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

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

.. _sphx_glr_auto_examples_proc_tune_capture.py:


Processing the Captured Tuning Curve
====================================

Takes the npz file of the captured tuning curve at different
zoom levels and plots them on the same plot, allowing us to 
look at any drift or discrepancies of the tuning curve.

This also fits the tuning curves to a Lorentzian for more
detailed comparison, and gives the width in the legend.
Note that if we converted the x-axis here (which is
determined by Bruker XEPR) to actual frequency units,
then we could convert this width to a Q factor.

.. GENERATED FROM PYTHON SOURCE LINES 15-84



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


    *

      .. image-sg:: /auto_examples/images/sphx_glr_proc_tune_capture_001.png
         :alt: Tuning curve comparison-zoom1
         :srcset: /auto_examples/images/sphx_glr_proc_tune_capture_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_proc_tune_capture_002.png
         :alt: Tuning curve comparison-zoom2
         :srcset: /auto_examples/images/sphx_glr_proc_tune_capture_002.png
         :class: sphx-glr-multi-img


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

 .. code-block:: none

    1: Tuning curve comparison-zoom1 |||None
    2: Tuning curve comparison-zoom2 |||None







|

.. code-block:: Python

    import sympy as sp
    from pyspecdata import figlist_var, nddata, search_filename, lmfitdata
    import numpy as np

    c_re, c_im, A, ll, nu, nu0 = sp.symbols(
        "c_re c_im A lambda_L nu nu_0", real=True
    )

    with figlist_var() as fl:
        for filename, thislabel in [
            ("220808_150uM_TEMPOL.npz", "150 uM"),
            (
                "220114_3mM_TEMPOL_3b.npz",
                "std 3 mM",
            ),  # this is our control so don't change! You want your tuning curve
            #     to match this one
        ]:
            # {{{Load in npz file
            thisfile = search_filename(
                filename,
                exp_type="francklab_esr/alex",  # adjust the exp_type according to
                #                                 your personal folder
                unique=True,
            )
            data = np.load(thisfile)
            # }}}
            # {{{Go over range of zooms and plot against control
            for j in range(3):
                if j == 0:
                    pass
                else:
                    nd_data = {}
                    zoom_data = data["zoom%d" % j].squeeze()
                    zoom_data_nd = nddata(zoom_data[0], "frequency")
                    zoom_data_nd.setaxis("frequency", zoom_data[1])
                    nd_data["zoom%d" % j] = zoom_data_nd
                    shift_val = zoom_data_nd.argmin()
                    zoom_data_nd.setaxis(
                        "frequency", lambda x: x - shift_val["frequency"]
                    )
                    fl.next("Tuning curve comparison-zoom%d" % j)
                    zoom_data_nd.rename("frequency", "nu")
                    zoom_data_nd = lmfitdata(zoom_data_nd)
                    zoom_data_nd.set_plot_color_next()
                    zoom_data_nd.functional_form = abs(
                        c_re + 1j * c_im - A / (1 + 1j * 2 * (nu - nu0) / ll)
                    )
                    zoom_data_nd.set_guess(
                        A=200, c_re=200, c_im=0, lambda_L=0.03, nu_0=0
                    )
                    zoom_data_nd.fit(use_jacobian=False) # the Jacobian seems to
                    #                                      contain NaN values.  The
                    #                                      rigorous way to rectify
                    #                                      this would be to use the
                    #                                      transform methodology
                    #                                      and define the
                    #                                      Lorentzian in the
                    #                                      inverse Fourier domain,
                    #                                      but that seems overkill
                    #                                      for this application.
                    fl.plot(
                        zoom_data_nd,
                        "o",
                        label=thislabel
                        + f": width={zoom_data_nd.output()['lambda_L']:0.5g}",
                        alpha=0.5,
                    )
                    fl.plot(zoom_data_nd.eval(500), label=thislabel, alpha=0.5)
            # }}}


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

   **Total running time of the script:** (0 minutes 2.276 seconds)


.. _sphx_glr_download_auto_examples_proc_tune_capture.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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