Datasets:

kejian

/

codesearchnet-python-raw

like 1

saltstack/salt

salt/cloud/clouds/digitalocean.py

https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/cloud/clouds/digitalocean.py#L1342-L1353

def _get_full_output(node, for_output=False): ''' Helper function for _list_nodes to loop through all node information. Returns a dictionary containing the full information of a node. ''' ret = {} for item in six.iterkeys(node): value = node[item] if value is not None and for_output: value = six.text_type(value) ret[item] = value return ret

Helper function for _list_nodes to loop through all node information. Returns a dictionary containing the full information of a node.

python

train

SatelliteQE/nailgun

nailgun/entities.py

https://github.com/SatelliteQE/nailgun/blob/c36d8c20862e87bf6975bd48ac1ca40a9e634eaa/nailgun/entities.py#L1722-L1734

def create_missing(self): """Customize the process of auto-generating instance attributes. Populate ``template_kind`` if: * this template is not a snippet, and * the ``template_kind`` instance attribute is unset. """ super(ProvisioningTemplate, self).create_missing() if (getattr(self, 'snippet', None) is False and not hasattr(self, 'template_kind')): self.template_kind = TemplateKind(self._server_config, id=1)

Customize the process of auto-generating instance attributes. Populate ``template_kind`` if: * this template is not a snippet, and * the ``template_kind`` instance attribute is unset.

python

train

google/grr

grr/client/grr_response_client/comms.py

https://github.com/google/grr/blob/5cef4e8e2f0d5df43ea4877e9c798e0bf60bfe74/grr/client/grr_response_client/comms.py#L1220-L1277

def Run(self): """The main run method of the client. This method does not normally return. Only if there have been more than connection_error_limit failures, the method returns and allows the client to exit. """ while True: if self.http_manager.ErrorLimitReached(): return # Check if there is a message from the nanny to be sent. self.client_worker.SendNannyMessage() now = time.time() # Check with the foreman if we need to if (now > self.last_foreman_check + config.CONFIG["Client.foreman_check_frequency"]): # We must not queue messages from the comms thread with blocking=True # or we might deadlock. If the output queue is full, we can't accept # more work from the foreman anyways so it's ok to drop the message. try: self.client_worker.SendReply( rdf_protodict.DataBlob(), session_id=rdfvalue.FlowSessionID(flow_name="Foreman"), require_fastpoll=False, blocking=False) self.last_foreman_check = now except queue.Full: pass try: self.RunOnce() except Exception: # pylint: disable=broad-except # Catch everything, yes, this is terrible but necessary logging.warning("Uncaught exception caught: %s", traceback.format_exc()) if flags.FLAGS.pdb_post_mortem: pdb.post_mortem() # We suicide if our memory is exceeded, and there is no more work to do # right now. Our death should not result in loss of messages since we are # not holding any requests in our input queues. if (self.client_worker.MemoryExceeded() and not self.client_worker.IsActive() and self.client_worker.InQueueSize() == 0 and self.client_worker.OutQueueSize() == 0): logging.warning("Memory exceeded - exiting.") self.client_worker.SendClientAlert("Memory limit exceeded, exiting.") # Make sure this will return True so we don't get more work. # pylint: disable=g-bad-name self.client_worker.MemoryExceeded = lambda: True # pylint: enable=g-bad-name # Now send back the client message. self.RunOnce() # And done for now. sys.exit(-1) self.timer.Wait()

The main run method of the client. This method does not normally return. Only if there have been more than connection_error_limit failures, the method returns and allows the client to exit.

python

train

signalfx/signalfx-python

signalfx/signalflow/computation.py

https://github.com/signalfx/signalfx-python/blob/650eb9a2b301bcc795e4e3a8c031574ade69849d/signalfx/signalflow/computation.py#L76-L169

def stream(self): """Iterate over the messages from the computation's output. Control and metadata messages are intercepted and interpreted to enhance this Computation's object knowledge of the computation's context. Data and event messages are yielded back to the caller as a generator. """ iterator = iter(self._stream) while self._state < Computation.STATE_COMPLETED: try: message = next(iterator) except StopIteration: if self._state < Computation.STATE_COMPLETED: self._stream = self._execute() iterator = iter(self._stream) continue if isinstance(message, messages.StreamStartMessage): self._state = Computation.STATE_STREAM_STARTED continue if isinstance(message, messages.JobStartMessage): self._state = Computation.STATE_COMPUTATION_STARTED self._id = message.handle yield message continue if isinstance(message, messages.JobProgressMessage): yield message continue if isinstance(message, messages.ChannelAbortMessage): self._state = Computation.STATE_ABORTED raise errors.ComputationAborted(message.abort_info) if isinstance(message, messages.EndOfChannelMessage): self._state = Computation.STATE_COMPLETED continue # Intercept metadata messages to accumulate received metadata... if isinstance(message, messages.MetadataMessage): self._metadata[message.tsid] = message.properties yield message continue # ...as well as expired-tsid messages to clean it up. if isinstance(message, messages.ExpiredTsIdMessage): if message.tsid in self._metadata: del self._metadata[message.tsid] yield message continue if isinstance(message, messages.InfoMessage): self._process_info_message(message.message) self._batch_count_detected = True if self._current_batch_message: yield self._get_batch_to_yield() continue # Accumulate data messages and release them when we have received # all batches for the same logical timestamp. if isinstance(message, messages.DataMessage): self._state = Computation.STATE_DATA_RECEIVED if not self._batch_count_detected: self._expected_batches += 1 if not self._current_batch_message: self._current_batch_message = message self._current_batch_count = 1 elif (message.logical_timestamp_ms == self._current_batch_message.logical_timestamp_ms): self._current_batch_message.add_data(message.data) self._current_batch_count += 1 else: self._batch_count_detected = True if (self._batch_count_detected and self._current_batch_count == self._expected_batches): yield self._get_batch_to_yield() continue if isinstance(message, messages.EventMessage): yield message continue if isinstance(message, messages.ErrorMessage): raise errors.ComputationFailed(message.errors) # Yield last batch, even if potentially incomplete. if self._current_batch_message: yield self._get_batch_to_yield()

Iterate over the messages from the computation's output. Control and metadata messages are intercepted and interpreted to enhance this Computation's object knowledge of the computation's context. Data and event messages are yielded back to the caller as a generator.

python

train

mLewisLogic/foursquare

foursquare/__init__.py

https://github.com/mLewisLogic/foursquare/blob/420f3b588b9af154688ec82649f24a70f96c1665/foursquare/__init__.py#L776-L787

def _process_response(response): """Make the request and handle exception processing""" # Read the response as JSON try: data = response.json() except ValueError: _log_and_raise_exception('Invalid response', response.text) # Default case, Got proper response if response.status_code == 200: return { 'headers': response.headers, 'data': data } return _raise_error_from_response(data)

Make the request and handle exception processing

python

train

happyleavesaoc/python-limitlessled

limitlessled/__init__.py

https://github.com/happyleavesaoc/python-limitlessled/blob/70307c2bf8c91430a99579d2ad18b228ec7a8488/limitlessled/__init__.py#L36-L42

def add_bridge(self, bridge): """ Add bridge groups. :param bridge: Add groups from this bridge. """ for group in bridge.groups: self._groups[group.name] = group

Add bridge groups. :param bridge: Add groups from this bridge.

python

train

pantsbuild/pants

src/python/pants/util/dirutil.py

https://github.com/pantsbuild/pants/blob/b72e650da0df685824ffdcc71988b8c282d0962d/src/python/pants/util/dirutil.py#L341-L357

def safe_rm_oldest_items_in_dir(root_dir, num_of_items_to_keep, excludes=frozenset()): """ Keep `num_of_items_to_keep` newly modified items besides `excludes` in `root_dir` then remove the rest. :param root_dir: the folder to examine :param num_of_items_to_keep: number of files/folders/symlinks to keep after the cleanup :param excludes: absolute paths excluded from removal (must be prefixed with `root_dir`) :return: none """ if os.path.isdir(root_dir): found_files = [] for old_file in os.listdir(root_dir): full_path = os.path.join(root_dir, old_file) if full_path not in excludes: found_files.append((full_path, os.path.getmtime(full_path))) found_files = sorted(found_files, key=lambda x: x[1], reverse=True) for cur_file, _ in found_files[num_of_items_to_keep:]: rm_rf(cur_file)

Keep `num_of_items_to_keep` newly modified items besides `excludes` in `root_dir` then remove the rest. :param root_dir: the folder to examine :param num_of_items_to_keep: number of files/folders/symlinks to keep after the cleanup :param excludes: absolute paths excluded from removal (must be prefixed with `root_dir`) :return: none

python

train

talkincode/toughlib

toughlib/btforms/net.py

https://github.com/talkincode/toughlib/blob/1c2f7dde3a7f101248f1b5f5d428cc85466995cf/toughlib/btforms/net.py#L100-L114

def urlquote(val): """ Quotes a string for use in a URL. >>> urlquote('://?f=1&j=1') '%3A//%3Ff%3D1%26j%3D1' >>> urlquote(None) '' >>> urlquote(u'\u203d') '%E2%80%BD' """ if val is None: return '' if not isinstance(val, unicode): val = str(val) else: val = val.encode('utf-8') return urllib.quote(val)

Quotes a string for use in a URL. >>> urlquote('://?f=1&j=1') '%3A//%3Ff%3D1%26j%3D1' >>> urlquote(None) '' >>> urlquote(u'\u203d') '%E2%80%BD'

python

train

davgeo/clear

clear/util.py

https://github.com/davgeo/clear/blob/5ec85d27efd28afddfcd4c3f44df17f0115a77aa/clear/util.py#L162-L240

def UserAcceptance( matchList, recursiveLookup = True, promptComment = None, promptOnly = False, xStrOverride = "to skip this selection" ): """ Prompt user to select a entry from a given match list or to enter a new string to look up. If the match list is empty user must enter a new string or exit. Parameters ---------- matchList : list A list of entries which the user can select a valid match from. recursiveLookup : boolean [optional: default = True] Allow user to enter a new string to look up. promptComment : string [optional: default = None] Add an additional comment on the end of the prompt message. promptOnly : boolean [optional: default = False] Set to true if match list is expected to be empty. In which case the presence of an empty match list will not be mentioned and user will be expected to enter a new response to look up. xStrOverride : string [optional: default = "to skip this selection"] Override the string for 'x' response. This can be used if the behaviour of the 'x' response is changed. Returns ---------- string or None Either a entry from matchList, another valid response or a new string to look up. If match list is empty and recursive lookup is disabled or if the user response is 'x' this will return None. """ matchString = ', '.join(matchList) if len(matchList) == 1: goodlogging.Log.Info("UTIL", "Match found: {0}".format(matchString)) prompt = "Enter 'y' to accept this match or e" elif len(matchList) > 1: goodlogging.Log.Info("UTIL", "Multiple possible matches found: {0}".format(matchString)) prompt = "Enter correct match from list or e" else: if promptOnly is False: goodlogging.Log.Info("UTIL", "No match found") prompt = "E" if not recursiveLookup: return None if recursiveLookup: prompt = prompt + "nter a different string to look up or e" prompt = prompt + "nter 'x' {0} or enter 'exit' to quit this program".format(xStrOverride) if promptComment is None: prompt = prompt + ": " else: prompt = prompt + " ({0}): ".format(promptComment) while(1): response = goodlogging.Log.Input('UTIL', prompt) if response.lower() == 'exit': goodlogging.Log.Fatal("UTIL", "Program terminated by user 'exit'") if response.lower() == 'x': return None elif response.lower() == 'y' and len(matchList) == 1: return matchList[0] elif len(matchList) > 1: for match in matchList: if response.lower() == match.lower(): return match if recursiveLookup: return response

Prompt user to select a entry from a given match list or to enter a new string to look up. If the match list is empty user must enter a new string or exit. Parameters ---------- matchList : list A list of entries which the user can select a valid match from. recursiveLookup : boolean [optional: default = True] Allow user to enter a new string to look up. promptComment : string [optional: default = None] Add an additional comment on the end of the prompt message. promptOnly : boolean [optional: default = False] Set to true if match list is expected to be empty. In which case the presence of an empty match list will not be mentioned and user will be expected to enter a new response to look up. xStrOverride : string [optional: default = "to skip this selection"] Override the string for 'x' response. This can be used if the behaviour of the 'x' response is changed. Returns ---------- string or None Either a entry from matchList, another valid response or a new string to look up. If match list is empty and recursive lookup is disabled or if the user response is 'x' this will return None.

python

train

Kortemme-Lab/klab

klab/bio/clustalo.py

https://github.com/Kortemme-Lab/klab/blob/6d410ad08f1bd9f7cbbb28d7d946e94fbaaa2b6b/klab/bio/clustalo.py#L664-L679

def _get_uniparc_sequences_through_uniprot_ACs(self, mapping_pdb_id, uniprot_ACs, cache_dir): '''Get the UniParc sequences associated with the UniProt accession number.''' # Map the UniProt ACs to the UniParc IDs m = uniprot_map('ACC', 'UPARC', uniprot_ACs, cache_dir = cache_dir) UniParcIDs = [] for _, v in m.iteritems(): UniParcIDs.extend(v) # Create a mapping from the mapping_pdb_id to the UniParcEntry objects. This must match the return type from pdb_to_uniparc. mapping = {mapping_pdb_id : []} for UniParcID in UniParcIDs: entry = UniParcEntry(UniParcID, cache_dir = cache_dir) mapping[mapping_pdb_id].append(entry) return mapping

Get the UniParc sequences associated with the UniProt accession number.

python

train

dwavesystems/dwavebinarycsp

dwavebinarycsp/compilers/stitcher.py

https://github.com/dwavesystems/dwavebinarycsp/blob/d6b1e70ceaa8f451d7afaa87ea10c7fc948a64e2/dwavebinarycsp/compilers/stitcher.py#L34-L196

