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openpermissions/chub
chub/api.py
https://github.com/openpermissions/chub/blob/00762aa17015f4b3010673d1570c708eab3c34ed/chub/api.py#L61-L69
def _sub_resource(self, path): """ get or create sub resource """ if path not in self.resource_map: self.resource_map[path] = Resource( path, self.fetch, self.resource_map, default_headers=self.default_headers) return self.resource_map[path]
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get or create sub resource
[ "get", "or", "create", "sub", "resource" ]
python
train
basho/riak-python-client
riak/codecs/pbuf.py
https://github.com/basho/riak-python-client/blob/91de13a16607cdf553d1a194e762734e3bec4231/riak/codecs/pbuf.py#L847-L895
def decode_timeseries_row(self, tsrow, tscols=None, convert_timestamp=False): """ Decodes a TsRow into a list :param tsrow: the protobuf TsRow to decode. :type tsrow: riak.pb.riak_ts_pb2.TsRow :param tscols: the protobuf TsColumn data to help decode. :type tscols: list :rtype list """ row = [] for i, cell in enumerate(tsrow.cells): col = None if tscols is not None: col = tscols[i] if cell.HasField('varchar_value'): if col and not (col.type == TsColumnType.Value('VARCHAR') or col.type == TsColumnType.Value('BLOB')): raise TypeError('expected VARCHAR or BLOB column') else: row.append(cell.varchar_value) elif cell.HasField('sint64_value'): if col and col.type != TsColumnType.Value('SINT64'): raise TypeError('expected SINT64 column') else: row.append(cell.sint64_value) elif cell.HasField('double_value'): if col and col.type != TsColumnType.Value('DOUBLE'): raise TypeError('expected DOUBLE column') else: row.append(cell.double_value) elif cell.HasField('timestamp_value'): if col and col.type != TsColumnType.Value('TIMESTAMP'): raise TypeError('expected TIMESTAMP column') else: dt = cell.timestamp_value if convert_timestamp: dt = datetime_from_unix_time_millis( cell.timestamp_value) row.append(dt) elif cell.HasField('boolean_value'): if col and col.type != TsColumnType.Value('BOOLEAN'): raise TypeError('expected BOOLEAN column') else: row.append(cell.boolean_value) else: row.append(None) return row
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Decodes a TsRow into a list :param tsrow: the protobuf TsRow to decode. :type tsrow: riak.pb.riak_ts_pb2.TsRow :param tscols: the protobuf TsColumn data to help decode. :type tscols: list :rtype list
[ "Decodes", "a", "TsRow", "into", "a", "list" ]
python
train
yohell/python-tui
tui/__init__.py
https://github.com/yohell/python-tui/blob/de2e678e2f00e5940de52c000214dbcb8812a222/tui/__init__.py#L1513-L1533
def strsettings(self, indent=0, maxindent=25, width=0): """Return user friendly help on positional arguments. indent is the number of spaces preceeding the text on each line. The indent of the documentation is dependent on the length of the longest label that is shorter than maxindent. A label longer than maxindent will be printed on its own line. width is maximum allowed page width, use self.width if 0. """ out = [] makelabel = lambda name: ' ' * indent + name + ': ' settingsindent = _autoindent([makelabel(s) for s in self.options], indent, maxindent) for name in self.option_order: option = self.options[name] label = makelabel(name) settingshelp = "%s(%s): %s" % (option.formatname, option.strvalue, option.location) wrapped = self._wrap_labelled(label, settingshelp, settingsindent, width) out.extend(wrapped) return '\n'.join(out)
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Return user friendly help on positional arguments. indent is the number of spaces preceeding the text on each line. The indent of the documentation is dependent on the length of the longest label that is shorter than maxindent. A label longer than maxindent will be printed on its own line. width is maximum allowed page width, use self.width if 0.
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python
valid
saltstack/salt
salt/states/pagerduty_service.py
https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/states/pagerduty_service.py#L87-L98
def absent(profile='pagerduty', subdomain=None, api_key=None, **kwargs): ''' Ensure a pagerduty service does not exist. Name can be the service name or pagerduty service id. ''' r = __salt__['pagerduty_util.resource_absent']('services', ['name', 'id'], profile, subdomain, api_key, **kwargs) return r
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Ensure a pagerduty service does not exist. Name can be the service name or pagerduty service id.
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python
train
twilio/twilio-python
twilio/rest/notify/v1/service/__init__.py
https://github.com/twilio/twilio-python/blob/c867895f55dcc29f522e6e8b8868d0d18483132f/twilio/rest/notify/v1/service/__init__.py#L38-L87
def create(self, friendly_name=values.unset, apn_credential_sid=values.unset, gcm_credential_sid=values.unset, messaging_service_sid=values.unset, facebook_messenger_page_id=values.unset, default_apn_notification_protocol_version=values.unset, default_gcm_notification_protocol_version=values.unset, fcm_credential_sid=values.unset, default_fcm_notification_protocol_version=values.unset, log_enabled=values.unset, alexa_skill_id=values.unset, default_alexa_notification_protocol_version=values.unset): """ Create a new ServiceInstance :param unicode friendly_name: A string to describe the resource :param unicode apn_credential_sid: The SID of the Credential to use for APN Bindings :param unicode gcm_credential_sid: The SID of the Credential to use for GCM Bindings :param unicode messaging_service_sid: The SID of the Messaging Service to use for SMS Bindings :param unicode facebook_messenger_page_id: Deprecated :param unicode default_apn_notification_protocol_version: The protocol version to use for sending APNS notifications :param unicode default_gcm_notification_protocol_version: The protocol version to use for sending GCM notifications :param unicode fcm_credential_sid: The SID of the Credential to use for FCM Bindings :param unicode default_fcm_notification_protocol_version: The protocol version to use for sending FCM notifications :param bool log_enabled: Whether to log notifications :param unicode alexa_skill_id: Deprecated :param unicode default_alexa_notification_protocol_version: Deprecated :returns: Newly created ServiceInstance :rtype: twilio.rest.notify.v1.service.ServiceInstance """ data = values.of({ 'FriendlyName': friendly_name, 'ApnCredentialSid': apn_credential_sid, 'GcmCredentialSid': gcm_credential_sid, 'MessagingServiceSid': messaging_service_sid, 'FacebookMessengerPageId': facebook_messenger_page_id, 'DefaultApnNotificationProtocolVersion': default_apn_notification_protocol_version, 'DefaultGcmNotificationProtocolVersion': default_gcm_notification_protocol_version, 'FcmCredentialSid': fcm_credential_sid, 'DefaultFcmNotificationProtocolVersion': default_fcm_notification_protocol_version, 'LogEnabled': log_enabled, 'AlexaSkillId': alexa_skill_id, 'DefaultAlexaNotificationProtocolVersion': default_alexa_notification_protocol_version, }) payload = self._version.create( 'POST', self._uri, data=data, ) return ServiceInstance(self._version, payload, )
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Create a new ServiceInstance :param unicode friendly_name: A string to describe the resource :param unicode apn_credential_sid: The SID of the Credential to use for APN Bindings :param unicode gcm_credential_sid: The SID of the Credential to use for GCM Bindings :param unicode messaging_service_sid: The SID of the Messaging Service to use for SMS Bindings :param unicode facebook_messenger_page_id: Deprecated :param unicode default_apn_notification_protocol_version: The protocol version to use for sending APNS notifications :param unicode default_gcm_notification_protocol_version: The protocol version to use for sending GCM notifications :param unicode fcm_credential_sid: The SID of the Credential to use for FCM Bindings :param unicode default_fcm_notification_protocol_version: The protocol version to use for sending FCM notifications :param bool log_enabled: Whether to log notifications :param unicode alexa_skill_id: Deprecated :param unicode default_alexa_notification_protocol_version: Deprecated :returns: Newly created ServiceInstance :rtype: twilio.rest.notify.v1.service.ServiceInstance
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python
train
Josef-Friedrich/tmep
tmep/doc.py
https://github.com/Josef-Friedrich/tmep/blob/326de14f5b9498696a1f06a8be3d39e33e376102/tmep/doc.py#L33-L52
def extract_value(self, string, key, inline_code=True): """Extract strings from the docstrings .. code-block:: text * synopsis: ``%shorten{text, max_size}`` * example: ``%shorten{$title, 32}`` * description: Shorten “text” on word boundarys. """ regex = r'\* ' + key + ': ' if inline_code: regex = regex + '``(.*)``' else: regex = regex + '(.*)' value = re.findall(regex, string) if value: return value[0].replace('``', '') else: return False
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Extract strings from the docstrings .. code-block:: text * synopsis: ``%shorten{text, max_size}`` * example: ``%shorten{$title, 32}`` * description: Shorten “text” on word boundarys.
[ "Extract", "strings", "from", "the", "docstrings" ]
python
train
jstitch/MambuPy
MambuPy/mambuutil.py
https://github.com/jstitch/MambuPy/blob/2af98cc12e7ed5ec183b3e97644e880e70b79ee8/MambuPy/mambuutil.py#L548-L562
def getproductsurl(idproduct, *args, **kwargs): """Request loan Products URL. If idproduct is set, you'll get a response adequate for a MambuProduct object. If not set, you'll get a response adequate for a MambuProducts object. See mambuproduct module and pydoc for further information. No current implemented filter parameters. See Mambu official developer documentation for further details, and info on parameters that may be implemented here in the future. """ productidparam = "" if idproduct == "" else "/"+idproduct url = getmambuurl(*args,**kwargs) + "loanproducts" + productidparam return url
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Request loan Products URL. If idproduct is set, you'll get a response adequate for a MambuProduct object. If not set, you'll get a response adequate for a MambuProducts object. See mambuproduct module and pydoc for further information. No current implemented filter parameters. See Mambu official developer documentation for further details, and info on parameters that may be implemented here in the future.
[ "Request", "loan", "Products", "URL", "." ]
python
train
pandas-dev/pandas
pandas/core/groupby/base.py
https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/groupby/base.py#L33-L67
def _gotitem(self, key, ndim, subset=None): """ Sub-classes to define. Return a sliced object. Parameters ---------- key : string / list of selections ndim : 1,2 requested ndim of result subset : object, default None subset to act on """ # create a new object to prevent aliasing if subset is None: subset = self.obj # we need to make a shallow copy of ourselves # with the same groupby kwargs = {attr: getattr(self, attr) for attr in self._attributes} # Try to select from a DataFrame, falling back to a Series try: groupby = self._groupby[key] except IndexError: groupby = self._groupby self = self.__class__(subset, groupby=groupby, parent=self, **kwargs) self._reset_cache() if subset.ndim == 2: if is_scalar(key) and key in subset or is_list_like(key): self._selection = key return self
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Sub-classes to define. Return a sliced object. Parameters ---------- key : string / list of selections ndim : 1,2 requested ndim of result subset : object, default None subset to act on
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python
train
ajyoon/blur
blur/markov/graph.py
https://github.com/ajyoon/blur/blob/25fcf083af112bb003956a7a7e1c6ff7d8fef279/blur/markov/graph.py#L152-L206
def feather_links(self, factor=0.01, include_self=False): """ Feather the links of connected nodes. Go through every node in the network and make it inherit the links of the other nodes it is connected to. Because the link weight sum for any given node can be very different within a graph, the weights of inherited links are made proportional to the sum weight of the parent nodes. Args: factor (float): multiplier of neighbor links include_self (bool): whether nodes can inherit links pointing to themselves Returns: None Example: >>> from blur.markov.node import Node >>> node_1 = Node('One') >>> node_2 = Node('Two') >>> node_1.add_link(node_2, 1) >>> node_2.add_link(node_1, 1) >>> graph = Graph([node_1, node_2]) >>> for link in graph.node_list[0].link_list: ... print('{} {}'.format(link.target.value, link.weight)) Two 1 >>> graph.feather_links(include_self=True) >>> for link in graph.node_list[0].link_list: ... print('{} {}'.format(link.target.value, link.weight)) Two 1 One 0.01 """ def feather_node(node): node_weight_sum = sum(l.weight for l in node.link_list) # Iterate over a copy of the original link list since we will # need to refer to this while modifying node.link_list for original_link in node.link_list[:]: neighbor_node = original_link.target neighbor_weight = original_link.weight feather_weight = neighbor_weight / node_weight_sum neighbor_node_weight_sum = sum(l.weight for l in neighbor_node.link_list) # Iterate over the links belonging to the neighbor_node, # copying its links to ``node`` with proportional weights for neighbor_link in neighbor_node.link_list: if (not include_self) and (neighbor_link.target == node): continue relative_link_weight = (neighbor_link.weight / neighbor_node_weight_sum) feathered_link_weight = round((relative_link_weight * feather_weight * factor), 2) node.add_link(neighbor_link.target, feathered_link_weight) for n in self.node_list: feather_node(n)
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Feather the links of connected nodes. Go through every node in the network and make it inherit the links of the other nodes it is connected to. Because the link weight sum for any given node can be very different within a graph, the weights of inherited links are made proportional to the sum weight of the parent nodes. Args: factor (float): multiplier of neighbor links include_self (bool): whether nodes can inherit links pointing to themselves Returns: None Example: >>> from blur.markov.node import Node >>> node_1 = Node('One') >>> node_2 = Node('Two') >>> node_1.add_link(node_2, 1) >>> node_2.add_link(node_1, 1) >>> graph = Graph([node_1, node_2]) >>> for link in graph.node_list[0].link_list: ... print('{} {}'.format(link.target.value, link.weight)) Two 1 >>> graph.feather_links(include_self=True) >>> for link in graph.node_list[0].link_list: ... print('{} {}'.format(link.target.value, link.weight)) Two 1 One 0.01
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python
train
pmichali/whodunit
whodunit/__init__.py
https://github.com/pmichali/whodunit/blob/eed9107533766d716469e35fbb647a39dfa07035/whodunit/__init__.py#L306-L319
def sort(self): """Sort by commit size, per author.""" # First sort commits by author email users = [] # Group commits by author email, so they can be merged for _, group in itertools.groupby(sorted(self.commits), operator.attrgetter('author_mail')): if group: users.append(self.merge_user_commits(group)) # Finally sort by the (aggregated) commits' line counts self.sorted_commits = sorted(users, key=operator.attrgetter('line_count'), reverse=True) return self.sorted_commits
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Sort by commit size, per author.
