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# Licensed under the Apache License, Version 2.0 (the "License"). You
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from __future__ import annotations

from braket.ahs.discretization_types import DiscretizationProperties
from braket.ahs.field import Field
from braket.ahs.hamiltonian import Hamiltonian
from braket.ahs.pattern import Pattern
from braket.timings.time_series import StitchBoundaryCondition, TimeSeries


class LocalDetuning(Hamiltonian):
    def __init__(self, magnitude: Field) -> None:
        r"""Creates a Hamiltonian term :math:`H_{shift}` representing the local detuning
        that changes the energy of the Rydberg level in an AnalogHamiltonianSimulation,
        defined by the formula

        .. math::
            H_{shift} (t) := -\Delta(t) \sum_k h_k | r_k \rangle \langle r_k |

        where

            :math:`\Delta(t)` is the magnitude of the frequency shift in rad/s,

            :math:`h_k` is the site coefficient of atom :math:`k`,
            a dimensionless real number between 0 and 1,

            :math:`|r_k \rangle` is the Rydberg state of atom :math:`k`.

        with the sum :math:`\sum_k` taken over all target atoms.

        Args:
            magnitude (Field): containing the global magnitude time series :math:`\Delta(t)`,
                where time is measured in seconds (s) and values are measured in rad/s, and the
                local pattern :math:`h_k` of dimensionless real numbers between 0 and 1.
        """
        super().__init__()
        self._magnitude = magnitude

    @property
    def terms(self) -> list[Hamiltonian]:
        return [self]

    @property
    def magnitude(self) -> Field:
        r"""Field: containing the global magnitude time series :math:`\Delta(t)`,
        where time is measured in seconds (s) and values measured in rad/s)
        and the local pattern :math:`h_k` of dimensionless real numbers between 0 and 1.
        """
        return self._magnitude

    @staticmethod
    def from_lists(times: list[float], values: list[float], pattern: list[float]) -> LocalDetuning:
        """Get the shifting field from a set of time points, values and pattern

        Args:
            times (list[float]): The time points of the shifting field
            values (list[float]): The values of the shifting field
            pattern (list[float]): The pattern of the shifting field

        Raises:
            ValueError: If the length of times and values differs.

        Returns:
            LocalDetuning: The shifting field obtained
        """
        if len(times) != len(values):
            raise ValueError("The length of the times and values lists must be equal.")

        magnitude = TimeSeries()
        for t, v in zip(times, values, strict=True):
            magnitude.put(t, v)
        return LocalDetuning(Field(magnitude, Pattern(pattern)))

    def stitch(
        self, other: LocalDetuning, boundary: StitchBoundaryCondition = StitchBoundaryCondition.MEAN
    ) -> LocalDetuning:
        """Stitches two shifting fields based on TimeSeries.stitch method.
        The time points of the second LocalDetuning are shifted such that the first time point of
        the second LocalDetuning coincides with the last time point of the first LocalDetuning.
        The boundary point value is handled according to StitchBoundaryCondition argument value.

        Args:
            other (LocalDetuning): The second local detuning to be stitched with.
            boundary (StitchBoundaryCondition): {"mean", "left", "right"}. Boundary point handler.

                Possible options are
              - "mean" - take the average of the boundary value points of the first
                and the second time series.
              - "left" - use the last value from the left time series as the boundary point.
              - "right" - use the first value from the right time series as the boundary point.

        Raises:
            ValueError: The LocalDetuning patterns differ.

        Returns:
            LocalDetuning: The stitched LocalDetuning object.

        Example (StitchBoundaryCondition.MEAN):
        ::
            time_series_1 = TimeSeries.from_lists(times=[0, 0.1], values=[1, 2])
            time_series_2 = TimeSeries.from_lists(times=[0.2, 0.4], values=[4, 5])

            stitch_ts = time_series_1.stitch(time_series_2, boundary=StitchBoundaryCondition.MEAN)

            Result:
                stitch_ts.times() = [0, 0.1, 0.3]
                stitch_ts.values() = [1, 3, 5]

        Example (StitchBoundaryCondition.LEFT):
        ::
            stitch_ts = time_series_1.stitch(time_series_2, boundary=StitchBoundaryCondition.LEFT)

            Result:
                stitch_ts.times() = [0, 0.1, 0.3]
                stitch_ts.values() = [1, 2, 5]

        Example (StitchBoundaryCondition.RIGHT):
        ::
            stitch_ts = time_series_1.stitch(time_series_2, boundary=StitchBoundaryCondition.RIGHT)

            Result:
                stitch_ts.times() = [0, 0.1, 0.3]
                stitch_ts.values() = [1, 4, 5]
        """
        if self.magnitude.pattern.series != other.magnitude.pattern.series:
            raise ValueError("The LocalDetuning pattern for both fields must be equal.")

        new_ts = self.magnitude.time_series.stitch(other.magnitude.time_series, boundary)
        return LocalDetuning(Field(new_ts, self.magnitude.pattern))

    def discretize(self, properties: DiscretizationProperties) -> LocalDetuning:
        """Creates a discretized version of the LocalDetuning.

        Args:
            properties (DiscretizationProperties): Capabilities of a device that represent the
                resolution with which the device can implement the parameters.

        Returns:
            LocalDetuning: A new discretized LocalDetuning.
        """
        local_detuning_parameters = properties.rydberg.rydbergLocal
        time_resolution = (
            local_detuning_parameters.timeResolution if local_detuning_parameters else None
        )
        discretized_magnitude = self.magnitude.discretize(
            time_resolution=time_resolution,
        )
        return LocalDetuning(discretized_magnitude)
