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spencerahill/aospy | aospy/automate.py | https://github.com/spencerahill/aospy/blob/2f6e775b9b9956c54af117fdcdce2c87196afb6c/aospy/automate.py#L101-L115 | def _get_all_objs_of_type(type_, parent):
"""Get all attributes of the given type from the given object.
Parameters
----------
type_ : The desired type
parent : The object from which to get the attributes with type matching
'type_'
Returns
-------
A list (possibly empty) of attributes from 'parent'
"""
return set([obj for obj in parent.__dict__.values()
if isinstance(obj, type_)]) | [
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theislab/scanpy | scanpy/plotting/_utils.py | https://github.com/theislab/scanpy/blob/9e4e5ee02e04cf618872d9b098e24f0542e8b227/scanpy/plotting/_utils.py#L212-L236 | def savefig(writekey, dpi=None, ext=None):
"""Save current figure to file.
The `filename` is generated as follows:
filename = settings.figdir + writekey + settings.plot_suffix + '.' + settings.file_format_figs
"""
if dpi is None:
# we need this as in notebooks, the internal figures are also influenced by 'savefig.dpi' this...
if not isinstance(rcParams['savefig.dpi'], str) and rcParams['savefig.dpi'] < 150:
if settings._low_resolution_warning:
logg.warn(
'You are using a low resolution (dpi<150) for saving figures.\n'
'Consider running `set_figure_params(dpi_save=...)`, which will '
'adjust `matplotlib.rcParams[\'savefig.dpi\']`')
settings._low_resolution_warning = False
else:
dpi = rcParams['savefig.dpi']
if not os.path.exists(settings.figdir): os.makedirs(settings.figdir)
if settings.figdir[-1] != '/': settings.figdir += '/'
if ext is None: ext = settings.file_format_figs
filename = settings.figdir + writekey + settings.plot_suffix + '.' + ext
# output the following msg at warning level; it's really important for the user
logg.msg('saving figure to file', filename, v=1)
pl.savefig(filename, dpi=dpi, bbox_inches='tight') | [
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brettcannon/gidgethub | gidgethub/abc.py | https://github.com/brettcannon/gidgethub/blob/24feb6c35bba3966c6cc9ec2896729578f6d7ccc/gidgethub/abc.py#L34-L89 | async def _make_request(self, method: str, url: str, url_vars: Dict[str, str],
data: Any, accept: str,
jwt: Opt[str] = None,
oauth_token: Opt[str] = None,
) -> Tuple[bytes, Opt[str]]:
"""Construct and make an HTTP request."""
if oauth_token is not None and jwt is not None:
raise ValueError("Cannot pass both oauth_token and jwt.")
filled_url = sansio.format_url(url, url_vars)
if jwt is not None:
request_headers = sansio.create_headers(
self.requester, accept=accept,
jwt=jwt)
elif oauth_token is not None:
request_headers = sansio.create_headers(
self.requester, accept=accept,
oauth_token=oauth_token)
else:
# fallback to using oauth_token
request_headers = sansio.create_headers(
self.requester, accept=accept,
oauth_token=self.oauth_token)
cached = cacheable = False
# Can't use None as a "no body" sentinel as it's a legitimate JSON type.
if data == b"":
body = b""
request_headers["content-length"] = "0"
if method == "GET" and self._cache is not None:
cacheable = True
try:
etag, last_modified, data, more = self._cache[filled_url]
cached = True
except KeyError:
pass
else:
if etag is not None:
request_headers["if-none-match"] = etag
if last_modified is not None:
request_headers["if-modified-since"] = last_modified
else:
charset = "utf-8"
body = json.dumps(data).encode(charset)
request_headers['content-type'] = f"application/json; charset={charset}"
request_headers['content-length'] = str(len(body))
if self.rate_limit is not None:
self.rate_limit.remaining -= 1
response = await self._request(method, filled_url, request_headers, body)
if not (response[0] == 304 and cached):
data, self.rate_limit, more = sansio.decipher_response(*response)
has_cache_details = ("etag" in response[1]
or "last-modified" in response[1])
if self._cache is not None and cacheable and has_cache_details:
etag = response[1].get("etag")
last_modified = response[1].get("last-modified")
self._cache[filled_url] = etag, last_modified, data, more
return data, more | [
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brainiak/brainiak | brainiak/utils/utils.py | https://github.com/brainiak/brainiak/blob/408f12dec2ff56559a26873a848a09e4c8facfeb/brainiak/utils/utils.py#L634-L675 | def center_mass_exp(interval, scale=1.0):
""" Calculate the center of mass of negative exponential distribution
p(x) = exp(-x / scale) / scale
in the interval of (interval_left, interval_right).
scale is the same scale parameter as scipy.stats.expon.pdf
Parameters
----------
interval: size 2 tuple, float
interval must be in the form of (interval_left, interval_right),
where interval_left/interval_right is the starting/end point of the
interval in which the center of mass is calculated for exponential
distribution.
Note that interval_left must be non-negative, since exponential is
not supported in the negative domain, and interval_right must be
bigger than interval_left (thus positive) to form a well-defined
interval.
scale: float, positive
The scale parameter of the exponential distribution. See above.
Returns
-------
m: float
The center of mass in the interval of (interval_left,
interval_right) for exponential distribution.
"""
assert isinstance(interval, tuple), 'interval must be a tuple'
assert len(interval) == 2, 'interval must be length two'
(interval_left, interval_right) = interval
assert interval_left >= 0, 'interval_left must be non-negative'
assert interval_right > interval_left, \
'interval_right must be bigger than interval_left'
assert scale > 0, 'scale must be positive'
if interval_right < np.inf:
return ((interval_left + scale) * np.exp(-interval_left / scale) - (
scale + interval_right) * np.exp(-interval_right / scale)) / (
np.exp(-interval_left / scale) - np.exp(-interval_right / scale))
else:
return interval_left + scale | [
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urinieto/msaf | msaf/algorithms/sf/segmenter.py | https://github.com/urinieto/msaf/blob/9dbb57d77a1310465a65cc40f1641d083ca74385/msaf/algorithms/sf/segmenter.py#L63-L80 | def pick_peaks(nc, L=16, offset_denom=0.1):
"""Obtain peaks from a novelty curve using an adaptive threshold."""
offset = nc.mean() * float(offset_denom)
th = filters.median_filter(nc, size=L) + offset
#th = filters.gaussian_filter(nc, sigma=L/2., mode="nearest") + offset
#import pylab as plt
#plt.plot(nc)
#plt.plot(th)
#plt.show()
# th = np.ones(nc.shape[0]) * nc.mean() - 0.08
peaks = []
for i in range(1, nc.shape[0] - 1):
# is it a peak?
if nc[i - 1] < nc[i] and nc[i] > nc[i + 1]:
# is it above the threshold?
if nc[i] > th[i]:
peaks.append(i)
return peaks | [
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rraadd88/rohan | rohan/dandage/db/uniprot.py | https://github.com/rraadd88/rohan/blob/b0643a3582a2fffc0165ace69fb80880d92bfb10/rohan/dandage/db/uniprot.py#L46-L67 | def map_ids(queries,frm='ACC',to='ENSEMBL_PRO_ID',
organism_taxid=9606,test=False):
"""
https://www.uniprot.org/help/api_idmapping
"""
url = 'https://www.uniprot.org/uploadlists/'
params = {
'from':frm,
'to':to,
'format':'tab',
'organism':organism_taxid,
'query':' '.join(queries),
}
response = requests.get(url, params=params)
if test:
print(response.url)
if response.ok:
df=pd.read_table(response.url)
df.columns=[frm,to]
return df
else:
print('Something went wrong ', response.status_code) | [
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inveniosoftware/invenio-webhooks | invenio_webhooks/models.py | https://github.com/inveniosoftware/invenio-webhooks/blob/f407cb2245464543ee474a81189fb9d3978bdde5/invenio_webhooks/models.py#L87-L112 | def get_hook_url(self, access_token):
"""Get URL for webhook.
In debug and testing mode the hook URL can be overwritten using
``WEBHOOKS_DEBUG_RECEIVER_URLS`` configuration variable to allow
testing webhooks via services such as e.g. Ultrahook.
.. code-block:: python
WEBHOOKS_DEBUG_RECEIVER_URLS = dict(
github='http://github.userid.ultrahook.com',
)
"""
# Allow overwriting hook URL in debug mode.
if (current_app.debug or current_app.testing) and \
current_app.config.get('WEBHOOKS_DEBUG_RECEIVER_URLS', None):
url_pattern = current_app.config[
'WEBHOOKS_DEBUG_RECEIVER_URLS'].get(self.receiver_id, None)
if url_pattern:
return url_pattern % dict(token=access_token)
return url_for(
'invenio_webhooks.event_list',
receiver_id=self.receiver_id,
access_token=access_token,
_external=True
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python-diamond/Diamond | src/diamond/handler/sentry.py | https://github.com/python-diamond/Diamond/blob/0f3eb04327d6d3ed5e53a9967d6c9d2c09714a47/src/diamond/handler/sentry.py#L349-L357 | def process(self, metric):
"""
process a single metric
@type metric: diamond.metric.Metric
@param metric: metric to process
@rtype None
"""
for rule in self.rules:
rule.process(metric, self) | [
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edx/edx-organizations | organizations/serializers.py | https://github.com/edx/edx-organizations/blob/51000d5d359d880a6eb3a79345f60744f1982c00/organizations/serializers.py#L18-L28 | def serialize_organization(organization):
"""
Organization object-to-dict serialization
"""
return {
'id': organization.id,
'name': organization.name,
'short_name': organization.short_name,
'description': organization.description,
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bitesofcode/projexui | projexui/widgets/xganttwidget/xganttwidgetitem.py | https://github.com/bitesofcode/projexui/blob/f18a73bec84df90b034ca69b9deea118dbedfc4d/projexui/widgets/xganttwidget/xganttwidgetitem.py#L303-L329 | def property(self, key, default=None):
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Returns the custom data that is stored on this object.
:param key | <str>
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:return <variant>
"""
if key == 'Name':
return self.name()
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return self.timeEnd()
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peri-source/peri | peri/states.py | https://github.com/peri-source/peri/blob/61beed5deaaf978ab31ed716e8470d86ba639867/peri/states.py#L447-L454 | def crb(self, params=None, *args, **kwargs):
"""
Calculate the diagonal elements of the minimum covariance of the model
with respect to parameters params. ``*args`` and ``**kwargs`` go to
``fisherinformation``.
"""
fish = self.fisherinformation(params=params, *args, **kwargs)
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IdentityPython/SATOSA | src/satosa/micro_services/consent.py | https://github.com/IdentityPython/SATOSA/blob/49da5d4c0ac1a5ebf1a71b4f7aaf04f0e52d8fdb/src/satosa/micro_services/consent.py#L206-L219 | def _end_consent(self, context, internal_response):
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Clear the state for consent and end the consent step
:type context: satosa.context.Context
:type internal_response: satosa.internal.InternalData
:rtype: satosa.response.Response
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ternaris/marv | marv_node/io.py | https://github.com/ternaris/marv/blob/c221354d912ff869bbdb4f714a86a70be30d823e/marv_node/io.py#L34-L40 | def create_stream(name, **header):
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assert isinstance(name, basestring), name
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ethereum/py-evm | eth/vm/computation.py | https://github.com/ethereum/py-evm/blob/58346848f076116381d3274bbcea96b9e2cfcbdf/eth/vm/computation.py#L369-L390 | def prepare_child_message(self,
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to: Address,
value: int,
data: BytesOrView,
code: bytes,
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"""
Helper method for creating a child computation.
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CivicSpleen/ambry | ambry/orm/database.py | https://github.com/CivicSpleen/ambry/blob/d7f2be4bf1f7ffd086f3fadd4fcae60c32473e42/ambry/orm/database.py#L1013-L1030 | def _get_all_migrations():
""" Returns sorted list of all migrations.
Returns:
list of (int, str) tuples: first elem of the tuple is migration number, second if module name.
"""
from . import migrations
package = migrations
prefix = package.__name__ + '.'
all_migrations = []
for importer, modname, ispkg in pkgutil.iter_modules(package.__path__, prefix):
version = int(modname.split('.')[-1].split('_')[0])
all_migrations.append((version, modname))
all_migrations = sorted(all_migrations, key=lambda x: x[0])
return all_migrations | [
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rckclmbr/pyportify | pyportify/pkcs1/primitives.py | https://github.com/rckclmbr/pyportify/blob/696a1caad8a47b191f3bec44cc8fc3c437779512/pyportify/pkcs1/primitives.py#L67-L79 | def i2osp(x, x_len):
'''Converts the integer x to its big-endian representation of length
x_len.
