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rmax/scrapydo | scrapydo/api.py | https://github.com/rmax/scrapydo/blob/b0f9e6d50a5ea9d2ba8335bffa877003109c3af5/scrapydo/api.py#L130-L153 | def _fetch_in_reactor(url, spider_cls=DefaultSpider, **kwargs):
"""Fetches an URL and returns the response.
Parameters
----------
url : str
An URL to fetch.
spider_cls : scrapy.Spider (default: DefaultSpider)
A spider class to be used in the crawler.
kwargs : dict, optional
Additional arguments to be passed to ``_run_spider_in_reactor``.
Returns
-------
crochet.EventualResult
"""
def parse(self, response):
self.response = response
req = Request(url) if isinstance(url, six.string_types) else url
req.dont_filter = True
req.meta['handle_httpstatus_all'] = True
spider_cls = override_start_requests(spider_cls, [req], parse=parse)
return _run_spider_in_reactor(spider_cls, **kwargs) | [
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| python | train |
neuropsychology/NeuroKit.py | neurokit/statistics/statistics.py | https://github.com/neuropsychology/NeuroKit.py/blob/c9589348fbbde0fa7e986048c48f38e6b488adfe/neurokit/statistics/statistics.py#L125-L165 | def find_outliers(data, treshold=2.58):
"""
Identify outliers (abnormal values) using the standart deviation.
Parameters
----------
data : list or ndarray
Data array
treshold : float
Maximum deviation (in terms of standart deviation). Rule of thumb of a gaussian distribution: 2.58 = rejecting 1%, 2.33 = rejecting 2%, 1.96 = 5% and 1.28 = rejecting 10%.
Returns
----------
outliers : ndarray
A list of True/False with True being the outliers.
Example
----------
>>> import neurokit as nk
>>> outliers = nk.find_outliers([1, 2, 1, 5, 666, 4, 1 ,3, 5])
Notes
----------
*Authors*
- `Dominique Makowski <https://dominiquemakowski.github.io/>`_
*Dependencies*
- numpy
"""
outliers = []
mean = np.mean(data)
std = np.std(data)
for i in data:
if abs(i - mean)/std < treshold:
outliers.append(False)
else:
outliers.append(True)
outliers = np.array(outliers)
return (outliers) | [
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----------
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| python | train |
chaoss/grimoirelab-perceval-mozilla | perceval/backends/mozilla/crates.py | https://github.com/chaoss/grimoirelab-perceval-mozilla/blob/4514f8d3d609d3cb79d83c72d51fcc4b4a7daeb4/perceval/backends/mozilla/crates.py#L91-L104 | def fetch_items(self, category, **kwargs):
"""Fetch packages and summary from Crates.io
:param category: the category of items to fetch
:param kwargs: backend arguments
:returns: a generator of items
"""
from_date = kwargs['from_date']
if category == CATEGORY_CRATES:
return self.__fetch_crates(from_date)
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| python | test |
Workiva/furious | furious/extras/appengine/ndb_persistence.py | https://github.com/Workiva/furious/blob/c29823ec8b98549e7439d7273aa064d1e5830632/furious/extras/appengine/ndb_persistence.py#L297-L307 | def store_async_result(async_id, async_result):
"""Persist the Async's result to the datastore."""
logging.debug("Storing result for %s", async_id)
key = FuriousAsyncMarker(
id=async_id, result=json.dumps(async_result.to_dict()),
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logging.debug("Setting Async result %s using marker: %s.", async_result,
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| python | train |
mattja/nsim | nsim/analyses1/freq.py | https://github.com/mattja/nsim/blob/ed62c41cd56b918fd97e09f7ad73c12c76a8c3e0/nsim/analyses1/freq.py#L207-L210 | def hilbert_phase(ts):
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output = np.angle(signal.hilbert(signal.detrend(ts, axis=0), axis=0))
return Timeseries(output, ts.tspan, labels=ts.labels) | [
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| python | train |
rbarrois/restricted_pkg | restricted_pkg/base.py | https://github.com/rbarrois/restricted_pkg/blob/abbd3cb33ed85af02fbb531fd85dda9c1b070c85/restricted_pkg/base.py#L78-L91 | def full_url(self):
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| python | train |
elastic/elasticsearch-py | elasticsearch/client/indices.py | https://github.com/elastic/elasticsearch-py/blob/2aab285c8f506f3863cbdaba3c90a685c510ba00/elasticsearch/client/indices.py#L988-L1015 | def shrink(self, index, target, body=None, params=None):
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index with fewer primary shards. The number of primary shards in the
target index must be a factor of the shards in the source index. For
example an index with 8 primary shards can be shrunk into 4, 2 or 1
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:arg master_timeout: Specify timeout for connection to master
:arg request_timeout: Explicit operation timeout
:arg wait_for_active_shards: Set the number of active shards to wait for
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"""
for param in (index, target):
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raise ValueError("Empty value passed for a required argument.")
return self.transport.perform_request(
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| python | train |
saltstack/salt | salt/modules/inspectlib/query.py | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/inspectlib/query.py#L195-L211 | def _configuration(self, *args, **kwargs):
'''
Return configuration files.
'''
data = dict()
self.db.open()
for pkg in self.db.get(Package):
configs = list()
for pkg_cfg in self.db.get(PackageCfgFile, eq={'pkgid': pkg.id}):
configs.append(pkg_cfg.path)
data[pkg.name] = configs
if not data:
raise InspectorQueryException("No inspected configuration yet available.")
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| python | train |
pygeobuf/pygeobuf | geobuf/scripts/cli.py | https://github.com/pygeobuf/pygeobuf/blob/c9e055ab47532781626cfe2c931a8444820acf05/geobuf/scripts/cli.py#L65-L78 | def decode():
"""Given a Geobuf byte string on stdin, write a GeoJSON feature
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logger = logging.getLogger('geobuf')
stdin = click.get_binary_stream('stdin')
sink = click.get_text_stream('stdout')
try:
pbf = stdin.read()
data = geobuf.decode(pbf)
json.dump(data, sink)
sys.exit(0)
except Exception:
logger.exception("Failed. Exception caught")
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| python | train |
HewlettPackard/python-hpOneView | hpOneView/resources/servers/server_profiles.py | https://github.com/HewlettPackard/python-hpOneView/blob/3c6219723ef25e6e0c83d44a89007f89bc325b89/hpOneView/resources/servers/server_profiles.py#L302-L320 | def get_available_targets(self, **kwargs):
"""
Retrieves a list of the target servers and empty device bays that are available for assignment to the server
profile.
Args:
enclosureGroupUri (str): The URI of the enclosure group associated with the resource.
serverHardwareTypeUri (str): The URI of the server hardware type associated with the resource.
profileUri (str): The URI of the server profile associated with the resource.
scopeUris (str): An expression to restrict the resources returned according to
the scopes to which they are assigned.
filter (list or str): A general filter/query string to narrow the list of items returned.
The default is no filter, all resources are returned.
Returns:
list: List of available servers and bays.
"""
uri = self._helper.build_uri_with_query_string(kwargs, '/available-targets')
return self._helper.do_get(uri) | [
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| python | train |
kyuupichan/aiorpcX | aiorpcx/jsonrpc.py | https://github.com/kyuupichan/aiorpcX/blob/707c989ed1c67ac9a40cd20b0161b1ce1f4d7db0/aiorpcx/jsonrpc.py#L330-L336 | def encode_payload(cls, payload):
'''Encode a Python object as JSON and convert it to bytes.'''
try:
return json.dumps(payload).encode()
except TypeError:
msg = f'JSON payload encoding error: {payload}'
raise ProtocolError(cls.INTERNAL_ERROR, msg) from None | [
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| python | train |
BlueBrain/nat | nat/zotero_wrap.py | https://github.com/BlueBrain/nat/blob/0934f06e48e6efedf55a9617b15becae0d7b277c/nat/zotero_wrap.py#L70-L79 | def create_distant_reference(self, ref_data):
"""Validate and create the reference in Zotero and return the created item."""
self.validate_reference_data(ref_data)
creation_status = self._zotero_lib.create_items([ref_data])
try:
created_item = creation_status["successful"]["0"]
return created_item
except KeyError as e:
print(creation_status)
raise CreateZoteroItemError from e | [
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| python | train |
pydata/xarray | xarray/core/formatting.py | https://github.com/pydata/xarray/blob/6d93a95d05bdbfc33fff24064f67d29dd891ab58/xarray/core/formatting.py#L139-L150 | def format_item(x, timedelta_format=None, quote_strings=True):
"""Returns a succinct summary of an object as a string"""
if isinstance(x, (np.datetime64, datetime)):
return format_timestamp(x)
if isinstance(x, (np.timedelta64, timedelta)):
return format_timedelta(x, timedelta_format=timedelta_format)
elif isinstance(x, (str, bytes)):
return repr(x) if quote_strings else x
elif isinstance(x, (float, np.float)):
return '{0:.4}'.format(x)
else:
return str(x) | [
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| python | train |
bcbio/bcbio-nextgen | bcbio/illumina/demultiplex.py | https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/illumina/demultiplex.py#L11-L30 | def run_bcl2fastq(run_folder, ss_csv, config):
"""Run bcl2fastq for de-multiplexing and fastq generation.
run_folder -- directory of Illumina outputs
ss_csv -- Samplesheet CSV file describing samples.
"""
bc_dir = os.path.join(run_folder, "Data", "Intensities", "BaseCalls")
output_dir = os.path.join(run_folder, "fastq")
if not os.path.exists(os.path.join(output_dir, "Makefile")):
subprocess.check_call(["configureBclToFastq.pl", "--no-eamss",
"--input-dir", bc_dir, "--output-dir", output_dir,
"--sample-sheet", ss_csv])
with utils.chdir(output_dir):
cores = str(utils.get_in(config, ("algorithm", "num_cores"), 1))
cmd = ["make", "-j", cores]
if "submit_cmd" in config["process"] and "bcl2fastq_batch" in config["process"]:
_submit_and_wait(cmd, cores, config, output_dir)
else:
subprocess.check_call(cmd)
return output_dir | [
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run_folder -- directory of Illumina outputs
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| python | train |
pmneila/morphsnakes | morphsnakes_v1.py | https://github.com/pmneila/morphsnakes/blob/aab66e70f86308d7b1927d76869a1a562120f849/morphsnakes_v1.py#L74-L91 | def operator_si(u):
"""operator_si operator."""
global _aux
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P = _P2
elif np.ndim(u) == 3:
P = _P3
else:
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_aux = np.zeros((len(P),) + u.shape)
for _aux_i, P_i in zip(_aux, P):
_aux_i[:] = binary_erosion(u, P_i)
return _aux.max(0) | [
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| python | train |
rflamary/POT | ot/dr.py | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/dr.py#L25-L35 | def sinkhorn(w1, w2, M, reg, k):
"""Sinkhorn algorithm with fixed number of iteration (autograd)
"""
K = np.exp(-M / reg)
ui = np.ones((M.shape[0],))
vi = np.ones((M.shape[1],))
for i in range(k):
vi = w2 / (np.dot(K.T, ui))
ui = w1 / (np.dot(K, vi))
G = ui.reshape((M.shape[0], 1)) * K * vi.reshape((1, M.shape[1]))
return G | [
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| python | train |
hitchtest/hitchserve | hitchserve/service_bundle.py | https://github.com/hitchtest/hitchserve/blob/a2def19979264186d283e76f7f0c88f3ed97f2e0/hitchserve/service_bundle.py#L261-L285 | def time_travel(self, datetime=None, timedelta=None, seconds=0, minutes=0, hours=0, days=0):
"""Mock moving forward or backward in time by shifting the system clock fed to the services tested.
Note that all of these arguments can be used together, individually or not at all. The time
traveled to will be the sum of all specified time deltas from datetime. If no datetime is specified,
the deltas will be added to the current time.
Args:
datetime (Optional[datetime]): Time travel to specific datetime.
timedelta (Optional[timedelta]): Time travel to 'timedelta' from now.
seconds (Optional[number]): Time travel 'seconds' seconds from now.
minutes (Optional[number]): Time travel 'minutes' minutes from now.
hours (Optional[number]): Time travel 'hours' hours from now.
days (Optional[number]): Time travel 'days' days from now.
"""
if datetime is not None:
self.timedelta = datetime - python_datetime.now()
if timedelta is not None:
self.timedelta = self.timedelta + timedelta
self.timedelta = self.timedelta + python_timedelta(seconds=seconds)
self.timedelta = self.timedelta + python_timedelta(minutes=minutes)
self.timedelta = self.timedelta + python_timedelta(hours=hours)
self.timedelta = self.timedelta + python_timedelta(days=days)
log("Time traveling to {}\n".format(humanize.naturaltime(self.now())))
faketime.change_time(self.hitch_dir.faketime(), self.now()) | [
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seconds (Optional[number]): Time travel 'seconds' seconds from now.
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| python | train |
CTPUG/wafer | wafer/registration/sso.py | https://github.com/CTPUG/wafer/blob/a20af3c399267f76373dc342f4d542a9bc457c35/wafer/registration/sso.py#L24-L41 | def sso(user, desired_username, name, email, profile_fields=None):
"""
Create a user, if the provided `user` is None, from the parameters.
Then log the user in, and return it.
