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values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1
value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | TrimmedTraceGraph._populate_issues_from_affected_conditions | Helper for populating reachable issue instances from the initial
pre/postconditions. Also populates conditions/traces reachable from
these instances. Traces are populated only in the direction this is
called from: i.e. if initial_conditions are preconditions, only the
backward trace is p... | sapp/sapp/trimmed_trace_graph.py | def _populate_issues_from_affected_conditions(
self,
initial_conditions,
graph: TraceGraph,
get_issue_instances_from_condition_id,
get_condition_parent,
get_instance_leaves,
get_condition_leaves,
add_instance_condition_assoc,
add_condition,
... | def _populate_issues_from_affected_conditions(
self,
initial_conditions,
graph: TraceGraph,
get_issue_instances_from_condition_id,
get_condition_parent,
get_instance_leaves,
get_condition_leaves,
add_instance_condition_assoc,
add_condition,
... | [
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train | TrimmedTraceGraph._populate_issue_and_traces | Copies an issue over from the given trace graph, including all its
traces and assocs. | sapp/sapp/trimmed_trace_graph.py | def _populate_issue_and_traces(self, graph: TraceGraph, instance_id: int) -> None:
""" Copies an issue over from the given trace graph, including all its
traces and assocs.
"""
self._populate_issue(graph, instance_id)
self._populate_issue_trace(graph, instance_id) | def _populate_issue_and_traces(self, graph: TraceGraph, instance_id: int) -> None:
""" Copies an issue over from the given trace graph, including all its
traces and assocs.
"""
self._populate_issue(graph, instance_id)
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train | TrimmedTraceGraph._populate_issue | Adds an issue to the trace graph along with relevant information
pertaining to the issue (e.g. instance, fix_info, sources/sinks)
The issue is identified by its corresponding instance's ID in the input
trace graph. | sapp/sapp/trimmed_trace_graph.py | def _populate_issue(self, graph: TraceGraph, instance_id: int) -> None:
"""Adds an issue to the trace graph along with relevant information
pertaining to the issue (e.g. instance, fix_info, sources/sinks)
The issue is identified by its corresponding instance's ID in the input
trace graph... | def _populate_issue(self, graph: TraceGraph, instance_id: int) -> None:
"""Adds an issue to the trace graph along with relevant information
pertaining to the issue (e.g. instance, fix_info, sources/sinks)
The issue is identified by its corresponding instance's ID in the input
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train | TrimmedTraceGraph._populate_trace | Populates (from the given trace graph) the forward and backward
traces reachable from the given traces (including input trace frames).
Make sure to respect trace kind in successors | sapp/sapp/trimmed_trace_graph.py | def _populate_trace(self, graph: TraceGraph, trace_frame_ids: List[int]) -> None:
""" Populates (from the given trace graph) the forward and backward
traces reachable from the given traces (including input trace frames).
Make sure to respect trace kind in successors
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while len... | def _populate_trace(self, graph: TraceGraph, trace_frame_ids: List[int]) -> None:
""" Populates (from the given trace graph) the forward and backward
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train | TrimmedTraceGraph._add_trace_frame | Copies the trace frame from 'graph' to this (self) graph.
Also copies all the trace_frame-leaf assocs since we don't
know which ones are needed until we know the issue that reaches it | sapp/sapp/trimmed_trace_graph.py | def _add_trace_frame(self, graph: TraceGraph, trace_frame: TraceFrame) -> None:
""" Copies the trace frame from 'graph' to this (self) graph.
Also copies all the trace_frame-leaf assocs since we don't
know which ones are needed until we know the issue that reaches it
"""
trace_fr... | def _add_trace_frame(self, graph: TraceGraph, trace_frame: TraceFrame) -> None:
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Also copies all the trace_frame-leaf assocs since we don't
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train | _compute_symbolic_link_mapping | Given a shared analysis directory, produce a mapping from actual source files
to files contained within this directory. Only includes files which have
one of the provided extensions.
Watchman watches actual source files, so when a change is detected to a
file, this mapping can be used t... | client/filesystem.py | def _compute_symbolic_link_mapping(
directory: str, extensions: Iterable[str]
) -> Dict[str, str]:
"""
Given a shared analysis directory, produce a mapping from actual source files
to files contained within this directory. Only includes files which have
one of the provided extensions.
... | def _compute_symbolic_link_mapping(
directory: str, extensions: Iterable[str]
) -> Dict[str, str]:
"""
Given a shared analysis directory, produce a mapping from actual source files
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train | acquire_lock | Raises an OSError if the lock can't be acquired | client/filesystem.py | def acquire_lock(path: str, blocking: bool) -> Generator[Optional[int], None, None]:
"""Raises an OSError if the lock can't be acquired"""
try:
with open(path, "w+") as lockfile:
if not blocking:
lock_command = fcntl.LOCK_EX | fcntl.LOCK_NB
else:
l... | def acquire_lock(path: str, blocking: bool) -> Generator[Optional[int], None, None]:
"""Raises an OSError if the lock can't be acquired"""
try:
with open(path, "w+") as lockfile:
if not blocking:
lock_command = fcntl.LOCK_EX | fcntl.LOCK_NB
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train | AnalysisDirectory.process_updated_files | Process a list of paths which were added/removed/updated, making any
necessary changes to the directory:
- For an AnalysisDirectory, nothing needs to be changed, since
the mapping from source file to analysis file is 1:1.
