Cython Spaces
Warning
Add text describing the inheritance structure, and the need to hold a union.
- class Euclidean2D(*args, **kwargs)
\(\mathbb{R}^2\) with \(L_2\)-induced metric (Euclidean).
- Parameters:
velocity – constant velocity to compute travel time, optional. default: 1
- __reduce__(self)
- asdict(self)
- random_point(self)
- class Manhattan2D(*args, **kwargs)
\(\mathbb{R}^2\) with \(L_1\)-induced metric (Manhattan).
- Parameters:
velocity – constant velocity to compute travel time, optional. default: 1
- __reduce__(self)
- asdict(self)
- random_point(self)
- class Graph(vertices, edges, weights=None, double velocity=1)
Weighted directed graph with integer node labels.
- Parameters:
vertices (Sequence[int]) – sequence of vertices
weights (Union[None, float, Sequence[float]]) – Edge weights. - if None is supplied, the resulting graph is unweighted (unit edge length) - if a single float is supplied, every edge length will be equal to this number - if a sequence is supplied, this will be mapped onto the edge sequence
velocity – constant velocity to compute travel time, optional. default: 1
- __reduce__(self)
- asdict(self)
- classmethod from_nx(cls, G, double velocity: float = 1.0, unicode make_attribute_distance: Optional[str] = u'distance')
Create a Graph from a networkx.Graph with a mandatory distance edge attribute.
- Parameters:
- Returns:
Graph instance
- random_point(self)
- class TransportSpace(loc_type)
Base class for extension types wrapping C++ TransportSpace class template. Since there’s no elegant way of wrapping templates in cython and exposing them to python, we will use the [Explicit Run-Time Dispatch approach] (https://martinralbrecht.wordpress.com/2017/07/23/adventures-in-cython-templating/). See the docstring of ridepy/data_structures_cython/data_structures.pyx for details.
- asdict(self)
- interp_dist(self, u, v, double dist_to_dest)
- interp_time(self, u, v, double time_to_dest)
Warning
Add text describing how the graphs can only hold integers as nodes and how we automatically relabel the nodes.