"""
if not user:
if not settings.REGISTRATION_OPEN:
raise SSOError('Account registration is closed')
user = _create_desired_user(desired_username)
_configure_user(user, name, email, profile_fields)
if not user.is_active:
raise SSOError('Account disabled')
# login() expects the logging in backend to be set on the user.
# We are bypassing login, so fake it.
user.backend = settings.AUTHENTICATION_BACKENDS[0]
return user | [
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| python | train |
dh1tw/pyhamtools | pyhamtools/locator.py | https://github.com/dh1tw/pyhamtools/blob/ee7e4b8732e23c298da10e07163748156c16d0fa/pyhamtools/locator.py#L128-L167 | def calculate_distance(locator1, locator2):
"""calculates the (shortpath) distance between two Maidenhead locators
Args:
locator1 (string): Locator, either 4 or 6 characters
locator2 (string): Locator, either 4 or 6 characters
Returns:
float: Distance in km
Raises:
ValueError: When called with wrong or invalid input arg
AttributeError: When args are not a string
Example:
The following calculates the distance between two Maidenhead locators in km
>>> from pyhamtools.locator import calculate_distance
>>> calculate_distance("JN48QM", "QF67bf")
16466.413
"""
R = 6371 #earh radius
lat1, long1 = locator_to_latlong(locator1)
lat2, long2 = locator_to_latlong(locator2)
d_lat = radians(lat2) - radians(lat1)
d_long = radians(long2) - radians(long1)
r_lat1 = radians(lat1)
r_long1 = radians(long1)
r_lat2 = radians(lat2)
r_long2 = radians(long2)
a = sin(d_lat/2) * sin(d_lat/2) + cos(r_lat1) * cos(r_lat2) * sin(d_long/2) * sin(d_long/2)
c = 2 * atan2(sqrt(a), sqrt(1-a))
d = R * c #distance in km
return d; | [
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| calculates the (shortpath) distance between two Maidenhead locators
Args:
locator1 (string): Locator, either 4 or 6 characters
locator2 (string): Locator, either 4 or 6 characters
Returns:
float: Distance in km
Raises:
ValueError: When called with wrong or invalid input arg
AttributeError: When args are not a string
Example:
The following calculates the distance between two Maidenhead locators in km
>>> from pyhamtools.locator import calculate_distance
>>> calculate_distance("JN48QM", "QF67bf")
16466.413 | [
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| python | train |
Cue/scales | src/greplin/scales/__init__.py | https://github.com/Cue/scales/blob/0aced26eb050ceb98ee9d5d6cdca8db448666986/src/greplin/scales/__init__.py#L415-L418 | def update(self, instance, oldValue, newValue):
"""Updates the aggregate based on a change in the child value."""
self.__set__(instance,
self.__get__(instance, None) + newValue - (oldValue or 0)) | [
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| python | train |
inspirehep/inspire-dojson | inspire_dojson/cds/rules.py | https://github.com/inspirehep/inspire-dojson/blob/17f3789cd3d5ae58efa1190dc0eea9efb9c8ca59/inspire_dojson/cds/rules.py#L400-L453 | def urls(self, key, value):
"""Populate the ``8564`` MARC field.
Also populate the ``FFT`` field through side effects.
"""
def _is_preprint(value):
return value.get('y', '').lower() == 'preprint'
def _is_fulltext(value):
return value['u'].endswith('.pdf') and value['u'].startswith('http://cds.cern.ch')
def _is_local_copy(value):
return 'local copy' in value.get('y', '')
def _is_ignored_domain(value):
ignored_domains = ['http://cdsweb.cern.ch', 'http://cms.cern.ch',
'http://cmsdoc.cern.ch', 'http://documents.cern.ch',
'http://preprints.cern.ch', 'http://cds.cern.ch',
'http://arxiv.org']
return any(value['u'].startswith(domain) for domain in ignored_domains)
field_8564 = self.get('8564_', [])
field_FFT = self.get('FFT__', [])
if 'u' not in value:
return field_8564
url = escape_url(value['u'])
if _is_fulltext(value) and not _is_preprint(value):
if _is_local_copy(value):
description = value.get('y', '').replace('local copy', 'on CERN Document Server')
field_8564.append({
'u': url,
'y': description,
})
else:
_, file_name = os.path.split(urllib.parse.urlparse(value['u']).path)
_, extension = os.path.splitext(file_name)
field_FFT.append({
't': 'CDS',
'a': url,
'd': value.get('y', ''),
'n': file_name,
'f': extension,
})
elif not _is_ignored_domain(value):
field_8564.append({
'u': url,
'y': value.get('y'),
})
self['FFT__'] = field_FFT
return field_8564 | [
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Also populate the ``FFT`` field through side effects. | [
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| python | train |
coderholic/pyradio | pyradio/player.py | https://github.com/coderholic/pyradio/blob/c5219d350bccbccd49dbd627c1f886a952ea1963/pyradio/player.py#L285-L319 | def play(self, name, streamUrl, encoding = ''):
""" use a multimedia player to play a stream """
self.close()
self.name = name
self.oldUserInput = {'Input': '', 'Volume': '', 'Title': ''}
self.muted = False
self.show_volume = True
self.title_prefix = ''
self.playback_is_on = False
self.outputStream.write('Station: "{}"'.format(name), self.status_update_lock)
if logger.isEnabledFor(logging.INFO):
logger.info('Selected Station: "{}"'.format(name))
if encoding:
self._station_encoding = encoding
else:
self._station_encoding = 'utf-8'
opts = []
isPlayList = streamUrl.split("?")[0][-3:] in ['m3u', 'pls']
opts = self._buildStartOpts(streamUrl, isPlayList)
self.process = subprocess.Popen(opts, shell=False,
stdout=subprocess.PIPE,
stdin=subprocess.PIPE,
stderr=subprocess.STDOUT)
t = threading.Thread(target=self.updateStatus, args=(self.status_update_lock, ))
t.start()
# start playback check timer thread
try:
self.connection_timeout_thread = threading.Timer(self.playback_timeout, self.playback_timeout_handler)
self.connection_timeout_thread.start()
except:
self.connection_timeout_thread = None
if (logger.isEnabledFor(logging.ERROR)):
logger.error("playback detection thread start failed")
if logger.isEnabledFor(logging.INFO):
logger.info("Player started") | [
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| python | train |
the01/python-paps | paps/si/app/message.py | https://github.com/the01/python-paps/blob/2dde5a71913e4c7b22901cf05c6ecedd890919c4/paps/si/app/message.py#L362-L401 | def unpack(cls, data):
"""
Unpack packed data into an instance
:param data: Packed data
:type data: str
:return: Object instance and remaining data
:rtype: (APPHeader, str)
"""
size = struct.calcsize(APPHeader.fmt_header)
(
version, msg_type, payload_len,
timestamp, device_id, flags,
), payload = struct.unpack(
APPHeader.fmt_header, data[:size]
), data[size:]
ack_sequence_number = None
sequence_number = None
if flags & Flag.ACKSEQ:
size = struct.calcsize(APPHeader.fmt_seq_ack)
(ack_sequence_number,), payload = struct.unpack(
APPHeader.fmt_seq_ack, payload[:size]
), payload[size:]
if flags & Flag.SEQ:
size = struct.calcsize(APPHeader.fmt_seq)
(sequence_number,), payload = struct.unpack(
APPHeader.fmt_seq, payload[:size]
), payload[size:]
return cls(
message_type=msg_type,
version_major=version >> 4,
version_minor=version & 0xf,
payload_length=payload_len,
device_id=device_id,
sequence_number=sequence_number,
flags=flags,
timestamp=timestamp,
ack_sequence_number=ack_sequence_number
), payload | [
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| python | train |
greyli/flask-dropzone | flask_dropzone/__init__.py | https://github.com/greyli/flask-dropzone/blob/eb1d5ef16d8f83a12e6fed1bb9412a0c12c6d584/flask_dropzone/__init__.py#L29-L134 | def load(js_url='', css_url='', version='5.2.0'):
"""Load Dropzone resources with given version and init dropzone configuration.
.. versionchanged:: 1.4.3
Added ``js_url`` and ``css_url`` parameters to pass custom resource URL.
.. versionchanged:: 1.4.4
This method was deprecated due to inflexible. Now it's divided into three methods:
1. Use ``load_css()`` to load css resources.
2. Use ``load_js()`` to load js resources.
3. Use ``config()`` to configure Dropzone.
:param js_url: The JavaScript url for Dropzone.js.
:param css_url: The CSS url for Dropzone.js.
:param version: The version of Dropzone.js.
"""
warnings.warn('The method will be removed in 2.0, see docs for more details.')
js_filename = 'dropzone.min.js'
css_filename = 'dropzone.min.css'
upload_multiple = current_app.config['DROPZONE_UPLOAD_MULTIPLE']
parallel_uploads = current_app.config['DROPZONE_PARALLEL_UPLOADS']
if upload_multiple in [True, 'true', 'True', 1]:
upload_multiple = 'true'
else:
upload_multiple = 'false'
serve_local = current_app.config['DROPZONE_SERVE_LOCAL']
size = current_app.config['DROPZONE_MAX_FILE_SIZE']
param = current_app.config['DROPZONE_INPUT_NAME']
redirect_view = current_app.config['DROPZONE_REDIRECT_VIEW']
if redirect_view is not None:
redirect_js = '''
this.on("queuecomplete", function(file) {
// Called when all files in the queue finish uploading.
window.location = "%s";
});''' % url_for(redirect_view)
else:
redirect_js = ''
if not current_app.config['DROPZONE_ALLOWED_FILE_CUSTOM']:
allowed_type = allowed_file_extensions[
current_app.config['DROPZONE_ALLOWED_FILE_TYPE']]
else:
allowed_type = current_app.config['DROPZONE_ALLOWED_FILE_TYPE']
max_files = current_app.config['DROPZONE_MAX_FILES']
default_message = current_app.config['DROPZONE_DEFAULT_MESSAGE']
invalid_file_type = current_app.config['DROPZONE_INVALID_FILE_TYPE']
file_too_big = current_app.config['DROPZONE_FILE_TOO_BIG']
server_error = current_app.config['DROPZONE_SERVER_ERROR']
browser_unsupported = current_app.config['DROPZONE_BROWSER_UNSUPPORTED']
max_files_exceeded = current_app.config['DROPZONE_MAX_FILE_EXCEED']
cancelUpload = current_app.config['DROPZONE_CANCEL_UPLOAD']
removeFile = current_app.config['DROPZONE_REMOVE_FILE']
cancelConfirmation = current_app.config['DROPZONE_CANCEL_CONFIRMATION']
uploadCanceled = current_app.config['DROPZONE_UPLOAD_CANCELED']
timeout = current_app.config['DROPZONE_TIMEOUT']
if timeout:
timeout_js = 'timeout: %d,' % timeout
else:
timeout_js = ''
if serve_local:
js = '<script src="%s"></script>\n' % url_for('dropzone.static', filename=js_filename)
css = '<link rel="stylesheet" href="%s" type="text/css">\n' % \
url_for('dropzone.static', filename=css_filename)
else:
js = '<script src="https://cdn.jsdelivr.net/npm/dropzone@%s/dist/%s"></script>\n' % (version, js_filename)
css = '<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/dropzone@%s/dist/min/%s"' \
' type="text/css">\n' % (version, css_filename)
if js_url:
js = '<script src="%s"></script>\n' % js_url
if css_url:
css = '<link rel="stylesheet" href="%s" type="text/css">\n' % css_url
return Markup('''
%s%s<script>
Dropzone.options.myDropzone = {
init: function() {%s},
uploadMultiple: %s,
parallelUploads: %d,
paramName: "%s", // The name that will be used to transfer the file
maxFilesize: %d, // MB
acceptedFiles: "%s",
maxFiles: %s,
dictDefaultMessage: "%s", // message display on drop area
dictFallbackMessage: "%s",
dictInvalidFileType: "%s",
dictFileTooBig: "%s",
dictResponseError: "%s",
dictMaxFilesExceeded: "%s",
dictCancelUpload: "%s",
dictRemoveFile: "%s",
dictCancelUploadConfirmation: "%s",
dictUploadCanceled: "%s",
%s // timeout
};
</script>
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default_message, browser_unsupported, invalid_file_type, file_too_big, server_error,
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1. Use ``load_css()`` to load css resources.
2. Use ``load_js()`` to load js resources.
3. Use ``config()`` to configure Dropzone.
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:param css_url: The CSS url for Dropzone.js.
:param version: The version of Dropzone.js. | [
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| python | train |
CivicSpleen/ambry | ambry/orm/column.py | https://github.com/CivicSpleen/ambry/blob/d7f2be4bf1f7ffd086f3fadd4fcae60c32473e42/ambry/orm/column.py#L444-L457 | def mangle_name(name):
"""Mangles a column name to a standard form, remoing illegal
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:param name:
:return:
"""
import re
try:
return re.sub('_+', '_', re.sub('[^\w_]', '_', name).lower()).rstrip('_')
except TypeError:
raise TypeError(
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| python | train |
lawsie/guizero | guizero/tkmixins.py | https://github.com/lawsie/guizero/blob/84c7f0b314fa86f9fc88eb11c9a0f6c4b57155e2/guizero/tkmixins.py#L8-L11 | def after(self, time, function, args = []):
"""Call `function` after `time` milliseconds."""
callback_id = self.tk.after(time, self._call_wrapper, time, function, *args)
self._callback[function] = [callback_id, False] | [
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| python | train |
Loudr/pale | pale/doc.py | https://github.com/Loudr/pale/blob/dc002ee6032c856551143af222ff8f71ed9853fe/pale/doc.py#L818-L833 | def document_endpoint(endpoint):
"""Extract the full documentation dictionary from the endpoint."""
descr = clean_description(py_doc_trim(endpoint.__doc__))
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'name': endpoint._route_name,
'http_method': endpoint._http_method,
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if hasattr(endpoint, "_success"):
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| python | train |
VingtCinq/python-mailchimp | mailchimp3/entities/reportunsubscribes.py | https://github.com/VingtCinq/python-mailchimp/blob/1b472f1b64fdde974732ac4b7ed48908bb707260/mailchimp3/entities/reportunsubscribes.py#L25-L45 | def all(self, campaign_id, get_all=False, **queryparams):
"""
Get information about members who have unsubscribed from a specific
campaign.
