positive stringlengths 100 30.3k | anchor stringlengths 1 15k |
|---|---|
def yieldable(self):
"""Return True if there is a line that the buffer can return, False otherwise."""
if self.read_buffer is None:
return False
t = _remove_trailing_new_line(self.read_buffer)
n = _find_furthest_new_line(t)
if n >= 0:
return True
# we have read in entire file and have some unprocessed lines
if self.read_position == 0 and self.read_buffer is not None:
return True
return False | Return True if there is a line that the buffer can return, False otherwise. |
def _is_ignorable_404(uri):
"""
Returns True if the given request *shouldn't* notify the site managers.
"""
urls = getattr(django_settings, "IGNORABLE_404_URLS", ())
return any(pattern.search(uri) for pattern in urls) | Returns True if the given request *shouldn't* notify the site managers. |
def reply(self):
"""Reply to the selected status"""
status = self.get_selected_status()
app, user = self.app, self.user
if not app or not user:
self.footer.draw_message("You must be logged in to reply", Color.RED)
return
compose_modal = ComposeModal(self.stdscr, default_cw='\n'.join(status['spoiler_text']) or None, resize_callback=self.on_resize)
content, cw = compose_modal.loop()
self.full_redraw()
if content is None:
return
elif len(content) == 0:
self.footer.draw_message("Status must contain content", Color.RED)
return
self.footer.draw_message("Submitting reply...", Color.YELLOW)
response = api.post_status(app, user, content, spoiler_text=cw, sensitive=cw is not None, in_reply_to_id=status['id'])
status = parse_status(response)
self.statuses.insert(0, status)
self.selected += 1
self.left.draw_statuses(self.statuses, self.selected)
self.footer.draw_message("✓ Reply posted", Color.GREEN) | Reply to the selected status |
def get_mapping(session, table, candidates, generator, key_map):
"""Generate map of keys and values for the candidate from the generator.
:param session: The database session.
:param table: The table we will be inserting into (i.e. Feature or Label).
:param candidates: The candidates to get mappings for.
:param generator: A generator yielding (candidate_id, key, value) tuples.
:param key_map: A mutable dict which values will be added to as {key:
[relations]}.
:type key_map: Dict
:return: Generator of dictionaries of {"candidate_id": _, "keys": _, "values": _}
:rtype: generator of dict
"""
for cand in candidates:
# Grab the old values currently in the DB
try:
temp = session.query(table).filter(table.candidate_id == cand.id).one()
cand_map = dict(zip(temp.keys, temp.values))
except NoResultFound:
cand_map = {}
map_args = {"candidate_id": cand.id}
for cid, key, value in generator(cand):
if value == 0:
continue
cand_map[key] = value
# Assemble label arguments
map_args["keys"] = [*cand_map.keys()]
map_args["values"] = [*cand_map.values()]
# Update key_map by adding the candidate class for each key
for key in map_args["keys"]:
try:
key_map[key].add(cand.__class__.__tablename__)
except KeyError:
key_map[key] = {cand.__class__.__tablename__}
yield map_args | Generate map of keys and values for the candidate from the generator.
:param session: The database session.
:param table: The table we will be inserting into (i.e. Feature or Label).
:param candidates: The candidates to get mappings for.
:param generator: A generator yielding (candidate_id, key, value) tuples.
:param key_map: A mutable dict which values will be added to as {key:
[relations]}.
:type key_map: Dict
:return: Generator of dictionaries of {"candidate_id": _, "keys": _, "values": _}
:rtype: generator of dict |
def output(self, msg, indent, status=None):
""" Alias for print_indent_msg with color determined by status."""
color = None
if self.use_color:
color = get_color_from_status(status)
print_indent_msg(msg, indent, color) | Alias for print_indent_msg with color determined by status. |
def from_config(cls, cp, model, nprocesses=1, use_mpi=False):
"""Loads the sampler from the given config file."""
section = "sampler"
# check name
assert cp.get(section, "name") == cls.name, (
"name in section [sampler] must match mine")
# get the number of walkers to use
nwalkers = int(cp.get(section, "nwalkers"))
# get the checkpoint interval, if it's specified
checkpoint_interval = cls.checkpoint_from_config(cp, section)
checkpoint_signal = cls.ckpt_signal_from_config(cp, section)
# get the logpost function
lnpost = get_optional_arg_from_config(cp, section, 'logpost-function')
obj = cls(model, nwalkers,
checkpoint_interval=checkpoint_interval,
checkpoint_signal=checkpoint_signal,
logpost_function=lnpost, nprocesses=nprocesses,
use_mpi=use_mpi)
# set target
obj.set_target_from_config(cp, section)
# add burn-in if it's specified
obj.set_burn_in_from_config(cp)
# set prethin options
obj.set_thin_interval_from_config(cp, section)
return obj | Loads the sampler from the given config file. |
def create_node(kwargs):
'''
Convenience function to make the rest api call for node creation.
'''
if not isinstance(kwargs, dict):
kwargs = {}
# Required parameters
params = {
'Action': 'CreateInstance',
'InstanceType': kwargs.get('size_id', ''),
'RegionId': kwargs.get('region_id', DEFAULT_LOCATION),
'ImageId': kwargs.get('image_id', ''),
'SecurityGroupId': kwargs.get('securitygroup_id', ''),
'InstanceName': kwargs.get('name', ''),
}
# Optional parameters'
optional = [
'InstanceName', 'InternetChargeType',
'InternetMaxBandwidthIn', 'InternetMaxBandwidthOut',
'HostName', 'Password', 'SystemDisk.Category', 'VSwitchId'
# 'DataDisk.n.Size', 'DataDisk.n.Category', 'DataDisk.n.SnapshotId'
]
for item in optional:
if item in kwargs:
params.update({item: kwargs[item]})
# invoke web call
result = query(params)
return result['InstanceId'] | Convenience function to make the rest api call for node creation. |
def get_history_by_tail_number(self, tail_number, page=1, limit=100):
"""Fetch the history of a particular aircraft by its tail number.
This method can be used to get the history of a particular aircraft by its tail number.
It checks the user authentication and returns the data accordingly.
Args:
tail_number (str): The tail number, e.g. VT-ANL
page (int): Optional page number; for users who are on a plan with flightradar24 they can pass in higher page numbers to get more data
limit (int): Optional limit on number of records returned
Returns:
A list of dicts with the data; one dict for each row of data from flightradar24
Example::
from pyflightdata import FlightData
f=FlightData()
#optional login
f.login(myemail,mypassword)
f.get_history_by_flight_number('VT-ANL')
f.get_history_by_flight_number('VT-ANL',page=1,limit=10)
"""
url = REG_BASE.format(tail_number, str(self.AUTH_TOKEN), page, limit)
return self._fr24.get_data(url, True) | Fetch the history of a particular aircraft by its tail number.
This method can be used to get the history of a particular aircraft by its tail number.
It checks the user authentication and returns the data accordingly.
Args:
tail_number (str): The tail number, e.g. VT-ANL
page (int): Optional page number; for users who are on a plan with flightradar24 they can pass in higher page numbers to get more data
limit (int): Optional limit on number of records returned
Returns:
A list of dicts with the data; one dict for each row of data from flightradar24
Example::
from pyflightdata import FlightData
f=FlightData()
#optional login
f.login(myemail,mypassword)
f.get_history_by_flight_number('VT-ANL')
f.get_history_by_flight_number('VT-ANL',page=1,limit=10) |
def calculate(cls, byte_arr, crc=0):
"""Compute CRC for input bytes."""
for byte in byte_iter(byte_arr):
# Taken verbatim from FIT SDK docs
tmp = cls.CRC_TABLE[crc & 0xF]
crc = (crc >> 4) & 0x0FFF
crc = crc ^ tmp ^ cls.CRC_TABLE[byte & 0xF]
tmp = cls.CRC_TABLE[crc & 0xF]
crc = (crc >> 4) & 0x0FFF
crc = crc ^ tmp ^ cls.CRC_TABLE[(byte >> 4) & 0xF]
return crc | Compute CRC for input bytes. |
def exists(name, attributes):
'''
Make sure the given attributes exist on the file/directory
name
The path to the file/directory
attributes
The attributes that should exist on the file/directory, this is accepted as
an array, with key and value split with an equals sign, if you want to specify
a hex value then add 0x to the beginning of the value.
'''
ret = {'name': name,
'result': True,
'comment': '',
'changes': {}}
if not os.path.exists(name):
ret['result'] = False
ret['comment'] = "File or directory doesn't exist"
return ret
current_attrs = __salt__['xattr.list'](name)
current_ids = current_attrs.keys()
for attr in attributes:
attr_id, attr_val = attr.split("=")
attr_hex = attr_val.startswith("0x")
if attr_hex:
# Remove spaces and new lines so we can match these
current_attrs[attr_id] = __salt__['xattr.read'](name, attr_id, hex=True).replace(" ", "").replace("\n", "")
attr_val = attr_val[2:].replace(" ", "")
if attr_id not in current_attrs:
value_matches = False
else:
value_matches = ((current_attrs[attr_id] == attr_val) or
(attr_hex and current_attrs[attr_id] == attr_val))
if attr_id in current_ids and value_matches:
continue
else:
ret['changes'][attr_id] = attr_val
__salt__['xattr.write'](name, attr_id, attr_val, attr_hex)
if not ret['changes']:
ret['comment'] = 'All values existed correctly.'
return ret | Make sure the given attributes exist on the file/directory
name
The path to the file/directory
attributes
The attributes that should exist on the file/directory, this is accepted as
an array, with key and value split with an equals sign, if you want to specify
a hex value then add 0x to the beginning of the value. |
def factory(**default_opts):
"""
Factory function to create decorators for tasks' run methods. Default options for the decorator
function can be given in *default_opts*. The returned decorator can be used with or without
function invocation. Example:
.. code-block:: python
@factory(digits=2)
def runtime(fn, opts, task, *args, **kwargs):
t0 = time.time()
try:
return fn(task, *args, **kwargs)
finally:
t1 = time.time()
diff = round(t1 - t0, opts["digits"])
print("runtime:")
print(diff)
...
class MyTask(law.Task):
@runtime
def run(self):
...
# or
@runtime(digits=3):
def run(self):
...
.. note::
Decorators might not have the expected behavior when used to decorate generator functions
such as ``Task.run()`` methods that yield dynamic dependencies.
"""
def wrapper(decorator):
@functools.wraps(decorator)
def wrapper(fn=None, **opts):
_opts = default_opts.copy()
_opts.update(opts)
def wrapper(fn):
@functools.wraps(fn)
def wrapper(*args, **kwargs):
return decorator(fn, _opts, *args, **kwargs)
return wrapper
return wrapper if fn is None else wrapper(fn)
return wrapper
return wrapper | Factory function to create decorators for tasks' run methods. Default options for the decorator
function can be given in *default_opts*. The returned decorator can be used with or without
function invocation. Example:
.. code-block:: python
@factory(digits=2)
def runtime(fn, opts, task, *args, **kwargs):
t0 = time.time()
try:
return fn(task, *args, **kwargs)
finally:
t1 = time.time()
diff = round(t1 - t0, opts["digits"])
print("runtime:")
print(diff)
...
class MyTask(law.Task):
@runtime
def run(self):
...
# or
@runtime(digits=3):
def run(self):
...
.. note::
Decorators might not have the expected behavior when used to decorate generator functions
such as ``Task.run()`` methods that yield dynamic dependencies. |
async def get_stats(self, battletag: str, regions=(EUROPE, KOREA, AMERICAS, CHINA, JAPAN, ANY),
platform=None, _session=None, handle_ratelimit=None, max_tries=None, request_timeout=None):
"""Returns the stats for the profiles on the specified regions and platform. The format for regions without a matching user, the format is the same as get_profile.
The stats are returned in a dictionary with a similar format to what https://github.com/SunDwarf/OWAPI/blob/master/api.md#get-apiv3ubattletagstats specifies."""
if platform is None:
platform = self.default_platform
try:
blob_dict = await self._base_request(battletag, "stats", _session, platform=platform,
handle_ratelimit=handle_ratelimit, max_tries=max_tries,
request_timeout=request_timeout)
except ProfileNotFoundError as e:
# The battletag doesn't exist
blob_dict = {}
existing_regions = {key: val for key, val in blob_dict.items() if ((val is not None) and (key != "_request"))}
return {key: [inner_val for inner_key, inner_val in val.items() if inner_key == "stats"][0] for key, val in
existing_regions.items() if key in regions} | Returns the stats for the profiles on the specified regions and platform. The format for regions without a matching user, the format is the same as get_profile.
The stats are returned in a dictionary with a similar format to what https://github.com/SunDwarf/OWAPI/blob/master/api.md#get-apiv3ubattletagstats specifies. |
def is_restricted(self, assets, dt):
"""
Returns whether or not an asset or iterable of assets is restricted
on a dt.
