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jessevdk/cldoc | fc7f59405c4a891b8367c80a700f5aa3c5c9230c | cldoc/clang/cindex.py | python | Type.is_function_variadic | (self) | return conf.lib.clang_isFunctionTypeVariadic(self) | Determine whether this function Type is a variadic function type. | Determine whether this function Type is a variadic function type. | [
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] | def is_function_variadic(self):
"""Determine whether this function Type is a variadic function type."""
assert self.kind == TypeKind.FUNCTIONPROTO
return conf.lib.clang_isFunctionTypeVariadic(self) | [
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/modules/sensehat.py | python | get_temperature | () | return _sensehat.get_temperature() | Gets the temperature in degrees Celsius from the humidity sensor.
Equivalent to calling ``get_temperature_from_humidity``.
If you get strange results try using ``get_temperature_from_pressure``. | Gets the temperature in degrees Celsius from the humidity sensor.
Equivalent to calling ``get_temperature_from_humidity``. | [
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"""
Gets the temperature in degrees Celsius from the humidity sensor.
Equivalent to calling ``get_temperature_from_humidity``.
If you get strange results try using ``get_temperature_from_pressure``.
"""
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hhursev/recipe-scrapers | 478b9ddb0dda02b17b14f299eea729bef8131aa9 | recipe_scrapers/bowlofdelicious.py | python | BowlOfDelicious.host | (cls) | return "bowlofdelicious.com" | [] | def host(cls):
return "bowlofdelicious.com" | [
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VirtueSecurity/aws-extender | d123b7e1a845847709ba3a481f11996bddc68a1c | BappModules/boto/cognito/identity/layer1.py | python | CognitoIdentityConnection.get_id | (self, account_id, identity_pool_id, logins=None) | return self.make_request(action='GetId',
body=json.dumps(params)) | Generates (or retrieves) a Cognito ID. Supplying multiple
logins will create an implicit linked account.
:type account_id: string
:param account_id: A standard AWS account ID (9+ digits).
:type identity_pool_id: string
:param identity_pool_id: An identity pool ID in the format REGION:GUID.
:type logins: map
:param logins: A set of optional name-value pairs that map provider
names to provider tokens.
The available provider names for `Logins` are as follows:
+ Facebook: `graph.facebook.com`
+ Google: `accounts.google.com`
+ Amazon: `www.amazon.com` | Generates (or retrieves) a Cognito ID. Supplying multiple
logins will create an implicit linked account. | [
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] | def get_id(self, account_id, identity_pool_id, logins=None):
"""
Generates (or retrieves) a Cognito ID. Supplying multiple
logins will create an implicit linked account.
:type account_id: string
:param account_id: A standard AWS account ID (9+ digits).
:type identity_pool_id: string
:param identity_pool_id: An identity pool ID in the format REGION:GUID.
:type logins: map
:param logins: A set of optional name-value pairs that map provider
names to provider tokens.
The available provider names for `Logins` are as follows:
+ Facebook: `graph.facebook.com`
+ Google: `accounts.google.com`
+ Amazon: `www.amazon.com`
"""
params = {
'AccountId': account_id,
'IdentityPoolId': identity_pool_id,
}
if logins is not None:
params['Logins'] = logins
return self.make_request(action='GetId',
body=json.dumps(params)) | [
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instaloader/instaloader | d6e5e310054aa42d3fd8d881389f04d1b4cdbe72 | instaloader/structures.py | python | Profile.from_username | (cls, context: InstaloaderContext, username: str) | return profile | Create a Profile instance from a given username, raise exception if it does not exist.
See also :meth:`Instaloader.check_profile_id`.
:param context: :attr:`Instaloader.context`
:param username: Username
:raises: :class:`ProfileNotExistsException` | Create a Profile instance from a given username, raise exception if it does not exist. | [
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"""Create a Profile instance from a given username, raise exception if it does not exist.
See also :meth:`Instaloader.check_profile_id`.
:param context: :attr:`Instaloader.context`
:param username: Username
:raises: :class:`ProfileNotExistsException`
"""
# pylint:disable=protected-access
profile = cls(context, {'username': username.lower()})
profile._obtain_metadata() # to raise ProfileNotExistsException now in case username is invalid
return profile | [
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tenzir/threatbus | a26096e7b61b3eddf25c445d40a6cd2ea4420558 | apps/zmq-app-template/zmq_app_template/template.py | python | subscribe | (endpoint: str, topic: str, snapshot: int, timeout: int = 5) | return send_manage_message(endpoint, action, timeout) | Subscribes this app to Threat Bus for the given topic. Requests an optional
snapshot of historical indicators.
@param endpoint The ZMQ management endpoint of Threat Bus ('host:port')
@param topic The topic to subscribe to
@param snapshot An integer value to request n days of historical IoC items
@param timeout The period after which the connection attempt is aborted | Subscribes this app to Threat Bus for the given topic. Requests an optional
snapshot of historical indicators. | [
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"""
Subscribes this app to Threat Bus for the given topic. Requests an optional
snapshot of historical indicators.
@param endpoint The ZMQ management endpoint of Threat Bus ('host:port')
@param topic The topic to subscribe to
@param snapshot An integer value to request n days of historical IoC items
@param timeout The period after which the connection attempt is aborted
"""
global logger
logger.info(f"Subscribing to topic '{topic}'...")
action = {"action": "subscribe", "topic": topic, "snapshot": snapshot}
return send_manage_message(endpoint, action, timeout) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/mako/pygen.py | python | PythonPrinter.__init__ | (self, stream) | [] | def __init__(self, stream):
# indentation counter
self.indent = 0
# a stack storing information about why we incremented
# the indentation counter, to help us determine if we
# should decrement it
self.indent_detail = []
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self.line_buffer = []
self.in_indent_lines = False
self._reset_multi_line_flags()
# mapping of generated python lines to template
# source lines
self.source_map = {} | [
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CvvT/dumpDex | 92ab3b7e996194a06bf1dd5538a4954e8a5ee9c1 | python/idc.py | python | GetDisasm | (ea) | return GetDisasmEx(ea, 0) | Get disassembly line
@param ea: linear address of instruction
@return: "" - could not decode instruction at the specified location
@note: this function may not return exactly the same mnemonics
as you see on the screen. | Get disassembly line | [
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] | def GetDisasm(ea):
"""
Get disassembly line
@param ea: linear address of instruction
@return: "" - could not decode instruction at the specified location
@note: this function may not return exactly the same mnemonics
as you see on the screen.
"""
return GetDisasmEx(ea, 0) | [
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ricequant/rqalpha | d8b345ca3fde299e061c6a89c1f2c362c3584c96 | rqalpha/portfolio/__init__.py | python | Portfolio.accounts | (self) | return self._accounts | 账户字典 | 账户字典 | [
"账户字典"
] | def accounts(self):
# type: () -> Dict[DEFAULT_ACCOUNT_TYPE, Account]
"""
账户字典
"""
return self._accounts | [
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googlefonts/fontbakery | cb8196c3a636b63654f8370636cb3f438b60d5b1 | Lib/fontbakery/profiles/googlefonts.py | python | com_google_fonts_check_old_ttfautohint | (ttFont) | Font has old ttfautohint applied? | Font has old ttfautohint applied? | [
"Font",
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"""Font has old ttfautohint applied?"""
from fontbakery.utils import get_name_entry_strings
def ttfautohint_version(values):
import re
for value in values:
results = re.search(r'ttfautohint \(v(.*)\)', value)
if results:
return results.group(1)
version_strings = get_name_entry_strings(ttFont, NameID.VERSION_STRING)
ttfa_version = ttfautohint_version(version_strings)
if len(version_strings) == 0:
yield FAIL,\
Message("lacks-version-strings",
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elif ttfa_version is None:
yield INFO,\
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yield WARN,\
Message("old-ttfa",
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f" latest = {LATEST_TTFAUTOHINT_VERSION};"
f" Need to re-run with the newer version!")
else:
yield PASS, (f"Font has been hinted with ttfautohint {ttfa_version}"
f" which is greater than or equal to the latest"
f" known version {LATEST_TTFAUTOHINT_VERSION}")
except ValueError:
yield FAIL,\
Message("parse-error",
f"Failed to parse ttfautohint version values:"
f" latest = '{LATEST_TTFAUTOHINT_VERSION}';"
f" used_in_font = '{ttfa_version}'") | [
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hudson-and-thames/mlfinlab | 79dcc7120ec84110578f75b025a75850eb72fc73 | mlfinlab/microstructural_features/entropy.py | python | get_konto_entropy | (message: str, window: int = 0) | Advances in Financial Machine Learning, Snippet 18.4, page 268.
Implementations of Algorithms Discussed in Gao et al.[2008]
Get Kontoyiannis entropy
:param message: (str or array) Encoded message
:param window: (int) Expanding window length, can be negative
:return: (float) Kontoyiannis entropy | Advances in Financial Machine Learning, Snippet 18.4, page 268. | [
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] | def get_konto_entropy(message: str, window: int = 0) -> float:
"""
Advances in Financial Machine Learning, Snippet 18.4, page 268.
Implementations of Algorithms Discussed in Gao et al.[2008]
Get Kontoyiannis entropy
:param message: (str or array) Encoded message
:param window: (int) Expanding window length, can be negative
:return: (float) Kontoyiannis entropy
"""
pass | [
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spack/spack | 675210bd8bd1c5d32ad1cc83d898fb43b569ed74 | var/spack/repos/builtin/packages/catalyst/package.py | python | Catalyst.root_cmakelists_dir | (self) | return os.path.join(self.stage.source_path,
'Catalyst-v' + str(self.version)) | The relative path to the directory containing CMakeLists.txt
This path is relative to the root of the extracted tarball,
not to the ``build_directory``. Defaults to the current directory.
:return: directory containing CMakeLists.txt | The relative path to the directory containing CMakeLists.txt | [
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"""The relative path to the directory containing CMakeLists.txt
This path is relative to the root of the extracted tarball,
not to the ``build_directory``. Defaults to the current directory.
:return: directory containing CMakeLists.txt
"""
return os.path.join(self.stage.source_path,
'Catalyst-v' + str(self.version)) | [
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robhagemans/pcbasic | c3a043b46af66623a801e18a38175be077251ada | pcbasic/basic/devices/cassette.py | python | TapeBitStream.write_intro | (self) | Write some noise to give the reader something to get started. | Write some noise to give the reader something to get started. | [
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"""Write some noise to give the reader something to get started."""
# We just need some bits here
# however on a new CAS file this works like a magic-sequence...
for b in bytearray(self.intro):
self.write_byte(b)
# Write seven bits, so that we are byte-aligned after the sync bit
# (after the 256-byte pilot). Makes CAS-files easier to read in hex.
for _ in range(7):
self.write_bit(0)
self.write_pause(100) | [
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CvvT/dumpDex | 92ab3b7e996194a06bf1dd5538a4954e8a5ee9c1 | python/idaapi.py | python | idc_get_local_type_name | (*args) | return _idaapi.idc_get_local_type_name(*args) | idc_get_local_type_name(ordinal) -> char | idc_get_local_type_name(ordinal) -> char | [
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"""
idc_get_local_type_name(ordinal) -> char
"""
return _idaapi.idc_get_local_type_name(*args) | [
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dragnet-org/dragnet | 4a1649d9b29bf64ccc5a86200e415e8b04cd257b | dragnet/extractor.py | python | Extractor._has_enough_blocks | (self, blocks) | return True | [] | def _has_enough_blocks(self, blocks):
if len(blocks) < 3:
logging.warning(
'extraction failed: too few blocks (%s)', len(blocks))
return False
return True | [
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python-social-auth/social-core | 1ea27e8989657bb35dd37b6ee2e038e1358fbc96 | social_core/backends/cilogon.py | python | CILogonOAuth2.user_data | (self, token, *args, **kwargs) | Loads user data from endpoint | Loads user data from endpoint | [
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"""Loads user data from endpoint"""
url = 'https://cilogon.org/oauth2/userinfo'
data = {'access_token': token}
try:
return self.get_json(url, method='POST', data=data)
except ValueError:
return None | [
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... | https://github.com/python-social-auth/social-core/blob/1ea27e8989657bb35dd37b6ee2e038e1358fbc96/social_core/backends/cilogon.py#L18-L25 | ||
mozillazg/pypy | 2ff5cd960c075c991389f842c6d59e71cf0cb7d0 | rpython/rlib/objectmodel.py | python | _Specialize.ll | (self) | return decorated_func | This is version of argtypes that cares about low-level types
(so it'll get additional copies for two different types of pointers
for example). Same warnings about exponential behavior apply. | This is version of argtypes that cares about low-level types
(so it'll get additional copies for two different types of pointers
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caiiiac/Machine-Learning-with-Python | 1a26c4467da41ca4ebc3d5bd789ea942ef79422f | MachineLearning/venv/lib/python3.5/site-packages/setuptools/sandbox.py | python | _needs_hiding | (mod_name) | return bool(pattern.match(mod_name)) | >>> _needs_hiding('setuptools')
True
>>> _needs_hiding('pkg_resources')
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>>> _needs_hiding('setuptools_plugin')
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>>> _needs_hiding('setuptools.__init__')
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>>> _needs_hiding('distutils')
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>>> _needs_hiding('os')
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>>> _needs_hiding('Cython')
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>>> _needs_hiding('setuptools_plugin')
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>>> _needs_hiding('setuptools.__init__')
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>>> _needs_hiding('distutils')
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>>> _needs_hiding('os')
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>>> _needs_hiding('Cython')
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IronLanguages/ironpython3 | 7a7bb2a872eeab0d1009fc8a6e24dca43f65b693 | Src/StdLib/Lib/cgi.py | python | parse_qsl | (qs, keep_blank_values=0, strict_parsing=0) | return urllib.parse.parse_qsl(qs, keep_blank_values, strict_parsing) | Parse a query given as a string argument. | Parse a query given as a string argument. | [
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] | def parse_qsl(qs, keep_blank_values=0, strict_parsing=0):
"""Parse a query given as a string argument."""
