<gh_stars>0
"""
The algorithm finds distance between closest pair of points
in the given n points.
Approach used -> Divide and conquer
The points are sorted based on Xco-ords and
then based on Yco-ords separately.
And by applying divide and conquer approach,
minimum distance is obtained recursively.

>> Closest points can lie on different sides of partition.
This case handled by forming a strip of points
whose Xco-ords distance is less than closest_pair_dis
from mid-point's Xco-ords. Points sorted based on Yco-ords
are used in this step to reduce sorting time.
Closest pair distance is found in the strip of points. (closest_in_strip)

min(closest_pair_dis, closest_in_strip) would be the final answer.

Time complexity: O(n * log n)
"""


def euclidean_distance_sqr(point1, point2):
    """
    >>> euclidean_distance_sqr([1,2],[2,4])
    5
    """
    return (point1[0] - point2[0]) ** 2 + (point1[1] - point2[1]) ** 2


def column_based_sort(array, column=0):
    """
    >>> column_based_sort([(5, 1), (4, 2), (3, 0)], 1)
    [(3, 0), (5, 1), (4, 2)]
    """
    return sorted(array, key=lambda x: x[column])


def dis_between_closest_pair(points, points_counts, min_dis=float("inf")):
    """
    brute force approach to find distance between closest pair points

    Parameters :
    points, points_count, min_dis (list(tuple(int, int)), int, int)

    Returns :
    min_dis (float):  distance between closest pair of points

    >>> dis_between_closest_pair([[1,2],[2,4],[5,7],[8,9],[11,0]],5)
    5

    """

    for i in range(points_counts - 1):
        for j in range(i + 1, points_counts):
            current_dis = euclidean_distance_sqr(points[i], points[j])
            if current_dis < min_dis:
                min_dis = current_dis
    return min_dis


def dis_between_closest_in_strip(points, points_counts, min_dis=float("inf")):
    """
    closest pair of points in strip

    Parameters :
    points, points_count, min_dis (list(tuple(int, int)), int, int)

    Returns :
    min_dis (float):  distance btw closest pair of points in the strip (< min_dis)

    >>> dis_between_closest_in_strip([[1,2],[2,4],[5,7],[8,9],[11,0]],5)
    85
    """

    for i in range(min(6, points_counts - 1), points_counts):
        for j in range(max(0, i - 6), i):
            current_dis = euclidean_distance_sqr(points[i], points[j])
            if current_dis < min_dis:
                min_dis = current_dis
    return min_dis


def closest_pair_of_points_sqr(points_sorted_on_x, points_sorted_on_y, points_counts):
    """divide and conquer approach

    Parameters :
    points, points_count (list(tuple(int, int)), int)

    Returns :
    (float):  distance btw closest pair of points

    >>> closest_pair_of_points_sqr([(1, 2), (3, 4)], [(5, 6), (7, 8)], 2)
    8
    """

    # base case
    if points_counts <= 3:
        return dis_between_closest_pair(points_sorted_on_x, points_counts)

    # recursion
    mid = points_counts // 2
    closest_in_left = closest_pair_of_points_sqr(
        points_sorted_on_x, points_sorted_on_y[:mid], mid
    )
    closest_in_right = closest_pair_of_points_sqr(
        points_sorted_on_y, points_sorted_on_y[mid:], points_counts - mid
    )
    closest_pair_dis = min(closest_in_left, closest_in_right)

    """
    cross_strip contains the points, whose Xcoords are at a
    distance(< closest_pair_dis) from mid's Xcoord
    """

    cross_strip = [
        point
        for point in points_sorted_on_x
        if abs(point[0] - points_sorted_on_x[mid][0]) < closest_pair_dis
    ]

    closest_in_strip = dis_between_closest_in_strip(
        cross_strip, len(cross_strip), closest_pair_dis
    )
    return min(closest_pair_dis, closest_in_strip)


def closest_pair_of_points(points, points_counts):
    """
    >>> closest_pair_of_points([(2, 3), (12, 30)], len([(2, 3), (12, 30)]))
    28.792360097775937
    """
    points_sorted_on_x = column_based_sort(points, column=0)
    points_sorted_on_y = column_based_sort(points, column=1)
    return (
        closest_pair_of_points_sqr(
            points_sorted_on_x, points_sorted_on_y, points_counts
        )
    ) ** 0.5


if __name__ == "__main__":
    points = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
    print("Distance:", closest_pair_of_points(points, len(points)))

"""
The algorithm finds distance between closest pair of points
in the given n points.
Approach used -> Divide and conquer
The points are sorted based on Xco-ords and
then based on Yco-ords separately.
And by applying divide and conquer approach,
minimum distance is obtained recursively.

>> Closest points can lie on different sides of partition.
This case handled by forming a strip of points
whose Xco-ords distance is less than closest_pair_dis
from mid-point's Xco-ords. Points sorted based on Yco-ords
are used in this step to reduce sorting time.
Closest pair distance is found in the strip of points. (closest_in_strip)

min(closest_pair_dis, closest_in_strip) would be the final answer.