:param campaign_id: The unique id for the campaign.
:type campaign_id: :py:class:`str`
:param get_all: Should the query get all results
:type get_all: :py:class:`bool`
:param queryparams: The query string parameters
queryparams['fields'] = []
queryparams['exclude_fields'] = []
queryparams['count'] = integer
queryparams['offset'] = integer
"""
self.campaign_id = campaign_id
self.subscriber_hash = None
if get_all:
return self._iterate(url=self._build_path(campaign_id, 'unsubscribed'), **queryparams)
else:
return self._mc_client._get(url=self._build_path(campaign_id, 'unsubscribed'), **queryparams) | [
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| python | valid |
FNNDSC/med2image | med2image/systemMisc.py | https://github.com/FNNDSC/med2image/blob/638d5d230de47608af20f9764acf8e382c2bf2ff/med2image/systemMisc.py#L183-L215 | def com_find2D(ar_grid, **kwargs):
"""
ARGS
**kwargs
ordering = 'rc' or 'xy' order the return either in (x,y)
or (row, col) order.
indexing = 'zero' or 'one' return positions relative to zero (i.e.
python addressing) or one (i.e. MatLAB
addressing)
DESC
Find the center of mass in 2D array grid <ar_grid>. Mass elements
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By using python idioms, his version is MUCH faster than the com_find()
"""
b_reorder = True
b_oneOffset = True
for key, value in kwargs.iteritems():
if key == 'ordering' and value == 'rc': b_reorder = False
if key == 'ordering' and value == 'xy': b_reorder = True
if key == 'indexing' and value == 'zero': b_oneOffset = False
if key == 'indexing' and value == 'one': b_oneOffset = True
f_Smass = ar_grid.sum()
f_comX = (ar_grid[nonzero(ar_grid)] * (nonzero(ar_grid)[1] + 1)).sum() / f_Smass
f_comY = (ar_grid[nonzero(ar_grid)] * (nonzero(ar_grid)[0] + 1)).sum() / f_Smass
if b_reorder: ar_ret = array( (f_comX, f_comY) )
if not b_reorder: ar_ret = array( (f_comY, f_comX) )
if not b_oneOffset: ar_ret -= 1.0
return ar_ret | [
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| python | train |
bcbio/bcbio-nextgen | bcbio/variation/vcfutils.py | https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L383-L400 | def _run_concat_variant_files_gatk4(input_file_list, out_file, config):
"""Use GATK4 GatherVcfs for concatenation of scattered VCFs.
"""
if not utils.file_exists(out_file):
with file_transaction(config, out_file) as tx_out_file:
params = ["-T", "GatherVcfs", "-I", input_file_list, "-O", tx_out_file]
# Use GATK4 for merging, tools_off: [gatk4] applies to variant calling
config = utils.deepish_copy(config)
if "gatk4" in dd.get_tools_off({"config": config}):
config["algorithm"]["tools_off"].remove("gatk4")
# Allow specification of verbosity in the unique style this tool uses
resources = config_utils.get_resources("gatk", config)
opts = [str(x) for x in resources.get("options", [])]
if "--verbosity" in opts:
params += ["--VERBOSITY:%s" % opts[opts.index("--verbosity") + 1]]
broad_runner = broad.runner_from_config(config)
broad_runner.run_gatk(params)
return out_file | [
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| python | train |
saltstack/salt | salt/modules/boto_kinesis.py | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/boto_kinesis.py#L124-L153 | def get_stream_when_active(stream_name, region=None, key=None, keyid=None, profile=None):
'''
Get complete stream info from AWS, returning only when the stream is in the ACTIVE state.
Continues to retry when stream is updating or creating.
If the stream is deleted during retries, the loop will catch the error and break.
CLI example::
salt myminion boto_kinesis.get_stream_when_active my_stream region=us-east-1
'''
conn = _get_conn(region=region, key=key, keyid=keyid, profile=profile)
stream_status = None
# only get basic stream until it's active,
# so we don't pull the full list of shards repeatedly (in case of very large stream)
attempt = 0
max_retry_delay = 10
while stream_status != "ACTIVE":
time.sleep(_jittered_backoff(attempt, max_retry_delay))
attempt += 1
stream_response = _get_basic_stream(stream_name, conn)
if 'error' in stream_response:
return stream_response
stream_status = stream_response['result']["StreamDescription"]["StreamStatus"]
# now it's active, get the full stream if necessary
if stream_response['result']["StreamDescription"]["HasMoreShards"]:
stream_response = _get_full_stream(stream_name, region, key, keyid, profile)
return stream_response | [
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CLI example::
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| python | train |
litl/rauth | rauth/oauth.py | https://github.com/litl/rauth/blob/a6d887d7737cf21ec896a8104f25c2754c694011/rauth/oauth.py#L115-L155 | def sign(self,
consumer_secret,
access_token_secret,
method,
url,
oauth_params,
req_kwargs):
'''Sign request parameters.
:param consumer_secret: Consumer secret.
:type consumer_secret: str
:param access_token_secret: Access token secret.
:type access_token_secret: str
:param method: The method of this particular request.
:type method: str
:param url: The URL of this particular request.
:type url: str
:param oauth_params: OAuth parameters.
:type oauth_params: dict
:param req_kwargs: Keyworded args that will be sent to the request
method.
:type req_kwargs: dict
'''
url = self._remove_qs(url)
oauth_params = \
self._normalize_request_parameters(oauth_params, req_kwargs)
parameters = map(self._escape, [method, url, oauth_params])
key = self._escape(consumer_secret) + b'&'
if access_token_secret is not None:
key += self._escape(access_token_secret)
# build a Signature Base String
signature_base_string = b'&'.join(parameters)
# hash the string with HMAC-SHA1
hashed = hmac.new(key, signature_base_string, sha1)
# return the signature
return base64.b64encode(hashed.digest()).decode() | [
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:type method: str
:param url: The URL of this particular request.
:type url: str
:param oauth_params: OAuth parameters.
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| python | train |
JdeRobot/base | src/drivers/MAVLinkServer/MAVProxy/modules/mavproxy_map/__init__.py | https://github.com/JdeRobot/base/blob/303b18992785b2fe802212f2d758a60873007f1f/src/drivers/MAVLinkServer/MAVProxy/modules/mavproxy_map/__init__.py#L182-L189 | def remove_rally(self, key):
'''remove a rally point'''
a = key.split(' ')
if a[0] != 'Rally' or len(a) != 2:
print("Bad rally object %s" % key)
return
i = int(a[1])
self.mpstate.functions.process_stdin('rally remove %u' % i) | [
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| python | train |
googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/trainer/util.py | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/structured_data/mltoolbox/_structured_data/trainer/util.py#L367-L445 | def get_estimator(output_dir, train_config, args):
"""Returns a tf learn estimator.
We only support {DNN, Linear}Regressor and {DNN, Linear}Classifier. This is
controlled by the values of model_type in the args.
Args:
output_dir: Modes are saved into outputdir/train
train_config: our training config
args: command line parameters
Returns:
TF lean estimator
Raises:
ValueError: if config is wrong.
"""
# Check the requested mode fits the preprocessed data.
target_name = train_config['target_column']
if is_classification_model(args.model_type) and target_name not in \
train_config['categorical_columns']:
raise ValueError('When using a classification model, the target must be a '
'categorical variable.')
if is_regression_model(args.model_type) and target_name not in \
train_config['numerical_columns']:
raise ValueError('When using a regression model, the target must be a '
'numerical variable.')
# Check layers used for dnn models.
if is_dnn_model(args.model_type) and not args.layer_sizes:
raise ValueError('--layer-size* must be used with DNN models')
if is_linear_model(args.model_type) and args.layer_sizes:
raise ValueError('--layer-size* cannot be used with linear models')
# Build tf.learn features
feature_columns = _tflearn_features(train_config, args)
# Set how often to run checkpointing in terms of time.
config = tf.contrib.learn.RunConfig(
save_checkpoints_secs=args.save_checkpoints_secs)
train_dir = os.path.join(output_dir, 'train')
if args.model_type == 'dnn_regression':
estimator = tf.contrib.learn.DNNRegressor(
feature_columns=feature_columns,
hidden_units=args.layer_sizes,
config=config,
model_dir=train_dir,
optimizer=tf.train.AdamOptimizer(
args.learning_rate, epsilon=args.epsilon))
elif args.model_type == 'linear_regression':
estimator = tf.contrib.learn.LinearRegressor(
feature_columns=feature_columns,
config=config,
model_dir=train_dir,
optimizer=tf.train.AdamOptimizer(
args.learning_rate, epsilon=args.epsilon))
elif args.model_type == 'dnn_classification':
estimator = tf.contrib.learn.DNNClassifier(
feature_columns=feature_columns,
hidden_units=args.layer_sizes,
n_classes=train_config['vocab_stats'][target_name]['n_classes'],
config=config,
model_dir=train_dir,
optimizer=tf.train.AdamOptimizer(
args.learning_rate, epsilon=args.epsilon))
elif args.model_type == 'linear_classification':
estimator = tf.contrib.learn.LinearClassifier(
feature_columns=feature_columns,
n_classes=train_config['vocab_stats'][target_name]['n_classes'],
config=config,
model_dir=train_dir,
optimizer=tf.train.AdamOptimizer(
args.learning_rate, epsilon=args.epsilon))
else:
raise ValueError('bad --model-type value')
return estimator | [
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Args:
output_dir: Modes are saved into outputdir/train
train_config: our training config
args: command line parameters
Returns:
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| python | train |
DataBiosphere/toil | src/toil/jobStores/aws/jobStore.py | https://github.com/DataBiosphere/toil/blob/a8252277ff814e7bee0971139c2344f88e44b644/src/toil/jobStores/aws/jobStore.py#L1226-L1241 | def __getBucketVersioning(self, bucket):
"""
For newly created buckets get_versioning_status returns an empty dict. In the past we've
seen None in this case. We map both to a return value of False.
Otherwise, the 'Versioning' entry in the dictionary returned by get_versioning_status can
be 'Enabled', 'Suspended' or 'Disabled' which we map to True, None and False
respectively. Note that we've never seen a versioning status of 'Disabled', only the
empty dictionary. Calling configure_versioning with False on a bucket will cause
get_versioning_status to then return 'Suspended' even on a new bucket that never had
versioning enabled.
"""
for attempt in retry_s3():
with attempt:
status = bucket.get_versioning_status()
return self.versionings[status['Versioning']] if status else False | [
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| python | train |
jbloomlab/phydms | phydmslib/models.py | https://github.com/jbloomlab/phydms/blob/9cdebc10bafbe543c552d79486c7f950780ed3c0/phydmslib/models.py#L2226-L2229 | def branchScale(self):
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bscales = [m.branchScale for m in self._models]
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| See docs for `Model` abstract base class. | [
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| python | train |
tjguk/networkzero | networkzero/messenger.py | https://github.com/tjguk/networkzero/blob/0e3e81d2e9200b25a83ac07741612283599486d7/networkzero/messenger.py#L59-L69 | def wait_for_news_from(address, prefix=config.EVERYTHING, wait_for_s=config.FOREVER, is_raw=False):
"""Wait for news whose topic starts with `prefix`.
:param address: a nw0 address, eg from `nw0.discover`
:param prefix: any text object [default: all messages]
:param wait_for_s: how many seconds to wait before giving up [default: forever]
:returns: a 2-tuple of (topic, data) or (None, None) if out of time
"""
_logger.info("Listen on %s for news matching %s waiting for %s secs", address, prefix, wait_for_s)
return sockets._sockets.wait_for_news_from(address, prefix, wait_for_s, is_raw) | [
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| python | train |
RJT1990/pyflux | pyflux/ssm/nllm.py | https://github.com/RJT1990/pyflux/blob/297f2afc2095acd97c12e827dd500e8ea5da0c0f/pyflux/ssm/nllm.py#L628-L678 | def plot_fit(self,intervals=True,**kwargs):
""" Plots the fit of the model
Returns
----------
None (plots data and the fit)
"""
import matplotlib.pyplot as plt
import seaborn as sns
figsize = kwargs.get('figsize',(10,7))
if self.latent_variables.estimated is False:
raise Exception("No latent variables estimated!")
else:
date_index = copy.deepcopy(self.index)
date_index = date_index[self.integ:self.data_original.shape[0]+1]
t_params = self.transform_z()
if self.model_name2 == 'Skewt':
states_upper_95 = (self.states + 1.98*np.sqrt(self.states_ses)) + ((t_params[-3] - (1.0/t_params[-3]))*t_params[-2]*gas.SkewtScore.tv_variate_exp(t_params[-1]))
states_lower_95 = (self.states - 1.98*np.sqrt(self.states_ses)) + ((t_params[-3] - (1.0/t_params[-3]))*t_params[-2]*gas.SkewtScore.tv_variate_exp(t_params[-1]))
mean_states = self.states + ((t_params[-3] - (1.0/t_params[-3]))*t_params[-2]*gas.SkewtScore.tv_variate_exp(t_params[-1]))
else:
states_upper_95 = (self.states + 1.98*np.sqrt(self.states_ses))
states_lower_95 = (self.states - 1.98*np.sqrt(self.states_ses))
mean_states = self.states
plt.figure(figsize=figsize)
plt.subplot(2, 1, 1)
plt.title(self.data_name + " Raw and Smoothed")
if intervals == True:
alpha =[0.15*i/float(100) for i in range(50,12,-2)]
plt.fill_between(date_index, self.link(states_lower_95), self.link(states_upper_95), alpha=0.15,label='95% C.I.')
plt.plot(date_index, self.data, label='Data')
plt.plot(date_index, self.link(mean_states), label='Smoothed', c='black')
plt.legend(loc=2)
plt.subplot(2, 1, 2)
plt.title(self.data_name + " Local Level")
if intervals == True:
alpha =[0.15*i/float(100) for i in range(50,12,-2)]
plt.fill_between(date_index, self.link(states_lower_95), self.link(states_upper_95), alpha=0.15,label='95% C.I.')
plt.plot(date_index, self.link(mean_states), label='Smoothed State')
plt.legend(loc=2)
plt.show() | [
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| python | train |
mezz64/pyHik | pyhik/hikvision.py | https://github.com/mezz64/pyHik/blob/1e7afca926e2b045257a43cbf8b1236a435493c2/pyhik/hikvision.py#L225-L231 | def _do_update_callback(self, msg):
"""Call registered callback functions."""
for callback, sensor in self._updateCallbacks:
if sensor == msg:
_LOGGING.debug('Update callback %s for sensor %s',
callback, sensor)
callback(msg) | [
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| python | train |
ktbyers/netmiko | netmiko/_textfsm/_texttable.py | https://github.com/ktbyers/netmiko/blob/54e6116c0b4664de2123081937e0a9a27bdfdfea/netmiko/_textfsm/_texttable.py#L1036-L1103 | def CsvToTable(self, buf, header=True, separator=","):
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Strips off comments (preceded by '#').