warn("cgi.parse_qsl is deprecated, use urllib.parse.parse_qsl instead",
DeprecationWarning, 2)
return urllib.parse.parse_qsl(qs, keep_blank_values, strict_parsing) | [
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/states/dvs.py | python | _get_diff_dict | (dict1, dict2) | return ret_dict | Returns a dictionary with the diffs between two dictionaries
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Returns a dictionary with the diffs between two dictionaries
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"""
ret_dict = {}
for p in dict2.keys():
if p not in dict1:
ret_dict.update({p: {"val1": None, "val2": dict2[p]}})
elif dict1[p] != dict2[p]:
if isinstance(dict1[p], dict) and isinstance(dict2[p], dict):
sub_diff_dict = _get_diff_dict(dict1[p], dict2[p])
if sub_diff_dict:
ret_dict.update({p: sub_diff_dict})
else:
ret_dict.update({p: {"val1": dict1[p], "val2": dict2[p]}})
return ret_dict | [
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rlvaugh/Impractical_Python_Projects | ff9065e94430dc4ecf76d2c9e78f05fae499213e | Chapter_13/tvashtar.py | python | Particle.vector | (self) | Calculate particle vector at launch. | Calculate particle vector at launch. | [
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"""Calculate particle vector at launch."""
orient = random.uniform(60, 120) # 90 is vertical
radians = math.radians(orient)
self.dx = self.vel * math.cos(radians)
self.dy = -self.vel * math.sin(radians) | [
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realpython/book2-exercises | cde325eac8e6d8cff2316601c2e5b36bb46af7d0 | py2manager/gluon/languages.py | python | read_possible_plural_rules | () | return plurals | Creates list of all possible plural rules files
The result is cached in PLURAL_RULES dictionary to increase speed | Creates list of all possible plural rules files
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] | def read_possible_plural_rules():
"""
Creates list of all possible plural rules files
The result is cached in PLURAL_RULES dictionary to increase speed
"""
plurals = {}
try:
import gluon.contrib.plural_rules as package
for importer, modname, ispkg in pkgutil.iter_modules(package.__path__):
if len(modname) == 2:
module = __import__(package.__name__ + '.' + modname,
fromlist=[modname])
lang = modname
pname = modname + '.py'
nplurals = getattr(module, 'nplurals', DEFAULT_NPLURALS)
get_plural_id = getattr(
module, 'get_plural_id',
DEFAULT_GET_PLURAL_ID)
construct_plural_form = getattr(
module, 'construct_plural_form',
DEFAULT_CONSTRUCT_PLURAL_FORM)
plurals[lang] = (lang, nplurals, get_plural_id,
construct_plural_form)
except ImportError:
e = sys.exc_info()[1]
logging.warn('Unable to import plural rules: %s' % e)
return plurals | [
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apache/libcloud | 90971e17bfd7b6bb97b2489986472c531cc8e140 | libcloud/compute/drivers/hostvirtual.py | python | HostVirtualNodeDriver._wait_for_node | (self, node_id, timeout=30, interval=5.0) | :param node_id: ID of the node to wait for.
:type node_id: ``int``
:param timeout: Timeout (in seconds).
:type timeout: ``int``
:param interval: How long to wait (in seconds) between each attempt.
:type interval: ``float``
:return: Node representing the newly built server
:rtype: :class:`Node` | :param node_id: ID of the node to wait for.
:type node_id: ``int`` | [
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"""
:param node_id: ID of the node to wait for.
:type node_id: ``int``
:param timeout: Timeout (in seconds).
:type timeout: ``int``
:param interval: How long to wait (in seconds) between each attempt.
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:return: Node representing the newly built server
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"""
# poll until we get a node
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try:
node = self.ex_get_node(node_id)
return node
except HostVirtualException:
time.sleep(interval)
raise HostVirtualException(412, "Timeout on getting node details") | [
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prody/ProDy | b24bbf58aa8fffe463c8548ae50e3955910e5b7f | prody/utilities/logger.py | python | PackageLogger.exit | (self, status=0) | Exit the interpreter. | Exit the interpreter. | [
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pyparallel/pyparallel | 11e8c6072d48c8f13641925d17b147bf36ee0ba3 | Lib/tkinter/font.py | python | Font.actual | (self, option=None, displayof=None) | Return actual font attributes | Return actual font attributes | [
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] | def actual(self, option=None, displayof=None):
"Return actual font attributes"
args = ()
if displayof:
args = ('-displayof', displayof)
if option:
args = args + ('-' + option, )
return self._call("font", "actual", self.name, *args)
else:
return self._mkdict(
self._split(self._call("font", "actual", self.name, *args))) | [
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m-labs/nmigen | 6c5afdc3c37e82854b08861e8b6d95f644b45f6e | nmigen/hdl/ast.py | python | UserValue.lower | (self) | Conversion to a concrete representation. | Conversion to a concrete representation. | [
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openstack/barbican | a9d2b133c8dc3307974f119f9a2b23a4ba82e8ce | barbican/api/controllers/secretmeta.py | python | SecretMetadataController.__init__ | (self, secret) | [] | def __init__(self, secret):
LOG.debug('=== Creating SecretMetadataController ===')
self.secret = secret
self.secret_project_id = self.secret.project.external_id
self.secret_repo = repo.get_secret_repository()
self.user_meta_repo = repo.get_secret_user_meta_repository()
self.metadata_validator = validators.NewSecretMetadataValidator()
self.metadatum_validator = validators.NewSecretMetadatumValidator() | [
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thu-ml/tianshou | a2d76d1276bef334bba537a355a5ea12f4279410 | tianshou/policy/modelfree/npg.py | python | NPGPolicy._MVP | (self, v: torch.Tensor, flat_kl_grad: torch.Tensor) | return flat_kl_grad_grad + v * self._damping | Matrix vector product. | Matrix vector product. | [
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"""Matrix vector product."""
# caculate second order gradient of kl with respect to theta
kl_v = (flat_kl_grad * v).sum()
flat_kl_grad_grad = self._get_flat_grad(kl_v, self.actor,
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return flat_kl_grad_grad + v * self._damping | [
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mozillazg/pypy | 2ff5cd960c075c991389f842c6d59e71cf0cb7d0 | pypy/objspace/std/bytesobject.py | python | W_AbstractBytesObject.descr_swapcase | (self, space) | S.swapcase() -> string
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Blizzard/heroprotocol | 3d36eaf44fc4c8ff3331c2ae2f1dc08a94535f1c | heroprotocol/versions/protocol47903.py | python | decode_replay_initdata | (contents) | return decoder.instance(replay_initdata_typeid) | Decodes and return the replay init data from the contents byte string. | Decodes and return the replay init data from the contents byte string. | [
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] | def decode_replay_initdata(contents):
"""Decodes and return the replay init data from the contents byte string."""
decoder = BitPackedDecoder(contents, typeinfos)
return decoder.instance(replay_initdata_typeid) | [
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replit-archive/empythoned | 977ec10ced29a3541a4973dc2b59910805695752 | cpython/Lib/mailbox.py | python | MaildirMessage.get_subdir | (self) | return self._subdir | Return 'new' or 'cur'. | Return 'new' or 'cur'. | [
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return self._subdir | [
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junfu1115/DANet | 56a612ec1ed5c2573ebc8df04ad08475fbf13a52 | encoding/functions/customize.py | python | NonMaxSuppression | (boxes, scores, threshold) | r"""Non-Maximum Suppression
The algorithm begins by storing the highest-scoring bounding
box, and eliminating any box whose intersection-over-union (IoU)
with it is too great. The procedure repeats on the surviving
boxes, and so on until there are no boxes left.
The stored boxes are returned.
NB: The function returns a tuple (mask, indices), where
indices index into the input boxes and are sorted
according to score, from higest to lowest.
indices[i][mask[i]] gives the indices of the surviving
boxes from the ith batch, sorted by score.
Args:
- boxes :math:`(N, n_boxes, 4)`
- scroes :math:`(N, n_boxes)`
- threshold (float): IoU above which to eliminate boxes
Outputs:
- mask: :math:`(N, n_boxes)`
- indicies: :math:`(N, n_boxes)`
Examples::
>>> boxes = torch.Tensor([[[10., 20., 20., 15.],
>>> [24., 22., 50., 54.],
>>> [10., 21., 20. 14.5]]])
>>> scores = torch.abs(torch.randn([1, 3]))
>>> mask, indices = NonMaxSuppression(boxes, scores, 0.7)
>>> #indices are SORTED according to score.
>>> surviving_box_indices = indices[mask] | r"""Non-Maximum Suppression
The algorithm begins by storing the highest-scoring bounding
box, and eliminating any box whose intersection-over-union (IoU)
with it is too great. The procedure repeats on the surviving
boxes, and so on until there are no boxes left.
The stored boxes are returned. | [
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r"""Non-Maximum Suppression
The algorithm begins by storing the highest-scoring bounding
box, and eliminating any box whose intersection-over-union (IoU)
with it is too great. The procedure repeats on the surviving
boxes, and so on until there are no boxes left.
The stored boxes are returned.
NB: The function returns a tuple (mask, indices), where
indices index into the input boxes and are sorted
according to score, from higest to lowest.
indices[i][mask[i]] gives the indices of the surviving
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Args:
- boxes :math:`(N, n_boxes, 4)`
- scroes :math:`(N, n_boxes)`
- threshold (float): IoU above which to eliminate boxes
Outputs:
- mask: :math:`(N, n_boxes)`
- indicies: :math:`(N, n_boxes)`
Examples::
>>> boxes = torch.Tensor([[[10., 20., 20., 15.],
>>> [24., 22., 50., 54.],
>>> [10., 21., 20. 14.5]]])
>>> scores = torch.abs(torch.randn([1, 3]))
>>> mask, indices = NonMaxSuppression(boxes, scores, 0.7)
>>> #indices are SORTED according to score.
>>> surviving_box_indices = indices[mask]
"""
if boxes.is_cuda:
return lib.gpu.non_max_suppression(boxes, scores, threshold)
else:
return lib.cpu.non_max_suppression(boxes, scores, threshold) | [
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aroberge/friendly | 0b82326ba1cb982f8612885ac60957f095d7476f | friendly/core.py | python | TracebackData.get_source_info | (self) | Retrieves the file name and the line of code where the exception
was raised. | Retrieves the file name and the line of code where the exception
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"""Retrieves the file name and the line of code where the exception
was raised.
"""
if issubclass(self.exception_type, SyntaxError):
self.filename = self.value.filename
# Python 3.10 introduced new arguments. For simplicity,
# we give them some default values for other Python versions
# so that we can use these elsewhere without having to perform
# additional checks.
if not hasattr(self.value, "end_offset"):
self.value.end_offset = (
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if self.value.text is not None:
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if len(self.records) > 1:
self.program_stopped_frame, filename, linenumber, *_rest = self.records[
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_, line = cache.get_formatted_partial_source(filename, linenumber)
self.program_stopped_bad_line = line.rstrip()
else:
self.program_stopped_bad_line = self.bad_line
self.program_stopped_frame = self.exception_frame
return
# We should never reach this stage.
def _log_error(): # pragma: no cover
debug_helper.log("Internal error in TracebackData.get_source_info.")
debug_helper.log("No records found.")
debug_helper.log("self.exception_type:" + str(self.exception_type))
debug_helper.log("self.value:" + str(self.value))
debug_helper.log_error()
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ifwe/digsby | f5fe00244744aa131e07f09348d10563f3d8fa99 | digsby/src/gui/uberwidgets/connectionlist.py | python | ConnectionPanel.UpdateSkin | (self) | The usual update skin | The usual update skin | [
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self.bg = s('backgrounds.account',lambda: SkinColor(wx.WHITE))
self.majorfont = s('Fonts.Account', default_font)
self.minorfont = s('Fonts.StateAndLink',default_font)
self.linkfont = CopyFont(self.minorfont, underline=True)
self.majorfc = s('FontColors.Account', wx.BLACK)
self.statefc = s('FontColors.State',lambda: wx.Colour(125,125,125))
self.linkfc = s('FontColors.Link', wx.BLUE)
self.closeicon = [None]*3
self.closeicon[0] = s('Icons.Close',skin.get('AppDefaults.removeicon')).ResizedSmaller(16)
self.closeicon[1] = s('Icons.CloseHover',self.closeicon[0]).ResizedSmaller(16)
self.closeicon[2] = s('Icons.CloseDown',self.closeicon[1]).ResizedSmaller(16)
self.liconsize = s('ServiceIconSize',16)
self.padding = s('Padding',lambda: wx.Point(3,3))
self.stateicon = self.account.statusicon.ResizedSmaller(16)
self.licon = self.account.serviceicon.Resized(self.liconsize)
if self.initover:
self.CalcLayout() | [
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psd-tools/psd-tools | 00241f3aed2ca52a8012e198a0f390ff7d8edca9 | src/psd_tools/api/psd_image.py | python | PSDImage.open | (cls, fp, **kwargs) | return self | Open a PSD document.
