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googleapis/google-cloud-python
bigquery/google/cloud/bigquery/client.py
https://github.com/googleapis/google-cloud-python/blob/85e80125a59cb10f8cb105f25ecc099e4b940b50/bigquery/google/cloud/bigquery/client.py#L293-L309
def dataset(self, dataset_id, project=None): """Construct a reference to a dataset. :type dataset_id: str :param dataset_id: ID of the dataset. :type project: str :param project: (Optional) project ID for the dataset (defaults to the project of the client). :rtype: :class:`google.cloud.bigquery.dataset.DatasetReference` :returns: a new ``DatasetReference`` instance """ if project is None: project = self.project return DatasetReference(project, dataset_id)
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Construct a reference to a dataset. :type dataset_id: str :param dataset_id: ID of the dataset. :type project: str :param project: (Optional) project ID for the dataset (defaults to the project of the client). :rtype: :class:`google.cloud.bigquery.dataset.DatasetReference` :returns: a new ``DatasetReference`` instance
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python
train
honzamach/pydgets
pydgets/widgets.py
https://github.com/honzamach/pydgets/blob/5ca4ce19fc2d9b5f41441fb9163810f8ca502e79/pydgets/widgets.py#L1294-L1308
def _render_content(self, content, **settings): """ Perform widget rendering, but do not print anything. """ bar_len = int(settings[self.SETTING_BAR_WIDTH]) if not bar_len: bar_len = TERMINAL_WIDTH - 10 percent = content progress = "" progress += str(settings[self.SETTING_BAR_CHAR]) * int(bar_len * percent) s = {k: settings[k] for k in (self.SETTING_FLAG_PLAIN,)} s.update(settings[self.SETTING_BAR_FORMATING]) progress = self.fmt_text(progress, **s) progress += ' ' * int(bar_len - int(bar_len * percent)) return "{:6.2f}% [{:s}]".format(percent * 100, progress)
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Perform widget rendering, but do not print anything.
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python
train
alvations/pywsd
pywsd/utils.py
https://github.com/alvations/pywsd/blob/4c12394c8adbcfed71dd912bdbef2e36370821bf/pywsd/utils.py#L29-L34
def remove_tags(text: str) -> str: """ Removes <tags> in angled brackets from text. """ tags = {i:" " for i in re.findall("(<[^>\n]*>)",text.strip())} no_tag_text = reduce(lambda x, kv:x.replace(*kv), tags.iteritems(), text) return " ".join(no_tag_text.split())
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Removes <tags> in angled brackets from text.
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python
train
frictionlessdata/tableschema-pandas-py
tableschema_pandas/mapper.py
https://github.com/frictionlessdata/tableschema-pandas-py/blob/ef941dbc12f5d346e9612f8fec1b4b356b8493ca/tableschema_pandas/mapper.py#L156-L176
def restore_row(self, row, schema, pk): """Restore row from Pandas """ result = [] for field in schema.fields: if schema.primary_key and schema.primary_key[0] == field.name: if field.type == 'number' and np.isnan(pk): pk = None if pk and field.type == 'integer': pk = int(pk) result.append(field.cast_value(pk)) else: value = row[field.name] if field.type == 'number' and np.isnan(value): value = None if value and field.type == 'integer': value = int(value) elif field.type == 'datetime': value = value.to_pydatetime() result.append(field.cast_value(value)) return result
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Restore row from Pandas
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python
train
klen/muffin-debugtoolbar
muffin_debugtoolbar/tbtools/tbtools.py
https://github.com/klen/muffin-debugtoolbar/blob/b650b35fbe2035888f6bba5dac3073ef01c94dc6/muffin_debugtoolbar/tbtools/tbtools.py#L231-L256
def render_full(self, request, lodgeit_url=None): """Render the Full HTML page with the traceback info.""" app = request.app root_path = request.app.ps.debugtoolbar.cfg.prefix exc = escape(self.exception) summary = self.render_summary(include_title=False, request=request) token = request.app['debugtoolbar']['pdbt_token'] vars = { 'evalex': app.ps.debugtoolbar.cfg.intercept_exc == 'debug' and 'true' or 'false', 'console': 'console', 'lodgeit_url': lodgeit_url and escape(lodgeit_url) or '', 'title': exc, 'exception': exc, 'exception_type': escape(self.exception_type), 'summary': summary, 'plaintext': self.plaintext, 'plaintext_cs': re.sub('-{2,}', '-', self.plaintext), 'traceback_id': self.id, 'static_path': root_path + 'static/', 'token': token, 'root_path': root_path, 'url': root_path + 'exception?token=%s&tb=%s' % (token, self.id), } template = app.ps.jinja2.env.get_template('debugtoolbar/exception.html') return template.render(app=app, request=request, **vars)
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Render the Full HTML page with the traceback info.
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python
train
pypa/pipenv
pipenv/vendor/click/globals.py
https://github.com/pypa/pipenv/blob/cae8d76c210b9777e90aab76e9c4b0e53bb19cde/pipenv/vendor/click/globals.py#L39-L48
def resolve_color_default(color=None): """"Internal helper to get the default value of the color flag. If a value is passed it's returned unchanged, otherwise it's looked up from the current context. """ if color is not None: return color ctx = get_current_context(silent=True) if ctx is not None: return ctx.color
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Internal helper to get the default value of the color flag. If a value is passed it's returned unchanged, otherwise it's looked up from the current context.
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python
train
watson-developer-cloud/python-sdk
ibm_watson/visual_recognition_v3.py
https://github.com/watson-developer-cloud/python-sdk/blob/4c2c9df4466fcde88975da9ecd834e6ba95eb353/ibm_watson/visual_recognition_v3.py#L789-L809
def _from_dict(cls, _dict): """Initialize a ClassifiedImage object from a json dictionary.""" args = {} if 'source_url' in _dict: args['source_url'] = _dict.get('source_url') if 'resolved_url' in _dict: args['resolved_url'] = _dict.get('resolved_url') if 'image' in _dict: args['image'] = _dict.get('image') if 'error' in _dict: args['error'] = ErrorInfo._from_dict(_dict.get('error')) if 'classifiers' in _dict: args['classifiers'] = [ ClassifierResult._from_dict(x) for x in (_dict.get('classifiers')) ] else: raise ValueError( 'Required property \'classifiers\' not present in ClassifiedImage JSON' ) return cls(**args)
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Initialize a ClassifiedImage object from a json dictionary.
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python
train
PrefPy/prefpy
prefpy/mechanismMcmc.py
https://github.com/PrefPy/prefpy/blob/f395ba3782f05684fa5de0cece387a6da9391d02/prefpy/mechanismMcmc.py#L140-L171
def getRankingBruteForce(self, profile): """ Returns a list that orders all candidates from best to worst when we use brute force to compute Bayesian utilities for an election profile. This function assumes that getCandScoresMapBruteForce(profile) is implemented for the child Mechanism class. Note that the returned list gives no indication of ties between candidates. :ivar Profile profile: A Profile object that represents an election profile. """ # We generate a map that associates each score with the candidates that have that score. candScoresMapBruteForce = self.getCandScoresMapBruteForce(profile) reverseCandScoresMap = dict() for key, value in candScoresMapBruteForce.items(): if value not in reverseCandScoresMap.keys(): reverseCandScoresMap[value] = [key] else: reverseCandScoresMap[value].append(key) # We sort the scores by either decreasing order or increasing order. if self.maximizeCandScore == True: sortedCandScores = sorted(reverseCandScoresMap.keys(), reverse=True) else: sortedCandScores = sorted(reverseCandScoresMap.keys()) # We put the candidates into our ranking based on the order in which their score appears ranking = [] for candScore in sortedCandScores: for cand in reverseCandScoresMap[candScore]: ranking.append(cand) return ranking
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Returns a list that orders all candidates from best to worst when we use brute force to compute Bayesian utilities for an election profile. This function assumes that getCandScoresMapBruteForce(profile) is implemented for the child Mechanism class. Note that the returned list gives no indication of ties between candidates. :ivar Profile profile: A Profile object that represents an election profile.
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python
train
ministryofjustice/money-to-prisoners-common
mtp_common/build_tasks/tasks.py
https://github.com/ministryofjustice/money-to-prisoners-common/blob/33c43a2912cb990d9148da7c8718f480f07d90a1/mtp_common/build_tasks/tasks.py#L131-L139
def python_dependencies(context: Context, common_path=None): """ Updates python dependencies """ context.pip_command('install', '-r', context.requirements_file) if common_path: context.pip_command('uninstall', '--yes', 'money-to-prisoners-common') context.pip_command('install', '--force-reinstall', '-e', common_path) context.shell('rm', '-rf', 'webpack.config.js')
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Updates python dependencies
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python
train
flo-compbio/xlmhg
xlmhg/result.py
https://github.com/flo-compbio/xlmhg/blob/8e5929ee1dc91b95e343b7a2b1b1d6664c4540a1/xlmhg/result.py#L181-L188
def escore(self): """(property) Returns the E-score associated with the result.""" hg_pval_thresh = self.escore_pval_thresh or self.pval escore_tol = self.escore_tol or mhg_cython.get_default_tol() es = mhg_cython.get_xlmhg_escore( self.indices, self.N, self.K, self.X, self.L, hg_pval_thresh, escore_tol) return es
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(property) Returns the E-score associated with the result.
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python
train
KelSolaar/Foundations
foundations/exceptions.py
https://github.com/KelSolaar/Foundations/blob/5c141330faf09dad70a12bc321f4c564917d0a91/foundations/exceptions.py#L171-L196
def extract_locals(trcback): """ Extracts the frames locals of given traceback. :param trcback: Traceback. :type trcback: Traceback :return: Frames locals. :rtype: list """ output = [] stack = extract_stack(get_inner_most_frame(trcback)) for frame, file_name, line_number, name, context, index in stack: args_names, nameless, keyword = extract_arguments(frame) arguments, nameless_args, keyword_args, locals = OrderedDict(), [], {}, {} for key, data in frame.f_locals.iteritems(): if key == nameless: nameless_args = map(repr, frame.f_locals.get(nameless, ())) elif key == keyword: keyword_args = dict((arg, repr(value)) for arg, value in frame.f_locals.get(keyword, {}).iteritems()) elif key in args_names: arguments[key] = repr(data) else: locals[key] = repr(data) output.append(((name, file_name, line_number), (arguments, nameless_args, keyword_args, locals))) return output
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python
train
Nachtfeuer/pipeline
spline/tools/report/generator.py
https://github.com/Nachtfeuer/pipeline/blob/04ca18c4e95e4349532bb45b768206393e1f2c13/spline/tools/report/generator.py#L26-L45
def generate_html(store): """ Generating HTML report. Args: store (Store): report data. Returns: str: rendered HTML template. """ spline = { 'version': VERSION, 'url': 'https://github.com/Nachtfeuer/pipeline', 'generated': datetime.now().strftime("%A, %d. %B %Y - %I:%M:%S %p") } html_template_file = os.path.join(os.path.dirname(__file__), 'templates/report.html.j2') with open(html_template_file) as handle: html_template = handle.read() return render(html_template, spline=spline, store=store)
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Generating HTML report. Args: store (Store): report data. Returns: str: rendered HTML template.
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python
train
rackerlabs/simpl
simpl/rest.py
https://github.com/rackerlabs/simpl/blob/60ed3336a931cd6a7a7246e60f26165d9dc7c99c/simpl/rest.py#L169-L175
def validate_range_values(request, label, kwargs): """Ensure value contained in label is a positive integer.""" value = kwargs.get(label, request.query.get(label)) if value: kwargs[label] = int(value) if kwargs[label] < 0 or kwargs[label] > MAX_PAGE_SIZE: raise ValueError
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Ensure value contained in label is a positive integer.
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python
train
portantier/habu
habu/cli/cmd_hasher.py
https://github.com/portantier/habu/blob/87091e389dc6332fe1b82830c22b2eefc55816f2/habu/cli/cmd_hasher.py#L11-L43
def cmd_hasher(f, algorithm): """Compute various hashes for the input data, that can be a file or a stream. Example: \b $ habu.hasher README.rst md5 992a833cd162047daaa6a236b8ac15ae README.rst ripemd160 0566f9141e65e57cae93e0e3b70d1d8c2ccb0623 README.rst sha1 d7dbfd2c5e2828eb22f776550c826e4166526253 README.rst sha256 6bb22d927e1b6307ced616821a1877b6cc35e... README.rst sha512 8743f3eb12a11cf3edcc16e400fb14d599b4a... README.rst whirlpool 96bcc083242e796992c0f3462f330811f9e8c... README.rst You can also specify which algorithm to use. In such case, the output is only the value of the calculated hash: \b $ habu.hasher -a md5 README.rst 992a833cd162047daaa6a236b8ac15ae README.rst """ data = f.read() if not data: print("Empty file or string!") return 1 if algorithm: print(hasher(data, algorithm)[algorithm], f.name) else: for algo, result in hasher(data).items(): print("{:<12} {} {}".format(algo, result, f.name))
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Compute various hashes for the input data, that can be a file or a stream. Example: \b $ habu.hasher README.rst md5 992a833cd162047daaa6a236b8ac15ae README.rst ripemd160 0566f9141e65e57cae93e0e3b70d1d8c2ccb0623 README.rst sha1 d7dbfd2c5e2828eb22f776550c826e4166526253 README.rst sha256 6bb22d927e1b6307ced616821a1877b6cc35e... README.rst sha512 8743f3eb12a11cf3edcc16e400fb14d599b4a... README.rst whirlpool 96bcc083242e796992c0f3462f330811f9e8c... README.rst You can also specify which algorithm to use. In such case, the output is only the value of the calculated hash: \b $ habu.hasher -a md5 README.rst 992a833cd162047daaa6a236b8ac15ae README.rst
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python
train
DLR-RM/RAFCON
source/rafcon/gui/controllers/graphical_editor_gaphas.py
https://github.com/DLR-RM/RAFCON/blob/24942ef1a904531f49ab8830a1dbb604441be498/source/rafcon/gui/controllers/graphical_editor_gaphas.py#L380-L627
def state_machine_change_after(self, model, prop_name, info): """Called on any change within th state machine This method is called, when any state, transition, data flow, etc. within the state machine changes. This then typically requires a redraw of the graphical editor, to display these changes immediately. :param rafcon.gui.models.state_machine.StateMachineModel model: The state machine model :param str prop_name: The property that was changed :param dict info: Information about the change """ if 'method_name' in info and info['method_name'] == 'root_state_change': method_name, model, result, arguments, instance = self._extract_info_data(info['kwargs']) if self.model.ongoing_complex_actions: return # The method causing the change raised an exception, thus nothing was changed if (isinstance(result, string_types) and "CRASH" in result) or isinstance(result, Exception): return # avoid to remove views of elements of states which parent state is destroyed recursively if 'remove' in method_name: # for remove the model is always a state and in case of remove_state it is the container_state # that performs the operation therefore if is_about_to_be_destroyed_recursively is False # the child state can be removed and for True ignored because its parent will create a notification if model.is_about_to_be_destroyed_recursively: return # only react to the notification if the model is a model, which has to be drawn # if it is a model inside a library state, this is eventually not the case if isinstance(model, AbstractStateModel): library_root_state = model.state.get_next_upper_library_root_state() if library_root_state: parent_library_root_state_m = self.model.get_state_model_by_path(library_root_state.get_path()) if not parent_library_root_state_m.parent.show_content(): return if method_name == 'state_execution_status': state_v = self.canvas.get_view_for_model(model) if state_v: # Children of LibraryStates are not modeled, yet self.canvas.request_update(state_v, matrix=False) elif method_name == 'add_state': new_state = arguments[1] new_state_m = model.states[new_state.state_id] self.add_state_view_with_meta_data_for_model(new_state_m, model) if not self.perform_drag_and_drop: self.canvas.wait_for_update() elif method_name == 'remove_state': state_v = self.canvas.get_view_for_core_element(result) if state_v: parent_v = self.canvas.get_parent(state_v) state_v.remove() if parent_v: self.canvas.request_update(parent_v) self.canvas.wait_for_update() # ---------------------------------- # TRANSITIONS # ---------------------------------- elif method_name == 'add_transition': transitions_models = model.transitions transition_id = result for transition_m in transitions_models: if transition_m.transition.transition_id == transition_id: self.add_transition_view_for_model(transition_m, model) self.canvas.wait_for_update() break elif method_name == 'remove_transition': transition_v = self.canvas.get_view_for_core_element(result) if transition_v: state_m = model state_v = self.canvas.get_view_for_model(state_m) transition_v.remove() self.canvas.request_update(state_v, matrix=False) self.canvas.wait_for_update() elif method_name == 'transition_change': transition_m = model transition_v = self.canvas.get_view_for_model(transition_m) self._reconnect_transition(transition_v, transition_m, transition_m.parent) self.canvas.wait_for_update() # ---------------------------------- # DATA FLOW # ---------------------------------- elif method_name == 'add_data_flow': data_flow_models = model.data_flows data_flow_id = result for data_flow_m in data_flow_models: if data_flow_m.data_flow.data_flow_id == data_flow_id: self.add_data_flow_view_for_model(data_flow_m, model) self.canvas.wait_for_update() break elif method_name == 'remove_data_flow': data_flow_v = self.canvas.get_view_for_core_element(result) if data_flow_v: state_m = model state_v = self.canvas.get_view_for_model(state_m) self.canvas.request_update(state_v, matrix=False) data_flow_v.remove() self.canvas.wait_for_update() elif method_name == 'data_flow_change': data_flow_m = model data_flow_v = self.canvas.get_view_for_model(data_flow_m) self._reconnect_data_flow(data_flow_v, data_flow_m, data_flow_m.parent) self.canvas.wait_for_update() # ---------------------------------- # OUTCOMES # ---------------------------------- elif method_name == 'add_outcome': state_m = model state_v = self.canvas.get_view_for_model(state_m) for outcome_m in state_m.outcomes: if outcome_m.outcome.outcome_id == result: state_v.add_outcome(outcome_m) self.canvas.request_update(state_v, matrix=False) self.canvas.wait_for_update() break elif method_name == 'remove_outcome': state_m = model state_v = self.canvas.get_view_for_model(state_m) if state_v is None: logger.debug("no state_v found for method_name '{}'".format(method_name)) else: outcome_v = self.canvas.get_view_for_core_element(result) if outcome_v: state_v.remove_outcome(outcome_v) self.canvas.request_update(state_v, matrix=False) self.canvas.wait_for_update() # ---------------------------------- # DATA PORTS # ---------------------------------- elif method_name == 'add_input_data_port': state_m = model state_v = self.canvas.get_view_for_model(state_m) for input_data_port_m in state_m.input_data_ports: if input_data_port_m.data_port.data_port_id == result: state_v.add_input_port(input_data_port_m) self.canvas.request_update(state_v, matrix=False) self.canvas.wait_for_update() break elif method_name == 'add_output_data_port': state_m = model state_v = self.canvas.get_view_for_model(state_m) for output_data_port_m in state_m.output_data_ports: if output_data_port_m.data_port.data_port_id == result: state_v.add_output_port(output_data_port_m) self.canvas.request_update(state_v, matrix=False) self.canvas.wait_for_update() break elif method_name == 'remove_input_data_port': state_m = model state_v = self.canvas.get_view_for_model(state_m) if state_v is None: logger.debug("no state_v found for method_name '{}'".format(method_name)) else: input_port_v = self.canvas.get_view_for_core_element(result) if input_port_v: state_v.remove_input_port(input_port_v) self.canvas.request_update(state_v, matrix=False) self.canvas.wait_for_update() elif method_name == 'remove_output_data_port': state_m = model state_v = self.canvas.get_view_for_model(state_m) if state_v is None: logger.debug("no state_v found for method_name '{}'".format(method_name)) else: output_port_v = self.canvas.get_view_for_core_element(result) if output_port_v: state_v.remove_output_port(output_port_v) self.canvas.request_update(state_v, matrix=False) self.canvas.wait_for_update() elif method_name in ['data_type', 'change_data_type']: pass elif method_name == 'default_value': pass # ---------------------------------- # SCOPED VARIABLES # ---------------------------------- elif method_name == 'add_scoped_variable': state_m = model state_v = self.canvas.get_view_for_model(state_m) for scoped_variable_m in state_m.scoped_variables: if scoped_variable_m.scoped_variable.data_port_id == result: state_v.add_scoped_variable(scoped_variable_m) self.canvas.request_update(state_v, matrix=False) self.canvas.wait_for_update() break elif method_name == 'remove_scoped_variable': state_m = model state_v = self.canvas.get_view_for_model(state_m) if state_v is None: logger.debug("no state_v found for method_name '{}'".format(method_name)) else: scoped_variable_v = self.canvas.get_view_for_core_element(result) if scoped_variable_v: state_v.remove_scoped_variable(scoped_variable_v) self.canvas.request_update(state_v, matrix=False) self.canvas.wait_for_update() # ---------------------------------- # STATE MISCELLANEOUS # ---------------------------------- elif method_name == 'name': # The name of a state was changed if not isinstance(model, AbstractStateModel): parent_model = model.parent # The name of a port (input, output, scoped var, outcome) was changed else: parent_model = model state_v = self.canvas.get_view_for_model(parent_model) if parent_model is model: state_v.name_view.name = arguments[1] self.canvas.request_update(state_v.name_view, matrix=False) else: self.canvas.request_update(state_v, matrix=False) self.canvas.wait_for_update() elif method_name == 'parent': pass elif method_name == 'description': pass elif method_name == 'script_text': pass # TODO handle the following method calls -> for now those are explicit (in the past implicit) ignored # TODO -> correct the complex actions which are used in some test (by test calls or by adapting the model) elif method_name in ['input_data_ports', 'output_data_ports', 'outcomes', 'change_root_state_type', 'change_state_type', 'group_states', 'ungroup_state', 'substitute_state']: pass else: known_ignore_list = ['set_input_runtime_value', 'set_use_input_runtime_value', # from library State 'set_output_runtime_value', 'set_use_output_runtime_value', 'input_data_port_runtime_values', 'use_runtime_value_input_data_ports', 'output_data_port_runtime_values', 'use_runtime_value_output_data_ports', 'semantic_data', 'add_semantic_data', 'remove_semantic_data', 'remove_income'] if method_name not in known_ignore_list: logger.warning("Method {0} not caught in GraphicalViewer, details: {1}".format(method_name, info)) if method_name in ['add_state', 'add_transition', 'add_data_flow', 'add_outcome', 'add_input_data_port', 'add_output_data_port', 'add_scoped_variable', 'data_flow_change', 'transition_change']: try: self._meta_data_changed(None, model, 'append_to_last_change', True) except Exception as e: logger.exception('Error while trying to emit meta data signal {0} {1}'.format(e, model))
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"canvas", ".", "get_view_for_model", "(", "state_m", ")", "for", "scoped_variable_m", "in", "state_m", ".", "scoped_variables", ":", "if", "scoped_variable_m", ".", "scoped_variable", ".", "data_port_id", "==", "result", ":", "state_v", ".", "add_scoped_variable", "(", "scoped_variable_m", ")", "self", ".", "canvas", ".", "request_update", "(", "state_v", ",", "matrix", "=", "False", ")", "self", ".", "canvas", ".", "wait_for_update", "(", ")", "break", "elif", "method_name", "==", "'remove_scoped_variable'", ":", "state_m", "=", "model", "state_v", "=", "self", ".", "canvas", ".", "get_view_for_model", "(", "state_m", ")", "if", "state_v", "is", "None", ":", "logger", ".", "debug", "(", "\"no state_v found for method_name '{}'\"", ".", "format", "(", "method_name", ")", ")", "else", ":", "scoped_variable_v", "=", "self", ".", "canvas", ".", "get_view_for_core_element", "(", "result", ")", "if", "scoped_variable_v", ":", "state_v", ".", "remove_scoped_variable", "(", "scoped_variable_v", ")", "self", ".", "canvas", ".", "request_update", "(", "state_v", ",", "matrix", "=", "False", ")", "self", ".", "canvas", ".", "wait_for_update", "(", ")", "# ----------------------------------", "# STATE MISCELLANEOUS", "# ----------------------------------", "elif", "method_name", "==", "'name'", ":", "# The name of a state was changed", "if", "not", "isinstance", "(", "model", ",", "AbstractStateModel", ")", ":", "parent_model", "=", "model", ".", "parent", "# The name of a port (input, output, scoped var, outcome) was changed", "else", ":", "parent_model", "=", "model", "state_v", "=", "self", ".", "canvas", ".", "get_view_for_model", "(", "parent_model", ")", "if", "parent_model", "is", "model", ":", "state_v", ".", "name_view", ".", "name", "=", "arguments", "[", "1", "]", "self", ".", "canvas", ".", "request_update", "(", "state_v", ".", "name_view", ",", "matrix", "=", "False", ")", "else", ":", "self", ".", "canvas", ".", "request_update", "(", "state_v", ",", "matrix", "=", "False", ")", "self", ".", "canvas", ".", "wait_for_update", "(", ")", "elif", "method_name", "==", "'parent'", ":", "pass", "elif", "method_name", "==", "'description'", ":", "pass", "elif", "method_name", "==", "'script_text'", ":", "pass", "# TODO handle the following method calls -> for now those are explicit (in the past implicit) ignored", "# TODO -> correct the complex actions which are used in some test (by test calls or by adapting the model)", "elif", "method_name", "in", "[", "'input_data_ports'", ",", "'output_data_ports'", ",", "'outcomes'", ",", "'change_root_state_type'", ",", "'change_state_type'", ",", "'group_states'", ",", "'ungroup_state'", ",", "'substitute_state'", "]", ":", "pass", "else", ":", "known_ignore_list", "=", "[", "'set_input_runtime_value'", ",", "'set_use_input_runtime_value'", ",", "# from library State", "'set_output_runtime_value'", ",", "'set_use_output_runtime_value'", ",", "'input_data_port_runtime_values'", ",", "'use_runtime_value_input_data_ports'", ",", "'output_data_port_runtime_values'", ",", "'use_runtime_value_output_data_ports'", ",", "'semantic_data'", ",", "'add_semantic_data'", ",", "'remove_semantic_data'", ",", "'remove_income'", "]", "if", "method_name", "not", "in", "known_ignore_list", ":", "logger", ".", "warning", "(", "\"Method {0} not caught in GraphicalViewer, details: {1}\"", ".", "format", "(", "method_name", ",", "info", ")", ")", "if", "method_name", "in", "[", "'add_state'", ",", "'add_transition'", ",", "'add_data_flow'", ",", "'add_outcome'", ",", "'add_input_data_port'", ",", "'add_output_data_port'", ",", "'add_scoped_variable'", ",", "'data_flow_change'", ",", "'transition_change'", "]", ":", "try", ":", "self", ".", "_meta_data_changed", "(", "None", ",", "model", ",", "'append_to_last_change'", ",", "True", ")", "except", "Exception", "as", "e", ":", "logger", ".", "exception", "(", "'Error while trying to emit meta data signal {0} {1}'", ".", "format", "(", "e", ",", "model", ")", ")" ]
Called on any change within th state machine This method is called, when any state, transition, data flow, etc. within the state machine changes. This then typically requires a redraw of the graphical editor, to display these changes immediately. :param rafcon.gui.models.state_machine.StateMachineModel model: The state machine model :param str prop_name: The property that was changed :param dict info: Information about the change
[ "Called", "on", "any", "change", "within", "th", "state", "machine" ]
python
train
eyeseast/propublica-congress
congress/members.py
https://github.com/eyeseast/propublica-congress/blob/03e519341063c5703080b4723112f1831816c77e/congress/members.py#L51-L60
def compare(self, first, second, chamber, type='votes', congress=CURRENT_CONGRESS): """ See how often two members voted together in a given Congress. Takes two member IDs, a chamber and a Congress number. """ check_chamber(chamber) path = "members/{first}/{type}/{second}/{congress}/{chamber}.json" path = path.format(first=first, second=second, type=type, congress=congress, chamber=chamber) return self.fetch(path)
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See how often two members voted together in a given Congress. Takes two member IDs, a chamber and a Congress number.
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python
train
MillionIntegrals/vel
vel/rl/algo/policy_gradient/trpo.py
https://github.com/MillionIntegrals/vel/blob/e0726e1f63742b728966ccae0c8b825ea0ba491a/vel/rl/algo/policy_gradient/trpo.py#L263-L272
def create(max_kl, cg_iters, line_search_iters, cg_damping, entropy_coef, vf_iters, discount_factor, gae_lambda=1.0, improvement_acceptance_ratio=0.1, max_grad_norm=0.5): """ Vel factory function """ return TrpoPolicyGradient( max_kl, int(cg_iters), int(line_search_iters), cg_damping, entropy_coef, vf_iters, discount_factor=discount_factor, gae_lambda=gae_lambda, improvement_acceptance_ratio=improvement_acceptance_ratio, max_grad_norm=max_grad_norm )
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Vel factory function
[ "Vel", "factory", "function" ]
python
train
dereneaton/ipyrad
ipyrad/analysis/tetrad2.py
https://github.com/dereneaton/ipyrad/blob/5eeb8a178160f45faf71bf47cec4abe998a575d1/ipyrad/analysis/tetrad2.py#L1651-L1689
def find_clades(trees, names): """ A subfunc of consensus_tree(). Traverses trees to count clade occurrences. Names are ordered by names, else they are in the order of the first tree. """ ## index names from the first tree if not names: names = trees[0].get_leaf_names() ndict = {j:i for i, j in enumerate(names)} namedict = {i:j for i, j in enumerate(names)} ## store counts clade_counts = defaultdict(int) ## count as bitarray clades in each tree for tree in trees: tree.unroot() for node in tree.traverse('postorder'): #bits = bitarray('0'*len(tree)) bits = np.zeros(len(tree), dtype=np.bool_) for child in node.iter_leaf_names(): bits[ndict[child]] = True ## if parent is root then mirror flip one child (where bit[0]=0) # if not node.is_root(): # if node.up.is_root(): # if bits[0]: # bits.invert() bitstring = "".join([np.binary_repr(i) for i in bits]) clade_counts[bitstring] += 1 ## convert to freq for key, val in clade_counts.items(): clade_counts[key] = val / float(len(trees)) ## return in sorted order clade_counts = sorted(clade_counts.items(), key=lambda x: x[1], reverse=True) return namedict, clade_counts
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A subfunc of consensus_tree(). Traverses trees to count clade occurrences. Names are ordered by names, else they are in the order of the first tree.
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python
valid
josiah-wolf-oberholtzer/uqbar
uqbar/sphinx/api.py
https://github.com/josiah-wolf-oberholtzer/uqbar/blob/eca7fefebbbee1e2ae13bf5d6baa838be66b1db6/uqbar/sphinx/api.py#L116-L134
def setup(app) -> Dict[str, Any]: """ Sets up Sphinx extension. """ app.add_config_value("uqbar_api_directory_name", "api", "env") app.add_config_value("uqbar_api_document_empty_modules", False, "env") app.add_config_value("uqbar_api_document_private_members", False, "env") app.add_config_value("uqbar_api_document_private_modules", False, "env") app.add_config_value("uqbar_api_member_documenter_classes", None, "env") app.add_config_value("uqbar_api_module_documenter_class", None, "env") app.add_config_value("uqbar_api_root_documenter_class", None, "env") app.add_config_value("uqbar_api_source_paths", None, "env") app.add_config_value("uqbar_api_title", "API", "html") app.connect("builder-inited", on_builder_inited) return { "version": uqbar.__version__, "parallel_read_safe": True, "parallel_write_safe": True, }
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Sets up Sphinx extension.
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python
train
SKA-ScienceDataProcessor/integration-prototype
sip/tango_control/tango_subarray/app/subarray_device.py
https://github.com/SKA-ScienceDataProcessor/integration-prototype/blob/8c8006de6ad71dcd44114b0338780738079c87d4/sip/tango_control/tango_subarray/app/subarray_device.py#L16-L20
def init_device(self): """Initialise the device.""" Device.init_device(self) time.sleep(0.1) self.set_state(DevState.STANDBY)
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Initialise the device.
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python
train
zetaops/zengine
zengine/messaging/views.py
https://github.com/zetaops/zengine/blob/b5bc32d3b37bca799f8985be916f04528ac79e4a/zengine/messaging/views.py#L794-L822
def edit_message(current): """ Edit a message a user own. .. code-block:: python # request: { 'view':'_zops_edit_message', 'message': { 'body': string, # message text 'key': key } } # response: { 'status': string, # 'OK' for success 'code': int, # 200 for success } """ current.output = {'status': 'OK', 'code': 200} in_msg = current.input['message'] try: msg = Message(current).objects.get(sender_id=current.user_id, key=in_msg['key']) msg.body = in_msg['body'] msg.save() except ObjectDoesNotExist: raise HTTPError(404, "")
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Edit a message a user own. .. code-block:: python # request: { 'view':'_zops_edit_message', 'message': { 'body': string, # message text 'key': key } } # response: { 'status': string, # 'OK' for success 'code': int, # 200 for success }
[ "Edit", "a", "message", "a", "user", "own", "." ]
python
train
Atomistica/atomistica
src/python/atomistica/mdcore_io.py
https://github.com/Atomistica/atomistica/blob/5ed79d776c92b91a566be22615bfb304ecc75db7/src/python/atomistica/mdcore_io.py#L164-L190
def read_cyc(this, fn, conv=1.0): """ Read the lattice information from a cyc.dat file (i.e., tblmd input file) """ f = paropen(fn, "r") f.readline() f.readline() f.readline() f.readline() cell = np.array( [ [ 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0 ] ] ) l = f.readline() s = map(float, l.split()) cell[0, 0] = s[0]*conv cell[1, 0] = s[1]*conv cell[2, 0] = s[2]*conv l = f.readline() s = map(float, l.split()) cell[0, 1] = s[0]*conv cell[1, 1] = s[1]*conv cell[2, 1] = s[2]*conv l = f.readline() s = map(float, l.split()) cell[0, 2] = s[0]*conv cell[1, 2] = s[1]*conv cell[2, 2] = s[2]*conv this.set_cell(cell) this.set_pbc(True) f.close()
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Read the lattice information from a cyc.dat file (i.e., tblmd input file)
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python
train
aouyar/PyMunin
pymunin/plugins/memcachedstats.py
https://github.com/aouyar/PyMunin/blob/4f58a64b6b37c85a84cc7e1e07aafaa0321b249d/pymunin/plugins/memcachedstats.py#L305-L440
def retrieveVals(self): """Retrieve values for graphs.""" if self._stats is None: serverInfo = MemcachedInfo(self._host, self._port, self._socket_file) stats = serverInfo.getStats() else: stats = self._stats if stats is None: raise Exception("Undetermined error accesing stats.") stats['set_hits'] = stats.get('total_items') if stats.has_key('cmd_set') and stats.has_key('total_items'): stats['set_misses'] = stats['cmd_set'] - stats['total_items'] self.saveState(stats) if self.hasGraph('memcached_connections'): self.setGraphVal('memcached_connections', 'conn', stats.get('curr_connections')) if self.hasGraph('memcached_items'): self.setGraphVal('memcached_items', 'items', stats.get('curr_items')) if self.hasGraph('memcached_memory'): self.setGraphVal('memcached_memory', 'bytes', stats.get('bytes')) if self.hasGraph('memcached_connrate'): self.setGraphVal('memcached_connrate', 'conn', stats.get('total_connections')) if self.hasGraph('memcached_traffic'): self.setGraphVal('memcached_traffic', 'rxbytes', stats.get('bytes_read')) self.setGraphVal('memcached_traffic', 'txbytes', stats.get('bytes_written')) if self.hasGraph('memcached_reqrate'): self.setGraphVal('memcached_reqrate', 'set', stats.get('cmd_set')) self.setGraphVal('memcached_reqrate', 'get', stats.get('cmd_get')) if self.graphHasField('memcached_reqrate', 'del'): self.setGraphVal('memcached_reqrate', 'del', safe_sum([stats.get('delete_hits'), stats.get('delete_misses')])) if self.graphHasField('memcached_reqrate', 'cas'): self.setGraphVal('memcached_reqrate', 'cas', safe_sum([stats.get('cas_hits'), stats.get('cas_misses'), stats.get('cas_badval')])) if self.graphHasField('memcached_reqrate', 'incr'): self.setGraphVal('memcached_reqrate', 'incr', safe_sum([stats.get('incr_hits'), stats.get('incr_misses')])) if self.graphHasField('memcached_reqrate', 'decr'): self.setGraphVal('memcached_reqrate', 'decr', safe_sum([stats.get('decr_hits'), stats.get('decr_misses')])) if self.hasGraph('memcached_statget'): self.setGraphVal('memcached_statget', 'hit', stats.get('get_hits')) self.setGraphVal('memcached_statget', 'miss', stats.get('get_misses')) self.setGraphVal('memcached_statget', 'total', safe_sum([stats.get('get_hits'), stats.get('get_misses')])) if self.hasGraph('memcached_statset'): self.setGraphVal('memcached_statset', 'hit', stats.get('set_hits')) self.setGraphVal('memcached_statset', 'miss', stats.get('set_misses')) self.setGraphVal('memcached_statset', 'total', safe_sum([stats.get('set_hits'), stats.get('set_misses')])) if self.hasGraph('memcached_statdel'): self.setGraphVal('memcached_statdel', 'hit', stats.get('delete_hits')) self.setGraphVal('memcached_statdel', 'miss', stats.get('delete_misses')) self.setGraphVal('memcached_statdel', 'total', safe_sum([stats.get('delete_hits'), stats.get('delete_misses')])) if self.hasGraph('memcached_statcas'): self.setGraphVal('memcached_statcas', 'hit', stats.get('cas_hits')) self.setGraphVal('memcached_statcas', 'miss', stats.get('cas_misses')) self.setGraphVal('memcached_statcas', 'badval', stats.get('cas_badval')) self.setGraphVal('memcached_statcas', 'total', safe_sum([stats.get('cas_hits'), stats.get('cas_misses'), stats.get('cas_badval')])) if self.hasGraph('memcached_statincrdecr'): self.setGraphVal('memcached_statincrdecr', 'incr_hit', stats.get('incr_hits')) self.setGraphVal('memcached_statincrdecr', 'decr_hit', stats.get('decr_hits')) self.setGraphVal('memcached_statincrdecr', 'incr_miss', stats.get('incr_misses')) self.setGraphVal('memcached_statincrdecr', 'decr_miss', stats.get('decr_misses')) self.setGraphVal('memcached_statincrdecr', 'total', safe_sum([stats.get('incr_hits'), stats.get('decr_hits'), stats.get('incr_misses'), stats.get('decr_misses')])) if self.hasGraph('memcached_statevict'): self.setGraphVal('memcached_statevict', 'evict', stats.get('evictions')) if self.graphHasField('memcached_statevict', 'reclaim'): self.setGraphVal('memcached_statevict', 'reclaim', stats.get('reclaimed')) if self.hasGraph('memcached_statauth'): self.setGraphVal('memcached_statauth', 'reqs', stats.get('auth_cmds')) self.setGraphVal('memcached_statauth', 'errors', stats.get('auth_errors')) if self.hasGraph('memcached_hitpct'): prev_stats = self._prev_stats for (field_name, field_hits, field_misses) in ( ('set', 'set_hits', 'set_misses'), ('get', 'get_hits', 'get_misses'), ('del', 'delete_hits', 'delete_misses'), ('cas', 'cas_hits', 'cas_misses'), ('incr', 'incr_hits', 'incr_misses'), ('decr', 'decr_hits', 'decr_misses') ): if prev_stats: if (stats.has_key(field_hits) and prev_stats.has_key(field_hits) and stats.has_key(field_misses) and prev_stats.has_key(field_misses)): hits = stats[field_hits] - prev_stats[field_hits] misses = stats[field_misses] - prev_stats[field_misses] total = hits + misses if total > 0: val = 100.0 * hits / total else: val = 0 self.setGraphVal('memcached_hitpct', field_name, round(val, 2))
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Retrieve values for graphs.
[ "Retrieve", "values", "for", "graphs", "." ]
python
train
watson-developer-cloud/python-sdk
ibm_watson/assistant_v1.py
https://github.com/watson-developer-cloud/python-sdk/blob/4c2c9df4466fcde88975da9ecd834e6ba95eb353/ibm_watson/assistant_v1.py#L5485-L5492
def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'intents') and self.intents is not None: _dict['intents'] = [x._to_dict() for x in self.intents] if hasattr(self, 'pagination') and self.pagination is not None: _dict['pagination'] = self.pagination._to_dict() return _dict
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Return a json dictionary representing this model.
[ "Return", "a", "json", "dictionary", "representing", "this", "model", "." ]
python
train
michael-lazar/rtv
rtv/packages/praw/__init__.py
https://github.com/michael-lazar/rtv/blob/ccef2af042566ad384977028cf0bde01bc524dda/rtv/packages/praw/__init__.py#L491-L500
def evict(self, urls): """Evict url(s) from the cache. :param urls: An iterable containing normalized urls. :returns: The number of items removed from the cache. """ if isinstance(urls, six.string_types): urls = (urls,) return self.handler.evict(urls)
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Evict url(s) from the cache. :param urls: An iterable containing normalized urls. :returns: The number of items removed from the cache.
[ "Evict", "url", "(", "s", ")", "from", "the", "cache", "." ]
python
train
evonove/django-stored-messages
stored_messages/api.py
https://github.com/evonove/django-stored-messages/blob/23b71f952d5d3fd03285f5e700879d05796ef7ba/stored_messages/api.py#L12-L28
def add_message_for(users, level, message_text, extra_tags='', date=None, url=None, fail_silently=False): """ Send a message to a list of users without passing through `django.contrib.messages` :param users: an iterable containing the recipients of the messages :param level: message level :param message_text: the string containing the message :param extra_tags: like the Django api, a string containing extra tags for the message :param date: a date, different than the default timezone.now :param url: an optional url :param fail_silently: not used at the moment """ BackendClass = stored_messages_settings.STORAGE_BACKEND backend = BackendClass() m = backend.create_message(level, message_text, extra_tags, date, url) backend.archive_store(users, m) backend.inbox_store(users, m)
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Send a message to a list of users without passing through `django.contrib.messages` :param users: an iterable containing the recipients of the messages :param level: message level :param message_text: the string containing the message :param extra_tags: like the Django api, a string containing extra tags for the message :param date: a date, different than the default timezone.now :param url: an optional url :param fail_silently: not used at the moment
[ "Send", "a", "message", "to", "a", "list", "of", "users", "without", "passing", "through", "django", ".", "contrib", ".", "messages" ]
python
valid
etcher-be/emiz
emiz/avwx/__init__.py
https://github.com/etcher-be/emiz/blob/1c3e32711921d7e600e85558ffe5d337956372de/emiz/avwx/__init__.py#L148-L154
def summary(self): # type: ignore """ Condensed summary for each forecast created from translations """ if not self.translations: self.update() return [summary.taf(trans) for trans in self.translations.forecast]
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Condensed summary for each forecast created from translations
[ "Condensed", "summary", "for", "each", "forecast", "created", "from", "translations" ]
python
train
modin-project/modin
modin/pandas/indexing.py
https://github.com/modin-project/modin/blob/5b77d242596560c646b8405340c9ce64acb183cb/modin/pandas/indexing.py#L127-L140
def _compute_ndim(row_loc, col_loc): """Compute the ndim of result from locators """ row_scaler = is_scalar(row_loc) col_scaler = is_scalar(col_loc) if row_scaler and col_scaler: ndim = 0 elif row_scaler ^ col_scaler: ndim = 1 else: ndim = 2 return ndim
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Compute the ndim of result from locators
[ "Compute", "the", "ndim", "of", "result", "from", "locators" ]
python
train
sampsyo/confuse
setup.py
https://github.com/sampsyo/confuse/blob/9ff0992e30470f6822824711950e6dd906e253fb/setup.py#L21-L30
def export_live_eggs(self, env=False): """Adds all of the eggs in the current environment to PYTHONPATH.""" path_eggs = [p for p in sys.path if p.endswith('.egg')] command = self.get_finalized_command("egg_info") egg_base = path.abspath(command.egg_base) unique_path_eggs = set(path_eggs + [egg_base]) os.environ['PYTHONPATH'] = ':'.join(unique_path_eggs)
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Adds all of the eggs in the current environment to PYTHONPATH.
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python
train
volafiled/python-volapi
volapi/volapi.py
https://github.com/volafiled/python-volapi/blob/5f0bc03dbde703264ac6ed494e2050761f688a3e/volapi/volapi.py#L232-L267
def on_message(self, new_data): """Processes incoming messages according to engine-io rules""" # https://github.com/socketio/engine.io-protocol LOGGER.debug("new frame [%r]", new_data) try: what = int(new_data[0]) data = new_data[1:] data = data and from_json(data) if what == 0: self.ping_interval = float(data["pingInterval"]) / 1000 LOGGER.debug("adjusted ping interval") return if what == 1: LOGGER.debug("received close") self.reraise(IOError("Connection closed remotely")) return if what == 3: self.__lastpong = time.time() LOGGER.debug("received a pong") return if what == 4: self._on_frame(data) return if what == 6: LOGGER.debug("received noop") self.send_message("5") return LOGGER.debug("unhandled message: [%d] [%r]", what, data) except Exception as ex: self.reraise(ex)
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Processes incoming messages according to engine-io rules
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python
train
rflamary/POT
ot/dr.py
https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/dr.py#L110-L203
def wda(X, y, p=2, reg=1, k=10, solver=None, maxiter=100, verbose=0, P0=None): """ Wasserstein Discriminant Analysis [11]_ The function solves the following optimization problem: .. math:: P = \\text{arg}\min_P \\frac{\\sum_i W(PX^i,PX^i)}{\\sum_{i,j\\neq i} W(PX^i,PX^j)} where : - :math:`P` is a linear projection operator in the Stiefel(p,d) manifold - :math:`W` is entropic regularized Wasserstein distances - :math:`X^i` are samples in the dataset corresponding to class i Parameters ---------- X : numpy.ndarray (n,d) Training samples y : np.ndarray (n,) labels for training samples p : int, optional size of dimensionnality reduction reg : float, optional Regularization term >0 (entropic regularization) solver : str, optional None for steepest decsent or 'TrustRegions' for trust regions algorithm else shoudl be a pymanopt.solvers P0 : numpy.ndarray (d,p) Initial starting point for projection verbose : int, optional Print information along iterations Returns ------- P : (d x p) ndarray Optimal transportation matrix for the given parameters proj : fun projection function including mean centering References ---------- .. [11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). Wasserstein Discriminant Analysis. arXiv preprint arXiv:1608.08063. """ # noqa mx = np.mean(X) X -= mx.reshape((1, -1)) # data split between classes d = X.shape[1] xc = split_classes(X, y) # compute uniform weighs wc = [np.ones((x.shape[0]), dtype=np.float32) / x.shape[0] for x in xc] def cost(P): # wda loss loss_b = 0 loss_w = 0 for i, xi in enumerate(xc): xi = np.dot(xi, P) for j, xj in enumerate(xc[i:]): xj = np.dot(xj, P) M = dist(xi, xj) G = sinkhorn(wc[i], wc[j + i], M, reg, k) if j == 0: loss_w += np.sum(G * M) else: loss_b += np.sum(G * M) # loss inversed because minimization return loss_w / loss_b # declare manifold and problem manifold = Stiefel(d, p) problem = Problem(manifold=manifold, cost=cost) # declare solver and solve if solver is None: solver = SteepestDescent(maxiter=maxiter, logverbosity=verbose) elif solver in ['tr', 'TrustRegions']: solver = TrustRegions(maxiter=maxiter, logverbosity=verbose) Popt = solver.solve(problem, x=P0) def proj(X): return (X - mx.reshape((1, -1))).dot(Popt) return Popt, proj
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Wasserstein Discriminant Analysis [11]_ The function solves the following optimization problem: .. math:: P = \\text{arg}\min_P \\frac{\\sum_i W(PX^i,PX^i)}{\\sum_{i,j\\neq i} W(PX^i,PX^j)} where : - :math:`P` is a linear projection operator in the Stiefel(p,d) manifold - :math:`W` is entropic regularized Wasserstein distances - :math:`X^i` are samples in the dataset corresponding to class i Parameters ---------- X : numpy.ndarray (n,d) Training samples y : np.ndarray (n,) labels for training samples p : int, optional size of dimensionnality reduction reg : float, optional Regularization term >0 (entropic regularization) solver : str, optional None for steepest decsent or 'TrustRegions' for trust regions algorithm else shoudl be a pymanopt.solvers P0 : numpy.ndarray (d,p) Initial starting point for projection verbose : int, optional Print information along iterations Returns ------- P : (d x p) ndarray Optimal transportation matrix for the given parameters proj : fun projection function including mean centering References ---------- .. [11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). Wasserstein Discriminant Analysis. arXiv preprint arXiv:1608.08063.
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python
train
ankitmathur3193/song-cli
song/commands/FileDownload.py
https://github.com/ankitmathur3193/song-cli/blob/ca8ccfe547e9d702313ff6d14e81ae4355989a67/song/commands/FileDownload.py#L27-L74
def file_download_using_requests(self,url): '''It will download file specified by url using requests module''' file_name=url.split('/')[-1] if os.path.exists(os.path.join(os.getcwd(),file_name)): print 'File already exists' return #print 'Downloading file %s '%file_name #print 'Downloading from %s'%url try: r=requests.get(url,stream=True,timeout=200) except requests.exceptions.SSLError: try: response=requests.get(url,stream=True,verify=False,timeout=200) except requests.exceptions.RequestException as e: print e quit() except requests.exceptions.RequestException as e: print e quit() chunk_size = 1024 total_size = int(r.headers['Content-Length']) total_chunks = total_size/chunk_size file_iterable = r.iter_content(chunk_size = chunk_size) tqdm_iter = tqdm(iterable = file_iterable,total = total_chunks,unit = 'KB', leave = False ) with open(file_name,'wb') as f: for data in tqdm_iter: f.write(data) #total_size=float(r.headers['Content-Length'])/(1024*1024) '''print 'Total size of file to be downloaded %.2f MB '%total_size total_downloaded_size=0.0 with open(file_name,'wb') as f: for chunk in r.iter_content(chunk_size=1*1024*1024): if chunk: size_of_chunk=float(len(chunk))/(1024*1024) total_downloaded_size+=size_of_chunk print '{0:.0%} Downloaded'.format(total_downloaded_size/total_size) f.write(chunk)''' print 'Downloaded file %s '%file_name
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It will download file specified by url using requests module
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python
test
GoogleCloudPlatform/cloud-debug-python
src/googleclouddebugger/capture_collector.py
https://github.com/GoogleCloudPlatform/cloud-debug-python/blob/89ce3782c98b814838a3ecb5479ed3882368cbee/src/googleclouddebugger/capture_collector.py#L360-L383
def CaptureFrameLocals(self, frame): """Captures local variables and arguments of the specified frame. Args: frame: frame to capture locals and arguments. Returns: (arguments, locals) tuple. """ # Capture all local variables (including method arguments). variables = {n: self.CaptureNamedVariable(n, v, 1, self.default_capture_limits) for n, v in six.viewitems(frame.f_locals)} # Split between locals and arguments (keeping arguments in the right order). nargs = frame.f_code.co_argcount if frame.f_code.co_flags & inspect.CO_VARARGS: nargs += 1 if frame.f_code.co_flags & inspect.CO_VARKEYWORDS: nargs += 1 frame_arguments = [] for argname in frame.f_code.co_varnames[:nargs]: if argname in variables: frame_arguments.append(variables.pop(argname)) return (frame_arguments, list(six.viewvalues(variables)))
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Captures local variables and arguments of the specified frame. Args: frame: frame to capture locals and arguments. Returns: (arguments, locals) tuple.
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python
train
pyviz/holoviews
holoviews/core/data/interface.py
https://github.com/pyviz/holoviews/blob/ae0dd2f3de448b0ca5e9065aabd6ef8d84c7e655/holoviews/core/data/interface.py#L278-L314
def select_mask(cls, dataset, selection): """ Given a Dataset object and a dictionary with dimension keys and selection keys (i.e tuple ranges, slices, sets, lists or literals) return a boolean mask over the rows in the Dataset object that have been selected. """ mask = np.ones(len(dataset), dtype=np.bool) for dim, k in selection.items(): if isinstance(k, tuple): k = slice(*k) arr = cls.values(dataset, dim) if isinstance(k, slice): with warnings.catch_warnings(): warnings.filterwarnings('ignore', r'invalid value encountered') if k.start is not None: mask &= k.start <= arr if k.stop is not None: mask &= arr < k.stop elif isinstance(k, (set, list)): iter_slcs = [] for ik in k: with warnings.catch_warnings(): warnings.filterwarnings('ignore', r'invalid value encountered') iter_slcs.append(arr == ik) mask &= np.logical_or.reduce(iter_slcs) elif callable(k): mask &= k(arr) else: index_mask = arr == k if dataset.ndims == 1 and np.sum(index_mask) == 0: data_index = np.argmin(np.abs(arr - k)) mask = np.zeros(len(dataset), dtype=np.bool) mask[data_index] = True else: mask &= index_mask return mask
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Given a Dataset object and a dictionary with dimension keys and selection keys (i.e tuple ranges, slices, sets, lists or literals) return a boolean mask over the rows in the Dataset object that have been selected.
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python
train
gwastro/pycbc
pycbc/psd/analytical.py
https://github.com/gwastro/pycbc/blob/7a64cdd104d263f1b6ea0b01e6841837d05a4cb3/pycbc/psd/analytical.py#L124-L144
def flat_unity(length, delta_f, low_freq_cutoff): """ Returns a FrequencySeries of ones above the low_frequency_cutoff. Parameters ---------- length : int Length of output Frequencyseries. delta_f : float Frequency step for output FrequencySeries. low_freq_cutoff : int Low-frequency cutoff for output FrequencySeries. Returns ------- FrequencySeries Returns a FrequencySeries containing the unity PSD model. """ fseries = FrequencySeries(numpy.ones(length), delta_f=delta_f) kmin = int(low_freq_cutoff / fseries.delta_f) fseries.data[:kmin] = 0 return fseries
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Returns a FrequencySeries of ones above the low_frequency_cutoff. Parameters ---------- length : int Length of output Frequencyseries. delta_f : float Frequency step for output FrequencySeries. low_freq_cutoff : int Low-frequency cutoff for output FrequencySeries. Returns ------- FrequencySeries Returns a FrequencySeries containing the unity PSD model.
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python
train
projectatomic/atomic-reactor
atomic_reactor/util.py
https://github.com/projectatomic/atomic-reactor/blob/fd31c01b964097210bf169960d051e5f04019a80/atomic_reactor/util.py#L1584-L1592
def update_from_dict(self, source): """Update records of the digests of images from a dictionary (no validation is performed) :param dict source: data """ assert isinstance(source, dict) source_copy = deepcopy(source) # no mutable side effects self._images_digests.update(source_copy)
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Update records of the digests of images from a dictionary (no validation is performed) :param dict source: data
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python
train
internetarchive/brozzler
brozzler/model.py
https://github.com/internetarchive/brozzler/blob/411b3f266a38b9bb942021c0121ebd8e5ca66447/brozzler/model.py#L74-L108
def new_job(frontier, job_conf): '''Returns new Job.''' validate_conf(job_conf) job = Job(frontier.rr, { "conf": job_conf, "status": "ACTIVE", "started": doublethink.utcnow()}) if "id" in job_conf: job.id = job_conf["id"] if "max_claimed_sites" in job_conf: job.max_claimed_sites = job_conf["max_claimed_sites"] job.save() sites = [] pages = [] for seed_conf in job_conf["seeds"]: merged_conf = merge(seed_conf, job_conf) merged_conf.pop("seeds") merged_conf["job_id"] = job.id merged_conf["seed"] = merged_conf.pop("url") site = brozzler.Site(frontier.rr, merged_conf) site.id = str(uuid.uuid4()) sites.append(site) pages.append(new_seed_page(frontier, site)) # insert in batches to avoid this error # rethinkdb.errors.ReqlDriverError: Query size (167883036) greater than maximum (134217727) in: for batch in (pages[i:i+500] for i in range(0, len(pages), 500)): logging.info('inserting batch of %s pages', len(batch)) result = frontier.rr.table('pages').insert(batch).run() for batch in (sites[i:i+100] for i in range(0, len(sites), 100)): logging.info('inserting batch of %s sites', len(batch)) result = frontier.rr.table('sites').insert(batch).run() logging.info('job %s fully started', job.id) return job
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Returns new Job.
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python
train
threeML/astromodels
astromodels/core/model.py
https://github.com/threeML/astromodels/blob/9aac365a372f77603039533df9a6b694c1e360d5/astromodels/core/model.py#L392-L403
def remove_independent_variable(self, variable_name): """ Remove an independent variable which was added with add_independent_variable :param variable_name: name of variable to remove :return: """ self._remove_child(variable_name) # Remove also from the list of independent variables self._independent_variables.pop(variable_name)
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Remove an independent variable which was added with add_independent_variable :param variable_name: name of variable to remove :return:
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python
train
senaite/senaite.core
bika/lims/content/abstractanalysis.py
https://github.com/senaite/senaite.core/blob/7602ce2ea2f9e81eb34e20ce17b98a3e70713f85/bika/lims/content/abstractanalysis.py#L1123-L1129
def getAttachmentUIDs(self): """Used to populate metadata, so that we don't need full objects of analyses when working with their attachments. """ attachments = self.getAttachment() uids = [att.UID() for att in attachments] return uids
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Used to populate metadata, so that we don't need full objects of analyses when working with their attachments.
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python
train
titusjan/argos
argos/utils/masks.py
https://github.com/titusjan/argos/blob/20d0a3cae26c36ea789a5d219c02ca7df21279dd/argos/utils/masks.py#L338-L367
def maskedEqual(array, missingValue): """ Mask an array where equal to a given (missing)value. Unfortunately ma.masked_equal does not work with structured arrays. See: https://mail.scipy.org/pipermail/numpy-discussion/2011-July/057669.html If the data is a structured array the mask is applied for every field (i.e. forming a logical-and). Otherwise ma.masked_equal is called. """ if array_is_structured(array): # Enforce the array to be masked if not isinstance(array, ma.MaskedArray): array = ma.MaskedArray(array) # Set the mask separately per field for nr, field in enumerate(array.dtype.names): if hasattr(missingValue, '__len__'): fieldMissingValue = missingValue[nr] else: fieldMissingValue = missingValue array[field] = ma.masked_equal(array[field], fieldMissingValue) check_class(array, ma.MaskedArray) # post-condition check return array else: # masked_equal works with missing is None result = ma.masked_equal(array, missingValue, copy=False) check_class(result, ma.MaskedArray) # post-condition check return result
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Mask an array where equal to a given (missing)value. Unfortunately ma.masked_equal does not work with structured arrays. See: https://mail.scipy.org/pipermail/numpy-discussion/2011-July/057669.html If the data is a structured array the mask is applied for every field (i.e. forming a logical-and). Otherwise ma.masked_equal is called.
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python
train
agile-geoscience/striplog
striplog/legend.py
https://github.com/agile-geoscience/striplog/blob/8033b673a151f96c29802b43763e863519a3124c/striplog/legend.py#L637-L682
def to_csv(self): """ Renders a legend as a CSV string. No arguments. Returns: str: The legend as a CSV. """ # We can't delegate this to Decor because we need to know the superset # of all Decor properties. There may be lots of blanks. header = [] component_header = [] for row in self: for j in row.__dict__.keys(): if j == '_colour': j = 'colour' header.append(j) for k in row.component.__dict__.keys(): component_header.append(k) header = set(header) component_header = set(component_header) header.remove('component') header_row = '' if 'colour' in header: header_row += 'colour,' header.remove('colour') has_colour = True for item in header: header_row += item + ',' for item in component_header: header_row += 'component ' + item + ',' # Now we have a header row! Phew. # Next we'll go back over the legend and collect everything. result = header_row.strip(',') + '\n' for row in self: if has_colour: result += row.__dict__.get('_colour', '') + ',' for item in header: result += str(row.__dict__.get(item, '')) + ',' for item in component_header: result += str(row.component.__dict__.get(item, '')) + ',' result += '\n' return result
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Renders a legend as a CSV string. No arguments. Returns: str: The legend as a CSV.
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python
test
benedictpaten/sonLib
bioio.py
https://github.com/benedictpaten/sonLib/blob/1decb75bb439b70721ec776f685ce98e25217d26/bioio.py#L199-L213
def popenCatch(command, stdinString=None): """Runs a command and return standard out. """ logger.debug("Running the command: %s" % command) if stdinString != None: process = subprocess.Popen(command, shell=True, stdin=subprocess.PIPE, stdout=subprocess.PIPE, bufsize=-1) output, nothing = process.communicate(stdinString) else: process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=sys.stderr, bufsize=-1) output, nothing = process.communicate() #process.stdout.read().strip() sts = process.wait() if sts != 0: raise RuntimeError("Command: %s with stdin string '%s' exited with non-zero status %i" % (command, stdinString, sts)) return output
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Runs a command and return standard out.
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python
train
jwodder/javaproperties
javaproperties/reading.py
https://github.com/jwodder/javaproperties/blob/8b48f040305217ebeb80c98c4354691bbb01429b/javaproperties/reading.py#L38-L66
def loads(s, object_pairs_hook=dict): """ Parse the contents of the string ``s`` as a simple line-oriented ``.properties`` file and return a `dict` of the key-value pairs. ``s`` may be either a text string or bytes string. If it is a bytes string, its contents are decoded as Latin-1. By default, the key-value pairs extracted from ``s`` are combined into a `dict` with later occurrences of a key overriding previous occurrences of the same key. To change this behavior, pass a callable as the ``object_pairs_hook`` argument; it will be called with one argument, a generator of ``(key, value)`` pairs representing the key-value entries in ``s`` (including duplicates) in order of occurrence. `loads` will then return the value returned by ``object_pairs_hook``. .. versionchanged:: 0.5.0 Invalid ``\\uXXXX`` escape sequences will now cause an `InvalidUEscapeError` to be raised :param string s: the string from which to read the ``.properties`` document :param callable object_pairs_hook: class or function for combining the key-value pairs :rtype: `dict` of text strings or the return value of ``object_pairs_hook`` :raises InvalidUEscapeError: if an invalid ``\\uXXXX`` escape sequence occurs in the input """ fp = BytesIO(s) if isinstance(s, binary_type) else StringIO(s) return load(fp, object_pairs_hook=object_pairs_hook)
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Parse the contents of the string ``s`` as a simple line-oriented ``.properties`` file and return a `dict` of the key-value pairs. ``s`` may be either a text string or bytes string. If it is a bytes string, its contents are decoded as Latin-1. By default, the key-value pairs extracted from ``s`` are combined into a `dict` with later occurrences of a key overriding previous occurrences of the same key. To change this behavior, pass a callable as the ``object_pairs_hook`` argument; it will be called with one argument, a generator of ``(key, value)`` pairs representing the key-value entries in ``s`` (including duplicates) in order of occurrence. `loads` will then return the value returned by ``object_pairs_hook``. .. versionchanged:: 0.5.0 Invalid ``\\uXXXX`` escape sequences will now cause an `InvalidUEscapeError` to be raised :param string s: the string from which to read the ``.properties`` document :param callable object_pairs_hook: class or function for combining the key-value pairs :rtype: `dict` of text strings or the return value of ``object_pairs_hook`` :raises InvalidUEscapeError: if an invalid ``\\uXXXX`` escape sequence occurs in the input
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python
train
obulpathi/cdn-fastly-python
fastly/__init__.py
https://github.com/obulpathi/cdn-fastly-python/blob/db2564b047e8af4bce72c3b88d6c27d3d0291425/fastly/__init__.py#L965-L968
def deactivate_version(self, service_id, version_number): """Deactivate the current version.""" content = self._fetch("/service/%s/version/%d/deactivate" % (service_id, version_number), method="PUT") return FastlyVersion(self, content)
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Deactivate the current version.
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python
train
AltSchool/dynamic-rest
dynamic_rest/viewsets.py
https://github.com/AltSchool/dynamic-rest/blob/5b0338c3dd8bc638d60c3bb92645857c5b89c920/dynamic_rest/viewsets.py#L94-L144
def initialize_request(self, request, *args, **kargs): """ Override DRF initialize_request() method to swap request.GET (which is aliased by request.query_params) with a mutable instance of QueryParams, and to convert request MergeDict to a subclass of dict for consistency (MergeDict is not a subclass of dict) """ def handle_encodings(request): """ WSGIRequest does not support Unicode values in the query string. WSGIRequest handling has a history of drifting behavior between combinations of Python versions, Django versions and DRF versions. Django changed its QUERY_STRING handling here: https://goo.gl/WThXo6. DRF 3.4.7 changed its behavior here: https://goo.gl/0ojIIO. """ try: return QueryParams(request.GET) except UnicodeEncodeError: pass s = request.environ.get('QUERY_STRING', '') try: s = s.encode('utf-8') except UnicodeDecodeError: pass return QueryParams(s) request.GET = handle_encodings(request) request = super(WithDynamicViewSetMixin, self).initialize_request( request, *args, **kargs ) try: # Django<1.9, DRF<3.2 # MergeDict doesn't have the same API as dict. # Django has deprecated MergeDict and DRF is moving away from # using it - thus, were comfortable replacing it with a QueryDict # This will allow the data property to have normal dict methods. from django.utils.datastructures import MergeDict if isinstance(request._full_data, MergeDict): data_as_dict = request.data.dicts[0] for d in request.data.dicts[1:]: data_as_dict.update(d) request._full_data = data_as_dict except: pass return request
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Override DRF initialize_request() method to swap request.GET (which is aliased by request.query_params) with a mutable instance of QueryParams, and to convert request MergeDict to a subclass of dict for consistency (MergeDict is not a subclass of dict)
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python
train
inspirehep/harvesting-kit
harvestingkit/ftp_utils.py
https://github.com/inspirehep/harvesting-kit/blob/33a7f8aa9dade1d863110c6d8b27dfd955cb471f/harvestingkit/ftp_utils.py#L143-L162
def ls(self, folder=''): """ Lists the files and folders of a specific directory default is the current working directory. :param folder: the folder to be listed. :type folder: string :returns: a tuple with the list of files in the folder and the list of subfolders in the folder. """ current_folder = self._ftp.pwd() self.cd(folder) contents = [] self._ftp.retrlines('LIST', lambda a: contents.append(a)) files = filter(lambda a: a.split()[0].startswith('-'), contents) folders = filter(lambda a: a.split()[0].startswith('d'), contents) files = map(lambda a: ' '.join(a.split()[8:]), files) folders = map(lambda a: ' '.join(a.split()[8:]), folders) self._ftp.cwd(current_folder) return files, folders
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Lists the files and folders of a specific directory default is the current working directory. :param folder: the folder to be listed. :type folder: string :returns: a tuple with the list of files in the folder and the list of subfolders in the folder.
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python
valid
urinieto/msaf
msaf/base.py
https://github.com/urinieto/msaf/blob/9dbb57d77a1310465a65cc40f1641d083ca74385/msaf/base.py#L112-L140
def estimate_beats(self): """Estimates the beats using librosa. Returns ------- times: np.array Times of estimated beats in seconds. frames: np.array Frame indeces of estimated beats. """ # Compute harmonic-percussive source separation if needed if self._audio_percussive is None: self._audio_harmonic, self._audio_percussive = self.compute_HPSS() # Compute beats tempo, frames = librosa.beat.beat_track( y=self._audio_percussive, sr=self.sr, hop_length=self.hop_length) # To times times = librosa.frames_to_time(frames, sr=self.sr, hop_length=self.hop_length) # TODO: Is this really necessary? if len(times) > 0 and times[0] == 0: times = times[1:] frames = frames[1:] return times, frames
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Estimates the beats using librosa. Returns ------- times: np.array Times of estimated beats in seconds. frames: np.array Frame indeces of estimated beats.
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python
test
astrorafael/twisted-mqtt
examples/subscriber.py
https://github.com/astrorafael/twisted-mqtt/blob/5b322f7c2b82a502b1e1b70703ae45f1f668d07d/examples/subscriber.py#L72-L87
def connectToBroker(self, protocol): ''' Connect to MQTT broker ''' self.protocol = protocol self.protocol.onPublish = self.onPublish self.protocol.onDisconnection = self.onDisconnection self.protocol.setWindowSize(3) try: yield self.protocol.connect("TwistedMQTT-subs", keepalive=60) yield self.subscribe() except Exception as e: log.error("Connecting to {broker} raised {excp!s}", broker=BROKER, excp=e) else: log.info("Connected and subscribed to {broker}", broker=BROKER)
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Connect to MQTT broker
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python
test
openstack/networking-arista
networking_arista/common/db_lib.py
https://github.com/openstack/networking-arista/blob/07ce6b1fc62ff74308a6eabfc4cc0ee09fb7b0fe/networking_arista/common/db_lib.py#L204-L216
def get_tenants(tenant_id=None): """Returns list of all project/tenant ids that may be relevant on CVX""" if tenant_id == '': return [] session = db.get_reader_session() project_ids = set() with session.begin(): for m in [models_v2.Network, models_v2.Port]: q = session.query(m.project_id).filter(m.project_id != '') if tenant_id: q = q.filter(m.project_id == tenant_id) project_ids.update(pid[0] for pid in q.distinct()) return [{'project_id': project_id} for project_id in project_ids]
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Returns list of all project/tenant ids that may be relevant on CVX
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python
train
Skype4Py/Skype4Py
Skype4Py/utils.py
https://github.com/Skype4Py/Skype4Py/blob/c48d83f7034109fe46315d45a066126002c6e0d4/Skype4Py/utils.py#L493-L507
def _AddEvents(cls, Class): """Adds events based on the attributes of the given ``...Events`` class. :Parameters: Class : class An `...Events` class whose methods define events that may occur in the instances of the current class. """ def make_event(event): return property(lambda self: self._GetDefaultEventHandler(event), lambda self, Value: self._SetDefaultEventHandler(event, Value)) for event in dir(Class): if not event.startswith('_'): setattr(cls, 'On%s' % event, make_event(event)) cls._EventNames.append(event)
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Adds events based on the attributes of the given ``...Events`` class. :Parameters: Class : class An `...Events` class whose methods define events that may occur in the instances of the current class.
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python
train
jepegit/cellpy
cellpy/utils/batch_tools/engines.py
https://github.com/jepegit/cellpy/blob/9f4a84cdd11f72cfa02cda8c2d7b5174abbb7370/cellpy/utils/batch_tools/engines.py#L87-L131
def simple_db_engine(reader=None, srnos=None): """engine that gets values from the simple excel 'db'""" if reader is None: reader = dbreader.Reader() logger.debug("No reader provided. Creating one myself.") info_dict = dict() info_dict["filenames"] = [reader.get_cell_name(srno) for srno in srnos] info_dict["masses"] = [reader.get_mass(srno) for srno in srnos] info_dict["total_masses"] = [reader.get_total_mass(srno) for srno in srnos] info_dict["loadings"] = [reader.get_loading(srno) for srno in srnos] info_dict["fixed"] = [reader.inspect_hd5f_fixed(srno) for srno in srnos] info_dict["labels"] = [reader.get_label(srno) for srno in srnos] info_dict["cell_type"] = [reader.get_cell_type(srno) for srno in srnos] info_dict["raw_file_names"] = [] info_dict["cellpy_file_names"] = [] logger.debug("created info-dict") for key in list(info_dict.keys()): logger.debug("%s: %s" % (key, str(info_dict[key]))) _groups = [reader.get_group(srno) for srno in srnos] logger.debug(">\ngroups: %s" % str(_groups)) groups = helper.fix_groups(_groups) info_dict["groups"] = groups my_timer_start = time.time() filename_cache = [] info_dict = helper.find_files(info_dict, filename_cache) my_timer_end = time.time() if (my_timer_end - my_timer_start) > 5.0: logger.info( "The function _find_files was very slow. " "Save your info_df so you don't have to run it again!" ) info_df = pd.DataFrame(info_dict) info_df = info_df.sort_values(["groups", "filenames"]) info_df = helper.make_unique_groups(info_df) info_df["labels"] = info_df["filenames"].apply(helper.create_labels) info_df.set_index("filenames", inplace=True) return info_df
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engine that gets values from the simple excel 'db
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python
train
django-auth-ldap/django-auth-ldap
django_auth_ldap/backend.py
https://github.com/django-auth-ldap/django-auth-ldap/blob/9ce3c2825527f8faa1793958b041816e63d839af/django_auth_ldap/backend.py#L688-L742
def _normalize_mirror_settings(self): """ Validates the group mirroring settings and converts them as necessary. """ def malformed_mirror_groups_except(): return ImproperlyConfigured( "{} must be a collection of group names".format( self.settings._name("MIRROR_GROUPS_EXCEPT") ) ) def malformed_mirror_groups(): return ImproperlyConfigured( "{} must be True or a collection of group names".format( self.settings._name("MIRROR_GROUPS") ) ) mge = self.settings.MIRROR_GROUPS_EXCEPT mg = self.settings.MIRROR_GROUPS if mge is not None: if isinstance(mge, (set, frozenset)): pass elif isinstance(mge, (list, tuple)): mge = self.settings.MIRROR_GROUPS_EXCEPT = frozenset(mge) else: raise malformed_mirror_groups_except() if not all(isinstance(value, str) for value in mge): raise malformed_mirror_groups_except() elif mg: warnings.warn( ConfigurationWarning( "Ignoring {} in favor of {}".format( self.settings._name("MIRROR_GROUPS"), self.settings._name("MIRROR_GROUPS_EXCEPT"), ) ) ) mg = self.settings.MIRROR_GROUPS = None if mg is not None: if isinstance(mg, (bool, set, frozenset)): pass elif isinstance(mg, (list, tuple)): mg = self.settings.MIRROR_GROUPS = frozenset(mg) else: raise malformed_mirror_groups() if isinstance(mg, (set, frozenset)) and ( not all(isinstance(value, str) for value in mg) ): raise malformed_mirror_groups()
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Validates the group mirroring settings and converts them as necessary.
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python
train
proycon/pynlpl
pynlpl/formats/sonar.py
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/sonar.py#L235-L244
def validate(self, formats_dir="../formats/"): """checks if the document is valid""" #TODO: download XSD from web if self.inline: xmlschema = ElementTree.XMLSchema(ElementTree.parse(StringIO("\n".join(open(formats_dir+"dcoi-dsc.xsd").readlines())))) xmlschema.assertValid(self.tree) #return xmlschema.validate(self) else: xmlschema = ElementTree.XMLSchema(ElementTree.parse(StringIO("\n".join(open(formats_dir+"dutchsemcor-standalone.xsd").readlines())))) xmlschema.assertValid(self.tree)
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checks if the document is valid
[ "checks", "if", "the", "document", "is", "valid" ]
python
train
faucamp/python-gsmmodem
gsmmodem/modem.py
https://github.com/faucamp/python-gsmmodem/blob/834c68b1387ca2c91e2210faa8f75526b39723b5/gsmmodem/modem.py#L1088-L1112
def deleteMultipleStoredSms(self, delFlag=4, memory=None): """ Deletes all SMS messages that have the specified read status. The messages are read from the memory set by the "memory" parameter. The value of the "delFlag" paramater is the same as the "DelFlag" parameter of the +CMGD command: 1: Delete All READ messages 2: Delete All READ and SENT messages 3: Delete All READ, SENT and UNSENT messages 4: Delete All messages (this is the default) :param delFlag: Controls what type of messages to delete; see description above. :type delFlag: int :param memory: The memory type to delete from. If None, use the current default SMS read/delete memory :type memory: str or None :param delete: If True, delete returned messages from the device/SIM card :type delete: bool :raise ValueErrror: if "delFlag" is not in range [1,4] :raise CommandError: if unable to delete the stored messages """ if 0 < delFlag <= 4: self._setSmsMemory(readDelete=memory) self.write('AT+CMGD=1,{0}'.format(delFlag)) else: raise ValueError('"delFlag" must be in range [1,4]')
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Deletes all SMS messages that have the specified read status. The messages are read from the memory set by the "memory" parameter. The value of the "delFlag" paramater is the same as the "DelFlag" parameter of the +CMGD command: 1: Delete All READ messages 2: Delete All READ and SENT messages 3: Delete All READ, SENT and UNSENT messages 4: Delete All messages (this is the default) :param delFlag: Controls what type of messages to delete; see description above. :type delFlag: int :param memory: The memory type to delete from. If None, use the current default SMS read/delete memory :type memory: str or None :param delete: If True, delete returned messages from the device/SIM card :type delete: bool :raise ValueErrror: if "delFlag" is not in range [1,4] :raise CommandError: if unable to delete the stored messages
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python
train
CalebBell/thermo
thermo/viscosity.py
https://github.com/CalebBell/thermo/blob/3857ed023a3e64fd3039a32d53576c24990ef1c3/thermo/viscosity.py#L1503-L1538
def calculate(self, T, method): r'''Method to calculate low-pressure gas viscosity at tempearture `T` with a given method. This method has no exception handling; see `T_dependent_property` for that. Parameters ---------- T : float Temperature of the gas, [K] method : str Name of the method to use Returns ------- mu : float Viscosity of the gas at T and a low pressure, [Pa*S] ''' if method == GHARAGHEIZI: mu = Gharagheizi_gas_viscosity(T, self.Tc, self.Pc, self.MW) elif method == COOLPROP: mu = CoolProp_T_dependent_property(T, self.CASRN, 'V', 'g') elif method == DIPPR_PERRY_8E: mu = EQ102(T, *self.Perrys2_312_coeffs) elif method == VDI_PPDS: mu = horner(self.VDI_PPDS_coeffs, T) elif method == YOON_THODOS: mu = Yoon_Thodos(T, self.Tc, self.Pc, self.MW) elif method == STIEL_THODOS: mu = Stiel_Thodos(T, self.Tc, self.Pc, self.MW) elif method == LUCAS_GAS: mu = lucas_gas(T, self.Tc, self.Pc, self.Zc, self.MW, self.dipole, CASRN=self.CASRN) elif method in self.tabular_data: mu = self.interpolate(T, method) return mu
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r'''Method to calculate low-pressure gas viscosity at tempearture `T` with a given method. This method has no exception handling; see `T_dependent_property` for that. Parameters ---------- T : float Temperature of the gas, [K] method : str Name of the method to use Returns ------- mu : float Viscosity of the gas at T and a low pressure, [Pa*S]
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python
valid
jasonrbriggs/stomp.py
stomp/transport.py
https://github.com/jasonrbriggs/stomp.py/blob/643843c5fbf25fd24339dd0e69a9411c3d8b94c7/stomp/transport.py#L103-L113
def start(self): """ Start the connection. This should be called after all listeners have been registered. If this method is not called, no frames will be received by the connection. """ self.running = True self.attempt_connection() receiver_thread = self.create_thread_fc(self.__receiver_loop) receiver_thread.name = "StompReceiver%s" % getattr(receiver_thread, "name", "Thread") self.notify('connecting')
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Start the connection. This should be called after all listeners have been registered. If this method is not called, no frames will be received by the connection.
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python
train
divio/django-filer
filer/admin/folderadmin.py
https://github.com/divio/django-filer/blob/946629087943d41eff290f07bfdf240b8853dd88/filer/admin/folderadmin.py#L504-L581
def response_action(self, request, files_queryset, folders_queryset): """ Handle an admin action. This is called if a request is POSTed to the changelist; it returns an HttpResponse if the action was handled, and None otherwise. """ # There can be multiple action forms on the page (at the top # and bottom of the change list, for example). Get the action # whose button was pushed. try: action_index = int(request.POST.get('index', 0)) except ValueError: action_index = 0 # Construct the action form. data = request.POST.copy() data.pop(helpers.ACTION_CHECKBOX_NAME, None) data.pop("index", None) # Use the action whose button was pushed try: data.update({'action': data.getlist('action')[action_index]}) except IndexError: # If we didn't get an action from the chosen form that's invalid # POST data, so by deleting action it'll fail the validation check # below. So no need to do anything here pass action_form = self.action_form(data, auto_id=None) action_form.fields['action'].choices = self.get_action_choices(request) # If the form's valid we can handle the action. if action_form.is_valid(): action = action_form.cleaned_data['action'] select_across = action_form.cleaned_data['select_across'] func, name, description = self.get_actions(request)[action] # Get the list of selected PKs. If nothing's selected, we can't # perform an action on it, so bail. Except we want to perform # the action explicitly on all objects. selected = request.POST.getlist(helpers.ACTION_CHECKBOX_NAME) if not selected and not select_across: # Reminder that something needs to be selected or nothing # will happen msg = _("Items must be selected in order to perform " "actions on them. No items have been changed.") self.message_user(request, msg) return None if not select_across: selected_files = [] selected_folders = [] for pk in selected: if pk[:5] == "file-": selected_files.append(pk[5:]) else: selected_folders.append(pk[7:]) # Perform the action only on the selected objects files_queryset = files_queryset.filter(pk__in=selected_files) folders_queryset = folders_queryset.filter( pk__in=selected_folders) response = func(self, request, files_queryset, folders_queryset) # Actions may return an HttpResponse, which will be used as the # response from the POST. If not, we'll be a good little HTTP # citizen and redirect back to the changelist page. if isinstance(response, HttpResponse): return response else: return HttpResponseRedirect(request.get_full_path()) else: msg = _("No action selected.") self.message_user(request, msg) return None
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Handle an admin action. This is called if a request is POSTed to the changelist; it returns an HttpResponse if the action was handled, and None otherwise.
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python
train
materialsproject/pymatgen
pymatgen/analysis/fragmenter.py
https://github.com/materialsproject/pymatgen/blob/4ca558cf72f8d5f8a1f21dfdfc0181a971c186da/pymatgen/analysis/fragmenter.py#L90-L126
def _fragment_one_level(self, mol_graphs): """ Perform one step of iterative fragmentation on a list of molecule graphs. Loop through the graphs, then loop through each graph's edges and attempt to remove that edge in order to obtain two disconnected subgraphs, aka two new fragments. If successful, check to see if the new fragments are already present in self.unique_fragments, and append them if not. If unsucessful, we know that edge belongs to a ring. If we are opening rings, do so with that bond, and then again check if the resulting fragment is present in self.unique_fragments and add it if it is not. """ unique_fragments_on_this_level = [] for mol_graph in mol_graphs: for edge in mol_graph.graph.edges: bond = [(edge[0],edge[1])] try: fragments = mol_graph.split_molecule_subgraphs(bond, allow_reverse=True) for fragment in fragments: found = False for unique_fragment in self.unique_fragments: if unique_fragment.isomorphic_to(fragment): found = True break if not found: self.unique_fragments.append(fragment) unique_fragments_on_this_level.append(fragment) except MolGraphSplitError: if self.open_rings: fragment = open_ring(mol_graph, bond, self.opt_steps) found = False for unique_fragment in self.unique_fragments: if unique_fragment.isomorphic_to(fragment): found = True break if not found: self.unique_fragments.append(fragment) self.unique_fragments_from_ring_openings.append(fragment) unique_fragments_on_this_level.append(fragment) return unique_fragments_on_this_level
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Perform one step of iterative fragmentation on a list of molecule graphs. Loop through the graphs, then loop through each graph's edges and attempt to remove that edge in order to obtain two disconnected subgraphs, aka two new fragments. If successful, check to see if the new fragments are already present in self.unique_fragments, and append them if not. If unsucessful, we know that edge belongs to a ring. If we are opening rings, do so with that bond, and then again check if the resulting fragment is present in self.unique_fragments and add it if it is not.
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python
train
awslabs/sockeye
sockeye/image_captioning/inference.py
https://github.com/awslabs/sockeye/blob/5d64a1ee1ef3cbba17c6d1d94bc061020c43f6ab/sockeye/image_captioning/inference.py#L102-L130
def translate(self, trans_inputs: List[TranslatorInput]) -> List[TranslatorOutput]: """ Batch-translates a list of TranslatorInputs, returns a list of TranslatorOutputs. Splits oversized sentences to sentence chunks of size less than max_input_length. :param trans_inputs: List of TranslatorInputs as returned by make_input(). :return: List of translation results. """ batch_size = self.max_batch_size # translate in batch-sized blocks over input chunks translations = [] for batch_id, batch in enumerate(utils.grouper(trans_inputs, batch_size)): logger.debug("Translating batch %d", batch_id) # underfilled batch will be filled to a full batch size with copies of the 1st input rest = batch_size - len(batch) if rest > 0: logger.debug("Extending the last batch to the full batch size (%d)", batch_size) batch = batch + [batch[0]] * rest batch_translations = self._translate_nd(*self._get_inference_input(batch)) # truncate to remove filler translations if rest > 0: batch_translations = batch_translations[:-rest] translations.extend(batch_translations) # Concatenate results results = [] # type: List[TranslatorOutput] for trans_input, translation in zip(trans_inputs, translations): results.append(self._make_result(trans_input, translation)) return results
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Batch-translates a list of TranslatorInputs, returns a list of TranslatorOutputs. Splits oversized sentences to sentence chunks of size less than max_input_length. :param trans_inputs: List of TranslatorInputs as returned by make_input(). :return: List of translation results.
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python
train
ivanyu/idx2numpy
idx2numpy/converters.py
https://github.com/ivanyu/idx2numpy/blob/9b88698314973226212181d1747dfad6c6974e51/idx2numpy/converters.py#L49-L59
def convert_from_file(file): """ Reads the content of file in IDX format, converts it into numpy.ndarray and returns it. file is a file-like object (with read() method) or a file name. """ if isinstance(file, six_string_types): with open(file, 'rb') as f: return _internal_convert(f) else: return _internal_convert(file)
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Reads the content of file in IDX format, converts it into numpy.ndarray and returns it. file is a file-like object (with read() method) or a file name.
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python
train
armstrong/armstrong.dev
armstrong/dev/tasks.py
https://github.com/armstrong/armstrong.dev/blob/6fd8b863038d9e5ebfd52dfe5ce6c85fb441c267/armstrong/dev/tasks.py#L64-L74
def replaced_by_django_migrations(func, *args, **kwargs): """Decorator to preempt South requirement""" DjangoSettings() # trigger helpful messages if Django is missing import django if django.VERSION >= (1, 7): print("Django 1.7+ has its own migrations system.") print("Use this instead: `invoke managepy makemigrations`") sys.exit(1) return func(*args, **kwargs)
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Decorator to preempt South requirement
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python
train
mozillazg/python-pinyin
pypinyin/core.py
https://github.com/mozillazg/python-pinyin/blob/b44756c852e0d2f50f251e3098cbbfef51774979/pypinyin/core.py#L251-L283
def slug(hans, style=Style.NORMAL, heteronym=False, separator='-', errors='default', strict=True): """生成 slug 字符串. :param hans: 汉字 :type hans: unicode or list :param style: 指定拼音风格,默认是 :py:attr:`~pypinyin.Style.NORMAL` 风格。 更多拼音风格详见 :class:`~pypinyin.Style` :param heteronym: 是否启用多音字 :param separstor: 两个拼音间的分隔符/连接符 :param errors: 指定如何处理没有拼音的字符,详情请参考 :py:func:`~pypinyin.pinyin` :param strict: 是否严格遵照《汉语拼音方案》来处理声母和韵母,详见 :ref:`strict` :return: slug 字符串. :raise AssertionError: 当传入的字符串不是 unicode 字符时会抛出这个异常 :: >>> import pypinyin >>> from pypinyin import Style >>> pypinyin.slug('中国人') 'zhong-guo-ren' >>> pypinyin.slug('中国人', separator=' ') 'zhong guo ren' >>> pypinyin.slug('中国人', style=Style.FIRST_LETTER) 'z-g-r' >>> pypinyin.slug('中国人', style=Style.CYRILLIC) 'чжун1-го2-жэнь2' """ return separator.join(chain(*pinyin(hans, style=style, heteronym=heteronym, errors=errors, strict=strict) ))
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生成 slug 字符串. :param hans: 汉字 :type hans: unicode or list :param style: 指定拼音风格,默认是 :py:attr:`~pypinyin.Style.NORMAL` 风格。 更多拼音风格详见 :class:`~pypinyin.Style` :param heteronym: 是否启用多音字 :param separstor: 两个拼音间的分隔符/连接符 :param errors: 指定如何处理没有拼音的字符,详情请参考 :py:func:`~pypinyin.pinyin` :param strict: 是否严格遵照《汉语拼音方案》来处理声母和韵母,详见 :ref:`strict` :return: slug 字符串. :raise AssertionError: 当传入的字符串不是 unicode 字符时会抛出这个异常 :: >>> import pypinyin >>> from pypinyin import Style >>> pypinyin.slug('中国人') 'zhong-guo-ren' >>> pypinyin.slug('中国人', separator=' ') 'zhong guo ren' >>> pypinyin.slug('中国人', style=Style.FIRST_LETTER) 'z-g-r' >>> pypinyin.slug('中国人', style=Style.CYRILLIC) 'чжун1-го2-жэнь2'
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python
train
NASA-AMMOS/AIT-Core
ait/core/server/stream.py
https://github.com/NASA-AMMOS/AIT-Core/blob/9d85bd9c738e7a6a6fbdff672bea708238b02a3a/ait/core/server/stream.py#L54-L68
def process(self, input_data, topic=None): """ Invokes each handler in sequence. Publishes final output data. Params: input_data: message received by stream topic: name of plugin or stream message received from, if applicable """ for handler in self.handlers: output = handler.handle(input_data) input_data = output self.publish(input_data)
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Invokes each handler in sequence. Publishes final output data. Params: input_data: message received by stream topic: name of plugin or stream message received from, if applicable
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python
train
libtcod/python-tcod
tcod/libtcodpy.py
https://github.com/libtcod/python-tcod/blob/8ba10c5cfb813eaf3e834de971ba2d6acb7838e4/tcod/libtcodpy.py#L762-L780
def color_lerp( c1: Tuple[int, int, int], c2: Tuple[int, int, int], a: float ) -> Color: """Return the linear interpolation between two colors. ``a`` is the interpolation value, with 0 returing ``c1``, 1 returning ``c2``, and 0.5 returing a color halfway between both. Args: c1 (Union[Tuple[int, int, int], Sequence[int]]): The first color. At a=0. c2 (Union[Tuple[int, int, int], Sequence[int]]): The second color. At a=1. a (float): The interpolation value, Returns: Color: The interpolated Color. """ return Color._new_from_cdata(lib.TCOD_color_lerp(c1, c2, a))
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Return the linear interpolation between two colors. ``a`` is the interpolation value, with 0 returing ``c1``, 1 returning ``c2``, and 0.5 returing a color halfway between both. Args: c1 (Union[Tuple[int, int, int], Sequence[int]]): The first color. At a=0. c2 (Union[Tuple[int, int, int], Sequence[int]]): The second color. At a=1. a (float): The interpolation value, Returns: Color: The interpolated Color.
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python
train
pypa/pipenv
pipenv/patched/notpip/_internal/cli/cmdoptions.py
https://github.com/pypa/pipenv/blob/cae8d76c210b9777e90aab76e9c4b0e53bb19cde/pipenv/patched/notpip/_internal/cli/cmdoptions.py#L623-L643
def no_use_pep517_callback(option, opt, value, parser): """ Process a value provided for the --no-use-pep517 option. This is an optparse.Option callback for the no_use_pep517 option. """ # Since --no-use-pep517 doesn't accept arguments, the value argument # will be None if --no-use-pep517 is passed via the command-line. # However, the value can be non-None if the option is triggered e.g. # by an environment variable, for example "PIP_NO_USE_PEP517=true". if value is not None: msg = """A value was passed for --no-use-pep517, probably using either the PIP_NO_USE_PEP517 environment variable or the "no-use-pep517" config file option. Use an appropriate value of the PIP_USE_PEP517 environment variable or the "use-pep517" config file option instead. """ raise_option_error(parser, option=option, msg=msg) # Otherwise, --no-use-pep517 was passed via the command-line. parser.values.use_pep517 = False
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Process a value provided for the --no-use-pep517 option. This is an optparse.Option callback for the no_use_pep517 option.
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python
train
saltstack/salt
salt/modules/state.py
https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/state.py#L527-L576
def template(tem, queue=False, **kwargs): ''' Execute the information stored in a template file on the minion. This function does not ask a master for a SLS file to render but instead directly processes the file at the provided path on the minion. CLI Example: .. code-block:: bash salt '*' state.template '<Path to template on the minion>' ''' if 'env' in kwargs: # "env" is not supported; Use "saltenv". kwargs.pop('env') conflict = _check_queue(queue, kwargs) if conflict is not None: return conflict opts = salt.utils.state.get_sls_opts(__opts__, **kwargs) try: st_ = salt.state.HighState(opts, context=__context__, proxy=__proxy__, initial_pillar=_get_initial_pillar(opts)) except NameError: st_ = salt.state.HighState(opts, context=__context__, initial_pillar=_get_initial_pillar(opts)) errors = _get_pillar_errors(kwargs, pillar=st_.opts['pillar']) if errors: __context__['retcode'] = salt.defaults.exitcodes.EX_PILLAR_FAILURE raise CommandExecutionError('Pillar failed to render', info=errors) if not tem.endswith('.sls'): tem = '{sls}.sls'.format(sls=tem) high_state, errors = st_.render_state(tem, kwargs.get('saltenv', ''), '', None, local=True) if errors: __context__['retcode'] = salt.defaults.exitcodes.EX_STATE_COMPILER_ERROR return errors ret = st_.state.call_high(high_state) _set_retcode(ret, highstate=high_state) return ret
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Execute the information stored in a template file on the minion. This function does not ask a master for a SLS file to render but instead directly processes the file at the provided path on the minion. CLI Example: .. code-block:: bash salt '*' state.template '<Path to template on the minion>'
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python
train
tensorflow/probability
tensorflow_probability/python/distributions/mixture_same_family.py
https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/mixture_same_family.py#L566-L583
def _prevent_2nd_derivative(x): """Disables computation of the second derivatives for a tensor. NB: you need to apply a non-identity function to the output tensor for the exception to be raised. Arguments: x: A tensor. Returns: A tensor with the same value and the same derivative as x, but that raises LookupError when trying to compute the second derivatives. """ def grad(dy): return array_ops.prevent_gradient( dy, message="Second derivative is not implemented.") return tf.identity(x), grad
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Disables computation of the second derivatives for a tensor. NB: you need to apply a non-identity function to the output tensor for the exception to be raised. Arguments: x: A tensor. Returns: A tensor with the same value and the same derivative as x, but that raises LookupError when trying to compute the second derivatives.
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python
test
python-rope/rope
rope/base/oi/type_hinting/utils.py
https://github.com/python-rope/rope/blob/1c9f9cd5964b099a99a9111e998f0dc728860688/rope/base/oi/type_hinting/utils.py#L77-L95
def resolve_type(type_name, pyobject): """ :type type_name: str :type pyobject: rope.base.pyobjects.PyDefinedObject | rope.base.pyobjects.PyObject :rtype: rope.base.pyobjects.PyDefinedObject | rope.base.pyobjects.PyObject or None """ if '.' not in type_name: try: return pyobject.get_module().get_scope().get_name(type_name).get_object() except Exception: pass else: mod_name, attr_name = type_name.rsplit('.', 1) try: mod_finder = ScopeNameFinder(pyobject.get_module()) mod = mod_finder._find_module(mod_name).get_object() return mod.get_attribute(attr_name).get_object() except Exception: pass
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:type type_name: str :type pyobject: rope.base.pyobjects.PyDefinedObject | rope.base.pyobjects.PyObject :rtype: rope.base.pyobjects.PyDefinedObject | rope.base.pyobjects.PyObject or None
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python
train
PythonCharmers/python-future
src/future/backports/http/server.py
https://github.com/PythonCharmers/python-future/blob/c423752879acc05eebc29b0bb9909327bd5c7308/src/future/backports/http/server.py#L688-L727
def send_head(self): """Common code for GET and HEAD commands. This sends the response code and MIME headers. Return value is either a file object (which has to be copied to the outputfile by the caller unless the command was HEAD, and must be closed by the caller under all circumstances), or None, in which case the caller has nothing further to do. """ path = self.translate_path(self.path) f = None if os.path.isdir(path): if not self.path.endswith('/'): # redirect browser - doing basically what apache does self.send_response(301) self.send_header("Location", self.path + "/") self.end_headers() return None for index in "index.html", "index.htm": index = os.path.join(path, index) if os.path.exists(index): path = index break else: return self.list_directory(path) ctype = self.guess_type(path) try: f = open(path, 'rb') except IOError: self.send_error(404, "File not found") return None self.send_response(200) self.send_header("Content-type", ctype) fs = os.fstat(f.fileno()) self.send_header("Content-Length", str(fs[6])) self.send_header("Last-Modified", self.date_time_string(fs.st_mtime)) self.end_headers() return f
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python
train
amadeus4dev/amadeus-python
amadeus/shopping/_hotel_offer.py
https://github.com/amadeus4dev/amadeus-python/blob/afb93667d2cd486ddc7f4a7f29f222f04453a44a/amadeus/shopping/_hotel_offer.py#L9-L21
def get(self, **params): ''' Returns details for a specific offer. .. code-block:: python amadeus.shopping.hotel_offer('XXX').get :rtype: amadeus.Response :raises amadeus.ResponseError: if the request could not be completed ''' return self.client.get('/v2/shopping/hotel-offers/{0}' .format(self.offer_id), **params)
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Returns details for a specific offer. .. code-block:: python amadeus.shopping.hotel_offer('XXX').get :rtype: amadeus.Response :raises amadeus.ResponseError: if the request could not be completed
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python
train
alex-sherman/unsync
examples/mixing_methods.py
https://github.com/alex-sherman/unsync/blob/a52a0b04980dcaf6dc2fd734aa9d7be9d8960bbe/examples/mixing_methods.py#L25-L32
async def result_processor(tasks): """An async result aggregator that combines all the results This gets executed in unsync.loop and unsync.thread""" output = {} for task in tasks: num, res = await task output[num] = res return output
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An async result aggregator that combines all the results This gets executed in unsync.loop and unsync.thread
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python
train
materialsproject/pymatgen
pymatgen/analysis/magnetism/analyzer.py
https://github.com/materialsproject/pymatgen/blob/4ca558cf72f8d5f8a1f21dfdfc0181a971c186da/pymatgen/analysis/magnetism/analyzer.py#L538-L559
def get_exchange_group_info(self, symprec=1e-2, angle_tolerance=5.0): """ Returns the information on the symmetry of the Hamiltonian describing the exchange energy of the system, taking into account relative direction of magnetic moments but not their absolute direction. This is not strictly accurate (e.g. some/many atoms will have zero magnetic moments), but defining symmetry this way is a useful way of keeping track of distinct magnetic orderings within pymatgen. :param symprec: same as SpacegroupAnalyzer :param angle_tolerance: same as SpacegroupAnalyzer :return: spacegroup_symbol, international_number """ structure = self.get_structure_with_spin() return structure.get_space_group_info( symprec=symprec, angle_tolerance=angle_tolerance )
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Returns the information on the symmetry of the Hamiltonian describing the exchange energy of the system, taking into account relative direction of magnetic moments but not their absolute direction. This is not strictly accurate (e.g. some/many atoms will have zero magnetic moments), but defining symmetry this way is a useful way of keeping track of distinct magnetic orderings within pymatgen. :param symprec: same as SpacegroupAnalyzer :param angle_tolerance: same as SpacegroupAnalyzer :return: spacegroup_symbol, international_number
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python
train
pantsbuild/pants
contrib/buildgen/src/python/pants/contrib/buildgen/build_file_manipulator.py
https://github.com/pantsbuild/pants/blob/b72e650da0df685824ffdcc71988b8c282d0962d/contrib/buildgen/src/python/pants/contrib/buildgen/build_file_manipulator.py#L400-L405
def build_file_lines(self): """Like `target_lines`, the entire BUILD file's lines after dependency manipulation.""" build_file_lines = self._build_file_source_lines[:] target_begin, target_end = self._target_interval build_file_lines[target_begin:target_end] = self.target_lines() return build_file_lines
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Like `target_lines`, the entire BUILD file's lines after dependency manipulation.
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python
train
quantumlib/Cirq
dev_tools/incremental_coverage.py
https://github.com/quantumlib/Cirq/blob/0827da80dd7880e5b923eb69407e980ed9bc0bd2/dev_tools/incremental_coverage.py#L48-L105
def diff_to_new_interesting_lines(unified_diff_lines: List[str] ) -> Dict[int, str]: """ Extracts a set of 'interesting' lines out of a GNU unified diff format. Format: gnu.org/software/diffutils/manual/html_node/Detailed-Unified.html @@ from-line-numbers to-line-numbers @@ line-from-either-file ... @@ start,count start,count @@ line-from-either-file ... @@ single start,count @@ line-from-either-file ... Examples: Deleted line (5 is the deleted LOC, 7 is the guessed would-have-been loc in the updated file given other changes pushing the line around): @@ 5 7,0 @@ - content-of-line Added line: @@ 5,0 7 @@ + content-of-line Modified chunk: @@ 10,15 11,5 @@ - removed-line + added-line ... Args: unified_diff_lines: Lines of output from git diff. Returns: A dictionary of "touched lines", with key equal to the line number and value equal to the reason the line was touched. Includes added lines and lines near changes (including removals). """ interesting_lines = dict() for diff_line in unified_diff_lines: # Parse the 'new file' range parts of the unified diff. if not diff_line.startswith('@@ '): continue change = diff_line[3:diff_line.index(' @@', 3)] new = change.split(' ')[1] start = int(new.split(',')[0]) count = 1 if ',' not in new else int(new.split(',')[1]) # The lines before and after a deletion should still be covered. if count == 0: for i in range(start, start + 2): interesting_lines[i] = 'is near a removal' else: for i in range(start, start + count): interesting_lines[i] = 'is new or changed' return interesting_lines
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Extracts a set of 'interesting' lines out of a GNU unified diff format. Format: gnu.org/software/diffutils/manual/html_node/Detailed-Unified.html @@ from-line-numbers to-line-numbers @@ line-from-either-file ... @@ start,count start,count @@ line-from-either-file ... @@ single start,count @@ line-from-either-file ... Examples: Deleted line (5 is the deleted LOC, 7 is the guessed would-have-been loc in the updated file given other changes pushing the line around): @@ 5 7,0 @@ - content-of-line Added line: @@ 5,0 7 @@ + content-of-line Modified chunk: @@ 10,15 11,5 @@ - removed-line + added-line ... Args: unified_diff_lines: Lines of output from git diff. Returns: A dictionary of "touched lines", with key equal to the line number and value equal to the reason the line was touched. Includes added lines and lines near changes (including removals).
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python
train
ThreatConnect-Inc/tcex
tcex/tcex_data_filter.py
https://github.com/ThreatConnect-Inc/tcex/blob/dd4d7a1ef723af1561687120191886b9a2fd4b47/tcex/tcex_data_filter.py#L28-L57
def _build_indexes(self): """Build indexes from data for fast filtering of data. Building indexes of data when possible. This is only supported when dealing with a List of Dictionaries with String values. """ if isinstance(self._data, list): for d in self._data: if not isinstance(d, dict): err = u'Cannot build index for non Dict type.' self._tcex.log.error(err) raise RuntimeError(err) data_obj = DataObj(d) self._master_index.setdefault(id(data_obj), data_obj) for key, value in d.items(): # bcs - update this # if not isinstance(value, (types.StringType, float, int)): # TODO: This is not Python 3 ready if not isinstance(value, (float, int, str)): # For comparison operators the value needs to be a StringType self._tcex.log.debug(u'Can only build index String Types.') continue self._indexes.setdefault(key, {}).setdefault(value, []).append(data_obj) else: err = u'Only *List* data type is currently supported' self._tcex.log.error(err) raise RuntimeError(err)
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Build indexes from data for fast filtering of data. Building indexes of data when possible. This is only supported when dealing with a List of Dictionaries with String values.
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python
train
saltstack/salt
salt/runners/ddns.py
https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/runners/ddns.py#L187-L229
def add_host(zone, name, ttl, ip, keyname, keyfile, nameserver, timeout, port=53, keyalgorithm='hmac-md5'): ''' Create both A and PTR (reverse) records for a host. CLI Example: .. code-block:: bash salt-run ddns.add_host domain.com my-test-vm 3600 10.20.30.40 my-tsig-key /etc/salt/tsig.keyring 10.0.0.1 5 ''' res = [] if zone in name: name = name.replace(zone, '').rstrip('.') fqdn = '{0}.{1}'.format(name, zone) ret = create(zone, name, ttl, 'A', ip, keyname, keyfile, nameserver, timeout, port, keyalgorithm) res.append(ret[fqdn]) parts = ip.split('.')[::-1] i = len(parts) popped = [] # Iterate over possible reverse zones while i > 1: p = parts.pop(0) i -= 1 popped.append(p) zone = '{0}.{1}'.format('.'.join(parts), 'in-addr.arpa.') name = '.'.join(popped) rev_fqdn = '{0}.{1}'.format(name, zone) ret = create(zone, name, ttl, 'PTR', "{0}.".format(fqdn), keyname, keyfile, nameserver, timeout, port, keyalgorithm) if "Created" in ret[rev_fqdn]: res.append(ret[rev_fqdn]) return {fqdn: res} res.append(ret[rev_fqdn]) return {fqdn: res}
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Create both A and PTR (reverse) records for a host. CLI Example: .. code-block:: bash salt-run ddns.add_host domain.com my-test-vm 3600 10.20.30.40 my-tsig-key /etc/salt/tsig.keyring 10.0.0.1 5
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python
train
sosy-lab/benchexec
benchexec/tablegenerator/columns.py
https://github.com/sosy-lab/benchexec/blob/44428f67f41384c03aea13e7e25f884764653617/benchexec/tablegenerator/columns.py#L348-L364
def get_column_type(column, column_values): """ Returns the type of the given column based on its row values on the given RunSetResult. @param column: the column to return the correct ColumnType for @param column_values: the column values to consider @return: a tuple of a type object describing the column - the concrete ColumnType is stored in the attribute 'type', the display unit of the column, which may be None, the source unit of the column, which may be None, and the scale factor to convert from the source unit to the display unit. If no scaling is necessary for conversion, this value is 1. """ try: return _get_column_type_heur(column, column_values) except util.TableDefinitionError as e: logging.error("Column type couldn't be determined: {}".format(e.message)) return ColumnType.text, None, None, 1
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Returns the type of the given column based on its row values on the given RunSetResult. @param column: the column to return the correct ColumnType for @param column_values: the column values to consider @return: a tuple of a type object describing the column - the concrete ColumnType is stored in the attribute 'type', the display unit of the column, which may be None, the source unit of the column, which may be None, and the scale factor to convert from the source unit to the display unit. If no scaling is necessary for conversion, this value is 1.
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python
train
Microsoft/LightGBM
python-package/lightgbm/basic.py
https://github.com/Microsoft/LightGBM/blob/8d2ec69f4f685b0ab1c4624d59ee2d3287bb3147/python-package/lightgbm/basic.py#L234-L252
def c_int_array(data): """Get pointer of int numpy array / list.""" if is_1d_list(data): data = np.array(data, copy=False) if is_numpy_1d_array(data): data = convert_from_sliced_object(data) assert data.flags.c_contiguous if data.dtype == np.int32: ptr_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)) type_data = C_API_DTYPE_INT32 elif data.dtype == np.int64: ptr_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_int64)) type_data = C_API_DTYPE_INT64 else: raise TypeError("Expected np.int32 or np.int64, met type({})" .format(data.dtype)) else: raise TypeError("Unknown type({})".format(type(data).__name__)) return (ptr_data, type_data, data)
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Get pointer of int numpy array / list.
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python
train
datajoint/datajoint-python
datajoint/declare.py
https://github.com/datajoint/datajoint-python/blob/4f29bb154a7ed2b8b64b4d3a9c8be4c16b39621c/datajoint/declare.py#L255-L325
def compile_attribute(line, in_key, foreign_key_sql): """ Convert attribute definition from DataJoint format to SQL :param line: attribution line :param in_key: set to True if attribute is in primary key set :param foreign_key_sql: :returns: (name, sql, is_external) -- attribute name and sql code for its declaration """ try: match = attribute_parser.parseString(line+'#', parseAll=True) except pp.ParseException as err: raise DataJointError('Declaration error in position {pos} in line:\n {line}\n{msg}'.format( line=err.args[0], pos=err.args[1], msg=err.args[2])) match['comment'] = match['comment'].rstrip('#') if 'default' not in match: match['default'] = '' match = {k: v.strip() for k, v in match.items()} match['nullable'] = match['default'].lower() == 'null' accepted_datatype = r'time|date|year|enum|(var)?char|float|real|double|decimal|numeric|' \ r'(tiny|small|medium|big)?int|bool|' \ r'(tiny|small|medium|long)?blob|external|attach' if re.match(accepted_datatype, match['type'], re.I) is None: raise DataJointError('DataJoint does not support datatype "{type}"'.format(**match)) literals = ['CURRENT_TIMESTAMP'] # not to be enclosed in quotes if match['nullable']: if in_key: raise DataJointError('Primary key attributes cannot be nullable in line %s' % line) match['default'] = 'DEFAULT NULL' # nullable attributes default to null else: if match['default']: quote = match['default'].upper() not in literals and match['default'][0] not in '"\'' match['default'] = ('NOT NULL DEFAULT ' + ('"%s"' if quote else "%s") % match['default']) else: match['default'] = 'NOT NULL' match['comment'] = match['comment'].replace('"', '\\"') # escape double quotes in comment is_external = match['type'].startswith('external') is_attachment = match['type'].startswith('attachment') if not is_external: sql = ('`{name}` {type} {default}' + (' COMMENT "{comment}"' if match['comment'] else '')).format(**match) else: # process externally stored attribute if in_key: raise DataJointError('External attributes cannot be primary in:\n%s' % line) store_name = match['type'].split('-') if store_name[0] != 'external': raise DataJointError('External store types must be specified as "external" or "external-<name>"') store_name = '-'.join(store_name[1:]) if store_name != '' and not store_name.isidentifier(): raise DataJointError( 'The external store name `{type}` is invalid. Make like a python identifier.'.format(**match)) if len(store_name) > STORE_NAME_LENGTH: raise DataJointError( 'The external store name `{type}` is too long. Must be <={max_len} characters.'.format( max_len=STORE_NAME_LENGTH, **match)) if not match['default'] in ('DEFAULT NULL', 'NOT NULL'): raise DataJointError('The only acceptable default value for an external field is null in:\n%s' % line) if match['type'] not in config: raise DataJointError('The external store `{type}` is not configured.'.format(**match)) # append external configuration name to the end of the comment sql = '`{name}` {hash_type} {default} COMMENT ":{type}:{comment}"'.format( hash_type=HASH_DATA_TYPE, **match) foreign_key_sql.append( "FOREIGN KEY (`{name}`) REFERENCES {{external_table}} (`hash`) " "ON UPDATE RESTRICT ON DELETE RESTRICT".format(**match)) return match['name'], sql, is_external
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Convert attribute definition from DataJoint format to SQL :param line: attribution line :param in_key: set to True if attribute is in primary key set :param foreign_key_sql: :returns: (name, sql, is_external) -- attribute name and sql code for its declaration
[ "Convert", "attribute", "definition", "from", "DataJoint", "format", "to", "SQL" ]
python
train
bloomberg/bqplot
bqplot/pyplot.py
https://github.com/bloomberg/bqplot/blob/8eb8b163abe9ee6306f6918067e2f36c1caef2ef/bqplot/pyplot.py#L871-L897
def bin(sample, options={}, **kwargs): """Draw a histogram in the current context figure. Parameters ---------- sample: numpy.ndarray, 1d The sample for which the histogram must be generated. options: dict (default: {}) Options for the scales to be created. If a scale labeled 'x' is required for that mark, options['x'] contains optional keyword arguments for the constructor of the corresponding scale type. axes_options: dict (default: {}) Options for the axes to be created. If an axis labeled 'x' is required for that mark, axes_options['x'] contains optional keyword arguments for the constructor of the corresponding axis type. """ kwargs['sample'] = sample scales = kwargs.pop('scales', {}) for xy in ['x', 'y']: if xy not in scales: dimension = _get_attribute_dimension(xy, Bars) if dimension in _context['scales']: scales[xy] = _context['scales'][dimension] else: scales[xy] = LinearScale(**options.get(xy, {})) _context['scales'][dimension] = scales[xy] kwargs['scales'] = scales return _draw_mark(Bins, options=options, **kwargs)
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Draw a histogram in the current context figure. Parameters ---------- sample: numpy.ndarray, 1d The sample for which the histogram must be generated. options: dict (default: {}) Options for the scales to be created. If a scale labeled 'x' is required for that mark, options['x'] contains optional keyword arguments for the constructor of the corresponding scale type. axes_options: dict (default: {}) Options for the axes to be created. If an axis labeled 'x' is required for that mark, axes_options['x'] contains optional keyword arguments for the constructor of the corresponding axis type.
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python
train
ska-sa/katcp-python
katcp/fake_clients.py
https://github.com/ska-sa/katcp-python/blob/9127c826a1d030c53b84d0e95743e20e5c5ea153/katcp/fake_clients.py#L16-L59
def fake_KATCP_client_resource_factory( KATCPClientResourceClass, fake_options, resource_spec, *args, **kwargs): """Create a fake KATCPClientResource-like class and a fake-manager Parameters ---------- KATCPClientResourceClass : class Subclass of :class:`katcp.resource_client.KATCPClientResource` fake_options : dict Options for the faking process. Keys: allow_any_request : bool, default False (TODO not implemented behaves as if it were True) resource_spec, *args, **kwargs : passed to KATCPClientResourceClass A subclass of the passed-in KATCPClientResourceClass is created that replaces the internal InspecingClient instances with fakes using fake_inspecting_client_factory() based on the InspectingClient class used by KATCPClientResourceClass. Returns ------- (fake_katcp_client_resource, fake_katcp_client_resource_manager): fake_katcp_client_resource : instance of faked subclass of KATCPClientResourceClass fake_katcp_client_resource_manager : :class:`FakeKATCPClientResourceManager` instance Bound to the `fake_katcp_client_resource` instance. """ # TODO Implement allow_any_request functionality. When True, any unknown request (even # if there is no fake implementation) should succeed allow_any_request = fake_options.get('allow_any_request', False) class FakeKATCPClientResource(KATCPClientResourceClass): def inspecting_client_factory(self, host, port, ioloop_set_to): real_instance = (super(FakeKATCPClientResource, self) .inspecting_client_factory(host, port, ioloop_set_to) ) fic, fic_manager = fake_inspecting_client_factory( real_instance.__class__, fake_options, host, port, ioloop=ioloop_set_to, auto_reconnect=self.auto_reconnect) self.fake_inspecting_client_manager = fic_manager return fic fkcr = FakeKATCPClientResource(resource_spec, *args, **kwargs) fkcr_manager = FakeKATCPClientResourceManager(fkcr) return (fkcr, fkcr_manager)
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Create a fake KATCPClientResource-like class and a fake-manager Parameters ---------- KATCPClientResourceClass : class Subclass of :class:`katcp.resource_client.KATCPClientResource` fake_options : dict Options for the faking process. Keys: allow_any_request : bool, default False (TODO not implemented behaves as if it were True) resource_spec, *args, **kwargs : passed to KATCPClientResourceClass A subclass of the passed-in KATCPClientResourceClass is created that replaces the internal InspecingClient instances with fakes using fake_inspecting_client_factory() based on the InspectingClient class used by KATCPClientResourceClass. Returns ------- (fake_katcp_client_resource, fake_katcp_client_resource_manager): fake_katcp_client_resource : instance of faked subclass of KATCPClientResourceClass fake_katcp_client_resource_manager : :class:`FakeKATCPClientResourceManager` instance Bound to the `fake_katcp_client_resource` instance.
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python
train
linkedin/luminol
src/luminol/correlator.py
https://github.com/linkedin/luminol/blob/42e4ab969b774ff98f902d064cb041556017f635/src/luminol/correlator.py#L72-L90
def _get_algorithm_and_params(self, algorithm_name, algorithm_params): """ Get the specific algorithm and merge the algorithm params. :param str algorithm: name of the algorithm to use. :param dict algorithm_params: additional params for the specific algorithm. """ algorithm_name = algorithm_name or CORRELATOR_ALGORITHM try: self.algorithm = correlator_algorithms[algorithm_name] except KeyError: raise exceptions.AlgorithmNotFound('luminol.Correlator: ' + str(algorithm_name) + ' not found.') # Merge parameters. if algorithm_params: if not isinstance(algorithm_params, dict): raise exceptions.InvalidDataFormat('luminol.Correlator: algorithm_params passed is not a dictionary.') else: # self.algorithm_params = dict(algorithm_params.items() + self.algorithm_params.items()) self.algorithm_params = self.algorithm_params.copy() self.algorithm_params.update(algorithm_params)
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Get the specific algorithm and merge the algorithm params. :param str algorithm: name of the algorithm to use. :param dict algorithm_params: additional params for the specific algorithm.
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python
train
summa-tx/riemann
riemann/encoding/base58.py
https://github.com/summa-tx/riemann/blob/04ae336dfd4007ceaed748daadc91cc32fa278ec/riemann/encoding/base58.py#L42-L55
def decode(s, checksum=True): """Convert base58 to binary using BASE58_ALPHABET.""" v, prefix = to_long( BASE58_BASE, lambda c: BASE58_LOOKUP[c], s.encode("utf8")) data = from_long(v, prefix, 256, lambda x: x) if checksum: data, the_hash = data[:-4], data[-4:] if utils.hash256(data)[:4] == the_hash: return data raise ValueError("hashed base58 has bad checksum %s" % s) return data
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Convert base58 to binary using BASE58_ALPHABET.
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python
train
ANTsX/ANTsPy
ants/learn/decomposition.py
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/learn/decomposition.py#L19-L248
def sparse_decom2(inmatrix, inmask=(None, None), sparseness=(0.01, 0.01), nvecs=3, its=20, cthresh=(0,0), statdir=None, perms=0, uselong=0, z=0, smooth=0, robust=0, mycoption=0, initialization_list=[], initialization_list2=[], ell1=10, prior_weight=0, verbose=False, rejector=0, max_based=False, version=1): """ Decomposes two matrices into paired sparse eigenevectors to maximize canonical correlation - aka Sparse CCA. Note: we do not scale the matrices internally. We leave scaling choices to the user. ANTsR function: `sparseDecom2` Arguments --------- inmatrix : 2-tuple of ndarrays input as inmatrix=(mat1,mat2). n by p input matrix and n by q input matrix , spatial variable lies along columns. inmask : 2-tuple of ANTsImage types (optional - one or both) optional pair of image masks sparseness : tuple a pair of float values e.g c(0.01,0.1) enforces an unsigned 99 percent and 90 percent sparse solution for each respective view nvecs : integer number of eigenvector pairs its : integer number of iterations, 10 or 20 usually sufficient cthresh : 2-tuple cluster threshold pair statdir : string (optional) temporary directory if you want to look at full output perms : integer number of permutations. settings permutations greater than 0 will estimate significance per vector empirically. For small datasets, these may be conservative. p-values depend on how one scales the input matrices. uselong : boolean enforce solutions of both views to be the same - requires matrices to be the same size z : float subject space (low-dimensional space) sparseness value smooth : float smooth the data (only available when mask is used) robust : boolean rank transform input matrices mycoption : integer enforce 1 - spatial orthogonality, 2 - low-dimensional orthogonality or 0 - both initialization_list : list initialization for first view initialization_list2 : list initialization for 2nd view ell1 : float gradient descent parameter, if negative then l0 otherwise use l1 prior_weight : scalar Scalar value weight on prior between 0 (prior is weak) and 1 (prior is strong). Only engaged if initialization is used verbose : boolean activates verbose output to screen rejector : scalar rejects small correlation solutions max_based : boolean whether to choose max-based thresholding Returns ------- dict w/ following key/value pairs: `projections` : ndarray X projections `projections2` : ndarray Y projections `eig1` : ndarray X components `eig2` : ndarray Y components `summary` : pd.DataFrame first column is canonical correlations, second column is p-values (these are `None` if perms > 0) Example ------- >>> import numpy as np >>> import ants >>> mat = np.random.randn(20, 100) >>> mat2 = np.random.randn(20, 90) >>> mydecom = ants.sparse_decom2(inmatrix = (mat,mat2), sparseness=(0.1,0.3), nvecs=3, its=3, perms=0) """ if inmatrix[0].shape[0] != inmatrix[1].shape[0]: raise ValueError('Matrices must have same number of rows (samples)') idim = 3 if isinstance(inmask[0], iio.ANTsImage): maskx = inmask[0].clone('float') idim = inmask[0].dimension hasmaskx = 1 elif isinstance(inmask[0], np.ndarray): maskx = core.from_numpy(inmask[0], pixeltype='float') idim = inmask[0].ndim hasmaskx = 1 else: maskx = core.make_image([1]*idim, pixeltype='float') hasmaskx = -1 if isinstance(inmask[1], iio.ANTsImage): masky = inmask[1].clone('float') idim = inmask[1].dimension hasmasky = 1 elif isinstance(inmask[1], np.ndarray): masky = core.from_numpy(inmask[1], pixeltype='float') idim = inmask[1].ndim hasmasky = 1 else: masky = core.make_image([1]*idim, pixeltype='float') hasmasky = -1 inmask = [maskx, masky] if robust > 0: raise NotImplementedError('robust > 0 not currently implemented') else: input_matrices = inmatrix if idim == 2: if version == 1: sccancpp_fn = utils.get_lib_fn('sccanCpp2D') elif version == 2: sccancpp_fn = utils.get_lib_fn('sccanCpp2DV2') input_matrices = (input_matrices[0].tolist(), input_matrices[1].tolist()) elif idim ==3: if version == 1: sccancpp_fn = utils.get_lib_fn('sccanCpp3D') elif version == 2: sccancpp_fn = utils.get_lib_fn('sccanCpp3DV2') input_matrices = (input_matrices[0].tolist(), input_matrices[1].tolist()) outval = sccancpp_fn(input_matrices[0], input_matrices[1], inmask[0].pointer, inmask[1].pointer, hasmaskx, hasmasky, sparseness[0], sparseness[1], nvecs, its, cthresh[0], cthresh[1], z, smooth, initialization_list, initialization_list2, ell1, verbose, prior_weight, mycoption, max_based) p1 = np.dot(input_matrices[0], outval['eig1'].T) p2 = np.dot(input_matrices[1], outval['eig2'].T) outcorrs = np.array([pearsonr(p1[:,i],p2[:,i])[0] for i in range(p1.shape[1])]) if prior_weight < 1e-10: myord = np.argsort(np.abs(outcorrs))[::-1] outcorrs = outcorrs[myord] p1 = p1[:, myord] p2 = p2[:, myord] outval['eig1'] = outval['eig1'][myord,:] outval['eig2'] = outval['eig2'][myord,:] cca_summary = np.vstack((outcorrs,[None]*len(outcorrs))).T if perms > 0: cca_summary[:,1] = 0 nsubs = input_matrices[0].shape[0] for permer in range(perms): m1 = input_matrices[0][np.random.permutation(nsubs),:] m2 = input_matrices[1][np.random.permutation(nsubs),:] outvalperm = sccancpp_fn(m1, m2, inmask[0].pointer, inmask[1].pointer, hasmaskx, hasmasky, sparseness[0], sparseness[1], nvecs, its, cthresh[0], cthresh[1], z, smooth, initialization_list, initialization_list2, ell1, verbose, prior_weight, mycoption, max_based) p1perm = np.dot(m1, outvalperm['eig1'].T) p2perm = np.dot(m2, outvalperm['eig2'].T) outcorrsperm = np.array([pearsonr(p1perm[:,i],p2perm[:,i])[0] for i in range(p1perm.shape[1])]) if prior_weight < 1e-10: myord = np.argsort(np.abs(outcorrsperm))[::-1] outcorrsperm = outcorrsperm[myord] counter = np.abs(cca_summary[:,0]) < np.abs(outcorrsperm) counter = counter.astype('int') cca_summary[:,1] = cca_summary[:,1] + counter cca_summary[:,1] = cca_summary[:,1] / float(perms) return {'projections': p1, 'projections2': p2, 'eig1': outval['eig1'].T, 'eig2': outval['eig2'].T, 'summary': pd.DataFrame(cca_summary,columns=['corrs','pvalues'])}
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Decomposes two matrices into paired sparse eigenevectors to maximize canonical correlation - aka Sparse CCA. Note: we do not scale the matrices internally. We leave scaling choices to the user. ANTsR function: `sparseDecom2` Arguments --------- inmatrix : 2-tuple of ndarrays input as inmatrix=(mat1,mat2). n by p input matrix and n by q input matrix , spatial variable lies along columns. inmask : 2-tuple of ANTsImage types (optional - one or both) optional pair of image masks sparseness : tuple a pair of float values e.g c(0.01,0.1) enforces an unsigned 99 percent and 90 percent sparse solution for each respective view nvecs : integer number of eigenvector pairs its : integer number of iterations, 10 or 20 usually sufficient cthresh : 2-tuple cluster threshold pair statdir : string (optional) temporary directory if you want to look at full output perms : integer number of permutations. settings permutations greater than 0 will estimate significance per vector empirically. For small datasets, these may be conservative. p-values depend on how one scales the input matrices. uselong : boolean enforce solutions of both views to be the same - requires matrices to be the same size z : float subject space (low-dimensional space) sparseness value smooth : float smooth the data (only available when mask is used) robust : boolean rank transform input matrices mycoption : integer enforce 1 - spatial orthogonality, 2 - low-dimensional orthogonality or 0 - both initialization_list : list initialization for first view initialization_list2 : list initialization for 2nd view ell1 : float gradient descent parameter, if negative then l0 otherwise use l1 prior_weight : scalar Scalar value weight on prior between 0 (prior is weak) and 1 (prior is strong). Only engaged if initialization is used verbose : boolean activates verbose output to screen rejector : scalar rejects small correlation solutions max_based : boolean whether to choose max-based thresholding Returns ------- dict w/ following key/value pairs: `projections` : ndarray X projections `projections2` : ndarray Y projections `eig1` : ndarray X components `eig2` : ndarray Y components `summary` : pd.DataFrame first column is canonical correlations, second column is p-values (these are `None` if perms > 0) Example ------- >>> import numpy as np >>> import ants >>> mat = np.random.randn(20, 100) >>> mat2 = np.random.randn(20, 90) >>> mydecom = ants.sparse_decom2(inmatrix = (mat,mat2), sparseness=(0.1,0.3), nvecs=3, its=3, perms=0)
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python
train
adrn/gala
gala/potential/frame/builtin/transformations.py
https://github.com/adrn/gala/blob/ea95575a0df1581bb4b0986aebd6eea8438ab7eb/gala/potential/frame/builtin/transformations.py#L10-L29
def rodrigues_axis_angle_rotate(x, vec, theta): """ Rotated the input vector or set of vectors `x` around the axis `vec` by the angle `theta`. Parameters ---------- x : array_like The vector or array of vectors to transform. Must have shape """ x = np.array(x).T vec = np.array(vec).T theta = np.array(theta).T[...,None] out = np.cos(theta)*x + np.sin(theta)*np.cross(vec, x) + \ (1 - np.cos(theta)) * (vec * x).sum(axis=-1)[...,None] * vec return out.T
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python
train
fboender/ansible-cmdb
lib/mako/_ast_util.py
https://github.com/fboender/ansible-cmdb/blob/ebd960ac10684e8c9ec2b12751bba2c4c9504ab7/lib/mako/_ast_util.py#L107-L122
def dump(node): """ A very verbose representation of the node passed. This is useful for debugging purposes. """ def _format(node): if isinstance(node, AST): return '%s(%s)' % (node.__class__.__name__, ', '.join('%s=%s' % (a, _format(b)) for a, b in iter_fields(node))) elif isinstance(node, list): return '[%s]' % ', '.join(_format(x) for x in node) return repr(node) if not isinstance(node, AST): raise TypeError('expected AST, got %r' % node.__class__.__name__) return _format(node)
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python
train
scanny/python-pptx
pptx/chart/axis.py
https://github.com/scanny/python-pptx/blob/d6ab8234f8b03953d2f831ff9394b1852db34130/pptx/chart/axis.py#L164-L172
def minor_tick_mark(self): """ Read/write :ref:`XlTickMark` value specifying the type of minor tick mark for this axis. """ minorTickMark = self._element.minorTickMark if minorTickMark is None: return XL_TICK_MARK.CROSS return minorTickMark.val
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Read/write :ref:`XlTickMark` value specifying the type of minor tick mark for this axis.
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python
train
EwilDawe/typy
typy/mouse.py
https://github.com/EwilDawe/typy/blob/0349e7176567a4dbef318e75d9b3d6868950a1a9/typy/mouse.py#L43-L69
def move_arc(x, y, r, speed = 1, orientation = True): # WARNING: This function currently contains inaccuracy likely due to the rounding of trigonometric functions """ Moves the cursor in an arc of radius r to (x, y) at a certain speed :param x: target x-ordinate :param y: target y-ordinate :param r: radius :param speed: pixel traversal rate :param orientation: direction of arc :return: None """ _x, _y = win32api.GetCursorPos() c_len = (r**2 - (((x - _x)/2)**2 + ((y - _y)/2)**2))**0.5 t = (c_len**2/((y - _y)**2 + (x - _x)**2))**0.5 t = t if orientation else -t centre = ((_x + x)/2 + t*(_x - x), (_y + y)/2 + t*(y - _y)) if any(isinstance(ordinate, complex) for ordinate in centre): raise ValueError("Radius too low - minimum: {}".format(((x - _x)**2 + (y - _y)**2)**0.5/2)) theta = math.atan2(_y - centre[1], _x - centre[0]) end = math.atan2(y - centre[1], x - centre[0]) while theta < end: move(*list(map(round, (centre[0] + r*math.cos(theta), centre[1] + r*math.sin(theta))))) theta += speed/100 time.sleep(0.01) move(x, y)
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Moves the cursor in an arc of radius r to (x, y) at a certain speed :param x: target x-ordinate :param y: target y-ordinate :param r: radius :param speed: pixel traversal rate :param orientation: direction of arc :return: None
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python
train
mblayman/httpony
httpony/application.py
https://github.com/mblayman/httpony/blob/5af404d647a8dac8a043b64ea09882589b3b5247/httpony/application.py#L15-L55
def make_app(): """Make a WSGI app that has all the HTTPie pieces baked in.""" env = Environment() # STDIN is ignored because HTTPony runs a server that doesn't care. # Additionally, it is needed or else pytest blows up. args = parser.parse_args(args=['/', '--ignore-stdin'], env=env) args.output_options = 'HB' # Output only requests. server = 'HTTPony/{0}'.format(__version__) def application(environ, start_response): # The WSGI server puts content length and type in the environment # even when not provided with the request. Drop them if they are empty. if environ.get('CONTENT_LENGTH') == '': del environ['CONTENT_LENGTH'] if environ.get('CONTENT_TYPE') == '': del environ['CONTENT_TYPE'] wrequest = WerkzeugRequest(environ) data = wrequest.get_data() request = Request( method=wrequest.method, url=wrequest.url, headers=wrequest.headers, data=data, ) prepared = request.prepare() stream = streams.build_output_stream( args, env, prepared, response=None, output_options=args.output_options) streams.write_stream(stream, env.stdout, env.stdout_isatty) # When there is data in the request, give the next one breathing room. if data: print("\n", file=env.stdout) # Make dreams come true. response = Response(headers={'Server': server}) return response(environ, start_response) return application
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python
test
merll/docker-fabric
dockerfabric/apiclient.py
https://github.com/merll/docker-fabric/blob/785d84e40e17265b667d8b11a6e30d8e6b2bf8d4/dockerfabric/apiclient.py#L147-L156
def build(self, tag, **kwargs): """ Identical to :meth:`dockermap.client.base.DockerClientWrapper.build` with additional logging. """ self.push_log("Building image '{0}'.".format(tag)) set_raise_on_error(kwargs) try: return super(DockerFabricClient, self).build(tag, **kwargs) except DockerStatusError as e: error(e.message)
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Identical to :meth:`dockermap.client.base.DockerClientWrapper.build` with additional logging.
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python
train
plivo/plivohelper-python
plivohelper.py
https://github.com/plivo/plivohelper-python/blob/a2f706d69e2138fbb973f792041341f662072d26/plivohelper.py#L244-L249
def schedule_play(self, call_params): """REST Schedule playing something on a call Helper """ path = '/' + self.api_version + '/SchedulePlay/' method = 'POST' return self.request(path, method, call_params)
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REST Schedule playing something on a call Helper
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python
valid
gem/oq-engine
openquake/hazardlib/gsim/boore_1997.py
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/boore_1997.py#L121-L141
def _compute_style_of_faulting_term(self, rup, C): """ Computes the coefficient to scale for reverse or strike-slip events Fault type (Strike-slip, Normal, Thrust/reverse) is derived from rake angle. Rakes angles within 30 of horizontal are strike-slip, angles from 30 to 150 are reverse, and angles from -30 to -150 are normal. See paragraph 'Predictor Variables' pag 103. Note that 'Unspecified' case is used to refer to all other rake angles. """ if np.abs(rup.rake) <= 30.0 or (180.0 - np.abs(rup.rake)) <= 30.0: # strike-slip return C['B1ss'] elif rup.rake > 30.0 and rup.rake < 150.0: # reverse return C['B1rv'] else: # unspecified (also includes Normal faulting!) return C['B1all']
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Computes the coefficient to scale for reverse or strike-slip events Fault type (Strike-slip, Normal, Thrust/reverse) is derived from rake angle. Rakes angles within 30 of horizontal are strike-slip, angles from 30 to 150 are reverse, and angles from -30 to -150 are normal. See paragraph 'Predictor Variables' pag 103. Note that 'Unspecified' case is used to refer to all other rake angles.
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python
train
Infinidat/infi.clickhouse_orm
src/infi/clickhouse_orm/models.py
https://github.com/Infinidat/infi.clickhouse_orm/blob/595f2023e334e3925a5c3fbfdd6083a5992a7169/src/infi/clickhouse_orm/models.py#L207-L228
def from_tsv(cls, line, field_names, timezone_in_use=pytz.utc, database=None): ''' Create a model instance from a tab-separated line. The line may or may not include a newline. The `field_names` list must match the fields defined in the model, but does not have to include all of them. - `line`: the TSV-formatted data. - `field_names`: names of the model fields in the data. - `timezone_in_use`: the timezone to use when parsing dates and datetimes. - `database`: if given, sets the database that this instance belongs to. ''' from six import next values = iter(parse_tsv(line)) kwargs = {} for name in field_names: field = getattr(cls, name) kwargs[name] = field.to_python(next(values), timezone_in_use) obj = cls(**kwargs) if database is not None: obj.set_database(database) return obj
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Create a model instance from a tab-separated line. The line may or may not include a newline. The `field_names` list must match the fields defined in the model, but does not have to include all of them. - `line`: the TSV-formatted data. - `field_names`: names of the model fields in the data. - `timezone_in_use`: the timezone to use when parsing dates and datetimes. - `database`: if given, sets the database that this instance belongs to.
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python
train
brutasse/rache
rache/__init__.py
https://github.com/brutasse/rache/blob/fa9cf073376a8c731a13924b84fb8422a771a4ab/rache/__init__.py#L48-L87
def schedule_job(job_id, schedule_in, connection=None, **kwargs): """Schedules a job. :param job_id: unique identifier for this job :param schedule_in: number of seconds from now in which to schedule the job or timedelta object. :param **kwargs: parameters to attach to the job, key-value structure. >>> schedule_job('http://example.com/test', schedule_in=10, num_retries=10) """ if not isinstance(schedule_in, int): # assumed to be a timedelta schedule_in = schedule_in.days * 3600 * 24 + schedule_in.seconds schedule_at = int(time.time()) + schedule_in if connection is None: connection = r if 'id' in kwargs: raise RuntimeError("'id' is a reserved key for the job ID") with connection.pipeline() as pipe: if schedule_at is not None: args = (schedule_at, job_id) if isinstance(connection, redis.Redis): # StrictRedis or Redis don't have the same argument order args = (job_id, schedule_at) pipe.zadd(REDIS_KEY, *args) delete = [] hmset = {} for key, value in kwargs.items(): if value is None: delete.append(key) else: hmset[key] = value if hmset: pipe.hmset(job_key(job_id), hmset) if len(delete) > 0: pipe.hdel(job_key(job_id), *delete) pipe.execute()
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Schedules a job. :param job_id: unique identifier for this job :param schedule_in: number of seconds from now in which to schedule the job or timedelta object. :param **kwargs: parameters to attach to the job, key-value structure. >>> schedule_job('http://example.com/test', schedule_in=10, num_retries=10)
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python
train
UCBerkeleySETI/blimpy
blimpy/file_wrapper.py
https://github.com/UCBerkeleySETI/blimpy/blob/b8822d3e3e911944370d84371a91fa0c29e9772e/blimpy/file_wrapper.py#L147-L158
def _calc_selection_shape(self): """Calculate shape of data of interest. """ #Check how many integrations requested n_ints = int(self.t_stop - self.t_start) #Check how many frequency channels requested n_chan = int(np.round((self.f_stop - self.f_start) / abs(self.header[b'foff']))) selection_shape = (n_ints,int(self.header[b'nifs']),n_chan) return selection_shape
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Calculate shape of data of interest.
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python
test
rodluger/everest
everest/missions/k2/utils.py
https://github.com/rodluger/everest/blob/6779591f9f8b3556847e2fbf761bdfac7520eaea/everest/missions/k2/utils.py#L545-L615
def GetHiResImage(ID): ''' Queries the Palomar Observatory Sky Survey II catalog to obtain a higher resolution optical image of the star with EPIC number :py:obj:`ID`. ''' # Get the TPF info client = kplr.API() star = client.k2_star(ID) k2ra = star.k2_ra k2dec = star.k2_dec tpf = star.get_target_pixel_files()[0] with tpf.open() as f: k2wcs = WCS(f[2].header) shape = np.array(f[1].data.field('FLUX'), dtype='float64')[0].shape # Get the POSS URL hou = int(k2ra * 24 / 360.) min = int(60 * (k2ra * 24 / 360. - hou)) sec = 60 * (60 * (k2ra * 24 / 360. - hou) - min) ra = '%02d+%02d+%.2f' % (hou, min, sec) sgn = '' if np.sign(k2dec) >= 0 else '-' deg = int(np.abs(k2dec)) min = int(60 * (np.abs(k2dec) - deg)) sec = 3600 * (np.abs(k2dec) - deg - min / 60) dec = '%s%02d+%02d+%.1f' % (sgn, deg, min, sec) url = 'https://archive.stsci.edu/cgi-bin/dss_search?v=poss2ukstu_red&' + \ 'r=%s&d=%s&e=J2000&h=3&w=3&f=fits&c=none&fov=NONE&v3=' % (ra, dec) # Query the server r = urllib.request.Request(url) handler = urllib.request.urlopen(r) code = handler.getcode() if int(code) != 200: # Unavailable return None data = handler.read() # Atomically write to a temp file f = NamedTemporaryFile("wb", delete=False) f.write(data) f.flush() os.fsync(f.fileno()) f.close() # Now open the POSS fits file with pyfits.open(f.name) as ff: img = ff[0].data # Map POSS pixels onto K2 pixels xy = np.empty((img.shape[0] * img.shape[1], 2)) z = np.empty(img.shape[0] * img.shape[1]) pwcs = WCS(f.name) k = 0 for i in range(img.shape[0]): for j in range(img.shape[1]): ra, dec = pwcs.all_pix2world(float(j), float(i), 0) xy[k] = k2wcs.all_world2pix(ra, dec, 0) z[k] = img[i, j] k += 1 # Resample grid_x, grid_y = np.mgrid[-0.5:shape[1] - 0.5:0.1, -0.5:shape[0] - 0.5:0.1] resampled = griddata(xy, z, (grid_x, grid_y), method='cubic') # Rotate to align with K2 image. Not sure why, but it is necessary resampled = np.rot90(resampled) return resampled
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Queries the Palomar Observatory Sky Survey II catalog to obtain a higher resolution optical image of the star with EPIC number :py:obj:`ID`.
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python
train
OpenHydrology/floodestimation
floodestimation/analysis.py
https://github.com/OpenHydrology/floodestimation/blob/782da7c5abd1348923129efe89fb70003ebb088c/floodestimation/analysis.py#L973-L981
def _solve_location_param(self): """ We're lazy here and simply iterate to find the location parameter such that growth_curve(0.5)=1. """ params = copy.copy(self.params) del params['loc'] f = lambda location: self.distr_f.ppf(0.5, loc=location, **params) - 1 return optimize.brentq(f, -10, 10)
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We're lazy here and simply iterate to find the location parameter such that growth_curve(0.5)=1.
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python
train
awslabs/serverless-application-model
samtranslator/sdk/parameter.py
https://github.com/awslabs/serverless-application-model/blob/cccb0c96b5c91e53355ebc07e542467303a5eedd/samtranslator/sdk/parameter.py#L19-L59
def add_default_parameter_values(self, sam_template): """ Method to read default values for template parameters and merge with user supplied values. Example: If the template contains the following parameters defined Parameters: Param1: Type: String Default: default_value Param2: Type: String Default: default_value And, the user explicitly provided the following parameter values: { Param2: "new value" } then, this method will grab default value for Param1 and return the following result: { Param1: "default_value", Param2: "new value" } :param dict sam_template: SAM template :param dict parameter_values: Dictionary of parameter values provided by the user :return dict: Merged parameter values """ parameter_definition = sam_template.get("Parameters", None) if not parameter_definition or not isinstance(parameter_definition, dict): return self.parameter_values for param_name, value in parameter_definition.items(): if param_name not in self.parameter_values and isinstance(value, dict) and "Default" in value: self.parameter_values[param_name] = value["Default"]
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Method to read default values for template parameters and merge with user supplied values. Example: If the template contains the following parameters defined Parameters: Param1: Type: String Default: default_value Param2: Type: String Default: default_value And, the user explicitly provided the following parameter values: { Param2: "new value" } then, this method will grab default value for Param1 and return the following result: { Param1: "default_value", Param2: "new value" } :param dict sam_template: SAM template :param dict parameter_values: Dictionary of parameter values provided by the user :return dict: Merged parameter values
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python
train
tensorflow/cleverhans
cleverhans/model_zoo/deep_k_nearest_neighbors/dknn.py
https://github.com/tensorflow/cleverhans/blob/97488e215760547b81afc53f5e5de8ba7da5bd98/cleverhans/model_zoo/deep_k_nearest_neighbors/dknn.py#L170-L190
def nonconformity(self, knns_labels): """ Given an dictionary of nb_data x nb_classes dimension, compute the nonconformity of each candidate label for each data point: i.e. the number of knns whose label is different from the candidate label. """ nb_data = knns_labels[self.layers[0]].shape[0] knns_not_in_class = np.zeros((nb_data, self.nb_classes), dtype=np.int32) for i in range(nb_data): # Compute number of nearest neighbors per class knns_in_class = np.zeros( (len(self.layers), self.nb_classes), dtype=np.int32) for layer_id, layer in enumerate(self.layers): knns_in_class[layer_id, :] = np.bincount( knns_labels[layer][i], minlength=self.nb_classes) # Compute number of knns in other class than class_id for class_id in range(self.nb_classes): knns_not_in_class[i, class_id] = np.sum( knns_in_class) - np.sum(knns_in_class[:, class_id]) return knns_not_in_class
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Given an dictionary of nb_data x nb_classes dimension, compute the nonconformity of each candidate label for each data point: i.e. the number of knns whose label is different from the candidate label.
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python
train
Rediker-Software/doac
doac/contrib/rest_framework/authentication.py
https://github.com/Rediker-Software/doac/blob/398fdd64452e4ff8662297b0381926addd77505a/doac/contrib/rest_framework/authentication.py#L6-L25
def authenticate(self, request): """ Send the request through the authentication middleware that is provided with DOAC and grab the user and token from it. """ from doac.middleware import AuthenticationMiddleware try: response = AuthenticationMiddleware().process_request(request) except: raise exceptions.AuthenticationFailed("Invalid handler") if not hasattr(request, "user") or not request.user.is_authenticated(): return None if not hasattr(request, "access_token"): raise exceptions.AuthenticationFailed("Access token was not valid") return request.user, request.access_token
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Send the request through the authentication middleware that is provided with DOAC and grab the user and token from it.
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python
train