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avocado-framework/avocado | 1f9b3192e8ba47d029c33fe21266bd113d17811f | avocado/utils/script.py | python | make_temp_script | (name, content, prefix='avocado_script', mode=DEFAULT_MODE) | return scpt.path | Creates a new temporary script stored in the file system.
:param path: the script file name.
:param content: the script content.
:param prefix: the directory prefix Default to 'avocado_script'.
:param mode: set file mode, default to 0775.
:return: the script path. | Creates a new temporary script stored in the file system. | [
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] | def make_temp_script(name, content, prefix='avocado_script', mode=DEFAULT_MODE):
"""
Creates a new temporary script stored in the file system.
:param path: the script file name.
:param content: the script content.
:param prefix: the directory prefix Default to 'avocado_script'.
:param mode: set file mode, default to 0775.
:return: the script path.
"""
scpt = TemporaryScript(name, content, prefix=prefix, mode=mode)
scpt.save()
return scpt.path | [
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Cog-Creators/Red-DiscordBot | b05933274a11fb097873ab0d1b246d37b06aa306 | redbot/cogs/audio/apis/api_utils.py | python | prepare_config_scope_for_migration23 | ( # TODO: remove me in a future version ?
scope, author: Union[discord.abc.User, int] = None, guild: discord.Guild = None
) | return config_scope | Return the scope used by Playlists. | Return the scope used by Playlists. | [
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] | def prepare_config_scope_for_migration23( # TODO: remove me in a future version ?
scope, author: Union[discord.abc.User, int] = None, guild: discord.Guild = None
):
"""Return the scope used by Playlists."""
scope = standardize_scope(scope)
if scope == PlaylistScope.GLOBAL.value:
config_scope = [PlaylistScope.GLOBAL.value]
elif scope == PlaylistScope.USER.value:
if author is None:
raise MissingAuthor("Invalid author for user scope.")
config_scope = [PlaylistScope.USER.value, str(getattr(author, "id", author))]
else:
if guild is None:
raise MissingGuild("Invalid guild for guild scope.")
config_scope = [PlaylistScope.GUILD.value, str(getattr(guild, "id", guild))]
return config_scope | [
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lovelylain/pyctp | fd304de4b50c4ddc31a4190b1caaeb5dec66bc5d | example/pyctp/dac.py | python | date2week | (iday) | return (day+2*month+3*(month+1)/5+year+year/4-year/100+year/400)%7 + 1 | [] | def date2week(iday):
#http://blog.csdn.net/hawkfeifei/article/details/4337181
year = iday/10000
month = iday/100%100
day = iday%100
if month <= 2:
month += 12
year -= 1
return (day+2*month+3*(month+1)/5+year+year/4-year/100+year/400)%7 + 1 | [
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flasgger/flasgger | beb9fa781fc6b063fe3f3081b9677dd70184a2da | examples/marshmallow_apispec.py | python | UserPostView.post | (self) | return jsonify(request.json) | A simple post
Do it
---
# This value overwrites the attributes above
deprecated: true | A simple post
Do it
---
# This value overwrites the attributes above
deprecated: true | [
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"""
A simple post
Do it
---
# This value overwrites the attributes above
deprecated: true
"""
return jsonify(request.json) | [
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uccser/cs-unplugged | f83593f872792e71a9fab3f2d77a0f489205926b | csunplugged/topics/utils/add_lesson_ages_to_objects.py | python | add_lesson_ages_to_objects | (objects) | return objects | Add lesson min and max ages to given list of objects.
Args:
objects: List of objects of lessons.
Returns:
Modified list of objects. | Add lesson min and max ages to given list of objects. | [
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] | def add_lesson_ages_to_objects(objects):
"""Add lesson min and max ages to given list of objects.
Args:
objects: List of objects of lessons.
Returns:
Modified list of objects.
"""
age_groups = AgeGroup.objects.distinct()
for item in objects:
item_age_groups = list(age_groups.filter(lessons__id__in=item.lessons.all()))
item.min_age = item_age_groups[0].ages.lower
item.max_age = item_age_groups[-1].ages.upper
return objects | [
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bigmlcom/python | 35f69d2f3121f1b3dde43495cf145d4992796ad5 | bigml/fields.py | python | get_fields_structure | (resource, errors=False) | return (None, None, None, None, None) if errors else \
(None, None, None, None) | Returns the field structure for a resource, its locale and
missing_tokens | Returns the field structure for a resource, its locale and
missing_tokens | [
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] | def get_fields_structure(resource, errors=False):
"""Returns the field structure for a resource, its locale and
missing_tokens
"""
try:
resource_type = get_resource_type(resource)
except ValueError:
raise ValueError("Unknown resource structure")
field_errors = None
resource = resource.get('object', resource)
# locale and missing tokens
if resource_type == SOURCE_PATH:
resource_locale = resource['source_parser']['locale']
missing_tokens = resource[
'source_parser']['missing_tokens']
else:
resource_locale = resource.get('locale', DEFAULT_LOCALE)
missing_tokens = resource.get('missing_tokens',
DEFAULT_MISSING_TOKENS)
fields = get_fields(resource)
if resource_type in RESOURCES_WITH_FIELDS:
# Check whether there's an objective id
objective_column = None
if resource_type == DATASET_PATH:
objective_column = resource.get( \
'objective_field', {}).get('id')
if errors:
field_errors = resource.get("status", {}).get("field_errors")
elif resource_type in SUPERVISED_PATHS and \
resource_type != FUSION_PATH:
objective_id = resource.get( \
'objective_fields', [None])[0]
objective_column = fields.get( \
objective_id, {}).get('column_number')
result = fields, resource_locale, missing_tokens, objective_column
if errors:
result = result + (field_errors,)
return result
return (None, None, None, None, None) if errors else \
(None, None, None, None) | [
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kupferlauncher/kupfer | 1c1e9bcbce05a82f503f68f8b3955c20b02639b3 | waflib/Utils.py | python | writef | (fname, data, m='w', encoding='latin-1') | Writes an entire file from a string.
See also :py:meth:`waflib.Node.Node.writef`::
def build(ctx):
from waflib import Utils
txt = Utils.writef(self.path.make_node('i_like_kittens').abspath(), 'some data')
self.path.make_node('i_like_kittens').write('some data')
:type fname: string
:param fname: Path to file
:type data: string
:param data: The contents to write to the file
:type m: string
:param m: Open mode
:type encoding: string
:param encoding: encoding value, only used for python 3 | Writes an entire file from a string.
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"""
Writes an entire file from a string.
See also :py:meth:`waflib.Node.Node.writef`::
def build(ctx):
from waflib import Utils
txt = Utils.writef(self.path.make_node('i_like_kittens').abspath(), 'some data')
self.path.make_node('i_like_kittens').write('some data')
:type fname: string
:param fname: Path to file
:type data: string
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:param m: Open mode
:type encoding: string
:param encoding: encoding value, only used for python 3
"""
if sys.hexversion > 0x3000000 and not 'b' in m:
data = data.encode(encoding)
m += 'b'
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oracle/oci-python-sdk | 3c1604e4e212008fb6718e2f68cdb5ef71fd5793 | src/oci/_vendor/requests/structures.py | python | CaseInsensitiveDict.__setitem__ | (self, key, value) | [] | def __setitem__(self, key, value):
# Use the lowercased key for lookups, but store the actual
# key alongside the value.
self._store[key.lower()] = (key, value) | [
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weechat/scripts | 99ec0e7eceefabb9efb0f11ec26d45d6e8e84335 | python/autosort.py | python | command_helper_set | (buffer, command, args) | return weechat.WEECHAT_RC_OK | Add/update a helper to the helper list. | Add/update a helper to the helper list. | [
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''' Add/update a helper to the helper list. '''
name, expression = split_args(args, 2)
config.helpers[name] = expression
config.save_helpers()
command_helper_list(buffer, command, '')
return weechat.WEECHAT_RC_OK | [
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qtile/qtile | 803dc06fc1f8b121a1d8fe047f26a43812cd427f | libqtile/widget/base.py | python | _Widget._remove_dead_timers | (self) | Remove completed and cancelled timers from the list. | Remove completed and cancelled timers from the list. | [
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"""Remove completed and cancelled timers from the list."""
self._futures = [
timer
for timer in self._futures
if not (timer.cancelled() or timer.when() < self.qtile._eventloop.time())
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riptideio/pymodbus | c5772b35ae3f29d1947f3ab453d8d00df846459f | examples/tools/convert.py | python | ConversionException.__init__ | (self, string) | Initialize a ConversionException instance
:param string: Additional information to append to exception | Initialize a ConversionException instance | [
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""" Initialize a ConversionException instance
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Exception.__init__(self, string)
self.string = string | [
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numba/numba | bf480b9e0da858a65508c2b17759a72ee6a44c51 | numba/np/npyfuncs.py | python | np_int_smax_impl | (context, builder, sig, args) | return builder.select(arg1_sge_arg2, arg1, arg2) | [] | def np_int_smax_impl(context, builder, sig, args):
_check_arity_and_homogeneity(sig, args, 2)
arg1, arg2 = args
arg1_sge_arg2 = builder.icmp(lc.ICMP_SGE, arg1, arg2)
return builder.select(arg1_sge_arg2, arg1, arg2) | [
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/compiler/transformer.py | python | Transformer.print_stmt | (self, nodelist) | return Printnl(items, dest, lineno=nodelist[0][2]) | [] | def print_stmt(self, nodelist):
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items = []
if len(nodelist) == 1:
start = 1
dest = None
elif nodelist[1][0] == token.RIGHTSHIFT:
assert len(nodelist) == 3 \
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dest = self.com_node(nodelist[2])
start = 4
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dest = None
start = 1
for i in range(start, len(nodelist), 2):
items.append(self.com_node(nodelist[i]))
if nodelist[-1][0] == token.COMMA:
return Print(items, dest, lineno=nodelist[0][2])
return Printnl(items, dest, lineno=nodelist[0][2]) | [
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pypa/bandersnatch | 2e3eb53029ddb8f205f85242d724ae492040c1ce | src/bandersnatch_storage_plugins/filesystem.py | python | FilesystemStorage.mkdir | (
self, path: PATH_TYPES, exist_ok: bool = False, parents: bool = False
) | return path.mkdir(exist_ok=exist_ok, parents=parents) | Create the provided directory | Create the provided directory | [
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"""Create the provided directory"""
if not isinstance(path, pathlib.Path):
path = pathlib.Path(path)
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sripathikrishnan/redis-rdb-tools | 548b11ec3c81a603f5b321228d07a61a0b940159 | rdbtools/parser.py | python | RdbParser.skip_stream | (self, f) | [] | def skip_stream(self, f):
listpacks = self.read_length(f)
for _lp in range(listpacks):
self.skip_string(f)
self.skip_string(f)
self.read_length(f)
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consumers = self.read_length(f)
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f.read(8)
pending = self.read_length(f)
f.read(pending*16) | [
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microsoft/NimbusML | f6be39ce9359786976429bab0ccd837e849b4ba5 | src/python/nimbusml/preprocessing/schema/columnconcatenator.py | python | ColumnConcatenator.get_params | (self, deep=False) | return core.get_params(self) | Get the parameters for this operator. | Get the parameters for this operator. | [
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"""
Get the parameters for this operator.
