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ska-sa/purr
Purr/Plugins/local_pychart/arrow.py
https://github.com/ska-sa/purr/blob/4c848768d0485d0f88b30850d0d5372221b21b66/Purr/Plugins/local_pychart/arrow.py#L111-L146
def draw(self, points, can=None): """Parameter <points> specifies the list of points the arrow traverses through. It should contain at least two points, i.e., the tail and tip. Parameter <can> is an optional parameter that specifies the output. <<canvas>> """ if can == None: can = canvas.default_canvas() assert self.check_integrity() xtip = points[-1][0] ytip = points[-1][1] xtail = points[-2][0] ytail = points[-2][1] can.newpath() can.set_line_style(self.line_style) if len(points) > 2: can.moveto(points[0][0], points[0][1]) for i in range(1, len(points) - 1): can.lineto(points[i][0], points[i][1]) draw_arrowbody(can, xscale(xtail), yscale(ytail), yscale(xtip), yscale(ytip), nscale(self.head_len)) can.set_fill_color(self.head_color) draw_arrowhead(can, xscale(xtail), yscale(ytail), xscale(xtip), yscale(ytip), nscale(self.thickness), nscale(self.head_len), self.head_style) can.setbb(xtail, ytail) can.setbb(xtip, ytip)
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Parameter <points> specifies the list of points the arrow traverses through. It should contain at least two points, i.e., the tail and tip. Parameter <can> is an optional parameter that specifies the output. <<canvas>>
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python
train
nosedjango/nosedjango
nosedjango/nosedjango.py
https://github.com/nosedjango/nosedjango/blob/cd4d06857c88291769bc38e5c9573f43b7ffcd6a/nosedjango/nosedjango.py#L232-L317
def begin(self): """ Create the test database and schema, if needed, and switch the connection over to that database. Then call install() to install all apps listed in the loaded settings module. """ for plugin in self.nose_config.plugins.plugins: if getattr(plugin, 'django_plugin', False): self.django_plugins.append(plugin) os.environ['DJANGO_SETTINGS_MODULE'] = self.settings_module if self.conf.addPaths: map(add_path, self.conf.where) try: __import__(self.settings_module) self.settings_path = self.settings_module except ImportError: # Settings module is not found in PYTHONPATH. Try to do # some funky backwards crawling in directory tree, ie. add # the working directory (and any package parents) to # sys.path before trying to import django modules; # otherwise, they won't be able to find project.settings # if the working dir is project/ or project/.. self.settings_path = get_settings_path(self.settings_module) if not self.settings_path: # short circuit if no settings file can be found raise RuntimeError("Can't find Django settings file!") add_path(self.settings_path) sys.path.append(self.settings_path) from django.conf import settings # Some Django code paths evaluate differently # between DEBUG and not DEBUG. Example of this include the url # dispatcher when 404's are hit. Django's own test runner forces DEBUG # to be off. settings.DEBUG = False self.call_plugins_method('beforeConnectionSetup', settings) from django.core import management from django.test.utils import setup_test_environment if hasattr(settings, 'DATABASES'): self.old_db = settings.DATABASES['default']['NAME'] else: self.old_db = settings.DATABASE_NAME from django.db import connections self._monkeypatch_test_classes() for connection in connections.all(): self.call_plugins_method( 'beforeTestSetup', settings, setup_test_environment, connection) try: setup_test_environment() except RuntimeError: # Django 1.11 + multiprocess this happens. pass import django if hasattr(django, 'setup'): django.setup() self.call_plugins_method('afterTestSetup', settings) management.get_commands() # Ensure that nothing (eg. South) steals away our syncdb command if self.django_version < self.DJANGO_1_7: management._commands['syncdb'] = 'django.core' for connection in connections.all(): self.call_plugins_method( 'beforeTestDb', settings, connection, management) connection.creation.create_test_db( verbosity=self.verbosity, autoclobber=True, ) logger.debug("Running syncdb") self._num_syncdb_calls += 1 self.call_plugins_method('afterTestDb', settings, connection) self.store_original_transaction_methods()
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Create the test database and schema, if needed, and switch the connection over to that database. Then call install() to install all apps listed in the loaded settings module.
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python
valid
mwgielen/jackal
jackal/scripts/eternalblue.py
https://github.com/mwgielen/jackal/blob/7fe62732eb5194b7246215d5277fb37c398097bf/jackal/scripts/eternalblue.py#L123-L134
def detect_os(self, ip): """ Runs the checker.py scripts to detect the os. """ process = subprocess.run(['python2', os.path.join(self.datadir, 'MS17-010', 'checker.py'), str(ip)], stdout=subprocess.PIPE) out = process.stdout.decode('utf-8').split('\n') system_os = '' for line in out: if line.startswith('Target OS:'): system_os = line.replace('Target OS: ', '') break return system_os
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Runs the checker.py scripts to detect the os.
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python
valid
Capitains/MyCapytain
MyCapytain/resolvers/cts/api.py
https://github.com/Capitains/MyCapytain/blob/b11bbf6b6ae141fc02be70471e3fbf6907be6593/MyCapytain/resolvers/cts/api.py#L44-L65
def getTextualNode(self, textId, subreference=None, prevnext=False, metadata=False): """ Retrieve a text node from the API :param textId: CtsTextMetadata Identifier :type textId: str :param subreference: CapitainsCtsPassage Reference :type subreference: str :param prevnext: Retrieve graph representing previous and next passage :type prevnext: boolean :param metadata: Retrieve metadata about the passage and the text :type metadata: boolean :return: CapitainsCtsPassage :rtype: CapitainsCtsPassage """ text = CtsText( urn=textId, retriever=self.endpoint ) if metadata or prevnext: return text.getPassagePlus(reference=subreference) else: return text.getTextualNode(subreference=subreference)
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Retrieve a text node from the API :param textId: CtsTextMetadata Identifier :type textId: str :param subreference: CapitainsCtsPassage Reference :type subreference: str :param prevnext: Retrieve graph representing previous and next passage :type prevnext: boolean :param metadata: Retrieve metadata about the passage and the text :type metadata: boolean :return: CapitainsCtsPassage :rtype: CapitainsCtsPassage
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python
train
HewlettPackard/python-hpOneView
hpOneView/oneview_client.py
https://github.com/HewlettPackard/python-hpOneView/blob/3c6219723ef25e6e0c83d44a89007f89bc325b89/hpOneView/oneview_client.py#L1117-L1126
def appliance_device_snmp_v1_trap_destinations(self): """ Gets the ApplianceDeviceSNMPv1TrapDestinations API client. Returns: ApplianceDeviceSNMPv1TrapDestinations: """ if not self.__appliance_device_snmp_v1_trap_destinations: self.__appliance_device_snmp_v1_trap_destinations = ApplianceDeviceSNMPv1TrapDestinations(self.__connection) return self.__appliance_device_snmp_v1_trap_destinations
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Gets the ApplianceDeviceSNMPv1TrapDestinations API client. Returns: ApplianceDeviceSNMPv1TrapDestinations:
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python
train
apple/turicreate
deps/src/libxml2-2.9.1/python/libxml2.py
https://github.com/apple/turicreate/blob/74514c3f99e25b46f22c6e02977fe3da69221c2e/deps/src/libxml2-2.9.1/python/libxml2.py#L6628-L6639
def GetRemainder(self): """Method to get the remainder of the buffered XML. this method stops the parser, set its state to End Of File and return the input stream with what is left that the parser did not use. The implementation is not good, the parser certainly procgressed past what's left in reader->input, and there is an allocation problem. Best would be to rewrite it differently. """ ret = libxml2mod.xmlTextReaderGetRemainder(self._o) if ret is None:raise treeError('xmlTextReaderGetRemainder() failed') __tmp = inputBuffer(_obj=ret) return __tmp
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Method to get the remainder of the buffered XML. this method stops the parser, set its state to End Of File and return the input stream with what is left that the parser did not use. The implementation is not good, the parser certainly procgressed past what's left in reader->input, and there is an allocation problem. Best would be to rewrite it differently.
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python
train
projecthamster/hamster
src/hamster/lib/graphics.py
https://github.com/projecthamster/hamster/blob/ca5254eff53172796ddafc72226c394ed1858245/src/hamster/lib/graphics.py#L58-L89
def parse(self, color): """parse string or a color tuple into color usable for cairo (all values in the normalized (0..1) range""" assert color is not None #parse color into rgb values if isinstance(color, str): match = self.hex_color_long.match(color) if match: color = [int(color, 16) / 65535.0 for color in match.groups()] else: match = self.hex_color_normal.match(color) if match: color = [int(color, 16) / 255.0 for color in match.groups()] else: match = self.hex_color_short.match(color) color = [int(color + color, 16) / 255.0 for color in match.groups()] elif isinstance(color, gdk.Color): color = [color.red / 65535.0, color.green / 65535.0, color.blue / 65535.0] elif isinstance(color, (list, tuple)): # otherwise we assume we have color components in 0..255 range if color[0] > 1 or color[1] > 1 or color[2] > 1: color = [c / 255.0 for c in color] else: color = [color.red, color.green, color.blue] return color
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parse string or a color tuple into color usable for cairo (all values in the normalized (0..1) range
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python
train
bioidiap/bob.ip.facedetect
bob/ip/facedetect/script/plot_froc.py
https://github.com/bioidiap/bob.ip.facedetect/blob/601da5141ca7302ad36424d1421b33190ba46779/bob/ip/facedetect/script/plot_froc.py#L168-L225
def main(command_line_arguments=None): """Reads score files, computes error measures and plots curves.""" args = command_line_options(command_line_arguments) # get some colors for plotting cmap = mpl.cm.get_cmap(name='hsv') count = len(args.files) + (len(args.baselines) if args.baselines else 0) colors = [cmap(i) for i in numpy.linspace(0, 1.0, count+1)] # First, read the score files logger.info("Loading %d score files" % len(args.files)) scores = [read_score_file(os.path.join(args.directory, f)) for f in args.files] false_alarms = [] detection_rate = [] logger.info("Computing FROC curves") for score in scores: # compute some thresholds tmin = min(score[2]) tmax = max(score[2]) count = 100 thresholds = [tmin + float(x)/count * (tmax - tmin) for x in range(count+2)] false_alarms.append([]) detection_rate.append([]) for threshold in thresholds: detection_rate[-1].append(numpy.count_nonzero(numpy.array(score[1]) >= threshold) / float(score[0])) false_alarms[-1].append(numpy.count_nonzero(numpy.array(score[2]) >= threshold)) # to display 0 in a semilogx plot, we have to add a little # false_alarms[-1][-1] += 1e-8 # also read baselines if args.baselines is not None: for baseline in args.baselines: dr = [] fa = [] with open(os.path.join(args.baseline_directory, baseline)) as f: for line in f: splits = line.rstrip().split() dr.append(float(splits[0])) fa.append(int(splits[1])) false_alarms.append(fa) detection_rate.append(dr) logger.info("Plotting FROC curves to file '%s'", args.output) # create a multi-page PDF for the ROC curve pdf = PdfPages(args.output) figure = _plot_froc(false_alarms, detection_rate, colors, args.legends, args.title, args.max) mpl.xlabel('False Alarm (of %d pruned)' % len(scores[0][2])) mpl.ylabel('Detection Rate in \%% (total %d faces)' % scores[0][0]) pdf.savefig(figure) pdf.close() if args.count_detections: for i, f in enumerate(args.files): det, all = count_detections(f) print("The number of detected faces for %s is %d out of %d" % (args.legends[i], det, all))
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Reads score files, computes error measures and plots curves.
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python
train
TankerHQ/python-cli-ui
cli_ui/__init__.py
https://github.com/TankerHQ/python-cli-ui/blob/4c9928827cea06cf80e6a1f5bd86478d8566863f/cli_ui/__init__.py#L608-L621
def did_you_mean(message: str, user_input: str, choices: Sequence[str]) -> str: """ Given a list of choices and an invalid user input, display the closest items in the list that match the input. """ if not choices: return message else: result = { difflib.SequenceMatcher(a=user_input, b=choice).ratio(): choice for choice in choices } message += "\nDid you mean: %s?" % result[max(result)] return message
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Given a list of choices and an invalid user input, display the closest items in the list that match the input.
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python
train
spotify/gordon-gcp
src/gordon_gcp/clients/http.py
https://github.com/spotify/gordon-gcp/blob/5ab19e3c2fe6ace72ee91e2ef1a1326f90b805da/src/gordon_gcp/clients/http.py#L72-L82
async def valid_token_set(self): """Check for validity of token, and refresh if none or expired.""" is_valid = False if self._auth_client.token: # Account for a token near expiration now = datetime.datetime.utcnow() skew = datetime.timedelta(seconds=60) if self._auth_client.expiry > (now + skew): is_valid = True return is_valid
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Check for validity of token, and refresh if none or expired.
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python
train
WZBSocialScienceCenter/tmtoolkit
tmtoolkit/topicmod/visualize.py
https://github.com/WZBSocialScienceCenter/tmtoolkit/blob/ca8b9d072e37ccc82b533f47d48bd9755722305b/tmtoolkit/topicmod/visualize.py#L124-L179
def plot_doc_topic_heatmap(fig, ax, doc_topic_distrib, doc_labels, topic_labels=None, which_documents=None, which_document_indices=None, which_topics=None, which_topic_indices=None, xaxislabel=None, yaxislabel=None, **kwargs): """ Plot a heatmap for a document-topic distribution `doc_topic_distrib` to a matplotlib Figure `fig` and Axes `ax` using `doc_labels` as document labels on the y-axis and topics from 1 to `n_topics=doc_topic_distrib.shape[1]` on the x-axis. Custom topic labels can be passed as `topic_labels`. A subset of documents can be specified either with a sequence `which_documents` containing a subset of document labels from `doc_labels` or `which_document_indices` containing a sequence of document indices. A subset of topics can be specified either with a sequence `which_topics` containing sequence of numbers between [1, n_topics] or `which_topic_indices` which is a number between [0, n_topics-1] Additional arguments can be passed via `kwargs` to `plot_heatmap`. Please note that it is almost always necessary to select a subset of your document-topic distribution with the `which_documents` or `which_topics` parameters, as otherwise the amount of data to be plotted will be too high to give a reasonable picture. """ if which_documents is not None and which_document_indices is not None: raise ValueError('only `which_documents` or `which_document_indices` can be set, not both') if which_topics is not None and which_topic_indices is not None: raise ValueError('only `which_topics` or `which_topic_indices` can be set, not both') if which_documents is not None: which_document_indices = np.where(np.isin(doc_labels, which_documents))[0] if which_topics is not None: which_topic_indices = np.array(which_topics) - 1 select_distrib_subset = False if topic_labels is None: topic_labels = np.array(range(1, doc_topic_distrib.shape[1]+1)) elif not isinstance(topic_labels, np.ndarray): topic_labels = np.array(topic_labels) if which_document_indices is not None: select_distrib_subset = True doc_labels = np.array(doc_labels)[which_document_indices] if which_topic_indices is not None: select_distrib_subset = True topic_labels = topic_labels[which_topic_indices] if select_distrib_subset: doc_topic_distrib = mat2d_window_from_indices(doc_topic_distrib, which_document_indices, which_topic_indices) return plot_heatmap(fig, ax, doc_topic_distrib, xaxislabel=xaxislabel or 'topic', yaxislabel=yaxislabel or 'document', xticklabels=topic_labels, yticklabels=doc_labels, **kwargs)
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python
train
TheGhouls/oct
oct/results/output.py
https://github.com/TheGhouls/oct/blob/7e9bddeb3b8495a26442b1c86744e9fb187fe88f/oct/results/output.py#L24-L33
def print_infos(results): """Print informations in standard output :param ReportResults results: the report result containing all compiled informations """ print('transactions: %i' % results.total_transactions) print('timers: %i' % results.total_timers) print('errors: %i' % results.total_errors) print('test start: %s' % results.start_datetime) print('test finish: %s\n' % results.finish_datetime)
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Print informations in standard output :param ReportResults results: the report result containing all compiled informations
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python
train
sixty-north/cosmic-ray
src/cosmic_ray/modules.py
https://github.com/sixty-north/cosmic-ray/blob/c654e074afbb7b7fcbc23359083c1287c0d3e991/src/cosmic_ray/modules.py#L7-L20
def find_modules(module_path): """Find all modules in the module (possibly package) represented by `module_path`. Args: module_path: A pathlib.Path to a Python package or module. Returns: An iterable of paths Python modules (i.e. *py files). """ if module_path.is_file(): if module_path.suffix == '.py': yield module_path elif module_path.is_dir(): pyfiles = glob.glob('{}/**/*.py'.format(module_path), recursive=True) yield from (Path(pyfile) for pyfile in pyfiles)
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Find all modules in the module (possibly package) represented by `module_path`. Args: module_path: A pathlib.Path to a Python package or module. Returns: An iterable of paths Python modules (i.e. *py files).
