_id stringlengths 2 7 | title stringlengths 1 88 | partition stringclasses 3
values | text stringlengths 75 19.8k | language stringclasses 1
value | meta_information dict |
|---|---|---|---|---|---|
q262100 | bond_task | validation | def bond_task(
perc_graph_result, seeds, ps, convolution_factors_tasks_iterator
):
"""
Perform a number of runs
The number of runs is the number of seeds
convolution_factors_tasks_iterator needs to be an iterator
We shield the convolution factors tasks from jug value/result mechanism
by s... | python | {
"resource": ""
} |
q262101 | get_peripheral_successor_edges | validation | def get_peripheral_successor_edges(graph: BELGraph, subgraph: BELGraph) -> EdgeIterator:
"""Get the set of possible successor edges peripheral to the sub-graph.
The source nodes in this iterable are all inside the sub-graph, while the targets are outside.
"""
for u in subgraph:
for _, v, k in g... | python | {
"resource": ""
} |
q262102 | get_peripheral_predecessor_edges | validation | def get_peripheral_predecessor_edges(graph: BELGraph, subgraph: BELGraph) -> EdgeIterator:
"""Get the set of possible predecessor edges peripheral to the sub-graph.
The target nodes in this iterable are all inside the sub-graph, while the sources are outside.
"""
for v in subgraph:
for u, _, k ... | python | {
"resource": ""
} |
q262103 | count_sources | validation | def count_sources(edge_iter: EdgeIterator) -> Counter:
"""Count the source nodes in an edge iterator with keys and data.
:return: A counter of source nodes in the iterable
"""
return Counter(u for u, _, _ in edge_iter) | python | {
"resource": ""
} |
q262104 | count_targets | validation | def count_targets(edge_iter: EdgeIterator) -> Counter:
"""Count the target nodes in an edge iterator with keys and data.
:return: A counter of target nodes in the iterable
"""
return Counter(v for _, v, _ in edge_iter) | python | {
"resource": ""
} |
q262105 | get_subgraph_edges | validation | def get_subgraph_edges(graph: BELGraph,
annotation: str,
value: str,
source_filter=None,
target_filter=None,
):
"""Gets all edges from a given subgraph whose source and target nodes pass all of the giv... | python | {
"resource": ""
} |
q262106 | get_subgraph_peripheral_nodes | validation | def get_subgraph_peripheral_nodes(graph: BELGraph,
subgraph: Iterable[BaseEntity],
node_predicates: NodePredicates = None,
edge_predicates: EdgePredicates = None,
):
"""Get a summa... | python | {
"resource": ""
} |
q262107 | enrich_complexes | validation | def enrich_complexes(graph: BELGraph) -> None:
"""Add all of the members of the complex abundances to the graph."""
nodes = list(get_nodes_by_function(graph, COMPLEX))
for u in nodes:
for v in u.members:
graph.add_has_component(u, v) | python | {
"resource": ""
} |
q262108 | enrich_composites | validation | def enrich_composites(graph: BELGraph):
"""Adds all of the members of the composite abundances to the graph."""
nodes = list(get_nodes_by_function(graph, COMPOSITE))
for u in nodes:
for v in u.members:
graph.add_has_component(u, v) | python | {
"resource": ""
} |
q262109 | enrich_reactions | validation | def enrich_reactions(graph: BELGraph):
"""Adds all of the reactants and products of reactions to the graph."""
nodes = list(get_nodes_by_function(graph, REACTION))
for u in nodes:
for v in u.reactants:
graph.add_has_reactant(u, v)
for v in u.products:
graph.add_has_p... | python | {
"resource": ""
} |
q262110 | enrich_variants | validation | def enrich_variants(graph: BELGraph, func: Union[None, str, Iterable[str]] = None):
"""Add the reference nodes for all variants of the given function.
:param graph: The target BEL graph to enrich
:param func: The function by which the subject of each triple is filtered. Defaults to the set of protein, rna,... | python | {
"resource": ""
} |
q262111 | enrich_unqualified | validation | def enrich_unqualified(graph: BELGraph):
"""Enrich the sub-graph with the unqualified edges from the graph.
The reason you might want to do this is you induce a sub-graph from the original graph based on an annotation
filter, but the unqualified edges that don't have annotations that most likely connect el... | python | {
"resource": ""
} |
q262112 | expand_internal | validation | def expand_internal(universe: BELGraph, graph: BELGraph, edge_predicates: EdgePredicates = None) -> None:
"""Edges between entities in the sub-graph that pass the given filters.
