AssertionError when starting the main function

I am implementing kmeans algorithm in python, and the following code. I am testing the code using some simple data. same as the following, which is stored in a file called data.txt
2 5
3 7
-1 -2
-3 -3
5 4
4 -4
3 -7
3,5 -9

my problem is that during the iteration, some cluster seems empty, that is (the number of clusters) <k, and after my analysis this seems to happen, but after searching the internet I did not find that kmeans in this algorithm the body does not work.

So I do not know where the error is? is that my test data is so simple

import sys
import numpy as np
from math import sqrt

"""
useage: python mykmeans.py mydata.txt k

"""

GAP = 2
MIN_VAL = 1000000

def get_distance(point1, point2):
    dis = sqrt(pow(point1[0] - point2[0], 2) + pow(point1[1] - point2[1], 2))

    return dis


def cluster_dis(centroid, cluster):
    dis = 0.0
    for point in cluster:
        dis += get_distance(centroid, point)

    return dis

def update_centroids(centroids, cluster_id, cluster):
    x, y = 0.0, 0.0
    length = len(cluster)
    if length == 0:  # TODO： this is my question? do we need to examine this?
        return

    for item in cluster:
        x += item[0]
        y += item[1]
    centroids[cluster_id] = (x / length, y / length)


def kmeans(data, k):
    assert k <= len(data)

    seed_ids = np.random.randint(0, len(data), k)
    centroids = [data[idx] for idx in seed_ids]
    clusters = [[] for _ in xrange(k)]
    cluster_idx = [-1] * len(data)

    pre_dis = 0
    while True:
        for point_id, point in enumerate(data):
            min_distance, tmp_id = MIN_VAL, -1
            for seed_id, seed in enumerate(centroids):
                distance = get_distance(seed, point)
                if distance < min_distance:
                    min_distance = distance
                    tmp_id = seed_id
            if cluster_idx[point_id] != -1:
                dex = clusters[cluster_idx[point_id]].index(point)
                del clusters[cluster_idx[point_id]][dex]
            clusters[tmp_id].append(point)
            cluster_idx[point_id] = tmp_id

        now_dis = 0.0
        for cluster_id, cluster in enumerate(clusters):
            now_dis += cluster_dis(centroids[cluster_id], cluster)
            update_centroids(centroids, cluster_id, cluster)

        delta_dis = now_dis - pre_dis
        pre_dis = now_dis

        if delta_dis < GAP:
            break

    print(centroids)
    print(clusters)

    return centroids, clusters

def get_data(file_name):
    try:
        fr = open(file_name)
        lines = fr.read().splitlines()
    except IOError, e:
        pass
    finally:
        fr.close()

    data = []
    for line in lines:
        tmp = line.split()
        x, y = float(tmp[0]), float(tmp[1])
        data.append([x, y])

    return data

def main():
    args = sys.argv[1:]
    assert len(args) > 1
    file_name, k = args[0], int(args[1])

    data = get_data(file_name)
    kmeans(data, k)


if __name__ == '__main__':
    main()