Optionally parses and indexes by first line (header).
Args:
buf: String file buffer containing CSV data.
header: Is the first line of buffer a header.
separator: String that CSV is separated by.
Returns:
int, the size of the table created.
Raises:
TableError: A parsing error occurred.
"""
self.Reset()
header_row = self.row_class()
if header:
line = buf.readline()
header_str = ""
while not header_str:
# Remove comments.
header_str = line.split("#")[0].strip()
if not header_str:
line = buf.readline()
header_list = header_str.split(separator)
header_length = len(header_list)
for entry in header_list:
entry = entry.strip()
if entry in header_row:
raise TableError("Duplicate header entry %r." % entry)
header_row[entry] = entry
header_row.row = 0
self._table[0] = header_row
# xreadlines would be better but not supported by StringIO for testing.
for line in buf:
# Support commented lines, provide '#' is first character of line.
if line.startswith("#"):
continue
lst = line.split(separator)
lst = [l.strip() for l in lst]
if header and len(lst) != header_length:
# Silently drop illegal line entries
continue
if not header:
header_row = self.row_class()
header_length = len(lst)
header_row.values = dict(
zip(range(header_length), range(header_length))
)
self._table[0] = header_row
header = True
continue
new_row = self.NewRow()
new_row.values = lst
header_row.row = self.size + 1
self._table.append(new_row)
return self.size | [
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| python | train |
Gbps/fastlog | fastlog/log.py | https://github.com/Gbps/fastlog/blob/8edb2327d72191510302c4654ffaa1691fe31277/fastlog/log.py#L67-L78 | def _log(self, lvl, msg, type, args, kwargs):
"""
Internal method to filter into the formatter before being passed to the main Python logger
"""
extra = kwargs.get('extra', {})
extra.setdefault("fastlog-type", type)
extra.setdefault("fastlog-indent", self._indent)
kwargs['extra'] = extra
self._lastlevel = lvl
self.inner.log(lvl, msg, *args, **kwargs) | [
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| python | train |
phoikoi/sisy | src/sisy/models.py | https://github.com/phoikoi/sisy/blob/840c5463ab65488d34e99531f230e61f755d2d69/src/sisy/models.py#L359-L364 | def taskinfo_with_label(label):
"""Return task info dictionary from task label. Internal function,
pretty much only used in migrations since the model methods aren't there."""
task = Task.objects.get(label=label)
info = json.loads(task._func_info)
return info | [
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| python | test |
icometrix/dicom2nifti | dicom2nifti/convert_dicom.py | https://github.com/icometrix/dicom2nifti/blob/1462ae5dd979fa3f276fe7a78ceb9b028121536f/dicom2nifti/convert_dicom.py#L54-L96 | def dicom_series_to_nifti(original_dicom_directory, output_file=None, reorient_nifti=True):
""" Converts dicom single series (see pydicom) to nifty, mimicking SPM
Examples: See unit test
will return a dictionary containing
- the NIFTI under key 'NIFTI'
- the NIFTI file path under 'NII_FILE'
- the BVAL file path under 'BVAL_FILE' (only for dti)
- the BVEC file path under 'BVEC_FILE' (only for dti)
IMPORTANT:
If no specific sequence type can be found it will default to anatomical and try to convert.
You should check that the data you are trying to convert is supported by this code
Inspired by http://nipy.sourceforge.net/nibabel/dicom/spm_dicom.html
Inspired by http://code.google.com/p/pydicom/source/browse/source/dicom/contrib/pydicom_series.py
:param reorient_nifti: if True the nifti affine and data will be updated so the data is stored LAS oriented
:param output_file: file path to write to if not set to None
:param original_dicom_directory: directory with the dicom files for a single series/scan
:return nibabel image
"""
# copy files so we can can modify without altering the original
temp_directory = tempfile.mkdtemp()
try:
dicom_directory = os.path.join(temp_directory, 'dicom')
shutil.copytree(original_dicom_directory, dicom_directory)
dicom_input = common.read_dicom_directory(dicom_directory)
return dicom_array_to_nifti(dicom_input, output_file, reorient_nifti)
except AttributeError as exception:
reraise(
tp=ConversionError,
value=ConversionError(str(exception)),
tb=sys.exc_info()[2])
finally:
# remove the copied data
shutil.rmtree(temp_directory) | [
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- the BVAL file path under 'BVAL_FILE' (only for dti)
- the BVEC file path under 'BVEC_FILE' (only for dti)
IMPORTANT:
If no specific sequence type can be found it will default to anatomical and try to convert.
You should check that the data you are trying to convert is supported by this code
Inspired by http://nipy.sourceforge.net/nibabel/dicom/spm_dicom.html
Inspired by http://code.google.com/p/pydicom/source/browse/source/dicom/contrib/pydicom_series.py
:param reorient_nifti: if True the nifti affine and data will be updated so the data is stored LAS oriented
:param output_file: file path to write to if not set to None
:param original_dicom_directory: directory with the dicom files for a single series/scan
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| python | train |
explosion/spaCy | spacy/pipeline/functions.py | https://github.com/explosion/spaCy/blob/8ee4100f8ffb336886208a1ea827bf4c745e2709/spacy/pipeline/functions.py#L39-L55 | def merge_subtokens(doc, label="subtok"):
"""Merge subtokens into a single token.
doc (Doc): The Doc object.
label (unicode): The subtoken dependency label.
RETURNS (Doc): The Doc object with merged subtokens.
DOCS: https://spacy.io/api/pipeline-functions#merge_subtokens
"""
merger = Matcher(doc.vocab)
merger.add("SUBTOK", None, [{"DEP": label, "op": "+"}])
matches = merger(doc)
spans = [doc[start : end + 1] for _, start, end in matches]
with doc.retokenize() as retokenizer:
for span in spans:
retokenizer.merge(span)
return doc | [
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RETURNS (Doc): The Doc object with merged subtokens.
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| python | train |
googleapis/google-cloud-python | logging/google/cloud/logging/client.py | https://github.com/googleapis/google-cloud-python/blob/85e80125a59cb10f8cb105f25ecc099e4b940b50/logging/google/cloud/logging/client.py#L294-L318 | def list_metrics(self, page_size=None, page_token=None):
"""List metrics for the project associated with this client.
See
https://cloud.google.com/logging/docs/reference/v2/rest/v2/projects.metrics/list
:type page_size: int
:param page_size:
Optional. The maximum number of metrics in each page of results
from this request. Non-positive values are ignored. Defaults to a
sensible value set by the API.
:type page_token: str
:param page_token:
Optional. If present, return the next batch of metrics, using the
value, which must correspond to the ``nextPageToken`` value
returned in the previous response. Deprecated: use the ``pages``
property of the returned iterator instead of manually passing the
token.
:rtype: :class:`~google.api_core.page_iterator.Iterator`
:returns: Iterator of :class:`~google.cloud.logging.metric.Metric`
accessible to the current client.
"""
return self.metrics_api.list_metrics(self.project, page_size, page_token) | [
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:param page_size:
Optional. The maximum number of metrics in each page of results
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:rtype: :class:`~google.api_core.page_iterator.Iterator`
:returns: Iterator of :class:`~google.cloud.logging.metric.Metric`
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| python | train |
openstack/horizon | openstack_dashboard/api/neutron.py | https://github.com/openstack/horizon/blob/5601ea9477323e599d9b766fcac1f8be742935b2/openstack_dashboard/api/neutron.py#L361-L371 | def list(self, **params):
"""Fetches a list all security groups.
:returns: List of SecurityGroup objects
"""
# This is to ensure tenant_id key is not populated
# if tenant_id=None is specified.
tenant_id = params.pop('tenant_id', self.request.user.tenant_id)
if tenant_id:
params['tenant_id'] = tenant_id
return self._list(**params) | [
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| python | train |
CalebBell/fluids | fluids/fittings.py | https://github.com/CalebBell/fluids/blob/57f556752e039f1d3e5a822f408c184783db2828/fluids/fittings.py#L3693-L3758 | def K_angle_stop_check_valve_Crane(D1, D2, fd=None, style=0):
r'''Returns the loss coefficient for a angle stop check valve as shown in
[1]_.
If β = 1:
.. math::
K = K_1 = K_2 = N\cdot f_d
Otherwise:
.. math::
K_2 = \frac{K + \left[0.5(1-\beta^2) + (1-\beta^2)^2\right]}{\beta^4}
Style 0 is the standard form; style 1 has a restrition to force
the flow up through the valve; style 2 is has the clearest flow area with
no guides for the angle valve. N is 200, 350, and 55 for those cases
respectively.
Parameters
----------
D1 : float
Diameter of the valve seat bore (must be smaller or equal to `D2`), [m]
D2 : float
Diameter of the pipe attached to the valve, [m]
fd : float, optional
Darcy friction factor calculated for the actual pipe flow in clean
steel (roughness = 0.0018 inch) in the fully developed turbulent
region; do not specify this to use the original Crane friction factor!,
[-]
style : int, optional
One of 0, 1, or 2; refers to three different types of angle valves
as shown in [1]_ [-]
Returns
-------
K : float
Loss coefficient with respect to the pipe inside diameter [-]
Notes
-----
This method is not valid in the laminar regime and the pressure drop will
be underestimated in those conditions.
Examples
--------
>>> K_angle_stop_check_valve_Crane(.1, .02, style=1)
4.525425593879809
References
----------
.. [1] Crane Co. Flow of Fluids Through Valves, Fittings, and Pipe. Crane,
2009.
'''
if fd is None:
fd = ft_Crane(D2)
try:
K = angle_stop_check_valve_Crane_coeffs[style]*fd
except KeyError:
raise KeyError('Accepted valve styles are 0, 1, and 2 only')
beta = D1/D2
if beta == 1:
return K
else:
return (K + beta*(0.5*(1.0 - beta**2) + (1.0 - beta**2)**2))/beta**4 | [
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Style 0 is the standard form; style 1 has a restrition to force
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Parameters
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D1 : float
Diameter of the valve seat bore (must be smaller or equal to `D2`), [m]
D2 : float
Diameter of the pipe attached to the valve, [m]
fd : float, optional
Darcy friction factor calculated for the actual pipe flow in clean
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style : int, optional
One of 0, 1, or 2; refers to three different types of angle valves
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Returns
-------
K : float
Loss coefficient with respect to the pipe inside diameter [-]
Notes
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This method is not valid in the laminar regime and the pressure drop will
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Examples
--------
>>> K_angle_stop_check_valve_Crane(.1, .02, style=1)
4.525425593879809
References
----------
.. [1] Crane Co. Flow of Fluids Through Valves, Fittings, and Pipe. Crane,
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| python | train |
pybel/pybel-tools | src/pybel_tools/analysis/neurommsig/algorithm.py | https://github.com/pybel/pybel-tools/blob/3491adea0ac4ee60f57275ef72f9b73da6dbfe0c/src/pybel_tools/analysis/neurommsig/algorithm.py#L166-L172 | def neurommsig_gene_ora(graph: BELGraph, genes: List[Gene]) -> float:
"""Calculate the percentage of target genes mappable to the graph.
Assume: graph central dogma inferred, collapsed to genes, collapsed variants
"""
graph_genes = set(get_nodes_by_function(graph, GENE))
return len(graph_genes.intersection(genes)) / len(graph_genes) | [
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| python | valid |
Stewori/pytypes | pytypes/type_util.py | https://github.com/Stewori/pytypes/blob/b814d38709e84c0e0825caf8b721c20eb5a8ab3b/pytypes/type_util.py#L125-L170 | def get_iterable_itemtype(obj):
"""Attempts to get an iterable's itemtype without iterating over it,
not even partly. Note that iterating over an iterable might modify
its inner state, e.g. if it is an iterator.
Note that obj is expected to be an iterable, not a typing.Iterable.
This function leverages various alternative ways to obtain that
info, e.g. by looking for type annotations of '__iter__' or '__getitem__'.
It is intended for (unknown) iterables, where the type cannot be obtained
via sampling without the risk of modifying inner state.
"""
# support further specific iterables on demand
try:
if isinstance(obj, range):
tpl = tuple(deep_type(obj.start), deep_type(obj.stop), deep_type(obj.step))
return Union[tpl]
except TypeError:
# We're running Python 2
pass
if type(obj) is tuple:
tpl = tuple(deep_type(t) for t in obj)
return Union[tpl]
elif type(obj) is types.GeneratorType:
return get_generator_yield_type(obj)
else:
tp = deep_type(obj)
if is_Generic(tp):
if issubclass(tp.__origin__, typing.Iterable):
if len(tp.__args__) == 1:
return tp.__args__[0]
return _select_Generic_superclass_parameters(tp, typing.Iterable)[0]
if is_iterable(obj):
if type(obj) is str:
return str
if hasattr(obj, '__iter__'):
if has_type_hints(obj.__iter__):
itrator = _funcsigtypes(obj.__iter__, True, obj.__class__)[1]
if is_Generic(itrator) and itrator.__origin__ is typing.Iterator:
return itrator.__args__[0]
if hasattr(obj, '__getitem__'):
if has_type_hints(obj.__getitem__):
itrator = _funcsigtypes(obj.__getitem__, True, obj.__class__)[1]
if is_Generic(itrator) and itrator.__origin__ is typing.Iterator:
return itrator.__args__[0]
return None # means that type is unknown
else:
raise TypeError('Not an iterable: '+str(type(obj))) | [
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| python | train |
kensho-technologies/graphql-compiler | graphql_compiler/compiler/emit_sql.py | https://github.com/kensho-technologies/graphql-compiler/blob/f6079c6d10f64932f6b3af309b79bcea2123ca8f/graphql_compiler/compiler/emit_sql.py#L273-L286 | def _transform_local_field_to_expression(expression, node, context):
"""Transform a LocalField compiler expression into its SQLAlchemy expression representation.
Args:
expression: expression, LocalField compiler expression.
node: SqlNode, the SqlNode the expression applies to.
context: CompilationContext, global compilation state and metadata.
Returns:
Expression, SQLAlchemy expression.
"""
column_name = expression.field_name
column = sql_context_helpers.get_column(column_name, node, context)
return column | [
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| python | train |
dpkp/kafka-python | kafka/client_async.py | https://github.com/dpkp/kafka-python/blob/f6a8a38937688ea2cc5dc13d3d1039493be5c9b5/kafka/client_async.py#L704-L738 | def least_loaded_node(self):
"""Choose the node with fewest outstanding requests, with fallbacks.
This method will prefer a node with an existing connection and no
in-flight-requests. If no such node is found, a node will be chosen
randomly from disconnected nodes that are not "blacked out" (i.e.,
are not subject to a reconnect backoff). If no node metadata has been
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Returns:
node_id or None if no suitable node was found
"""
nodes = [broker.nodeId for broker in self.cluster.brokers()]
random.shuffle(nodes)
inflight = float('inf')
found = None
for node_id in nodes:
conn = self._conns.get(node_id)
connected = conn is not None and conn.connected()
blacked_out = conn is not None and conn.blacked_out()
curr_inflight = len(conn.in_flight_requests) if conn is not None else 0
if connected and curr_inflight == 0:
# if we find an established connection
# with no in-flight requests, we can stop right away
return node_id
elif not blacked_out and curr_inflight < inflight:
# otherwise if this is the best we have found so far, record that
inflight = curr_inflight
found = node_id
if found is not None:
return found
return None | [
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| python | train |
DataDog/integrations-core | tokumx/datadog_checks/tokumx/vendor/pymongo/common.py | https://github.com/DataDog/integrations-core/blob/ebd41c873cf9f97a8c51bf9459bc6a7536af8acd/tokumx/datadog_checks/tokumx/vendor/pymongo/common.py#L178-L187 | def validate_readable(option, value):
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"""
if value is None:
return value
# First make sure its a string py3.3 open(True, 'r') succeeds
# Used in ssl cert checking due to poor ssl module error reporting
value = validate_string(option, value)
open(value, 'r').close()
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| python | train |
cisco-sas/kitty | kitty/model/low_level/field.py | https://github.com/cisco-sas/kitty/blob/cb0760989dcdfe079e43ac574d872d0b18953a32/kitty/model/low_level/field.py#L938-L944 | def hash(self):
'''
:rtype: int
:return: hash of the field
'''
hashed = super(_LibraryBitField, self).hash()
return khash(hashed, self._length, self._signed, self._min_value, self._max_value) | [
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| python | train |
gem/oq-engine | openquake/hazardlib/gsim/nga_east.py | https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/nga_east.py#L530-L534 | def _get_tau(self, imt, mag):
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| python | train |
pyQode/pyqode.core | pyqode/core/widgets/splittable_tab_widget.py | https://github.com/pyQode/pyqode.core/blob/a99ec6cd22d519394f613309412f8329dc4e90cb/pyqode/core/widgets/splittable_tab_widget.py#L971-L979 | def count(self):
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Returns the number of widgets currently displayed (takes child splits
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c = self.main_tab_widget.count()
for child in self.child_splitters:
c += child.count()
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| python | train |
JarryShaw/PyPCAPKit | src/const/ipv6/routing.py | https://github.com/JarryShaw/PyPCAPKit/blob/c7f0da9aebc2cf210bf8f8b912f7d3cbb98ca10e/src/const/ipv6/routing.py#L21-L27 | def get(key, default=-1):
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if key not in Routing._member_map_:
extend_enum(Routing, key, default)
return Routing[key] | [
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| python | train |
improbable-research/keanu | keanu-python/keanu/vertex/generated.py | https://github.com/improbable-research/keanu/blob/73189a8f569078e156168e795f82c7366c59574b/keanu-python/keanu/vertex/generated.py#L523-L529 | def Sum(input_vertex: vertex_constructor_param_types, label: Optional[str]=None) -> Vertex:
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Performs a sum across all dimensions
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| python | train |
f3at/feat | src/feat/simulation/driver.py | https://github.com/f3at/feat/blob/15da93fc9d6ec8154f52a9172824e25821195ef8/src/feat/simulation/driver.py#L288-L297 | def freeze_all(self):
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| python | train |
Skype4Py/Skype4Py | Skype4Py/skype.py | https://github.com/Skype4Py/Skype4Py/blob/c48d83f7034109fe46315d45a066126002c6e0d4/Skype4Py/skype.py#L555-L567 | def CreateChatWith(self, *Usernames):
"""Creates a chat with one or more users.
:Parameters:
Usernames : str
One or more Skypenames of the users.
:return: A chat object
:rtype: `Chat`
:see: `Chat.AddMembers`
"""
return Chat(self, chop(self._DoCommand('CHAT CREATE %s' % ', '.join(Usernames)), 2)[1]) | [
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| python | train |
peterldowns/lggr | lggr/__init__.py | https://github.com/peterldowns/lggr/blob/622968f17133e02d9a46a4900dd20fb3b19fe961/lggr/__init__.py#L322-L332 | def Printer(open_file=sys.stdout, closing=False):
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open_file.write(logstr)
open_file.write('\n') # new line
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| python | train |
gunthercox/ChatterBot | chatterbot/storage/sql_storage.py | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/storage/sql_storage.py#L176-L217 | def create(self, **kwargs):
"""
Creates a new statement matching the keyword arguments specified.
Returns the created statement.
"""
Statement = self.get_model('statement')
Tag = self.get_model('tag')
session = self.Session()
tags = set(kwargs.pop('tags', []))
if 'search_text' not in kwargs:
kwargs['search_text'] = self.tagger.get_bigram_pair_string(kwargs['text'])
if 'search_in_response_to' not in kwargs:
in_response_to = kwargs.get('in_response_to')
if in_response_to:
kwargs['search_in_response_to'] = self.tagger.get_bigram_pair_string(in_response_to)
statement = Statement(**kwargs)
for tag_name in tags:
tag = session.query(Tag).filter_by(name=tag_name).first()
if not tag:
# Create the tag
tag = Tag(name=tag_name)
statement.tags.append(tag)
session.add(statement)
session.flush()
session.refresh(statement)
statement_object = self.model_to_object(statement)
self._session_finish(session)
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| python | train |
danilobellini/audiolazy | audiolazy/lazy_auditory.py | https://github.com/danilobellini/audiolazy/blob/dba0a278937909980ed40b976d866b8e97c35dee/audiolazy/lazy_auditory.py#L225-L290 | def phon2dB(loudness=None):
"""
Loudness in phons to Sound Pressure Level (SPL) in dB using the
ISO/FDIS 226:2003 model.
This function needs Scipy, as ``scipy.interpolate.UnivariateSpline``
objects are used as interpolators.
Parameters
----------
loudness :
The loudness value in phons to be converted, or None (default) to get
the threshold of hearing.
Returns
-------
A callable that returns the SPL dB value for each given frequency in hertz.
Note
----
See ``phon2dB.iso226.schema`` and ``phon2dB.iso226.table`` to know the
original frequency used for the result. The result for any other value is
an interpolation (spline). Don't trust on values nor lower nor higher than
the frequency limits there (20Hz and 12.5kHz) as they're not part of
ISO226 and no value was collected to estimate them (they're just a spline
interpolation to reach 1000dB at -30Hz and 32kHz). Likewise, the trustful
loudness input range is from 20 to 90 phon, as written on ISO226, although
other values aren't found by a spline interpolation but by using the
formula on section 4.1 of ISO226.
Hint
----
The ``phon2dB.iso226.table`` also have other useful information, such as
the threshold values in SPL dB.
"""
from scipy.interpolate import UnivariateSpline
table = phon2dB.iso226.table
schema = phon2dB.iso226.schema
freqs = [row[schema.index("freq")] for row in table]
if loudness is None: # Threshold levels
spl = [row[schema.index("threshold")] for row in table]
else: # Curve for a specific phon value
def get_pressure_level(freq, alpha, loudness_base, threshold):
return 10 / alpha * math.log10(
4.47e-3 * (10 ** (.025 * loudness) - 1.14) +
(.4 * 10 ** ((threshold + loudness_base) / 10 - 9)) ** alpha
) - loudness_base + 94
spl = [get_pressure_level(**dict(xzip(schema, args))) for args in table]
interpolator = UnivariateSpline(freqs, spl, s=0)
interpolator_low = UnivariateSpline([-30] + freqs, [1e3] + spl, s=0)
interpolator_high = UnivariateSpline(freqs + [32000], spl + [1e3], s=0)
@elementwise("freq", 0)
def freq2dB_spl(freq):
if freq < 20:
return interpolator_low(freq).tolist()
if freq > 12500:
return interpolator_high(freq).tolist()
return interpolator(freq).tolist()
return freq2dB_spl | [
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Parameters
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loudness :
The loudness value in phons to be converted, or None (default) to get
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Returns
-------
A callable that returns the SPL dB value for each given frequency in hertz.
Note
----
See ``phon2dB.iso226.schema`` and ``phon2dB.iso226.table`` to know the
original frequency used for the result. The result for any other value is
an interpolation (spline). Don't trust on values nor lower nor higher than
the frequency limits there (20Hz and 12.5kHz) as they're not part of
ISO226 and no value was collected to estimate them (they're just a spline
interpolation to reach 1000dB at -30Hz and 32kHz). Likewise, the trustful
loudness input range is from 20 to 90 phon, as written on ISO226, although
other values aren't found by a spline interpolation but by using the
formula on section 4.1 of ISO226.
Hint
----
The ``phon2dB.iso226.table`` also have other useful information, such as
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| python | train |
diffeo/rejester | rejester/_registry.py | https://github.com/diffeo/rejester/blob/5438a4a18be2801d7826c46e2079ba9639d2ecb4/rejester/_registry.py#L953-L979 | def delete(self, dict_name):
'''Delete an entire dictionary.
This operation on its own is atomic and does not require a
session lock, but a session lock is honored.
:param str dict_name: name of the dictionary to delete
:raises rejester.exceptions.LockError: if called with a session
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'''
conn = redis.Redis(connection_pool=self.pool)
script = conn.register_script('''
if redis.call("get", KEYS[1]) == ARGV[1]
then
redis.call("del", KEYS[2], KEYS[3])
return 0
else
return -1
end
''')
res = script(keys=[self._lock_name,
self._namespace(dict_name),
self._namespace(dict_name) + 'keys'],
args=[self._session_lock_identifier])
if res == -1:
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| python | train |
fermiPy/fermipy | fermipy/jobs/file_archive.py | https://github.com/fermiPy/fermipy/blob/9df5e7e3728307fd58c5bba36fd86783c39fbad4/fermipy/jobs/file_archive.py#L632-L678 | def register_file(self, filepath, creator, status=FileStatus.no_file, flags=FileFlags.no_flags):
"""Register a file in the archive.