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python
train
hollenstein/maspy
maspy/sil.py
https://github.com/hollenstein/maspy/blob/f15fcfd24df306d8420540460d902aa3073ec133/maspy/sil.py#L271-L327
def expectedLabelPosition(peptide, labelStateInfo, sequence=None, modPositions=None): """Returns a modification description of a certain label state of a peptide. :param peptide: Peptide sequence used to calculat the expected label state modifications :param labelStateInfo: An entry of :attr:`LabelDescriptor.labels` that describes a label state :param sequence: unmodified amino acid sequence of :var:`peptide`, if None it is generated by :func:`maspy.peptidemethods.removeModifications()` :param modPositions: dictionary describing the modification state of "peptide", if None it is generated by :func:`maspy.peptidemethods.returnModPositions()` :returns: {sequence position: sorted list of expected label modifications on that position, ... } """ if modPositions is None: modPositions = maspy.peptidemethods.returnModPositions(peptide, indexStart=0 ) if sequence is None: sequence = maspy.peptidemethods.removeModifications(peptide) currLabelMods = dict() for labelPosition, labelSymbols in viewitems(labelStateInfo['aminoAcidLabels']): labelSymbols = aux.toList(labelSymbols) if labelSymbols == ['']: pass elif labelPosition == 'nTerm': currLabelMods.setdefault(0, list()) currLabelMods[0].extend(labelSymbols) else: for sequencePosition in aux.findAllSubstrings(sequence, labelPosition): currLabelMods.setdefault(sequencePosition, list()) currLabelMods[sequencePosition].extend(labelSymbols) if labelStateInfo['excludingModifications'] is not None: for excludingMod, excludedLabelSymbol in viewitems(labelStateInfo['excludingModifications']): if excludingMod not in modPositions: continue for excludingModPos in modPositions[excludingMod]: if excludingModPos not in currLabelMods: continue if excludedLabelSymbol not in currLabelMods[excludingModPos]: continue if len(currLabelMods[excludingModPos]) == 1: del(currLabelMods[excludingModPos]) else: excludedModIndex = currLabelMods[excludingModPos].index(excludedLabelSymbol) currLabelMods[excludingModPos].pop(excludedModIndex) for sequencePosition in list(viewkeys(currLabelMods)): currLabelMods[sequencePosition] = sorted(currLabelMods[sequencePosition]) return currLabelMods
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Returns a modification description of a certain label state of a peptide. :param peptide: Peptide sequence used to calculat the expected label state modifications :param labelStateInfo: An entry of :attr:`LabelDescriptor.labels` that describes a label state :param sequence: unmodified amino acid sequence of :var:`peptide`, if None it is generated by :func:`maspy.peptidemethods.removeModifications()` :param modPositions: dictionary describing the modification state of "peptide", if None it is generated by :func:`maspy.peptidemethods.returnModPositions()` :returns: {sequence position: sorted list of expected label modifications on that position, ... }
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python
train
ask/carrot
carrot/messaging.py
https://github.com/ask/carrot/blob/5889a25cd2e274642071c9bba39772f4b3e3d9da/carrot/messaging.py#L712-L772
def send(self, message_data, routing_key=None, delivery_mode=None, mandatory=False, immediate=False, priority=0, content_type=None, content_encoding=None, serializer=None, exchange=None): """Send a message. :param message_data: The message data to send. Can be a list, dictionary or a string. :keyword routing_key: A custom routing key for the message. If not set, the default routing key set in the :attr:`routing_key` attribute is used. :keyword mandatory: If set, the message has mandatory routing. By default the message is silently dropped by the server if it can't be routed to a queue. However - If the message is mandatory, an exception will be raised instead. :keyword immediate: Request immediate delivery. If the message cannot be routed to a queue consumer immediately, an exception will be raised. This is instead of the default behaviour, where the server will accept and queue the message, but with no guarantee that the message will ever be consumed. :keyword delivery_mode: Override the default :attr:`delivery_mode`. :keyword priority: The message priority, ``0`` to ``9``. :keyword content_type: The messages content_type. If content_type is set, no serialization occurs as it is assumed this is either a binary object, or you've done your own serialization. Leave blank if using built-in serialization as our library properly sets content_type. :keyword content_encoding: The character set in which this object is encoded. Use "binary" if sending in raw binary objects. Leave blank if using built-in serialization as our library properly sets content_encoding. :keyword serializer: Override the default :attr:`serializer`. :keyword exchange: Override the exchange to publish to. Note that this exchange must have been declared. """ headers = None routing_key = routing_key or self.routing_key if self.exchange_type == "headers": headers, routing_key = routing_key, "" exchange = exchange or self.exchange message = self.create_message(message_data, priority=priority, delivery_mode=delivery_mode, content_type=content_type, content_encoding=content_encoding, serializer=serializer) self.backend.publish(message, exchange=exchange, routing_key=routing_key, mandatory=mandatory, immediate=immediate, headers=headers)
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Send a message. :param message_data: The message data to send. Can be a list, dictionary or a string. :keyword routing_key: A custom routing key for the message. If not set, the default routing key set in the :attr:`routing_key` attribute is used. :keyword mandatory: If set, the message has mandatory routing. By default the message is silently dropped by the server if it can't be routed to a queue. However - If the message is mandatory, an exception will be raised instead. :keyword immediate: Request immediate delivery. If the message cannot be routed to a queue consumer immediately, an exception will be raised. This is instead of the default behaviour, where the server will accept and queue the message, but with no guarantee that the message will ever be consumed. :keyword delivery_mode: Override the default :attr:`delivery_mode`. :keyword priority: The message priority, ``0`` to ``9``. :keyword content_type: The messages content_type. If content_type is set, no serialization occurs as it is assumed this is either a binary object, or you've done your own serialization. Leave blank if using built-in serialization as our library properly sets content_type. :keyword content_encoding: The character set in which this object is encoded. Use "binary" if sending in raw binary objects. Leave blank if using built-in serialization as our library properly sets content_encoding. :keyword serializer: Override the default :attr:`serializer`. :keyword exchange: Override the exchange to publish to. Note that this exchange must have been declared.
[ "Send", "a", "message", "." ]
python
train
apple/turicreate
deps/src/boost_1_68_0/libs/predef/tools/ci/common.py
https://github.com/apple/turicreate/blob/74514c3f99e25b46f22c6e02977fe3da69221c2e/deps/src/boost_1_68_0/libs/predef/tools/ci/common.py#L683-L709
def install_toolset(self, toolset): ''' Installs specific toolset on CI system. ''' info = toolset_info[toolset] if sys.platform.startswith('linux'): os.chdir(self.work_dir) if 'ppa' in info: for ppa in info['ppa']: utils.check_call( 'sudo','add-apt-repository','--yes',ppa) if 'deb' in info: utils.make_file('sources.list', "deb %s"%(' '.join(info['deb'])), "deb-src %s"%(' '.join(info['deb']))) utils.check_call('sudo','bash','-c','cat sources.list >> /etc/apt/sources.list') if 'apt-key' in info: for key in info['apt-key']: utils.check_call('wget',key,'-O','apt.key') utils.check_call('sudo','apt-key','add','apt.key') utils.check_call( 'sudo','apt-get','update','-qq') utils.check_call( 'sudo','apt-get','install','-qq',info['package']) if 'debugpackage' in info and info['debugpackage']: utils.check_call( 'sudo','apt-get','install','-qq',info['debugpackage'])
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Installs specific toolset on CI system.
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python
train
tensorflow/probability
tensorflow_probability/python/distributions/poisson_lognormal.py
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/poisson_lognormal.py#L45-L85
def quadrature_scheme_lognormal_gauss_hermite( loc, scale, quadrature_size, validate_args=False, name=None): # pylint: disable=unused-argument """Use Gauss-Hermite quadrature to form quadrature on positive-reals. Note: for a given `quadrature_size`, this method is generally less accurate than `quadrature_scheme_lognormal_quantiles`. Args: loc: `float`-like (batch of) scalar `Tensor`; the location parameter of the LogNormal prior. scale: `float`-like (batch of) scalar `Tensor`; the scale parameter of the LogNormal prior. quadrature_size: Python `int` scalar representing the number of quadrature points. validate_args: Python `bool`, default `False`. When `True` distribution parameters are checked for validity despite possibly degrading runtime performance. When `False` invalid inputs may silently render incorrect outputs. name: Python `str` name prefixed to Ops created by this class. Returns: grid: (Batch of) length-`quadrature_size` vectors representing the `log_rate` parameters of a `Poisson`. probs: (Batch of) length-`quadrature_size` vectors representing the weight associate with each `grid` value. """ with tf.name_scope( name or "vector_diffeomixture_quadrature_gauss_hermite"): grid, probs = np.polynomial.hermite.hermgauss(deg=quadrature_size) npdt = dtype_util.as_numpy_dtype(loc.dtype) grid = grid.astype(npdt) probs = probs.astype(npdt) probs /= np.linalg.norm(probs, ord=1, keepdims=True) probs = tf.convert_to_tensor(value=probs, name="probs", dtype=loc.dtype) # The following maps the broadcast of `loc` and `scale` to each grid # point, i.e., we are creating several log-rates that correspond to the # different Gauss-Hermite quadrature points and (possible) batches of # `loc` and `scale`. grid = (loc[..., tf.newaxis] + np.sqrt(2.) * scale[..., tf.newaxis] * grid) return grid, probs
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Use Gauss-Hermite quadrature to form quadrature on positive-reals. Note: for a given `quadrature_size`, this method is generally less accurate than `quadrature_scheme_lognormal_quantiles`. Args: loc: `float`-like (batch of) scalar `Tensor`; the location parameter of the LogNormal prior. scale: `float`-like (batch of) scalar `Tensor`; the scale parameter of the LogNormal prior. quadrature_size: Python `int` scalar representing the number of quadrature points. validate_args: Python `bool`, default `False`. When `True` distribution parameters are checked for validity despite possibly degrading runtime performance. When `False` invalid inputs may silently render incorrect outputs. name: Python `str` name prefixed to Ops created by this class. Returns: grid: (Batch of) length-`quadrature_size` vectors representing the `log_rate` parameters of a `Poisson`. probs: (Batch of) length-`quadrature_size` vectors representing the weight associate with each `grid` value.
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python
test
cbrand/vpnchooser
src/vpnchooser/helpers/parser.py
https://github.com/cbrand/vpnchooser/blob/d153e3d05555c23cf5e8e15e507eecad86465923/src/vpnchooser/helpers/parser.py#L9-L44
def id_from_url(url, param_name: str) -> int: """ Parses an object and tries to extract a url. Tries to parse if a resource_url has been given it as a url. :raise ValueError: If no id could be extracted. """ if url is None: raise ValueError('url is none') elif isinstance(url, int): # Seems to already be the url. return url if not url: raise ValueError('Seems to be empty') try: return int(url) except ValueError: pass parsed = urlparse(url) try: resource_url = app.url_map.bind(parsed.netloc).match( parsed.path ) except NotFound: raise ValueError('No URL found') if param_name in resource_url[1]: return resource_url[1][param_name] else: raise ValueError( 'Parameter {name} could not be extracted'.format( name=param_name ) )
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Parses an object and tries to extract a url. Tries to parse if a resource_url has been given it as a url. :raise ValueError: If no id could be extracted.
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python
train
GoogleCloudPlatform/python-repo-tools
gcp_devrel/tools/requirements.py
https://github.com/GoogleCloudPlatform/python-repo-tools/blob/87422ba91814529848a2b8bf8be4294283a3e041/gcp_devrel/tools/requirements.py#L85-L112
def update_req(req): """Updates a given req object with the latest version.""" if not req.name: return req, None info = get_package_info(req.name) if info['info'].get('_pypi_hidden'): print('{} is hidden on PyPI and will not be updated.'.format(req)) return req, None if _is_pinned(req) and _is_version_range(req): print('{} is pinned to a range and will not be updated.'.format(req)) return req, None newest_version = _get_newest_version(info) current_spec = next(iter(req.specifier)) if req.specifier else None current_version = current_spec.version if current_spec else None new_spec = Specifier(u'=={}'.format(newest_version)) if not current_spec or current_spec._spec != new_spec._spec: req.specifier = new_spec update_info = ( req.name, current_version, newest_version) return req, update_info return req, None
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Updates a given req object with the latest version.
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python
train
markuskiller/textblob-de
textblob_de/ext/_pattern/text/search.py
https://github.com/markuskiller/textblob-de/blob/1b427b2cdd7e5e9fd3697677a98358fae4aa6ad1/textblob_de/ext/_pattern/text/search.py#L571-L650
def match(self, word): """ Return True if the given Word is part of the constraint: - the word (or lemma) occurs in Constraint.words, OR - the word (or lemma) occurs in Constraint.taxa taxonomy tree, AND - the word and/or chunk tags match those defined in the constraint. Individual terms in Constraint.words or the taxonomy can contain wildcards (*). Some part-of-speech-tags can also contain wildcards: NN*, VB*, JJ*, RB* If the given word contains spaces (e.g., proper noun), the entire chunk will also be compared. For example: Constraint(words=["Mac OS X*"]) matches the word "Mac" if the word occurs in a Chunk("Mac OS X 10.5"). """ # If the constraint has a custom function it must return True. if self.custom is not None and self.custom(word) is False: return False # If the constraint can only match the first word, Word.index must be 0. if self.first and word.index > 0: return False # If the constraint defines excluded options, Word can not match any of these. if self.exclude and self.exclude.match(word): return False # If the constraint defines allowed tags, Word.tag needs to match one of these. if self.tags: if find(lambda w: _match(word.tag, w), self.tags) is None: return False # If the constraint defines allowed chunks, Word.chunk.tag needs to match one of these. if self.chunks: ch = word.chunk and word.chunk.tag or None if find(lambda w: _match(ch, w), self.chunks) is None: return False # If the constraint defines allowed role, Word.chunk.tag needs to match one of these. if self.roles: R = word.chunk and [r2 for r1, r2 in word.chunk.relations] or [] if find(lambda w: w in R, self.roles) is None: return False # If the constraint defines allowed words, # Word.string.lower() OR Word.lemma needs to match one of these. b = True # b==True when word in constraint (or Constraints.words=[]). if len(self.words) + len(self.taxa) > 0: s1 = word.string.lower() s2 = word.lemma b = False for w in itertools.chain(self.words, self.taxa): # If the constraint has a word with spaces (e.g., a proper noun), # compare it to the entire chunk. try: if " " in w and (s1 in w or s2 and s2 in w or "*" in w): s1 = word.chunk and word.chunk.string.lower() or s1 s2 = word.chunk and " ".join([x or "" for x in word.chunk.lemmata]) or s2 except: s1 = s1 s2 = None # Compare the word to the allowed words (which can contain wildcards). if _match(s1, w): b=True; break # Compare the word lemma to the allowed words, e.g., # if "was" is not in the constraint, perhaps "be" is, which is a good match. if s2 and _match(s2, w): b=True; break # If the constraint defines allowed taxonomy terms, # and the given word did not match an allowed word, traverse the taxonomy. # The search goes up from the given word to its parents in the taxonomy. # This is faster than traversing all the children of terms in Constraint.taxa. # The drawback is that: # 1) Wildcards in the taxonomy are not detected (use classifiers instead), # 2) Classifier.children() has no effect, only Classifier.parent(). if self.taxa and (not self.words or (self.words and not b)): for s in ( word.string, # "ants" word.lemma, # "ant" word.chunk and word.chunk.string or None, # "army ants" word.chunk and " ".join([x or "" for x in word.chunk.lemmata]) or None): # "army ant" if s is not None: if self.taxonomy.case_sensitive is False: s = s.lower() # Compare ancestors of the word to each term in Constraint.taxa. for p in self.taxonomy.parents(s, recursive=True): if find(lambda s: p==s, self.taxa): # No wildcards. return True return b
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Return True if the given Word is part of the constraint: - the word (or lemma) occurs in Constraint.words, OR - the word (or lemma) occurs in Constraint.taxa taxonomy tree, AND - the word and/or chunk tags match those defined in the constraint. Individual terms in Constraint.words or the taxonomy can contain wildcards (*). Some part-of-speech-tags can also contain wildcards: NN*, VB*, JJ*, RB* If the given word contains spaces (e.g., proper noun), the entire chunk will also be compared. For example: Constraint(words=["Mac OS X*"]) matches the word "Mac" if the word occurs in a Chunk("Mac OS X 10.5").
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python
train
ThreatConnect-Inc/tcex
tcex/tcex_ti/mappings/group/group_types/document.py
https://github.com/ThreatConnect-Inc/tcex/blob/dd4d7a1ef723af1561687120191886b9a2fd4b47/tcex/tcex_ti/mappings/group/group_types/document.py#L55-L67
def file_name(self, file_name): """ Updates the file_name. Args: file_name: """ if not self.can_update(): self._tcex.handle_error(910, [self.type]) self._data['fileName'] = file_name request = {'fileName': file_name} return self.tc_requests.update(self.api_type, self.api_sub_type, self.unique_id, request)
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Updates the file_name. Args: file_name:
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python
train
saltstack/salt
salt/modules/inspectlib/fsdb.py
https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/inspectlib/fsdb.py#L104-L113
def flush(self, table): ''' Flush table. :param table: :return: ''' table_path = os.path.join(self.db_path, table) if os.path.exists(table_path): os.unlink(table_path)
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Flush table. :param table: :return:
[ "Flush", "table", "." ]
python
train
zarr-developers/zarr
zarr/core.py
https://github.com/zarr-developers/zarr/blob/fb8e6d5ea6bc26e451e5cf0eaaee36977556d5b5/zarr/core.py#L2244-L2302
def astype(self, dtype): """Returns a view that does on the fly type conversion of the underlying data. Parameters ---------- dtype : string or dtype NumPy dtype. Notes ----- This method returns a new Array object which is a view on the same underlying chunk data. Modifying any data via the view is currently not permitted and will result in an error. This is an experimental feature and its behavior is subject to change in the future. See Also -------- Array.view Examples -------- >>> import zarr >>> import numpy as np >>> data = np.arange(100, dtype=np.uint8) >>> a = zarr.array(data, chunks=10) >>> a[:] array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99], dtype=uint8) >>> v = a.astype(np.float32) >>> v.is_view True >>> v[:] array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98., 99.], dtype=float32) """ dtype = np.dtype(dtype) filters = [] if self._filters: filters.extend(self._filters) filters.insert(0, AsType(encode_dtype=self._dtype, decode_dtype=dtype)) return self.view(filters=filters, dtype=dtype, read_only=True)
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Returns a view that does on the fly type conversion of the underlying data. Parameters ---------- dtype : string or dtype NumPy dtype. Notes ----- This method returns a new Array object which is a view on the same underlying chunk data. Modifying any data via the view is currently not permitted and will result in an error. This is an experimental feature and its behavior is subject to change in the future. See Also -------- Array.view Examples -------- >>> import zarr >>> import numpy as np >>> data = np.arange(100, dtype=np.uint8) >>> a = zarr.array(data, chunks=10) >>> a[:] array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99], dtype=uint8) >>> v = a.astype(np.float32) >>> v.is_view True >>> v[:] array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98., 99.], dtype=float32)
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python
train
stephantul/somber
somber/plsom.py
https://github.com/stephantul/somber/blob/b7a13e646239500cc393668c01a7169c3e50b7b5/somber/plsom.py#L139-L147
def _update_params(self, constants): """Update the params.""" constants = np.max(np.min(constants, 1)) self.params['r']['value'] = max([self.params['r']['value'], constants]) epsilon = constants / self.params['r']['value'] influence = self._calculate_influence(epsilon) # Account for learning rate return influence * epsilon
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Update the params.