- For a SharedAnalysisDirectory, the... | client/filesystem.py | def process_updated_files(self, paths: List[str]) -> List[str]:
"""
Process a list of paths which were added/removed/updated, making any
necessary changes to the directory:
- For an AnalysisDirectory, nothing needs to be changed, since
the mapping from s... | def process_updated_files(self, paths: List[str]) -> List[str]:
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Process a list of paths which were added/removed/updated, making any
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- For an AnalysisDirectory, nothing needs to be changed, since
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Result also includes any files which are within a tracked directory.
This method will remove/add symbolic links for deleted/new files. | client/filesystem.py | def process_updated_files(self, paths: List[str]) -> List[str]:
"""
Return the paths in the analysis directory (symbolic links)
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Result also includes any files which are within a tracked directory.
This method will remove/add symb... | def process_updated_files(self, paths: List[str]) -> List[str]:
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Return the paths in the analysis directory (symbolic links)
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train | Filesystem.list | Return the list of files that match any of the patterns within root.
If exclude is provided, files that match an exclude pattern are omitted.
Note: The `find` command does not understand globs properly.
e.g. 'a/*.py' will match 'a/b/c.py'
For this reason, avoid calli... | client/filesystem.py | def list(
self, root: str, patterns: List[str], exclude: Optional[List[str]] = None
) -> List[str]:
"""
Return the list of files that match any of the patterns within root.
If exclude is provided, files that match an exclude pattern are omitted.
Note: The `find` ... | def list(
self, root: str, patterns: List[str], exclude: Optional[List[str]] = None
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train | ModelGenerator._compute_callables_count | Iterate over all issues and count the number of times each callable
is seen. | sapp/sapp/model_generator.py | def _compute_callables_count(self, iters: Dict[str, Any]):
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count = dict.fromkeys([issue["callable"] for issue in iters["issues"]], 0)
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count = dict.fromkeys([issue["callable"] for issue in iters["issues"]], 0)
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train | ModelGenerator._create_empty_run | setting boilerplate when creating a Run object | sapp/sapp/model_generator.py | def _create_empty_run(
self, status=RunStatus.FINISHED, status_description=None
) -> Run:
"""setting boilerplate when creating a Run object"""
run = Run(
job_id=self.summary["job_id"],
issue_instances=[],
date=datetime.datetime.now(),
status=st... | def _create_empty_run(
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run = Run(
job_id=self.summary["job_id"],
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train | ModelGenerator._generate_issue | Insert the issue instance into a run. This includes creating (for
new issues) or finding (for existing issues) Issue objects to associate
with the instances.
Also create sink entries and associate related issues | sapp/sapp/model_generator.py | def _generate_issue(self, run, entry, callablesCount):
"""Insert the issue instance into a run. This includes creating (for
new issues) or finding (for existing issues) Issue objects to associate
with the instances.
Also create sink entries and associate related issues"""
trace_... | def _generate_issue(self, run, entry, callablesCount):
"""Insert the issue instance into a run. This includes creating (for
new issues) or finding (for existing issues) Issue objects to associate
with the instances.
Also create sink entries and associate related issues"""
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train | HistoryContext.get_history_without_ipython_calls | Remove all lines in execution history added by iPython calls. | client/pyre_ipython.py | def get_history_without_ipython_calls(self):
"""
Remove all lines in execution history added by iPython calls.
"""
line_count = 0
filtered_history = []
for line in self.history:
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... | def get_history_without_ipython_calls(self):
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Remove all lines in execution history added by iPython calls.
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line_count = 0
filtered_history = []
for line in self.history:
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train | DatabaseSaver._prep_save | Prepares the bulk saver to load the trace graph info into the
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train | DatabaseSaver._save | Saves bulk saver's info into the databases in bulk. | sapp/sapp/database_saver.py | def _save(self) -> RunSummary:
""" Saves bulk saver's info into the databases in bulk.
"""
assert self.summary["run"] is not None, "Must have called process before"
trace_frames = self.bulk_saver.get_items_to_add(TraceFrame)
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train | Parser._parse_leaves | Returns a list of pairs (leaf_name, distance) | sapp/sapp/pysa_taint_parser.py | def _parse_leaves(self, leaves) -> List[Tuple[str, int]]:
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return [(self._leaf_name(leaf), 0) for leaf in leaves] | def _parse_leaves(self, leaves) -> List[Tuple[str, int]]:
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train | AnalysisOutput.file_handles | Generates all file handles represented by the analysis.
Callee owns file handle and closes it when the next is yielded or the
generator ends. | sapp/sapp/analysis_output.py | def file_handles(self) -> Iterable[IO[str]]:
"""Generates all file handles represented by the analysis.
Callee owns file handle and closes it when the next is yielded or the
generator ends.
"""
if self.file_handle:
yield self.file_handle
self.file_handle.c... | def file_handles(self) -> Iterable[IO[str]]:
"""Generates all file handles represented by the analysis.
Callee owns file handle and closes it when the next is yielded or the
generator ends.
"""
if self.file_handle:
yield self.file_handle
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train | AnalysisOutput.file_names | Generates all file names that are used to generate file_handles. | sapp/sapp/analysis_output.py | def file_names(self) -> Iterable[str]:
"""Generates all file names that are used to generate file_handles.
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train | PrepareMixin.prepare | This is called immediately before the items are written to the
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session: Session object for querying the DB.
items: Iterator of items to be added to the DB.
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session: Session object for querying the DB.
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train | SourceLocationType.process_result_value | SQLAlchemy uses this to convert a string into a SourceLocation object.