If the file already exists, this raises a `KeyError`
Parameters
----------
filepath : str
The path to the file
creatror : int
A unique key for the job that created this file
status : `FileStatus`
Enumeration giving current status of file
flags : `FileFlags`
Enumeration giving flags set on this file
Returns `FileHandle`
"""
# check to see if the file already exists
try:
file_handle = self.get_handle(filepath)
raise KeyError("File %s already exists in archive" % filepath)
except KeyError:
pass
localpath = self._get_localpath(filepath)
if status == FileStatus.exists:
# Make sure the file really exists
fullpath = self._get_fullpath(filepath)
if not os.path.exists(fullpath):
print("register_file called on called on mising file %s" % fullpath)
status = FileStatus.missing
timestamp = 0
else:
timestamp = int(os.stat(fullpath).st_mtime)
else:
timestamp = 0
key = len(self._table) + 1
file_handle = FileHandle(path=localpath,
key=key,
creator=creator,
timestamp=timestamp,
status=status,
flags=flags)
file_handle.append_to_table(self._table)
self._cache[localpath] = file_handle
return file_handle | [
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| python | train |
HDI-Project/ballet | ballet/util/fs.py | https://github.com/HDI-Project/ballet/blob/6f4d4b87b8234cb6bb38b9e9484a58ef8fe8fdb2/ballet/util/fs.py#L51-L62 | def splitext2(filepath):
"""Split filepath into root, filename, ext
Args:
filepath (str, path): file path
Returns:
str
"""
root, filename = os.path.split(safepath(filepath))
filename, ext = os.path.splitext(safepath(filename))
return root, filename, ext | [
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| python | train |
saltstack/salt | salt/modules/snapper.py | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/snapper.py#L607-L643 | def status(config='root', num_pre=None, num_post=None):
'''
Returns a comparison between two snapshots
config
Configuration name.
num_pre
first snapshot ID to compare. Default is last snapshot
num_post
last snapshot ID to compare. Default is 0 (current state)
CLI example:
.. code-block:: bash
salt '*' snapper.status
salt '*' snapper.status num_pre=19 num_post=20
'''
try:
pre, post = _get_num_interval(config, num_pre, num_post)
snapper.CreateComparison(config, int(pre), int(post))
files = snapper.GetFiles(config, int(pre), int(post))
status_ret = {}
SUBVOLUME = list_configs()[config]['SUBVOLUME']
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status_ret[os.path.normpath(SUBVOLUME + _filepath)] = {'status': status_to_string(file[1])}
return status_ret
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last snapshot ID to compare. Default is 0 (current state)
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| python | train |
skorch-dev/skorch | skorch/cli.py | https://github.com/skorch-dev/skorch/blob/5b9b8b7b7712cb6e5aaa759d9608ea6269d5bcd3/skorch/cli.py#L138-L157 | def parse_net_kwargs(kwargs):
"""Parse arguments for the estimator.
Resolves dotted names and instantiated classes.
Examples
--------
>>> kwargs = {'lr': 0.1, 'module__nonlin': 'torch.nn.Hardtanh(-2, max_val=3)'}
>>> parse_net_kwargs(kwargs)
{'lr': 0.1, 'module__nonlin': Hardtanh(min_val=-2, max_val=3)}
"""
if not kwargs:
return kwargs
resolved = {}
for k, v in kwargs.items():
resolved[k] = _resolve_dotted_name(v)
return resolved | [
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| python | train |
apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/engine.py | https://github.com/apple/turicreate/blob/74514c3f99e25b46f22c6e02977fe3da69221c2e/deps/src/boost_1_68_0/tools/build/src/build/engine.py#L76-L91 | def add_dependency (self, targets, sources):
"""Adds a dependency from 'targets' to 'sources'
Both 'targets' and 'sources' can be either list
of target names, or a single target name.
"""
if isinstance (targets, str):
targets = [targets]
if isinstance (sources, str):
sources = [sources]
assert is_iterable(targets)
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for target in targets:
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| python | train |
open511/open511 | open511/converter/tmdd.py | https://github.com/open511/open511/blob/3d573f59d7efa06ff1b5419ea5ff4d90a90b3cf8/open511/converter/tmdd.py#L64-L75 | def _generate_automatic_headline(c):
"""The only field that maps closely to Open511 <headline>, a required field, is optional
in TMDD. So we sometimes need to generate our own."""
# Start with the event type, e.g. "Incident"
headline = c.data['event_type'].replace('_', ' ').title()
if c.data['roads']:
# Add the road name
headline += ' on ' + c.data['roads'][0]['name']
direction = c.data['roads'][0].get('direction')
if direction and direction not in ('BOTH', 'NONE'):
headline += ' ' + direction
return headline | [
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| python | valid |
lemieuxl/pyGenClean | pyGenClean/run_data_clean_up.py | https://github.com/lemieuxl/pyGenClean/blob/6173a48ccc0cf3a3bd711b1f2a1fa16248b8bf55/pyGenClean/run_data_clean_up.py#L3162-L3277 | def check_input_files(prefix, the_type, required_type):
"""Check that the file is of a certain file type.
:param prefix: the prefix of the input files.
:param the_type: the type of the input files (bfile, tfile or file).
:param required_type: the required type of the input files (bfile, tfile or
file).
:type prefix: str
:type the_type: str
:type required_type: str
:returns: ``True`` if everything is OK.
Checks if the files are of the required type, according to their current
type. The available types are ``bfile`` (binary), ``tfile`` (transposed)
and ``file`` (normal).
"""
# The files required for each type
bfile_type = {".bed", ".bim", ".fam"}
tfile_type = {".tped", ".tfam"}
file_type = {".ped", ".map"}
# Check if of bfile, tfile and file
plink_command = ["plink", "--noweb", "--out", prefix]
if required_type == "bfile":
# We need bfile
plink_command += ["--make-bed"]
if the_type == "bfile":
return True
elif the_type == "tfile":
# We have tfile, we need to create bfile from tfile
plink_command += ["--tfile", prefix]
elif the_type == "file":
# We have file, we need to create bfile from file
plink_command += ["--file", prefix]
else:
msg = "{}: no suitable input format...".format(prefix)
raise ProgramError(msg)
# We create the required files
if os.path.isfile(prefix + ".log"):
# There is a log file... we need to copy it
shutil.copyfile(prefix + ".log", prefix + ".olog")
logger.info("Converting {} from {} to {}".format(
prefix,
the_type,
required_type,
))
run_command(plink_command)
# Everything is now fine
return True
elif required_type == "tfile":
# We need a tfile
plink_command += ["--recode", "--transpose", "--tab"]
if the_type == "tfile":
return True
elif the_type == "bfile":
# We have bfile, we need to create tfile from bfile
plink_command += ["--bfile", prefix]
elif the_type == "file":
# We have file, we need to create tfile from file
plink_command += ["--file", prefix]
else:
msg = "{}: no suitable input format...".format(prefix)
raise ProgramError(msg)
# We create the required files
if os.path.isfile(prefix + ".log"):
# There is a log file... we need to copy it
shutil.copyfile(prefix + ".log", prefix + ".olog")
logger.info("Converting {} from {} to {}".format(
prefix,
the_type,
required_type,
))
run_command(plink_command)
# Everything is now fine
return True
elif required_type == "file":
# We need a file
plink_command += ["--recode", "--tab"]
if the_type == "file":
return True
elif the_type == "bfile":
# We have bfile, we need to create file from bfile
plink_command += ["--bfile", prefix]
elif the_type == "tfile":
# We have tfile, we need to create file from tfile
plink_command += ["--tfile", prefix]
else:
msg = "{}: no suitable input format...".format(prefix)
raise ProgramError(msg)
# We create the required files
if os.path.isfile(prefix + ".log"):
# There is a log file... we need to copy it
shutil.copyfile(prefix + ".log", prefix + ".olog")
logger.info("Converting {} from {} to {}".format(
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the_type,
required_type,
))
run_command(plink_command)
# Everything is now fine
return True
else:
msg = "{}: unknown file format".format(required_type)
raise ProgramError(msg) | [
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| python | train |
elifesciences/elife-tools | elifetools/utils.py | https://github.com/elifesciences/elife-tools/blob/4b9e38cbe485c61a4ed7cbd8970c6b318334fd86/elifetools/utils.py#L78-L85 | def coerce_to_int(val, default=0xDEADBEEF):
"""Attempts to cast given value to an integer, return the original value if failed or the default if one provided."""
try:
return int(val)
except (TypeError, ValueError):
if default != 0xDEADBEEF:
return default
return val | [
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| python | train |
lrq3000/pyFileFixity | pyFileFixity/lib/reedsolomon/reedsolo.py | https://github.com/lrq3000/pyFileFixity/blob/fd5ef23bb13835faf1e3baa773619b86a1cc9bdf/pyFileFixity/lib/reedsolomon/reedsolo.py#L126-L178 | def find_prime_polys(generator=2, c_exp=8, fast_primes=False, single=False):
'''Compute the list of prime polynomials for the given generator and galois field characteristic exponent.'''
# fast_primes will output less results but will be significantly faster.
# single will output the first prime polynomial found, so if all you want is to just find one prime polynomial to generate the LUT for Reed-Solomon to work, then just use that.
# A prime polynomial (necessarily irreducible) is necessary to reduce the multiplications in the Galois Field, so as to avoid overflows.
# Why do we need a "prime polynomial"? Can't we just reduce modulo 255 (for GF(2^8) for example)? Because we need the values to be unique.
# For example: if the generator (alpha) = 2 and c_exp = 8 (GF(2^8) == GF(256)), then the generated Galois Field (0, 1, α, α^1, α^2, ..., α^(p-1)) will be galois field it becomes 0, 1, 2, 4, 8, 16, etc. However, upon reaching 128, the next value will be doubled (ie, next power of 2), which will give 256. Then we must reduce, because we have overflowed above the maximum value of 255. But, if we modulo 255, this will generate 256 == 1. Then 2, 4, 8, 16, etc. giving us a repeating pattern of numbers. This is very bad, as it's then not anymore a bijection (ie, a non-zero value doesn't have a unique index). That's why we can't just modulo 255, but we need another number above 255, which is called the prime polynomial.
# Why so much hassle? Because we are using precomputed look-up tables for multiplication: instead of multiplying a*b, we precompute alpha^a, alpha^b and alpha^(a+b), so that we can just use our lookup table at alpha^(a+b) and get our result. But just like in our original field we had 0,1,2,...,p-1 distinct unique values, in our "LUT" field using alpha we must have unique distinct values (we don't care that they are different from the original field as long as they are unique and distinct). That's why we need to avoid duplicated values, and to avoid duplicated values we need to use a prime irreducible polynomial.
# Here is implemented a bruteforce approach to find all these prime polynomials, by generating every possible prime polynomials (ie, every integers between field_charac+1 and field_charac*2), and then we build the whole Galois Field, and we reject the candidate prime polynomial if it duplicates even one value or if it generates a value above field_charac (ie, cause an overflow).
# Note that this algorithm is slow if the field is too big (above 12), because it's an exhaustive search algorithm. There are probabilistic approaches, and almost surely prime approaches, but there is no determistic polynomial time algorithm to find irreducible monic polynomials. More info can be found at: http://people.mpi-inf.mpg.de/~csaha/lectures/lec9.pdf
# Another faster algorithm may be found at Adleman, Leonard M., and Hendrik W. Lenstra. "Finding irreducible polynomials over finite fields." Proceedings of the eighteenth annual ACM symposium on Theory of computing. ACM, 1986.
# Prepare the finite field characteristic (2^p - 1), this also represent the maximum possible value in this field
root_charac = 2 # we're in GF(2)
field_charac = int(root_charac**c_exp - 1)
field_charac_next = int(root_charac**(c_exp+1) - 1)
prim_candidates = []
if fast_primes:
prim_candidates = rwh_primes1(field_charac_next) # generate maybe prime polynomials and check later if they really are irreducible
prim_candidates = [x for x in prim_candidates if x > field_charac] # filter out too small primes
else:
prim_candidates = xrange(field_charac+2, field_charac_next, root_charac) # try each possible prime polynomial, but skip even numbers (because divisible by 2 so necessarily not irreducible)
# Start of the main loop
correct_primes = []
for prim in prim_candidates: # try potential candidates primitive irreducible polys
seen = bytearray(field_charac+1) # memory variable to indicate if a value was already generated in the field (value at index x is set to 1) or not (set to 0 by default)
conflict = False # flag to know if there was at least one conflict
# Second loop, build the whole Galois Field
x = 1
for i in xrange(field_charac):
# Compute the next value in the field (ie, the next power of alpha/generator)
x = gf_mult_noLUT(x, generator, prim, field_charac+1)
# Rejection criterion: if the value overflowed (above field_charac) or is a duplicate of a previously generated power of alpha, then we reject this polynomial (not prime)
if x > field_charac or seen[x] == 1:
conflict = True
break
# Else we flag this value as seen (to maybe detect future duplicates), and we continue onto the next power of alpha
else:
seen[x] = 1
# End of the second loop: if there's no conflict (no overflow nor duplicated value), this is a prime polynomial!
if not conflict:
correct_primes.append(prim)
if single: return prim
# Return the list of all prime polynomials
return correct_primes | [
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"# For example: if the generator (alpha) = 2 and c_exp = 8 (GF(2^8) == GF(256)), then the generated Galois Field (0, 1, α, α^1, α^2, ..., α^(p-1)) will be galois field it becomes 0, 1, 2, 4, 8, 16, etc. However, upon reaching 128, the next value will be doubled (ie, next power of 2), which will give 256. Then we must reduce, because we have overflowed above the maximum value of 255. But, if we modulo 255, this will generate 256 == 1. Then 2, 4, 8, 16, etc. giving us a repeating pattern of numbers. This is very bad, as it's then not anymore a bijection (ie, a non-zero value doesn't have a unique index). That's why we can't just modulo 255, but we need another number above 255, which is called the prime polynomial.",
"# Why so much hassle? Because we are using precomputed look-up tables for multiplication: instead of multiplying a*b, we precompute alpha^a, alpha^b and alpha^(a+b), so that we can just use our lookup table at alpha^(a+b) and get our result. But just like in our original field we had 0,1,2,...,p-1 distinct unique values, in our \"LUT\" field using alpha we must have unique distinct values (we don't care that they are different from the original field as long as they are unique and distinct). That's why we need to avoid duplicated values, and to avoid duplicated values we need to use a prime irreducible polynomial.",
"# Here is implemented a bruteforce approach to find all these prime polynomials, by generating every possible prime polynomials (ie, every integers between field_charac+1 and field_charac*2), and then we build the whole Galois Field, and we reject the candidate prime polynomial if it duplicates even one value or if it generates a value above field_charac (ie, cause an overflow).",
"# Note that this algorithm is slow if the field is too big (above 12), because it's an exhaustive search algorithm. There are probabilistic approaches, and almost surely prime approaches, but there is no determistic polynomial time algorithm to find irreducible monic polynomials. More info can be found at: http://people.mpi-inf.mpg.de/~csaha/lectures/lec9.pdf",
"# Another faster algorithm may be found at Adleman, Leonard M., and Hendrik W. Lenstra. \"Finding irreducible polynomials over finite fields.\" Proceedings of the eighteenth annual ACM symposium on Theory of computing. ACM, 1986.",
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| python | train |
nion-software/nionswift | nion/typeshed/API_1_0.py | https://github.com/nion-software/nionswift/blob/d43693eaf057b8683b9638e575000f055fede452/nion/typeshed/API_1_0.py#L1110-L1119 | def create_data_and_metadata_from_data(self, data: numpy.ndarray, intensity_calibration: Calibration.Calibration=None, dimensional_calibrations: typing.List[Calibration.Calibration]=None, metadata: dict=None, timestamp: str=None) -> DataAndMetadata.DataAndMetadata:
"""Create a data_and_metadata object from data.