"""
if isinstance(assets, Asset):
return self._is_restricted_for_asset(assets, dt)
is_restricted = partial(self._is_restricted_for_asset, dt=dt)
return pd.Series(
index=pd.Index(assets),
data=vectorize(is_restricted, otypes=[bool])(assets)
) | Returns whether or not an asset or iterable of assets is restricted
on a dt. |
def groups_moderators(self, room_id=None, group=None, **kwargs):
"""Lists all moderators of a group."""
if room_id:
return self.__call_api_get('groups.moderators', roomId=room_id, kwargs=kwargs)
elif group:
return self.__call_api_get('groups.moderators', roomName=group, kwargs=kwargs)
else:
raise RocketMissingParamException('roomId or group required') | Lists all moderators of a group. |
def export(self, node):
"""Export tree starting at `node`."""
attriter = self.attriter or (lambda attr_values: attr_values)
return self.__export(node, self.dictcls, attriter, self.childiter) | Export tree starting at `node`. |
def __get_config(self):
""" Really connect """
if not self.name:
room_resp = self.conn.get(BASE_URL + "/new")
room_resp.raise_for_status()
url = room_resp.url
try:
self.name = re.search(r"r/(.+?)$", url).group(1)
except Exception:
raise IOError("Failed to create room")
params = {"room": self.name}
if self.key:
params["roomKey"] = self.key
if self.password:
params["password"] = self.password
config = self.conn.make_api_call("getRoomConfig", params)
if "error" in config:
raise RuntimeError(
f"Failed to get room config for {self.name}\n"
f"{config['error'].get('message') or config['error']}"
)
self.config.update(config)
self.__add_prop("private")
self.__add_prop("title")
self.__add_prop("motd")
self.__add_prop("adult")
self.__add_prop("disabled", True)
self.__add_prop("file_ttl", True)
return (self.config.room_id, self.config.owner, config["checksum2"]) | Really connect |
def update_balances(self, recursive=True):
"""
Calculate tree balance factor
"""
if self.node:
if recursive:
if self.node.left:
self.node.left.update_balances()
if self.node.right:
self.node.right.update_balances()
self.balance = self.node.left.height - self.node.right.height
else:
self.balance = 0 | Calculate tree balance factor |
def _get_metadata_path(self, key):
""" return the metadata pathname for this key """
return "{group}/meta/{key}/meta".format(group=self.group._v_pathname,
key=key) | return the metadata pathname for this key |
async def withdraw_bulk(self, *args, **kwargs):
"""
Withdraw funds requests to user wallet
Accepts:
- coinid [string] (blockchain id (example: BTCTEST, LTCTEST))
- address [string] withdrawal address (in hex for tokens)
- amount [int] withdrawal amount multiplied by decimals_k (10**8)
Returns dictionary with following fields:
- success [bool]
"""
await self.db.withdraw_requests.insert_one({
'coinid': kwargs.get("coinid"),
'address': kwargs.get("address"),
'amount': int(kwargs.get("amount")),
'timestamp': datetime.datetime.utcnow()
})
return {'success': True} | Withdraw funds requests to user wallet
Accepts:
- coinid [string] (blockchain id (example: BTCTEST, LTCTEST))
- address [string] withdrawal address (in hex for tokens)
- amount [int] withdrawal amount multiplied by decimals_k (10**8)
Returns dictionary with following fields:
- success [bool] |
def update(self, enabled=values.unset, webhook_url=values.unset,
webhook_method=values.unset):
"""
Update the ExportConfigurationInstance
:param bool enabled: The enabled
:param unicode webhook_url: The webhook_url
:param unicode webhook_method: The webhook_method
:returns: Updated ExportConfigurationInstance
:rtype: twilio.rest.preview.bulk_exports.export_configuration.ExportConfigurationInstance
"""
data = values.of({'Enabled': enabled, 'WebhookUrl': webhook_url, 'WebhookMethod': webhook_method, })
payload = self._version.update(
'POST',
self._uri,
data=data,
)
return ExportConfigurationInstance(
self._version,
payload,
resource_type=self._solution['resource_type'],
) | Update the ExportConfigurationInstance
:param bool enabled: The enabled
:param unicode webhook_url: The webhook_url
:param unicode webhook_method: The webhook_method
:returns: Updated ExportConfigurationInstance
:rtype: twilio.rest.preview.bulk_exports.export_configuration.ExportConfigurationInstance |
def poll(self):
""" Poll the job status.
Returns the changes in this iteration."""
self.runner.module_name = 'async_status'
self.runner.module_args = "jid=%s" % self.jid
self.runner.pattern = "*"
self.runner.background = 0
self.runner.inventory.restrict_to(self.hosts_to_poll)
results = self.runner.run()
self.runner.inventory.lift_restriction()
hosts = []
poll_results = { 'contacted': {}, 'dark': {}, 'polled': {}}
for (host, res) in results['contacted'].iteritems():
if res.get('started',False):
hosts.append(host)
poll_results['polled'][host] = res
else:
self.results['contacted'][host] = res
poll_results['contacted'][host] = res
if 'failed' in res:
self.runner.callbacks.on_async_failed(host, res, self.jid)
else:
self.runner.callbacks.on_async_ok(host, res, self.jid)
for (host, res) in results['dark'].iteritems():
self.results['dark'][host] = res
poll_results['dark'][host] = res
self.runner.callbacks.on_async_failed(host, res, self.jid)
self.hosts_to_poll = hosts
if len(hosts)==0:
self.completed = True
return poll_results | Poll the job status.
Returns the changes in this iteration. |
def modify(name, **kwargs):
'''
Modify an existing job in the schedule
CLI Example:
.. code-block:: bash
salt '*' schedule.modify job1 function='test.ping' seconds=3600
'''
ret = {'comment': '',
'changes': {},
'result': True}
time_conflict = False
for item in ['seconds', 'minutes', 'hours', 'days']:
if item in kwargs and 'when' in kwargs:
time_conflict = True
if item in kwargs and 'cron' in kwargs:
time_conflict = True
if time_conflict:
ret['result'] = False
ret['comment'] = 'Error: Unable to use "seconds", "minutes", "hours", or "days" with "when" option.'
return ret
if 'when' in kwargs and 'cron' in kwargs:
ret['result'] = False
ret['comment'] = 'Unable to use "when" and "cron" options together. Ignoring.'
return ret
current_schedule = list_(show_all=True, return_yaml=False)
if name not in current_schedule:
ret['comment'] = 'Job {0} does not exist in schedule.'.format(name)
ret['result'] = False
return ret
_current = current_schedule[name]
if '_seconds' in _current:
_current['seconds'] = _current['_seconds']
del _current['_seconds']
_new = build_schedule_item(name, **kwargs)
if 'result' in _new and not _new['result']:
return _new
if _new == _current:
ret['comment'] = 'Job {0} in correct state'.format(name)
return ret
_current_lines = ['{0}:{1}\n'.format(key, value)
for (key, value) in sorted(_current.items())]
_new_lines = ['{0}:{1}\n'.format(key, value)
for (key, value) in sorted(_new.items())]
_diff = difflib.unified_diff(_current_lines, _new_lines)
ret['changes']['diff'] = ''.join(_diff)
if 'test' in kwargs and kwargs['test']:
ret['comment'] = 'Job: {0} would be modified in schedule.'.format(name)
else:
persist = True
if 'persist' in kwargs:
persist = kwargs['persist']
if name in list_(show_all=True, where='opts', return_yaml=False):
event_data = {'name': name,
'schedule': _new,
'func': 'modify',
'persist': persist}
elif name in list_(show_all=True, where='pillar', return_yaml=False):
event_data = {'name': name,
'schedule': _new,
'where': 'pillar',
'func': 'modify',
'persist': False}
out = __salt__['event.fire'](event_data, 'manage_schedule')
if out:
ret['comment'] = 'Modified job: {0} in schedule.'.format(name)
else:
ret['comment'] = 'Failed to modify job {0} in schedule.'.format(name)
ret['result'] = False
return ret | Modify an existing job in the schedule
CLI Example:
.. code-block:: bash
salt '*' schedule.modify job1 function='test.ping' seconds=3600 |
def close(self):
""" Close all connections and empty the pool.
This method is thread safe.
"""
if self._closed:
return
try:
with self.lock:
if not self._closed:
self._closed = True
for address in list(self.connections):
self.remove(address)
except TypeError as e:
pass | Close all connections and empty the pool.
This method is thread safe. |
def setup_scrollarea(self):
"""Setup the scrollarea that will contain the FigureThumbnails."""
self.view = QWidget()
self.scene = QGridLayout(self.view)
self.scene.setColumnStretch(0, 100)
self.scene.setColumnStretch(2, 100)
self.scrollarea = QScrollArea()
self.scrollarea.setWidget(self.view)
self.scrollarea.setWidgetResizable(True)
self.scrollarea.setFrameStyle(0)
self.scrollarea.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.scrollarea.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.scrollarea.setSizePolicy(QSizePolicy(QSizePolicy.Ignored,
QSizePolicy.Preferred))
# Set the vertical scrollbar explicitely :
# This is required to avoid a "RuntimeError: no access to protected
# functions or signals for objects not created from Python" in Linux.
self.scrollarea.setVerticalScrollBar(QScrollBar())
return self.scrollarea | Setup the scrollarea that will contain the FigureThumbnails. |
def glob(self, pattern):
""" Return a list of Path objects that match the pattern.
`pattern` - a path relative to this directory, with wildcards.
For example, ``Path('/users').glob('*/bin/*')`` returns a list
of all the files users have in their :file:`bin` directories.
.. seealso:: :func:`glob.glob`
.. note:: Glob is **not** recursive, even when using ``**``.
To do recursive globbing see :func:`walk`,
:func:`walkdirs` or :func:`walkfiles`.
"""
cls = self._next_class
return [cls(s) for s in glob.glob(self / pattern)] | Return a list of Path objects that match the pattern.
`pattern` - a path relative to this directory, with wildcards.
For example, ``Path('/users').glob('*/bin/*')`` returns a list
of all the files users have in their :file:`bin` directories.
.. seealso:: :func:`glob.glob`
.. note:: Glob is **not** recursive, even when using ``**``.
To do recursive globbing see :func:`walk`,
:func:`walkdirs` or :func:`walkfiles`. |
def fcoe_get_login_output_fcoe_login_list_fcoe_login_fcoe_interface_name(self, **kwargs):
"""Auto Generated Code
"""
config = ET.Element("config")
fcoe_get_login = ET.Element("fcoe_get_login")
config = fcoe_get_login
output = ET.SubElement(fcoe_get_login, "output")
fcoe_login_list = ET.SubElement(output, "fcoe-login-list")
fcoe_login_session_mac_key = ET.SubElement(fcoe_login_list, "fcoe-login-session-mac")
fcoe_login_session_mac_key.text = kwargs.pop('fcoe_login_session_mac')
fcoe_login_fcoe_interface_name = ET.SubElement(fcoe_login_list, "fcoe-login-fcoe-interface-name")
fcoe_login_fcoe_interface_name.text = kwargs.pop('fcoe_login_fcoe_interface_name')
callback = kwargs.pop('callback', self._callback)
return callback(config) | Auto Generated Code |
def fetch_and_transform(
transformed_filename,
transformer,
loader,
source_filename,
source_url,
subdir=None):
"""
Fetch a remote file from `source_url`, save it locally as `source_filename` and then use
the `loader` and `transformer` function arguments to turn this saved data into an in-memory
object.
"""
transformed_path = build_path(transformed_filename, subdir)
if not os.path.exists(transformed_path):
source_path = fetch_file(source_url, source_filename, subdir)
logger.info("Generating data file %s from %s", transformed_path, source_path)
result = transformer(source_path, transformed_path)
else:
logger.info("Cached data file: %s", transformed_path)
result = loader(transformed_path)
assert os.path.exists(transformed_path)
return result | Fetch a remote file from `source_url`, save it locally as `source_filename` and then use
the `loader` and `transformer` function arguments to turn this saved data into an in-memory
object. |
def get_logger(name=None, format_string=None):
"""
:type name: str
:param name: used for declaring log channels.
:type format_string: str
:param format_string: for custom formatting
"""
logging.captureWarnings(True)
log = logging.getLogger(name)
# Bind custom method to instance.
# Source: https://stackoverflow.com/a/2982
log.set_debug_level = _set_debug_level.__get__(log)
log.set_debug_level(0)
if not format_string:
format_string = '[%(module)s]\t%(levelname)s\t%(message)s'
# Setting up log formats
log.handlers = []
handler = logging.StreamHandler(sys.stderr)
handler.setFormatter(
logging.Formatter(format_string),
)
log.addHandler(handler)
return log | :type name: str
:param name: used for declaring log channels.
:type format_string: str
:param format_string: for custom formatting |
def utcoffset(self):
"""
:return:
None or a datetime.timedelta() of the offset from UTC
"""
if self.tzinfo is None:
return None
return self.tzinfo.utcoffset(self.replace(year=2000)) | :return:
None or a datetime.timedelta() of the offset from UTC |
def _dnsname_match(dn, hostname, max_wildcards=1):
"""Matching according to RFC 6125, section 6.4.3
http://tools.ietf.org/html/rfc6125#section-6.4.3
"""
pats = []
if not dn:
return False
split_dn = dn.split(r'.')
leftmost, remainder = split_dn[0], split_dn[1:]
wildcards = leftmost.count('*')
if wildcards > max_wildcards:
# Issue #17980: avoid denials of service by refusing more
# than one wildcard per fragment. A survey of established
# policy among SSL implementations showed it to be a
# reasonable choice.
raise CertificateError(
"too many wildcards in certificate DNS name: " + repr(dn))
# speed up common case w/o wildcards
if not wildcards:
return dn.lower() == hostname.lower()
# RFC 6125, section 6.4.3, subitem 1.
# The client SHOULD NOT attempt to match a presented identifier in which
# the wildcard character comprises a label other than the left-most label.
if leftmost == '*':
# When '*' is a fragment by itself, it matches a non-empty dotless
# fragment.
pats.append('[^.]+')
elif leftmost.startswith('xn--') or hostname.startswith('xn--'):
# RFC 6125, section 6.4.3, subitem 3.
# The client SHOULD NOT attempt to match a presented identifier
# where the wildcard character is embedded within an A-label or
# U-label of an internationalized domain name.
pats.append(re.escape(leftmost))
else:
# Otherwise, '*' matches any dotless string, e.g. www*
pats.append(re.escape(leftmost).replace(r'\*', '[^.]*'))
# add the remaining fragments, ignore any wildcards
for frag in remainder:
pats.append(re.escape(frag))
pat = re.compile(r'\A' + r'\.'.join(pats) + r'\Z', re.IGNORECASE)
return pat.match(hostname) | Matching according to RFC 6125, section 6.4.3
http://tools.ietf.org/html/rfc6125#section-6.4.3 |
def captureQuery(self, query, params=(), engine=None, **kwargs):
"""
Creates an event for a SQL query.
>>> client.captureQuery('SELECT * FROM foo')
"""
return self.capture(
'raven.events.Query', query=query, params=params, engine=engine,
**kwargs) | Creates an event for a SQL query.
>>> client.captureQuery('SELECT * FROM foo') |
def create_segments(self, segments):
"""Enqueue segment creates"""
for segment in segments:
s_res = MechResource(segment['id'], a_const.SEGMENT_RESOURCE,
a_const.CREATE)
self.provision_queue.put(s_res) | Enqueue segment creates |
def get(self, arg):
"""
Return instance object with given EC2 ID or nametag.