:param fp: filename or file-like object.
:param encoding: charset encoding of the pascal string within the file,
default 'macroman'. Some psd files need explicit encoding option.
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Open a PSD document.
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default 'macroman'. Some psd files need explicit encoding option.
:return: A :py:class:`~psd_tools.api.psd_image.PSDImage` object.
"""
if hasattr(fp, 'read'):
self = cls(PSD.read(fp, **kwargs))
else:
with open(fp, 'rb') as f:
self = cls(PSD.read(f, **kwargs))
return self | [
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pculture/miro | d8e4594441939514dd2ac29812bf37087bb3aea5 | tv/lib/downloader.py | python | RemoteDownloader.get_filename | (self) | return self.filename | Returns the filename that we're downloading to. Should not be
called until state is "finished." | Returns the filename that we're downloading to. Should not be
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self.confirm_db_thread()
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edfungus/Crouton | ada98b3930192938a48909072b45cb84b945f875 | clients/python_clients/cf_demo_client/cf_env/lib/python2.7/site-packages/pip/_vendor/requests/cookies.py | python | RequestsCookieJar.multiple_domains | (self) | return False | Returns True if there are multiple domains in the jar.
Returns False otherwise. | Returns True if there are multiple domains in the jar.
Returns False otherwise. | [
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] | def multiple_domains(self):
"""Returns True if there are multiple domains in the jar.
Returns False otherwise."""
domains = []
for cookie in iter(self):
if cookie.domain is not None and cookie.domain in domains:
return True
domains.append(cookie.domain)
return False | [
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CharlesBlonde/libpurecoollink | a91362c57a0bc4126279c8c51c407dd713b08e10 | libpurecoollink/dyson_pure_state.py | python | DysonPureHotCoolState.__init__ | (self, payload) | Create a new Dyson Hot+Cool state.
:param product_type: Product type
:param payload: Message payload | Create a new Dyson Hot+Cool state. | [
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] | def __init__(self, payload):
"""Create a new Dyson Hot+Cool state.
:param product_type: Product type
:param payload: Message payload
"""
super().__init__(payload)
self._tilt = DysonPureCoolState._get_field_value(self._state, 'tilt')
self._fan_focus = DysonPureCoolState._get_field_value(self._state,
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self._heat_target = DysonPureCoolState._get_field_value(self._state,
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self._heat_mode = DysonPureCoolState._get_field_value(self._state,
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self._heat_state = DysonPureCoolState._get_field_value(self._state,
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urwid/urwid | e2423b5069f51d318ea1ac0f355a0efe5448f7eb | urwid/wimp.py | python | CheckBox._repr_words | (self) | return self.__super._repr_words() + [
python3_repr(self.label)] | [] | def _repr_words(self):
return self.__super._repr_words() + [
python3_repr(self.label)] | [
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mozilla/telemetry-airflow | 8162470e6eaad5688715ee53f32336ebc00bf352 | jobs/taar_lite_guidguid.py | python | is_valid_addon | (broadcast_amo_whitelist, guid, addon) | return not (
addon.is_system
or addon.app_disabled
or addon.type != "extension"
or addon.user_disabled
or addon.foreign_install
or
# make sure the amo_whitelist has been broadcast to worker nodes.
guid not in broadcast_amo_whitelist.value
or
# Make sure that the Pioneer addon is explicitly
# excluded
guid == "pioneer-opt-in@mozilla.org"
) | Filter individual addons out to exclude, system addons,
legacy addons, disabled addons, sideloaded addons. | Filter individual addons out to exclude, system addons,
legacy addons, disabled addons, sideloaded addons. | [
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"."
] | def is_valid_addon(broadcast_amo_whitelist, guid, addon):
""" Filter individual addons out to exclude, system addons,
legacy addons, disabled addons, sideloaded addons.
"""
return not (
addon.is_system
or addon.app_disabled
or addon.type != "extension"
or addon.user_disabled
or addon.foreign_install
or
# make sure the amo_whitelist has been broadcast to worker nodes.
guid not in broadcast_amo_whitelist.value
or
# Make sure that the Pioneer addon is explicitly
# excluded
guid == "pioneer-opt-in@mozilla.org"
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owid/covid-19-data | 936aeae6cfbdc0163939ed7bd8ecdbb2582c0a92 | scripts/src/cowidev/vax/manual/twitter/maldives.py | python | main | (api) | [] | def main(api):
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wanggrun/Adaptively-Connected-Neural-Networks | e27066ef52301bdafa5932f43af8feeb23647edb | tensorpack-installed/tensorpack/callbacks/monitor.py | python | TFEventWriter.__init__ | (self, logdir=None, max_queue=10, flush_secs=120) | Args:
Same as in :class:`tf.summary.FileWriter`.
logdir will be ``logger.get_logger_dir()`` by default. | Args:
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logdir will be ``logger.get_logger_dir()`` by default. | [
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"""
Args:
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logdir will be ``logger.get_logger_dir()`` by default.
"""
if logdir is None:
logdir = logger.get_logger_dir()
assert tf.gfile.IsDirectory(logdir), logdir
self._logdir = logdir
self._max_queue = max_queue
self._flush_secs = flush_secs | [
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maxjiang93/ugscnn | 89cdd512e21a2d0cbb884e52ee75645c39ad6ed7 | baseline/exp3_2d3ds/models/vgg.py | python | vgg19 | (pretrained=False, **kwargs) | return model | VGG 19-layer model (configuration "E")
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet | VGG 19-layer model (configuration "E") | [
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] | def vgg19(pretrained=False, **kwargs):
"""VGG 19-layer model (configuration "E")
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if pretrained:
kwargs['init_weights'] = False
model = VGG(make_layers(cfg['E']), **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['vgg19']))
return model | [
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tenzir/threatbus | a26096e7b61b3eddf25c445d40a6cd2ea4420558 | apps/suricata/suricata_threatbus/suricata.py | python | stop_signal | () | Implements Python's asyncio eventloop signal handler
https://docs.python.org/3/library/asyncio-eventloop.html
Cancels all running tasks and exits the app. | Implements Python's asyncio eventloop signal handler
https://docs.python.org/3/library/asyncio-eventloop.html
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"""
Implements Python's asyncio eventloop signal handler
https://docs.python.org/3/library/asyncio-eventloop.html
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"""
global user_exit
user_exit = True
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TristenHarr/MyPyBuilder | 5823ae9e5fcd2d745a70425c37a3aaa432c0389d | GuiBuilder/BUILDER/ProjectTemplate/Tabs/FrameTab/FrameTabBuild.py | python | FrameTab.refresh_edit_frames | (self) | This is used to refresh the edit frame
:return: None | This is used to refresh the edit frame | [
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"""
This is used to refresh the edit frame
:return: None
"""
tmp = FrameWidget()
for key in list(self.window.containers['edit_frame_frame'].containers.keys()):
garbage = self.window.containers['edit_frame_frame'].containers.pop(key)
garbage.destroy()
del garbage
for item in tmp.edit_components:
tmp_args = []
for arg in item['args']:
if arg is None:
tmp_args.append(self.commands[item['id']])
elif arg == 'hotfix': # TODO: Remove hotfix for something more stable
tmp_args.append(self.choose_edit_frame)
else:
tmp_args.append(arg)
item['args'] = tmp_args
self.window.containers['edit_frame_frame'].add_widget(**item) | [
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debian-calibre/calibre | 020fc81d3936a64b2ac51459ecb796666ab6a051 | src/calibre/ebooks/rtf2xml/list_numbers.py | python | ListNumbers.__init__ | (self,
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run_level=1,
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Optional:
'copy'-- whether to make a copy of result for debugging
'temp_dir' --where to output temporary results (default is
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Returns:
nothing | Required:
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Required:
'file'
Optional:
'copy'-- whether to make a copy of result for debugging
'temp_dir' --where to output temporary results (default is
directory from which the script is run.)
Returns:
nothing
"""
self.__file = in_file
self.__bug_handler = bug_handler
self.__copy = copy
self.__write_to = better_mktemp() | [
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sony/nnabla | 5b022f309bf2fa3ade0b6cb9bf05da9ddc67f104 | python/src/nnabla/utils/converter/onnx/importer.py | python | generate_stack | (node_name, in_name, out_name, axis, base_name, func_counter) | return sp | Generate a Stack operator to stack specified buffer | Generate a Stack operator to stack specified buffer | [
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sp.type = "Stack"
set_function_name(sp, node_name, base_name, func_counter)
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/vod/v20180717/models.py | python | DescribeSampleSnapshotTemplatesResponse.__init__ | (self) | r"""
:param TotalCount: 符合过滤条件的记录总数。
:type TotalCount: int
:param SampleSnapshotTemplateSet: 采样截图模板详情列表。
:type SampleSnapshotTemplateSet: list of SampleSnapshotTemplate
:param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。
:type RequestId: str | r"""
:param TotalCount: 符合过滤条件的记录总数。
:type TotalCount: int
:param SampleSnapshotTemplateSet: 采样截图模板详情列表。
:type SampleSnapshotTemplateSet: list of SampleSnapshotTemplate
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JimmXinu/FanFicFare | bc149a2deb2636320fe50a3e374af6eef8f61889 | included_dependencies/html5lib/_inputstream.py | python | EncodingParser.handlePossibleTag | (self, endTag) | return True | [] | def handlePossibleTag(self, endTag):
data = self.data
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# Read all attributes
attr = self.getAttribute()
while attr is not None:
attr = self.getAttribute()
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xonsh/xonsh | b76d6f994f22a4078f602f8b386f4ec280c8461f | xonsh/jobs.py | python | fg | (args, stdin=None) | return resume_job(args, wording="fg") | xonsh command: fg
Bring the currently active job to the foreground, or, if a single number is
given as an argument, bring that job to the foreground. Additionally,
specify "+" for the most recent job and "-" for the second most recent job. | xonsh command: fg | [
"xonsh",
"command",
":",
"fg"
] | def fg(args, stdin=None):
"""
xonsh command: fg
Bring the currently active job to the foreground, or, if a single number is
given as an argument, bring that job to the foreground. Additionally,
specify "+" for the most recent job and "-" for the second most recent job.
"""
return resume_job(args, wording="fg") | [
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tomplus/kubernetes_asyncio | f028cc793e3a2c519be6a52a49fb77ff0b014c9b | kubernetes_asyncio/client/api/apps_v1_api.py | python | AppsV1Api.read_namespaced_deployment_scale | (self, name, namespace, **kwargs) | return self.read_namespaced_deployment_scale_with_http_info(name, namespace, **kwargs) | read_namespaced_deployment_scale # noqa: E501
read scale of the specified Deployment # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.read_namespaced_deployment_scale(name, namespace, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str name: name of the Scale (required)
:param str namespace: object name and auth scope, such as for teams and projects (required)
:param str pretty: If 'true', then the output is pretty printed.
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: V1Scale
If the method is called asynchronously,
returns the request thread. | read_namespaced_deployment_scale # noqa: E501 | [
"read_namespaced_deployment_scale",
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"noqa",
":",
"E501"
] | def read_namespaced_deployment_scale(self, name, namespace, **kwargs): # noqa: E501
"""read_namespaced_deployment_scale # noqa: E501
read scale of the specified Deployment # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.read_namespaced_deployment_scale(name, namespace, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str name: name of the Scale (required)
:param str namespace: object name and auth scope, such as for teams and projects (required)
:param str pretty: If 'true', then the output is pretty printed.
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: V1Scale
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.read_namespaced_deployment_scale_with_http_info(name, namespace, **kwargs) | [
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natashamjaques/neural_chat | ddb977bb4602a67c460d02231e7bbf7b2cb49a97 | ParlAI/parlai/mturk/core/dev/mturk_manager.py | python | MTurkManager.remove_worker_qualification | (self, worker_id, qual_name, reason='') | Remove a qualification from a worker | Remove a qualification from a worker | [
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"""Remove a qualification from a worker"""
qual_id = mturk_utils.find_qualification(qual_name, self.is_sandbox)
if qual_id is False or qual_id is None:
shared_utils.print_and_log(
logging.WARN,
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should_print=True,
)
return
try:
mturk_utils.remove_worker_qualification(
worker_id, qual_id, self.is_sandbox, reason
)
shared_utils.print_and_log(
logging.INFO,
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should_print=True,
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SeldonIO/alibi | ce961caf995d22648a8338857822c90428af4765 | alibi/explainers/backends/cfrl_tabular.py | python | sample | (X_hat_split: List[np.ndarray],
X_ohe: np.ndarray,
C: Optional[np.ndarray],
category_map: Dict[int, List[str]],
stats: Dict[int, Dict[str, float]]) | return X_ohe_hat_split | Samples an instance from the given reconstruction according to the conditional vector and
the dictionary of statistics.