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python
train
treycucco/pyebnf
pyebnf/_hand_written_parser.py
https://github.com/treycucco/pyebnf/blob/3634ddabbe5d73508bcc20f4a591f86a46634e1d/pyebnf/_hand_written_parser.py#L226-L234
def operator(self, text): """operator = "|" | "." | "," | "-";""" self._attempting(text) return alternation([ "|", ".", ",", "-" ])(text).retyped(TokenType.operator)
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operator = "|" | "." | "," | "-";
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python
test
mlperf/training
reinforcement/tensorflow/minigo/validate.py
https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/validate.py#L47-L65
def validate(*tf_records): """Validate a model's performance on a set of holdout data.""" if FLAGS.use_tpu: def _input_fn(params): return preprocessing.get_tpu_input_tensors( params['batch_size'], tf_records, filter_amount=1.0) else: def _input_fn(): return preprocessing.get_input_tensors( FLAGS.train_batch_size, tf_records, filter_amount=1.0, shuffle_examples=False) steps = FLAGS.examples_to_validate // FLAGS.train_batch_size if FLAGS.use_tpu: steps //= FLAGS.num_tpu_cores estimator = dual_net.get_estimator() with utils.logged_timer("Validating"): estimator.evaluate(_input_fn, steps=steps, name=FLAGS.validate_name)
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Validate a model's performance on a set of holdout data.
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python
train
charlesthomas/proauth2
proauth2/async_proauth2.py
https://github.com/charlesthomas/proauth2/blob/f88c8df966a1802414047ed304d02df1dd520097/proauth2/async_proauth2.py#L109-L120
def authenticate_token(self, token, callback): ''' authenticate_token checks the passed token and returns the user_id it is associated with. it is assumed that this method won't be directly exposed to the oauth client, but some kind of framework or wrapper. this allows the framework to have the user_id without doing additional DB calls. ''' token_data = yield Task(self.data_store.fetch, 'tokens', token=token) if not token_data: raise Proauth2Error('access_denied', 'token does not exist or has been revoked') callback(token_data['user_id'])
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authenticate_token checks the passed token and returns the user_id it is associated with. it is assumed that this method won't be directly exposed to the oauth client, but some kind of framework or wrapper. this allows the framework to have the user_id without doing additional DB calls.
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python
valid
johnwheeler/flask-ask
flask_ask/cache.py
https://github.com/johnwheeler/flask-ask/blob/fe407646ae404a8c90b363c86d5c4c201b6a5580/flask_ask/cache.py#L65-L80
def top_stream(cache, user_id): """ Peek at the top of the stack in the cache. :param cache: werkzeug BasicCache-like object :param user_id: id of user, used as key in cache :return: top item in user's cached stack, otherwise None """ if not user_id: return None stack = cache.get(user_id) if stack is None: return None return stack.pop()
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Peek at the top of the stack in the cache. :param cache: werkzeug BasicCache-like object :param user_id: id of user, used as key in cache :return: top item in user's cached stack, otherwise None
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python
train
latchset/jwcrypto
jwcrypto/jwk.py
https://github.com/latchset/jwcrypto/blob/961df898dc08f63fe3d900f2002618740bc66b4a/jwcrypto/jwk.py#L490-L501
def from_json(cls, key): """Creates a RFC 7517 JWK from the standard JSON format. :param key: The RFC 7517 representation of a JWK. """ obj = cls() try: jkey = json_decode(key) except Exception as e: # pylint: disable=broad-except raise InvalidJWKValue(e) obj.import_key(**jkey) return obj
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Creates a RFC 7517 JWK from the standard JSON format. :param key: The RFC 7517 representation of a JWK.
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python
train
twisted/mantissa
xmantissa/people.py
https://github.com/twisted/mantissa/blob/53e5502aba23ce99be78b27f923a276593033fe8/xmantissa/people.py#L798-L819
def _gatherPluginMethods(self, methodName): """ Walk through each L{IOrganizerPlugin} powerup, yielding the bound method if the powerup implements C{methodName}. Upon encountering a plugin which fails to implement it, issue a L{PendingDeprecationWarning}. @param methodName: The name of a L{IOrganizerPlugin} method. @type methodName: C{str} @return: Iterable of methods. """ for plugin in self.getOrganizerPlugins(): implementation = getattr(plugin, methodName, None) if implementation is not None: yield implementation else: warn( ('IOrganizerPlugin now has the %r method, %s' ' did not implement it') % ( methodName, plugin.__class__), category=PendingDeprecationWarning)
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Walk through each L{IOrganizerPlugin} powerup, yielding the bound method if the powerup implements C{methodName}. Upon encountering a plugin which fails to implement it, issue a L{PendingDeprecationWarning}. @param methodName: The name of a L{IOrganizerPlugin} method. @type methodName: C{str} @return: Iterable of methods.
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python
train
huggingface/pytorch-pretrained-BERT
examples/run_squad.py
https://github.com/huggingface/pytorch-pretrained-BERT/blob/b832d5bb8a6dfc5965015b828e577677eace601e/examples/run_squad.py#L741-L761
def _compute_softmax(scores): """Compute softmax probability over raw logits.""" if not scores: return [] max_score = None for score in scores: if max_score is None or score > max_score: max_score = score exp_scores = [] total_sum = 0.0 for score in scores: x = math.exp(score - max_score) exp_scores.append(x) total_sum += x probs = [] for score in exp_scores: probs.append(score / total_sum) return probs
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Compute softmax probability over raw logits.
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python
train
iotile/coretools
transport_plugins/bled112/iotile_transport_bled112/bgapi_structures.py
https://github.com/iotile/coretools/blob/2d794f5f1346b841b0dcd16c9d284e9bf2f3c6ec/transport_plugins/bled112/iotile_transport_bled112/bgapi_structures.py#L27-L41
def process_gatt_service(services, event): """Process a BGAPI event containing a GATT service description and add it to a dictionary Args: services (dict): A dictionary of discovered services that is updated with this event event (BGAPIPacket): An event containing a GATT service """ length = len(event.payload) - 5 handle, start, end, uuid = unpack('<BHH%ds' % length, event.payload) uuid = process_uuid(uuid) services[uuid] = {'uuid_raw': uuid, 'start_handle': start, 'end_handle': end}
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Process a BGAPI event containing a GATT service description and add it to a dictionary Args: services (dict): A dictionary of discovered services that is updated with this event event (BGAPIPacket): An event containing a GATT service
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python
train
bokeh/bokeh
bokeh/models/transforms.py
https://github.com/bokeh/bokeh/blob/dc8cf49e4e4302fd38537ad089ece81fbcca4737/bokeh/models/transforms.py#L170-L206
def from_coffeescript(cls, func, v_func, args={}): ''' Create a ``CustomJSTransform`` instance from a pair of CoffeeScript snippets. The function bodies are translated to JavaScript functions using node and therefore require return statements. The ``func`` snippet namespace will contain the variable ``x`` (the untransformed value) at render time. The ``v_func`` snippet namespace will contain the variable ``xs`` (the untransformed vector) at render time. Example: .. code-block:: coffeescript func = "return Math.cos(x)" v_func = "return [Math.cos(x) for x in xs]" transform = CustomJSTransform.from_coffeescript(func, v_func) Args: func (str) : a coffeescript snippet to transform a single ``x`` value v_func (str) : a coffeescript snippet function to transform a vector ``xs`` Returns: CustomJSTransform ''' compiled = nodejs_compile(func, lang="coffeescript", file="???") if "error" in compiled: raise CompilationError(compiled.error) v_compiled = nodejs_compile(v_func, lang="coffeescript", file="???") if "error" in v_compiled: raise CompilationError(v_compiled.error) return cls(func=compiled.code, v_func=v_compiled.code, args=args)
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Create a ``CustomJSTransform`` instance from a pair of CoffeeScript snippets. The function bodies are translated to JavaScript functions using node and therefore require return statements. The ``func`` snippet namespace will contain the variable ``x`` (the untransformed value) at render time. The ``v_func`` snippet namespace will contain the variable ``xs`` (the untransformed vector) at render time. Example: .. code-block:: coffeescript func = "return Math.cos(x)" v_func = "return [Math.cos(x) for x in xs]" transform = CustomJSTransform.from_coffeescript(func, v_func) Args: func (str) : a coffeescript snippet to transform a single ``x`` value v_func (str) : a coffeescript snippet function to transform a vector ``xs`` Returns: CustomJSTransform
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python
train
JoelBender/bacpypes
sandbox/io.py
https://github.com/JoelBender/bacpypes/blob/4111b8604a16fa2b7f80d8104a43b9f3e28dfc78/sandbox/io.py#L1287-L1297
def abort(err): """Abort everything, everywhere.""" if _debug: abort._debug("abort %r", err) # start with the server if IOServer._highlander: IOServer._highlander.abort(err) # now do everything local for controller in _local_controllers.values(): controller.abort(err)
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Abort everything, everywhere.
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python
train
dwavesystems/dimod
dimod/binary_quadratic_model.py
https://github.com/dwavesystems/dimod/blob/beff1b7f86b559d923ac653c1de6d593876d6d38/dimod/binary_quadratic_model.py#L459-L504
def add_variables_from(self, linear, vartype=None): """Add variables and/or linear biases to a binary quadratic model. Args: linear (dict[variable, bias]/iterable[(variable, bias)]): A collection of variables and their linear biases to add to the model. If a dict, keys are variables in the binary quadratic model and values are biases. Alternatively, an iterable of (variable, bias) pairs. Variables can be any python object that is a valid dict key. Many methods and functions expect the biases to be numbers but this is not explicitly checked. If any variable already exists in the model, its bias is added to the variable's current linear bias. vartype (:class:`.Vartype`, optional, default=None): Vartype of the given bias. If None, the vartype of the binary quadratic model is used. Valid values are :class:`.Vartype.SPIN` or :class:`.Vartype.BINARY`. Examples: This example creates creates an empty Ising model, adds two variables, and subsequently adds to the bias of the one while adding a new, third, variable. >>> import dimod ... >>> bqm = dimod.BinaryQuadraticModel({}, {}, 0.0, dimod.SPIN) >>> len(bqm.linear) 0 >>> bqm.add_variables_from({'a': .5, 'b': -1.}) >>> 'b' in bqm True >>> bqm.add_variables_from({'b': -1., 'c': 2.0}) >>> bqm.linear['b'] -2.0 """ if isinstance(linear, abc.Mapping): for v, bias in iteritems(linear): self.add_variable(v, bias, vartype=vartype) else: try: for v, bias in linear: self.add_variable(v, bias, vartype=vartype) except TypeError: raise TypeError("expected 'linear' to be a dict or an iterable of 2-tuples.")
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Add variables and/or linear biases to a binary quadratic model. Args: linear (dict[variable, bias]/iterable[(variable, bias)]): A collection of variables and their linear biases to add to the model. If a dict, keys are variables in the binary quadratic model and values are biases. Alternatively, an iterable of (variable, bias) pairs. Variables can be any python object that is a valid dict key. Many methods and functions expect the biases to be numbers but this is not explicitly checked. If any variable already exists in the model, its bias is added to the variable's current linear bias. vartype (:class:`.Vartype`, optional, default=None): Vartype of the given bias. If None, the vartype of the binary quadratic model is used. Valid values are :class:`.Vartype.SPIN` or :class:`.Vartype.BINARY`. Examples: This example creates creates an empty Ising model, adds two variables, and subsequently adds to the bias of the one while adding a new, third, variable. >>> import dimod ... >>> bqm = dimod.BinaryQuadraticModel({}, {}, 0.0, dimod.SPIN) >>> len(bqm.linear) 0 >>> bqm.add_variables_from({'a': .5, 'b': -1.}) >>> 'b' in bqm True >>> bqm.add_variables_from({'b': -1., 'c': 2.0}) >>> bqm.linear['b'] -2.0
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python
train
dyve/django-bootstrap3
bootstrap3/forms.py
https://github.com/dyve/django-bootstrap3/blob/1d4095ba113a1faff228f9592bdad4f0b3aed653/bootstrap3/forms.py#L73-L78
def render_field(field, **kwargs): """ Render a field to a Bootstrap layout """ renderer_cls = get_field_renderer(**kwargs) return renderer_cls(field, **kwargs).render()
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Render a field to a Bootstrap layout
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python
train
bcbio/bcbio-nextgen
bcbio/variation/strelka2.py
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/strelka2.py#L20-L34
def run(align_bams, items, ref_file, assoc_files, region, out_file): """Run strelka2 variant calling, either paired tumor/normal or germline calling. region can be a single region or list of multiple regions for multicore calling. """ call_file = "%s-raw.vcf.gz" % utils.splitext_plus(out_file)[0] strelka_work_dir = "%s-work" % utils.splitext_plus(out_file)[0] paired = vcfutils.get_paired_bams(align_bams, items) if paired: assert paired.normal_bam, "Strelka2 requires a normal sample" call_file = _run_somatic(paired, ref_file, assoc_files, region, call_file, strelka_work_dir) else: call_file = _run_germline(align_bams, items, ref_file, assoc_files, region, call_file, strelka_work_dir) return _af_annotate_and_filter(paired, items, call_file, out_file)
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Run strelka2 variant calling, either paired tumor/normal or germline calling. region can be a single region or list of multiple regions for multicore calling.
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python
train
spookylukey/django-paypal
paypal/pro/creditcard.py
https://github.com/spookylukey/django-paypal/blob/b07d0a3ad91b5c5fe7bb27be3e5d70aabcdef76f/paypal/pro/creditcard.py#L68-L72
def verify(self): """Returns the card type if valid else None.""" if self.is_number() and not self.is_test() and self.is_mod10(): return self.get_type() return None
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Returns the card type if valid else None.