"""
try:
reservations = self.get_all_instances(filters={'tag:Name': [arg]})
instance = reservations[0].instances[0]
except IndexError:
try:
instance = self.get_all_instances([arg])[0].instances[0]
except (_ResponseError, IndexError):
# TODO: encapsulate actual exception for debugging
err = "Can't find any instance with name or ID '%s'" % arg
raise ValueError(err)
return instance | Return instance object with given EC2 ID or nametag. |
def indices_to_labels(self, indices: Sequence[int]) -> List[str]:
""" Converts a sequence of indices into their corresponding labels."""
return [(self.INDEX_TO_LABEL[index]) for index in indices] | Converts a sequence of indices into their corresponding labels. |
def _generate_examples(self, data_dir):
"""Generate corrupted Cifar10 test data.
Apply corruptions to the raw images according to self.corruption_type.
Args:
data_dir: root directory of downloaded dataset
Yields:
dictionary with image file and label.
"""
corruption = self.builder_config.corruption
severity = self.builder_config.severity
images_file = os.path.join(data_dir, _CORRUPTIONS_TO_FILENAMES[corruption])
labels_file = os.path.join(data_dir, _LABELS_FILENAME)
with tf.io.gfile.GFile(labels_file, mode='rb') as f:
labels = np.load(f)
num_images = labels.shape[0] // 5
# Labels are stacked 5 times so we can just read the first iteration
labels = labels[:num_images]
with tf.io.gfile.GFile(images_file, mode='rb') as f:
images = np.load(f)
# Slice images corresponding to correct severity level
images = images[(severity - 1) * num_images:severity * num_images]
for image, label in zip(images, labels):
yield {
'image': image,
'label': label,
} | Generate corrupted Cifar10 test data.
Apply corruptions to the raw images according to self.corruption_type.
Args:
data_dir: root directory of downloaded dataset
Yields:
dictionary with image file and label. |
def par_relax_AX(i):
"""Parallel implementation of relaxation if option ``RelaxParam`` !=
1.0.
"""
global mp_X
global mp_Xnr
global mp_DX
global mp_DXnr
mp_Xnr[mp_grp[i]:mp_grp[i+1]] = mp_X[mp_grp[i]:mp_grp[i+1]]
mp_DXnr[i] = mp_DX[i]
if mp_rlx != 1.0:
grpind = slice(mp_grp[i], mp_grp[i+1])
mp_X[grpind] = mp_rlx * mp_X[grpind] + (1-mp_rlx)*mp_Y1[grpind]
mp_DX[i] = mp_rlx*mp_DX[i] + (1-mp_rlx)*mp_Y0[i] | Parallel implementation of relaxation if option ``RelaxParam`` !=
1.0. |
def _ExtractMetadataFromFileEntry(self, mediator, file_entry, data_stream):
"""Extracts metadata from a file entry.
Args:
mediator (ParserMediator): mediates the interactions between
parsers and other components, such as storage and abort signals.
file_entry (dfvfs.FileEntry): file entry to extract metadata from.
data_stream (dfvfs.DataStream): data stream or None if the file entry
has no data stream.
"""
# Do not extract metadata from the root file entry when it is virtual.
if file_entry.IsRoot() and file_entry.type_indicator not in (
self._TYPES_WITH_ROOT_METADATA):
return
# We always want to extract the file entry metadata but we only want
# to parse it once per file entry, so we only use it if we are
# processing the default data stream of regular files.
if data_stream and not data_stream.IsDefault():
return
display_name = mediator.GetDisplayName()
logger.debug(
'[ExtractMetadataFromFileEntry] processing file entry: {0:s}'.format(
display_name))
self.processing_status = definitions.STATUS_INDICATOR_EXTRACTING
if self._processing_profiler:
self._processing_profiler.StartTiming('extracting')
self._event_extractor.ParseFileEntryMetadata(mediator, file_entry)
if self._processing_profiler:
self._processing_profiler.StopTiming('extracting')
self.processing_status = definitions.STATUS_INDICATOR_RUNNING | Extracts metadata from a file entry.
Args:
mediator (ParserMediator): mediates the interactions between
parsers and other components, such as storage and abort signals.
file_entry (dfvfs.FileEntry): file entry to extract metadata from.
data_stream (dfvfs.DataStream): data stream or None if the file entry
has no data stream. |
def strip_prompt(self, a_string):
""" Strip 'Done' from command output """
output = super(NetscalerSSH, self).strip_prompt(a_string)
lines = output.split(self.RESPONSE_RETURN)
if "Done" in lines[-1]:
return self.RESPONSE_RETURN.join(lines[:-1])
else:
return output | Strip 'Done' from command output |
def _create_significance_table(self,data):
"""
Create a table containing p-values for significance tests. Add features of
the distributions and the p-values to the dataframe.
Parameters
----------
data : pandas DataFrame
The input dataset.
Returns
----------
df : pandas DataFrame
A table containing the p-values, test name, etc.
"""
# list features of the variable e.g. matched, paired, n_expected
df=pd.DataFrame(index=self._continuous+self._categorical,
columns=['continuous','nonnormal','min_observed','pval','ptest'])
df.index.rename('variable', inplace=True)
df['continuous'] = np.where(df.index.isin(self._continuous),True,False)
df['nonnormal'] = np.where(df.index.isin(self._nonnormal),True,False)
# list values for each variable, grouped by groupby levels
for v in df.index:
is_continuous = df.loc[v]['continuous']
is_categorical = ~df.loc[v]['continuous']
is_normal = ~df.loc[v]['nonnormal']
# if continuous, group data into list of lists
if is_continuous:
catlevels = None
grouped_data = []
for s in self._groupbylvls:
lvl_data = data.loc[data[self._groupby]==s, v]
# coerce to numeric and drop non-numeric data
lvl_data = lvl_data.apply(pd.to_numeric, errors='coerce').dropna()
# append to overall group data
grouped_data.append(lvl_data.values)
min_observed = len(min(grouped_data,key=len))
# if categorical, create contingency table
elif is_categorical:
catlevels = sorted(data[v].astype('category').cat.categories)
grouped_data = pd.crosstab(data[self._groupby].rename('_groupby_var_'),data[v])
min_observed = grouped_data.sum(axis=1).min()
# minimum number of observations across all levels
df.loc[v,'min_observed'] = min_observed
# compute pvalues
df.loc[v,'pval'],df.loc[v,'ptest'] = self._p_test(v,
grouped_data,is_continuous,is_categorical,
is_normal,min_observed,catlevels)
return df | Create a table containing p-values for significance tests. Add features of
the distributions and the p-values to the dataframe.
Parameters
----------
data : pandas DataFrame
The input dataset.
Returns
----------
df : pandas DataFrame
A table containing the p-values, test name, etc. |
def rlmb_tiny_stochastic():
"""Tiny setting with a stochastic next-frame model."""
hparams = rlmb_ppo_tiny()
hparams.epochs = 1 # Too slow with 2 for regular runs.
hparams.generative_model = "next_frame_basic_stochastic"
hparams.generative_model_params = "next_frame_basic_stochastic"
return hparams | Tiny setting with a stochastic next-frame model. |
def cli(**args):
""" Shakedown is a DC/OS test-harness wrapper for the pytest tool.
"""
import shakedown
# Read configuration options from ~/.shakedown (if exists)
args = read_config(args)
# Set configuration defaults
args = set_config_defaults(args)
if args['quiet']:
shakedown.cli.quiet = True
if not args['dcos_url']:
try:
args['dcos_url'] = dcos_url()
except:
click.secho('error: cluster URL not set, use --dcos-url or see --help for more information.', fg='red', bold=True)
sys.exit(1)
if not args['dcos_url']:
click.secho('error: --dcos-url is a required option; see --help for more information.', fg='red', bold=True)
sys.exit(1)
if args['ssh_key_file']:
shakedown.cli.ssh_key_file = args['ssh_key_file']
if args['ssh_user']:
shakedown.cli.ssh_user = args['ssh_user']
if not args['no_banner']:
echo(banner(), n=False)
echo('Running pre-flight checks...', d='step-maj')
# required modules and their 'version' method
imported = {}
requirements = {
'pytest': '__version__',
'dcos': 'version'
}
for req in requirements:
ver = requirements[req]
echo("Checking for {} library...".format(req), d='step-min', n=False)
try:
imported[req] = importlib.import_module(req, package=None)
except ImportError:
click.secho("error: {p} is not installed; run 'pip install {p}'.".format(p=req), fg='red', bold=True)
sys.exit(1)
echo(getattr(imported[req], requirements[req]))
if shakedown.attach_cluster(args['dcos_url']):
echo('Checking DC/OS cluster version...', d='step-min', n=False)
echo(shakedown.dcos_version())
else:
with imported['dcos'].cluster.setup_directory() as temp_path:
imported['dcos'].cluster.set_attached(temp_path)
imported['dcos'].config.set_val('core.dcos_url', args['dcos_url'])
if args['ssl_no_verify']:
imported['dcos'].config.set_val('core.ssl_verify', 'False')
try:
imported['dcos'].cluster.setup_cluster_config(args['dcos_url'], temp_path, False)
except:
echo('Authenticating with DC/OS cluster...', d='step-min')
authenticated = False
token = imported['dcos'].config.get_config_val("core.dcos_acs_token")
if token is not None:
echo('trying existing ACS token...', d='step-min', n=False)
try:
shakedown.dcos_leader()
authenticated = True
echo(fchr('PP'), d='pass')
except imported['dcos'].errors.DCOSException:
echo(fchr('FF'), d='fail')
if not authenticated and args['oauth_token']:
try:
echo('trying OAuth token...', d='item-maj', n=False)
token = shakedown.authenticate_oauth(args['oauth_token'])
with stdchannel_redirected(sys.stderr, os.devnull):
imported['dcos'].config.set_val('core.dcos_acs_token', token)
authenticated = True
echo(fchr('PP'), d='pass')
except:
echo(fchr('FF'), d='fail')
if not authenticated and args['username'] and args['password']:
try:
echo('trying username and password...', d='item-maj', n=False)
token = shakedown.authenticate(args['username'], args['password'])
with stdchannel_redirected(sys.stderr, os.devnull):
imported['dcos'].config.set_val('core.dcos_acs_token', token)
authenticated = True
echo(fchr('PP'), d='pass')
except:
echo(fchr('FF'), d='fail')
if authenticated:
imported['dcos'].cluster.setup_cluster_config(args['dcos_url'], temp_path, False)
echo('Checking DC/OS cluster version...', d='step-min', n=False)
echo(shakedown.dcos_version())
else:
click.secho("error: no authentication credentials or token found.", fg='red', bold=True)
sys.exit(1)
class shakedown:
""" This encapsulates a PyTest wrapper plugin
"""
state = {}
stdout = []
tests = {
'file': {},
'test': {}
}
report_stats = {
'passed':[],
'skipped':[],
'failed':[],
'total_passed':0,
'total_skipped':0,
'total_failed':0,
}
def output(title, state, text, status=True):
""" Capture and display stdout/stderr output
:param title: the title of the output box (eg. test name)
:type title: str
:param state: state of the result (pass, fail)
:type state: str
:param text: the stdout/stderr output
:type text: str
:param status: whether to output a status marker
:type status: bool
"""
if state == 'fail':
schr = fchr('FF')
elif state == 'pass':
schr = fchr('PP')
elif state == 'skip':
schr = fchr('SK')
else:
schr = ''
if status:
if not args['stdout_inline']:
if state == 'fail':
echo(schr, d='fail')
elif state == 'pass':
echo(schr, d='pass')
else:
if not text:
if state == 'fail':
echo(schr, d='fail')
elif state == 'pass':
if '::' in title:
echo(title.split('::')[-1], d='item-min', n=False)
echo(schr, d='pass')
if text and args['stdout'] in [state, 'all']:
o = decorate(schr + ': ', 'quote-head-' + state)
o += click.style(decorate(title, style=state), bold=True) + "\n"
o += decorate(str(text).strip(), style='quote-' + state)
if args['stdout_inline']:
echo(o)
else:
shakedown.stdout.append(o)
def pytest_collectreport(self, report):
""" Collect and validate individual test files
"""
if not 'collect' in shakedown.state:
shakedown.state['collect'] = 1
echo('Collecting and validating test files...', d='step-min')
if report.nodeid:
echo(report.nodeid, d='item-maj', n=False)
state = None
if report.failed:
state = 'fail'
if report.passed:
state = 'pass'
if report.skipped:
state = 'skip'
if state:
if report.longrepr:
shakedown.output(report.nodeid, state, report.longrepr)
else:
shakedown.output(report.nodeid, state, None)
def pytest_sessionstart(self):
""" Tests have been collected, begin running them...
