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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... |
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... |
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(... |
def pairwise(iterable):
"s -> (s0,s1), (s1,s2), (s2, s3), ..."
a, b = itt.tee(iterable)
next(b, None)
return zip(a, b) |
def rank_path(graph, path, edge_ranking=None):
"""Takes in a path (a list of nodes in the graph) and calculates a score
:param pybel.BELGraph graph: A BEL graph
:param list[tuple] path: A list of nodes in the path (includes terminal nodes)
:param dict edge_ranking: A dictionary of {relationship: score}... |
def find_root_in_path(graph, path_nodes):
"""Find the 'root' of the path -> The node with the lowest out degree, if multiple:
root is the one with the highest out degree among those with lowest out degree
:param pybel.BELGraph graph: A BEL Graph
:param list[tuple] path_nodes: A list of nodes i... |
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(),
(... |
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... |
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... |
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 |
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 |
def build_source_namespace_filter(namespaces: Strings) -> EdgePredicate:
"""Pass for edges whose source nodes have the given namespace or one of the given namespaces.
:param namespaces: The namespace or namespaces to filter by
"""
if isinstance(namespaces, str):
def source_namespace_filter(_, u... |
def build_target_namespace_filter(namespaces: Strings) -> EdgePredicate:
"""Only passes for edges whose target nodes have the given namespace or one of the given namespaces
:param namespaces: The namespace or namespaces to filter by
"""
if isinstance(namespaces, str):
def target_namespace_filte... |
def search_node_namespace_names(graph, query, namespace):
"""Search for nodes with the given namespace(s) and whose names containing a given string(s).
:param pybel.BELGraph graph: A BEL graph
:param query: The search query
:type query: str or iter[str]
:param namespace: The namespace(s) to filter
... |
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 |
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... |
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... |
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) |
def calculate_concordance_probability(graph: BELGraph,
key: str,
cutoff: Optional[float] = None,
permutations: Optional[int] = None,
percentage: Optional[float] = None,... |
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... |
def calculate_concordance_probability_by_annotation(graph, annotation, key, cutoff=None, permutations=None,
percentage=None,
use_ambiguous=False):
"""Returns the results of concordance analysis on each subgraph, ... |
def _get_drug_target_interactions(manager: Optional['bio2bel_drugbank.manager'] = None) -> Mapping[str, List[str]]:
"""Get a mapping from drugs to their list of gene."""
if manager is None:
import bio2bel_drugbank
manager = bio2bel_drugbank.Manager()
if not manager.is_populated():
m... |
def multi_run_epicom(graphs: Iterable[BELGraph], path: Union[None, str, TextIO]) -> None:
"""Run EpiCom analysis on many graphs."""
if isinstance(path, str):
with open(path, 'w') as file:
_multi_run_helper_file_wrapper(graphs, file)
else:
_multi_run_helper_file_wrapper(graphs, p... |
def main():
"""Convert the Alzheimer's and Parkinson's disease NeuroMMSig excel sheets to BEL."""
logging.basicConfig(level=logging.INFO)
log.setLevel(logging.INFO)
bms_base = get_bms_base()
neurommsig_base = get_neurommsig_base()
neurommsig_excel_dir = os.path.join(neurommsig_base, 'resources'... |
def remove_inconsistent_edges(graph: BELGraph) -> None:
"""Remove all edges between node pairs with inconsistent edges.
This is the all-or-nothing approach. It would be better to do more careful investigation of the evidences during
curation.
"""
for u, v in get_inconsistent_edges(graph):
e... |
def get_walks_exhaustive(graph, node, length):
"""Gets all walks under a given length starting at a given node
:param networkx.Graph graph: A graph
:param node: Starting node
:param int length: The length of walks to get
:return: A list of paths
:rtype: list[tuple]
"""
if 0 == length:
... |
def match_simple_metapath(graph, node, simple_metapath):
"""Matches a simple metapath starting at the given node
:param pybel.BELGraph graph: A BEL graph
:param tuple node: A BEL node
:param list[str] simple_metapath: A list of BEL Functions
:return: An iterable over paths from the node matching th... |
def build_database(manager: pybel.Manager, annotation_url: Optional[str] = None) -> None:
"""Build a database of scores for NeuroMMSig annotated graphs.
