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

.. only:: html

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

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

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

.. _sphx_glr_gen_examples_free_bound_plot_bohme_kraus_relevance_of_bound_free.py:


Showcase of the relavance of including free-bound scattering
============================================================

It was suggested by :cite:`Bohme.2023` that the evaluation of some experiments
need to consider free-bound conditions to fully understand the scattering
spectrum. One of the seminal examples of the aforementioned paper is fitting
the LCLS data from :cite:`Kraus.2018`.

Here, we present the stated best-fit results, to show the relevance of these
contributions for certain conditions.

.. GENERATED FROM PYTHON SOURCE LINES 13-105



.. image-sg:: /gen_examples/free_bound/images/sphx_glr_plot_bohme_kraus_relevance_of_bound_free_001.svg
   :alt: plot bohme kraus relevance of bound free
   :srcset: /gen_examples/free_bound/images/sphx_glr_plot_bohme_kraus_relevance_of_bound_free_001.svg
   :class: sphx-glr-single-img


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

 .. code-block:: none

    /home/runner/work/jaxrts/jaxrts/doc/examples/free_bound/plot_bohme_kraus_relevance_of_bound_free.py:45: DeprecationWarning: `trapz` is deprecated. Use `trapezoid` instead, or one of the numerical integration functions in `scipy.integrate`.
      onp.trapz(PSF_I, (PSF_E / ureg.hbar).m_as(1 / ureg.second)) / ureg.second






|

.. code-block:: Python


    from pathlib import Path

    import jax.numpy as jnp
    import jpu.numpy as jnpu
    import matplotlib.pyplot as plt
    import numpy as onp
    import scienceplots  # noqa: F401

    import jaxrts
    from jaxrts.hnc_potentials import CoulombPotential
    from jaxrts.models import (
        ArbitraryDegeneracyScreeningLength,
        ConstantIPD,
        DetailedBalance,
        OnePotentialHNCIonFeat,
        RPA,
        SchumacherImpulse,
    )

    ureg = jaxrts.ureg

    try:
        current_folder = Path(__file__).parent
    except NameError:
        current_folder = Path.cwd()

    PSF_E, PSF_I = onp.genfromtxt(
        current_folder / "kraus2018/PSF.csv", unpack=True, delimiter=","
    )
    PSF_E *= ureg.electron_volt
    PSF_I /= (
        onp.trapz(PSF_I, (PSF_E / ureg.hbar).m_as(1 / ureg.second)) / ureg.second
    )
    central_E = 5909 * ureg.electron_volt


    def PSF(omega):
        return jnpu.interp(
            omega, (PSF_E - central_E) / ureg.hbar, PSF_I, left=0, right=0
        )


    state = jaxrts.PlasmaState(
        ions=[jaxrts.Element("C")],
        Z_free=jnp.array([1.71]),
        mass_density=([2]) * ureg.gram / ureg.centimeter**3,
        T_e=jnp.array([21.7]) * ureg.electron_volt / ureg.k_B,
        # T_e=jnp.array([16.6]) * ureg.electron_volt / ureg.k_B,
    )

    setup = jaxrts.setup.Setup(
        ureg("160°"),
        central_E,
        # Make this a little bitter, to avoid the edge effects of the convolution
        jnp.linspace(5450, 6100, 500) * ureg.electron_volt,
        PSF,
    )


    state["electron-ion Potential"] = CoulombPotential()
    state["screening length"] = ArbitraryDegeneracyScreeningLength()
    state["ionic scattering"] = OnePotentialHNCIonFeat()
    state["ipd"] = ConstantIPD(jnp.array([-24]) * ureg.electron_volt)
    state["free-free scattering"] = RPA()
    state["bound-free scattering"] = SchumacherImpulse(r_k=1)
    state["free-bound scattering"] = DetailedBalance()

    probed = state.probe(setup)
    norm = jnpu.max(probed)
    plt.plot(
        (setup.measured_energy).m_as(ureg.electron_volt),
        (probed / norm).m_as(ureg.dimensionless),
    )
    plt.plot(
        (setup.measured_energy).m_as(ureg.electron_volt),
        (state.evaluate("bound-free scattering", setup) / norm).m_as(
            ureg.dimensionless
        ),
    )

    Data_E, Data_I = onp.genfromtxt(
        current_folder / "kraus2018/data.csv", unpack=True, delimiter=","
    )
    plt.plot(Data_E, Data_I, ls="none", marker="o", alpha=0.5)
    plt.yscale("log")

    plt.xlabel("Energy [eV]")
    plt.ylabel("Scattering Intensity [a. u.]")
    plt.xlim(5550, 6000)
    plt.ylim(1e-3, 1.2)
    plt.show()


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

   **Total running time of the script:** (0 minutes 12.338 seconds)


.. _sphx_glr_download_gen_examples_free_bound_plot_bohme_kraus_relevance_of_bound_free.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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