def stitch(csp, min_classical_gap=2.0, max_graph_size=8): """Build a binary quadratic model with minimal energy levels at solutions to the specified constraint satisfaction problem. Args: csp (:obj:`.ConstraintSatisfactionProblem`): Constraint satisfaction problem. min_classical_gap (float, optional, default=2.0): Minimum energy gap from ground. Each constraint violated by the solution increases the energy level of the binary quadratic model by at least this much relative to ground energy. max_graph_size (int, optional, default=8): Maximum number of variables in the binary quadratic model that can be used to represent a single constraint. Returns: :class:`~dimod.BinaryQuadraticModel` Notes: For a `min_classical_gap` > 2 or constraints with more than two variables, requires access to factories from the penaltymodel_ ecosystem to construct the binary quadratic model. .. _penaltymodel: https://github.com/dwavesystems/penaltymodel Examples: This example creates a binary-valued constraint satisfaction problem with two constraints, :math:`a = b` and :math:`b \\ne c`, and builds a binary quadratic model with a minimum energy level of -2 such that each constraint violation by a solution adds the default minimum energy gap. >>> import dwavebinarycsp >>> import operator >>> csp = dwavebinarycsp.ConstraintSatisfactionProblem(dwavebinarycsp.BINARY) >>> csp.add_constraint(operator.eq, ['a', 'b']) # a == b >>> csp.add_constraint(operator.ne, ['b', 'c']) # b != c >>> bqm = dwavebinarycsp.stitch(csp) >>> bqm.energy({'a': 0, 'b': 0, 'c': 1}) # satisfies csp -2.0 >>> bqm.energy({'a': 0, 'b': 0, 'c': 0}) # violates one constraint 0.0 >>> bqm.energy({'a': 1, 'b': 0, 'c': 0}) # violates two constraints 2.0 This example creates a binary-valued constraint satisfaction problem with two constraints, :math:`a = b` and :math:`b \\ne c`, and builds a binary quadratic model with a minimum energy gap of 4. Note that in this case the conversion to binary quadratic model adds two ancillary variables that must be minimized over when solving. >>> import dwavebinarycsp >>> import operator >>> import itertools >>> csp = dwavebinarycsp.ConstraintSatisfactionProblem(dwavebinarycsp.BINARY) >>> csp.add_constraint(operator.eq, ['a', 'b']) # a == b >>> csp.add_constraint(operator.ne, ['b', 'c']) # b != c >>> bqm = dwavebinarycsp.stitch(csp, min_classical_gap=4.0) >>> list(bqm) # # doctest: +SKIP ['a', 'aux1', 'aux0', 'b', 'c'] >>> min([bqm.energy({'a': 0, 'b': 0, 'c': 1, 'aux0': aux0, 'aux1': aux1}) for ... aux0, aux1 in list(itertools.product([0, 1], repeat=2))]) # satisfies csp -6.0 >>> min([bqm.energy({'a': 0, 'b': 0, 'c': 0, 'aux0': aux0, 'aux1': aux1}) for ... aux0, aux1 in list(itertools.product([0, 1], repeat=2))]) # violates one constraint -2.0 >>> min([bqm.energy({'a': 1, 'b': 0, 'c': 0, 'aux0': aux0, 'aux1': aux1}) for ... aux0, aux1 in list(itertools.product([0, 1], repeat=2))]) # violates two constraints 2.0 This example finds for the previous example the minimum graph size. >>> import dwavebinarycsp >>> import operator >>> csp = dwavebinarycsp.ConstraintSatisfactionProblem(dwavebinarycsp.BINARY) >>> csp.add_constraint(operator.eq, ['a', 'b']) # a == b >>> csp.add_constraint(operator.ne, ['b', 'c']) # b != c >>> for n in range(8, 1, -1): ... try: ... bqm = dwavebinarycsp.stitch(csp, min_classical_gap=4.0, max_graph_size=n) ... except dwavebinarycsp.exceptions.ImpossibleBQM: ... print(n+1) ... 3 """ # ensure we have penaltymodel factory available try: dwavebinarycsp.assert_penaltymodel_factory_available() except AssertionError as e: raise RuntimeError(e) def aux_factory(): for i in count(): yield 'aux{}'.format(i) aux = aux_factory() bqm = dimod.BinaryQuadraticModel.empty(csp.vartype) # developer note: we could cache them and relabel, for now though let's do the simple thing # penalty_models = {} for const in csp.constraints: configurations = const.configurations if len(const.variables) > max_graph_size: msg = ("The given csp contains a constraint {const} with {num_var} variables. " "This cannot be mapped to a graph with {max_graph_size} nodes. " "Consider checking whether your constraint is irreducible." "").format(const=const, num_var=len(const.variables), max_graph_size=max_graph_size) raise ImpossibleBQM(msg) pmodel = None if len(const) == 0: # empty constraint continue if min_classical_gap <= 2.0: if len(const) == 1 and max_graph_size >= 1: bqm.update(_bqm_from_1sat(const)) continue elif len(const) == 2 and max_graph_size >= 2: bqm.update(_bqm_from_2sat(const)) continue # developer note: we could cache them and relabel, for now though let's do the simple thing # if configurations in penalty_models: # raise NotImplementedError for G in iter_complete_graphs(const.variables, max_graph_size + 1, aux): # construct a specification spec = pm.Specification( graph=G, decision_variables=const.variables, feasible_configurations=configurations, min_classical_gap=min_classical_gap, vartype=csp.vartype ) # try to use the penaltymodel ecosystem try: pmodel = pm.get_penalty_model(spec) except pm.ImpossiblePenaltyModel: # hopefully adding more variables will make it possible continue if pmodel.classical_gap >= min_classical_gap: break # developer note: we could cache them and relabel, for now though let's do the simple thing # penalty_models[configurations] = pmodel else: msg = ("No penalty model can be build for constraint {}".format(const)) raise ImpossibleBQM(msg) bqm.update(pmodel.model) return bqm

Build a binary quadratic model with minimal energy levels at solutions to the specified constraint satisfaction problem. Args: csp (:obj:`.ConstraintSatisfactionProblem`): Constraint satisfaction problem. min_classical_gap (float, optional, default=2.0): Minimum energy gap from ground. Each constraint violated by the solution increases the energy level of the binary quadratic model by at least this much relative to ground energy. max_graph_size (int, optional, default=8): Maximum number of variables in the binary quadratic model that can be used to represent a single constraint. Returns: :class:`~dimod.BinaryQuadraticModel` Notes: For a `min_classical_gap` > 2 or constraints with more than two variables, requires access to factories from the penaltymodel_ ecosystem to construct the binary quadratic model. .. _penaltymodel: https://github.com/dwavesystems/penaltymodel Examples: This example creates a binary-valued constraint satisfaction problem with two constraints, :math:`a = b` and :math:`b \\ne c`, and builds a binary quadratic model with a minimum energy level of -2 such that each constraint violation by a solution adds the default minimum energy gap. >>> import dwavebinarycsp >>> import operator >>> csp = dwavebinarycsp.ConstraintSatisfactionProblem(dwavebinarycsp.BINARY) >>> csp.add_constraint(operator.eq, ['a', 'b']) # a == b >>> csp.add_constraint(operator.ne, ['b', 'c']) # b != c >>> bqm = dwavebinarycsp.stitch(csp) >>> bqm.energy({'a': 0, 'b': 0, 'c': 1}) # satisfies csp -2.0 >>> bqm.energy({'a': 0, 'b': 0, 'c': 0}) # violates one constraint 0.0 >>> bqm.energy({'a': 1, 'b': 0, 'c': 0}) # violates two constraints 2.0 This example creates a binary-valued constraint satisfaction problem with two constraints, :math:`a = b` and :math:`b \\ne c`, and builds a binary quadratic model with a minimum energy gap of 4. Note that in this case the conversion to binary quadratic model adds two ancillary variables that must be minimized over when solving. >>> import dwavebinarycsp >>> import operator >>> import itertools >>> csp = dwavebinarycsp.ConstraintSatisfactionProblem(dwavebinarycsp.BINARY) >>> csp.add_constraint(operator.eq, ['a', 'b']) # a == b >>> csp.add_constraint(operator.ne, ['b', 'c']) # b != c >>> bqm = dwavebinarycsp.stitch(csp, min_classical_gap=4.0) >>> list(bqm) # # doctest: +SKIP ['a', 'aux1', 'aux0', 'b', 'c'] >>> min([bqm.energy({'a': 0, 'b': 0, 'c': 1, 'aux0': aux0, 'aux1': aux1}) for ... aux0, aux1 in list(itertools.product([0, 1], repeat=2))]) # satisfies csp -6.0 >>> min([bqm.energy({'a': 0, 'b': 0, 'c': 0, 'aux0': aux0, 'aux1': aux1}) for ... aux0, aux1 in list(itertools.product([0, 1], repeat=2))]) # violates one constraint -2.0 >>> min([bqm.energy({'a': 1, 'b': 0, 'c': 0, 'aux0': aux0, 'aux1': aux1}) for ... aux0, aux1 in list(itertools.product([0, 1], repeat=2))]) # violates two constraints 2.0 This example finds for the previous example the minimum graph size. >>> import dwavebinarycsp >>> import operator >>> csp = dwavebinarycsp.ConstraintSatisfactionProblem(dwavebinarycsp.BINARY) >>> csp.add_constraint(operator.eq, ['a', 'b']) # a == b >>> csp.add_constraint(operator.ne, ['b', 'c']) # b != c >>> for n in range(8, 1, -1): ... try: ... bqm = dwavebinarycsp.stitch(csp, min_classical_gap=4.0, max_graph_size=n) ... except dwavebinarycsp.exceptions.ImpossibleBQM: ... print(n+1) ... 3

python

valid

rochacbruno/dynaconf

docs/customexts/aafig.py

https://github.com/rochacbruno/dynaconf/blob/5a7cc8f8252251cbdf4f4112965801f9dfe2831d/docs/customexts/aafig.py#L153-L212

def render_aafigure(app, text, options): """ Render an ASCII art figure into the requested format output file. """ if aafigure is None: raise AafigError('aafigure module not installed') fname = get_basename(text, options) fname = '%s.%s' % (get_basename(text, options), options['format']) if app.builder.format == 'html': # HTML imgpath = relative_uri(app.builder.env.docname, '_images') relfn = posixpath.join(imgpath, fname) outfn = path.join(app.builder.outdir, '_images', fname) else: # Non-HTML if app.builder.format != 'latex': app.builder.warn('aafig: the builder format %s is not officially ' 'supported, aafigure images could not work. Please report ' 'problems and working builder to avoid this warning in ' 'the future' % app.builder.format) relfn = fname outfn = path.join(app.builder.outdir, fname) metadata_fname = '%s.aafig' % outfn try: if path.isfile(outfn): extra = None if options['format'].lower() == 'svg': f = None try: try: f = open(metadata_fname, 'r') extra = f.read() except: raise AafigError() finally: if f is not None: f.close() return relfn, outfn, id, extra except AafigError: pass ensuredir(path.dirname(outfn)) try: (visitor, output) = aafigure.render(text, outfn, options) output.close() except aafigure.UnsupportedFormatError as e: raise AafigError(str(e)) extra = None if options['format'].lower() == 'svg': extra = visitor.get_size_attrs() f = open(metadata_fname, 'w') f.write(extra) f.close() return relfn, outfn, id, extra

Render an ASCII art figure into the requested format output file.

python

train

sirfoga/pyhal

hal/data/linked_list.py

https://github.com/sirfoga/pyhal/blob/4394d8a1f7e45bea28a255ec390f4962ee64d33a/hal/data/linked_list.py#L141-L163

def remove_last(self): """Removes last :return: True iff last element has been removed """ if self.length() <= 1: self.head = None return True node = self.head while node is not None: is_last_but_one = node.next_node is not None and \ node.next_node.next_node is None print(node.val, is_last_but_one) if is_last_but_one: # this is the last node.next_node = None # get to last but one element return True node = node.next_node return False

Removes last :return: True iff last element has been removed

python

train

dslackw/slpkg

slpkg/sbo/network.py

https://github.com/dslackw/slpkg/blob/dd2e08a80e944d337d157b992167ba631a4343de/slpkg/sbo/network.py#L388-L394

def install(self): """Install SBo package found in /tmp directory. """ binary = slack_package(self.prgnam) print("[ {0}Installing{1} ] --> {2}".format(self.green, self.endc, self.name)) PackageManager(binary).upgrade(flag="--install-new")

Install SBo package found in /tmp directory.

python

train

pyviz/holoviews

holoviews/operation/datashader.py

https://github.com/pyviz/holoviews/blob/ae0dd2f3de448b0ca5e9065aabd6ef8d84c7e655/holoviews/operation/datashader.py#L270-L340

def get_agg_data(cls, obj, category=None): """ Reduces any Overlay or NdOverlay of Elements into a single xarray Dataset that can be aggregated. """ paths = [] if isinstance(obj, Graph): obj = obj.edgepaths kdims = list(obj.kdims) vdims = list(obj.vdims) dims = obj.dimensions()[:2] if isinstance(obj, Path): glyph = 'line' for p in obj.split(datatype='dataframe'): paths.append(p) elif isinstance(obj, CompositeOverlay): element = None for key, el in obj.data.items(): x, y, element, glyph = cls.get_agg_data(el) dims = (x, y) df = PandasInterface.as_dframe(element) if isinstance(obj, NdOverlay): df = df.assign(**dict(zip(obj.dimensions('key', True), key))) paths.append(df) if element is None: dims = None else: kdims += element.kdims vdims = element.vdims elif isinstance(obj, Element): glyph = 'line' if isinstance(obj, Curve) else 'points' paths.append(PandasInterface.as_dframe(obj)) if dims is None or len(dims) != 2: return None, None, None, None else: x, y = dims if len(paths) > 1: if glyph == 'line': path = paths[0][:1] if isinstance(path, dd.DataFrame): path = path.compute() empty = path.copy() empty.iloc[0, :] = (np.NaN,) * empty.shape[1] paths = [elem for p in paths for elem in (p, empty)][:-1] if all(isinstance(path, dd.DataFrame) for path in paths): df = dd.concat(paths) else: paths = [p.compute() if isinstance(p, dd.DataFrame) else p for p in paths] df = pd.concat(paths) else: df = paths[0] if paths else pd.DataFrame([], columns=[x.name, y.name]) if category and df[category].dtype.name != 'category': df[category] = df[category].astype('category') is_dask = isinstance(df, dd.DataFrame) if any((not is_dask and len(df[d.name]) and isinstance(df[d.name].values[0], cftime_types)) or df[d.name].dtype.kind == 'M' for d in (x, y)): df = df.copy() for d in (x, y): vals = df[d.name] if not is_dask and len(vals) and isinstance(vals.values[0], cftime_types): vals = cftime_to_timestamp(vals, 'ns') elif df[d.name].dtype.kind == 'M': vals = vals.astype('datetime64[ns]') else: continue df[d.name] = vals.astype('int64') return x, y, Dataset(df, kdims=kdims, vdims=vdims), glyph

Reduces any Overlay or NdOverlay of Elements into a single xarray Dataset that can be aggregated.

python

train

data-8/datascience

datascience/tables.py

https://github.com/data-8/datascience/blob/4cee38266903ca169cea4a53b8cc39502d85c464/datascience/tables.py#L246-L285

def column(self, index_or_label): """Return the values of a column as an array. table.column(label) is equivalent to table[label]. >>> tiles = Table().with_columns( ... 'letter', make_array('c', 'd'), ... 'count', make_array(2, 4), ... ) >>> list(tiles.column('letter')) ['c', 'd'] >>> tiles.column(1) array([2, 4]) Args: label (int or str): The index or label of a column Returns: An instance of ``numpy.array``. Raises: ``ValueError``: When the ``index_or_label`` is not in the table. """ if (isinstance(index_or_label, str) and index_or_label not in self.labels): raise ValueError( 'The column "{}" is not in the table. The table contains ' 'these columns: {}' .format(index_or_label, ', '.join(self.labels)) ) if (isinstance(index_or_label, int) and not 0 <= index_or_label < len(self.labels)): raise ValueError( 'The index {} is not in the table. Only indices between ' '0 and {} are valid' .format(index_or_label, len(self.labels) - 1) ) return self._columns[self._as_label(index_or_label)]

Return the values of a column as an array. table.column(label) is equivalent to table[label]. >>> tiles = Table().with_columns( ... 'letter', make_array('c', 'd'), ... 'count', make_array(2, 4), ... ) >>> list(tiles.column('letter')) ['c', 'd'] >>> tiles.column(1) array([2, 4]) Args: label (int or str): The index or label of a column Returns: An instance of ``numpy.array``. Raises: ``ValueError``: When the ``index_or_label`` is not in the table.

python

train

stephen-bunn/file-config

src/file_config/contrib/ini_parser.py

https://github.com/stephen-bunn/file-config/blob/93429360c949985202e1f2b9cd0340731819ba75/src/file_config/contrib/ini_parser.py#L81-L117

def _build_dict( cls, parser_dict, delimiter=DEFAULT_DELIMITER, dict_type=collections.OrderedDict ): """ Builds a dictionary of ``dict_type`` given the ``parser._sections`` dict. :param dict parser_dict: The ``parser._sections`` mapping :param str delimiter: The delimiter for nested dictionaries, defaults to ":", optional :param class dict_type: The dictionary type to use for building the dict, defaults to :class:`collections.OrderedDict`, optional :return: The resulting dictionary :rtype: dict """ result = dict_type() for (key, value) in parser_dict.items(): if isinstance(value, dict): nestings = key.split(delimiter) # build nested dictionaries if they don't exist (up to 2nd to last key) base_dict = result for nested_key in nestings[:-1]: if nested_key not in base_dict: base_dict[nested_key] = dict_type() base_dict = base_dict[nested_key] base_dict[nestings[-1]] = cls._build_dict( parser_dict.get(key), delimiter=delimiter, dict_type=dict_type ) else: if "\n" in value: result[key] = [ cls._decode_var(_) for _ in value.lstrip("\n").split("\n") ] else: result[key] = cls._decode_var(value) return result