We separate the fields by a | | sapp/sapp/models.py | def process_result_value(self, value, dialect):
"""
SQLAlchemy uses this to convert a string into a SourceLocation object.
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"""
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return None
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train | PrimaryKeyGenerator.reserve | session - Session for DB operations.
saving_classes - class objects that need to be saved e.g. Issue, Run
item_counts - map from class name to the number of items, for preallocating
id ranges | sapp/sapp/models.py | def reserve(
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train | BaseParser.analysis_output_to_dict_entries | Here we take input generators and return a dict with issues,
preconditions, and postconditions separated. If there is only a single
generator file, it's simple. If we also pass in a generator from a
previous inputfile then there are a couple extra steps:
1. If an issue was seen in the p... | sapp/sapp/base_parser.py | def analysis_output_to_dict_entries(
self,
inputfile: AnalysisOutput,
previous_inputfile: Optional[AnalysisOutput],
previous_issue_handles: Optional[AnalysisOutput],
linemapfile: Optional[str],
) -> DictEntries:
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linemapfile: Optional[str],
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train | BaseParser.compute_diff_handle | Uses the absolute line and ignores the callable/character offsets.
Used only in determining whether new issues are old issues. | sapp/sapp/base_parser.py | def compute_diff_handle(filename, old_line, code):
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Used only in determining whether new issues are old issues.
"""
key = "{filename}:{old_line}:{code}".format(
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key = "{filename}:{old_line}:{code}".format(
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train | is_capable_terminal | Determine whether we are connected to a capable terminal. | client/__init__.py | def is_capable_terminal(file=sys.stderr) -> bool:
"""
Determine whether we are connected to a capable terminal.
"""
if not os.isatty(file.fileno()):
return False
terminal = os.getenv("TERM", "dumb")
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return terminal not in ["dumb", "emacs"] | def is_capable_terminal(file=sys.stderr) -> bool:
"""
Determine whether we are connected to a capable terminal.
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if not os.isatty(file.fileno()):
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train | _find_directory_upwards | Walk directories upwards from base, until the root directory is
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train | FastBuckBuilder.compute_targets_to_build | Compute the set of targets to build for the given targets using a
breadth-first traversal. | client/buck_project_builder/__init__.py | def compute_targets_to_build(self, targets: Iterable[str]) -> Iterable[BuildTarget]:
"""
Compute the set of targets to build for the given targets using a
breadth-first traversal.
"""
normalized_targets = set()
for target in targets:
normalized_targets... | def compute_targets_to_build(self, targets: Iterable[str]) -> Iterable[BuildTarget]:
"""
Compute the set of targets to build for the given targets using a
breadth-first traversal.
"""
normalized_targets = set()
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train | FastBuckBuilder.compute_reverse_dependencies | Compute the set of targets which depend on each target. | client/buck_project_builder/__init__.py | def compute_reverse_dependencies(
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"""
Compute the set of targets which depend on each target.
"""
result = defaultdict(list)
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train | Interactive.analysis_output | Sets the location of the output from the static analysis tool.
Parameters:
location: str Filesystem location for the results. | sapp/sapp/interactive.py | def analysis_output(self, location: Optional[str] = None) -> None:
"""Sets the location of the output from the static analysis tool.
Parameters:
location: str Filesystem location for the results.
"""
try:
if not location:
location = self.prompt(... | def analysis_output(self, location: Optional[str] = None) -> None:
"""Sets the location of the output from the static analysis tool.
Parameters:
location: str Filesystem location for the results.
"""
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train | Interactive.issue | Select an issue.
Parameters:
issue_instance_id: int id of the issue instance to select
Note: We are selecting issue instances, even though the command is called
issue. | sapp/sapp/interactive.py | def issue(self, issue_instance_id):
"""Select an issue.
Parameters:
issue_instance_id: int id of the issue instance to select
Note: We are selecting issue instances, even though the command is called
issue.
"""
with self.db.make_session() as session:
... | def issue(self, issue_instance_id):
"""Select an issue.
Parameters:
issue_instance_id: int id of the issue instance to select
Note: We are selecting issue instances, even though the command is called
issue.
"""
with self.db.make_session() as session:
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train | Interactive.show | More details about the selected issue or trace frame. | sapp/sapp/interactive.py | def show(self):
""" More details about the selected issue or trace frame.
"""
self._verify_entrypoint_selected()
if self.current_issue_instance_id != -1:
self._show_current_issue_instance()
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self._show_current_issue_instance()
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train | Interactive.issues | Lists issues for the selected run.
Parameters (all optional):
use_pager: bool use a unix style pager for output
codes: int or list[int] issue codes to filter on
callables: str or list[str] callables to filter on (supports wildcards)
filen... | sapp/sapp/interactive.py | def issues(
self,
use_pager: bool = None,
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callables: Optional[Union[str, List[str]]] = None,
filenames: Optional[Union[str, List[str]]] = None,
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self,
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*,
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callables: Optional[Union[str, List[str]]] = None,
filenames: Optional[Union[str, List[str]]] = None,
):
"""Lists issues for the selected run.
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train | Interactive.frames | Display trace frames independent of the current issue.
Parameters (all optional):
callers: str or list[str] filter traces by this caller name
callees: str or list[str] filter traces by this callee name
kind: precondition|postcondition the type of tra... | sapp/sapp/interactive.py | def frames(
self,
*,
callers: Optional[Union[str, List[str]]] = None,
callees: Optional[Union[str, List[str]]] = None,
kind: Optional[TraceKind] = None,
limit: Optional[int] = 10,
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"""Display trace frames independent of the current issue.