:param universe: The full graph
:param graph: A sub-graph to find the upstream information
:param edge_predicates: Optional l... | python | {
"resource": ""
} |
q262113 | expand_internal_causal | validation | def expand_internal_causal(universe: BELGraph, graph: BELGraph) -> None:
"""Add causal edges between entities in the sub-graph.
Is an extremely thin wrapper around :func:`expand_internal`.
:param universe: A BEL graph representing the universe of all knowledge
:param graph: The target BEL graph to enr... | python | {
"resource": ""
} |
q262114 | get_namespaces_with_incorrect_names | validation | def get_namespaces_with_incorrect_names(graph: BELGraph) -> Set[str]:
"""Return the set of all namespaces with incorrect names in the graph."""
return {
exc.namespace
for _, exc, _ in graph.warnings
if isinstance(exc, (MissingNamespaceNameWarning, MissingNamespaceRegexWarning))
} | python | {
"resource": ""
} |
q262115 | get_undefined_namespaces | validation | def get_undefined_namespaces(graph: BELGraph) -> Set[str]:
"""Get all namespaces that are used in the BEL graph aren't actually defined."""
return {
exc.namespace
for _, exc, _ in graph.warnings
if isinstance(exc, UndefinedNamespaceWarning)
} | python | {
"resource": ""
} |
q262116 | get_incorrect_names_by_namespace | validation | def get_incorrect_names_by_namespace(graph: BELGraph, namespace: str) -> Set[str]:
"""Return the set of all incorrect names from the given namespace in the graph.
:return: The set of all incorrect names from the given namespace in the graph
"""
return {
exc.name
for _, exc, _ in graph.w... | python | {
"resource": ""
} |
q262117 | get_undefined_namespace_names | validation | def get_undefined_namespace_names(graph: BELGraph, namespace: str) -> Set[str]:
"""Get the names from a namespace that wasn't actually defined.
:return: The set of all names from the undefined namespace
"""
return {
exc.name
for _, exc, _ in graph.warnings
if isinstance(exc, Und... | python | {
"resource": ""
} |
q262118 | get_incorrect_names | validation | def get_incorrect_names(graph: BELGraph) -> Mapping[str, Set[str]]:
"""Return the dict of the sets of all incorrect names from the given namespace in the graph.
:return: The set of all incorrect names from the given namespace in the graph
"""
return {
namespace: get_incorrect_names_by_namespace... | python | {
"resource": ""
} |
q262119 | group_errors | validation | def group_errors(graph: BELGraph) -> Mapping[str, List[int]]:
"""Group the errors together for analysis of the most frequent error.
:return: A dictionary of {error string: list of line numbers}
"""
warning_summary = defaultdict(list)
for _, exc, _ in graph.warnings:
warning_summary[str(exc... | python | {
"resource": ""
} |
q262120 | get_names_including_errors | validation | def get_names_including_errors(graph: BELGraph) -> Mapping[str, Set[str]]:
"""Takes the names from the graph in a given namespace and the erroneous names from the same namespace and returns
them together as a unioned set
:return: The dict of the sets of all correct and incorrect names from the given namesp... | python | {
"resource": ""
} |
q262121 | count_defaultdict | validation | def count_defaultdict(dict_of_lists: Mapping[X, List[Y]]) -> Mapping[X, typing.Counter[Y]]:
"""Count the number of elements in each value of the dictionary."""
return {
k: Counter(v)
for k, v in dict_of_lists.items()
} | python | {
"resource": ""
} |
q262122 | set_percentage | validation | def set_percentage(x: Iterable[X], y: Iterable[X]) -> float:
"""What percentage of x is contained within y?
:param set x: A set
:param set y: Another set
:return: The percentage of x contained within y
"""
a, b = set(x), set(y)
if not a:
return 0.0
return len(a & b) / len(a) | python | {
"resource": ""
} |
q262123 | tanimoto_set_similarity | validation | def tanimoto_set_similarity(x: Iterable[X], y: Iterable[X]) -> float:
"""Calculate the tanimoto set similarity."""
a, b = set(x), set(y)
union = a | b
if not union:
return 0.0
return len(a & b) / len(union) | python | {
"resource": ""
} |
q262124 | min_tanimoto_set_similarity | validation | def min_tanimoto_set_similarity(x: Iterable[X], y: Iterable[X]) -> float:
"""Calculate the tanimoto set similarity using the minimum size.