"""
return core.get_params(self) | [
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andresriancho/w3af | cd22e5252243a87aaa6d0ddea47cf58dacfe00a9 | w3af/core/controllers/plugins/plugin.py | python | Plugin.kb_append | (self, location_a, location_b, info) | kb.kb.append a vulnerability to the KB | kb.kb.append a vulnerability to the KB | [
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"""
kb.kb.append a vulnerability to the KB
"""
kb.kb.append(location_a, location_b, info)
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siddharth-agrawal/Convolutional-Neural-Network | b06a04e7c33b8cb40116be0a8498bc59a78848fb | convolutionalNeuralNetwork.py | python | loadTrainingDataset | () | return [train_images, train_labels] | Loads the images and labels as numpy arrays
The dataset is originally read as a dictionary | Loads the images and labels as numpy arrays
The dataset is originally read as a dictionary | [
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""" Loads the images and labels as numpy arrays
The dataset is originally read as a dictionary """
train_data = scipy.io.loadmat('stlTrainSubset.mat')
train_images = numpy.array(train_data['trainImages'])
train_labels = numpy.array(train_data['trainLabels'])
return [train_images, train_labels] | [
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pymedusa/Medusa | 1405fbb6eb8ef4d20fcca24c32ddca52b11f0f38 | ext/boto/cloudsearch/layer1.py | python | Layer1.update_service_access_policies | (self, domain_name, access_policies) | return self.get_response(doc_path, 'UpdateServiceAccessPolicies',
params, verb='POST') | Updates the policies controlling access to the services in
this search domain.
:type domain_name: string
:param domain_name: A string that represents the name of a
domain. Domain names must be unique across the domains
owned by an account within an AWS region. Domain names
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following characters: a-z (lowercase), 0-9, and -
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:type access_policies: string
:param access_policies: An IAM access policy as described in
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document is 100KB.
:raises: BaseException, InternalException, LimitExceededException,
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"""
Updates the policies controlling access to the services in
this search domain.
:type domain_name: string
:param domain_name: A string that represents the name of a
domain. Domain names must be unique across the domains
owned by an account within an AWS region. Domain names
must start with a letter or number and can contain the
following characters: a-z (lowercase), 0-9, and -
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:type access_policies: string
:param access_policies: An IAM access policy as described in
The Access Policy Language in Using AWS Identity and
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:raises: BaseException, InternalException, LimitExceededException,
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"""
doc_path = ('update_service_access_policies_response',
'update_service_access_policies_result',
'access_policies')
params = {'AccessPolicies': access_policies,
'DomainName': domain_name}
return self.get_response(doc_path, 'UpdateServiceAccessPolicies',
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kanzure/nanoengineer | 874e4c9f8a9190f093625b267f9767e19f82e6c4 | cad/src/utilities/debug_prefs.py | python | iconset_from_color | (color) | return iconset | Return a QIcon suitable for showing the given color in a menu item or (###k untested, unreviewed) some other widget.
The color can be a QColor or any python type we use for colors (out of the few our helper funcs understand). | Return a QIcon suitable for showing the given color in a menu item or (###k untested, unreviewed) some other widget.
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Return a QIcon suitable for showing the given color in a menu item or (###k untested, unreviewed) some other widget.
The color can be a QColor or any python type we use for colors (out of the few our helper funcs understand).
"""
# figure out desired size of a small icon
from PyQt4.Qt import QIcon
#size = QIcon.iconSize(QIcon.Small) # a QSize object
#w, h = size.width(), size.height()
w, h = 16, 16 # hardwire it and be done
# get pixmap of that color
pixmap = pixmap_from_color_and_size( color, (w,h) )
# for now, let Qt figure out the Active appearance, etc. Later we can draw our own black outline, or so. ##e
iconset = QIcon(pixmap)
checkmark = ("checkmark" == debug_pref("color checkmark", Choice(["checkmark","box"])))
modify_iconset_On_states( iconset, color = color, checkmark = checkmark )
return iconset | [
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lilydjwg/winterpy | 3864a463386202b9c9c6ce8ba902878e40f3084c | pylib/lilypath.py | python | path.open | (self, mode='r', buffering=2, encoding=None, errors=None,
newline=None, closefd=True) | return open(self.value, mode, buffering, encoding, errors, newline, closefd) | 打开该文件 | 打开该文件 | [
"打开该文件"
] | def open(self, mode='r', buffering=2, encoding=None, errors=None,
newline=None, closefd=True):
'''打开该文件'''
#XXX 文档说buffering默认值为 None，但事实并非如此。使用full buffering好了
return open(self.value, mode, buffering, encoding, errors, newline, closefd) | [
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yihui-he/KL-Loss | 66c0ed9e886a2218f4cf88c0efd4f40199bff54a | detectron/core/config.py | python | get_output_dir | (datasets, training=True) | return outdir | Get the output directory determined by the current global config. | Get the output directory determined by the current global config. | [
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"""Get the output directory determined by the current global config."""
assert isinstance(datasets, tuple([tuple, list] + list(six.string_types))), \
'datasets argument must be of type tuple, list or string'
is_string = isinstance(datasets, six.string_types)
dataset_name = datasets if is_string else ':'.join(datasets)
tag = 'train' if training else 'test'
# <output-dir>/<train|test>/<dataset-name>/<model-type>/
outdir = osp.join(__C.OUTPUT_DIR, tag, dataset_name, __C.MODEL.TYPE)
if not osp.exists(outdir):
os.makedirs(outdir)
return outdir | [
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seopbo/nlp_classification | 21ea6e3f5737e7074bdd8dd190e5f5172f86f6bf | A_Structured_Self-attentive_Sentence_Embedding_ptc/model/utils.py | python | PadSequence.__call__ | (self, sample) | [] | def __call__(self, sample):
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F8LEFT/DecLLVM | d38e45e3d0dd35634adae1d0cf7f96f3bd96e74c | python/idc.py | python | GuessType | (ea) | return idaapi.idc_guess_type(ea) | Guess type of function/variable
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@return: type string or None if failed | Guess type of function/variable | [
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"""
Guess type of function/variable
@param ea: the address of the object, can be the structure member id too
@return: type string or None if failed
"""
return idaapi.idc_guess_type(ea) | [
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Altinity/clickhouse-mysql-data-reader | 3b1b7088751b05e5bbf45890c5949b58208c2343 | clickhouse_mysql/pumper.py | python | Pumper.write_rows_event_each_row | (self, event=None) | WriteRowsEvent.EachRow handler
:param event: | WriteRowsEvent.EachRow handler
:param event: | [
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WriteRowsEvent.EachRow handler
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mbj4668/pyang | 97523476e7ada8609d27fd47880e1b5061073dc3 | pyang/repository.py | python | FileRepository.__init__ | (self, path="", use_env=True, no_path_recurse=False,
verbose=False) | Create a Repository which searches the filesystem for modules
`path` is a `os.pathsep`-separated string of directories | Create a Repository which searches the filesystem for modules | [
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verbose=False):
"""Create a Repository which searches the filesystem for modules
`path` is a `os.pathsep`-separated string of directories
"""
Repository.__init__(self)
self.dirs = []
self.no_path_recurse = no_path_recurse
self.modules = None
self.verbose = verbose
for directory in path.split(os.pathsep):
self._add_directory(directory)
while use_env:
use_env = False
modpath = os.getenv('YANG_MODPATH')
if modpath is not None:
for directory in modpath.split(os.pathsep):
self._add_directory(directory)
home = os.getenv('HOME')
if home is not None:
self._add_directory(os.path.join(home, 'yang', 'modules'))
inst = os.getenv('YANG_INSTALL')
if inst is not None:
self._add_directory(os.path.join(inst, 'yang', 'modules'))
break # skip search if install location is indicated
default_install = os.path.join(
sys.prefix, 'share', 'yang', 'modules')
if os.path.exists(default_install):
self._add_directory(default_install)
break # end search if default location exists
# for some systems, sys.prefix returns `/usr`
# but the real location is `/usr/local`
# if the package is installed with pip
# this information can be easily retrieved
import pkgutil
if not pkgutil.find_loader('pip'):
break # abort search if pip is not installed
# hack below to handle pip 10 internals
# if someone knows pip and how to fix this, it would be great!
location = None
try:
import pip.locations as locations
location = locations.distutils_scheme('pyang')
except:
try:
import pip._internal.locations as locations
location = locations.distutils_scheme('pyang')
except:
pass
if location is not None:
self._add_directory(
os.path.join(location['data'], 'share', 'yang', 'modules'))
if verbose:
sys.stderr.write('# module search path: %s\n'
% os.pathsep.join(self.dirs)) | [
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kubernetes-client/python | 47b9da9de2d02b2b7a34fbe05afb44afd130d73a | kubernetes/client/models/v1beta1_endpoint_slice_list.py | python | V1beta1EndpointSliceList.kind | (self, kind) | Sets the kind of this V1beta1EndpointSliceList.
Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds # noqa: E501
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"""
self._kind = kind | [
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duo-labs/isthislegit | 5d51fd2e0fe070cacd1ee169ca8a371a72e005ef | dashboard/lib/flanker/mime/message/fallback/part.py | python | FallbackMimePart.was_changed | (self) | return False | [] | def was_changed(self):
return False | [
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deepgully/me | f7ad65edc2fe435310c6676bc2e322cfe5d4c8f0 | libs/flask_sqlalchemy.py | python | SQLAlchemy.apply_driver_hacks | (self, app, info, options) | This method is called before engine creation and used to inject
driver specific hacks into the options. The `options` parameter is
a dictionary of keyword arguments that will then be used to call
the :func:`sqlalchemy.create_engine` function.
The default implementation provides some saner defaults for things
like pool sizes for MySQL and sqlite. Also it injects the setting of
`SQLALCHEMY_NATIVE_UNICODE`. | This method is called before engine creation and used to inject
driver specific hacks into the options. The `options` parameter is
a dictionary of keyword arguments that will then be used to call
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"""This method is called before engine creation and used to inject
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a dictionary of keyword arguments that will then be used to call
the :func:`sqlalchemy.create_engine` function.
The default implementation provides some saner defaults for things
like pool sizes for MySQL and sqlite. Also it injects the setting of
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"""
if info.drivername == 'mysql':
info.query.setdefault('charset', 'utf8')
options.setdefault('pool_size', 10)
options.setdefault('pool_recycle', 7200)
elif info.drivername == 'sqlite':
pool_size = options.get('pool_size')
detected_in_memory = False
# we go to memory and the pool size was explicitly set to 0
# which is fail. Let the user know that
if info.database in (None, '', ':memory:'):
detected_in_memory = True
if pool_size == 0:
raise RuntimeError('SQLite in memory database with an '
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'loss.')
# if pool size is None or explicitly set to 0 we assume the
# user did not want a queue for this sqlite connection and
# hook in the null pool.
elif not pool_size:
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options['poolclass'] = NullPool
# if it's not an in memory database we make the path absolute.
if not detected_in_memory:
info.database = os.path.join(app.root_path, info.database)
unu = app.config['SQLALCHEMY_NATIVE_UNICODE']
if unu is None:
unu = self.use_native_unicode
if not unu:
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jython/jython3 | def4f8ec47cb7a9c799ea4c745f12badf92c5769 | lib-python/3.5.1/concurrent/futures/_base.py | python | Executor.submit | (self, fn, *args, **kwargs) | Submits a callable to be executed with the given arguments.
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] | def submit(self, fn, *args, **kwargs):
"""Submits a callable to be executed with the given arguments.
Schedules the callable to be executed as fn(*args, **kwargs) and returns
a Future instance representing the execution of the callable.
Returns:
A Future representing the given call.