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python
train
pybel/pybel-tools
src/pybel_tools/mutation/bound.py
https://github.com/pybel/pybel-tools/blob/3491adea0ac4ee60f57275ef72f9b73da6dbfe0c/src/pybel_tools/mutation/bound.py#L29-L38
def build_delete_node_by_hash(manager: Manager) -> Callable[[BELGraph, str], None]: """Make a delete function that's bound to the manager.""" @in_place_transformation def delete_node_by_hash(graph: BELGraph, node_hash: str) -> None: """Remove a node by identifier.""" node = manager.get_dsl_by_hash(node_hash) graph.remove_node(node) return delete_node_by_hash
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Make a delete function that's bound to the manager.
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python
valid
ellisonbg/vizarray
vizarray/__init__.py
https://github.com/ellisonbg/vizarray/blob/3221a232ecf54e8348094aacfc5719b40d89a451/vizarray/__init__.py#L129-L137
def disable_notebook(): """Disable automatic visualization of NumPy arrays in the IPython Notebook.""" try: from IPython.core.getipython import get_ipython except ImportError: raise ImportError('This feature requires IPython 1.0+') ip = get_ipython() f = ip.display_formatter.formatters['text/html'] f.type_printers.pop(np.ndarray, None)
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Disable automatic visualization of NumPy arrays in the IPython Notebook.
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python
train
mogproject/mog-commons-python
src/mog_commons/command.py
https://github.com/mogproject/mog-commons-python/blob/951cf0fa9a56248b4d45be720be25f1d4b7e1bff/src/mog_commons/command.py#L41-L49
def __convert_env(env, encoding): """Environment variables should be bytes not unicode on Windows.""" d = dict(os.environ, **(oget(env, {}))) # workaround for Windows+Python3 environment if not SHOULD_NOT_ENCODE_ARGS: return dict((k.encode(encoding), v.encode(encoding)) for k, v in d.items()) else: return d
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Environment variables should be bytes not unicode on Windows.
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python
train
arne-cl/discoursegraphs
src/discoursegraphs/readwrite/rst/dplp.py
https://github.com/arne-cl/discoursegraphs/blob/842f0068a3190be2c75905754521b176b25a54fb/src/discoursegraphs/readwrite/rst/dplp.py#L93-L114
def dplptree2dgparentedtree(self): """Convert the tree from DPLP's format into a conventional binary tree, which can be easily converted into output formats like RS3. """ def transform(dplp_tree): """Transform a DPLP parse tree into a more conventional parse tree.""" if isinstance(dplp_tree, basestring) or not hasattr(dplp_tree, 'label'): return dplp_tree assert len(dplp_tree) == 2, "We can only handle binary trees." match = DPLP_REL_RE.match(dplp_tree.label()) assert match, "Relation '{}' does not match regex '{}'".format(dplp_tree.label(), DPLP_REL_RE) left_child_nuc, right_child_nuc, relname = match.groups() dplp_tree._label = relname for i, child_nuclearity in enumerate([left_child_nuc, right_child_nuc]): child = dplp_tree[i] dplp_tree[i] = Tree(child_nuclearity, [transform(child)]) return dplp_tree tree = transform(self.parsetree) return DGParentedTree.convert(tree)
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Convert the tree from DPLP's format into a conventional binary tree, which can be easily converted into output formats like RS3.
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python
train
dossier/dossier.label
dossier/label/label.py
https://github.com/dossier/dossier.label/blob/d445e56b02ffd91ad46b0872cfbff62b9afef7ec/dossier/label/label.py#L852-L884
def everything(self, include_deleted=False, content_id=None, subtopic_id=None, prefix=None): '''Returns a generator of all labels in the store. If `include_deleted` is :const:`True`, labels that have been overwritten with more recent labels are also included. If `content_id` is not :const:`None`, only labels for that content ID are retrieved; and then if `subtopic_id` is not :const:`None`, only that subtopic is retrieved, else all subtopics are retrieved. If `content_id` is :const:`None` but `prefix` is not, then only labels with at least one content ID beginning with `prefix` will be returned. The returned labels will always be q in sorted order, content IDs first, and with those with the same content, subtopic, and annotator IDs sorted newest first. :rtype: generator of :class:`Label` ''' if content_id is not None: ranges = [((content_id,), (content_id,))] elif prefix is not None: # This is the cheap, easy, and wrong way to do this ranges = [((prefix,), (prefix + b'\xff',))] else: ranges = [] labels = self.kvl.scan(self.TABLE, *ranges) labels = ifilter(self._filter_keys(content_id, prefix, subtopic_id), labels) labels = imap(lambda p: self._label_from_kvlayer(*p), labels) if not include_deleted: labels = Label.most_recent(labels) return labels
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Returns a generator of all labels in the store. If `include_deleted` is :const:`True`, labels that have been overwritten with more recent labels are also included. If `content_id` is not :const:`None`, only labels for that content ID are retrieved; and then if `subtopic_id` is not :const:`None`, only that subtopic is retrieved, else all subtopics are retrieved. If `content_id` is :const:`None` but `prefix` is not, then only labels with at least one content ID beginning with `prefix` will be returned. The returned labels will always be q in sorted order, content IDs first, and with those with the same content, subtopic, and annotator IDs sorted newest first. :rtype: generator of :class:`Label`
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python
train
foremast/foremast
src/foremast/utils/generate_s3_tags.py
https://github.com/foremast/foremast/blob/fb70f29b8ce532f061685a17d120486e47b215ba/src/foremast/utils/generate_s3_tags.py#L4-L20
def generated_tag_data(tags): """Convert :obj:`dict` to S3 Tag list. Args: tags (dict): Dictonary of tag key and tag value passed. Returns: list: List of dictionaries. """ generated_tags = [] for key, value in tags.items(): generated_tags.append({ 'Key': key, 'Value': value, }) return generated_tags
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Convert :obj:`dict` to S3 Tag list. Args: tags (dict): Dictonary of tag key and tag value passed. Returns: list: List of dictionaries.
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python
train
andycasey/sick
sick/models/create.py
https://github.com/andycasey/sick/blob/6c37686182794c4cafea45abf7062b30b789b1a2/sick/models/create.py#L49-L161
def create(output_prefix, grid_flux_filename, wavelength_filenames, clobber=False, grid_flux_filename_format="csv", **kwargs): """ Create a new *sick* model from files describing the parameter names, fluxes, and wavelengths. """ if not clobber: # Check to make sure the output files won't exist already. output_suffixes = (".yaml", ".pkl", "-wavelengths.memmap", "-intensities.memmap") for path in [output_prefix + suffix for suffix in output_suffixes]: if os.path.exists(path): raise IOError("output filename {} already exists".format(path)) # Read the grid_flux filename. # param1 param2 param3 param4 channelname1 channelname2 kwds = kwargs.pop("__grid_flux_filename_kwargs", {}) kwds.update({"format": grid_flux_filename_format}) grid_flux_tbl = Table.read(grid_flux_filename, **kwds) # Distinguish column names between parameters (real numbers) and filenames str_columns = \ np.array([_[1].startswith("|S") for _ in grid_flux_tbl.dtype.descr]) # Check the number of channels provided. if str_columns.sum() != len(wavelength_filenames): raise ValueError("expected {0} wavelength filenames because {1} has {0}" " string columns ({2}) but found {3} wavelength filenames".format( sum(str_columns), grid_flux_filename, ", ".join(np.array(grid_flux_tbl.colnames)[str_columns]), len(wavelength_filenames))) # Create a record array of the grid points. grid_points = \ grid_flux_tbl.as_array()[np.array(grid_flux_tbl.colnames)[~str_columns]] # To-do: make sure they are all floats. # Sort the grid points. grid_indices = grid_points.argsort(order=grid_points.dtype.names) grid_points = grid_points[grid_indices] grid_flux_tbl = grid_flux_tbl[grid_indices] # Check the wavelength filenames. channel_wavelengths = np.array(map(load_simple_data, wavelength_filenames)) # Sort the channels by starting wavelength. c_indices = np.argsort([each.min() for each in channel_wavelengths]) channel_names = np.array(grid_flux_tbl.colnames)[str_columns][c_indices] channel_wavelengths = channel_wavelengths[c_indices] channel_sizes = [len(_) for _ in channel_wavelengths] num_pixels = sum(channel_sizes) # Create the model YAML file. with open(output_prefix + ".yaml", "w") as fp: header = "\n".join([ "# Model created on {0}".format(strftime("%Y-%m-%d %H:%M:%S")), "# Grid parameters: {0}".format(", ".join(grid_points.dtype.names)), "# Channel names: {0}".format(", ".join(channel_names)) ]) fp.write(header + "\n" + yaml.safe_dump({ "model_grid": { "grid_points": output_prefix + ".pkl", "intensities": output_prefix + "-intensities.memmap", "wavelengths": output_prefix + "-wavelengths.memmap" }}, stream=None, allow_unicode=True, default_flow_style=False)) # Create the pickled model file, with meta data. metadata = { "grid_flux_filename": grid_flux_filename, "wavelength_filenames": wavelength_filenames, "channel_names": channel_names, "channel_sizes": channel_sizes, "channel_resolutions": [float("inf")] * len(channel_names), "sick_version": sick_version } logger.debug("Dumping grid points and metadata to file") with open(output_prefix + ".pkl", "wb") as fp: pickle.dump((grid_points, metadata), fp, -1) # Create the memory-mapped dispersion file. logger.debug("Creating memory-mapped dispersion file.") wavelengths_memmap = np.memmap(output_prefix + "-wavelengths.memmap", dtype="float32", mode="w+", shape=(num_pixels, )) wavelengths_memmap[:] = np.hstack(channel_wavelengths) wavelengths_memmap.flush() del wavelengths_memmap # Create the memory-mapped intensities file. logger.debug("Creating memory-mapped intensities file.") intensities_memmap = np.memmap(output_prefix + "-intensities.memmap", shape=(grid_points.size, num_pixels), dtype="float32", mode="w+") n = len(grid_flux_tbl) for i, row in enumerate(grid_flux_tbl): logger.debug("Loading point {0}/{1} into the intensities map"\ .format(i + 1, n)) j = 0 for channel_name in channel_names: try: data = load_simple_data(row[channel_name]) except: logger.exception("Could not load data from {0} for channel {1}"\ .format(row[channel_name], channel_name)) raise intensities_memmap[i, j:j + data.size] = data j += data.size intensities_memmap.flush() del intensities_memmap return True
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"\"-wavelengths.memmap\"", "}", "}", ",", "stream", "=", "None", ",", "allow_unicode", "=", "True", ",", "default_flow_style", "=", "False", ")", ")", "# Create the pickled model file, with meta data.", "metadata", "=", "{", "\"grid_flux_filename\"", ":", "grid_flux_filename", ",", "\"wavelength_filenames\"", ":", "wavelength_filenames", ",", "\"channel_names\"", ":", "channel_names", ",", "\"channel_sizes\"", ":", "channel_sizes", ",", "\"channel_resolutions\"", ":", "[", "float", "(", "\"inf\"", ")", "]", "*", "len", "(", "channel_names", ")", ",", "\"sick_version\"", ":", "sick_version", "}", "logger", ".", "debug", "(", "\"Dumping grid points and metadata to file\"", ")", "with", "open", "(", "output_prefix", "+", "\".pkl\"", ",", "\"wb\"", ")", "as", "fp", ":", "pickle", ".", "dump", "(", "(", "grid_points", ",", "metadata", ")", ",", "fp", ",", "-", "1", ")", "# Create the memory-mapped dispersion file.", "logger", ".", "debug", "(", "\"Creating memory-mapped dispersion file.\"", 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Create a new *sick* model from files describing the parameter names, fluxes, and wavelengths.
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python
train
Parquery/icontract
icontract/_recompute.py
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/icontract/_recompute.py#L275-L285
def visit_IfExp(self, node: ast.IfExp) -> Any: """Visit the ``test``, and depending on its outcome, the ``body`` or ``orelse``.""" test = self.visit(node=node.test) if test: result = self.visit(node=node.body) else: result = self.visit(node=node.orelse) self.recomputed_values[node] = result return result
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Visit the ``test``, and depending on its outcome, the ``body`` or ``orelse``.
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python
train
robotpy/pyfrc
lib/pyfrc/physics/visionsim.py
https://github.com/robotpy/pyfrc/blob/7672ea3f17c8d4b702a9f18a7372d95feee7e37d/lib/pyfrc/physics/visionsim.py#L190-L236
def compute(self, now, x, y, angle): """ Call this when vision processing should be enabled :param now: The value passed to ``update_sim`` :param x: Returned from physics_controller.get_position :param y: Returned from physics_controller.get_position :param angle: Returned from physics_controller.get_position :returns: None or list of tuples of (found=0 or 1, capture_time, offset_degrees, distance). The tuples are ordered by absolute offset from the target. If a list is returned, it is guaranteed to have at least one element in it. Note: If your vision targeting doesn't have the ability to focus on multiple targets, then you should ignore the other elements. """ # Normalize angle to [-180,180] output = [] angle = ((angle + math.pi) % (math.pi * 2)) - math.pi for target in self.targets: proposed = target.compute(now, x, y, angle) if proposed: output.append(proposed) if not output: output.append((0, now, inf, 0)) self.distance = None else: # order by absolute offset output.sort(key=lambda i: abs(i[2])) self.distance = output[-1][3] # Only store stuff every once in awhile if now - self.last_compute_time > self.update_period: self.last_compute_time = now self.send_queue.appendleft(output) # simulate latency by delaying camera output if self.send_queue: output = self.send_queue[-1] if now - output[0][1] > self.data_lag: return self.send_queue.pop()
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Call this when vision processing should be enabled :param now: The value passed to ``update_sim`` :param x: Returned from physics_controller.get_position :param y: Returned from physics_controller.get_position :param angle: Returned from physics_controller.get_position :returns: None or list of tuples of (found=0 or 1, capture_time, offset_degrees, distance). The tuples are ordered by absolute offset from the target. If a list is returned, it is guaranteed to have at least one element in it. Note: If your vision targeting doesn't have the ability to focus on multiple targets, then you should ignore the other elements.
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python
train
caffeinehit/django-oauth2-provider
provider/views.py
https://github.com/caffeinehit/django-oauth2-provider/blob/6b5bc0d3ad706d2aaa47fa476f38406cddd01236/provider/views.py#L469-L492
def access_token_response(self, access_token): """ Returns a successful response after creating the access token as defined in :rfc:`5.1`. """ response_data = { 'access_token': access_token.token, 'token_type': constants.TOKEN_TYPE, 'expires_in': access_token.get_expire_delta(), 'scope': ' '.join(scope.names(access_token.scope)), } # Not all access_tokens are given a refresh_token # (for example, public clients doing password auth) try: rt = access_token.refresh_token response_data['refresh_token'] = rt.token except ObjectDoesNotExist: pass return HttpResponse( json.dumps(response_data), mimetype='application/json' )
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Returns a successful response after creating the access token as defined in :rfc:`5.1`.
