.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/proc_nutation.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_proc_nutation.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_proc_nutation.py:


Process nutation data
=====================

`py proc_nutation.py NODENAME FILENAME EXP_TYPE`

Fourier transforms (and any needed data corrections for older data) are
performed according to the `postproc_type` attribute of the data node.
This script plots the result as well as examines the phase variation along the
indirect dimension.
Finally, the data is integrated and fit to a sin**3 function to find the
optimal beta_ninety.

Tested with:

``py proc_nutation.py nutation_1 240805_amp0p1_27mM_TEMPOL_nutation.h5        ODNP_NMR_comp/nutation``

.. GENERATED FROM PYTHON SOURCE LINES 19-98



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/images/sphx_glr_proc_nutation_001.png
         :alt: autoslicing!
         :srcset: /auto_examples/images/sphx_glr_proc_nutation_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_proc_nutation_002.png
         :alt: 240805_amp0p1_27mM_TEMPOL_nutation.h5, extract signal pathway, check phase variation along indirect, FID sliced, phased, and aligned, Integrated and fit
         :srcset: /auto_examples/images/sphx_glr_proc_nutation_002.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_proc_nutation_003.png
         :alt: power terms
         :srcset: /auto_examples/images/sphx_glr_proc_nutation_003.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_proc_nutation_004.png
         :alt: check covariance test
         :srcset: /auto_examples/images/sphx_glr_proc_nutation_004.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_proc_nutation_005.png
         :alt: residual after shift
         :srcset: /auto_examples/images/sphx_glr_proc_nutation_005.png
         :class: sphx-glr-multi-img


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

 .. code-block:: none

    1: autoslicing!
    2: Raw Data with averaged scans
    3: power terms |||ms
    4: check covariance test
    5: residual after shift |||('Hz', 'μs√W')







|

.. code-block:: Python


    import pyspecdata as psd
    import pyspecProcScripts as prscr
    import sympy as sp
    import sys, os
    from numpy import r_

    # even though the following comes from pint, import the instance from
    # pyspecdata, because we might want to mess with it.
    from pyspecdata import Q_

    if (
        "SPHINX_GALLERY_RUNNING" in os.environ
        and os.environ["SPHINX_GALLERY_RUNNING"] == "True"
    ):
        sys.argv = [
            sys.argv[0],
            "nutation_1",
            "240805_amp0p1_27mM_TEMPOL_nutation.h5",
            "ODNP_NMR_comp/nutation",
        ]

    slice_expansion = 5
    assert len(sys.argv) == 4
    s = psd.find_file(
        sys.argv[2],
        exp_type=sys.argv[3],
        expno=sys.argv[1],
        lookup=prscr.lookup_table,
    )
    with psd.figlist_var() as fl:
        frq_center, frq_half = prscr.find_peakrange(s, fl=fl)
        signal_range = tuple(slice_expansion * r_[-1, 1] * frq_half + frq_center)
        if "nScans" in s.dimlabels:
            s.mean("nScans")
        s.set_plot_color(
            "g"
        )  # this affects the 1D plots, but not the images, etc.
        # {{{ generate the table of integrals and fit
        s, ax_last = prscr.rough_table_of_integrals(
            s, signal_range, fl=fl, title=sys.argv[2], echo_like=True
        )
        prefactor_scaling = 10 ** psd.det_unit_prefactor(s.get_units("beta"))
        A, R, beta_ninety, beta = sp.symbols("A R beta_ninety beta", real=True)
        s = psd.lmfitdata(s)
        s.functional_form = (
            A * sp.exp(-R * beta) * sp.sin(beta / beta_ninety * sp.pi / 2) ** 3
        )
        s.set_guess(
            A=dict(
                value=s.data.max(),
                min=s.data.max() * 0.8,
                max=s.data.max() * 1.5,
            ),
            R=dict(
                value=1e3 * prefactor_scaling, min=0, max=3e4 * prefactor_scaling
            ),
            beta_ninety=dict(
                value=20e-6 / prefactor_scaling,
                min=0,
                max=1000e-6 / prefactor_scaling,
            ),
        )
        s.fit()
        # }}}
        # {{{ show the fit and the β₉₀
        fit = s.eval(500)
        psd.plot(fit, ax=ax_last, alpha=0.5)
        ax_last.set_title("Integrated and fit")
        beta_90 = s.output("beta_ninety")
        ax_last.axvline(beta_90, color="b")
        ax_last.text(
            beta_90 + 5,
            5e4,
            r"$\beta_{90} = " + f"{Q_(beta_90,s.get_units('beta')):0.1f~L}$",
            color="b",
        )
        # }}}
        ax_last.grid()


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

   **Total running time of the script:** (0 minutes 9.550 seconds)


.. _sphx_glr_download_auto_examples_proc_nutation.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

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

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

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

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


.. only:: html

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

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