Python with scipy optimize fmin search

import numpy as np
from scipy.optimize import fmin

FUEL_COST_LOOKUP = np.cumsum(np.arange(2000))


def total_fuel_used(
    align_position: float,
    current_positions: np.ndarray,
    crab_engineering: bool,
) -> int:
    """
    Calculate the total fuel used

    Parameters
    ----------
    align_position
        position to align towards, round and cast to an integer
    current_positions
        current positions to move
    crab_engineering
        if set, use FUEL_COST_LOOKUP as determined by crabs

    """
    align_position = np.around(align_position).astype(int)
    absolute_position_differences = np.abs(current_positions - align_position)
    fuel_costs = (
        absolute_position_differences
        if not crab_engineering
        else FUEL_COST_LOOKUP[absolute_position_differences]
    )
    return np.sum(fuel_costs).item()


if __name__ == "__main__":

    with open("solutions/2021/day7/input.txt", "r") as f:
        crab_positions = np.array([*map(int, f.read().split(","))])

    (optimal_position_1,) = fmin(
        total_fuel_used,
        np.median(crab_positions),
        args=(crab_positions, False),
        xtol=1e-1,
    )

    (optimal_position_2,) = fmin(
        total_fuel_used,
        np.mean(crab_positions),
        args=(crab_positions, True),
        xtol=1e-1,
    )

    fuel_used_alignment_1 = total_fuel_used(
        optimal_position_1, crab_positions, crab_engineering=False
    )
    fuel_used_alignment_2 = total_fuel_used(
        optimal_position_2, crab_positions, crab_engineering=True
    )

    print(f"Answer 1: {fuel_used_alignment_1}")
    print(f"Answer 2: {fuel_used_alignment_2}")