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python
train
JoelBender/bacpypes
py25/bacpypes/debugging.py
https://github.com/JoelBender/bacpypes/blob/4111b8604a16fa2b7f80d8104a43b9f3e28dfc78/py25/bacpypes/debugging.py#L91-L212
def debug_contents(self, indent=1, file=sys.stdout, _ids=None): """Debug the contents of an object.""" if _debug: _log.debug("debug_contents indent=%r file=%r _ids=%r", indent, file, _ids) klasses = list(self.__class__.__mro__) klasses.reverse() if _debug: _log.debug(" - klasses: %r", klasses) # loop through the classes and look for _debug_contents attrs = [] cids = [] ownFn = [] for klass in klasses: if klass is DebugContents: continue if not issubclass(klass, DebugContents) and hasattr(klass, 'debug_contents'): for i, seenAlready in enumerate(ownFn): if issubclass(klass, seenAlready): del ownFn[i] break ownFn.append(klass) continue # look for a tuple of attribute names if not hasattr(klass, '_debug_contents'): continue debugContents = klass._debug_contents if not isinstance(debugContents, tuple): raise RuntimeError("%s._debug_contents must be a tuple" % (klass.__name__,)) # already seen it? if id(debugContents) in cids: continue cids.append(id(debugContents)) for attr in debugContents: if attr not in attrs: attrs.append(attr) # a bit of debugging if _debug: _log.debug(" - attrs: %r", attrs) _log.debug(" - ownFn: %r", ownFn) # make/extend the list of objects already seen if _ids is None: _ids = [] # loop through the attributes for attr in attrs: # assume you're going deep, but not into lists and dicts goDeep = True goListDict = False goHexed = False # attribute list might want to go deep if attr.endswith("-"): goDeep = False attr = attr[:-1] elif attr.endswith("*"): goHexed = True attr = attr[:-1] elif attr.endswith("+"): goDeep = False goListDict = True attr = attr[:-1] if attr.endswith("+"): goDeep = True attr = attr[:-1] value = getattr(self, attr, None) # skip None if value is None: continue # standard output if goListDict and isinstance(value, list) and value: file.write("%s%s = [\n" % (' ' * indent, attr)) indent += 1 for i, elem in enumerate(value): file.write("%s[%d] %r\n" % (' ' * indent, i, elem)) if goDeep and hasattr(elem, 'debug_contents'): if id(elem) not in _ids: _ids.append(id(elem)) elem.debug_contents(indent + 1, file, _ids) indent -= 1 file.write("%s ]\n" % (' ' * indent,)) elif goListDict and isinstance(value, dict) and value: file.write("%s%s = {\n" % (' ' * indent, attr)) indent += 1 for key, elem in value.items(): file.write("%s%r : %r\n" % (' ' * indent, key, elem)) if goDeep and hasattr(elem, 'debug_contents'): if id(elem) not in _ids: _ids.append(id(elem)) elem.debug_contents(indent + 1, file, _ids) indent -= 1 file.write("%s }\n" % (' ' * indent,)) elif goHexed and isinstance(value, str): if len(value) > 20: hexed = btox(value[:20], '.') + "..." else: hexed = btox(value, '.') file.write("%s%s = x'%s'\n" % (' ' * indent, attr, hexed)) # elif goHexed and isinstance(value, int): # file.write("%s%s = 0x%X\n" % (' ' * indent, attr, value)) else: file.write("%s%s = %r\n" % (' ' * indent, attr, value)) # go nested if it is debugable if goDeep and hasattr(value, 'debug_contents'): if id(value) not in _ids: _ids.append(id(value)) value.debug_contents(indent + 1, file, _ids) # go through the functions ownFn.reverse() for klass in ownFn: klass.debug_contents(self, indent, file, _ids)
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attrs: %r\"", ",", "attrs", ")", "_log", ".", "debug", "(", "\" - ownFn: %r\"", ",", "ownFn", ")", "# make/extend the list of objects already seen", "if", "_ids", "is", "None", ":", "_ids", "=", "[", "]", "# loop through the attributes", "for", "attr", "in", "attrs", ":", "# assume you're going deep, but not into lists and dicts", "goDeep", "=", "True", "goListDict", "=", "False", "goHexed", "=", "False", "# attribute list might want to go deep", "if", "attr", ".", "endswith", "(", "\"-\"", ")", ":", "goDeep", "=", "False", "attr", "=", "attr", "[", ":", "-", "1", "]", "elif", "attr", ".", "endswith", "(", "\"*\"", ")", ":", "goHexed", "=", "True", "attr", "=", "attr", "[", ":", "-", "1", "]", "elif", "attr", ".", "endswith", "(", "\"+\"", ")", ":", "goDeep", "=", "False", "goListDict", "=", "True", "attr", "=", "attr", "[", ":", "-", "1", "]", "if", "attr", ".", "endswith", "(", "\"+\"", ")", ":", "goDeep", "=", "True", "attr", "=", "attr", "[", ":", "-", "1", "]", "value", "=", "getattr", "(", "self", ",", "attr", ",", "None", ")", "# skip None", "if", "value", "is", "None", ":", "continue", "# standard output", "if", "goListDict", "and", "isinstance", "(", "value", ",", "list", ")", "and", "value", ":", "file", ".", "write", "(", "\"%s%s = [\\n\"", "%", "(", "' '", "*", "indent", ",", "attr", ")", ")", "indent", "+=", "1", "for", "i", ",", "elem", "in", "enumerate", "(", "value", ")", ":", "file", ".", "write", "(", "\"%s[%d] %r\\n\"", "%", "(", "' '", "*", "indent", ",", "i", ",", "elem", ")", ")", "if", "goDeep", "and", "hasattr", "(", "elem", ",", "'debug_contents'", ")", ":", "if", "id", "(", "elem", ")", "not", "in", "_ids", ":", "_ids", ".", "append", "(", "id", "(", "elem", ")", ")", "elem", ".", "debug_contents", "(", "indent", "+", "1", ",", "file", ",", "_ids", ")", "indent", "-=", "1", "file", ".", "write", "(", "\"%s ]\\n\"", "%", "(", "' '", "*", "indent", ",", ")", ")", "elif", "goListDict", "and", "isinstance", "(", "value", ",", "dict", ")", "and", "value", ":", "file", ".", "write", "(", "\"%s%s = {\\n\"", "%", "(", "' '", "*", "indent", ",", "attr", ")", ")", "indent", "+=", "1", "for", "key", ",", "elem", "in", "value", ".", "items", "(", ")", ":", "file", ".", "write", "(", "\"%s%r : %r\\n\"", "%", "(", "' '", "*", "indent", ",", "key", ",", "elem", ")", ")", "if", "goDeep", "and", "hasattr", "(", "elem", ",", "'debug_contents'", ")", ":", "if", "id", "(", "elem", ")", "not", "in", "_ids", ":", "_ids", ".", "append", "(", "id", "(", "elem", ")", ")", "elem", ".", "debug_contents", "(", "indent", "+", "1", ",", "file", ",", "_ids", ")", "indent", "-=", "1", "file", ".", "write", "(", "\"%s }\\n\"", "%", "(", "' '", "*", "indent", ",", ")", ")", "elif", "goHexed", "and", "isinstance", "(", "value", ",", "str", ")", ":", "if", "len", "(", "value", ")", ">", "20", ":", "hexed", "=", "btox", "(", "value", "[", ":", "20", "]", ",", "'.'", ")", "+", "\"...\"", "else", ":", "hexed", "=", "btox", "(", "value", ",", "'.'", ")", "file", ".", "write", "(", "\"%s%s = x'%s'\\n\"", "%", "(", "' '", "*", "indent", ",", "attr", ",", "hexed", ")", ")", "# elif goHexed and isinstance(value, int):", "# file.write(\"%s%s = 0x%X\\n\" % (' ' * indent, attr, value))", "else", ":", "file", ".", "write", "(", "\"%s%s = %r\\n\"", "%", "(", "' '", "*", "indent", ",", "attr", ",", "value", ")", ")", "# go nested if it is debugable", "if", "goDeep", "and", "hasattr", "(", "value", ",", "'debug_contents'", ")", ":", "if", "id", "(", "value", ")", "not", "in", "_ids", ":", "_ids", ".", "append", "(", "id", "(", "value", ")", ")", "value", ".", "debug_contents", "(", "indent", "+", "1", ",", "file", ",", "_ids", ")", "# go through the functions", "ownFn", ".", "reverse", "(", ")", "for", "klass", "in", "ownFn", ":", "klass", ".", "debug_contents", "(", "self", ",", "indent", ",", "file", ",", "_ids", ")" ]
Debug the contents of an object.
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python
train
jobovy/galpy
galpy/util/bovy_coords.py
https://github.com/jobovy/galpy/blob/9c5b9fe65d58835624dffe432be282060918ee08/galpy/util/bovy_coords.py#L372-L415
def sphergal_to_rectgal(l,b,d,vr,pmll,pmbb,degree=False): """ NAME: sphergal_to_rectgal PURPOSE: transform phase-space coordinates in spherical Galactic coordinates to rectangular Galactic coordinates (can take vector inputs) INPUT: l - Galactic longitude (rad) b - Galactic lattitude (rad) d - distance (kpc) vr - line-of-sight velocity (km/s) pmll - proper motion in the Galactic longitude direction (mu_l*cos(b) ) (mas/yr) pmbb - proper motion in the Galactic lattitude (mas/yr) degree - (bool) if True, l and b are in degrees OUTPUT: (X,Y,Z,vx,vy,vz) in (kpc,kpc,kpc,km/s,km/s,km/s) HISTORY: 2009-10-25 - Written - Bovy (NYU) """ XYZ= lbd_to_XYZ(l,b,d,degree=degree) vxvyvz= vrpmllpmbb_to_vxvyvz(vr,pmll,pmbb,l,b,d,XYZ=False,degree=degree) if sc.array(l).shape == (): return sc.array([XYZ[0],XYZ[1],XYZ[2],vxvyvz[0],vxvyvz[1],vxvyvz[2]]) else: out=sc.zeros((len(l),6)) out[:,0:3]= XYZ out[:,3:6]= vxvyvz return out
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NAME: sphergal_to_rectgal PURPOSE: transform phase-space coordinates in spherical Galactic coordinates to rectangular Galactic coordinates (can take vector inputs) INPUT: l - Galactic longitude (rad) b - Galactic lattitude (rad) d - distance (kpc) vr - line-of-sight velocity (km/s) pmll - proper motion in the Galactic longitude direction (mu_l*cos(b) ) (mas/yr) pmbb - proper motion in the Galactic lattitude (mas/yr) degree - (bool) if True, l and b are in degrees OUTPUT: (X,Y,Z,vx,vy,vz) in (kpc,kpc,kpc,km/s,km/s,km/s) HISTORY: 2009-10-25 - Written - Bovy (NYU)
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python
train
ianmiell/shutit
emailer.py
https://github.com/ianmiell/shutit/blob/19cd64cdfb23515b106b40213dccff4101617076/emailer.py#L88-L125
def __set_config(self, cfg_section): """Set a local config array up according to defaults and main shutit configuration cfg_section - see __init__ """ defaults = [ 'shutit.core.alerting.emailer.mailto', None, 'shutit.core.alerting.emailer.mailfrom', '[email protected]', 'shutit.core.alerting.emailer.smtp_server', 'localhost', 'shutit.core.alerting.emailer.smtp_port', 25, 'shutit.core.alerting.emailer.use_tls', True, 'shutit.core.alerting.emailer.send_mail', True, 'shutit.core.alerting.emailer.subject', 'Shutit Report', 'shutit.core.alerting.emailer.signature', '--Angry Shutit', 'shutit.core.alerting.emailer.compress', True, 'shutit.core.alerting.emailer.username', '', 'shutit.core.alerting.emailer.password', '', 'shutit.core.alerting.emailer.safe_mode', True, 'shutit.core.alerting.emailer.maintainer','', 'shutit.core.alerting.emailer.mailto_maintainer', True ] for cfg_name, cfg_default in zip(defaults[0::2], defaults[1::2]): try: self.config[cfg_name] = self.shutit.cfg[cfg_section][cfg_name] except KeyError: if cfg_default is None: raise Exception(cfg_section + ' ' + cfg_name + ' must be set') else: self.config[cfg_name] = cfg_default # only send a mail to the module's maintainer if configured correctly if self.config['shutit.core.alerting.emailer.mailto_maintainer'] and \ (self.config['shutit.core.alerting.emailer.maintainer'] == "" or \ self.config['shutit.core.alerting.emailer.maintainer'] == self.config['shutit.core.alerting.emailer.mailto']): self.config['shutit.core.alerting.emailer.mailto_maintainer'] = False self.config['shutit.core.alerting.emailer.maintainer'] = ""
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Set a local config array up according to defaults and main shutit configuration cfg_section - see __init__
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python
train
hydpy-dev/hydpy
hydpy/core/sequencetools.py
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/sequencetools.py#L198-L222
def load_conditions(self, filename=None): """Read the initial conditions from a file and assign them to the respective |StateSequence| and/or |LogSequence| objects handled by the actual |Sequences| object. If no filename or dirname is passed, the ones defined by the |ConditionManager| stored in module |pub| are used. """ if self.hasconditions: if not filename: filename = self._conditiondefaultfilename namespace = locals() for seq in self.conditionsequences: namespace[seq.name] = seq namespace['model'] = self code = hydpy.pub.conditionmanager.load_file(filename) try: # ToDo: raises an escape sequence deprecation sometimes # ToDo: use runpy instead? # ToDo: Move functionality to filetools.py? exec(code) except BaseException: objecttools.augment_excmessage( 'While trying to gather initial conditions of element %s' % objecttools.devicename(self))
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Read the initial conditions from a file and assign them to the respective |StateSequence| and/or |LogSequence| objects handled by the actual |Sequences| object. If no filename or dirname is passed, the ones defined by the |ConditionManager| stored in module |pub| are used.
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python
train
deepmind/sonnet
sonnet/python/modules/base_info.py
https://github.com/deepmind/sonnet/blob/00612ca3178964d86b556e062694d808ff81fcca/sonnet/python/modules/base_info.py#L119-L142
def _from_proto_sparse_tensor(sparse_tensor_proto, process_leafs): """Deserializes a `tf.SparseTensor` from `sparse_tensor_proto`. Args: sparse_tensor_proto: A proto representing a `tf.SparseTensor`. process_leafs: A function to be applied to the leaf valued of the nested structure. Returns: An instance of `tf.SparseTensor`. """ if not sparse_tensor_proto.HasField("named_tuple"): raise base_errors.ModuleInfoError( "Error while deserializing a SparseTensor: expected proto tuple.") if sparse_tensor_proto.named_tuple.name != _SPARSE_TENSOR_NAME: raise base_errors.ModuleInfoError( "Error while deserializing a SparseTensor: The name of the tuple " "should have been {} but was {}.".format( _SPARSE_TENSOR_NAME, sparse_tensor_proto.named_tuple.name)) named_tuple_map = sparse_tensor_proto.named_tuple.map return tf.SparseTensor( indices=process_leafs(named_tuple_map["indices"].value), values=process_leafs(named_tuple_map["values"].value), dense_shape=process_leafs(named_tuple_map["dense_shape"].value))
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Deserializes a `tf.SparseTensor` from `sparse_tensor_proto`. Args: sparse_tensor_proto: A proto representing a `tf.SparseTensor`. process_leafs: A function to be applied to the leaf valued of the nested structure. Returns: An instance of `tf.SparseTensor`.
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python
train
RJT1990/pyflux
pyflux/tsm.py
https://github.com/RJT1990/pyflux/blob/297f2afc2095acd97c12e827dd500e8ea5da0c0f/pyflux/tsm.py#L187-L221
def _laplace_fit(self,obj_type): """ Performs a Laplace approximation to the posterior Parameters ---------- obj_type : method Whether a likelihood or a posterior Returns ---------- None (plots posterior) """ # Get Mode and Inverse Hessian information y = self.fit(method='PML',printer=False) if y.ihessian is None: raise Exception("No Hessian information - Laplace approximation cannot be performed") else: self.latent_variables.estimation_method = 'Laplace' theta, Y, scores, states, states_var, X_names = self._categorize_model_output(self.latent_variables.get_z_values()) # Change this in future try: latent_variables_store = self.latent_variables.copy() except: latent_variables_store = self.latent_variables return LaplaceResults(data_name=self.data_name,X_names=X_names,model_name=self.model_name, model_type=self.model_type, latent_variables=latent_variables_store,data=Y,index=self.index, multivariate_model=self.multivariate_model,objective_object=obj_type, method='Laplace',ihessian=y.ihessian,signal=theta,scores=scores, z_hide=self._z_hide,max_lag=self.max_lag,states=states,states_var=states_var)
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Performs a Laplace approximation to the posterior Parameters ---------- obj_type : method Whether a likelihood or a posterior Returns ---------- None (plots posterior)
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python
train
ministryofjustice/money-to-prisoners-common
mtp_common/build_tasks/executor.py
https://github.com/ministryofjustice/money-to-prisoners-common/blob/33c43a2912cb990d9148da7c8718f480f07d90a1/mtp_common/build_tasks/executor.py#L206-L214
def update_from(self, mapping): """ Updates the set of parameters from a mapping for keys that already exist """ for key, value in mapping.items(): if key in self: if isinstance(value, Parameter): value = value.value self[key].value = value
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Updates the set of parameters from a mapping for keys that already exist
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python
train
trailofbits/manticore
manticore/ethereum/solidity.py
https://github.com/trailofbits/manticore/blob/54c5a15b1119c523ae54c09972413e8b97f11629/manticore/ethereum/solidity.py#L196-L205
def get_func_argument_types(self, hsh: bytes): """Returns the tuple type signature for the arguments of the function associated with the selector ``hsh``. If no normal contract function has the specified selector, the empty tuple type signature ``'()'`` is returned. """ if not isinstance(hsh, (bytes, bytearray)): raise TypeError('The selector argument must be a concrete byte array') sig = self._function_signatures_by_selector.get(hsh) return '()' if sig is None else sig[sig.find('('):]
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Returns the tuple type signature for the arguments of the function associated with the selector ``hsh``. If no normal contract function has the specified selector, the empty tuple type signature ``'()'`` is returned.
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python
valid
larsyencken/csvdiff
csvdiff/patch.py
https://github.com/larsyencken/csvdiff/blob/163dd9da676a8e5f926a935803726340261f03ae/csvdiff/patch.py#L326-L337
def _is_significant(change, significance): """ Return True if a change is genuinely significant given our tolerance. """ try: a = float(change['from']) b = float(change['to']) except ValueError: return True return abs(a - b) > 10 ** (-significance)
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Return True if a change is genuinely significant given our tolerance.
