Quench of the quantum Ising model starting from the ground state.

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

import os

# pylint: disable-next=no-member, no-name-in-module
import os.path

import matplotlib.pyplot as plt
import numpy as np

import qtealeaves as qtl
from qtealeaves.models import get_quantum_ising_1d

# Keys are L, g_init, tau,  symmetry sector, value is final energy at g_final=0
ref_osmps = {
    (8, 2.0, 10, None): -6.88725731743711,
    (8, 2.0, 10, 0): None,
    (8, 2.0, 10, 1): None,
}


# pylint: disable-next=too-many-locals
def main(tn_type=5, output_folder=None, timesteps=200, plot=True):
    """
    Main method for quenching the quantum Ising model starting in a
    ground 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.

    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: "QI1dDyn_L%d" % (params["L"])

    model, my_ops = get_quantum_ising_1d()

    my_conv = qtl.convergence_parameters.TNConvergenceParameters(
        max_iter=7, max_bond_dimension=20
    )
    my_obs = qtl.observables.TNObservables()

    # Dynamics
    nn_meas = 10
    quench = qtl.DynamicsQuench(
        "t_grid", measurement_period=nn_meas, time_evolution_mode=1
    )
    quench["g"] = lambda tt, params: 2.0 - 2.0 * (tt / 10.0)

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

    params = []

    params.append(
        {
            "L": 8,
            "J": 1.0,
            "g": 2.0,
            "t_grid": [0.05] * timesteps,
            "Quenches": [quench],
            "exclude_from_hash": ["Quenches"],
        }
    )

    simulation.run(params, delete_existing_folder=True)

    for elem in params:
        obs_quench_1 = simulation.get_dynamic_obs(elem)
        energies = obs_quench_1["energy"]

        if plot:
            fig = plt.figure()
            ax1 = fig.add_subplot(111)
            ax1.plot(
                np.cumsum(elem["Quenches"][0].get_dt_grid(elem))[::nn_meas],
                energies,
                "b-",
            )
            ax1.set_xlabel("Time t")
            ax1.set_ylabel("Energy E")
            # pylint: disable-next=no-member
            plt.savefig(os.path.join(output_folder(elem), "dyn_energy.pdf"))

    print(
        f"\nExample `{__file__}` ran successfully; "
        + "pdf-figures are saved to QI1dDyn; "
        + "no asserts are implemented for this example."
    )

    return


if __name__ == "__main__":
    main()