"""
raise NotImplementedError() | [
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ladybug-tools/honeybee-legacy | bd62af4862fe022801fb87dbc8794fdf1dff73a9 | src/Honeybee_Export To OpenStudio.py | python | WriteOPS.updateDXCoolingCoil | (self, model, coolCoil, coolingAvailSched, coolingCOP) | [] | def updateDXCoolingCoil(self, model, coolCoil, coolingAvailSched, coolingCOP):
if coolingAvailSched != 'ALWAYS ON':
coolAvailSch = self.getOSSchedule(coolingAvailSched, model)
coolCoil.setAvailabilitySchedule(coolAvailSch)
if coolingCOP != 'Default':
coolCoil.setRatedCOP(ops.OptionalDouble(coolingCOP)) | [
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makerbot/ReplicatorG | d6f2b07785a5a5f1e172fb87cb4303b17c575d5d | skein_engines/skeinforge-47/skeinforge_application/skeinforge_plugins/craft_plugins/fillet.py | python | getNewRepository | () | return FilletRepository() | Get new repository. | Get new repository. | [
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] | def getNewRepository():
'Get new repository.'
return FilletRepository() | [
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Unidata/siphon | d8ede355114801bf7a05db20dfe49ab132723f86 | src/siphon/simplewebservice/ndbc.py | python | NDBC.realtime_observations | (cls, buoy, data_type='txt') | return parsers[data_type](raw_data) | Retrieve the realtime buoy data from NDBC.
Parameters
----------
buoy : str
Name of buoy
data_type : str
Type of data requested, must be one of
'txt' standard meteorological data
'drift' meteorological data from drifting buoys and limited moored buoy data
mainly from international partners
'cwind' continuous winds data (10 minute average)
'spec' spectral wave summaries
'ocean' oceanographic data
'srad' solar radiation data
'dart' water column height
'supl' supplemental measurements data
'rain' hourly rain data
Returns
-------
Raw data string | Retrieve the realtime buoy data from NDBC. | [
"Retrieve",
"the",
"realtime",
"buoy",
"data",
"from",
"NDBC",
"."
] | def realtime_observations(cls, buoy, data_type='txt'):
"""Retrieve the realtime buoy data from NDBC.
Parameters
----------
buoy : str
Name of buoy
data_type : str
Type of data requested, must be one of
'txt' standard meteorological data
'drift' meteorological data from drifting buoys and limited moored buoy data
mainly from international partners
'cwind' continuous winds data (10 minute average)
'spec' spectral wave summaries
'ocean' oceanographic data
'srad' solar radiation data
'dart' water column height
'supl' supplemental measurements data
'rain' hourly rain data
Returns
-------
Raw data string
"""
endpoint = cls()
parsers = {'txt': endpoint._parse_met,
'drift': endpoint._parse_drift,
'cwind': endpoint._parse_cwind,
'spec': endpoint._parse_spec,
'ocean': endpoint._parse_ocean,
'srad': endpoint._parse_srad,
'dart': endpoint._parse_dart,
'supl': endpoint._parse_supl,
'rain': endpoint._parse_rain}
if data_type not in parsers:
raise KeyError('Data type must be txt, drift, cwind, spec, ocean, srad, dart,'
'supl, or rain for parsed realtime data.')
raw_data = endpoint.raw_buoy_data(buoy, data_type=data_type)
return parsers[data_type](raw_data) | [
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pytroll/satpy | 09e51f932048f98cce7919a4ff8bd2ec01e1ae98 | satpy/dataset/data_dict.py | python | DatasetDict.get | (self, key, default=None) | return super(DatasetDict, self).get(key, default) | Get value with optional default. | Get value with optional default. | [
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] | def get(self, key, default=None):
"""Get value with optional default."""
try:
key = self.get_key(key)
except KeyError:
return default
return super(DatasetDict, self).get(key, default) | [
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mesonbuild/meson | a22d0f9a0a787df70ce79b05d0c45de90a970048 | docs/refman/main.py | python | main | () | return 0 | [] | def main() -> int:
parser = argparse.ArgumentParser(description='Meson reference manual generator')
parser.add_argument('-l', '--loader', type=str, default='yaml', choices=['yaml', 'pickle'], help='Information loader backend')
parser.add_argument('-g', '--generator', type=str, choices=['print', 'pickle', 'md', 'json', 'man'], required=True, help='Generator backend')
parser.add_argument('-s', '--sitemap', type=Path, default=meson_root / 'docs' / 'sitemap.txt', help='Path to the input sitemap.txt')
parser.add_argument('-o', '--out', type=Path, required=True, help='Output directory for generated files')
parser.add_argument('-i', '--input', type=Path, default=meson_root / 'docs' / 'yaml', help='Input path for the selected loader')
parser.add_argument('--link-defs', type=Path, help='Output file for the MD generator link definition file')
parser.add_argument('--depfile', type=Path, default=None, help='Set to generate a depfile')
parser.add_argument('--force-color', action='store_true', help='Force enable colors')
parser.add_argument('--no-modules', action='store_true', help='Disable building modules')
args = parser.parse_args()
if args.force_color:
mlog.colorize_console = lambda: True
loaders: T.Dict[str, T.Callable[[], LoaderBase]] = {
'yaml': lambda: LoaderYAML(args.input),
'pickle': lambda: LoaderPickle(args.input),
}
loader = loaders[args.loader]()
refMan = loader.load()
generators: T.Dict[str, T.Callable[[], GeneratorBase]] = {
'print': lambda: GeneratorPrint(refMan),
'pickle': lambda: GeneratorPickle(refMan, args.out),
'md': lambda: GeneratorMD(refMan, args.out, args.sitemap, args.link_defs, not args.no_modules),
'json': lambda: GeneratorJSON(refMan, args.out, not args.no_modules),
'man': lambda: GeneratorMan(refMan, args.out, not args.no_modules),
}
generator = generators[args.generator]()
# Generate the depfile if required
if args.depfile is not None:
assert isinstance(args.depfile, Path)
assert isinstance(args.out, Path)
# Also add all files of this package
script_files = list(Path(__file__).resolve().parent.glob('**/*.py'))
templates = list(Path(__file__).resolve().parent.glob('**/*.mustache'))
out_text = f'{args.out.resolve().as_posix()}: \\\n'
for input in loader.input_files + script_files + templates:
out_text += f' {input.resolve().as_posix():<93} \\\n'
args.depfile.write_text(out_text, encoding='utf-8')
generator.generate()
return 0 | [
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IJDykeman/wangTiles | 7c1ee2095ebdf7f72bce07d94c6484915d5cae8b | experimental_code/tiles_3d/venv_mac_py3/lib/python2.7/site-packages/setuptools/_vendor/pyparsing.py | python | pyparsing_common.stripHTMLTags | (s, l, tokens) | return pyparsing_common._html_stripper.transformString(tokens[0]) | Parse action to remove HTML tags from web page HTML source
Example::
# strip HTML links from normal text
text = '<td>More info at the <a href="http://pyparsing.wikispaces.com">pyparsing</a> wiki page</td>'
td,td_end = makeHTMLTags("TD")
table_text = td + SkipTo(td_end).setParseAction(pyparsing_common.stripHTMLTags)("body") + td_end
print(table_text.parseString(text).body) # -> 'More info at the pyparsing wiki page' | Parse action to remove HTML tags from web page HTML source | [
"Parse",
"action",
"to",
"remove",
"HTML",
"tags",
"from",
"web",
"page",
"HTML",
"source"
] | def stripHTMLTags(s, l, tokens):
"""
Parse action to remove HTML tags from web page HTML source
Example::
# strip HTML links from normal text
text = '<td>More info at the <a href="http://pyparsing.wikispaces.com">pyparsing</a> wiki page</td>'
td,td_end = makeHTMLTags("TD")
table_text = td + SkipTo(td_end).setParseAction(pyparsing_common.stripHTMLTags)("body") + td_end
print(table_text.parseString(text).body) # -> 'More info at the pyparsing wiki page'
"""
return pyparsing_common._html_stripper.transformString(tokens[0]) | [
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intel/virtual-storage-manager | 00706ab9701acbd0d5e04b19cc80c6b66a2973b8 | source/vsm/vsm/api/v1/agents.py | python | AgentsController._write_zone_info | (self) | return True | Write zone into DB.
Zone list:
zone_a
zone_b
zone_c
Just write names here. | Write zone into DB. | [
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"into",
"DB",
"."
] | def _write_zone_info(self):
"""Write zone into DB.
Zone list:
zone_a
zone_b
zone_c
Just write names here.
"""
#TODO add zone table two variables. cluster_id, storage_group_id.
zone_list = self._cluster_info['zone']
for zone_name in zone_list:
zone_ref = db.\
zone_get_by_name(self._context, zone_name)
if not zone_ref:
LOG.info('Have not find zone = %s' % zone_name)
db.zone_create(self._context,
{'name': zone_name,
'deleted': False})
else:
LOG.info('Find zone = %s in zone Table.' % zone_name)
return True | [
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rembo10/headphones | b3199605be1ebc83a7a8feab6b1e99b64014187c | lib/mako/runtime.py | python | Context.pop_caller | (self) | Pop a ``caller`` callable onto the callstack for this
:class:`.Context`. | Pop a ``caller`` callable onto the callstack for this
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"""Pop a ``caller`` callable onto the callstack for this
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CouchPotato/CouchPotatoServer | 7260c12f72447ddb6f062367c6dfbda03ecd4e9c | libs/requests/packages/urllib3/__init__.py | python | disable_warnings | (category=exceptions.HTTPWarning) | Helper for quickly disabling all urllib3 warnings. | Helper for quickly disabling all urllib3 warnings. | [
"Helper",
"for",
"quickly",
"disabling",
"all",
"urllib3",
"warnings",
"."
] | def disable_warnings(category=exceptions.HTTPWarning):
"""
Helper for quickly disabling all urllib3 warnings.