Parameters... | def frames(
self,
*,
callers: Optional[Union[str, List[str]]] = None,
callees: Optional[Union[str, List[str]]] = None,
kind: Optional[TraceKind] = None,
limit: Optional[int] = 10,
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train | Interactive.next_cursor_location | Move cursor to the next trace frame. | sapp/sapp/interactive.py | def next_cursor_location(self):
"""Move cursor to the next trace frame.
"""
self._verify_entrypoint_selected()
self.current_trace_frame_index = min(
self.current_trace_frame_index + 1, len(self.trace_tuples) - 1
)
self.trace() | def next_cursor_location(self):
"""Move cursor to the next trace frame.
"""
self._verify_entrypoint_selected()
self.current_trace_frame_index = min(
self.current_trace_frame_index + 1, len(self.trace_tuples) - 1
)
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train | Interactive.prev_cursor_location | Move cursor to the previous trace frame. | sapp/sapp/interactive.py | def prev_cursor_location(self):
"""Move cursor to the previous trace frame.
"""
self._verify_entrypoint_selected()
self.current_trace_frame_index = max(self.current_trace_frame_index - 1, 0)
self.trace() | def prev_cursor_location(self):
"""Move cursor to the previous trace frame.
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self._verify_entrypoint_selected()
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train | Interactive.jump | Jump to a specific trace frame in a trace.
Parameters:
selected_number: int the trace frame number from trace output | sapp/sapp/interactive.py | def jump(self, selected_number: int) -> None:
"""Jump to a specific trace frame in a trace.
Parameters:
selected_number: int the trace frame number from trace output
"""
self._verify_entrypoint_selected()
if selected_number < 1 or selected_number > len(self.trace_... | def jump(self, selected_number: int) -> None:
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train | Interactive.branch | Show and select branches for a branched trace.
- [*] signifies the current branch that is selected
- will prompt for branch selection if called with no argument
- will automatically select a branch if called with an argument
Parameters (optional):
selected_number: int bra... | sapp/sapp/interactive.py | def branch(self, selected_number: Optional[int] = None) -> None:
"""Show and select branches for a branched trace.
- [*] signifies the current branch that is selected
- will prompt for branch selection if called with no argument
- will automatically select a branch if called with an argu... | def branch(self, selected_number: Optional[int] = None) -> None:
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train | Interactive.list_source_code | Show source code around the current trace frame location.
Parameters:
context: int number of lines to show above and below trace location
(default: 5) | sapp/sapp/interactive.py | def list_source_code(self, context: int = 5) -> None:
"""Show source code around the current trace frame location.
Parameters:
context: int number of lines to show above and below trace location
(default: 5)
"""
self._verify_entrypoint_selected... | def list_source_code(self, context: int = 5) -> None:
"""Show source code around the current trace frame location.
Parameters:
context: int number of lines to show above and below trace location
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train | Interactive.details | Show additional info about the current trace frame.
Parameters (all optional):
limit: int number of related post/pre conditions to show
(pass limit=None to show all) | sapp/sapp/interactive.py | def details(
self, *, limit: Optional[int] = 5, kind: Optional[TraceKind] = None
) -> None:
"""Show additional info about the current trace frame.
Parameters (all optional):
limit: int number of related post/pre conditions to show
(pass limit=None to... | def details(
self, *, limit: Optional[int] = 5, kind: Optional[TraceKind] = None
) -> None:
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Parameters (all optional):
limit: int number of related post/pre conditions to show
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train | Interactive._group_trace_frames | Buckets together trace frames that have the same caller:caller_port. | sapp/sapp/interactive.py | def _group_trace_frames(
self, trace_frames: Iterable[TraceFrameQueryResult], limit: int
) -> Dict[Tuple[str, str], List[TraceFrameQueryResult]]:
"""Buckets together trace frames that have the same caller:caller_port.
"""
# pyre-fixme[9]: caller_buckets has type `DefaultDict[Tuple[st... | def _group_trace_frames(
self, trace_frames: Iterable[TraceFrameQueryResult], limit: int
) -> Dict[Tuple[str, str], List[TraceFrameQueryResult]]:
"""Buckets together trace frames that have the same caller:caller_port.
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train | Interactive._next_trace_frames | Finds all trace frames that the given trace_frame flows to.
When backwards=True, the result will include the parameter trace_frame,
since we are filtering on the parameter's callee. | sapp/sapp/interactive.py | def _next_trace_frames(
self,
session: Session,
trace_frame: TraceFrameQueryResult,
visited_ids: Set[int],
backwards: bool = False,
) -> List[TraceFrameQueryResult]:
"""Finds all trace frames that the given trace_frame flows to.
When backwards=True, the resul... | def _next_trace_frames(
self,
session: Session,
trace_frame: TraceFrameQueryResult,
visited_ids: Set[int],
backwards: bool = False,
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"""Finds all trace frames that the given trace_frame flows to.
