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

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

.. _sphx_glr_auto_examples_proc_Ep.py:


Process Enhancement experiment 
====================================================
Opens .h5 results file, uses rough_table_of_integrals() to roughly process
dataset including generating a table of integrals

.. GENERATED FROM PYTHON SOURCE LINES 7-66



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


    *

      .. image-sg:: /auto_examples/images/sphx_glr_proc_Ep_001.png
         :alt: autoslicing!
         :srcset: /auto_examples/images/sphx_glr_proc_Ep_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_proc_Ep_002.png
         :alt: , extract signal pathway, check phase variation along indirect, FID sliced, phased, and aligned, table of integrals
         :srcset: /auto_examples/images/sphx_glr_proc_Ep_002.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_proc_Ep_003.png
         :alt: power terms
         :srcset: /auto_examples/images/sphx_glr_proc_Ep_003.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_proc_Ep_004.png
         :alt: check covariance test
         :srcset: /auto_examples/images/sphx_glr_proc_Ep_004.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_proc_Ep_005.png
         :alt: normalized $E(p(t))$
         :srcset: /auto_examples/images/sphx_glr_proc_Ep_005.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_proc_Ep_006.png
         :alt: power log
         :srcset: /auto_examples/images/sphx_glr_proc_Ep_006.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_proc_Ep_007.png
         :alt: normalized $E(p)$
         :srcset: /auto_examples/images/sphx_glr_proc_Ep_007.png
         :class: sphx-glr-multi-img


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

 .. code-block:: none

    You didn't set units for indirect before saving the data!!!
    /home/jmfranck/git_repos/pyspecdata/pyspecdata/core.py:8693: RuntimeWarning: invalid value encountered in sqrt
      return np.sqrt(arg)
    1: autoslicing!
    2: Raw Data with averaged scans
    3: power terms |||ms
    4: check covariance test
    5: normalized $E(p(t))$ |||s
    6: power log
    7: normalized $E(p)$ |||mW







|

.. code-block:: Python


    import pyspecProcScripts as prscr
    import pyspecdata as psd
    import datetime
    import matplotlib.pyplot as plt

    plt.rcParams["image.aspect"] = "auto"  # needed for sphinx gallery
    # sphinx_gallery_thumbnail_number = 2
    plt.rcParams.update({
        "errorbar.capsize": 2,
        "figure.facecolor": (1.0, 1.0, 1.0, 0.0),  # clear
        "axes.facecolor": (1.0, 1.0, 1.0, 0.9),  # 90% transparent white
        "savefig.facecolor": (1.0, 1.0, 1.0, 0.0),  # clear
        "savefig.bbox": "tight",
        "savefig.dpi": 300,
        "figure.figsize": (6, 5),
    })


    with psd.figlist_var() as fl:
        thisfile, exptype, nodename = (
            "240924_13p5mM_TEMPOL_ODNP_1.h5",
            "ODNP_NMR_comp/ODNP",
            "ODNP",
        )
        s = psd.find_file(
            thisfile,
            exp_type=exptype,
            expno=nodename,
            lookup=prscr.lookup_table,
        )
        orig_axis = s["indirect"]  # let's save this so we
        #                           can pass it to the log
        s["indirect"] = (
            s["indirect"]["start_times"] - s["indirect"]["start_times"][0]
        )
        s.set_units("indirect", "s")
        s, _ = prscr.rough_table_of_integrals(s, fl=fl)
        assert psd.det_unit_prefactor(s.get_units("indirect")) == 0
        s.set_error(s["indirect", 0].item() * 0.01)  # We are not calculating the
        #                                              errors in rough table of
        #                                              integrals, so just make up a
        #                                              reasonable sized random
        #                                              number so that I can see the
        #                                              relative errors!
        s /= s["indirect", 0:1]
        fl.next("normalized $E(p(t))$")
        s["indirect"] -= s["indirect"][0]
        fl.plot(s, "o")
        # {{{ this is just matplotlib time formatting
        ax = plt.gca()
        ax.xaxis.set_major_formatter(
            plt.FuncFormatter(lambda x, _: str(datetime.timedelta(seconds=x)))
        )
        # }}}
        s["indirect"] = orig_axis
        s = prscr.convert_to_power(s, thisfile, exptype, fl=fl)
        fl.next("normalized $E(p)$")
        fl.plot(s, "o")


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

   **Total running time of the script:** (0 minutes 5.166 seconds)


.. _sphx_glr_download_auto_examples_proc_Ep.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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