:param set x: A set
:param set y: Another set
:return: The similarity between
"""
a, b = set(x), set(y)
if not a or not b:
return 0.0
... | python | {
"resource": ""
} |
q262125 | calculate_single_tanimoto_set_distances | validation | def calculate_single_tanimoto_set_distances(target: Iterable[X], dict_of_sets: Mapping[Y, Set[X]]) -> Mapping[Y, float]:
"""Return a dictionary of distances keyed by the keys in the given dict.
Distances are calculated based on pairwise tanimoto similarity of the sets contained
:param set target: A set
... | python | {
"resource": ""
} |
q262126 | calculate_tanimoto_set_distances | validation | def calculate_tanimoto_set_distances(dict_of_sets: Mapping[X, Set]) -> Mapping[X, Mapping[X, float]]:
"""Return a distance matrix keyed by the keys in the given dict.
Distances are calculated based on pairwise tanimoto similarity of the sets contained.
:param dict_of_sets: A dict of {x: set of y}
:ret... | python | {
"resource": ""
} |
q262127 | calculate_global_tanimoto_set_distances | validation | def calculate_global_tanimoto_set_distances(dict_of_sets: Mapping[X, Set]) -> Mapping[X, Mapping[X, float]]:
r"""Calculate an alternative distance matrix based on the following equation.
.. math:: distance(A, B)=1- \|A \cup B\| / \| \cup_{s \in S} s\|
:param dict_of_sets: A dict of {x: set of y}
:retu... | python | {
"resource": ""
} |
q262128 | barh | validation | def barh(d, plt, title=None):
"""A convenience function for plotting a horizontal bar plot from a Counter"""
labels = sorted(d, key=d.get)
index = range(len(labels))
plt.yticks(index, labels)
plt.barh(index, [d[v] for v in labels])
if title is not None:
plt.title(title) | python | {
"resource": ""
} |
q262129 | barv | validation | def barv(d, plt, title=None, rotation='vertical'):
"""A convenience function for plotting a vertical bar plot from a Counter"""
labels = sorted(d, key=d.get, reverse=True)
index = range(len(labels))
plt.xticks(index, labels, rotation=rotation)
plt.bar(index, [d[v] for v in labels])
if title is ... | python | {
"resource": ""
} |
q262130 | safe_add_edge | validation | def safe_add_edge(graph, u, v, key, attr_dict, **attr):
"""Adds an edge while preserving negative keys, and paying no respect to positive ones
:param pybel.BELGraph graph: A BEL Graph
:param tuple u: The source BEL node
:param tuple v: The target BEL node
:param int key: The edge key. If less than ... | python | {
"resource": ""
} |
q262131 | prepare_c3 | validation | def prepare_c3(data: Union[List[Tuple[str, int]], Mapping[str, int]],
y_axis_label: str = 'y',
x_axis_label: str = 'x',
) -> str:
"""Prepares C3 JSON for making a bar chart from a Counter
:param data: A dictionary of {str: int} to display as bar chart
:param y_a... | python | {
"resource": ""
} |
q262132 | prepare_c3_time_series | validation | def prepare_c3_time_series(data: List[Tuple[int, int]], y_axis_label: str = 'y', x_axis_label: str = 'x') -> str:
"""Prepare C3 JSON string dump for a time series.
:param data: A list of tuples [(year, count)]
:param y_axis_label: The Y axis label
:param x_axis_label: X axis internal label. Should be l... | python | {
"resource": ""
} |
q262133 | calculate_betweenness_centality | validation | def calculate_betweenness_centality(graph: BELGraph, number_samples: int = CENTRALITY_SAMPLES) -> Counter:
"""Calculate the betweenness centrality over nodes in the graph.
Tries to do it with a certain number of samples, but then tries a complete approach if it fails.
"""
try:
res = nx.betweenn... | python | {
"resource": ""
} |
q262134 | canonical_circulation | validation | def canonical_circulation(elements: T, key: Optional[Callable[[T], bool]] = None) -> T:
"""Get get a canonical representation of the ordered collection by finding its minimum circulation with the
given sort key
"""
return min(get_circulations(elements), key=key) | python | {
"resource": ""
} |
q262135 | pair_has_contradiction | validation | def pair_has_contradiction(graph: BELGraph, u: BaseEntity, v: BaseEntity) -> bool:
"""Check if a pair of nodes has any contradictions in their causal relationships.
Assumes both nodes are in the graph.