1. Get all networks that use the Subgraph annotation
2. run on each
"""
annotation_url = annotation_url or NEUROMMSIG_DEFAULT_URL
annotation ... |
def calculate_average_scores_on_graph(
graph: BELGraph,
key: Optional[str] = None,
tag: Optional[str] = None,
default_score: Optional[float] = None,
runs: Optional[int] = None,
use_tqdm: bool = False,
):
"""Calculate the scores over all biological processes in the sub... |
def calculate_average_scores_on_subgraphs(
subgraphs: Mapping[H, BELGraph],
key: Optional[str] = None,
tag: Optional[str] = None,
default_score: Optional[float] = None,
runs: Optional[int] = None,
use_tqdm: bool = False,
tqdm_kwargs: Optional[Mapping[str, Any]] = ... |
def workflow(
graph: BELGraph,
node: BaseEntity,
key: Optional[str] = None,
tag: Optional[str] = None,
default_score: Optional[float] = None,
runs: Optional[int] = None,
minimum_nodes: int = 1,
) -> List['Runner']:
"""Generate candidate mechanisms and run the ... |
def multirun(graph: BELGraph,
node: BaseEntity,
key: Optional[str] = None,
tag: Optional[str] = None,
default_score: Optional[float] = None,
runs: Optional[int] = None,
use_tqdm: bool = False,
) -> Iterable['Runner']:
"""Run ... |
def workflow_aggregate(graph: BELGraph,
node: BaseEntity,
key: Optional[str] = None,
tag: Optional[str] = None,
default_score: Optional[float] = None,
runs: Optional[int] = None,
agg... |
def workflow_all(graph: BELGraph,
key: Optional[str] = None,
tag: Optional[str] = None,
default_score: Optional[float] = None,
runs: Optional[int] = None,
) -> Mapping[BaseEntity, List[Runner]]:
"""Run the heat diffusion workflow a... |
def workflow_all_aggregate(graph: BELGraph,
key: Optional[str] = None,
tag: Optional[str] = None,
default_score: Optional[float] = None,
runs: Optional[int] = None,
aggregator: Optional... |
def calculate_average_score_by_annotation(
graph: BELGraph,
annotation: str,
key: Optional[str] = None,
runs: Optional[int] = None,
use_tqdm: bool = False,
) -> Mapping[str, float]:
"""For each sub-graph induced over the edges matching the annotation, calculate the average sc... |
def iter_leaves(self) -> Iterable[BaseEntity]:
"""Return an iterable over all nodes that are leaves.
A node is a leaf if either:
- it doesn't have any predecessors, OR
- all of its predecessors have a score in their data dictionaries
"""
for node in self.graph:
... |
def in_out_ratio(self, node: BaseEntity) -> float:
"""Calculate the ratio of in-degree / out-degree of a node."""
return self.graph.in_degree(node) / float(self.graph.out_degree(node)) |
def unscored_nodes_iter(self) -> BaseEntity:
"""Iterate over all nodes without a score."""
for node, data in self.graph.nodes(data=True):
if self.tag not in data:
yield node |
def get_random_edge(self):
"""This function should be run when there are no leaves, but there are still unscored nodes. It will introduce
a probabilistic element to the algorithm, where some edges are disregarded randomly to eventually get a score
for the network. This means that the score can b... |
def remove_random_edge(self):
"""Remove a random in-edge from the node with the lowest in/out degree ratio."""
u, v, k = self.get_random_edge()
log.log(5, 'removing %s, %s (%s)', u, v, k)
self.graph.remove_edge(u, v, k) |
def remove_random_edge_until_has_leaves(self) -> None:
"""Remove random edges until there is at least one leaf node."""
while True:
leaves = set(self.iter_leaves())
if leaves:
return
self.remove_random_edge() |
def score_leaves(self) -> Set[BaseEntity]:
"""Calculate the score for all leaves.