"""
warnings.simplefilter('ignore', category) | [
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onnx/onnx-tensorflow | 6194294c9f2f1c9270a614f6ae5078f2095587b7 | onnx_tf/handlers/backend/dilated_pooling.py | python | DilatedPooling._calc_argmax_without_padding | (self, ind) | return new_ind | Calculate the original indices as they would be without padding | Calculate the original indices as they would be without padding | [
"Calculate",
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] | def _calc_argmax_without_padding(self, ind):
"""
Calculate the original indices as they would be without padding
"""
in_width = self.orig_input_shape[3]
padded_width = self.input_shape[3]
num_channels = self.input_shape[1]
# mod_floor op is not implemented on GPU
# implement it using: a % b = a - (a // b) * b
# ind_nhw = ind // num_channels
# ind_channel = ind % num_channels
ind_nhw = ind // num_channels
ind_channel = ind - ind_nhw * num_channels
new_ind = (ind_nhw // padded_width) * (self.pads[2] + self.pads[3])
new_ind = ind_nhw - new_ind - self.pads[0] * in_width - self.pads[2]
new_ind = num_channels * new_ind + ind_channel
return new_ind | [
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PINTO0309/PINTO_model_zoo | 2924acda7a7d541d8712efd7cc4fd1c61ef5bddd | 024_yolov3-lite/utils/data.py | python | Data.__create_label | (self, bboxes) | return label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes | :param bboxes: 一张图对应的所有bbox和每个bbox所属的类别，以及mixup的权重，
bbox的坐标为(xmin, ymin, xmax, ymax, class_ind, mixup_weight)
:return:
label_sbbox: shape为(input_size / 8, input_size / 8, anchor_per_scale, 6 + num_classes)
label_mbbox: shape为(input_size / 16, input_size / 16, anchor_per_scale, 6 + num_classes)
label_lbbox: shape为(input_size / 32, input_size / 32, anchor_per_scale, 6 + num_classes)
只要某个GT落入grid中，那么这个grid就负责预测它，最多负责预测gt_per_grid个GT，
那么该grid中对应位置的数据为(xmin, ymin, xmax, ymax, 1, classes, mixup_weights),
其他grid对应位置的数据都为(0, 0, 0, 0, 0, 0..., 1)
sbboxes：shape为(max_bbox_per_scale, 4)
mbboxes：shape为(max_bbox_per_scale, 4)
lbboxes：shape为(max_bbox_per_scale, 4)
存储的坐标为(xmin, ymin, xmax, ymax)，大小都是bbox纠正后的原始大小 | :param bboxes: 一张图对应的所有bbox和每个bbox所属的类别，以及mixup的权重，
bbox的坐标为(xmin, ymin, xmax, ymax, class_ind, mixup_weight)
:return:
label_sbbox: shape为(input_size / 8, input_size / 8, anchor_per_scale, 6 + num_classes)
label_mbbox: shape为(input_size / 16, input_size / 16, anchor_per_scale, 6 + num_classes)
label_lbbox: shape为(input_size / 32, input_size / 32, anchor_per_scale, 6 + num_classes)
只要某个GT落入grid中，那么这个grid就负责预测它，最多负责预测gt_per_grid个GT，
那么该grid中对应位置的数据为(xmin, ymin, xmax, ymax, 1, classes, mixup_weights),
其他grid对应位置的数据都为(0, 0, 0, 0, 0, 0..., 1)
sbboxes：shape为(max_bbox_per_scale, 4)
mbboxes：shape为(max_bbox_per_scale, 4)
lbboxes：shape为(max_bbox_per_scale, 4)
存储的坐标为(xmin, ymin, xmax, ymax)，大小都是bbox纠正后的原始大小 | [
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"""
:param bboxes: 一张图对应的所有bbox和每个bbox所属的类别，以及mixup的权重，
bbox的坐标为(xmin, ymin, xmax, ymax, class_ind, mixup_weight)
:return:
label_sbbox: shape为(input_size / 8, input_size / 8, anchor_per_scale, 6 + num_classes)
label_mbbox: shape为(input_size / 16, input_size / 16, anchor_per_scale, 6 + num_classes)
label_lbbox: shape为(input_size / 32, input_size / 32, anchor_per_scale, 6 + num_classes)
只要某个GT落入grid中，那么这个grid就负责预测它，最多负责预测gt_per_grid个GT，
那么该grid中对应位置的数据为(xmin, ymin, xmax, ymax, 1, classes, mixup_weights),
其他grid对应位置的数据都为(0, 0, 0, 0, 0, 0..., 1)
sbboxes：shape为(max_bbox_per_scale, 4)
mbboxes：shape为(max_bbox_per_scale, 4)
lbboxes：shape为(max_bbox_per_scale, 4)
存储的坐标为(xmin, ymin, xmax, ymax)，大小都是bbox纠正后的原始大小
"""
label = [np.zeros((self.__train_output_sizes[i], self.__train_output_sizes[i],
self.__gt_per_grid, 6 + self.__num_classes)) for i in range(3)]
# mixup weight位默认为1.0
for i in range(3):
label[i][:, :, :, -1] = 1.0
bboxes_coor = [[] for _ in range(3)]
bboxes_count = [np.zeros((self.__train_output_sizes[i], self.__train_output_sizes[i])) for i in range(3)]
for bbox in bboxes:
# (1)获取bbox在原图上的顶点坐标、类别索引、mix up权重、中心坐标、高宽、尺度
bbox_coor = bbox[:4]
bbox_class_ind = int(bbox[4])
bbox_mixw = bbox[5]
bbox_xywh = np.concatenate([(bbox_coor[2:] + bbox_coor[:2]) * 0.5,
bbox_coor[2:] - bbox_coor[:2]], axis=-1)
bbox_scale = np.sqrt(np.multiply.reduce(bbox_xywh[2:]))
# label smooth
onehot = np.zeros(self.__num_classes, dtype=np.float)
onehot[bbox_class_ind] = 1.0
uniform_distribution = np.full(self.__num_classes, 1.0 / self.__num_classes)
deta = 0.01
smooth_onehot = onehot * (1 - deta) + deta * uniform_distribution
if bbox_scale <= 30:
match_branch = 0
elif (30 < bbox_scale) and (bbox_scale <= 90):
match_branch = 1
else:
match_branch = 2
xind, yind = np.floor(1.0 * bbox_xywh[:2] / self.__strides[match_branch]).astype(np.int32)
gt_count = int(bboxes_count[match_branch][yind, xind])
if gt_count < self.__gt_per_grid:
if gt_count == 0:
gt_count = slice(None)
bbox_label = np.concatenate([bbox_coor, [1.0], smooth_onehot, [bbox_mixw]], axis=-1)
label[match_branch][yind, xind, gt_count, :] = 0
label[match_branch][yind, xind, gt_count, :] = bbox_label
bboxes_count[match_branch][yind, xind] += 1
bboxes_coor[match_branch].append(bbox_coor)
label_sbbox, label_mbbox, label_lbbox = label
sbboxes, mbboxes, lbboxes = bboxes_coor
return label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes | [
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Hellowlol/bw_plex | 86768d6ee89ee1c08d2f6e6468976e4c51135915 | bw_plex/audfprint/hash_table.py | python | HashTable.totalhashes | (self) | return np.sum(self.counts) | Return the total count of hashes stored in the table | Return the total count of hashes stored in the table | [
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lsbardel/python-stdnet | 78db5320bdedc3f28c5e4f38cda13a4469e35db7 | stdnet/odm/query.py | python | get_lookups | (attname, field_lookups) | return lookups | [] | def get_lookups(attname, field_lookups):
lookups = field_lookups.get(attname)
if lookups is None:
lookups = []
field_lookups[attname] = lookups
return lookups | [
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larryhastings/gilectomy | 4315ec3f1d6d4f813cc82ce27a24e7f784dbfc1a | Lib/weakref.py | python | finalize.__call__ | (self, _=None) | If alive then mark as dead and return func(*args, **kwargs);
otherwise return None | If alive then mark as dead and return func(*args, **kwargs);
otherwise return None | [
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"""If alive then mark as dead and return func(*args, **kwargs);
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info = self._registry.pop(self, None)
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hydroshare/hydroshare | 7ba563b55412f283047fb3ef6da367d41dec58c6 | hs_file_types/models/netcdf.py | python | NetCDFFileMetaData.get_variable_formset | (self) | return variable_formset | [] | def get_variable_formset(self):
VariableFormSetEdit = formset_factory(
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openstack/barbican | a9d2b133c8dc3307974f119f9a2b23a4ba82e8ce | barbican/api/app.py | python | get_api_wsgi_script | () | return application | [] | def get_api_wsgi_script():
conf = '/etc/barbican/barbican-api-paste.ini'
application = deploy.loadapp('config:%s' % conf)
return application | [
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aouyar/PyMunin | 94624d4f56340cb2ed7e96ca3c5d9533a0721306 | pysysinfo/lighttpd.py | python | LighttpdInfo.__init__ | (self, host=None, port=None, user=None, password=None,
statuspath = None, ssl=False, autoInit=True) | Initialize Lighttpd server-status URL access.
@param host: Lighttpd Web Server Host. (Default: 127.0.0.1)
@param port: Lighttpd Web Server Port. (Default: 80, SSL: 443)
@param user: Username. (Not needed unless authentication is required
to access server-status page.
@param password: Password. (Not needed unless authentication is required
to access server-status page.
@statuspath: Path of status page. (Default: server-status)
@param ssl: Use SSL if True. (Default: False)
@param autoInit: If True connect to Lighttpd Web Server on instantiation. | Initialize Lighttpd server-status URL access. | [
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] | def __init__(self, host=None, port=None, user=None, password=None,
statuspath = None, ssl=False, autoInit=True):
"""Initialize Lighttpd server-status URL access.
@param host: Lighttpd Web Server Host. (Default: 127.0.0.1)
@param port: Lighttpd Web Server Port. (Default: 80, SSL: 443)
@param user: Username. (Not needed unless authentication is required
to access server-status page.
@param password: Password. (Not needed unless authentication is required
to access server-status page.
@statuspath: Path of status page. (Default: server-status)
@param ssl: Use SSL if True. (Default: False)
@param autoInit: If True connect to Lighttpd Web Server on instantiation.
"""
if host is not None:
self._host = host
else:
self._host = '127.0.0.1'
if port is not None:
self._port = int(port)
else:
if ssl:
self._port = defaultHTTPSport
else:
self._port = defaultHTTPport
self._user = user
self._password = password
if statuspath is not None:
self._statuspath = statuspath
else:
self._statuspath = 'server-status'
if ssl:
self._proto = 'https'
else:
self._proto = 'http'
self._statusDict = None
if autoInit:
self.initStats() | [
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intrig-unicamp/mininet-wifi | 3c8a8f63bd4aa043aa9c1ad16f304dec2916f5ba | mn_wifi/sumo/traci/_vehicletype.py | python | VehicleTypeDomain.setApparentDecel | (self, typeID, decel) | setDecel(string, double) -> None
Sets the apparent deceleration in m/s^2 of vehicles of this type. | setDecel(string, double) -> None
Sets the apparent deceleration in m/s^2 of vehicles of this type. | [
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"""setDecel(string, double) -> None
Sets the apparent deceleration in m/s^2 of vehicles of this type.
"""
self._connection._sendDoubleCmd(
tc.CMD_SET_VEHICLETYPE_VARIABLE, tc.VAR_APPARENT_DECEL, typeID, decel) | [
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golismero/golismero | 7d605b937e241f51c1ca4f47b20f755eeefb9d76 | thirdparty_libs/django/db/models/sql/query.py | python | Query.add_q | (self, q_object, used_aliases=None, force_having=False) | Adds a Q-object to the current filter.
Can also be used to add anything that has an 'add_to_query()' method. | Adds a Q-object to the current filter. | [
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"""
Adds a Q-object to the current filter.
Can also be used to add anything that has an 'add_to_query()' method.
