.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_examples_1D_simulation(macro_amorphous)_plot_0_protein_GB1.py>`     to download the full example code or to run this example in your browser via Binder
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_examples_1D_simulation(macro_amorphous)_plot_0_protein_GB1.py:


Protein GB1, 13C and 15N (I=1/2)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

13C/15N (I=1/2) spinning sideband simulation.

The following is the spinning sideband simulation of a macromolecule, protein GB1. The
:math:`^{13}\text{C}` and :math:`^{15}\text{N}` CSA tensor parameters were obtained
from Hung `et. al.` [#f1]_, which consists of 42 :math:`^{13}\text{C}\alpha`,
44 :math:`^{13}\text{CO}`, and 44 :math:`^{15}\text{NH}` tensors. In the following
example, instead of creating 130 spin systems, we download the spin systems from
a remote file and load it directly to the Simulator object.


.. 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
    from mrsimulator.methods import BlochDecaySpectrum

    # global plot configuration
    mpl.rcParams["figure.figsize"] = [9, 4]








Create the Simulator object and load the spin systems from an external file.


.. code-block:: python

    sim = Simulator()

    file_ = "https://sandbox.zenodo.org/record/687656/files/protein_GB1_15N_13CA_13CO.mrsys"
    sim.load_spin_systems(file_)  # load the spin systems.
    print(f"number of spin systems = {len(sim.spin_systems)}")





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    number of spin systems = 130




Create a :math:`^{13}\text{C}` Bloch decay spectrum method.


.. code-block:: python

    method_13C = BlochDecaySpectrum(
        channels=["13C"],
        magnetic_flux_density=11.7,  # in T
        rotor_frequency=3000,  # in Hz
        spectral_dimensions=[
            {
                "count": 8192,
                "spectral_width": 5e4,  # in Hz
                "reference_offset": 2e4,  # in Hz
                "label": r"$^{13}$C resonances",
            }
        ],
    )








Since the spin systems contain both :math:`^{13}\text{C}` and :math:`^{15}\text{N}`
sites, let's also create a :math:`^{15}\text{N}` Bloch decay spectrum method.


.. code-block:: python

    method_15N = BlochDecaySpectrum(
        channels=["15N"],
        magnetic_flux_density=11.7,  # in T
        rotor_frequency=3000,  # in Hz
        spectral_dimensions=[
            {
                "count": 8192,
                "spectral_width": 4e4,  # in Hz
                "reference_offset": 7e3,  # in Hz
                "label": r"$^{15}$N resonances",
            }
        ],
    )








Add the methods to the Simulator object and run the simulation


.. code-block:: python


    # Add the methods.
    sim.methods = [method_13C, method_15N]

    # Run the simulation.
    sim.run()

    # Get the simulation data from the respective methods.
    data_13C = sim.methods[0].simulation  # method at index 0 is 13C Bloch decay method.
    data_15N = sim.methods[1].simulation  # method at index 1 is 15N Bloch decay method.








Add post-simulation signal processing.


.. code-block:: python

    processor = sp.SignalProcessor(
        operations=[sp.IFFT(), apo.Exponential(FWHM="10 Hz"), sp.FFT()]
    )
    # apply post-simulation processing to data_13C
    processed_data_13C = processor.apply_operations(data=data_13C).real

    # apply post-simulation processing to data_15N
    processed_data_15N = processor.apply_operations(data=data_15N).real








The plot of the simulation after signal processing.


.. code-block:: python

    fig, ax = plt.subplots(1, 2, subplot_kw={"projection": "csdm"}, sharey=True)

    ax[0].plot(processed_data_13C, color="black", linewidth=0.5)
    ax[0].invert_xaxis()

    ax[1].plot(processed_data_15N, color="black", linewidth=0.5)
    ax[1].set_ylabel(None)
    ax[1].invert_xaxis()

    plt.tight_layout()
    plt.show()




.. image:: /examples/1D_simulation(macro_amorphous)/images/sphx_glr_plot_0_protein_GB1_001.png
    :alt: plot 0 protein GB1
    :class: sphx-glr-single-img





.. [#f1] Hung I., Ge Y., Liu X., Liu M., Li C., Gan Z., Measuring
    :math:`^{13}\text{C}`/:math:`^{15}\text{N}` chemical shift anisotropy in
    [:math:`^{13}\text{C}`, :math:`^{15}\text{N}`] uniformly enriched proteins using
    CSA amplification, Solid State Nuclear Magnetic Resonance. 2015, **72**, 96-103.
    `DOI: 10.1016/j.ssnmr.2015.09.002 <https://doi.org/10.1016/j.ssnmr.2015.09.002>`_


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  3.302 seconds)


.. _sphx_glr_download_examples_1D_simulation(macro_amorphous)_plot_0_protein_GB1.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/examples/1D_simulation(macro_amorphous)/plot_0_protein_GB1.ipynb
      :alt: Launch binder
      :width: 150 px


  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_0_protein_GB1.py <plot_0_protein_GB1.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_0_protein_GB1.ipynb <plot_0_protein_GB1.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_