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python
train
Microsoft/azure-devops-python-api
azure-devops/azure/devops/v5_0/work_item_tracking_process/work_item_tracking_process_client.py
https://github.com/Microsoft/azure-devops-python-api/blob/4777ffda2f5052fabbaddb2abe9cb434e0cf1aa8/azure-devops/azure/devops/v5_0/work_item_tracking_process/work_item_tracking_process_client.py#L489-L505
def get_form_layout(self, process_id, wit_ref_name): """GetFormLayout. [Preview API] Gets the form layout. :param str process_id: The ID of the process. :param str wit_ref_name: The reference name of the work item type. :rtype: :class:`<FormLayout> <azure.devops.v5_0.work_item_tracking_process.models.FormLayout>` """ route_values = {} if process_id is not None: route_values['processId'] = self._serialize.url('process_id', process_id, 'str') if wit_ref_name is not None: route_values['witRefName'] = self._serialize.url('wit_ref_name', wit_ref_name, 'str') response = self._send(http_method='GET', location_id='fa8646eb-43cd-4b71-9564-40106fd63e40', version='5.0-preview.1', route_values=route_values) return self._deserialize('FormLayout', response)
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python
train
ejeschke/ginga
ginga/trcalc.py
https://github.com/ejeschke/ginga/blob/a78c893ec6f37a837de851947e9bb4625c597915/ginga/trcalc.py#L556-L617
def calc_image_merge_clip(p1, p2, dst, q1, q2): """ p1 (x1, y1, z1) and p2 (x2, y2, z2) define the extent of the (non-scaled) data shown. The image, defined by region q1, q2 is to be placed at dst in the image (destination may be outside of the actual data array). Refines the modified points (q1', q2') defining the clipped rectangle needed to be cut from the source array and scaled. """ x1, y1 = p1[:2] x2, y2 = p2[:2] dst_x, dst_y = dst[:2] a1, b1 = q1[:2] a2, b2 = q2[:2] src_wd, src_ht = a2 - a1, b2 - b1 # Trim off parts of srcarr that would be "hidden" # to the left and above the dstarr edge. ex = y1 - dst_y if ex > 0: src_ht -= ex dst_y += ex b1 += ex ex = x1 - dst_x if ex > 0: src_wd -= ex dst_x += ex a1 += ex # Trim off parts of srcarr that would be "hidden" # to the right and below dstarr edge. ex = dst_y + src_ht - y2 if ex > 0: src_ht -= ex b2 -= ex ex = dst_x + src_wd - x2 if ex > 0: src_wd -= ex a2 -= ex if len(p1) > 2: # 3D image z1, z2, dst_z, c1, c2 = p1[2], p2[2], dst[2], q1[2], q2[2] src_dp = c2 - c1 ex = z1 - dst_z if ex > 0: src_dp -= ex dst_z += ex c1 += ex ex = dst_z + src_dp - z2 if ex > 0: src_dp -= ex c2 -= ex return ((dst_x, dst_y, dst_z), (a1, b1, c1), (a2, b2, c2)) else: return ((dst_x, dst_y), (a1, b1), (a2, b2))
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p1 (x1, y1, z1) and p2 (x2, y2, z2) define the extent of the (non-scaled) data shown. The image, defined by region q1, q2 is to be placed at dst in the image (destination may be outside of the actual data array). Refines the modified points (q1', q2') defining the clipped rectangle needed to be cut from the source array and scaled.
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python
train
adafruit/Adafruit_Python_GPIO
Adafruit_GPIO/PWM.py
https://github.com/adafruit/Adafruit_Python_GPIO/blob/a92a23d6b5869663b2bc1ccf78bb11585076a9c4/Adafruit_GPIO/PWM.py#L55-L63
def set_duty_cycle(self, pin, dutycycle): """Set percent duty cycle of PWM output on specified pin. Duty cycle must be a value 0.0 to 100.0 (inclusive). """ if dutycycle < 0.0 or dutycycle > 100.0: raise ValueError('Invalid duty cycle value, must be between 0.0 to 100.0 (inclusive).') if pin not in self.pwm: raise ValueError('Pin {0} is not configured as a PWM. Make sure to first call start for the pin.'.format(pin)) self.pwm[pin].ChangeDutyCycle(dutycycle)
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Set percent duty cycle of PWM output on specified pin. Duty cycle must be a value 0.0 to 100.0 (inclusive).
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python
valid
PyCQA/pylint
pylint/pyreverse/diagrams.py
https://github.com/PyCQA/pylint/blob/2bf5c61a3ff6ae90613b81679de42c0f19aea600/pylint/pyreverse/diagrams.py#L241-L247
def add_from_depend(self, node, from_module): """add dependencies created by from-imports """ mod_name = node.root().name obj = self.module(mod_name) if from_module not in obj.node.depends: obj.node.depends.append(from_module)
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add dependencies created by from-imports
[ "add", "dependencies", "created", "by", "from", "-", "imports" ]
python
test
uw-it-aca/uw-restclients-canvas
uw_canvas/sections.py
https://github.com/uw-it-aca/uw-restclients-canvas/blob/9845faf33d49a8f06908efc22640c001116d6ea2/uw_canvas/sections.py#L9-L16
def get_section(self, section_id, params={}): """ Return section resource for given canvas section id. https://canvas.instructure.com/doc/api/sections.html#method.sections.show """ url = SECTIONS_API.format(section_id) return CanvasSection(data=self._get_resource(url, params=params))
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Return section resource for given canvas section id. https://canvas.instructure.com/doc/api/sections.html#method.sections.show
[ "Return", "section", "resource", "for", "given", "canvas", "section", "id", "." ]
python
test
Yelp/kafka-utils
kafka_utils/kafka_cluster_manager/cmds/command.py
https://github.com/Yelp/kafka-utils/blob/cdb4d64308f3079ee0873250bf7b34d0d94eca50/kafka_utils/kafka_cluster_manager/cmds/command.py#L107-L119
def execute_plan(self, plan, allow_rf_change=False): """Save proposed-plan and execute the same if requested.""" if self.should_execute(): result = self.zk.execute_plan(plan, allow_rf_change=allow_rf_change) if not result: self.log.error('Plan execution unsuccessful.') sys.exit(1) else: self.log.info( 'Plan sent to zookeeper for reassignment successfully.', ) else: self.log.info('Proposed plan won\'t be executed (--apply and confirmation needed).')
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Save proposed-plan and execute the same if requested.
[ "Save", "proposed", "-", "plan", "and", "execute", "the", "same", "if", "requested", "." ]
python
train
cloud9ers/gurumate
environment/lib/python2.7/site-packages/IPython/core/fakemodule.py
https://github.com/cloud9ers/gurumate/blob/075dc74d1ee62a8c6b7a8bf2b271364f01629d1e/environment/lib/python2.7/site-packages/IPython/core/fakemodule.py#L18-L46
def init_fakemod_dict(fm,adict=None): """Initialize a FakeModule instance __dict__. Kept as a standalone function and not a method so the FakeModule API can remain basically empty. This should be considered for private IPython use, used in managing namespaces for %run. Parameters ---------- fm : FakeModule instance adict : dict, optional """ dct = {} # It seems pydoc (and perhaps others) needs any module instance to # implement a __nonzero__ method, so we add it if missing: dct.setdefault('__nonzero__',lambda : True) dct.setdefault('__file__',__file__) if adict is not None: dct.update(adict) # Hard assignment of the object's __dict__. This is nasty but deliberate. fm.__dict__.clear() fm.__dict__.update(dct)
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Initialize a FakeModule instance __dict__. Kept as a standalone function and not a method so the FakeModule API can remain basically empty. This should be considered for private IPython use, used in managing namespaces for %run. Parameters ---------- fm : FakeModule instance adict : dict, optional
[ "Initialize", "a", "FakeModule", "instance", "__dict__", "." ]
python
test
rosenbrockc/fortpy
fortpy/config.py
https://github.com/rosenbrockc/fortpy/blob/1ed0757c52d549e41d9d44bdea68cb89529293a5/fortpy/config.py#L85-L89
def getenvar(self, envar): from os import getenv """Retrieves the value of an environment variable if it exists.""" if getenv(envar) is not None: self._vardict[envar] = getenv(envar)
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Retrieves the value of an environment variable if it exists.
[ "Retrieves", "the", "value", "of", "an", "environment", "variable", "if", "it", "exists", "." ]
python
train
theolind/pymysensors
mysensors/gateway_tcp.py
https://github.com/theolind/pymysensors/blob/a139ab6e2f6b71ebaf37282f69bfd0f7fe6193b6/mysensors/gateway_tcp.py#L76-L108
def _connect(self): """Connect to socket. This should be run in a new thread.""" while self.protocol: _LOGGER.info('Trying to connect to %s', self.server_address) try: sock = socket.create_connection( self.server_address, self.reconnect_timeout) except socket.timeout: _LOGGER.error( 'Connecting to socket timed out for %s', self.server_address) _LOGGER.info( 'Waiting %s secs before trying to connect again', self.reconnect_timeout) time.sleep(self.reconnect_timeout) except OSError: _LOGGER.error( 'Failed to connect to socket at %s', self.server_address) _LOGGER.info( 'Waiting %s secs before trying to connect again', self.reconnect_timeout) time.sleep(self.reconnect_timeout) else: self.tcp_check_timer = time.time() self.tcp_disconnect_timer = time.time() transport = TCPTransport( sock, lambda: self.protocol, self._check_connection) poll_thread = threading.Thread(target=self._poll_queue) self._stop_event.clear() poll_thread.start() transport.start() transport.connect() return
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Connect to socket. This should be run in a new thread.
[ "Connect", "to", "socket", ".", "This", "should", "be", "run", "in", "a", "new", "thread", "." ]
python
train
Robpol86/libnl
libnl/socket_.py
https://github.com/Robpol86/libnl/blob/274e9fdaa39822d06ef70b799ed4a95937a4d923/libnl/socket_.py#L276-L308
def nl_socket_set_buffer_size(sk, rxbuf, txbuf): """Set socket buffer size of Netlink socket. https://github.com/thom311/libnl/blob/libnl3_2_25/lib/socket.c#L675 Sets the socket buffer size of a Netlink socket to the specified values `rxbuf` and `txbuf`. Providing a value of 0 assumes a good default value. Positional arguments: sk -- Netlink socket (nl_sock class instance). rxbuf -- new receive socket buffer size in bytes (integer). txbuf -- new transmit socket buffer size in bytes (integer). Returns: 0 on success or a negative error code. """ rxbuf = 32768 if rxbuf <= 0 else rxbuf txbuf = 32768 if txbuf <= 0 else txbuf if sk.s_fd == -1: return -NLE_BAD_SOCK try: sk.socket_instance.setsockopt(socket.SOL_SOCKET, socket.SO_SNDBUF, txbuf) except OSError as exc: return -nl_syserr2nlerr(exc.errno) try: sk.socket_instance.setsockopt(socket.SOL_SOCKET, socket.SO_RCVBUF, rxbuf) except OSError as exc: return -nl_syserr2nlerr(exc.errno) sk.s_flags |= NL_SOCK_BUFSIZE_SET return 0
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Set socket buffer size of Netlink socket. https://github.com/thom311/libnl/blob/libnl3_2_25/lib/socket.c#L675 Sets the socket buffer size of a Netlink socket to the specified values `rxbuf` and `txbuf`. Providing a value of 0 assumes a good default value. Positional arguments: sk -- Netlink socket (nl_sock class instance). rxbuf -- new receive socket buffer size in bytes (integer). txbuf -- new transmit socket buffer size in bytes (integer). Returns: 0 on success or a negative error code.
[ "Set", "socket", "buffer", "size", "of", "Netlink", "socket", "." ]
python
train
libyal/dtfabric
dtfabric/runtime/data_maps.py
https://github.com/libyal/dtfabric/blob/0d2b5719fa257f6e5c661a406737ebcf8c8db266/dtfabric/runtime/data_maps.py#L1055-L1088
def _LinearMapByteStream( self, byte_stream, byte_offset=0, context=None, **unused_kwargs): """Maps a data type sequence on a byte stream. Args: byte_stream (bytes): byte stream. byte_offset (Optional[int]): offset into the byte stream where to start. context (Optional[DataTypeMapContext]): data type map context. Returns: tuple[object, ...]: mapped values. Raises: MappingError: if the data type definition cannot be mapped on the byte stream. """ elements_data_size = self._data_type_definition.GetByteSize() self._CheckByteStreamSize(byte_stream, byte_offset, elements_data_size) try: struct_tuple = self._operation.ReadFrom(byte_stream[byte_offset:]) mapped_values = map(self._element_data_type_map.MapValue, struct_tuple) except Exception as exception: error_string = ( 'Unable to read: {0:s} from byte stream at offset: {1:d} ' 'with error: {2!s}').format( self._data_type_definition.name, byte_offset, exception) raise errors.MappingError(error_string) if context: context.byte_size = elements_data_size return tuple(mapped_values)
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Maps a data type sequence on a byte stream. Args: byte_stream (bytes): byte stream. byte_offset (Optional[int]): offset into the byte stream where to start. context (Optional[DataTypeMapContext]): data type map context. Returns: tuple[object, ...]: mapped values. Raises: MappingError: if the data type definition cannot be mapped on the byte stream.
[ "Maps", "a", "data", "type", "sequence", "on", "a", "byte", "stream", "." ]
python
train
saltstack/salt
salt/utils/win_update.py
https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/utils/win_update.py#L101-L167
def list(self): ''' Create a dictionary with the details for the updates in the collection. Returns: dict: Details about each update .. code-block:: cfg List of Updates: {'<GUID>': {'Title': <title>, 'KB': <KB>, 'GUID': <the globally unique identifier for the update> 'Description': <description>, 'Downloaded': <has the update been downloaded>, 'Installed': <has the update been installed>, 'Mandatory': <is the update mandatory>, 'UserInput': <is user input required>, 'EULAAccepted': <has the EULA been accepted>, 'Severity': <update severity>, 'NeedsReboot': <is the update installed and awaiting reboot>, 'RebootBehavior': <will the update require a reboot>, 'Categories': [ '<category 1>', '<category 2>', ...] } } Code Example: .. code-block:: python import salt.utils.win_update updates = salt.utils.win_update.Updates() updates.list() ''' # https://msdn.microsoft.com/en-us/library/windows/desktop/aa386099(v=vs.85).aspx if self.count() == 0: return 'Nothing to return' log.debug('Building a detailed report of the results.') # Build a dictionary containing details for each update results = {} for update in self.updates: results[update.Identity.UpdateID] = { 'guid': update.Identity.UpdateID, 'Title': six.text_type(update.Title), 'Type': self.update_types[update.Type], 'Description': update.Description, 'Downloaded': bool(update.IsDownloaded), 'Installed': bool(update.IsInstalled), 'Mandatory': bool(update.IsMandatory), 'EULAAccepted': bool(update.EulaAccepted), 'NeedsReboot': bool(update.RebootRequired), 'Severity': six.text_type(update.MsrcSeverity), 'UserInput': bool(update.InstallationBehavior.CanRequestUserInput), 'RebootBehavior': self.reboot_behavior[ update.InstallationBehavior.RebootBehavior], 'KBs': ['KB' + item for item in update.KBArticleIDs], 'Categories': [item.Name for item in update.Categories] } return results
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python
train
RudolfCardinal/pythonlib
cardinal_pythonlib/dicts.py
https://github.com/RudolfCardinal/pythonlib/blob/0b84cb35f38bd7d8723958dae51b480a829b7227/cardinal_pythonlib/dicts.py#L152-L167
def set_null_values_in_dict(d: Dict[str, Any], null_literals: List[Any]) -> None: """ Within ``d`` (in place), replace any values found in ``null_literals`` with ``None``. """ if not null_literals: return # DO NOT add/delete values to/from a dictionary during iteration, but it # is OK to modify existing keys: # https://stackoverflow.com/questions/6777485 # https://stackoverflow.com/questions/2315520 # https://docs.python.org/3/library/stdtypes.html#dict-views for k, v in d.items(): if v in null_literals: d[k] = None
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Within ``d`` (in place), replace any values found in ``null_literals`` with ``None``.