"""
echo('Initiating testing phase...', d='step-maj')
def pytest_report_teststatus(self, report):
""" Print report results to the console as they are run
"""
try:
report_file, report_test = report.nodeid.split('::', 1)
except ValueError:
return
if not 'test' in shakedown.state:
shakedown.state['test'] = 1
echo('Running individual tests...', d='step-min')
if not report_file in shakedown.tests['file']:
shakedown.tests['file'][report_file] = 1
echo(report_file, d='item-maj')
if not report.nodeid in shakedown.tests['test']:
shakedown.tests['test'][report.nodeid] = {}
if args['stdout_inline']:
echo('')
echo(report_test + ':', d='item-min')
else:
echo(report_test, d='item-min', n=False)
if report.failed:
shakedown.tests['test'][report.nodeid]['fail'] = True
if report.when == 'teardown' and not 'tested' in shakedown.tests['test'][report.nodeid]:
shakedown.output(report.nodeid, 'pass', None)
# Suppress excess terminal output
return report.outcome, None, None
def pytest_runtest_logreport(self, report):
""" Log the [stdout, stderr] results of tests if desired
"""
state = None
for secname, content in report.sections:
if report.failed:
state = 'fail'
if report.passed:
state = 'pass'
if report.skipped:
state = 'skip'
if state and secname != 'Captured stdout call':
module = report.nodeid.split('::', 1)[0]
cap_type = secname.split(' ')[-1]
if not 'setup' in shakedown.tests['test'][report.nodeid]:
shakedown.tests['test'][report.nodeid]['setup'] = True
shakedown.output(module + ' ' + cap_type, state, content, False)
elif cap_type == 'teardown':
shakedown.output(module + ' ' + cap_type, state, content, False)
elif state and report.when == 'call':
if 'tested' in shakedown.tests['test'][report.nodeid]:
shakedown.output(report.nodeid, state, content, False)
else:
shakedown.tests['test'][report.nodeid]['tested'] = True
shakedown.output(report.nodeid, state, content)
# Capture execution crashes
if hasattr(report.longrepr, 'reprcrash'):
longreport = report.longrepr
if 'tested' in shakedown.tests['test'][report.nodeid]:
shakedown.output(report.nodeid, 'fail', 'error: ' + str(longreport.reprcrash), False)
else:
shakedown.tests['test'][report.nodeid]['tested'] = True
shakedown.output(report.nodeid, 'fail', 'error: ' + str(longreport.reprcrash))
def pytest_sessionfinish(self, session, exitstatus):
""" Testing phase is complete; print extra reports (stdout/stderr, JSON) as requested
"""
echo('Test phase completed.', d='step-maj')
if ('stdout' in args and args['stdout']) and shakedown.stdout:
for output in shakedown.stdout:
echo(output)
opts = ['-q', '--tb=no', "--timeout={}".format(args['timeout'])]
if args['fail'] == 'fast':
opts.append('-x')
if args['pytest_option']:
for opt in args['pytest_option']:
opts.append(opt)
if args['stdout_inline']:
opts.append('-s')
if args['tests']:
tests_to_run = []
for test in args['tests']:
tests_to_run.extend(test.split())
for test in tests_to_run:
opts.append(test)
exitstatus = imported['pytest'].main(opts, plugins=[shakedown()])
sys.exit(exitstatus) | Shakedown is a DC/OS test-harness wrapper for the pytest tool. |
def _parse_network_settings(opts, current):
'''
Filters given options and outputs valid settings for
the global network settings file.
'''
# Normalize keys
opts = dict((k.lower(), v) for (k, v) in six.iteritems(opts))
current = dict((k.lower(), v) for (k, v) in six.iteritems(current))
# Check for supported parameters
retain_settings = opts.get('retain_settings', False)
result = current if retain_settings else {}
# Default quote type is an empty string, which will not quote values
quote_type = ''
valid = _CONFIG_TRUE + _CONFIG_FALSE
if 'enabled' not in opts:
try:
opts['networking'] = current['networking']
# If networking option is quoted, use its quote type
quote_type = salt.utils.stringutils.is_quoted(opts['networking'])
_log_default_network('networking', current['networking'])
except ValueError:
_raise_error_network('networking', valid)
else:
opts['networking'] = opts['enabled']
true_val = '{0}yes{0}'.format(quote_type)
false_val = '{0}no{0}'.format(quote_type)
networking = salt.utils.stringutils.dequote(opts['networking'])
if networking in valid:
if networking in _CONFIG_TRUE:
result['networking'] = true_val
elif networking in _CONFIG_FALSE:
result['networking'] = false_val
else:
_raise_error_network('networking', valid)
if 'hostname' not in opts:
try:
opts['hostname'] = current['hostname']
_log_default_network('hostname', current['hostname'])
except Exception:
_raise_error_network('hostname', ['server1.example.com'])
if opts['hostname']:
result['hostname'] = '{1}{0}{1}'.format(
salt.utils.stringutils.dequote(opts['hostname']), quote_type)
else:
_raise_error_network('hostname', ['server1.example.com'])
if 'nozeroconf' in opts:
nozeroconf = salt.utils.stringutils.dequote(opts['nozeroconf'])
if nozeroconf in valid:
if nozeroconf in _CONFIG_TRUE:
result['nozeroconf'] = true_val
elif nozeroconf in _CONFIG_FALSE:
result['nozeroconf'] = false_val
else:
_raise_error_network('nozeroconf', valid)
for opt in opts:
if opt not in ['networking', 'hostname', 'nozeroconf']:
result[opt] = '{1}{0}{1}'.format(
salt.utils.stringutils.dequote(opts[opt]), quote_type)
return result | Filters given options and outputs valid settings for
the global network settings file. |
def delete_connector_c_pool(name, target='server', cascade=True, server=None):
'''
Delete a connection pool
'''
data = {'target': target, 'cascade': cascade}
return _delete_element(name, 'resources/connector-connection-pool', data, server) | Delete a connection pool |
def _check_lookup_prop(self, result_data):
"""Checks that selected lookup property can be used for this testcase."""
if not self._lookup_prop:
return False
if not result_data.get("id") and self._lookup_prop != "name":
return False
if not result_data.get("title") and self._lookup_prop == "name":
return False
return True | Checks that selected lookup property can be used for this testcase. |
def list_path_traversal(path):
'''
Returns a full list of directories leading up to, and including, a path.
So list_path_traversal('/path/to/salt') would return:
['/', '/path', '/path/to', '/path/to/salt']
in that order.
This routine has been tested on Windows systems as well.
list_path_traversal('c:\\path\\to\\salt') on Windows would return:
['c:\\', 'c:\\path', 'c:\\path\\to', 'c:\\path\\to\\salt']
'''
out = [path]
(head, tail) = os.path.split(path)
if tail == '':
# paths with trailing separators will return an empty string
out = [head]
(head, tail) = os.path.split(head)
while head != out[0]:
# loop until head is the same two consecutive times
out.insert(0, head)
(head, tail) = os.path.split(head)
return out | Returns a full list of directories leading up to, and including, a path.
So list_path_traversal('/path/to/salt') would return:
['/', '/path', '/path/to', '/path/to/salt']
in that order.
This routine has been tested on Windows systems as well.
list_path_traversal('c:\\path\\to\\salt') on Windows would return:
['c:\\', 'c:\\path', 'c:\\path\\to', 'c:\\path\\to\\salt'] |
def check_call(*popenargs, **kwargs):
"""Run command with arguments. Wait for command to complete. If
the exit code was zero then return, otherwise raise
CalledProcessError. The CalledProcessError object will have the
return code in the returncode attribute.
The arguments are the same as for the Popen constructor. Example:
check_call(["ls", "-l"])
"""
retcode = call(*popenargs, **kwargs)
cmd = kwargs.get("args")
if cmd is None:
cmd = popenargs[0]
if retcode:
raise CalledProcessError(retcode, cmd)
return retcode | Run command with arguments. Wait for command to complete. If
the exit code was zero then return, otherwise raise
CalledProcessError. The CalledProcessError object will have the
return code in the returncode attribute.
The arguments are the same as for the Popen constructor. Example:
check_call(["ls", "-l"]) |
def path(self):
"""Timestamp for placing into filepaths."""
out = self.datetime.strftime("%Y-%m-%d")
out += " "
ssm = (
self.datetime - self.datetime.replace(hour=0, minute=0, second=0, microsecond=0)
).total_seconds()
out += str(int(ssm)).zfill(5)
return out | Timestamp for placing into filepaths. |
def register_event(self, direction, verb, child_fn, priority=10):
"""Register an event with all servers.
Args:
direction (str): `in`, `out`, `both`, or `girc`.
verb (str): Event name, `all`, or `raw`.
child_fn (function): Handler function.
priority (int): Handler priority (lower priority executes first).
Note: `all` will not match `raw` events. If you wish to receive both
`raw` and all other events, you need to register these separately.
"""
event_managers = []
if direction in ('in', 'both'):
event_managers.append(self._events_in)
if direction in ('out', 'both'):
event_managers.append(self._events_out)
if direction == 'girc':
event_managers.append(self._girc_events)
for event_manager in event_managers:
event_manager.register(verb, child_fn, priority=priority) | Register an event with all servers.
Args:
direction (str): `in`, `out`, `both`, or `girc`.
verb (str): Event name, `all`, or `raw`.
child_fn (function): Handler function.
priority (int): Handler priority (lower priority executes first).
Note: `all` will not match `raw` events. If you wish to receive both
`raw` and all other events, you need to register these separately. |
def _refine_v(seq, species):
'''
Completes the 5' end of a a truncated sequence with germline nucleotides.
Input is a MongoDB dict (seq) and the species.
'''
vgerm = germlines.get_germline(seq['v_gene']['full'], species)
aln = global_alignment(seq['vdj_nt'], vgerm)
prepend = ''
for s, g in zip(aln.aligned_query, aln.aligned_target):
if s != '-':
break
else:
prepend += g
seq['vdj_nt'] = prepend + seq['vdj_nt'] | Completes the 5' end of a a truncated sequence with germline nucleotides.
Input is a MongoDB dict (seq) and the species. |
def format_datetime(d: PotentialDatetimeType,
fmt: str,
default: str = None) -> Optional[str]:
"""
Format a datetime with a ``strftime`` format specification string, or
return ``default`` if the input is ``None``.
"""
d = coerce_to_pendulum(d)
if d is None:
return default
return d.strftime(fmt) | Format a datetime with a ``strftime`` format specification string, or
return ``default`` if the input is ``None``. |
def index():
"""
This is not served anywhere in the web application.
It is used explicitly in the context of generating static files since
flask-frozen requires url_for's to crawl content.
url_for's are not used with file.show_file directly and are instead
dynamically generated through javascript for performance purposes.
"""
if current_app.config['ARA_PLAYBOOK_OVERRIDE'] is not None:
override = current_app.config['ARA_PLAYBOOK_OVERRIDE']
files = (models.File.query
.filter(models.File.playbook_id.in_(override)))
else:
files = models.File.query.all()
return render_template('file_index.html', files=files) | This is not served anywhere in the web application.
It is used explicitly in the context of generating static files since
flask-frozen requires url_for's to crawl content.
url_for's are not used with file.show_file directly and are instead
dynamically generated through javascript for performance purposes. |
def load(parser, serializer):
"""Returns a dictionary of builtin functions for Fortran. Checks the
cache first to see if we have a serialized version. If we don't, it
loads it from the XML file.
:arg parser: the DocParser instance for parsing the XML tags.
:arg serializer: a Serializer instance from the CodeParser to cache
the loaded XML file.
"""
fortdir = os.path.dirname(fortpy.__file__)
xmlpath = os.path.join(fortdir, "isense", "builtin.xml")
if not os.path.isfile(xmlpath):
return {}
changed_time = os.path.getmtime(xmlpath)
cached = serializer.load_module("builtin.xml", changed_time)
if cached is None:
result = _load_builtin_xml(xmlpath, parser)
serializer.save_module("builtin.xml", result, changed_time)
else:
result = cached
return result | Returns a dictionary of builtin functions for Fortran. Checks the
cache first to see if we have a serialized version. If we don't, it
loads it from the XML file.
:arg parser: the DocParser instance for parsing the XML tags.
:arg serializer: a Serializer instance from the CodeParser to cache
the loaded XML file. |
def get_instance(self, payload):
"""
Build an instance of MessageInstance
:param dict payload: Payload response from the API
:returns: twilio.rest.messaging.v1.session.message.MessageInstance
:rtype: twilio.rest.messaging.v1.session.message.MessageInstance
"""
return MessageInstance(self._version, payload, session_sid=self._solution['session_sid'], ) | Build an instance of MessageInstance
:param dict payload: Payload response from the API
:returns: twilio.rest.messaging.v1.session.message.MessageInstance
:rtype: twilio.rest.messaging.v1.session.message.MessageInstance |
def _is_version_duplicate(self):
""" Define should new version be created for object or no.
Reasons to provide custom check instead of default `ignore_revision_duplicates`:
- no need to compare all revisions - it is OK if right object version exists in any revision;
- need to compare object attributes (not serialized data) to avoid
version creation on wrong <float> vs <int> comparison;
"""
if self.id is None:
return False
try:
latest_version = Version.objects.get_for_object(self).latest('revision__date_created')
except Version.DoesNotExist:
return False
latest_version_object = latest_version._object_version.object
fields = self.get_version_fields()
return all([getattr(self, f) == getattr(latest_version_object, f) for f in fields]) | Define should new version be created for object or no.
Reasons to provide custom check instead of default `ignore_revision_duplicates`:
- no need to compare all revisions - it is OK if right object version exists in any revision;
- need to compare object attributes (not serialized data) to avoid
version creation on wrong <float> vs <int> comparison; |
def getModulePath(project_path,module_name,verbose):
'''Searches for module_name in searchpath and returns the filepath.
If no filepath was found, returns None.'''
if not module_name:
return None
sys.path.append(project_path)
try:
package = pkgutil.get_loader(module_name)
except ImportError:
if verbose:
print("Parent module for "+module_name+" not found.")
return None
except:
if verbose:
print(module_name+" not loaded for bizarre reasons")
try:
if package:
if package.get_code(module_name):
filename = package.get_code(module_name).co_filename
return filename
elif package.find_spec(module_name).has_location==False:
return None #built-in module such as itertools
else:
pass #perhaps filename is in package.find_spec(module_name).origin?
pass #a good reference is https://www.python.org/dev/peps/pep-0302/
except ImportError:
if verbose:
print("Code object unavailable for "+module_name)
return None
except AttributeError:
if verbose:
print(module_name+" is an ExtensionFileLoader object")
return None
except:
if verbose:
print(module_name+" not loaded for bizarre reasons")
return None
else:
if verbose:
print ("Module "+module_name+" not found.")
return None | Searches for module_name in searchpath and returns the filepath.
If no filepath was found, returns None. |
def _read_protocol_line(self):
"""Reads the next line of instrumentation output relevant to snippets.
This method will skip over lines that don't start with 'SNIPPET' or
'INSTRUMENTATION_RESULT'.
Returns:
(str) Next line of snippet-related instrumentation output, stripped.
Raises:
jsonrpc_client_base.AppStartError: If EOF is reached without any
protocol lines being read.
"""
while True:
line = self._proc.stdout.readline().decode('utf-8')
if not line:
raise jsonrpc_client_base.AppStartError(
self._ad, 'Unexpected EOF waiting for app to start')
# readline() uses an empty string to mark EOF, and a single newline
# to mark regular empty lines in the output. Don't move the strip()
# call above the truthiness check, or this method will start
# considering any blank output line to be EOF.
line = line.strip()
if (line.startswith('INSTRUMENTATION_RESULT:')
or line.startswith('SNIPPET ')):
self.log.debug(
'Accepted line from instrumentation output: "%s"', line)
return line
self.log.debug('Discarded line from instrumentation output: "%s"',
line) | Reads the next line of instrumentation output relevant to snippets.