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python
train
muatik/naive-bayes-classifier
naiveBayesClassifier/trainer.py
https://github.com/muatik/naive-bayes-classifier/blob/cdc1d8681ef6674e946cff38e87ce3b00c732fbb/naiveBayesClassifier/trainer.py#L11-L21
def train(self, text, className): """ enhances trained data using the given text and class """ self.data.increaseClass(className) tokens = self.tokenizer.tokenize(text) for token in tokens: token = self.tokenizer.remove_stop_words(token) token = self.tokenizer.remove_punctuation(token) self.data.increaseToken(token, className)
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enhances trained data using the given text and class
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python
train
TkTech/Jawa
jawa/util/utf.py
https://github.com/TkTech/Jawa/blob/94c8424e699029ac33fbc0e866fff0ecb2742289/jawa/util/utf.py#L12-L52
def decode_modified_utf8(s: bytes) -> str: """ Decodes a bytestring containing modified UTF-8 as defined in section 4.4.7 of the JVM specification. :param s: bytestring to be converted. :returns: A unicode representation of the original string. """ s = bytearray(s) buff = [] buffer_append = buff.append ix = 0 while ix < len(s): x = s[ix] ix += 1 if x >> 7 == 0: # Just an ASCII character, nothing else to do. pass elif x >> 6 == 6: y = s[ix] ix += 1 x = ((x & 0x1F) << 6) + (y & 0x3F) elif x >> 4 == 14: y, z = s[ix:ix+2] ix += 2 x = ((x & 0xF) << 12) + ((y & 0x3F) << 6) + (z & 0x3F) elif x == 0xED: v, w, x, y, z = s[ix:ix+6] ix += 5 x = 0x10000 + ( ((v & 0x0F) << 16) + ((w & 0x3F) << 10) + ((y & 0x0F) << 6) + (z & 0x3F) ) elif x == 0xC0 and s[ix] == 0x80: ix += 1 x = 0 buffer_append(x) return u''.join(chr(b) for b in buff)
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Decodes a bytestring containing modified UTF-8 as defined in section 4.4.7 of the JVM specification. :param s: bytestring to be converted. :returns: A unicode representation of the original string.
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python
train
google/apitools
apitools/base/py/transfer.py
https://github.com/google/apitools/blob/f3745a7ea535aa0e88b0650c16479b696d6fd446/apitools/base/py/transfer.py#L286-L297
def __SetTotal(self, info): """Sets the total size based off info if possible otherwise 0.""" if 'content-range' in info: _, _, total = info['content-range'].rpartition('/') if total != '*': self.__total_size = int(total) # Note "total_size is None" means we don't know it; if no size # info was returned on our initial range request, that means we # have a 0-byte file. (That last statement has been verified # empirically, but is not clearly documented anywhere.) if self.total_size is None: self.__total_size = 0
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Sets the total size based off info if possible otherwise 0.
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python
train
astropy/photutils
photutils/psf/sandbox.py
https://github.com/astropy/photutils/blob/cc9bb4534ab76bac98cb5f374a348a2573d10401/photutils/psf/sandbox.py#L365-L383
def to_rectified(self, x, y): """ Convert the input (x, y) positions from the original (unrectified) image to the rectified image. Parameters ---------- x, y: float or array-like of float The zero-index pixel coordinates in the original (unrectified) image. Returns ------- x, y: float or array-like The zero-index pixel coordinates in the rectified image. """ return self._reproject(self.wcs_original, self.wcs_rectified)(x, y)
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Convert the input (x, y) positions from the original (unrectified) image to the rectified image. Parameters ---------- x, y: float or array-like of float The zero-index pixel coordinates in the original (unrectified) image. Returns ------- x, y: float or array-like The zero-index pixel coordinates in the rectified image.
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python
train
linkhub-sdk/popbill.py
popbill/faxService.py
https://github.com/linkhub-sdk/popbill.py/blob/68a0dd7f7a937603318e93be321fde73c50b96cc/popbill/faxService.py#L389-L417
def resendFaxRN(self, CorpNum, OrgRequestNum, SenderNum, SenderName, ReceiverNum, ReceiverName, ReserveDT=None, UserID=None, title=None, RequestNum=None): """ 팩스 단건 전송 args CorpNum : 팝빌회원 사업자번호 OrgRequestNum : 원본 팩스 전송시 할당한 전송요청번호 ReceiptNum : 팩스 접수번호 SenderNum : 발신자 번호 SenderName : 발신자명 ReceiverNum : 수신번호 ReceiverName : 수신자명 ReserveDT : 예약시간(형식 yyyyMMddHHmmss) UserID : 팝빌회원 아이디 title : 팩스제목 RequestNum : 전송요청시 할당한 전송요청번호 return 접수번호 (receiptNum) raise PopbillException """ receivers = None if ReceiverNum != "" or ReceiverName != "": receivers = [] receivers.append(FaxReceiver(receiveNum=ReceiverNum, receiveName=ReceiverName) ) return self.resendFaxRN_multi(CorpNum, OrgRequestNum, SenderNum, SenderName, receivers, ReserveDT, UserID, title, RequestNum)
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팩스 단건 전송 args CorpNum : 팝빌회원 사업자번호 OrgRequestNum : 원본 팩스 전송시 할당한 전송요청번호 ReceiptNum : 팩스 접수번호 SenderNum : 발신자 번호 SenderName : 발신자명 ReceiverNum : 수신번호 ReceiverName : 수신자명 ReserveDT : 예약시간(형식 yyyyMMddHHmmss) UserID : 팝빌회원 아이디 title : 팩스제목 RequestNum : 전송요청시 할당한 전송요청번호 return 접수번호 (receiptNum) raise PopbillException
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python
train
mottosso/be
be/vendor/requests/sessions.py
https://github.com/mottosso/be/blob/0f3d4f3597c71223f616d78c6d9b2c8dffcd8a71/be/vendor/requests/sessions.py#L92-L201
def resolve_redirects(self, resp, req, stream=False, timeout=None, verify=True, cert=None, proxies=None): """Receives a Response. Returns a generator of Responses.""" i = 0 hist = [] # keep track of history while resp.is_redirect: prepared_request = req.copy() if i > 0: # Update history and keep track of redirects. hist.append(resp) new_hist = list(hist) resp.history = new_hist try: resp.content # Consume socket so it can be released except (ChunkedEncodingError, ContentDecodingError, RuntimeError): resp.raw.read(decode_content=False) if i >= self.max_redirects: raise TooManyRedirects('Exceeded %s redirects.' % self.max_redirects) # Release the connection back into the pool. resp.close() url = resp.headers['location'] method = req.method # Handle redirection without scheme (see: RFC 1808 Section 4) if url.startswith('//'): parsed_rurl = urlparse(resp.url) url = '%s:%s' % (parsed_rurl.scheme, url) # The scheme should be lower case... parsed = urlparse(url) url = parsed.geturl() # Facilitate relative 'location' headers, as allowed by RFC 7231. # (e.g. '/path/to/resource' instead of 'http://domain.tld/path/to/resource') # Compliant with RFC3986, we percent encode the url. if not parsed.netloc: url = urljoin(resp.url, requote_uri(url)) else: url = requote_uri(url) prepared_request.url = to_native_string(url) # Cache the url, unless it redirects to itself. if resp.is_permanent_redirect and req.url != prepared_request.url: self.redirect_cache[req.url] = prepared_request.url # http://tools.ietf.org/html/rfc7231#section-6.4.4 if (resp.status_code == codes.see_other and method != 'HEAD'): method = 'GET' # Do what the browsers do, despite standards... # First, turn 302s into GETs. if resp.status_code == codes.found and method != 'HEAD': method = 'GET' # Second, if a POST is responded to with a 301, turn it into a GET. # This bizarre behaviour is explained in Issue 1704. if resp.status_code == codes.moved and method == 'POST': method = 'GET' prepared_request.method = method # https://github.com/kennethreitz/requests/issues/1084 if resp.status_code not in (codes.temporary_redirect, codes.permanent_redirect): if 'Content-Length' in prepared_request.headers: del prepared_request.headers['Content-Length'] prepared_request.body = None headers = prepared_request.headers try: del headers['Cookie'] except KeyError: pass # Extract any cookies sent on the response to the cookiejar # in the new request. Because we've mutated our copied prepared # request, use the old one that we haven't yet touched. extract_cookies_to_jar(prepared_request._cookies, req, resp.raw) prepared_request._cookies.update(self.cookies) prepared_request.prepare_cookies(prepared_request._cookies) # Rebuild auth and proxy information. proxies = self.rebuild_proxies(prepared_request, proxies) self.rebuild_auth(prepared_request, resp) # Override the original request. req = prepared_request resp = self.send( req, stream=stream, timeout=timeout, verify=verify, cert=cert, proxies=proxies, allow_redirects=False, ) extract_cookies_to_jar(self.cookies, prepared_request, resp.raw) i += 1 yield resp
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Receives a Response. Returns a generator of Responses.
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python
train
NORDUnet/python-norduniclient
norduniclient/core.py
https://github.com/NORDUnet/python-norduniclient/blob/ee5084a6f45caac614b4fda4a023749ca52f786c/norduniclient/core.py#L117-L135
def get_db_driver(uri, username=None, password=None, encrypted=True, max_pool_size=50, trust=0): """ :param uri: Bolt uri :type uri: str :param username: Neo4j username :type username: str :param password: Neo4j password :type password: str :param encrypted: Use TLS :type encrypted: Boolean :param max_pool_size: Maximum number of idle sessions :type max_pool_size: Integer :param trust: Trust cert on first use (0) or do not accept unknown cert (1) :type trust: Integer :return: Neo4j driver :rtype: neo4j.v1.session.Driver """ return GraphDatabase.driver(uri, auth=basic_auth(username, password), encrypted=encrypted, max_pool_size=max_pool_size, trust=trust)
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:param uri: Bolt uri :type uri: str :param username: Neo4j username :type username: str :param password: Neo4j password :type password: str :param encrypted: Use TLS :type encrypted: Boolean :param max_pool_size: Maximum number of idle sessions :type max_pool_size: Integer :param trust: Trust cert on first use (0) or do not accept unknown cert (1) :type trust: Integer :return: Neo4j driver :rtype: neo4j.v1.session.Driver
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python
train
chemlab/chemlab
chemlab/core/base.py
https://github.com/chemlab/chemlab/blob/c8730966316d101e24f39ac3b96b51282aba0abe/chemlab/core/base.py#L575-L634
def where(self, inplace=False, **kwargs): """Return indices over every dimension that met the conditions. Condition syntax: *attribute* = value Return indices that satisfy the condition where the attribute is equal to the value e.g. type_array = 'H' *attribute* = list(value1, value2) Return indices that satisfy the condition where the attribute is equal to any of the value in the list. e.g. type_array = ['H', 'O'] *dimension_index* = value: int *dimension_index* = value: list(int) Return only elements that correspond to the index in the specified dimension: atom_index = 0 atom_index = [0, 1] """ masks = {k: np.ones(v, dtype='bool') for k,v in self.dimensions.items()} def index_to_mask(index, n): val = np.zeros(n, dtype='bool') val[index] = True return val def masks_and(dict1, dict2): return {k: dict1[k] & index_to_mask(dict2[k], len(dict1[k])) for k in dict1 } for key in kwargs: value = kwargs[key] if key.endswith('_index'): if isinstance(value, int): value = [value] dim = key[:-len('_index')] m = self._propagate_dim(value, dim) masks = masks_and(masks, m) else: attribute = self.get_attribute(key) if isinstance(value, list): mask = reduce(operator.or_, [attribute.value == m for m in value]) else: mask = attribute.value == value m = self._propagate_dim(mask, attribute.dim) masks = masks_and(masks, m) return masks
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python
train
DataDog/integrations-core
datadog_checks_dev/datadog_checks/dev/tooling/commands/agent/integrations.py
https://github.com/DataDog/integrations-core/blob/ebd41c873cf9f97a8c51bf9459bc6a7536af8acd/datadog_checks_dev/datadog_checks/dev/tooling/commands/agent/integrations.py#L25-L59
def integrations(since, to, write, force): """ Generates a markdown file containing the list of integrations shipped in a given Agent release. Agent version numbers are derived inspecting tags on `integrations-core` so running this tool might provide unexpected results if the repo is not up to date with the Agent release process. If neither `--since` or `--to` are passed (the most common use case), the tool will generate the list for every Agent since version 6.3.0 (before that point we don't have enough information to build the log). """ agent_tags = get_agent_tags(since, to) # get the list of integrations shipped with the agent from the requirements file req_file_name = os.path.basename(get_agent_release_requirements()) integrations_contents = StringIO() for tag in agent_tags: integrations_contents.write('## Datadog Agent version {}\n\n'.format(tag)) # Requirements for current tag file_contents = git_show_file(req_file_name, tag) for name, ver in iteritems(parse_agent_req_file(file_contents)): integrations_contents.write('* {}: {}\n'.format(name, ver)) integrations_contents.write('\n') # save the changelog on disk if --write was passed if write: dest = get_agent_integrations_file() # don't overwrite an existing file if os.path.exists(dest) and not force: msg = "Output file {} already exists, run the command again with --force to overwrite" abort(msg.format(dest)) write_file(dest, integrations_contents.getvalue()) else: echo_info(integrations_contents.getvalue())
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Generates a markdown file containing the list of integrations shipped in a given Agent release. Agent version numbers are derived inspecting tags on `integrations-core` so running this tool might provide unexpected results if the repo is not up to date with the Agent release process. If neither `--since` or `--to` are passed (the most common use case), the tool will generate the list for every Agent since version 6.3.0 (before that point we don't have enough information to build the log).
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python
train
log2timeline/plaso
plaso/analysis/windows_services.py
https://github.com/log2timeline/plaso/blob/9c564698d2da3ffbe23607a3c54c0582ea18a6cc/plaso/analysis/windows_services.py#L171-L186
def AddService(self, new_service): """Add a new service to the list of ones we know about. Args: new_service (WindowsService): the service to add. """ for service in self._services: if new_service == service: # If this service is the same as one we already know about, we # just want to add where it came from. service.sources.append(new_service.sources[0]) return # We only add a new object to our list if we don't have # an identical one already. self._services.append(new_service)
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Add a new service to the list of ones we know about. Args: new_service (WindowsService): the service to add.
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python
train
leancloud/python-sdk
leancloud/message.py
https://github.com/leancloud/python-sdk/blob/fea3240257ce65e6a32c7312a5cee1f94a51a587/leancloud/message.py#L75-L95
def find_by_client(cls, from_client, limit=None, before_time=None, before_message_id=None): # type: (str, Optional[int], Optional[Union[datetime, float]], Optional[str]) -> List[Message] """获取某个 client 的聊天记录 :param from_client: 要获取聊天记录的 client id :param limit: 返回条数限制,可选,服务端默认 100 条,最大 1000 条 :param before_time: 查询起始的时间戳,返回小于这个时间(不包含)的记录,服务端默认是当前时间 :param before_message_id: 起始的消息 id,使用时必须加上对应消息的时间 before_time 参数,一起作为查询的起点 :return: 符合条件的聊天记录 """ query_params = {} # type: Dict[str, Any] query_params['from'] = from_client if limit is not None: query_params['limit'] = limit if isinstance(before_time, datetime): query_params['max_ts'] = round(before_time.timestamp() * 1000) elif isinstance(before_time, six.integer_types) or isinstance(before_time, float): query_params['max_ts'] = round(before_time * 1000) if before_message_id is not None: query_params['msgid'] = before_message_id return list(cls._find(query_params))
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获取某个 client 的聊天记录 :param from_client: 要获取聊天记录的 client id :param limit: 返回条数限制,可选,服务端默认 100 条,最大 1000 条 :param before_time: 查询起始的时间戳,返回小于这个时间(不包含)的记录,服务端默认是当前时间 :param before_message_id: 起始的消息 id,使用时必须加上对应消息的时间 before_time 参数,一起作为查询的起点 :return: 符合条件的聊天记录
[ "获取某个", "client", "的聊天记录" ]
python
train
DeepHorizons/iarm
iarm/arm_instructions/data_movement.py
https://github.com/DeepHorizons/iarm/blob/b913c9fd577b793a6bbced78b78a5d8d7cd88de4/iarm/arm_instructions/data_movement.py#L11-L25
def MOV(self, params): """ MOV Rx, Ry MOV PC, Ry Move the value of Ry into Rx or PC """ Rx, Ry = self.get_two_parameters(self.TWO_PARAMETER_COMMA_SEPARATED, params) self.check_arguments(any_registers=(Rx, Ry)) def MOV_func(): self.register[Rx] = self.register[Ry] return MOV_func
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MOV Rx, Ry MOV PC, Ry Move the value of Ry into Rx or PC
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python
train
dlintott/gns3-converter
gns3converter/converter.py
https://github.com/dlintott/gns3-converter/blob/acbc55da51de86388dc5b5f6da55809b3c86b7ca/gns3converter/converter.py#L318-L332
def device_id_from_name(device_name, nodes): """ Get the device ID when given a device name :param str device_name: device name :param list nodes: list of nodes from :py:meth:`generate_nodes` :return: device ID :rtype: int """ device_id = None for node in nodes: if device_name == node['properties']['name']: device_id = node['id'] break return device_id
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Get the device ID when given a device name :param str device_name: device name :param list nodes: list of nodes from :py:meth:`generate_nodes` :return: device ID :rtype: int
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python
train
google/apitools
apitools/base/py/encoding_helper.py
https://github.com/google/apitools/blob/f3745a7ea535aa0e88b0650c16479b696d6fd446/apitools/base/py/encoding_helper.py#L137-L147
def DictToAdditionalPropertyMessage(properties, additional_property_type, sort_items=False): """Convert the given dictionary to an AdditionalProperty message.""" items = properties.items() if sort_items: items = sorted(items) map_ = [] for key, value in items: map_.append(additional_property_type.AdditionalProperty( key=key, value=value)) return additional_property_type(additionalProperties=map_)
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Convert the given dictionary to an AdditionalProperty message.