.. versionadded:: 1.0
.. deprecated:: 1.1
Use :py:meth:`~nion.swift.Facade.DataItem.create_data_and_metadata` instead.
Scriptable: No
"""
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| python | train |
Esri/ArcREST | src/arcrest/agol/services.py | https://github.com/Esri/ArcREST/blob/ab240fde2b0200f61d4a5f6df033516e53f2f416/src/arcrest/agol/services.py#L796-L825 | def synchronizeReplica(self,
replicaID,
transportType="esriTransportTypeUrl",
replicaServerGen=None,
returnIdsForAdds=False,
edits=None,
returnAttachmentDatabyURL=False,
async=False,
syncDirection="snapshot",
syncLayers="perReplica",
editsUploadID=None,
editsUploadFormat=None,
dataFormat="json",
rollbackOnFailure=True):
"""
TODO: implement synchronize replica
http://resources.arcgis.com/en/help/arcgis-rest-api/index.html#//02r3000000vv000000
"""
params = {
"f" : "json",
"replicaID" : replicaID,
"transportType" : transportType,
"dataFormat" : dataFormat,
"rollbackOnFailure" : rollbackOnFailure,
"async" : async,
"returnIdsForAdds": returnIdsForAdds,
"syncDirection" : syncDirection,
"returnAttachmentDatabyURL" : returnAttachmentDatabyURL
}
return | [
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| python | train |
OCA/openupgradelib | openupgradelib/openupgrade.py | https://github.com/OCA/openupgradelib/blob/b220b6498075d62c1b64073cc934513a465cfd85/openupgradelib/openupgrade.py#L1193-L1268 | def map_values(
cr, source_column, target_column, mapping,
model=None, table=None, write='sql'):
"""
Map old values to new values within the same model or table. Old values
presumably come from a legacy column.
You will typically want to use it in post-migration scripts.
:param cr: The database cursor
:param source_column: the database column that contains old values to be \
mapped
:param target_column: the database column, or model field (if 'write' is \
'orm') that the new values are written to
:param mapping: list of tuples [(old value, new value)]
Old value True represents "is set", False "is not set".
:param model: used for writing if 'write' is 'orm', or to retrieve the \
table if 'table' is not given.
:param table: the database table used to query the old values, and write \
the new values (if 'write' is 'sql')
:param write: Either 'orm' or 'sql'. Note that old ids are always \
identified by an sql read.
This method does not support mapping m2m, o2m or property fields. \
For o2m you can migrate the inverse field's column instead.
.. versionadded:: 8.0
"""
if write not in ('sql', 'orm'):
logger.exception(
"map_values is called with unknown value for write param: %s",
write)
if not table:
if not model:
logger.exception("map_values is called with no table and no model")
table = model._table
if source_column == target_column:
logger.exception(
"map_values is called with the same value for source and old"
" columns : %s",
source_column)
for old, new in mapping:
new = "'%s'" % new
if old is True:
old = 'NOT NULL'
op = 'IS'
elif old is False:
old = 'NULL'
op = 'IS'
else:
old = "'%s'" % old
op = '='
values = {
'table': table,
'source': source_column,
'target': target_column,
'old': old,
'new': new,
'op': op,
}
if write == 'sql':
query = """UPDATE %(table)s
SET %(target)s = %(new)s
WHERE %(source)s %(op)s %(old)s""" % values
else:
query = """SELECT id FROM %(table)s
WHERE %(source)s %(op)s %(old)s""" % values
logged_query(cr, query, values)
if write == 'orm':
model.write(
cr, SUPERUSER_ID,
[row[0] for row in cr.fetchall()],
{target_column: new}) | [
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:param target_column: the database column, or model field (if 'write' is \
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:param mapping: list of tuples [(old value, new value)]
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:param write: Either 'orm' or 'sql'. Note that old ids are always \
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| python | train |
bcbio/bcbio-nextgen | bcbio/upload/irods.py | https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/upload/irods.py#L33-L47 | def _upload_dir_icommands_cli(local_dir, irods_dir, config=None, metadata=None):
"""
Upload directory recursively via the standard icommands CLI.
example: irsync -Kvar -R $resource $local_dir i:$irods_dir
go to https://docs.irods.org/4.2.0/icommands/user/#irsync for more info
"""
args = ["-K","-v","-a","-r"]
if config:
if config.get("resource"):
args += ["-R", config.get("resource")]
_check_create_collection(irods_dir,isdir=True)
cmd = ["irsync"] + args + [local_dir, "i:"+irods_dir]
do.run(cmd, "Uploading to iRODS") | [
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| python | train |
ska-sa/katcp-python | katcp/resource_client.py | https://github.com/ska-sa/katcp-python/blob/9127c826a1d030c53b84d0e95743e20e5c5ea153/katcp/resource_client.py#L1408-L1411 | def until_not_synced(self, timeout=None):
"""Return a tornado Future; resolves when any subordinate client is not synced"""
yield until_any(*[r.until_not_synced() for r in dict.values(self.children)],
timeout=timeout) | [
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| python | train |
marcelcaraciolo/foursquare | examples/django/example/djfoursquare/views.py | https://github.com/marcelcaraciolo/foursquare/blob/a8bda33cc2d61e25aa8df72011246269fd98aa13/examples/django/example/djfoursquare/views.py#L22-L30 | def unauth(request):
"""
logout and remove all session data
"""
if check_key(request):
api = get_api(request)
request.session.clear()
logout(request)
return HttpResponseRedirect(reverse('main')) | [
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| python | train |
dwavesystems/dimod | dimod/reference/composites/higherordercomposites.py | https://github.com/dwavesystems/dimod/blob/beff1b7f86b559d923ac653c1de6d593876d6d38/dimod/reference/composites/higherordercomposites.py#L132-L161 | def penalty_satisfaction(response, bqm):
""" Creates a penalty satisfaction list
Given a sampleSet and a bqm object, will create a binary list informing
whether the penalties introduced during degree reduction are satisfied for
each sample in sampleSet
Args:
response (:obj:`.SampleSet`): Samples corresponding to provided bqm
bqm (:obj:`.BinaryQuadraticModel`): a bqm object that contains
its reduction info.
Returns:
:obj:`numpy.ndarray`: a binary array of penalty satisfaction information
"""
record = response.record
label_dict = response.variables.index
if len(bqm.info['reduction']) == 0:
return np.array([1] * len(record.sample))
penalty_vector = np.prod([record.sample[:, label_dict[qi]] *
record.sample[:, label_dict[qj]]
== record.sample[:,
label_dict[valdict['product']]]
for (qi, qj), valdict in
bqm.info['reduction'].items()], axis=0)
return penalty_vector | [
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Args:
response (:obj:`.SampleSet`): Samples corresponding to provided bqm
bqm (:obj:`.BinaryQuadraticModel`): a bqm object that contains
its reduction info.
Returns:
:obj:`numpy.ndarray`: a binary array of penalty satisfaction information | [
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| python | train |
pycontribs/pyrax | pyrax/clouddns.py | https://github.com/pycontribs/pyrax/blob/9ddfd5064b3a292d7337906f3b2d5dce95b50b99/pyrax/clouddns.py#L676-L698 | def update_domain(self, domain, emailAddress=None, ttl=None, comment=None):
"""
Provides a way to modify the following attributes of a domain
record:
- email address
- ttl setting
- comment
"""
if not any((emailAddress, ttl, comment)):
raise exc.MissingDNSSettings(
"No settings provided to update_domain().")
uri = "/domains/%s" % utils.get_id(domain)
body = {"comment": comment,
"ttl": ttl,
"emailAddress": emailAddress,
}
none_keys = [key for key, val in body.items()
if val is None]
for none_key in none_keys:
body.pop(none_key)
resp, resp_body = self._async_call(uri, method="PUT", body=body,
error_class=exc.DomainUpdateFailed, has_response=False)
return resp_body | [
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| python | train |
fracpete/python-weka-wrapper3 | python/weka/core/stemmers.py | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/stemmers.py#L43-L52 | def stem(self, s):
"""
Performs stemming on the string.
:param s: the string to stem
:type s: str
:return: the stemmed string
:rtype: str
"""
return javabridge.get_env().get_string(self.__stem(javabridge.get_env().new_string_utf(s))) | [
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| python | train |
yougov/mongo-connector | mongo_connector/namespace_config.py | https://github.com/yougov/mongo-connector/blob/557cafd4b54c848cd54ef28a258391a154650cb4/mongo_connector/namespace_config.py#L560-L567 | def namespace_to_regex(namespace):
"""Create a RegexObject from a wildcard namespace."""
db_name, coll_name = namespace.split(".", 1)
# A database name cannot contain a '.' character
db_regex = re.escape(db_name).replace(r"\*", "([^.]*)")
# But a collection name can.
coll_regex = re.escape(coll_name).replace(r"\*", "(.*)")
return re.compile(r"\A" + db_regex + r"\." + coll_regex + r"\Z") | [
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| python | train |
pvlib/pvlib-python | pvlib/iotools/crn.py | https://github.com/pvlib/pvlib-python/blob/2e844a595b820b43d1170269781fa66bd0ccc8a3/pvlib/iotools/crn.py#L43-L106 | def read_crn(filename):
"""
Read NOAA USCRN [1]_ [2]_ fixed-width file into pandas dataframe.
Parameters
----------
filename: str
filepath or url to read for the fixed-width file.
Returns
-------
data: Dataframe
A dataframe with DatetimeIndex and all of the variables in the
file.
Notes
-----
CRN files contain 5 minute averages labeled by the interval ending
time. Here, missing data is flagged as NaN, rather than the lowest
possible integer for a field (e.g. -999 or -99). Air temperature in
deg C. Wind speed in m/s at a height of 1.5 m above ground level.
Variables corresponding to standard pvlib variables are renamed,
e.g. `SOLAR_RADIATION` becomes `ghi`. See the
`pvlib.iotools.crn.VARIABLE_MAP` dict for the complete mapping.
References
----------
.. [1] U.S. Climate Reference Network
`https://www.ncdc.noaa.gov/crn/qcdatasets.html
<https://www.ncdc.noaa.gov/crn/qcdatasets.html>`_
.. [2] Diamond, H. J. et. al., 2013: U.S. Climate Reference Network
after one decade of operations: status and assessment. Bull.
Amer. Meteor. Soc., 94, 489-498. :doi:`10.1175/BAMS-D-12-00170.1`
"""
# read in data
data = pd.read_fwf(filename, header=None, names=HEADERS.split(' '),
widths=WIDTHS)
# loop here because dtype kwarg not supported in read_fwf until 0.20
for (col, _dtype) in zip(data.columns, DTYPES):
data[col] = data[col].astype(_dtype)
# set index
# UTC_TIME does not have leading 0s, so must zfill(4) to comply
# with %H%M format
dts = data[['UTC_DATE', 'UTC_TIME']].astype(str)
dtindex = pd.to_datetime(dts['UTC_DATE'] + dts['UTC_TIME'].str.zfill(4),
format='%Y%m%d%H%M', utc=True)
data = data.set_index(dtindex)
try:
# to_datetime(utc=True) does not work in older versions of pandas
data = data.tz_localize('UTC')
except TypeError:
pass
# set nans
for val in [-99, -999, -9999]:
data = data.where(data != val, np.nan)
data = data.rename(columns=VARIABLE_MAP)
return data | [
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Parameters
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filename: str
filepath or url to read for the fixed-width file.
Returns
-------
data: Dataframe
A dataframe with DatetimeIndex and all of the variables in the
file.
Notes
-----
CRN files contain 5 minute averages labeled by the interval ending
time. Here, missing data is flagged as NaN, rather than the lowest
possible integer for a field (e.g. -999 or -99). Air temperature in
deg C. Wind speed in m/s at a height of 1.5 m above ground level.
Variables corresponding to standard pvlib variables are renamed,
e.g. `SOLAR_RADIATION` becomes `ghi`. See the
`pvlib.iotools.crn.VARIABLE_MAP` dict for the complete mapping.
References
----------
.. [1] U.S. Climate Reference Network
`https://www.ncdc.noaa.gov/crn/qcdatasets.html
<https://www.ncdc.noaa.gov/crn/qcdatasets.html>`_
.. [2] Diamond, H. J. et. al., 2013: U.S. Climate Reference Network
after one decade of operations: status and assessment. Bull.