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python
train
Rapptz/discord.py
discord/ext/commands/cog.py
https://github.com/Rapptz/discord.py/blob/05d4f7f9620ef33635d6ac965b26528e09cdaf5b/discord/ext/commands/cog.py#L223-L225
def get_listeners(self): """Returns a :class:`list` of (name, function) listener pairs that are defined in this cog.""" return [(name, getattr(self, method_name)) for name, method_name in self.__cog_listeners__]
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Returns a :class:`list` of (name, function) listener pairs that are defined in this cog.
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python
train
staugur/Flask-PluginKit
flask_pluginkit/flask_pluginkit.py
https://github.com/staugur/Flask-PluginKit/blob/512aabf87fa13f4dc1082abd08d1d1dcf3b03f16/flask_pluginkit/flask_pluginkit.py#L411-L467
def __getPluginInfo(self, plugin, package_abspath, package_name): """ Organize plugin information. :returns: dict: plugin info """ if not isValidSemver(plugin.__version__): raise VersionError("The plugin version does not conform to the standard named %s" % package_name) try: url = plugin.__url__ except AttributeError: url = None try: license = plugin.__license__ except AttributeError: license = None try: license_file = plugin.__license_file__ except AttributeError: license_file = None try: readme_file = plugin.__readme_file__ except AttributeError: readme_file = None try: plugin_state = plugin.__state__ except AttributeError: plugin_state = "enabled" # 插件状态首先读取`__state`状态值,优先级低于状态文件,ENABLED文件优先级低于DISABLED文件 if os.path.isfile(os.path.join(package_abspath, "ENABLED")): plugin_state = "enabled" if os.path.isfile(os.path.join(package_abspath, "DISABLED")): plugin_state = "disabled" return { "plugin_name": plugin.__plugin_name__, "plugin_package_name": package_name, "plugin_package_abspath": package_abspath, "plugin_description": plugin.__description__, "plugin_version": plugin.__version__, "plugin_author": plugin.__author__, "plugin_url": url, "plugin_license": license, "plugin_license_file": license_file, "plugin_readme_file": readme_file, "plugin_state": plugin_state, "plugin_tpl_path": os.path.join(package_abspath, "templates"), "plugin_ats_path": os.path.join(package_abspath, "static"), "plugin_tep": {}, "plugin_hep": {}, "plugin_bep": {}, "plugin_yep": {} }
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Organize plugin information. :returns: dict: plugin info
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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#L2314-L2339
def create_comment(self, comment, repository_id, pull_request_id, thread_id, project=None): """CreateComment. [Preview API] Create a comment on a specific thread in a pull request (up to 500 comments can be created per thread). :param :class:`<Comment> <azure.devops.v5_1.git.models.Comment>` comment: The comment to create. Comments can be up to 150,000 characters. :param str repository_id: The repository ID of the pull request's target branch. :param int pull_request_id: ID of the pull request. :param int thread_id: ID of the thread that the desired comment is in. :param str project: Project ID or project name :rtype: :class:`<Comment> <azure.devops.v5_1.git.models.Comment>` """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if repository_id is not None: route_values['repositoryId'] = self._serialize.url('repository_id', repository_id, 'str') if pull_request_id is not None: route_values['pullRequestId'] = self._serialize.url('pull_request_id', pull_request_id, 'int') if thread_id is not None: route_values['threadId'] = self._serialize.url('thread_id', thread_id, 'int') content = self._serialize.body(comment, 'Comment') response = self._send(http_method='POST', location_id='965a3ec7-5ed8-455a-bdcb-835a5ea7fe7b', version='5.1-preview.1', route_values=route_values, content=content) return self._deserialize('Comment', response)
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CreateComment. [Preview API] Create a comment on a specific thread in a pull request (up to 500 comments can be created per thread). :param :class:`<Comment> <azure.devops.v5_1.git.models.Comment>` comment: The comment to create. Comments can be up to 150,000 characters. :param str repository_id: The repository ID of the pull request's target branch. :param int pull_request_id: ID of the pull request. :param int thread_id: ID of the thread that the desired comment is in. :param str project: Project ID or project name :rtype: :class:`<Comment> <azure.devops.v5_1.git.models.Comment>`
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python
train
richardcornish/django-pygmentify
pygmentify/utils/pygmentify.py
https://github.com/richardcornish/django-pygmentify/blob/a2d3f6b3c3019d810d46f6ff6beb4e9f53190e7b/pygmentify/utils/pygmentify.py#L12-L33
def bits_to_dict(bits): """Convert a Django template tag's kwargs into a dictionary of Python types. The only necessary types are number, boolean, list, and string. http://pygments.org/docs/formatters/#HtmlFormatter from: ["style='monokai'", "cssclass='cssclass',", "boolean='true',", 'num=0,', "list='[]'"] to: {'style': 'monokai', 'cssclass': 'cssclass', 'boolean': True, 'num': 0, 'list': [],} """ # Strip any trailing commas cleaned_bits = [bit[:-1] if bit.endswith(',') else bit for bit in bits] # Create dictionary by splitting on equal signs options = dict(bit.split('=') for bit in cleaned_bits) # Coerce strings of types to Python types for key in options: if options[key] == "'true'" or options[key] == "'false'": options[key] = options[key].title() options[key] = ast.literal_eval(options[key]) return options
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python
train
tmux-python/libtmux
libtmux/common.py
https://github.com/tmux-python/libtmux/blob/8eb2f8bbea3a025c1567b1516653414dbc24e1fc/libtmux/common.py#L324-L351
def where(self, attrs, first=False): """ Return objects matching child objects properties. Parameters ---------- attrs : dict tmux properties to match values of Returns ------- list """ # from https://github.com/serkanyersen/underscore.py def by(val, *args): for key, value in attrs.items(): try: if attrs[key] != val[key]: return False except KeyError: return False return True if first: return list(filter(by, self.children))[0] else: return list(filter(by, self.children))
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Return objects matching child objects properties. Parameters ---------- attrs : dict tmux properties to match values of Returns ------- list
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python
train
lordmauve/lepton
lepton/texturizer.py
https://github.com/lordmauve/lepton/blob/bf03f2c20ea8c51ade632f692d0a21e520fbba7c/lepton/texturizer.py#L52-L67
def from_images(cls, images, weights=None, filter=None, wrap=None, aspect_adjust_width=False, aspect_adjust_height=False): """Create a SpriteTexturizer from a sequence of Pyglet images. Note all the images must be able to fit into a single OpenGL texture, so their combined size should typically be less than 1024x1024 """ import pyglet atlas, textures = _atlas_from_images(images) texturizer = cls( atlas.texture.id, [tex.tex_coords for tex in textures], weights, filter or pyglet.gl.GL_LINEAR, wrap or pyglet.gl.GL_CLAMP, aspect_adjust_width, aspect_adjust_height) texturizer.atlas = atlas texturizer.textures = textures return texturizer
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Create a SpriteTexturizer from a sequence of Pyglet images. Note all the images must be able to fit into a single OpenGL texture, so their combined size should typically be less than 1024x1024
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python
train
Alignak-monitoring/alignak
alignak/external_command.py
https://github.com/Alignak-monitoring/alignak/blob/f3c145207e83159b799d3714e4241399c7740a64/alignak/external_command.py#L1701-L1714
def delay_svc_notification(self, service, notification_time): """Modify service first notification delay Format of the line that triggers function call:: DELAY_SVC_NOTIFICATION;<host_name>;<service_description>;<notification_time> :param service: service to edit :type service: alignak.objects.service.Service :param notification_time: new value to set :type notification_time: :return: None """ service.first_notification_delay = notification_time self.send_an_element(service.get_update_status_brok())
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Modify service first notification delay Format of the line that triggers function call:: DELAY_SVC_NOTIFICATION;<host_name>;<service_description>;<notification_time> :param service: service to edit :type service: alignak.objects.service.Service :param notification_time: new value to set :type notification_time: :return: None
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python
train
Dentosal/python-sc2
sc2/unit.py
https://github.com/Dentosal/python-sc2/blob/608bd25f04e89d39cef68b40101d8e9a8a7f1634/sc2/unit.py#L133-L141
def can_attack_air(self) -> bool: """ Does not include upgrades """ if self._weapons: weapon = next( (weapon for weapon in self._weapons if weapon.type in {TargetType.Air.value, TargetType.Any.value}), None, ) return weapon is not None return False
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Does not include upgrades
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python
train
jobovy/galpy
galpy/df/evolveddiskdf.py
https://github.com/jobovy/galpy/blob/9c5b9fe65d58835624dffe432be282060918ee08/galpy/df/evolveddiskdf.py#L2282-L2286
def _vmomentsurfaceIntegrand(vR,vT,R,az,df,n,m,sigmaR1,sigmaT1,t,initvmoment): """Internal function that is the integrand for the velocity moment times surface mass integration""" o= Orbit([R,vR*sigmaR1,vT*sigmaT1,az]) return vR**n*vT**m*df(o,t)/initvmoment
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Internal function that is the integrand for the velocity moment times surface mass integration
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python
train
blockstack/virtualchain
virtualchain/lib/blockchain/bitcoin_blockchain/blocks.py
https://github.com/blockstack/virtualchain/blob/fcfc970064ca7dfcab26ebd3ab955870a763ea39/virtualchain/lib/blockchain/bitcoin_blockchain/blocks.py#L450-L492
def parse_tx( self, txn, block_header, block_hash, txindex ): """ Given a transaction message and its index in the block, go and create a "verbose" transaction structure containing all the information in a nice, easy-to-read dict (i.e. like what bitcoind would give us). Does not work on coinbase transactions. Does not include segwit witnesses """ txn_serializer = TxSerializer() tx_bin = txn_serializer.serialize(txn) txdata = { "version": txn.version, "locktime": txn.lock_time, "hex": binascii.hexlify( tx_bin ), "txid": txn.calculate_hash(), "size": len( tx_bin ), "blockhash": block_hash, "blocktime": block_header.get('timestamp', 0), # non-standard; added by us for virtualchain "txindex": txindex, "relindex": None, "senders": None, "fee": 0, "nulldata": None, "ins": None, # library-specific field, to be passed to the state engine "outs": None, # library-specific field, to be passed to the state engine "tx_merkle_path": None } # keep these around too, since this is what gets fed into the virtualchain state engine implementation virtualchain_btc_tx_data = bits.btc_tx_deserialize(txdata['hex']) txdata['ins'] = virtualchain_btc_tx_data['ins'] txdata['outs'] = virtualchain_btc_tx_data['outs'] # we know how many senders there have to be txdata['senders'] = [None] * len(txdata['ins']) return txdata
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Given a transaction message and its index in the block, go and create a "verbose" transaction structure containing all the information in a nice, easy-to-read dict (i.e. like what bitcoind would give us). Does not work on coinbase transactions. Does not include segwit witnesses
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python
train
SuperCowPowers/workbench
workbench/workers/view_memory_deep.py
https://github.com/SuperCowPowers/workbench/blob/710232756dd717f734253315e3d0b33c9628dafb/workbench/workers/view_memory_deep.py#L11-L20
def execute(self, input_data): ''' Execute the ViewMemoryDeep worker ''' # Aggregate the output from all the memory workers, clearly this could be kewler output = input_data['view_memory'] output['tables'] = {} for data in [input_data[key] for key in ViewMemoryDeep.dependencies]: for name,table in data['tables'].iteritems(): output['tables'].update({name: table}) return output
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Execute the ViewMemoryDeep worker
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python
train
google/grr
grr/core/grr_response_core/stats/default_stats_collector.py
https://github.com/google/grr/blob/5cef4e8e2f0d5df43ea4877e9c798e0bf60bfe74/grr/core/grr_response_core/stats/default_stats_collector.py#L108-L115
def Record(self, value, fields=None): """Records the given observation in a distribution.""" key = _FieldsToKey(fields) metric_value = self._metric_values.get(key) if metric_value is None: metric_value = self._DefaultValue() self._metric_values[key] = metric_value metric_value.Record(value)
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Records the given observation in a distribution.
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python
train
JnyJny/Geometry
Geometry/triangle.py
https://github.com/JnyJny/Geometry/blob/3500f815fa56c535b36d1b6fd0afe69ce5d055be/Geometry/triangle.py#L335-L345
def CB(self): ''' Vertices C and B, list. ''' try: return self._CB except AttributeError: pass self._CB = [self.C, self.B] return self._CB
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Vertices C and B, list.
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python
train
decryptus/sonicprobe
sonicprobe/libs/threading_tcp_server.py
https://github.com/decryptus/sonicprobe/blob/72f73f3a40d2982d79ad68686e36aa31d94b76f8/sonicprobe/libs/threading_tcp_server.py#L123-L145
def process_request_thread(self, mainthread): """obtain request from queue instead of directly from server socket""" life_time = time.time() nb_requests = 0 while not mainthread.killed(): if self.max_life_time > 0: if (time.time() - life_time) >= self.max_life_time: mainthread.add_worker(1) return try: SocketServer.ThreadingTCPServer.process_request_thread(self, *self.requests.get(True, 0.5)) except Queue.Empty: continue else: SocketServer.ThreadingTCPServer.process_request_thread(self, *self.requests.get()) LOG.debug("nb_requests: %d, max_requests: %d", nb_requests, self.max_requests) nb_requests += 1 if self.max_requests > 0 and nb_requests >= self.max_requests: mainthread.add_worker(1) return
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obtain request from queue instead of directly from server socket
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python
train
spyder-ide/spyder
spyder/api/plugins.py
https://github.com/spyder-ide/spyder/blob/f76836ce1b924bcc4efd3f74f2960d26a4e528e0/spyder/api/plugins.py#L102-L119
def initialize_plugin(self): """ Initialize plugin: connect signals, setup actions, etc. It must be run at the end of __init__ """ self.create_toggle_view_action() self.plugin_actions = self.get_plugin_actions() + [MENU_SEPARATOR, self.undock_action] add_actions(self.options_menu, self.plugin_actions) self.options_button.setMenu(self.options_menu) self.options_menu.aboutToShow.connect(self.refresh_actions) self.sig_show_message.connect(self.show_message) self.sig_update_plugin_title.connect(self.update_plugin_title) self.sig_option_changed.connect(self.set_option) self.setWindowTitle(self.get_plugin_title())
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Initialize plugin: connect signals, setup actions, etc. It must be run at the end of __init__
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python
train
cykl/infoqscraper
infoqscraper/client.py
https://github.com/cykl/infoqscraper/blob/4fc026b994f98a0a7fe8578e0c9a3a9664982b2e/infoqscraper/client.py#L92-L105
def fetch_no_cache(self, url): """ Fetch the resource specified and return its content. DownloadError is raised if the resource cannot be fetched. """ try: with contextlib.closing(self.opener.open(url)) as response: # InfoQ does not send a 404 but a 302 redirecting to a valid URL... if response.code != 200 or response.url == INFOQ_404_URL: raise DownloadError("%s not found" % url) return response.read() except urllib.error.URLError as e: raise DownloadError("Failed to get %s: %s" % (url, e))
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Fetch the resource specified and return its content. DownloadError is raised if the resource cannot be fetched.
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python
train
genialis/resolwe
resolwe/flow/views/entity.py
https://github.com/genialis/resolwe/blob/f7bb54932c81ec0cfc5b5e80d238fceaeaa48d86/resolwe/flow/views/entity.py#L163-L175
def move_to_collection(self, request, *args, **kwargs): """Move samples from source to destination collection.""" ids = self.get_ids(request.data) src_collection_id = self.get_id(request.data, 'source_collection') dst_collection_id = self.get_id(request.data, 'destination_collection') src_collection = self._get_collection_for_user(src_collection_id, request.user) dst_collection = self._get_collection_for_user(dst_collection_id, request.user) entity_qs = self._get_entities(request.user, ids) entity_qs.move_to_collection(src_collection, dst_collection) return Response()
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Move samples from source to destination collection.
