.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/2D_simulation(crystalline)/plot_2_SAS_Rb2SO4.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_2D_simulation(crystalline)_plot_2_SAS_Rb2SO4.py: Rb₂SO₄, ⁸⁷Rb (I=3/2) SAS ^^^^^^^^^^^^^^^^^^^^^^^^ ⁸⁷Rb (I=3/2) Switched-angle spinning (SAS) simulation. .. GENERATED FROM PYTHON SOURCE LINES 9-12 The following is an example of Switched-Angle Spinning (SAS) simulation of :math:`\text{Rb}_2\text{SO}_4`, which has two distinct rubidium sites. The NMR tensor parameters for these sites are taken from Shore `et al.` [#f1]_. .. GENERATED FROM PYTHON SOURCE LINES 12-21 .. code-block:: Python import numpy as np import matplotlib.pyplot as plt from mrsimulator import Simulator, SpinSystem, Site from mrsimulator.method import Method, SpectralDimension, SpectralEvent from mrsimulator import signal_processor as sp from mrsimulator.spin_system.tensors import SymmetricTensor .. GENERATED FROM PYTHON SOURCE LINES 23-24 Generate the site and spin system objects. .. GENERATED FROM PYTHON SOURCE LINES 24-38 .. code-block:: Python sites = [ Site( isotope="87Rb", isotropic_chemical_shift=16, # in ppm quadrupolar=SymmetricTensor(Cq=5.3e6, eta=0.1), # Cq in Hz ), Site( isotope="87Rb", isotropic_chemical_shift=40, # in ppm quadrupolar=SymmetricTensor(Cq=2.6e6, eta=1.0), # Cq in Hz ), ] spin_systems = [SpinSystem(sites=[s]) for s in sites] .. GENERATED FROM PYTHON SOURCE LINES 39-41 Use the generic `Method` class to simulate a 2D SAS spectrum by customizing the method parameters, as shown below. .. GENERATED FROM PYTHON SOURCE LINES 41-79 .. code-block:: Python sas = Method( name="Switched Angle Spinning", channels=["87Rb"], magnetic_flux_density=9.4, # in T rotor_frequency=np.inf, spectral_dimensions=[ SpectralDimension( count=256, spectral_width=3.5e4, # in Hz reference_offset=1e3, # in Hz label="90 dimension", events=[ SpectralEvent( rotor_angle=90 * np.pi / 180, # in radians transition_queries=[{"ch1": {"P": [-1], "D": [0]}}], ) ], ), SpectralDimension( count=256, spectral_width=22e3, # in Hz reference_offset=-4e3, # in Hz label="MAS dimension", events=[ SpectralEvent( rotor_angle=54.74 * np.pi / 180, # in radians transition_queries=[{"ch1": {"P": [-1], "D": [0]}}], ) ], ), ], ) # A graphical representation of the method object. plt.figure(figsize=(5, 2.5)) sas.plot() plt.show() .. image-sg:: /examples/2D_simulation(crystalline)/images/sphx_glr_plot_2_SAS_Rb2SO4_001.png :alt: Switched Angle Spinning :srcset: /examples/2D_simulation(crystalline)/images/sphx_glr_plot_2_SAS_Rb2SO4_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 80-82 Create the Simulator object, add the method and spin system objects, and run the simulation. .. GENERATED FROM PYTHON SOURCE LINES 82-85 .. code-block:: Python sim = Simulator(spin_systems=spin_systems, methods=[sas]) sim.run() .. GENERATED FROM PYTHON SOURCE LINES 86-87 The plot of the simulation. .. GENERATED FROM PYTHON SOURCE LINES 87-97 .. code-block:: Python dataset = sim.methods[0].simulation plt.figure(figsize=(4.25, 3.0)) ax = plt.subplot(projection="csdm") cb = ax.imshow(dataset.real / dataset.real.max(), aspect="auto", cmap="gist_ncar_r") plt.colorbar(cb) ax.invert_xaxis() plt.tight_layout() plt.show() .. image-sg:: /examples/2D_simulation(crystalline)/images/sphx_glr_plot_2_SAS_Rb2SO4_002.png :alt: plot 2 SAS Rb2SO4 :srcset: /examples/2D_simulation(crystalline)/images/sphx_glr_plot_2_SAS_Rb2SO4_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 98-99 Add post-simulation signal processing. .. GENERATED FROM PYTHON SOURCE LINES 99-111 .. code-block:: Python processor = sp.SignalProcessor( operations=[ # Gaussian convolution along both dimensions. sp.IFFT(dim_index=(0, 1)), sp.apodization.Gaussian(FWHM="0.4 kHz", dim_index=0), sp.apodization.Gaussian(FWHM="0.4 kHz", dim_index=1), sp.FFT(dim_index=(0, 1)), ] ) processed_dataset = processor.apply_operations(dataset=dataset) processed_dataset /= processed_dataset.max() .. GENERATED FROM PYTHON SOURCE LINES 112-113 The plot of the simulation after signal processing. .. GENERATED FROM PYTHON SOURCE LINES 113-121 .. code-block:: Python plt.figure(figsize=(4.25, 3.0)) ax = plt.subplot(projection="csdm") cb = ax.imshow(processed_dataset.real, cmap="gist_ncar_r", aspect="auto") plt.colorbar(cb) ax.invert_xaxis() plt.tight_layout() plt.show() .. image-sg:: /examples/2D_simulation(crystalline)/images/sphx_glr_plot_2_SAS_Rb2SO4_003.png :alt: plot 2 SAS Rb2SO4 :srcset: /examples/2D_simulation(crystalline)/images/sphx_glr_plot_2_SAS_Rb2SO4_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 122-126 .. [#f1] Shore, J.S., Wang, S.H., Taylor, R.E., Bell, A.T., Pines, A. Determination of quadrupolar and chemical shielding tensors using solid-state two-dimensional NMR spectroscopy, J. Chem. Phys. (1996) **105** *21*, 9412. `DOI: 10.1063/1.472776 `_ .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.100 seconds) .. _sphx_glr_download_examples_2D_simulation(crystalline)_plot_2_SAS_Rb2SO4.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_2_SAS_Rb2SO4.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_2_SAS_Rb2SO4.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_