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python
train
jbeluch/xbmcswift2
xbmcswift2/cli/create.py
https://github.com/jbeluch/xbmcswift2/blob/0e7a3642499554edc8265fdf1ba6c5ee567daa78/xbmcswift2/cli/create.py#L60-L63
def validate_pluginid(value): '''Returns True if the provided value is a valid pluglin id''' valid = string.ascii_letters + string.digits + '.' return all(c in valid for c in value)
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Returns True if the provided value is a valid pluglin id
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python
train
Hackerfleet/hfos
hfos/ui/auth.py
https://github.com/Hackerfleet/hfos/blob/b6df14eacaffb6be5c844108873ff8763ec7f0c9/hfos/ui/auth.py#L277-L302
def _get_profile(self, user_account): """Retrieves a user's profile""" try: # TODO: Load active profile, not just any user_profile = objectmodels['profile'].find_one( {'owner': str(user_account.uuid)}) self.log("Profile: ", user_profile, user_account.uuid, lvl=debug) except Exception as e: self.log("No profile due to error: ", e, type(e), lvl=error) user_profile = None if not user_profile: default = { 'uuid': std_uuid(), 'owner': user_account.uuid, 'userdata': { 'notes': 'Default profile of ' + user_account.name } } user_profile = objectmodels['profile'](default) user_profile.save() return user_profile
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Retrieves a user's profile
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python
train
saltstack/salt
salt/modules/pyenv.py
https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/pyenv.py#L105-L117
def install(runas=None, path=None): ''' Install pyenv systemwide CLI Example: .. code-block:: bash salt '*' pyenv.install ''' path = path or _pyenv_path(runas) path = os.path.expanduser(path) return _install_pyenv(path, runas)
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Install pyenv systemwide CLI Example: .. code-block:: bash salt '*' pyenv.install
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python
train
rosenbrockc/fortpy
fortpy/isense/classes.py
https://github.com/rosenbrockc/fortpy/blob/1ed0757c52d549e41d9d44bdea68cb89529293a5/fortpy/isense/classes.py#L155-L165
def _type_description(self): """Gets the completion description for a TypeExecutable.""" #This is a little tricker because the docstring is housed #inside of the module that contains the actual executable. #These TypeExecutables are just pointers. iexec = self._element.target if iexec is not None: result = "method() | " + iexec.summary else: result = "Type Method: points to executable in module." return result
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Gets the completion description for a TypeExecutable.
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python
train
vtemian/buffpy
buffpy/managers/profiles.py
https://github.com/vtemian/buffpy/blob/6c9236fd3b6a8f9e2d70dbf1bc01529242b73075/buffpy/managers/profiles.py#L27-L40
def filter(self, **kwargs): ''' Based on some criteria, filter the profiles and return a new Profiles Manager containing only the chosen items If the manager doen't have any items, get all the profiles from Buffer ''' if not len(self): self.all() new_list = filter(lambda item: [True for arg in kwargs if item[arg] == kwargs[arg]] != [], self) return Profiles(self.api, new_list)
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Based on some criteria, filter the profiles and return a new Profiles Manager containing only the chosen items If the manager doen't have any items, get all the profiles from Buffer
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python
valid
limodou/uliweb
uliweb/contrib/auth/__init__.py
https://github.com/limodou/uliweb/blob/34472f25e4bc0b954a35346672f94e84ef18b076/uliweb/contrib/auth/__init__.py#L40-L51
def check_password(raw_password, enc_password): """ Returns a boolean of whether the raw_password was correct. Handles encryption formats behind the scenes. """ l = enc_password.split('$') #only password of built-in user can split to 3 if len(l)==3: algo, salt, hsh = l return hsh == get_hexdigest(algo, salt, raw_password) else: return False
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Returns a boolean of whether the raw_password was correct. Handles encryption formats behind the scenes.
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python
train
sdispater/eloquent
eloquent/schema/grammars/grammar.py
https://github.com/sdispater/eloquent/blob/0638b688d5fd0c1a46b7471dd465eeb4c2f84666/eloquent/schema/grammars/grammar.py#L125-L135
def _add_modifiers(self, sql, blueprint, column): """ Add the column modifiers to the deifinition """ for modifier in self._modifiers: method = '_modify_%s' % modifier if hasattr(self, method): sql += getattr(self, method)(blueprint, column) return sql
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Add the column modifiers to the deifinition
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python
train
pylast/pylast
src/pylast/__init__.py
https://github.com/pylast/pylast/blob/a52f66d316797fc819b5f1d186d77f18ba97b4ff/src/pylast/__init__.py#L976-L984
def execute(self, cacheable=False): """Returns the XML DOM response of the POST Request from the server""" if self.network.is_caching_enabled() and cacheable: response = self._get_cached_response() else: response = self._download_response() return minidom.parseString(_string(response).replace("opensearch:", ""))
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Returns the XML DOM response of the POST Request from the server
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python
train
pgjones/quart
quart/blueprints.py
https://github.com/pgjones/quart/blob/7cb2d3bd98e8746025764f2b933abc12041fa175/quart/blueprints.py#L220-L235
def add_app_template_filter(self, func: Callable, name: Optional[str]=None) -> None: """Add an application wide template filter. This is designed to be used on the blueprint directly, and has the same arguments as :meth:`~quart.Quart.add_template_filter`. An example usage, .. code-block:: python def filter(): ... blueprint = Blueprint(__name__) blueprint.add_app_template_filter(filter) """ self.record_once(lambda state: state.register_template_filter(func, name))
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Add an application wide template filter. This is designed to be used on the blueprint directly, and has the same arguments as :meth:`~quart.Quart.add_template_filter`. An example usage, .. code-block:: python def filter(): ... blueprint = Blueprint(__name__) blueprint.add_app_template_filter(filter)
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python
train
cosven/feeluown-core
fuocore/player.py
https://github.com/cosven/feeluown-core/blob/62dc64638f62971b16be0a75c0b8c7ae2999869e/fuocore/player.py#L296-L303
def state(self, value): """set player state, emit state changed signal outer object should not set state directly, use ``pause`` / ``resume`` / ``stop`` / ``play`` method instead. """ self._state = value self.state_changed.emit(value)
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set player state, emit state changed signal outer object should not set state directly, use ``pause`` / ``resume`` / ``stop`` / ``play`` method instead.
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python
train
spyder-ide/spyder
spyder/utils/workers.py
https://github.com/spyder-ide/spyder/blob/f76836ce1b924bcc4efd3f74f2960d26a4e528e0/spyder/utils/workers.py#L249-L289
def _start(self, worker=None): """Start threads and check for inactive workers.""" if worker: self._queue_workers.append(worker) if self._queue_workers and self._running_threads < self._max_threads: #print('Queue: {0} Running: {1} Workers: {2} ' # 'Threads: {3}'.format(len(self._queue_workers), # self._running_threads, # len(self._workers), # len(self._threads))) self._running_threads += 1 worker = self._queue_workers.popleft() thread = QThread() if isinstance(worker, PythonWorker): worker.moveToThread(thread) worker.sig_finished.connect(thread.quit) thread.started.connect(worker._start) thread.start() elif isinstance(worker, ProcessWorker): thread.quit() worker._start() self._threads.append(thread) else: self._timer.start() if self._workers: for w in self._workers: if w.is_finished(): self._bag_collector.append(w) self._workers.remove(w) if self._threads: for t in self._threads: if t.isFinished(): self._threads.remove(t) self._running_threads -= 1 if len(self._threads) == 0 and len(self._workers) == 0: self._timer.stop() self._timer_worker_delete.start()
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Start threads and check for inactive workers.
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python
train
ZELLMECHANIK-DRESDEN/dclab
dclab/downsampling.py
https://github.com/ZELLMECHANIK-DRESDEN/dclab/blob/79002c4356e7020c2ba73ab0a3819c9abd4affec/dclab/downsampling.py#L72-L167
def downsample_grid(a, b, samples, ret_idx=False): """Content-based downsampling for faster visualization The arrays `a` and `b` make up a 2D scatter plot with high and low density values. This method takes out points at indices with high density. Parameters ---------- a, b: 1d ndarrays The input arrays to downsample samples: int The desired number of samples remove_invalid: bool Remove nan and inf values before downsampling ret_idx: bool Also return a boolean array that corresponds to the downsampled indices in `a` and `b`. Returns ------- dsa, dsb: 1d ndarrays of shape (samples,) The arrays `a` and `b` downsampled by evenly selecting points and pseudo-randomly adding or removing points to match `samples`. idx: 1d boolean array with same shape as `a` Only returned if `ret_idx` is True. A boolean array such that `a[idx] == dsa` """ # fixed random state for this method rs = np.random.RandomState(seed=47).get_state() samples = int(samples) if samples and samples < a.size: # The events to keep keep = np.zeros_like(a, dtype=bool) # 1. Produce evenly distributed samples # Choosing grid-size: # - large numbers tend to show actual structures of the sample, # which is not desired for plotting # - small numbers tend will not result in too few samples and, # in order to reach the desired samples, the data must be # upsampled again. # 300 is about the size of the plot in marker sizes and yields # good results. grid_size = 300 xpx = norm(a, a, b) * grid_size ypx = norm(b, b, a) * grid_size # The events on the grid to process toproc = np.ones((grid_size, grid_size), dtype=bool) for ii in range(xpx.size): xi = xpx[ii] yi = ypx[ii] # filter for overlapping events if valid(xi, yi) and toproc[int(xi-1), int(yi-1)]: toproc[int(xi-1), int(yi-1)] = False # include event keep[ii] = True # 2. Make sure that we reach `samples` by adding or # removing events. diff = np.sum(keep) - samples if diff > 0: # Too many samples rem_indices = np.where(keep)[0] np.random.set_state(rs) rem = np.random.choice(rem_indices, size=diff, replace=False) keep[rem] = False elif diff < 0: # Not enough samples add_indices = np.where(~keep)[0] np.random.set_state(rs) add = np.random.choice(add_indices, size=abs(diff), replace=False) keep[add] = True assert np.sum(keep) == samples, "sanity check" asd = a[keep] bsd = b[keep] assert np.allclose(a[keep], asd, equal_nan=True), "sanity check" assert np.allclose(b[keep], bsd, equal_nan=True), "sanity check" else: keep = np.ones_like(a, dtype=bool) asd = a bsd = b if ret_idx: return asd, bsd, keep else: return asd, bsd
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Content-based downsampling for faster visualization The arrays `a` and `b` make up a 2D scatter plot with high and low density values. This method takes out points at indices with high density. Parameters ---------- a, b: 1d ndarrays The input arrays to downsample samples: int The desired number of samples remove_invalid: bool Remove nan and inf values before downsampling ret_idx: bool Also return a boolean array that corresponds to the downsampled indices in `a` and `b`. Returns ------- dsa, dsb: 1d ndarrays of shape (samples,) The arrays `a` and `b` downsampled by evenly selecting points and pseudo-randomly adding or removing points to match `samples`. idx: 1d boolean array with same shape as `a` Only returned if `ret_idx` is True. A boolean array such that `a[idx] == dsa`
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python
train
glue-viz/glue-vispy-viewers
glue_vispy_viewers/extern/vispy/util/fourier.py
https://github.com/glue-viz/glue-vispy-viewers/blob/54a4351d98c1f90dfb1a557d1b447c1f57470eea/glue_vispy_viewers/extern/vispy/util/fourier.py#L59-L69
def fft_freqs(n_fft, fs): """Return frequencies for DFT Parameters ---------- n_fft : int Number of points in the FFT. fs : float The sampling rate. """ return np.arange(0, (n_fft // 2 + 1)) / float(n_fft) * float(fs)
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Return frequencies for DFT Parameters ---------- n_fft : int Number of points in the FFT. fs : float The sampling rate.
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python
train
smdabdoub/phylotoast
bin/diversity.py
https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/diversity.py#L54-L66
def print_MannWhitneyU(div_calc): """ Compute the Mann-Whitney U test for unequal group sample sizes. """ try: x = div_calc.values()[0].values() y = div_calc.values()[1].values() except: return "Error setting up input arrays for Mann-Whitney U Test. Skipping "\ "significance testing." T, p = stats.mannwhitneyu(x, y) print "\nMann-Whitney U test statistic:", T print "Two-tailed p-value: {}".format(2 * p)
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Compute the Mann-Whitney U test for unequal group sample sizes.
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python
train
go-macaroon-bakery/py-macaroon-bakery
macaroonbakery/httpbakery/_client.py
https://github.com/go-macaroon-bakery/py-macaroon-bakery/blob/63ce1ef1dabe816eb8aaec48fbb46761c34ddf77/macaroonbakery/httpbakery/_client.py#L123-L156
def acquire_discharge(self, cav, payload): ''' Request a discharge macaroon from the caveat location as an HTTP URL. @param cav Third party {pymacaroons.Caveat} to be discharged. @param payload External caveat data {bytes}. @return The acquired macaroon {macaroonbakery.Macaroon} ''' resp = self._acquire_discharge_with_token(cav, payload, None) # TODO Fabrice what is the other http response possible ?? if resp.status_code == 200: return bakery.Macaroon.from_dict(resp.json().get('Macaroon')) cause = Error.from_dict(resp.json()) if cause.code != ERR_INTERACTION_REQUIRED: raise DischargeError(cause.message) if cause.info is None: raise DischargeError( 'interaction-required response with no info: {}'.format( resp.json()) ) loc = cav.location if not loc.endswith('/'): loc = loc + '/' token, m = self._interact(loc, cause, payload) if m is not None: # We've acquired the macaroon directly via legacy interaction. return m # Try to acquire the discharge again, but this time with # the token acquired by the interaction method. resp = self._acquire_discharge_with_token(cav, payload, token) if resp.status_code == 200: return bakery.Macaroon.from_dict(resp.json().get('Macaroon')) else: raise DischargeError( 'discharge failed with code {}'.format(resp.status_code))
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Request a discharge macaroon from the caveat location as an HTTP URL. @param cav Third party {pymacaroons.Caveat} to be discharged. @param payload External caveat data {bytes}. @return The acquired macaroon {macaroonbakery.Macaroon}
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python
train
wummel/linkchecker
linkcheck/__init__.py
https://github.com/wummel/linkchecker/blob/c2ce810c3fb00b895a841a7be6b2e78c64e7b042/linkcheck/__init__.py#L114-L133
def init_i18n (loc=None): """Initialize i18n with the configured locale dir. The environment variable LOCPATH can also specify a locale dir. @return: None """ if 'LOCPATH' in os.environ: locdir = os.environ['LOCPATH'] else: locdir = os.path.join(get_install_data(), 'share', 'locale') i18n.init(configdata.name.lower(), locdir, loc=loc) # install translated log level names import logging logging.addLevelName(logging.CRITICAL, _('CRITICAL')) logging.addLevelName(logging.ERROR, _('ERROR')) logging.addLevelName(logging.WARN, _('WARN')) logging.addLevelName(logging.WARNING, _('WARNING')) logging.addLevelName(logging.INFO, _('INFO')) logging.addLevelName(logging.DEBUG, _('DEBUG')) logging.addLevelName(logging.NOTSET, _('NOTSET'))
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Initialize i18n with the configured locale dir. The environment variable LOCPATH can also specify a locale dir. @return: None
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python
train
mathiasertl/django-ca
ca/django_ca/models.py
https://github.com/mathiasertl/django-ca/blob/976d7ea05276320f20daed2a6d59c8f5660fe976/ca/django_ca/models.py#L598-L606
def get_authority_key_identifier(self): """Return the AuthorityKeyIdentifier extension used in certificates signed by this CA.""" try: ski = self.x509.extensions.get_extension_for_class(x509.SubjectKeyIdentifier) except x509.ExtensionNotFound: return x509.AuthorityKeyIdentifier.from_issuer_public_key(self.x509.public_key()) else: return x509.AuthorityKeyIdentifier.from_issuer_subject_key_identifier(ski)
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Return the AuthorityKeyIdentifier extension used in certificates signed by this CA.
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python
train
dbcli/athenacli
athenacli/packages/prompt_utils.py
https://github.com/dbcli/athenacli/blob/bcab59e4953145866430083e902ed4d042d4ebba/athenacli/packages/prompt_utils.py#L22-L27
def confirm(*args, **kwargs): """Prompt for confirmation (yes/no) and handle any abort exceptions.""" try: return click.confirm(*args, **kwargs) except click.Abort: return False
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Prompt for confirmation (yes/no) and handle any abort exceptions.
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python
train
spookylukey/django-paypal
paypal/pro/helpers.py
https://github.com/spookylukey/django-paypal/blob/b07d0a3ad91b5c5fe7bb27be3e5d70aabcdef76f/paypal/pro/helpers.py#L66-L75
def express_endpoint_for_token(token, commit=False): """ Returns the PayPal Express Checkout endpoint for a token. Pass 'commit=True' if you will not prompt for confirmation when the user returns to your site. """ pp_params = dict(token=token) if commit: pp_params['useraction'] = 'commit' return express_endpoint() % urlencode(pp_params)
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Returns the PayPal Express Checkout endpoint for a token. Pass 'commit=True' if you will not prompt for confirmation when the user returns to your site.