Time complexity: O(n * log n)
"""


def euclidean_distance_sqr(point1, point2):
    """
    >>> euclidean_distance_sqr([1,2],[2,4])
    5
    """
    return (point1[0] - point2[0]) ** 2 + (point1[1] - point2[1]) ** 2


def column_based_sort(array, column=0):
    """
    >>> column_based_sort([(5, 1), (4, 2), (3, 0)], 1)
    [(3, 0), (5, 1), (4, 2)]
    """
    return sorted(array, key=lambda x: x[column])


def dis_between_closest_pair(points, points_counts, min_dis=float("inf")):
    """
    brute force approach to find distance between closest pair points

    Parameters :
    points, points_count, min_dis (list(tuple(int, int)), int, int)

    Returns :
    min_dis (float):  distance between closest pair of points

    >>> dis_between_closest_pair([[1,2],[2,4],[5,7],[8,9],[11,0]],5)
    5

    """

    for i in range(points_counts - 1):
        for j in range(i + 1, points_counts):
            current_dis = euclidean_distance_sqr(points[i], points[j])
            if current_dis < min_dis:
                min_dis = current_dis
    return min_dis


def dis_between_closest_in_strip(points, points_counts, min_dis=float("inf")):
    """
    closest pair of points in strip

    Parameters :
    points, points_count, min_dis (list(tuple(int, int)), int, int)

    Returns :
    min_dis (float):  distance btw closest pair of points in the strip (< min_dis)

    >>> dis_between_closest_in_strip([[1,2],[2,4],[5,7],[8,9],[11,0]],5)
    85
    """

    for i in range(min(6, points_counts - 1), points_counts):
        for j in range(max(0, i - 6), i):
            current_dis = euclidean_distance_sqr(points[i], points[j])
            if current_dis < min_dis:
                min_dis = current_dis
    return min_dis


def closest_pair_of_points_sqr(points_sorted_on_x, points_sorted_on_y, points_counts):
    """divide and conquer approach

    Parameters :
    points, points_count (list(tuple(int, int)), int)

    Returns :
    (float):  distance btw closest pair of points

    >>> closest_pair_of_points_sqr([(1, 2), (3, 4)], [(5, 6), (7, 8)], 2)
    8
    """

    # base case
    if points_counts <= 3:
        return dis_between_closest_pair(points_sorted_on_x, points_counts)

    # recursion
    mid = points_counts // 2
    closest_in_left = closest_pair_of_points_sqr(
        points_sorted_on_x, points_sorted_on_y[:mid], mid
    )
    closest_in_right = closest_pair_of_points_sqr(
        points_sorted_on_y, points_sorted_on_y[mid:], points_counts - mid
    )
    closest_pair_dis = min(closest_in_left, closest_in_right)

    """
    cross_strip contains the points, whose Xcoords are at a
    distance(< closest_pair_dis) from mid's Xcoord
    """

    cross_strip = [
        point
        for point in points_sorted_on_x
        if abs(point[0] - points_sorted_on_x[mid][0]) < closest_pair_dis
    ]

    closest_in_strip = dis_between_closest_in_strip(
        cross_strip, len(cross_strip), closest_pair_dis
    )
    return min(closest_pair_dis, closest_in_strip)


def closest_pair_of_points(points, points_counts):
    """
    >>> closest_pair_of_points([(2, 3), (12, 30)], len([(2, 3), (12, 30)]))
    28.792360097775937
    """
    points_sorted_on_x = column_based_sort(points, column=0)
    points_sorted_on_y = column_based_sort(points, column=1)
    return (
        closest_pair_of_points_sqr(
            points_sorted_on_x, points_sorted_on_y, points_counts
        )
    ) ** 0.5


if __name__ == "__main__":
    points = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
    print("Distance:", closest_pair_of_points(points, len(points)))
The algorithm finds distance between closest pair of points in the given n points. Approach used -> Divide and conquer The points are sorted based on Xco-ords and then based on Yco-ords separately. And by applying divide and conquer approach, minimum distance is obtained recursively.  >> Closest points can lie on different sides of partition. This case handled by forming a strip of points whose Xco-ords distance is less than closest_pair_dis from mid-point's Xco-ords. Points sorted based on Yco-ords are used in this step to reduce sorting time. Closest pair distance is found in the strip of points. (closest_in_strip)  min(closest_pair_dis, closest_in_strip) would be the final answer.  Time complexity: O(n * log n) >>> euclidean_distance_sqr([1,2],[2,4])     5 >>> column_based_sort([(5, 1), (4, 2), (3, 0)], 1)     [(3, 0), (5, 1), (4, 2)] brute force approach to find distance between closest pair points      Parameters :     points, points_count, min_dis (list(tuple(int, int)), int, int)      Returns :     min_dis (float):  distance between closest pair of points      >>> dis_between_closest_pair([[1,2],[2,4],[5,7],[8,9],[11,0]],5)     5 closest pair of points in strip      Parameters :     points, points_count, min_dis (list(tuple(int, int)), int, int)      Returns :     min_dis (float):  distance btw closest pair of points in the strip (< min_dis)      >>> dis_between_closest_in_strip([[1,2],[2,4],[5,7],[8,9],[11,0]],5)     85 divide and conquer approach      Parameters :     points, points_count (list(tuple(int, int)), int)      Returns :     (float):  distance btw closest pair of points      >>> closest_pair_of_points_sqr([(1, 2), (3, 4)], [(5, 6), (7, 8)], 2)     8 # base case # recursion cross_strip contains the points, whose Xcoords are at a     distance(< closest_pair_dis) from mid's Xcoord >>> closest_pair_of_points([(2, 3), (12, 30)], len([(2, 3), (12, 30)]))     28.792360097775937