Builds a dictionary of ``dict_type`` given the ``parser._sections`` dict. :param dict parser_dict: The ``parser._sections`` mapping :param str delimiter: The delimiter for nested dictionaries, defaults to ":", optional :param class dict_type: The dictionary type to use for building the dict, defaults to :class:`collections.OrderedDict`, optional :return: The resulting dictionary :rtype: dict

python

train

vaexio/vaex

packages/vaex-core/vaex/export.py

https://github.com/vaexio/vaex/blob/a45b672f8287afca2ada8e36b74b604b9b28dd85/packages/vaex-core/vaex/export.py#L270-L330

def export_fits(dataset, path, column_names=None, shuffle=False, selection=False, progress=None, virtual=True, sort=None, ascending=True): """ :param DatasetLocal dataset: dataset to export :param str path: path for file :param lis[str] column_names: list of column names to export or None for all columns :param bool shuffle: export rows in random order :param bool selection: export selection or not :param progress: progress callback that gets a progress fraction as argument and should return True to continue, or a default progress bar when progress=True :param: bool virtual: When True, export virtual columns :return: """ if shuffle: random_index_name = "random_index" while random_index_name in dataset.get_column_names(): random_index_name += "_new" column_names = column_names or dataset.get_column_names(virtual=virtual, strings=True) logger.debug("exporting columns(fits): %r" % column_names) N = len(dataset) if not selection else dataset.selected_length(selection) data_types = [] data_shapes = [] ucds = [] units = [] for column_name in column_names: if column_name in dataset.get_column_names(strings=True): column = dataset.columns[column_name] shape = (N,) + column.shape[1:] dtype = column.dtype if dataset.dtype(column_name) == str_type: max_length = dataset[column_name].apply(lambda x: len(x)).max(selection=selection) dtype = np.dtype('S'+str(int(max_length))) else: dtype = np.float64().dtype shape = (N,) ucds.append(dataset.ucds.get(column_name)) units.append(dataset.units.get(column_name)) data_types.append(dtype) data_shapes.append(shape) if shuffle: column_names.append(random_index_name) data_types.append(np.int64().dtype) data_shapes.append((N,)) ucds.append(None) units.append(None) else: random_index_name = None # TODO: all expressions can have missing values.. how to support that? null_values = {key: dataset.columns[key].fill_value for key in dataset.get_column_names() if dataset.is_masked(key) and dataset.dtype(key).kind != "f"} vaex.file.colfits.empty(path, N, column_names, data_types, data_shapes, ucds, units, null_values=null_values) if shuffle: del column_names[-1] del data_types[-1] del data_shapes[-1] dataset_output = vaex.file.other.FitsBinTable(path, write=True) _export(dataset_input=dataset, dataset_output=dataset_output, path=path, random_index_column=random_index_name, column_names=column_names, selection=selection, shuffle=shuffle, progress=progress, sort=sort, ascending=ascending) dataset_output.close_files()

:param DatasetLocal dataset: dataset to export :param str path: path for file :param lis[str] column_names: list of column names to export or None for all columns :param bool shuffle: export rows in random order :param bool selection: export selection or not :param progress: progress callback that gets a progress fraction as argument and should return True to continue, or a default progress bar when progress=True :param: bool virtual: When True, export virtual columns :return:

python

test

helium/helium-python

helium/resource.py

https://github.com/helium/helium-python/blob/db73480b143da4fc48e95c4414bd69c576a3a390/helium/resource.py#L201-L225

def find(cls, session, resource_id, include=None): """Retrieve a single resource. This should only be called from sub-classes. Args: session(Session): The session to find the resource in resource_id: The ``id`` for the resource to look up Keyword Args: include: Resource classes to include Returns: Resource: An instance of a resource, or throws a :class:`NotFoundError` if the resource can not be found. """ url = session._build_url(cls._resource_path(), resource_id) params = build_request_include(include, None) process = cls._mk_one(session, include=include) return session.get(url, CB.json(200, process), params=params)

Retrieve a single resource. This should only be called from sub-classes. Args: session(Session): The session to find the resource in resource_id: The ``id`` for the resource to look up Keyword Args: include: Resource classes to include Returns: Resource: An instance of a resource, or throws a :class:`NotFoundError` if the resource can not be found.

python

train

Anaconda-Platform/anaconda-client

binstar_client/inspect_package/pypi.py

https://github.com/Anaconda-Platform/anaconda-client/blob/b276f0572744c73c184a8b43a897cfa7fc1dc523/binstar_client/inspect_package/pypi.py#L111-L126

def get_header_description(filedata): """Get description from metadata file and remove any empty lines at end.""" python_version = sys.version_info.major if python_version == 3: filedata = Parser().parsestr(filedata) else: filedata = Parser().parsestr(filedata.encode("UTF-8", "replace")) payload = filedata.get_payload() lines = payload.split('\n') while True: if lines and lines[-1] == '': lines.pop() else: break return '\n'.join(lines)

Get description from metadata file and remove any empty lines at end.

python

train

Fantomas42/django-blog-zinnia

zinnia/templatetags/zinnia.py

https://github.com/Fantomas42/django-blog-zinnia/blob/b4949304b104a8e1a7a7a0773cbfd024313c3a15/zinnia/templatetags/zinnia.py#L411-L420

def week_number(date): """ Return the Python week number of a date. The django \|date:"W" returns incompatible value with the view implementation. """ week_number = date.strftime('%W') if int(week_number) < 10: week_number = week_number[-1] return week_number

Return the Python week number of a date. The django \|date:"W" returns incompatible value with the view implementation.

python

train

tornadoweb/tornado

tornado/gen.py

https://github.com/tornadoweb/tornado/blob/b8b481770bcdb333a69afde5cce7eaa449128326/tornado/gen.py#L619-L637

def sleep(duration: float) -> "Future[None]": """Return a `.Future` that resolves after the given number of seconds. When used with ``yield`` in a coroutine, this is a non-blocking analogue to `time.sleep` (which should not be used in coroutines because it is blocking):: yield gen.sleep(0.5) Note that calling this function on its own does nothing; you must wait on the `.Future` it returns (usually by yielding it). .. versionadded:: 4.1 """ f = _create_future() IOLoop.current().call_later( duration, lambda: future_set_result_unless_cancelled(f, None) ) return f

Return a `.Future` that resolves after the given number of seconds. When used with ``yield`` in a coroutine, this is a non-blocking analogue to `time.sleep` (which should not be used in coroutines because it is blocking):: yield gen.sleep(0.5) Note that calling this function on its own does nothing; you must wait on the `.Future` it returns (usually by yielding it). .. versionadded:: 4.1

python

train

apache/incubator-mxnet

python/mxnet/image/image.py

https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/image/image.py#L86-L140

def imresize(src, w, h, *args, **kwargs): r"""Resize image with OpenCV. .. note:: `imresize` uses OpenCV (not the CV2 Python library). MXNet must have been built with USE_OPENCV=1 for `imresize` to work. Parameters ---------- src : NDArray source image w : int, required Width of resized image. h : int, required Height of resized image. interp : int, optional, default=1 Interpolation method (default=cv2.INTER_LINEAR). Possible values: 0: Nearest Neighbors Interpolation. 1: Bilinear interpolation. 2: Area-based (resampling using pixel area relation). It may be a preferred method for image decimation, as it gives moire-free results. But when the image is zoomed, it is similar to the Nearest Neighbors method. (used by default). 3: Bicubic interpolation over 4x4 pixel neighborhood. 4: Lanczos interpolation over 8x8 pixel neighborhood. 9: Cubic for enlarge, area for shrink, bilinear for others 10: Random select from interpolation method metioned above. Note: When shrinking an image, it will generally look best with AREA-based interpolation, whereas, when enlarging an image, it will generally look best with Bicubic (slow) or Bilinear (faster but still looks OK). More details can be found in the documentation of OpenCV, please refer to http://docs.opencv.org/master/da/d54/group__imgproc__transform.html. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. Example ------- >>> with open("flower.jpeg", 'rb') as fp: ... str_image = fp.read() ... >>> image = mx.img.imdecode(str_image) >>> image <NDArray 2321x3482x3 @cpu(0)> >>> new_image = mx.img.resize(image, 240, 360) >>> new_image <NDArray 240x360x3 @cpu(0)> """ return _internal._cvimresize(src, w, h, *args, **kwargs)

r"""Resize image with OpenCV. .. note:: `imresize` uses OpenCV (not the CV2 Python library). MXNet must have been built with USE_OPENCV=1 for `imresize` to work. Parameters ---------- src : NDArray source image w : int, required Width of resized image. h : int, required Height of resized image. interp : int, optional, default=1 Interpolation method (default=cv2.INTER_LINEAR). Possible values: 0: Nearest Neighbors Interpolation. 1: Bilinear interpolation. 2: Area-based (resampling using pixel area relation). It may be a preferred method for image decimation, as it gives moire-free results. But when the image is zoomed, it is similar to the Nearest Neighbors method. (used by default). 3: Bicubic interpolation over 4x4 pixel neighborhood. 4: Lanczos interpolation over 8x8 pixel neighborhood. 9: Cubic for enlarge, area for shrink, bilinear for others 10: Random select from interpolation method metioned above. Note: When shrinking an image, it will generally look best with AREA-based interpolation, whereas, when enlarging an image, it will generally look best with Bicubic (slow) or Bilinear (faster but still looks OK). More details can be found in the documentation of OpenCV, please refer to http://docs.opencv.org/master/da/d54/group__imgproc__transform.html. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. Example ------- >>> with open("flower.jpeg", 'rb') as fp: ... str_image = fp.read() ... >>> image = mx.img.imdecode(str_image) >>> image <NDArray 2321x3482x3 @cpu(0)> >>> new_image = mx.img.resize(image, 240, 360) >>> new_image <NDArray 240x360x3 @cpu(0)>

python

train

cloud9ers/gurumate

environment/lib/python2.7/site-packages/IPython/parallel/client/asyncresult.py

https://github.com/cloud9ers/gurumate/blob/075dc74d1ee62a8c6b7a8bf2b271364f01629d1e/environment/lib/python2.7/site-packages/IPython/parallel/client/asyncresult.py#L443-L543

def display_outputs(self, groupby="type"): """republish the outputs of the computation Parameters ---------- groupby : str [default: type] if 'type': Group outputs by type (show all stdout, then all stderr, etc.): [stdout:1] foo [stdout:2] foo [stderr:1] bar [stderr:2] bar if 'engine': Display outputs for each engine before moving on to the next: [stdout:1] foo [stderr:1] bar [stdout:2] foo [stderr:2] bar if 'order': Like 'type', but further collate individual displaypub outputs. This is meant for cases of each command producing several plots, and you would like to see all of the first plots together, then all of the second plots, and so on. """ if self._single_result: self._display_single_result() return stdouts = self.stdout stderrs = self.stderr pyouts = self.pyout output_lists = self.outputs results = self.get() targets = self.engine_id if groupby == "engine": for eid,stdout,stderr,outputs,r,pyout in zip( targets, stdouts, stderrs, output_lists, results, pyouts ): self._display_stream(stdout, '[stdout:%i] ' % eid) self._display_stream(stderr, '[stderr:%i] ' % eid, file=sys.stderr) try: get_ipython() except NameError: # displaypub is meaningless outside IPython return if outputs or pyout is not None: _raw_text('[output:%i]' % eid) for output in outputs: self._republish_displaypub(output, eid) if pyout is not None: display(r) elif groupby in ('type', 'order'): # republish stdout: for eid,stdout in zip(targets, stdouts): self._display_stream(stdout, '[stdout:%i] ' % eid) # republish stderr: for eid,stderr in zip(targets, stderrs): self._display_stream(stderr, '[stderr:%i] ' % eid, file=sys.stderr) try: get_ipython() except NameError: # displaypub is meaningless outside IPython return if groupby == 'order': output_dict = dict((eid, outputs) for eid,outputs in zip(targets, output_lists)) N = max(len(outputs) for outputs in output_lists) for i in range(N): for eid in targets: outputs = output_dict[eid] if len(outputs) >= N: _raw_text('[output:%i]' % eid) self._republish_displaypub(outputs[i], eid) else: # republish displaypub output for eid,outputs in zip(targets, output_lists): if outputs: _raw_text('[output:%i]' % eid) for output in outputs: self._republish_displaypub(output, eid) # finally, add pyout: for eid,r,pyout in zip(targets, results, pyouts): if pyout is not None: display(r) else: raise ValueError("groupby must be one of 'type', 'engine', 'collate', not %r" % groupby)

republish the outputs of the computation Parameters ---------- groupby : str [default: type] if 'type': Group outputs by type (show all stdout, then all stderr, etc.): [stdout:1] foo [stdout:2] foo [stderr:1] bar [stderr:2] bar if 'engine': Display outputs for each engine before moving on to the next: [stdout:1] foo [stderr:1] bar [stdout:2] foo [stderr:2] bar if 'order': Like 'type', but further collate individual displaypub outputs. This is meant for cases of each command producing several plots, and you would like to see all of the first plots together, then all of the second plots, and so on.

python

test

eonpatapon/contrail-api-cli

contrail_api_cli/resource.py

https://github.com/eonpatapon/contrail-api-cli/blob/1571bf523fa054f3d6bf83dba43a224fea173a73/contrail_api_cli/resource.py#L669-L686

def remove_ref(self, ref): """Remove reference from self to ref >>> iip = Resource('instance-ip', uuid='30213cf9-4b03-4afc-b8f9-c9971a216978', fetch=True) >>> for vmi in iip['virtual_machine_interface_refs']: iip.remove_ref(vmi) >>> iip['virtual_machine_interface_refs'] KeyError: u'virtual_machine_interface_refs' :param ref: reference to remove :type ref: Resource :rtype: Resource """ self.session.remove_ref(self, ref) return self.fetch()

Remove reference from self to ref >>> iip = Resource('instance-ip', uuid='30213cf9-4b03-4afc-b8f9-c9971a216978', fetch=True) >>> for vmi in iip['virtual_machine_interface_refs']: iip.remove_ref(vmi) >>> iip['virtual_machine_interface_refs'] KeyError: u'virtual_machine_interface_refs' :param ref: reference to remove :type ref: Resource :rtype: Resource

python

train

brutasse/graphite-api

graphite_api/render/glyph.py

https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/render/glyph.py#L2219-L2246

def format_units(v, step=None, system="si", units=None): """Format the given value in standardized units. ``system`` is either 'binary' or 'si' For more info, see: http://en.wikipedia.org/wiki/SI_prefix http://en.wikipedia.org/wiki/Binary_prefix """ if v is None: return 0, '' for prefix, size in UnitSystems[system]: if condition(v, size, step): v2 = v / size if v2 - math.floor(v2) < 0.00000000001 and v > 1: v2 = float(math.floor(v2)) if units: prefix = "%s%s" % (prefix, units) return v2, prefix if v - math.floor(v) < 0.00000000001 and v > 1: v = float(math.floor(v)) if units: prefix = units else: prefix = '' return v, prefix

Format the given value in standardized units. ``system`` is either 'binary' or 'si' For more info, see: http://en.wikipedia.org/wiki/SI_prefix http://en.wikipedia.org/wiki/Binary_prefix

python

train

numenta/nupic

src/nupic/frameworks/opf/opf_task_driver.py

https://github.com/numenta/nupic/blob/5922fafffdccc8812e72b3324965ad2f7d4bbdad/src/nupic/frameworks/opf/opf_task_driver.py#L120-L129

def _getImpl(self, model): """ Creates and returns the _IterationPhase-based instance corresponding to this phase specification model: Model instance """ impl = _IterationPhaseInferOnly(model=model, nIters=self.__nIters, inferenceArgs=self.__inferenceArgs) return impl

Creates and returns the _IterationPhase-based instance corresponding to this phase specification model: Model instance

python

valid

limodou/uliweb

uliweb/contrib/rbac/rbac.py

https://github.com/limodou/uliweb/blob/34472f25e4bc0b954a35346672f94e84ef18b076/uliweb/contrib/rbac/rbac.py#L42-L78

def has_role(user, *roles, **kwargs): """ Judge is the user belongs to the role, and if does, then return the role object if not then return False. kwargs will be passed to role_func. """ Role = get_model('role') if isinstance(user, (unicode, str)): User = get_model('user') user = User.get(User.c.username==user) for role in roles: if isinstance(role, (str, unicode)): role = Role.get(Role.c.name==role) if not role: continue name = role.name func = __role_funcs__.get(name, None) if func: if isinstance(func, (unicode, str)): func = import_attr(func) assert callable(func) para = kwargs.copy() para['user'] = user flag = call_func(func, para) if flag: return role flag = role.users.has(user) if flag: return role flag = role.usergroups_has_user(user) if flag: return role return False

Judge is the user belongs to the role, and if does, then return the role object if not then return False. kwargs will be passed to role_func.

python

train

arcus-io/puppetdb-python

puppetdb/v2/facts.py

https://github.com/arcus-io/puppetdb-python/blob/d772eb80a1dfb1154a1f421c7ecdc1ac951b5ea2/puppetdb/v2/facts.py#L34-L42

def get_facts_by_name(api_url=None, fact_name=None, verify=False, cert=list()): """ Returns facts by name :param api_url: Base PuppetDB API url :param fact_name: Name of fact """ return utils._make_api_request(api_url, '/facts/{0}'.format(fact_name), verify, cert)