:return: The set of leaf nodes that were scored
"""
leaves = set(self.iter_leaves())
if not leaves:
log.warning('no leaves.')
return set()
for leaf in leaves:
... |
def run_with_graph_transformation(self) -> Iterable[BELGraph]:
"""Calculate scores for all leaves until there are none, removes edges until there are, and repeats until
all nodes have been scored. Also, yields the current graph at every step so you can make a cool animation
of how the graph chan... |
def done_chomping(self) -> bool:
"""Determines if the algorithm is complete by checking if the target node of this analysis has been scored
yet. Because the algorithm removes edges when it gets stuck until it is un-stuck, it is always guaranteed to
finish.
:return: Is the algorithm done... |
def get_final_score(self) -> float:
"""Return the final score for the target node.
:return: The final score for the target node
"""
if not self.done_chomping():
raise ValueError('algorithm has not yet completed')
return self.graph.nodes[self.target_node][self.tag] |
def calculate_score(self, node: BaseEntity) -> float:
"""Calculate the new score of the given node."""
score = (
self.graph.nodes[node][self.tag]
if self.tag in self.graph.nodes[node] else
self.default_score
)
for predecessor, _, d in self.graph.in_ed... |
def microcanonical_statistics_dtype(spanning_cluster=True):
"""
Return the numpy structured array data type for sample states
Helper function
Parameters
----------
spanning_cluster : bool, optional
Whether to detect a spanning cluster or not.
Defaults to ``True``.
Returns
... |
def bond_sample_states(
perc_graph, num_nodes, num_edges, seed, spanning_cluster=True,
auxiliary_node_attributes=None, auxiliary_edge_attributes=None,
spanning_sides=None,
**kwargs
):
'''
Generate successive sample states of the bond percolation model
This is a :ref:`generator function <pyt... |
def bond_microcanonical_statistics(
perc_graph, num_nodes, num_edges, seed,
spanning_cluster=True,
auxiliary_node_attributes=None, auxiliary_edge_attributes=None,
spanning_sides=None,
**kwargs
):
"""
Evolve a single run over all microstates (bond occupation numbers)
Return the cluster s... |
def canonical_statistics_dtype(spanning_cluster=True):
"""
The NumPy Structured Array type for canonical statistics
Helper function
Parameters
----------
spanning_cluster : bool, optional
Whether to detect a spanning cluster or not.
Defaults to ``True``.
Returns
------... |
def bond_canonical_statistics(
microcanonical_statistics,
convolution_factors,
**kwargs
):
"""
canonical cluster statistics for a single run and a single probability
Parameters
----------
microcanonical_statistics : ndarray
Return value of `bond_microcanonical_statistics`
... |
def canonical_averages_dtype(spanning_cluster=True):
"""
The NumPy Structured Array type for canonical averages over several
runs
Helper function
Parameters
----------
spanning_cluster : bool, optional
Whether to detect a spanning cluster or not.
Defaults to ``True``.
... |
def bond_initialize_canonical_averages(
canonical_statistics, **kwargs
):
"""
Initialize the canonical averages from a single-run cluster statistics
Parameters
----------
canonical_statistics : 1-D structured ndarray
Typically contains the canonical statistics for a range of values
... |
def bond_reduce(row_a, row_b):
"""
Reduce the canonical averages over several runs
This is a "true" reducer.
It is associative and commutative.
This is a wrapper around `simoa.stats.online_variance`.
Parameters
----------
row_a, row_b : structured ndarrays
Output of this funct... |
def finalized_canonical_averages_dtype(spanning_cluster=True):
"""
The NumPy Structured Array type for finalized canonical averages over
several runs
Helper function
Parameters
----------
spanning_cluster : bool, optional
Whether to detect a spanning cluster or not.