This method will skip over lines that don't start with 'SNIPPET' or
'INSTRUMENTATION_RESULT'.
Returns:
(str) Next line of snippet-related instrumentation output, stripped.
Raises:
jsonrpc_client_base.AppStartError: If EOF is reached without any
protocol lines being read. |
def generate_signature(method, version, endpoint,
date, rel_url, content_type, content,
access_key, secret_key, hash_type):
'''
Generates the API request signature from the given parameters.
'''
hash_type = hash_type
hostname = endpoint._val.netloc # FIXME: migrate to public API
if version >= 'v4.20181215':
content = b''
else:
if content_type.startswith('multipart/'):
content = b''
body_hash = hashlib.new(hash_type, content).hexdigest()
sign_str = '{}\n{}\n{}\nhost:{}\ncontent-type:{}\nx-backendai-version:{}\n{}'.format( # noqa
method.upper(),
rel_url,
date.isoformat(),
hostname,
content_type.lower(),
version,
body_hash
)
sign_bytes = sign_str.encode()
sign_key = hmac.new(secret_key.encode(),
date.strftime('%Y%m%d').encode(), hash_type).digest()
sign_key = hmac.new(sign_key, hostname.encode(), hash_type).digest()
signature = hmac.new(sign_key, sign_bytes, hash_type).hexdigest()
headers = {
'Authorization': 'BackendAI signMethod=HMAC-{}, credential={}:{}'.format(
hash_type.upper(),
access_key,
signature
),
}
return headers, signature | Generates the API request signature from the given parameters. |
def list_to_serialized(ref, the_list):
"""Serialize the list of elements
Used for the retention store
:param ref: Not used
:type ref:
:param the_list: dictionary to convert
:type the_list: dict
:return: dict of serialized
:rtype: dict
"""
result = []
for elt in the_list:
if not getattr(elt, 'serialize', None):
continue
result.append(elt.serialize())
return result | Serialize the list of elements
Used for the retention store
:param ref: Not used
:type ref:
:param the_list: dictionary to convert
:type the_list: dict
:return: dict of serialized
:rtype: dict |
def _compute_distance(self, dists, C):
"""
Compute the second term of the equation described on p. 1144:
`` c4 * np.log(sqrt(R ** 2. + h ** 2.)
"""
return C["c4"] * np.log(np.sqrt(dists.rrup ** 2. + C["h"] ** 2.)) | Compute the second term of the equation described on p. 1144:
`` c4 * np.log(sqrt(R ** 2. + h ** 2.) |
def proj_l2(v, gamma, axis=None):
r"""Compute the projection operator of the :math:`\ell_2` norm.
The projection operator of the uncentered :math:`\ell_2` norm,
.. math::
\mathrm{argmin}_{\mathbf{x}} (1/2) \| \mathbf{x} - \mathbf{v} \|_2^2 \;
\text{ s.t. } \; \| \mathbf{x} - \mathbf{s} \|_2 \leq \gamma
can be computed as :math:`\mathbf{s} + \mathrm{proj}_{f,\gamma}
(\mathbf{v} - \mathbf{s})` where :math:`f(\mathbf{x}) =
\| \mathbf{x} \|_2`.
Parameters
----------
v : array_like
Input array :math:`\mathbf{v}`
gamma : float
Parameter :math:`\gamma`
axis : None or int or tuple of ints, optional (default None)
Axes of `v` over which to compute the :math:`\ell_2` norm. If
`None`, an entire multi-dimensional array is treated as a vector.
If axes are specified, then distinct norm values are computed
over the indices of the remaining axes of input array `v`.
Returns
-------
x : ndarray
Output array
"""
d = np.sqrt(np.sum(v**2, axis=axis, keepdims=True))
return np.asarray((d <= gamma) * v +
(d > gamma) * (gamma * sl.zdivide(v, d)),
dtype=v.dtype) | r"""Compute the projection operator of the :math:`\ell_2` norm.
The projection operator of the uncentered :math:`\ell_2` norm,
.. math::
\mathrm{argmin}_{\mathbf{x}} (1/2) \| \mathbf{x} - \mathbf{v} \|_2^2 \;
\text{ s.t. } \; \| \mathbf{x} - \mathbf{s} \|_2 \leq \gamma
can be computed as :math:`\mathbf{s} + \mathrm{proj}_{f,\gamma}
(\mathbf{v} - \mathbf{s})` where :math:`f(\mathbf{x}) =
\| \mathbf{x} \|_2`.
Parameters
----------
v : array_like
Input array :math:`\mathbf{v}`
gamma : float
Parameter :math:`\gamma`
axis : None or int or tuple of ints, optional (default None)
Axes of `v` over which to compute the :math:`\ell_2` norm. If
`None`, an entire multi-dimensional array is treated as a vector.
If axes are specified, then distinct norm values are computed
over the indices of the remaining axes of input array `v`.
Returns
-------
x : ndarray
Output array |
def find_max_and_min_frequencies(name, mass_range_params, freqs):
"""
ADD DOCS
"""
cutoff_fns = pnutils.named_frequency_cutoffs
if name not in cutoff_fns.keys():
err_msg = "%s not recognized as a valid cutoff frequency choice." %name
err_msg += "Recognized choices: " + " ".join(cutoff_fns.keys())
raise ValueError(err_msg)
# Can I do this quickly?
total_mass_approxs = {
"SchwarzISCO": pnutils.f_SchwarzISCO,
"LightRing" : pnutils.f_LightRing,
"ERD" : pnutils.f_ERD
}
if name in total_mass_approxs.keys():
# This can be done quickly if the cutoff only depends on total mass
# Assumes that lower total mass = higher cutoff frequency
upper_f_cutoff = total_mass_approxs[name](mass_range_params.minTotMass)
lower_f_cutoff = total_mass_approxs[name](mass_range_params.maxTotMass)
else:
# Do this numerically
# FIXME: Is 1000000 the right choice? I think so, but just highlighting
mass1, mass2, spin1z, spin2z = \
get_random_mass(1000000, mass_range_params)
mass_dict = {}
mass_dict['mass1'] = mass1
mass_dict['mass2'] = mass2
mass_dict['spin1z'] = spin1z
mass_dict['spin2z'] = spin2z
tmp_freqs = cutoff_fns[name](mass_dict)
upper_f_cutoff = tmp_freqs.max()
lower_f_cutoff = tmp_freqs.min()
cutoffs = numpy.array([lower_f_cutoff,upper_f_cutoff])
if lower_f_cutoff < freqs.min():
warn_msg = "WARNING: "
warn_msg += "Lowest frequency cutoff is %s Hz " %(lower_f_cutoff,)
warn_msg += "which is lower than the lowest frequency calculated "
warn_msg += "for the metric: %s Hz. " %(freqs.min())
warn_msg += "Distances for these waveforms will be calculated at "
warn_msg += "the lowest available metric frequency."
logging.warn(warn_msg)
if upper_f_cutoff > freqs.max():
warn_msg = "WARNING: "
warn_msg += "Highest frequency cutoff is %s Hz " %(upper_f_cutoff,)
warn_msg += "which is larger than the highest frequency calculated "
warn_msg += "for the metric: %s Hz. " %(freqs.max())
warn_msg += "Distances for these waveforms will be calculated at "
warn_msg += "the largest available metric frequency."
logging.warn(warn_msg)
return find_closest_calculated_frequencies(cutoffs, freqs) | ADD DOCS |
def select_eps(xmrs, nodeid=None, iv=None, label=None, pred=None):
"""
Return the list of matching elementary predications in *xmrs*.
:class:`~delphin.mrs.components.ElementaryPredication` objects for
*xmrs* match if their `nodeid` matches *nodeid*,
`intrinsic_variable` matches *iv*, `label` matches *label*, and
`pred` to *pred*. The *nodeid*, *iv*, *label*, and *pred* filters
are ignored if they are `None`.
Args:
xmrs (:class:`~delphin.mrs.xmrs.Xmrs`): semantic structure to
query
nodeid (optional): nodeid to match
iv (str, optional): intrinsic variable to match
label (str, optional): label to match
pred (str, :class:`~delphin.mrs.components.Pred`, optional):
predicate to match
Returns:
list: matching elementary predications
"""
epmatch = lambda n: ((nodeid is None or n.nodeid == nodeid) and
(iv is None or n.iv == iv) and
(label is None or n.label == label) and
(pred is None or n.pred == pred))
return list(filter(epmatch, xmrs.eps())) | Return the list of matching elementary predications in *xmrs*.
:class:`~delphin.mrs.components.ElementaryPredication` objects for
*xmrs* match if their `nodeid` matches *nodeid*,
`intrinsic_variable` matches *iv*, `label` matches *label*, and
`pred` to *pred*. The *nodeid*, *iv*, *label*, and *pred* filters
are ignored if they are `None`.
Args:
xmrs (:class:`~delphin.mrs.xmrs.Xmrs`): semantic structure to
query
nodeid (optional): nodeid to match
iv (str, optional): intrinsic variable to match
label (str, optional): label to match
pred (str, :class:`~delphin.mrs.components.Pred`, optional):
predicate to match
Returns:
list: matching elementary predications |
def file_list(*packages, **kwargs):
'''
List the files that belong to a package. Not specifying any packages will
return a list of _every_ file on the system's rpm database (not generally
recommended).
root
use root as top level directory (default: "/")
CLI Examples:
.. code-block:: bash
salt '*' lowpkg.file_list httpd
salt '*' lowpkg.file_list httpd postfix
salt '*' lowpkg.file_list
'''
cmd = ['rpm']
if kwargs.get('root'):
cmd.extend(['--root', kwargs['root']])
cmd.append('-ql' if packages else '-qla')
if packages:
# Can't concatenate a tuple, must do a list.extend()
cmd.extend(packages)
ret = __salt__['cmd.run'](
cmd,
output_loglevel='trace',
python_shell=False).splitlines()
return {'errors': [], 'files': ret} | List the files that belong to a package. Not specifying any packages will
return a list of _every_ file on the system's rpm database (not generally
recommended).
root
use root as top level directory (default: "/")
CLI Examples:
.. code-block:: bash
salt '*' lowpkg.file_list httpd
salt '*' lowpkg.file_list httpd postfix
salt '*' lowpkg.file_list |
def do_stop_role(self, role):
"""
Stop a role
Usage:
> stop_role <role> Stops this role
"""
if not role:
return None
if not self.has_cluster():
return None
if '-' not in role:
print("Please enter a valid role name")
return None
try:
service = api.get_cluster(self.cluster).get_service(role.split('-')[0])
service.stop_roles(role)
print("Stopping Role")
except ApiException:
print("Error: Role or Service Not Found") | Stop a role
Usage:
> stop_role <role> Stops this role |
def tx2genefile(gtf, out_file=None, data=None, tsv=True, keep_version=False):
"""
write out a file of transcript->gene mappings.
"""
if tsv:
extension = ".tsv"
sep = "\t"
else:
extension = ".csv"
sep = ","
if file_exists(out_file):
return out_file
with file_transaction(data, out_file) as tx_out_file:
with open(tx_out_file, "w") as out_handle:
for k, v in tx2genedict(gtf, keep_version).items():
out_handle.write(sep.join([k, v]) + "\n")
logger.info("tx2gene file %s created from %s." % (out_file, gtf))
return out_file | write out a file of transcript->gene mappings. |
def close(self, filehandle):
"""Close openend file if no longer used."""
with self.lock:
if filehandle in self.files:
self.files[filehandle] -= 1
# trim the file cache
index = 0
size = len(self.past)
while size > self.size and index < size:
filehandle = self.past[index]
if self.files[filehandle] == 0:
filehandle.close()
del self.files[filehandle]
del self.past[index]
size -= 1
else:
index += 1 | Close openend file if no longer used. |
def zone_data(self):
"""Get zone data"""
if self._zone_data is None:
self._zone_data = self._get('/zones/' + self.domain).json()
return self._zone_data | Get zone data |
def load_module(self, name):
"""Load and return a module"""
if name in sys.modules:
return sys.modules[name]
# load the actual import hook module
module_name = self.mount2name(name)
__import__(module_name)
# alias the import hook module to the mount, so both can be used interchangeably
module = sys.modules[name] = sys.modules[module_name]
module.install()
return module | Load and return a module |
def _get_approved_attributes(self, idp, idp_policy, sp_entity_id, state):
"""
Returns a list of approved attributes
:type idp: saml.server.Server
:type idp_policy: saml2.assertion.Policy
:type sp_entity_id: str
:type state: satosa.state.State
:rtype: list[str]
:param idp: The saml frontend idp server
:param idp_policy: The idp policy
:param sp_entity_id: The requesting sp entity id
:param state: The current state
:return: A list containing approved attributes
"""
name_format = idp_policy.get_name_form(sp_entity_id)
attrconvs = idp.config.attribute_converters
idp_policy.acs = attrconvs
attribute_filter = []
for aconv in attrconvs:
if aconv.name_format == name_format:
all_attributes = {v: None for v in aconv._fro.values()}
attribute_filter = list(idp_policy.restrict(all_attributes, sp_entity_id, idp.metadata).keys())
break
attribute_filter = self.converter.to_internal_filter(self.attribute_profile, attribute_filter)
satosa_logging(logger, logging.DEBUG, "Filter: %s" % attribute_filter, state)
return attribute_filter | Returns a list of approved attributes
:type idp: saml.server.Server
:type idp_policy: saml2.assertion.Policy
:type sp_entity_id: str
:type state: satosa.state.State
:rtype: list[str]
:param idp: The saml frontend idp server
:param idp_policy: The idp policy
:param sp_entity_id: The requesting sp entity id
:param state: The current state
:return: A list containing approved attributes |
def from_path(kls, vertices):
"""
Given an Nx3 array of vertices that constitute a single path,
generate a skeleton with appropriate edges.