Returns facts by name :param api_url: Base PuppetDB API url :param fact_name: Name of fact

python

train

fermiPy/fermipy

fermipy/jobs/job_archive.py

https://github.com/fermiPy/fermipy/blob/9df5e7e3728307fd58c5bba36fd86783c39fbad4/fermipy/jobs/job_archive.py#L362-L368

def make_dict(cls, table): """Build a dictionary map int to `JobDetails` from an `astropy.table.Table`""" ret_dict = {} for row in table: job_details = cls.create_from_row(row) ret_dict[job_details.dbkey] = job_details return ret_dict

Build a dictionary map int to `JobDetails` from an `astropy.table.Table`

python

train

peri-source/peri

peri/comp/psfcalc.py

https://github.com/peri-source/peri/blob/61beed5deaaf978ab31ed716e8470d86ba639867/peri/comp/psfcalc.py#L421-L462

def get_polydisp_pts_wts(kfki, sigkf, dist_type='gaussian', nkpts=3): """ Calculates a set of Gauss quadrature points & weights for polydisperse light. Returns a list of points and weights of the final wavevector's distri- bution, in units of the initial wavevector. Parameters ---------- kfki : Float The mean of the polydisperse outgoing wavevectors. sigkf : Float The standard dev. of the polydisperse outgoing wavevectors. dist_type : {`gaussian`, `gamma`}, optional The distribution, gaussian or gamma, of the wavevectors. Default is `gaussian` nkpts : Int, optional The number of quadrature points to use. Default is 3 Returns ------- kfkipts : numpy.ndarray The Gauss quadrature points at which to calculate kfki. wts : numpy.ndarray The associated Gauss quadrature weights. """ if dist_type.lower() == 'gaussian': pts, wts = np.polynomial.hermite.hermgauss(nkpts) kfkipts = np.abs(kfki + sigkf*np.sqrt(2)*pts) elif dist_type.lower() == 'laguerre' or dist_type.lower() == 'gamma': k_scale = sigkf**2/kfki associated_order = kfki**2/sigkf**2 - 1 #Associated Laguerre with alpha >~170 becomes numerically unstable, so: max_order=150 if associated_order > max_order or associated_order < (-1+1e-3): warnings.warn('Numerically unstable sigk, clipping', RuntimeWarning) associated_order = np.clip(associated_order, -1+1e-3, max_order) kfkipts, wts = la_roots(nkpts, associated_order) kfkipts *= k_scale else: raise ValueError('dist_type must be either gaussian or laguerre') return kfkipts, wts/wts.sum()

Calculates a set of Gauss quadrature points & weights for polydisperse light. Returns a list of points and weights of the final wavevector's distri- bution, in units of the initial wavevector. Parameters ---------- kfki : Float The mean of the polydisperse outgoing wavevectors. sigkf : Float The standard dev. of the polydisperse outgoing wavevectors. dist_type : {`gaussian`, `gamma`}, optional The distribution, gaussian or gamma, of the wavevectors. Default is `gaussian` nkpts : Int, optional The number of quadrature points to use. Default is 3 Returns ------- kfkipts : numpy.ndarray The Gauss quadrature points at which to calculate kfki. wts : numpy.ndarray The associated Gauss quadrature weights.

python

valid

cons3rt/pycons3rt

pycons3rt/awsapi/s3util.py

https://github.com/cons3rt/pycons3rt/blob/f004ab3a35c5bff2f698131fef3b2a8ed5a7596d/pycons3rt/awsapi/s3util.py#L305-L341

def upload_file(self, filepath, key): """Uploads a file using the passed S3 key This method uploads a file specified by the filepath to S3 using the provided S3 key. :param filepath: (str) Full path to the file to be uploaded :param key: (str) S3 key to be set for the upload :return: True if upload is successful, False otherwise. """ log = logging.getLogger(self.cls_logger + '.upload_file') log.info('Attempting to upload file %s to S3 bucket %s as key %s...', filepath, self.bucket_name, key) if not isinstance(filepath, basestring): log.error('filepath argument is not a string') return False if not isinstance(key, basestring): log.error('key argument is not a string') return False if not os.path.isfile(filepath): log.error('File not found on file system: %s', filepath) return False try: self.s3client.upload_file( Filename=filepath, Bucket=self.bucket_name, Key=key) except ClientError as e: log.error('Unable to upload file %s to bucket %s as key %s:\n%s', filepath, self.bucket_name, key, e) return False else: log.info('Successfully uploaded file to S3 bucket %s as key %s', self.bucket_name, key) return True

Uploads a file using the passed S3 key This method uploads a file specified by the filepath to S3 using the provided S3 key. :param filepath: (str) Full path to the file to be uploaded :param key: (str) S3 key to be set for the upload :return: True if upload is successful, False otherwise.

python

train

airbus-cert/mispy

mispy/misp.py

https://github.com/airbus-cert/mispy/blob/6d523d6f134d2bd38ec8264be74e73b68403da65/mispy/misp.py#L800-L811

def GET(self, path): """ Raw GET to the MISP server :param path: URL fragment (ie /events/) :returns: HTTP raw content (as seen by :class:`requests.Response`) """ url = self._absolute_url(path) resp = requests.get(url, headers=self.headers, verify=self.verify_ssl) if resp.status_code != 200: raise MispTransportError('GET %s: returned status=%d', path, resp.status_code) return resp.content

Raw GET to the MISP server :param path: URL fragment (ie /events/) :returns: HTTP raw content (as seen by :class:`requests.Response`)

python

train

intake/intake

intake/gui/base.py

https://github.com/intake/intake/blob/277b96bfdee39d8a3048ea5408c6d6716d568336/intake/gui/base.py#L202-L214

def add(self, items): """Add items to options""" options = self._create_options(items) for k, v in options.items(): if k in self.labels and v not in self.items: options.pop(k) count = 0 while f'{k}_{count}' in self.labels: count += 1 options[f'{k}_{count}'] = v self.widget.options.update(options) self.widget.param.trigger('options') self.widget.value = list(options.values())[:1]

Add items to options

python

train

mjirik/imtools

imtools/tools.py

https://github.com/mjirik/imtools/blob/eb29fa59df0e0684d8334eb3bc5ef36ea46d1d3a/imtools/tools.py#L1421-L1451

def split_to_tiles(img, columns, rows): """ Split an image into a specified number of tiles. Args: img (ndarray): The image to split. number_tiles (int): The number of tiles required. Returns: Tuple of tiles """ # validate_image(img, number_tiles) im_w, im_h = img.shape # columns, rows = calc_columns_rows(number_tiles) # extras = (columns * rows) - number_tiles tile_w, tile_h = int(np.floor(im_w / columns)), int(np.floor(im_h / rows)) tiles = [] # number = 1 for pos_y in range(0, im_h - rows, tile_h): # -rows for rounding error. for pos_x in range(0, im_w - columns, tile_w): # as above. roi = (pos_x, pos_y, pos_x + tile_w, pos_y + tile_h) # image = img.crop(area) tile = img[roi[1]:roi[3], roi[0]:roi[2]] # position = (int(floor(pos_x / tile_w)) + 1, # int(floor(pos_y / tile_h)) + 1) # coords = (pos_x, pos_y) # tile = Tile(image, number, position, coords) tiles.append(tile) # number += 1 return tuple(tiles)

Split an image into a specified number of tiles. Args: img (ndarray): The image to split. number_tiles (int): The number of tiles required. Returns: Tuple of tiles

python

train

scivision/pymap3d

pymap3d/lox.py

https://github.com/scivision/pymap3d/blob/c9cf676594611cdb52ff7e0eca6388c80ed4f63f/pymap3d/lox.py#L130-L202

def loxodrome_inverse(lat1: float, lon1: float, lat2: float, lon2: float, ell: Ellipsoid = None, deg: bool = True): """ computes the arc length and azimuth of the loxodrome between two points on the surface of the reference ellipsoid Parameters ---------- lat1 : float or numpy.ndarray of float geodetic latitude of first point lon1 : float or numpy.ndarray of float geodetic longitude of first point lat2 : float or numpy.ndarray of float geodetic latitude of second point lon2 : float or numpy.ndarray of float geodetic longitude of second point ell : Ellipsoid, optional reference ellipsoid (default WGS84) deg : bool, optional degrees input/output (False: radians in/out) Results ------- lox_s : float or numpy.ndarray of float distance along loxodrome az12 : float or numpy.ndarray of float azimuth of loxodrome (degrees/radians) Based on Deakin, R.E., 2010, 'The Loxodrome on an Ellipsoid', Lecture Notes, School of Mathematical and Geospatial Sciences, RMIT University, January 2010 [1] Bowring, B.R., 1985, 'The geometry of the loxodrome on the ellipsoid', The Canadian Surveyor, Vol. 39, No. 3, Autumn 1985, pp.223-230. [2] Snyder, J.P., 1987, Map Projections-A Working Manual. U.S. Geological Survey Professional Paper 1395. Washington, DC: U.S. Government Printing Office, pp.15-16 and pp. 44-45. [3] Thomas, P.D., 1952, Conformal Projections in Geodesy and Cartography, Special Publication No. 251, Coast and Geodetic Survey, U.S. Department of Commerce, Washington, DC: U.S. Government Printing Office, p. 66. """ # set ellipsoid parameters if ell is None: ell = Ellipsoid() if deg is True: lat1, lon1, lat2, lon2 = np.radians([lat1, lon1, lat2, lon2]) # compute isometric latitude of P1 and P2 isolat1 = isometric(lat1, deg=False, ell=ell) isolat2 = isometric(lat2, deg=False, ell=ell) # compute changes in isometric latitude and longitude between points disolat = isolat2 - isolat1 dlon = lon2 - lon1 # compute azimuth az12 = np.arctan2(dlon, disolat) # compute distance along loxodromic curve m1 = meridian_dist(lat1, deg=False, ell=ell) m2 = meridian_dist(lat2, deg=False, ell=ell) dm = m2 - m1 lox_s = dm / np.cos(az12) if deg is True: az12 = np.degrees(az12) % 360. return lox_s, az12

computes the arc length and azimuth of the loxodrome between two points on the surface of the reference ellipsoid Parameters ---------- lat1 : float or numpy.ndarray of float geodetic latitude of first point lon1 : float or numpy.ndarray of float geodetic longitude of first point lat2 : float or numpy.ndarray of float geodetic latitude of second point lon2 : float or numpy.ndarray of float geodetic longitude of second point ell : Ellipsoid, optional reference ellipsoid (default WGS84) deg : bool, optional degrees input/output (False: radians in/out) Results ------- lox_s : float or numpy.ndarray of float distance along loxodrome az12 : float or numpy.ndarray of float azimuth of loxodrome (degrees/radians) Based on Deakin, R.E., 2010, 'The Loxodrome on an Ellipsoid', Lecture Notes, School of Mathematical and Geospatial Sciences, RMIT University, January 2010 [1] Bowring, B.R., 1985, 'The geometry of the loxodrome on the ellipsoid', The Canadian Surveyor, Vol. 39, No. 3, Autumn 1985, pp.223-230. [2] Snyder, J.P., 1987, Map Projections-A Working Manual. U.S. Geological Survey Professional Paper 1395. Washington, DC: U.S. Government Printing Office, pp.15-16 and pp. 44-45. [3] Thomas, P.D., 1952, Conformal Projections in Geodesy and Cartography, Special Publication No. 251, Coast and Geodetic Survey, U.S. Department of Commerce, Washington, DC: U.S. Government Printing Office, p. 66.

python

train

robotools/extractor

Lib/extractor/formats/opentype.py

https://github.com/robotools/extractor/blob/da3c2c92bfd3da863dd5de29bd8bc94cbbf433df/Lib/extractor/formats/opentype.py#L702-L711

def _replaceRenamedPairMembers(kerning, leftRename, rightRename): """ Populate the renamed pair members into the kerning. """ renamedKerning = {} for (left, right), value in kerning.items(): left = leftRename.get(left, left) right = rightRename.get(right, right) renamedKerning[left, right] = value return renamedKerning

Populate the renamed pair members into the kerning.

python

train

ylogx/universal

universal/builder.py

https://github.com/ylogx/universal/blob/1be04c2e828d9f97a94d48bff64031b14c2b8463/universal/builder.py#L41-L53

def compile_files(args, mem_test=False): ''' Copiles the files and runs memory tests if needed. PARAM args: list of files passed as CMD args to be compiled. PARAM mem_test: Weither to perform memory test ? ''' for filename in args: if not os.path.isfile(filename): print('The file doesn\'t exits') return build_and_run_file(filename) print("")

Copiles the files and runs memory tests if needed. PARAM args: list of files passed as CMD args to be compiled. PARAM mem_test: Weither to perform memory test ?

python

train

Microsoft/azure-devops-python-api

azure-devops/azure/devops/v5_1/git/git_client_base.py

https://github.com/Microsoft/azure-devops-python-api/blob/4777ffda2f5052fabbaddb2abe9cb434e0cf1aa8/azure-devops/azure/devops/v5_1/git/git_client_base.py#L405-L429

def get_commit(self, commit_id, repository_id, project=None, change_count=None): """GetCommit. [Preview API] Retrieve a particular commit. :param str commit_id: The id of the commit. :param str repository_id: The id or friendly name of the repository. To use the friendly name, projectId must also be specified. :param str project: Project ID or project name :param int change_count: The number of changes to include in the result. :rtype: :class:`<GitCommit> <azure.devops.v5_1.git.models.GitCommit>` """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if commit_id is not None: route_values['commitId'] = self._serialize.url('commit_id', commit_id, 'str') if repository_id is not None: route_values['repositoryId'] = self._serialize.url('repository_id', repository_id, 'str') query_parameters = {} if change_count is not None: query_parameters['changeCount'] = self._serialize.query('change_count', change_count, 'int') response = self._send(http_method='GET', location_id='c2570c3b-5b3f-41b8-98bf-5407bfde8d58', version='5.1-preview.1', route_values=route_values, query_parameters=query_parameters) return self._deserialize('GitCommit', response)

GetCommit. [Preview API] Retrieve a particular commit. :param str commit_id: The id of the commit. :param str repository_id: The id or friendly name of the repository. To use the friendly name, projectId must also be specified. :param str project: Project ID or project name :param int change_count: The number of changes to include in the result. :rtype: :class:`<GitCommit> <azure.devops.v5_1.git.models.GitCommit>`

python

train

kylef/refract.py

refract/elements/base.py

https://github.com/kylef/refract.py/blob/f58ddf619038b580ab50c2e7f867d59d153eabbb/refract/elements/base.py#L104-L132

def defract(self): """ Returns the underlying (unrefracted) value of element >>> Element(content='Hello').defract 'Hello' >>> Element(content=Element(content='Hello')).defract 'Hello' >>> Element(content=[Element(content='Hello')]).defract ['Hello'] """ from refract.elements.object import Object def get_value(item): if isinstance(item, KeyValuePair): return (get_value(item.key), get_value(item.value)) elif isinstance(item, list): return [get_value(element) for element in item] elif isinstance(item, Element): if isinstance(item, Object) or item.element == 'object': return dict(get_value(item.content)) return get_value(item.content) return item return get_value(self)

Returns the underlying (unrefracted) value of element >>> Element(content='Hello').defract 'Hello' >>> Element(content=Element(content='Hello')).defract 'Hello' >>> Element(content=[Element(content='Hello')]).defract ['Hello']

python

train

obriencj/python-javatools

javatools/report.py

https://github.com/obriencj/python-javatools/blob/9e2332b452ddc508bed0615937dddcb2cf051557/javatools/report.py#L81-L93

def add_formats_by_name(self, rfmt_list): """ adds formats by short label descriptors, such as 'txt', 'json', or 'html' """ for fmt in rfmt_list: if fmt == "json": self.add_report_format(JSONReportFormat) elif fmt in ("txt", "text"): self.add_report_format(TextReportFormat) elif fmt in ("htm", "html"): self.add_report_format(CheetahReportFormat)

adds formats by short label descriptors, such as 'txt', 'json', or 'html'

python

train

openthread/openthread

tools/harness-automation/autothreadharness/harness_case.py

https://github.com/openthread/openthread/blob/0208d10563aa21c518092985c78ecf9cd223ab74/tools/harness-automation/autothreadharness/harness_case.py#L262-L285

def _init_browser(self): """Open harness web page. Open a quiet chrome which: 1. disables extensions, 2. ignore certificate errors and 3. always allow notifications. """ chrome_options = webdriver.ChromeOptions() chrome_options.add_argument('--disable-extensions') chrome_options.add_argument('--disable-infobars') chrome_options.add_argument('--ignore-certificate-errors') chrome_options.add_experimental_option('prefs', { 'profile.managed_default_content_settings.notifications': 1 }) browser = webdriver.Chrome(chrome_options=chrome_options) browser.set_page_load_timeout(10) browser.implicitly_wait(1) browser.maximize_window() browser.get(settings.HARNESS_URL) self._browser = browser if not wait_until(lambda: 'Thread' in browser.title, 30): self.assertIn('Thread', browser.title)