"""
if used_aliases is None:
used_aliases = self.used_aliases
if hasattr(q_object, 'add_to_query'):
# Complex custom objects are responsible for adding themselves.
q_object.add_to_query(self, used_aliases)
else:
if self.where and q_object.connector != AND and len(q_object) > 1:
self.where.start_subtree(AND)
subtree = True
else:
subtree = False
connector = AND
if q_object.connector == OR and not force_having:
force_having = self.need_force_having(q_object)
for child in q_object.children:
if connector == OR:
refcounts_before = self.alias_refcount.copy()
if force_having:
self.having.start_subtree(connector)
else:
self.where.start_subtree(connector)
if isinstance(child, Node):
self.add_q(child, used_aliases, force_having=force_having)
else:
self.add_filter(child, connector, q_object.negated,
can_reuse=used_aliases, force_having=force_having)
if force_having:
self.having.end_subtree()
else:
self.where.end_subtree()
if connector == OR:
# Aliases that were newly added or not used at all need to
# be promoted to outer joins if they are nullable relations.
# (they shouldn't turn the whole conditional into the empty
# set just because they don't match anything).
self.promote_unused_aliases(refcounts_before, used_aliases)
connector = q_object.connector
if q_object.negated:
self.where.negate()
if subtree:
self.where.end_subtree()
if self.filter_is_sticky:
self.used_aliases = used_aliases | [
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NVIDIA/DeepLearningExamples | 589604d49e016cd9ef4525f7abcc9c7b826cfc5e | TensorFlow/Translation/GNMT/model_helper.py | python | avg_checkpoints | (model_dir, num_last_checkpoints, global_step_name) | return avg_model_dir | Average the last N checkpoints in the model_dir. | Average the last N checkpoints in the model_dir. | [
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"""Average the last N checkpoints in the model_dir."""
checkpoint_state = tf.train.get_checkpoint_state(model_dir)
if not checkpoint_state:
utils.print_out("# No checkpoint file found in directory: %s" % model_dir)
return None
# Checkpoints are ordered from oldest to newest.
checkpoints = (
checkpoint_state.all_model_checkpoint_paths[-num_last_checkpoints:])
if len(checkpoints) < num_last_checkpoints:
utils.print_out(
"# Skipping averaging checkpoints because not enough checkpoints is "
"available.")
return None
avg_model_dir = os.path.join(model_dir, "avg_checkpoints")
if not tf.gfile.Exists(avg_model_dir):
utils.print_out(
"# Creating new directory %s for saving averaged checkpoints." %
avg_model_dir)
tf.gfile.MakeDirs(avg_model_dir)
utils.print_out("# Reading and averaging variables in checkpoints:")
var_list = tf.contrib.framework.list_variables(checkpoints[0])
var_values, var_dtypes = {}, {}
for (name, shape) in var_list:
if name != global_step_name:
var_values[name] = np.zeros(shape)
for checkpoint in checkpoints:
utils.print_out(" %s" % checkpoint)
reader = tf.contrib.framework.load_checkpoint(checkpoint)
for name in var_values:
tensor = reader.get_tensor(name)
var_dtypes[name] = tensor.dtype
var_values[name] += tensor
for name in var_values:
var_values[name] /= len(checkpoints)
# Build a graph with same variables in the checkpoints, and save the averaged
# variables into the avg_model_dir.
with tf.Graph().as_default():
tf_vars = [
tf.get_variable(v, shape=var_values[v].shape, dtype=var_dtypes[name])
for v in var_values
]
placeholders = [tf.placeholder(v.dtype, shape=v.shape) for v in tf_vars]
assign_ops = [tf.assign(v, p) for (v, p) in zip(tf_vars, placeholders)]
saver = tf.train.Saver(tf.all_variables(), save_relative_paths=True)
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
for p, assign_op, (name, value) in zip(placeholders, assign_ops,
six.iteritems(var_values)):
sess.run(assign_op, {p: value})
# Use the built saver to save the averaged checkpoint. Only keep 1
# checkpoint and the best checkpoint will be moved to avg_best_metric_dir.
saver.save(
sess,
os.path.join(avg_model_dir, "translate.ckpt"))
return avg_model_dir | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/combinat/partition_algebra.py | python | SetPartitionsBk | (k) | return SetPartitionsBk_k(k) | r"""
Return the combinatorial class of set partitions of type `B_k`.
These are the set partitions where every block has size 2.
EXAMPLES::
sage: B3 = SetPartitionsBk(3); B3
Set partitions of {1, ..., 3, -1, ..., -3} with block size 2
sage: B3.first() #random
{{2, -2}, {1, -3}, {3, -1}}
sage: B3.last() #random
{{1, 2}, {3, -2}, {-3, -1}}
sage: B3.random_element() #random
{{2, -1}, {1, -3}, {3, -2}}
sage: B3.cardinality()
15
sage: B2p5 = SetPartitionsBk(2.5); B2p5
Set partitions of {1, ..., 3, -1, ..., -3} with 3 and -3 in the same block and with block size 2
sage: B2p5.first() #random
{{2, -1}, {3, -3}, {1, -2}}
sage: B2p5.last() #random
{{1, 2}, {3, -3}, {-1, -2}}
sage: B2p5.random_element() #random
{{2, -2}, {3, -3}, {1, -1}}
sage: B2p5.cardinality()
3 | r"""
Return the combinatorial class of set partitions of type `B_k`. | [
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r"""
Return the combinatorial class of set partitions of type `B_k`.
These are the set partitions where every block has size 2.
EXAMPLES::
sage: B3 = SetPartitionsBk(3); B3
Set partitions of {1, ..., 3, -1, ..., -3} with block size 2
sage: B3.first() #random
{{2, -2}, {1, -3}, {3, -1}}
sage: B3.last() #random
{{1, 2}, {3, -2}, {-3, -1}}
sage: B3.random_element() #random
{{2, -1}, {1, -3}, {3, -2}}
sage: B3.cardinality()
15
sage: B2p5 = SetPartitionsBk(2.5); B2p5
Set partitions of {1, ..., 3, -1, ..., -3} with 3 and -3 in the same block and with block size 2
sage: B2p5.first() #random
{{2, -1}, {3, -3}, {1, -2}}
sage: B2p5.last() #random
{{1, 2}, {3, -3}, {-1, -2}}
sage: B2p5.random_element() #random
{{2, -2}, {3, -3}, {1, -1}}
sage: B2p5.cardinality()
3
"""
is_int, k = _int_or_half_int(k)
if not is_int:
return SetPartitionsBkhalf_k(k)
return SetPartitionsBk_k(k) | [
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IJDykeman/wangTiles | 7c1ee2095ebdf7f72bce07d94c6484915d5cae8b | experimental_code/tiles_3d/venv_mac_py3/lib/python2.7/site-packages/setuptools/command/egg_info.py | python | egg_info.save_version_info | (self, filename) | Materialize the value of date into the
build tag. Install build keys in a deterministic order
to avoid arbitrary reordering on subsequent builds. | Materialize the value of date into the
build tag. Install build keys in a deterministic order
to avoid arbitrary reordering on subsequent builds. | [
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"""
Materialize the value of date into the
build tag. Install build keys in a deterministic order
to avoid arbitrary reordering on subsequent builds.
"""
egg_info = collections.OrderedDict()
# follow the order these keys would have been added
# when PYTHONHASHSEED=0
egg_info['tag_build'] = self.tags()
egg_info['tag_date'] = 0
edit_config(filename, dict(egg_info=egg_info)) | [
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kLabUM/rrcf | 34504c14bba233f86a7dcae35d55fc84cc5b7508 | rrcf/rrcf.py | python | RCTree.codisp | (self, leaf) | return co_displacement | Compute collusive displacement at leaf
Parameters:
-----------
leaf: index of leaf or Leaf instance
Returns:
--------
codisplacement: float
Collusive displacement if leaf is removed.
Example:
--------
# Create RCTree
>>> X = np.random.randn(100, 2)
>>> tree = rrcf.RCTree(X)
>>> new_point = np.array([4, 4])
>>> tree.insert_point(new_point, index=100)
# Compute collusive displacement
>>> tree.codisp(100)
31.667 | Compute collusive displacement at leaf | [
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] | def codisp(self, leaf):
"""
Compute collusive displacement at leaf
Parameters:
-----------
leaf: index of leaf or Leaf instance
Returns:
--------
codisplacement: float
Collusive displacement if leaf is removed.
Example:
--------
# Create RCTree
>>> X = np.random.randn(100, 2)
>>> tree = rrcf.RCTree(X)
>>> new_point = np.array([4, 4])
>>> tree.insert_point(new_point, index=100)
# Compute collusive displacement
>>> tree.codisp(100)
31.667
"""
if not isinstance(leaf, Leaf):
try:
leaf = self.leaves[leaf]
except KeyError:
raise KeyError(
'leaf must be a Leaf instance or key to self.leaves')
# Handle case where leaf is root
if leaf is self.root:
return 0
node = leaf
results = []
for _ in range(node.d):
parent = node.u
if parent is None:
break
if node is parent.l:
sibling = parent.r
else:
sibling = parent.l
num_deleted = node.n
displacement = sibling.n
result = (displacement / num_deleted)
results.append(result)
node = parent
co_displacement = max(results)
return co_displacement | [
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cloudtools/stacker | f563a6f5a23550c7a668a1500bcea2b4e94f5bbf | stacker/hooks/iam.py | python | create_ecs_service_role | (provider, context, **kwargs) | return True | Used to create the ecsServieRole, which has to be named exactly that
currently, so cannot be created via CloudFormation. See:
http://docs.aws.amazon.com/AmazonECS/latest/developerguide/IAM_policies.html#service_IAM_role
Args:
provider (:class:`stacker.providers.base.BaseProvider`): provider
instance
context (:class:`stacker.context.Context`): context instance
Returns: boolean for whether or not the hook succeeded. | Used to create the ecsServieRole, which has to be named exactly that
currently, so cannot be created via CloudFormation. See: | [
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] | def create_ecs_service_role(provider, context, **kwargs):
"""Used to create the ecsServieRole, which has to be named exactly that
currently, so cannot be created via CloudFormation. See:
http://docs.aws.amazon.com/AmazonECS/latest/developerguide/IAM_policies.html#service_IAM_role
Args:
provider (:class:`stacker.providers.base.BaseProvider`): provider
instance
context (:class:`stacker.context.Context`): context instance
Returns: boolean for whether or not the hook succeeded.
"""
role_name = kwargs.get("role_name", "ecsServiceRole")
client = get_session(provider.region).client('iam')
try:
client.create_role(
RoleName=role_name,
AssumeRolePolicyDocument=get_ecs_assumerole_policy().to_json()
)
except ClientError as e:
if "already exists" in str(e):
pass
else:
raise
policy = Policy(
Version='2012-10-17',
Statement=[
Statement(
Effect=Allow,
Resource=["*"],
Action=[ecs.CreateCluster, ecs.DeregisterContainerInstance,
ecs.DiscoverPollEndpoint, ecs.Poll,
ecs.Action("Submit*")]
)
])
client.put_role_policy(
RoleName=role_name,
PolicyName="AmazonEC2ContainerServiceRolePolicy",
PolicyDocument=policy.to_json()
)
return True | [
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aliyun/aliyun-odps-python-sdk | 20b391c8d6eb1a689eedf950c2fc702be5f057c9 | odps/ml/algolib/loader.py | python | load_classifiers | (algo_defs, env) | Load an algorithm into a module. The algorithm is an instance of ``AlgorithmDef`` class.
:param algo_defs: algorithm definitions
:type algo_defs: AlgorithmDef | list[AlgorithmDef]
:param env: environment
:Example:
>>> import sys
>>> from odps.ml.algolib.loader import *
>>> a = XflowAlgorithmDef('SampleAlgorithm')
>>> a.add_param(ParamDef('param1', 'val1'))
>>> a.add_port(PortDef('input'))
>>> a.add_port(PortDef('output', PortDirection.OUTPUT))
>>> load_classifiers(a, sys.modules[__name__]) | Load an algorithm into a module. The algorithm is an instance of ``AlgorithmDef`` class. | [
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] | def load_classifiers(algo_defs, env):
"""
Load an algorithm into a module. The algorithm is an instance of ``AlgorithmDef`` class.