"""
if vertices.shape[0] == 0:
return PrecomputedSkeleton()
skel = PrecomputedSkeleton(vertices)
edges = np.zeros(shape=(skel.vertices.shape[0] - 1, 2), dtype=np.uint32)
edges[:,0] = np.arange(skel.vertices.shape[0] - 1)
edges[:,1] = np.arange(1, skel.vertices.shape[0])
skel.edges = edges
return skel | Given an Nx3 array of vertices that constitute a single path,
generate a skeleton with appropriate edges. |
def replicate(source, model, cache=None):
'''Replicates the `source` object to `model` class and returns its
reflection.'''
target = replicate_no_merge(source, model, cache=cache)
if target is not None:
db = object_session(source)
target = db.merge(target)
return target | Replicates the `source` object to `model` class and returns its
reflection. |
def _get_sorted_section(self, nts_section):
"""Sort GO IDs in each section, if requested by user."""
#pylint: disable=unnecessary-lambda
if self.section_sortby is True:
return sorted(nts_section, key=lambda nt: self.sortgos.usrgo_sortby(nt))
if self.section_sortby is False or self.section_sortby is None:
return nts_section
# print('SORT GO IDS IN A SECTION')
return sorted(nts_section, key=lambda nt: self.section_sortby(nt)) | Sort GO IDs in each section, if requested by user. |
def add_data(self, metric, value, ts=None):
"""
Add data to queue
:param metric: the metric name
:type metric: str
:param value: the value of data
:type value: int
:param ts: the timestamp
:type ts: int | None
:return: True if added successfully, otherwise False
:rtype: bool
"""
if not ts:
ts = time.time()
if self.__data_lock.acquire():
self.__data.append((metric, (ts, value)))
self.__data_lock.release()
return True
return False | Add data to queue
:param metric: the metric name
:type metric: str
:param value: the value of data
:type value: int
:param ts: the timestamp
:type ts: int | None
:return: True if added successfully, otherwise False
:rtype: bool |
def load_zip_data(zipname, f_sino_real, f_sino_imag,
f_angles=None, f_phantom=None, f_info=None):
"""Load example sinogram data from a .zip file"""
ret = []
with zipfile.ZipFile(str(zipname)) as arc:
sino_real = np.loadtxt(arc.open(f_sino_real))
sino_imag = np.loadtxt(arc.open(f_sino_imag))
sino = sino_real + 1j * sino_imag
ret.append(sino)
if f_angles:
angles = np.loadtxt(arc.open(f_angles))
ret.append(angles)
if f_phantom:
phantom = np.loadtxt(arc.open(f_phantom))
ret.append(phantom)
if f_info:
with arc.open(f_info) as info:
cfg = {}
for li in info.readlines():
li = li.decode()
if li.count("=") == 1:
key, val = li.split("=")
cfg[key.strip()] = float(val.strip())
ret.append(cfg)
return ret | Load example sinogram data from a .zip file |
def value_counts(expr, sort=True, ascending=False, dropna=False):
"""
Return object containing counts of unique values.
The resulting object will be in descending order so that the first element is the most frequently-occuring
element. Exclude NA values by default
:param expr: sequence
:param sort: if sort
:type sort: bool
:param dropna: Don’t include counts of None, default False
:return: collection with two columns
:rtype: :class:`odps.df.expr.expressions.CollectionExpr`
"""
names = [expr.name, 'count']
typos = [expr.dtype, types.int64]
return ValueCounts(_input=expr, _schema=Schema.from_lists(names, typos),
_sort=sort, _ascending=ascending, _dropna=dropna) | Return object containing counts of unique values.
The resulting object will be in descending order so that the first element is the most frequently-occuring
element. Exclude NA values by default
:param expr: sequence
:param sort: if sort
:type sort: bool
:param dropna: Don’t include counts of None, default False
:return: collection with two columns
:rtype: :class:`odps.df.expr.expressions.CollectionExpr` |
def trainHMM_fromFile(wav_file, gt_file, hmm_model_name, mt_win, mt_step):
'''
This function trains a HMM model for segmentation-classification using a single annotated audio file
ARGUMENTS:
- wav_file: the path of the audio filename
- gt_file: the path of the ground truth filename
(a csv file of the form <segment start in seconds>,<segment end in seconds>,<segment label> in each row
- hmm_model_name: the name of the HMM model to be stored
- mt_win: mid-term window size
- mt_step: mid-term window step
RETURNS:
- hmm: an object to the resulting HMM
- class_names: a list of class_names
After training, hmm, class_names, along with the mt_win and mt_step values are stored in the hmm_model_name file
'''
[seg_start, seg_end, seg_labs] = readSegmentGT(gt_file)
flags, class_names = segs2flags(seg_start, seg_end, seg_labs, mt_step)
[fs, x] = audioBasicIO.readAudioFile(wav_file)
[F, _, _] = aF.mtFeatureExtraction(x, fs, mt_win * fs, mt_step * fs,
round(fs * 0.050), round(fs * 0.050))
start_prob, transmat, means, cov = trainHMM_computeStatistics(F, flags)
hmm = hmmlearn.hmm.GaussianHMM(start_prob.shape[0], "diag")
hmm.startprob_ = start_prob
hmm.transmat_ = transmat
hmm.means_ = means
hmm.covars_ = cov
fo = open(hmm_model_name, "wb")
cPickle.dump(hmm, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(class_names, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(mt_win, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(mt_step, fo, protocol=cPickle.HIGHEST_PROTOCOL)
fo.close()
return hmm, class_names | This function trains a HMM model for segmentation-classification using a single annotated audio file
ARGUMENTS:
- wav_file: the path of the audio filename
- gt_file: the path of the ground truth filename
(a csv file of the form <segment start in seconds>,<segment end in seconds>,<segment label> in each row
- hmm_model_name: the name of the HMM model to be stored
- mt_win: mid-term window size
- mt_step: mid-term window step
RETURNS:
- hmm: an object to the resulting HMM
- class_names: a list of class_names
After training, hmm, class_names, along with the mt_win and mt_step values are stored in the hmm_model_name file |
def reset_small(self, eq):
"""Reset numbers smaller than 1e-12 in f and g equations"""
assert eq in ('f', 'g')
for idx, var in enumerate(self.__dict__[eq]):
if abs(var) <= 1e-12:
self.__dict__[eq][idx] = 0 | Reset numbers smaller than 1e-12 in f and g equations |
def _get_tag_match(self, ele, tree):
"""
Match tag
:param ele:
:type ele:
:param tree:
:type tree: None, list
:return:
:rtype: None | list
"""
if tree in [None, []]:
return [ele]
res = []
t = tree[0]
branch = tree[1:]
attributes = {}
for attr in t:
if isinstance(t[attr], dict):
if t[attr].get("type", None) == "reg":
t[attr] = re.compile(t[attr]['reg'])
attributes.update(t)
if "name" in attributes:
del attributes['name']
if "text" in attributes:
del attributes['text']
if "recursive" in attributes:
del attributes['recursive']
if "[]" in attributes:
del attributes['[]']
possibles = ele.find_all(
t.get('name', None),
text=t.get('text', None),
attrs=attributes,
recursive=t.get('recursive', True)
)
if not possibles:
return None
else:
pass
if "[]" in t:
try:
possibles = eval("possibles[{}]".format(t["[]"]))
except:
# no possibles
return None
if not isinstance(possibles, list):
possibles = [possibles]
for a in possibles:
match = self._get_tag_match(a, branch)
if match:
res.extend(match)
if not res:
return None
else:
return res | Match tag
:param ele:
:type ele:
:param tree:
:type tree: None, list
:return:
:rtype: None | list |
def export(self, nidm_version, export_dir):
"""
Create prov entities and activities.
"""
if nidm_version['major'] < 1 or \
(nidm_version['major'] == 1 and nidm_version['minor'] < 3):
self.type = NLX_OLD_FSL
atts = (
(PROV['type'], self.type),
(PROV['type'], PROV['SoftwareAgent']),
(PROV['label'], Literal(self.label, datatype=XSD_STRING)),
(NIDM_SOFTWARE_VERSION, self.version)
)
if self.feat_version:
atts = atts + ((FSL_FEAT_VERSION, self.feat_version),)
self.add_attributes(atts) | Create prov entities and activities. |
def _multitaper_cross_spectrum(self, clm, slm, k, convention='power',
unit='per_l', clat=None, clon=None,
coord_degrees=True, lmax=None,
taper_wt=None):
"""
Return the multitaper cross-spectrum estimate and standard error for
two input SHCoeffs class instances.
"""
if lmax is None:
lmax = min(clm.lmax, slm.lmax)
if (clat is not None and clon is not None and clat == self.clat and
clon == self.clon and coord_degrees is self.coord_degrees and
k <= self.nwinrot):
# use the already stored coeffs
pass
elif (clat is None and clon is None) and \
(self.clat is not None and self.clon is not None and
k <= self.nwinrot):
# use the already stored coeffs
pass
else:
if clat is None:
clat = self.clat
if clon is None:
clon = self.clon
if (clat is None and clon is not None) or \
(clat is not None and clon is None):
raise ValueError('clat and clon must both be input. ' +
'clat = {:s}, clon = {:s}'
.format(repr(clat), repr(clon)))
if clat is None and clon is None:
self.rotate(clat=90., clon=0., coord_degrees=True, nwinrot=k)
else:
self.rotate(clat=clat, clon=clon, coord_degrees=coord_degrees,
nwinrot=k)
sh1 = clm.to_array(normalization='4pi', csphase=1, lmax=lmax)
sh2 = slm.to_array(normalization='4pi', csphase=1, lmax=lmax)
if taper_wt is None:
mtse, sd = _shtools.SHMultiTaperMaskCSE(sh1, sh2, self.coeffs,
lmax1=lmax, lmax2=lmax,
k=k)
else:
mtse, sd = _shtools.SHMultiTaperMaskCSE(sh1, sh2, self.coeffs,
lmax1=lmax, lmax2=lmax,
k=k, taper_wt=taper_wt)
if (unit == 'per_l'):
pass
elif (unit == 'per_lm'):
degree_l = _np.arange(len(mtse))
mtse /= (2.0 * degree_l + 1.0)
sd /= (2.0 * degree_l + 1.0)
else:
raise ValueError(
"unit must be 'per_l' or 'per_lm'." +
"Input value was {:s}".format(repr(unit)))
if (convention == 'power'):
return mtse, sd
elif (convention == 'energy'):
return mtse * 4.0 * _np.pi, sd * 4.0 * _np.pi
else:
raise ValueError(
"convention must be 'power' or 'energy'." +
"Input value was {:s}".format(repr(convention))) | Return the multitaper cross-spectrum estimate and standard error for
two input SHCoeffs class instances. |
def post_periodic_filtered(values, repeat_after, block):
"""
After every *repeat_after* items, blocks the next *block* items from
*values*. Note that unlike :func:`pre_periodic_filtered`, *repeat_after*
can't be 0. For example, to block every tenth item read from an ADC::
from gpiozero import MCP3008
from gpiozero.tools import post_periodic_filtered
adc = MCP3008(channel=0)
for value in post_periodic_filtered(adc, 9, 1):
print(value)
"""
values = _normalize(values)
if repeat_after < 1:
raise ValueError("repeat_after must be 1 or larger")
if block < 1:
raise ValueError("block must be 1 or larger")
it = iter(values)
try:
while True:
for _ in range(repeat_after):
yield next(it)
for _ in range(block):
next(it)
except StopIteration:
pass | After every *repeat_after* items, blocks the next *block* items from
*values*. Note that unlike :func:`pre_periodic_filtered`, *repeat_after*
can't be 0. For example, to block every tenth item read from an ADC::
from gpiozero import MCP3008
from gpiozero.tools import post_periodic_filtered
adc = MCP3008(channel=0)
for value in post_periodic_filtered(adc, 9, 1):
print(value) |
def has_attr(self, table_name, attr_name):
"""
:param str table_name: Table name that the attribute exists.
:param str attr_name: Attribute name to be tested.
:return: |True| if the table has the attribute.
:rtype: bool
:raises simplesqlite.TableNotFoundError:
|raises_verify_table_existence|
:Sample Code:
.. code:: python
import simplesqlite
table_name = "sample_table"
con = simplesqlite.SimpleSQLite("sample.sqlite", "w")
con.create_table_from_data_matrix(
table_name,
["attr_a", "attr_b"],
[[1, "a"], [2, "b"]])
print(con.has_attr(table_name, "attr_a"))
print(con.has_attr(table_name, "not_existing"))
try:
print(con.has_attr("not_existing", "attr_a"))
except simplesqlite.TableNotFoundError as e:
print(e)
:Output:
.. parsed-literal::
True
False
'not_existing' table not found in /tmp/sample.sqlite
"""
self.verify_table_existence(table_name)
if typepy.is_null_string(attr_name):
return False
return attr_name in self.fetch_attr_names(table_name) | :param str table_name: Table name that the attribute exists.
:param str attr_name: Attribute name to be tested.
:return: |True| if the table has the attribute.
:rtype: bool
:raises simplesqlite.TableNotFoundError:
|raises_verify_table_existence|
:Sample Code:
.. code:: python
import simplesqlite
table_name = "sample_table"
con = simplesqlite.SimpleSQLite("sample.sqlite", "w")
con.create_table_from_data_matrix(
table_name,
["attr_a", "attr_b"],
[[1, "a"], [2, "b"]])
print(con.has_attr(table_name, "attr_a"))
print(con.has_attr(table_name, "not_existing"))
try:
print(con.has_attr("not_existing", "attr_a"))
except simplesqlite.TableNotFoundError as e:
print(e)
:Output:
.. parsed-literal::
True
False
'not_existing' table not found in /tmp/sample.sqlite |
def render_povray(scene, filename='ipython', width=600, height=600,
antialiasing=0.01, extra_opts={}):
'''Render the scene with povray for publication.
:param dict scene: The scene to render
:param string filename: Output filename or 'ipython' to render in the notebook.
:param int width: Width in pixels.
:param int height: Height in pixels.
:param dict extra_opts: Dictionary to merge/override with the passed scene.
'''
if not vapory_available:
raise Exception("To render with povray, you need to have the vapory"
" package installed.")
