<h1> Summary of Research</h1> This project investigates the usefulness of applying techniques from graph theory to configurations of points. These point patterns are often used as stimuli in studies of perception of numerosity. We used a wide range of graph indices, along with an index from the occupancy model. The scripts here will compute any indices listed below, when given a valid input of dot patterns, as well as the maximum standard deviation. Class computes a number of different indices for a random geometric graph $G(V,E)_{d}$ - G is a random geometric graph - V is the vertex set, and each vertex has coordinates x-y - E is the edge set - d is the connectivity distance parameters that is used to determine if two vertices are connected The following indices are computed: :: DN - Domination number :: IN - Independence number :: CC - Number of connected components :: LC - Local clustering coefficient :: GC - Global clustering coefficient :: RW - Random walk :: EG - EigenVector centrality :: CL - Maximum Clique :: TD - Total degree :: TL - Total edge length :: OC - Occupancy Model For more information on each Indices please see preprint on [PsyArXiv][1] **Example** import indices as idx # Inputfile is MS Excel file # target is the name of the sheet in Input_file # distance_range is python list of distances to be computed i.e. range(15, 50, 5)} indice_instance = idx.Indices(Input_file, target, distance_range) # Use instance to compute one of the indices below in the distance range given indices_instance.computeindices(graph_index) **Returns** Saves a excel file with name <graphindex>_<targetsheet>.xlsx Prints out the column name that has the maximum standard deviation [1]: https://psyarxiv.com/g3zcq/