"""
relations = {data[RELATION] for data in graph[u][v].values()}
return relation_set_has_contrad... | python | {
"resource": ""
} |
q262136 | relation_set_has_contradictions | validation | def relation_set_has_contradictions(relations: Set[str]) -> bool:
"""Return if the set of BEL relations contains a contradiction."""
has_increases = any(relation in CAUSAL_INCREASE_RELATIONS for relation in relations)
has_decreases = any(relation in CAUSAL_DECREASE_RELATIONS for relation in relations)
h... | python | {
"resource": ""
} |
q262137 | single_run_arrays | validation | def single_run_arrays(spanning_cluster=True, **kwargs):
r'''
Generate statistics for a single run
This is a stand-alone helper function to evolve a single sample state
(realization) and return the cluster statistics.
Parameters
----------
spanning_cluster : bool, optional
Whether t... | python | {
"resource": ""
} |
q262138 | _microcanonical_average_spanning_cluster | validation | def _microcanonical_average_spanning_cluster(has_spanning_cluster, alpha):
r'''
Compute the average number of runs that have a spanning cluster
Helper function for :func:`microcanonical_averages`
Parameters
----------
has_spanning_cluster : 1-D :py:class:`numpy.ndarray` of bool
Each e... | python | {
"resource": ""
} |
q262139 | _microcanonical_average_max_cluster_size | validation | def _microcanonical_average_max_cluster_size(max_cluster_size, alpha):
"""
Compute the average size of the largest cluster
Helper function for :func:`microcanonical_averages`
Parameters
----------
max_cluster_size : 1-D :py:class:`numpy.ndarray` of int
Each entry is the ``max_cluster_... | python | {
"resource": ""
} |
q262140 | _microcanonical_average_moments | validation | def _microcanonical_average_moments(moments, alpha):
"""
Compute the average moments of the cluster size distributions
Helper function for :func:`microcanonical_averages`
Parameters
----------
moments : 2-D :py:class:`numpy.ndarray` of int
``moments.shape[1] == 5`.
Each array ... | python | {
"resource": ""
} |
q262141 | microcanonical_averages | validation | def microcanonical_averages(
graph, runs=40, spanning_cluster=True, model='bond', alpha=alpha_1sigma,
copy_result=True
):
r'''
Generate successive microcanonical percolation ensemble averages
This is a :ref:`generator function <python:tut-generators>` to successively
add one edge at a time from... | python | {
"resource": ""
} |
q262142 | spanning_1d_chain | validation | def spanning_1d_chain(length):
"""
Generate a linear chain with auxiliary nodes for spanning cluster detection
Parameters
----------
length : int
Number of nodes in the chain, excluding the auxiliary nodes.
Returns
-------
networkx.Graph
A linear chain graph with auxili... | python | {
"resource": ""
} |
q262143 | spanning_2d_grid | validation | def spanning_2d_grid(length):
"""
Generate a square lattice with auxiliary nodes for spanning detection
Parameters
----------
length : int
Number of nodes in one dimension, excluding the auxiliary nodes.
Returns
-------
networkx.Graph
A square lattice graph with auxilia... | python | {
"resource": ""
} |
q262144 | microcanonical_averages_arrays | validation | def microcanonical_averages_arrays(microcanonical_averages):
"""
Compile microcanonical averages over all iteration steps into single arrays
Helper function to aggregate the microcanonical averages over all iteration
steps into single arrays for further processing
Parameters
----------
mi... | python | {
"resource": ""
} |
q262145 | _binomial_pmf | validation | def _binomial_pmf(n, p):
"""
Compute the binomial PMF according to Newman and Ziff
Helper function for :func:`canonical_averages`
See Also
--------
canonical_averages
Notes
-----
See Newman & Ziff, Equation (10) [10]_
References
----------
.. [10] Newman, M. E. J. ... | python | {
"resource": ""
} |
q262146 | canonical_averages | validation | def canonical_averages(ps, microcanonical_averages_arrays):
"""
Compute the canonical cluster statistics from microcanonical statistics
This is according to Newman and Ziff, Equation (2).
Note that we also simply average the bounds of the confidence intervals
according to this formula.
Paramet... | python | {
"resource": ""
} |
q262147 | statistics | validation | def statistics(
graph, ps, spanning_cluster=True, model='bond', alpha=alpha_1sigma, runs=40
):
"""
Helper function to compute percolation statistics
See Also
--------
canonical_averages
microcanonical_averages
sample_states
"""
my_microcanonical_averages = microcanonical_av... | python | {
"resource": ""
} |
q262148 | rank_causalr_hypothesis | validation | def rank_causalr_hypothesis(graph, node_to_regulation, regulator_node):
"""Test the regulator hypothesis of the given node on the input data using the algorithm.