# Adding extra options
scene = normalize_scene(scene)
scene.update(extra_opts)
# Camera target
aspect = scene['camera']['aspect']
up = np.dot(rmatrixquaternion(scene['camera']['quaternion']), [0, 1, 0])
v_fov = scene['camera']['vfov'] / 180.0 * np.pi
h_fov = 2.0 * np.arctan(np.tan(v_fov/2.0) * aspect) / np.pi * 180
# Setup camera position
camera = vp.Camera( 'location', scene['camera']['location'],
'direction', [0, 0, -1],
'sky', up,
'look_at', scene['camera']['target'],
'angle', h_fov )
global_settings = []
# Setup global illumination
if scene.get('radiosity', False):
# Global Illumination
radiosity = vp.Radiosity(
'brightness', 2.0,
'count', 100,
'error_bound', 0.15,
'gray_threshold', 0.0,
'low_error_factor', 0.2,
'minimum_reuse', 0.015,
'nearest_count', 10,
'recursion_limit', 1, #Docs say 1 is enough
'adc_bailout', 0.01,
'max_sample', 0.5,
'media off',
'normal off',
'always_sample', 1,
'pretrace_start', 0.08,
'pretrace_end', 0.01)
light_sources = []
global_settings.append(radiosity)
else:
# Lights
light_sources = [
vp.LightSource( np.array([2,4,-3]) * 1000, 'color', [1,1,1] ),
vp.LightSource( np.array([-2,-4,3]) * 1000, 'color', [1,1,1] ),
vp.LightSource( np.array([-1,2,3]) * 1000, 'color', [1,1,1] ),
vp.LightSource( np.array([1,-2,-3]) * 1000, 'color', [1,1,1] )
]
# Background -- white for now
background = vp.Background([1, 1, 1])
# Things to display
stuff = _generate_objects(scene['representations'])
scene = vp.Scene( camera, objects = light_sources + stuff + [background],
global_settings=global_settings)
return scene.render(filename, width=width, height=height,
antialiasing = antialiasing) | Render the scene with povray for publication.
:param dict scene: The scene to render
:param string filename: Output filename or 'ipython' to render in the notebook.
:param int width: Width in pixels.
:param int height: Height in pixels.
:param dict extra_opts: Dictionary to merge/override with the passed scene. |
def subnetpool_create(request, name, prefixes, **kwargs):
"""Create a subnetpool.
ip_version is auto-detected in back-end.
Parameters:
request -- Request context
name -- Name for subnetpool
prefixes -- List of prefixes for pool
Keyword Arguments (optional):
min_prefixlen -- Minimum prefix length for allocations from pool
max_prefixlen -- Maximum prefix length for allocations from pool
default_prefixlen -- Default prefix length for allocations from pool
default_quota -- Default quota for allocations from pool
shared -- Subnetpool should be shared (Admin-only)
tenant_id -- Owner of subnetpool
Returns:
SubnetPool object
"""
LOG.debug("subnetpool_create(): name=%(name)s, prefixes=%(prefixes)s, "
"kwargs=%(kwargs)s", {'name': name, 'prefixes': prefixes,
'kwargs': kwargs})
body = {'subnetpool':
{'name': name,
'prefixes': prefixes,
}
}
if 'tenant_id' not in kwargs:
kwargs['tenant_id'] = request.user.project_id
body['subnetpool'].update(kwargs)
subnetpool = \
neutronclient(request).create_subnetpool(body=body).get('subnetpool')
return SubnetPool(subnetpool) | Create a subnetpool.
ip_version is auto-detected in back-end.
Parameters:
request -- Request context
name -- Name for subnetpool
prefixes -- List of prefixes for pool
Keyword Arguments (optional):
min_prefixlen -- Minimum prefix length for allocations from pool
max_prefixlen -- Maximum prefix length for allocations from pool
default_prefixlen -- Default prefix length for allocations from pool
default_quota -- Default quota for allocations from pool
shared -- Subnetpool should be shared (Admin-only)
tenant_id -- Owner of subnetpool
Returns:
SubnetPool object |
def populate_user(self):
"""
Populates the Django user object using the default bind credentials.
"""
user = None
try:
# self.attrs will only be non-None if we were able to load this user
# from the LDAP directory, so this filters out nonexistent users.
if self.attrs is not None:
self._get_or_create_user(force_populate=True)
user = self._user
except ldap.LDAPError as e:
results = ldap_error.send(
self.backend.__class__,
context="populate_user",
user=self._user,
exception=e,
)
if len(results) == 0:
logger.warning(
"Caught LDAPError while authenticating {}: {}".format(
self._username, pprint.pformat(e)
)
)
except Exception as e:
logger.warning("{} while authenticating {}".format(e, self._username))
raise
return user | Populates the Django user object using the default bind credentials. |
def create(cls, statement_format, date_start, date_end,
monetary_account_id=None, regional_format=None,
custom_headers=None):
"""
:type user_id: int
:type monetary_account_id: int
:param statement_format: The format type of statement. Allowed values:
MT940, CSV, PDF.
:type statement_format: str
:param date_start: The start date for making statements.
:type date_start: str
:param date_end: The end date for making statements.
:type date_end: str
:param regional_format: Required for CSV exports. The regional format of
the statement, can be UK_US (comma-separated) or EUROPEAN
(semicolon-separated).
:type regional_format: str
:type custom_headers: dict[str, str]|None
:rtype: BunqResponseInt
"""
if custom_headers is None:
custom_headers = {}
request_map = {
cls.FIELD_STATEMENT_FORMAT: statement_format,
cls.FIELD_DATE_START: date_start,
cls.FIELD_DATE_END: date_end,
cls.FIELD_REGIONAL_FORMAT: regional_format
}
request_map_string = converter.class_to_json(request_map)
request_map_string = cls._remove_field_for_request(request_map_string)
api_client = client.ApiClient(cls._get_api_context())
request_bytes = request_map_string.encode()
endpoint_url = cls._ENDPOINT_URL_CREATE.format(cls._determine_user_id(),
cls._determine_monetary_account_id(
monetary_account_id))
response_raw = api_client.post(endpoint_url, request_bytes,
custom_headers)
return BunqResponseInt.cast_from_bunq_response(
cls._process_for_id(response_raw)
) | :type user_id: int
:type monetary_account_id: int
:param statement_format: The format type of statement. Allowed values:
MT940, CSV, PDF.
:type statement_format: str
:param date_start: The start date for making statements.
:type date_start: str
:param date_end: The end date for making statements.
:type date_end: str
:param regional_format: Required for CSV exports. The regional format of
the statement, can be UK_US (comma-separated) or EUROPEAN
(semicolon-separated).
:type regional_format: str
:type custom_headers: dict[str, str]|None
:rtype: BunqResponseInt |
def reaction_signature(eq, direction=False, stoichiometry=False):
"""Return unique signature object for :class:`Reaction`.
Signature objects are hashable, and compare equal only if the reactions
are considered the same according to the specified rules.
Args:
direction: Include reaction directionality when considering equality.
stoichiometry: Include stoichiometry when considering equality.
"""
def compounds_sig(compounds):
if stoichiometry:
return tuple(sorted(compounds))
else:
return tuple(sorted(compound for compound, _ in compounds))
left = compounds_sig(eq.left)
right = compounds_sig(eq.right)
if left < right:
reaction_sig = left, right
direction_sig = eq.direction
else:
reaction_sig = right, left
direction_sig = eq.direction.flipped()
if direction:
return reaction_sig, direction_sig
return reaction_sig | Return unique signature object for :class:`Reaction`.
Signature objects are hashable, and compare equal only if the reactions
are considered the same according to the specified rules.
Args:
direction: Include reaction directionality when considering equality.
stoichiometry: Include stoichiometry when considering equality. |
def crop_to_fit(self, image_size, view_size):
"""
Set cropping values in `p:blipFill/a:srcRect` such that an image of
*image_size* will stretch to exactly fit *view_size* when its aspect
ratio is preserved.
"""
self.blipFill.crop(self._fill_cropping(image_size, view_size)) | Set cropping values in `p:blipFill/a:srcRect` such that an image of
*image_size* will stretch to exactly fit *view_size* when its aspect
ratio is preserved. |
def ecs_idsKEGG(organism):
"""
Uses KEGG to retrieve all ids and respective ecs for a given KEGG organism
:param organism: an organisms as listed in organismsKEGG()
:returns: a Pandas dataframe of with 'ec' and 'KEGGid'.
"""
kegg_ec=urlopen("http://rest.kegg.jp/link/"+organism+"/enzyme").read()
kegg_ec=kegg_ec.split("\n")
final=[]
for k in kegg_ec:
final.append(k.split("\t"))
df=pd.DataFrame(final[0:len(final)-1])[[0,1]]
df.columns=['ec','KEGGid']
return df | Uses KEGG to retrieve all ids and respective ecs for a given KEGG organism
:param organism: an organisms as listed in organismsKEGG()
:returns: a Pandas dataframe of with 'ec' and 'KEGGid'. |
def get_content(self, renderer, data,
accepted_media_type, renderer_context):
"""
Get the content as if it had been rendered by the default
non-documenting renderer.
"""
if not renderer:
return '[No renderers were found]'
renderer_context['indent'] = 4
content = renderer.render(data, accepted_media_type, renderer_context)
render_style = getattr(renderer, 'render_style', 'text')
assert render_style in ['text', 'binary'], 'Expected .render_style ' \
'"text" or "binary", but got "%s"' % render_style
if render_style == 'binary':
return '[%d bytes of binary content]' % len(content)
return content | Get the content as if it had been rendered by the default
non-documenting renderer. |
def replace(self, to_replace, value=_NoValue, subset=None):
"""Returns a new :class:`DataFrame` replacing a value with another value.
:func:`DataFrame.replace` and :func:`DataFrameNaFunctions.replace` are
aliases of each other.
Values to_replace and value must have the same type and can only be numerics, booleans,
or strings. Value can have None. When replacing, the new value will be cast
to the type of the existing column.
For numeric replacements all values to be replaced should have unique
floating point representation. In case of conflicts (for example with `{42: -1, 42.0: 1}`)
and arbitrary replacement will be used.
:param to_replace: bool, int, long, float, string, list or dict.
Value to be replaced.
If the value is a dict, then `value` is ignored or can be omitted, and `to_replace`
must be a mapping between a value and a replacement.
:param value: bool, int, long, float, string, list or None.
The replacement value must be a bool, int, long, float, string or None. If `value` is a
list, `value` should be of the same length and type as `to_replace`.
If `value` is a scalar and `to_replace` is a sequence, then `value` is
used as a replacement for each item in `to_replace`.
:param subset: optional list of column names to consider.
Columns specified in subset that do not have matching data type are ignored.
For example, if `value` is a string, and subset contains a non-string column,
then the non-string column is simply ignored.
>>> df4.na.replace(10, 20).show()
+----+------+-----+
| age|height| name|
+----+------+-----+
| 20| 80|Alice|
| 5| null| Bob|
|null| null| Tom|
|null| null| null|
+----+------+-----+
>>> df4.na.replace('Alice', None).show()
+----+------+----+
| age|height|name|
+----+------+----+
| 10| 80|null|
| 5| null| Bob|
|null| null| Tom|
|null| null|null|
+----+------+----+
>>> df4.na.replace({'Alice': None}).show()
+----+------+----+
| age|height|name|
+----+------+----+
| 10| 80|null|
| 5| null| Bob|
|null| null| Tom|
|null| null|null|
+----+------+----+
>>> df4.na.replace(['Alice', 'Bob'], ['A', 'B'], 'name').show()
+----+------+----+
| age|height|name|
+----+------+----+
| 10| 80| A|
| 5| null| B|
|null| null| Tom|
|null| null|null|
+----+------+----+
"""
if value is _NoValue:
if isinstance(to_replace, dict):
value = None
else:
raise TypeError("value argument is required when to_replace is not a dictionary.")
# Helper functions
def all_of(types):
"""Given a type or tuple of types and a sequence of xs
check if each x is instance of type(s)
>>> all_of(bool)([True, False])
True
>>> all_of(basestring)(["a", 1])
False
"""
def all_of_(xs):
return all(isinstance(x, types) for x in xs)
return all_of_
all_of_bool = all_of(bool)
all_of_str = all_of(basestring)
all_of_numeric = all_of((float, int, long))
# Validate input types
valid_types = (bool, float, int, long, basestring, list, tuple)
if not isinstance(to_replace, valid_types + (dict, )):
raise ValueError(
"to_replace should be a bool, float, int, long, string, list, tuple, or dict. "
"Got {0}".format(type(to_replace)))
if not isinstance(value, valid_types) and value is not None \
and not isinstance(to_replace, dict):
raise ValueError("If to_replace is not a dict, value should be "
"a bool, float, int, long, string, list, tuple or None. "
"Got {0}".format(type(value)))
if isinstance(to_replace, (list, tuple)) and isinstance(value, (list, tuple)):
if len(to_replace) != len(value):
raise ValueError("to_replace and value lists should be of the same length. "
"Got {0} and {1}".format(len(to_replace), len(value)))
if not (subset is None or isinstance(subset, (list, tuple, basestring))):
raise ValueError("subset should be a list or tuple of column names, "
"column name or None. Got {0}".format(type(subset)))
# Reshape input arguments if necessary
if isinstance(to_replace, (float, int, long, basestring)):
to_replace = [to_replace]
if isinstance(to_replace, dict):
rep_dict = to_replace
if value is not None:
warnings.warn("to_replace is a dict and value is not None. value will be ignored.")
else:
if isinstance(value, (float, int, long, basestring)) or value is None:
value = [value for _ in range(len(to_replace))]
rep_dict = dict(zip(to_replace, value))
if isinstance(subset, basestring):
subset = [subset]
# Verify we were not passed in mixed type generics.
if not any(all_of_type(rep_dict.keys())
and all_of_type(x for x in rep_dict.values() if x is not None)
for all_of_type in [all_of_bool, all_of_str, all_of_numeric]):
raise ValueError("Mixed type replacements are not supported")
if subset is None:
return DataFrame(self._jdf.na().replace('*', rep_dict), self.sql_ctx)
else:
return DataFrame(
self._jdf.na().replace(self._jseq(subset), self._jmap(rep_dict)), self.sql_ctx) | Returns a new :class:`DataFrame` replacing a value with another value.
:func:`DataFrame.replace` and :func:`DataFrameNaFunctions.replace` are
aliases of each other.
Values to_replace and value must have the same type and can only be numerics, booleans,
or strings. Value can have None. When replacing, the new value will be cast
to the type of the existing column.