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python
train
apache/incubator-mxnet
python/mxnet/gluon/trainer.py
https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/gluon/trainer.py#L429-L456
def save_states(self, fname): """Saves trainer states (e.g. optimizer, momentum) to a file. Parameters ---------- fname : str Path to output states file. Note ---- `optimizer.param_dict`, which contains Parameter information (such as `lr_mult` and `wd_mult`) will not be saved. """ assert self._optimizer is not None if not self._kv_initialized: self._init_kvstore() if self._params_to_init: self._init_params() if self._update_on_kvstore: assert not self._params_to_init, "Cannot save trainer states when some " \ "parameters are not yet initialized in kvstore." self._kvstore.save_optimizer_states(fname, dump_optimizer=True) else: with open(fname, 'wb') as fout: fout.write(self._updaters[0].get_states(dump_optimizer=True))
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Saves trainer states (e.g. optimizer, momentum) to a file. Parameters ---------- fname : str Path to output states file. Note ---- `optimizer.param_dict`, which contains Parameter information (such as `lr_mult` and `wd_mult`) will not be saved.
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python
train
praekeltfoundation/seed-stage-based-messaging
subscriptions/views.py
https://github.com/praekeltfoundation/seed-stage-based-messaging/blob/6f0cacf0727ac2ed19877de214d58009c685b8fa/subscriptions/views.py#L125-L153
def post(self, request, *args, **kwargs): """ Validates subscription data before creating Subscription message """ # Ensure that we check for the 'data' key in the request object before # attempting to reference it if "data" in request.data: # This is a workaround for JSONField not liking blank/null refs if "metadata" not in request.data["data"]: request.data["data"]["metadata"] = {} if "initial_sequence_number" not in request.data["data"]: request.data["data"]["initial_sequence_number"] = request.data[ "data" ].get("next_sequence_number") subscription = SubscriptionSerializer(data=request.data["data"]) if subscription.is_valid(): subscription.save() # Return status = 201 accepted = {"accepted": True} return Response(accepted, status=status) else: status = 400 return Response(subscription.errors, status=status) else: status = 400 message = {"data": ["This field is required."]} return Response(message, status=status)
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Validates subscription data before creating Subscription message
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python
train
hobson/pug-invest
pug/invest/sandbox/sim.py
https://github.com/hobson/pug-invest/blob/836911258a0e920083a88c91beae88eefdebb20c/pug/invest/sandbox/sim.py#L103-L140
def chart( symbols=("AAPL", "GLD", "GOOG", "$SPX", "XOM", "msft"), start=datetime.datetime(2008, 1, 1), end=datetime.datetime(2009, 12, 31), # data stops at 2013/1/1 normalize=True, ): """Display a graph of the price history for the list of ticker symbols provided Arguments: symbols (list of str): Ticker symbols like "GOOG", "AAPL", etc start (datetime): The date at the start of the period being analyzed. end (datetime): The date at the end of the period being analyzed. normalize (bool): Whether to normalize prices to 1 at the start of the time series. """ start = util.normalize_date(start or datetime.date(2008, 1, 1)) end = util.normalize_date(end or datetime.date(2009, 12, 31)) symbols = [s.upper() for s in symbols] timeofday = datetime.timedelta(hours=16) timestamps = du.getNYSEdays(start, end, timeofday) ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close'] ldf_data = da.get_data(timestamps, symbols, ls_keys) d_data = dict(zip(ls_keys, ldf_data)) na_price = d_data['close'].values if normalize: na_price /= na_price[0, :] plt.clf() plt.plot(timestamps, na_price) plt.legend(symbols) plt.ylabel('Adjusted Close') plt.xlabel('Date') plt.savefig('chart.pdf', format='pdf') plt.grid(True) plt.show() return na_price
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Display a graph of the price history for the list of ticker symbols provided Arguments: symbols (list of str): Ticker symbols like "GOOG", "AAPL", etc start (datetime): The date at the start of the period being analyzed. end (datetime): The date at the end of the period being analyzed. normalize (bool): Whether to normalize prices to 1 at the start of the time series.
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python
train
HPAC/matchpy
matchpy/matching/one_to_one.py
https://github.com/HPAC/matchpy/blob/06b2ec50ee0efdf3dd183768c0ffdb51b7efc393/matchpy/matching/one_to_one.py#L179-L216
def _build_full_partition( optional_parts, sequence_var_partition: Sequence[int], subjects: Sequence[Expression], operation: Operation ) -> List[Sequence[Expression]]: """Distribute subject operands among pattern operands. Given a partitoning for the variable part of the operands (i.e. a list of how many extra operands each sequence variable gets assigned). """ i = 0 var_index = 0 opt_index = 0 result = [] for operand in op_iter(operation): wrap_associative = False if isinstance(operand, Wildcard): count = operand.min_count if operand.optional is None else 0 if not operand.fixed_size or isinstance(operation, AssociativeOperation): count += sequence_var_partition[var_index] var_index += 1 wrap_associative = operand.fixed_size and operand.min_count elif operand.optional is not None: count = optional_parts[opt_index] opt_index += 1 else: count = 1 operand_expressions = list(op_iter(subjects))[i:i + count] i += count if wrap_associative and len(operand_expressions) > wrap_associative: fixed = wrap_associative - 1 operand_expressions = tuple(operand_expressions[:fixed]) + ( create_operation_expression(operation, operand_expressions[fixed:]), ) result.append(operand_expressions) return result
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Distribute subject operands among pattern operands. Given a partitoning for the variable part of the operands (i.e. a list of how many extra operands each sequence variable gets assigned).
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python
train
wandb/client
wandb/vendor/prompt_toolkit/layout/containers.py
https://github.com/wandb/client/blob/7d08954ed5674fee223cd85ed0d8518fe47266b2/wandb/vendor/prompt_toolkit/layout/containers.py#L1508-L1585
def _scroll_without_linewrapping(self, ui_content, width, height, cli): """ Scroll to make sure the cursor position is visible and that we maintain the requested scroll offset. Set `self.horizontal_scroll/vertical_scroll`. """ cursor_position = ui_content.cursor_position or Point(0, 0) # Without line wrapping, we will never have to scroll vertically inside # a single line. self.vertical_scroll_2 = 0 if ui_content.line_count == 0: self.vertical_scroll = 0 self.horizontal_scroll = 0 return else: current_line_text = token_list_to_text(ui_content.get_line(cursor_position.y)) def do_scroll(current_scroll, scroll_offset_start, scroll_offset_end, cursor_pos, window_size, content_size): " Scrolling algorithm. Used for both horizontal and vertical scrolling. " # Calculate the scroll offset to apply. # This can obviously never be more than have the screen size. Also, when the # cursor appears at the top or bottom, we don't apply the offset. scroll_offset_start = int(min(scroll_offset_start, window_size / 2, cursor_pos)) scroll_offset_end = int(min(scroll_offset_end, window_size / 2, content_size - 1 - cursor_pos)) # Prevent negative scroll offsets. if current_scroll < 0: current_scroll = 0 # Scroll back if we scrolled to much and there's still space to show more of the document. if (not self.allow_scroll_beyond_bottom(cli) and current_scroll > content_size - window_size): current_scroll = max(0, content_size - window_size) # Scroll up if cursor is before visible part. if current_scroll > cursor_pos - scroll_offset_start: current_scroll = max(0, cursor_pos - scroll_offset_start) # Scroll down if cursor is after visible part. if current_scroll < (cursor_pos + 1) - window_size + scroll_offset_end: current_scroll = (cursor_pos + 1) - window_size + scroll_offset_end return current_scroll # When a preferred scroll is given, take that first into account. if self.get_vertical_scroll: self.vertical_scroll = self.get_vertical_scroll(self) assert isinstance(self.vertical_scroll, int) if self.get_horizontal_scroll: self.horizontal_scroll = self.get_horizontal_scroll(self) assert isinstance(self.horizontal_scroll, int) # Update horizontal/vertical scroll to make sure that the cursor # remains visible. offsets = self.scroll_offsets self.vertical_scroll = do_scroll( current_scroll=self.vertical_scroll, scroll_offset_start=offsets.top, scroll_offset_end=offsets.bottom, cursor_pos=ui_content.cursor_position.y, window_size=height, content_size=ui_content.line_count) self.horizontal_scroll = do_scroll( current_scroll=self.horizontal_scroll, scroll_offset_start=offsets.left, scroll_offset_end=offsets.right, cursor_pos=get_cwidth(current_line_text[:ui_content.cursor_position.x]), window_size=width, # We can only analyse the current line. Calculating the width off # all the lines is too expensive. content_size=max(get_cwidth(current_line_text), self.horizontal_scroll + width))
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Scroll to make sure the cursor position is visible and that we maintain the requested scroll offset. Set `self.horizontal_scroll/vertical_scroll`.
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python
train
twilio/twilio-python
twilio/rest/taskrouter/v1/workspace/task_queue/task_queue_statistics.py
https://github.com/twilio/twilio-python/blob/c867895f55dcc29f522e6e8b8868d0d18483132f/twilio/rest/taskrouter/v1/workspace/task_queue/task_queue_statistics.py#L36-L47
def get(self): """ Constructs a TaskQueueStatisticsContext :returns: twilio.rest.taskrouter.v1.workspace.task_queue.task_queue_statistics.TaskQueueStatisticsContext :rtype: twilio.rest.taskrouter.v1.workspace.task_queue.task_queue_statistics.TaskQueueStatisticsContext """ return TaskQueueStatisticsContext( self._version, workspace_sid=self._solution['workspace_sid'], task_queue_sid=self._solution['task_queue_sid'], )
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Constructs a TaskQueueStatisticsContext :returns: twilio.rest.taskrouter.v1.workspace.task_queue.task_queue_statistics.TaskQueueStatisticsContext :rtype: twilio.rest.taskrouter.v1.workspace.task_queue.task_queue_statistics.TaskQueueStatisticsContext
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python
train
fabioz/PyDev.Debugger
third_party/pep8/lib2to3/lib2to3/pgen2/conv.py
https://github.com/fabioz/PyDev.Debugger/blob/ed9c4307662a5593b8a7f1f3389ecd0e79b8c503/third_party/pep8/lib2to3/lib2to3/pgen2/conv.py#L47-L51
def run(self, graminit_h, graminit_c): """Load the grammar tables from the text files written by pgen.""" self.parse_graminit_h(graminit_h) self.parse_graminit_c(graminit_c) self.finish_off()
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Load the grammar tables from the text files written by pgen.
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python
train
ANTsX/ANTsPy
ants/utils/crop_image.py
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/utils/crop_image.py#L14-L56
def crop_image(image, label_image=None, label=1): """ Use a label image to crop a smaller ANTsImage from within a larger ANTsImage ANTsR function: `cropImage` Arguments --------- image : ANTsImage image to crop label_image : ANTsImage image with label values. If not supplied, estimated from data. label : integer the label value to use Returns ------- ANTsImage Example ------- >>> import ants >>> fi = ants.image_read( ants.get_ants_data('r16') ) >>> cropped = ants.crop_image(fi) >>> cropped = ants.crop_image(fi, fi, 100 ) """ inpixeltype = image.pixeltype ndim = image.dimension if image.pixeltype != 'float': image = image.clone('float') if label_image is None: label_image = get_mask(image) if label_image.pixeltype != 'float': label_image = label_image.clone('float') libfn = utils.get_lib_fn('cropImageF%i' % ndim) itkimage = libfn(image.pointer, label_image.pointer, label, 0, [], []) return iio.ANTsImage(pixeltype='float', dimension=ndim, components=image.components, pointer=itkimage).clone(inpixeltype)
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Use a label image to crop a smaller ANTsImage from within a larger ANTsImage ANTsR function: `cropImage` Arguments --------- image : ANTsImage image to crop label_image : ANTsImage image with label values. If not supplied, estimated from data. label : integer the label value to use Returns ------- ANTsImage Example ------- >>> import ants >>> fi = ants.image_read( ants.get_ants_data('r16') ) >>> cropped = ants.crop_image(fi) >>> cropped = ants.crop_image(fi, fi, 100 )
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python
train
lago-project/lago
lago/plugins/vm.py
https://github.com/lago-project/lago/blob/5b8970f7687e063e4619066d5b8093ca997678c9/lago/plugins/vm.py#L464-L468
def extract_paths(self, paths, *args, **kwargs): """ Thin method that just uses the provider """ return self.provider.extract_paths(paths, *args, **kwargs)
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Thin method that just uses the provider
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python
train
totalgood/nlpia
src/nlpia/book/examples/ch09.py
https://github.com/totalgood/nlpia/blob/efa01126275e9cd3c3a5151a644f1c798a9ec53f/src/nlpia/book/examples/ch09.py#L479-L486
def create_dicts(data): """ Modified from Keras LSTM example""" chars = set() for sample in data: chars.update(set(sample)) char_indices = dict((c, i) for i, c in enumerate(chars)) indices_char = dict((i, c) for i, c in enumerate(chars)) return char_indices, indices_char
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Modified from Keras LSTM example
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python
train
KarchinLab/probabilistic2020
prob2020/python/permutation.py
https://github.com/KarchinLab/probabilistic2020/blob/5d70583b0a7c07cfe32e95f3a70e05df412acb84/prob2020/python/permutation.py#L99-L207
def position_permutation(obs_stat, context_counts, context_to_mut, seq_context, gene_seq, gene_vest=None, num_permutations=10000, stop_criteria=100, pseudo_count=0, max_batch=25000): """Performs null-permutations for position-based mutation statistics in a single gene. Parameters ---------- obs_stat : tuple, (recur ct, entropy, delta entropy, mean vest) tuple containing the observed statistics context_counts : pd.Series number of mutations for each context context_to_mut : dict dictionary mapping nucleotide context to a list of observed somatic base changes. seq_context : SequenceContext Sequence context for the entire gene sequence (regardless of where mutations occur). The nucleotide contexts are identified at positions along the gene. gene_seq : GeneSequence Sequence of gene of interest num_permutations : int, default: 10000 number of permutations to create for null stop_criteria : int stop after stop_criteria iterations are more significant then the observed statistic. pseudo_count : int, default: 0 Pseudo-count for number of recurrent missense mutations for each permutation for the null distribution. Increasing pseudo_count makes the statistical test more stringent. Returns ------- num_recur_list : list list of recurrent mutation counts under the null entropy_list : list list of position entropy values under the null """ # get contexts and somatic base mycontexts = context_counts.index.tolist() somatic_base = [base for one_context in mycontexts for base in context_to_mut[one_context]] # calculate the # of batches for simulations max_batch = min(num_permutations, max_batch) num_batches = num_permutations // max_batch remainder = num_permutations % max_batch batch_sizes = [max_batch] * num_batches if remainder: batch_sizes += [remainder] obs_recur, obs_ent, obs_delta_ent, obs_vest = obs_stat num_sim = 0 # number of simulations null_num_recur_ct, null_entropy_ct, null_delta_entropy_ct, null_vest_ct = 0, 0, 0, 0 for j, batch_size in enumerate(batch_sizes): # stop iterations if reached sufficient precision if null_vest_ct >= stop_criteria and null_entropy_ct >= stop_criteria: break # get random positions determined by sequence context tmp_contxt_pos = seq_context.random_pos(context_counts.iteritems(), batch_size) tmp_mut_pos = np.hstack(pos_array for base, pos_array in tmp_contxt_pos) # calculate position-based statistics as a result of random positions for i, row in enumerate(tmp_mut_pos): # get info about mutations tmp_mut_info = mc.get_aa_mut_info(row, somatic_base, gene_seq) # calculate position info tmp_recur_ct, tmp_entropy, tmp_delta_entropy, _ = cutils.calc_pos_info(tmp_mut_info['Codon Pos'], tmp_mut_info['Reference AA'], tmp_mut_info['Somatic AA'], pseudo_count=pseudo_count, is_obs=0) # get vest scores if gene_vest: tmp_vest = scores.compute_vest_stat(gene_vest, tmp_mut_info['Reference AA'], tmp_mut_info['Somatic AA'], tmp_mut_info['Codon Pos']) else: tmp_vest = 0.0 # update empirical null distribution counts if tmp_entropy-utils.epsilon <= obs_ent: null_entropy_ct += 1 if tmp_vest+utils.epsilon >= obs_vest: null_vest_ct += 1 # stop iterations if reached sufficient precision if null_vest_ct >= stop_criteria and null_entropy_ct >= stop_criteria: break # update the number of simulations num_sim += i+1 # calculate p-value from empirical null-distribution ent_pval = float(null_entropy_ct) / (num_sim) vest_pval = float(null_vest_ct) / (num_sim) return ent_pval, vest_pval
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Performs null-permutations for position-based mutation statistics in a single gene. Parameters ---------- obs_stat : tuple, (recur ct, entropy, delta entropy, mean vest) tuple containing the observed statistics context_counts : pd.Series number of mutations for each context context_to_mut : dict dictionary mapping nucleotide context to a list of observed somatic base changes. seq_context : SequenceContext Sequence context for the entire gene sequence (regardless of where mutations occur). The nucleotide contexts are identified at positions along the gene. gene_seq : GeneSequence Sequence of gene of interest num_permutations : int, default: 10000 number of permutations to create for null stop_criteria : int stop after stop_criteria iterations are more significant then the observed statistic. pseudo_count : int, default: 0 Pseudo-count for number of recurrent missense mutations for each permutation for the null distribution. Increasing pseudo_count makes the statistical test more stringent. Returns ------- num_recur_list : list list of recurrent mutation counts under the null entropy_list : list list of position entropy values under the null
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python
train
bitesofcode/projexui
projexui/widgets/xviewwidget/xviewprofilemanagermenu.py
https://github.com/bitesofcode/projexui/blob/f18a73bec84df90b034ca69b9deea118dbedfc4d/projexui/widgets/xviewwidget/xviewprofilemanagermenu.py#L68-L79
def saveProfileAs( self ): """ Saves the current profile as a new profile to the manager. """ name, ok = QInputDialog.getText(self, 'Create Profile', 'Name:') if ( not name ): return manager = self.parent() prof = manager.viewWidget().saveProfile() prof.setName(nativestring(name)) self.parent().addProfile(prof)
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Saves the current profile as a new profile to the manager.