Note: this method returns both +/- signed hypotheses evaluated
Algorithm:
1. Calculate the shortest path between the regulator node and eac... | python | {
"resource": ""
} |
q262149 | get_path_effect | validation | def get_path_effect(graph, path, relationship_dict):
"""Calculate the final effect of the root node to the sink node in the path.
:param pybel.BELGraph graph: A BEL graph
:param list path: Path from root to sink node
:param dict relationship_dict: dictionary with relationship effects
:rtype: Effect... | python | {
"resource": ""
} |
q262150 | rank_edges | validation | def rank_edges(edges, edge_ranking=None):
"""Return the highest ranked edge from a multiedge.
:param dict edges: dictionary with all edges between two nodes
:param dict edge_ranking: A dictionary of {relationship: score}
:return: Highest ranked edge
:rtype: tuple: (edge id, relation, score given ra... | python | {
"resource": ""
} |
q262151 | group_nodes_by_annotation | validation | def group_nodes_by_annotation(graph: BELGraph, annotation: str = 'Subgraph') -> Mapping[str, Set[BaseEntity]]:
"""Group the nodes occurring in edges by the given annotation."""
result = defaultdict(set)
for u, v, d in graph.edges(data=True):
if not edge_has_annotation(d, annotation):
co... | python | {
"resource": ""
} |
q262152 | average_node_annotation | validation | def average_node_annotation(graph: BELGraph,
key: str,
annotation: str = 'Subgraph',
aggregator: Optional[Callable[[Iterable[X]], X]] = None,
) -> Mapping[str, X]:
"""Groups graph into subgraphs and assig... | python | {
"resource": ""
} |
q262153 | group_nodes_by_annotation_filtered | validation | def group_nodes_by_annotation_filtered(graph: BELGraph,
node_predicates: NodePredicates = None,
annotation: str = 'Subgraph',
) -> Mapping[str, Set[BaseEntity]]:
"""Group the nodes occurring in edges... | python | {
"resource": ""
} |
q262154 | build_expand_node_neighborhood_by_hash | validation | def build_expand_node_neighborhood_by_hash(manager: Manager) -> Callable[[BELGraph, BELGraph, str], None]:
"""Make an expand function that's bound to the manager."""
@uni_in_place_transformation
def expand_node_neighborhood_by_hash(universe: BELGraph, graph: BELGraph, node_hash: str) -> None:
"""Ex... | python | {
"resource": ""
} |
q262155 | build_delete_node_by_hash | validation | def build_delete_node_by_hash(manager: Manager) -> Callable[[BELGraph, str], None]:
"""Make a delete function that's bound to the manager."""
@in_place_transformation
def delete_node_by_hash(graph: BELGraph, node_hash: str) -> None:
"""Remove a node by identifier."""
node = manager.get_dsl_... | python | {
"resource": ""
} |
q262156 | bel_to_spia_matrices | validation | def bel_to_spia_matrices(graph: BELGraph) -> Mapping[str, pd.DataFrame]:
"""Create an excel sheet ready to be used in SPIA software.
:param graph: BELGraph
:return: dictionary with matrices
"""
index_nodes = get_matrix_index(graph)
spia_matrices = build_spia_matrices(index_nodes)
for u, v,... | python | {
"resource": ""
} |
q262157 | build_spia_matrices | validation | def build_spia_matrices(nodes: Set[str]) -> Dict[str, pd.DataFrame]:
"""Build an adjacency matrix for each KEGG relationship and return in a dictionary.
:param nodes: A set of HGNC gene symbols
:return: Dictionary of adjacency matrix for each relationship
"""
nodes = list(sorted(nodes))
# Crea... | python | {
"resource": ""
} |
q262158 | update_spia_matrices | validation | def update_spia_matrices(spia_matrices: Dict[str, pd.DataFrame],
u: CentralDogma,
v: CentralDogma,
edge_data: EdgeData,
) -> None:
"""Populate the adjacency matrix."""
if u.namespace.upper() != 'HGNC' or v.namesp... | python | {
"resource": ""
} |
q262159 | spia_matrices_to_excel | validation | def spia_matrices_to_excel(spia_matrices: Mapping[str, pd.DataFrame], path: str) -> None:
"""Export a SPIA data dictionary into an Excel sheet at the given path.