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python
train
pygalle-io/pygalle.core.base.klass
src/pygalle/core/base/klass/__init__.py
https://github.com/pygalle-io/pygalle.core.base.klass/blob/fa683f7f88b63ca46a0970af81a558c9efbbe942/src/pygalle/core/base/klass/__init__.py#L245-L258
def instance_of(self, kls: Any) -> bool: """ Return true if the current object is an instance of passed type. # Arguments kls: The class. # Returns: bool: * Return true if the current object is an instance of passed type. * False else. """ if not kls: raise ValueError return isinstance(self, kls)
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Return true if the current object is an instance of passed type. # Arguments kls: The class. # Returns: bool: * Return true if the current object is an instance of passed type. * False else.
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python
train
googleapis/google-cloud-python
datastore/google/cloud/datastore/client.py
https://github.com/googleapis/google-cloud-python/blob/85e80125a59cb10f8cb105f25ecc099e4b940b50/datastore/google/cloud/datastore/client.py#L542-L624
def query(self, **kwargs): """Proxy to :class:`google.cloud.datastore.query.Query`. Passes our ``project``. Using query to search a datastore: .. testsetup:: query import os import uuid from google.cloud import datastore unique = os.getenv('CIRCLE_BUILD_NUM', str(uuid.uuid4())[0:8]) client = datastore.Client(namespace='ns{}'.format(unique)) query = client.query(kind='_Doctest') def do_something(entity): pass .. doctest:: query >>> query = client.query(kind='MyKind') >>> query.add_filter('property', '=', 'val') Using the query iterator .. doctest:: query >>> query_iter = query.fetch() >>> for entity in query_iter: ... do_something(entity) or manually page through results .. testsetup:: query-page import os import uuid from google.cloud import datastore from tests.system.test_system import Config # system tests unique = os.getenv('CIRCLE_BUILD_NUM', str(uuid.uuid4())[0:8]) client = datastore.Client(namespace='ns{}'.format(unique)) key = client.key('_Doctest') entity1 = datastore.Entity(key=key) entity1['foo'] = 1337 entity2 = datastore.Entity(key=key) entity2['foo'] = 42 Config.TO_DELETE.extend([entity1, entity2]) client.put_multi([entity1, entity2]) query = client.query(kind='_Doctest') cursor = None .. doctest:: query-page >>> query_iter = query.fetch(start_cursor=cursor) >>> pages = query_iter.pages >>> >>> first_page = next(pages) >>> first_page_entities = list(first_page) >>> query_iter.next_page_token is None True :type kwargs: dict :param kwargs: Parameters for initializing and instance of :class:`~google.cloud.datastore.query.Query`. :rtype: :class:`~google.cloud.datastore.query.Query` :returns: A query object. """ if "client" in kwargs: raise TypeError("Cannot pass client") if "project" in kwargs: raise TypeError("Cannot pass project") kwargs["project"] = self.project if "namespace" not in kwargs: kwargs["namespace"] = self.namespace return Query(self, **kwargs)
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Proxy to :class:`google.cloud.datastore.query.Query`. Passes our ``project``. Using query to search a datastore: .. testsetup:: query import os import uuid from google.cloud import datastore unique = os.getenv('CIRCLE_BUILD_NUM', str(uuid.uuid4())[0:8]) client = datastore.Client(namespace='ns{}'.format(unique)) query = client.query(kind='_Doctest') def do_something(entity): pass .. doctest:: query >>> query = client.query(kind='MyKind') >>> query.add_filter('property', '=', 'val') Using the query iterator .. doctest:: query >>> query_iter = query.fetch() >>> for entity in query_iter: ... do_something(entity) or manually page through results .. testsetup:: query-page import os import uuid from google.cloud import datastore from tests.system.test_system import Config # system tests unique = os.getenv('CIRCLE_BUILD_NUM', str(uuid.uuid4())[0:8]) client = datastore.Client(namespace='ns{}'.format(unique)) key = client.key('_Doctest') entity1 = datastore.Entity(key=key) entity1['foo'] = 1337 entity2 = datastore.Entity(key=key) entity2['foo'] = 42 Config.TO_DELETE.extend([entity1, entity2]) client.put_multi([entity1, entity2]) query = client.query(kind='_Doctest') cursor = None .. doctest:: query-page >>> query_iter = query.fetch(start_cursor=cursor) >>> pages = query_iter.pages >>> >>> first_page = next(pages) >>> first_page_entities = list(first_page) >>> query_iter.next_page_token is None True :type kwargs: dict :param kwargs: Parameters for initializing and instance of :class:`~google.cloud.datastore.query.Query`. :rtype: :class:`~google.cloud.datastore.query.Query` :returns: A query object.
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python
train
saltstack/salt
salt/cloud/clouds/msazure.py
https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/cloud/clouds/msazure.py#L2253-L2286
def show_deployment(kwargs=None, conn=None, call=None): ''' .. versionadded:: 2015.8.0 Return information about a deployment CLI Example: .. code-block:: bash salt-cloud -f show_deployment my-azure name=my_deployment ''' if call != 'function': raise SaltCloudSystemExit( 'The get_deployment function must be called with -f or --function.' ) if not conn: conn = get_conn() if kwargs is None: kwargs = {} if 'service_name' not in kwargs: raise SaltCloudSystemExit('A service name must be specified as "service_name"') if 'deployment_name' not in kwargs: raise SaltCloudSystemExit('A deployment name must be specified as "deployment_name"') data = conn.get_deployment_by_name( service_name=kwargs['service_name'], deployment_name=kwargs['deployment_name'], ) return object_to_dict(data)
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.. versionadded:: 2015.8.0 Return information about a deployment CLI Example: .. code-block:: bash salt-cloud -f show_deployment my-azure name=my_deployment
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python
train
bitesofcode/projexui
projexui/widgets/xorbcolumnnavigator.py
https://github.com/bitesofcode/projexui/blob/f18a73bec84df90b034ca69b9deea118dbedfc4d/projexui/widgets/xorbcolumnnavigator.py#L155-L168
def currentSchemaPath(self): """ Returns the column path for the current item. This will be a '.' joined path based on the root schema to the given column. :return <str> """ item = self.currentItem() path = [] while item: path.append(nativestring(item.text(0))) item = item.parent() return '.'.join(reversed(path))
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Returns the column path for the current item. This will be a '.' joined path based on the root schema to the given column. :return <str>
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python
train
sorgerlab/indra
indra/tools/gene_network.py
https://github.com/sorgerlab/indra/blob/79a70415832c5702d7a820c7c9ccc8e25010124b/indra/tools/gene_network.py#L177-L198
def get_statements(self, filter=False): """Return the combined list of statements from BEL and Pathway Commons. Internally calls :py:meth:`get_biopax_stmts` and :py:meth:`get_bel_stmts`. Parameters ---------- filter : bool If True, includes only those statements that exclusively mention genes in :py:attr:`gene_list`. Default is False. Returns ------- list of :py:class:`indra.statements.Statement` List of INDRA statements extracted the BEL large corpus and Pathway Commons. """ bp_stmts = self.get_biopax_stmts(filter=filter) bel_stmts = self.get_bel_stmts(filter=filter) return bp_stmts + bel_stmts
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Return the combined list of statements from BEL and Pathway Commons. Internally calls :py:meth:`get_biopax_stmts` and :py:meth:`get_bel_stmts`. Parameters ---------- filter : bool If True, includes only those statements that exclusively mention genes in :py:attr:`gene_list`. Default is False. Returns ------- list of :py:class:`indra.statements.Statement` List of INDRA statements extracted the BEL large corpus and Pathway Commons.
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python
train
tensorflow/probability
tensorflow_probability/python/mcmc/sample.py
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/sample.py#L81-L372
def sample_chain( num_results, current_state, previous_kernel_results=None, kernel=None, num_burnin_steps=0, num_steps_between_results=0, trace_fn=lambda current_state, kernel_results: kernel_results, return_final_kernel_results=False, parallel_iterations=10, name=None, ): """Implements Markov chain Monte Carlo via repeated `TransitionKernel` steps. This function samples from an Markov chain at `current_state` and whose stationary distribution is governed by the supplied `TransitionKernel` instance (`kernel`). This function can sample from multiple chains, in parallel. (Whether or not there are multiple chains is dictated by the `kernel`.) The `current_state` can be represented as a single `Tensor` or a `list` of `Tensors` which collectively represent the current state. Since MCMC states are correlated, it is sometimes desirable to produce additional intermediate states, and then discard them, ending up with a set of states with decreased autocorrelation. See [Owen (2017)][1]. Such "thinning" is made possible by setting `num_steps_between_results > 0`. The chain then takes `num_steps_between_results` extra steps between the steps that make it into the results. The extra steps are never materialized (in calls to `sess.run`), and thus do not increase memory requirements. Warning: when setting a `seed` in the `kernel`, ensure that `sample_chain`'s `parallel_iterations=1`, otherwise results will not be reproducible. In addition to returning the chain state, this function supports tracing of auxiliary variables used by the kernel. The traced values are selected by specifying `trace_fn`. By default, all kernel results are traced but in the future the default will be changed to no results being traced, so plan accordingly. See below for some examples of this feature. Args: num_results: Integer number of Markov chain draws. current_state: `Tensor` or Python `list` of `Tensor`s representing the current state(s) of the Markov chain(s). previous_kernel_results: A `Tensor` or a nested collection of `Tensor`s representing internal calculations made within the previous call to this function (or as returned by `bootstrap_results`). kernel: An instance of `tfp.mcmc.TransitionKernel` which implements one step of the Markov chain. num_burnin_steps: Integer number of chain steps to take before starting to collect results. Default value: 0 (i.e., no burn-in). num_steps_between_results: Integer number of chain steps between collecting a result. Only one out of every `num_steps_between_samples + 1` steps is included in the returned results. The number of returned chain states is still equal to `num_results`. Default value: 0 (i.e., no thinning). trace_fn: A callable that takes in the current chain state and the previous kernel results and return a `Tensor` or a nested collection of `Tensor`s that is then traced along with the chain state. return_final_kernel_results: If `True`, then the final kernel results are returned alongside the chain state and the trace specified by the `trace_fn`. parallel_iterations: The number of iterations allowed to run in parallel. It must be a positive integer. See `tf.while_loop` for more details. name: Python `str` name prefixed to Ops created by this function. Default value: `None` (i.e., "mcmc_sample_chain"). Returns: checkpointable_states_and_trace: if `return_final_kernel_results` is `True`. The return value is an instance of `CheckpointableStatesAndTrace`. all_states: if `return_final_kernel_results` is `False` and `trace_fn` is `None`. The return value is a `Tensor` or Python list of `Tensor`s representing the state(s) of the Markov chain(s) at each result step. Has same shape as input `current_state` but with a prepended `num_results`-size dimension. states_and_trace: if `return_final_kernel_results` is `False` and `trace_fn` is not `None`. The return value is an instance of `StatesAndTrace`. #### Examples ##### Sample from a diagonal-variance Gaussian. I.e., ```none for i=1..n: x[i] ~ MultivariateNormal(loc=0, scale=diag(true_stddev)) # likelihood ``` ```python import tensorflow as tf import tensorflow_probability as tfp tfd = tfp.distributions dims = 10 true_stddev = np.sqrt(np.linspace(1., 3., dims)) likelihood = tfd.MultivariateNormalDiag(loc=0., scale_diag=true_stddev) states = tfp.mcmc.sample_chain( num_results=1000, num_burnin_steps=500, current_state=tf.zeros(dims), kernel=tfp.mcmc.HamiltonianMonteCarlo( target_log_prob_fn=likelihood.log_prob, step_size=0.5, num_leapfrog_steps=2), trace_fn=None) sample_mean = tf.reduce_mean(states, axis=0) # ==> approx all zeros sample_stddev = tf.sqrt(tf.reduce_mean( tf.squared_difference(states, sample_mean), axis=0)) # ==> approx equal true_stddev ``` ##### Sampling from factor-analysis posteriors with known factors. I.e., ```none # prior w ~ MultivariateNormal(loc=0, scale=eye(d)) for i=1..n: # likelihood x[i] ~ Normal(loc=w^T F[i], scale=1) ``` where `F` denotes factors. ```python import tensorflow as tf import tensorflow_probability as tfp tfd = tfp.distributions # Specify model. def make_prior(dims): return tfd.MultivariateNormalDiag( loc=tf.zeros(dims)) def make_likelihood(weights, factors): return tfd.MultivariateNormalDiag( loc=tf.matmul(weights, factors, adjoint_b=True)) def joint_log_prob(num_weights, factors, x, w): return (make_prior(num_weights).log_prob(w) + make_likelihood(w, factors).log_prob(x)) def unnormalized_log_posterior(w): # Posterior is proportional to: `p(W, X=x | factors)`. return joint_log_prob(num_weights, factors, x, w) # Setup data. num_weights = 10 # == d num_factors = 40 # == n num_chains = 100 weights = make_prior(num_weights).sample(1) factors = tf.random_normal([num_factors, num_weights]) x = make_likelihood(weights, factors).sample() # Sample from Hamiltonian Monte Carlo Markov Chain. # Get `num_results` samples from `num_chains` independent chains. chains_states, kernels_results = tfp.mcmc.sample_chain( num_results=1000, num_burnin_steps=500, current_state=tf.zeros([num_chains, num_weights], name='init_weights'), kernel=tfp.mcmc.HamiltonianMonteCarlo( target_log_prob_fn=unnormalized_log_posterior, step_size=0.1, num_leapfrog_steps=2)) # Compute sample stats. sample_mean = tf.reduce_mean(chains_states, axis=[0, 1]) # ==> approx equal to weights sample_var = tf.reduce_mean( tf.squared_difference(chains_states, sample_mean), axis=[0, 1]) # ==> less than 1 ``` ##### Custom tracing functions. ```python import tensorflow as tf import tensorflow_probability as tfp tfd = tfp.distributions likelihood = tfd.Normal(loc=0., scale=1.) def sample_chain(trace_fn): return tfp.mcmc.sample_chain( num_results=1000, num_burnin_steps=500, current_state=0., kernel=tfp.mcmc.HamiltonianMonteCarlo( target_log_prob_fn=likelihood.log_prob, step_size=0.5, num_leapfrog_steps=2), trace_fn=trace_fn) def trace_log_accept_ratio(states, previous_kernel_results): return previous_kernel_results.log_accept_ratio def trace_everything(states, previous_kernel_results): return previous_kernel_results _, log_accept_ratio = sample_chain(trace_fn=trace_log_accept_ratio) _, kernel_results = sample_chain(trace_fn=trace_everything) acceptance_prob = tf.exp(tf.minimum(log_accept_ratio_, 0.)) # Equivalent to, but more efficient than: acceptance_prob = tf.exp(tf.minimum(kernel_results.log_accept_ratio_, 0.)) ``` #### References [1]: Art B. Owen. Statistically efficient thinning of a Markov chain sampler. _Technical Report_, 2017. http://statweb.stanford.edu/~owen/reports/bestthinning.pdf """ if not kernel.is_calibrated: warnings.warn("supplied `TransitionKernel` is not calibrated. Markov " "chain may not converge to intended target distribution.") with tf.compat.v1.name_scope( name, "mcmc_sample_chain", [num_results, num_burnin_steps, num_steps_between_results]): num_results = tf.convert_to_tensor( value=num_results, dtype=tf.int32, name="num_results") num_burnin_steps = tf.convert_to_tensor( value=num_burnin_steps, dtype=tf.int32, name="num_burnin_steps") num_steps_between_results = tf.convert_to_tensor( value=num_steps_between_results, dtype=tf.int32, name="num_steps_between_results") current_state = tf.nest.map_structure( lambda x: tf.convert_to_tensor(value=x, name="current_state"), current_state) if previous_kernel_results is None: previous_kernel_results = kernel.bootstrap_results(current_state) if trace_fn is None: # It simplifies the logic to use a dummy function here. trace_fn = lambda *args: () no_trace = True else: no_trace = False if trace_fn is sample_chain.