HitResult

def flag(self, flag: Union[TrajFlag, int]) -> Optional[TrajectoryData]:
    """Get first TrajectoryData row with the specified flag.

    Args:
        flag: The flag to search for.

    Returns:
        First TrajectoryData row with the specified flag.

    Raises:
        AttributeError: If flag was not requested.
    """
    self._check_flag(flag)
    for row in self.trajectory:
        if row.flag & flag:
            return row
    return None

def get_at(
    self,
    key_attribute: TRAJECTORY_DATA_ATTRIBUTES,
    value: Union[float, GenericDimension],
    *,
    epsilon: float = 1e-9,
    start_from_time: float = 0.0,
) -> TrajectoryData:
    """Get TrajectoryData where key_attribute==value.

    Interpolates to create new object if necessary. Preserves the units of the original trajectory data.

    Args:
        key_attribute: The name of the TrajectoryData attribute to key on (e.g., 'time', 'distance').
        value: The value of the key attribute to find. If a float is provided
               for a dimensioned attribute, it's assumed to be a .raw_value.
        epsilon: Allowed key value difference to match existing TrajectoryData object without interpolating.
        start_from_time: The time to center the search from (default is 0.0).  If the target value is
                         at a local extremum then the search will only go forward in time.

    Returns:
        TrajectoryData where key_attribute==value.

    Raises:
        AttributeError: If TrajectoryData doesn't have the specified attribute.
        KeyError: If the key_attribute is 'flag'.
        ValueError: If interpolation is required and len(self.trajectory) < 3.
        ArithmeticError: If trajectory doesn't reach the requested value.

    Notes:
        * Not all attributes are monotonic: Height typically goes up and then down.
            Velocity typically goes down, but for lofted trajectories can begin to increase.
            Windage can wander back and forth in complex winds. We even have (see ExtremeExamples.ipynb)
            backward-bending scenarios in which distance reverses!
        * The only guarantee is that time is strictly increasing.
    """
    key_attribute = TRAJECTORY_DATA_SYNONYMS.get(key_attribute, key_attribute)  # Resolve synonyms
    if not hasattr(TrajectoryData, key_attribute):
        raise AttributeError(f"TrajectoryData has no attribute '{key_attribute}'")
    if key_attribute == "flag":
        raise KeyError("Cannot interpolate based on 'flag' attribute")

    traj = self.trajectory
    n = len(traj)
    key_value = value.raw_value if isinstance(value, GenericDimension) else value

    def get_key_val(td):
        """Helper to get the raw value of the key attribute from a TrajectoryData point."""
        val = getattr(td, key_attribute)
        return val.raw_value if hasattr(val, "raw_value") else val

    if n < 3:  # We won't interpolate on less than 3 points, but check for an exact match in the existing rows.
        if abs(get_key_val(traj[0]) - key_value) < epsilon:
            return traj[0]
        if n > 1 and abs(get_key_val(traj[1]) - key_value) < epsilon:
            return traj[1]
        raise ValueError("Interpolation requires at least 3 TrajectoryData points.")

    # Find the starting index based on start_from_time
    start_idx = 0
    if start_from_time > 0:
        start_idx = next((i for i, td in enumerate(traj) if td.time >= start_from_time), 0)
    curr_val = get_key_val(traj[start_idx])
    if abs(curr_val - key_value) < epsilon:  # Check for exact match
        return traj[start_idx]
    # Determine search direction from the starting point
    search_forward = True  # Default to forward search
    if start_idx == n - 1:  # We're at the last point, search backwards
        search_forward = False
    if 0 < start_idx < n - 1:
        # We're in the middle of the trajectory, determine local direction towards key_value
        next_val = get_key_val(traj[start_idx + 1])
        if (next_val > curr_val and key_value > curr_val) or (next_val < curr_val and key_value < curr_val):
            search_forward = True
        else:
            search_forward = False

    # Search for the target value in the determined direction
    target_idx = -1
    if search_forward:  # Search forward from start_idx
        for i in range(start_idx, n - 1):
            curr_val = get_key_val(traj[i])
            next_val = get_key_val(traj[i + 1])
            # Check if key_value is between curr_val and next_val
            if (curr_val < key_value <= next_val) or (next_val <= key_value < curr_val):
                target_idx = i + 1
                break
    if not search_forward or target_idx == -1:  # Search backward from start_idx
        for i in range(start_idx, 0, -1):
            curr_val = get_key_val(traj[i])
            prev_val = get_key_val(traj[i - 1])
            # Check if key_value is between prev_val and curr_val
            if (prev_val <= key_value < curr_val) or (curr_val < key_value <= prev_val):
                target_idx = i
                break

    # Check if we found a valid index
    if target_idx == -1:
        raise ArithmeticError(f"Trajectory does not reach {key_attribute} = {value}")
    # Check for exact match here
    if abs(get_key_val(traj[target_idx]) - key_value) < epsilon:
        return traj[target_idx]
    if target_idx == 0:  # Step forward from first point so we can interpolate
        target_idx = 1
    # Choose three bracketing points (p0, p1, p2)
    if target_idx >= n - 1:  # At or after the last point
        p0, p1, p2 = traj[n - 3], traj[n - 2], traj[n - 1]
    else:
        p0, p1, p2 = traj[target_idx - 1], traj[target_idx], traj[target_idx + 1]
    return TrajectoryData.interpolate(key_attribute, value, p0, p1, p2)

def zeros(self) -> list[TrajectoryData]:
    """Get all zero crossing points.

    Returns:
        Zero crossing points.