'''
if x > 256**x_len:
raise exceptions.IntegerTooLarge
h = hex(x)[2:]
if h[-1] == 'L':
h = h[:-1]
if len(h) & 1 == 1:
h = '0%s' % h
x = binascii.unhexlify(h)
return b'\x00' * int(x_len-len(x)) + x | [
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spyder-ide/spyder | spyder/plugins/findinfiles/widgets.py | https://github.com/spyder-ide/spyder/blob/f76836ce1b924bcc4efd3f74f2960d26a4e528e0/spyder/plugins/findinfiles/widgets.py#L1000-L1027 | def find(self):
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options = self.find_options.get_options()
if options is None:
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self.stop_and_reset_thread(ignore_results=True)
self.search_thread = SearchThread(self)
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django-danceschool/django-danceschool | danceschool/financial/views.py | https://github.com/django-danceschool/django-danceschool/blob/bb08cbf39017a812a5a94bdb4ea34170bf1a30ba/danceschool/financial/views.py#L428-L473 | def get(self,request,*args,**kwargs):
'''
Pass any permissable GET data. URL parameters override GET parameters
'''
try:
year = int(self.kwargs.get('year'))
except (ValueError, TypeError):
year = getIntFromGet(request,'year')
if self.kwargs.get('month'):
try:
month = int(self.kwargs.get('month'))
except (ValueError, TypeError):
try:
month = list(month_name).index(self.kwargs.get('month').title())
except (ValueError, TypeError):
month = None
else:
month = getIntFromGet(request, 'month')
try:
event_id = int(self.kwargs.get('event'))
except (ValueError, TypeError):
event_id = getIntFromGet(request, 'event')
event = None
if event_id:
try:
event = Event.objects.get(id=event_id)
except ObjectDoesNotExist:
pass
kwargs.update({
'year': year,
'month': month,
'startDate': getDateTimeFromGet(request,'startDate'),
'endDate': getDateTimeFromGet(request,'endDate'),
'basis': request.GET.get('basis'),
'event': event,
})
if kwargs.get('basis') not in EXPENSE_BASES.keys():
kwargs['basis'] = 'accrualDate'
context = self.get_context_data(**kwargs)
return self.render_to_response(context) | [
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jamieleshaw/lurklib | lurklib/core.py | https://github.com/jamieleshaw/lurklib/blob/a861f35d880140422103dd78ec3239814e85fd7e/lurklib/core.py#L187-L198 | def stepback(self, append=False):
"""
Stepbacks/reverses the buffer.
Optional arguments:
* append=False - If True, appends the data onto the buffer;
else, it just steps the index back.
"""
if append:
data = self._buffer[self._index - 1]
self._buffer.append(data)
else:
self._index -= 1 | [
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GNS3/gns3-server | gns3server/compute/dynamips/hypervisor.py | https://github.com/GNS3/gns3-server/blob/a221678448fb5d24e977ef562f81d56aacc89ab1/gns3server/compute/dynamips/hypervisor.py#L174-L187 | def read_stdout(self):
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Reads the standard output of the Dynamips process.
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"""
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log.warn("could not read {}: {}".format(self._stdout_file, e))
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fvdsn/py-xml-escpos | xmlescpos/escpos.py | https://github.com/fvdsn/py-xml-escpos/blob/7f77e039c960d5773fb919aed02ba392dccbc360/xmlescpos/escpos.py#L467-L475 | def qr(self,text):
""" Print QR Code for the provided string """
qr_code = qrcode.QRCode(version=4, box_size=4, border=1)
qr_code.add_data(text)
qr_code.make(fit=True)
qr_img = qr_code.make_image()
im = qr_img._img.convert("RGB")
# Convert the RGB image in printable image
self._convert_image(im) | [
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hanguokai/youku | youku/youku_upload.py | https://github.com/hanguokai/youku/blob/b2df060c7dccfad990bcfa289fff68bb77d1e69b/youku/youku_upload.py#L291-L321 | def upload(self, params={}):
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This is the main method to used, If you do not care about
state of process.
Args:
params: a dict object describe video info, eg title,
tags, description, category.
all video params see the doc of prepare_video_params.
Returns:
return video_id if upload successfully
"""
if self.upload_token is not None:
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status = self.check()
if status['status'] != 4:
return self.commit()
else:
self.new_slice()
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mathandy/svgpathtools | svgpathtools/path.py | https://github.com/mathandy/svgpathtools/blob/fd7348a1dfd88b65ea61da02325c6605aedf8c4f/svgpathtools/path.py#L680-L687 | def bbox(self):
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googleapis/google-cloud-python | datastore/google/cloud/datastore/client.py | https://github.com/googleapis/google-cloud-python/blob/85e80125a59cb10f8cb105f25ecc099e4b940b50/datastore/google/cloud/datastore/client.py#L54-L77 | def _determine_default_project(project=None):
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In implicit case, supports four environments. In order of precedence, the
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* DATASTORE_DATASET environment variable (for ``gcd`` / emulator testing)
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MisterY/gnucash-portfolio | gnucash_portfolio/lib/settings.py | https://github.com/MisterY/gnucash-portfolio/blob/bfaad8345a5479d1cd111acee1939e25c2a638c2/gnucash_portfolio/lib/settings.py#L89-L93 | def file_path(self) -> str:
""" Settings file absolute path"""
user_dir = self.__get_user_path()
file_path = path.abspath(path.join(user_dir, self.FILENAME))
return file_path | [
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sdispater/orator | orator/orm/relations/morph_pivot.py | https://github.com/sdispater/orator/blob/bd90bf198ee897751848f9a92e49d18e60a74136/orator/orm/relations/morph_pivot.py#L25-L35 | def delete(self):
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Delete the pivot model record from the database.
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query.where(self._morph_type, self._morph_name)
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ph4r05/monero-serialize | monero_serialize/xmrboost.py | https://github.com/ph4r05/monero-serialize/blob/cebb3ba2aaf2e9211b1dcc6db2bab02946d06e42/monero_serialize/xmrboost.py#L232-L244 | async def version(self, tp, params, version=None, elem=None):
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Symmetric version management
:param tp:
:param params:
:param version:
:return:
"""
if self.writing:
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cloudmesh/cloudmesh-common | cloudmesh/common/FlatDict.py | https://github.com/cloudmesh/cloudmesh-common/blob/ae4fae09cd78205d179ea692dc58f0b0c8fea2b8/cloudmesh/common/FlatDict.py#L41-L67 | def flatten(d, parent_key='', sep='__'):
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if type(d) == list:
flat = []
for entry in d:
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else:
items = []
for k, v in d.items():
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odlgroup/odl | odl/solvers/functional/functional.py | https://github.com/odlgroup/odl/blob/b8443f6aca90e191ba36c91d32253c5a36249a6c/odl/solvers/functional/functional.py#L628-L657 | def gradient(self):
"""Gradient of the compositon according to the chain rule."""
func = self.left
op = self.right
class FunctionalCompositionGradient(Operator):
"""Gradient of the compositon according to the chain rule."""
def __init__(self):
"""Initialize a new instance."""
super(FunctionalCompositionGradient, self).__init__(
op.domain, op.domain, linear=False)
def _call(self, x):
"""Apply the gradient operator to the given point."""
return op.derivative(x).adjoint(func.gradient(op(x)))
def derivative(self, x):
"""The derivative in point ``x``.
This is only defined
"""
if not op.is_linear:
raise NotImplementedError('derivative only implemented '
'for linear opertors.')
else:
return (op.adjoint * func.gradient * op).derivative(x)
return FunctionalCompositionGradient() | [
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maweigert/gputools | gputools/fft/oclfft_convolve.py | https://github.com/maweigert/gputools/blob/6ab26efeb05dceef74cf13aadeeeb9b009b529dd/gputools/fft/oclfft_convolve.py#L15-L45 | def fft_convolve(data, h, res_g = None,
plan = None, inplace = False,
kernel_is_fft = False,
kernel_is_fftshifted = False):
""" convolves data with kernel h via FFTs
data should be either a numpy array or a OCLArray (see doc for fft)
both data and h should be same shape
if data/h are OCLArrays, then:
- type should be complex64
- shape should be equal and power of two
- h is assumed to be already fftshifted
(otherwise set kernel_is_fftshifted to true)
"""
if isinstance(data,np.ndarray):
return _fft_convolve_numpy(data, h,
plan = plan,
kernel_is_fft = kernel_is_fft,
kernel_is_fftshifted = kernel_is_fftshifted)
elif isinstance(data,OCLArray):
return _fft_convolve_gpu(data,h, res_g = res_g,
plan = plan, inplace = inplace,
kernel_is_fft = kernel_is_fft)
else:
raise TypeError("array argument (1) has bad type: %s"%type(data)) | [
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duniter/duniter-python-api | examples/save_revoke_document.py | https://github.com/duniter/duniter-python-api/blob/3a1e5d61a2f72f5afaf29d010c6cf4dff3648165/examples/save_revoke_document.py#L77-L91 | def get_signed_raw_revocation_document(identity: Identity, salt: str, password: str) -> str:
"""
Generate account revocation document for given identity
:param identity: Self Certification of the identity
:param salt: Salt
:param password: Password
:rtype: str
"""
revocation = Revocation(PROTOCOL_VERSION, identity.currency, identity, "")
key = SigningKey.from_credentials(salt, password)
revocation.sign([key])
return revocation.signed_raw() | [
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scanny/python-pptx | pptx/chart/xmlwriter.py | https://github.com/scanny/python-pptx/blob/d6ab8234f8b03953d2f831ff9394b1852db34130/pptx/chart/xmlwriter.py#L123-L141 | def numRef_xml(self, wksht_ref, number_format, values):
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Return the ``<c:numRef>`` element specified by the parameters as
unicode text.
"""
pt_xml = self.pt_xml(values)
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' <c:f>{wksht_ref}</c:f>\n'
' <c:numCache>\n'
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'number_format': number_format,
'pt_xml': pt_xml,
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the01/python-floscraper | floscraper/webscraper.py | https://github.com/the01/python-floscraper/blob/d578cd3d6381070d9a07dade1e10387ae33e9a65/floscraper/webscraper.py#L642-L655 | def shrink(self, shrink):
"""
Remove unnecessary parts
:param shrink: Object to shringk
:type shrink: dict | list
:return: Shrunk object
:rtype: dict | list
"""
if isinstance(shrink, list):
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if isinstance(shrink, dict):
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return shrink | [
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nvbn/thefuck | thefuck/output_readers/read_log.py | https://github.com/nvbn/thefuck/blob/40ab4eb62db57627bff10cf029d29c94704086a2/thefuck/output_readers/read_log.py#L70-L105 | def get_output(script):
"""Reads script output from log.
:type script: str
:rtype: str | None
"""
if six.PY2:
logs.warn('Experimental instant mode is Python 3+ only')
return None
if 'THEFUCK_OUTPUT_LOG' not in os.environ:
logs.warn("Output log isn't specified")
return None
if const.USER_COMMAND_MARK not in os.environ.get('PS1', ''):
logs.warn(
"PS1 doesn't contain user command mark, please ensure "
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return None
try:
with logs.debug_time(u'Read output from log'):
fd = os.open(os.environ['THEFUCK_OUTPUT_LOG'], os.O_RDONLY)
buffer = mmap.mmap(fd, const.LOG_SIZE_IN_BYTES, mmap.MAP_SHARED, mmap.PROT_READ)
_skip_old_lines(buffer)
lines = _get_output_lines(script, buffer)
output = '\n'.join(lines).strip()
logs.debug(u'Received output: {}'.format(output))
return output
except OSError:
logs.warn("Can't read output log")
return None
except ScriptNotInLog:
logs.warn("Script not found in output log")
return None | [
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twisted/twistedchecker | check_pyflakes.py | https://github.com/twisted/twistedchecker/blob/80060e1c07cf5d67d747dbec8ec0e5ee913e8929/check_pyflakes.py#L117-L132 | def checkRecursive(paths, reporter):
"""
Recursively check all source files in C{paths}.
@param paths: A list of paths to Python source files and directories
containing Python source files.
@param reporter: A L{Reporter} where all of the warnings and errors
will be reported to.
@return: The number of warnings found.
"""
warnings = 0
for sourcePath in iterSourceCode(paths):
if re.search(RE_EXCLUDE, sourcePath):
continue
warnings += checkPath(sourcePath, reporter)
return warnings | [
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SmartTeleMax/iktomi | iktomi/unstable/utils/image_resizers.py | https://github.com/SmartTeleMax/iktomi/blob/80bc0f1408d63efe7f5844367d1f6efba44b35f2/iktomi/unstable/utils/image_resizers.py#L34-L55 | def transform(self, img, transformation, params):
'''
Apply transformations to the image.