Parameters
----------
X_hat_split
List of reconstructed columns from the auto-encoder. The categorical columns contain logits.
X_ohe
One-hot encoded representation of the input.
C
Conditional vector.
category_map
Dictionary of category mapping. The keys are column indexes and the values are lists containing the possible
values for a feature.
stats
Dictionary of statistic of the training data. Contains the minimum and maximum value of each numerical
feature in the training set. Each key is an index of the column and each value is another dictionary
containing `min` and `max` keys.
Returns
-------
X_ohe_hat_split
Most probable reconstruction sample according to the autoencoder, sampled according to the conditional vector
and the dictionary of statistics. This method assumes that the input array, `X_ohe` , has the first columns
corresponding to the numerical features, and the rest are one-hot encodings of the categorical columns. | Samples an instance from the given reconstruction according to the conditional vector and
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X_ohe: np.ndarray,
C: Optional[np.ndarray],
category_map: Dict[int, List[str]],
stats: Dict[int, Dict[str, float]]) -> List[np.ndarray]:
"""
Samples an instance from the given reconstruction according to the conditional vector and
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List of reconstructed columns from the auto-encoder. The categorical columns contain logits.
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One-hot encoded representation of the input.
C
Conditional vector.
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Dictionary of category mapping. The keys are column indexes and the values are lists containing the possible
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Dictionary of statistic of the training data. Contains the minimum and maximum value of each numerical
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Most probable reconstruction sample according to the autoencoder, sampled according to the conditional vector
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"""
X_ohe_num_split, X_ohe_cat_split = split_ohe(X_ohe, category_map)
C_num_split, C_cat_split = split_ohe(C, category_map) if (C is not None) else (None, None)
X_ohe_hat_split = [] # list of sampled numerical columns and sampled categorical columns
num_feat, cat_feat = len(X_ohe_num_split), len(X_ohe_cat_split)
if num_feat > 0:
# Sample numerical columns
X_ohe_hat_split += sample_numerical(X_hat_num_split=X_hat_split[:num_feat],
X_ohe_num_split=X_ohe_num_split,
C_num_split=C_num_split,
stats=stats)
if cat_feat > 0:
# Sample categorical columns
X_ohe_hat_split += sample_categorical(X_hat_cat_split=X_hat_split[-cat_feat:],
C_cat_split=C_cat_split)
return X_ohe_hat_split | [
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getnikola/nikola | 2da876e9322e42a93f8295f950e336465c6a4ee5 | nikola/plugins/command/github_deploy.py | python | CommandGitHubDeploy._run_command | (self, command, xfail=False) | Run a command that may or may not fail. | Run a command that may or may not fail. | [
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return 0
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caiiiac/Machine-Learning-with-Python | 1a26c4467da41ca4ebc3d5bd789ea942ef79422f | MachineLearning/venv/lib/python3.5/site-packages/setuptools/msvc.py | python | SystemInfo.WindowsSdkDir | (self) | return sdkdir | Microsoft Windows SDK directory. | Microsoft Windows SDK directory. | [
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"""
Microsoft Windows SDK directory.
"""
sdkdir = ''
for ver in self.WindowsSdkVersion:
# Try to get it from registry
loc = os.path.join(self.ri.windows_sdk, 'v%s' % ver)
sdkdir = self.ri.lookup(loc, 'installationfolder')
if sdkdir:
break
if not sdkdir or not os.path.isdir(sdkdir):
# Try to get "VC++ for Python" version from registry
path = os.path.join(self.ri.vc_for_python, '%0.1f' % self.vc_ver)
install_base = self.ri.lookup(path, 'installdir')
if install_base:
sdkdir = os.path.join(install_base, 'WinSDK')
if not sdkdir or not os.path.isdir(sdkdir):
# If fail, use default new path
for ver in self.WindowsSdkVersion:
intver = ver[:ver.rfind('.')]
path = r'Microsoft SDKs\Windows Kits\%s' % (intver)
d = os.path.join(self.ProgramFiles, path)
if os.path.isdir(d):
sdkdir = d
if not sdkdir or not os.path.isdir(sdkdir):
# If fail, use default old path
for ver in self.WindowsSdkVersion:
path = r'Microsoft SDKs\Windows\v%s' % ver
d = os.path.join(self.ProgramFiles, path)
if os.path.isdir(d):
sdkdir = d
if not sdkdir:
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sdkdir = os.path.join(self.VCInstallDir, 'PlatformSDK')
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pyvisa/pyvisa-py | 91e3475883b0f6e20dd308f0925f9940fcf818ea | pyvisa_py/sessions.py | python | Session.register_unavailable | (
cls, interface_type: constants.InterfaceType, resource_class: str, msg: str
) | Register that no session class exists.
This creates a fake session that will raise a ValueError if called.
Parameters
----------
interface_type : constants.InterfaceType
Type of interface.
resource_class : str
Class of the resource
msg : str
Message detailing why no session class exists for this particular
interface type, resource class pair.
Returns
-------
Type[Session]
Fake session. | Register that no session class exists. | [
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cls, interface_type: constants.InterfaceType, resource_class: str, msg: str
) -> None:
"""Register that no session class exists.
This creates a fake session that will raise a ValueError if called.
Parameters
----------
interface_type : constants.InterfaceType
Type of interface.
resource_class : str
Class of the resource
msg : str
Message detailing why no session class exists for this particular
interface type, resource class pair.
Returns
-------
Type[Session]
Fake session.
"""
class _internal(Session):
#: Message detailing why no session is available.
session_issue: str = msg
def __init__(self, *args, **kwargs) -> None:
raise ValueError(msg)
def _get_attribute(self, attr):
raise NotImplementedError()
def _set_attribute(self, attr, value):
raise NotImplementedError()
def close(self):
raise NotImplementedError()
if (interface_type, resource_class) in cls._session_classes:
logger.warning(
"%s is already registered in the ResourceManager. "
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((interface_type, resource_class), msg),
)
cls._session_classes[(interface_type, resource_class)] = _internal | [
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/modules/marathon.py | python | apps | () | return {"apps": [app["id"] for app in response["dict"]["apps"]]} | Return a list of the currently installed app ids.
CLI Example:
.. code-block:: bash
salt marathon-minion-id marathon.apps | Return a list of the currently installed app ids. | [
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"""
Return a list of the currently installed app ids.
CLI Example:
.. code-block:: bash
salt marathon-minion-id marathon.apps
"""
response = salt.utils.http.query(
"{}/v2/apps".format(_base_url()),
decode_type="json",
decode=True,
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return {"apps": [app["id"] for app in response["dict"]["apps"]]} | [
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georgesung/ssd_tensorflow_traffic_sign_detection | d9a16cc330842c1d5b93bd1c64df8079cd9197e8 | model.py | python | nms | (y_pred_conf, y_pred_loc, prob) | return boxes | Non-Maximum Suppression (NMS)
Performs NMS on all boxes of each class where predicted probability > CONF_THRES
For all boxes exceeding IOU threshold, select the box with highest confidence
Returns a lsit of box coordinates post-NMS
Arguments:
* y_pred_conf: Class predictions, numpy array of shape (num_feature_map_cells * num_defaul_boxes,)
* y_pred_loc: Bounding box coordinates, numpy array of shape (num_feature_map_cells * num_defaul_boxes * 4,)
These coordinates are normalized coordinates relative to center of feature map cell
* prob: Class probabilities, numpy array of shape (num_feature_map_cells * num_defaul_boxes,)
Returns:
* boxes: Numpy array of boxes, with shape (num_boxes, 6). shape[0] is interpreted as:
[x1, y1, x2, y2, class, probability], where x1/y1/x2/y2 are the coordinates of the
upper-left and lower-right corners. Box coordinates assume the image size is IMG_W x IMG_H.
Remember to rescale box coordinates if your target image has different dimensions. | Non-Maximum Suppression (NMS)
Performs NMS on all boxes of each class where predicted probability > CONF_THRES
For all boxes exceeding IOU threshold, select the box with highest confidence
Returns a lsit of box coordinates post-NMS | [
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"""
Non-Maximum Suppression (NMS)
Performs NMS on all boxes of each class where predicted probability > CONF_THRES
For all boxes exceeding IOU threshold, select the box with highest confidence
Returns a lsit of box coordinates post-NMS
Arguments:
* y_pred_conf: Class predictions, numpy array of shape (num_feature_map_cells * num_defaul_boxes,)
* y_pred_loc: Bounding box coordinates, numpy array of shape (num_feature_map_cells * num_defaul_boxes * 4,)
These coordinates are normalized coordinates relative to center of feature map cell
* prob: Class probabilities, numpy array of shape (num_feature_map_cells * num_defaul_boxes,)
Returns:
* boxes: Numpy array of boxes, with shape (num_boxes, 6). shape[0] is interpreted as:
[x1, y1, x2, y2, class, probability], where x1/y1/x2/y2 are the coordinates of the
upper-left and lower-right corners. Box coordinates assume the image size is IMG_W x IMG_H.
Remember to rescale box coordinates if your target image has different dimensions.
"""
# Keep track of boxes for each class
class_boxes = {} # class -> [(x1, y1, x2, y2, prob), (...), ...]
with open('signnames.csv', 'r') as f:
for line in f:
cls, _ = line.split(',')
class_boxes[float(cls)] = []
# Go through all possible boxes and perform class-based greedy NMS (greedy based on class prediction confidence)
y_idx = 0
for fm_size in FM_SIZES:
fm_h, fm_w = fm_size # feature map height and width
for row in range(fm_h):
for col in range(fm_w):
for db in DEFAULT_BOXES:
# Only perform calculations if class confidence > CONF_THRESH and not background class
if prob[y_idx] > CONF_THRESH and y_pred_conf[y_idx] > 0.:
# Calculate absolute coordinates of predicted bounding box
xc, yc = col + 0.5, row + 0.5 # center of current feature map cell
center_coords = np.array([xc, yc, xc, yc])
abs_box_coords = center_coords + y_pred_loc[y_idx*4 : y_idx*4 + 4] # predictions are offsets to center of fm cell
# Calculate predicted box coordinates in actual image
scale = np.array([IMG_W/fm_w, IMG_H/fm_h, IMG_W/fm_w, IMG_H/fm_h])
box_coords = abs_box_coords * scale
box_coords = [int(round(x)) for x in box_coords]
# Compare this box to all previous boxes of this class
cls = y_pred_conf[y_idx]
cls_prob = prob[y_idx]
box = (*box_coords, cls, cls_prob)
if len(class_boxes[cls]) == 0:
class_boxes[cls].append(box)
else:
suppressed = False # did this box suppress other box(es)?
overlapped = False # did this box overlap with other box(es)?
for other_box in class_boxes[cls]:
iou = calc_iou(box[:4], other_box[:4])
if iou > NMS_IOU_THRESH:
overlapped = True
# If current box has higher confidence than other box
if box[5] > other_box[5]:
class_boxes[cls].remove(other_box)
suppressed = True
if suppressed or not overlapped:
class_boxes[cls].append(box)
y_idx += 1
# Gather all the pruned boxes and return them
boxes = []
for cls in class_boxes.keys():
for class_box in class_boxes[cls]:
boxes.append(class_box)
boxes = np.array(boxes)
return boxes | [
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f-dangel/backpack | 1da7e53ebb2c490e2b7dd9f79116583641f3cca1 | backpack/core/derivatives/linear.py | python | LinearDerivatives._jac_t_mat_prod | (
self,
module: Linear,
g_inp: Tuple[Tensor],
g_out: Tuple[Tensor],
mat: Tensor,
subsampling: List[int] = None,
) | return einsum("vn...o,oi->vn...i", mat, module.weight) | Batch-apply transposed Jacobian of the output w.r.t. the input.
Args:
module: Linear layer.
g_inp: Gradients w.r.t. module input. Not required by the implementation.
g_out: Gradients w.r.t. module output. Not required by the implementation.
mat: Batch of ``V`` vectors of same shape as the layer output
(``[N, *, out_features]``) to which the transposed output-input Jacobian
is applied. Has shape ``[V, N, *, out_features]``; but if used with
sub-sampling, ``N`` is replaced by ``len(subsampling)``.
subsampling: Indices of active samples. ``None`` means all samples.
Returns:
Batched transposed Jacobian vector products. Has shape
``[V, N, *, in_features]``. If used with sub-sampling, ``N`` is replaced
by ``len(subsampling)``. | Batch-apply transposed Jacobian of the output w.r.t. the input. | [
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] | def _jac_t_mat_prod(
self,
module: Linear,
g_inp: Tuple[Tensor],
g_out: Tuple[Tensor],
mat: Tensor,
subsampling: List[int] = None,
) -> Tensor:
"""Batch-apply transposed Jacobian of the output w.r.t. the input.
