Filters
This module contains functions for filtering node and edge iterables.
It relies heavily on the concepts of functional programming and the concept of predicates.
- pybel.struct.filters.invert_edge_predicate(edge_predicate)[source]
Build an edge predicate that is the inverse of the given edge predicate.
- Return type
Callable
[[BELGraph
,BaseEntity
,BaseEntity
,str
],bool
]
- pybel.struct.filters.and_edge_predicates(edge_predicates)[source]
Concatenate multiple edge predicates to a new predicate that requires all predicates to be met.
- Return type
Callable
[[BELGraph
,BaseEntity
,BaseEntity
,str
],bool
]
- pybel.struct.filters.filter_edges(graph, edge_predicates)[source]
Apply a set of filters to the edges iterator of a BEL graph.
- Return type
- Returns
An iterable of edges that pass all predicates
- pybel.struct.filters.count_passed_edge_filter(graph, edge_predicates)[source]
Return the number of edges passing a given set of predicates.
- Return type
- pybel.struct.filters.build_pmid_exclusion_filter(pmids)[source]
Fail for edges with citations whose references are one of the given PubMed identifiers.
- Parameters
pmids (
Union
[str
,Iterable
[str
]]) – A PubMed identifier or list of PubMed identifiers to filter against- Return type
Callable
[[BELGraph
,BaseEntity
,BaseEntity
,str
],bool
]
- pybel.struct.filters.build_annotation_dict_all_filter(annotations)[source]
Build an edge predicate for edges whose annotations are super-dictionaries of the given dictionary.
If no annotations are given, will always evaluate to true.
- Parameters
annotations (
Mapping
[str
,Iterable
[str
]]) – The annotation query dict to match- Return type
Callable
[[BELGraph
,BaseEntity
,BaseEntity
,str
],bool
]
- pybel.struct.filters.build_annotation_dict_any_filter(annotations)[source]
Build an edge predicate that passes for edges whose data dictionaries match the given dictionary.
If the given dictionary is empty, will always evaluate to true.
- Parameters
annotations (
Mapping
[str
,Iterable
[str
]]) – The annotation query dict to match- Return type
Callable
[[BELGraph
,BaseEntity
,BaseEntity
,str
],bool
]
- pybel.struct.filters.build_upstream_edge_predicate(nodes)[source]
Build an edge predicate that pass for relations for which one of the given nodes is the object.
- Return type
Callable
[[BELGraph
,BaseEntity
,BaseEntity
,str
],bool
]
- pybel.struct.filters.build_downstream_edge_predicate(nodes)[source]
Build an edge predicate that passes for edges for which one of the given nodes is the subject.
- Return type
Callable
[[BELGraph
,BaseEntity
,BaseEntity
,str
],bool
]
- pybel.struct.filters.build_relation_predicate(relations)[source]
Build an edge predicate that passes for edges with the given relation.
- Return type
Callable
[[BELGraph
,BaseEntity
,BaseEntity
,str
],bool
]
- pybel.struct.filters.build_pmid_inclusion_filter(pmids)[source]
Build an edge predicate that passes for edges with citations from the given PubMed identifier(s).
- Parameters
pmids (
Union
[str
,Iterable
[str
]]) – A PubMed identifier or list of PubMed identifiers to filter for- Return type
Callable
[[BELGraph
,BaseEntity
,BaseEntity
,str
],bool
]
- pybel.struct.filters.build_author_inclusion_filter(authors)[source]
Build an edge predicate that passes for edges with citations written by the given author(s).
- Return type
Callable
[[BELGraph
,BaseEntity
,BaseEntity
,str
],bool
]
- pybel.struct.filters.edge_predicate(func)[source]
Decorate an edge predicate function that only takes a dictionary as its singular argument.
Apply this as a decorator to a function that takes a single argument, a PyBEL node data dictionary, to make sure that it can also accept a pair of arguments, a BELGraph and a PyBEL node tuple as well.