New transformations can be defined as methods::
def do__transformationname(self, img, transformation, params):
'returns new image with transformation applied'
...
def new_size__transformationname(self, size, target_size, params):
'dry run, returns a size of image if transformation is applied'
...
'''
# Transformations MUST be idempotent.
# The limitation is caused by implementation of
# image upload in iktomi.cms.
# The transformation can be applied twice:
# on image upload after crop (when TransientFile is created)
# and on object save (when PersistentFile is created).
method = getattr(self, 'do__' + transformation)
return method(img, transformation, params) | [
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blockcypher/blockcypher-python | blockcypher/api.py | https://github.com/blockcypher/blockcypher-python/blob/7601ea21916957ff279384fd699527ff9c28a56e/blockcypher/api.py#L571-L581 | def get_broadcast_transaction_hashes(coin_symbol='btc', api_key=None, limit=10):
'''
Warning, slow!
'''
transactions = get_broadcast_transactions(
coin_symbol=coin_symbol,
api_key=api_key,
limit=limit,
)
return [tx['hash'] for tx in transactions] | [
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Fantomas42/django-blog-zinnia | zinnia/models_bases/entry.py | https://github.com/Fantomas42/django-blog-zinnia/blob/b4949304b104a8e1a7a7a0773cbfd024313c3a15/zinnia/models_bases/entry.py#L383-L391 | def save(self, *args, **kwargs):
"""
Overrides the save method to create an excerpt
from the content field if void.
"""
if not self.excerpt and self.status == PUBLISHED:
self.excerpt = Truncator(strip_tags(
getattr(self, 'content', ''))).words(50)
super(ExcerptEntry, self).save(*args, **kwargs) | [
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SheffieldML/GPy | GPy/plotting/gpy_plot/latent_plots.py | https://github.com/SheffieldML/GPy/blob/54c32d79d289d622fb18b898aee65a2a431d90cf/GPy/plotting/gpy_plot/latent_plots.py#L238-L280 | def plot_latent(self, labels=None, which_indices=None,
resolution=60, legend=True,
plot_limits=None,
updates=False,
kern=None, marker='<>^vsd',
num_samples=1000, projection='2d',
scatter_kwargs=None, **imshow_kwargs):
"""
Plot the latent space of the GP on the inputs. This is the
density of the GP posterior as a grey scale and the
scatter plot of the input dimemsions selected by which_indices.
:param array-like labels: a label for each data point (row) of the inputs
:param (int, int) which_indices: which input dimensions to plot against each other
:param int resolution: the resolution at which we predict the magnification factor
:param bool legend: whether to plot the legend on the figure
:param plot_limits: the plot limits for the plot
:type plot_limits: (xmin, xmax, ymin, ymax) or ((xmin, xmax), (ymin, ymax))
:param bool updates: if possible, make interactive updates using the specific library you are using
:param :py:class:`~GPy.kern.Kern` kern: the kernel to use for prediction
:param str marker: markers to use - cycle if more labels then markers are given
:param int num_samples: the number of samples to plot maximally. We do a stratified subsample from the labels, if the number of samples (in X) is higher then num_samples.
:param imshow_kwargs: the kwargs for the imshow (magnification factor)
:param scatter_kwargs: the kwargs for the scatter plots
"""
if projection != '2d':
raise ValueError('Cannot plot latent in other then 2 dimensions, consider plot_scatter')
input_1, input_2 = which_indices = self.get_most_significant_input_dimensions(which_indices)[:2]
X = get_x_y_var(self)[0]
_, _, Xgrid, _, _, xmin, xmax, resolution = helper_for_plot_data(self, X, plot_limits, which_indices, None, resolution)
canvas, imshow_kwargs = pl().new_canvas(xlim=(xmin[0], xmax[0]), ylim=(xmin[1], xmax[1]),
xlabel='latent dimension %i' % input_1, ylabel='latent dimension %i' % input_2, **imshow_kwargs)
if legend:
if (labels is not None):
legend = find_best_layout_for_subplots(len(np.unique(labels)))[1]
else:
labels = np.ones(self.num_data)
legend = False
scatters = _plot_latent_scatter(canvas, X, which_indices, labels, marker, num_samples, projection='2d', **scatter_kwargs or {})
view = _plot_latent(self, canvas, which_indices, Xgrid, xmin, xmax, resolution, updates, kern, **imshow_kwargs)
retval = pl().add_to_canvas(canvas, dict(scatter=scatters, imshow=view), legend=legend)
_wait_for_updates(view, updates)
return retval | [
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:param int resolution: the resolution at which we predict the magnification factor
:param bool legend: whether to plot the legend on the figure
:param plot_limits: the plot limits for the plot
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:param bool updates: if possible, make interactive updates using the specific library you are using
:param :py:class:`~GPy.kern.Kern` kern: the kernel to use for prediction
:param str marker: markers to use - cycle if more labels then markers are given
:param int num_samples: the number of samples to plot maximally. We do a stratified subsample from the labels, if the number of samples (in X) is higher then num_samples.
:param imshow_kwargs: the kwargs for the imshow (magnification factor)
:param scatter_kwargs: the kwargs for the scatter plots | [
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saltstack/salt | salt/modules/win_dacl.py | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/win_dacl.py#L653-L674 | def enable_inheritance(path, objectType, clear=False):
'''
enable/disable inheritance on an object
Args:
path: The path to the object
objectType: The type of object (FILE, DIRECTORY, REGISTRY)
clear: True will remove non-Inherited ACEs from the ACL
Returns (dict): A dictionary containing the results
CLI Example:
.. code-block:: bash
salt 'minion-id' win_dacl.enable_inheritance c:\temp directory
'''
dc = daclConstants()
objectType = dc.getObjectTypeBit(objectType)
path = dc.processPath(path, objectType)
return _set_dacl_inheritance(path, objectType, True, None, clear) | [
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.. code-block:: bash
salt 'minion-id' win_dacl.enable_inheritance c:\temp directory | [
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angr/angr | angr/analyses/forward_analysis.py | https://github.com/angr/angr/blob/4e2f97d56af5419ee73bdb30482c8dd8ff5f3e40/angr/analyses/forward_analysis.py#L129-L149 | def all_successors(self, node, skip_reached_fixedpoint=False):
"""
Returns all successors to the specific node.
:param node: A node in the graph.
:return: A set of nodes that are all successors to the given node.
:rtype: set
"""
successors = set()
stack = [ node ]
while stack:
n = stack.pop()
successors.add(n)
stack.extend(succ for succ in self.successors(n) if
succ not in successors and
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return successors | [
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sibson/vncdotool | vncdotool/client.py | https://github.com/sibson/vncdotool/blob/e133a8916efaa0f5ed421e0aa737196624635b0c/vncdotool/client.py#L318-L342 | def mouseDrag(self, x, y, step=1):
""" Move the mouse point to position (x, y) in increments of step
"""
log.debug('mouseDrag %d,%d', x, y)
if x < self.x:
xsteps = [self.x - i for i in range(step, self.x - x + 1, step)]
else:
xsteps = range(self.x, x, step)
if y < self.y:
ysteps = [self.y - i for i in range(step, self.y - y + 1, step)]
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ysteps = range(self.y, y, step)
for ypos in ysteps:
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for xpos in xsteps:
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self.mouseMove(xpos, self.y)
self.mouseMove(x, y)
return self | [
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mar10/pyftpsync | ftpsync/synchronizers.py | https://github.com/mar10/pyftpsync/blob/bbdc94186975cdc1cc4f678474bdce08bce7bb76/ftpsync/synchronizers.py#L936-L990 | def on_conflict(self, pair):
"""Return False to prevent visiting of children."""
# self._log_action("skip", "conflict", "!", pair.local, min_level=2)
# print("on_conflict", pair)
any_entry = pair.any_entry
if not self._test_match_or_print(any_entry):
return
resolve = self._interactive_resolve(pair)
if resolve == "skip":
self._log_action("skip", "conflict", "*?*", any_entry)
self._inc_stat("conflict_files_skipped")
return
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assert pair.local.is_file()
is_newer = pair.local > pair.remote
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self._copy_file(self.local, self.remote, pair.local)
elif (
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self._log_action("copy", "conflict", "*<*", pair.local)
self._copy_file(self.remote, self.local, pair.remote)
else:
raise NotImplementedError
elif pair.local:
assert pair.local.is_file()
if resolve == "local":
self._log_action("restore", "conflict", "*>x", pair.local)
self._copy_file(self.local, self.remote, pair.local)
elif resolve == "remote":
self._log_action("delete", "conflict", "*<x", pair.local)
self._remove_file(pair.local)
else:
raise NotImplementedError
else:
assert pair.remote.is_file()
if resolve == "local":
self._log_action("delete", "conflict", "x>*", pair.remote)
self._remove_file(pair.remote)
elif resolve == "remote":
self._log_action("restore", "conflict", "x<*", pair.remote)
self._copy_file(self.remote, self.local, pair.remote)
else:
raise NotImplementedError
return | [
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] | python | train |
stevearc/dql | dql/cli.py | https://github.com/stevearc/dql/blob/e9d3aa22873076dae5ebd02e35318aa996b1e56a/dql/cli.py#L504-L518 | def do_use(self, region):
"""
Switch the AWS region
> use us-west-1
> use us-east-1
"""
if self._local_endpoint is not None:
host, port = self._local_endpoint # pylint: disable=W0633
self.engine.connect(
region, session=self.session, host=host, port=port, is_secure=False
)
else:
self.engine.connect(region, session=self.session) | [
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aliyun/aliyun-odps-python-sdk | odps/models/instance.py | https://github.com/aliyun/aliyun-odps-python-sdk/blob/4b0de18f5864386df6068f26f026e62f932c41e4/odps/models/instance.py#L632-L651 | def get_worker_log(self, log_id, log_type, size=0):
"""
Get logs from worker.
:param log_id: id of log, can be retrieved from details.
:param log_type: type of logs. Possible log types contains {log_types}
:param size: length of the log to retrieve
:return: log content
"""
params = OrderedDict([('log', ''), ('id', log_id)])
if log_type is not None:
log_type = log_type.lower()
if log_type not in LOG_TYPES_MAPPING:
raise ValueError('log_type should choose a value in ' +
' '.join(six.iterkeys(LOG_TYPES_MAPPING)))
params['logtype'] = LOG_TYPES_MAPPING[log_type]
if size > 0:
params['size'] = str(size)
resp = self._client.get(self.resource(), params=params)
return resp.text | [
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pmelchior/proxmin | examples/parabola.py | https://github.com/pmelchior/proxmin/blob/60e49d90c67c46329cc1d3b5c484951dc8bd2c3f/examples/parabola.py#L53-L60 | def prox_yline(y, step):
"""Projection onto line in y"""
if not np.isscalar(y):
y= y[0]
if y > -0.75:
return np.array([-0.75])
else:
return np.array([y]) | [
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pyca/pynacl | src/nacl/bindings/crypto_box.py | https://github.com/pyca/pynacl/blob/0df0c2c7693fa5d316846111ce510702756f5feb/src/nacl/bindings/crypto_box.py#L234-L269 | def crypto_box_seal(message, pk):
"""
Encrypts and returns a message ``message`` using an ephemeral secret key
and the public key ``pk``.
The ephemeral public key, which is embedded in the sealed box, is also
used, in combination with ``pk``, to derive the nonce needed for the
underlying box construct.
:param message: bytes
:param pk: bytes
:rtype: bytes
.. versionadded:: 1.2
"""
ensure(isinstance(message, bytes),
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raising=TypeError)
ensure(isinstance(pk, bytes),
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raising=TypeError)
if len(pk) != crypto_box_PUBLICKEYBYTES:
raise exc.ValueError("Invalid public key")
_mlen = len(message)
_clen = crypto_box_SEALBYTES + _mlen
ciphertext = ffi.new("unsigned char[]", _clen)
rc = lib.crypto_box_seal(ciphertext, message, _mlen, pk)
ensure(rc == 0,
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raising=exc.RuntimeError)
return ffi.buffer(ciphertext, _clen)[:] | [
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saltstack/salt | salt/thorium/__init__.py | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/thorium/__init__.py#L70-L102 | def gather_cache(self):
'''
Gather the specified data from the minion data cache
'''
cache = {'grains': {}, 'pillar': {}}
if self.grains or self.pillar:
if self.opts.get('minion_data_cache'):
minions = self.cache.list('minions')
if not minions:
return cache
for minion in minions:
total = self.cache.fetch('minions/{0}'.format(minion), 'data')
if 'pillar' in total:
if self.pillar_keys:
for key in self.pillar_keys:
if key in total['pillar']:
cache['pillar'][minion][key] = total['pillar'][key]
else:
cache['pillar'][minion] = total['pillar']
else:
cache['pillar'][minion] = {}
if 'grains' in total:
if self.grain_keys:
for key in self.grain_keys:
if key in total['grains']:
cache['grains'][minion][key] = total['grains'][key]
else:
cache['grains'][minion] = total['grains']
else:
cache['grains'][minion] = {}
return cache | [
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bitesofcode/projexui | projexui/widgets/xcalendarwidget/xcalendaritem.py | https://github.com/bitesofcode/projexui/blob/f18a73bec84df90b034ca69b9deea118dbedfc4d/projexui/widgets/xcalendarwidget/xcalendaritem.py#L240-L259 | def rebuild( self ):
"""
Rebuilds the current item in the scene.