.. note::
# The R import should add the values:
# ["nodes"] from the columns
# ["title"] from the name of the file
#... | python | {
"resource": ""
} |
q262160 | spia_matrices_to_tsvs | validation | def spia_matrices_to_tsvs(spia_matrices: Mapping[str, pd.DataFrame], directory: str) -> None:
"""Export a SPIA data dictionary into a directory as several TSV documents."""
os.makedirs(directory, exist_ok=True)
for relation, df in spia_matrices.items():
df.to_csv(os.path.join(directory, f'{relation}... | python | {
"resource": ""
} |
q262161 | main | validation | def main(graph: BELGraph, xlsx: str, tsvs: str):
"""Export the graph to a SPIA Excel sheet."""
if not xlsx and not tsvs:
click.secho('Specify at least one option --xlsx or --tsvs', fg='red')
sys.exit(1)
spia_matrices = bel_to_spia_matrices(graph)
if xlsx:
spia_matrices_to_excel... | python | {
"resource": ""
} |
q262162 | overlay_data | validation | def overlay_data(graph: BELGraph,
data: Mapping[BaseEntity, Any],
label: Optional[str] = None,
overwrite: bool = False,
) -> None:
"""Overlays tabular data on the network
:param graph: A BEL Graph
:param data: A dictionary of {tuple node: ... | python | {
"resource": ""
} |
q262163 | overlay_type_data | validation | def overlay_type_data(graph: BELGraph,
data: Mapping[str, float],
func: str,
namespace: str,
label: Optional[str] = None,
overwrite: bool = False,
impute: Optional[float] = None,
... | python | {
"resource": ""
} |
q262164 | load_differential_gene_expression | validation | def load_differential_gene_expression(path: str,
gene_symbol_column: str = 'Gene.symbol',
logfc_column: str = 'logFC',
aggregator: Optional[Callable[[List[float]], float]] = None,
... | python | {
"resource": ""
} |
q262165 | get_merged_namespace_names | validation | def get_merged_namespace_names(locations, check_keywords=True):
"""Loads many namespaces and combines their names.
:param iter[str] locations: An iterable of URLs or file paths pointing to BEL namespaces.
:param bool check_keywords: Should all the keywords be the same? Defaults to ``True``
:return: A d... | python | {
"resource": ""
} |
q262166 | merge_namespaces | validation | def merge_namespaces(input_locations, output_path, namespace_name, namespace_keyword, namespace_domain, author_name,
citation_name, namespace_description=None, namespace_species=None, namespace_version=None,
namespace_query_url=None, namespace_created=None, author_contact=None,... | python | {
"resource": ""
} |
q262167 | run_rcr | validation | def run_rcr(graph, tag='dgxp'):
"""Run the reverse causal reasoning algorithm on a graph.
Steps:
1. Get all downstream controlled things into map (that have at least 4 downstream things)
2. calculate population of all things that are downstream controlled
.. note:: Assumes all nodes have been pre... | python | {
"resource": ""
} |
q262168 | export_namespace | validation | def export_namespace(graph, namespace, directory=None, cacheable=False):
"""Exports all names and missing names from the given namespace to its own BEL Namespace files in the given
directory.
Could be useful during quick and dirty curation, where planned namespace building is not a priority.
:param py... | python | {
"resource": ""
} |
q262169 | lint_file | validation | def lint_file(in_file, out_file=None):
"""Helps remove extraneous whitespace from the lines of a file
:param file in_file: A readable file or file-like
:param file out_file: A writable file or file-like
"""
for line in in_file:
print(line.strip(), file=out_file) | python | {
"resource": ""
} |
q262170 | lint_directory | validation | def lint_directory(source, target):
"""Adds a linted version of each document in the source directory to the target directory
:param str source: Path to directory to lint
:param str target: Path to directory to output
"""
for path in os.listdir(source):
if not path.endswith('.bel'):
... | python | {
"resource": ""
} |
q262171 | get_entrez_gene_data | validation | def get_entrez_gene_data(entrez_ids: Iterable[Union[str, int]]):
"""Get gene info from Entrez."""
url = PUBMED_GENE_QUERY_URL.format(','.join(str(x).strip() for x in entrez_ids))
response = requests.get(url)
tree = ElementTree.fromstring(response.content)
return {
element.attrib['uid']: {
... | python | {
"resource": ""
} |
q262172 | make_pubmed_gene_group | validation | def make_pubmed_gene_group(entrez_ids: Iterable[Union[str, int]]) -> Iterable[str]:
"""Builds a skeleton for gene summaries
:param entrez_ids: A list of Entrez Gene identifiers to query the PubMed service
:return: An iterator over statement lines for NCBI Entrez Gene summaries
"""
url = PUBMED_GENE... | python | {
"resource": ""
} |
q262173 | write_boilerplate | validation | def write_boilerplate(name: str,
version: Optional[str] = None,
description: Optional[str] = None,
authors: Optional[str] = None,
contact: Optional[str] = None,
copyright: Optional[str] = None,
... | python | {
"resource": ""
} |
q262174 | get_subgraph_by_node_search | validation | def get_subgraph_by_node_search(graph: BELGraph, query: Strings) -> BELGraph:
"""Get a sub-graph induced over all nodes matching the query string.
