spaghetti.Network.allneighbordistances¶

Network.allneighbordistances(self, sourcepattern, destpattern=None, fill_diagonal=None, n_processes=None, gen_tree=False, snap_dist=False)[source]¶

Compute either all distances between i and j in a single point pattern or all distances between each i from a source pattern and all j from a destination pattern.

Parameters

sourcepattern{str, spaghetti.network.PointPattern}

The key of a point pattern snapped to the network OR the full spaghetti.network.PointPattern object.

destpatternstr

(Optional) The key of a point pattern snapped to the network OR the full spaghetti.network.PointPattern object.

fill_diagonal{float, int}

(Optional) Fill the diagonal of the cost matrix. Default is None and will populate the diagonal with numpy.nan. Do not declare a destpattern for a custom fill_diagonal.

n_processes{int, str}

(Optional) Specify the number of cores to utilize. Default is 1 core. Use int to specify an exact number or cores. Use "all" to request all available cores.

gen_treebool

Rebuild shortest path True, or skip False.

snap_distbool

Flag as True to include the distance from the original location to the snapped location along the network. Default is False.

Returns

nearestnumpy.ndarray

An array of shape (n,n) storing distances between all points.

tree_nearestdict

Nearest network node to point pattern vertex shortest path lookup. The values of the dictionary are a tuple of the nearest source vertex and the near destination vertex to query the lookup tree. If two observations are snapped to the same network arc a flag of -.1 is set for both the source and destination network vertex indicating the same arc is used while also raising an IndexError when rebuilding the path.

Examples

>>> import spaghetti as spgh
>>> ntw = spgh.Network(examples.get_path('streets.shp'))
>>> ntw.snapobservations(examples.get_path('crimes.shp'),
...                                        'crimes',
...                                         attribute=True)

>>> s2s_dist = ntw.allneighbordistances('crimes')
>>> s2s_dist[0,0], s2s_dist[1,0]
(nan, 3105.189475447081)

>>> ntw.snapobservations(examples.get_path('schools.shp'),
...                                        'schools',
...                                        attribute=False)

>>> s2d_dist = ntw.allneighbordistances('crimes',
...                                     destpattern='schools')
>>> s2d_dist[0,0], s2d_dist[1,0]
(4520.72353741989, 6340.422971967315)

>>> s2d_dist, tree = ntw.allneighbordistances('schools',
...                                           gen_tree=True)
>>> tree[(6, 7)]
(173, 64)