"""
self.markForRebuild(False)
self._textData = []
if ( self.rebuildBlocked() ):
return
scene = self.scene()
if ( not scene ):
return
# rebuild a month look
if ( scene.currentMode() == scene.Mode.Month ):
self.rebuildMonth()
elif ( scene.currentMode() in (scene.Mode.Day, scene.Mode.Week) ):
self.rebuildDay() | [
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alvations/lazyme | lazyme/fileio.py | https://github.com/alvations/lazyme/blob/961a8282198588ff72e15643f725ce895e51d06d/lazyme/fileio.py#L9-L20 | def find_files(dir_path, extension="*"):
"""
From https://stackoverflow.com/a/2186565/610569
"""
if sys.version_info.major == 3 and sys.version_info.minor >= 5:
pattern = '/'.join([dir_path, '**', extension])
for filename in glob.iglob(pattern, recursive=True):
yield filename
else:
for root, dirnames, filenames in os.walk(dir_path):
for filename in fnmatch.filter(filenames, extension):
yield os.path.join(root, filename) | [
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saltstack/salt | salt/auth/rest.py | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/auth/rest.py#L50-L70 | def auth(username, password):
'''
REST authentication
'''
url = rest_auth_setup()
data = {'username': username, 'password': password}
# Post to the API endpoint. If 200 is returned then the result will be the ACLs
# for this user
result = salt.utils.http.query(url, method='POST', data=data, status=True,
decode=True)
if result['status'] == 200:
log.debug('eauth REST call returned 200: %s', result)
if result['dict'] is not None:
return result['dict']
return True
else:
log.debug('eauth REST call failed: %s', result)
return False | [
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sleepyfran/itunespy | itunespy/music_artist.py | https://github.com/sleepyfran/itunespy/blob/0e7e931b135b5e0daae49ba68e9167ff4ac73eb5/itunespy/music_artist.py#L29-L34 | def get_albums(self):
"""
Retrieves all the albums by the artist
:return: List. Albums published by the artist
"""
return itunespy.lookup(id=self.artist_id, entity=itunespy.entities['album'])[1:] | [
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saltstack/salt | salt/config/__init__.py | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/config/__init__.py#L1971-L1980 | def _validate_pillar_roots(pillar_roots):
'''
If the pillar_roots option has a key that is None then we will error out,
just replace it with an empty list
'''
if not isinstance(pillar_roots, dict):
log.warning('The pillar_roots parameter is not properly formatted,'
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return {'base': _expand_glob_path([salt.syspaths.BASE_PILLAR_ROOTS_DIR])}
return _normalize_roots(pillar_roots) | [
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tomer8007/kik-bot-api-unofficial | kik_unofficial/client.py | https://github.com/tomer8007/kik-bot-api-unofficial/blob/2ae5216bc05e7099a41895382fc8e428a7a5c3ac/kik_unofficial/client.py#L152-L167 | def send_chat_message(self, peer_jid: str, message: str, bot_mention_jid=None):
"""
Sends a text chat message to another person or a group with the given JID/username.
:param peer_jid: The Jabber ID for which to send the message (looks like [email protected])
If you don't know the JID of someone, you can also specify a kik username here.
:param message: The actual message body
:param bot_mention_jid: If an official bot is referenced, their jid must be embedded as mention for them
to respond.
"""
if self.is_group_jid(peer_jid):
log.info("[+] Sending chat message '{}' to group '{}'...".format(message, peer_jid))
return self._send_xmpp_element(chatting.OutgoingGroupChatMessage(peer_jid, message, bot_mention_jid))
else:
log.info("[+] Sending chat message '{}' to user '{}'...".format(message, peer_jid))
return self._send_xmpp_element(chatting.OutgoingChatMessage(peer_jid, message, False, bot_mention_jid)) | [
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gem/oq-engine | openquake/commonlib/shapefileparser.py | https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commonlib/shapefileparser.py#L994-L1030 | def read(self, input_shapefile, validate=False,
simple_fault_spacing=1.0, complex_mesh_spacing=5.0,
mfd_spacing=0.1):
"""
Build the source model from nrml format
"""
reader = shapefile.Reader(input_shapefile)
fields = [field[0] for field in reader.fields[1:]]
shapes = reader.shapes()
records = reader.records()
sources = []
if validate:
converter = SourceConverter(1.0, simple_fault_spacing,
complex_mesh_spacing,
mfd_spacing,
10.0)
for iloc in range(0, reader.numRecords):
# Build record dictionary
record = record_to_dict(records[iloc], fields)
shape = shapes[iloc]
if "pointSource" in record["sourcetype"]:
src = build_point_source_from_shp(shape, record)
elif "areaSource" in record["sourcetype"]:
src = build_area_source_from_shp(shape, record)
elif "simpleFaultSource" in record["sourcetype"]:
src = build_simple_fault_source_from_shp(shape, record)
elif "complexFaultSource" in record["sourcetype"]:
src = build_complex_fault_source_from_shp(shape, record)
elif "characteristicFaultSource" in record["sourcetype"]:
print("Characteristic Fault Source Not Yet Supported - Sorry!")
src = None
if src and validate:
print("Validating Source %s" % src.attrib["id"])
converter.convert_node(src)
if src:
sources.append(src)
return SourceModel(sources) | [
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rohankapoorcom/zm-py | zoneminder/zm.py | https://github.com/rohankapoorcom/zm-py/blob/bd3a9f6b2f7b84b37589e2939f628b479a5531bf/zoneminder/zm.py#L117-L130 | def get_run_states(self) -> List[RunState]:
"""Get a list of RunStates from the ZoneMinder API."""
raw_states = self.get_state('api/states.json')
if not raw_states:
_LOGGER.warning("Could not fetch runstates from ZoneMinder")
return []
run_states = []
for i in raw_states['states']:
raw_state = i['State']
_LOGGER.info("Initializing runstate %s", raw_state['Id'])
run_states.append(RunState(self, raw_state))
return run_states | [
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fprimex/zdesk | zdesk/zdesk_api.py | https://github.com/fprimex/zdesk/blob/851611c13b4d530e9df31390b3ec709baf0a0188/zdesk/zdesk_api.py#L4158-L4169 | def users_autocomplete(self, name=None, **kwargs):
"https://developer.zendesk.com/rest_api/docs/core/users#autocomplete-users"
api_path = "/api/v2/users/autocomplete.json"
api_query = {}
if "query" in kwargs.keys():
api_query.update(kwargs["query"])
del kwargs["query"]
if name:
api_query.update({
"name": name,
})
return self.call(api_path, query=api_query, **kwargs) | [
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raphaelvallat/pingouin | pingouin/external/qsturng.py | https://github.com/raphaelvallat/pingouin/blob/58b19fa4fffbfe09d58b456e3926a148249e4d9b/pingouin/external/qsturng.py#L666-L725 | def _qsturng(p, r, v):
"""scalar version of qsturng"""
## print 'q',p
# r is interpolated through the q to y here we only need to
# account for when p and/or v are not found in the table.
global A, p_keys, v_keys
if p < .1 or p > .999:
raise ValueError('p must be between .1 and .999')
if p < .9:
if v < 2:
raise ValueError('v must be > 2 when p < .9')
else:
if v < 1:
raise ValueError('v must be > 1 when p >= .9')
# The easy case. A tabled value is requested.
#numpy 1.4.1: TypeError: unhashable type: 'numpy.ndarray' :
p = float(p)
if isinstance(v, np.ndarray):
v = v.item()
if (p,v) in A:
y = _func(A[(p,v)], p, r, v) + 1.
elif p not in p_keys and v not in v_keys+([],[1])[p>=.90]:
# find the 3 closest v values
v0, v1, v2 = _select_vs(v, p)
# find the 3 closest p values
p0, p1, p2 = _select_ps(p)
# calculate r0, r1, and r2
r0_sq = _interpolate_p(p, r, v0)**2
r1_sq = _interpolate_p(p, r, v1)**2
r2_sq = _interpolate_p(p, r, v2)**2
# transform v
v_, v0_, v1_, v2_ = 1./v, 1./v0, 1./v1, 1./v2
# calculate derivatives for quadratic interpolation
d2 = 2.*((r2_sq-r1_sq)/(v2_-v1_) - \
(r0_sq-r1_sq)/(v0_-v1_)) / (v2_-v0_)
if (v2_ + v0_) >= (v1_ + v1_):
d1 = (r2_sq-r1_sq) / (v2_-v1_) - 0.5*d2*(v2_-v1_)
else:
d1 = (r1_sq-r0_sq) / (v1_-v0_) + 0.5*d2*(v1_-v0_)
d0 = r1_sq
# calculate y
y = math.sqrt((d2/2.)*(v_-v1_)**2. + d1*(v_-v1_)+ d0)
elif v not in v_keys+([],[1])[p>=.90]:
y = _interpolate_v(p, r, v)
elif p not in p_keys:
y = _interpolate_p(p, r, v)
return math.sqrt(2) * -y * \
scipy.stats.t.isf((1. + p) / 2., max(v, 1e38)) | [
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samuelcolvin/arq | docs/examples/job_results.py | https://github.com/samuelcolvin/arq/blob/1434646b48c45bd27e392f0162976404e4d8021d/docs/examples/job_results.py#L12-L50 | async def main():
redis = await create_pool(RedisSettings())
job = await redis.enqueue_job('the_task')
# get the job's id
print(job.job_id)
"""
> 68362958a244465b9be909db4b7b5ab4 (or whatever)
"""
# get information about the job, will include results if the job has finished, but
# doesn't await the job's result
debug(await job.info())
"""
> docs/examples/job_results.py:23 main
JobDef(
function='the_task',
args=(),
kwargs={},
job_try=None,
enqueue_time=datetime.datetime(2019, 4, 23, 13, 58, 56, 781000),
score=1556027936781
) (JobDef)
"""
# get the Job's status
print(await job.status())
"""
> JobStatus.queued
"""
# poll redis for the job result, if the job raised an exception,
# it will be raised here
# (You'll need the worker running at the same time to get a result here)
print(await job.result(timeout=5))
"""
> 42
""" | [
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locationlabs/mockredis | mockredis/client.py | https://github.com/locationlabs/mockredis/blob/fd4e3117066ff0c24e86ebca007853a8092e3254/mockredis/client.py#L1069-L1080 | def srem(self, key, *values):
"""Emulate srem."""
redis_set = self._get_set(key, 'SREM')
if not redis_set:
return 0
before_count = len(redis_set)
for value in values:
redis_set.discard(self._encode(value))
after_count = len(redis_set)
if before_count > 0 and len(redis_set) == 0:
self.delete(key)
return before_count - after_count | [
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quantopian/alphalens | alphalens/utils.py | https://github.com/quantopian/alphalens/blob/d43eac871bb061e956df936794d3dd514da99e44/alphalens/utils.py#L420-L629 | def get_clean_factor(factor,
forward_returns,
groupby=None,
binning_by_group=False,
quantiles=5,
bins=None,
groupby_labels=None,
max_loss=0.35,
zero_aware=False):
"""
Formats the factor data, forward return data, and group mappings into a
DataFrame that contains aligned MultiIndex indices of timestamp and asset.
The returned data will be formatted to be suitable for Alphalens functions.
It is safe to skip a call to this function and still make use of Alphalens
functionalities as long as the factor data conforms to the format returned
from get_clean_factor_and_forward_returns and documented here
Parameters
----------
factor : pd.Series - MultiIndex
A MultiIndex Series indexed by timestamp (level 0) and asset
(level 1), containing the values for a single alpha factor.
::
-----------------------------------
date | asset |
-----------------------------------
| AAPL | 0.5
-----------------------
| BA | -1.1
-----------------------
2014-01-01 | CMG | 1.7
-----------------------
| DAL | -0.1
-----------------------
| LULU | 2.7
-----------------------
forward_returns : pd.DataFrame - MultiIndex
A MultiIndex DataFrame indexed by timestamp (level 0) and asset
(level 1), containing the forward returns for assets.
Forward returns column names must follow the format accepted by
pd.Timedelta (e.g. '1D', '30m', '3h15m', '1D1h', etc).
'date' index freq property must be set to a trading calendar
(pandas DateOffset), see infer_trading_calendar for more details.
This information is currently used only in cumulative returns
computation
::
---------------------------------------
| | 1D | 5D | 10D
---------------------------------------
date | asset | | |
---------------------------------------
| AAPL | 0.09|-0.01|-0.079
----------------------------
| BA | 0.02| 0.06| 0.020
----------------------------
2014-01-01 | CMG | 0.03| 0.09| 0.036
----------------------------
| DAL |-0.02|-0.06|-0.029
----------------------------
| LULU |-0.03| 0.05|-0.009
----------------------------
groupby : pd.Series - MultiIndex or dict
Either A MultiIndex Series indexed by date and asset,
containing the period wise group codes for each asset, or
a dict of asset to group mappings. If a dict is passed,
it is assumed that group mappings are unchanged for the
entire time period of the passed factor data.
binning_by_group : bool
If True, compute quantile buckets separately for each group.
This is useful when the factor values range vary considerably
across gorups so that it is wise to make the binning group relative.
You should probably enable this if the factor is intended
to be analyzed for a group neutral portfolio
quantiles : int or sequence[float]
Number of equal-sized quantile buckets to use in factor bucketing.
Alternately sequence of quantiles, allowing non-equal-sized buckets
e.g. [0, .10, .5, .90, 1.] or [.05, .5, .95]
Only one of 'quantiles' or 'bins' can be not-None
bins : int or sequence[float]
Number of equal-width (valuewise) bins to use in factor bucketing.
Alternately sequence of bin edges allowing for non-uniform bin width
e.g. [-4, -2, -0.5, 0, 10]
Chooses the buckets to be evenly spaced according to the values
themselves. Useful when the factor contains discrete values.
Only one of 'quantiles' or 'bins' can be not-None
groupby_labels : dict
A dictionary keyed by group code with values corresponding
to the display name for each group.
max_loss : float, optional
Maximum percentage (0.00 to 1.00) of factor data dropping allowed,
computed comparing the number of items in the input factor index and
the number of items in the output DataFrame index.
Factor data can be partially dropped due to being flawed itself
(e.g. NaNs), not having provided enough price data to compute
forward returns for all factor values, or because it is not possible
to perform binning.
Set max_loss=0 to avoid Exceptions suppression.
zero_aware : bool, optional
If True, compute quantile buckets separately for positive and negative
signal values. This is useful if your signal is centered and zero is
the separation between long and short signals, respectively.
'quantiles' is None.
Returns
-------
merged_data : pd.DataFrame - MultiIndex
A MultiIndex Series indexed by date (level 0) and asset (level 1),
containing the values for a single alpha factor, forward returns for
each period, the factor quantile/bin that factor value belongs to, and
(optionally) the group the asset belongs to.
- forward returns column names follow the format accepted by
pd.Timedelta (e.g. '1D', '30m', '3h15m', '1D1h', etc)
- 'date' index freq property (merged_data.index.levels[0].freq) is the
same as that of the input forward returns data. This is currently
used only in cumulative returns computation
::
-------------------------------------------------------------------
| | 1D | 5D | 10D |factor|group|factor_quantile
-------------------------------------------------------------------
date | asset | | | | | |
-------------------------------------------------------------------
| AAPL | 0.09|-0.01|-0.079| 0.5 | G1 | 3
--------------------------------------------------------
| BA | 0.02| 0.06| 0.020| -1.1 | G2 | 5
--------------------------------------------------------
2014-01-01 | CMG | 0.03| 0.09| 0.036| 1.7 | G2 | 1
--------------------------------------------------------
| DAL |-0.02|-0.06|-0.029| -0.1 | G3 | 5
--------------------------------------------------------
| LULU |-0.03| 0.05|-0.009| 2.7 | G1 | 2
--------------------------------------------------------
"""
initial_amount = float(len(factor.index))
factor_copy = factor.copy()
factor_copy.index = factor_copy.index.rename(['date', 'asset'])
merged_data = forward_returns.copy()
merged_data['factor'] = factor_copy
if groupby is not None:
if isinstance(groupby, dict):
diff = set(factor_copy.index.get_level_values(
'asset')) - set(groupby.keys())
if len(diff) > 0:
raise KeyError(
"Assets {} not in group mapping".format(
list(diff)))
ss = pd.Series(groupby)
groupby = pd.Series(index=factor_copy.index,
data=ss[factor_copy.index.get_level_values(
'asset')].values)
if groupby_labels is not None:
diff = set(groupby.values) - set(groupby_labels.keys())
if len(diff) > 0:
raise KeyError(
"groups {} not in passed group names".format(
list(diff)))
sn = pd.Series(groupby_labels)
groupby = pd.Series(index=groupby.index,
data=sn[groupby.values].values)
merged_data['group'] = groupby.astype('category')
merged_data = merged_data.dropna()
fwdret_amount = float(len(merged_data.index))
no_raise = False if max_loss == 0 else True
quantile_data = quantize_factor(
merged_data,
quantiles,
bins,
binning_by_group,
no_raise,
zero_aware
)
merged_data['factor_quantile'] = quantile_data
merged_data = merged_data.dropna()
binning_amount = float(len(merged_data.index))
tot_loss = (initial_amount - binning_amount) / initial_amount
fwdret_loss = (initial_amount - fwdret_amount) / initial_amount
bin_loss = tot_loss - fwdret_loss
print("Dropped %.1f%% entries from factor data: %.1f%% in forward "
"returns computation and %.1f%% in binning phase "
"(set max_loss=0 to see potentially suppressed Exceptions)." %
(tot_loss * 100, fwdret_loss * 100, bin_loss * 100))
if tot_loss > max_loss:
message = ("max_loss (%.1f%%) exceeded %.1f%%, consider increasing it."
% (max_loss * 100, tot_loss * 100))
raise MaxLossExceededError(message)
else:
print("max_loss is %.1f%%, not exceeded: OK!" % (max_loss * 100))
return merged_data | [
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The returned data will be formatted to be suitable for Alphalens functions.
It is safe to skip a call to this function and still make use of Alphalens
functionalities as long as the factor data conforms to the format returned
from get_clean_factor_and_forward_returns and documented here
Parameters
----------
factor : pd.Series - MultiIndex
A MultiIndex Series indexed by timestamp (level 0) and asset
(level 1), containing the values for a single alpha factor.
::
-----------------------------------
date | asset |
-----------------------------------
| AAPL | 0.5
-----------------------
| BA | -1.1
-----------------------
2014-01-01 | CMG | 1.7
-----------------------
| DAL | -0.1
-----------------------
| LULU | 2.7
-----------------------
forward_returns : pd.DataFrame - MultiIndex
A MultiIndex DataFrame indexed by timestamp (level 0) and asset
(level 1), containing the forward returns for assets.
Forward returns column names must follow the format accepted by
pd.Timedelta (e.g. '1D', '30m', '3h15m', '1D1h', etc).
'date' index freq property must be set to a trading calendar
(pandas DateOffset), see infer_trading_calendar for more details.
This information is currently used only in cumulative returns
computation
::
---------------------------------------
| | 1D | 5D | 10D
---------------------------------------
date | asset | | |
---------------------------------------
| AAPL | 0.09|-0.01|-0.079
----------------------------
| BA | 0.02| 0.06| 0.020
----------------------------
2014-01-01 | CMG | 0.03| 0.09| 0.036
----------------------------
| DAL |-0.02|-0.06|-0.029
----------------------------
| LULU |-0.03| 0.05|-0.009
----------------------------
groupby : pd.Series - MultiIndex or dict
Either A MultiIndex Series indexed by date and asset,
containing the period wise group codes for each asset, or
a dict of asset to group mappings. If a dict is passed,
it is assumed that group mappings are unchanged for the
entire time period of the passed factor data.
binning_by_group : bool
If True, compute quantile buckets separately for each group.
This is useful when the factor values range vary considerably
across gorups so that it is wise to make the binning group relative.
You should probably enable this if the factor is intended
to be analyzed for a group neutral portfolio
quantiles : int or sequence[float]
Number of equal-sized quantile buckets to use in factor bucketing.
Alternately sequence of quantiles, allowing non-equal-sized buckets
e.g. [0, .10, .5, .90, 1.] or [.05, .5, .95]
Only one of 'quantiles' or 'bins' can be not-None
bins : int or sequence[float]
Number of equal-width (valuewise) bins to use in factor bucketing.
Alternately sequence of bin edges allowing for non-uniform bin width
e.g. [-4, -2, -0.5, 0, 10]
Chooses the buckets to be evenly spaced according to the values
themselves. Useful when the factor contains discrete values.
Only one of 'quantiles' or 'bins' can be not-None
groupby_labels : dict
A dictionary keyed by group code with values corresponding
to the display name for each group.
max_loss : float, optional
Maximum percentage (0.00 to 1.00) of factor data dropping allowed,
computed comparing the number of items in the input factor index and
the number of items in the output DataFrame index.
Factor data can be partially dropped due to being flawed itself
(e.g. NaNs), not having provided enough price data to compute
forward returns for all factor values, or because it is not possible
to perform binning.
Set max_loss=0 to avoid Exceptions suppression.
zero_aware : bool, optional
If True, compute quantile buckets separately for positive and negative
signal values. This is useful if your signal is centered and zero is
the separation between long and short signals, respectively.
'quantiles' is None.
Returns
-------
merged_data : pd.DataFrame - MultiIndex
A MultiIndex Series indexed by date (level 0) and asset (level 1),
containing the values for a single alpha factor, forward returns for
each period, the factor quantile/bin that factor value belongs to, and
(optionally) the group the asset belongs to.
- forward returns column names follow the format accepted by
pd.Timedelta (e.g. '1D', '30m', '3h15m', '1D1h', etc)
- 'date' index freq property (merged_data.index.levels[0].freq) is the
same as that of the input forward returns data. This is currently
used only in cumulative returns computation
::
-------------------------------------------------------------------
| | 1D | 5D | 10D |factor|group|factor_quantile
-------------------------------------------------------------------
date | asset | | | | | |
-------------------------------------------------------------------
| AAPL | 0.09|-0.01|-0.079| 0.5 | G1 | 3
--------------------------------------------------------
| BA | 0.02| 0.06| 0.020| -1.1 | G2 | 5
--------------------------------------------------------
2014-01-01 | CMG | 0.03| 0.09| 0.036| 1.7 | G2 | 1
--------------------------------------------------------
| DAL |-0.02|-0.06|-0.029| -0.1 | G3 | 5
--------------------------------------------------------
| LULU |-0.03| 0.05|-0.009| 2.7 | G1 | 2
-------------------------------------------------------- | [
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RedHatInsights/insights-core | insights/client/collection_rules.py | https://github.com/RedHatInsights/insights-core/blob/b57cbf8ed7c089672426ede0441e0a4f789ef4a1/insights/client/collection_rules.py#L149-L158 | def get_collection_rules_gpg(self, collection_rules):
"""
Download the collection rules gpg signature
"""
sig_text = self.fetch_gpg()
sig_response = NamedTemporaryFile(suffix=".asc")
sig_response.write(sig_text.encode('utf-8'))
sig_response.file.flush()
self.validate_gpg_sig(collection_rules.name, sig_response.name)
self.write_collection_data(self.collection_rules_file + ".asc", sig_text) | [
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micha030201/aionationstates | aionationstates/wa_.py | https://github.com/micha030201/aionationstates/blob/dc86b86d994cbab830b69ab8023601c73e778b3a/aionationstates/wa_.py#L68-L83 | def repeal_target(self):
"""The resolution this resolution has repealed, or is attempting
to repeal.
Returns
-------
:class:`ApiQuery` of :class:`Resolution`
Raises
------
TypeError:
If the resolution doesn't repeal anything.
"""
if not self.category == 'Repeal':
raise TypeError("This resolution doesn't repeal anything")
return wa.resolution(int(self.option) + 1) | [
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] | The resolution this resolution has repealed, or is attempting
to repeal.
Returns
-------
:class:`ApiQuery` of :class:`Resolution`
Raises
------
TypeError:
If the resolution doesn't repeal anything. | [
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tensorflow/tensorboard | tensorboard/lazy.py | https://github.com/tensorflow/tensorboard/blob/8e5f497b48e40f2a774f85416b8a35ac0693c35e/tensorboard/lazy.py#L27-L76 | def lazy_load(name):
"""Decorator to define a function that lazily loads the module 'name'.
This can be used to defer importing troublesome dependencies - e.g. ones that
are large and infrequently used, or that cause a dependency cycle -
until they are actually used.
Args:
name: the fully-qualified name of the module; typically the last segment
of 'name' matches the name of the decorated function
Returns:
Decorator function that produces a lazy-loading module 'name' backed by the
underlying decorated function.
"""
def wrapper(load_fn):
# Wrap load_fn to call it exactly once and update __dict__ afterwards to
# make future lookups efficient (only failed lookups call __getattr__).
@_memoize
def load_once(self):
if load_once.loading:
raise ImportError("Circular import when resolving LazyModule %r" % name)
load_once.loading = True
try:
module = load_fn()
finally:
load_once.loading = False
self.__dict__.update(module.__dict__)
load_once.loaded = True
return module
load_once.loading = False
load_once.loaded = False
# Define a module that proxies getattr() and dir() to the result of calling
# load_once() the first time it's needed. The class is nested so we can close
# over load_once() and avoid polluting the module's attrs with our own state.
class LazyModule(types.ModuleType):
def __getattr__(self, attr_name):
return getattr(load_once(self), attr_name)
def __dir__(self):
return dir(load_once(self))
def __repr__(self):
if load_once.loaded:
return '<%r via LazyModule (loaded)>' % load_once(self)
return '<module %r via LazyModule (not yet loaded)>' % self.__name__
return LazyModule(name)
return wrapper | [
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saltstack/salt | salt/utils/network.py | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/utils/network.py#L576-L592 | def natural_ipv4_netmask(ip, fmt='prefixlen'):
'''
Returns the "natural" mask of an IPv4 address
'''
bits = _ipv4_to_bits(ip)
if bits.startswith('11'):
mask = '24'
elif bits.startswith('1'):
mask = '16'
else:
mask = '8'
if fmt == 'netmask':
return cidr_to_ipv4_netmask(mask)
else:
return '/' + mask | [
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kislyuk/ensure | ensure/main.py | https://github.com/kislyuk/ensure/blob/0a562a4b469ffbaf71c75dc4d394e94334c831f0/ensure/main.py#L243-L249 | def contains_one_of(self, elements):
"""
Ensures :attr:`subject` contains exactly one of *elements*, which must be an iterable.
"""
if sum(e in self._subject for e in elements) != 1:
raise self._error_factory(_format("Expected {} to have exactly one of {}", self._subject, elements))
return ChainInspector(self._subject) | [
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awslabs/aws-sam-cli | samcli/local/layers/layer_downloader.py | https://github.com/awslabs/aws-sam-cli/blob/c05af5e7378c6f05f7d82ad3f0bca17204177db6/samcli/local/layers/layer_downloader.py#L54-L74 | def download_all(self, layers, force=False):
"""
Download a list of layers to the cache
Parameters
----------
layers list(samcli.commands.local.lib.provider.Layer)
List of Layers representing the layer to be downloaded
force bool
True to download the layer even if it exists already on the system
Returns
-------
List(Path)
List of Paths to where the layer was cached
"""
layer_dirs = []
for layer in layers:
layer_dirs.append(self.download(layer, force))
return layer_dirs | [
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List of Layers representing the layer to be downloaded
force bool
True to download the layer even if it exists already on the system
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sebp/scikit-survival | sksurv/datasets/base.py | https://github.com/sebp/scikit-survival/blob/cfc99fd20454cdd6f4f20fe331b39f2191ccaabc/sksurv/datasets/base.py#L46-L88 | def get_x_y(data_frame, attr_labels, pos_label=None, survival=True):
"""Split data frame into features and labels.
Parameters
----------
data_frame : pandas.DataFrame, shape = (n_samples, n_columns)
A data frame.
attr_labels : sequence of str or None
A list of one or more columns that are considered the label.
If `survival` is `True`, then attr_labels has two elements:
1) the name of the column denoting the event indicator, and
2) the name of the column denoting the survival time.
If the sequence contains `None`, then labels are not retrieved
and only a data frame with features is returned.
pos_label : any, optional
Which value of the event indicator column denotes that a
patient experienced an event. This value is ignored if
`survival` is `False`.
survival : bool, optional, default: True
Whether to return `y` that can be used for survival analysis.
Returns
-------
X : pandas.DataFrame, shape = (n_samples, n_columns - len(attr_labels))
Data frame containing features.
y : None or pandas.DataFrame, shape = (n_samples, len(attr_labels))
Data frame containing columns with supervised information.
If `survival` was `True`, then the column denoting the event
indicator will be boolean and survival times will be float.
If `attr_labels` contains `None`, y is set to `None`.
"""
if survival:
if len(attr_labels) != 2:
raise ValueError("expected sequence of length two for attr_labels, but got %d" % len(attr_labels))
if pos_label is None:
raise ValueError("pos_label needs to be specified if survival=True")
return _get_x_y_survival(data_frame, attr_labels[0], attr_labels[1], pos_label)
return _get_x_y_other(data_frame, attr_labels) | [
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A list of one or more columns that are considered the label.
If `survival` is `True`, then attr_labels has two elements:
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2) the name of the column denoting the survival time.
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Which value of the event indicator column denotes that a
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survival : bool, optional, default: True
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Data frame containing features.
y : None or pandas.DataFrame, shape = (n_samples, len(attr_labels))
Data frame containing columns with supervised information.
If `survival` was `True`, then the column denoting the event
indicator will be boolean and survival times will be float.
If `attr_labels` contains `None`, y is set to `None`. | [
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ecell/ecell4 | ecell4/extra/ensemble.py | https://github.com/ecell/ecell4/blob/a4a1229661c39b2059adbbacae9090e5ba664e01/ecell4/extra/ensemble.py#L503-L523 | def run_azure(target, jobs, n=1, nproc=None, path='.', delete=True, config=None, **kwargs):
"""
Evaluate the given function with each set of arguments, and return a list of results.
This function does in parallel with Microsoft Azure Batch.
This function is the work in progress.
The argument `nproc` doesn't work yet.
See `ecell4.extra.azure_batch.run_azure` for details.
See Also
--------
ecell4.extra.ensemble.run_serial
ecell4.extra.ensemble.run_sge
ecell4.extra.ensemble.run_slurm
ecell4.extra.ensemble.run_multiprocessing
ecell4.extra.ensemble.run_azure
ecell4.extra.azure_batch.run_azure
"""
import ecell4.extra.azure_batch as azure_batch
return azure_batch.run_azure(target, jobs, n, path, delete, config) | [
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ecell4.extra.ensemble.run_slurm
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saltstack/salt | salt/states/boto_dynamodb.py | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/states/boto_dynamodb.py#L617-L668 | def _alarms_present(name, alarms, alarms_from_pillar,
write_capacity_units, read_capacity_units,
region, key, keyid, profile):
'''helper method for present. ensure that cloudwatch_alarms are set'''
# load data from alarms_from_pillar
tmp = copy.deepcopy(
__salt__['config.option'](alarms_from_pillar, {})
)
# merge with data from alarms
if alarms:
tmp = dictupdate.update(tmp, alarms)
# set alarms, using boto_cloudwatch_alarm.present
merged_return_value = {'name': name, 'result': True, 'comment': '', 'changes': {}}
for _, info in six.iteritems(tmp):
# add dynamodb table to name and description
info["name"] = name + " " + info["name"]
info["attributes"]["description"] = name + " " + info["attributes"]["description"]
# add dimension attribute
info["attributes"]["dimensions"] = {"TableName": [name]}
if info["attributes"]["metric"] == "ConsumedWriteCapacityUnits" \
and "threshold" not in info["attributes"]:
info["attributes"]["threshold"] = math.ceil(write_capacity_units * info["attributes"]["threshold_percent"])
del info["attributes"]["threshold_percent"]
# the write_capacity_units is given in unit / second. So we need
# to multiply by the period to get the proper threshold.
# http://docs.aws.amazon.com/amazondynamodb/latest/developerguide/MonitoringDynamoDB.html
info["attributes"]["threshold"] *= info["attributes"]["period"]
if info["attributes"]["metric"] == "ConsumedReadCapacityUnits" \
and "threshold" not in info["attributes"]:
info["attributes"]["threshold"] = math.ceil(read_capacity_units * info["attributes"]["threshold_percent"])
del info["attributes"]["threshold_percent"]
# the read_capacity_units is given in unit / second. So we need
# to multiply by the period to get the proper threshold.
# http://docs.aws.amazon.com/amazondynamodb/latest/developerguide/MonitoringDynamoDB.html
info["attributes"]["threshold"] *= info["attributes"]["period"]
# set alarm
kwargs = {
"name": info["name"],
"attributes": info["attributes"],
"region": region,
"key": key,
"keyid": keyid,
"profile": profile,
}
results = __states__['boto_cloudwatch_alarm.present'](**kwargs)
if not results["result"]:
merged_return_value["result"] = results["result"]
if results.get("changes", {}) != {}:
merged_return_value["changes"][info["name"]] = results["changes"]
if "comment" in results:
merged_return_value["comment"] += results["comment"]
return merged_return_value | [
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KrzyHonk/bpmn-python | bpmn_python/bpmn_process_csv_export.py | https://github.com/KrzyHonk/bpmn-python/blob/6e5e28e3d656dbf5bd3d85d78fe8e3f2fb462629/bpmn_python/bpmn_process_csv_export.py#L44-L75 | def export_process_to_csv(bpmn_diagram, directory, filename):
"""
Root method of CSV export functionality.
:param bpmn_diagram: an instance of BpmnDiagramGraph class,
:param directory: a string object, which is a path of output directory,
:param filename: a string object, which is a name of output file.
"""
nodes = copy.deepcopy(bpmn_diagram.get_nodes())
start_nodes = []
export_elements = []
for node in nodes:
incoming_list = node[1].get(consts.Consts.incoming_flow)
if len(incoming_list) == 0:
start_nodes.append(node)
if len(start_nodes) != 1:
raise bpmn_exception.BpmnPythonError("Exporting to CSV format accepts only one start event")
nodes_classification = utils.BpmnImportUtils.generate_nodes_clasification(bpmn_diagram)
start_node = start_nodes.pop()
BpmnDiagramGraphCsvExport.export_node(bpmn_diagram, export_elements, start_node, nodes_classification)
try:
os.makedirs(directory)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
file_object = open(directory + filename, "w")
file_object.write("Order,Activity,Condition,Who,Subprocess,Terminated\n")
BpmnDiagramGraphCsvExport.write_export_node_to_file(file_object, export_elements)
file_object.close() | [
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luckydonald/pytgbot | code_generation/output/teleflask_messages.py | https://github.com/luckydonald/pytgbot/blob/67f4b5a1510d4583d40b5477e876b1ef0eb8971b/code_generation/output/teleflask_messages.py#L1936-L1991 | def to_array(self):
"""
Serializes this VoiceMessage to a dictionary.
:return: dictionary representation of this object.
:rtype: dict
"""
array = super(VoiceMessage, self).to_array()
if isinstance(self.voice, InputFile):
array['voice'] = self.voice.to_array() # type InputFile
elif isinstance(self.voice, str):
array['voice'] = u(self.voice) # py2: type unicode, py3: type str
else:
raise TypeError('Unknown type, must be one of InputFile, str.')
# end if
if self.receiver is not None:
if isinstance(self.receiver, None):
array['chat_id'] = None(self.receiver) # type Noneelif isinstance(self.receiver, str):
array['chat_id'] = u(self.receiver) # py2: type unicode, py3: type str
elif isinstance(self.receiver, int):
array['chat_id'] = int(self.receiver) # type intelse:
raise TypeError('Unknown type, must be one of None, str, int.')
# end if
if self.reply_id is not None:
if isinstance(self.reply_id, DEFAULT_MESSAGE_ID):
array['reply_to_message_id'] = DEFAULT_MESSAGE_ID(self.reply_id) # type DEFAULT_MESSAGE_IDelif isinstance(self.reply_id, int):
array['reply_to_message_id'] = int(self.reply_id) # type intelse:
raise TypeError('Unknown type, must be one of DEFAULT_MESSAGE_ID, int.')
