) = 0.109
C(sys_2_site_0_isotropic_chemical_shift, operation_1_Gaussian_FWHM) = -0.103
Simulate the spectrum corresponding to the optimum parameters
.. code-block:: python
sim.run()
processed_data = processor.apply_operations(data=sim.methods[0].simulation).real
Plot the spectrum
.. code-block:: python
ax = plt.subplot(projection="csdm")
ax.contour(processed_data, colors="r", levels=levels, alpha=0.75, linewidths=0.5)
cb = ax.contour(experiment, colors="k", levels=levels, alpha=0.5, linewidths=0.5)
plt.colorbar(cb)
ax.invert_xaxis()
ax.set_ylim(30, -30)
plt.tight_layout()
plt.show()
.. image:: /fitting/2D_fitting/images/sphx_glr_plot_2_Coesite_DAS_003.png
:alt: plot 2 Coesite DAS
:class: sphx-glr-single-img
.. [#f1] Grandinetti, P. J., Baltisberger, J. H., Farnan, I., Stebbins, J. F.,
Werner, U. and Pines, A.
Solid-State :math:`^{17}\text{O}` Magic-Angle and Dynamic-Angle Spinning NMR
Study of the :math:`\text{SiO}_2` Polymorph Coesite, J. Phys. Chem. 1995,
**99**, *32*, 12341-12348.
`DOI: 10.1021/j100032a045 `_
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 1 minutes 50.062 seconds)
.. _sphx_glr_download_fitting_2D_fitting_plot_2_Coesite_DAS.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: binder-badge
.. image:: images/binder_badge_logo.svg
:target: https://mybinder.org/v2/gh/DeepanshS/mrsimulator/master?urlpath=lab/tree/docs/_build/html/../../notebooks/fitting/2D_fitting/plot_2_Coesite_DAS.ipynb
:alt: Launch binder
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_2_Coesite_DAS.py `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_2_Coesite_DAS.ipynb `
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery `_