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belbio/bel | bel/utils.py | download_file | def download_file(url):
"""Download file"""
response = requests.get(url, stream=True)
fp = tempfile.NamedTemporaryFile()
for chunk in response.iter_content(chunk_size=1024):
if chunk: # filter out keep-alive new chunks
fp.write(chunk)
# log.info(f'Download file - tmp file: {fp... | python | def download_file(url):
"""Download file"""
response = requests.get(url, stream=True)
fp = tempfile.NamedTemporaryFile()
for chunk in response.iter_content(chunk_size=1024):
if chunk: # filter out keep-alive new chunks
fp.write(chunk)
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belbio/bel | bel/utils.py | first_true | def first_true(iterable, default=False, pred=None):
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# first_true([a,b... | python | def first_true(iterable, default=False, pred=None):
"""Returns the first true value in the iterable.
If no true value is found, returns *default*
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for which pred(item) is true.
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belbio/bel | bel/utils.py | _create_hash_from_doc | def _create_hash_from_doc(doc: Mapping[str, Any]) -> str:
"""Create hash Id from edge record
Args:
edge (Mapping[str, Any]): edge record to create hash from
Returns:
str: Murmur3 128 bit hash
"""
doc_string = json.dumps(doc, sort_keys=True)
return _create_hash(doc_string) | python | def _create_hash_from_doc(doc: Mapping[str, Any]) -> str:
"""Create hash Id from edge record
Args:
edge (Mapping[str, Any]): edge record to create hash from
Returns:
str: Murmur3 128 bit hash
"""
doc_string = json.dumps(doc, sort_keys=True)
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belbio/bel | bel/utils.py | Timer.elapsed | def elapsed(self):
""" Return the current elapsed time since start
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the elapsed time bewteen start and property access is returned.
However, if it is accessed outside of the context manager scope,
it returns the ela... | python | def elapsed(self):
""" Return the current elapsed time since start
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belbio/bel | bel/edge/pipeline.py | load_edges_into_db | def load_edges_into_db(
nanopub_id: str,
nanopub_url: str,
edges: list = [],
edges_coll_name: str = edges_coll_name,
nodes_coll_name: str = nodes_coll_name,
):
"""Load edges into Edgestore"""
start_time = datetime.datetime.now()
# Clean out edges for nanopub in edgestore
query = f"... | python | def load_edges_into_db(
nanopub_id: str,
nanopub_url: str,
edges: list = [],
edges_coll_name: str = edges_coll_name,
nodes_coll_name: str = nodes_coll_name,
):
"""Load edges into Edgestore"""
start_time = datetime.datetime.now()
# Clean out edges for nanopub in edgestore
query = f"... | [
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belbio/bel | bel/edge/pipeline.py | edge_iterator | def edge_iterator(edges=[], edges_fn=None):
"""Yield documents from edge for loading into ArangoDB"""
for edge in itertools.chain(edges, files.read_edges(edges_fn)):
subj = copy.deepcopy(edge["edge"]["subject"])
subj_id = str(utils._create_hash_from_doc(subj))
subj["_key"] = subj_id
... | python | def edge_iterator(edges=[], edges_fn=None):
"""Yield documents from edge for loading into ArangoDB"""
for edge in itertools.chain(edges, files.read_edges(edges_fn)):
subj = copy.deepcopy(edge["edge"]["subject"])
subj_id = str(utils._create_hash_from_doc(subj))
subj["_key"] = subj_id
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belbio/bel | bel/nanopub/nanopubstore.py | update_nanopubstore_start_dt | def update_nanopubstore_start_dt(url: str, start_dt: str):
"""Add nanopubstore start_dt to belapi.state_mgmt collection
Args:
url: url of nanopubstore
start_dt: datetime of last query against nanopubstore for new ID's
"""
hostname = urllib.parse.urlsplit(url)[1]
start_dates_doc = ... | python | def update_nanopubstore_start_dt(url: str, start_dt: str):
"""Add nanopubstore start_dt to belapi.state_mgmt collection
Args:
url: url of nanopubstore
start_dt: datetime of last query against nanopubstore for new ID's
"""
hostname = urllib.parse.urlsplit(url)[1]
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belbio/bel | bel/nanopub/nanopubstore.py | get_nanopubstore_start_dt | def get_nanopubstore_start_dt(url: str):
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hostname = urllib.parse.urlsplit(url)[1]
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... | python | def get_nanopubstore_start_dt(url: str):
"""Get last start_dt recorded for getting new nanopub ID's"""
hostname = urllib.parse.urlsplit(url)[1]
start_dates_doc = state_mgmt.get(start_dates_doc_key)
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belbio/bel | bel/nanopub/nanopubstore.py | get_nanopub_urls | def get_nanopub_urls(ns_root_url: str = None, start_dt: str = None) -> dict:
"""Get modified and deleted nanopub urls
Limited by last datetime retrieved (start_dt). Modified includes new and updated nanopubs
Returns:
dict: {'modified': [], 'deleted': []}
"""
if not ns_root_url:
ns... | python | def get_nanopub_urls(ns_root_url: str = None, start_dt: str = None) -> dict:
"""Get modified and deleted nanopub urls
Limited by last datetime retrieved (start_dt). Modified includes new and updated nanopubs
Returns:
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belbio/bel | bel/nanopub/nanopubstore.py | get_nanopub | def get_nanopub(url):
"""Get Nanopub from nanopubstore given url"""
r = bel.utils.get_url(url, cache=False)
if r and r.json():
return r.json()
else:
return {} | python | def get_nanopub(url):
"""Get Nanopub from nanopubstore given url"""
r = bel.utils.get_url(url, cache=False)
if r and r.json():
return r.json()
else:
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belbio/bel | bel/scripts.py | pipeline | def pipeline(
ctx,
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db_delete,
output_fn,
rules,
species,
namespace_targets,
version,
api,
config_fn,
):
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This will process BEL Nanopubs into BEL Edges by validating, orthologizing (if requested),
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output_fn,
rules,
species,
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config_fn,
):
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belbio/bel | bel/scripts.py | nanopub_validate | def nanopub_validate(ctx, input_fn, output_fn, api, config_fn):
"""Validate nanopubs"""
if config_fn:
config = bel.db.Config.merge_config(ctx.config, override_config_fn=config_fn)
else:
config = ctx.config
api = utils.first_true(
[api, config["bel_api"]["servers"].get("api_url"... | python | def nanopub_validate(ctx, input_fn, output_fn, api, config_fn):
"""Validate nanopubs"""
if config_fn:
config = bel.db.Config.merge_config(ctx.config, override_config_fn=config_fn)
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belbio/bel | bel/scripts.py | convert_belscript | def convert_belscript(ctx, input_fn, output_fn):
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nanopub_bel-1.0.0 format.