# end if
if self.caption is not None:
array['caption'] = u(self.caption) # py2: type unicode, py3: type str
if self.parse_mode is not None:
array['parse_mode'] = u(self.parse_mode) # py2: type unicode, py3: type str
if self.duration is not None:
array['duration'] = int(self.duration) # type int
if self.disable_notification is not None:
array['disable_notification'] = bool(self.disable_notification) # type bool
if self.reply_markup is not None:
if isinstance(self.reply_markup, InlineKeyboardMarkup):
array['reply_markup'] = self.reply_markup.to_array() # type InlineKeyboardMarkup
elif isinstance(self.reply_markup, ReplyKeyboardMarkup):
array['reply_markup'] = self.reply_markup.to_array() # type ReplyKeyboardMarkup
elif isinstance(self.reply_markup, ReplyKeyboardRemove):
array['reply_markup'] = self.reply_markup.to_array() # type ReplyKeyboardRemove
elif isinstance(self.reply_markup, ForceReply):
array['reply_markup'] = self.reply_markup.to_array() # type ForceReply
else:
raise TypeError('Unknown type, must be one of InlineKeyboardMarkup, ReplyKeyboardMarkup, ReplyKeyboardRemove, ForceReply.')
# end if
return array | [
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pyusb/pyusb | usb/legacy.py | https://github.com/pyusb/pyusb/blob/ffe6faf42c6ad273880b0b464b9bbf44c1d4b2e9/usb/legacy.py#L131-L141 | def bulkWrite(self, endpoint, buffer, timeout = 100):
r"""Perform a bulk write request to the endpoint specified.
Arguments:
endpoint: endpoint number.
buffer: sequence data buffer to write.
This parameter can be any sequence type.
timeout: operation timeout in milliseconds. (default: 100)
Returns the number of bytes written.
"""
return self.dev.write(endpoint, buffer, timeout) | [
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IdentityPython/pysaml2 | src/saml2/validate.py | https://github.com/IdentityPython/pysaml2/blob/d3aa78eeb7d37c12688f783cb4db1c7263a14ad6/src/saml2/validate.py#L255-L265 | def valid_qname(val):
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return valid_ncname(prefix) and valid_ncname(localpart)
except ValueError:
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modin-project/modin | modin/pandas/base.py | https://github.com/modin-project/modin/blob/5b77d242596560c646b8405340c9ce64acb183cb/modin/pandas/base.py#L457-L506 | def apply(
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convert_dtype=True,
args=(),
**kwds
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Args:
func: The function to apply
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broadcast: Whether or not to broadcast.
raw: Whether or not to convert to a Series.
reduce: Whether or not to try to apply reduction procedures.
Returns:
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"""
axis = self._get_axis_number(axis)
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if isinstance(func, string_types):
if axis == 1:
kwds["axis"] = axis
result = self._string_function(func, *args, **kwds)
# Sometimes we can return a scalar here
if isinstance(result, BasePandasDataset):
return result._query_compiler
return result
elif isinstance(func, dict):
if axis == 1:
raise TypeError(
"(\"'dict' object is not callable\", "
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)
if len(self.columns) != len(set(self.columns)):
warnings.warn(
"duplicate column names not supported with apply().",
FutureWarning,
stacklevel=2,
)
elif not callable(func) and not is_list_like(func):
raise TypeError("{} object is not callable".format(type(func)))
query_compiler = self._query_compiler.apply(func, axis, *args, **kwds)
return query_compiler | [
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proteanhq/protean | src/protean/core/usecase/generic.py | https://github.com/proteanhq/protean/blob/0e29873f4aa634aa93cc08ed675dd749c7ed4b0f/src/protean/core/usecase/generic.py#L157-L168 | def process_request(self, request_object):
"""Process the Delete Resource Request"""
# Delete the object by its identifier
entity = request_object.entity_cls.get(request_object.identifier)
entity.delete()
# FIXME Check for return value of `delete()`
# We have successfully deleted the object.
# Sending a 204 Response code.
return ResponseSuccessWithNoContent() | [
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openknowledge-archive/datapackage-registry-py | datapackage_registry/registry.py | https://github.com/openknowledge-archive/datapackage-registry-py/blob/02ba4d1ae6a75d8960abef0ffec3e9ec49ed26f9/datapackage_registry/registry.py#L75-L87 | def _get_profile(self, profile_id):
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url = profile_metadata.get('schema')
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balloob/pychromecast | pychromecast/discovery.py | https://github.com/balloob/pychromecast/blob/831b09c4fed185a7bffe0ea330b7849d5f4e36b6/pychromecast/discovery.py#L147-L159 | def get_info_from_service(service, zconf):
""" Resolve service_info from service. """
service_info = None
try:
service_info = zconf.get_service_info('_googlecast._tcp.local.',
service)
if service_info:
_LOGGER.debug(
"get_info_from_service resolved service %s to service_info %s",
service, service_info)
except IOError:
pass
return service_info | [
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jstitch/MambuPy | MambuPy/rest/mambuclient.py | https://github.com/jstitch/MambuPy/blob/2af98cc12e7ed5ec183b3e97644e880e70b79ee8/MambuPy/rest/mambuclient.py#L136-L162 | def setGroups(self, *args, **kwargs):
"""Adds the groups to which this client belongs.
The 'groupKeys' field of the client holds a list of the
encodedKeys of the groups to which this client belongs.
Returns the number of requests done to Mambu.
"""
requests = 0
groups = []
try:
for gk in self['groupKeys']:
try:
g = self.mambugroupclass(entid=gk, *args, **kwargs)
except AttributeError as ae:
from .mambugroup import MambuGroup
self.mambugroupclass = MambuGroup
g = self.mambugroupclass(entid=gk, *args, **kwargs)
requests += 1
groups.append(g)
except KeyError:
pass
self['groups'] = groups
return requests | [
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apple/turicreate | src/unity/python/turicreate/toolkits/object_detector/_detection.py | https://github.com/apple/turicreate/blob/74514c3f99e25b46f22c6e02977fe3da69221c2e/src/unity/python/turicreate/toolkits/object_detector/_detection.py#L17-L24 | def bbox_to_ybox(bbox):
"""Convert from corner bounding box to center/shape"""
return [
(bbox[1] + bbox[3]) / 2,
(bbox[0] + bbox[2]) / 2,
(bbox[3] - bbox[1]),
(bbox[2] - bbox[0]),
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johntruckenbrodt/spatialist | spatialist/raster.py | https://github.com/johntruckenbrodt/spatialist/blob/007f49296a156de8d7168ad235b5a5b8e8d3633d/spatialist/raster.py#L619-L625 | def load(self):
"""
load all raster data to internal memory arrays.
This shortens the read time of other methods like :meth:`matrix`.
"""
for i in range(1, self.bands + 1):
self.__data[i - 1] = self.matrix(i) | [
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owncloud/pyocclient | owncloud/owncloud.py | https://github.com/owncloud/pyocclient/blob/b9e1f04cdbde74588e86f1bebb6144571d82966c/owncloud/owncloud.py#L678-L695 | def accept_remote_share(self, share_id):
"""Accepts a remote share
:param share_id: Share ID (int)
:returns: True if the operation succeeded, False otherwise
:raises: HTTPResponseError in case an HTTP error status was returned
"""
if not isinstance(share_id, int):
return False
res = self._make_ocs_request(
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self.OCS_SERVICE_SHARE,
'remote_shares/pending/' + str(share_id)
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if res.status_code == 200:
return res
raise HTTPResponseError(res) | [
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/data/build_image_data.py | https://github.com/yahoo/TensorFlowOnSpark/blob/5e4b6c185ab722fd0104ede0377e1149ea8d6f7c/examples/imagenet/inception/data/build_image_data.py#L287-L328 | def _process_image_files(name, filenames, texts, labels, num_shards):
"""Process and save list of images as TFRecord of Example protos.
Args:
name: string, unique identifier specifying the data set
filenames: list of strings; each string is a path to an image file
texts: list of strings; each string is human readable, e.g. 'dog'
labels: list of integer; each integer identifies the ground truth
num_shards: integer number of shards for this data set.
"""
assert len(filenames) == len(texts)
assert len(filenames) == len(labels)
# Break all images into batches with a [ranges[i][0], ranges[i][1]].
spacing = np.linspace(0, len(filenames), FLAGS.num_threads + 1).astype(np.int)
ranges = []
for i in range(len(spacing) - 1):
ranges.append([spacing[i], spacing[i+1]])
# Launch a thread for each batch.
print('Launching %d threads for spacings: %s' % (FLAGS.num_threads, ranges))
sys.stdout.flush()
# Create a mechanism for monitoring when all threads are finished.
coord = tf.train.Coordinator()
# Create a generic TensorFlow-based utility for converting all image codings.
coder = ImageCoder()
threads = []
for thread_index in range(len(ranges)):
args = (coder, thread_index, ranges, name, filenames,
texts, labels, num_shards)
t = threading.Thread(target=_process_image_files_batch, args=args)
t.start()
threads.append(t)
# Wait for all the threads to terminate.
coord.join(threads)
print('%s: Finished writing all %d images in data set.' %
(datetime.now(), len(filenames)))
sys.stdout.flush() | [
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texts: list of strings; each string is human readable, e.g. 'dog'
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glormph/msstitch | src/app/lookups/sqlite/searchspace.py | https://github.com/glormph/msstitch/blob/ded7e5cbd813d7797dc9d42805778266e59ff042/src/app/lookups/sqlite/searchspace.py#L39-L61 | def check_seq_exists(self, seq, amount_ntermwildcards):
"""Look up sequence in sqlite DB. Returns True or False if it
exists (or not). When looking up a reversed DB with
ntermwildcards: we reverse the sequence of the pep and add
a LIKE and %-suffix to the query.
"""
cursor = self.get_cursor()
if amount_ntermwildcards > 0:
seq = seq[::-1]
sqlseq = '{}{}'.format(seq, '%')
# FIXME non-parametrized string binding because if ? binding is
# used the INDEX is not used when looking up, apparently because
# the query cant be optimized when using LIKE and binding.
sql = ('select seqs from known_searchspace where seqs LIKE '
'"{}"'.format(sqlseq))
for match in cursor.execute(sql):
if match[0][:-amount_ntermwildcards] in seq:
return True
return False
else:
sql = ('select exists(select seqs from known_searchspace '
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return cursor.execute(sql, (seq, )).fetchone()[0] == 1 | [
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Spinmob/spinmob | egg/_temporary_fixes.py | https://github.com/Spinmob/spinmob/blob/f037f5df07f194bcd4a01f4d9916e57b9e8fb45a/egg/_temporary_fixes.py#L196-L202 | def setMaximum(self, m, update=True):
"""Set the maximum allowed value (or None for no limit)"""
if m is not None:
m = D(asUnicode(m))
self.opts['bounds'][1] = m
if update:
self.setValue() | [
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jhermann/rituals | src/rituals/util/notify.py | https://github.com/jhermann/rituals/blob/1534f50d81e19bbbe799e2eba0acdefbce047c06/src/rituals/util/notify.py#L43-L47 | def info(msg):
"""Emit a normal message."""
_flush()
sys.stdout.write(msg + '\n')
sys.stdout.flush() | [
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mohamedattahri/PyXMLi | pyxmli/__init__.py | https://github.com/mohamedattahri/PyXMLi/blob/a81a245be822d62f1a20c734ca14b42c786ae81e/pyxmli/__init__.py#L1252-L1263 | def __set_unit_price(self, value):
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Sets the unit price
@param value:str
'''
try:
if value < 0:
raise ValueError()
self.__unit_price = Decimal(str(value))
except ValueError:
raise ValueError("Unit Price must be a positive number") | [
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ev3dev/ev3dev-lang-python | ev3dev2/sensor/lego.py | https://github.com/ev3dev/ev3dev-lang-python/blob/afc98d35004b533dc161a01f7c966e78607d7c1e/ev3dev2/sensor/lego.py#L944-L950 | def sound_pressure_low(self):
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A measurement of the measured sound pressure level, as a
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self._ensure_mode(self.MODE_DBA)
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acutesoftware/AIKIF | aikif/project.py | https://github.com/acutesoftware/AIKIF/blob/fcf1582dc5f884b9a4fa7c6e20e9de9d94d21d03/aikif/project.py#L35-L40 | def get_by_name(self, name):
""" returns an object Project which matches name """
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delph-in/pydelphin | delphin/repp.py | https://github.com/delph-in/pydelphin/blob/7bd2cd63ab7cf74803e1d6547b9ebc014b382abd/delphin/repp.py#L410-L424 | def apply(self, s, active=None):
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Args:
s (str): the input string to process
active (optional): a collection of external module names
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quantopian/alphalens | alphalens/utils.py | https://github.com/quantopian/alphalens/blob/d43eac871bb061e956df936794d3dd514da99e44/alphalens/utils.py#L386-L417 | def print_table(table, name=None, fmt=None):
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Pretty print a pandas DataFrame.