:param algo_defs: algorithm definitions
:type algo_defs: AlgorithmDef | list[AlgorithmDef]
:param env: environment
:Example:
>>> import sys
>>> from odps.ml.algolib.loader import *
>>> a = XflowAlgorithmDef('SampleAlgorithm')
>>> a.add_param(ParamDef('param1', 'val1'))
>>> a.add_port(PortDef('input'))
>>> a.add_port(PortDef('output', PortDirection.OUTPUT))
>>> load_classifiers(a, sys.modules[__name__])
"""
if not isinstance(algo_defs, Iterable):
algo_defs = [algo_defs, ]
load_algorithms(algo_defs, 'BaseTrainingAlgorithm', env) | [
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aleju/imgaug | 0101108d4fed06bc5056c4a03e2bcb0216dac326 | imgaug/random.py | python | _derive_generators_np116_ | (random_state, n) | return [_convert_seed_to_generator_np116(seed_ + i) for i in sm.xrange(n)] | [] | def _derive_generators_np116_(random_state, n):
seed_ = random_state.randint(SEED_MIN_VALUE, SEED_MAX_VALUE)
return [_convert_seed_to_generator_np116(seed_ + i) for i in sm.xrange(n)] | [
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spectacles/CodeComplice | 8ca8ee4236f72b58caa4209d2fbd5fa56bd31d62 | libs/codeintel2/pythoncile2.py | python | Scanner._iter_scopes | (self) | [] | def _iter_scopes(self):
for item in reversed(self._scope_stack):
yield item | [
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tensorflow/models | 6b8bb0cbeb3e10415c7a87448f08adc3c484c1d3 | research/object_detection/tpu_exporters/ssd.py | python | get_prediction_tensor_shapes | (pipeline_config) | return {
BOX_ENCODINGS:
prediction_dict[BOX_ENCODINGS].shape.as_list(),
CLASS_PREDICTIONS_WITH_BACKGROUND:
prediction_dict[CLASS_PREDICTIONS_WITH_BACKGROUND].shape.as_list(),
ANCHORS:
prediction_dict[ANCHORS].shape.as_list(),
} | Gets static shapes of tensors by building the graph on CPU.
This function builds the graph on CPU and obtain static shapes of output
tensors from TPUPartitionedCall. Shapes information are later used for setting
shapes of tensors when TPU graphs are built. This is necessary because tensors
coming out of TPUPartitionedCall lose their shape information, which are
needed for a lot of CPU operations later.
Args:
pipeline_config: A TrainEvalPipelineConfig proto.
Returns:
A python dict of tensors' names and their shapes. | Gets static shapes of tensors by building the graph on CPU. | [
"Gets",
"static",
"shapes",
"of",
"tensors",
"by",
"building",
"the",
"graph",
"on",
"CPU",
"."
] | def get_prediction_tensor_shapes(pipeline_config):
"""Gets static shapes of tensors by building the graph on CPU.
This function builds the graph on CPU and obtain static shapes of output
tensors from TPUPartitionedCall. Shapes information are later used for setting
shapes of tensors when TPU graphs are built. This is necessary because tensors
coming out of TPUPartitionedCall lose their shape information, which are
needed for a lot of CPU operations later.
Args:
pipeline_config: A TrainEvalPipelineConfig proto.
Returns:
A python dict of tensors' names and their shapes.
"""
detection_model = model_builder.build(
pipeline_config.model, is_training=False)
_, input_tensors = exporter.input_placeholder_fn_map['image_tensor']()
inputs = tf.cast(input_tensors, dtype=tf.float32)
preprocessed_inputs, true_image_shapes = detection_model.preprocess(inputs)
prediction_dict = detection_model.predict(preprocessed_inputs,
true_image_shapes)
return {
BOX_ENCODINGS:
prediction_dict[BOX_ENCODINGS].shape.as_list(),
CLASS_PREDICTIONS_WITH_BACKGROUND:
prediction_dict[CLASS_PREDICTIONS_WITH_BACKGROUND].shape.as_list(),
ANCHORS:
prediction_dict[ANCHORS].shape.as_list(),
} | [
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kuhnertdm/wow-addon-updater | 9df13c70873f5e93f30eedb89010d99d42c7baf4 | packages/requests/cookies.py | python | cookiejar_from_dict | (cookie_dict, cookiejar=None, overwrite=True) | return cookiejar | Returns a CookieJar from a key/value dictionary.
:param cookie_dict: Dict of key/values to insert into CookieJar.
:param cookiejar: (optional) A cookiejar to add the cookies to.
:param overwrite: (optional) If False, will not replace cookies
already in the jar with new ones. | Returns a CookieJar from a key/value dictionary. | [
"Returns",
"a",
"CookieJar",
"from",
"a",
"key",
"/",
"value",
"dictionary",
"."
] | def cookiejar_from_dict(cookie_dict, cookiejar=None, overwrite=True):
"""Returns a CookieJar from a key/value dictionary.
:param cookie_dict: Dict of key/values to insert into CookieJar.
:param cookiejar: (optional) A cookiejar to add the cookies to.
:param overwrite: (optional) If False, will not replace cookies
already in the jar with new ones.
"""
if cookiejar is None:
cookiejar = RequestsCookieJar()
if cookie_dict is not None:
names_from_jar = [cookie.name for cookie in cookiejar]
for name in cookie_dict:
if overwrite or (name not in names_from_jar):
cookiejar.set_cookie(create_cookie(name, cookie_dict[name]))
return cookiejar | [
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Nuitka/Nuitka | 39262276993757fa4e299f497654065600453fc9 | nuitka/build/inline_copy/lib/scons-4.3.0/SCons/Node/Alias.py | python | AliasNodeInfo.__setstate__ | (self, state) | Restore the attributes from a pickled state. | Restore the attributes from a pickled state. | [
"Restore",
"the",
"attributes",
"from",
"a",
"pickled",
"state",
"."
] | def __setstate__(self, state):
"""
Restore the attributes from a pickled state.
"""
# TODO check or discard version
del state['_version_id']
for key, value in state.items():
if key not in ('__weakref__',):
setattr(self, key, value) | [
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WXinlong/SOLO | 95f3732d5fbb0d7c7044c7dd074f439d48a72ce5 | mmdet/core/anchor/guided_anchor_target.py | python | ga_shape_target | (approx_list,
inside_flag_list,
square_list,
gt_bboxes_list,
img_metas,
approxs_per_octave,
cfg,
gt_bboxes_ignore_list=None,
sampling=True,
unmap_outputs=True) | return (bbox_anchors_list, bbox_gts_list, bbox_weights_list, num_total_pos,
num_total_neg) | Compute guided anchoring targets.
Args:
approx_list (list[list]): Multi level approxs of each image.
inside_flag_list (list[list]): Multi level inside flags of each image.
square_list (list[list]): Multi level squares of each image.
gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
img_metas (list[dict]): Meta info of each image.
approxs_per_octave (int): number of approxs per octave
cfg (dict): RPN train configs.
gt_bboxes_ignore_list (list[Tensor]): ignore list of gt bboxes.
sampling (bool): sampling or not.
unmap_outputs (bool): unmap outputs or not.
Returns:
tuple | Compute guided anchoring targets. | [
"Compute",
"guided",
"anchoring",
"targets",
"."
] | def ga_shape_target(approx_list,
inside_flag_list,
square_list,
gt_bboxes_list,
img_metas,
approxs_per_octave,
cfg,
gt_bboxes_ignore_list=None,
sampling=True,
unmap_outputs=True):
"""Compute guided anchoring targets.
Args:
approx_list (list[list]): Multi level approxs of each image.
inside_flag_list (list[list]): Multi level inside flags of each image.
square_list (list[list]): Multi level squares of each image.
gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
img_metas (list[dict]): Meta info of each image.
approxs_per_octave (int): number of approxs per octave
cfg (dict): RPN train configs.
gt_bboxes_ignore_list (list[Tensor]): ignore list of gt bboxes.
sampling (bool): sampling or not.
unmap_outputs (bool): unmap outputs or not.
Returns:
tuple
"""
num_imgs = len(img_metas)
assert len(approx_list) == len(inside_flag_list) == len(
square_list) == num_imgs
# anchor number of multi levels
num_level_squares = [squares.size(0) for squares in square_list[0]]
# concat all level anchors and flags to a single tensor
inside_flag_flat_list = []
approx_flat_list = []
square_flat_list = []
for i in range(num_imgs):
assert len(square_list[i]) == len(inside_flag_list[i])
inside_flag_flat_list.append(torch.cat(inside_flag_list[i]))
approx_flat_list.append(torch.cat(approx_list[i]))
square_flat_list.append(torch.cat(square_list[i]))
# compute targets for each image
if gt_bboxes_ignore_list is None:
gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
(all_bbox_anchors, all_bbox_gts, all_bbox_weights, pos_inds_list,
neg_inds_list) = multi_apply(
ga_shape_target_single,
approx_flat_list,
inside_flag_flat_list,
square_flat_list,
gt_bboxes_list,
gt_bboxes_ignore_list,
img_metas,
approxs_per_octave=approxs_per_octave,
cfg=cfg,
sampling=sampling,
unmap_outputs=unmap_outputs)
# no valid anchors
if any([bbox_anchors is None for bbox_anchors in all_bbox_anchors]):
return None
# sampled anchors of all images
num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
# split targets to a list w.r.t. multiple levels
bbox_anchors_list = images_to_levels(all_bbox_anchors, num_level_squares)
bbox_gts_list = images_to_levels(all_bbox_gts, num_level_squares)
bbox_weights_list = images_to_levels(all_bbox_weights, num_level_squares)
return (bbox_anchors_list, bbox_gts_list, bbox_weights_list, num_total_pos,
num_total_neg) | [
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/lib-graalpython/faulthandler.py | python | enable | (file=None, all_threads=True) | [] | def enable(file=None, all_threads=True):
global _enabled
_enabled = True | [
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quantumlib/Cirq | 89f88b01d69222d3f1ec14d649b7b3a85ed9211f | cirq-core/cirq/sim/simulator.py | python | SimulatesIntermediateState._create_simulator_trial_result | (
self,
params: 'cirq.ParamResolver',
measurements: Dict[str, np.ndarray],
final_step_result: TStepResult,
) | This method can be implemented to create a trial result.
Args:
params: The ParamResolver for this trial.
measurements: The measurement results for this trial.
final_step_result: The final step result of the simulation.
Returns:
The SimulationTrialResult. | This method can be implemented to create a trial result. | [
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] | def _create_simulator_trial_result(
self,
params: 'cirq.ParamResolver',
measurements: Dict[str, np.ndarray],
final_step_result: TStepResult,
) -> TSimulationTrialResult:
"""This method can be implemented to create a trial result.
Args:
params: The ParamResolver for this trial.
measurements: The measurement results for this trial.
final_step_result: The final step result of the simulation.
Returns:
The SimulationTrialResult.
"""
raise NotImplementedError() | [
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pgq/skytools-legacy | 8b7e6c118572a605d28b7a3403c96aeecfd0d272 | scripts/scriptmgr.py | python | launch_cmd | (job, cmd) | return os.system(cmd) | [] | def launch_cmd(job, cmd):
if job['user']:
cmd = 'sudo -nH -u "%s" %s' % (job['user'], cmd)
return os.system(cmd) | [
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caiiiac/Machine-Learning-with-Python | 1a26c4467da41ca4ebc3d5bd789ea942ef79422f | MachineLearning/venv/lib/python3.5/site-packages/scipy/constants/codata.py | python | value | (key) | return physical_constants[key][0] | Value in physical_constants indexed by key
Parameters
----------
key : Python string or unicode
Key in dictionary `physical_constants`
Returns
-------
value : float
Value in `physical_constants` corresponding to `key`
See Also
--------
codata : Contains the description of `physical_constants`, which, as a
dictionary literal object, does not itself possess a docstring.
Examples
--------
>>> from scipy import constants
>>> constants.value(u'elementary charge')
1.6021766208e-19 | Value in physical_constants indexed by key | [
"Value",
"in",
"physical_constants",
"indexed",
"by",
"key"
] | def value(key):
"""
Value in physical_constants indexed by key
Parameters
----------
key : Python string or unicode
Key in dictionary `physical_constants`
Returns
-------
value : float
Value in `physical_constants` corresponding to `key`
See Also
--------
codata : Contains the description of `physical_constants`, which, as a
dictionary literal object, does not itself possess a docstring.