__defaults__[4]: warnings.warn("Tracing all kernel results by default is deprecated. Set " "the `trace_fn` argument to None (the future default " "value) or an explicit callback that traces the values " "you are interested in.") def _trace_scan_fn(state_and_results, num_steps): next_state, current_kernel_results = mcmc_util.smart_for_loop( loop_num_iter=num_steps, body_fn=kernel.one_step, initial_loop_vars=list(state_and_results), parallel_iterations=parallel_iterations) return next_state, current_kernel_results (_, final_kernel_results), (all_states, trace) = mcmc_util.trace_scan( loop_fn=_trace_scan_fn, initial_state=(current_state, previous_kernel_results), elems=tf.one_hot( indices=0, depth=num_results, on_value=1 + num_burnin_steps, off_value=1 + num_steps_between_results, dtype=tf.int32), # pylint: disable=g-long-lambda trace_fn=lambda state_and_results: (state_and_results[0], trace_fn(*state_and_results)), # pylint: enable=g-long-lambda parallel_iterations=parallel_iterations) if return_final_kernel_results: return CheckpointableStatesAndTrace( all_states=all_states, trace=trace, final_kernel_results=final_kernel_results) else: if no_trace: return all_states else: return StatesAndTrace(all_states=all_states, trace=trace)
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Implements Markov chain Monte Carlo via repeated `TransitionKernel` steps. This function samples from an Markov chain at `current_state` and whose stationary distribution is governed by the supplied `TransitionKernel` instance (`kernel`). This function can sample from multiple chains, in parallel. (Whether or not there are multiple chains is dictated by the `kernel`.) The `current_state` can be represented as a single `Tensor` or a `list` of `Tensors` which collectively represent the current state. Since MCMC states are correlated, it is sometimes desirable to produce additional intermediate states, and then discard them, ending up with a set of states with decreased autocorrelation. See [Owen (2017)][1]. Such "thinning" is made possible by setting `num_steps_between_results > 0`. The chain then takes `num_steps_between_results` extra steps between the steps that make it into the results. The extra steps are never materialized (in calls to `sess.run`), and thus do not increase memory requirements. Warning: when setting a `seed` in the `kernel`, ensure that `sample_chain`'s `parallel_iterations=1`, otherwise results will not be reproducible. In addition to returning the chain state, this function supports tracing of auxiliary variables used by the kernel. The traced values are selected by specifying `trace_fn`. By default, all kernel results are traced but in the future the default will be changed to no results being traced, so plan accordingly. See below for some examples of this feature. Args: num_results: Integer number of Markov chain draws. current_state: `Tensor` or Python `list` of `Tensor`s representing the current state(s) of the Markov chain(s). previous_kernel_results: A `Tensor` or a nested collection of `Tensor`s representing internal calculations made within the previous call to this function (or as returned by `bootstrap_results`). kernel: An instance of `tfp.mcmc.TransitionKernel` which implements one step of the Markov chain. num_burnin_steps: Integer number of chain steps to take before starting to collect results. Default value: 0 (i.e., no burn-in). num_steps_between_results: Integer number of chain steps between collecting a result. Only one out of every `num_steps_between_samples + 1` steps is included in the returned results. The number of returned chain states is still equal to `num_results`. Default value: 0 (i.e., no thinning). trace_fn: A callable that takes in the current chain state and the previous kernel results and return a `Tensor` or a nested collection of `Tensor`s that is then traced along with the chain state. return_final_kernel_results: If `True`, then the final kernel results are returned alongside the chain state and the trace specified by the `trace_fn`. parallel_iterations: The number of iterations allowed to run in parallel. It must be a positive integer. See `tf.while_loop` for more details. name: Python `str` name prefixed to Ops created by this function. Default value: `None` (i.e., "mcmc_sample_chain"). Returns: checkpointable_states_and_trace: if `return_final_kernel_results` is `True`. The return value is an instance of `CheckpointableStatesAndTrace`. all_states: if `return_final_kernel_results` is `False` and `trace_fn` is `None`. The return value is a `Tensor` or Python list of `Tensor`s representing the state(s) of the Markov chain(s) at each result step. Has same shape as input `current_state` but with a prepended `num_results`-size dimension. states_and_trace: if `return_final_kernel_results` is `False` and `trace_fn` is not `None`. The return value is an instance of `StatesAndTrace`. #### Examples ##### Sample from a diagonal-variance Gaussian. I.e., ```none for i=1..n: x[i] ~ MultivariateNormal(loc=0, scale=diag(true_stddev)) # likelihood ``` ```python import tensorflow as tf import tensorflow_probability as tfp tfd = tfp.distributions dims = 10 true_stddev = np.sqrt(np.linspace(1., 3., dims)) likelihood = tfd.MultivariateNormalDiag(loc=0., scale_diag=true_stddev) states = tfp.mcmc.sample_chain( num_results=1000, num_burnin_steps=500, current_state=tf.zeros(dims), kernel=tfp.mcmc.HamiltonianMonteCarlo( target_log_prob_fn=likelihood.log_prob, step_size=0.5, num_leapfrog_steps=2), trace_fn=None) sample_mean = tf.reduce_mean(states, axis=0) # ==> approx all zeros sample_stddev = tf.sqrt(tf.reduce_mean( tf.squared_difference(states, sample_mean), axis=0)) # ==> approx equal true_stddev ``` ##### Sampling from factor-analysis posteriors with known factors. I.e., ```none # prior w ~ MultivariateNormal(loc=0, scale=eye(d)) for i=1..n: # likelihood x[i] ~ Normal(loc=w^T F[i], scale=1) ``` where `F` denotes factors. ```python import tensorflow as tf import tensorflow_probability as tfp tfd = tfp.distributions # Specify model. def make_prior(dims): return tfd.MultivariateNormalDiag( loc=tf.zeros(dims)) def make_likelihood(weights, factors): return tfd.MultivariateNormalDiag( loc=tf.matmul(weights, factors, adjoint_b=True)) def joint_log_prob(num_weights, factors, x, w): return (make_prior(num_weights).log_prob(w) + make_likelihood(w, factors).log_prob(x)) def unnormalized_log_posterior(w): # Posterior is proportional to: `p(W, X=x | factors)`. return joint_log_prob(num_weights, factors, x, w) # Setup data. num_weights = 10 # == d num_factors = 40 # == n num_chains = 100 weights = make_prior(num_weights).sample(1) factors = tf.random_normal([num_factors, num_weights]) x = make_likelihood(weights, factors).sample() # Sample from Hamiltonian Monte Carlo Markov Chain. # Get `num_results` samples from `num_chains` independent chains. chains_states, kernels_results = tfp.mcmc.sample_chain( num_results=1000, num_burnin_steps=500, current_state=tf.zeros([num_chains, num_weights], name='init_weights'), kernel=tfp.mcmc.HamiltonianMonteCarlo( target_log_prob_fn=unnormalized_log_posterior, step_size=0.1, num_leapfrog_steps=2)) # Compute sample stats. sample_mean = tf.reduce_mean(chains_states, axis=[0, 1]) # ==> approx equal to weights sample_var = tf.reduce_mean( tf.squared_difference(chains_states, sample_mean), axis=[0, 1]) # ==> less than 1 ``` ##### Custom tracing functions. ```python import tensorflow as tf import tensorflow_probability as tfp tfd = tfp.distributions likelihood = tfd.Normal(loc=0., scale=1.) def sample_chain(trace_fn): return tfp.mcmc.sample_chain( num_results=1000, num_burnin_steps=500, current_state=0., kernel=tfp.mcmc.HamiltonianMonteCarlo( target_log_prob_fn=likelihood.log_prob, step_size=0.5, num_leapfrog_steps=2), trace_fn=trace_fn) def trace_log_accept_ratio(states, previous_kernel_results): return previous_kernel_results.log_accept_ratio def trace_everything(states, previous_kernel_results): return previous_kernel_results _, log_accept_ratio = sample_chain(trace_fn=trace_log_accept_ratio) _, kernel_results = sample_chain(trace_fn=trace_everything) acceptance_prob = tf.exp(tf.minimum(log_accept_ratio_, 0.)) # Equivalent to, but more efficient than: acceptance_prob = tf.exp(tf.minimum(kernel_results.log_accept_ratio_, 0.)) ``` #### References [1]: Art B. Owen. Statistically efficient thinning of a Markov chain sampler. _Technical Report_, 2017. http://statweb.stanford.edu/~owen/reports/bestthinning.pdf
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python
test
prompt-toolkit/pymux
pymux/commands/commands.py
https://github.com/prompt-toolkit/pymux/blob/3f66e62b9de4b2251c7f9afad6c516dc5a30ec67/pymux/commands/commands.py#L466-L474
def send_prefix(pymux, variables): """ Send prefix to active pane. """ process = pymux.arrangement.get_active_pane().process for k in pymux.key_bindings_manager.prefix: vt100_data = prompt_toolkit_key_to_vt100_key(k) process.write_input(vt100_data)
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Send prefix to active pane.
[ "Send", "prefix", "to", "active", "pane", "." ]
python
train
6809/MC6809
MC6809/components/cpu_utils/MC6809_registers.py
https://github.com/6809/MC6809/blob/6ba2f5106df46689017b5d0b6d84d43b7ee6a240/MC6809/components/cpu_utils/MC6809_registers.py#L122-L150
def convert_differend_width(src_reg, dst_reg): """ e.g.: 8bit $cd TFR into 16bit, results in: $ffcd 16bit $1234 TFR into 8bit, results in: $34 >>> reg8 = ValueStorage8Bit(name="bar", initial_value=0xcd) >>> reg16 = ValueStorage16Bit(name="foo", initial_value=0x0000) >>> hex(convert_differend_width(src_reg=reg8, dst_reg=reg16)) '0xffcd' >>> reg16 = ValueStorage16Bit(name="foo", initial_value=0x1234) >>> reg8 = ValueStorage8Bit(name="bar", initial_value=0xcd) >>> hex(convert_differend_width(src_reg=reg16, dst_reg=reg8)) '0x34' TODO: verify this behaviour on real hardware see: http://archive.worldofdragon.org/phpBB3/viewtopic.php?f=8&t=4886 """ src_value = src_reg.value if src_reg.WIDTH == 8 and dst_reg.WIDTH == 16: # e.g.: $cd -> $ffcd src_value += 0xff00 elif src_reg.WIDTH == 16 and dst_reg.WIDTH == 8: # This not not really needed, because all 8Bit register will # limit the value, too. # e.g.: $1234 -> $34 src_value = src_value & 0xff return src_value
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e.g.: 8bit $cd TFR into 16bit, results in: $ffcd 16bit $1234 TFR into 8bit, results in: $34 >>> reg8 = ValueStorage8Bit(name="bar", initial_value=0xcd) >>> reg16 = ValueStorage16Bit(name="foo", initial_value=0x0000) >>> hex(convert_differend_width(src_reg=reg8, dst_reg=reg16)) '0xffcd' >>> reg16 = ValueStorage16Bit(name="foo", initial_value=0x1234) >>> reg8 = ValueStorage8Bit(name="bar", initial_value=0xcd) >>> hex(convert_differend_width(src_reg=reg16, dst_reg=reg8)) '0x34' TODO: verify this behaviour on real hardware see: http://archive.worldofdragon.org/phpBB3/viewtopic.php?f=8&t=4886
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python
train
ulule/django-badgify
badgify/utils.py
https://github.com/ulule/django-badgify/blob/1bf233ffeb6293ee659454de7b3794682128b6ca/badgify/utils.py#L115-L120
def chunks(l, n): """ Yields successive n-sized chunks from l. """ for i in _range(0, len(l), n): yield l[i:i + n]
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Yields successive n-sized chunks from l.
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python
train
achedeuzot/django-timestampable
django_timestampable/models.py
https://github.com/achedeuzot/django-timestampable/blob/44d774655c383e43d931e64ac5e1d41dd737873d/django_timestampable/models.py#L31-L41
def update_timestampable_model(sender, instance, *args, **kwargs): ''' Using signals guarantees that timestamps are set no matter what: loading fixtures, bulk inserts, bulk updates, etc. Indeed, the `save()` method is *not* called when using fixtures. ''' if not isinstance(instance, TimestampableModel): return if not instance.pk: instance.created_at = now() instance.updated_at = now()
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Using signals guarantees that timestamps are set no matter what: loading fixtures, bulk inserts, bulk updates, etc. Indeed, the `save()` method is *not* called when using fixtures.
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python
train
persandstrom/python-verisure
verisure/session.py
https://github.com/persandstrom/python-verisure/blob/babd25e7f8fb2b24f12e4109dfa8a04041e8dcb8/verisure/session.py#L127-L150
def _get_installations(self): """ Get information about installations """ response = None for base_url in urls.BASE_URLS: urls.BASE_URL = base_url try: response = requests.get( urls.get_installations(self._username), headers={ 'Cookie': 'vid={}'.format(self._vid), 'Accept': 'application/json,' 'text/javascript, */*; q=0.01', }) if 2 == response.status_code // 100: break elif 503 == response.status_code: continue else: raise ResponseError(response.status_code, response.text) except requests.exceptions.RequestException as ex: raise RequestError(ex) _validate_response(response) self.installations = json.loads(response.text)
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Get information about installations
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python
train
stphivos/django-mock-queries
django_mock_queries/mocks.py
https://github.com/stphivos/django-mock-queries/blob/1522a0debfa78f4a986818d92eef826410becc85/django_mock_queries/mocks.py#L174-L181
def find_all_models(models): """ Yield all models and their parents. """ for model in models: yield model # noinspection PyProtectedMember for parent in model._meta.parents.keys(): for parent_model in find_all_models((parent,)): yield parent_model
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Yield all models and their parents.
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python
train
tanghaibao/goatools
goatools/cli/wr_hierarchy.py
https://github.com/tanghaibao/goatools/blob/407682e573a108864a79031f8ca19ee3bf377626/goatools/cli/wr_hierarchy.py#L129-L137
def prt_hier(self, prt=sys.stdout): """Write hierarchy below specfied GO IDs.""" objwr = WrHierGO(self.gosubdag, **self.kws) assert self.goids, "NO VALID GO IDs WERE PROVIDED" if 'up' not in objwr.usrset: for goid in self.goids: objwr.prt_hier_down(goid, prt) else: objwr.prt_hier_up(self.goids, prt)
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Write hierarchy below specfied GO IDs.
