:py:mod:`gnn_tracking.analysis.graphs` ====================================== .. py:module:: gnn_tracking.analysis.graphs Module Contents --------------- Classes ~~~~~~~ .. autoapisummary:: gnn_tracking.analysis.graphs.TrackGraphInfo gnn_tracking.analysis.graphs.OrphanCount Functions ~~~~~~~~~ .. autoapisummary:: gnn_tracking.analysis.graphs.shortest_path_length_catch_no_path gnn_tracking.analysis.graphs.shortest_path_length_multi gnn_tracking.analysis.graphs.get_n_reachable gnn_tracking.analysis.graphs.get_track_graph_info gnn_tracking.analysis.graphs.get_track_graph_info_from_data gnn_tracking.analysis.graphs.summarize_track_graph_info gnn_tracking.analysis.graphs.get_orphan_counts gnn_tracking.analysis.graphs.get_basic_counts gnn_tracking.analysis.graphs.get_all_graph_construction_stats gnn_tracking.analysis.graphs.get_largest_segment_fracs gnn_tracking.analysis.graphs.get_cc_labels .. py:function:: shortest_path_length_catch_no_path(graph: networkx.Graph, source, target) -> int | float Same as nx.shortest_path_length but return inf if no path exists .. py:function:: shortest_path_length_multi(graph: networkx.Graph, sources: Iterable[int], targets: Iterable[int]) Shortest path for source to reach any of targets from any of the sources. If no connection exists, returns inf. If only target is source itself, returns 0. .. py:function:: get_n_reachable(graph: networkx.Graph, source: int, targets: Sequence[int]) -> int Get the number of targets that are reachable from source. The source node itself will not be counted! .. py:class:: TrackGraphInfo Bases: :py:obj:`NamedTuple` Information about how well connected the hits of a track are in the graph. Here, "component" means connected component of the graph. "segment" means connected component of the graph that only contains hits of the track with the given particle ID. .. attribute:: pid The particle ID of the track. .. attribute:: n_hits The number of hits in the track. .. attribute:: n_segments The number of segments of the track. .. attribute:: n_hits_largest_segment The number of hits in the largest segment of the track. .. attribute:: distance_largest_segments The shortest path length between the two largest segments .. attribute:: n_hits_largest_component The number of hits of the track of the biggest component of the track. .. py:attribute:: pid :type: int .. py:attribute:: n_hits :type: int .. py:attribute:: n_segments :type: int .. py:attribute:: n_hits_largest_segment :type: int .. py:attribute:: distance_largest_segments :type: int .. py:attribute:: n_hits_largest_component :type: int .. py:function:: get_track_graph_info(graph: networkx.Graph, particle_ids: numpy.ndarray, pid: int) -> TrackGraphInfo Get information about how well connected the hits of a single particle are in the graph. :param graph: networkx graph of the data :param particle_ids: The particle IDs of the hits. :param pid: Particle ID of the true track :returns: `TrackGraphInfo` .. py:function:: get_track_graph_info_from_data(data: torch_geometric.data.Data, *, w: torch.Tensor | None = None, pt_thld=0.9, threshold: float | None = None, max_eta: float = 4.0) -> pandas.DataFrame Get DataFrame of track graph information for every particle ID in the data. This function basically applies `get_track_graph_info` to every particle ID. :param data: Data :param w: Edge weights. If None, no cut on edge weights :param pt_thld: pt threshold for particle IDs to consider :param threshold: Edge classification cutoff (if w is given) :returns: DataFrame with columns as in `TrackGraphInfo` .. py:function:: summarize_track_graph_info(tgi: pandas.DataFrame) -> dict[str, float] Summarize track graph information returned by `get_track_graph_info_from_data`. .. py:class:: OrphanCount Bases: :py:obj:`NamedTuple` Stats about the number of orphan nodes in a graph .. attribute:: n_orphan_correct Number of orphan nodes that are actually bad nodes (low pt or noise) .. attribute:: n_orphan_incorrect Number of orphan nodes that are actually good nodes .. attribute:: n_orphan_total Total number of orphan nodes .. py:attribute:: n_orphan_correct :type: int .. py:attribute:: n_orphan_incorrect :type: int .. py:attribute:: n_orphan_total :type: int .. py:function:: get_orphan_counts(data: torch_geometric.data.Data, *, pt_thld=0.9, max_eta: float = 4.0) -> OrphanCount Count unmber of orphan nodes in a graph. See `OrphanCount` for details. .. py:function:: get_basic_counts(data: torch_geometric.data.Data, *, pt_thld: float = 0.9, max_eta: float = 4.0) -> dict[str, int] Get basic counts of edges and nodes .. py:function:: get_all_graph_construction_stats(data: torch_geometric.data.Data, pt_thld=0.9, max_eta: float = 4.0) -> dict[str, float] Evaluate graph construction performance for a single batch. .. py:function:: get_largest_segment_fracs(data: torch_geometric.data.Data, *, pt_thld=0.9, n_particles_sampled=None, max_eta=4) -> numpy.ndarray A fast way to get the fraction of hits in the largest segment for each track. :param data: :param pt_thld: :param n_particles_sampled: If not None, only consider a subsample of the particles. This speeds up calculation but introduces statistical fluctuations. :param max_eta: Maximum pseudorapidity :returns: Array of fractions. .. py:function:: get_cc_labels(edge_index: torch.Tensor, num_nodes: int) -> torch.Tensor Get labels for connected components of a graph.