- Return type
Callable
[[BELGraph
,BaseEntity
,BaseEntity
,str
],bool
]
- pybel.struct.filters.true_edge_predicate(graph, u, v, k)[source]
Return true for all edges.
- Return type
- pybel.struct.filters.false_edge_predicate(graph, u, v, k)[source]
Return false for all edges.
- Return type
- pybel.struct.filters.has_provenance(edge_data)[source]
Check if the edge has provenance information (i.e. citation and evidence).
- Return type
- pybel.struct.filters.has_pubmed(edge_data)[source]
Check if the edge has a PubMed citation.
- Return type
- pybel.struct.filters.has_authors(edge_data)[source]
Check if the edge contains author information for its citation.
- Return type
- pybel.struct.filters.is_causal_relation(edge_data)[source]
Check if the given relation is causal.
- Return type
- pybel.struct.filters.not_causal_relation(edge_data)[source]
Check if the given relation is not causal.
- Return type
- pybel.struct.filters.is_direct_causal_relation(edge_data)[source]
Check if the edge is a direct causal relation.
- Return type
- pybel.struct.filters.is_associative_relation(edge_data)[source]
Check if the edge has an association relation.
- Return type
- pybel.struct.filters.edge_has_activity(edge_data)[source]
Check if the edge contains an activity in either the subject or object.
- Return type
- pybel.struct.filters.edge_has_degradation(edge_data)[source]
Check if the edge contains a degradation in either the subject or object.
- Return type
- pybel.struct.filters.edge_has_translocation(edge_data)[source]
Check if the edge has a translocation in either the subject or object.
- Return type
- pybel.struct.filters.edge_has_annotation(edge_data, key)[source]
Check if an edge has the given annotation.
- Parameters
- Return type
- Returns
If the annotation key is present in the current data dictionary
For example, it might be useful to print all edges that are annotated with ‘Subgraph’:
>>> from pybel.examples import sialic_acid_graph >>> from pybel.examples.sialic_acid_example import sialic_acid_cd33_complex, cd33 >>> edges = { ... (u, v) ... for u, v, data in sialic_acid_graph.edges(data=True) ... if edge_has_annotation(data, 'Species') ... } >>> assert (sialic_acid_cd33_complex, cd33) in edges
- pybel.struct.filters.has_pathology_causal(graph, u, v, k)[source]
Check if the subject is a pathology and has a causal relationship with a non bioprocess/pathology.
- Return type
- Returns
If the subject of this edge is a pathology and it participates in a causal reaction.
- pybel.struct.filters.filter_nodes(graph, node_predicates)[source]
Apply a set of predicates to the nodes iterator of a BEL graph.
- Return type
- pybel.struct.filters.get_nodes(graph, node_predicates)[source]
Get the set of all nodes that pass the predicates.
- Return type
- pybel.struct.filters.count_passed_node_filter(graph, node_predicates)[source]
Count how many nodes pass a given set of node predicates.
- Return type
- pybel.struct.filters.summarize_node_filter(graph, node_filters)[source]
Print a summary of the number of nodes passing a given set of filters.
- pybel.struct.filters.get_nodes_by_function(graph, func)[source]
Get all nodes with the given function(s).
- Return type
- pybel.struct.filters.get_nodes_by_namespace(graph, namespaces)[source]
Get all nodes identified by the given namespace(s).
- Return type
- pybel.struct.filters.function_inclusion_filter_builder(func)[source]
Build a filter that only passes on nodes of the given function(s).
- pybel.struct.filters.function_exclusion_filter_builder(func)[source]
Build a filter that fails on nodes of the given function(s).
- pybel.struct.filters.data_missing_key_builder(key)[source]
Build a filter that passes only on nodes that don’t have the given key in their data dictionary.
- Parameters
key (str) – A key for the node’s data dictionary
- Return type
Callable
[[BELGraph
,BaseEntity
],bool
]
- pybel.struct.filters.build_node_data_search(key, data_predicate)[source]
Build a filter for nodes whose associated data with the given key passes the given predicate.