Open harness web page. Open a quiet chrome which: 1. disables extensions, 2. ignore certificate errors and 3. always allow notifications.

python

train

twisted/axiom

axiom/sequence.py

https://github.com/twisted/axiom/blob/7de70bc8fe1bb81f9c2339fba8daec9eb2e92b68/axiom/sequence.py#L40-L60

def _fixIndex(self, index, truncate=False): """ @param truncate: If true, negative indices which go past the beginning of the list will be evaluated as zero. For example:: >>> L = List([1,2,3,4,5]) >>> len(L) 5 >>> L._fixIndex(-9, truncate=True) 0 """ assert not isinstance(index, slice), 'slices are not supported (yet)' if index < 0: index += self.length if index < 0: if not truncate: raise IndexError('stored List index out of range') else: index = 0 return index

@param truncate: If true, negative indices which go past the beginning of the list will be evaluated as zero. For example:: >>> L = List([1,2,3,4,5]) >>> len(L) 5 >>> L._fixIndex(-9, truncate=True) 0

python

train

inasafe/inasafe

safe/utilities/settings.py

https://github.com/inasafe/inasafe/blob/831d60abba919f6d481dc94a8d988cc205130724/safe/utilities/settings.py#L42-L58

def set_general_setting(key, value, qsettings=None): """Set value to QSettings based on key. :param key: Unique key for setting. :type key: basestring :param value: Value to be saved. :type value: QVariant :param qsettings: A custom QSettings to use. If it's not defined, it will use the default one. :type qsettings: qgis.PyQt.QtCore.QSettings """ if not qsettings: qsettings = QSettings() qsettings.setValue(key, deep_convert_dict(value))

Set value to QSettings based on key. :param key: Unique key for setting. :type key: basestring :param value: Value to be saved. :type value: QVariant :param qsettings: A custom QSettings to use. If it's not defined, it will use the default one. :type qsettings: qgis.PyQt.QtCore.QSettings

python

train

helixyte/everest

everest/resources/utils.py

https://github.com/helixyte/everest/blob/70c9b93c3061db5cb62428349d18b8fb8566411b/everest/resources/utils.py#L176-L183

def get_registered_collection_resources(): """ Returns a list of all registered collection resource classes. """ reg = get_current_registry() return [util.component for util in reg.registeredUtilities() if util.name == 'collection-class']

Returns a list of all registered collection resource classes.

python

train

jaywink/federation

federation/protocols/activitypub/protocol.py

https://github.com/jaywink/federation/blob/59d31bb37e662891dbea72c1dee05dc53146c78b/federation/protocols/activitypub/protocol.py#L26-L37

def identify_request(request: RequestType) -> bool: """ Try to identify whether this is an ActivityPub request. """ # noinspection PyBroadException try: data = json.loads(decode_if_bytes(request.body)) if "@context" in data: return True except Exception: pass return False

Try to identify whether this is an ActivityPub request.

python

train

dylanaraps/pywal

pywal/colors.py

https://github.com/dylanaraps/pywal/blob/c823e3c9dbd0100ca09caf824e77d296685a1c1e/pywal/colors.py#L86-L94

def cache_fname(img, backend, light, cache_dir, sat=""): """Create the cache file name.""" color_type = "light" if light else "dark" file_name = re.sub("[/|\\|.]", "_", img) file_size = os.path.getsize(img) file_parts = [file_name, color_type, backend, sat, file_size, __cache_version__] return [cache_dir, "schemes", "%s_%s_%s_%s_%s_%s.json" % (*file_parts,)]

Create the cache file name.

python

train

aegirhall/console-menu

consolemenu/prompt_utils.py

https://github.com/aegirhall/console-menu/blob/1a28959d6f1dd6ac79c87b11efd8529d05532422/consolemenu/prompt_utils.py#L116-L130

def input_password(self, message=None): """ Prompt the user for a password. This is equivalent to the input() method, but does not echo inputted characters to the screen. :param message: the prompt message. """ message = self.__prompt_formatter.format_prompt(message) try: if message: return getpass.getpass(message) else: return getpass.getpass() except BaseException: self.__screen.println('Warning: Unable to mask input; characters will be echoed to console') return self.input(message)

Prompt the user for a password. This is equivalent to the input() method, but does not echo inputted characters to the screen. :param message: the prompt message.

python

train

open-homeautomation/pknx

knxip/core.py

https://github.com/open-homeautomation/pknx/blob/a8aed8271563923c447aa330ba7c1c2927286f7a/knxip/core.py#L141-L162

def to_frame(self): """Convert the object to its frame format.""" self.sanitize() res = [] res.append((1 << 7) + (1 << 4) + (self.repeat << 5) + (self.priority << 2)) res.append(self.src_addr >> 8) res.append(self.src_addr % 0x100) res.append(self.dst_addr >> 8) res.append(self.dst_addr % 0x100) res.append((self.multicast << 7) + (self.routing << 4) + self.length) for i in range(0, self.length - 1): res.append(self.data[i]) checksum = 0 for i in range(0, 5 + self.length): checksum += res[i] res.append(checksum % 0x100) return bytearray(res)

Convert the object to its frame format.

python

train

duguyue100/minesweeper

minesweeper/msboard.py

https://github.com/duguyue100/minesweeper/blob/38b1910f4c34d0275ac10a300285aba6f1d91d61/minesweeper/msboard.py#L144-L155

def check_board(self): """Check the board status and give feedback.""" num_mines = np.sum(self.info_map == 12) num_undiscovered = np.sum(self.info_map == 11) num_questioned = np.sum(self.info_map == 10) if num_mines > 0: return 0 elif np.array_equal(self.info_map == 9, self.mine_map): return 1 elif num_undiscovered > 0 or num_questioned > 0: return 2

Check the board status and give feedback.

python

train

Capitains/flask-capitains-nemo

flask_nemo/common.py

https://github.com/Capitains/flask-capitains-nemo/blob/8d91f2c05b925a6c8ea8c997baf698c87257bc58/flask_nemo/common.py#L12-L21

def resource_qualifier(resource): """ Split a resource in (filename, directory) tuple with taking care of external resources :param resource: A file path or a URI :return: (Filename, Directory) for files, (URI, None) for URI """ if resource.startswith("//") or resource.startswith("http"): return resource, None else: return reversed(op.split(resource))

Split a resource in (filename, directory) tuple with taking care of external resources :param resource: A file path or a URI :return: (Filename, Directory) for files, (URI, None) for URI

python

valid

slackapi/python-slackclient

tutorial/PythOnBoardingBot/app.py

https://github.com/slackapi/python-slackclient/blob/901341c0284fd81e6d2719d6a0502308760d83e4/tutorial/PythOnBoardingBot/app.py#L38-L53

def onboarding_message(**payload): """Create and send an onboarding welcome message to new users. Save the time stamp of this message so we can update this message in the future. """ # Get WebClient so you can communicate back to Slack. web_client = payload["web_client"] # Get the id of the Slack user associated with the incoming event user_id = payload["data"]["user"]["id"] # Open a DM with the new user. response = web_client.im_open(user_id) channel = response["channel"]["id"] # Post the onboarding message. start_onboarding(web_client, user_id, channel)

Create and send an onboarding welcome message to new users. Save the time stamp of this message so we can update this message in the future.

python

train

priendeau/UnderscoreX

setup.py

https://github.com/priendeau/UnderscoreX/blob/ac83e13627cfa009dc5731a1fb31f9c6145d983a/setup.py#L68-L82

def Kargs2Attr( ): """ This Decorator Will: Read **kwargs key and add it to current Object-class ClassTinyDecl under current name readed from **kwargs key name. """ def decorator( func ): def inner( **kwargs ): for ItemName in kwargs.keys(): setattr( __builtins__, ItemName , kwargs[ItemName] ) func( **kwargs ) return inner return decorator

This Decorator Will: Read **kwargs key and add it to current Object-class ClassTinyDecl under current name readed from **kwargs key name.

python

train

glomex/gcdt

gcdt/iam.py

https://github.com/glomex/gcdt/blob/cd67cf416371337b83cb9ca3f696277125703339/gcdt/iam.py#L190-L221

def build_bucket(self, name, lifecycle_configuration=False, use_plain_name=False): """ Generate S3 bucket statement :param name: Name of the bucket :param lifecycle_configuration: Additional lifecycle configuration (default=False) :param use_plain_name: Just use the given name and do not add prefix :return: Ref to new bucket """ if use_plain_name: name_aws = name_bucket = name name_aws = name_aws.title() name_aws = name_aws.replace('-', '') else: name_aws = self.name_strip(name, False, False) name_bucket = self.name_build(name) if lifecycle_configuration: return self.__template.add_resource( Bucket( name_aws, BucketName=name_bucket, LifecycleConfiguration=lifecycle_configuration ) ) else: return self.__template.add_resource( Bucket( name_aws, BucketName=name_bucket, ) )

Generate S3 bucket statement :param name: Name of the bucket :param lifecycle_configuration: Additional lifecycle configuration (default=False) :param use_plain_name: Just use the given name and do not add prefix :return: Ref to new bucket

python

train

rbarrois/mpdlcd

mpdlcd/mpdwrapper.py

https://github.com/rbarrois/mpdlcd/blob/85f16c8cc0883f8abb4c2cc7f69729c3e2f857da/mpdlcd/mpdwrapper.py#L135-L146

def get(self, tags): """Find an adequate value for this field from a dict of tags.""" # Try to find our name value = tags.get(self.name, '') for name in self.alternate_tags: # Iterate of alternates until a non-empty value is found value = value or tags.get(name, '') # If we still have nothing, return our default value = value or self.default return value

Find an adequate value for this field from a dict of tags.

python

train

priestc/moneywagon

moneywagon/tx.py

https://github.com/priestc/moneywagon/blob/00518f1f557dcca8b3031f46d3564c2baa0227a3/moneywagon/tx.py#L31-L43

def from_unit_to_satoshi(self, value, unit='satoshi'): """ Convert a value to satoshis. units can be any fiat currency. By default the unit is satoshi. """ if not unit or unit == 'satoshi': return value if unit == 'bitcoin' or unit == 'btc': return value * 1e8 # assume fiat currency that we can convert convert = get_current_price(self.crypto, unit) return int(value / convert * 1e8)

Convert a value to satoshis. units can be any fiat currency. By default the unit is satoshi.

python

train

inspirehep/inspire-dojson

inspire_dojson/hep/rules/bd5xx.py

https://github.com/inspirehep/inspire-dojson/blob/17f3789cd3d5ae58efa1190dc0eea9efb9c8ca59/inspire_dojson/hep/rules/bd5xx.py#L221-L244

def license(self, key, value): """Populate the ``license`` key.""" def _get_license(value): a_values = force_list(value.get('a')) oa_licenses = [el for el in a_values if el == 'OA' or el == 'Open Access'] other_licenses = [el for el in a_values if el != 'OA' and el != 'Open Access'] if not other_licenses: return force_single_element(oa_licenses) return force_single_element(other_licenses) def _get_material(value): material = value.get('3', '').lower() if material == 'article': return 'publication' return material return { 'imposing': value.get('b'), 'license': _get_license(value), 'material': _get_material(value), 'url': value.get('u'), }

Populate the ``license`` key.

python

train

synw/dataswim

dataswim/data/count.py

https://github.com/synw/dataswim/blob/4a4a53f80daa7cd8e8409d76a19ce07296269da2/dataswim/data/count.py#L10-L22

def count_nulls(self, field): """ Count the number of null values in a column """ try: n = self.df[field].isnull().sum() except KeyError: self.warning("Can not find column", field) return except Exception as e: self.err(e, "Can not count nulls") return self.ok("Found", n, "nulls in column", field)

Count the number of null values in a column

python

train

limpyd/redis-limpyd

limpyd/collection.py

https://github.com/limpyd/redis-limpyd/blob/3c745dde1390a0bd09690b77a089dcc08c6c7e43/limpyd/collection.py#L206-L222

def _prepare_sort_options(self, has_pk): """ Prepare "sort" options to use when calling the collection, depending on "_sort" and "_sort_limits" attributes """ sort_options = {} if self._sort is not None and not has_pk: sort_options.update(self._sort) if self._sort_limits is not None: if 'start' in self._sort_limits and 'num' not in self._sort_limits: self._sort_limits['num'] = -1 elif 'num' in self._sort_limits and 'start' not in self._sort_limits: self._sort_limits['start'] = 0 sort_options.update(self._sort_limits) if not sort_options and self._sort is None: sort_options = None return sort_options

Prepare "sort" options to use when calling the collection, depending on "_sort" and "_sort_limits" attributes

python

train

spulec/moto

moto/ec2/utils.py

https://github.com/spulec/moto/blob/4a286c4bc288933bb023396e2784a6fdbb966bc9/moto/ec2/utils.py#L204-L240

def dhcp_configuration_from_querystring(querystring, option=u'DhcpConfiguration'): """ turn: {u'AWSAccessKeyId': [u'the_key'], u'Action': [u'CreateDhcpOptions'], u'DhcpConfiguration.1.Key': [u'domain-name'], u'DhcpConfiguration.1.Value.1': [u'example.com'], u'DhcpConfiguration.2.Key': [u'domain-name-servers'], u'DhcpConfiguration.2.Value.1': [u'10.0.0.6'], u'DhcpConfiguration.2.Value.2': [u'10.0.0.7'], u'Signature': [u'uUMHYOoLM6r+sT4fhYjdNT6MHw22Wj1mafUpe0P0bY4='], u'SignatureMethod': [u'HmacSHA256'], u'SignatureVersion': [u'2'], u'Timestamp': [u'2014-03-18T21:54:01Z'], u'Version': [u'2013-10-15']} into: {u'domain-name': [u'example.com'], u'domain-name-servers': [u'10.0.0.6', u'10.0.0.7']} """ key_needle = re.compile(u'{0}.[0-9]+.Key'.format(option), re.UNICODE) response_values = {} for key, value in querystring.items(): if key_needle.match(key): values = [] key_index = key.split(".")[1] value_index = 1 while True: value_key = u'{0}.{1}.Value.{2}'.format( option, key_index, value_index) if value_key in querystring: values.extend(querystring[value_key]) else: break value_index += 1 response_values[value[0]] = values return response_values

turn: {u'AWSAccessKeyId': [u'the_key'], u'Action': [u'CreateDhcpOptions'], u'DhcpConfiguration.1.Key': [u'domain-name'], u'DhcpConfiguration.1.Value.1': [u'example.com'], u'DhcpConfiguration.2.Key': [u'domain-name-servers'], u'DhcpConfiguration.2.Value.1': [u'10.0.0.6'], u'DhcpConfiguration.2.Value.2': [u'10.0.0.7'], u'Signature': [u'uUMHYOoLM6r+sT4fhYjdNT6MHw22Wj1mafUpe0P0bY4='], u'SignatureMethod': [u'HmacSHA256'], u'SignatureVersion': [u'2'], u'Timestamp': [u'2014-03-18T21:54:01Z'], u'Version': [u'2013-10-15']} into: {u'domain-name': [u'example.com'], u'domain-name-servers': [u'10.0.0.6', u'10.0.0.7']}

python

train

numenta/htmresearch

htmresearch/frameworks/pytorch/sparse_speech_experiment.py

https://github.com/numenta/htmresearch/blob/70c096b09a577ea0432c3f3bfff4442d4871b7aa/htmresearch/frameworks/pytorch/sparse_speech_experiment.py#L266-L282

def createOptimizer(self, params, model): """ Create a new instance of the optimizer """ lr = params["learning_rate"] print("Creating optimizer with learning rate=", lr) if params["optimizer"] == "SGD": optimizer = optim.SGD(model.parameters(), lr=lr, momentum=params["momentum"], weight_decay=params["weight_decay"], ) elif params["optimizer"] == "Adam": optimizer = optim.Adam(model.parameters(), lr=lr) else: raise LookupError("Incorrect optimizer value") return optimizer