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python
train
SheffieldML/GPy
GPy/util/mocap.py
https://github.com/SheffieldML/GPy/blob/54c32d79d289d622fb18b898aee65a2a431d90cf/GPy/util/mocap.py#L634-L643
def set_rotation_matrices(self): """Set the meta information at each vertex to contain the correct matrices C and Cinv as prescribed by the rotations and rotation orders.""" for i in range(len(self.vertices)): self.vertices[i].meta['C'] = rotation_matrix(self.vertices[i].meta['axis'][0], self.vertices[i].meta['axis'][1], self.vertices[i].meta['axis'][2], self.vertices[i].meta['axis_order'], degrees=True) # Todo: invert this by applying angle operations in reverse order self.vertices[i].meta['Cinv'] = np.linalg.inv(self.vertices[i].meta['C'])
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Set the meta information at each vertex to contain the correct matrices C and Cinv as prescribed by the rotations and rotation orders.
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python
train
pyBookshelf/bookshelf
bookshelf/api_v2/pkg.py
https://github.com/pyBookshelf/bookshelf/blob/a6770678e735de95b194f6e6989223970db5f654/bookshelf/api_v2/pkg.py#L124-L130
def install_gem(gem): """ install a particular gem """ with settings(hide('warnings', 'running', 'stdout', 'stderr'), warn_only=False, capture=True): # convert 0 into True, any errors will always raise an exception return not bool( run("gem install %s --no-rdoc --no-ri" % gem).return_code)
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install a particular gem
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python
train
unt-libraries/pyuntl
pyuntl/highwire_structure.py
https://github.com/unt-libraries/pyuntl/blob/f92413302897dab948aac18ee9e482ace0187bd4/pyuntl/highwire_structure.py#L52-L68
def get_author(self, **kwargs): """Determine the authors from the creator field.""" qualifier = kwargs.get('qualifier', '') children = kwargs.get('children', []) creator_type_per = False author_name = None # Find the creator type in children. for child in children: if child.tag == 'type' and child.content == 'per': creator_type_per = True # Get the author name. elif child.tag == 'name': author_name = child.content if qualifier == 'aut' and creator_type_per and author_name: return author_name return None
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Determine the authors from the creator field.
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python
train
bwohlberg/sporco
sporco/admm/parcbpdn.py
https://github.com/bwohlberg/sporco/blob/8946a04331106f4e39904fbdf2dc7351900baa04/sporco/admm/parcbpdn.py#L250-L262
def par_u1step(i): r"""Dual variable update for :math:`\mathbf{u}_{1,G_i}`, one of the disjoint problems for updating :math:`\mathbf{u}_1`. Parameters ---------- i : int Index of grouping to update """ global mp_U1 grpind = slice(mp_grp[i], mp_grp[i+1]) mp_U1[grpind] += mp_alpha*(mp_X[grpind] - mp_Y1[grpind])
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r"""Dual variable update for :math:`\mathbf{u}_{1,G_i}`, one of the disjoint problems for updating :math:`\mathbf{u}_1`. Parameters ---------- i : int Index of grouping to update
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python
train
dourvaris/nano-python
src/nano/rpc.py
https://github.com/dourvaris/nano-python/blob/f26b8bc895b997067780f925049a70e82c0c2479/src/nano/rpc.py#L1194-L1228
def delegators(self, account): """ Returns a list of pairs of delegator names given **account** a representative and its balance .. version 8.0 required :param account: Account to return delegators for :type account: str :raises: :py:exc:`nano.rpc.RPCException` >>> rpc.delegators( ... account="xrb_1111111111111111111111111111111111111111111111111117353trpda" ... ) { "xrb_13bqhi1cdqq8yb9szneoc38qk899d58i5rcrgdk5mkdm86hekpoez3zxw5sd": "500000000000000000000000000000000000", "xrb_17k6ug685154an8gri9whhe5kb5z1mf5w6y39gokc1657sh95fegm8ht1zpn": "961647970820730000000000000000000000" } """ account = self._process_value(account, 'account') payload = {"account": account} resp = self.call('delegators', payload) delegators = resp.get('delegators') or {} for k, v in delegators.items(): delegators[k] = int(v) return delegators
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Returns a list of pairs of delegator names given **account** a representative and its balance .. version 8.0 required :param account: Account to return delegators for :type account: str :raises: :py:exc:`nano.rpc.RPCException` >>> rpc.delegators( ... account="xrb_1111111111111111111111111111111111111111111111111117353trpda" ... ) { "xrb_13bqhi1cdqq8yb9szneoc38qk899d58i5rcrgdk5mkdm86hekpoez3zxw5sd": "500000000000000000000000000000000000", "xrb_17k6ug685154an8gri9whhe5kb5z1mf5w6y39gokc1657sh95fegm8ht1zpn": "961647970820730000000000000000000000" }
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python
train
zalando/patroni
patroni/ha.py
https://github.com/zalando/patroni/blob/f6d29081c90af52064b981cdd877a07338d86038/patroni/ha.py#L1153-L1190
def handle_starting_instance(self): """Starting up PostgreSQL may take a long time. In case we are the leader we may want to fail over to.""" # Check if we are in startup, when paused defer to main loop for manual failovers. if not self.state_handler.check_for_startup() or self.is_paused(): self.set_start_timeout(None) if self.is_paused(): self.state_handler.set_state(self.state_handler.is_running() and 'running' or 'stopped') return None # state_handler.state == 'starting' here if self.has_lock(): if not self.update_lock(): logger.info("Lost lock while starting up. Demoting self.") self.demote('immediate-nolock') return 'stopped PostgreSQL while starting up because leader key was lost' timeout = self._start_timeout or self.patroni.config['master_start_timeout'] time_left = timeout - self.state_handler.time_in_state() if time_left <= 0: if self.is_failover_possible(self.cluster.members): logger.info("Demoting self because master startup is taking too long") self.demote('immediate') return 'stopped PostgreSQL because of startup timeout' else: return 'master start has timed out, but continuing to wait because failover is not possible' else: msg = self.process_manual_failover_from_leader() if msg is not None: return msg return 'PostgreSQL is still starting up, {0:.0f} seconds until timeout'.format(time_left) else: # Use normal processing for standbys logger.info("Still starting up as a standby.") return None
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Starting up PostgreSQL may take a long time. In case we are the leader we may want to fail over to.
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python
train
sigmaris/python-gssapi
gssapi/creds.py
https://github.com/sigmaris/python-gssapi/blob/a8ca577b3ccf9d9fa48f16f4954a1eddd5896236/gssapi/creds.py#L218-L226
def mechs(self): """ The set of mechanisms supported by the credential. :type: :class:`~gssapi.oids.OIDSet` """ if not self._mechs: self._mechs = self._inquire(False, False, False, True)[3] return self._mechs
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The set of mechanisms supported by the credential. :type: :class:`~gssapi.oids.OIDSet`
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python
test
quantopian/zipline
zipline/data/hdf5_daily_bars.py
https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/data/hdf5_daily_bars.py#L424-L443
def from_file(cls, h5_file, country_code): """ Construct from an h5py.File and a country code. Parameters ---------- h5_file : h5py.File An HDF5 daily pricing file. country_code : str The ISO 3166 alpha-2 country code for the country to read. """ if h5_file.attrs['version'] != VERSION: raise ValueError( 'mismatched version: file is of version %s, expected %s' % ( h5_file.attrs['version'], VERSION, ), ) return cls(h5_file[country_code])
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Construct from an h5py.File and a country code. Parameters ---------- h5_file : h5py.File An HDF5 daily pricing file. country_code : str The ISO 3166 alpha-2 country code for the country to read.
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python
train
twilio/twilio-python
twilio/rest/autopilot/v1/assistant/task/sample.py
https://github.com/twilio/twilio-python/blob/c867895f55dcc29f522e6e8b8868d0d18483132f/twilio/rest/autopilot/v1/assistant/task/sample.py#L63-L80
def list(self, language=values.unset, limit=None, page_size=None): """ Lists SampleInstance records from the API as a list. Unlike stream(), this operation is eager and will load `limit` records into memory before returning. :param unicode language: The ISO language-country string that specifies the language used for the sample :param int limit: Upper limit for the number of records to return. list() guarantees never to return more than limit. Default is no limit :param int page_size: Number of records to fetch per request, when not set will use the default value of 50 records. If no page_size is defined but a limit is defined, list() will attempt to read the limit with the most efficient page size, i.e. min(limit, 1000) :returns: Generator that will yield up to limit results :rtype: list[twilio.rest.autopilot.v1.assistant.task.sample.SampleInstance] """ return list(self.stream(language=language, limit=limit, page_size=page_size, ))
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Lists SampleInstance records from the API as a list. Unlike stream(), this operation is eager and will load `limit` records into memory before returning. :param unicode language: The ISO language-country string that specifies the language used for the sample :param int limit: Upper limit for the number of records to return. list() guarantees never to return more than limit. Default is no limit :param int page_size: Number of records to fetch per request, when not set will use the default value of 50 records. If no page_size is defined but a limit is defined, list() will attempt to read the limit with the most efficient page size, i.e. min(limit, 1000) :returns: Generator that will yield up to limit results :rtype: list[twilio.rest.autopilot.v1.assistant.task.sample.SampleInstance]
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python
train
Azure/azure-storage-python
azure-storage-queue/azure/storage/queue/queueservice.py
https://github.com/Azure/azure-storage-python/blob/52327354b192cbcf6b7905118ec6b5d57fa46275/azure-storage-queue/azure/storage/queue/queueservice.py#L586-L611
def get_queue_metadata(self, queue_name, timeout=None): ''' Retrieves user-defined metadata and queue properties on the specified queue. Metadata is associated with the queue as name-value pairs. :param str queue_name: The name of an existing queue. :param int timeout: The server timeout, expressed in seconds. :return: A dictionary representing the queue metadata with an approximate_message_count int property on the dict estimating the number of messages in the queue. :rtype: dict(str, str) ''' _validate_not_none('queue_name', queue_name) request = HTTPRequest() request.method = 'GET' request.host_locations = self._get_host_locations(secondary=True) request.path = _get_path(queue_name) request.query = { 'comp': 'metadata', 'timeout': _int_to_str(timeout), } return self._perform_request(request, _parse_metadata_and_message_count)
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Retrieves user-defined metadata and queue properties on the specified queue. Metadata is associated with the queue as name-value pairs. :param str queue_name: The name of an existing queue. :param int timeout: The server timeout, expressed in seconds. :return: A dictionary representing the queue metadata with an approximate_message_count int property on the dict estimating the number of messages in the queue. :rtype: dict(str, str)
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python
train
lowandrew/OLCTools
spadespipeline/legacy_vtyper.py
https://github.com/lowandrew/OLCTools/blob/88aa90ac85f84d0bbeb03e43c29b0a9d36e4ce2a/spadespipeline/legacy_vtyper.py#L399-L419
def filer(args): """ Create metadata objects with necessary attributes for each FASTA file found in the sequence path :param args: Argument parser object with necessary variables :return: samples: List of metadata objects """ # List to store all the metadata objects samples = list() # Find all the sequence files in the path fastas = sorted(glob(os.path.join(args.sequencepath, '*.fa*'))) for fasta in fastas: # Create a metadata object for each sample metadata = MetadataObject() # Populate the metadata object with the required attributes metadata.name = os.path.splitext(os.path.basename(fasta))[0] metadata.general = GenObject() metadata.commands = GenObject() metadata.general.bestassemblyfile = fasta metadata.general.outputdirectory = os.path.join(args.sequencepath, metadata.name) samples.append(metadata) return samples
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Create metadata objects with necessary attributes for each FASTA file found in the sequence path :param args: Argument parser object with necessary variables :return: samples: List of metadata objects
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python
train
mojaie/chorus
chorus/rdkit.py
https://github.com/mojaie/chorus/blob/fc7fe23a0272554c67671645ab07830b315eeb1b/chorus/rdkit.py#L20-L39
def to_rdmol(mol): """Convert molecule to RDMol""" rwmol = Chem.RWMol(Chem.MolFromSmiles('')) key_to_idx = {} bond_type = {1: Chem.BondType.SINGLE, 2: Chem.BondType.DOUBLE, 3: Chem.BondType.TRIPLE} conf = Chem.Conformer(rwmol.GetNumAtoms()) for k, a in mol.atoms_iter(): i = rwmol.AddAtom(Chem.Atom(atom_number(a.symbol))) key_to_idx[k] = i conf.SetAtomPosition(i, a.coords) rwmol.AddConformer(conf) for u, v, b in mol.bonds_iter(): ui = key_to_idx[u] vi = key_to_idx[v] rwmol.AddBond(ui, vi, bond_type[b.order]) Chem.GetSSSR(rwmol) # Ring recognition is required for fingerprint rwmol.UpdatePropertyCache(strict=False) return rwmol.GetMol()
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Convert molecule to RDMol
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python
train
moonso/extract_vcf
extract_vcf/config_parser.py
https://github.com/moonso/extract_vcf/blob/c8381b362fa6734cd2ee65ef260738868d981aaf/extract_vcf/config_parser.py#L196-L290
def check_plugin(self, plugin): """ Check if the section is in the proper format vcf format. Args: vcf_section (dict): The information from a vcf section Returns: True is it is in the proper format """ vcf_section = self[plugin] try: vcf_field = vcf_section['field'] if not vcf_field in self.vcf_columns: raise ValidateError( "field has to be in {0}\n" "Wrong field name in plugin: {1}".format( self.vcf_columns, plugin )) if vcf_field == 'INFO': try: info_key = vcf_section['info_key'] if info_key == 'CSQ': try: csq_key = vcf_section['csq_key'] except KeyError: raise ValidateError( "CSQ entrys has to refer to an csq field.\n" "Refer with keyword 'csq_key'\n" "csq_key is missing in section: {0}".format( plugin ) ) except KeyError: raise ValidateError( "INFO entrys has to refer to an INFO field.\n" "Refer with keyword 'info_key'\n" "info_key is missing in section: {0}".format( plugin ) ) except KeyError: raise ValidateError( "Vcf entrys have to refer to a field in the VCF with keyword" " 'field'.\nMissing keyword 'field' in plugin: {0}".format( plugin )) try: data_type = vcf_section['data_type'] if not data_type in self.data_types: raise ValidateError( "data_type has to be in {0}\n" "Wrong data_type in plugin: {1}".format( self.data_types, plugin) ) except KeyError: raise ValidateError( "Vcf entrys have to refer to a data type in the VCF with " "keyword 'data_type'.\n" "Missing data_type in plugin: {0}".format(plugin) ) separators = vcf_section.get('separators', None) if separators: if len(separators) == 1: self[plugin]['separators'] = list(separators) else: if data_type != 'flag': raise ValidateError( "If data_type != flag the separators have to be defined" "Missing separators in plugin: {0}".format(plugin) ) record_rule = vcf_section.get('record_rule', None) if record_rule: if not record_rule in ['min', 'max']: raise ValidateError( "Record rules have to be in {0}\n" "Wrong record_rule in plugin: {1}".format( ['min', 'max'], plugin) ) else: self.logger.info("Setting record rule to default: 'max'") return True
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Check if the section is in the proper format vcf format. Args: vcf_section (dict): The information from a vcf section Returns: True is it is in the proper format
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python
train
OCHA-DAP/hdx-python-api
src/hdx/data/dataset.py
https://github.com/OCHA-DAP/hdx-python-api/blob/212440f54f73805826a16db77dbcb6033b18a313/src/hdx/data/dataset.py#L753-L767
def get_all_resources(datasets): # type: (List['Dataset']) -> List[hdx.data.resource.Resource] """Get all resources from a list of datasets (such as returned by search) Args: datasets (List[Dataset]): list of datasets Returns: List[hdx.data.resource.Resource]: list of resources within those datasets """ resources = [] for dataset in datasets: for resource in dataset.get_resources(): resources.append(resource) return resources
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Get all resources from a list of datasets (such as returned by search) Args: datasets (List[Dataset]): list of datasets Returns: List[hdx.data.resource.Resource]: list of resources within those datasets
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python
train
bitesofcode/projexui
projexui/widgets/xorbrecordbox.py
https://github.com/bitesofcode/projexui/blob/f18a73bec84df90b034ca69b9deea118dbedfc4d/projexui/widgets/xorbrecordbox.py#L125-L142
def addRecord(self, record): """ Adds the given record to the system. :param record | <str> """ label_mapper = self.labelMapper() icon_mapper = self.iconMapper() self.addItem(label_mapper(record)) self.setItemData(self.count() - 1, wrapVariant(record), Qt.UserRole) # load icon if icon_mapper: self.setItemIcon(self.count() - 1, icon_mapper(record)) if self.showTreePopup(): XOrbRecordItem(self.treePopupWidget(), record)
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Adds the given record to the system. :param record | <str>
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python
train
ansible/tower-cli
tower_cli/resources/host.py
https://github.com/ansible/tower-cli/blob/a2b151fed93c47725018d3034848cb3a1814bed7/tower_cli/resources/host.py#L69-L86
def list_facts(self, pk=None, **kwargs): """Return a JSON object of all available facts of the given host. Note global option --format is not available here, as the output would always be JSON-formatted. =====API DOCS===== List all available facts of the given host. :param pk: Primary key of the target host. :type pk: int :param `**kwargs`: Keyword arguments list of available fields used for searching resource objects. :returns: A JSON object of all available facts of the given host. :rtype: dict =====API DOCS===== """ res = self.get(pk=pk, **kwargs) url = self.endpoint + '%d/%s/' % (res['id'], 'ansible_facts') return client.get(url, params={}).json()
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Return a JSON object of all available facts of the given host. Note global option --format is not available here, as the output would always be JSON-formatted. =====API DOCS===== List all available facts of the given host. :param pk: Primary key of the target host. :type pk: int :param `**kwargs`: Keyword arguments list of available fields used for searching resource objects. :returns: A JSON object of all available facts of the given host. :rtype: dict =====API DOCS=====
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python
valid
pyviz/holoviews
holoviews/core/boundingregion.py
https://github.com/pyviz/holoviews/blob/ae0dd2f3de448b0ca5e9065aabd6ef8d84c7e655/holoviews/core/boundingregion.py#L303-L308
def centroid(self): """ Return the centroid of the rectangle. """ left, bottom, right, top = self.lbrt() return (right + left) / 2.0, (top + bottom) / 2.0
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Return the centroid of the rectangle.
