A simple Monte Carlo simulation was written in Python to analyze the protein distribution at the oil/water interface. First, virtual fluorescently tagged proteins were randomly placed on rings representing 2D cross-sections of oil/water droplet interfaces. In the next step, each fluorophore was allowed to blink to generate several localizations per protein. To enable a quantitative analysis of the experimental localizations at the interface, we simulated homogeneous and heterogeneous distributions of proteins around N = 100 non-overlapping droplets in a 30 μm by 30 μm field of view. The homogeneous distribution was simulated using an even distribution ranging from 0 to 2π, representing the Φ value in polar coordinates. The radius r of each droplet in the simulation was randomly chosen from a normal distribution with a mean of 750 nm and a standard deviation of 200 nm. To determine the number of proteins present at the interface of each droplet, we take into account the droplet's circumference, the size of the protein (d = 22 nm diameter), and a quantity we define as the density  of proteins adsorbed at the interface. We multiply the droplet's cross-sectional circumference by the protein density  divided by the protein size to calculate the number of proteins occupying a specific perimeter. The value of  can vary between 0 and 1, with 0 indicating no proteins present and 1 indicating that proteins occupy the entire interface (a circle in our two-dimensional representation). For example, a droplet with a radius of 500 nm diameter and a protein density  of 0.5 would have 71 proteins distributed (71 = 2 500[nm]  /d). The ground truth position rgt of each protein relative to the droplet's interface is set by the polar coordinate Φ, the radius of the simulated droplet, and an additional sigma error in x and y representing the finite localization accuracy σac of 30 nm for each position. Moreover, the position of each fluorophore (localization) was calculated using the positions of each protein and an additional sigma error representing the finite localization precision σ lp of 50 nm unless otherwise specified. To model the number of localizations for each protein, a random number is generated from an exponential distribution with a mean of 5. Together with assuming an average of five localizations per protein, this will lead to a total number of 355 localisations for this simulated droplet.