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python
train
apacha/OMR-Datasets
omrdatasettools/converters/csv_to_crop_object_conversion.py
https://github.com/apacha/OMR-Datasets/blob/d0a22a03ae35caeef211729efa340e1ec0e01ea5/omrdatasettools/converters/csv_to_crop_object_conversion.py#L14-L48
def convert_csv_annotations_to_cropobject(annotations_path: str, image_path: str) -> List[CropObject]: """ Converts a normalized dataset of objects into crop-objects. :param annotations_path: Path to the csv-file that contains bounding boxes in the following format for a single image: image_name,top,left,bottom,right,class_name,confidence CVC-MUSCIMA_W-01_N-10_D-ideal_1.png,138.93,2286.36,185.20,2316.52,8th_flag,1.00 :param image_path: Image that is being described by the file given under the annotations_path :return: A list of CropObjects as being used by the MUSCIMA++ dataset including the binary image-masks """ annotations = pd.read_csv(annotations_path) image = Image.open(image_path) # type: Image.Image crop_objects = [] node_id = 0 for index, annotation in annotations.iterrows(): # Annotation example: # image_name,top,left,bottom,right,class_name,confidence # CVC-MUSCIMA_W-01_N-10_D-ideal_1.png,138.93,2286.36,185.20,2316.52,8th_flag,1.00 image_name = annotation["image_name"] class_name = annotation["class_name"] top = round(annotation["top"]) left = round(annotation["left"]) width = round(annotation["right"] - annotation["left"]) heigth = round(annotation["bottom"] - annotation["top"]) crop_object = CropObject(node_id, class_name, top, left, width, heigth) crop_object.set_doc(image_name) crop_image = image.crop((left, top, crop_object.right, crop_object.bottom)).convert("1") # noinspection PyTypeChecker cropped_image_mask = np.array(crop_image) crop_object.set_mask(cropped_image_mask) crop_objects.append(crop_object) node_id += 1 return crop_objects
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Converts a normalized dataset of objects into crop-objects. :param annotations_path: Path to the csv-file that contains bounding boxes in the following format for a single image: image_name,top,left,bottom,right,class_name,confidence CVC-MUSCIMA_W-01_N-10_D-ideal_1.png,138.93,2286.36,185.20,2316.52,8th_flag,1.00 :param image_path: Image that is being described by the file given under the annotations_path :return: A list of CropObjects as being used by the MUSCIMA++ dataset including the binary image-masks
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python
train
ThreatConnect-Inc/tcex
tcex/tcex_playbook.py
https://github.com/ThreatConnect-Inc/tcex/blob/dd4d7a1ef723af1561687120191886b9a2fd4b47/tcex/tcex_playbook.py#L1089-L1101
def indicator_arrays(tc_entity_array): """Convert TCEntityArray to Indicator Type dictionary. Args: tc_entity_array (dictionary): The TCEntityArray to convert. Returns: (dictionary): Dictionary containing arrays of indicators for each indicator type. """ type_dict = {} for ea in tc_entity_array: type_dict.setdefault(ea['type'], []).append(ea['value']) return type_dict
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Convert TCEntityArray to Indicator Type dictionary. Args: tc_entity_array (dictionary): The TCEntityArray to convert. Returns: (dictionary): Dictionary containing arrays of indicators for each indicator type.
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python
train
svetlyak40wt/python-repr
src/magic_repr/__init__.py
https://github.com/svetlyak40wt/python-repr/blob/49e358e77b97d74f29f4977ea009ab2d64c254e8/src/magic_repr/__init__.py#L125-L177
def format_value(value): """This function should return unicode representation of the value """ value_id = id(value) if value_id in recursion_breaker.processed: return u'<recursion>' recursion_breaker.processed.add(value_id) try: if isinstance(value, six.binary_type): # suppose, all byte strings are in unicode # don't know if everybody in the world uses anything else? return u"'{0}'".format(value.decode('utf-8')) elif isinstance(value, six.text_type): return u"u'{0}'".format(value) elif isinstance(value, (list, tuple)): # long lists or lists with multiline items # will be shown vertically values = list(map(format_value, value)) result = serialize_list(u'[', values, delimiter=u',') + u']' return force_unicode(result) elif isinstance(value, dict): items = six.iteritems(value) # format each key/value pair as a text, # calling format_value recursively items = (tuple(map(format_value, item)) for item in items) items = list(items) # sort by keys for readability items.sort() # for each item value items = [ serialize_text( u'{0}: '.format(key), item_value) for key, item_value in items] # and serialize these pieces as a list, enclosing # them into a curve brackets result = serialize_list(u'{', items, delimiter=u',') + u'}' return force_unicode(result) return force_unicode(repr(value)) finally: recursion_breaker.processed.remove(value_id)
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This function should return unicode representation of the value
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python
valid
hhatto/pgmagick
pgmagick/api.py
https://github.com/hhatto/pgmagick/blob/5dce5fa4681400b4c059431ad69233e6a3e5799a/pgmagick/api.py#L868-L875
def matte(self, x, y, paint_method): """ :param paint_method: 'point' or 'replace' or 'floodfill' or 'filltoborder' or 'reset' :type paint_method: str or pgmagick.PaintMethod """ paint_method = _convert_paintmethod(paint_method) self.drawer.append(pgmagick.DrawableMatte(x, y, paint_method))
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:param paint_method: 'point' or 'replace' or 'floodfill' or 'filltoborder' or 'reset' :type paint_method: str or pgmagick.PaintMethod
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python
valid
gem/oq-engine
openquake/calculators/export/hazard.py
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/export/hazard.py#L223-L235
def export_hmaps_csv(key, dest, sitemesh, array, comment): """ Export the hazard maps of the given realization into CSV. :param key: output_type and export_type :param dest: name of the exported file :param sitemesh: site collection :param array: a composite array of dtype hmap_dt :param comment: comment to use as header of the exported CSV file """ curves = util.compose_arrays(sitemesh, array) writers.write_csv(dest, curves, comment=comment) return [dest]
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Export the hazard maps of the given realization into CSV. :param key: output_type and export_type :param dest: name of the exported file :param sitemesh: site collection :param array: a composite array of dtype hmap_dt :param comment: comment to use as header of the exported CSV file
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python
train
cocaine/cocaine-tools
cocaine/tools/cli.py
https://github.com/cocaine/cocaine-tools/blob/d8834f8e04ca42817d5f4e368d471484d4b3419f/cocaine/tools/cli.py#L245-L250
def loop(self): """Lazy event loop initialization""" if not self._loop: self._loop = IOLoop.current() return self._loop return self._loop
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Lazy event loop initialization
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python
train
saltstack/salt
salt/modules/pip.py
https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/pip.py#L259-L274
def _resolve_requirements_chain(requirements): ''' Return an array of requirements file paths that can be used to complete the no_chown==False && user != None conundrum ''' chain = [] if isinstance(requirements, six.string_types): requirements = [requirements] for req_file in requirements: chain.append(req_file) chain.extend(_resolve_requirements_chain(_find_req(req_file))) return chain
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Return an array of requirements file paths that can be used to complete the no_chown==False && user != None conundrum
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python
train
casebeer/audiogen
audiogen/util.py
https://github.com/casebeer/audiogen/blob/184dee2ca32c2bb4315a0f18e62288728fcd7881/audiogen/util.py#L252-L266
def channelize(gen, channels): ''' Break multi-channel generator into one sub-generator per channel Takes a generator producing n-tuples of samples and returns n generators, each producing samples for a single channel. Since multi-channel generators are the only reasonable way to synchronize samples across channels, and the sampler functions only take tuples of generators, you must use this function to process synchronized streams for output. ''' def pick(g, channel): for samples in g: yield samples[channel] return [pick(gen_copy, channel) for channel, gen_copy in enumerate(itertools.tee(gen, channels))]
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Break multi-channel generator into one sub-generator per channel Takes a generator producing n-tuples of samples and returns n generators, each producing samples for a single channel. Since multi-channel generators are the only reasonable way to synchronize samples across channels, and the sampler functions only take tuples of generators, you must use this function to process synchronized streams for output.
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python
train
quantmind/pulsar
pulsar/apps/wsgi/content.py
https://github.com/quantmind/pulsar/blob/fee44e871954aa6ca36d00bb5a3739abfdb89b26/pulsar/apps/wsgi/content.py#L168-L182
def http_response(self, request): '''Return a :class:`.WsgiResponse` or a :class:`~asyncio.Future`. This method asynchronously wait for :meth:`stream` and subsequently returns a :class:`.WsgiResponse`. ''' content_types = request.content_types if not content_types or self._content_type in content_types: response = request.response response.content_type = self._content_type response.encoding = self.charset response.content = self.to_bytes() return response else: raise HttpException(status=415, msg=request.content_types)
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Return a :class:`.WsgiResponse` or a :class:`~asyncio.Future`. This method asynchronously wait for :meth:`stream` and subsequently returns a :class:`.WsgiResponse`.
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python
train
knipknap/SpiffWorkflow
SpiffWorkflow/bpmn/parser/TaskParser.py
https://github.com/knipknap/SpiffWorkflow/blob/f0af7f59a332e0619e4f3c00a7d4a3d230760e00/SpiffWorkflow/bpmn/parser/TaskParser.py#L158-L170
def connect_outgoing(self, outgoing_task, outgoing_task_node, sequence_flow_node, is_default): """ Connects this task to the indicating outgoing task, with the details in the sequence flow. A subclass can override this method to get extra information from the node. """ self.task.connect_outgoing( outgoing_task, sequence_flow_node.get('id'), sequence_flow_node.get( 'name', None), self.parser._parse_documentation(sequence_flow_node, task_parser=self))
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Connects this task to the indicating outgoing task, with the details in the sequence flow. A subclass can override this method to get extra information from the node.
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python
valid
pyblish/pyblish-nuke
pyblish_nuke/vendor/Qt.py
https://github.com/pyblish/pyblish-nuke/blob/5fbd766774e999e5e3015201094a07a92d800c4f/pyblish_nuke/vendor/Qt.py#L77-L108
def _remap(object, name, value, safe=True): """Prevent accidental assignment of existing members Arguments: object (object): Parent of new attribute name (str): Name of new attribute value (object): Value of new attribute safe (bool): Whether or not to guarantee that the new attribute was not overwritten. Can be set to False under condition that it is superseded by extensive testing. """ if os.getenv("QT_TESTING") is not None and safe: # Cannot alter original binding. if hasattr(object, name): raise AttributeError("Cannot override existing name: " "%s.%s" % (object.__name__, name)) # Cannot alter classes of functions if type(object).__name__ != "module": raise AttributeError("%s != 'module': Cannot alter " "anything but modules" % object) elif hasattr(object, name): # Keep track of modifications self.__modified__.append(name) self.__remapped__.append(name) setattr(object, name, value)
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Prevent accidental assignment of existing members Arguments: object (object): Parent of new attribute name (str): Name of new attribute value (object): Value of new attribute safe (bool): Whether or not to guarantee that the new attribute was not overwritten. Can be set to False under condition that it is superseded by extensive testing.
[ "Prevent", "accidental", "assignment", "of", "existing", "members" ]
python
train
ddorn/GUI
GUI/gui_examples/bezier.py
https://github.com/ddorn/GUI/blob/e1fcb5286d24e0995f280d5180222e51895c368c/GUI/gui_examples/bezier.py#L17-L105
def gui(): """Main function""" # ####### # setup all objects # ####### zones = [ALL] last_zones = [] COLORS.remove(WHITE) screen = pygame.display.set_mode(SCREEN_SIZE, DOUBLEBUF) pygame.display.set_caption('Bezier simulator') pygame.event.set_allowed([QUIT, KEYDOWN, MOUSEBUTTONDOWN]) points = [ (40, 40), (100, 400), (200, 100), (650, 420) ] bezier = Bezier((0, 0), SCREEN_SIZE, points, ORANGE, 8) points = [Point(p, 24, choice(COLORS)) for p in points] clock = pygame.time.Clock() fps = FPSIndicator(clock) dragging = None render = True while True: # ####### # Input loop # ####### mouse = pygame.mouse.get_pos() for e in pygame.event.get(): if e.type == QUIT: return 0 elif e.type == KEYDOWN: if e.key == K_ESCAPE: return 0 if e.key == K_F4 and e.mod & KMOD_ALT: return 0 elif e.type == MOUSEBUTTONDOWN: if e.button == 1: dragging = not dragging if e.button == 3: points.append(Point(mouse, 24, choice(COLORS))) bezier.points.append(V2(mouse)) render = True if dragging: mdist = 10000 the_p = None for i, p in enumerate(points): if p.dist_to(mouse) < mdist: mdist = p.dist_to(mouse) the_p = i render = points[the_p].pos != mouse points[the_p].pos = mouse bezier.points[the_p] = V2(mouse) # ####### # Draw all # ####### if render: render = False screen.fill(WHITE) bezier.render(screen) for p in points: p.render(screen) zones.append(ALL) _ = fps.render(screen) zones.append(_) pygame.display.update(zones + last_zones) last_zones = zones[:] zones.clear() clock.tick(FPS)
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Main function
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python
train
google/grr
grr/core/grr_response_core/lib/util/filesystem.py
https://github.com/google/grr/blob/5cef4e8e2f0d5df43ea4877e9c798e0bf60bfe74/grr/core/grr_response_core/lib/util/filesystem.py#L187-L211
def Get(self, path, follow_symlink = True): """Stats given file or returns a cached result if available. Args: path: A path to the file to perform `stat` on. follow_symlink: True if `stat` of a symlink should be returned instead of a file that it points to. For non-symlinks this setting has no effect. Returns: `Stat` object corresponding to the given path. """ key = self._Key(path=path, follow_symlink=follow_symlink) try: return self._cache[key] except KeyError: value = Stat.FromPath(path, follow_symlink=follow_symlink) self._cache[key] = value # If we are not following symlinks and the file is a not symlink then # the stat result for this file stays the same even if we want to follow # symlinks. if not follow_symlink and not value.IsSymlink(): self._cache[self._Key(path=path, follow_symlink=True)] = value return value
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python
train
seequent/properties
properties/images.py
https://github.com/seequent/properties/blob/096b07012fff86b0a880c8c018320c3b512751b9/properties/images.py#L86-L97
def to_json(value, **kwargs): """Convert a PNG Image to base64-encoded JSON to_json assumes that value has passed validation. """ b64rep = base64.b64encode(value.read()) value.seek(0) jsonrep = '{preamble}{b64}'.format( preamble=PNG_PREAMBLE, b64=b64rep.decode(), ) return jsonrep
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Convert a PNG Image to base64-encoded JSON to_json assumes that value has passed validation.
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python
train
belbio/bel
bel/lang/semantics.py
https://github.com/belbio/bel/blob/60333e8815625b942b4836903f3b618cf44b3771/bel/lang/semantics.py#L46-L88
def validate_functions(ast: BELAst, bo): """Recursively validate function signatures Determine if function matches one of the available signatures. Also, 1. Add entity types to AST NSArg, e.g. Abundance, ... 2. Add optional to AST Arg (optional means it is not a fixed, required argument and needs to be sorted for canonicalization, e.g. reactants(A, B, C) ) Args: bo: bel object Returns: bel object """ if isinstance(ast, Function): log.debug(f"Validating: {ast.name}, {ast.function_type}, {ast.args}") function_signatures = bo.spec["functions"]["signatures"][ast.name]["signatures"] function_name = ast.name (valid_function, messages) = check_function_args( ast.args, function_signatures, function_name ) if not valid_function: message = ", ".join(messages) bo.validation_messages.append( ( "ERROR", "Invalid BEL Statement function {} - problem with function signatures: {}".format( ast.to_string(), message ), ) ) bo.parse_valid = False # Recursively process every NSArg by processing BELAst and Functions if hasattr(ast, "args"): for arg in ast.args: validate_functions(arg, bo) return bo
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python
train