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train | Interactive.callable | Show the name of the current callable in the trace | sapp/sapp/interactive.py | def callable(self) -> Optional[str]:
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train | Interactive._get_callable_from_trace_tuple | Returns either (caller, caller_port) or (callee, callee_port). | sapp/sapp/interactive.py | def _get_callable_from_trace_tuple(
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train | require_option | Throw an exception if an option wasn't required. This is useful when its
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train | default_database | Try to guess a reasonable database name by looking at the repository path | sapp/sapp/cli_lib.py | def default_database(ctx: click.Context, _param: Parameter, value: Optional[str]):
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train | lint | Output DB models in a lint-friendly format | sapp/sapp/lint.py | def lint(click_ctx: click.Context, run_id: int, filenames: List[str]) -> None:
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train | resolve_source_mapping | Returns a mapping from absolute source path to absolute output path as specified
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train | resolve_relative_paths | Query buck to obtain a mapping from each absolute path to the relative
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"""
Query buck to obtain a mapping from each absolute path to the relative
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train | SimpleBuckBuilder.build | Shell out to buck to build the targets, then yield the paths to the
link trees. | client/buck.py | def build(self, targets: Iterable[str]) -> Iterable[str]:
"""
Shell out to buck to build the targets, then yield the paths to the
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"""
return generate_source_directories(
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train | FunctionStub._get_parameter_string | Depending on if an argument has a type, the style for default values
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def fun(x=5)
def fun(x : int = 5) | client/infer.py | def _get_parameter_string(self) -> str:
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train | FunctionStub.is_complete | Determines if a stub completely types a function | client/infer.py | def is_complete(self) -> bool:
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train | StubFile.to_string | We currently ignore nested classes, i.e.:
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train | download | Download a trained weights, config and preprocessor.
Args:
url (str): target url. | anago/utils.py | def download(url):
"""Download a trained weights, config and preprocessor.
Args:
url (str): target url.
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base_dir = os.path.dirname(filepath)
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url (str): target url.
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encoding (str): file encoding format.
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filename (str): path to the file.
encoding (str): file encoding format.
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encoding (str): file encoding format.
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train | filter_embeddings | Loads word vectors in numpy array.
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embeddings (dict): a dictionary of numpy array.
vocab (dict): word_index lookup table.
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vocab (dict): word_index lookup table.
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numpy array: an array of word embeddings.
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embeddings (dict): a dictionary of numpy array.
vocab (dict): word_index lookup table.
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numpy array: an array of word embeddings.
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train | load_glove | Loads GloVe vectors in numpy array.
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file (str): a path to a glove file.
Return:
dict: a dict of numpy arrays. | anago/utils.py | def load_glove(file):
"""Loads GloVe vectors in numpy array.
Args:
file (str): a path to a glove file.
Return:
dict: a dict of numpy arrays.
"""
model = {}
with open(file, encoding="utf8", errors='ignore') as f:
for line in f:
line = line.split(' ')
... | def load_glove(file):
"""Loads GloVe vectors in numpy array.
Args:
file (str): a path to a glove file.
Return:
dict: a dict of numpy arrays.
"""
model = {}
with open(file, encoding="utf8", errors='ignore') as f:
for line in f:
line = line.split(' ')
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train | Vocabulary.add_token | Add token to vocabulary.
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"""Add token to vocabulary.
Args:
token (str): token to add.
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token (str): token to add.
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train | Vocabulary.add_documents | Update dictionary from a collection of documents. Each document is a list
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docs (list): documents to add.
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train | Vocabulary.doc2id | Get the list of token_id given doc.
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doc (list): document.
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"""Get the list of token_id given doc.
Args:
doc (list): document.
Returns:
list: int id of doc.
"""
doc = map(self.process_token, doc)
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train | Vocabulary.build | Build vocabulary. | anago/utils.py | def build(self):
"""
Build vocabulary.
"""
token_freq = self._token_count.most_common(self._max_size)
idx = len(self.vocab)
for token, _ in token_freq:
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self._id2token.append(token)
idx += 1
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Build vocabulary.
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token_freq = self._token_count.most_common(self._max_size)
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train | Vocabulary.token_to_id | Get the token_id of given token.
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token (str): token from vocabulary.
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Args:
token (str): token from vocabulary.
Returns:
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"""
token = self.process_token(token)
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train | Tagger.predict_proba | Probability estimates.
The returned estimates for all classes are ordered by the
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Args:
text : string, the input text.
Returns:
y : array-like, shape = [num_words, num_classes]
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The returned estimates for all classes are ordered by the
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Args:
text : string, the input text.
Returns:
y : array-like, shape = [num_words, num_classes]
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Args:
text : string, the input text.
Returns:
y : array-like, shape = [num_words, num_classes]
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train | Tagger.analyze | Analyze text and return pretty format.
Args:
text: string, the input text.
Returns:
res: dict.
Examples:
>>> text = 'President Obama is speaking at the White House.'
>>> model.analyze(text)
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"""Analyze text and return pretty format.
Args:
text: string, the input text.
Returns:
res: dict.
Examples:
>>> text = 'President Obama is speaking at the White House.'
>>> model.analyze(text)
{
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text: string, the input text.
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res: dict.
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>>> text = 'President Obama is speaking at the White House.'
>>> model.analyze(text)
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train | Tagger.predict | Predict using the model.
Args:
text: string, the input text.
Returns:
tags: list, shape = (num_words,)
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Args:
text: string, the input text.
Returns:
tags: list, shape = (num_words,)
Returns predicted values.
"""
pred = self.predict_proba(text)
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Args:
text: string, the input text.
Returns:
tags: list, shape = (num_words,)
Returns predicted values.
"""
pred = self.predict_proba(text)
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train | pad_nested_sequences | Pads nested sequences to the same length.
This function transforms a list of list sequences
into a 3D Numpy array of shape `(num_samples, max_sent_len, max_word_len)`.
