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ronhanson/python-tbx
tbx/file.py
https://github.com/ronhanson/python-tbx/blob/87f72ae0cadecafbcd144f1e930181fba77f6b83/tbx/file.py#L45-L65
def full_file_list_with_sequence(scan_path): """ Returns a list of all files in a folder and its subfolders (only files). """ file_list = [] path = os.path.abspath(scan_path) for root, dirs, files in os.walk(path): if len(files) != 0 and not '.svn' in root and not '.git' in root: try: sc = sequential.SequentialFolder(str(root)) if sc.sequence: file_list.append(sc) continue except Exception as e: pass for f in files: file_list.append(os.path.join(root, f)) return file_list
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Returns a list of all files in a folder and its subfolders (only files).
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python
train
msfrank/cifparser
cifparser/valuetree.py
https://github.com/msfrank/cifparser/blob/ecd899ba2e7b990e2cec62b115742d830e7e4384/cifparser/valuetree.py#L228-L246
def get_field(self, path, name): """ Retrieves the value of the field at the specified path. :param path: str or Path instance :param name: :type name: str :return: :raises ValueError: A component of path is a field name. :raises KeyError: A component of path doesn't exist. :raises TypeError: The field name is a component of a path. """ try: value = self.get(path, name) if not isinstance(value, str): raise TypeError() return value except KeyError: raise KeyError()
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Retrieves the value of the field at the specified path. :param path: str or Path instance :param name: :type name: str :return: :raises ValueError: A component of path is a field name. :raises KeyError: A component of path doesn't exist. :raises TypeError: The field name is a component of a path.
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python
train
nickhand/classylss
classylss/__init__.py
https://github.com/nickhand/classylss/blob/b297cb25bc47ffed845470fe1c052346ea96cddd/classylss/__init__.py#L60-L99
def load_ini(filename): """ Read a CLASS ``.ini`` file, returning a dictionary of parameters Parameters ---------- filename : str the name of an existing parameter file to load, or one included as part of the CLASS source Returns ------- dict : the input parameters loaded from file """ # also look in data dir path = _find_file(filename) pars = {} with open(path, 'r') as ff: # loop over lines for lineno, line in enumerate(ff): if not line: continue # skip any commented lines with # if '#' in line: line = line[line.index('#')+1:] # must have an equals sign to be valid if "=" not in line: continue # extract key and value pairs fields = line.split("=") if len(fields) != 2: import warnings warnings.warn("skipping line number %d: '%s'" %(lineno,line)) continue pars[fields[0].strip()] = fields[1].strip() return pars
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Read a CLASS ``.ini`` file, returning a dictionary of parameters Parameters ---------- filename : str the name of an existing parameter file to load, or one included as part of the CLASS source Returns ------- dict : the input parameters loaded from file
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python
train
iotile/coretools
iotilebuild/iotile/build/config/scons-local-3.0.1/SCons/Taskmaster.py
https://github.com/iotile/coretools/blob/2d794f5f1346b841b0dcd16c9d284e9bf2f3c6ec/iotilebuild/iotile/build/config/scons-local-3.0.1/SCons/Taskmaster.py#L271-L285
def executed_without_callbacks(self): """ Called when the task has been successfully executed and the Taskmaster instance doesn't want to call the Node's callback methods. """ T = self.tm.trace if T: T.write(self.trace_message('Task.executed_without_callbacks()', self.node)) for t in self.targets: if t.get_state() == NODE_EXECUTING: for side_effect in t.side_effects: side_effect.set_state(NODE_NO_STATE) t.set_state(NODE_EXECUTED)
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Called when the task has been successfully executed and the Taskmaster instance doesn't want to call the Node's callback methods.
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python
train
Azure/azure-cosmos-python
azure/cosmos/auth.py
https://github.com/Azure/azure-cosmos-python/blob/dd01b3c5d308c6da83cfcaa0ab7083351a476353/azure/cosmos/auth.py#L69-L114
def __GetAuthorizationTokenUsingMasterKey(verb, resource_id_or_fullname, resource_type, headers, master_key): """Gets the authorization token using `master_key. :param str verb: :param str resource_id_or_fullname: :param str resource_type: :param dict headers: :param str master_key: :return: The authorization token. :rtype: dict """ # decodes the master key which is encoded in base64 key = base64.b64decode(master_key) # Skipping lower casing of resource_id_or_fullname since it may now contain "ID" of the resource as part of the fullname text = '{verb}\n{resource_type}\n{resource_id_or_fullname}\n{x_date}\n{http_date}\n'.format( verb=(verb.lower() or ''), resource_type=(resource_type.lower() or ''), resource_id_or_fullname=(resource_id_or_fullname or ''), x_date=headers.get(http_constants.HttpHeaders.XDate, '').lower(), http_date=headers.get(http_constants.HttpHeaders.HttpDate, '').lower()) if six.PY2: body = text.decode('utf-8') digest = hmac.new(key, body, sha256).digest() signature = digest.encode('base64') else: # python 3 support body = text.encode('utf-8') digest = hmac.new(key, body, sha256).digest() signature = base64.encodebytes(digest).decode('utf-8') master_token = 'master' token_version = '1.0' return 'type={type}&ver={ver}&sig={sig}'.format(type=master_token, ver=token_version, sig=signature[:-1])
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Gets the authorization token using `master_key. :param str verb: :param str resource_id_or_fullname: :param str resource_type: :param dict headers: :param str master_key: :return: The authorization token. :rtype: dict
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python
train
rsmuc/health_monitoring_plugins
health_monitoring_plugins/check_snmp_ilo4/check_snmp_ilo4.py
https://github.com/rsmuc/health_monitoring_plugins/blob/7ac29dfb9fe46c055b018cb72ad0d7d8065589b9/health_monitoring_plugins/check_snmp_ilo4/check_snmp_ilo4.py#L350-L369
def check_power_redundancy(): """ Check if the power supplies are redundant The check is skipped if --noPowerRedundancy is set """ # skip the check if --noPowerRedundancy is set if power_redundancy_flag: # walk the data ps_redundant_data = walk_data(sess, oid_ps_redundant, helper)[0] for x, state in enumerate(ps_redundant_data, 1): # human readable status hr_status = ps_redundant_state[int(state)] if hr_status != "redundant": # if the power supply is not redundant, we will set a critical status and add it to the summary helper.add_summary('Power supply %s: %s' % (x, hr_status)) helper.status(critical) # we always want to see the redundancy status in the long output helper.add_long_output('Power supply %s: %s' % (x, hr_status)) helper.add_long_output('')
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Check if the power supplies are redundant The check is skipped if --noPowerRedundancy is set
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python
train
pywbem/pywbem
attic/cimxml_parse.py
https://github.com/pywbem/pywbem/blob/e54ecb82c2211e289a268567443d60fdd489f1e4/attic/cimxml_parse.py#L319-L334
def parse_namespace(parser, event, node): #pylint: disable=unused-argument """Parse the CIM/XML NAMESPACE element and return the value of the CIMName attribute <!ELEMENT NAMESPACE EMPTY> <!ATTLIST NAMESPACE %CIMName; """ name = _get_required_attribute(node, 'NAME') (next_event, next_node) = six.next(parser) if not _is_end(next_event, next_node, 'NAMESPACE'): raise ParseError('Expecting end NAMESPACE') return name
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Parse the CIM/XML NAMESPACE element and return the value of the CIMName attribute <!ELEMENT NAMESPACE EMPTY> <!ATTLIST NAMESPACE %CIMName;
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python
train
assamite/creamas
creamas/util.py
https://github.com/assamite/creamas/blob/54dc3e31c97a3f938e58272f8ab80b6bcafeff58/creamas/util.py#L173-L190
def split_addrs(addrs): '''Split addresses into dictionaries by hosts and ports. :param list addrs: A list of addresses. :returns: A dictionary of dictionaries, where ``dict[HOST][PORT]`` holds a list of all agent addresses in that environment. ''' splitted = {} for addr in addrs: host, port, _ = _addr_key(addr) if host not in splitted: splitted[host] = {} if port not in splitted[host]: splitted[host][port] = [] splitted[host][port].append(addr) return splitted
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Split addresses into dictionaries by hosts and ports. :param list addrs: A list of addresses. :returns: A dictionary of dictionaries, where ``dict[HOST][PORT]`` holds a list of all agent addresses in that environment.
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python
train
fabioz/PyDev.Debugger
_pydevd_bundle/pydevd_api.py
https://github.com/fabioz/PyDev.Debugger/blob/ed9c4307662a5593b8a7f1f3389ecd0e79b8c503/_pydevd_bundle/pydevd_api.py#L513-L518
def request_get_variable_json(self, py_db, request, thread_id): ''' :param VariablesRequest request: ''' py_db.post_method_as_internal_command( thread_id, internal_get_variable_json, request)
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:param VariablesRequest request:
[ ":", "param", "VariablesRequest", "request", ":" ]
python
train
frispete/keyrings.cryptfile
keyrings/cryptfile/convert.py
https://github.com/frispete/keyrings.cryptfile/blob/cfa80d4848a5c3c0aeee41a954b2b120c80e69b2/keyrings/cryptfile/convert.py#L132-L142
def main(argv=None): """Main command line interface.""" if argv is None: argv = sys.argv[1:] cli = CommandLineTool() try: return cli.run(argv) except KeyboardInterrupt: print('Canceled') return 3
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Main command line interface.
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python
test
yyuu/botornado
boto/sqs/queue.py
https://github.com/yyuu/botornado/blob/fffb056f5ff2324d1d5c1304014cfb1d899f602e/boto/sqs/queue.py#L287-L302
def count_slow(self, page_size=10, vtimeout=10): """ Deprecated. This is the old 'count' method that actually counts the messages by reading them all. This gives an accurate count but is very slow for queues with non-trivial number of messasges. Instead, use get_attribute('ApproximateNumberOfMessages') to take advantage of the new SQS capability. This is retained only for the unit tests. """ n = 0 l = self.get_messages(page_size, vtimeout) while l: for m in l: n += 1 l = self.get_messages(page_size, vtimeout) return n
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Deprecated. This is the old 'count' method that actually counts the messages by reading them all. This gives an accurate count but is very slow for queues with non-trivial number of messasges. Instead, use get_attribute('ApproximateNumberOfMessages') to take advantage of the new SQS capability. This is retained only for the unit tests.
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python
train
mlouielu/twstock
twstock/legacy.py
https://github.com/mlouielu/twstock/blob/cddddcc084d2d00497d591ab3059e3205b755825/twstock/legacy.py#L119-L122
def best_buy_3(self): """三日均價由下往上 """ return self.data.continuous(self.data.moving_average(self.data.price, 3)) == 1
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三日均價由下往上
[ "三日均價由下往上" ]
python
train
AtteqCom/zsl
src/zsl/resource/json_server_resource.py
https://github.com/AtteqCom/zsl/blob/ab51a96da1780ff642912396d4b85bdcb72560c1/src/zsl/resource/json_server_resource.py#L47-L71
def _get_link_pages(page, per_page, count, page_url): # type: (int, int, int, str) -> Dict[str, str] """Create link header for page metadata. :param page: current page :param per_page: page limit :param count: count of all resources :param page_url: url for resources :return: dictionary with name of the link as key and its url as value """ current_page = _page_arg(page) links = {} end = page * per_page if page > 1: links['prev'] = page_url.replace(current_page, _page_arg(page - 1)) if end < count: links['next'] = page_url.replace(current_page, _page_arg(page + 1)) if per_page < count: links['first'] = page_url.replace(current_page, _page_arg(1)) links['last'] = page_url.replace(current_page, _page_arg((count + per_page - 1) // per_page)) return links
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Create link header for page metadata. :param page: current page :param per_page: page limit :param count: count of all resources :param page_url: url for resources :return: dictionary with name of the link as key and its url as value
[ "Create", "link", "header", "for", "page", "metadata", "." ]
python
train
google/grr
grr/core/grr_response_core/lib/rdfvalues/paths.py
https://github.com/google/grr/blob/5cef4e8e2f0d5df43ea4877e9c798e0bf60bfe74/grr/core/grr_response_core/lib/rdfvalues/paths.py#L298-L314
def InterpolateGrouping(self, pattern): """Interpolate inline globbing groups.""" components = [] offset = 0 for match in GROUPING_PATTERN.finditer(pattern): components.append([pattern[offset:match.start()]]) # Expand the attribute into the set of possibilities: alternatives = match.group(1).split(",") components.append(set(alternatives)) offset = match.end() components.append([pattern[offset:]]) # Now calculate the cartesian products of all these sets to form all # strings. for vector in itertools.product(*components): yield u"".join(vector)
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Interpolate inline globbing groups.
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python
train
PmagPy/PmagPy
dev_setup.py
https://github.com/PmagPy/PmagPy/blob/c7984f8809bf40fe112e53dcc311a33293b62d0b/dev_setup.py#L56-L96
def unix_install(): """ Edits or creates .bashrc, .bash_profile, and .profile files in the users HOME directory in order to add your current directory (hopefully your PmagPy directory) and assorted lower directories in the PmagPy/programs directory to your PATH environment variable. It also adds the PmagPy and the PmagPy/programs directories to PYTHONPATH. """ PmagPyDir = os.path.abspath(".") COMMAND = """\n for d in %s/programs/*/ "%s/programs/"; do case ":$PATH:" in *":$d:"*) :;; # already there *) PMAGPATHS="$PMAGPATHS:$d";; # or PATH="$PATH:$new_entry" esac done export PYTHONPATH="$PYTHONPATH:%s:%s/programs/" export PATH="$PATH:$PMAGPATHS" """ % (PmagPyDir, PmagPyDir, PmagPyDir, PmagPyDir) frc_path = os.path.join( os.environ["HOME"], ".bashrc") # not recommended, but hey it freaking works fbprof_path = os.path.join(os.environ["HOME"], ".bash_profile") fprof_path = os.path.join(os.environ["HOME"], ".profile") all_paths = [frc_path, fbprof_path, fprof_path] for f_path in all_paths: open_type = 'a' if not os.path.isfile(f_path): open_type = 'w+' fout = open(f_path, open_type) fout.write(COMMAND) fout.close() else: fin = open(f_path, 'r') current_f = fin.read() fin.close() if COMMAND not in current_f: fout = open(f_path, open_type) fout.write(COMMAND) fout.close() print("Install complete. Please restart the shell to complete install.\nIf you are seeing strange or non-existent paths in your PATH or PYTHONPATH variable please manually check your .bashrc, .bash_profile, and .profile or attempt to reinstall.")
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Edits or creates .bashrc, .bash_profile, and .profile files in the users HOME directory in order to add your current directory (hopefully your PmagPy directory) and assorted lower directories in the PmagPy/programs directory to your PATH environment variable. It also adds the PmagPy and the PmagPy/programs directories to PYTHONPATH.
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python
train
lucasmaystre/choix
choix/mm.py
https://github.com/lucasmaystre/choix/blob/05a57a10bb707338113a9d91601ca528ead7a881/choix/mm.py#L198-L252
def choicerank( digraph, traffic_in, traffic_out, weight=None, initial_params=None, alpha=1.0, max_iter=10000, tol=1e-8): """Compute the MAP estimate of a network choice model's parameters. This function computes the maximum-a-posteriori (MAP) estimate of model parameters given a network structure and node-level traffic data (see :ref:`data-network`), using the ChoiceRank algorithm [MG17]_, [KTVV15]_. The nodes are assumed to be labeled using consecutive integers starting from 0. Parameters ---------- digraph : networkx.DiGraph Directed graph representing the network. traffic_in : array_like Number of arrivals at each node. traffic_out : array_like Number of departures at each node. weight : str, optional The edge attribute that holds the numerical value used for the edge weight. If None (default) then all edge weights are 1. initial_params : array_like, optional Parameters used to initialize the iterative procedure. alpha : float, optional Regularization parameter. max_iter : int, optional Maximum number of iterations allowed. tol : float, optional Maximum L1-norm of the difference between successive iterates to declare convergence. Returns ------- params : numpy.ndarray The MAP estimate of model parameters. Raises ------ ImportError If the NetworkX library cannot be imported. """ import networkx as nx # Compute the (sparse) adjacency matrix. n_items = len(digraph) nodes = np.arange(n_items) adj = nx.to_scipy_sparse_matrix(digraph, nodelist=nodes, weight=weight) adj_t = adj.T.tocsr() # Process the data into a standard form. traffic_in = np.asarray(traffic_in) traffic_out = np.asarray(traffic_out) data = (adj, adj_t, traffic_in, traffic_out) return _mm( n_items, data, initial_params, alpha, max_iter, tol, _choicerank)
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Compute the MAP estimate of a network choice model's parameters. This function computes the maximum-a-posteriori (MAP) estimate of model parameters given a network structure and node-level traffic data (see :ref:`data-network`), using the ChoiceRank algorithm [MG17]_, [KTVV15]_. The nodes are assumed to be labeled using consecutive integers starting from 0. Parameters ---------- digraph : networkx.DiGraph Directed graph representing the network. traffic_in : array_like Number of arrivals at each node. traffic_out : array_like Number of departures at each node. weight : str, optional The edge attribute that holds the numerical value used for the edge weight. If None (default) then all edge weights are 1. initial_params : array_like, optional Parameters used to initialize the iterative procedure. alpha : float, optional Regularization parameter. max_iter : int, optional Maximum number of iterations allowed. tol : float, optional Maximum L1-norm of the difference between successive iterates to declare convergence. Returns ------- params : numpy.ndarray The MAP estimate of model parameters. Raises ------ ImportError If the NetworkX library cannot be imported.