\b
input_fn:
If input fn has *.gz, will read as a gzip file
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output_fn:
If output fn has *.gz, wi... | python | def convert_belscript(ctx, input_fn, output_fn):
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input_fn:
If input fn has *.gz, will read as a gzip file
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belbio/bel | bel/scripts.py | reformat | def reformat(ctx, input_fn, output_fn):
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input_fn:
If input fn has *.gz, will read as a gzip file
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output_fn:
If output fn has *.gz, will written as a gzip file
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"""Reformat between JSON, YAML, JSONLines formats
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input_fn:
If input fn has *.gz, will read as a gzip file
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belbio/bel | bel/scripts.py | nanopub_stats | def nanopub_stats(ctx, input_fn):
"""Collect statistics on nanopub file
input_fn can be json, jsonl or yaml and additionally gzipped
"""
counts = {
"nanopubs": 0,
"assertions": {"total": 0, "subject_only": 0, "nested": 0, "relations": {}},
}
for np in bnf.read_nanopubs(input_f... | python | def nanopub_stats(ctx, input_fn):
"""Collect statistics on nanopub file
input_fn can be json, jsonl or yaml and additionally gzipped
"""
counts = {
"nanopubs": 0,
"assertions": {"total": 0, "subject_only": 0, "nested": 0, "relations": {}},
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belbio/bel | bel/scripts.py | canonicalize | def canonicalize(ctx, statement, namespace_targets, version, api, config_fn):
"""Canonicalize statement
Target namespaces can be provided in the following manner:
bel stmt canonicalize "<BELStmt>" --namespace_targets '{"HGNC": ["EG", "SP"], "CHEMBL": ["CHEBI"]}'
the value of target_namespa... | python | def canonicalize(ctx, statement, namespace_targets, version, api, config_fn):
"""Canonicalize statement
Target namespaces can be provided in the following manner:
bel stmt canonicalize "<BELStmt>" --namespace_targets '{"HGNC": ["EG", "SP"], "CHEMBL": ["CHEBI"]}'
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the value of target_namespaces must be JSON and embedded in single quotes
reserving double quotes fo... | [
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belbio/bel | bel/scripts.py | edges | def edges(ctx, statement, rules, species, namespace_targets, version, api, config_fn):
"""Create BEL Edges from BEL Statement"""
if config_fn:
config = bel.db.Config.merge_config(ctx.config, override_config_fn=config_fn)
else:
config = ctx.config
# Configuration - will return the first... | python | def edges(ctx, statement, rules, species, namespace_targets, version, api, config_fn):
"""Create BEL Edges from BEL Statement"""
if config_fn:
config = bel.db.Config.merge_config(ctx.config, override_config_fn=config_fn)
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config = ctx.config
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belbio/bel | bel/scripts.py | elasticsearch | def elasticsearch(delete, index_name):
"""Setup Elasticsearch namespace indexes
This will by default only create the indexes and run the namespace index mapping
if the indexes don't exist. The --delete option will force removal of the
index if it exists.
The index_name should be aliased to the in... | python | def elasticsearch(delete, index_name):
"""Setup Elasticsearch namespace indexes
This will by default only create the indexes and run the namespace index mapping
if the indexes don't exist. The --delete option will force removal of the
index if it exists.
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belbio/bel | bel/scripts.py | arangodb | def arangodb(delete, db_name):
"""Setup ArangoDB database
db_name: Either 'belns' or 'edgestore' - must be one or the other
This will create the database, collections and indexes on the collection if it doesn't exist.
The --delete option will force removal of the database if it exists."""
if del... | python | def arangodb(delete, db_name):
"""Setup ArangoDB database
db_name: Either 'belns' or 'edgestore' - must be one or the other
This will create the database, collections and indexes on the collection if it doesn't exist.
The --delete option will force removal of the database if it exists."""