Amer. Meteor. Soc., 94, 489-498. :doi:`10.1175/BAMS-D-12-00170.1` | [
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| python | train |
Koed00/django-q | django_q/tasks.py | https://github.com/Koed00/django-q/blob/c84fd11a67c9a47d821786dfcdc189bb258c6f54/django_q/tasks.py#L532-L540 | def run(self):
"""
Start queueing the chain to the worker cluster
:return: the chain's group id
"""
self.group = async_chain(chain=self.chain[:], group=self.group, cached=self.cached, sync=self.sync,
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self.started = True
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| python | train |
codelv/enaml-native | src/enamlnative/android/android_coordinator_layout.py | https://github.com/codelv/enaml-native/blob/c33986e9eda468c508806e0a3e73c771401e5718/src/enamlnative/android/android_coordinator_layout.py#L35-L40 | def create_widget(self):
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| python | train |
googlefonts/glyphsLib | Lib/glyphsLib/affine/__init__.py | https://github.com/googlefonts/glyphsLib/blob/9c12dc70c8d13f08d92b824e6710f6e3bb5037bb/Lib/glyphsLib/affine/__init__.py#L214-L243 | def rotation(cls, angle, pivot=None):
"""Create a rotation transform at the specified angle,
optionally about the specified pivot point.
:param angle: Rotation angle in degrees
:type angle: float
:param pivot: Point to rotate about, if omitted the
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:type pivot: sequence
:rtype: Affine
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| python | train |
saltstack/salt | salt/modules/macdefaults.py | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/macdefaults.py#L93-L116 | def delete(domain, key, user=None):
'''
Delete a default from the system
CLI Example:
.. code-block:: bash
salt '*' macdefaults.delete com.apple.CrashReporter DialogType
salt '*' macdefaults.delete NSGlobalDomain ApplePersistence
domain
The name of the domain to delete from
key
The key of the given domain to delete
user
The user to delete the defaults with
'''
cmd = 'defaults delete "{0}" "{1}"'.format(domain, key)
return __salt__['cmd.run_all'](cmd, runas=user, output_loglevel='debug') | [
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| python | train |
tell-k/django-modelsdoc | modelsdoc/templatetags/modelsdoc_tags.py | https://github.com/tell-k/django-modelsdoc/blob/c9d336e76251feb142347b3a41365430d3365436/modelsdoc/templatetags/modelsdoc_tags.py#L31-L54 | def emptylineless(parser, token):
"""
Removes empty line.
Example usage::
{% emptylineless %}
test1
test2
test3
{% endemptylineless %}
This example would return this HTML::
test1
test2
test3
"""
nodelist = parser.parse(('endemptylineless',))
parser.delete_first_token()
return EmptylinelessNode(nodelist) | [
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{% endemptylineless %}
This example would return this HTML::
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| python | train |
luckydonald/pytgbot | pytgbot/api_types/sendable/passport.py | https://github.com/luckydonald/pytgbot/blob/67f4b5a1510d4583d40b5477e876b1ef0eb8971b/pytgbot/api_types/sendable/passport.py#L91-L104 | def to_array(self):
"""
Serializes this PassportElementErrorDataField to a dictionary.
:return: dictionary representation of this object.
:rtype: dict
"""
array = super(PassportElementErrorDataField, self).to_array()
array['source'] = u(self.source) # py2: type unicode, py3: type str
array['type'] = u(self.type) # py2: type unicode, py3: type str
array['field_name'] = u(self.field_name) # py2: type unicode, py3: type str
array['data_hash'] = u(self.data_hash) # py2: type unicode, py3: type str
array['message'] = u(self.message) # py2: type unicode, py3: type str
return array | [
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| python | train |
fhcrc/seqmagick | seqmagick/transform.py | https://github.com/fhcrc/seqmagick/blob/1642bb87ba5c171fbd307f9da0f8a0ee1d69d5ed/seqmagick/transform.py#L380-L389 | def _update_id(record, new_id):
"""
Update a record id to new_id, also modifying the ID in record.description
"""
old_id = record.id
record.id = new_id
# At least for FASTA, record ID starts the description
record.description = re.sub('^' + re.escape(old_id), new_id, record.description)
return record | [
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| python | train |
spacetelescope/drizzlepac | drizzlepac/wcs_functions.py | https://github.com/spacetelescope/drizzlepac/blob/15bec3c929a6a869d9e71b9398ced43ede0620f1/drizzlepac/wcs_functions.py#L580-L708 | def mergeWCS(default_wcs, user_pars):
""" Merges the user specified WCS values given as dictionary derived from
the input configObj object with the output PyWCS object computed
using distortion.output_wcs().
The user_pars dictionary needs to have the following set of keys::
user_pars = {'ra':None,'dec':None,'scale':None,'rot':None,
'outnx':None,'outny':None,'crpix1':None,'crpix2':None}
"""
#
# Start by making a copy of the input WCS...
#
outwcs = default_wcs.deepcopy()
# If there are no user set parameters, just return a copy of
# the original WCS:
if all([upar is None for upar in user_pars.values()]):
return outwcs
if _check_custom_WCS_pars('ra', 'dec', user_pars):
_crval = (user_pars['ra'], user_pars['dec'])
else:
_crval = None
if ('scale' in user_pars and user_pars['scale'] is not None and
not _check_close_scale(user_pars['scale'], outwcs.pscale)):
_scale = user_pars['scale']
_ratio = outwcs.pscale / _scale
else:
_ratio = None
_scale = None
if ('rot' not in user_pars) or user_pars['rot'] is None:
_delta_rot = None
else:
_delta_rot = outwcs.orientat - user_pars['rot']
if _delta_rot == 0.0:
_delta_rot = None
if _check_custom_WCS_pars('crpix1', 'crpix2', user_pars):
_crpix = (user_pars['crpix1'], user_pars['crpix2'])
else:
_crpix = None
shape = None
if _check_custom_WCS_pars('outnx', 'outny', user_pars):
shape = (
int(_py2round(user_pars['outnx'])),
int(_py2round(user_pars['outny']))
)
if shape[0] < 1 or shape[1] < 1:
raise ValueError("Custom WCS output image size smaller than 1")
if _crpix is None:
# make sure new image is centered on the CRPIX of the old WCS:
_crpix = ((shape[0] + 1.0) / 2.0, (shape[1] + 1.0) / 2.0)
else:
naxis1, naxis2 = outwcs.pixel_shape
if _delta_rot is None:
# no rotation is involved
if _ratio is not None:
# apply scale only:
# compute output image shape:
shape = (
max(1, int(_py2round(_ratio * naxis1))),
max(1, int(_py2round(_ratio * naxis2)))
)
# update CRPIX:
if _crpix is None:
_crpix = 1.0 + _ratio * (outwcs.wcs.crpix - 1.0)
else:
_corners = np.array(
[[0.5, 0.5],
[naxis1 + 0.5, 0.5],
[0.5, naxis2 + 0.5],
[naxis1 + 0.5, naxis2 + 0.5]]
) - outwcs.wcs.crpix
if _ratio is not None:
# scale corners:
_corners *= _ratio
# rotate corners and find new image range:
((_xmin, _xmax), (_ymin, _ymax)) = util.getRotatedSize(_corners,
_delta_rot)
# compute output image shape:
# NOTE: _py2round may be replaced with np.ceil
shape = (
max(1, int(_py2round(_xmax - _xmin))),
max(1, int(_py2round(_ymax - _ymin)))
)
if _crpix is None:
# update CRPIX:
_crpix = (-_xmin + 0.5, -_ymin + 0.5)
# Set up the new WCS based on values from old one:
if _ratio is not None:
# Update plate scale
outwcs.wcs.cd = outwcs.wcs.cd / _ratio
outwcs.pscale = _scale
# update orientation
if _delta_rot is not None:
outwcs.wcs.cd = _rotateCD(outwcs.wcs.cd, _delta_rot)
outwcs.orientat -= _delta_rot
if shape is not None:
# update size:
outwcs.pixel_shape = shape
# update reference position
if _crpix is not None:
outwcs.wcs.crpix = np.array(_crpix, dtype=np.float64)
if _crval is not None:
outwcs.wcs.crval = np.array(_crval, dtype=np.float64)
return outwcs | [
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| python | train |
siznax/wptools | wptools/core.py | https://github.com/siznax/wptools/blob/100eaea585c34aa9ad87a9eda8982bb4898f6ec9/wptools/core.py#L107-L118 | def _continue_params(self):
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| python | train |
LogicalDash/LiSE | allegedb/allegedb/query.py | https://github.com/LogicalDash/LiSE/blob/fe6fd4f0a7c1780e065f4c9babb9bc443af6bb84/allegedb/allegedb/query.py#L599-L605 | def commit(self):
"""Commit the transaction"""
self.flush()
if hasattr(self, 'transaction') and self.transaction.is_active:
self.transaction.commit()
elif hasattr(self, 'connection'):
self.connection.commit() | [
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| python | train |
anti1869/sunhead | src/sunhead/conf.py | https://github.com/anti1869/sunhead/blob/5117ec797a38eb82d955241d20547d125efe80f3/src/sunhead/conf.py#L120-L137 | def discover_config_path(self, config_filename: str) -> str:
"""
Search for config file in a number of places.
If there is no config file found, will return None.
:param config_filename: Config file name or custom path to filename with config.
:return: Path to the discovered config file or None.
"""
if config_filename and os.path.isfile(config_filename):
return config_filename
for place in _common_places:
config_path = os.path.join(place, config_filename)
if os.path.isfile(config_path):
return config_path
return | [
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| python | train |
jason-weirather/py-seq-tools | seqtools/format/sam/__init__.py | https://github.com/jason-weirather/py-seq-tools/blob/f642c2c73ffef2acc83656a78059a476fc734ca1/seqtools/format/sam/__init__.py#L145-L163 | def target_sequence_length(self):
""" Get the length of the target sequence. length of the entire chromosome
throws an error if there is no information available
:return: length
:rtype: int
"""
if not self.is_aligned():
raise ValueError("no length for reference when read is not not aligned")
if self.entries.tlen: return self.entries.tlen #simplest is if tlen is set
if self.header:
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elif self.reference:
return len(self.reference[self.entries.rname])
else:
raise ValueError("some reference needs to be set to go from psl to bam\n")
raise ValueError("No reference available") | [
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:return: length
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| python | train |
StanfordVL/robosuite | robosuite/utils/mjcf_utils.py | https://github.com/StanfordVL/robosuite/blob/65cd16810e2ed647e3ec88746af3412065b7f278/robosuite/utils/mjcf_utils.py#L82-L97 | def new_site(name, rgba=RED, pos=(0, 0, 0), size=(0.005,), **kwargs):
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Creates a site element with attributes specified by @**kwargs.
Args:
name (str): site name.
rgba: color and transparency. Defaults to solid red.
pos: 3d position of the site.
size ([float]): site size (sites are spherical by default).
"""
kwargs["rgba"] = array_to_string(rgba)
kwargs["pos"] = array_to_string(pos)
kwargs["size"] = array_to_string(size)
kwargs["name"] = name
element = ET.Element("site", attrib=kwargs)
return element | [
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| python | train |
brocade/pynos | pynos/versions/ver_6/ver_6_0_1/yang/brocade_ntp.py | https://github.com/brocade/pynos/blob/bd8a34e98f322de3fc06750827d8bbc3a0c00380/pynos/versions/ver_6/ver_6_0_1/yang/brocade_ntp.py#L12-L23 | def show_ntp_input_rbridge_id(self, **kwargs):
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"""
config = ET.Element("config")
show_ntp = ET.Element("show_ntp")
config = show_ntp
input = ET.SubElement(show_ntp, "input")
rbridge_id = ET.SubElement(input, "rbridge-id")
rbridge_id.text = kwargs.pop('rbridge_id')
callback = kwargs.pop('callback', self._callback)
return callback(config) | [
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| python | train |
Yubico/yubikey-manager | ykman/cli/oath.py | https://github.com/Yubico/yubikey-manager/blob/3ac27bc59ae76a59db9d09a530494add2edbbabf/ykman/cli/oath.py#L403-L429 | def delete(ctx, query, force):
"""
Delete a credential.
Delete a credential from your YubiKey.
Provide a query string to match the credential to delete.
"""
ensure_validated(ctx)
controller = ctx.obj['controller']
creds = controller.list()
hits = _search(creds, query)
if len(hits) == 0:
click.echo('No matches, nothing to be done.')
elif len(hits) == 1:
cred = hits[0]
if force or (click.confirm(
u'Delete credential: {} ?'.format(cred.printable_key),
default=False, err=True
)):
controller.delete(cred)
click.echo(u'Deleted {}.'.format(cred.printable_key))
else:
click.echo('Deletion aborted by user.')
else:
_error_multiple_hits(ctx, hits) | [
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| python | train |
tamasgal/km3pipe | km3pipe/utils/streamds.py | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/utils/streamds.py#L56-L80 | def get_data(stream, parameters, fmt):
"""Retrieve data for given stream and parameters, or None if not found"""
sds = kp.db.StreamDS()
if stream not in sds.streams:
log.error("Stream '{}' not found in the database.".format(stream))
return
params = {}
if parameters:
for parameter in parameters:
if '=' not in parameter:
log.error(
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format(parameter)
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continue
key, value = parameter.split('=')
params[key] = value
data = sds.get(stream, fmt, **params)
if data is not None:
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print(data)
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sds.help(stream) | [
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| python | train |
brocade/pynos | pynos/versions/ver_6/ver_6_0_1/yang/brocade_system_monitor_ext.py | https://github.com/brocade/pynos/blob/bd8a34e98f322de3fc06750827d8bbc3a0c00380/pynos/versions/ver_6/ver_6_0_1/yang/brocade_system_monitor_ext.py#L154-L167 | def show_system_monitor_output_switch_status_port_status_port_name(self, **kwargs):
"""Auto Generated Code
"""
config = ET.Element("config")
show_system_monitor = ET.Element("show_system_monitor")
config = show_system_monitor
output = ET.SubElement(show_system_monitor, "output")
switch_status = ET.SubElement(output, "switch-status")
port_status = ET.SubElement(switch_status, "port-status")
port_name = ET.SubElement(port_status, "port-name")
port_name.text = kwargs.pop('port_name')
callback = kwargs.pop('callback', self._callback)
return callback(config) | [
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| python | train |
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