Examples
--------
>>> from scipy import constants
>>> constants.value(u'elementary charge')
1.6021766208e-19
"""
_check_obsolete(key)
return physical_constants[key][0] | [
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googleads/google-ads-python | 2a1d6062221f6aad1992a6bcca0e7e4a93d2db86 | google/ads/googleads/v9/services/services/combined_audience_service/client.py | python | CombinedAudienceServiceClient.common_project_path | (project: str,) | return "projects/{project}".format(project=project,) | Return a fully-qualified project string. | Return a fully-qualified project string. | [
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openstack/barbican | a9d2b133c8dc3307974f119f9a2b23a4ba82e8ce | barbican/cmd/pkcs11_migrate_kek_signatures.py | python | main | () | [] | def main():
script_desc = (
'Utility to migrate existing project KEK signatures to include IV.'
)
parser = argparse.ArgumentParser(description=script_desc)
parser.add_argument(
'--dry-run',
action='store_true',
help='Displays changes that will be made (Non-destructive)'
)
args = parser.parse_args()
migrator = KekSignatureMigrator(
db_connection=CONF.sql_connection,
library_path=CONF.p11_crypto_plugin.library_path,
login=CONF.p11_crypto_plugin.login,
slot_id=CONF.p11_crypto_plugin.slot_id
)
migrator.execute(args.dry_run) | [
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mozillazg/pypy | 2ff5cd960c075c991389f842c6d59e71cf0cb7d0 | lib-python/2.7/idlelib/EditorWindow.py | python | EditorWindow.help_dialog | (self, event=None) | Handle Help 'IDLE Help' event. | Handle Help 'IDLE Help' event. | [
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# Synchronize with macosxSupport.overrideRootMenu.help_dialog.
if self.root:
parent = self.root
else:
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NTMC-Community/MatchZoo-py | 0e5c04e1e948aa9277abd5c85ff99d9950d8527f | matchzoo/preprocessors/units/matching_histogram.py | python | MatchingHistogram._normalize_embedding | (self) | Normalize the embedding matrix. | Normalize the embedding matrix. | [
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] | def _normalize_embedding(self):
"""Normalize the embedding matrix."""
l2_norm = np.sqrt(
(self._embedding_matrix * self._embedding_matrix).sum(axis=1)
)
self._embedding_matrix = \
self._embedding_matrix / l2_norm[:, np.newaxis] | [
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CedricGuillemet/Imogen | ee417b42747ed5b46cb11b02ef0c3630000085b3 | bin/Lib/asyncio/events.py | python | AbstractEventLoop.is_running | (self) | Return whether the event loop is currently running. | Return whether the event loop is currently running. | [
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derv82/wifite2 | e190794149f488f9c4a2801962e5165b29e71b5e | wifite/tools/hashcat.py | python | Hashcat.crack_pmkid | (pmkid_file, verbose=False) | Cracks a given pmkid_file using the PMKID/WPA2 attack (-m 16800)
Returns:
Key (str) if found; `None` if not found. | Cracks a given pmkid_file using the PMKID/WPA2 attack (-m 16800)
Returns:
Key (str) if found; `None` if not found. | [
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'''
Cracks a given pmkid_file using the PMKID/WPA2 attack (-m 16800)
Returns:
Key (str) if found; `None` if not found.
'''
# Run hashcat once normally, then with --show if it failed
# To catch cases where the password is already in the pot file.
for additional_arg in ([], ['--show']):
command = [
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'-m', '16800', # WPA-PMKID-PBKDF2
'-a', '0', # Wordlist attack-mode
pmkid_file,
Configuration.wordlist
]
if Hashcat.should_use_force():
command.append('--force')
command.extend(additional_arg)
if verbose and additional_arg == []:
Color.pl('{+} {D}Running: {W}{P}%s{W}' % ' '.join(command))
# TODO: Check status of hashcat (%); it's impossible with --quiet
hashcat_proc = Process(command)
hashcat_proc.wait()
stdout = hashcat_proc.stdout()
if ':' not in stdout:
# Failed
continue
else:
# Cracked
key = stdout.strip().split(':', 1)[1]
return key | [
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avirambh/MSDNet-GCN | 818246e3e38030e0d97c47f8aa1f39e125a0091c | models/msdnet_layers.py | python | MSDLayer.build_densenet | (self, in_channels, out_channels, bottleneck, bn_width) | return _DynamicInputDenseBlock(nn.ModuleList([conv_module]),
self.debug) | Builds a scale sub-network for the first layer
:param in_channels: number of input channels
:param out_channels: number of output channels
:param bottleneck: A flag to perform a channel dimension bottleneck
:param bn_width: The width of the bottleneck factor
:return: A scale module | Builds a scale sub-network for the first layer | [
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"""
Builds a scale sub-network for the first layer
:param in_channels: number of input channels
:param out_channels: number of output channels
:param bottleneck: A flag to perform a channel dimension bottleneck
:param bn_width: The width of the bottleneck factor
:return: A scale module
"""
conv_module = self.convolve(in_channels, out_channels, 'normal',
bottleneck, bn_width)
return _DynamicInputDenseBlock(nn.ModuleList([conv_module]),
self.debug) | [
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lingtengqiu/Deeperlab-pytorch | 5c500780a6655ff343d147477402aa20e0ed7a7c | engine/engine.py | python | Engine.__enter__ | (self) | return self | [] | def __enter__(self):
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FederatedAI/FATE | 32540492623568ecd1afcb367360133616e02fa3 | python/federatedml/linear_model/logistic_regression/hetero_logistic_regression/hetero_lr_host.py | python | HeteroLRHost.fit | (self, data_instances, validate_data=None) | Train lr model of role host
Parameters
----------
data_instances: Table of Instance, input data | Train lr model of role host
Parameters
----------
data_instances: Table of Instance, input data | [
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"""
Train lr model of role host
Parameters
----------
data_instances: Table of Instance, input data
"""
LOGGER.info("Enter hetero_logistic_regression host")
self.header = self.get_header(data_instances)
classes = self.one_vs_rest_obj.get_data_classes(data_instances)
if len(classes) > 2:
self.need_one_vs_rest = True
self.need_call_back_loss = False
self.one_vs_rest_fit(train_data=data_instances, validate_data=validate_data)
else:
self.need_one_vs_rest = False
self.fit_binary(data_instances, validate_data) | [
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pyopenapi/pyswagger | 333c4ca08e758cd2194943d9904a3eda3fe43977 | pyswagger/scanner/v1_2/validate.py | python | Validate._validate_prop | (self, path, obj, _) | return path, obj.__class__.__name__, errs | validate option combination of Property object | validate option combination of Property object | [
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""" validate option combination of Property object """
errs = []
if obj.type == 'void':
errs.append('void is only allowed in Operation object.')
return path, obj.__class__.__name__, errs | [
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suurjaak/Skyperious | 6a4f264dbac8d326c2fa8aeb5483dbca987860bf | skyperious/gui.py | python | MainWindow.on_sys_colour_change | (self, event) | Handler for system colour change, updates filesystem images. | Handler for system colour change, updates filesystem images. | [
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] | def on_sys_colour_change(self, event):
"""Handler for system colour change, updates filesystem images."""
event.Skip()
self.adapt_colours()
def after():
self.load_fs_images()
for i in range(self.list_db.GetItemCount()):
self.list_db.SetItemTextColour(i, self.list_db.ForegroundColour)
self.list_db.SetItemBackgroundColour(i, self.list_db.BackgroundColour)
wx.CallAfter(after) | [
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spesmilo/electrum | bdbd59300fbd35b01605e66145458e5f396108e8 | electrum/wallet.py | python | Deterministic_Wallet.get_seed | (self, password) | return self.keystore.get_seed(password) | [] | def get_seed(self, password):
return self.keystore.get_seed(password) | [
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magenta/magenta | be6558f1a06984faff6d6949234f5fe9ad0ffdb5 | magenta/models/image_stylization/model.py | python | upsampling | (input_,
kernel_size,
stride,
num_outputs,
scope,
activation_fn=tf.nn.relu) | A smooth replacement of a same-padded transposed convolution.
This function first computes a nearest-neighbor upsampling of the input by a
factor of `stride`, then applies a mirror-padded, same-padded convolution.
It expects `kernel_size` to be odd.
Args:
input_: 4-D Tensor input.
kernel_size: int (odd-valued) representing the kernel size.
stride: int representing the strides.
num_outputs: int. Number of output feature maps.
scope: str. Scope under which to operate.
activation_fn: activation function.
Returns:
4-D Tensor output.
Raises:
ValueError: if `kernel_size` is even. | A smooth replacement of a same-padded transposed convolution. | [
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] | def upsampling(input_,
kernel_size,
stride,
num_outputs,
scope,
activation_fn=tf.nn.relu):
"""A smooth replacement of a same-padded transposed convolution.
This function first computes a nearest-neighbor upsampling of the input by a
factor of `stride`, then applies a mirror-padded, same-padded convolution.
It expects `kernel_size` to be odd.
Args:
input_: 4-D Tensor input.
kernel_size: int (odd-valued) representing the kernel size.
stride: int representing the strides.
num_outputs: int. Number of output feature maps.
scope: str. Scope under which to operate.
activation_fn: activation function.
Returns:
4-D Tensor output.
Raises:
ValueError: if `kernel_size` is even.
"""
if kernel_size % 2 == 0:
raise ValueError('kernel_size is expected to be odd.')
with tf.variable_scope(scope):
shape = tf.shape(input_)
height = shape[1]
width = shape[2]
upsampled_input = tf.image.resize_nearest_neighbor(
input_, [stride * height, stride * width])
return conv2d(
upsampled_input,
kernel_size,
1,
num_outputs,
'conv',
activation_fn=activation_fn) | [
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cloudera/impyla | 0c736af4cad2bade9b8e313badc08ec50e81c948 | impala/_thrift_gen/TCLIService/TCLIService.py | python | GetResultSetMetadata_result.__eq__ | (self, other) | return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ | [] | def __eq__(self, other):
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cleverhans-lab/cleverhans | e5d00e537ce7ad6119ed5a8db1f0e9736d1f6e1d | cleverhans_v3.1.0/cleverhans/confidence_report.py | python | ConfidenceReportEntry.__getitem__ | (self, key) | return self.__dict__[key] | [] | def __getitem__(self, key):
warnings.warn(
"Dictionary confidence report entries are deprecated. "
"Switch to accessing the appropriate field of "
"ConfidenceReportEntry. "
"Dictionary-style access will be removed on or after "
"2019-04-24."
