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I'm trying to get python to return, as close as possible, the center of the most obvious clustering in an image like the one below: In my previous question I asked how to get the global maximum and the local maximums of a 2d array, and the answers given worked perfectly. The issue is that the center estimation I can get by averaging the global maximum obtained with different bin sizes is always slightly off than the one I would set by eye , because I'm only accounting for the biggest bin instead of a group of biggest bins (like one does by eye). I tried adapting the answer to this question to my problem, but it turns out my image is too noisy for that algorithm to work. Here's my code implementing that answer: import numpy as np from scipy.ndimage.filters import maximum_filter from scipy.ndimage.morphology import generate_binary_structure, binary_erosion import matplotlib.pyplot as pp from os import getcwd from os.path import join, realpath, dirname # Save path to dir where this code exists. mypath = realpath(join(getcwd(), dirname(__file__))) myfile = 'data_file.dat' x, y = np.loadtxt(join(mypath,myfile), usecols=(1, 2), unpack=True) xmin, xmax = min(x), max(x) ymin, ymax = min(y), max(y) rang = [[xmin, xmax], [ymin, ymax]] paws = [] for d_b in range(25, 110, 25): # Number of bins in x,y given the bin width 'd_b' binsxy = [int((xmax - xmin) / d_b), int((ymax - ymin) / d_b)] H, xedges, yedges = np.histogram2d(x, y, range=rang, bins=binsxy) paws.append(H) def detect_peaks(image): """ Takes an image and detect the peaks usingthe local maximum filter. Returns a boolean mask of the peaks (

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