For numeric replacements all values to be replaced should have unique
floating point representation. In case of conflicts (for example with `{42: -1, 42.0: 1}`)
and arbitrary replacement will be used.
:param to_replace: bool, int, long, float, string, list or dict.
Value to be replaced.
If the value is a dict, then `value` is ignored or can be omitted, and `to_replace`
must be a mapping between a value and a replacement.
:param value: bool, int, long, float, string, list or None.
The replacement value must be a bool, int, long, float, string or None. If `value` is a
list, `value` should be of the same length and type as `to_replace`.
If `value` is a scalar and `to_replace` is a sequence, then `value` is
used as a replacement for each item in `to_replace`.
:param subset: optional list of column names to consider.
Columns specified in subset that do not have matching data type are ignored.
For example, if `value` is a string, and subset contains a non-string column,
then the non-string column is simply ignored.
>>> df4.na.replace(10, 20).show()
+----+------+-----+
| age|height| name|
+----+------+-----+
| 20| 80|Alice|
| 5| null| Bob|
|null| null| Tom|
|null| null| null|
+----+------+-----+
>>> df4.na.replace('Alice', None).show()
+----+------+----+
| age|height|name|
+----+------+----+
| 10| 80|null|
| 5| null| Bob|
|null| null| Tom|
|null| null|null|
+----+------+----+
>>> df4.na.replace({'Alice': None}).show()
+----+------+----+
| age|height|name|
+----+------+----+
| 10| 80|null|
| 5| null| Bob|
|null| null| Tom|
|null| null|null|
+----+------+----+
>>> df4.na.replace(['Alice', 'Bob'], ['A', 'B'], 'name').show()
+----+------+----+
| age|height|name|
+----+------+----+
| 10| 80| A|
| 5| null| B|
|null| null| Tom|
|null| null|null|
+----+------+----+ |
def silenceRemoval(x, fs, st_win, st_step, smoothWindow=0.5, weight=0.5, plot=False):
'''
Event Detection (silence removal)
ARGUMENTS:
- x: the input audio signal
- fs: sampling freq
- st_win, st_step: window size and step in seconds
- smoothWindow: (optinal) smooth window (in seconds)
- weight: (optinal) weight factor (0 < weight < 1) the higher, the more strict
- plot: (optinal) True if results are to be plotted
RETURNS:
- seg_limits: list of segment limits in seconds (e.g [[0.1, 0.9], [1.4, 3.0]] means that
the resulting segments are (0.1 - 0.9) seconds and (1.4, 3.0) seconds
'''
if weight >= 1:
weight = 0.99
if weight <= 0:
weight = 0.01
# Step 1: feature extraction
x = audioBasicIO.stereo2mono(x)
st_feats, _ = aF.stFeatureExtraction(x, fs, st_win * fs,
st_step * fs)
# Step 2: train binary svm classifier of low vs high energy frames
# keep only the energy short-term sequence (2nd feature)
st_energy = st_feats[1, :]
en = numpy.sort(st_energy)
# number of 10% of the total short-term windows
l1 = int(len(en) / 10)
# compute "lower" 10% energy threshold
t1 = numpy.mean(en[0:l1]) + 0.000000000000001
# compute "higher" 10% energy threshold
t2 = numpy.mean(en[-l1:-1]) + 0.000000000000001
# get all features that correspond to low energy
class1 = st_feats[:, numpy.where(st_energy <= t1)[0]]
# get all features that correspond to high energy
class2 = st_feats[:, numpy.where(st_energy >= t2)[0]]
# form the binary classification task and ...
faets_s = [class1.T, class2.T]
# normalize and train the respective svm probabilistic model
# (ONSET vs SILENCE)
[faets_s_norm, means_s, stds_s] = aT.normalizeFeatures(faets_s)
svm = aT.trainSVM(faets_s_norm, 1.0)
# Step 3: compute onset probability based on the trained svm
prob_on_set = []
for i in range(st_feats.shape[1]):
# for each frame
cur_fv = (st_feats[:, i] - means_s) / stds_s
# get svm probability (that it belongs to the ONSET class)
prob_on_set.append(svm.predict_proba(cur_fv.reshape(1,-1))[0][1])
prob_on_set = numpy.array(prob_on_set)
# smooth probability:
prob_on_set = smoothMovingAvg(prob_on_set, smoothWindow / st_step)
# Step 4A: detect onset frame indices:
prog_on_set_sort = numpy.sort(prob_on_set)
# find probability Threshold as a weighted average
# of top 10% and lower 10% of the values
Nt = int(prog_on_set_sort.shape[0] / 10)
T = (numpy.mean((1 - weight) * prog_on_set_sort[0:Nt]) +
weight * numpy.mean(prog_on_set_sort[-Nt::]))
max_idx = numpy.where(prob_on_set > T)[0]
# get the indices of the frames that satisfy the thresholding
i = 0
time_clusters = []
seg_limits = []
# Step 4B: group frame indices to onset segments
while i < len(max_idx):
# for each of the detected onset indices
cur_cluster = [max_idx[i]]
if i == len(max_idx)-1:
break
while max_idx[i+1] - cur_cluster[-1] <= 2:
cur_cluster.append(max_idx[i+1])
i += 1
if i == len(max_idx)-1:
break
i += 1
time_clusters.append(cur_cluster)
seg_limits.append([cur_cluster[0] * st_step,
cur_cluster[-1] * st_step])
# Step 5: Post process: remove very small segments:
min_dur = 0.2
seg_limits_2 = []
for s in seg_limits:
if s[1] - s[0] > min_dur:
seg_limits_2.append(s)
seg_limits = seg_limits_2
if plot:
timeX = numpy.arange(0, x.shape[0] / float(fs), 1.0 / fs)
plt.subplot(2, 1, 1)
plt.plot(timeX, x)
for s in seg_limits:
plt.axvline(x=s[0])
plt.axvline(x=s[1])
plt.subplot(2, 1, 2)
plt.plot(numpy.arange(0, prob_on_set.shape[0] * st_step, st_step),
prob_on_set)
plt.title('Signal')
for s in seg_limits:
plt.axvline(x=s[0])
plt.axvline(x=s[1])
plt.title('svm Probability')
plt.show()
return seg_limits | Event Detection (silence removal)
ARGUMENTS:
- x: the input audio signal
- fs: sampling freq
- st_win, st_step: window size and step in seconds
- smoothWindow: (optinal) smooth window (in seconds)
- weight: (optinal) weight factor (0 < weight < 1) the higher, the more strict
- plot: (optinal) True if results are to be plotted
RETURNS:
- seg_limits: list of segment limits in seconds (e.g [[0.1, 0.9], [1.4, 3.0]] means that
the resulting segments are (0.1 - 0.9) seconds and (1.4, 3.0) seconds |
def receive_empty(self, message):
"""
Pair ACKs with requests.
:type message: Message
:param message: the received message
:rtype : Transaction
:return: the transaction to which the message belongs to
"""
logger.debug("receive_empty - " + str(message))
try:
host, port = message.source
except AttributeError:
return
key_mid = str_append_hash(host, port, message.mid)
key_mid_multicast = str_append_hash(defines.ALL_COAP_NODES, port, message.mid)
key_token = str_append_hash(host, port, message.token)
key_token_multicast = str_append_hash(defines.ALL_COAP_NODES, port, message.token)
if key_mid in list(self._transactions.keys()):
transaction = self._transactions[key_mid]
elif key_token in self._transactions_token:
transaction = self._transactions_token[key_token]
elif key_mid_multicast in list(self._transactions.keys()):
transaction = self._transactions[key_mid_multicast]
elif key_token_multicast in self._transactions_token:
transaction = self._transactions_token[key_token_multicast]
else:
logger.warning("Un-Matched incoming empty message " + str(host) + ":" + str(port))
return None
if message.type == defines.Types["ACK"]:
if not transaction.request.acknowledged:
transaction.request.acknowledged = True
elif (transaction.response is not None) and (not transaction.response.acknowledged):
transaction.response.acknowledged = True
elif message.type == defines.Types["RST"]:
if not transaction.request.acknowledged:
transaction.request.rejected = True
elif not transaction.response.acknowledged:
transaction.response.rejected = True
elif message.type == defines.Types["CON"]:
#implicit ACK (might have been lost)
logger.debug("Implicit ACK on received CON for waiting transaction")
transaction.request.acknowledged = True
else:
logger.warning("Unhandled message type...")
if transaction.retransmit_stop is not None:
transaction.retransmit_stop.set()
return transaction | Pair ACKs with requests.
:type message: Message
:param message: the received message
:rtype : Transaction
:return: the transaction to which the message belongs to |
def post(self, endpoint, **kwargs):
"""Create a resource.
Args:
endpoint: resource endpoint.
"""
return self._request(requests.post, endpoint, **kwargs) | Create a resource.
Args:
endpoint: resource endpoint. |
def get_hotkey_name(names=None):
"""
Returns a string representation of hotkey from the given key names, or
the currently pressed keys if not given. This function:
- normalizes names;
- removes "left" and "right" prefixes;
- replaces the "+" key name with "plus" to avoid ambiguity;
- puts modifier keys first, in a standardized order;
- sort remaining keys;
- finally, joins everything with "+".
Example:
get_hotkey_name(['+', 'left ctrl', 'shift'])
# "ctrl+shift+plus"
"""
if names is None:
_listener.start_if_necessary()
with _pressed_events_lock:
names = [e.name for e in _pressed_events.values()]
else:
names = [normalize_name(name) for name in names]
clean_names = set(e.replace('left ', '').replace('right ', '').replace('+', 'plus') for e in names)
# https://developer.apple.com/macos/human-interface-guidelines/input-and-output/keyboard/
# > List modifier keys in the correct order. If you use more than one modifier key in a
# > hotkey, always list them in this order: Control, Option, Shift, Command.
modifiers = ['ctrl', 'alt', 'shift', 'windows']
sorting_key = lambda k: (modifiers.index(k) if k in modifiers else 5, str(k))
return '+'.join(sorted(clean_names, key=sorting_key)) | Returns a string representation of hotkey from the given key names, or
the currently pressed keys if not given. This function:
- normalizes names;
- removes "left" and "right" prefixes;
- replaces the "+" key name with "plus" to avoid ambiguity;
- puts modifier keys first, in a standardized order;
- sort remaining keys;
- finally, joins everything with "+".
Example:
get_hotkey_name(['+', 'left ctrl', 'shift'])
# "ctrl+shift+plus" |
def run(self):
"""
the main loop
"""
try:
master_process = BackgroundProcess.objects.filter(pk=self.process_id).first()
if master_process:
master_process.last_update = now()
master_process.message = 'init child processes'
master_process.save()
else:
self.delete_pid(force_del=True)
self.stderr.write("no such process in BackgroundProcesses")
sys.exit(0)
self.manage_processes()
while True:
# handle signals
sig = self.SIG_QUEUE.pop(0) if len(self.SIG_QUEUE) else None
# check the DB connection
check_db_connection()
# update the P
BackgroundProcess.objects.filter(pk=self.process_id).update(
last_update=now(),
message='running..')
if sig is None:
self.manage_processes()
elif sig not in self.SIGNALS:
logger.error('%s, unhandled signal %d' % (self.label, sig))
continue
elif sig == signal.SIGTERM:
logger.debug('%s, termination signal' % self.label)
raise StopIteration
elif sig == signal.SIGHUP:
# todo handle sighup
pass
elif sig == signal.SIGUSR1:
# restart all child processes
logger.debug('PID %d, processed SIGUSR1 (%d) signal' % (self.pid, sig))
self.restart()
elif sig == signal.SIGUSR2:
# write the process status to stdout
self.status()
pass
sleep(5)
except StopIteration:
self.stop()
self.delete_pid()
sys.exit(0)
except SystemExit:
raise
except:
logger.error('%s(%d), unhandled exception\n%s' % (self.label, getpid(), traceback.format_exc())) | the main loop |
def write_batch_json(self, content):
"""Write batch json data to a file."""
timestamp = str(time.time()).replace('.', '')
batch_json_file = os.path.join(
self.tcex.args.tc_temp_path, 'batch-{}.json'.format(timestamp)
)
with open(batch_json_file, 'w') as fh:
json.dump(content, fh, indent=2) | Write batch json data to a file. |
def session_preparation(self):
"""Prepare the session after the connection has been established."""
self._test_channel_read(pattern=r"[>#]")
self.ansi_escape_codes = True
self.set_base_prompt()
self.disable_paging()
# Clear the read buffer
time.sleep(0.3 * self.global_delay_factor)
self.clear_buffer() | Prepare the session after the connection has been established. |
def _load_yaml_config(cls, config_data, filename="(unknown)"):
"""Load a yaml config file."""
try:
config = yaml.safe_load(config_data)
except yaml.YAMLError as err:
if hasattr(err, 'problem_mark'):
mark = err.problem_mark
errmsg = ("Invalid YAML syntax in Configuration file "
"%(file)s at line: %(line)s, column: %(column)s."
% dict(file=filename,
line=mark.line + 1,
column=mark.column + 1))
else:
errmsg = ("YAML error reading Configuration file "
"%(file)s"
% dict(file=filename))
logger.error(errmsg)
raise
logger.info("Configuration: %s", config)
return config | Load a yaml config file. |
def filter(self, obj, *args, **kwargs):
"""
Filter the given object through the filter chain.
:param obj: The object to filter
:param args: Additional arguments to pass to each filter function.
:param kwargs: Additional keyword arguments to pass to each filter
function.
:return: The filtered object or :data:`None`
See the documentation of :class:`Filter` on how filtering operates.
Returns the object returned by the last function in the filter chain or
:data:`None` if any function returned :data:`None`.
"""
for _, _, func in self._filter_order:
obj = func(obj, *args, **kwargs)
if obj is None:
return None
return obj | Filter the given object through the filter chain.
:param obj: The object to filter
:param args: Additional arguments to pass to each filter function.
:param kwargs: Additional keyword arguments to pass to each filter
function.
:return: The filtered object or :data:`None`
See the documentation of :class:`Filter` on how filtering operates.
Returns the object returned by the last function in the filter chain or
:data:`None` if any function returned :data:`None`. |
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