Uses HTML output if running inside Jupyter Notebook, otherwise
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Parameters
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table : pd.Series or pd.DataFrame
Table to pretty-print.
name : str, optional
Table name to display in upper left corner.
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Formatter to use for displaying table elements.
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glue-viz/glue-vispy-viewers | glue_vispy_viewers/extern/vispy/visuals/transforms/base_transform.py | https://github.com/glue-viz/glue-vispy-viewers/blob/54a4351d98c1f90dfb1a557d1b447c1f57470eea/glue_vispy_viewers/extern/vispy/visuals/transforms/base_transform.py#L99-L104 | def inverse(self):
""" The inverse of this transform.
"""
if self._inverse is None:
self._inverse = InverseTransform(self)
return self._inverse | [
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SmileyChris/easy-thumbnails | easy_thumbnails/namers.py | https://github.com/SmileyChris/easy-thumbnails/blob/b08ab44883bf7b221a98dadb9b589cb95d35b0bf/easy_thumbnails/namers.py#L34-L44 | def hashed(source_filename, prepared_options, thumbnail_extension, **kwargs):
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Generate a short hashed thumbnail filename.
Creates a 12 character url-safe base64 sha1 filename (plus the extension),
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parts = ':'.join([source_filename] + prepared_options)
short_sha = hashlib.sha1(parts.encode('utf-8')).digest()
short_hash = base64.urlsafe_b64encode(short_sha[:9]).decode('utf-8')
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mitsei/dlkit | dlkit/json_/assessment/managers.py | https://github.com/mitsei/dlkit/blob/445f968a175d61c8d92c0f617a3c17dc1dc7c584/dlkit/json_/assessment/managers.py#L822-L836 | def get_item_query_session(self):
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return: (osid.assessment.ItemQuerySession) - an
``ItemQuerySession``
raise: OperationFailed - unable to complete request
raise: Unimplemented - ``supports_item_query()`` is ``false``
*compliance: optional -- This method must be implemented if
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"""
if not self.supports_item_query():
raise errors.Unimplemented()
# pylint: disable=no-member
return sessions.ItemQuerySession(runtime=self._runtime) | [
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wakatime/wakatime | wakatime/packages/pygments/scanner.py | https://github.com/wakatime/wakatime/blob/74519ace04e8472f3a3993269963732b9946a01d/wakatime/packages/pygments/scanner.py#L55-L65 | def check(self, pattern):
"""
Apply `pattern` on the current position and return
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"""
if self.eos:
raise EndOfText()
if pattern not in self._re_cache:
self._re_cache[pattern] = re.compile(pattern, self.flags)
return self._re_cache[pattern].match(self.data, self.pos) | [
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twisted/mantissa | xmantissa/people.py | https://github.com/twisted/mantissa/blob/53e5502aba23ce99be78b27f923a276593033fe8/xmantissa/people.py#L1422-L1443 | def contactInfo(self, request, tag):
"""
Render the result of calling L{IContactType.getReadOnlyView} on the
corresponding L{IContactType} for each piece of contact info
associated with L{person}. Arrange the result by group, using
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"""
groupPattern = inevow.IQ(tag).patternGenerator('contact-group')
groupedViews = self.organizer.groupReadOnlyViews(self.person)
for (groupName, views) in sorted(groupedViews.items()):
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yield views
else:
yield groupPattern().fillSlots(
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theislab/scvelo | scvelo/preprocessing/utils.py | https://github.com/theislab/scvelo/blob/c7a96d70edfe705e86bf364434a9527d4fd8df11/scvelo/preprocessing/utils.py#L99-L185 | def filter_genes(data, min_counts=None, min_cells=None, max_counts=None, max_cells=None,
min_counts_u=None, min_cells_u=None, max_counts_u=None, max_cells_u=None,
min_shared_counts=None, min_shared_cells=None, copy=False):
"""Filter genes based on number of cells or counts.
Keep genes that have at least `min_counts` counts or are expressed in at
least `min_cells` cells or have at most `max_counts` counts or are expressed
in at most `max_cells` cells.
Only provide one of the optional parameters `min_counts`, `min_cells`,
`max_counts`, `max_cells` per call.
Parameters
----------
data : :class:`~anndata.AnnData`, `np.ndarray`, `sp.spmatrix`
The (annotated) data matrix of shape `n_obs` × `n_vars`. Rows correspond
to cells and columns to genes.
min_counts : `int`, optional (default: `None`)
Minimum number of counts required for a gene to pass filtering.
min_cells : `int`, optional (default: `None`)
Minimum number of cells expressed required for a gene to pass filtering.
max_counts : `int`, optional (default: `None`)
Maximum number of counts required for a gene to pass filtering.
max_cells : `int`, optional (default: `None`)
Maximum number of cells expressed required for a gene to pass filtering.
min_counts_u : `int`, optional (default: `None`)
Minimum number of unspliced counts required for a gene to pass filtering.
min_cells_u : `int`, optional (default: `None`)
Minimum number of unspliced cells expressed required for a gene to pass filtering.
max_counts_u : `int`, optional (default: `None`)
Maximum number of unspliced counts required for a gene to pass filtering.
max_cells_u : `int`, optional (default: `None`)
Maximum number of unspliced cells expressed required for a gene to pass filtering.
min_shared_counts: `int`, optional (default: `None`)
Minimum number of counts (in cells expressed simultaneously in unspliced and spliced) required for a gene.
min_shared_cells: `int`, optional (default: `None`)
Minimum number of cells required for a gene to be expressed simultaneously in unspliced and spliced.
copy : `bool`, optional (default: `False`)
Determines whether a copy is returned.
Returns
-------
Filters the object and adds `n_counts` to `adata.var`.
"""
adata = data.copy() if copy else data
# set initial cell sizes before filtering
set_initial_size(adata)
layers = [layer for layer in ['spliced', 'unspliced'] if layer in adata.layers.keys()]
if min_shared_counts is not None or min_shared_cells is not None: layers.extend(['shared'])
for layer in layers:
if layer is 'spliced':
_min_counts, _min_cells, _max_counts, _max_cells = min_counts, min_cells, max_counts, max_cells
elif layer is 'unspliced':
_min_counts, _min_cells, _max_counts, _max_cells = min_counts_u, min_cells_u, max_counts_u, max_cells_u
else: # shared counts/cells
_min_counts, _min_cells, _max_counts, _max_cells = min_shared_counts, min_shared_cells, None, None
if layer in adata.layers.keys():
X = adata.layers[layer]
else: # shared counts/cells
Xs, Xu = adata.layers['spliced'], adata.layers['unspliced']
nonzeros = (Xs > 0).multiply(Xu > 0) if issparse(Xs) else (Xs > 0) * (Xu > 0)
X = nonzeros.multiply(Xs) + nonzeros.multiply(Xu) if issparse(nonzeros) else nonzeros * (Xs + Xu)
gene_subset = np.ones(adata.n_vars, dtype=bool)
if _min_counts is not None or _max_counts is not None:
gene_subset, _ = filter(X, min_counts=_min_counts, max_counts=_max_counts)
adata._inplace_subset_var(gene_subset)
if _min_cells is not None or _max_cells is not None:
gene_subset, _ = filter(X, min_cells=_min_cells, max_cells=_max_cells)
adata._inplace_subset_var(gene_subset)
s = np.sum(~gene_subset)
if s > 0:
logg.info('Filtered out {} genes that are detected'.format(s), end=' ')
if _min_cells is not None or _min_counts is not None:
logg.info('in less than', str(_min_cells) + ' cells (' + str(layer) + ').' if _min_counts is None
else str(_min_counts) + ' counts (' + str(layer) + ').', no_indent=True)
if max_cells is not None or max_counts is not None:
logg.info('in more than ', str(_max_cells) + ' cells(' + str(layer) + ').' if _max_counts is None
else str(_max_counts) + ' counts (' + str(layer) + ').', no_indent=True)
return adata if copy else None | [
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Keep genes that have at least `min_counts` counts or are expressed in at
least `min_cells` cells or have at most `max_counts` counts or are expressed
in at most `max_cells` cells.
Only provide one of the optional parameters `min_counts`, `min_cells`,
`max_counts`, `max_cells` per call.
Parameters
----------
data : :class:`~anndata.AnnData`, `np.ndarray`, `sp.spmatrix`
The (annotated) data matrix of shape `n_obs` × `n_vars`. Rows correspond
to cells and columns to genes.
min_counts : `int`, optional (default: `None`)
Minimum number of counts required for a gene to pass filtering.
min_cells : `int`, optional (default: `None`)
Minimum number of cells expressed required for a gene to pass filtering.
max_counts : `int`, optional (default: `None`)
Maximum number of counts required for a gene to pass filtering.
max_cells : `int`, optional (default: `None`)
Maximum number of cells expressed required for a gene to pass filtering.
min_counts_u : `int`, optional (default: `None`)
Minimum number of unspliced counts required for a gene to pass filtering.
min_cells_u : `int`, optional (default: `None`)
Minimum number of unspliced cells expressed required for a gene to pass filtering.
max_counts_u : `int`, optional (default: `None`)
Maximum number of unspliced counts required for a gene to pass filtering.
max_cells_u : `int`, optional (default: `None`)
Maximum number of unspliced cells expressed required for a gene to pass filtering.
min_shared_counts: `int`, optional (default: `None`)
Minimum number of counts (in cells expressed simultaneously in unspliced and spliced) required for a gene.
min_shared_cells: `int`, optional (default: `None`)
Minimum number of cells required for a gene to be expressed simultaneously in unspliced and spliced.
copy : `bool`, optional (default: `False`)
Determines whether a copy is returned.
Returns
-------
Filters the object and adds `n_counts` to `adata.var`. | [
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dslackw/slpkg | slpkg/slack/patches.py | https://github.com/dslackw/slpkg/blob/dd2e08a80e944d337d157b992167ba631a4343de/slpkg/slack/patches.py#L159-L185 | def dialog_checklist(self):
"""Create checklist to choose packages for upgrade
"""
data = []
for upg in self.upgrade_all:
data.append(upg[:-4])
text = "Press 'spacebar' to unchoose packages from upgrade"
title = " Upgrade "
backtitle = "{0} {1}".format(self.meta.__all__,
self.meta.__version__)
status = True
pkgs = DialogUtil(data, text, title, backtitle,
status).checklist()
index = 0
for pkg, comp, uncomp in zip(self.upgrade_all, self.comp_sum,
self.uncomp_sum):
if pkg[:-4] not in pkgs:
self.dwn_links.pop(index)
self.upgrade_all.pop(index)
self.comp_sum.pop(index)
self.uncomp_sum.pop(index)
self.count_upg -= 1
del comp, uncomp
index -= 1
index += 1
if not self.upgrade_all:
raise SystemExit() | [
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] | python | train |
alex-kostirin/pyatomac | atomac/ldtpd/value.py | https://github.com/alex-kostirin/pyatomac/blob/3f46f6feb4504315eec07abb18bb41be4d257aeb/atomac/ldtpd/value.py#L146-L162 | def scrollleft(self, window_name, object_name):
"""
Scroll left
@param window_name: Window name to type in, either full name,
LDTP's name convention, or a Unix glob.
@type window_name: string
@param object_name: Object name to type in, either full name,
LDTP's name convention, or a Unix glob.
@type object_name: string
@return: 1 on success.
@rtype: integer
"""
if not self.verifyscrollbarhorizontal(window_name, object_name):
raise LdtpServerException('Object not horizontal scrollbar')
return self.setmin(window_name, object_name) | [
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@param window_name: Window name to type in, either full name,
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@type window_name: string
@param object_name: Object name to type in, either full name,
LDTP's name convention, or a Unix glob.
@type object_name: string
@return: 1 on success.
@rtype: integer | [
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google/openhtf | openhtf/plugs/usb/adb_protocol.py | https://github.com/google/openhtf/blob/655e85df7134db7bdf8f8fdd6ff9a6bf932e7b09/openhtf/plugs/usb/adb_protocol.py#L277-L285 | def _set_or_check_remote_id(self, remote_id):
"""Set or check the remote id."""
if not self.remote_id:
assert self.closed_state == self.ClosedState.PENDING, 'Bad ClosedState!'
self.remote_id = remote_id
self.closed_state = self.ClosedState.OPEN
elif self.remote_id != remote_id:
raise usb_exceptions.AdbProtocolError(
'%s remote-id change to %s', self, remote_id) | [
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Subsets and Splits