.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/XXZModel_1d_oqs.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_XXZModel_1d_oqs.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_XXZModel_1d_oqs.py:


Open system dynamics of the XXZ model with a Lindblad decay flipping the spin.
==============================================================================

.. GENERATED FROM PYTHON SOURCE LINES 15-148

.. code-block:: Python


    # pylint: disable=invalid-name

    import matplotlib.pyplot as plt
    import numpy as np

    import qtealeaves as qtl
    from qtealeaves import modeling
    from qtealeaves.models import get_xxz_1d

    plt.rcParams.update({"text.usetex": True, "font.size": 12, "font.family": "lmodern"})


    # pylint: disable-next=too-many-locals
    def main(tn_type=5, output_folder=None, plot=True):
        """
        Main method for the dynamics of the XXZ model as Lindblad master equation.

        **Arguments**

        tn_type : int, optional
            Choose 5 for python-TTN, 6 for python-MPS.
            Default to 5.

        output_folder : str | None, optional
            Output folder. Default to None.

        plot : bool, optional
            If True, plot the results at the end of the example. Default tot True
        """

        def maska(params):
            ll = params["L"]
            tmp = np.zeros((ll), dtype=bool)
            tmp[2:3] = True
            return tmp

        def maskb(params, maska=maska):
            return np.logical_not(maska(params))

        if output_folder is None:
            output_folder = lambda params: "XXZ1d/oqs"

        model, my_ops = get_xxz_1d()

        model += modeling.LocalTerm("n", strength="prep", prefactor=-1, mask=maska)
        model += modeling.LocalTerm("n", strength="prep", prefactor=1, mask=maskb)

        # We need splus as a Lindblad operator
        model += modeling.LindbladTerm("splus", strength="gamma")

        my_conv = qtl.convergence_parameters.TNConvergenceParameters(
            max_iter=7, max_bond_dimension=20
        )
        my_ops["splus"] = np.array([[0, 1], [0, 0.0]])
        my_ops["sminus"] = np.array([[0, 0], [1, 0.0]])

        my_obs = qtl.observables.TNObservables()
        my_obs += qtl.observables.Local("<n>", "n")

        # the oqs_mode=2 evolves as quantum trajectory without any jumps
        quench = qtl.DynamicsQuench([0.05] * 20, time_evolution_mode=2, oqs_mode=2)
        quench["prep"] = lambda tt, params: 0.0
        quench["g"] = lambda tt, params: params["g_t"]
        quench["Jx"] = lambda tt, params: params["Jx_t"]
        quench["Jz"] = lambda tt, params: params["Jz_t"]

        params = [
            {
                "L": 8,
                "g": 0.0,
                "Jx": 1e-5,
                "Jz": 1e-5,
                "prep": 200,
                "gamma": 0.125,
                "Jx_t": 0.5,
                "Jz_t": 1,
                "g_t": 0.0,
                "Quenches": [quench],
                "has_log_file": True,
                "exclude_from_hash": ["Quenches"],
            }
        ]

        simulation = qtl.QuantumGreenTeaSimulation(
            model,
            my_ops,
            my_conv,
            my_obs,
            tn_type=tn_type,
            folder_name_output=output_folder,
            has_log_file=False,
            store_checkpoints=False,
        )

        simulation.run(params, delete_existing_folder=True)
        results = simulation.get_static_obs(params[0])

        print("<n(t=0)>", np.sum(results["<n>"]))

        results = simulation.get_dynamic_obs(params[0])
        pop_norm = results["norm"]

        if plot:
            fig = plt.figure()
            axis = fig.add_subplot(111)

            tgrid = np.cumsum(quench.dt_grid)
            axis.plot(tgrid, pop_norm, "c-", label="TTN")
            axis.plot(tgrid, np.exp(-0.125 * tgrid), "k:", label="Theory")

            axis.set_xlabel("\\textbf{Evolution time} $\\tau$")
            axis.set_ylabel("\\textbf{Norm}")

            axis.set_yscale("log")

            plt.legend(loc="upper right")
            pdf_name = "XXZ1d/oqs/XXZ_Excitation"
            plt.savefig(pdf_name + ".pdf")

        print("Norm over time", pop_norm)

        print(
            f"\nExample `{__file__}` ran successfully; "
            + "pdf-plots are saved to XXZ1d/oqs folder; "
            + "no asserts implemented."
        )

        return


    if __name__ == "__main__":
        main()


.. _sphx_glr_download_auto_examples_XXZModel_1d_oqs.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: XXZModel_1d_oqs.ipynb <XXZModel_1d_oqs.ipynb>`

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

      :download:`Download Python source code: XXZModel_1d_oqs.py <XXZModel_1d_oqs.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: XXZModel_1d_oqs.zip <XXZModel_1d_oqs.zip>`


.. only:: html

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

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