Args:
sequences: List of lists of lists.
dtype: Type of the output sequences.
# Returns
x: Numpy array. | anago/preprocessing.py | def pad_nested_sequences(sequences, dtype='int32'):
"""Pads nested sequences to the same length.
This function transforms a list of list sequences
into a 3D Numpy array of shape `(num_samples, max_sent_len, max_word_len)`.
Args:
sequences: List of lists of lists.
dtype: Type of the out... | def pad_nested_sequences(sequences, dtype='int32'):
"""Pads nested sequences to the same length.
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sequences: List of lists of lists.
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train | IndexTransformer.fit | Learn vocabulary from training set.
Args:
X : iterable. An iterable which yields either str, unicode or file objects.
Returns:
self : IndexTransformer. | anago/preprocessing.py | def fit(self, X, y):
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Args:
X : iterable. An iterable which yields either str, unicode or file objects.
Returns:
self : IndexTransformer.
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self._word_vocab.add_documents(X)
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X : iterable. An iterable which yields either str, unicode or file objects.
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self : IndexTransformer.
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X : iterable
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y : iterabl, label strings.
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X : iterable
an iterable which yields either str, unicode or file objects.
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Args:
X : iterable
an iterable which yields either str, unicode or file objects.
Returns:
list : document id matrix.
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Args:
X : iterable
an iterable which yields either str, unicode or file objects.
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an iterable which yields either str, unicode or file objects.
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train | IndexTransformer.inverse_transform | Return label strings.
Args:
y: label id matrix.
lengths: sentences length.
Returns:
list: list of list of strings. | anago/preprocessing.py | def inverse_transform(self, y, lengths=None):
"""Return label strings.
Args:
y: label id matrix.
lengths: sentences length.
Returns:
list: list of list of strings.
"""
y = np.argmax(y, -1)
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Args:
y: label id matrix.
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list: list of list of strings.
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train | ELMoTransformer.transform | Transform documents to document ids.
Uses the vocabulary learned by fit.
Args:
X : iterable
an iterable which yields either str, unicode or file objects.
y : iterabl, label strings.
Returns:
features: document id matrix.
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X : iterable
an iterable which yields either str, unicode or file objects.
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train | Trainer.train | Trains the model for a fixed number of epochs (iterations on a dataset).
Args:
x_train: list of training data.
y_train: list of training target (label) data.
x_valid: list of validation data.
y_valid: list of validation target (label) data.
batch_size... | anago/trainer.py | def train(self, x_train, y_train, x_valid=None, y_valid=None,
epochs=1, batch_size=32, verbose=1, callbacks=None, shuffle=True):
"""Trains the model for a fixed number of epochs (iterations on a dataset).
Args:
x_train: list of training data.
y_train: list of train... | def train(self, x_train, y_train, x_valid=None, y_valid=None,
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train | Sequence.fit | Fit the model for a fixed number of epochs.
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y_train: list of training target (label) data.
x_valid: list of validation data.
y_valid: list of validation target (label) data.
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"""Fit the model for a fixed number of epochs.
Args:
x_train: list of training data.
y_train: list of training target (label) data.
... | def fit(self, x_train, y_train, x_valid=None, y_valid=None,
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train | Sequence.predict | Returns the prediction of the model on the given test data.
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x_test : array-like, shape = (n_samples, sent_length)
Test samples.
Returns:
y_pred : array-like, shape = (n_smaples, sent_length)
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Args:
x_test : array-like, shape = (n_samples, sent_length)
Test samples.
Returns:
y_pred : array-like, shape = (n_smaples, sent_length)
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x_test : array-like, shape = (n_samples, sent_length)
Test samples.
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train | Sequence.analyze | Analyze text and return pretty format.
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tokenizer: Tokenize input sentence. Default tokenizer is `str.split`.
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text: string, the input text.
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Returns:
res: dict.