Create a new instance of the optimizer

python

train

materialsproject/pymatgen

pymatgen/io/phonopy.py

https://github.com/materialsproject/pymatgen/blob/4ca558cf72f8d5f8a1f21dfdfc0181a971c186da/pymatgen/io/phonopy.py#L174-L190

def get_ph_bs_symm_line(bands_path, has_nac=False, labels_dict=None): """ Creates a pymatgen PhononBandStructure from a band.yaml file. The labels will be extracted from the dictionary, if present. If the 'eigenvector' key is found the eigendisplacements will be calculated according to the formula: \\exp(2*pi*i*(frac_coords \\dot q) / sqrt(mass) * v and added to the object. Args: bands_path: path to the band.yaml file has_nac: True if the data have been obtained with the option --nac option. Default False. labels_dict: dict that links a qpoint in frac coords to a label. """ return get_ph_bs_symm_line_from_dict(loadfn(bands_path), has_nac, labels_dict)

Creates a pymatgen PhononBandStructure from a band.yaml file. The labels will be extracted from the dictionary, if present. If the 'eigenvector' key is found the eigendisplacements will be calculated according to the formula: \\exp(2*pi*i*(frac_coords \\dot q) / sqrt(mass) * v and added to the object. Args: bands_path: path to the band.yaml file has_nac: True if the data have been obtained with the option --nac option. Default False. labels_dict: dict that links a qpoint in frac coords to a label.

python

train

yyuu/botornado

boto/route53/connection.py

https://github.com/yyuu/botornado/blob/fffb056f5ff2324d1d5c1304014cfb1d899f602e/boto/route53/connection.py#L78-L108

def get_all_hosted_zones(self, start_marker=None, zone_list=None): """ Returns a Python data structure with information about all Hosted Zones defined for the AWS account. :param int start_marker: start marker to pass when fetching additional results after a truncated list :param list zone_list: a HostedZones list to prepend to results """ params = {} if start_marker: params = {'marker': start_marker} response = self.make_request('GET', '/%s/hostedzone' % self.Version, params=params) body = response.read() boto.log.debug(body) if response.status >= 300: raise exception.DNSServerError(response.status, response.reason, body) e = boto.jsonresponse.Element(list_marker='HostedZones', item_marker=('HostedZone',)) h = boto.jsonresponse.XmlHandler(e, None) h.parse(body) if zone_list: e['ListHostedZonesResponse']['HostedZones'].extend(zone_list) while e['ListHostedZonesResponse'].has_key('NextMarker'): next_marker = e['ListHostedZonesResponse']['NextMarker'] zone_list = e['ListHostedZonesResponse']['HostedZones'] e = self.get_all_hosted_zones(next_marker, zone_list) return e

Returns a Python data structure with information about all Hosted Zones defined for the AWS account. :param int start_marker: start marker to pass when fetching additional results after a truncated list :param list zone_list: a HostedZones list to prepend to results

python

train

apache/spark

python/pyspark/context.py

https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/context.py#L568-L579

def pickleFile(self, name, minPartitions=None): """ Load an RDD previously saved using L{RDD.saveAsPickleFile} method. >>> tmpFile = NamedTemporaryFile(delete=True) >>> tmpFile.close() >>> sc.parallelize(range(10)).saveAsPickleFile(tmpFile.name, 5) >>> sorted(sc.pickleFile(tmpFile.name, 3).collect()) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] """ minPartitions = minPartitions or self.defaultMinPartitions return RDD(self._jsc.objectFile(name, minPartitions), self)

Load an RDD previously saved using L{RDD.saveAsPickleFile} method. >>> tmpFile = NamedTemporaryFile(delete=True) >>> tmpFile.close() >>> sc.parallelize(range(10)).saveAsPickleFile(tmpFile.name, 5) >>> sorted(sc.pickleFile(tmpFile.name, 3).collect()) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

python

train

Guake/guake

guake/terminal.py

https://github.com/Guake/guake/blob/4153ef38f9044cbed6494075fce80acd5809df2b/guake/terminal.py#L299-L327

def button_press(self, terminal, event): """Handles the button press event in the terminal widget. If any match string is caught, another application is open to handle the matched resource uri. """ self.matched_value = '' if (Vte.MAJOR_VERSION, Vte.MINOR_VERSION) >= (0, 46): matched_string = self.match_check_event(event) else: matched_string = self.match_check( int(event.x / self.get_char_width()), int(event.y / self.get_char_height()) ) self.found_link = None if event.button == 1 and (event.get_state() & Gdk.ModifierType.CONTROL_MASK): if (Vte.MAJOR_VERSION, Vte.MINOR_VERSION) > (0, 50): s = self.hyperlink_check_event(event) else: s = None if s is not None: self._on_ctrl_click_matcher((s, None)) elif self.get_has_selection(): self.quick_open() elif matched_string and matched_string[0]: self._on_ctrl_click_matcher(matched_string) elif event.button == 3 and matched_string: self.found_link = self.handleTerminalMatch(matched_string) self.matched_value = matched_string[0]

Handles the button press event in the terminal widget. If any match string is caught, another application is open to handle the matched resource uri.

python

train

caseyjlaw/sdmreader

sdmreader/sdmreader.py

https://github.com/caseyjlaw/sdmreader/blob/b6c3498f1915138727819715ee00d2c46353382d/sdmreader/sdmreader.py#L351-L440

def _parse (self): """Parse the BDF mime structure and record the locations of the binary blobs. Sets up various data fields in the BDFData object.""" feedparser = FeedParser (Message) binarychunks = {} sizeinfo = None headxml = None self.fp.seek (0, 0) while True: data = self.fp.readline () if not data: break feedparser.feed (data) skip = (data == '\n' and len (feedparser._msgstack) == 3 and feedparser._msgstack[-1].get_content_type () in ('application/octet-stream', 'binary/octet-stream')) if skip: # We just finished reading the headers for a huge binary blob. # Time to remember where the data chunk is and pretend it doesn't # exist. msg = feedparser._msgstack[-1] ident = msg['Content-Location'] assert ident.endswith ('.bin'), 'confusion #1 in hacky MIME parsing!' binarychunks[ident] = self.fp.tell () if sizeinfo is None: headxml, sizeinfo, tagpfx = _extract_size_info (feedparser) kind = ident.split ('/')[-1] assert kind in sizeinfo, 'no size info for binary chunk kind %s in MIME!' % kind self.fp.seek (sizeinfo[kind] + 1, 1) # skip ahead by data chunk size sample = self.fp.read (16) assert sample.startswith ('--MIME'), 'crap, unexpected chunk size in MIME parsing: %r' % sample self.fp.seek (-16, 1) # go back # check that two major kinds of data are read at least once if any([k.split('/')[3] == '3' for k in binarychunks.iterkeys()]): break if headxml is None: raise RuntimeError ('never found any binary data') self.mimemsg = feedparser.close () self.headxml = headxml self.sizeinfo = sizeinfo self.binarychunks = binarychunks headsize, intsize = self.calc_intsize() # Compute some miscellaneous parameters that we'll need. # self.n_integrations = len (self.mimemsg.get_payload ()) - 1 self.n_integrations = os.stat(self.fp.name).st_size/intsize self.n_antennas = int (headxml.find (tagpfx + nanttag).text) self.n_baselines = (self.n_antennas * (self.n_antennas - 1)) // 2 ds = headxml.find (tagpfx + dstag) nbb = 0 nspw = 0 nchan = 0 crosspolstr = None for bb in ds.findall (tagpfx + basebandtag): nbb += 1 for spw in bb.getchildren (): nspw += 1 nchan += int (spw.get ('numSpectralPoint')) if crosspolstr is None: crosspolstr = spw.get ('crossPolProducts') elif spw.get ('crossPolProducts') != crosspolstr: raise Exception ('can only handle spectral windows with identical cross pol products') self.n_basebands = nbb self.n_spws = nspw self.n_channels = nchan self.crosspols = crosspolstr.split () self.n_pols = len(self.crosspols) # if bdf info pkl not present, write it if os.path.exists(os.path.dirname(self.pklname)) and self.pklname and (not os.path.exists(self.pklname)): logger.info('Writing bdf pkl info to %s...' % (self.pklname)) with open(self.pklname,'wb') as pkl: # Compute some miscellaneous parameters that we'll need. pickle.dump( (self.mimemsg, self.headxml, self.sizeinfo, self.binarychunks, self.n_integrations, self.n_antennas, self.n_baselines, self.n_basebands, self.n_spws, self.n_channels, self.crosspols), pkl) return self

Parse the BDF mime structure and record the locations of the binary blobs. Sets up various data fields in the BDFData object.

python

train

ucsb-cs/submit

submit/models.py

https://github.com/ucsb-cs/submit/blob/92810c81255a4fc6bbebac1ac8aae856fd576ffe/submit/models.py#L919-L926

def get_value(cls, value): '''Takes the class of the item that we want to query, along with a potential instance of that class. If the value is an instance of int or basestring, then we will treat it like an id for that instance.''' if isinstance(value, (basestring, int)): value = cls.fetch_by(id=value) return value if isinstance(value, cls) else None

Takes the class of the item that we want to query, along with a potential instance of that class. If the value is an instance of int or basestring, then we will treat it like an id for that instance.

python

train

openstack/horizon

openstack_dashboard/api/neutron.py

https://github.com/openstack/horizon/blob/5601ea9477323e599d9b766fcac1f8be742935b2/openstack_dashboard/api/neutron.py#L1795-L1808

def list_extensions(request): """List neutron extensions. :param request: django request object """ neutron_api = neutronclient(request) try: extensions_list = neutron_api.list_extensions() except exceptions.ServiceCatalogException: return {} if 'extensions' in extensions_list: return tuple(extensions_list['extensions']) else: return ()

List neutron extensions. :param request: django request object

python

train

coordt/django-alphabetfilter

alphafilter/templatetags/alphafilter.py

https://github.com/coordt/django-alphabetfilter/blob/a7bc21c0ea985c2021a4668241bf643c615c6c1f/alphafilter/templatetags/alphafilter.py#L13-L35

def _get_default_letters(model_admin=None): """ Returns the set of letters defined in the configuration variable DEFAULT_ALPHABET. DEFAULT_ALPHABET can be a callable, string, tuple, or list and returns a set. If a ModelAdmin class is passed, it will look for a DEFAULT_ALPHABET attribute and use it instead. """ from django.conf import settings import string default_ltrs = string.digits + string.ascii_uppercase default_letters = getattr(settings, 'DEFAULT_ALPHABET', default_ltrs) if model_admin and hasattr(model_admin, 'DEFAULT_ALPHABET'): default_letters = model_admin.DEFAULT_ALPHABET if callable(default_letters): return set(default_letters()) elif isinstance(default_letters, str): return set([x for x in default_letters]) elif isinstance(default_letters, str): return set([x for x in default_letters.decode('utf8')]) elif isinstance(default_letters, (tuple, list)): return set(default_letters)

Returns the set of letters defined in the configuration variable DEFAULT_ALPHABET. DEFAULT_ALPHABET can be a callable, string, tuple, or list and returns a set. If a ModelAdmin class is passed, it will look for a DEFAULT_ALPHABET attribute and use it instead.

python

train

hotdoc/hotdoc

hotdoc/core/project.py

https://github.com/hotdoc/hotdoc/blob/1067cdc8482b585b364a38fb52ca5d904e486280/hotdoc/core/project.py#L151-L160

def persist(self): """ Banana banana """ if self.app.dry: return for proj in self.subprojects.values(): proj.persist()

Banana banana

python

train

LuminosoInsight/ordered-set

ordered_set.py

https://github.com/LuminosoInsight/ordered-set/blob/a29eaedcedfe5072bcee11bdef61dea321d5e9f9/ordered_set.py#L310-L327

def union(self, *sets): """ Combines all unique items. Each items order is defined by its first appearance. Example: >>> oset = OrderedSet.union(OrderedSet([3, 1, 4, 1, 5]), [1, 3], [2, 0]) >>> print(oset) OrderedSet([3, 1, 4, 5, 2, 0]) >>> oset.union([8, 9]) OrderedSet([3, 1, 4, 5, 2, 0, 8, 9]) >>> oset | {10} OrderedSet([3, 1, 4, 5, 2, 0, 10]) """ cls = self.__class__ if isinstance(self, OrderedSet) else OrderedSet containers = map(list, it.chain([self], sets)) items = it.chain.from_iterable(containers) return cls(items)

Combines all unique items. Each items order is defined by its first appearance. Example: >>> oset = OrderedSet.union(OrderedSet([3, 1, 4, 1, 5]), [1, 3], [2, 0]) >>> print(oset) OrderedSet([3, 1, 4, 5, 2, 0]) >>> oset.union([8, 9]) OrderedSet([3, 1, 4, 5, 2, 0, 8, 9]) >>> oset | {10} OrderedSet([3, 1, 4, 5, 2, 0, 10])

python

train

MIT-LCP/wfdb-python

wfdb/io/annotation.py

https://github.com/MIT-LCP/wfdb-python/blob/cc8c9e9e44f10af961b7a9d8ae03708b31ac8a8c/wfdb/io/annotation.py#L781-L859

def get_contained_labels(self, inplace=True): """ Get the set of unique labels contained in this annotation. Returns a pandas dataframe or sets the contained_labels attribute of the object. Requires the label_store field to be set. Function will try to use attributes contained in the order: 1. label_store 2. symbol 3. description This function should also be called to summarize information about an annotation after it has been read. Should not be a helper function to others except rdann. """ if self.custom_labels is not None: self.check_field('custom_labels') # Create the label map label_map = ann_label_table.copy() # Convert the tuple triplets into a pandas dataframe if needed if isinstance(self.custom_labels, (list, tuple)): custom_labels = label_triplets_to_df(self.custom_labels) elif isinstance(self.custom_labels, pd.DataFrame): # Set the index just in case it doesn't already match the label_store self.custom_labels.set_index( self.custom_labels['label_store'].values, inplace=True) custom_labels = self.custom_labels else: custom_labels = None # Merge the standard wfdb labels with the custom labels. # custom labels values overwrite standard wfdb if overlap. if custom_labels is not None: for i in custom_labels.index: label_map.loc[i] = custom_labels.loc[i] # This doesn't work... # label_map.loc[custom_labels.index] = custom_labels.loc[custom_labels.index] # Get the labels using one of the features if self.label_store is not None: index_vals = set(self.label_store) reset_index = False counts = np.unique(self.label_store, return_counts=True) elif self.symbol is not None: index_vals = set(self.symbol) label_map.set_index(label_map['symbol'].values, inplace=True) reset_index = True counts = np.unique(self.symbol, return_counts=True) elif self.description is not None: index_vals = set(self.description) label_map.set_index(label_map['description'].values, inplace=True) reset_index = True counts = np.unique(self.description, return_counts=True) else: raise Exception('No annotation labels contained in object') contained_labels = label_map.loc[index_vals, :] # Add the counts for i in range(len(counts[0])): contained_labels.loc[counts[0][i], 'n_occurrences'] = counts[1][i] contained_labels['n_occurrences'] = pd.to_numeric(contained_labels['n_occurrences'], downcast='integer') if reset_index: contained_labels.set_index(contained_labels['label_store'].values, inplace=True) if inplace: self.contained_labels = contained_labels return else: return contained_labels

Get the set of unique labels contained in this annotation. Returns a pandas dataframe or sets the contained_labels attribute of the object. Requires the label_store field to be set. Function will try to use attributes contained in the order: 1. label_store 2. symbol 3. description This function should also be called to summarize information about an annotation after it has been read. Should not be a helper function to others except rdann.

python

train

jermnelson/flask-fedora-commons

flask_fedora_commons/__init__.py

https://github.com/jermnelson/flask-fedora-commons/blob/81cee0d8c9e79fa2bdd1a101facb9e8c0f307af4/flask_fedora_commons/__init__.py#L424-L461

def remove(self, entity_id, property_uri, value): """Method removes a triple for the given/subject. Args: entity_id(string): Fedora Object ID, ideally URI of the subject property_uri(string): value(string): Return: boolean: True if triple was removed from the object """ if not entity_id.startswith("http"): entity_uri = urllib.parse.urljoin(self.base_url, entity_id) else: entity_uri = entity_id sparql_template = Template("""$prefix DELETE { <$entity> $prop_name $value_str } WHERE { <$entity> $prop_name $value_str }""") sparql = sparql_template.substitute( prefix=build_prefixes(self.namespaces), entity=entity_uri, prop_name=property_uri, value_str=self.__value_format__(value)) delete_property_request = urllib.request.Request( entity_uri, data=sparql.encode(), method='PATCH', headers={'Content-Type': 'application/sparql-update'}) response = urllib.request.urlopen(delete_property_request) if response.code < 400: return True return False