:param graph: A BEL Graph
:param query: A query string or iterable of query strings for node names
Thinly wraps :func:`search_node_names` and :func:`get_su... | python | {
"resource": ""
} |
q262175 | get_largest_component | validation | def get_largest_component(graph: BELGraph) -> BELGraph:
"""Get the giant component of a graph."""
biggest_component_nodes = max(nx.weakly_connected_components(graph), key=len)
return subgraph(graph, biggest_component_nodes) | python | {
"resource": ""
} |
q262176 | random_by_nodes | validation | def random_by_nodes(graph: BELGraph, percentage: Optional[float] = None) -> BELGraph:
"""Get a random graph by inducing over a percentage of the original nodes.
:param graph: A BEL graph
:param percentage: The percentage of edges to keep
"""
percentage = percentage or 0.9
assert 0 < percentage... | python | {
"resource": ""
} |
q262177 | random_by_edges | validation | def random_by_edges(graph: BELGraph, percentage: Optional[float] = None) -> BELGraph:
"""Get a random graph by keeping a certain percentage of original edges.
:param graph: A BEL graph
:param percentage: What percentage of eges to take
"""
percentage = percentage or 0.9
assert 0 < percentage <=... | python | {
"resource": ""
} |
q262178 | shuffle_node_data | validation | def shuffle_node_data(graph: BELGraph, key: str, percentage: Optional[float] = None) -> BELGraph:
"""Shuffle the node's data.
Useful for permutation testing.
:param graph: A BEL graph
:param key: The node data dictionary key
:param percentage: What percentage of possible swaps to make
"""
... | python | {
"resource": ""
} |
q262179 | shuffle_relations | validation | def shuffle_relations(graph: BELGraph, percentage: Optional[str] = None) -> BELGraph:
"""Shuffle the relations.
Useful for permutation testing.
:param graph: A BEL graph
:param percentage: What percentage of possible swaps to make
"""
percentage = percentage or 0.3
assert 0 < percentage <=... | python | {
"resource": ""
} |
q262180 | is_edge_consistent | validation | def is_edge_consistent(graph, u, v):
"""Check if all edges between two nodes have the same relation.
:param pybel.BELGraph graph: A BEL Graph
:param tuple u: The source BEL node
:param tuple v: The target BEL node
:return: If all edges from the source to target node have the same relation
:rtyp... | python | {
"resource": ""
} |
q262181 | rewire_targets | validation | def rewire_targets(graph, rewiring_probability):
"""Rewire a graph's edges' target nodes.
- For BEL graphs, assumes edge consistency (all edges between two given nodes are have the same relation)
- Doesn't make self-edges
:param pybel.BELGraph graph: A BEL graph
:param float rewiring_probability: ... | python | {
"resource": ""
} |
q262182 | self_edge_filter | validation | def self_edge_filter(_: BELGraph, source: BaseEntity, target: BaseEntity, __: str) -> bool:
"""Check if the source and target nodes are the same."""
return source == target | python | {
"resource": ""
} |
q262183 | has_protein_modification_increases_activity | validation | def has_protein_modification_increases_activity(graph: BELGraph,
source: BaseEntity,
target: BaseEntity,
key: str,
) -> bool:
... | python | {
"resource": ""
} |
q262184 | has_degradation_increases_activity | validation | def has_degradation_increases_activity(data: Dict) -> bool:
"""Check if the degradation of source causes activity of target."""
return part_has_modifier(data, SUBJECT, DEGRADATION) and part_has_modifier(data, OBJECT, ACTIVITY) | python | {
"resource": ""
} |
q262185 | has_translocation_increases_activity | validation | def has_translocation_increases_activity(data: Dict) -> bool:
"""Check if the translocation of source causes activity of target."""
return part_has_modifier(data, SUBJECT, TRANSLOCATION) and part_has_modifier(data, OBJECT, ACTIVITY) | python | {
"resource": ""
} |
q262186 | complex_has_member | validation | def complex_has_member(graph: BELGraph, complex_node: ComplexAbundance, member_node: BaseEntity) -> bool:
"""Does the given complex contain the member?"""
