Understanding pattern variation and establishing equivalence between patterns is important to many areas of neuroscience and is made significantly more complex when the full extent of the pattern space is unknown or where it is infinite. Current studies of such patterns therefore often rely on the comparison of low dimensional pattern features to simplify comparison. We present a more comprehensive approach to pattern comparison based on statistical ensembles of circular pattern patches. We apply this approach to orientation preference maps for which previous feature-based comparisons supported the hypothesis of evolutionary invariance. First, using entropy methods, we calculate the number of possible distinguishable patches and the scaling of this quantity with measurement noise. This then provides estimates for the amount of data required to achieve a saturated sample of patches, at a specified noise level. This method successfully distinguishes between ensembles of orientation preference maps obtained from the Moiré interference model and the long-range interaction model which generate noisy hexagonal and quasi-periodic patterns respectively. Our approach can be used for other biological problems can be described as ordered 2D media, determine the consistency of models with data and to describe pattern development in active media.