Args:
module: Linear layer.
g_inp: Gradients w.r.t. module input. Not required by the implementation.
g_out: Gradients w.r.t. module output. Not required by the implementation.
mat: Batch of ``V`` vectors of same shape as the layer output
(``[N, *, out_features]``) to which the transposed output-input Jacobian
is applied. Has shape ``[V, N, *, out_features]``; but if used with
sub-sampling, ``N`` is replaced by ``len(subsampling)``.
subsampling: Indices of active samples. ``None`` means all samples.
Returns:
Batched transposed Jacobian vector products. Has shape
``[V, N, *, in_features]``. If used with sub-sampling, ``N`` is replaced
by ``len(subsampling)``.
"""
return einsum("vn...o,oi->vn...i", mat, module.weight) | [
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nucleic/enaml | 65c2a2a2d765e88f2e1103046680571894bb41ed | enaml/layout/dock_layout.py | python | DockLayout.children | (self) | return self.items[:] | Get the list of children of the dock layout. | Get the list of children of the dock layout. | [
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""" Get the list of children of the dock layout.
"""
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rbw/pysnow | 2b1c4accdf235675c4a4e610b31fb0a3cd4f9923 | docs/_themes/sphinx_rtd_theme/__init__.py | python | get_html_theme_path | () | return cur_dir | Return list of HTML theme paths. | Return list of HTML theme paths. | [
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] | def get_html_theme_path():
"""Return list of HTML theme paths."""
cur_dir = path.abspath(path.dirname(path.dirname(__file__)))
return cur_dir | [
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artetxem/monoses | 352c90120a543467b81175b94abaea65cd819933 | bli/induce-dictionary.py | python | induce_dictionary | (args) | [] | def induce_dictionary(args):
for src, trg in ('src', 'trg'), ('trg', 'src'):
bash('zcat ' + quote(args.working + '/step9/' + src + '2' + trg + '.phrase-table.gz') +
' | python3 ' + quote(PT2DICT) + ' -f ' + str(args.feature) + (' -r' if args.reverse else '') + (' --phrases' if args.phrases else '') +
' | ' + quote(MOSES + '/scripts/tokenizer/deescape-special-chars.perl') +
' | LC_ALL=C sort -k1,1 -k3,3gr' +
' > ' + quote(args.working + '/' + (trg + '2' + src if args.reverse else src + '2' + trg) + '.dic')) | [
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cylc/cylc-flow | 5ec221143476c7c616c156b74158edfbcd83794a | cylc/flow/task_pool.py | python | TaskPool.log_task_pool | (self, log_lvl=logging.DEBUG) | Log content of task and prerequisite pools in debug mode. | Log content of task and prerequisite pools in debug mode. | [
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] | def log_task_pool(self, log_lvl=logging.DEBUG):
"""Log content of task and prerequisite pools in debug mode."""
for pool, name in [
(self.main_pool_list, "Main"),
(self.hidden_pool_list, "Hidden")
]:
if pool:
LOG.log(
log_lvl,
f"{name} pool:\n"
+ "\n".join(
f"* {itask} status={itask.state.status}"
f" runahead={itask.state.is_runahead}"
f" queued={itask.state.is_queued}"
for itask in pool
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LinkedInAttic/indextank-service | 880c6295ce8e7a3a55bf9b3777cc35c7680e0d7e | storefront/api_linked_models.py | python | Deploy.is_writable | (self) | return self.status == Deploy.States.controllable or \
self.status == Deploy.States.recovering or \
self.status == Deploy.States.move_requested or \
self.status == Deploy.States.moving | Returns True if new data has to be written to this deployment. | Returns True if new data has to be written to this deployment. | [
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] | def is_writable(self):
'''Returns True if new data has to be written to this deployment.'''
return self.status == Deploy.States.controllable or \
self.status == Deploy.States.recovering or \
self.status == Deploy.States.move_requested or \
self.status == Deploy.States.moving | [
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oracle/oci-python-sdk | 3c1604e4e212008fb6718e2f68cdb5ef71fd5793 | src/oci/service_catalog/service_catalog_client.py | python | ServiceCatalogClient.list_private_application_packages | (self, private_application_id, **kwargs) | Lists the packages in the specified private application.
:param str private_application_id: (required)
The unique identifier for the private application.
:param str private_application_package_id: (optional)
The unique identifier for the private application package.
:param list[str] package_type: (optional)
Name of the package type. If multiple package types are provided, then any resource with
one or more matching package types will be returned.
Allowed values are: "STACK"
:param int limit: (optional)
How many records to return. Specify a value greater than zero and less than or equal to 1000. The default is 30.
:param str page: (optional)
The value of the `opc-next-page` response header from the previous \"List\" call.
:param str opc_request_id: (optional)
Unique Oracle-assigned identifier for the request. If you need to contact Oracle about a particular request,
please provide the request ID.
:param str sort_by: (optional)
The field to use to sort listed results. You can only specify one field to sort by.
`TIMECREATED` displays results in descending order by default. You can change your
preference by specifying a different sort order.
Allowed values are: "TIMECREATED", "VERSION"
:param str sort_order: (optional)
The sort order to apply, either `ASC` or `DESC`. Default is `ASC`.
Allowed values are: "ASC", "DESC"
:param str display_name: (optional)
Exact match name filter.
:param obj retry_strategy: (optional)
A retry strategy to apply to this specific operation/call. This will override any retry strategy set at the client-level.
This should be one of the strategies available in the :py:mod:`~oci.retry` module. This operation will not retry by default, users can also use the convenient :py:data:`~oci.retry.DEFAULT_RETRY_STRATEGY` provided by the SDK to enable retries for it.
The specifics of the default retry strategy are described `here <https://docs.oracle.com/en-us/iaas/tools/python/latest/sdk_behaviors/retries.html>`__.
To have this operation explicitly not perform any retries, pass an instance of :py:class:`~oci.retry.NoneRetryStrategy`.
:return: A :class:`~oci.response.Response` object with data of type :class:`~oci.service_catalog.models.PrivateApplicationPackageCollection`
:rtype: :class:`~oci.response.Response`
:example:
Click `here <https://docs.cloud.oracle.com/en-us/iaas/tools/python-sdk-examples/latest/servicecatalog/list_private_application_packages.py.html>`__ to see an example of how to use list_private_application_packages API. | Lists the packages in the specified private application. | [
"Lists",
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"in",
"the",
"specified",
"private",
"application",
"."
] | def list_private_application_packages(self, private_application_id, **kwargs):
"""
Lists the packages in the specified private application.
:param str private_application_id: (required)
The unique identifier for the private application.
:param str private_application_package_id: (optional)
The unique identifier for the private application package.
:param list[str] package_type: (optional)
Name of the package type. If multiple package types are provided, then any resource with
one or more matching package types will be returned.
Allowed values are: "STACK"
:param int limit: (optional)
How many records to return. Specify a value greater than zero and less than or equal to 1000. The default is 30.
:param str page: (optional)
The value of the `opc-next-page` response header from the previous \"List\" call.
:param str opc_request_id: (optional)
Unique Oracle-assigned identifier for the request. If you need to contact Oracle about a particular request,
please provide the request ID.
:param str sort_by: (optional)
The field to use to sort listed results. You can only specify one field to sort by.
`TIMECREATED` displays results in descending order by default. You can change your
preference by specifying a different sort order.
Allowed values are: "TIMECREATED", "VERSION"
:param str sort_order: (optional)
The sort order to apply, either `ASC` or `DESC`. Default is `ASC`.
Allowed values are: "ASC", "DESC"
:param str display_name: (optional)
Exact match name filter.
:param obj retry_strategy: (optional)
A retry strategy to apply to this specific operation/call. This will override any retry strategy set at the client-level.
This should be one of the strategies available in the :py:mod:`~oci.retry` module. This operation will not retry by default, users can also use the convenient :py:data:`~oci.retry.DEFAULT_RETRY_STRATEGY` provided by the SDK to enable retries for it.
The specifics of the default retry strategy are described `here <https://docs.oracle.com/en-us/iaas/tools/python/latest/sdk_behaviors/retries.html>`__.
To have this operation explicitly not perform any retries, pass an instance of :py:class:`~oci.retry.NoneRetryStrategy`.
:return: A :class:`~oci.response.Response` object with data of type :class:`~oci.service_catalog.models.PrivateApplicationPackageCollection`
:rtype: :class:`~oci.response.Response`
:example:
Click `here <https://docs.cloud.oracle.com/en-us/iaas/tools/python-sdk-examples/latest/servicecatalog/list_private_application_packages.py.html>`__ to see an example of how to use list_private_application_packages API.
"""
resource_path = "/privateApplicationPackages"
method = "GET"
# Don't accept unknown kwargs
expected_kwargs = [
"retry_strategy",
"private_application_package_id",
"package_type",
"limit",
"page",
"opc_request_id",
"sort_by",
"sort_order",
"display_name"
]
extra_kwargs = [_key for _key in six.iterkeys(kwargs) if _key not in expected_kwargs]
if extra_kwargs:
raise ValueError(
"list_private_application_packages got unknown kwargs: {!r}".format(extra_kwargs))
if 'package_type' in kwargs:
package_type_allowed_values = ["STACK"]
for package_type_item in kwargs['package_type']:
if package_type_item not in package_type_allowed_values:
raise ValueError(
"Invalid value for `package_type`, must be one of {0}".format(package_type_allowed_values)
)
if 'sort_by' in kwargs:
sort_by_allowed_values = ["TIMECREATED", "VERSION"]
if kwargs['sort_by'] not in sort_by_allowed_values:
raise ValueError(
"Invalid value for `sort_by`, must be one of {0}".format(sort_by_allowed_values)
)
if 'sort_order' in kwargs:
sort_order_allowed_values = ["ASC", "DESC"]
if kwargs['sort_order'] not in sort_order_allowed_values:
raise ValueError(
"Invalid value for `sort_order`, must be one of {0}".format(sort_order_allowed_values)
)
query_params = {
"privateApplicationId": private_application_id,
"privateApplicationPackageId": kwargs.get("private_application_package_id", missing),
"packageType": self.base_client.generate_collection_format_param(kwargs.get("package_type", missing), 'multi'),
"limit": kwargs.get("limit", missing),
"page": kwargs.get("page", missing),
"sortBy": kwargs.get("sort_by", missing),
"sortOrder": kwargs.get("sort_order", missing),
"displayName": kwargs.get("display_name", missing)
}
query_params = {k: v for (k, v) in six.iteritems(query_params) if v is not missing and v is not None}
header_params = {
"accept": "application/json",
"content-type": "application/json",
"opc-request-id": kwargs.get("opc_request_id", missing)
}
header_params = {k: v for (k, v) in six.iteritems(header_params) if v is not missing and v is not None}
retry_strategy = self.base_client.get_preferred_retry_strategy(
operation_retry_strategy=kwargs.get('retry_strategy'),
client_retry_strategy=self.retry_strategy
)
if retry_strategy:
if not isinstance(retry_strategy, retry.NoneRetryStrategy):
self.base_client.add_opc_client_retries_header(header_params)
retry_strategy.add_circuit_breaker_callback(self.circuit_breaker_callback)
return retry_strategy.make_retrying_call(
self.base_client.call_api,
resource_path=resource_path,
method=method,
query_params=query_params,
header_params=header_params,
response_type="PrivateApplicationPackageCollection")
else:
return self.base_client.call_api(
resource_path=resource_path,
method=method,
query_params=query_params,
header_params=header_params,
response_type="PrivateApplicationPackageCollection") | [
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idanr1986/cuckoo-droid | 1350274639473d3d2b0ac740cae133ca53ab7444 | analyzer/android_on_linux/lib/api/androguard/apk.py | python | APK.get_details_permissions | (self) | return l | Return permissions with details
:rtype: list of string | Return permissions with details | [
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] | def get_details_permissions(self) :
"""
Return permissions with details
:rtype: list of string
"""
l = {}
for i in self.permissions :
perm = i
pos = i.rfind(".")
if pos != -1 :
perm = i[pos+1:]
try :
l[ i ] = DVM_PERMISSIONS["MANIFEST_PERMISSION"][ perm ]
except KeyError :
l[ i ] = [ "dangerous", "Unknown permission from android reference", "Unknown permission from android reference" ]
return l | [
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CedricGuillemet/Imogen | ee417b42747ed5b46cb11b02ef0c3630000085b3 | bin/Lib/random.py | python | Random.choices | (self, population, weights=None, *, cum_weights=None, k=1) | return [population[bisect(cum_weights, random() * total, 0, hi)]
for i in range(k)] | Return a k sized list of population elements chosen with replacement.
If the relative weights or cumulative weights are not specified,
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"""Return a k sized list of population elements chosen with replacement.
If the relative weights or cumulative weights are not specified,
the selections are made with equal probability.