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python
train
openstack/horizon
openstack_dashboard/utils/config.py
https://github.com/openstack/horizon/blob/5601ea9477323e599d9b766fcac1f8be742935b2/openstack_dashboard/utils/config.py#L26-L38
def load_config(files=None, root_path=None, local_path=None): """Load the configuration from specified files.""" config = cfg.ConfigOpts() config.register_opts([ cfg.Opt('root_path', default=root_path), cfg.Opt('local_path', default=local_path), ]) # XXX register actual config groups here # theme_group = config_theme.register_config(config) if files is not None: config(args=[], default_config_files=files) return config
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Load the configuration from specified files.
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python
train
peerplays-network/python-peerplays
peerplays/peerplays.py
https://github.com/peerplays-network/python-peerplays/blob/188f04238e7e21d5f73e9b01099eea44289ef6b7/peerplays/peerplays.py#L1533-L1556
def bet_cancel(self, bet_to_cancel, account=None, **kwargs): """ Cancel a bet :param str bet_to_cancel: The identifier that identifies the bet to cancel :param str account: (optional) the account that owns the bet (defaults to ``default_account``) """ if not account: if "default_account" in self.config: account = self.config["default_account"] if not account: raise ValueError("You need to provide an account") account = Account(account) bet = Bet(bet_to_cancel) op = operations.Bet_cancel( **{ "fee": {"amount": 0, "asset_id": "1.3.0"}, "bettor_id": account["id"], "bet_to_cancel": bet["id"], "prefix": self.prefix, } ) return self.finalizeOp(op, account["name"], "active", **kwargs)
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Cancel a bet :param str bet_to_cancel: The identifier that identifies the bet to cancel :param str account: (optional) the account that owns the bet (defaults to ``default_account``)
[ "Cancel", "a", "bet" ]
python
train
JdeRobot/base
src/drivers/MAVLinkServer/MAVProxy/pymavlink/tools/mavplayback.py
https://github.com/JdeRobot/base/blob/303b18992785b2fe802212f2d758a60873007f1f/src/drivers/MAVLinkServer/MAVProxy/pymavlink/tools/mavplayback.py#L100-L109
def button(self, name, filename, command): '''add a button''' try: img = LoadImage(filename) b = Tkinter.Button(self.frame, image=img, command=command) b.image = img except Exception: b = Tkinter.Button(self.frame, text=filename, command=command) b.pack(side=Tkinter.LEFT) self.buttons[name] = b
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add a button
[ "add", "a", "button" ]
python
train
twitterdev/search-tweets-python
searchtweets/utils.py
https://github.com/twitterdev/search-tweets-python/blob/7875afb4f3ee125a9fdcf2e50b5ae761da5f46b5/searchtweets/utils.py#L60-L84
def merge_dicts(*dicts): """ Helpful function to merge / combine dictionaries and return a new dictionary. Args: dicts (list or Iterable): iterable set of dictionaries for merging. Returns: dict: dict with all keys from the passed list. Later dictionaries in the sequence will override duplicate keys from previous dictionaries. Example: >>> from searchtweets.utils import merge_dicts >>> d1 = {"rule": "something has:geo"} >>> d2 = {"maxResults": 1000} >>> merge_dicts(*[d1, d2]) {"maxResults": 1000, "rule": "something has:geo"} """ def _merge_dicts(dict1, dict2): merged = dict1.copy() merged.update(dict2) return merged return reduce(_merge_dicts, dicts)
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Helpful function to merge / combine dictionaries and return a new dictionary. Args: dicts (list or Iterable): iterable set of dictionaries for merging. Returns: dict: dict with all keys from the passed list. Later dictionaries in the sequence will override duplicate keys from previous dictionaries. Example: >>> from searchtweets.utils import merge_dicts >>> d1 = {"rule": "something has:geo"} >>> d2 = {"maxResults": 1000} >>> merge_dicts(*[d1, d2]) {"maxResults": 1000, "rule": "something has:geo"}
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python
train
SBRG/ssbio
ssbio/biopython/Bio/Struct/Protein.py
https://github.com/SBRG/ssbio/blob/e9449e64ffc1a1f5ad07e5849aa12a650095f8a2/ssbio/biopython/Bio/Struct/Protein.py#L92-L131
def check_missing_atoms(self, template=None, ha_only=True): """ Checks for missing atoms based on a template. Default: Searches for missing heavy atoms (not Hydrogen) based on Bio.Struct.protein_residues Arguments: - template, dictionary, keys are residue names, values list of atom names. - ha_only, boolean, default True, restrict check to heavy atoms. Returns a dictionary of tuples with the missing atoms per residue. """ missing_atoms = {} if not template: import protein_residues template = protein_residues.normal # Don't care for terminal residues here.. for residue in self.get_residues(): if not template.has_key(residue.resname): # Maybe add this as a warning instead of exception? raise ValueError('Residue name (%s) not in the template' %residue.resname ) if ha_only: heavy_atoms = [ atom for atom in template[residue.resname]['atoms'].keys() if atom[0] != 'H' and not (atom[0].isdigit() and atom[1] == 'H')] reference_set = set(heavy_atoms) else: reference_set = set(template[residue.resname]['atoms'].keys()) structure_set = set(residue.child_dict.keys()) diff = reference_set.difference(structure_set) if diff: residue_uniq_id = (residue.parent.id, residue.resname, residue.get_id()[1]) # Chain, Name, Number missing_atoms[residue_uniq_id] = list(diff) return missing_atoms
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Checks for missing atoms based on a template. Default: Searches for missing heavy atoms (not Hydrogen) based on Bio.Struct.protein_residues Arguments: - template, dictionary, keys are residue names, values list of atom names. - ha_only, boolean, default True, restrict check to heavy atoms. Returns a dictionary of tuples with the missing atoms per residue.
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python
train
SeabornGames/RequestClient
seaborn/request_client/api_call.py
https://github.com/SeabornGames/RequestClient/blob/21aeb951ddfdb6ee453ad0edc896ff224e06425d/seaborn/request_client/api_call.py#L568-L586
def time_report_item(self, label, message=None): """ This will return a dictionary for the given message based on timestamps :param label: :param message: str of the message to find the timestamp :return: dict of times """ next_ = TIMESTAMPS_ORDER[TIMESTAMPS_ORDER.index(label) + 1] while next_ not in self._timestamps: next_ = TIMESTAMPS_ORDER[TIMESTAMPS_ORDER.index(next_) + 1] assert label in TIMESTAMPS_ORDER start = self._timestamps[label] - self._timestamps[TIMESTAMPS_ORDER[0]] end = self._timestamps[next_] - self._timestamps[TIMESTAMPS_ORDER[0]] return {'Message': message, 'Start': start, 'End': end, 'Sum': end - start, 'Count': 1}
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This will return a dictionary for the given message based on timestamps :param label: :param message: str of the message to find the timestamp :return: dict of times
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python
train
limodou/uliweb
uliweb/core/SimpleFrame.py
https://github.com/limodou/uliweb/blob/34472f25e4bc0b954a35346672f94e84ef18b076/uliweb/core/SimpleFrame.py#L405-L424
def get_app_dir(app): """ Get an app's directory """ path = __app_dirs__.get(app) if path is not None: return path else: p = app.split('.') try: path = pkg.resource_filename(p[0], '') except ImportError as e: log.error("Can't import app %s" % app) log.exception(e) path = '' if len(p) > 1: path = os.path.join(path, *p[1:]) __app_dirs__[app] = path return path
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Get an app's directory
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python
train
noxdafox/vminspect
vminspect/timeline.py
https://github.com/noxdafox/vminspect/blob/e685282564877e2d1950f1e09b292f4f4db1dbcd/vminspect/timeline.py#L216-L233
def generate_timeline(usnjrnl, filesystem_content): """Aggregates the data collected from the USN journal and the filesystem content. """ journal_content = defaultdict(list) for event in usnjrnl: journal_content[event.inode].append(event) for event in usnjrnl: try: dirent = lookup_dirent(event, filesystem_content, journal_content) yield UsnJrnlEvent( dirent.inode, dirent.path, dirent.size, dirent.allocated, event.timestamp, event.changes, event.attributes) except LookupError as error: LOGGER.debug(error)
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Aggregates the data collected from the USN journal and the filesystem content.
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python
train
barrust/mediawiki
mediawiki/mediawiki.py
https://github.com/barrust/mediawiki/blob/292e0be6c752409062dceed325d74839caf16a9b/mediawiki/mediawiki.py#L543-L572
def prefixsearch(self, prefix, results=10): """ Perform a prefix search using the provided prefix string Args: prefix (str): Prefix string to use for search results (int): Number of pages with the prefix to return Returns: list: List of page titles Note: **Per the documentation:** "The purpose of this module is \ similar to action=opensearch: to take user input and provide \ the best-matching titles. Depending on the search engine \ backend, this might include typo correction, redirect \ avoidance, or other heuristics." """ self._check_query(prefix, "Prefix must be specified") query_params = { "list": "prefixsearch", "pssearch": prefix, "pslimit": ("max" if results > 500 else results), "psnamespace": 0, "psoffset": 0, # parameterize to skip to later in the list? } raw_results = self.wiki_request(query_params) self._check_error_response(raw_results, prefix) return [rec["title"] for rec in raw_results["query"]["prefixsearch"]]
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Perform a prefix search using the provided prefix string Args: prefix (str): Prefix string to use for search results (int): Number of pages with the prefix to return Returns: list: List of page titles Note: **Per the documentation:** "The purpose of this module is \ similar to action=opensearch: to take user input and provide \ the best-matching titles. Depending on the search engine \ backend, this might include typo correction, redirect \ avoidance, or other heuristics."
[ "Perform", "a", "prefix", "search", "using", "the", "provided", "prefix", "string" ]
python
train
ArchiveTeam/wpull
wpull/document/sitemap.py
https://github.com/ArchiveTeam/wpull/blob/ddf051aa3322479325ba20aa778cb2cb97606bf5/wpull/document/sitemap.py#L37-L41
def is_response(cls, response): '''Return whether the document is likely to be a Sitemap.''' if response.body: if cls.is_file(response.body): return True
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Return whether the document is likely to be a Sitemap.
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python
train
jasonrbriggs/stomp.py
stomp/protocol.py
https://github.com/jasonrbriggs/stomp.py/blob/643843c5fbf25fd24339dd0e69a9411c3d8b94c7/stomp/protocol.py#L68-L85
def begin(self, transaction=None, headers=None, **keyword_headers): """ Begin a transaction. :param str transaction: the identifier for the transaction (optional - if not specified a unique transaction id will be generated) :param dict headers: a map of any additional headers the broker requires :param keyword_headers: any additional headers the broker requires :return: the transaction id :rtype: str """ headers = utils.merge_headers([headers, keyword_headers]) if not transaction: transaction = utils.get_uuid() headers[HDR_TRANSACTION] = transaction self.send_frame(CMD_BEGIN, headers) return transaction
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Begin a transaction. :param str transaction: the identifier for the transaction (optional - if not specified a unique transaction id will be generated) :param dict headers: a map of any additional headers the broker requires :param keyword_headers: any additional headers the broker requires :return: the transaction id :rtype: str
[ "Begin", "a", "transaction", "." ]
python
train
evhub/coconut
coconut/compiler/util.py
https://github.com/evhub/coconut/blob/ff97177344e7604e89a0a98a977a87ed2a56fc6d/coconut/compiler/util.py#L65-L100
def evaluate_tokens(tokens): """Evaluate the given tokens in the computation graph.""" if isinstance(tokens, str): return tokens elif isinstance(tokens, ParseResults): # evaluate the list portion of the ParseResults toklist, name, asList, modal = tokens.__getnewargs__() new_toklist = [evaluate_tokens(toks) for toks in toklist] new_tokens = ParseResults(new_toklist, name, asList, modal) # evaluate the dictionary portion of the ParseResults new_tokdict = {} for name, occurrences in tokens._ParseResults__tokdict.items(): new_occurences = [] for value, position in occurrences: if isinstance(value, ParseResults) and value._ParseResults__toklist == toklist: new_value = new_tokens else: try: new_value = new_toklist[toklist.index(value)] except ValueError: complain(lambda: CoconutInternalException("inefficient reevaluation of tokens: {} not in {}".format( value, toklist, ))) new_value = evaluate_tokens(value) new_occurences.append(_ParseResultsWithOffset(new_value, position)) new_tokdict[name] = occurrences new_tokens._ParseResults__accumNames.update(tokens._ParseResults__accumNames) new_tokens._ParseResults__tokdict.update(new_tokdict) return new_tokens elif isinstance(tokens, ComputationNode): return tokens.evaluate() elif isinstance(tokens, (list, tuple)): return [evaluate_tokens(inner_toks) for inner_toks in tokens] else: raise CoconutInternalException("invalid computation graph tokens", tokens)
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Evaluate the given tokens in the computation graph.
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python
train
wbond/asn1crypto
asn1crypto/core.py
https://github.com/wbond/asn1crypto/blob/ecda20176f55d37021cbca1f6da9083a8e491197/asn1crypto/core.py#L3221-L3234
def _is_mutated(self): """ :return: A boolean - if the sequence or any children (recursively) have been mutated """ mutated = self._mutated if self.children is not None: for child in self.children: if isinstance(child, Sequence) or isinstance(child, SequenceOf): mutated = mutated or child._is_mutated() return mutated
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:return: A boolean - if the sequence or any children (recursively) have been mutated
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python
train
globocom/GloboNetworkAPI-client-python
networkapiclient/ApiV4VirtualInterface.py
https://github.com/globocom/GloboNetworkAPI-client-python/blob/cf34f913da48d9abbf750114f5d2ac4b2dde137d/networkapiclient/ApiV4VirtualInterface.py#L89-L99
def create(self, virtual_interfaces): """ Method to create Virtual Interfaces :param Virtual Interfaces: List containing Virtual Interfaces desired to be created on database :return: None """ data = {'virtual_interfaces': virtual_interfaces} return super(ApiV4VirtualInterface, self).post\ ('api/v4/virtual-interface/', data)
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Method to create Virtual Interfaces :param Virtual Interfaces: List containing Virtual Interfaces desired to be created on database :return: None
[ "Method", "to", "create", "Virtual", "Interfaces" ]
python
train
nion-software/nionswift
nion/swift/Facade.py
https://github.com/nion-software/nionswift/blob/d43693eaf057b8683b9638e575000f055fede452/nion/swift/Facade.py#L1156-L1172
def set_metadata_value(self, key: str, value: typing.Any) -> None: """Set the metadata value for the given key. There are a set of predefined keys that, when used, will be type checked and be interoperable with other applications. Please consult reference documentation for valid keys. If using a custom key, we recommend structuring your keys in the '<group>.<attribute>' format followed by the predefined keys. e.g. 'session.instrument' or 'camera.binning'. Also note that some predefined keys map to the metadata ``dict`` but others do not. For this reason, prefer using the ``metadata_value`` methods over directly accessing ``metadata``. .. versionadded:: 1.0 Scriptable: Yes """ self._data_item.set_metadata_value(key, value)
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Set the metadata value for the given key. There are a set of predefined keys that, when used, will be type checked and be interoperable with other applications. Please consult reference documentation for valid keys. If using a custom key, we recommend structuring your keys in the '<group>.<attribute>' format followed by the predefined keys. e.g. 'session.instrument' or 'camera.binning'. Also note that some predefined keys map to the metadata ``dict`` but others do not. For this reason, prefer using the ``metadata_value`` methods over directly accessing ``metadata``. .. versionadded:: 1.0 Scriptable: Yes
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python
train
spacetelescope/drizzlepac
drizzlepac/util.py
https://github.com/spacetelescope/drizzlepac/blob/15bec3c929a6a869d9e71b9398ced43ede0620f1/drizzlepac/util.py#L680-L743
def getDefaultConfigObj(taskname,configObj,input_dict={},loadOnly=True): """ Return default configObj instance for task updated with user-specified values from input_dict. Parameters ---------- taskname : string Name of task to load into TEAL configObj : string The valid values for 'configObj' would be:: None - loads last saved user .cfg file 'defaults' - loads task default .cfg file name of .cfg file (string)- loads user-specified .cfg file input_dict : dict Set of parameters and values specified by user to be different from what gets loaded in from the .cfg file for the task loadOnly : bool Setting 'loadOnly' to False causes the TEAL GUI to start allowing the user to edit the values further and then run the task if desired. """ if configObj is None: # Start by grabbing the default values without using the GUI # This insures that all subsequent use of the configObj includes # all parameters and their last saved values configObj = teal.load(taskname) elif isinstance(configObj,str): if configObj.lower().strip() == 'defaults': # Load task default .cfg file with all default values configObj = teal.load(taskname,defaults=True) # define default filename for configObj configObj.filename = taskname.lower()+'.cfg' else: # Load user-specified .cfg file with its special default values # we need to call 'fileutil.osfn()' to insure all environment # variables specified by the user in the configObj filename are # expanded to the full path configObj = teal.load(fileutil.osfn(configObj)) # merge in the user values for this run # this, though, does not save the results for use later if input_dict not in [None,{}]:# and configObj not in [None, {}]: # check to see whether any input parameters are unexpected. # Any unexpected parameters provided on input should be reported and # the code should stop validateUserPars(configObj,input_dict) # If everything looks good, merge user inputs with configObj and continue cfgpars.mergeConfigObj(configObj, input_dict) # Update the input .cfg file with the updated parameter values #configObj.filename = os.path.join(cfgpars.getAppDir(),os.path.basename(configObj.filename)) #configObj.write() if not loadOnly: # We want to run the GUI AFTER merging in any parameters # specified by the user on the command-line and provided in # input_dict configObj = teal.teal(configObj,loadOnly=False) return configObj
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python
train
albertz/py_better_exchook
better_exchook.py
https://github.com/albertz/py_better_exchook/blob/3d524a027d7fc4e83e47e39a1978849561da69b3/better_exchook.py#L219-L260
def simple_debug_shell(globals, locals): """ :param dict[str] globals: :param dict[str] locals: :return: nothing """ try: import readline except ImportError: pass # ignore compile_string_fn = "<simple_debug_shell input>" while True: try: s = raw_input("> ") except (KeyboardInterrupt, EOFError): print("breaked debug shell: " + sys.exc_info()[0].__name__) break if s.strip() == "": continue try: c = compile(s, compile_string_fn, "single") except Exception as e: print("%s : %s in %r" % (e.__class__.__name__, str(e), s)) else: set_linecache(compile_string_fn, s) # noinspection PyBroadException try: ret = eval(c, globals, locals) except (KeyboardInterrupt, SystemExit): print("debug shell exit: " + sys.exc_info()[0].__name__) break except Exception: print("Error executing %r" % s) better_exchook(*sys.exc_info(), autodebugshell=False) else: # noinspection PyBroadException try: if ret is not None: print(ret) except Exception: print("Error printing return value of %r" % s) better_exchook(*sys.exc_info(), autodebugshell=False)
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:param dict[str] globals: :param dict[str] locals: :return: nothing
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python
train
shaypal5/pdutil
pdutil/display/display.py
https://github.com/shaypal5/pdutil/blob/231059634643af2558d22070f89767410978cf56/pdutil/display/display.py#L9-L42
def df_string(df, percentage_columns=(), format_map=None, **kwargs): """Return a nicely formatted string for the given dataframe. Arguments --------- df : pandas.DataFrame A dataframe object. percentage_columns : iterable A list of cloumn names to be displayed with a percentage sign. Returns ------- str A nicely formatted string for the given dataframe. Example ------- >>> import pandas as pd >>> df = pd.DataFrame([[8,'a'],[5,'b']],[1,2],['num', 'char']) >>> print(df_string(df)) num char 1 8 a 2 5 b """ formatters_map = {} for col, dtype in df.dtypes.iteritems(): if col in percentage_columns: formatters_map[col] = '{:,.2f} %'.format elif dtype == 'float64': formatters_map[col] = '{:,.2f}'.format if format_map: for key in format_map: formatters_map[key] = format_map[key] return df.to_string(formatters=formatters_map, **kwargs)
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Return a nicely formatted string for the given dataframe. Arguments --------- df : pandas.DataFrame A dataframe object. percentage_columns : iterable A list of cloumn names to be displayed with a percentage sign. Returns ------- str A nicely formatted string for the given dataframe. Example ------- >>> import pandas as pd >>> df = pd.DataFrame([[8,'a'],[5,'b']],[1,2],['num', 'char']) >>> print(df_string(df)) num char 1 8 a 2 5 b
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python
train
sethmlarson/virtualbox-python
virtualbox/library_ext/virtual_system_description.py
https://github.com/sethmlarson/virtualbox-python/blob/706c8e3f6e3aee17eb06458e73cbb4bc2d37878b/virtualbox/library_ext/virtual_system_description.py#L18-L48
def set_final_value(self, description_type, value): """Set the value for the given description type. in description_type type :class:`VirtualSystemDescriptionType` in value type str """ types, _, _, vbox_values, extra_config = self.get_description() # find offset to description type for offset, t in enumerate(types): if t == description_type: break else: raise Exception("Failed to find type for %s" % description_type) enabled = [True] * len(types) vbox_values = list(vbox_values) extra_config = list(extra_config) if isinstance(value, basestring): final_value = value elif isinstance(value, Enum): final_value = str(value._value) elif isinstance(value, int): final_value = str(value) else: raise ValueError("Incorrect value type.") vbox_values[offset] = final_value self.set_final_values(enabled, vbox_values, extra_config)
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Set the value for the given description type. in description_type type :class:`VirtualSystemDescriptionType` in value type str
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python
train
fboender/ansible-cmdb
src/ansiblecmdb/ansible.py
https://github.com/fboender/ansible-cmdb/blob/ebd960ac10684e8c9ec2b12751bba2c4c9504ab7/src/ansiblecmdb/ansible.py#L363-L390
def get_hosts(self): """ Return a list of parsed hosts info, with the limit applied if required. """ limited_hosts = {} if self.limit is not None: # Find hosts and groups of hosts to include for include in self.limit['include']: # Include whole group for hostname in self.hosts_in_group(include): limited_hosts[hostname] = self.hosts[hostname] # Include individual host if include in self.hosts: limited_hosts[include] = self.hosts[include] # Find hosts and groups of hosts to exclude for exclude in self.limit["exclude"]: # Exclude whole group for hostname in self.hosts_in_group(exclude): if hostname in limited_hosts: limited_hosts.pop(hostname) # Exclude individual host if exclude in limited_hosts: limited_hosts.pop(exclude) return limited_hosts else: # Return all hosts return self.hosts
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Return a list of parsed hosts info, with the limit applied if required.
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python
train
anomaly/vishnu
vishnu/cipher.py
https://github.com/anomaly/vishnu/blob/5b3a6a69beedc8554cc506ddfab273760d61dc65/vishnu/cipher.py#L62-L72
def decrypt(self, encrypted): """ Base64 decodes the data and then decrypts using AES. :param encrypted: :return: """ decoded = b64decode(encrypted) init_vec = decoded[:AES.block_size] cipher = AES.new(self._key, AES.MODE_CBC, init_vec) return AESCipher.unpad(cipher.decrypt(decoded[AES.block_size:]))
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Base64 decodes the data and then decrypts using AES. :param encrypted: :return:
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python
train
bpsmith/tia
tia/bbg/bbg_com.py
https://github.com/bpsmith/tia/blob/a7043b6383e557aeea8fc7112bbffd6e36a230e9/tia/bbg/bbg_com.py#L406-L417
def on_security_data_node(self, node): """process a securityData node - FIXME: currently not handling relateDate node """ sid = XmlHelper.get_child_value(node, 'security') farr = node.GetElement('fieldData') dmap = defaultdict(list) for i in range(farr.NumValues): pt = farr.GetValue(i) [dmap[f].append(XmlHelper.get_child_value(pt, f, allow_missing=1)) for f in ['date'] + self.fields] idx = dmap.pop('date') frame = DataFrame(dmap, columns=self.fields, index=idx) frame.index.name = 'date' self.response[sid] = frame
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process a securityData node - FIXME: currently not handling relateDate node
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python
train
manns/pyspread
pyspread/src/gui/_dialogs.py
https://github.com/manns/pyspread/blob/0e2fd44c2e0f06605efc3058c20a43a8c1f9e7e0/pyspread/src/gui/_dialogs.py#L881-L907
def OnApply(self, event): """Event handler for Apply button""" # See if we have valid python try: ast.parse(self.macros) except: # Grab the traceback and print it for the user s = StringIO() e = exc_info() # usr_tb will more than likely be none because ast throws # SytnaxErrorsas occurring outside of the current # execution frame usr_tb = get_user_codeframe(e[2]) or None print_exception(e[0], e[1], usr_tb, None, s) post_command_event(self.parent, self.MacroErrorMsg, err=s.getvalue()) success = False else: self.result_ctrl.SetValue('') post_command_event(self.parent, self.MacroReplaceMsg, macros=self.macros) post_command_event(self.parent, self.MacroExecuteMsg) success = True event.Skip() return success
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Event handler for Apply button
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python
train
wndhydrnt/python-oauth2
oauth2/grant.py
https://github.com/wndhydrnt/python-oauth2/blob/abe3bf5f27bda2ff737cab387b040e2e6e85c2e2/oauth2/grant.py#L56-L66
def json_error_response(error, response, status_code=400): """ Formats an error as a response containing a JSON body. """ msg = {"error": error.error, "error_description": error.explanation} response.status_code = status_code response.add_header("Content-Type", "application/json") response.body = json.dumps(msg) return response
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Formats an error as a response containing a JSON body.
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python
train
saltstack/salt
salt/utils/win_pdh.py
https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/utils/win_pdh.py#L183-L191
def add_to_query(self, query): ''' Add the current path to the query Args: query (obj): The handle to the query to add the counter ''' self.handle = win32pdh.AddCounter(query, self.path)
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Add the current path to the query Args: query (obj): The handle to the query to add the counter
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python
train
fermiPy/fermipy
fermipy/irfs.py
https://github.com/fermiPy/fermipy/blob/9df5e7e3728307fd58c5bba36fd86783c39fbad4/fermipy/irfs.py#L915-L932
def calc_wtd_exp(skydir, ltc, event_class, event_types, egy_bins, cth_bins, fn, nbin=16): """Calculate the effective exposure. Parameters ---------- skydir : `~astropy.coordinates.SkyCoord` ltc : `~fermipy.irfs.LTCube` nbin : int Number of points per decade with which to sample true energy. """ cnts = calc_counts_edisp(skydir, ltc, event_class, event_types, egy_bins, cth_bins, fn, nbin=nbin) flux = fn.flux(egy_bins[:-1], egy_bins[1:]) return cnts / flux[:, None]
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Calculate the effective exposure. Parameters ---------- skydir : `~astropy.coordinates.SkyCoord` ltc : `~fermipy.irfs.LTCube` nbin : int Number of points per decade with which to sample true energy.
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python
train
bovee/Aston
aston/trace/math_chromatograms.py
https://github.com/bovee/Aston/blob/007630fdf074690373d03398fe818260d3d3cf5a/aston/trace/math_chromatograms.py#L6-L11
def molmz(df, noise=10000): """ The mz of the molecular ion. """ d = ((df.values > noise) * df.columns).max(axis=1) return Trace(d, df.index, name='molmz')
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The mz of the molecular ion.
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python
train
nikhilkumarsingh/gnewsclient
gnewsclient/gnewsclient.py
https://github.com/nikhilkumarsingh/gnewsclient/blob/65422f1dee9408f1b51ae6a2ee08ae478432e1d5/gnewsclient/gnewsclient.py#L36-L51
def params_dict(self): """ function to get params dict for HTTP request """ location_code = 'US' language_code = 'en' if len(self.location): location_code = locationMap[process.extractOne(self.location, self.locations)[0]] if len(self.language): language_code = langMap[process.extractOne(self.language, self.languages)[0]] params = { 'hl': language_code, 'gl': location_code, 'ceid': '{}:{}'.format(location_code, language_code) } return params
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function to get params dict for HTTP request
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python
train
ejeschke/ginga
ginga/ImageView.py
https://github.com/ejeschke/ginga/blob/a78c893ec6f37a837de851947e9bb4625c597915/ginga/ImageView.py#L1866-L1881
def offset_to_window(self, off_x, off_y): """Convert data offset to window coordinates. Parameters ---------- off_x, off_y : float or ndarray Data offsets. Returns ------- coord : tuple Offset in window coordinates in the form of ``(x, y)``. """ arr_pts = np.asarray((off_x, off_y)).T return self.tform['cartesian_to_native'].to_(arr_pts).T[:2]
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Convert data offset to window coordinates. Parameters ---------- off_x, off_y : float or ndarray Data offsets. Returns ------- coord : tuple Offset in window coordinates in the form of ``(x, y)``.
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python
train
titusjan/argos
argos/config/configitemdelegate.py
https://github.com/titusjan/argos/blob/20d0a3cae26c36ea789a5d219c02ca7df21279dd/argos/config/configitemdelegate.py#L92-L109
def setModelData(self, editor, model, index): """ Gets data from the editor widget and stores it in the specified model at the item index. Does this by calling getEditorValue of the config tree item at the index. :type editor: QWidget :type model: ConfigTreeModel :type index: QModelIndex Reimplemented from QStyledItemDelegate. """ try: data = editor.getData() except InvalidInputError as ex: logger.warn(ex) else: # The value is set via the model so that signals are emitted logger.debug("ConfigItemDelegate.setModelData: {}".format(data)) model.setData(index, data, Qt.EditRole)
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Gets data from the editor widget and stores it in the specified model at the item index. Does this by calling getEditorValue of the config tree item at the index. :type editor: QWidget :type model: ConfigTreeModel :type index: QModelIndex Reimplemented from QStyledItemDelegate.
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python
train
robotframework/Rammbock
src/Rammbock/rammbock.py
https://github.com/robotframework/Rammbock/blob/c906058d055a6f7c68fe1a6096d78c2e3f642b1c/src/Rammbock/rammbock.py#L164-L172
def embed_seqdiag_sequence(self): """Create a message sequence diagram png file to output folder and embed the image to log file. You need to have seqdiag installed to create the sequence diagram. See http://blockdiag.com/en/seqdiag/ """ test_name = BuiltIn().replace_variables('${TEST NAME}') outputdir = BuiltIn().replace_variables('${OUTPUTDIR}') path = os.path.join(outputdir, test_name + '.seqdiag') SeqdiagGenerator().compile(path, self._message_sequence)
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Create a message sequence diagram png file to output folder and embed the image to log file. You need to have seqdiag installed to create the sequence diagram. See http://blockdiag.com/en/seqdiag/
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python
train
log2timeline/plaso
plaso/cli/tools.py
https://github.com/log2timeline/plaso/blob/9c564698d2da3ffbe23607a3c54c0582ea18a6cc/plaso/cli/tools.py#L180-L190
def _PromptUserForInput(self, input_text): """Prompts user for an input. Args: input_text (str): text used for prompting the user for input. Returns: str: input read from the user. """ self._output_writer.Write('{0:s}: '.format(input_text)) return self._input_reader.Read()
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Prompts user for an input. Args: input_text (str): text used for prompting the user for input. Returns: str: input read from the user.
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python
train
tensorflow/cleverhans
examples/nips17_adversarial_competition/eval_infra/code/eval_lib/classification_results.py
https://github.com/tensorflow/cleverhans/blob/97488e215760547b81afc53f5e5de8ba7da5bd98/examples/nips17_adversarial_competition/eval_infra/code/eval_lib/classification_results.py#L286-L292
def init_from_datastore(self): """Initializes data by reading it from the datastore.""" self._data = {} client = self._datastore_client for entity in client.query_fetch(kind=KIND_CLASSIFICATION_BATCH): class_batch_id = entity.key.flat_path[-1] self.data[class_batch_id] = dict(entity)
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Initializes data by reading it from the datastore.
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python
train
mitsei/dlkit
dlkit/json_/learning/objects.py
https://github.com/mitsei/dlkit/blob/445f968a175d61c8d92c0f617a3c17dc1dc7c584/dlkit/json_/learning/objects.py#L877-L887
def get_courses_metadata(self): """Gets the metadata for the courses. return: (osid.Metadata) - metadata for the courses *compliance: mandatory -- This method must be implemented.* """ # Implemented from template for osid.learning.ActivityForm.get_assets_metadata_template metadata = dict(self._mdata['courses']) metadata.update({'existing_courses_values': self._my_map['courseIds']}) return Metadata(**metadata)
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Gets the metadata for the courses. return: (osid.Metadata) - metadata for the courses *compliance: mandatory -- This method must be implemented.*
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python
train
Erotemic/utool
utool/util_cache.py
https://github.com/Erotemic/utool/blob/3b27e1f4e6e6fb23cd8744af7b7195b57d99e03a/utool/util_cache.py#L906-L910
def get_global_shelf_fpath(appname='default', ensure=False): """ Returns the filepath to the global shelf """ global_cache_dir = get_global_cache_dir(appname, ensure=ensure) shelf_fpath = join(global_cache_dir, meta_util_constants.global_cache_fname) return shelf_fpath
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Returns the filepath to the global shelf
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python
train
hydpy-dev/hydpy
hydpy/cythons/modelutils.py
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/cythons/modelutils.py#L1142-L1168
def cleanlines(self): """Cleaned code lines. Implemented cleanups: * eventually remove method version * remove docstrings * remove comments * remove empty lines * remove line brackes within brackets * replace `modelutils` with nothing * remove complete lines containing `fastaccess` * replace shortcuts with complete references """ code = inspect.getsource(self.func) code = '\n'.join(code.split('"""')[::2]) code = code.replace('modelutils.', '') for (name, shortcut) in zip(self.collectornames, self.collectorshortcuts): code = code.replace('%s.' % shortcut, 'self.%s.' % name) code = self.remove_linebreaks_within_equations(code) lines = code.splitlines() self.remove_imath_operators(lines) lines[0] = 'def %s(self):' % self.funcname lines = [l.split('#')[0] for l in lines] lines = [l for l in lines if 'fastaccess' not in l] lines = [l.rstrip() for l in lines if l.rstrip()] return Lines(*lines)
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python
train
Danielhiversen/pyTibber
tibber/__init__.py
https://github.com/Danielhiversen/pyTibber/blob/114ebc3dd49f6affd93665b0862d4cbdea03e9ef/tibber/__init__.py#L493-L500
def price_unit(self): """Return the price unit.""" currency = self.currency consumption_unit = self.consumption_unit if not currency or not consumption_unit: _LOGGER.error("Could not find price_unit.") return " " return currency + "/" + consumption_unit
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Return the price unit.
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python
valid
saltstack/salt
salt/modules/boto_efs.py
https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/boto_efs.py#L340-L365
def delete_tags(filesystemid, tags, keyid=None, key=None, profile=None, region=None, **kwargs): ''' Deletes the specified tags from a file system. filesystemid (string) - ID of the file system for whose tags will be removed. tags (list[string]) - The tag keys to delete to the file system CLI Example: .. code-block:: bash salt 'my-minion' boto_efs.delete_tags ''' client = _get_conn(key=key, keyid=keyid, profile=profile, region=region) client.delete_tags(FileSystemId=filesystemid, Tags=tags)
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Deletes the specified tags from a file system. filesystemid (string) - ID of the file system for whose tags will be removed. tags (list[string]) - The tag keys to delete to the file system CLI Example: .. code-block:: bash salt 'my-minion' boto_efs.delete_tags
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python
train
IBMStreams/pypi.streamsx
streamsx/rest_primitives.py
https://github.com/IBMStreams/pypi.streamsx/blob/abd67b4757120f6f805787fba390f53e9df9cdd8/streamsx/rest_primitives.py#L1859-L1875
def create_application_configuration(self, name, properties, description=None): """Create an application configuration. Args: name (str, optional): Only return application configurations containing property **name** that matches `name`. `name` can be a .. versionadded 1.12 """ if not hasattr(self, 'applicationConfigurations'): raise NotImplementedError() cv = ApplicationConfiguration._props(name, properties, description) res = self.rest_client.session.post(self.applicationConfigurations, headers = {'Accept' : 'application/json'}, json=cv) _handle_http_errors(res) return ApplicationConfiguration(res.json(), self.rest_client)
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Create an application configuration. Args: name (str, optional): Only return application configurations containing property **name** that matches `name`. `name` can be a .. versionadded 1.12
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python
train
bitesofcode/projexui
projexui/widgets/xtreewidget/xtreewidget.py
https://github.com/bitesofcode/projexui/blob/f18a73bec84df90b034ca69b9deea118dbedfc4d/projexui/widgets/xtreewidget/xtreewidget.py#L321-L330
def __setUserMinimumSize( self, section, oldSize, newSize ): """ Records the user minimum size for a column. :param section | <int> oldSize | <int> newSize | <int> """ if self.isVisible(): self._columnMinimums[section] = newSize
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Records the user minimum size for a column. :param section | <int> oldSize | <int> newSize | <int>
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python
train
jtwhite79/pyemu
pyemu/utils/gw_utils.py
https://github.com/jtwhite79/pyemu/blob/c504d8e7a4097cec07655a6318d275739bd8148a/pyemu/utils/gw_utils.py#L2107-L2180
def modflow_sfr_gag_to_instruction_file(gage_output_file, ins_file=None, parse_filename=False): """writes an instruction file for an SFR gage output file to read Flow only at all times Parameters ---------- gage_output_file : str the gage output filename (ASCII). ins_file : str the name of the instruction file to create. If None, the name is <gage_output_file>.ins. Default is None parse_filename : bool if True, get the gage_num parameter by parsing the gage output file filename if False, get the gage number from the file itself Returns ------- df : pandas.DataFrame a dataframe with obsnme and obsval for the sfr simulated flows. If inschek was not successfully run, then returns None ins_file : str file name of instructions file relating to gage output. obs_file : str file name of processed gage output for all times Note ---- sets up observations for gage outputs only for the Flow column. if parse_namefile is true, only text up to first '.' is used as the gage_num TODO : allow other observation types and align explicitly with times - now returns all values """ if ins_file is None: ins_file = gage_output_file + '.ins' # navigate the file to be sure the header makes sense indat = [line.strip() for line in open(gage_output_file, 'r').readlines()] header = [i for i in indat if i.startswith('"')] # yank out the gage number to identify the observation names if parse_filename: gage_num = os.path.basename(gage_output_file).split('.')[0] else: gage_num = re.sub("[^0-9]", "", indat[0].lower().split("gage no.")[-1].strip().split()[0]) # get the column names cols = [i.lower() for i in header if 'data' in i.lower()][0].lower().replace('"', '').replace('data:', '').split() # make sure "Flow" is included in the columns if 'flow' not in cols: raise Exception('Requested field "Flow" not in gage output columns') # find which column is for "Flow" flowidx = np.where(np.array(cols) == 'flow')[0][0] # write out the instruction file lines inslines = ['l1 ' + (flowidx + 1) * 'w ' + '!g{0}_{1:d}!'.format(gage_num, j) for j in range(len(indat) - len(header))] inslines[0] = inslines[0].replace('l1', 'l{0:d}'.format(len(header) + 1)) # write the instruction file with open(ins_file, 'w') as ofp: ofp.write('pif ~\n') [ofp.write('{0}\n'.format(line)) for line in inslines] df = _try_run_inschek(ins_file, gage_output_file) if df is not None: return df, ins_file, gage_output_file else: print("Inschek didn't run so nothing returned") return None
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writes an instruction file for an SFR gage output file to read Flow only at all times Parameters ---------- gage_output_file : str the gage output filename (ASCII). ins_file : str the name of the instruction file to create. If None, the name is <gage_output_file>.ins. Default is None parse_filename : bool if True, get the gage_num parameter by parsing the gage output file filename if False, get the gage number from the file itself Returns ------- df : pandas.DataFrame a dataframe with obsnme and obsval for the sfr simulated flows. If inschek was not successfully run, then returns None ins_file : str file name of instructions file relating to gage output. obs_file : str file name of processed gage output for all times Note ---- sets up observations for gage outputs only for the Flow column. if parse_namefile is true, only text up to first '.' is used as the gage_num TODO : allow other observation types and align explicitly with times - now returns all values
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python
train
gwastro/pycbc-glue
pycbc_glue/pipeline.py
https://github.com/gwastro/pycbc-glue/blob/a3e906bae59fbfd707c3ff82e5d008d939ec5e24/pycbc_glue/pipeline.py#L1381-L1397
def write_job(self,fh): """ Write the DAG entry for this node's job to the DAG file descriptor. @param fh: descriptor of open DAG file. """ if isinstance(self.job(),CondorDAGManJob): # create an external subdag from this dag fh.write( ' '.join( ['SUBDAG EXTERNAL', self.__name, self.__job.get_sub_file()]) ) if self.job().get_dag_directory(): fh.write( ' DIR ' + self.job().get_dag_directory() ) else: # write a regular condor job fh.write( 'JOB ' + self.__name + ' ' + self.__job.get_sub_file() ) fh.write( '\n') fh.write( 'RETRY ' + self.__name + ' ' + str(self.__retry) + '\n' )
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Write the DAG entry for this node's job to the DAG file descriptor. @param fh: descriptor of open DAG file.
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python
train
shaypal5/strct
strct/sortedlists/sortedlist.py
https://github.com/shaypal5/strct/blob/f3a301692d052ddb79331230b3c00625db1d83fc/strct/sortedlists/sortedlist.py#L110-L151
def find_range_in_section_list(start, end, section_list): """Returns all sections belonging to the given range. The given list is assumed to contain start points of consecutive sections, except for the final point, assumed to be the end point of the last section. For example, the list [5, 8, 30, 31] is interpreted as the following list of sections: [5-8), [8-30), [30-31]. As such, this function will return [5,8] for the range (7,9) and [5,8,30] while for (7, 30). Parameters --------- start : float The start of the desired range. end : float The end of the desired range. section_list : sortedcontainers.SortedList A list of start points of consecutive sections. Returns ------- iterable The starting points of all sections belonging to the given range. Example ------- >>> from sortedcontainers import SortedList >>> seclist = SortedList([5, 8, 30, 31]) >>> find_range_in_section_list(3, 4, seclist) [] >>> find_range_in_section_list(6, 7, seclist) [5] >>> find_range_in_section_list(7, 9, seclist) [5, 8] >>> find_range_in_section_list(7, 30, seclist) [5, 8, 30] >>> find_range_in_section_list(7, 321, seclist) [5, 8, 30] >>> find_range_in_section_list(4, 321, seclist) [5, 8, 30] """ ind = find_range_ix_in_section_list(start, end, section_list) return section_list[ind[0]: ind[1]]
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Returns all sections belonging to the given range. The given list is assumed to contain start points of consecutive sections, except for the final point, assumed to be the end point of the last section. For example, the list [5, 8, 30, 31] is interpreted as the following list of sections: [5-8), [8-30), [30-31]. As such, this function will return [5,8] for the range (7,9) and [5,8,30] while for (7, 30). Parameters --------- start : float The start of the desired range. end : float The end of the desired range. section_list : sortedcontainers.SortedList A list of start points of consecutive sections. Returns ------- iterable The starting points of all sections belonging to the given range. Example ------- >>> from sortedcontainers import SortedList >>> seclist = SortedList([5, 8, 30, 31]) >>> find_range_in_section_list(3, 4, seclist) [] >>> find_range_in_section_list(6, 7, seclist) [5] >>> find_range_in_section_list(7, 9, seclist) [5, 8] >>> find_range_in_section_list(7, 30, seclist) [5, 8, 30] >>> find_range_in_section_list(7, 321, seclist) [5, 8, 30] >>> find_range_in_section_list(4, 321, seclist) [5, 8, 30]
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python
train
swimlane/swimlane-python
swimlane/core/fields/usergroup.py
https://github.com/swimlane/swimlane-python/blob/588fc503a76799bcdb5aecdf2f64a6ee05e3922d/swimlane/core/fields/usergroup.py#L105-L110
def set_swimlane(self, value): """Workaround for reports returning an empty usergroup field as a single element list with no id/name""" if value == [{"$type": "Core.Models.Utilities.UserGroupSelection, Core"}]: value = [] return super(UserGroupField, self).set_swimlane(value)
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Workaround for reports returning an empty usergroup field as a single element list with no id/name
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python
train
spacetelescope/pysynphot
pysynphot/observation.py
https://github.com/spacetelescope/pysynphot/blob/a125ff956f4d94beb157bd51899747a13234bb97/pysynphot/observation.py#L26-L78
def check_overlap(a, b): """Check for wavelength overlap between two spectra. .. note:: Generalized from :meth:`pysynphot.spectrum.SpectralElement.check_overlap`. Parameters ---------- a, b : `~pysynphot.spectrum.SourceSpectrum` or `~pysynphot.spectrum.SpectralElement` Typically a source spectrum, spectral element, observation, or bandpass from observation mode. Returns ------- result : {'full', 'partial', 'none'} Full, partial, or no overlap. Raises ------ AttributeError Given spectrum does not have flux or throughput. """ if a.isAnalytic or b.isAnalytic: #then it's defined everywhere result = 'full' else: #get the wavelength arrays waves = list() for x in (a, b): if hasattr(x,'throughput'): wv = x.wave[np.where(x.throughput != 0)] elif hasattr(x,'flux'): wv = x.wave else: raise AttributeError("neither flux nor throughput in %s"%x) waves.append(wv) #get the endpoints a1,a2 = waves[0].min(), waves[0].max() b1,b2 = waves[1].min(), waves[1].max() #do the comparison if (a1>=b1 and a2<=b2): result = 'full' elif (a2<b1) or (b2<a1): result = 'none' else: result = 'partial' return result
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Check for wavelength overlap between two spectra. .. note:: Generalized from :meth:`pysynphot.spectrum.SpectralElement.check_overlap`. Parameters ---------- a, b : `~pysynphot.spectrum.SourceSpectrum` or `~pysynphot.spectrum.SpectralElement` Typically a source spectrum, spectral element, observation, or bandpass from observation mode. Returns ------- result : {'full', 'partial', 'none'} Full, partial, or no overlap. Raises ------ AttributeError Given spectrum does not have flux or throughput.
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python
train
google/transitfeed
misc/import_ch_zurich.py
https://github.com/google/transitfeed/blob/eb2991a3747ba541b2cb66502b305b6304a1f85f/misc/import_ch_zurich.py#L387-L400
def Write(self, outpath): "Writes a .zip file in Google Transit format." out = zipfile.ZipFile(outpath, mode="w", compression=zipfile.ZIP_DEFLATED) for filename, func in [('agency.txt', self.WriteAgency), ('calendar.txt', self.WriteCalendar), ('calendar_dates.txt', self.WriteCalendarDates), ('routes.txt', self.WriteRoutes), ('trips.txt', self.WriteTrips), ('stops.txt', self.WriteStations), ('stop_times.txt', self.WriteStopTimes)]: s = cStringIO.StringIO() func(s) out.writestr(filename, s.getvalue()) out.close()
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Writes a .zip file in Google Transit format.
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python
train
pyviz/param
param/parameterized.py
https://github.com/pyviz/param/blob/8f0dafa78defa883247b40635f96cc6d5c1b3481/param/parameterized.py#L1453-L1475
def get_param_values(self_,onlychanged=False): """ Return a list of name,value pairs for all Parameters of this object. When called on an instance with onlychanged set to True, will only return values that are not equal to the default value (onlychanged has no effect when called on a class). """ self_or_cls = self_.self_or_cls # CEB: we'd actually like to know whether a value has been # explicitly set on the instance, but I'm not sure that's easy # (would need to distinguish instantiation of default from # user setting of value). vals = [] for name,val in self_or_cls.param.objects('existing').items(): value = self_or_cls.param.get_value_generator(name) # (this is pointless for cls) if not onlychanged or not all_equal(value,val.default): vals.append((name,value)) vals.sort(key=itemgetter(0)) return vals
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Return a list of name,value pairs for all Parameters of this object. When called on an instance with onlychanged set to True, will only return values that are not equal to the default value (onlychanged has no effect when called on a class).
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python
train
dh1tw/pyhamtools
pyhamtools/lookuplib.py
https://github.com/dh1tw/pyhamtools/blob/ee7e4b8732e23c298da10e07163748156c16d0fa/pyhamtools/lookuplib.py#L1084-L1119
def _extract_clublog_header(self, cty_xml_filename): """ Extract the header of the Clublog XML File """ cty_header = {} try: with open(cty_xml_filename, "r") as cty: raw_header = cty.readline() cty_date = re.search("date='.+'", raw_header) if cty_date: cty_date = cty_date.group(0).replace("date=", "").replace("'", "") cty_date = datetime.strptime(cty_date[:19], '%Y-%m-%dT%H:%M:%S') cty_date.replace(tzinfo=UTC) cty_header["Date"] = cty_date cty_ns = re.search("xmlns='.+[']", raw_header) if cty_ns: cty_ns = cty_ns.group(0).replace("xmlns=", "").replace("'", "") cty_header['NameSpace'] = cty_ns if len(cty_header) == 2: self._logger.debug("Header successfully retrieved from CTY File") elif len(cty_header) < 2: self._logger.warning("Header could only be partically retrieved from CTY File") self._logger.warning("Content of Header: ") for key in cty_header: self._logger.warning(str(key)+": "+str(cty_header[key])) return cty_header except Exception as e: self._logger.error("Clublog CTY File could not be opened / modified") self._logger.error("Error Message: " + str(e)) return
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Extract the header of the Clublog XML File
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python
train
jamescooke/flake8-aaa
src/flake8_aaa/helpers.py
https://github.com/jamescooke/flake8-aaa/blob/29938b96845fe32ced4358ba66af3b3be2a37794/src/flake8_aaa/helpers.py#L170-L178
def filter_arrange_nodes(nodes: List[ast.stmt], max_line_number: int) -> List[ast.stmt]: """ Finds all nodes that are before the ``max_line_number`` and are not docstrings or ``pass``. """ return [ node for node in nodes if node.lineno < max_line_number and not isinstance(node, ast.Pass) and not (isinstance(node, ast.Expr) and isinstance(node.value, ast.Str)) ]
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Finds all nodes that are before the ``max_line_number`` and are not docstrings or ``pass``.
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python
train
Azure/azure-sdk-for-python
azure-servicemanagement-legacy/azure/servicemanagement/servicemanagementservice.py
https://github.com/Azure/azure-sdk-for-python/blob/d7306fde32f60a293a7567678692bdad31e4b667/azure-servicemanagement-legacy/azure/servicemanagement/servicemanagementservice.py#L1950-L1970
def share_vm_image(self, vm_image_name, permission): ''' Share an already replicated OS image. This operation is only for publishers. You have to be registered as image publisher with Windows Azure to be able to call this. vm_image_name: The name of the virtual machine image to share permission: The sharing permission: public, msdn, or private ''' _validate_not_none('vm_image_name', vm_image_name) _validate_not_none('permission', permission) path = self._get_sharing_path_using_vm_image_name(vm_image_name) query = '&permission=' + permission path = path + '?' + query.lstrip('&') return self._perform_put( path, None, as_async=True, x_ms_version='2015-04-01' )
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Share an already replicated OS image. This operation is only for publishers. You have to be registered as image publisher with Windows Azure to be able to call this. vm_image_name: The name of the virtual machine image to share permission: The sharing permission: public, msdn, or private
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python
test
mongodb/mongo-python-driver
pymongo/client_session.py
https://github.com/mongodb/mongo-python-driver/blob/c29c21449e3aae74154207058cf85fd94018d4cd/pymongo/client_session.py#L382-L502
def with_transaction(self, callback, read_concern=None, write_concern=None, read_preference=None): """Execute a callback in a transaction. This method starts a transaction on this session, executes ``callback`` once, and then commits the transaction. For example:: def callback(session): orders = session.client.db.orders inventory = session.client.db.inventory orders.insert_one({"sku": "abc123", "qty": 100}, session=session) inventory.update_one({"sku": "abc123", "qty": {"$gte": 100}}, {"$inc": {"qty": -100}}, session=session) with client.start_session() as session: session.with_transaction(callback) To pass arbitrary arguments to the ``callback``, wrap your callable with a ``lambda`` like this:: def callback(session, custom_arg, custom_kwarg=None): # Transaction operations... with client.start_session() as session: session.with_transaction( lambda s: callback(s, "custom_arg", custom_kwarg=1)) In the event of an exception, ``with_transaction`` may retry the commit or the entire transaction, therefore ``callback`` may be invoked multiple times by a single call to ``with_transaction``. Developers should be mindful of this possiblity when writing a ``callback`` that modifies application state or has any other side-effects. Note that even when the ``callback`` is invoked multiple times, ``with_transaction`` ensures that the transaction will be committed at-most-once on the server. The ``callback`` should not attempt to start new transactions, but should simply run operations meant to be contained within a transaction. The ``callback`` should also not commit the transaction; this is handled automatically by ``with_transaction``. If the ``callback`` does commit or abort the transaction without error, however, ``with_transaction`` will return without taking further action. When ``callback`` raises an exception, ``with_transaction`` automatically aborts the current transaction. When ``callback`` or :meth:`~ClientSession.commit_transaction` raises an exception that includes the ``"TransientTransactionError"`` error label, ``with_transaction`` starts a new transaction and re-executes the ``callback``. When :meth:`~ClientSession.commit_transaction` raises an exception with the ``"UnknownTransactionCommitResult"`` error label, ``with_transaction`` retries the commit until the result of the transaction is known. This method will cease retrying after 120 seconds has elapsed. This timeout is not configurable and any exception raised by the ``callback`` or by :meth:`ClientSession.commit_transaction` after the timeout is reached will be re-raised. Applications that desire a different timeout duration should not use this method. :Parameters: - `callback`: The callable ``callback`` to run inside a transaction. The callable must accept a single argument, this session. Note, under certain error conditions the callback may be run multiple times. - `read_concern` (optional): The :class:`~pymongo.read_concern.ReadConcern` to use for this transaction. - `write_concern` (optional): The :class:`~pymongo.write_concern.WriteConcern` to use for this transaction. - `read_preference` (optional): The read preference to use for this transaction. If ``None`` (the default) the :attr:`read_preference` of this :class:`Database` is used. See :mod:`~pymongo.read_preferences` for options. :Returns: The return value of the ``callback``. .. versionadded:: 3.9 """ start_time = monotonic.time() while True: self.start_transaction( read_concern, write_concern, read_preference) try: ret = callback(self) except Exception as exc: if self._in_transaction: self.abort_transaction() if (isinstance(exc, PyMongoError) and exc.has_error_label("TransientTransactionError") and _within_time_limit(start_time)): # Retry the entire transaction. continue raise if self._transaction.state in ( _TxnState.NONE, _TxnState.COMMITTED, _TxnState.ABORTED): # Assume callback intentionally ended the transaction. return ret while True: try: self.commit_transaction() except PyMongoError as exc: if (exc.has_error_label("UnknownTransactionCommitResult") and _within_time_limit(start_time)): # Retry the commit. continue if (exc.has_error_label("TransientTransactionError") and _within_time_limit(start_time)): # Retry the entire transaction. break raise # Commit succeeded. return ret
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python
train
DLR-RM/RAFCON
source/rafcon/utils/geometry.py
https://github.com/DLR-RM/RAFCON/blob/24942ef1a904531f49ab8830a1dbb604441be498/source/rafcon/utils/geometry.py#L70-L89
def point_in_triangle(p, v1, v2, v3): """Checks whether a point is within the given triangle The function checks, whether the given point p is within the triangle defined by the the three corner point v1, v2 and v3. This is done by checking whether the point is on all three half-planes defined by the three edges of the triangle. :param p: The point to be checked (tuple with x any y coordinate) :param v1: First vertex of the triangle (tuple with x any y coordinate) :param v2: Second vertex of the triangle (tuple with x any y coordinate) :param v3: Third vertex of the triangle (tuple with x any y coordinate) :return: True if the point is within the triangle, False if not """ def _test(p1, p2, p3): return (p1[0] - p3[0]) * (p2[1] - p3[1]) - (p2[0] - p3[0]) * (p1[1] - p3[1]) b1 = _test(p, v1, v2) < 0.0 b2 = _test(p, v2, v3) < 0.0 b3 = _test(p, v3, v1) < 0.0 return (b1 == b2) and (b2 == b3)
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Checks whether a point is within the given triangle The function checks, whether the given point p is within the triangle defined by the the three corner point v1, v2 and v3. This is done by checking whether the point is on all three half-planes defined by the three edges of the triangle. :param p: The point to be checked (tuple with x any y coordinate) :param v1: First vertex of the triangle (tuple with x any y coordinate) :param v2: Second vertex of the triangle (tuple with x any y coordinate) :param v3: Third vertex of the triangle (tuple with x any y coordinate) :return: True if the point is within the triangle, False if not
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python
train
google/grr
grr/server/grr_response_server/signed_binary_utils.py
https://github.com/google/grr/blob/5cef4e8e2f0d5df43ea4877e9c798e0bf60bfe74/grr/server/grr_response_server/signed_binary_utils.py#L113-L149
def WriteSignedBinaryBlobs(binary_urn, blobs, token = None): """Saves signed blobs to the datastore. If a signed binary with the given URN already exists, its contents will get overwritten. Args: binary_urn: RDFURN that should serve as a unique identifier for the binary. blobs: An Iterable of signed blobs to write to the datastore. token: ACL token to use with the legacy (non-relational) datastore. """ if _ShouldUseLegacyDatastore(): aff4.FACTORY.Delete(binary_urn, token=token) with data_store.DB.GetMutationPool() as mutation_pool: with aff4.FACTORY.Create( binary_urn, collects.GRRSignedBlob, mode="w", mutation_pool=mutation_pool, token=token) as fd: for blob in blobs: fd.Add(blob, mutation_pool=mutation_pool) if data_store.RelationalDBEnabled(): blob_references = rdf_objects.BlobReferences() current_offset = 0 for blob in blobs: blob_id = data_store.BLOBS.WriteBlobWithUnknownHash( blob.SerializeToString()) blob_references.items.Append( rdf_objects.BlobReference( offset=current_offset, size=len(blob.data), blob_id=blob_id)) current_offset += len(blob.data) data_store.REL_DB.WriteSignedBinaryReferences( _SignedBinaryIDFromURN(binary_urn), blob_references)
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Saves signed blobs to the datastore. If a signed binary with the given URN already exists, its contents will get overwritten. Args: binary_urn: RDFURN that should serve as a unique identifier for the binary. blobs: An Iterable of signed blobs to write to the datastore. token: ACL token to use with the legacy (non-relational) datastore.
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python
train
slimkrazy/python-google-places
googleplaces/__init__.py
https://github.com/slimkrazy/python-google-places/blob/d4b7363e1655cdc091a6253379f6d2a95b827881/googleplaces/__init__.py#L922-L930
def vicinity(self): """Returns a feature name of a nearby location. Often this feature refers to a street or neighborhood within the given results. """ if self._vicinity == '' and self.details != None and 'vicinity' in self.details: self._vicinity = self.details['vicinity'] return self._vicinity
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Returns a feature name of a nearby location. Often this feature refers to a street or neighborhood within the given results.
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python
train
deep-compute/basescript
basescript/log.py
https://github.com/deep-compute/basescript/blob/f7233963c5291530fcb2444a7f45b556e6407b90/basescript/log.py#L177-L186
def critical(self, event=None, *args, **kw): """ Process event and call :meth:`logging.Logger.critical` with the result. """ if not self._logger.isEnabledFor(logging.CRITICAL): return kw = self._add_base_info(kw) kw['level'] = "critical" return self._proxy_to_logger('critical', event, *args, **kw)
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Process event and call :meth:`logging.Logger.critical` with the result.
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python
train
awacha/credolib
credolib/procedures.py
https://github.com/awacha/credolib/blob/11c0be3eea7257d3d6e13697d3e76ce538f2f1b2/credolib/procedures.py#L136-L367
def summarize(reintegrate=True, dist_tolerance=3, qranges=None, samples=None, raw=False, late_radavg=True, graph_ncols=3, std_multiplier=3, graph_extension='png', graph_dpi=80, correlmatrix_colormap='coolwarm', image_colormap='viridis', correlmatrix_logarithmic=True, cormaptest=True): """Summarize scattering patterns and curves for all samples defined by the global `allsamplenames`. Inputs: reintegrate (bool, default=True): if the curves are to be obained by reintegrating the patterns. Otherwise 1D curves are loaded. dist_tolerance (float, default=3): sample-to-detector distances nearer than this are considered the same qranges (dict): a dictionary mapping approximate sample-to-detector distances (within dist_tolerance) to one-dimensional np.ndarrays of the desired q-range of the reintegration. samples (list or None): the names of the samples to summarize. If None, all samples defined by ``allsamplenames`` are used. raw (bool, default=False): if raw images are to be treated instead the evaluated ones (default). late_radavg (bool, default=True): if the scattering curves are to be calculated from the summarized scattering pattern. If False, scattering curves are calculated from each pattern and will be averaged. graph_ncols: the number of columns in graphs (2D patterns, correlation matrices) std_multiplier: if the absolute value of the relative discrepancy is larger than this limit, the exposure is deemed an outlier. graph_extension: the extension of the produced hardcopy files. graph_dpi: resolution of the graphs correlmatrix_colormap: name of the colormap to be used for the correlation matrices (resolved by matplotlib.cm.get_cmap()) image_colormap: name of the colormap to be used for the scattering patterns (resolved by matplotlib.cm.get_cmap()) correlmatrix_logarithmic: if the correlation matrix has to be calculated from the logarithm of the intensity. """ if qranges is None: qranges = {} ip = get_ipython() data2d = {} data1d = {} headers_tosave = {} rowavg = {} if raw: writemarkdown('# Summarizing RAW images.') headers = ip.user_ns['_headers']['raw'] rawpart = '_raw' # this will be added in the filenames saved else: writemarkdown('# Summarizing CORRECTED images.') headers = ip.user_ns['_headers']['processed'] rawpart = '' # nothing will be added in the filenames saved if samples is None: samples = sorted(ip.user_ns['allsamplenames']) for samplename in samples: writemarkdown('## ' + samplename) headers_sample = [h for h in headers if h.title == samplename] data2d[samplename] = {} rowavg[samplename] = {} data1d[samplename] = {} headers_tosave[samplename] = {} dists = get_different_distances([h for h in headers if h.title == samplename], dist_tolerance) if not dists: writemarkdown('No measurements from sample, skipping.') continue fig_2d = plt.figure() fig_curves = plt.figure() fig_correlmatrices = plt.figure() distaxes = {} correlmatrixaxes = {} ncols = min(len(dists), graph_ncols) nrows = int(np.ceil(len(dists) / ncols)) onedimaxes = fig_curves.add_axes((0.1, 0.3, 0.8, 0.5)) onedimstdaxes = fig_curves.add_axes((0.1, 0.1, 0.8, 0.2)) for distidx, dist in enumerate(dists): writemarkdown("### Distance " + str(dist) + " mm") headers_narrowed = [h for h in headers_sample if abs(float(h.distance) - dist) < dist_tolerance] distaxes[dist] = fig_2d.add_subplot( nrows, ncols, distidx + 1) correlmatrixaxes[dist] = fig_correlmatrices.add_subplot( nrows, ncols, distidx + 1) # determine the q-range to be used from the qranges argument. try: distkey_min = min([np.abs(k - dist) for k in qranges if np.abs(k - dist) < dist_tolerance]) except ValueError: # no matching key in qranges dict qrange = None # request auto-determination of q-range else: distkey = [ k for k in qranges if np.abs(k - dist) == distkey_min][0] qrange = qranges[distkey] (data1d[samplename][dist], data2d[samplename][dist], headers_tosave[samplename][dist]) = \ _collect_data_for_summarization(headers_narrowed, raw, reintegrate, qrange) badfsns, badfsns_datcmp, tab, rowavg[samplename][dist] = _stabilityassessment( headers_tosave[samplename][dist], data1d[samplename][dist], dist, fig_correlmatrices, correlmatrixaxes[dist], std_multiplier, correlmatrix_colormap, os.path.join(ip.user_ns['saveto_dir'], 'correlmatrix_%s_%s' % ( samplename, ('%.2f' % dist).replace('.', '_')) + rawpart + '.npz'), logarithmic_correlmatrix=correlmatrix_logarithmic, cormaptest=cormaptest) if 'badfsns' not in ip.user_ns: ip.user_ns['badfsns'] = {} elif 'badfsns_datcmp' not in ip.user_ns: ip.user_ns['badfsns_datcmp'] = {} ip.user_ns['badfsns'] = set(ip.user_ns['badfsns']).union(badfsns) ip.user_ns['badfsns_datcmp'] = set(ip.user_ns['badfsns_datcmp']).union(badfsns_datcmp) display(tab) # Plot the image try: data2d[samplename][dist].imshow(axes=distaxes[dist], show_crosshair=False, norm=matplotlib.colors.LogNorm(), cmap=matplotlib.cm.get_cmap(image_colormap)) except ValueError: print('Error plotting 2D image for sample %s, distance %.2f' % (samplename, dist)) distaxes[dist].set_xlabel('q (' + qunit() + ')') distaxes[dist].set_ylabel('q (' + qunit() + ')') distaxes[dist].set_title( '%.2f mm (%d curve%s)' % (dist, len(headers_tosave[samplename][dist]), ['', 's'][len(headers_tosave[samplename][dist]) > 1])) # Plot the curves Istd = np.stack([c.Intensity for c in data1d[samplename][dist]], axis=1) for c, h in zip(data1d[samplename][dist], headers_tosave[samplename][dist]): color = 'green' if h.fsn in badfsns_datcmp: color = 'magenta' if h.fsn in badfsns: color = 'red' c.loglog(axes=onedimaxes, color=color) if Istd.shape[1] > 1: onedimstdaxes.loglog(data1d[samplename][dist][0].q, Istd.std(axis=1) / Istd.mean(axis=1) * 100, 'b-') if not late_radavg: data1d[samplename][dist] = Curve.average( *data1d[samplename][dist]) else: data1d[samplename][dist] = ( data2d[samplename][dist].radial_average( qrange, errorpropagation=3, abscissa_errorpropagation=3, raw_result=False)) data1d[samplename][dist].loglog( label='Average', lw=2, color='k', axes=onedimaxes) ##Saving image, headers, mask and curve # data2d[samplename][dist].write( # os.path.join(ip.user_ns['saveto_dir'], # samplename + '_'+( # '%.2f' % dist).replace('.', '_') + # rawpart + '.npz'), plugin='CREDO Reduced') # data2d[samplename][dist].header.write( # os.path.join(ip.user_ns['saveto_dir'], ### samplename + '_'+( # '%.2f' % dist).replace('.', '_') + # rawpart +'.log'), plugin='CREDO Reduced') # data2d[samplename][dist].mask.write_to_mat( # os.path.join(ip.user_ns['saveto_dir'], # data2d[samplename][dist].mask.maskid+'.mat')) data1d[samplename][dist].save(os.path.join(ip.user_ns['saveto_dir'], samplename + '_' + ('%.2f' % dist).replace('.', '_') + rawpart + '.txt')) # Report on qrange and flux q_ = data1d[samplename][dist].q qmin = q_[q_ > 0].min() writemarkdown('#### Q-range & flux') writemarkdown( '- $q_{min}$: ' + print_abscissavalue(qmin, headers_tosave[samplename][dist][0].wavelength, dist)) writemarkdown('- $q_{max}$: ' + print_abscissavalue(data1d[samplename][dist].q.max(), headers_tosave[samplename][dist][0].wavelength, dist)) writemarkdown('- Number of $q$ points: ' + str(len(data1d[samplename][dist]))) meastime = sum([h.exposuretime for h in headers_tosave[samplename][dist]]) writemarkdown("- from %d exposures, total exposure time %.0f sec <=> %.2f hr" % ( len(headers_tosave[samplename][dist]), meastime, meastime / 3600.)) try: flux = [h.flux for h in headers_tosave[samplename][dist]] flux = ErrorValue(np.mean(flux), np.std(flux)) writemarkdown("- beam flux (photon/sec): %s" % flux) except KeyError: writemarkdown("- *No information on beam flux: dealing with raw data.*") onedimaxes.set_xlabel('') onedimaxes.set_ylabel('$d\\Sigma/d\\Omega$ (cm$^{-1}$ sr$^{-1}$)') # plt.legend(loc='best') onedimaxes.grid(True, which='both') onedimaxes.axis('tight') onedimaxes.set_title(samplename) onedimstdaxes.set_xlabel('q (' + qunit() + ')') onedimstdaxes.set_ylabel('Rel.std.dev. of intensity (%)') onedimstdaxes.grid(True, which='both') onedimstdaxes.set_xlim(*onedimaxes.get_xlim()) onedimstdaxes.set_xscale(onedimaxes.get_xscale()) putlogo(fig_curves) putlogo(fig_2d) fig_2d.tight_layout() fig_correlmatrices.suptitle(samplename) fig_correlmatrices.tight_layout() fig_2d.savefig( os.path.join(ip.user_ns['auximages_dir'], 'averaging2D_' + samplename + rawpart + '.' + graph_extension), dpi=graph_dpi) fig_curves.savefig( os.path.join(ip.user_ns['auximages_dir'], 'averaging1D_' + samplename + rawpart + '.' + graph_extension), dpi=graph_dpi) putlogo(fig_correlmatrices) fig_correlmatrices.savefig( os.path.join(ip.user_ns['auximages_dir'], 'correlation_' + samplename + rawpart + '.' + graph_extension), dpi=graph_dpi) writemarkdown("### Collected images from all distances") plt.show() writemarkdown("Updated badfsns list:") writemarkdown('[' + ', '.join(str(f) for f in ip.user_ns['badfsns']) + ']') writemarkdown("Updated badfsns list using datcmp:") writemarkdown('[' + ', '.join(str(f) for f in ip.user_ns['badfsns_datcmp']) + ']') ip.user_ns['_data1d'] = data1d ip.user_ns['_data2d'] = data2d ip.user_ns['_headers_sample'] = headers_tosave ip.user_ns['_rowavg'] = rowavg
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"samplename", ",", "dist", ")", ")", "distaxes", "[", "dist", "]", ".", "set_xlabel", "(", "'q ('", "+", "qunit", "(", ")", "+", "')'", ")", "distaxes", "[", "dist", "]", ".", "set_ylabel", "(", "'q ('", "+", "qunit", "(", ")", "+", "')'", ")", "distaxes", "[", "dist", "]", ".", "set_title", "(", "'%.2f mm (%d curve%s)'", "%", "(", "dist", ",", "len", "(", "headers_tosave", "[", "samplename", "]", "[", "dist", "]", ")", ",", "[", "''", ",", "'s'", "]", "[", "len", "(", "headers_tosave", "[", "samplename", "]", "[", "dist", "]", ")", ">", "1", "]", ")", ")", "# Plot the curves", "Istd", "=", "np", ".", "stack", "(", "[", "c", ".", "Intensity", "for", "c", "in", "data1d", "[", "samplename", "]", "[", "dist", "]", "]", ",", "axis", "=", "1", ")", "for", "c", ",", "h", "in", "zip", "(", "data1d", "[", "samplename", "]", "[", "dist", "]", ",", "headers_tosave", "[", "samplename", "]", "[", "dist", "]", ")", ":", "color", "=", "'green'", "if", "h", ".", "fsn", "in", "badfsns_datcmp", ":", "color", "=", "'magenta'", "if", "h", ".", "fsn", "in", "badfsns", ":", "color", "=", "'red'", "c", ".", "loglog", "(", "axes", "=", "onedimaxes", ",", "color", "=", "color", ")", "if", "Istd", ".", "shape", "[", "1", "]", ">", "1", ":", "onedimstdaxes", ".", "loglog", "(", "data1d", "[", "samplename", "]", "[", "dist", "]", "[", "0", "]", ".", "q", ",", "Istd", ".", "std", "(", "axis", "=", "1", ")", "/", "Istd", ".", "mean", "(", "axis", "=", "1", ")", "*", "100", ",", "'b-'", ")", "if", "not", "late_radavg", ":", "data1d", "[", "samplename", "]", "[", "dist", "]", "=", "Curve", ".", "average", "(", "*", "data1d", "[", "samplename", "]", "[", "dist", "]", ")", "else", ":", "data1d", "[", "samplename", "]", "[", "dist", "]", "=", "(", "data2d", "[", "samplename", "]", "[", "dist", "]", ".", "radial_average", "(", "qrange", ",", "errorpropagation", "=", "3", ",", "abscissa_errorpropagation", "=", "3", ",", "raw_result", "=", "False", ")", ")", "data1d", "[", "samplename", "]", "[", "dist", "]", ".", "loglog", "(", "label", "=", "'Average'", ",", "lw", "=", "2", ",", "color", "=", "'k'", ",", "axes", "=", "onedimaxes", ")", "##Saving image, headers, mask and curve", "# data2d[samplename][dist].write(", "# os.path.join(ip.user_ns['saveto_dir'],", "# samplename + '_'+(", "# '%.2f' % dist).replace('.', '_') +", "# rawpart + '.npz'), plugin='CREDO Reduced')", "# data2d[samplename][dist].header.write(", "# os.path.join(ip.user_ns['saveto_dir'],", "### samplename + '_'+(", "# '%.2f' % dist).replace('.', '_') +", "# rawpart +'.log'), plugin='CREDO Reduced')", "# data2d[samplename][dist].mask.write_to_mat(", "# os.path.join(ip.user_ns['saveto_dir'],", "# data2d[samplename][dist].mask.maskid+'.mat'))", "data1d", "[", "samplename", "]", "[", "dist", "]", ".", "save", "(", "os", ".", "path", ".", "join", "(", "ip", ".", "user_ns", "[", "'saveto_dir'", "]", ",", "samplename", "+", "'_'", "+", "(", "'%.2f'", "%", "dist", ")", ".", "replace", "(", "'.'", ",", "'_'", ")", "+", "rawpart", "+", "'.txt'", ")", ")", "# Report on qrange and flux", "q_", "=", "data1d", "[", "samplename", "]", "[", "dist", "]", ".", "q", "qmin", "=", "q_", "[", "q_", ">", "0", "]", ".", "min", "(", ")", "writemarkdown", "(", "'#### Q-range & flux'", ")", "writemarkdown", "(", "'- $q_{min}$: '", "+", "print_abscissavalue", "(", "qmin", ",", "headers_tosave", "[", "samplename", "]", "[", "dist", "]", "[", "0", "]", ".", "wavelength", ",", "dist", ")", ")", "writemarkdown", "(", "'- $q_{max}$: '", "+", "print_abscissavalue", "(", "data1d", "[", "samplename", "]", "[", "dist", "]", ".", "q", ".", "max", "(", ")", ",", "headers_tosave", "[", "samplename", "]", "[", "dist", "]", "[", "0", "]", ".", "wavelength", ",", "dist", ")", ")", "writemarkdown", "(", "'- Number of $q$ points: '", "+", "str", "(", "len", "(", "data1d", "[", "samplename", "]", "[", "dist", "]", ")", ")", ")", "meastime", "=", "sum", "(", "[", "h", ".", "exposuretime", "for", "h", "in", "headers_tosave", "[", "samplename", "]", "[", "dist", "]", "]", ")", "writemarkdown", "(", "\"- from %d exposures, total exposure time %.0f sec <=> %.2f hr\"", "%", "(", "len", "(", "headers_tosave", "[", "samplename", "]", "[", "dist", "]", ")", ",", "meastime", ",", "meastime", "/", "3600.", ")", ")", "try", ":", "flux", "=", "[", "h", ".", "flux", "for", "h", "in", "headers_tosave", "[", "samplename", "]", "[", "dist", "]", "]", "flux", "=", "ErrorValue", "(", "np", ".", "mean", "(", "flux", ")", ",", "np", ".", "std", "(", "flux", ")", ")", "writemarkdown", "(", "\"- beam flux (photon/sec): %s\"", "%", "flux", ")", "except", "KeyError", ":", "writemarkdown", "(", "\"- *No information on beam flux: dealing with raw data.*\"", ")", "onedimaxes", ".", "set_xlabel", "(", "''", ")", "onedimaxes", ".", "set_ylabel", "(", "'$d\\\\Sigma/d\\\\Omega$ (cm$^{-1}$ sr$^{-1}$)'", ")", "# plt.legend(loc='best')", "onedimaxes", ".", "grid", "(", "True", ",", "which", "=", "'both'", ")", "onedimaxes", ".", "axis", "(", "'tight'", ")", "onedimaxes", ".", "set_title", "(", "samplename", ")", "onedimstdaxes", ".", "set_xlabel", "(", "'q ('", "+", "qunit", "(", ")", "+", "')'", ")", "onedimstdaxes", ".", "set_ylabel", "(", "'Rel.std.dev. of intensity (%)'", ")", "onedimstdaxes", ".", "grid", "(", "True", ",", "which", "=", "'both'", ")", "onedimstdaxes", ".", "set_xlim", "(", "*", "onedimaxes", ".", "get_xlim", "(", ")", ")", "onedimstdaxes", ".", "set_xscale", "(", "onedimaxes", ".", "get_xscale", "(", ")", ")", "putlogo", "(", "fig_curves", ")", "putlogo", "(", "fig_2d", ")", "fig_2d", ".", "tight_layout", "(", ")", "fig_correlmatrices", ".", "suptitle", "(", "samplename", ")", "fig_correlmatrices", ".", "tight_layout", "(", ")", "fig_2d", ".", "savefig", "(", "os", ".", "path", ".", "join", "(", "ip", ".", "user_ns", "[", "'auximages_dir'", "]", ",", "'averaging2D_'", "+", "samplename", "+", "rawpart", "+", "'.'", "+", "graph_extension", ")", ",", "dpi", "=", "graph_dpi", ")", "fig_curves", ".", "savefig", "(", "os", ".", "path", ".", "join", "(", "ip", ".", "user_ns", "[", "'auximages_dir'", "]", ",", "'averaging1D_'", "+", "samplename", "+", "rawpart", "+", "'.'", "+", "graph_extension", ")", ",", "dpi", "=", "graph_dpi", ")", "putlogo", "(", "fig_correlmatrices", ")", "fig_correlmatrices", ".", "savefig", "(", "os", ".", "path", ".", "join", "(", "ip", ".", "user_ns", "[", "'auximages_dir'", "]", ",", "'correlation_'", "+", "samplename", "+", "rawpart", "+", "'.'", "+", "graph_extension", ")", ",", "dpi", "=", "graph_dpi", ")", "writemarkdown", "(", "\"### Collected images from all distances\"", ")", "plt", ".", "show", "(", ")", "writemarkdown", "(", "\"Updated badfsns list:\"", ")", "writemarkdown", "(", "'['", "+", "', '", ".", "join", "(", "str", "(", "f", ")", "for", "f", "in", "ip", ".", "user_ns", "[", "'badfsns'", "]", ")", "+", "']'", ")", "writemarkdown", "(", "\"Updated badfsns list using datcmp:\"", ")", "writemarkdown", "(", "'['", "+", "', '", ".", "join", "(", "str", "(", "f", ")", "for", "f", "in", "ip", ".", "user_ns", "[", "'badfsns_datcmp'", "]", ")", "+", "']'", ")", "ip", ".", "user_ns", "[", "'_data1d'", "]", "=", "data1d", "ip", ".", "user_ns", "[", "'_data2d'", "]", "=", "data2d", "ip", ".", "user_ns", "[", "'_headers_sample'", "]", "=", "headers_tosave", "ip", ".", "user_ns", "[", "'_rowavg'", "]", "=", "rowavg" ]
Summarize scattering patterns and curves for all samples defined by the global `allsamplenames`. Inputs: reintegrate (bool, default=True): if the curves are to be obained by reintegrating the patterns. Otherwise 1D curves are loaded. dist_tolerance (float, default=3): sample-to-detector distances nearer than this are considered the same qranges (dict): a dictionary mapping approximate sample-to-detector distances (within dist_tolerance) to one-dimensional np.ndarrays of the desired q-range of the reintegration. samples (list or None): the names of the samples to summarize. If None, all samples defined by ``allsamplenames`` are used. raw (bool, default=False): if raw images are to be treated instead the evaluated ones (default). late_radavg (bool, default=True): if the scattering curves are to be calculated from the summarized scattering pattern. If False, scattering curves are calculated from each pattern and will be averaged. graph_ncols: the number of columns in graphs (2D patterns, correlation matrices) std_multiplier: if the absolute value of the relative discrepancy is larger than this limit, the exposure is deemed an outlier. graph_extension: the extension of the produced hardcopy files. graph_dpi: resolution of the graphs correlmatrix_colormap: name of the colormap to be used for the correlation matrices (resolved by matplotlib.cm.get_cmap()) image_colormap: name of the colormap to be used for the scattering patterns (resolved by matplotlib.cm.get_cmap()) correlmatrix_logarithmic: if the correlation matrix has to be calculated from the logarithm of the intensity.
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python
train
saltstack/salt
salt/modules/nova.py
https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/nova.py#L292-L324
def volume_create(name, size=100, snapshot=None, voltype=None, profile=None, **kwargs): ''' Create a block storage volume name Name of the new volume (must be first) size Volume size snapshot Block storage snapshot id voltype Type of storage profile Profile to build on CLI Example: .. code-block:: bash salt '*' nova.volume_create myblock size=300 profile=openstack ''' conn = _auth(profile, **kwargs) return conn.volume_create( name, size, snapshot, voltype )
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Create a block storage volume name Name of the new volume (must be first) size Volume size snapshot Block storage snapshot id voltype Type of storage profile Profile to build on CLI Example: .. code-block:: bash salt '*' nova.volume_create myblock size=300 profile=openstack
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python
train
justquick/django-native-tags
native_tags/registry.py
https://github.com/justquick/django-native-tags/blob/d40b976ee1cb13faeb04f0dedf02933d4274abf2/native_tags/registry.py#L100-L107
def unregister(self, bucket, name): """ Remove the function from the registry by name """ assert bucket in self, 'Bucket %s is unknown' % bucket if not name in self[bucket]: raise NotRegistered('The function %s is not registered' % name) del self[bucket][name]
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Remove the function from the registry by name
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python
train
Microsoft/nni
examples/trials/network_morphism/FashionMNIST/utils.py
https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/examples/trials/network_morphism/FashionMNIST/utils.py#L116-L137
def data_transforms_cifar10(args): """ data_transforms for cifar10 dataset """ cifar_mean = [0.49139968, 0.48215827, 0.44653124] cifar_std = [0.24703233, 0.24348505, 0.26158768] train_transform = transforms.Compose( [ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(cifar_mean, cifar_std), ] ) if args.cutout: train_transform.transforms.append(Cutout(args.cutout_length)) valid_transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize(cifar_mean, cifar_std)] ) return train_transform, valid_transform
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data_transforms for cifar10 dataset
[ "data_transforms", "for", "cifar10", "dataset" ]
python
train
ejeschke/ginga
ginga/Bindings.py
https://github.com/ejeschke/ginga/blob/a78c893ec6f37a837de851947e9bb4625c597915/ginga/Bindings.py#L1696-L1702
def ms_contrast_restore(self, viewer, event, data_x, data_y, msg=True): """An interactive way to restore the colormap contrast settings after a warp operation. """ if self.cancmap and (event.state == 'down'): self.restore_contrast(viewer, msg=msg) return True
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An interactive way to restore the colormap contrast settings after a warp operation.
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python
train
HazyResearch/fonduer
src/fonduer/candidates/mentions.py
https://github.com/HazyResearch/fonduer/blob/4520f86a716f03dcca458a9f4bddac75b4e7068f/src/fonduer/candidates/mentions.py#L246-L260
def apply(self, doc): """ Generate MentionCaptions from a Document by parsing all of its Captions. :param doc: The ``Document`` to parse. :type doc: ``Document`` :raises TypeError: If the input doc is not of type ``Document``. """ if not isinstance(doc, Document): raise TypeError( "Input Contexts to MentionCaptions.apply() must be of type Document" ) for caption in doc.captions: yield TemporaryCaptionMention(caption)
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Generate MentionCaptions from a Document by parsing all of its Captions. :param doc: The ``Document`` to parse. :type doc: ``Document`` :raises TypeError: If the input doc is not of type ``Document``.
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python
train
oseledets/ttpy
tt/amen/amen_mv.py
https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/amen/amen_mv.py#L480-L608
def _compute_next_Phi(Phi_prev, x, y, direction, A=None, extnrm=None, return_norm=True): ''' Performs the recurrent Phi (or Psi) matrix computation Phi = Phi_prev * (x'Ay). If direction is 'lr', computes Psi if direction is 'rl', computes Phi A can be empty, then only x'y is computed. Phi1: rx1, ry1, ra1, or {rx1, ry1}_ra, or rx1, ry1 Phi2: ry2, ra2, rx2, or {ry2, rx2}_ra, or ry2, rx2 ''' [rx1, n, rx2] = x.shape [ry1, m, ry2] = y.shape if A is not None: if isinstance(A, list): # ????????????????????????????????? # A is a canonical block ra = len(A) else: # Just full format [ra1, ra2] = A.shape ra1 = ra1 // n ra2 = ra2 // m # ????????????????????????????????????? else: [ra1, ra2] = [1, 1] if isinstance(Phi_prev, list): Phi = [None] * ra if return_norm: nrm = 0 if (direction == 'lr'): # lr: Phi1 x = _reshape(x, (rx1, n * rx2)) y = _reshape(y, (ry1 * m, ry2)) for i in xrange(ra): Phi[i] = _np.dot(_tconj(x), Phi_prev[i]) Phi[i] = _reshape(Phi[i], (n, rx2 * ry1)) Phi[i] = Phi[i].T Phi[i] = _np.dot(Phi[i], A[i]) Phi[i] = _reshape(Phi[i], (rx2, ry1 * m)) Phi[i] = _np.dot(Phi[i], y) if return_norm: nrm = max(nrm, _np.linalg.norm(Phi[i])) # , 'fro')) else: # rl: Phi2 y = _reshape(y, (ry1, m * ry2)) x = _reshape(x, (rx1 * n, rx2)) for i in xrange(ra): Phi[i] = _np.dot(Phi_prev[i], x.T) Phi[i] = _reshape(Phi[i], (ry2 * rx1, n)) Phi[i] = _np.dot(Phi[i], A[i]) Phi[i] = Phi[i].T Phi[i] = _reshape(Phi[i], (m * ry2, rx1)) Phi[i] = _np.dot(y, Phi[i]) if return_norm: nrm = max(nrm, _np.linalg.norm(Phi[i])) # , 'fro')) if return_norm: # Extract the scale to prevent overload if (nrm > 0): for i in xrange(ra): Phi[i] = Phi[i] / nrm else: nrm = 1 elif extnrm is not None: # Override the normalization for i in xrange(ra): Phi[i] = Phi[i] / extnrm else: if (direction == 'lr'): # lr: Phi1 x = _reshape(x, (rx1, n * rx2)) Phi = _reshape(Phi_prev, (rx1, ry1 * ra1)) Phi = _np.dot(_tconj(x), Phi) if A is not None: Phi = _reshape(Phi, (n * rx2 * ry1, ra1)) Phi = Phi.T Phi = _reshape(Phi, (ra1 * n, rx2 * ry1)) Phi = _np.dot(A.T, Phi) Phi = _reshape(Phi, (m, ra2 * rx2 * ry1)) else: Phi = _reshape(Phi, (n, rx2 * ry1)) Phi = Phi.T Phi = _reshape(Phi, (ra2 * rx2, ry1 * m)) y = _reshape(y, (ry1 * m, ry2)) Phi = _np.dot(Phi, y) if A is not None: Phi = _reshape(Phi, (ra2, rx2 * ry2)) Phi = Phi.T Phi = _reshape(Phi, (rx2, ry2, ra2)) else: Phi = _reshape(Phi, (rx2, ry2)) else: # rl: Phi2 y = _reshape(y, (ry1 * m, ry2)) Phi = _reshape(Phi_prev, (ry2, ra2 * rx2)) Phi = _np.dot(y, Phi) if A is not None: Phi = _reshape(Phi, (ry1, m * ra2 * rx2)) Phi = Phi.T Phi = _reshape(Phi, (m * ra2, rx2 * ry1)) Phi = _np.dot(A, Phi) Phi = _reshape(Phi, (ra1 * n * rx2, ry1)) Phi = Phi.T Phi = _reshape(Phi, (ry1 * ra1, n * rx2)) x = _reshape(x, (rx1, n * rx2)) Phi = _np.dot(Phi, _tconj(x)) if A is not None: Phi = _reshape(Phi, (ry1, ra1, rx1)) else: Phi = _reshape(Phi, (ry1, rx1)) if return_norm: # Extract the scale to prevent overload nrm = _np.linalg.norm(Phi) # , 'fro') if (nrm > 0): Phi = Phi / nrm else: nrm = 1 elif extnrm is not None: # Override the normalization by the external one Phi = Phi / extnrm if return_norm: return Phi, nrm else: return Phi
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Performs the recurrent Phi (or Psi) matrix computation Phi = Phi_prev * (x'Ay). If direction is 'lr', computes Psi if direction is 'rl', computes Phi A can be empty, then only x'y is computed. Phi1: rx1, ry1, ra1, or {rx1, ry1}_ra, or rx1, ry1 Phi2: ry2, ra2, rx2, or {ry2, rx2}_ra, or ry2, rx2
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python
train
juju/charm-helpers
charmhelpers/contrib/charmsupport/nrpe.py
https://github.com/juju/charm-helpers/blob/aa785c40c3b7a8c69dbfbc7921d6b9f30142e171/charmhelpers/contrib/charmsupport/nrpe.py#L355-L410
def add_init_service_checks(nrpe, services, unit_name, immediate_check=True): """ Add checks for each service in list :param NRPE nrpe: NRPE object to add check to :param list services: List of services to check :param str unit_name: Unit name to use in check description :param bool immediate_check: For sysv init, run the service check immediately """ for svc in services: # Don't add a check for these services from neutron-gateway if svc in ['ext-port', 'os-charm-phy-nic-mtu']: next upstart_init = '/etc/init/%s.conf' % svc sysv_init = '/etc/init.d/%s' % svc if host.init_is_systemd(): nrpe.add_check( shortname=svc, description='process check {%s}' % unit_name, check_cmd='check_systemd.py %s' % svc ) elif os.path.exists(upstart_init): nrpe.add_check( shortname=svc, description='process check {%s}' % unit_name, check_cmd='check_upstart_job %s' % svc ) elif os.path.exists(sysv_init): cronpath = '/etc/cron.d/nagios-service-check-%s' % svc checkpath = '%s/service-check-%s.txt' % (nrpe.homedir, svc) croncmd = ( '/usr/local/lib/nagios/plugins/check_exit_status.pl ' '-e -s /etc/init.d/%s status' % svc ) cron_file = '*/5 * * * * root %s > %s\n' % (croncmd, checkpath) f = open(cronpath, 'w') f.write(cron_file) f.close() nrpe.add_check( shortname=svc, description='service check {%s}' % unit_name, check_cmd='check_status_file.py -f %s' % checkpath, ) # if /var/lib/nagios doesn't exist open(checkpath, 'w') will fail # (LP: #1670223). if immediate_check and os.path.isdir(nrpe.homedir): f = open(checkpath, 'w') subprocess.call( croncmd.split(), stdout=f, stderr=subprocess.STDOUT ) f.close() os.chmod(checkpath, 0o644)
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Add checks for each service in list :param NRPE nrpe: NRPE object to add check to :param list services: List of services to check :param str unit_name: Unit name to use in check description :param bool immediate_check: For sysv init, run the service check immediately
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python
train
tcalmant/ipopo
pelix/internals/registry.py
https://github.com/tcalmant/ipopo/blob/2f9ae0c44cd9c34ef1a9d50837b3254e75678eb1/pelix/internals/registry.py#L163-L187
def _get_from_prototype(self, factory, svc_registration): # type: (Any, ServiceRegistration) -> Any """ Returns a service instance from a Prototype Service Factory :param factory: The service factory :param svc_registration: The ServiceRegistration object :return: The requested service instance returned by the factory """ svc_ref = svc_registration.get_reference() service = factory.get_service(self.__bundle, svc_registration) try: # Check if the service already exists services, counter = self.__factored[svc_ref] services.append(service) counter.inc() except KeyError: counter = _UsageCounter() counter.inc() # Store the counter self.__factored[svc_ref] = ([service], counter) return service
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Returns a service instance from a Prototype Service Factory :param factory: The service factory :param svc_registration: The ServiceRegistration object :return: The requested service instance returned by the factory
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python
train
vaexio/vaex
packages/vaex-core/vaex/ext/readcol.py
https://github.com/vaexio/vaex/blob/a45b672f8287afca2ada8e36b74b604b9b28dd85/packages/vaex-core/vaex/ext/readcol.py#L223-L236
def get_autotype(arr): """ Attempts to return a numpy array converted to the most sensible dtype Value errors will be caught and simply return the original array Tries to make dtype int, then float, then no change """ try: narr = arr.astype('float') if (narr < sys.maxsize).all() and (narr % 1).sum() == 0: return narr.astype('int') else: return narr except ValueError: return arr
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Attempts to return a numpy array converted to the most sensible dtype Value errors will be caught and simply return the original array Tries to make dtype int, then float, then no change
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python
test
PredixDev/predixpy
predix/service.py
https://github.com/PredixDev/predixpy/blob/a0cb34cf40f716229351bb6d90d6ecace958c81f/predix/service.py#L113-L126
def _delete(self, uri): """ Simple DELETE operation for a given path. """ headers = self._get_headers() response = self.session.delete(uri, headers=headers) # Will return a 204 on successful delete if response.status_code == 204: return response else: logging.error(response.content) response.raise_for_status()
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Simple DELETE operation for a given path.
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python
train
spyder-ide/spyder
spyder/plugins/explorer/widgets.py
https://github.com/spyder-ide/spyder/blob/f76836ce1b924bcc4efd3f74f2960d26a4e528e0/spyder/plugins/explorer/widgets.py#L1052-L1057
def set_scrollbar_position(self, position): """Set scrollbar positions""" # Scrollbars will be restored after the expanded state self._scrollbar_positions = position if self._to_be_loaded is not None and len(self._to_be_loaded) == 0: self.restore_scrollbar_positions()
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Set scrollbar positions
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python
train
python/performance
performance/benchmarks/bm_mdp.py
https://github.com/python/performance/blob/2a9524c0a5714e85106671bc61d750e800fe17db/performance/benchmarks/bm_mdp.py#L9-L33
def topoSort(roots, getParents): """Return a topological sorting of nodes in a graph. roots - list of root nodes to search from getParents - function which returns the parents of a given node """ results = [] visited = set() # Use iterative version to avoid stack limits for large datasets stack = [(node, 0) for node in roots] while stack: current, state = stack.pop() if state == 0: # before recursing if current not in visited: visited.add(current) stack.append((current, 1)) stack.extend((parent, 0) for parent in getParents(current)) else: # after recursing assert(current in visited) results.append(current) return results
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Return a topological sorting of nodes in a graph. roots - list of root nodes to search from getParents - function which returns the parents of a given node
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python
test
bxlab/bx-python
lib/bx/intervals/random_intervals.py
https://github.com/bxlab/bx-python/blob/09cb725284803df90a468d910f2274628d8647de/lib/bx/intervals/random_intervals.py#L98-L212
def throw_random_private( lengths, regions, save_interval_func, allow_overlap=False, three_args=True ): """ (Internal function; we expect calls only through the interface functions above) `lengths`: A list containing the length of each interval to be generated. `regions`: A list of regions in which intervals can be placed, sorted by decreasing length. Elements are triples of the form (length, start, extra), This list CAN BE MODIFIED by this function. `save_interval_func`: A function accepting three arguments which will be passed the (start,stop,extra) for each generated interval. """ # Implementation: # We keep a list of the regions, sorted from largest to smallest. We then # place each length by following steps: # (1) construct a candidate counts array (cc array) # (2) choose a candidate at random # (3) find region containing that candidate # (4) map candidate to position in that region # (5) split region if not allowing overlaps # (6) report placed segment # # The cc array is only constructed if there's a change (different length # to place, or the region list has changed). It contains, for each # region, the total number of number of candidate positions in regions # *preceding* it in the region list: # cc[i] = sum over k in 0..(i-1) of length[i] - L + 1 # where N is the number of regions and L is the length being thrown. # At the same time, we determine the total number of candidates (the total # number of places the current length can be placed) and the index range # of regions into which the length will fit. # # example: # for L = 20 # i = 0 1 2 3 4 5 6 7 8 9 # length[i] = 96 66 56 50 48 40 29 17 11 8 # cc[i] = 0 77 124 161 192 221 242 X X X # candidates = 252 # lo_rgn = 0 # hi_rgn = 6 # # The candidate is chosen in (0..candidates-1). The candidate counts # array allows us to do a binary search to locate the region that holds that # candidate. Continuing the example above, we choose a random candidate # s in (0..251). If s happens to be in (124..160), it will be mapped to # region 2 at start position s-124. # # During the binary search, if we are looking at region 3, if s < cc[3] # then the desired region is region 2 or lower. Otherwise it is region 3 or # higher. min_length = min( lengths ) prev_length = None # (force initial cc array construction) cc = [0] * (len( regions ) + len(lengths) - 1) num_thrown = 0 for length in lengths: # construct cc array (only needed if length has changed or region list has # changed) if length != prev_length: prev_length = length assert len( cc ) >= len( regions ) candidates = 0 hi_rgn = 0 for region in regions: rgn_len = region[0] if rgn_len < length: break cc[hi_rgn] = candidates candidates += rgn_len - length + 1 hi_rgn += 1 if candidates == 0: raise MaxtriesException( "No region can fit an interval of length %d (we threw %d of %d)" \ % ( length, num_thrown,len( lengths ) ) ) hi_rgn -= 1 # Select a candidate s = random.randrange( candidates ) #.. #..for ix in range( len( regions ) ): #.. region = regions[ix] #.. if ix <= hi_rgn: print "%2s: %5s %5s %5s" % ( ix, region[1], region[0], cc[ix] ) #.. else: print "%2s: %5s %5s %5s" % ( ix, region[1], region[0], "X" ) #..print "s = %s (of %s candidates)" % ( s, candidates ) # Locate region containing that candidate, by binary search lo = 0 hi = hi_rgn while hi > lo: mid = (lo + hi + 1) / 2 # (we round up to prevent infinite loop) if s < cc[mid]: hi = mid-1 # (s < num candidates from 0..mid-1) else: lo = mid # (s >= num candidates from 0..mid-1) s -= cc[lo] # If we are not allowing overlaps we will remove the placed interval # from the region list if allow_overlap: rgn_length, rgn_start, rgn_extra = regions[lo] else: # Remove the chosen region and split rgn_length, rgn_start, rgn_extra = regions.pop( lo ) rgn_end = rgn_start + rgn_length assert s >= 0 assert rgn_start + s + length <= rgn_end, "Expected: %d + %d + %d == %d <= %d" % ( rgn_start, s, length, rgn_start + s + length, rgn_end ) regions.reverse() if s >= min_length: bisect.insort( regions, ( s, rgn_start, rgn_extra ) ) if s + length <= rgn_length - min_length: bisect.insort( regions, ( rgn_length - ( s + length ), rgn_start + s + length, rgn_extra ) ) regions.reverse() prev_length = None # (force cc array construction) # Save the new interval if (three_args): save_interval_func( rgn_start + s, rgn_start + s + length, rgn_extra ) else: save_interval_func( rgn_start + s, rgn_start + s + length ) num_thrown += 1
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(Internal function; we expect calls only through the interface functions above) `lengths`: A list containing the length of each interval to be generated. `regions`: A list of regions in which intervals can be placed, sorted by decreasing length. Elements are triples of the form (length, start, extra), This list CAN BE MODIFIED by this function. `save_interval_func`: A function accepting three arguments which will be passed the (start,stop,extra) for each generated interval.