Default... |
def finalize_canonical_averages(
number_of_nodes, ps, canonical_averages, alpha,
):
"""
Finalize canonical averages
"""
spanning_cluster = (
(
'percolation_probability_mean' in
canonical_averages.dtype.names
) and
'percolation_probability_m2' in canon... |
def compare(graph: BELGraph, annotation: str = 'Subgraph') -> Mapping[str, Mapping[str, float]]:
"""Compare generated mechanisms to actual ones.
1. Generates candidate mechanisms for each biological process
2. Gets sub-graphs for all NeuroMMSig signatures
3. Make tanimoto similarity comparison for all ... |
def summarize_node_filter(graph: BELGraph, node_filters: NodePredicates) -> None:
"""Print a summary of the number of nodes passing a given set of filters.
:param graph: A BEL graph
:param node_filters: A node filter or list/tuple of node filters
"""
passed = count_passed_node_filter(graph, node_fi... |
def node_inclusion_filter_builder(nodes: Iterable[BaseEntity]) -> NodePredicate:
"""Build a filter that only passes on nodes in the given list.
:param nodes: An iterable of BEL nodes
"""
node_set = set(nodes)
def inclusion_filter(_: BELGraph, node: BaseEntity) -> bool:
"""Pass only for a n... |
def node_exclusion_filter_builder(nodes: Iterable[BaseEntity]) -> NodePredicate:
"""Build a filter that fails on nodes in the given list."""
node_set = set(nodes)
def exclusion_filter(_: BELGraph, node: BaseEntity) -> bool:
"""Pass only for a node that isn't in the enclosed node list
:retu... |
def function_exclusion_filter_builder(func: Strings) -> NodePredicate:
"""Build a filter that fails on nodes of the given function(s).
:param func: A BEL Function or list/set/tuple of BEL functions
"""
if isinstance(func, str):
def function_exclusion_filter(_: BELGraph, node: BaseEntity) -> boo... |
def function_namespace_inclusion_builder(func: str, namespace: Strings) -> NodePredicate:
"""Build a filter function for matching the given BEL function with the given namespace or namespaces.
:param func: A BEL function
:param namespace: The namespace to search by
"""
if isinstance(namespace, str)... |
def data_contains_key_builder(key: str) -> NodePredicate: # noqa: D202
"""Build a filter that passes only on nodes that have the given key in their data dictionary.
:param key: A key for the node's data dictionary
"""
def data_contains_key(_: BELGraph, node: BaseEntity) -> bool:
"""Pass only ... |
def variants_of(
graph: BELGraph,
node: Protein,
modifications: Optional[Set[str]] = None,
) -> Set[Protein]:
"""Returns all variants of the given node."""
if modifications:
return _get_filtered_variants_of(graph, node, modifications)
return {
v
for u, v, key... |
def get_variants_to_controllers(
graph: BELGraph,
node: Protein,
modifications: Optional[Set[str]] = None,
) -> Mapping[Protein, Set[Protein]]:
"""Get a mapping from variants of the given node to all of its upstream controllers."""
rv = defaultdict(set)
variants = variants_of(graph, ... |
def group_dict_set(iterator: Iterable[Tuple[A, B]]) -> Mapping[A, Set[B]]:
"""Make a dict that accumulates the values for each key in an iterator of doubles."""
d = defaultdict(set)
for key, value in iterator:
d[key].add(value)
return dict(d) |
def get_edge_relations(graph: BELGraph) -> Mapping[Tuple[BaseEntity, BaseEntity], Set[str]]:
"""Build a dictionary of {node pair: set of edge types}."""
return group_dict_set(
((u, v), d[RELATION])
for u, v, d in graph.edges(data=True)
) |
def count_unique_relations(graph: BELGraph) -> Counter:
"""Return a histogram of the different types of relations present in a graph.