"""
random = self.random
if cum_weights is None:
if weights is None:
_int = int
total = len(population)
return [population[_int(random() * total)] for i in range(k)]
cum_weights = list(_itertools.accumulate(weights))
elif weights is not None:
raise TypeError('Cannot specify both weights and cumulative weights')
if len(cum_weights) != len(population):
raise ValueError('The number of weights does not match the population')
bisect = _bisect.bisect
total = cum_weights[-1]
hi = len(cum_weights) - 1
return [population[bisect(cum_weights, random() * total, 0, hi)]
for i in range(k)] | [
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cenkalti/github-flask | 9f58d61b7d328cef857edbb5c64a5d3f716367cb | flask_github.py | python | GitHub.authorized_handler | (self, f) | return decorated | Decorator for the route that is used as the callback for authorizing
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jhpyle/docassemble | b90c84e57af59aa88b3404d44d0b125c70f832cc | docassemble_base/docassemble/base/functions.py | python | set_info | (**kwargs) | Used to set the values of global variables you wish to retrieve through get_info(). | Used to set the values of global variables you wish to retrieve through get_info(). | [
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"""Used to set the values of global variables you wish to retrieve through get_info()."""
for att, val in kwargs.items():
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ntasfi/PyGame-Learning-Environment | 3dbe79dc0c35559bb441b9359948aabf9bb3d331 | ple/games/pong.py | python | Pong.getScore | (self) | return self.score_sum | [] | def getScore(self):
return self.score_sum | [
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lfz/Guided-Denoise | 8881ab768d16eaf87342da4ff7dc8271e183e205 | Attackset/fgsm_v3_random/nets/mobilenet_v1.py | python | mobilenet_v1_base | (inputs,
final_endpoint='Conv2d_13_pointwise',
min_depth=8,
depth_multiplier=1.0,
conv_defs=None,
output_stride=None,
scope=None) | Mobilenet v1.
Constructs a Mobilenet v1 network from inputs to the given final endpoint.
Args:
inputs: a tensor of shape [batch_size, height, width, channels].
final_endpoint: specifies the endpoint to construct the network up to. It
can be one of ['Conv2d_0', 'Conv2d_1_pointwise', 'Conv2d_2_pointwise',
'Conv2d_3_pointwise', 'Conv2d_4_pointwise', 'Conv2d_5'_pointwise,
'Conv2d_6_pointwise', 'Conv2d_7_pointwise', 'Conv2d_8_pointwise',
'Conv2d_9_pointwise', 'Conv2d_10_pointwise', 'Conv2d_11_pointwise',
'Conv2d_12_pointwise', 'Conv2d_13_pointwise'].
min_depth: Minimum depth value (number of channels) for all convolution ops.
Enforced when depth_multiplier < 1, and not an active constraint when
depth_multiplier >= 1.
depth_multiplier: Float multiplier for the depth (number of channels)
for all convolution ops. The value must be greater than zero. Typical
usage will be to set this value in (0, 1) to reduce the number of
parameters or computation cost of the model.
conv_defs: A list of ConvDef namedtuples specifying the net architecture.
output_stride: An integer that specifies the requested ratio of input to
output spatial resolution. If not None, then we invoke atrous convolution
if necessary to prevent the network from reducing the spatial resolution
of the activation maps. Allowed values are 8 (accurate fully convolutional
mode), 16 (fast fully convolutional mode), 32 (classification mode).
scope: Optional variable_scope.
Returns:
tensor_out: output tensor corresponding to the final_endpoint.
end_points: a set of activations for external use, for example summaries or
losses.
Raises:
ValueError: if final_endpoint is not set to one of the predefined values,
or depth_multiplier <= 0, or the target output_stride is not
allowed. | Mobilenet v1. | [
"Mobilenet",
"v1",
"."
] | def mobilenet_v1_base(inputs,
final_endpoint='Conv2d_13_pointwise',
min_depth=8,
depth_multiplier=1.0,
conv_defs=None,
output_stride=None,
scope=None):
"""Mobilenet v1.
Constructs a Mobilenet v1 network from inputs to the given final endpoint.
Args:
inputs: a tensor of shape [batch_size, height, width, channels].
final_endpoint: specifies the endpoint to construct the network up to. It
can be one of ['Conv2d_0', 'Conv2d_1_pointwise', 'Conv2d_2_pointwise',
'Conv2d_3_pointwise', 'Conv2d_4_pointwise', 'Conv2d_5'_pointwise,
'Conv2d_6_pointwise', 'Conv2d_7_pointwise', 'Conv2d_8_pointwise',
'Conv2d_9_pointwise', 'Conv2d_10_pointwise', 'Conv2d_11_pointwise',
'Conv2d_12_pointwise', 'Conv2d_13_pointwise'].
min_depth: Minimum depth value (number of channels) for all convolution ops.
Enforced when depth_multiplier < 1, and not an active constraint when
depth_multiplier >= 1.
depth_multiplier: Float multiplier for the depth (number of channels)
for all convolution ops. The value must be greater than zero. Typical
usage will be to set this value in (0, 1) to reduce the number of
parameters or computation cost of the model.
conv_defs: A list of ConvDef namedtuples specifying the net architecture.
output_stride: An integer that specifies the requested ratio of input to
output spatial resolution. If not None, then we invoke atrous convolution
if necessary to prevent the network from reducing the spatial resolution
of the activation maps. Allowed values are 8 (accurate fully convolutional
mode), 16 (fast fully convolutional mode), 32 (classification mode).
scope: Optional variable_scope.
Returns:
tensor_out: output tensor corresponding to the final_endpoint.
end_points: a set of activations for external use, for example summaries or
losses.
Raises:
ValueError: if final_endpoint is not set to one of the predefined values,
or depth_multiplier <= 0, or the target output_stride is not
allowed.
"""
depth = lambda d: max(int(d * depth_multiplier), min_depth)
end_points = {}
# Used to find thinned depths for each layer.
if depth_multiplier <= 0:
raise ValueError('depth_multiplier is not greater than zero.')
if conv_defs is None:
conv_defs = _CONV_DEFS
if output_stride is not None and output_stride not in [8, 16, 32]:
raise ValueError('Only allowed output_stride values are 8, 16, 32.')
with tf.variable_scope(scope, 'MobilenetV1', [inputs]):
with slim.arg_scope([slim.conv2d, slim.separable_conv2d], padding='SAME'):
# The current_stride variable keeps track of the output stride of the
# activations, i.e., the running product of convolution strides up to the
# current network layer. This allows us to invoke atrous convolution
# whenever applying the next convolution would result in the activations
# having output stride larger than the target output_stride.
current_stride = 1
# The atrous convolution rate parameter.
rate = 1
net = inputs
for i, conv_def in enumerate(conv_defs):
end_point_base = 'Conv2d_%d' % i
if output_stride is not None and current_stride == output_stride:
# If we have reached the target output_stride, then we need to employ
# atrous convolution with stride=1 and multiply the atrous rate by the
# current unit's stride for use in subsequent layers.
layer_stride = 1
layer_rate = rate
rate *= conv_def.stride
else:
layer_stride = conv_def.stride
layer_rate = 1
current_stride *= conv_def.stride
if isinstance(conv_def, Conv):
end_point = end_point_base
net = slim.conv2d(net, depth(conv_def.depth), conv_def.kernel,
stride=conv_def.stride,
normalizer_fn=slim.batch_norm,
scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
elif isinstance(conv_def, DepthSepConv):
end_point = end_point_base + '_depthwise'
# By passing filters=None
# separable_conv2d produces only a depthwise convolution layer
net = slim.separable_conv2d(net, None, conv_def.kernel,
depth_multiplier=1,
stride=layer_stride,
rate=layer_rate,
normalizer_fn=slim.batch_norm,
scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
end_point = end_point_base + '_pointwise'
net = slim.conv2d(net, depth(conv_def.depth), [1, 1],
stride=1,
normalizer_fn=slim.batch_norm,
scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
else:
raise ValueError('Unknown convolution type %s for layer %d'
% (conv_def.ltype, i))
raise ValueError('Unknown final endpoint %s' % final_endpoint) | [
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arsaboo/homeassistant-config | 53c998986fbe84d793a0b174757154ab30e676e4 | custom_components/aarlo/pyaarlo/media.py | python | ArloVideo.media_duration_seconds | (self) | return self._attrs.get("mediaDurationSecond", None) | Returns how long the recording last. | Returns how long the recording last. | [
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qibinlou/SinaWeibo-Emotion-Classification | f336fc104abd68b0ec4180fe2ed80fafe49cb790 | nltk/classify/maxent.py | python | GISEncoding.__init__ | (self, labels, mapping, unseen_features=False,
alwayson_features=False, C=None) | :param C: The correction constant. The value of the correction
feature is based on this value. In particular, its value is
``C - sum([v for (f,v) in encoding])``.
:seealso: ``BinaryMaxentFeatureEncoding.__init__`` | :param C: The correction constant. The value of the correction
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makerbot/ReplicatorG | d6f2b07785a5a5f1e172fb87cb4303b17c575d5d | skein_engines/skeinforge-47/fabmetheus_utilities/svg_reader.py | python | PathReader.processPathWordA | (self) | Process path word A. | Process path word A. | [
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'Process path word A.'
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JaniceWuo/MovieRecommend | 4c86db64ca45598917d304f535413df3bc9fea65 | movierecommend/venv1/Lib/site-packages/django/contrib/admin/helpers.py | python | AdminReadonlyField.contents | (self) | return conditional_escape(result_repr) | [] | def contents(self):
from django.contrib.admin.templatetags.admin_list import _boolean_icon
field, obj, model_admin = self.field['field'], self.form.instance, self.model_admin
try:
f, attr, value = lookup_field(field, obj, model_admin)
except (AttributeError, ValueError, ObjectDoesNotExist):
result_repr = self.empty_value_display
else:
if f is None:
boolean = getattr(attr, "boolean", False)
if boolean:
result_repr = _boolean_icon(value)
else:
if hasattr(value, "__html__"):
result_repr = value
else:
result_repr = force_text(value)
if getattr(attr, "allow_tags", False):
warnings.warn(
"Deprecated allow_tags attribute used on %s. "
"Use django.utils.html.format_html(), format_html_join(), "
"or django.utils.safestring.mark_safe() instead." % attr,
RemovedInDjango20Warning
)
result_repr = mark_safe(value)
else:
result_repr = linebreaksbr(result_repr)
else:
if isinstance(f.remote_field, ManyToManyRel) and value is not None:
result_repr = ", ".join(map(six.text_type, value.all()))
else:
result_repr = display_for_field(value, f, self.empty_value_display)
result_repr = linebreaksbr(result_repr)
return conditional_escape(result_repr) | [
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arthurdejong/python-stdnum | 02dec52602ae0709b940b781fc1fcebfde7340b7 | stdnum/ch/vat.py | python | is_valid | (number) | Check if the number is a valid VAT number. | Check if the number is a valid VAT number. | [
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try:
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except ValidationError:
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tav/pylibs | 3c16b843681f54130ee6a022275289cadb2f2a69 | mako/runtime.py | python | Context._push_writer | (self) | return buf.write | push a capturing buffer onto this Context and return the new Writer function. | push a capturing buffer onto this Context and return the new Writer function. | [
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buf = util.FastEncodingBuffer()
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poljar/weechat-matrix | b88614e557399e8b3b623a3d29b2694acc019a44 | matrix/commands.py | python | grouper | (iterable, n, fillvalue=None) | return zip_longest(*args, fillvalue=fillvalue) | Collect data into fixed-length chunks or blocks | Collect data into fixed-length chunks or blocks | [
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"Collect data into fixed-length chunks or blocks"
# grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx"
args = [iter(iterable)] * n
return zip_longest(*args, fillvalue=fillvalue) | [
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tz28/deep-learning | 9baa081a487aac1e9dceb15b9a1a0ed73608280a | rnn.py | python | initialize_parameters | (n_a, n_x, n_y) | return parameters | Initialize parameters with small random values
Returns:
parameters -- python dictionary containing:
Wax -- Weight matrix multiplying the input, of shape (n_a, n_x)
Waa -- Weight matrix multiplying the hidden state, of shape (n_a, n_a)
Wya -- Weight matrix relating the hidden-state to the output, of shape (n_y, n_a)
b -- Bias, numpy array of shape (n_a, 1)
by -- Bias relating the hidden-state to the output, of shape (n_y, 1) | Initialize parameters with small random values
Returns:
parameters -- python dictionary containing:
Wax -- Weight matrix multiplying the input, of shape (n_a, n_x)
Waa -- Weight matrix multiplying the hidden state, of shape (n_a, n_a)
Wya -- Weight matrix relating the hidden-state to the output, of shape (n_y, n_a)
b -- Bias, numpy array of shape (n_a, 1)
by -- Bias relating the hidden-state to the output, of shape (n_y, 1) | [
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... | def initialize_parameters(n_a, n_x, n_y):
"""
Initialize parameters with small random values
Returns:
parameters -- python dictionary containing:
Wax -- Weight matrix multiplying the input, of shape (n_a, n_x)
Waa -- Weight matrix multiplying the hidden state, of shape (n_a, n_a)
Wya -- Weight matrix relating the hidden-state to the output, of shape (n_y, n_a)
b -- Bias, numpy array of shape (n_a, 1)
by -- Bias relating the hidden-state to the output, of shape (n_y, 1)
"""
np.random.seed(1)
Wax = np.random.randn(n_a, n_x) * 0.01 # input to hidden
Waa = np.random.randn(n_a, n_a) * 0.01 # hidden to hidden
Wya = np.random.randn(n_y, n_a) * 0.01 # hidden to output
ba = np.zeros((n_a, 1)) # hidden bias
by = np.zeros((n_y, 1)) # output bias
parameters = {"Wax": Wax, "Waa": Waa, "Wya": Wya, "ba": ba, "by": by}
return parameters | [
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deepset-ai/haystack | 79fdda8a7cf393d774803608a4874f2a6e63cf6f | haystack/schema.py | python | EvaluationResult._build_document_metrics_df | (
self,
documents: pd.DataFrame,
simulated_top_k_retriever: int = -1,
doc_relevance_col: str = "gold_id_match"
) | return metrics_df | Builds a dataframe containing document metrics (columns) per query (index).