- pybel.struct.filters.build_node_graph_data_search(key, data_predicate)[source]
Build a function for testing data associated with the node in the graph.
- pybel.struct.filters.build_node_key_search(query, key)[source]
Build a node filter for nodes whose values for the given key are superstrings of the query string(s).
- pybel.struct.filters.build_node_name_search(query)[source]
Search nodes’ names.
Is a thin wrapper around
build_node_key_search()
withpybel.constants.NAME
- pybel.struct.filters.namespace_inclusion_builder(namespace)[source]
Build a predicate for namespace inclusion.
- Return type
Callable
[[BELGraph
,BaseEntity
],bool
]
- pybel.struct.filters.has_activity(graph, node)[source]
Return true if over any of the node’s edges, it has a molecular activity.
- Return type
- pybel.struct.filters.has_edge_modifier(graph, node, modifier)[source]
Return true if over any of a nodes edges, it has a given modifier.
Modifier can be one of:
pybel.constants.ACTIVITY
,pybel.constants.DEGRADATION
pybel.constants.TRANSLOCATION
.
- pybel.struct.filters.is_degraded(graph, node)[source]
Return true if over any of the node’s edges, it is degraded.
- Return type
- pybel.struct.filters.is_translocated(graph, node)[source]
Return true if over any of the node’s edges, it is translocated.
- Return type
- pybel.struct.filters.is_isolated_list_abundance(graph, node, cls=<class 'pybel.dsl.node_classes.ListAbundance'>)[source]
Return if the node is a list abundance but has no qualified edges.
- Return type
- pybel.struct.filters.none_of(nodes)[source]
Build a node predicate that returns false for the given nodes.
- Return type
Callable
[[BELGraph
,BaseEntity
],bool
]
- pybel.struct.filters.one_of(nodes)[source]
Build a function that returns true for the given nodes.
- Return type
Callable
[[BELGraph
,BaseEntity
],bool
]
- pybel.struct.filters.has_fragment(node, variant_cls)
Return true if the node has at least one of the given variant.
- Return type
- pybel.struct.filters.has_gene_modification(node, variant_cls)
Return true if the node has at least one of the given variant.
- Return type
- pybel.struct.filters.has_hgvs(node, variant_cls)
Return true if the node has at least one of the given variant.
- Return type
- pybel.struct.filters.has_protein_modification(node, variant_cls)
Return true if the node has at least one of the given variant.
- Return type
- pybel.struct.filters.has_variant(node)[source]
Return true if the node has any variants.
- Return type
- pybel.struct.filters.has_causal_edges(graph, node)[source]
Check if the node has any causal out-edges or in-edges.
- Parameters
graph (
BELGraph
) – A BEL graphnode (
BaseEntity
) – A BEL term
- Return type
- pybel.struct.filters.has_causal_in_edges(graph, node)[source]
Check if the node has any causal in-edges.
- Parameters
graph (
BELGraph
) – A BEL graphnode (
BaseEntity
) – A BEL term
- Return type
- pybel.struct.filters.has_causal_out_edges(graph, node)[source]
Check if the node has any causal out-edges.
- Parameters
graph (
BELGraph
) – A BEL graphnode (
BaseEntity
) – A BEL term
- Return type
- pybel.struct.filters.has_in_edges(graph, node, edge_types)[source]
Check if the node has any in-edges in the given set.
- Parameters
graph (
BELGraph
) – A BEL graphnode (
BaseEntity
) – A BEL termedge_types (
Set
[str
]) – A collection of edge types to check against
- Return type
- pybel.struct.filters.has_out_edges(graph, node, edge_types)[source]
Check if the node has any out-edges in the given set.
- Parameters
graph (
BELGraph
) – A BEL graphnode (
BaseEntity
) – A BEL termedge_types (
Set
[str
]) – A collection of edge types to check against
- Return type
- pybel.struct.filters.is_causal_central(graph, node)[source]
Check if the node has both causal in-edges and also causal out-edges.