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belbio/bel | bel/nanopub/nanopubs.py | validate_to_schema | def validate_to_schema(nanopub, schema) -> Tuple[bool, List[Tuple[str, str]]]:
"""Validate nanopub against jsonschema for nanopub
Args:
nanopub (Mapping[str, Any]): nanopub dict
schema (Mapping[str, Any]): nanopub schema
Returns:
Tuple[bool, List[str]]:
bool: Is valid? ... | python | def validate_to_schema(nanopub, schema) -> Tuple[bool, List[Tuple[str, str]]]:
"""Validate nanopub against jsonschema for nanopub
Args:
nanopub (Mapping[str, Any]): nanopub dict
schema (Mapping[str, Any]): nanopub schema
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Tuple[bool, List[str]]:
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belbio/bel | bel/nanopub/nanopubs.py | hash_nanopub | def hash_nanopub(nanopub: Mapping[str, Any]) -> str:
"""Create CityHash64 from nanopub for duplicate check
TODO - check that this hash value is consistent between C# and Python running on
laptop and server
Build string to hash
Collect flat array of (all values.strip()):
nanopub.type.name
... | python | def hash_nanopub(nanopub: Mapping[str, Any]) -> str:
"""Create CityHash64 from nanopub for duplicate check
TODO - check that this hash value is consistent between C# and Python running on
laptop and server
Build string to hash
Collect flat array of (all values.strip()):
nanopub.type.name
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belbio/bel | bel/nanopub/nanopubs.py | Nanopub.validate | def validate(
self, nanopub: Mapping[str, Any]
) -> Tuple[bool, List[Tuple[str, str]]]:
"""Validates using the nanopub schema
Args:
nanopub (Mapping[str, Any]): nanopub dict
Returns:
Tuple[bool, List[Tuple[str, str]]]:
bool: Is valid? Yes = ... | python | def validate(
self, nanopub: Mapping[str, Any]
) -> Tuple[bool, List[Tuple[str, str]]]:
"""Validates using the nanopub schema
Args:
nanopub (Mapping[str, Any]): nanopub dict
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Tuple[bool, List[Tuple[str, str]]]:
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belbio/bel | bel/nanopub/nanopubs.py | Nanopub.validate_context | def validate_context(
self, context: Mapping[str, Any]
) -> Tuple[bool, List[Tuple[str, str]]]:
""" Validate context
Args:
context (Mapping[str, Any]): context dictionary of type, id and label
Returns:
Tuple[bool, List[Tuple[str, str]]]:
bool... | python | def validate_context(
self, context: Mapping[str, Any]
) -> Tuple[bool, List[Tuple[str, str]]]:
""" Validate context
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context (Mapping[str, Any]): context dictionary of type, id and label
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belbio/bel | bel/nanopub/nanopubs.py | Nanopub.bel_edges | def bel_edges(
self,
nanopub: Mapping[str, Any],
namespace_targets: Mapping[str, List[str]] = {},
rules: List[str] = [],
orthologize_target: str = None,
) -> List[Mapping[str, Any]]:
"""Create BEL Edges from BEL nanopub
Args:
nanopub (Mapping[str,... | python | def bel_edges(
self,
nanopub: Mapping[str, Any],
namespace_targets: Mapping[str, List[str]] = {},
rules: List[str] = [],
orthologize_target: str = None,
) -> List[Mapping[str, Any]]:
"""Create BEL Edges from BEL nanopub
Args:
nanopub (Mapping[str,... | [
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RockFeng0/rtsf-http | httpdriver/cli.py | main_hrun | def main_hrun():
""" parse command line options and run commands."""
parser = argparse.ArgumentParser(description="Tools for http(s) test. Base on rtsf.")
parser.add_argument(
'--log-level', default='INFO',
help="Specify logging level, default is INFO.")
p... | python | def main_hrun():
""" parse command line options and run commands."""
parser = argparse.ArgumentParser(description="Tools for http(s) test. Base on rtsf.")
parser.add_argument(
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malja/zroya | setup.py | find_pyd_file | def find_pyd_file():
"""
Return path to .pyd after successful build command.
:return: Path to .pyd file or None.
"""
if not os.path.isdir("./build"):
raise NotADirectoryError
for path, dirs, files in os.walk("./build"):
for file_name in files:
file_name_parts = os.p... | python | def find_pyd_file():
"""
Return path to .pyd after successful build command.
:return: Path to .pyd file or None.
"""
if not os.path.isdir("./build"):
raise NotADirectoryError
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for file_name in files:
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urschrei/simplification | simplification/util.py | _void_array_to_nested_list | def _void_array_to_nested_list(res, _func, _args):
""" Dereference the FFI result to a list of coordinates """
try:
shape = res.coords.len, 2
ptr = cast(res.coords.data, POINTER(c_double))
array = np.ctypeslib.as_array(ptr, shape)
return array.tolist()
finally:
drop_a... | python | def _void_array_to_nested_list(res, _func, _args):
""" Dereference the FFI result to a list of coordinates """
try:
shape = res.coords.len, 2
ptr = cast(res.coords.data, POINTER(c_double))
array = np.ctypeslib.as_array(ptr, shape)
return array.tolist()
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MacHu-GWU/dataIO-project | dataIO/js.py | is_json_file | def is_json_file(abspath):
"""Parse file extension.
