py3plex.core package¶
Subpackages¶
Submodules¶
py3plex.core.converters module¶
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py3plex.core.converters.
prepare_for_parsing
(multinet)¶ Compute layout for a hairball visualization
- Parameters
param1 (obj) – multilayer object
- Returns
(names, prepared network)
- Return type
tuple
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py3plex.core.converters.
prepare_for_visualization
(multinet, compute_layouts='force', layout_parameters=None, verbose=True, multiplex=False)¶ This functions takes a multilayer object and returns individual layers, their names, as well as multilayer edges spanning over multiple layers.
- Parameters
param1 (obj) – multilayer object
param2 (str) – Layout algorithm
param3 (dict) – Optional layout parameters
- Returns
(names,networks,multiedges)
- Return type
tuple
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py3plex.core.converters.
prepare_for_visualization_hairball
(multinet, compute_layouts=False)¶ Compute layout for a hairball visualization
- Parameters
param1 (obj) – multilayer object
- Returns
(names, prepared network)
- Return type
tuple
py3plex.core.multinet module¶
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class
py3plex.core.multinet.
multi_layer_network
(verbose=True, network_type='multilayer', directed=True, dummy_layer='null', label_delimiter='---', coupling_weight=1)¶ Bases:
object
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add_dummy_layers
()¶ Internal function, for conversion between objects
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add_edges
(edge_dict_list, input_type='dict')¶ A method for adding edges.. Types are: dict,list or px_edge. See examples for further use.
dict = {“source”: n1,”target”:n2,”type”:sth}
list = [[n1,t1,n2,t2] …]
px_edge = ((n1,t1)(n2,t2))
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add_nodes
(node_dict_list, input_type='dict')¶ A method for adding nodes..
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aggregate_edges
(metric='count', normalize_by='degree')¶
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basic_stats
(target_network=None)¶ A method for obtaining a network’s statistics
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edges_from_temporal_table
(edge_df)¶
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fill_tmp_with_edges
(edge_df)¶
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get_decomposition
(heuristic='all', cycle=None, parallel=False, alpha=1, beta=1)¶ Core method for obtaining a network’s decomposition in terms of relations
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get_decomposition_cycles
(cycle=None)¶ A supporting method for obtaining decomposition triplets
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get_degrees
()¶ A simple wrapper which computes node degrees.
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get_edges
(data=False, multiplex_edges=False)¶ A method for obtaining a network’s edges
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get_label_matrix
()¶ Return network labels
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get_layers
(style='diagonal', compute_layouts='force', layout_parameters=None, verbose=True)¶ A method for obtaining layerwise distributions
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get_neighbors
(node_id, layer_id=None)¶
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get_nodes
(data=False)¶ A method for obtaining a network’s nodes
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get_nx_object
()¶ Return only core network with proper annotations
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get_supra_adjacency_matrix
(mtype='sparse')¶ Get sparse representation of the supra matrix.
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get_tensor
(sparsity_type='bsr')¶ TODO
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get_unique_entity_counts
()¶ - Input
self object
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invert
(override_core=False)¶ invert the nodes to edges. Get the “edge graph”. Each node is here an edge.
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load_embedding
(embedding_file)¶ Embedding loading method
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load_layer_name_mapping
(mapping_name, header=False)¶ Layer-node mapping loader method
- Parameters
param1 – The name of the mapping file.
- Returns
self.layer_name_map is filled, returns nothing.
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load_network
(input_file=None, directed=False, input_type='gml', label_delimiter='---')¶ Main network loader
This method loads and prepares a given network.
- Parameters
param1 – network name
param2 – direction
param3 – input_type
- Returns
self.core_network along with self.labels(optional), self.activity etc.
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load_network_activity
(activity_file)¶ Network activity loader
- Args:
param1: The name of the generic activity file -> 65432 61888 1377688175 RE
, n1 n2 timestamp and layer name. Note that layer node mappings MUST be loaded in order to map nodes to activity properly.
- Returns:
self.activity is filled.
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load_temporal_edge_information
(input_file=None, input_type='edge_activity', directxed=False, layer_mapping=None)¶ A method for loading temporal edge information
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merge_with
(target_px_object)¶ Merge two px objects.
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monitor
(message)¶ A simple monithor method
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monoplex_nx_wrapper
(method, kwargs=None)¶ a generic networkx function wrapper
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read_ground_truth_communities
(cfile)¶ Parse ground truth community file and make mappings to the original nodes. This works based on node ID mappings, exact node,layer tuplets are to be added. :param param1: ground truth communities.
- Returns
self.ground_truth_communities
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remove_edges
(edge_dict_list, input_type='list')¶ A method for removing edges..