Document metrics are:
- mrr (Mean Reciprocal Rank: see https://en.wikipedia.org/wiki/Mean_reciprocal_rank)
- map (Mean Average Precision: see https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Mean_average_precision)
- precision (Precision: How many of the returned documents were relevant?)
- recall_multi_hit (Recall according to Information Retrieval definition: How many of the relevant documents were retrieved per query?)
- recall_single_hit (Recall for Question Answering: Did the query return at least one relevant document? -> 1.0 or 0.0) | Builds a dataframe containing document metrics (columns) per query (index).
Document metrics are:
- mrr (Mean Reciprocal Rank: see https://en.wikipedia.org/wiki/Mean_reciprocal_rank)
- map (Mean Average Precision: see https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Mean_average_precision)
- precision (Precision: How many of the returned documents were relevant?)
- recall_multi_hit (Recall according to Information Retrieval definition: How many of the relevant documents were retrieved per query?)
- recall_single_hit (Recall for Question Answering: Did the query return at least one relevant document? -> 1.0 or 0.0) | [
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"... | def _build_document_metrics_df(
self,
documents: pd.DataFrame,
simulated_top_k_retriever: int = -1,
doc_relevance_col: str = "gold_id_match"
) -> pd.DataFrame:
"""
Builds a dataframe containing document metrics (columns) per query (index).
Document metrics are:
- mrr (Mean Reciprocal Rank: see https://en.wikipedia.org/wiki/Mean_reciprocal_rank)
- map (Mean Average Precision: see https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Mean_average_precision)
- precision (Precision: How many of the returned documents were relevant?)
- recall_multi_hit (Recall according to Information Retrieval definition: How many of the relevant documents were retrieved per query?)
- recall_single_hit (Recall for Question Answering: Did the query return at least one relevant document? -> 1.0 or 0.0)
"""
if simulated_top_k_retriever != -1:
documents = documents[documents["rank"] <= simulated_top_k_retriever]
metrics = []
queries = documents["query"].unique()
for query in queries:
query_df = documents[documents["query"] == query]
gold_ids = query_df["gold_document_ids"].iloc[0]
retrieved = len(query_df)
relevance_criteria_ids = list(query_df[query_df[doc_relevance_col] == 1]["document_id"].values)
num_relevants = len(set(gold_ids + relevance_criteria_ids))
num_retrieved_relevants = query_df[doc_relevance_col].values.sum()
rank_retrieved_relevants = query_df[query_df[doc_relevance_col] == 1]["rank"].values
avp_retrieved_relevants = [query_df[doc_relevance_col].values[:rank].sum() / rank
for rank in rank_retrieved_relevants]
avg_precision = np.sum(avp_retrieved_relevants) / num_relevants if num_relevants > 0 else 0.0
recall_multi_hit = num_retrieved_relevants / num_relevants if num_relevants > 0 else 0.0
recall_single_hit = min(num_retrieved_relevants, 1)
precision = num_retrieved_relevants / retrieved if retrieved > 0 else 0.0
rr = 1.0 / rank_retrieved_relevants.min() if len(rank_retrieved_relevants) > 0 else 0.0
metrics.append({
"recall_multi_hit": recall_multi_hit,
"recall_single_hit": recall_single_hit,
"precision": precision,
"map": avg_precision,
"mrr": rr
})
metrics_df = pd.DataFrame.from_records(metrics, index=queries)
return metrics_df | [
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nodesign/weio | 1d67d705a5c36a2e825ad13feab910b0aca9a2e8 | weioLib/weioFiles.py | python | checkIfDirectoryExists | (path) | [] | def checkIfDirectoryExists(path):
if (os.path.isdir(path)):
return True
else :
return False | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/sqlalchemy/orm/collections.py | python | _set_decorators | () | return l | Tailored instrumentation wrappers for any set-like class. | Tailored instrumentation wrappers for any set-like class. | [
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] | def _set_decorators():
"""Tailored instrumentation wrappers for any set-like class."""
def _tidy(fn):
fn._sa_instrumented = True
fn.__doc__ = getattr(set, fn.__name__).__doc__
Unspecified = util.symbol('Unspecified')
def add(fn):
def add(self, value, _sa_initiator=None):
if value not in self:
value = __set(self, value, _sa_initiator)
# testlib.pragma exempt:__hash__
fn(self, value)
_tidy(add)
return add
def discard(fn):
def discard(self, value, _sa_initiator=None):
# testlib.pragma exempt:__hash__
if value in self:
__del(self, value, _sa_initiator)
# testlib.pragma exempt:__hash__
fn(self, value)
_tidy(discard)
return discard
def remove(fn):
def remove(self, value, _sa_initiator=None):
# testlib.pragma exempt:__hash__
if value in self:
__del(self, value, _sa_initiator)
# testlib.pragma exempt:__hash__
fn(self, value)
_tidy(remove)
return remove
def pop(fn):
def pop(self):
__before_delete(self)
item = fn(self)
__del(self, item)
return item
_tidy(pop)
return pop
def clear(fn):
def clear(self):
for item in list(self):
self.remove(item)
_tidy(clear)
return clear
def update(fn):
def update(self, value):
for item in value:
self.add(item)
_tidy(update)
return update
def __ior__(fn):
def __ior__(self, value):
if not _set_binops_check_strict(self, value):
return NotImplemented
for item in value:
self.add(item)
return self
_tidy(__ior__)
return __ior__
def difference_update(fn):
def difference_update(self, value):
for item in value:
self.discard(item)
_tidy(difference_update)
return difference_update
def __isub__(fn):
def __isub__(self, value):
if not _set_binops_check_strict(self, value):
return NotImplemented
for item in value:
self.discard(item)
return self
_tidy(__isub__)
return __isub__
def intersection_update(fn):
def intersection_update(self, other):
want, have = self.intersection(other), set(self)
remove, add = have - want, want - have
for item in remove:
self.remove(item)
for item in add:
self.add(item)
_tidy(intersection_update)
return intersection_update
def __iand__(fn):
def __iand__(self, other):
if not _set_binops_check_strict(self, other):
return NotImplemented
want, have = self.intersection(other), set(self)
remove, add = have - want, want - have
for item in remove:
self.remove(item)
for item in add:
self.add(item)
return self
_tidy(__iand__)
return __iand__
def symmetric_difference_update(fn):
def symmetric_difference_update(self, other):
want, have = self.symmetric_difference(other), set(self)
remove, add = have - want, want - have
for item in remove:
self.remove(item)
for item in add:
self.add(item)
_tidy(symmetric_difference_update)
return symmetric_difference_update
def __ixor__(fn):
def __ixor__(self, other):
if not _set_binops_check_strict(self, other):
return NotImplemented
want, have = self.symmetric_difference(other), set(self)
remove, add = have - want, want - have
for item in remove:
self.remove(item)
for item in add:
self.add(item)
return self
_tidy(__ixor__)
return __ixor__
l = locals().copy()
l.pop('_tidy')
l.pop('Unspecified')
return l | [
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khalim19/gimp-plugin-export-layers | b37255f2957ad322f4d332689052351cdea6e563 | export_layers/pygimplib/_lib/future/future/backports/urllib/request.py | python | getproxies_environment | () | return proxies | Return a dictionary of scheme -> proxy server URL mappings.
Scan the environment for variables named <scheme>_proxy;
this seems to be the standard convention. If you need a
different way, you can pass a proxies dictionary to the
[Fancy]URLopener constructor. | Return a dictionary of scheme -> proxy server URL mappings. | [
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"-",
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] | def getproxies_environment():
"""Return a dictionary of scheme -> proxy server URL mappings.
Scan the environment for variables named <scheme>_proxy;
this seems to be the standard convention. If you need a
different way, you can pass a proxies dictionary to the
[Fancy]URLopener constructor.
"""
proxies = {}
for name, value in os.environ.items():
name = name.lower()
if value and name[-6:] == '_proxy':
proxies[name[:-6]] = value
return proxies | [
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yiranran/Unpaired-Portrait-Drawing | b67591912a3e18622d56d3cd91cd6b71f1715649 | data/base_dataset.py | python | BaseDataset.__getitem__ | (self, index) | Return a data point and its metadata information.
Parameters:
index - - a random integer for data indexing
Returns:
a dictionary of data with their names. It ususally contains the data itself and its metadata information. | Return a data point and its metadata information. | [
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] | def __getitem__(self, index):
"""Return a data point and its metadata information.
Parameters:
index - - a random integer for data indexing
Returns:
a dictionary of data with their names. It ususally contains the data itself and its metadata information.
"""
pass | [
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naparuba/shinken | 8163d645e801fa43ee1704f099a4684f120e667b | shinken/scheduler.py | python | Scheduler.restore_object_retention_data | (self, o, data) | Now load interesting properties in hosts/services
Tagging retention=False prop that not be directly load
Items will be with theirs status, but not in checking, so
a new check will be launched like with a normal beginning (random distributed
scheduling)
:param Item o: The object to load data to
:param dict data: The object's loaded retention data | Now load interesting properties in hosts/services
Tagging retention=False prop that not be directly load
Items will be with theirs status, but not in checking, so
a new check will be launched like with a normal beginning (random distributed
scheduling) | [
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... | def restore_object_retention_data(self, o, data):
"""
Now load interesting properties in hosts/services
Tagging retention=False prop that not be directly load
Items will be with theirs status, but not in checking, so
a new check will be launched like with a normal beginning (random distributed
scheduling)
:param Item o: The object to load data to
:param dict data: The object's loaded retention data
"""
# First manage all running properties
running_properties = o.__class__.running_properties
for prop, entry in running_properties.items():
if entry.retention:
# Maybe the saved one was not with this value, so
# we just bypass this
if prop in data:
setattr(o, prop, data[prop])
# Ok, some are in properties too (like active check enabled
# or not. Will OVERRIDE THE CONFIGURATION VALUE!
properties = o.__class__.properties
for prop, entry in properties.items():
if entry.retention:
# Maybe the saved one was not with this value, so
# we just bypass this
if prop in data:
setattr(o, prop, data[prop])
# Now manage all linked oects load from previous run
for a in o.notifications_in_progress.values():
a.ref = o
self.add(a)
# Also raises the action id, so do not overlap ids
a.assume_at_least_id(a.id)
# And also add downtimes and comments
for dt in o.downtimes:
dt.ref = o
if hasattr(dt, 'extra_comment'):
dt.extra_comment.ref = o
else:
dt.extra_comment = None
# raises the downtime id to do not overlap
Downtime.id = max(Downtime.id, dt.id + 1)
self.add(dt)
for c in o.comments:
c.ref = o
self.add(c)
# raises comment id to do not overlap ids
Comment.id = max(Comment.id, c.id + 1)
if o.acknowledgement is not None:
o.acknowledgement.ref = o
# Raises the id of future ack so we don't overwrite
# these one
Acknowledge.id = max(Acknowledge.id, o.acknowledgement.id + 1)
# Relink the notified_contacts as a set() of true contacts objects
# it it was load from the retention, it's now a list of contacts
# names
if 'notified_contacts' in data:
new_notified_contacts = set()
for cname in o.notified_contacts:
c = self.contacts.find_by_name(cname)
# Maybe the contact is gone. Skip it
if c:
new_notified_contacts.add(c)
o.notified_contacts = new_notified_contacts | [
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... | https://github.com/naparuba/shinken/blob/8163d645e801fa43ee1704f099a4684f120e667b/shinken/scheduler.py#L1121-L1185 | ||
Tiiiger/SGC | 2c7a2727e82e462d8ef9d6e57f0b08888e16488f | downstream/TextSGC/utils.py | python | loadWord2Vec | (filename) | return vocab, embd, word_vector_map | Read Word Vectors | Read Word Vectors | [
"Read",
"Word",
"Vectors"
] | def loadWord2Vec(filename):
"""Read Word Vectors"""
vocab = []
embd = []
word_vector_map = {}
file = open(filename, 'r')
for line in file.readlines():
row = line.strip().split(' ')
if(len(row) > 2):
vocab.append(row[0])
vector = row[1:]
length = len(vector)
for i in range(length):
vector[i] = float(vector[i])
embd.append(vector)
word_vector_map[row[0]] = vector
print('Loaded Word Vectors!')
file.close()
return vocab, embd, word_vector_map | [
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mozilla/dxr | ef09324a32930e2769e6ff63eaa8de76fcf79ee3 | dxr/utils.py | python | decode_es_datetime | (es_datetime) | Turn an elasticsearch datetime into a datetime object. | Turn an elasticsearch datetime into a datetime object. | [
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] | def decode_es_datetime(es_datetime):
"""Turn an elasticsearch datetime into a datetime object."""
try:
return datetime.strptime(es_datetime, '%Y-%m-%dT%H:%M:%S')
except ValueError:
# For newer ES versions
return datetime.strptime(es_datetime, '%Y-%m-%dT%H:%M:%S.%f') | [
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"... | https://github.com/mozilla/dxr/blob/ef09324a32930e2769e6ff63eaa8de76fcf79ee3/dxr/utils.py#L151-L157 | ||
sagiebenaim/DistanceGAN | f827205ee45de351261f44505109395192894351 | cyclegan_arch/util/png.py | python | encode | (buf, width, height) | return b''.join(
[ SIGNATURE ] +
chunk(b'IHDR', struct.pack("!2I5B", width, height, bit_depth, COLOR_TYPE_RGB, 0, 0, 0)) +
chunk(b'IDAT', zlib.compress(b''.join(raw_data()), 9)) +
chunk(b'IEND', b'')
) | buf: must be bytes or a bytearray in py3, a regular string in py2. formatted RGBRGB... | buf: must be bytes or a bytearray in py3, a regular string in py2. formatted RGBRGB... | [
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"in",
"py2",
".",
"formatted",
"RGBRGB",
"..."