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python
train
adamrehn/ue4cli
ue4cli/UnrealManagerBase.py
https://github.com/adamrehn/ue4cli/blob/f1c34502c96059e36757b7433da7e98760a75a6f/ue4cli/UnrealManagerBase.py#L228-L244
def getThirdPartyLibCmakeFlags(self, libs): """ Retrieves the CMake invocation flags for building against the Unreal-bundled versions of the specified third-party libraries """ fmt = PrintingFormat.singleLine() if libs[0] == '--multiline': fmt = PrintingFormat.multiLine() libs = libs[1:] platformDefaults = True if libs[0] == '--nodefaults': platformDefaults = False libs = libs[1:] details = self.getThirdpartyLibs(libs, includePlatformDefaults=platformDefaults) CMakeCustomFlags.processLibraryDetails(details) return details.getCMakeFlags(self.getEngineRoot(), fmt)
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Retrieves the CMake invocation flags for building against the Unreal-bundled versions of the specified third-party libraries
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python
train
arviz-devs/arviz
arviz/plots/forestplot.py
https://github.com/arviz-devs/arviz/blob/d04d8da07f029fd2931f48d2f7f324cf393e5277/arviz/plots/forestplot.py#L270-L283
def make_plotters(self): """Initialize an object for each variable to be plotted.""" plotters, y = {}, 0 for var_name in self.var_names: plotters[var_name] = VarHandler( var_name, self.data, y, model_names=self.model_names, combined=self.combined, colors=self.colors, ) y = plotters[var_name].y_max() return plotters
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Initialize an object for each variable to be plotted.
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python
train
gem/oq-engine
openquake/hazardlib/sourceconverter.py
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/sourceconverter.py#L548-L595
def convert_mfdist(self, node): """ Convert the given node into a Magnitude-Frequency Distribution object. :param node: a node of kind incrementalMFD or truncGutenbergRichterMFD :returns: a :class:`openquake.hazardlib.mfd.EvenlyDiscretizedMFD.` or :class:`openquake.hazardlib.mfd.TruncatedGRMFD` instance """ with context(self.fname, node): [mfd_node] = [subnode for subnode in node if subnode.tag.endswith( ('incrementalMFD', 'truncGutenbergRichterMFD', 'arbitraryMFD', 'YoungsCoppersmithMFD', 'multiMFD'))] if mfd_node.tag.endswith('incrementalMFD'): return mfd.EvenlyDiscretizedMFD( min_mag=mfd_node['minMag'], bin_width=mfd_node['binWidth'], occurrence_rates=~mfd_node.occurRates) elif mfd_node.tag.endswith('truncGutenbergRichterMFD'): return mfd.TruncatedGRMFD( a_val=mfd_node['aValue'], b_val=mfd_node['bValue'], min_mag=mfd_node['minMag'], max_mag=mfd_node['maxMag'], bin_width=self.width_of_mfd_bin) elif mfd_node.tag.endswith('arbitraryMFD'): return mfd.ArbitraryMFD( magnitudes=~mfd_node.magnitudes, occurrence_rates=~mfd_node.occurRates) elif mfd_node.tag.endswith('YoungsCoppersmithMFD'): if "totalMomentRate" in mfd_node.attrib.keys(): # Return Youngs & Coppersmith from the total moment rate return mfd.YoungsCoppersmith1985MFD.from_total_moment_rate( min_mag=mfd_node["minMag"], b_val=mfd_node["bValue"], char_mag=mfd_node["characteristicMag"], total_moment_rate=mfd_node["totalMomentRate"], bin_width=mfd_node["binWidth"]) elif "characteristicRate" in mfd_node.attrib.keys(): # Return Youngs & Coppersmith from the total moment rate return mfd.YoungsCoppersmith1985MFD.\ from_characteristic_rate( min_mag=mfd_node["minMag"], b_val=mfd_node["bValue"], char_mag=mfd_node["characteristicMag"], char_rate=mfd_node["characteristicRate"], bin_width=mfd_node["binWidth"]) elif mfd_node.tag.endswith('multiMFD'): return mfd.multi_mfd.MultiMFD.from_node( mfd_node, self.width_of_mfd_bin)
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Convert the given node into a Magnitude-Frequency Distribution object. :param node: a node of kind incrementalMFD or truncGutenbergRichterMFD :returns: a :class:`openquake.hazardlib.mfd.EvenlyDiscretizedMFD.` or :class:`openquake.hazardlib.mfd.TruncatedGRMFD` instance
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python
train
heuer/segno
segno/utils.py
https://github.com/heuer/segno/blob/64d912a2bd17d0b5ff3e8b5d37098edfc663c2b3/segno/utils.py#L123-L149
def matrix_iter(matrix, version, scale=1, border=None): """\ Returns an iterator / generator over the provided matrix which includes the border and the scaling factor. If either the `scale` or `border` value is invalid, a :py:exc:`ValueError` is raised. :param matrix: An iterable of bytearrays. :param int version: A version constant. :param int scale: The scaling factor (default: ``1``). :param int border: The border size or ``None`` to specify the default quiet zone (4 for QR Codes, 2 for Micro QR Codes). :raises: :py:exc:`ValueError` if an illegal scale or border value is provided """ check_valid_border(border) scale = int(scale) check_valid_scale(scale) border = get_border(version, border) width, height = get_symbol_size(version, scale=1, border=0) def get_bit(i, j): return 0x1 if (0 <= i < height and 0 <= j < width and matrix[i][j]) else 0x0 for i in range(-border, height + border): for s in range(scale): yield chain.from_iterable(([get_bit(i, j)] * scale for j in range(-border, width + border)))
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\ Returns an iterator / generator over the provided matrix which includes the border and the scaling factor. If either the `scale` or `border` value is invalid, a :py:exc:`ValueError` is raised. :param matrix: An iterable of bytearrays. :param int version: A version constant. :param int scale: The scaling factor (default: ``1``). :param int border: The border size or ``None`` to specify the default quiet zone (4 for QR Codes, 2 for Micro QR Codes). :raises: :py:exc:`ValueError` if an illegal scale or border value is provided
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python
train
peterbrittain/asciimatics
asciimatics/renderers.py
https://github.com/peterbrittain/asciimatics/blob/f471427d7786ce2d5f1eeb2dae0e67d19e46e085/asciimatics/renderers.py#L226-L232
def _clear(self): """ Clear the current image. """ self._plain_image = [" " * self._width for _ in range(self._height)] self._colour_map = [[(None, 0, 0) for _ in range(self._width)] for _ in range(self._height)]
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Clear the current image.
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python
train
adamfast/faadata
faadata/airports/load.py
https://github.com/adamfast/faadata/blob/3c7d651b28160b7cb24724f67ebffd6bd0b490b9/faadata/airports/load.py#L9-L18
def clean_chars(value): "Hack to remove non-ASCII data. Should convert to Unicode: code page 437?" value = value.replace('\xb9', ' ') value = value.replace('\xf8', ' ') value = value.replace('\xab', ' ') value = value.replace('\xa7', ' ') value = value.replace('\xa8', ' ') value = value.replace('\xfb', ' ') value = value.replace('\xfc', ' ') return value
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Hack to remove non-ASCII data. Should convert to Unicode: code page 437?
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python
train
inspirehep/harvesting-kit
harvestingkit/elsevier_package.py
https://github.com/inspirehep/harvesting-kit/blob/33a7f8aa9dade1d863110c6d8b27dfd955cb471f/harvestingkit/elsevier_package.py#L194-L219
def _crawl_elsevier_and_find_issue_xml(self): """ Information about the current volume, issue, etc. is available in a file called issue.xml that is available in a higher directory. """ self._found_issues = [] if not self.path and not self.package_name: for issue in self.conn._get_issues(): dirname = issue.rstrip('/issue.xml') try: self._normalize_issue_dir_with_dtd(dirname) self._found_issues.append(dirname) except Exception as err: register_exception() print("ERROR: can't normalize %s: %s" % (dirname, err)) else: def visit(dummy, dirname, names): if "issue.xml" in names: try: self._normalize_issue_dir_with_dtd(dirname) self._found_issues.append(dirname) except Exception as err: register_exception() print("ERROR: can't normalize %s: %s" % (dirname, err)) walk(self.path, visit, None)
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Information about the current volume, issue, etc. is available in a file called issue.xml that is available in a higher directory.
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python
valid
ibelie/typy
typy/google/protobuf/json_format.py
https://github.com/ibelie/typy/blob/3616845fb91459aacd8df6bf82c5d91f4542bee7/typy/google/protobuf/json_format.py#L80-L94
def MessageToJson(message, including_default_value_fields=False): """Converts protobuf message to JSON format. Args: message: The protocol buffers message instance to serialize. including_default_value_fields: If True, singular primitive fields, repeated fields, and map fields will always be serialized. If False, only serialize non-empty fields. Singular message fields and oneof fields are not affected by this option. Returns: A string containing the JSON formatted protocol buffer message. """ js = _MessageToJsonObject(message, including_default_value_fields) return json.dumps(js, indent=2)
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Converts protobuf message to JSON format. Args: message: The protocol buffers message instance to serialize. including_default_value_fields: If True, singular primitive fields, repeated fields, and map fields will always be serialized. If False, only serialize non-empty fields. Singular message fields and oneof fields are not affected by this option. Returns: A string containing the JSON formatted protocol buffer message.
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python
valid
google/grr
grr/proto/setup.py
https://github.com/google/grr/blob/5cef4e8e2f0d5df43ea4877e9c798e0bf60bfe74/grr/proto/setup.py#L53-L63
def compile_protos(): """Builds necessary assets from sources.""" # If there's no makefile, we're likely installing from an sdist, # so there's no need to compile the protos (they should be already # compiled). if not os.path.exists(os.path.join(THIS_DIRECTORY, "makefile.py")): return # Only compile protobufs if we're inside GRR source tree. subprocess.check_call( ["python", "makefile.py", "--clean"], cwd=THIS_DIRECTORY)
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Builds necessary assets from sources.
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python
train
pyslackers/slack-sansio
slack/events.py
https://github.com/pyslackers/slack-sansio/blob/068ddd6480c6d2f9bf14fa4db498c9fe1017f4ab/slack/events.py#L278-L303
def dispatch(self, message: Message) -> Iterator[Any]: """ Yields handlers matching the routing of the incoming :class:`slack.events.Message` Args: message: :class:`slack.events.Message` Yields: handler """ if "text" in message: text = message["text"] or "" elif "message" in message: text = message["message"].get("text", "") else: text = "" msg_subtype = message.get("subtype") for subtype, matchs in itertools.chain( self._routes[message["channel"]].items(), self._routes["*"].items() ): if msg_subtype == subtype or subtype is None: for match, endpoints in matchs.items(): if match.search(text): yield from endpoints
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Yields handlers matching the routing of the incoming :class:`slack.events.Message` Args: message: :class:`slack.events.Message` Yields: handler
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python
train
chemlab/chemlab
chemlab/utils/__init__.py
https://github.com/chemlab/chemlab/blob/c8730966316d101e24f39ac3b96b51282aba0abe/chemlab/utils/__init__.py#L16-L23
def periodic_distance(a, b, periodic): '''Periodic distance between two arrays. Periodic is a 3 dimensional array containing the 3 box sizes. ''' delta = np.abs(a - b) delta = np.where(delta > 0.5 * periodic, periodic - delta, delta) return np.sqrt((delta ** 2).sum(axis=-1))
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Periodic distance between two arrays. Periodic is a 3 dimensional array containing the 3 box sizes.
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python
train
Esri/ArcREST
src/arcrest/webmap/symbols.py
https://github.com/Esri/ArcREST/blob/ab240fde2b0200f61d4a5f6df033516e53f2f416/src/arcrest/webmap/symbols.py#L229-L235
def outlineColor(self, value): """ sets the outline color """ if isinstance(value, (list, Color)): if value is list: self._outlineColor = value else: self._outlineColor = value.asList
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sets the outline color
[ "sets", "the", "outline", "color" ]
python
train
ubc/ubcpi
ubcpi/answer_pool.py
https://github.com/ubc/ubcpi/blob/7b6de03f93f3a4a8af4b92dfde7c69eeaf21f46e/ubcpi/answer_pool.py#L21-L40
def get_max_size(pool, num_option, item_length): """ Calculate the max number of item that an option can stored in the pool at give time. This is to limit the pool size to POOL_SIZE Args: option_index (int): the index of the option to calculate the size for pool (dict): answer pool num_option (int): total number of options available for the question item_length (int): the length of the item Returns: int: the max number of items that `option_index` can have """ max_items = POOL_SIZE / item_length # existing items plus the reserved for min size. If there is an option has 1 item, POOL_OPTION_MIN_SIZE - 1 space # is reserved. existing = POOL_OPTION_MIN_SIZE * num_option + sum([max(0, len(pool.get(i, {})) - 5) for i in xrange(num_option)]) return int(max_items - existing)
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Calculate the max number of item that an option can stored in the pool at give time. This is to limit the pool size to POOL_SIZE Args: option_index (int): the index of the option to calculate the size for pool (dict): answer pool num_option (int): total number of options available for the question item_length (int): the length of the item Returns: int: the max number of items that `option_index` can have
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python
train