.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/Singletfission_1d_quench.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_Singletfission_1d_quench.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_Singletfission_1d_quench.py:


Quench of the singlet fission model starting from a localised singlet state
===========================================================================

.. GENERATED FROM PYTHON SOURCE LINES 16-155

.. code-block:: Python


    # pylint: disable=invalid-name
    # pylint: disable=too-many-arguments
    # pylint: disable=too-many-locals

    import os
    import os.path

    import matplotlib.pyplot as plt
    import numpy as np

    import qtealeaves as qtl
    from qtealeaves.models import get_singlet_fission_1d


    def main(
        tn_type=5,
        output_folder=None,
        num_sites=8,
        max_bond_dimension=4,
        timesteps=10,
        plot=True,
    ):
        """
        Main method for quenching the singlet fission model starting
        from a localised singlet state

        **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.

        num_sites: int, optional
            number of sites in the system

        max_bond_dimension: int, optional
            maximum bond dimension

        timesteps : int, optional
            Number of timesteps. Default to 160.

        plot : bool, optional
            If True, plot the results at the end of the example. Default tot True
        """
        if output_folder is None:
            output_folder = lambda params: "SF1dDyn_L%d" % (params["L"])

        model, my_ops = get_singlet_fission_1d()

        my_conv = qtl.convergence_parameters.TNConvergenceParameters(
            max_iter=0,
            max_bond_dimension=max_bond_dimension,
            statics_method=4,
            min_bond_dimension=4,
        )

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

        # Dynamics
        quench = qtl.DynamicsQuench("t_grid", time_evolution_mode=2)
        quench["Et"] = lambda tt, params: 0.5
        quench["gamma"] = lambda tt, params: 0.4

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

        params = []

        # Initial state
        product = [np.array([1.0, 0.0, 0.0], dtype=np.complex128) for _ in range(num_sites)]
        product[0] = np.array([0.0, 1.0, 0.0])
        psi = qtl.emulator.ttn_simulator.TTN.product_state_from_local_states(
            np.array(product),
            convergence_parameters=my_conv,
            tensor_backend=qtl.tensors.TensorBackend(),
        )

        params.append(
            {
                "L": num_sites,
                "Eg": 0.0,
                "Es": 1.0,
                "Et": 0.5,
                "Js": -0.1,
                "Jt": 0.0,
                "chi": 0.0,
                "gamma": 0.0,
                "Quenches": [quench],
                "t_grid": [0.1] * timesteps,
                "continue_file": psi,
                "exclude_from_hash": ["Quenches", "continue_file"],
            }
        )

        simulation.run(params, delete_existing_folder=True)

        for param in params:
            results = simulation.get_dynamic_obs(param)

            tgrid = results["time"]
            avg_singlet = np.sum(results["<ns>"], axis=1)
            avg_triplet = np.sum(results["<nt>"], axis=1)

            if plot:
                fig = plt.figure()
                ax = fig.add_subplot(111)
                ax.plot(tgrid, avg_singlet, "b-", label="s")
                ax.plot(tgrid, avg_triplet, "r-", label="t")
                ax.set_xlabel("Time (t)")
                ax.set_ylabel("number of singlets, triplets")
                ax.legend()

                out_folder = (
                    output_folder(param) if callable(output_folder) else output_folder
                )
                pdf_name = os.path.join(out_folder, "number_over_time.pdf")
                plt.savefig(pdf_name)
                print(f"Saved number of singlets and triplets to {pdf_name}.")

        print("\nExample 'Singletfission_1d_quench.py' ran successfully; ")

        return


    if __name__ == "__main__":
        main()


.. _sphx_glr_download_auto_examples_Singletfission_1d_quench.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: Singletfission_1d_quench.ipynb <Singletfission_1d_quench.ipynb>`

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

      :download:`Download Python source code: Singletfission_1d_quench.py <Singletfission_1d_quench.py>`

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

      :download:`Download zipped: Singletfission_1d_quench.zip <Singletfission_1d_quench.zip>`


.. only:: html

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

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