cbongiorno/pednav: PEDNAV

PEDNAV is a python3 library for the stochastic vector-based navigation and stochastic shortest path navigation.

INSTALLATION Typical installations time: <10sec

On Linux: sudo pip3 install pednav

On Windows: pip3 install pednav

Requirements:

numpy>=1.14.2 python-igraph>=0.8.0

DATA data/graph_clusters_30m_bos.pk: It is a pickle igraph object with the pedestrian street network for Boston. g.vs["pos"] contains the lat,lon of the street intersections g.vs["ppos"] contains the projected lat,lon of the street intersection g.ws["weights"] contains the length in meters of the street

data/graph_clusters_30m_sf.pk: It is a pickle igraph object with the pedestrian street network for San Francisco.

data/Human_Paths_bos.pk: It is a pickle of a dictionary containing a sample of the pedestrian paths for Boston. The keys are the path IDs, the values are the sequence of index intersections of the path. The index intersections are matched with graph_clusters_30m_bos.pk

EXAMPLE import pickle as pk

initialize the module with the street network

nav = pednav.Navigation('data/graph_clusters_30m_bos.pk')

noise parameter

sigma = 0.7

Load pedestrian paths

with open('data/Human_Paths_bos.pk','rb') as handle:

H = pk.load(handle)

Select one human path

path0 = list(H.values())[0] origin,destination = path0[0], path0[-1]

print('Real Pedestrian Path:')

print(path0)

print('Stochastic Vector Path:')

print(nav.Stochastic_Vector_Path(destination,[origin],sigma)[0])

print('Stochastic Shortest Path:')

print(nav.Stochastic_Shortest_Path(destination,[origin],sigma)[0]) '''