] | def encode(buf, width, height):
""" buf: must be bytes or a bytearray in py3, a regular string in py2. formatted RGBRGB... """
assert (width * height * 3 == len(buf))
bpp = 3
def raw_data():
# reverse the vertical line order and add null bytes at the start
row_bytes = width * bpp
for row_start in range((height - 1) * width * bpp, -1, -row_bytes):
yield b'\x00'
yield buf[row_start:row_start + row_bytes]
def chunk(tag, data):
return [
struct.pack("!I", len(data)),
tag,
data,
struct.pack("!I", 0xFFFFFFFF & zlib.crc32(data, zlib.crc32(tag)))
]
SIGNATURE = b'\x89PNG\r\n\x1a\n'
COLOR_TYPE_RGB = 2
COLOR_TYPE_RGBA = 6
bit_depth = 8
return b''.join(
[ SIGNATURE ] +
chunk(b'IHDR', struct.pack("!2I5B", width, height, bit_depth, COLOR_TYPE_RGB, 0, 0, 0)) +
chunk(b'IDAT', zlib.compress(b''.join(raw_data()), 9)) +
chunk(b'IEND', b'')
) | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/plot/plot3d/shapes2.py | python | Line.tachyon_repr | (self, render_params) | return cmds | Return representation of the line suitable for plotting
using the Tachyon ray tracer.
TESTS::
sage: L = line3d([(cos(i),sin(i),i^2) for i in srange(0,10,.01)],color='red')
sage: L.tachyon_repr(L.default_render_params())[0]
'FCylinder base 1.0 0.0 0.0 apex 0.9999500004166653 0.009999833334166664 0.0001 rad 0.005 texture...' | Return representation of the line suitable for plotting
using the Tachyon ray tracer. | [
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] | def tachyon_repr(self, render_params):
"""
Return representation of the line suitable for plotting
using the Tachyon ray tracer.
TESTS::
sage: L = line3d([(cos(i),sin(i),i^2) for i in srange(0,10,.01)],color='red')
sage: L.tachyon_repr(L.default_render_params())[0]
'FCylinder base 1.0 0.0 0.0 apex 0.9999500004166653 0.009999833334166664 0.0001 rad 0.005 texture...'
"""
T = render_params.transform
cmds = []
px, py, pz = self.points[0] if T is None else T(self.points[0])
radius = self.thickness * TACHYON_PIXEL
for P in self.points[1:]:
x, y, z = P if T is None else T(P)
if self.arrow_head and P is self.points[-1]:
A = shapes.arrow3d((px, py, pz), (x, y, z), radius = radius, texture = self.texture)
render_params.push_transform(~T)
cmds.append(A.tachyon_repr(render_params))
render_params.pop_transform()
else:
cmd = ('FCylinder base {pos[0]!r} {pos[1]!r} {pos[2]!r} '
'apex {apex[0]!r} {apex[1]!r} {apex[2]!r} '
'rad {radius!r} {texture}').format(
pos=(px, py, pz), apex=(x, y, z), radius=radius,
texture=self.texture.id)
cmds.append(cmd)
px, py, pz = x, y, z
return cmds | [
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alecthomas/voluptuous | 980a55f57c0ecdd4fd3fec687dbbabf132f9e022 | voluptuous/schema_builder.py | python | _iterate_object | (obj) | Return iterator over object attributes. Respect objects with
defined __slots__. | Return iterator over object attributes. Respect objects with
defined __slots__. | [
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] | def _iterate_object(obj):
"""Return iterator over object attributes. Respect objects with
defined __slots__.
"""
d = {}
try:
d = vars(obj)
except TypeError:
# maybe we have named tuple here?
if hasattr(obj, '_asdict'):
d = obj._asdict()
for item in iteritems(d):
yield item
try:
slots = obj.__slots__
except AttributeError:
pass
else:
for key in slots:
if key != '__dict__':
yield (key, getattr(obj, key)) | [
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python-provy/provy | ca3d5e96a2210daf3c1fd4b96e047efff152db14 | provy/more/debian/database/postgresql.py | python | PostgreSQLRole.provision | (self) | Installs `PostgreSQL <http://www.postgresql.org/>`_ and its dependencies.
This method should be called upon if overriden in base classes, or PostgreSQL won't work properly in the remote server.
Example:
::
class MySampleRole(Role):
def provision(self):
self.provision_role(PostgreSQLRole) # no need to call this if using with block. | Installs `PostgreSQL <http://www.postgresql.org/>`_ and its dependencies.
This method should be called upon if overriden in base classes, or PostgreSQL won't work properly in the remote server. | [
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'''
Installs `PostgreSQL <http://www.postgresql.org/>`_ and its dependencies.
This method should be called upon if overriden in base classes, or PostgreSQL won't work properly in the remote server.
Example:
::
class MySampleRole(Role):
def provision(self):
self.provision_role(PostgreSQLRole) # no need to call this if using with block.
'''
with self.using(AptitudeRole) as role:
role.ensure_package_installed('postgresql')
role.ensure_package_installed('postgresql-server-dev-%s' % self.__get_version()) | [
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anki/cozmo-python-sdk | dd29edef18748fcd816550469195323842a7872e | examples/apps/quick_tap.py | python | QuickTapGame.report_scores | (self) | Prints the current scores of the game. | Prints the current scores of the game. | [
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] | def report_scores(self):
'''Prints the current scores of the game.'''
print('---------------------------------------------------')
print('Player score: {}'.format(self.player.score))
print('Cozmo score: {}'.format(self.cozmo_player.score))
print('---------------------------------------------------') | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/wagtail/wagtailusers/forms.py | python | BaseGroupPagePermissionFormSet.empty_form | (self) | return empty_form | [] | def empty_form(self):
empty_form = super(BaseGroupPagePermissionFormSet, self).empty_form
empty_form.fields['DELETE'].widget = forms.HiddenInput()
return empty_form | [
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larsyencken/csvdiff | 533326dd1db7252cd37a67eabf6b56e9f2646221 | csvdiff/patch.py | python | validate | (diff) | return jsonschema.validate(diff, SCHEMA) | Check the diff against the schema, raising an exception if it doesn't
match. | Check the diff against the schema, raising an exception if it doesn't
match. | [
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"."
] | def validate(diff):
"""
Check the diff against the schema, raising an exception if it doesn't
match.
"""
return jsonschema.validate(diff, SCHEMA) | [
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] | https://github.com/larsyencken/csvdiff/blob/533326dd1db7252cd37a67eabf6b56e9f2646221/csvdiff/patch.py#L98-L103 | |
wwqgtxx/wwqLyParse | 33136508e52821babd9294fdecffbdf02d73a6fc | wwqLyParse/lib/python-3.7.2-embed-win32/gevent/pywsgi.py | python | LoggingLogAdapter.__init__ | (self, logger, level=20) | Write information to the *logger* at the given *level* (default to INFO). | Write information to the *logger* at the given *level* (default to INFO). | [
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] | def __init__(self, logger, level=20):
"""
Write information to the *logger* at the given *level* (default to INFO).
"""
self._logger = logger
self._level = level | [
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/djangorestframework-3.9.4/rest_framework/fields.py | python | Field.get_initial | (self) | return self.initial | Return a value to use when the field is being returned as a primitive
value, without any object instance. | Return a value to use when the field is being returned as a primitive
value, without any object instance. | [
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] | def get_initial(self):
"""
Return a value to use when the field is being returned as a primitive
value, without any object instance.
"""
if callable(self.initial):
return self.initial()
return self.initial | [
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larryhastings/gilectomy | 4315ec3f1d6d4f813cc82ce27a24e7f784dbfc1a | Lib/distutils/cmd.py | python | Command.make_file | (self, infiles, outfile, func, args,
exec_msg=None, skip_msg=None, level=1) | Special case of 'execute()' for operations that process one or
more input files and generate one output file. Works just like
'execute()', except the operation is skipped and a different
message printed if 'outfile' already exists and is newer than all
files listed in 'infiles'. If the command defined 'self.force',
and it is true, then the command is unconditionally run -- does no
timestamp checks. | Special case of 'execute()' for operations that process one or
more input files and generate one output file. Works just like
'execute()', except the operation is skipped and a different
message printed if 'outfile' already exists and is newer than all
files listed in 'infiles'. If the command defined 'self.force',
and it is true, then the command is unconditionally run -- does no
timestamp checks. | [
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"operation",
"is",
"skipped",... | def make_file(self, infiles, outfile, func, args,
exec_msg=None, skip_msg=None, level=1):
"""Special case of 'execute()' for operations that process one or
more input files and generate one output file. Works just like
'execute()', except the operation is skipped and a different
message printed if 'outfile' already exists and is newer than all
files listed in 'infiles'. If the command defined 'self.force',
and it is true, then the command is unconditionally run -- does no
timestamp checks.
"""
if skip_msg is None:
skip_msg = "skipping %s (inputs unchanged)" % outfile
# Allow 'infiles' to be a single string
if isinstance(infiles, str):
infiles = (infiles,)
elif not isinstance(infiles, (list, tuple)):
raise TypeError(
"'infiles' must be a string, or a list or tuple of strings")
if exec_msg is None:
exec_msg = "generating %s from %s" % (outfile, ', '.join(infiles))
# If 'outfile' must be regenerated (either because it doesn't
# exist, is out-of-date, or the 'force' flag is true) then
# perform the action that presumably regenerates it
if self.force or dep_util.newer_group(infiles, outfile):
self.execute(func, args, exec_msg, level)
# Otherwise, print the "skip" message
else:
log.debug(skip_msg) | [
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tp4a/teleport | 1fafd34f1f775d2cf80ea4af6e44468d8e0b24ad | server/www/packages/packages-darwin/x64/tornado/platform/twisted.py | python | TwistedIOLoop.update_handler | (self, fd, events) | [] | def update_handler(self, fd, events):
fd, fileobj = self.split_fd(fd)
if events & tornado.ioloop.IOLoop.READ:
if not self.fds[fd].reading:
self.fds[fd].reading = True
self.reactor.addReader(self.fds[fd])
else:
if self.fds[fd].reading:
self.fds[fd].reading = False
self.reactor.removeReader(self.fds[fd])
if events & tornado.ioloop.IOLoop.WRITE:
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self.fds[fd].writing = True
self.reactor.addWriter(self.fds[fd])
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self.fds[fd].writing = False
self.reactor.removeWriter(self.fds[fd]) | [
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awslabs/sockeye | ec2d13f7beb42d8c4f389dba0172250dc9154d5a | sockeye/evaluate.py | python | raw_corpus_rouge1 | (hypotheses: Iterable[str], references: Iterable[str]) | return rouge.rouge_1(hypotheses, references) | Simple wrapper around ROUGE-1 implementation.
:param hypotheses: Hypotheses stream.
:param references: Reference stream.
:return: ROUGE-1 score as float between 0 and 1. | Simple wrapper around ROUGE-1 implementation. | [
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"ROUGE",
"-",
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] | def raw_corpus_rouge1(hypotheses: Iterable[str], references: Iterable[str]) -> float:
"""
Simple wrapper around ROUGE-1 implementation.
:param hypotheses: Hypotheses stream.
:param references: Reference stream.
:return: ROUGE-1 score as float between 0 and 1.
"""
return rouge.rouge_1(hypotheses, references) | [
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CedricGuillemet/Imogen | ee417b42747ed5b46cb11b02ef0c3630000085b3 | bin/Lib/distutils/ccompiler.py | python | CCompiler.set_library_dirs | (self, dirs) | Set the list of library search directories to 'dirs' (a list of
strings). This does not affect any standard library search path
that the linker may search by default. | Set the list of library search directories to 'dirs' (a list of
strings). This does not affect any standard library search path
that the linker may search by default. | [
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"""Set the list of library search directories to 'dirs' (a list of
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that the linker may search by default.
"""
self.library_dirs = dirs[:] | [
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