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train | CRF.get_log_normalization_constant | Compute logarithm of the normalization constant Z, where
Z = sum exp(-E) -> logZ = log sum exp(-E) =: -nlogZ | anago/layers.py | def get_log_normalization_constant(self, input_energy, mask, **kwargs):
"""Compute logarithm of the normalization constant Z, where
Z = sum exp(-E) -> logZ = log sum exp(-E) =: -nlogZ
"""
# should have logZ[:, i] == logZ[:, j] for any i, j
logZ = self.recursion(input_energy, mask... | def get_log_normalization_constant(self, input_energy, mask, **kwargs):
"""Compute logarithm of the normalization constant Z, where
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train | CRF.get_energy | Energy = a1' y1 + u1' y1 + y1' U y2 + u2' y2 + y2' U y3 + u3' y3 + an' y3 | anago/layers.py | def get_energy(self, y_true, input_energy, mask):
"""Energy = a1' y1 + u1' y1 + y1' U y2 + u2' y2 + y2' U y3 + u3' y3 + an' y3
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input_energy = K.sum(input_energy * y_true, 2) # (B, T)
chain_energy = K.sum(K.dot(y_true[:, :-1, :], self.chain_kernel) * y_true[:, 1:, :], 2) # (B, T-1)
... | def get_energy(self, y_true, input_energy, mask):
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input_energy = K.sum(input_energy * y_true, 2) # (B, T)
chain_energy = K.sum(K.dot(y_true[:, :-1, :], self.chain_kernel) * y_true[:, 1:, :], 2) # (B, T-1)
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train | CRF.get_negative_log_likelihood | Compute the loss, i.e., negative log likelihood (normalize by number of time steps)
likelihood = 1/Z * exp(-E) -> neg_log_like = - log(1/Z * exp(-E)) = logZ + E | anago/layers.py | def get_negative_log_likelihood(self, y_true, X, mask):
"""Compute the loss, i.e., negative log likelihood (normalize by number of time steps)
likelihood = 1/Z * exp(-E) -> neg_log_like = - log(1/Z * exp(-E)) = logZ + E
"""
input_energy = self.activation(K.dot(X, self.kernel) + self.... | def get_negative_log_likelihood(self, y_true, X, mask):
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likelihood = 1/Z * exp(-E) -> neg_log_like = - log(1/Z * exp(-E)) = logZ + E
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train | CRF.recursion | Forward (alpha) or backward (beta) recursion
If `return_logZ = True`, compute the logZ, the normalization constant:
\[ Z = \sum_{y1, y2, y3} exp(-E) # energy
= \sum_{y1, y2, y3} exp(-(u1' y1 + y1' W y2 + u2' y2 + y2' W y3 + u3' y3))
= sum_{y2, y3} (exp(-(u2' y2 + y2' W y3 + u3' y3)) ... | anago/layers.py | def recursion(self, input_energy, mask=None, go_backwards=False, return_sequences=True, return_logZ=True, input_length=None):
"""Forward (alpha) or backward (beta) recursion
If `return_logZ = True`, compute the logZ, the normalization constant:
\[ Z = \sum_{y1, y2, y3} exp(-E) # energy
... | def recursion(self, input_energy, mask=None, go_backwards=False, return_sequences=True, return_logZ=True, input_length=None):
"""Forward (alpha) or backward (beta) recursion
If `return_logZ = True`, compute the logZ, the normalization constant:
\[ Z = \sum_{y1, y2, y3} exp(-E) # energy
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train | extract_signature | Extracts a minimal signature from a given SQL query
:param sql: the SQL statement
:return: a string representing the signature | elasticapm/instrumentation/packages/dbapi2.py | def extract_signature(sql):
"""
Extracts a minimal signature from a given SQL query
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:return: a string representing the signature
"""
sql = force_text(sql)
sql = sql.strip()
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Extracts a minimal signature from a given SQL query
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sql = force_text(sql)
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train | AsyncWorker._timed_queue_join | implementation of Queue.join which takes a 'timeout' argument
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"""
implementation of Queue.join which takes a 'timeout' argument
returns True on success, False on timeout
"""
deadline = time.time() + timeout
with self._queue.all_tasks_done:
while self._queue.unfinished_tasks:
... | def _timed_queue_join(self, timeout):
"""
implementation of Queue.join which takes a 'timeout' argument
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deadline = time.time() + timeout
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train | AsyncWorker.start | Starts the task thread. | elasticapm/contrib/async_worker.py | def start(self):
"""
Starts the task thread.
"""
self._lock.acquire()
try:
if not self._thread:
self._thread = Thread(target=self._target)
self._thread.setDaemon(True)
self._thread.name = "elasticapm sender thread"
... | def start(self):
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Starts the task thread.
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self._lock.acquire()
try:
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self._thread.setDaemon(True)
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train | AsyncWorker.stop | Stops the task thread. Synchronous! | elasticapm/contrib/async_worker.py | def stop(self, timeout=None):
"""
Stops the task thread. Synchronous!
"""
self._lock.acquire()
try:
if self._thread:
self._queue.put_nowait(self._terminator)
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self._thread = None
... | def stop(self, timeout=None):
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Stops the task thread. Synchronous!
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self._lock.acquire()
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train | get_version | Get version without importing from elasticapm. This avoids any side effects
from importing while installing and/or building the module
:return: a string, indicating the version | setup.py | def get_version():
"""
Get version without importing from elasticapm. This avoids any side effects
from importing while installing and/or building the module
:return: a string, indicating the version
"""
version_file = open(os.path.join("elasticapm", "version.py"), encoding="utf-8")
for line... | def get_version():
"""
Get version without importing from elasticapm. This avoids any side effects
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:return: a string, indicating the version
"""
version_file = open(os.path.join("elasticapm", "version.py"), encoding="utf-8")
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train | get_headers | Returns only proper HTTP headers. | elasticapm/utils/wsgi.py | def get_headers(environ):
"""
Returns only proper HTTP headers.
"""
for key, value in compat.iteritems(environ):
key = str(key)
if key.startswith("HTTP_") and key not in ("HTTP_CONTENT_TYPE", "HTTP_CONTENT_LENGTH"):
yield key[5:].replace("_", "-").lower(), value
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"""
Returns only proper HTTP headers.
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train | get_current_url | A handy helper function that recreates the full URL for the current
request or parts of it. Here an example:
>>> from werkzeug import create_environ
>>> env = create_environ("/?param=foo", "http://localhost/script")
>>> get_current_url(env)
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>>> get_current_... | elasticapm/utils/wsgi.py | def get_current_url(environ, root_only=False, strip_querystring=False, host_only=False):
"""A handy helper function that recreates the full URL for the current
request or parts of it. Here an example:
>>> from werkzeug import create_environ
>>> env = create_environ("/?param=foo", "http://localhost/scr... | def get_current_url(environ, root_only=False, strip_querystring=False, host_only=False):
"""A handy helper function that recreates the full URL for the current
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>>> from werkzeug import create_environ
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train | Transport._flush | Flush the queue. This method should only be called from the event processing queue
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:return: None | elasticapm/transport/base.py | def _flush(self, buffer):
"""
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:return: None
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train | Transport.close | Cleans up resources and closes connection
:return: | elasticapm/transport/base.py | def close(self):
"""
Cleans up resources and closes connection
:return:
"""
if self._closed:
return
self._closed = True
self.queue("close", None)
if not self._flushed.wait(timeout=self._max_flush_time):
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Cleans up resources and closes connection
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train | Transport.flush | Trigger a flush of the queue.