return any( # TODO can't you look in the members of the complex object (if it's enumerated)
v == member_node
for _, v, data in graph.out_edg... | python | {
"resource": ""
} |
q262187 | complex_increases_activity | validation | def complex_increases_activity(graph: BELGraph, u: BaseEntity, v: BaseEntity, key: str) -> bool:
"""Return if the formation of a complex with u increases the activity of v."""
return (
isinstance(u, (ComplexAbundance, NamedComplexAbundance)) and
complex_has_member(graph, u, v) and
part_h... | python | {
"resource": ""
} |
q262188 | find_activations | validation | def find_activations(graph: BELGraph):
"""Find edges that are A - A, meaning that some conditions in the edge best describe the interaction."""
for u, v, key, data in graph.edges(keys=True, data=True):
if u != v:
continue
bel = graph.edge_to_bel(u, v, data)
line = data.get(... | python | {
"resource": ""
} |
q262189 | summarize_edge_filter | validation | def summarize_edge_filter(graph: BELGraph, edge_predicates: EdgePredicates) -> None:
"""Print a summary of the number of edges passing a given set of filters."""
passed = count_passed_edge_filter(graph, edge_predicates)
print('{}/{} edges passed {}'.format(
passed, graph.number_of_edges(),
(... | python | {
"resource": ""
} |
q262190 | build_edge_data_filter | validation | def build_edge_data_filter(annotations: Mapping, partial_match: bool = True) -> EdgePredicate: # noqa: D202
"""Build a filter that keeps edges whose data dictionaries are super-dictionaries to the given dictionary.
:param annotations: The annotation query dict to match
:param partial_match: Should the quer... | python | {
"resource": ""
} |
q262191 | build_pmid_exclusion_filter | validation | def build_pmid_exclusion_filter(pmids: Strings) -> EdgePredicate:
"""Fail for edges with citations whose references are one of the given PubMed identifiers.
:param pmids: A PubMed identifier or list of PubMed identifiers to filter against
"""
if isinstance(pmids, str):
@edge_predicate
d... | python | {
"resource": ""
} |
q262192 | node_has_namespace | validation | def node_has_namespace(node: BaseEntity, namespace: str) -> bool:
"""Pass for nodes that have the given namespace."""
ns = node.get(NAMESPACE)
return ns is not None and ns == namespace | python | {
"resource": ""
} |
q262193 | node_has_namespaces | validation | def node_has_namespaces(node: BaseEntity, namespaces: Set[str]) -> bool:
"""Pass for nodes that have one of the given namespaces."""
ns = node.get(NAMESPACE)
return ns is not None and ns in namespaces | python | {
"resource": ""
} |
q262194 | get_cutoff | validation | def get_cutoff(value: float, cutoff: Optional[float] = None) -> int:
"""Assign if a value is greater than or less than a cutoff."""
cutoff = cutoff if cutoff is not None else 0
if value > cutoff:
return 1
if value < (-1 * cutoff):
return - 1
return 0 | python | {
"resource": ""
} |
q262195 | calculate_concordance_helper | validation | def calculate_concordance_helper(graph: BELGraph,
key: str,
cutoff: Optional[float] = None,
) -> Tuple[int, int, int, int]:
"""Help calculate network-wide concordance
Assumes data already annotated with given key... | python | {
"resource": ""
} |
q262196 | calculate_concordance | validation | def calculate_concordance(graph: BELGraph, key: str, cutoff: Optional[float] = None,
use_ambiguous: bool = False) -> float:
"""Calculates network-wide concordance.
Assumes data already annotated with given key
:param graph: A BEL graph
:param key: The node data dictionary key... | python | {
"resource": ""
} |
q262197 | one_sided | validation | def one_sided(value: float, distribution: List[float]) -> float:
"""Calculate the one-sided probability of getting a value more extreme than the distribution."""
assert distribution
return sum(value < element for element in distribution) / len(distribution) | python | {
"resource": ""
} |
q262198 | calculate_concordance_probability | validation | def calculate_concordance_probability(graph: BELGraph,
key: str,
cutoff: Optional[float] = None,
permutations: Optional[int] = None,
percentage: Optional[float] = None,... | python | {
"resource": ""
} |
q262199 | calculate_concordance_by_annotation | validation | def calculate_concordance_by_annotation(graph, annotation, key, cutoff=None):
"""Returns the concordance scores for each stratified graph based on the given annotation
:param pybel.BELGraph graph: A BEL graph
:param str annotation: The annotation to group by.
:param str key: The node data dictionary ke... | python | {
"resource": ""
} |
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