    Raises:
        AttributeError: If extra_data was not requested.
        ArithmeticError: If zero crossing points are not found.
    """
    self._check_flag(TrajFlag.ZERO)
    data = [row for row in self.trajectory if row.flag & TrajFlag.ZERO]
    if len(data) < 1:
        raise ArithmeticError("Can't find zero crossing points")
    return data

@deprecated(reason="Use get_at() instead for better flexibility.")
def index_at_distance(self, d: Distance) -> int:
    """Deprecated. Use get_at() instead.

    Args:
        d: Distance for which we want Trajectory Data.

    Returns:
        Index of first trajectory row with .distance >= d; otherwise -1.
    """
    epsilon = 1e-1  # small value to avoid floating point issues
    return next(
        (i for i in range(len(self.trajectory)) if self.trajectory[i].distance.raw_value >= d.raw_value - epsilon),
        -1,
    )

@deprecated(reason="Use get_at('distance', d)")
def get_at_distance(self, d: Distance) -> TrajectoryData:
    """Deprecated. Use get_at('distance', d) instead.

    Args:
        d: Distance for which we want Trajectory Data.

    Returns:
        First trajectory row with .distance >= d.

    Raises:
        ArithmeticError: If trajectory doesn't reach requested distance.
    """
    if (i := self.index_at_distance(d)) < 0:
        raise ArithmeticError(f"Calculated trajectory doesn't reach requested distance {d}")
    return self.trajectory[i]

@deprecated(reason="Use get_at('time', t)")
def get_at_time(self, t: float) -> TrajectoryData:
    """Deprecated. Use get_at('time', t) instead.

    Args:
        t: Time for which we want Trajectory Data.

    Returns:
        First trajectory row with .time >= t.

    Raises:
        ArithmeticError: If trajectory doesn't reach requested time.
    """
    epsilon = 1e-6  # small value to avoid floating point issues
    idx = next((i for i in range(len(self.trajectory)) if self.trajectory[i].time >= t - epsilon), -1)
    if idx < 0:
        raise ArithmeticError(f"Calculated trajectory doesn't reach requested time {t}")
    return self.trajectory[idx]

def danger_space(
    self,
    at_range: Union[float, Distance],
    target_height: Union[float, Distance],
) -> DangerSpace:
    """Calculate the danger space for a target.

        Assumes that the trajectory hits the center of a target at any distance.
        Determines how much ranging error can be tolerated if the critical region
        of the target has target_height *h*. Finds how far forward and backward along the
        line of sight a target can move such that the trajectory is still within *h*/2
        of the original drop at_range.

    Args:
        at_range: Danger space is calculated for a target centered at this sight distance.
        target_height: Target height (*h*) determines danger space.

    Returns:
        DangerSpace: The calculated danger space.

    Raises:
        ArithmeticError: If trajectory doesn't reach requested distance.
    """
    target_at_range = PreferredUnits.distance(at_range)
    target_height = PreferredUnits.target_height(target_height)
    target_height_half = target_height.raw_value / 2.0

    target_row = self.get_at("slant_distance", target_at_range)
    is_climbing = ((target_row.angle >> Angular.Radian) - self.props.look_angle_rad) > 0
    slant_height_begin = target_row.slant_height.raw_value + (-1 if is_climbing else 1) * target_height_half
    slant_height_end = target_row.slant_height.raw_value - (-1 if is_climbing else 1) * target_height_half
    try:
        begin_row = self.get_at("slant_height", slant_height_begin, start_from_time=target_row.time)
    except ArithmeticError:
        begin_row = self.trajectory[0]
    try:
        end_row = self.get_at("slant_height", slant_height_end, start_from_time=target_row.time)
    except ArithmeticError:
        end_row = self.trajectory[-1]

    return DangerSpace(target_row, target_height, begin_row, end_row, Angular.Radian(self.props.look_angle_rad))

def dataframe(self, formatted: bool = False) -> DataFrame:
    """Return the trajectory table as a DataFrame.

    Args:
        formatted: False for values as floats; True for strings in PreferredUnits. Default is False.

    Returns:
        The trajectory table as a DataFrame.

    Raises:
        ImportError: If pandas or plotting dependencies are not installed.
    """
    try:
        from py_ballisticcalc.visualize.dataframe import hit_result_as_dataframe

        return hit_result_as_dataframe(self, formatted)
    except ImportError as err:
        raise ImportError(
            "Use `pip install py_ballisticcalc[charts]` to get trajectory as pandas.DataFrame"
        ) from err

def plot(self, look_angle: Optional[Angular] = None) -> Axes:
    """Return a graph of the trajectory.

    Args:
        look_angle (Optional[Angular], optional): Look angle for the plot. Defaults to None.

    Returns:
        The plot Axes object.

    Raises:
        ImportError: If plotting dependencies are not installed.
    """
    try:
        from py_ballisticcalc.visualize.plot import hit_result_as_plot  # type: ignore[attr-defined]

        return hit_result_as_plot(self, look_angle)
    except ImportError as err:
        raise ImportError("Use `pip install py_ballisticcalc[charts]` to get results as a plot") from err

def overlay(self, ax: Axes, label: Optional[str] = None) -> None:
    """Highlights danger-space region on plot.

    Args:
        ax: The axes to overlay on.
        label: Label for the overlay. Defaults to None.

    Raises:
        ImportError: If plotting dependencies are not installed.
    """
    try:
        from py_ballisticcalc.visualize.plot import add_danger_space_overlay  # type: ignore[attr-defined]

        add_danger_space_overlay(self, ax, label)
    except ImportError as err:
        raise ImportError("Use `pip install py_ballisticcalc[charts]` to get results as a plot") from err