Efficient algorithm for finding repeating bit patterns?

    public class Main {

        public static void main(String[] args) {
            int[][] seq={{ 42, 22, 40, 1, 11, 5, 43 },
                         { 13, 11, 18, 45, 3, 44, 19 },
                         { 46, 1, 32, 47, 35, 7, 36 },
                         { 48, 21, 38, 29, 3, 12, 11 },
            };

            for (int i=0; i < seq.length; i++) {
                int[] x=seq[i];

                Arrays.sort(x);
                for (int number : x) {
                        System.out.print(number  + " ");}
                        System.out.print("\n");
                    }
                }
            }

from itertools import combinations
from math import sqrt

def dist(x_vals, y_vals):
  " Distance of pair combinations of x_vals & y_vals "

  # Distance between zipped pairs
  dist2 = lambda z: sqrt((z[0][0] - z[1][0]) ** 2.0 + (z[0][1]- z[1][1]) ** 2.)

  # Use combinations to create desired distance pairs (i.e. 1-2, 1-3, 2-3, etc.)
  return list(map(dist2, combinations(zip(x_vals, y_vals), 2)))

x_vals = [2.3, 3.6, 1.8]
y_vals = [1.6, 4.8, 2.8]
print(dist(x_vals, y_vals))
# >> [3.4539832078341086, 1.2999999999999996, 2.69072480941474227422]

Original Data (Small): 
 x_vals = [2.3, 3.6, 1.8]
 y_vals = [1.6, 4.8, 2.8]

Data
N = 1000
x_vals = [random.randrange(N) for _ in range(N)]
y_vals = [random.randrange(N) for _ in range(N)]

library(vecsets)
f <- function(s) intToUtf8(Reduce(vintersect,lapply(s, utf8ToInt)))

> f(prec_names)
[1] "prec_0"

> f(tmean_names)
[1] "tmean"

> f(tmin_names)
[1] "tmin"