# Source code for tcod.los

```"""This modules holds functions for NumPy-based line of sight algorithms.
"""
from typing import Tuple

import numpy as np

[docs]def bresenham(start: Tuple[int, int], end: Tuple[int, int]) -> np.ndarray:
"""Return a thin Bresenham line as a NumPy array of shape (length, 2).

`start` and `end` are the endpoints of the line.
The result always includes both endpoints, and will always contain at
least one index.

You might want to use the results as is, convert them into a list with
:any:`numpy.ndarray.tolist` or transpose them and use that to index
another 2D array.

Example::

>>> import tcod
>>> tcod.los.bresenham((3, 5),(7, 7)).tolist()  # Convert into list.
[[3, 5], [4, 5], [5, 6], [6, 6], [7, 7]]
>>> tcod.los.bresenham((0, 0), (0, 0))
array([[0, 0]]...)
>>> tcod.los.bresenham((0, 0), (4, 4))[1:-1]  # Clip both endpoints.
array([[1, 1],
[2, 2],
[3, 3]]...)

>>> array = np.zeros((5, 5), dtype=np.int8)
>>> array
array([[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]], dtype=int8)
>>> tcod.los.bresenham((0, 0), (3, 4)).T  # Transposed results.
array([[0, 1, 1, 2, 3],
[0, 1, 2, 3, 4]]...)
>>> indexes_ij = tuple(tcod.los.bresenham((0, 0), (3, 4)).T)
>>> array[indexes_ij] = np.arange(len(indexes_ij[0]))
>>> array
array([[0, 0, 0, 0, 0],
[0, 1, 2, 0, 0],
[0, 0, 0, 3, 0],
[0, 0, 0, 0, 4],
[0, 0, 0, 0, 0]], dtype=int8)
>>> array[indexes_ij]
array([0, 1, 2, 3, 4], dtype=int8)