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python
train
LucidtechAI/las-sdk-python
las/api_client.py
https://github.com/LucidtechAI/las-sdk-python/blob/5f39dee7983baff28a1deb93c12d36414d835d12/las/api_client.py#L30-L57
def predict(self, document_path: str, model_name: str, consent_id: str = None) -> Prediction: """Run inference and create prediction on document. This method takes care of creating and uploading a document specified by document_path. as well as running inference using model specified by model_name to create prediction on the document. >>> from las import ApiClient >>> api_client = ApiClient(endpoint='<api endpoint>') >>> api_client.predict(document_path='document.jpeg', model_name='invoice') :param document_path: Path to document to run inference on :type document_path: str :param model_name: The name of the model to use for inference :type model_name: str :param consent_id: An identifier to mark the owner of the document handle :type consent_id: str :return: Prediction on document :rtype: Prediction :raises InvalidCredentialsException: If the credentials are invalid :raises TooManyRequestsException: If limit of requests per second is reached :raises LimitExceededException: If limit of total requests per month is reached :raises requests.exception.RequestException: If error was raised by requests """ content_type = self._get_content_type(document_path) consent_id = consent_id or str(uuid4()) document_id = self._upload_document(document_path, content_type, consent_id) prediction_response = self.post_predictions(document_id, model_name) return Prediction(document_id, consent_id, model_name, prediction_response)
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Run inference and create prediction on document. This method takes care of creating and uploading a document specified by document_path. as well as running inference using model specified by model_name to create prediction on the document. >>> from las import ApiClient >>> api_client = ApiClient(endpoint='<api endpoint>') >>> api_client.predict(document_path='document.jpeg', model_name='invoice') :param document_path: Path to document to run inference on :type document_path: str :param model_name: The name of the model to use for inference :type model_name: str :param consent_id: An identifier to mark the owner of the document handle :type consent_id: str :return: Prediction on document :rtype: Prediction :raises InvalidCredentialsException: If the credentials are invalid :raises TooManyRequestsException: If limit of requests per second is reached :raises LimitExceededException: If limit of total requests per month is reached :raises requests.exception.RequestException: If error was raised by requests
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python
train
sentinel-hub/sentinelhub-py
sentinelhub/geopedia.py
https://github.com/sentinel-hub/sentinelhub-py/blob/08a83b7f1e289187159a643336995d8369860fea/sentinelhub/geopedia.py#L220-L228
def get_request(self, request): """Get a list of DownloadRequests for all data that are under the given field in the table of a Geopedia layer. :return: list of items which have to be downloaded :rtype: list(DownloadRequest) """ request.layer = self._parse_layer(request.layer, return_wms_name=True) return super().get_request(request)
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Get a list of DownloadRequests for all data that are under the given field in the table of a Geopedia layer. :return: list of items which have to be downloaded :rtype: list(DownloadRequest)
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python
train
saltant-org/saltant-py
saltant/models/resource.py
https://github.com/saltant-org/saltant-py/blob/bf3bdbc4ec9c772c7f621f8bd6a76c5932af68be/saltant/models/resource.py#L194-L220
def validate_request_success( response_text, request_url, status_code, expected_status_code ): """Validates that a request was successful. Args: response_text (str): The response body of the request. request_url (str): The URL the request was made at. status_code (int): The status code of the response. expected_status_code (int): The expected status code of the response. Raises: :class:`saltant.exceptions.BadHttpRequestError`: The HTTP request failed. """ try: assert status_code == expected_status_code except AssertionError: msg = ( "Request to {url} failed with status {status_code}:\n" "The reponse from the request was as follows:\n\n" "{content}" ).format( url=request_url, status_code=status_code, content=response_text ) raise BadHttpRequestError(msg)
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Validates that a request was successful. Args: response_text (str): The response body of the request. request_url (str): The URL the request was made at. status_code (int): The status code of the response. expected_status_code (int): The expected status code of the response. Raises: :class:`saltant.exceptions.BadHttpRequestError`: The HTTP request failed.
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python
train
bitesofcode/projexui
projexui/widgets/xganttwidget/xganttwidget.py
https://github.com/bitesofcode/projexui/blob/f18a73bec84df90b034ca69b9deea118dbedfc4d/projexui/widgets/xganttwidget/xganttwidget.py#L255-L266
def centerOnDateTime(self, dtime): """ Centers the view on a given datetime for the gantt widget. :param dtime | <QDateTime> """ view = self.uiGanttVIEW scene = view.scene() point = view.mapToScene(0, 0) x = scene.datetimeXPos(dtime) y = point.y() view.centerOn(x, y)
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Centers the view on a given datetime for the gantt widget. :param dtime | <QDateTime>
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python
train
sonyxperiadev/pygerrit
pygerrit/events.py
https://github.com/sonyxperiadev/pygerrit/blob/756300120b0f4f4af19e0f985566d82bc80b4359/pygerrit/events.py#L40-L56
def register(cls, name): """ Decorator to register the event identified by `name`. Return the decorated class. Raise GerritError if the event is already registered. """ def decorate(klazz): """ Decorator. """ if name in cls._events: raise GerritError("Duplicate event: %s" % name) cls._events[name] = [klazz.__module__, klazz.__name__] klazz.name = name return klazz return decorate
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Decorator to register the event identified by `name`. Return the decorated class. Raise GerritError if the event is already registered.
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python
train
polyaxon/hestia
hestia/date_formatter.py
https://github.com/polyaxon/hestia/blob/382ed139cff8bf35c987cfc30a31b72c0d6b808e/hestia/date_formatter.py#L84-L96
def extract_datetime_hour(cls, datetime_str): """ Tries to extract a `datetime` object from the given string, including only hours. Raises `DateTimeFormatterException` if the extraction fails. """ if not datetime_str: raise DateTimeFormatterException('datetime_str must a valid string') try: return cls._extract_timestamp(datetime_str, cls.DATETIME_HOUR_FORMAT) except (TypeError, ValueError): raise DateTimeFormatterException('Invalid datetime string {}.'.format(datetime_str))
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Tries to extract a `datetime` object from the given string, including only hours. Raises `DateTimeFormatterException` if the extraction fails.
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python
train
jaraco/rst.linker
rst/linker.py
https://github.com/jaraco/rst.linker/blob/5d0ff09756c325c46c471c217bdefcfd11ce1de4/rst/linker.py#L142-L153
def from_definition(cls, defn, names={}): """ A definition may contain the following members: - using: a dictionary of variables available for substitution - replace: a list of replacement definitions. """ repls = map(Repl.from_defn, defn.get('replace', [])) self = cls(repls) vars(self).update(names) vars(self).update(defn.get('using', {})) return self
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A definition may contain the following members: - using: a dictionary of variables available for substitution - replace: a list of replacement definitions.
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python
train
maartenbreddels/ipyvolume
ipyvolume/examples.py
https://github.com/maartenbreddels/ipyvolume/blob/e68b72852b61276f8e6793bc8811f5b2432a155f/ipyvolume/examples.py#L161-L229
def brain( draw=True, show=True, fiducial=True, flat=True, inflated=True, subject='S1', interval=1000, uv=True, color=None ): """Show a human brain model. Requirement: $ pip install https://github.com/gallantlab/pycortex """ import ipyvolume as ipv try: import cortex except: warnings.warn("it seems pycortex is not installed, which is needed for this example") raise xlist, ylist, zlist = [], [], [] polys_list = [] def add(pts, polys): xlist.append(pts[:, 0]) ylist.append(pts[:, 1]) zlist.append(pts[:, 2]) polys_list.append(polys) def n(x): return (x - x.min()) / x.ptp() if fiducial or color is True: pts, polys = cortex.db.get_surf('S1', 'fiducial', merge=True) x, y, z = pts.T r = n(x) g = n(y) b = n(z) if color is True: color = np.array([r, g, b]).T.copy() else: color = None if fiducial: add(pts, polys) else: if color is False: color = None if inflated: add(*cortex.db.get_surf('S1', 'inflated', merge=True, nudge=True)) u = v = None if flat or uv: pts, polys = cortex.db.get_surf('S1', 'flat', merge=True, nudge=True) x, y, z = pts.T u = n(x) v = n(y) if flat: add(pts, polys) polys_list.sort(key=lambda x: len(x)) polys = polys_list[0] if draw: if color is None: mesh = ipv.plot_trisurf(xlist, ylist, zlist, polys, u=u, v=v) else: mesh = ipv.plot_trisurf(xlist, ylist, zlist, polys, color=color, u=u, v=v) if show: if len(x) > 1: ipv.animation_control(mesh, interval=interval) ipv.squarelim() ipv.show() return mesh else: return xlist, ylist, zlist, polys
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Show a human brain model. Requirement: $ pip install https://github.com/gallantlab/pycortex
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python
train
Parsl/parsl
parsl/providers/aws/aws.py
https://github.com/Parsl/parsl/blob/d7afb3bc37f50dcf224ae78637944172edb35dac/parsl/providers/aws/aws.py#L207-L220
def write_state_file(self): """Save information that must persist to a file. We do not want to create a new VPC and new identical security groups, so we save information about them in a file between runs. """ fh = open('awsproviderstate.json', 'w') state = {} state['vpcID'] = self.vpc_id state['sgID'] = self.sg_id state['snIDs'] = self.sn_ids state['instances'] = self.instances state["instanceState"] = self.instance_states fh.write(json.dumps(state, indent=4))
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Save information that must persist to a file. We do not want to create a new VPC and new identical security groups, so we save information about them in a file between runs.
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python
valid
edoburu/sphinxcontrib-django
sphinxcontrib_django/docstrings.py
https://github.com/edoburu/sphinxcontrib-django/blob/5116ac7f1510a76b1ff58cf7f8d2fab7d8bbe2a9/sphinxcontrib_django/docstrings.py#L88-L110
def improve_model_docstring(app, what, name, obj, options, lines): """Hook that improves the autodoc docstrings for Django models. :type app: sphinx.application.Sphinx :param what: The parent type, ``class`` or ``module`` :type what: str :param name: The dotted path to the child method/attribute. :type name: str :param obj: The Python object that i s being documented. :param options: The current autodoc settings. :type options: dict :param lines: The current documentation lines :type lines: list """ if what == 'class': _improve_class_docs(app, obj, lines) elif what == 'attribute': _improve_attribute_docs(obj, name, lines) elif what == 'method': _improve_method_docs(obj, name, lines) # Return the extended docstring return lines
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Hook that improves the autodoc docstrings for Django models. :type app: sphinx.application.Sphinx :param what: The parent type, ``class`` or ``module`` :type what: str :param name: The dotted path to the child method/attribute. :type name: str :param obj: The Python object that i s being documented. :param options: The current autodoc settings. :type options: dict :param lines: The current documentation lines :type lines: list
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python
train
singularityhub/singularity-cli
spython/main/parse/recipe.py
https://github.com/singularityhub/singularity-cli/blob/cb36b4504812ca87e29c6a40b222a545d1865799/spython/main/parse/recipe.py#L287-L305
def _write_script(path, lines, chmod=True): '''write a script with some lines content to path in the image. This is done by way of adding echo statements to the install section. Parameters ========== path: the path to the file to write lines: the lines to echo to the file chmod: If true, change permission to make u+x ''' if len(lines) > 0: lastline = lines.pop() for line in lines: self.install.append('echo "%s" >> %s' %path) self.install.append(lastline) if chmod is True: self.install.append('chmod u+x %s' %path)
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write a script with some lines content to path in the image. This is done by way of adding echo statements to the install section. Parameters ========== path: the path to the file to write lines: the lines to echo to the file chmod: If true, change permission to make u+x
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python
train
nschloe/matplotlib2tikz
matplotlib2tikz/save.py
https://github.com/nschloe/matplotlib2tikz/blob/ac5daca6f38b834d757f6c6ae6cc34121956f46b/matplotlib2tikz/save.py#L337-L415
def _recurse(data, obj): """Iterates over all children of the current object, gathers the contents contributing to the resulting PGFPlots file, and returns those. """ content = _ContentManager() for child in obj.get_children(): # Some patches are Spines, too; skip those entirely. # See <https://github.com/nschloe/matplotlib2tikz/issues/277>. if isinstance(child, mpl.spines.Spine): continue if isinstance(child, mpl.axes.Axes): ax = axes.Axes(data, child) if ax.is_colorbar: continue # add extra axis options if data["extra axis options [base]"]: ax.axis_options.extend(data["extra axis options [base]"]) data["current mpl axes obj"] = child data["current axes"] = ax # Run through the child objects, gather the content. data, children_content = _recurse(data, child) # populate content and add axis environment if desired if data["add axis environment"]: content.extend( ax.get_begin_code() + children_content + [ax.get_end_code(data)], 0 ) else: content.extend(children_content, 0) # print axis environment options, if told to show infos if data["show_info"]: print("=========================================================") print("These would have been the properties of the environment:") print("".join(ax.get_begin_code()[1:])) print("=========================================================") elif isinstance(child, mpl.lines.Line2D): data, cont = line2d.draw_line2d(data, child) content.extend(cont, child.get_zorder()) elif isinstance(child, mpl.image.AxesImage): data, cont = img.draw_image(data, child) content.extend(cont, child.get_zorder()) elif isinstance(child, mpl.patches.Patch): data, cont = patch.draw_patch(data, child) content.extend(cont, child.get_zorder()) elif isinstance( child, (mpl.collections.PatchCollection, mpl.collections.PolyCollection) ): data, cont = patch.draw_patchcollection(data, child) content.extend(cont, child.get_zorder()) elif isinstance(child, mpl.collections.PathCollection): data, cont = path.draw_pathcollection(data, child) content.extend(cont, child.get_zorder()) elif isinstance(child, mpl.collections.LineCollection): data, cont = line2d.draw_linecollection(data, child) content.extend(cont, child.get_zorder()) elif isinstance(child, mpl.collections.QuadMesh): data, cont = qmsh.draw_quadmesh(data, child) content.extend(cont, child.get_zorder()) elif isinstance(child, mpl.legend.Legend): data = legend.draw_legend(data, child) if data["legend colors"]: content.extend(data["legend colors"], 0) elif isinstance(child, (mpl.text.Text, mpl.text.Annotation)): data, cont = text.draw_text(data, child) content.extend(cont, child.get_zorder()) elif isinstance(child, (mpl.axis.XAxis, mpl.axis.YAxis)): pass else: warnings.warn( "matplotlib2tikz: Don't know how to handle object {}.".format( type(child) ) ) return data, content.flatten()
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Iterates over all children of the current object, gathers the contents contributing to the resulting PGFPlots file, and returns those.
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python
train
chrippa/python-librtmp
librtmp/stream.py
https://github.com/chrippa/python-librtmp/blob/6efefd5edd76cad7a3b53f7c87c1c7350448224d/librtmp/stream.py#L83-L92
def seek(self, time): """Attempts to seek in the stream. :param time: int, Time to seek to in seconds """ res = librtmp.RTMP_SendSeek(self.client.rtmp, time) if res < 1: raise RTMPError("Failed to seek")
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Attempts to seek in the stream. :param time: int, Time to seek to in seconds
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python
train
data61/clkhash
clkhash/cli.py
https://github.com/data61/clkhash/blob/ec6398d6708a063de83f7c3d6286587bff8e7121/clkhash/cli.py#L99-L110
def status(server, output, verbose): """Connect to an entity matching server and check the service status. Use "-" to output status to stdout. """ if verbose: log("Connecting to Entity Matching Server: {}".format(server)) service_status = server_get_status(server) if verbose: log("Status: {}".format(service_status['status'])) print(json.dumps(service_status), file=output)
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Connect to an entity matching server and check the service status. Use "-" to output status to stdout.
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python
train
marcomusy/vtkplotter
vtkplotter/actors.py
https://github.com/marcomusy/vtkplotter/blob/692c3396782722ec525bc1346a26999868c650c6/vtkplotter/actors.py#L62-L114
def isosurface(image, smoothing=0, threshold=None, connectivity=False): """Return a ``vtkActor`` isosurface extracted from a ``vtkImageData`` object. :param float smoothing: gaussian filter to smooth vtkImageData, in units of sigmas :param threshold: value or list of values to draw the isosurface(s) :type threshold: float, list :param bool connectivity: if True only keeps the largest portion of the polydata .. hint:: |isosurfaces| |isosurfaces.py|_ """ if smoothing: smImg = vtk.vtkImageGaussianSmooth() smImg.SetDimensionality(3) smImg.SetInputData(image) smImg.SetStandardDeviations(smoothing, smoothing, smoothing) smImg.Update() image = smImg.GetOutput() scrange = image.GetScalarRange() if scrange[1] > 1e10: print("Warning, high scalar range detected:", scrange) cf = vtk.vtkContourFilter() cf.SetInputData(image) cf.UseScalarTreeOn() cf.ComputeScalarsOn() if utils.isSequence(threshold): cf.SetNumberOfContours(len(threshold)) for i, t in enumerate(threshold): cf.SetValue(i, t) cf.Update() else: if not threshold: threshold = (2 * scrange[0] + scrange[1]) / 3.0 cf.SetValue(0, threshold) cf.Update() clp = vtk.vtkCleanPolyData() clp.SetInputConnection(cf.GetOutputPort()) clp.Update() poly = clp.GetOutput() if connectivity: conn = vtk.vtkPolyDataConnectivityFilter() conn.SetExtractionModeToLargestRegion() conn.SetInputData(poly) conn.Update() poly = conn.GetOutput() a = Actor(poly, c=None).phong() a.mapper.SetScalarRange(scrange[0], scrange[1]) return a
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Return a ``vtkActor`` isosurface extracted from a ``vtkImageData`` object. :param float smoothing: gaussian filter to smooth vtkImageData, in units of sigmas :param threshold: value or list of values to draw the isosurface(s) :type threshold: float, list :param bool connectivity: if True only keeps the largest portion of the polydata .. hint:: |isosurfaces| |isosurfaces.py|_
[ "Return", "a", "vtkActor", "isosurface", "extracted", "from", "a", "vtkImageData", "object", "." ]
python
train