Method removes a triple for the given/subject. Args: entity_id(string): Fedora Object ID, ideally URI of the subject property_uri(string): value(string): Return: boolean: True if triple was removed from the object

python

train

DataDog/integrations-core

tokumx/datadog_checks/tokumx/vendor/pymongo/message.py

https://github.com/DataDog/integrations-core/blob/ebd41c873cf9f97a8c51bf9459bc6a7536af8acd/tokumx/datadog_checks/tokumx/vendor/pymongo/message.py#L369-L379

def __pack_message(operation, data): """Takes message data and adds a message header based on the operation. Returns the resultant message string. """ request_id = _randint() message = struct.pack("<i", 16 + len(data)) message += struct.pack("<i", request_id) message += _ZERO_32 # responseTo message += struct.pack("<i", operation) return (request_id, message + data)

Takes message data and adds a message header based on the operation. Returns the resultant message string.

python

train

dougalsutherland/skl-groups

skl_groups/preprocessing.py

https://github.com/dougalsutherland/skl-groups/blob/2584c10a413626c6d5f9078cdbf3dcc84e4e9a5b/skl_groups/preprocessing.py#L78-L97

def fit_transform(self, X, y=None, **params): ''' Fit and transform the stacked points. Parameters ---------- X : :class:`Features` or list of bag feature arrays Data to train on and transform. any other keyword argument : Passed on as keyword arguments to the transformer's ``transform()``. Returns ------- X_new : :class:`Features` Transformed features. ''' X = as_features(X, stack=True) X_new = self.transformer.fit_transform(X.stacked_features, y, **params) return self._gather_outputs(X, X_new)

Fit and transform the stacked points. Parameters ---------- X : :class:`Features` or list of bag feature arrays Data to train on and transform. any other keyword argument : Passed on as keyword arguments to the transformer's ``transform()``. Returns ------- X_new : :class:`Features` Transformed features.

python

valid

ev3dev/ev3dev-lang-python

ev3dev2/sensor/lego.py

https://github.com/ev3dev/ev3dev-lang-python/blob/afc98d35004b533dc161a01f7c966e78607d7c1e/ev3dev2/sensor/lego.py#L935-L941

def sound_pressure(self): """ A measurement of the measured sound pressure level, as a percent. Uses a flat weighting. """ self._ensure_mode(self.MODE_DB) return self.value(0) * self._scale('DB')

A measurement of the measured sound pressure level, as a percent. Uses a flat weighting.

python

train

PSPC-SPAC-buyandsell/von_anchor

von_anchor/anchor/holderprover.py

https://github.com/PSPC-SPAC-buyandsell/von_anchor/blob/78ac1de67be42a676274f4bf71fe12f66e72f309/von_anchor/anchor/holderprover.py#L149-L199

async def _sync_revoc_for_proof(self, rr_id: str) -> None: """ Pick up tails file reader handle for input revocation registry identifier. If no symbolic link is present, get the revocation registry definition to retrieve its tails file hash, then find the tails file and link it. Raise AbsentTails for missing corresponding tails file. :param rr_id: revocation registry identifier """ LOGGER.debug('HolderProver._sync_revoc_for_proof >>> rr_id: %s', rr_id) if not ok_rev_reg_id(rr_id): LOGGER.debug('HolderProver._sync_revoc_for_proof <!< Bad rev reg id %s', rr_id) raise BadIdentifier('Bad rev reg id {}'.format(rr_id)) (cd_id, tag) = rev_reg_id2cred_def_id_tag(rr_id) try: json.loads(await self.get_cred_def(cd_id)) except AbsentCredDef: LOGGER.debug( 'HolderProver._sync_revoc_for_proof <!< corrupt tails tree %s may be for another ledger', self._dir_tails) raise AbsentCredDef('Corrupt tails tree {} may be for another ledger'.format(self._dir_tails)) except ClosedPool: pass # carry on, may be OK from cache only with REVO_CACHE.lock: revo_cache_entry = REVO_CACHE.get(rr_id, None) tails = revo_cache_entry.tails if revo_cache_entry else None if tails is None: # it's not yet set in cache try: tails = await Tails(self._dir_tails, cd_id, tag).open() except AbsentTails: # get hash from ledger and check for tails file rr_def = json.loads(await self.get_rev_reg_def(rr_id)) tails_hash = rr_def['value']['tailsHash'] path_tails = join(Tails.dir(self._dir_tails, rr_id), tails_hash) if not isfile(path_tails): LOGGER.debug('HolderProver._sync_revoc_for_proof <!< No tails file present at %s', path_tails) raise AbsentTails('No tails file present at {}'.format(path_tails)) Tails.associate(self._dir_tails, rr_id, tails_hash) tails = await Tails(self._dir_tails, cd_id, tag).open() # OK now since tails file present if revo_cache_entry is None: REVO_CACHE[rr_id] = RevoCacheEntry(None, tails) else: REVO_CACHE[rr_id].tails = tails LOGGER.debug('HolderProver._sync_revoc_for_proof <<<')

Pick up tails file reader handle for input revocation registry identifier. If no symbolic link is present, get the revocation registry definition to retrieve its tails file hash, then find the tails file and link it. Raise AbsentTails for missing corresponding tails file. :param rr_id: revocation registry identifier

python

train

ethereum/pyrlp

rlp/sedes/serializable.py

https://github.com/ethereum/pyrlp/blob/bb898f8056da3973204c699621350bf9565e43df/rlp/sedes/serializable.py#L82-L90

def _eq(left, right): """ Equality comparison that allows for equality between tuple and list types with equivalent elements. """ if isinstance(left, (tuple, list)) and isinstance(right, (tuple, list)): return len(left) == len(right) and all(_eq(*pair) for pair in zip(left, right)) else: return left == right

Equality comparison that allows for equality between tuple and list types with equivalent elements.

python

train

CodeReclaimers/neat-python

neat/statistics.py

https://github.com/CodeReclaimers/neat-python/blob/e3dbe77c0d776eae41d598e6439e6ac02ab90b18/neat/statistics.py#L124-L129

def save_species_count(self, delimiter=' ', filename='speciation.csv'): """ Log speciation throughout evolution. """ with open(filename, 'w') as f: w = csv.writer(f, delimiter=delimiter) for s in self.get_species_sizes(): w.writerow(s)

Log speciation throughout evolution.

python

train

chrisjrn/registrasion

registrasion/templatetags/registrasion_tags.py

https://github.com/chrisjrn/registrasion/blob/461d5846c6f9f3b7099322a94f5d9911564448e4/registrasion/templatetags/registrasion_tags.py#L124-L149

def sold_out_and_unregistered(context): ''' If the current user is unregistered, returns True if there are no products in the TICKET_PRODUCT_CATEGORY that are available to that user. If there *are* products available, the return False. If the current user *is* registered, then return None (it's not a pertinent question for people who already have a ticket). ''' user = user_for_context(context) if hasattr(user, "attendee") and user.attendee.completed_registration: # This user has completed registration, and so we don't need to answer # whether they have sold out yet. # TODO: what if a user has got to the review phase? # currently that user will hit the review page, click "Check out and # pay", and that will fail. Probably good enough for now. return None ticket_category = settings.TICKET_PRODUCT_CATEGORY categories = available_categories(context) return ticket_category not in [cat.id for cat in categories]

If the current user is unregistered, returns True if there are no products in the TICKET_PRODUCT_CATEGORY that are available to that user. If there *are* products available, the return False. If the current user *is* registered, then return None (it's not a pertinent question for people who already have a ticket).

python

test

sagemath/sage-package

sage_package/sphinx.py

https://github.com/sagemath/sage-package/blob/6e511753fb0667b202f497fc00b763647456a066/sage_package/sphinx.py#L72-L79

def themes_path(): """ Retrieve the location of the themes directory from the location of this package This is taken from Sphinx's theme documentation """ package_dir = os.path.abspath(os.path.dirname(__file__)) return os.path.join(package_dir, 'themes')

Retrieve the location of the themes directory from the location of this package This is taken from Sphinx's theme documentation

python

test

qweeze/wex-api-client

wex/client.py

https://github.com/qweeze/wex-api-client/blob/e84d139be229aab2c7c5eda5976b812be651807b/wex/client.py#L80-L88

def ticker(self, pair, ignore_invalid=0): """ This method provides all the information about currently active pairs, such as: the maximum price, the minimum price, average price, trade volume, trade volume in currency, the last trade, Buy and Sell price. All information is provided over the past 24 hours. :param str or iterable pair: pair (ex. 'btc_usd' or ['btc_usd', 'eth_usd']) :param int ignore_invalid: ignore non-existing pairs """ return self._public_api_call('ticker', pair=pair, ignore_invalid=ignore_invalid)

This method provides all the information about currently active pairs, such as: the maximum price, the minimum price, average price, trade volume, trade volume in currency, the last trade, Buy and Sell price. All information is provided over the past 24 hours. :param str or iterable pair: pair (ex. 'btc_usd' or ['btc_usd', 'eth_usd']) :param int ignore_invalid: ignore non-existing pairs

python

train

NASA-AMMOS/AIT-Core

ait/core/log.py

https://github.com/NASA-AMMOS/AIT-Core/blob/9d85bd9c738e7a6a6fbdff672bea708238b02a3a/ait/core/log.py#L66-L76

def formatTime (self, record, datefmt=None): """Return the creation time of the specified LogRecord as formatted text.""" if datefmt is None: datefmt = '%Y-%m-%d %H:%M:%S' ct = self.converter(record.created) t = time.strftime(datefmt, ct) s = '%s.%03d' % (t, record.msecs) return s

Return the creation time of the specified LogRecord as formatted text.

python

train

rabitt/pysox

sox/transform.py

https://github.com/rabitt/pysox/blob/eae89bde74567136ec3f723c3e6b369916d9b837/sox/transform.py#L3026-L3116

def vad(self, location=1, normalize=True, activity_threshold=7.0, min_activity_duration=0.25, initial_search_buffer=1.0, max_gap=0.25, initial_pad=0.0): '''Voice Activity Detector. Attempts to trim silence and quiet background sounds from the ends of recordings of speech. The algorithm currently uses a simple cepstral power measurement to detect voice, so may be fooled by other things, especially music. The effect can trim only from the front of the audio, so in order to trim from the back, the reverse effect must also be used. Parameters ---------- location : 1 or -1, default=1 If 1, trims silence from the beginning If -1, trims silence from the end normalize : bool, default=True If true, normalizes audio before processing. activity_threshold : float, default=7.0 The measurement level used to trigger activity detection. This may need to be cahnged depending on the noise level, signal level, and other characteristics of the input audio. min_activity_duration : float, default=0.25 The time constant (in seconds) used to help ignore short bursts of sound. initial_search_buffer : float, default=1.0 The amount of audio (in seconds) to search for quieter/shorter bursts of audio to include prior to the detected trigger point. max_gap : float, default=0.25 The allowed gap (in seconds) between quiteter/shorter bursts of audio to include prior to the detected trigger point initial_pad : float, default=0.0 The amount of audio (in seconds) to preserve before the trigger point and any found quieter/shorter bursts. See Also -------- silence Examples -------- >>> tfm = sox.Transformer() Remove silence from the beginning of speech >>> tfm.vad(initial_pad=0.3) Remove silence from the end of speech >>> tfm.vad(location=-1, initial_pad=0.2) ''' if location not in [-1, 1]: raise ValueError("location must be -1 or 1.") if not isinstance(normalize, bool): raise ValueError("normalize muse be a boolean.") if not is_number(activity_threshold): raise ValueError("activity_threshold must be a number.") if not is_number(min_activity_duration) or min_activity_duration < 0: raise ValueError("min_activity_duration must be a positive number") if not is_number(initial_search_buffer) or initial_search_buffer < 0: raise ValueError("initial_search_buffer must be a positive number") if not is_number(max_gap) or max_gap < 0: raise ValueError("max_gap must be a positive number.") if not is_number(initial_pad) or initial_pad < 0: raise ValueError("initial_pad must be a positive number.") effect_args = [] if normalize: effect_args.append('norm') if location == -1: effect_args.append('reverse') effect_args.extend([ 'vad', '-t', '{:f}'.format(activity_threshold), '-T', '{:f}'.format(min_activity_duration), '-s', '{:f}'.format(initial_search_buffer), '-g', '{:f}'.format(max_gap), '-p', '{:f}'.format(initial_pad) ]) if location == -1: effect_args.append('reverse') self.effects.extend(effect_args) self.effects_log.append('vad') return self

Voice Activity Detector. Attempts to trim silence and quiet background sounds from the ends of recordings of speech. The algorithm currently uses a simple cepstral power measurement to detect voice, so may be fooled by other things, especially music. The effect can trim only from the front of the audio, so in order to trim from the back, the reverse effect must also be used. Parameters ---------- location : 1 or -1, default=1 If 1, trims silence from the beginning If -1, trims silence from the end normalize : bool, default=True If true, normalizes audio before processing. activity_threshold : float, default=7.0 The measurement level used to trigger activity detection. This may need to be cahnged depending on the noise level, signal level, and other characteristics of the input audio. min_activity_duration : float, default=0.25 The time constant (in seconds) used to help ignore short bursts of sound. initial_search_buffer : float, default=1.0 The amount of audio (in seconds) to search for quieter/shorter bursts of audio to include prior to the detected trigger point. max_gap : float, default=0.25 The allowed gap (in seconds) between quiteter/shorter bursts of audio to include prior to the detected trigger point initial_pad : float, default=0.0 The amount of audio (in seconds) to preserve before the trigger point and any found quieter/shorter bursts. See Also -------- silence Examples -------- >>> tfm = sox.Transformer() Remove silence from the beginning of speech >>> tfm.vad(initial_pad=0.3) Remove silence from the end of speech >>> tfm.vad(location=-1, initial_pad=0.2)

python

valid

intelsdi-x/snap-plugin-lib-py

snap_plugin/v1/metric.py

https://github.com/intelsdi-x/snap-plugin-lib-py/blob/8da5d00ac5f9d2b48a7239563ac7788209891ca4/snap_plugin/v1/metric.py#L243-L277

def data(self): """Metric data Args: value (:obj:`bool` or :obj:`int` or :obj:`long` or :obj:`float` or :obj:`basestring` or :obj:`bytes`) Returns: value Raises: :obj:`TypeError` """ if self._data_type == int: if self._pb.HasField("int64_data"): return self._pb.int64_data if self._pb.HasField("int32_data"): return self._pb.int32_data if self._pb.HasField("uint64_data"): return self._pb.uint64_data if self._pb.HasField("uint32_data"): return self._pb.uint32_data elif self._data_type == float: if self._pb.HasField("float32_data"): return self._pb.float32_data if self._pb.HasField("float64_data"): return self._pb.float64_data elif self._data_type == str: return self._pb.string_data elif self._data_type == bool: return self._pb.bool_data elif self._data_type == bytes: return self._pb.bytes_data return None

Metric data Args: value (:obj:`bool` or :obj:`int` or :obj:`long` or :obj:`float` or :obj:`basestring` or :obj:`bytes`) Returns: value Raises: :obj:`TypeError`

python

train

nickmckay/LiPD-utilities

Python/lipd/directory.py

https://github.com/nickmckay/LiPD-utilities/blob/5dab6bbeffc5effd68e3a6beaca6b76aa928e860/Python/lipd/directory.py#L155-L189

def collect_metadata_files(cwd, new_files, existing_files): """ Collect all files from a given path. Separate by file type, and return one list for each type If 'files' contains specific :param str cwd: Directory w/ target files :param list new_files: Specific new files to load :param dict existing_files: Files currently loaded, separated by type :return list: All files separated by type """ obj = {} try: os.chdir(cwd) # Special case: User uses gui to mult-select 2+ files. You'll be given a list of file paths. if new_files: for full_path in new_files: # Create the file metadata for one file, and append it to the existing files. obj = collect_metadata_file(full_path) # directory: get all files in the directory and sort by type else: for file_type in [".lpd", ".xls", ".txt"]: # get all files in cwd of this file extension files_found = list_files(file_type) # if looking for excel files, also look for the alternate extension. if file_type == ".xls": files_found += list_files(".xlsx") # for each file found, build it's metadata and append it to files_by_type for file in files_found: fn = os.path.splitext(file)[0] existing_files[file_type].append({"full_path": os.path.join(cwd, file), "filename_ext": file, "filename_no_ext": fn, "dir": cwd}) except Exception: logger_directory.info("directory: collect_files: there's a problem") return obj