Note: this method will only return once the queue is empty. This means it can block indefinitely if more events
are produced in other threads than can be consumed. | elasticapm/transport/base.py | def flush(self):
"""
Trigger a flush of the queue.
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"""
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Trigger a flush of the queue.
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train | Transport.handle_transport_fail | Failure handler called by the transport on send failure | elasticapm/transport/base.py | def handle_transport_fail(self, exception=None, **kwargs):
"""
Failure handler called by the transport on send failure
"""
message = str(exception)
logger.error("Failed to submit message: %r", message, exc_info=getattr(exception, "print_trace", True))
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"""
Failure handler called by the transport on send failure
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message = str(exception)
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train | ChilledQueue.put | Put an item into the queue.
If optional args 'block' is true and 'timeout' is None (the default),
block if necessary until a free slot is available. If 'timeout' is
a non-negative number, it blocks at most 'timeout' seconds and raises
the Full exception if no free slot was available wit... | elasticapm/transport/base.py | def put(self, item, block=True, timeout=None, chill=True):
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train | request_host | Return request-host, as defined by RFC 2965.
Variation from RFC: returned value is lowercased, for convenient
comparison. | elasticapm/instrumentation/packages/urllib.py | def request_host(request):
"""Return request-host, as defined by RFC 2965.
Variation from RFC: returned value is lowercased, for convenient
comparison.
"""
url = request.get_full_url()
parse_result = compat.urlparse.urlparse(url)
scheme, host, port = parse_result.scheme, parse_result.hostn... | def request_host(request):
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url = request.get_full_url()
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train | get_client | Get an ElasticAPM client.
:param client:
:return:
:rtype: elasticapm.base.Client | elasticapm/contrib/django/client.py | def get_client(client=None):
"""
Get an ElasticAPM client.
:param client:
:return:
:rtype: elasticapm.base.Client
"""
global _client
tmp_client = client is not None
if not tmp_client:
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client = config.get("CLIENT",... | def get_client(client=None):
"""
Get an ElasticAPM client.
:param client:
:return:
:rtype: elasticapm.base.Client
"""
global _client
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train | _get_installed_apps_paths | Generate a list of modules in settings.INSTALLED_APPS. | elasticapm/contrib/django/client.py | def _get_installed_apps_paths():
"""
Generate a list of modules in settings.INSTALLED_APPS.
"""
out = set()
for app in django_settings.INSTALLED_APPS:
out.add(app)
return out | def _get_installed_apps_paths():
"""
Generate a list of modules in settings.INSTALLED_APPS.
"""
out = set()
for app in django_settings.INSTALLED_APPS:
out.add(app)
return out | [
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train | DjangoClient._get_stack_info_for_trace | If the stacktrace originates within the elasticapm module, it will skip
frames until some other module comes up. | elasticapm/contrib/django/client.py | def _get_stack_info_for_trace(
self,
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in_app_frame_context_lines=None,
with_locals=True,
locals_processor_func=None,
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train | DjangoClient.send | Serializes and signs ``data`` and passes the payload off to ``send_remote``
If ``server`` was passed into the constructor, this will serialize the data and pipe it to
the server using ``send_remote()``. | elasticapm/contrib/django/client.py | def send(self, url, **kwargs):
"""
Serializes and signs ``data`` and passes the payload off to ``send_remote``
If ``server`` was passed into the constructor, this will serialize the data and pipe it to
the server using ``send_remote()``.
"""
if self.config.server_url:
... | def send(self, url, **kwargs):
"""
Serializes and signs ``data`` and passes the payload off to ``send_remote``
If ``server`` was passed into the constructor, this will serialize the data and pipe it to
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train | deprecated | This is a decorator which can be used to mark functions
as deprecated. It will result in a warning being emitted
when the function is used. | elasticapm/utils/deprecation.py | def deprecated(alternative=None):
"""This is a decorator which can be used to mark functions
as deprecated. It will result in a warning being emitted
when the function is used."""
def real_decorator(func):
@functools.wraps(func)
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train | MetricsRegistry.register | Register a new metric set
:param class_path: a string with the import path of the metricset class | elasticapm/metrics/base_metrics.py | def register(self, class_path):
"""
Register a new metric set
:param class_path: a string with the import path of the metricset class
"""
if class_path in self._metricsets:
return
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try:
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... | def register(self, class_path):
"""
Register a new metric set
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if class_path in self._metricsets:
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train | MetricsRegistry.collect | Collect metrics from all registered metric sets
:return: | elasticapm/metrics/base_metrics.py | def collect(self):
"""
Collect metrics from all registered metric sets
:return:
"""
logger.debug("Collecting metrics")
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data = metricset.collect()
if data:
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"""
Collect metrics from all registered metric sets
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logger.debug("Collecting metrics")
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data = metricset.collect()
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"data",
"=",
"metricset",
".",
"collect",
"... | 2975663d7bd22282dc39336b2c37b37c12c7a774 |
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