- Parameters
graph (
BELGraph
) – A BEL graphnode (
BaseEntity
) – A BEL term
- Return type
- pybel.struct.filters.is_causal_sink(graph, node)[source]
Check if the node has causal in-edges but no causal out-edges.
- Parameters
graph (
BELGraph
) – A BEL graphnode (
BaseEntity
) – A BEL term
- Return type
- pybel.struct.filters.is_causal_source(graph, node)[source]
Check if the node has causal out-edges but no causal in-edges.
- Parameters
graph (
BELGraph
) – A BEL graphnode (
BaseEntity
) – A BEL term
- Return type
- pybel.struct.filters.no_causal_edges(graph, node)[source]
Check if the node does not have any causal out-edges or in-edges.
- Parameters
graph (
BELGraph
) – A BEL graphnode (
BaseEntity
) – A BEL term
- Return type
- pybel.struct.filters.no_causal_in_edges(graph, node)[source]
Check if the node does not have any causal in-edges.
- Parameters
graph (
BELGraph
) – A BEL graphnode (
BaseEntity
) – A BEL term
- Return type
- pybel.struct.filters.no_causal_out_edges(graph, node)[source]
Check if the node does not have any causal out-edges.
- Parameters
graph (
BELGraph
) – A BEL graphnode (
BaseEntity
) – A BEL term
- Return type
- pybel.struct.filters.no_in_edges(graph, node, edge_types)[source]
Check if the node does not have any in-edges in the given set.
- Parameters
graph (
BELGraph
) – A BEL graphnode (
BaseEntity
) – A BEL termedge_types (
Set
[str
]) – A collection of edge types to check against
- Return type
- pybel.struct.filters.no_out_edges(graph, node, edge_types)[source]
Check if the node does not have any out-edges in the given set.
- Parameters
graph (
BELGraph
) – A BEL graphnode (
BaseEntity
) – A BEL termedge_types (
Set
[str
]) – A collection of edge types to check against
- Return type
- pybel.struct.filters.concatenate_node_predicates(node_predicates)[source]
Concatenate multiple node predicates to a new predicate that requires all predicates to be met.
Example usage:
>>> from pybel import BELGraph >>> from pybel.dsl import Protein >>> from pybel.struct.filters import not_gene, not_rna >>> app_protein = Protein(name='APP', namespace='hgnc', identifier='620') >>> app_rna = app_protein.get_rna() >>> app_gene = app_rna.get_gene() >>> graph = BELGraph() >>> _ = graph.add_transcription(app_gene, app_rna) >>> _ = graph.add_translation(app_rna, app_protein) >>> node_predicate = concatenate_node_predicates([not_rna, not_gene]) >>> assert node_predicate(graph, app_protein) >>> assert not node_predicate(graph, app_rna) >>> assert not node_predicate(graph, app_gene)
- Return type
Callable
[[BELGraph
,BaseEntity
],bool
]
- pybel.struct.filters.invert_node_predicate(f)[source]
Build a node predicate that is the inverse of the given node predicate.
- Return type
Callable
[[BELGraph
,BaseEntity
],bool
]
- pybel.struct.filters.node_predicate(f)[source]
Tag a node predicate that takes a dictionary to also accept a pair of (BELGraph, node).
Apply this as a decorator to a function that takes a single argument, a PyBEL node, to make sure that it can also accept a pair of arguments, a BELGraph and a PyBEL node as well.
- Return type
Callable
[[BELGraph
,BaseEntity
],bool
]
- pybel.struct.filters.true_node_predicate(_)[source]
Return true for all nodes.
Given BEL graph
graph
, applyingtrue_predicate()
with a predicate on the nodes iterable as infilter(keep_node_permissive, graph)
will result in the same iterable as iterating directly over aBELGraph
- Return type