- *.json: uncompressed, utf-8 encode json file
- *.gz: compressed, utf-8 encode json file
"""
abspath = abspath.lower()
fname, ext = os.path.splitext(abspath)
if ext in [".json", ".js"]:
is_json = True
elif ext == ".gz":
... | python | def is_json_file(abspath):
"""Parse file extension.
- *.json: uncompressed, utf-8 encode json file
- *.gz: compressed, utf-8 encode json file
"""
abspath = abspath.lower()
fname, ext = os.path.splitext(abspath)
if ext in [".json", ".js"]:
is_json = True
elif ext == ".gz":
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MacHu-GWU/dataIO-project | dataIO/js.py | lower_ext | def lower_ext(abspath):
"""Convert file extension to lowercase.
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fname, ext = os.path.splitext(abspath)
return fname + ext.lower() | python | def lower_ext(abspath):
"""Convert file extension to lowercase.
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fname, ext = os.path.splitext(abspath)
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MacHu-GWU/dataIO-project | dataIO/js.py | load | def load(abspath, default=None, enable_verbose=True):
"""Load Json from file. If file are not exists, returns ``default``.
:param abspath: file path. use absolute path as much as you can.
extension has to be ``.json`` or ``.gz`` (for compressed Json).
:type abspath: string
:param default: defa... | python | def load(abspath, default=None, enable_verbose=True):
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MacHu-GWU/dataIO-project | dataIO/js.py | dump | def dump(data, abspath,
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float_precision=None,
ensure_ascii=True,
overwrite=False,
enable_verbose=True):
"""Dump Json serializable object to file.
Provides multiple choice to customize the behavior.
:param data: Serializable python object.
... | python | def dump(data, abspath,
indent_format=False,
float_precision=None,
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"""Dump Json serializable object to file.
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MacHu-GWU/dataIO-project | dataIO/js.py | safe_dump | def safe_dump(data, abspath,
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RI-imaging/nrefocus | nrefocus/pad.py | _get_pad_left_right | def _get_pad_left_right(small, large):
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RI-imaging/nrefocus | nrefocus/pad.py | _pad_add_1d | def _pad_add_1d(av, size, stlen):
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"""
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""" 2D component of `pad_add`
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RI-imaging/nrefocus | nrefocus/pad.py | pad_rem | def pad_rem(pv, size=None):
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Parameters
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pv : 1D or 2D ndarray
The array from which the padding will be removed.