Note: this operation only counts each type of edge once for each pair of nodes
"""
return Counter(itt.chain.from_iterable(get_edge_relations(graph).values())) |
def get_annotations_containing_keyword(graph: BELGraph, keyword: str) -> List[Mapping[str, str]]:
"""Get annotation/value pairs for values for whom the search string is a substring
:param graph: A BEL graph
:param keyword: Search for annotations whose values have this as a substring
"""
return [
... |
def count_annotation_values(graph: BELGraph, annotation: str) -> Counter:
"""Count in how many edges each annotation appears in a graph
:param graph: A BEL graph
:param annotation: The annotation to count
:return: A Counter from {annotation value: frequency}
"""
return Counter(iter_annotation_v... |
def count_annotation_values_filtered(graph: BELGraph,
annotation: str,
source_predicate: Optional[NodePredicate] = None,
target_predicate: Optional[NodePredicate] = None,
)... |
def pair_is_consistent(graph: BELGraph, u: BaseEntity, v: BaseEntity) -> Optional[str]:
"""Return if the edges between the given nodes are consistent, meaning they all have the same relation.
:return: If the edges aren't consistent, return false, otherwise return the relation type
"""
relations = {data... |
def get_contradictory_pairs(graph: BELGraph) -> Iterable[Tuple[BaseEntity, BaseEntity]]:
"""Iterates over contradictory node pairs in the graph based on their causal relationships
:return: An iterator over (source, target) node pairs that have contradictory causal edges
"""
for u, v in graph.edges(... |
def get_consistent_edges(graph: BELGraph) -> Iterable[Tuple[BaseEntity, BaseEntity]]:
"""Yield pairs of (source node, target node) for which all of their edges have the same type of relation.
:return: An iterator over (source, target) node pairs corresponding to edges with many inconsistent relations
"""
... |
def infer_missing_two_way_edges(graph):
"""Add edges to the graph when a two way edge exists, and the opposite direction doesn't exist.
Use: two way edges from BEL definition and/or axiomatic inverses of membership relations
:param pybel.BELGraph graph: A BEL graph
"""
for u, v, k, d in graph.edge... |
def infer_missing_backwards_edge(graph, u, v, k):
"""Add the same edge, but in the opposite direction if not already present.
:type graph: pybel.BELGraph
:type u: tuple
:type v: tuple
:type k: int
"""
if u in graph[v]:
for attr_dict in graph[v][u].values():
if attr_dict ... |
def enrich_internal_unqualified_edges(graph, subgraph):
"""Add the missing unqualified edges between entities in the subgraph that are contained within the full graph.
:param pybel.BELGraph graph: The full BEL graph
:param pybel.BELGraph subgraph: The query BEL subgraph
"""
for u, v in itt.combinat... |
def boilerplate(name, contact, description, pmids, version, copyright, authors, licenses, disclaimer, output):
"""Build a template BEL document with the given PubMed identifiers."""
from .document_utils import write_boilerplate
write_boilerplate(
name=name,
version=version,
descript... |
def serialize_namespaces(namespaces, connection: str, path, directory):
"""Parse a BEL document then serializes the given namespaces (errors and all) to the given directory."""
from .definition_utils import export_namespaces
graph = from_lines(path, manager=connection)
export_namespaces(namespaces, gra... |
def get_pmids(graph: BELGraph, output: TextIO):
"""Output PubMed identifiers from a graph to a stream."""
for pmid in get_pubmed_identifiers(graph):
click.echo(pmid, file=output) |
def getrowcount(self, window_name, object_name):
"""
Get count of rows in table object.
@param window_name: Window name to look for, either full name,
LDTP's name convention, or a Unix glob.
@type window_name: string
@param object_name: Object name to look for, either fu... |
def selectrow(self, window_name, object_name, row_text, partial_match=False):
"""
Select row
@param window_name: Window name to type in, either full name,
LDTP's name convention, or a Unix glob.
@type window_name: string
@param object_name: Object name to type in, either... |
def multiselect(self, window_name, object_name, row_text_list, partial_match=False):
"""
Select multiple row
@param window_name: Window name to type in, either full name,
LDTP's name convention, or a Unix glob.
