.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/epr_overlay2.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_epr_overlay2.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_epr_overlay2.py:

Another EPR Alignment
=====================
Please do see the other EPR alignment example.

However,
this EPR alignment represents a more challenging
case b/c:

- We are extracting 1D slices from a 2D
  dataset
- The spectra are somewhat more dissimilar, and so
  require progressive alignment against an
  average

.. GENERATED FROM PYTHON SOURCE LINES 15-55



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


    *

      .. image-sg:: /auto_examples/images/sphx_glr_epr_overlay2_001.png
         :alt: Raw
         :srcset: /auto_examples/images/sphx_glr_epr_overlay2_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_epr_overlay2_002.png
         :alt: correlation
         :srcset: /auto_examples/images/sphx_glr_epr_overlay2_002.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_epr_overlay2_003.png
         :alt: find center
         :srcset: /auto_examples/images/sphx_glr_epr_overlay2_003.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_epr_overlay2_004.png
         :alt: before centering -- ift
         :srcset: /auto_examples/images/sphx_glr_epr_overlay2_004.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_epr_overlay2_005.png
         :alt: after centering -- ift
         :srcset: /auto_examples/images/sphx_glr_epr_overlay2_005.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_epr_overlay2_006.png
         :alt: aligned, autoscaled
         :srcset: /auto_examples/images/sphx_glr_epr_overlay2_006.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_epr_overlay2_007.png
         :alt: epr overlay2
         :srcset: /auto_examples/images/sphx_glr_epr_overlay2_007.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_epr_overlay2_008.png
         :alt: normalized after alignment
         :srcset: /auto_examples/images/sphx_glr_epr_overlay2_008.png
         :class: sphx-glr-multi-img


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

 .. code-block:: none

    1: Raw |||mT
    2: correlation |||mT
    3: find center |||mT
    4: before centering -- ift |||kcyc · (T)$^{-1}$
    5: after centering -- ift |||kcyc · (T)$^{-1}$
    6: aligned, autoscaled |||mT
    7: centered spectra
    8: normalized after alignment |||mT

    /home/jmfranck/git_repos/pyspecdata/pyspecdata/figlist.py:782: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
      plt.gcf().tight_layout()






|

.. code-block:: Python


    from pyspecdata import find_file, figlist_var
    from pyspecProcScripts import align_esr
    import matplotlib.pyplot as plt

    d = {}
    d["250130 data: initial sample at 293K"] = find_file(
        "250130_20mM_TSO4_W20_Isooct_run1.DSC", exp_type="francklab_esr/romana"
    )  # cooling
    d["240220 data: initial sample at 293K"] = find_file(
        "240220_TempoSO4_W20_run1.DSC", exp_type="francklab_esr/romana"
    )  # cooling
    d["250206: Before cooling bath"] = find_file(
        "250206_20mM_TSO4_W20_C8.DSC", exp_type="francklab_esr/romana"
    )  # cooling
    for k, v in d.items():
        if "250206" in k:
            d[k] = v.chunk_auto("harmonic", "harmonic")["harmonic", 0]["phase", 0]
        else:
            d[k] = v.chunk_auto("harmonic", "harmonic")["harmonic", 0]["phase", 0]
            d[k].reorder("Temperature").sort("Temperature")
            d[k] = d[k]["Temperature":293]
    with figlist_var() as fl:
        d = align_esr(d, fl=fl)
        fl.next("normalized after alignment")
        # If the data very dissimilar, the least-squares scaling doesn't
        # work as well.  For these, it makes more sense to use a peak
        # normalization.
        for k, v in d.items():
            extra_norm = abs(d[k].data.max() - d[k].data.min())
            d[k] /= extra_norm
            d[k].set_prop("scaling", d[k].get_prop("scaling") * extra_norm)
            fl.plot(
                d[k],
                alpha=0.5,
                label=f"{k}\n÷ {d[k].get_prop('scaling'):#0.3g}",
            )
        fl.adjust_spines("bottom")
        plt.ylabel("")
        plt.gca().set_yticks([])


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

   **Total running time of the script:** (0 minutes 1.817 seconds)


.. _sphx_glr_download_auto_examples_epr_overlay2.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: epr_overlay2.ipynb <epr_overlay2.ipynb>`

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

      :download:`Download Python source code: epr_overlay2.py <epr_overlay2.py>`


.. only:: html

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

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