Collect all files from a given path. Separate by file type, and return one list for each type If 'files' contains specific :param str cwd: Directory w/ target files :param list new_files: Specific new files to load :param dict existing_files: Files currently loaded, separated by type :return list: All files separated by type

python

train

getfleety/coralillo

coralillo/hashing.py

https://github.com/getfleety/coralillo/blob/9cac101738a0fa7c1106f129604c00ef703370e1/coralillo/hashing.py#L146-L153

def mask_hash(hash, show=6, char="*"): """ Return the given hash, with only the first ``show`` number shown. The rest are masked with ``char`` for security reasons. """ masked = hash[:show] masked += char * len(hash[show:]) return masked

Return the given hash, with only the first ``show`` number shown. The rest are masked with ``char`` for security reasons.

python

train

adrianliaw/PyCuber

pycuber/solver/cfop/cross.py

https://github.com/adrianliaw/PyCuber/blob/e44b5ba48c831b964ce73d046fb813222771853f/pycuber/solver/cfop/cross.py#L82-L144

def cross_state_value(state): """ Compute the state value of the cross solving search. """ centres, edges = state value = 0 for edge in edges: if "U" in edge: if edge["U"] == centres["D"]["D"]: value += 1 else: value += 2 elif "D" in edge: if edge["D"] != centres["D"]["D"]: value += 3 else: value += 1 edgeposes = {} counts = {f: 0 for f in "LFRB"} ngedges = [] for edge in edges: if "U" in edge and edge["U"] == centres["D"]["D"]: k = "".join(edge.facings.keys()).replace("U", "") edgeposes[k] = edge[k] counts[k] += 1 elif "D" in edge and edge["D"] == centres["D"]["D"]: k = "".join(edge.facings.keys()).replace("D", "") edgeposes[k] = edge[k] counts[k] += 1 elif "U" in edge or "D" in edge: ngedges.append(edge) else: for k, s in edge: if s != centres["D"]["D"]: edgeposes[k] = s counts[k] += 1 break for edge in ngedges: idx = "LFRB".index(edge[centres["D"].colour]) for i in [-1, 1]: if "LFRB"[(idx+1)%4] not in edgeposes: k = "".join(edge.facings.keys()).replace("LFRB"[idx], "") edgeposes["LFRB"[(idx+1)%4]] = edge[k] counts["LFRB"[(idx+1)%4]] += 1 break else: k = "".join(edge.facings.keys()).replace("LFRB"[idx], "") if counts["LFRB"[(idx-1)%4]] > counts["LFRB"[(idx+1)%4]]: edgeposes["LFRB"[(idx-1)%4]] = edge[k] else: edgeposes["LFRB"[(idx+1)%4]] = edge[k] relative_pos = {f: centres[f][f] for f in "LFRB"} if len(edgeposes) == 4: for i in range(4): edgeposes["L"], edgeposes["F"], edgeposes["R"], edgeposes["B"] = \ edgeposes["F"], edgeposes["R"], edgeposes["B"], edgeposes["L"] if edgeposes == relative_pos: break else: value += 5 else: value += 3 return value

Compute the state value of the cross solving search.

python

train

saltstack/salt

salt/modules/nilrt_ip.py

https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/nilrt_ip.py#L85-L94

def _get_state(): ''' Returns the state of connman ''' try: return pyconnman.ConnManager().get_property('State') except KeyError: return 'offline' except dbus.DBusException as exc: raise salt.exceptions.CommandExecutionError('Connman daemon error: {0}'.format(exc))

Returns the state of connman

python

train

spyder-ide/spyder-notebook

spyder_notebook/widgets/client.py

https://github.com/spyder-ide/spyder-notebook/blob/54e626b9d2a3fccd3e4625b0f97fe06e5bb1a6db/spyder_notebook/widgets/client.py#L174-L194

def register(self, server_info): """Register attributes that can be computed with the server info.""" # Path relative to the server directory self.path = os.path.relpath(self.filename, start=server_info['notebook_dir']) # Replace backslashes on Windows if os.name == 'nt': self.path = self.path.replace('\\', '/') # Server url to send requests to self.server_url = server_info['url'] # Server token self.token = server_info['token'] url = url_path_join(self.server_url, 'notebooks', url_escape(self.path)) # Set file url to load this notebook self.file_url = self.add_token(url)

Register attributes that can be computed with the server info.

python

train

zblz/naima

naima/plot.py

https://github.com/zblz/naima/blob/d6a6781d73bf58fd8269e8b0e3b70be22723cd5b/naima/plot.py#L1282-L1376

def plot_data( input_data, xlabel=None, ylabel=None, sed=True, figure=None, e_unit=None, ulim_opts={}, errorbar_opts={}, ): """ Plot spectral data. Parameters ---------- input_data : `emcee.EnsembleSampler`, `astropy.table.Table`, or `dict` Spectral data to plot. Can be given as a data table, a dict generated with `validate_data_table` or a `emcee.EnsembleSampler` with a data property. xlabel : str, optional Label for the ``x`` axis of the plot. ylabel : str, optional Label for the ``y`` axis of the plot. sed : bool, optional Whether to plot SED or differential spectrum. figure : `matplotlib.figure.Figure`, optional `matplotlib` figure to plot on. If omitted a new one will be generated. e_unit : `astropy.unit.Unit`, optional Units for energy axis. Defaults to those of the data. ulim_opts : dict Options for upper-limit plotting. Available options are capsize (arrow width) and height_fraction (arrow length in fraction of flux value). errorbar_opts : dict Addtional options to pass to `matplotlib.plt.errorbar` for plotting the spectral flux points. """ import matplotlib.pyplot as plt try: data = validate_data_table(input_data) except TypeError as exc: if hasattr(input_data, "data"): data = input_data.data elif isinstance(input_data, dict) and "energy" in input_data.keys(): data = input_data else: log.warning( "input_data format unknown, no plotting data! " "Data loading exception: {}".format(exc) ) raise if figure is None: f = plt.figure() else: f = figure if len(f.axes) > 0: ax1 = f.axes[0] else: ax1 = f.add_subplot(111) # try to get units from previous plot in figure try: old_e_unit = u.Unit(ax1.get_xlabel().split("[")[-1].split("]")[0]) except ValueError: old_e_unit = u.Unit("") if e_unit is None and old_e_unit.physical_type == "energy": e_unit = old_e_unit elif e_unit is None: e_unit = data["energy"].unit _plot_data_to_ax( data, ax1, e_unit=e_unit, sed=sed, ylabel=ylabel, ulim_opts=ulim_opts, errorbar_opts=errorbar_opts, ) if xlabel is not None: ax1.set_xlabel(xlabel) elif xlabel is None and ax1.get_xlabel() == "": ax1.set_xlabel( r"$\mathrm{Energy}$" + " [{0}]".format(e_unit.to_string("latex_inline")) ) ax1.autoscale() return f

Plot spectral data. Parameters ---------- input_data : `emcee.EnsembleSampler`, `astropy.table.Table`, or `dict` Spectral data to plot. Can be given as a data table, a dict generated with `validate_data_table` or a `emcee.EnsembleSampler` with a data property. xlabel : str, optional Label for the ``x`` axis of the plot. ylabel : str, optional Label for the ``y`` axis of the plot. sed : bool, optional Whether to plot SED or differential spectrum. figure : `matplotlib.figure.Figure`, optional `matplotlib` figure to plot on. If omitted a new one will be generated. e_unit : `astropy.unit.Unit`, optional Units for energy axis. Defaults to those of the data. ulim_opts : dict Options for upper-limit plotting. Available options are capsize (arrow width) and height_fraction (arrow length in fraction of flux value). errorbar_opts : dict Addtional options to pass to `matplotlib.plt.errorbar` for plotting the spectral flux points.

python

train

rlisagor/freshen

freshen/stepregistry.py

https://github.com/rlisagor/freshen/blob/5578f7368e8d53b4cf51c589fb192090d3524968/freshen/stepregistry.py#L250-L263

def hook_decorator(cb_type): """ Decorator to wrap hook definitions in. Registers hook. """ def decorator_wrapper(*tags_or_func): if len(tags_or_func) == 1 and callable(tags_or_func[0]): # No tags were passed to this decorator func = tags_or_func[0] return HookImpl(cb_type, func) else: # We got some tags, so we need to produce the real decorator tags = tags_or_func def d(func): return HookImpl(cb_type, func, tags) return d return decorator_wrapper

Decorator to wrap hook definitions in. Registers hook.

python

train

pennersr/django-allauth

allauth/account/adapter.py

https://github.com/pennersr/django-allauth/blob/f70cb3d622f992f15fe9b57098e0b328445b664e/allauth/account/adapter.py#L139-L153

def get_login_redirect_url(self, request): """ Returns the default URL to redirect to after logging in. Note that URLs passed explicitly (e.g. by passing along a `next` GET parameter) take precedence over the value returned here. """ assert request.user.is_authenticated url = getattr(settings, "LOGIN_REDIRECT_URLNAME", None) if url: warnings.warn("LOGIN_REDIRECT_URLNAME is deprecated, simply" " use LOGIN_REDIRECT_URL with a URL name", DeprecationWarning) else: url = settings.LOGIN_REDIRECT_URL return resolve_url(url)

Returns the default URL to redirect to after logging in. Note that URLs passed explicitly (e.g. by passing along a `next` GET parameter) take precedence over the value returned here.

python

train

spotify/luigi

luigi/tools/range.py

https://github.com/spotify/luigi/blob/c5eca1c3c3ee2a7eb612486192a0da146710a1e9/luigi/tools/range.py#L316-L321

def parameters_to_datetime(self, p): """ Given a dictionary of parameters, will extract the ranged task parameter value """ dt = p[self._param_name] return datetime(dt.year, dt.month, dt.day)

Given a dictionary of parameters, will extract the ranged task parameter value

python

train

fossasia/knittingpattern

knittingpattern/convert/KnittingPatternToSVG.py

https://github.com/fossasia/knittingpattern/blob/8e608896b0ab82fea1ca9fbfa2b4ee023d8c8027/knittingpattern/convert/KnittingPatternToSVG.py#L74-L97

def _register_instruction_in_defs(self, instruction): """Create a definition for the instruction. :return: the id of a symbol in the defs for the specified :paramref:`instruction` :rtype: str If no symbol yet exists in the defs for the :paramref:`instruction` a symbol is created and saved using :meth:`_make_symbol`. """ type_ = instruction.type color_ = instruction.color instruction_to_svg_dict = \ self._instruction_to_svg.instruction_to_svg_dict instruction_id = "{}:{}".format(type_, color_) defs_id = instruction_id + ":defs" if instruction_id not in self._instruction_type_color_to_symbol: svg_dict = instruction_to_svg_dict(instruction) self._compute_scale(instruction_id, svg_dict) symbol = self._make_definition(svg_dict, instruction_id) self._instruction_type_color_to_symbol[defs_id] = \ symbol[DEFINITION_HOLDER].pop("defs", {}) self._instruction_type_color_to_symbol[instruction_id] = symbol return instruction_id

Create a definition for the instruction. :return: the id of a symbol in the defs for the specified :paramref:`instruction` :rtype: str If no symbol yet exists in the defs for the :paramref:`instruction` a symbol is created and saved using :meth:`_make_symbol`.

python

valid

Seeed-Studio/wio-cli

wio/commands/cmd_state.py

https://github.com/Seeed-Studio/wio-cli/blob/ce83f4c2d30be7f72d1a128acd123dfc5effa563/wio/commands/cmd_state.py#L6-L34

def cli(wio): ''' Login state. \b DOES: Display login email, token, server url. \b USE: wio state ''' user_token = wio.config.get("token", None) mserver_url = wio.config.get("mserver", None) if not mserver_url or not user_token: click.echo(click.style('>> ', fg='red') + "Please login, use " + click.style("wio login", fg='green')) return email = wio.config.get("email",None) server = wio.config.get("server",None) token = wio.config.get("token",None) click.secho('> ', fg='green', nl=False) click.echo("server: " + click.style(server, fg='green', bold=True) + ', ' + click.style(mserver_url, fg='green', bold=True)) click.secho('> ', fg='green', nl=False) click.echo("email: " + click.style(email, fg='green', bold=True)) click.secho('> ', fg='green', nl=False) click.echo("token: " + click.style(token, fg='green', bold=True))

Login state. \b DOES: Display login email, token, server url. \b USE: wio state

python

train

PmagPy/PmagPy

pmagpy/pmagplotlib.py

https://github.com/PmagPy/PmagPy/blob/c7984f8809bf40fe112e53dcc311a33293b62d0b/pmagpy/pmagplotlib.py#L1637-L1666

def plot_evec(fignum, Vs, symsize, title): """ plots eigenvector directions of S vectors Paramters ________ fignum : matplotlib figure number Vs : nested list of eigenvectors symsize : size in pts for symbol title : title for plot """ # plt.figure(num=fignum) plt.text(-1.1, 1.15, title) # plot V1s as squares, V2s as triangles and V3s as circles symb, symkey = ['s', 'v', 'o'], 0 col = ['r', 'b', 'k'] # plot V1s rec, V2s blue, V3s black for VEC in range(3): X, Y = [], [] for Vdirs in Vs: # # # plot the V1 data first # XY = pmag.dimap(Vdirs[VEC][0], Vdirs[VEC][1]) X.append(XY[0]) Y.append(XY[1]) plt.scatter(X, Y, s=symsize, marker=symb[VEC], c=col[VEC], edgecolors='none') plt.axis("equal")

plots eigenvector directions of S vectors Paramters ________ fignum : matplotlib figure number Vs : nested list of eigenvectors symsize : size in pts for symbol title : title for plot

python

train

slundberg/shap

shap/benchmark/models.py

https://github.com/slundberg/shap/blob/b280cb81d498b9d98565cad8dd16fc88ae52649f/shap/benchmark/models.py#L133-L141

def cric__lasso(): """ Lasso Regression """ model = sklearn.linear_model.LogisticRegression(penalty="l1", C=0.002) # we want to explain the raw probability outputs of the trees model.predict = lambda X: model.predict_proba(X)[:,1] return model

Lasso Regression

python

train

materialsproject/pymatgen

pymatgen/io/feff/inputs.py

https://github.com/materialsproject/pymatgen/blob/4ca558cf72f8d5f8a1f21dfdfc0181a971c186da/pymatgen/io/feff/inputs.py#L925-L941

def get_absorbing_atom_symbol_index(absorbing_atom, structure): """ Return the absorbing atom symboll and site index in the given structure. Args: absorbing_atom (str/int): symbol or site index structure (Structure) Returns: str, int: symbol and site index """ if isinstance(absorbing_atom, str): return absorbing_atom, structure.indices_from_symbol(absorbing_atom)[0] elif isinstance(absorbing_atom, int): return str(structure[absorbing_atom].specie), absorbing_atom else: raise ValueError("absorbing_atom must be either specie symbol or site index")

Return the absorbing atom symboll and site index in the given structure. Args: absorbing_atom (str/int): symbol or site index structure (Structure) Returns: str, int: symbol and site index

python

train

globocom/GloboNetworkAPI-client-python

networkapiclient/ApiV4As.py

https://github.com/globocom/GloboNetworkAPI-client-python/blob/cf34f913da48d9abbf750114f5d2ac4b2dde137d/networkapiclient/ApiV4As.py#L36-L49

def search(self, **kwargs): """ Method to search asns based on extends search. :param search: Dict containing QuerySets to find asns. :param include: Array containing fields to include on response. :param exclude: Array containing fields to exclude on response. :param fields: Array containing fields to override default fields. :param kind: Determine if result will be detailed ('detail') or basic ('basic'). :return: Dict containing asns """ return super(ApiV4As, self).get(self.prepare_url( 'api/v4/as/', kwargs))

Method to search asns based on extends search. :param search: Dict containing QuerySets to find asns. :param include: Array containing fields to include on response. :param exclude: Array containing fields to exclude on response. :param fields: Array containing fields to override default fields. :param kind: Determine if result will be detailed ('detail') or basic ('basic'). :return: Dict containing asns

python

train