@type window_name: string
@param object_name: Object name to... |
def selectrowpartialmatch(self, window_name, object_name, row_text):
"""
Select row partial match
@param window_name: Window name to type in, either full name,
LDTP's name convention, or a Unix glob.
@type window_name: string
@param object_name: Object name to type in, e... |
def selectrowindex(self, window_name, object_name, row_index):
"""
Select row index
@param window_name: Window name to type in, either full name,
LDTP's name convention, or a Unix glob.
@type window_name: string
@param object_name: Object name to type in, either full nam... |
def selectlastrow(self, window_name, object_name):
"""
Select last row
@param window_name: Window name to type in, either full name,
LDTP's name convention, or a Unix glob.
@type window_name: string
@param object_name: Object name to type in, either full name,
LD... |
def getcellvalue(self, window_name, object_name, row_index, column=0):
"""
Get cell value
@param window_name: Window name to type in, either full name,
LDTP's name convention, or a Unix glob.
@type window_name: string
@param object_name: Object name to type in, either fu... |
def gettablerowindex(self, window_name, object_name, row_text):
"""
Get table row index matching given text
@param window_name: Window name to type in, either full name,
LDTP's name convention, or a Unix glob.
@type window_name: string
@param object_name: Object name to ... |
def doubleclickrow(self, window_name, object_name, row_text):
"""
Double click row matching given text
@param window_name: Window name to type in, either full name,
LDTP's name convention, or a Unix glob.
@type window_name: string
@param object_name: Object name to type ... |
def doubleclickrowindex(self, window_name, object_name, row_index, col_index=0):
"""
Double click row matching given text
@param window_name: Window name to type in, either full name,
LDTP's name convention, or a Unix glob.
@type window_name: string
@param object_name: O... |
def verifytablecell(self, window_name, object_name, row_index,
column_index, row_text):
"""
Verify table cell value with given text
@param window_name: Window name to type in, either full name,
LDTP's name convention, or a Unix glob.
@type window_name: st... |
def doesrowexist(self, window_name, object_name, row_text,
partial_match=False):
"""
Verify table cell value with given text
@param window_name: Window name to type in, either full name,
LDTP's name convention, or a Unix glob.
@type window_name: string
... |
def verifypartialtablecell(self, window_name, object_name, row_index,
column_index, row_text):
"""
Verify partial table cell value
@param window_name: Window name to type in, either full name,
LDTP's name convention, or a Unix glob.
@type window_na... |
def getapplist(self):
"""
Get all accessibility application name that are currently running
@return: list of appliction name of string type on success.
@rtype: list
"""
app_list = []
# Update apps list, before parsing the list
self._update_apps()
... |
def startprocessmonitor(self, process_name, interval=2):
"""
Start memory and CPU monitoring, with the time interval between
each process scan
@param process_name: Process name, ex: firefox-bin.
@type process_name: string
@param interval: Time interval between each proce... |
def stopprocessmonitor(self, process_name):
"""
Stop memory and CPU monitoring
@param process_name: Process name, ex: firefox-bin.
@type process_name: string
@return: 1 on success
@rtype: integer
"""
if process_name in self._process_stats:
# ... |
def getcpustat(self, process_name):
"""
get CPU stat for the give process name
@param process_name: Process name, ex: firefox-bin.
@type process_name: string
@return: cpu stat list on success, else empty list
If same process name, running multiple instance,
... |
def getmemorystat(self, process_name):
"""
get memory stat
@param process_name: Process name, ex: firefox-bin.
@type process_name: string
@return: memory stat list on success, else empty list
If same process name, running multiple instance,
get t... |
def getobjectlist(self, window_name):
"""
Get list of items in given GUI.
@param window_name: Window name to look for, either full name,
LDTP's name convention, or a Unix glob.
@type window_name: string
@return: list of items in LDTP naming convention.
@rtype: l... |
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