)
assert key in ["correctness", "confidence"]
return self.__dict__[key] | [
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google-research/exoplanet-ml | 3dfe65f7ae44443b124ae87b000c317237c7dc00 | exoplanet-ml/experimental/beam/transit_search/bls_scorer.py | python | BlsScorer.ofir | (self,
window_size,
scatter_after_detrend=False,
sqrt_power=False,
normalize_by_bls_nbins=False) | return scores | Computes scores using the method of Ofir et al. | Computes scores using the method of Ofir et al. | [
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window_size,
scatter_after_detrend=False,
sqrt_power=False,
normalize_by_bls_nbins=False):
"""Computes scores using the method of Ofir et al."""
powers = self.power(sqrt_power, normalize_by_bls_nbins)
trend = scipy.signal.medfilt(powers, window_size)
scores = powers - trend
if scatter_after_detrend:
scatter = np.abs(np.diff(scores))
else:
scatter = np.abs(np.diff(powers))
# Diff returns a vector one less than the length of powers. We prepend the
# first diff to make the sizes match.
scatter = np.concatenate([[scatter[0]], scatter])
scatter_trend = scipy.signal.medfilt(scatter, window_size)
scores /= scatter_trend
return scores | [
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NetManAIOps/OmniAnomaly | 7fb0e0acf89ea49908896bcc9f9e80fcfff6baf4 | omni_anomaly/spot.py | python | dSPOT._quantile | (self, gamma, sigma) | Compute the quantile at level 1-q
Parameters
----------
gamma : float
GPD parameter
sigma : float
GPD parameter
Returns
----------
float
quantile at level 1-q for the GPD(γ,σ,μ=0) | Compute the quantile at level 1-q
Parameters
----------
gamma : float
GPD parameter
sigma : float
GPD parameter | [
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"""
Compute the quantile at level 1-q
Parameters
----------
gamma : float
GPD parameter
sigma : float
GPD parameter
Returns
----------
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quantile at level 1-q for the GPD(γ,σ,μ=0)
"""
r = self.n * self.proba / self.Nt
if gamma != 0:
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leoribeiro/struc2vec | 67da70bb75fdf17239e12bf90e1945256270acd8 | src/main.py | python | main | (args) | [] | def main(args):
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learn_embeddings() | [
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HewlettPackard/dlcookbook-dlbs | 863ac1d7e72ad2fcafc78d8a13f67d35bc00c235 | python/tf_cnn_benchmarks/models/resnet_model.py | python | residual_block | (cnn, depth, stride, pre_activation) | Residual block with identity short-cut.
Args:
cnn: the network to append residual blocks.
depth: the number of output filters for this residual block.
stride: Stride used in the first layer of the residual block.
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Args:
cnn: the network to append residual blocks.
depth: the number of output filters for this residual block.
stride: Stride used in the first layer of the residual block.
pre_activation: use pre_activation structure or not.
"""
input_layer = cnn.top_layer
in_size = cnn.top_size
if in_size != depth:
# Plan A of shortcut.
shortcut = cnn.apool(1, 1, stride, stride,
input_layer=input_layer,
num_channels_in=in_size)
padding = (depth - in_size) // 2
if cnn.channel_pos == 'channels_last':
shortcut = tf.pad(
shortcut, [[0, 0], [0, 0], [0, 0], [padding, padding]])
else:
shortcut = tf.pad(
shortcut, [[0, 0], [padding, padding], [0, 0], [0, 0]])
else:
shortcut = input_layer
if pre_activation:
res = cnn.batch_norm(input_layer)
res = tf.nn.relu(res)
else:
res = input_layer
cnn.conv(depth, 3, 3, stride, stride,
input_layer=res, num_channels_in=in_size,
use_batch_norm=True, bias=None)
if pre_activation:
res = cnn.conv(depth, 3, 3, 1, 1, activation=None,
use_batch_norm=False, bias=None)
output = shortcut + res
else:
res = cnn.conv(depth, 3, 3, 1, 1, activation=None,
use_batch_norm=True, bias=None)
output = tf.nn.relu(shortcut + res)
cnn.top_layer = output
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dropbox/dropbox-sdk-python | 015437429be224732990041164a21a0501235db1 | dropbox/base_team.py | python | DropboxTeamBase.team_legal_holds_release_policy | (self,
id) | return None | Releases a legal hold by Id. Note: Legal Holds is a paid add-on. Not all
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:param str id: The legal hold Id.
:rtype: None
:raises: :class:`.exceptions.ApiError`
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Releases a legal hold by Id. Note: Legal Holds is a paid add-on. Not all
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:param str id: The legal hold Id.
:rtype: None
:raises: :class:`.exceptions.ApiError`
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arg = team.LegalHoldsPolicyReleaseArg(id)
r = self.request(
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pwnieexpress/pwn_plug_sources | 1a23324f5dc2c3de20f9c810269b6a29b2758cad | src/miranda/miranda.py | python | upnp.listen | (self,size,socket) | [] | def listen(self,size,socket):
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IronLanguages/ironpython3 | 7a7bb2a872eeab0d1009fc8a6e24dca43f65b693 | Src/StdLib/Lib/tkinter/__init__.py | python | Listbox.scan_dragto | (self, x, y) | Adjust the view of the listbox to 10 times the
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rowliny/DiffHelper | ab3a96f58f9579d0023aed9ebd785f4edf26f8af | Tool/SitePackages/nltk/collocations.py | python | AbstractCollocationFinder._score_ngrams | (self, score_fn) | Generates of (ngram, score) pairs as determined by the scoring
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"""
for tup in self.ngram_fd:
score = self.score_ngram(score_fn, *tup)
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materialsproject/pymatgen | 8128f3062a334a2edd240e4062b5b9bdd1ae6f58 | pymatgen/core/units.py | python | obj_with_unit | (obj, unit) | return ArrayWithUnit(obj, unit=unit, unit_type=unit_type) | Returns a `FloatWithUnit` instance if obj is scalar, a dictionary of
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Args:
unit: Specific units (eV, Ha, m, ang, etc.).
"""
unit_type = _UNAME2UTYPE[unit]
if isinstance(obj, numbers.Number):
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googleanalytics/google-analytics-super-proxy | f5bad82eb1375d222638423e6ae302173a9a7948 | src/controllers/util/query_helper.py | python | DeleteApiQueryResponses | (api_query) | Deletes an API Query saved response.
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"""Deletes an API Query saved response.
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api_query: The API Query for which to delete the response.
"""
if api_query and api_query.api_query_responses:
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arrayfire/arrayfire-python | 96fa9768ee02e5fb5ffcaf3d1f744c898b141637 | arrayfire/signal.py | python | ifft | (signal, dim0 = None , scale = None) | return output | Inverse Fast Fourier Transform: 1D
Parameters
----------
signal: af.Array
A 1 dimensional signal or a batch of 1 dimensional signals.
dim0: optional: int. default: None.
- Specifies the size of the output.
- If None, dim0 is calculated to be the first dimension of `signal`.
scale: optional: scalar. default: None.
- Specifies the scaling factor.
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Returns
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output: af.Array
A complex af.Array containing the full output of the inverse fft.
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Inverse Fast Fourier Transform: 1D
Parameters
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signal: af.Array
A 1 dimensional signal or a batch of 1 dimensional signals.
dim0: optional: int. default: None.
- Specifies the size of the output.
- If None, dim0 is calculated to be the first dimension of `signal`.
scale: optional: scalar. default: None.
- Specifies the scaling factor.
- If None, scale is set to 1.0 / (dim0)
Returns
-------
output: af.Array
A complex af.Array containing the full output of the inverse fft.
Note
----
The output is always complex.
"""
if dim0 is None:
dim0 = signal.dims()[0]
if scale is None:
scale = 1.0/float(dim0)
output = Array()
safe_call(backend.get().af_ifft(c_pointer(output.arr), signal.arr, c_double_t(scale), c_dim_t(dim0)))
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chribsen/simple-machine-learning-examples | dc94e52a4cebdc8bb959ff88b81ff8cfeca25022 | venv/lib/python2.7/site-packages/sklearn/svm/base.py | python | BaseSVC.predict_proba | (self) | return self._predict_proba | Compute probabilities of possible outcomes for samples in X.
The model need to have probability information computed at training
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Parameters
----------
X : array-like, shape (n_samples, n_features)
For kernel="precomputed", the expected shape of X is
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Returns
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Parameters
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X : array-like, shape (n_samples, n_features)
For kernel="precomputed", the expected shape of X is
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Returns
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T : array-like, shape (n_samples, n_classes)
Returns the probability of the sample for each class in
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Notes
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self._check_proba()
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digidotcom/xbee-python | 0757f4be0017530c205175fbee8f9f61be9614d1 | digi/xbee/models/filesystem.py | python | OpenDirCmdResponse.is_last | (self) | return False | Returns whether there are more elements not included in this response.
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mpenning/ciscoconfparse | a6a176e6ceac7c5f3e974272fa70273476ba84a3 | ciscoconfparse/models_nxos.py | python | BaseNXOSIntfLine.has_ip_accessgroup_in | (self) | return bool(self.ipv4_accessgroup_in) | [] | def has_ip_accessgroup_in(self):
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/multiprocessing/util.py | python | debug | (msg, *args) | [] | def debug(msg, *args):
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awslabs/aws-servicebroker | c301912e7df3a2f09a9c34d3ae7ffe67c55aa3a0 | sample-apps/rds/sample-app/src/bottle.py | python | redirect | (url, code=None) | Aborts execution and causes a 303 or 302 redirect, depending on
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"("... | https://github.com/awslabs/aws-servicebroker/blob/c301912e7df3a2f09a9c34d3ae7ffe67c55aa3a0/sample-apps/rds/sample-app/src/bottle.py#L2423-L2432 | ||
pyqt/examples | 843bb982917cecb2350b5f6d7f42c9b7fb142ec1 | src/pyqt-official/itemviews/customsortfiltermodel.py | python | MySortFilterProxyModel.setFilterMaximumDate | (self, date) | [] | def setFilterMaximumDate(self, date):
self.maxDate = date
self.invalidateFilter() | [
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dr-costas/mad-twinnet | 446e49a423a4375e5ceedab5eb51bead1057d06b | helpers/arg_parsing.py | python | get_argument_parser | () | return cmd_arg_parser | Creates and return the CMD argument parser.
:return: The CMD argument parser.
:rtype: argparse.ArgumentParser | Creates and return the CMD argument parser. | [
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"parser",
"."
] | def get_argument_parser():
"""Creates and return the CMD argument parser.
:return: The CMD argument parser.
:rtype: argparse.ArgumentParser
"""
cmd_arg_parser = argparse.ArgumentParser(
usage='python scripts/use_me [-w the_file.wav]|[-l the_files.txt]',
description='Script to use the MaD TwinNet with your own files. Remember to set up properly'
'the PYTHONPATH environmental variable'
)
cmd_arg_parser.add_argument(
'--input-wav', '-w', action='store', dest='input_wav', default='',
help='Specify one wav file to be processed.'
)
cmd_arg_parser.add_argument(
'--input-list', '-l', action='store', dest='input_list', default=[],
help='Specify one txt file with each line to be one path for a wav file.'
)
return cmd_arg_parser | [
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Vector35/debugger | 4eb67fa10a8d58704bfbca42d23b5989e0212968 | DebugAdapter.py | python | DebugAdapter.breakpoint_list | (self) | return list of addresses | return list of addresses | [
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''' return list of addresses '''
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ganeti/ganeti | d340a9ddd12f501bef57da421b5f9b969a4ba905 | lib/rapi/client.py | python | GanetiRapiClient.RenameNetwork | (self, network, new_name, reason=None) | return self._SendRequest(HTTP_PUT,
("/%s/networks/%s/rename" %
(GANETI_RAPI_VERSION, network)), query, body) | Changes the name of a network.
@type network: string
@param network: Network name
@type new_name: string
@param new_name: New network name
@type reason: string
@param reason: the reason for executing this operation
@rtype: string
@return: job id | Changes the name of a network. | [
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] | def RenameNetwork(self, network, new_name, reason=None):
"""Changes the name of a network.
@type network: string
@param network: Network name
@type new_name: string
@param new_name: New network name
@type reason: string
@param reason: the reason for executing this operation
@rtype: string
@return: job id
"""
body = {
"new_name": new_name,
}
query = []
_AppendReason(query, reason)
return self._SendRequest(HTTP_PUT,
("/%s/networks/%s/rename" %
(GANETI_RAPI_VERSION, network)), query, body) | [
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