.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/proc_FID_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_FID_nutation.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_proc_FID_nutation.py:


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

`py proc_FID_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 function to find the optimal
:math:`\beta_{90}`.

Tested with:

``py proc_FID_nutation.py FID_nutation_1 240805_amp0p1_27mM_TEMPOL_FID_nutat        ion.h5 ODNP_NMR_comp/nutation``

.. GENERATED FROM PYTHON SOURCE LINES 19-95



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


    *

      .. image-sg:: /auto_examples/images/sphx_glr_proc_FID_nutation_001.png
         :alt: autoslicing!
         :srcset: /auto_examples/images/sphx_glr_proc_FID_nutation_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_proc_FID_nutation_002.png
         :alt: 240805_amp0p1_27mM_TEMPOL_FID_nutation.h5, extract signal pathway, check phase variation along indirect, FID sliced and aligned, Integrated and fit
         :srcset: /auto_examples/images/sphx_glr_proc_FID_nutation_002.png
         :class: sphx-glr-multi-img


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

 .. code-block:: none

    using postproc type spincore_FID_nutation_v1
    1: autoslicing!
    2: Raw Data with averaged scans







|

.. code-block:: Python


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

    if "SPHINX_GALLERY_RUNNING" in os.environ and os.environ['SPHINX_GALLERY_RUNNING'] == 'True':
        sys.argv = [
            sys.argv[0],
            "FID_nutation_1",
            "240805_amp0p1_27mM_TEMPOL_FID_nutation.h5",
            "ODNP_NMR_comp/nutation",
        ]

    slice_expansion = 5
    assert len(sys.argv) == 4, "intended to be called with file info at cmdline"
    s = psd.find_file(
        sys.argv[2],
        exp_type=sys.argv[3],
        expno=sys.argv[1],
        lookup=prscr.lookup_table,
    )
    print("using postproc type", s.get_prop("postproc_type"))
    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=False
        )
        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)
        )
        prefactor_scaling = 10 ** psd.det_unit_prefactor(s.get_units("beta"))
        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)
        fl.plot(fit, ax=ax_last)
        ax_last.set_title("Integrated and fit")
        beta_90 = s.output("beta_ninety")  # because we allow
        #                                   rough_table_of_integrals to convert to
        #                                   human units (with prefactors), this
        #                                   will be in human units
        ax_last.axvline(beta_90, color="b")
        ax_last.text(
            beta_90 + 5,
            5e4,
            r"$\beta_{90} = %0.1f\ \mathrm{μs \sqrt{W}}$" % beta_90,
            color="b",
        )
        # }}}
        ax_last.grid()


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

   **Total running time of the script:** (0 minutes 4.652 seconds)


.. _sphx_glr_download_auto_examples_proc_FID_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_FID_nutation.ipynb <proc_FID_nutation.ipynb>`

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

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


.. only:: html

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

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