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remove_layer_edges
()¶
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remove_nodes
(node_dict_list, input_type='dict')¶ Remove nodes from the network
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save_network
(output_file=None, output_type='edgelist')¶ A method for saving the network :param output_type – edgelist, multiedgelist or gpickle
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serialize_to_edgelist
(edgelist_file='./tmp/tmpedgelist.txt', tmp_folder='tmp', out_folder='out', multiplex=False)¶
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sparse_to_px
(directed=None)¶ convert to px format
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split_to_layers
(style='diagonal', compute_layouts='force', layout_parameters=None, verbose=True, multiplex=False, convert_to_simple=False)¶ A method for obtaining layerwise distributions
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subnetwork
(input_list=None, subset_by='node_layer_names')¶ Construct a subgraph based on a set of nodes.
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summary
()¶ Generate a short summary of the network in form of a dict.
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test_scale_free
()¶ Test the scale-free-nness of the network
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to_json
()¶ A method for exporting the graph to a json file
Args: self
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to_sparse_matrix
(replace_core=False, return_only=False)¶ Conver the matrix to scipy-sparse version. This is useful for classification.
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visualize_matrix
(kwargs={})¶ Plot the matrix – this plots the supra-adjacency matrix
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visualize_network
(style='diagonal', parameters_layers=None, parameters_multiedges=None, show=False, compute_layouts='force', layouts_parameters=None, verbose=True, orientation='upper', resolution=0.01, axis=None, fig=None, no_labels=False, linewidth=1.7, alphachannel=0.3, linepoints='-.', legend=False)¶
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py3plex.core.parsers module¶
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py3plex.core.parsers.
load_edge_activity_file
(fname, layer_mapping=None)¶
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py3plex.core.parsers.
load_edge_activity_raw
(activity_file, layer_mappings)¶ Basic parser for loading generic activity files. Here, temporal edges are given as tuples -> this can be easily transformed for example into a pandas dataframe!
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py3plex.core.parsers.
load_temporal_edge_information
(input_network, input_type, layer_mapping=None)¶
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py3plex.core.parsers.
parse_detangler_json
(file_path)¶ Parser for generic Detangler files :input: Detangler JSON
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py3plex.core.parsers.
parse_edgelist_multi_types
(input_name, directed)¶
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py3plex.core.parsers.
parse_embedding
(input_name)¶ Loader for generic embedding as outputed by GenSim
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py3plex.core.parsers.
parse_gml
(file_name, directed)¶ parse a gml network
- Parameters
param1 (obj) – file name
param2 (obj) – directed?
Returns: (multigraph, possible labels)
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py3plex.core.parsers.
parse_gpickle
(file_name, directed=False, layer_separator=None)¶ A parser for generic Gpickle as stored by Py3plex. :input: gpickle object
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py3plex.core.parsers.
parse_gpickle_biomine
(file_name, directed)¶ Gpickle parser for biomine graphs :input: Gpickle containing BioMine data
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py3plex.core.parsers.
parse_matrix
(file_name, directed)¶ Parser for matrices :input: a SciPy sparse matrix
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py3plex.core.parsers.
parse_multi_edgelist
(input_name, directed)¶ A generic multiedgelist parser n l n l w :input: a text file containing multiedges
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py3plex.core.parsers.
parse_multiedge_tuple_list
(network, directed)¶
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py3plex.core.parsers.
parse_multiplex_edges
(input_name, directed)¶
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py3plex.core.parsers.
parse_multiplex_folder
(input_folder, directed)¶
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py3plex.core.parsers.
parse_network
(input_name, f_type='gml', directed=False, label_delimiter=None, network_type='multilayer')¶ A wrapper method for available parsers!
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py3plex.core.parsers.
parse_nx
(nx_object, directed)¶ Core parser for networkx objects :input: a networkx graph
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py3plex.core.parsers.
parse_simple_edgelist
(input_name, directed)¶ Simple edgelist n n w :input: a text file
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py3plex.core.parsers.
parse_spin_edgelist
(input_name, directed)¶
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py3plex.core.parsers.
save_edgelist
(input_network, output_file, attributes=False)¶
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py3plex.core.parsers.
save_gpickle
(input_network, output_file)¶
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py3plex.core.parsers.
save_multiedgelist
(input_network, output_file, attributes=False, encode_with_ints=False)¶ Save multiedgelist – as n1, l1, n2, l2, w
py3plex.core.supporting module¶
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py3plex.core.supporting.
add_mpx_edges
(input_network)¶
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py3plex.core.supporting.
parse_gaf_to_uniprot_GO
(gaf_mappings, filter_terms=None)¶
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py3plex.core.supporting.
split_to_layers
(input_network)¶