size : tuple of length 1 (1D) or 2 (2D), optional
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pv : 1D or 2D ndarray
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anteater/anteater | anteater/src/virus_total.py | VirusTotal.rate_limit | def rate_limit(self):
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"""
Simple rate limit function using redis
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anteater/anteater | anteater/src/virus_total.py | VirusTotal.scan_file | def scan_file(self, filename, apikey):
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anteater/anteater | anteater/src/virus_total.py | VirusTotal.rescan_file | def rescan_file(self, filename, sha256hash, apikey):
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anteater/anteater | anteater/src/virus_total.py | VirusTotal.binary_report | def binary_report(self, sha256sum, apikey):
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retrieve report from file scan
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retrieve report from file scan
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anteater/anteater | anteater/src/virus_total.py | VirusTotal.send_ip | def send_ip(self, ipaddr, apikey):
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rapidpro/expressions | python/temba_expressions/functions/custom.py | read_digits | def read_digits(ctx, text):
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Takes a single parameter (administrative boundary as a string) and returns the name of the leaf boundary
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rapidpro/expressions | python/temba_expressions/functions/custom.py | regex_group | def regex_group(ctx, text, pattern, group_num):
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Tries to match the text with the given pattern and returns the value of matching group
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rapidpro/expressions | python/temba_expressions/utils.py | decimal_round | def decimal_round(number, num_digits, rounding=ROUND_HALF_UP):
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exp = Decimal(10) ** -num_digits
if num_digits >= 0:
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Rounding for decimals with support for negative digits
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exp = Decimal(10) ** -num_digits
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rapidpro/expressions | python/temba_expressions/utils.py | parse_json_date | def parse_json_date(value):
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Parses an ISO8601 formatted datetime from a string value
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rapidpro/expressions | python/temba_expressions/functions/excel.py | clean | def clean(ctx, text):
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Removes all non-printable characters from a text string
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Removes all non-printable characters from a text string
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Joins text strings into one text string
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Joins text strings into one text string
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rapidpro/expressions | python/temba_expressions/functions/excel.py | fixed | def fixed(ctx, number, decimals=2, no_commas=False):
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value = _round(ctx, number, decimals)
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rapidpro/expressions | python/temba_expressions/functions/excel.py | left | def left(ctx, text, num_chars):
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rapidpro/expressions | python/temba_expressions/functions/excel.py | rept | def rept(ctx, text, number_times):
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Repeats text a given number of times
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rapidpro/expressions | python/temba_expressions/functions/excel.py | right | def right(ctx, text, num_chars):
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rapidpro/expressions | python/temba_expressions/functions/excel.py | substitute | def substitute(ctx, text, old_text, new_text, instance_num=-1):
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rapidpro/expressions | python/temba_expressions/functions/excel.py | date | def date(ctx, year, month, day):
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rapidpro/expressions | python/temba_expressions/functions/excel.py | datedif | def datedif(ctx, start_date, end_date, unit):
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Moves a date by the given number of months
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Moves a date by the given number of months
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rapidpro/expressions | python/temba_expressions/functions/excel.py | time | def time(ctx, hours, minutes, seconds):
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rapidpro/expressions | python/temba_expressions/functions/excel.py | _abs | def _abs(ctx, number):
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Returns the absolute value of a number
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rapidpro/expressions | python/temba_expressions/functions/excel.py | _int | def _int(ctx, number):
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Rounds a number down to the nearest integer
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rapidpro/expressions | python/temba_expressions/functions/excel.py | _max | def _max(ctx, *number):
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... | python | def _max(ctx, *number):
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Returns the maximum value of all arguments
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rapidpro/expressions | python/temba_expressions/functions/excel.py | mod | def mod(ctx, number, divisor):
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Returns the remainder after number is divided by divisor
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Returns the result of a number raised to a power
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rapidpro/expressions | python/temba_expressions/functions/excel.py | randbetween | def randbetween(ctx, bottom, top):
"""
Returns a random integer number between the numbers you specify
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bottom = conversions.to_integer(bottom, ctx)
top = conversions.to_integer(top, ctx)
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rapidpro/expressions | python/temba_expressions/functions/excel.py | _round | def _round(ctx, number, num_digits):
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rapidpro/expressions | python/temba_expressions/functions/excel.py | rounddown | def rounddown(ctx, number, num_digits):
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rapidpro/expressions | python/temba_expressions/functions/excel.py | roundup | def roundup(ctx, number, num_digits):
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rapidpro/expressions | python/temba_expressions/functions/excel.py | _sum | def _sum(ctx, *number):
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Returns the sum of all arguments
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rapidpro/expressions | python/temba_expressions/functions/excel.py | trunc | def trunc(ctx, number):
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Truncates a number to an integer by removing the fractional part of the number
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rainwoodman/kdcount | kdcount/correlate.py | compute_sum_values | def compute_sum_values(i, j, data1, data2):
"""
Return the sum1_ij and sum2_ij values given
the input indices and data instances.
