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.. _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.
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]_.
.. code-block:: python
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, SpinSystem, Site
from mrsimulator.methods import Method2D
# global plot configuration
font = {"size": 9}
mpl.rc("font", **font)
mpl.rcParams["figure.figsize"] = [4.25, 3.0]
Generate the site and spin system objects.
.. code-block:: python
sites = [
Site(
isotope="87Rb",
isotropic_chemical_shift=16, # in ppm
quadrupolar={"Cq": 5.3e6, "eta": 0.1}, # Cq in Hz
),
Site(
isotope="87Rb",
isotropic_chemical_shift=40, # in ppm
quadrupolar={"Cq": 2.6e6, "eta": 1.0}, # Cq in Hz
),
]
spin_systems = [SpinSystem(sites=[s]) for s in sites]
Use the generic 2D method, `Method2D`, to simulate a SAS spectrum by customizing the
method parameters, as shown below. Note, the Method2D method simulates an infinite
spinning speed spectrum.
.. code-block:: python
sas = Method2D(
channels=["87Rb"],
magnetic_flux_density=9.4, # in T
spectral_dimensions=[
{
"count": 256,
"spectral_width": 3.5e4, # in Hz
"reference_offset": 1e3, # in Hz
"label": "90 dimension",
"events": [{"rotor_angle": 90 * 3.14159 / 180}], # in radians
},
{
"count": 256,
"spectral_width": 22e3, # in Hz
"reference_offset": -4e3, # in Hz
"label": "MAS dimension",
"events": [{"rotor_angle": 54.74 * 3.14159 / 180}], # in radians
},
],
)
Create the Simulator object, add the method and spin system objects, and
run the simulation.
.. code-block:: python
sim = Simulator()
sim.spin_systems = spin_systems # add the spin systems
sim.methods = [sas] # add the method.
sim.run()
The plot of the simulation.
.. code-block:: python
data = sim.methods[0].simulation
ax = plt.subplot(projection="csdm")
cb = ax.imshow(data / data.max(), aspect="auto", cmap="gist_ncar_r")
plt.colorbar(cb)
ax.invert_xaxis()
plt.tight_layout()
plt.show()
.. image:: /examples/2D_simulation(crystalline)/images/sphx_glr_plot_2_SAS_Rb2SO4_001.png
:alt: plot 2 SAS Rb2SO4
:class: sphx-glr-single-img
Add post-simulation signal processing.
.. code-block:: python
processor = sp.SignalProcessor(
operations=[
# Gaussian convolution along both dimensions.
sp.IFFT(dim_index=(0, 1)),
apo.Gaussian(FWHM="0.4 kHz", dim_index=0),
apo.Gaussian(FWHM="0.4 kHz", dim_index=1),
sp.FFT(dim_index=(0, 1)),
]
)
processed_data = processor.apply_operations(data=data)
processed_data /= processed_data.max()
The plot of the simulation after signal processing.
.. code-block:: python
ax = plt.subplot(projection="csdm")
cb = ax.imshow(processed_data.real, cmap="gist_ncar_r", aspect="auto")
plt.colorbar(cb)
ax.invert_xaxis()
plt.tight_layout()
plt.show()
.. image:: /examples/2D_simulation(crystalline)/images/sphx_glr_plot_2_SAS_Rb2SO4_002.png
:alt: plot 2 SAS Rb2SO4
:class: sphx-glr-single-img
.. [#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 `_
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.. _sphx_glr_download_examples_2D_simulation(crystalline)_plot_2_SAS_Rb2SO4.py:
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