Notes
-----
This is called in `Binning.update_sums` to compute
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Parameters
----------
i,j : a... | python | def compute_sum_values(i, j, data1, data2):
"""
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rainwoodman/kdcount | kdcount/correlate.py | Binning._setup | def _setup(self):
"""
Set the binning info we need from the `edges`
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self._info = numpy.empty(self.Ndim, dtype=dtype)
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"""
Set the binning info we need from the `edges`
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rainwoodman/kdcount | kdcount/correlate.py | Binning.linear | def linear(self, **paircoords):
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Linearize bin indices.
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args : list
a list of bin index, (xi, yi, zi, ..)
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"""
Linearize bin indices.
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rainwoodman/kdcount | kdcount/correlate.py | Binning.update_sums | def update_sums(self, r, i, j, data1, data2, sum1, sum2, N=None, centers_sum=None):
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rainwoodman/kdcount | kdcount/correlate.py | Binning.sum_shapes | def sum_shapes(self, data1, data2):
"""
Return the shapes of the summation arrays,
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"""
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linearshape = [-1] + list(self.shape)
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"""
Return the shapes of the summation arrays,
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linearshape = [-1] + list(self.shape)
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rainwoodman/kdcount | kdcount/correlate.py | Binning._update_mean_coords | def _update_mean_coords(self, dig, N, centers_sum, **paircoords):
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N.flat[:] += utils.bincount(dig, 1., minlength=N.size)
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"""
Update the mean coordinate sums
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N.flat[:] += utils.bincount(dig, 1., minlength=N.size)
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rainwoodman/kdcount | kdcount/correlate.py | paircount_queue.work | def work(self, i):
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Internal function that performs the pair-counting
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rainwoodman/kdcount | kdcount/correlate.py | paircount_queue.reduce | def reduce(self, sum1, sum2, *args):
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hammerlab/stanity | stanity/psis.py | psisloo | def psisloo(log_lik, **kwargs):
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lw : ndarray
Array of size n x m containing m sets of n log weights. It is also
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Reff : scalar, optional
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Returns empirical Bayes estimate for the parameters of the two-parameter
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Parameters
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hammerlab/stanity | stanity/psis.py | gpinv | def gpinv(p, k, sigma):
"""Inverse Generalised Pareto distribution function."""
x = np.empty(p.shape)
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if sigma <= 0:
return x
ok = (p > 0) & (p < 1)
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... | python | def gpinv(p, k, sigma):
"""Inverse Generalised Pareto distribution function."""
x = np.empty(p.shape)
x.fill(np.nan)
if sigma <= 0:
return x
ok = (p > 0) & (p < 1)
if np.all(ok):
if np.abs(k) < np.finfo(float).eps:
np.negative(p, out=x)
np.log1p(x, out=x)
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hammerlab/stanity | stanity/psis.py | sumlogs | def sumlogs(x, axis=None, out=None):
"""Sum of vector where numbers are represented by their logarithms.
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... | python | def sumlogs(x, axis=None, out=None):
"""Sum of vector where numbers are represented by their logarithms.
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rapidpro/expressions | python/temba_expressions/functions/__init__.py | FunctionManager.add_library | def add_library(self, library):
"""
Adds functions from a library module
:param library: the library module
:return:
"""
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# ignore imported methods and anything beginning __
if inspect.isfunction(fn) and i... | python | def add_library(self, library):
"""
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rapidpro/expressions | python/temba_expressions/functions/__init__.py | FunctionManager.invoke_function | def invoke_function(self, ctx, name, arguments):
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Invokes the given function
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rapidpro/expressions | python/temba_expressions/functions/__init__.py | FunctionManager.build_listing | def build_listing(self):
"""
Builds a listing of all functions sorted A-Z, with their names and descriptions
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def func_entry(name, func):
args, varargs, defaults = self._get_arg_spec(func)
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"""
Builds a listing of all functions sorted A-Z, with their names and descriptions
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def func_entry(name, func):
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