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def _inverse_permutation_indices(positions): """Like inverse_permutation, but also handles slices. Parameters ---------- positions : list of np.ndarray or slice objects. If slice objects, all are assumed to be slices. Returns ------- np.ndarray of indices or None, if no permutation is necessary. """ if not positions: return None if isinstance(positions[0], slice): positions = _consolidate_slices(positions) if positions == slice(None): return None positions = [np.arange(sl.start, sl.stop, sl.step) for sl in positions] indices = nputils.inverse_permutation(np.concatenate(positions)) return indices
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def _next_partition(self, topic, key=None): """get the next partition to which to publish Check with our client for the latest partitions for the topic, then ask our partitioner for the next partition to which we should publish for the give key. If needed, create a new partitioner for the topic. """ # check if the client has metadata for the topic while self.client.metadata_error_for_topic(topic): # client doesn't have good metadata for topic. ask to fetch... # check if we have request attempts left if self._req_attempts >= self._max_attempts: # No, no attempts left, so raise the error _check_error(self.client.metadata_error_for_topic(topic)) yield self.client.load_metadata_for_topics(topic) if not self.client.metadata_error_for_topic(topic): break self._req_attempts += 1 d = Deferred() self.client.reactor.callLater( self._retry_interval, d.callback, True) self._retry_interval *= self.RETRY_INTERVAL_FACTOR yield d # Ok, should be safe to get the partitions now... partitions = self.client.topic_partitions[topic] # Do we have a partitioner for this topic already? if topic not in self.partitioners: # No, create a new paritioner for topic, partitions self.partitioners[topic] = \ self.partitioner_class(topic, partitions) # Lookup the next partition partition = self.partitioners[topic].partition(key, partitions) returnValue(partition)
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def Builder(**kw): """A factory for builder objects.""" composite = None if 'generator' in kw: if 'action' in kw: raise UserError("You must not specify both an action and a generator.") kw['action'] = SCons.Action.CommandGeneratorAction(kw['generator'], {}) del kw['generator'] elif 'action' in kw: source_ext_match = kw.get('source_ext_match', 1) if 'source_ext_match' in kw: del kw['source_ext_match'] if SCons.Util.is_Dict(kw['action']): composite = DictCmdGenerator(kw['action'], source_ext_match) kw['action'] = SCons.Action.CommandGeneratorAction(composite, {}) kw['src_suffix'] = composite.src_suffixes() else: kw['action'] = SCons.Action.Action(kw['action']) if 'emitter' in kw: emitter = kw['emitter'] if SCons.Util.is_String(emitter): # This allows users to pass in an Environment # variable reference (like "$FOO") as an emitter. # We will look in that Environment variable for # a callable to use as the actual emitter. var = SCons.Util.get_environment_var(emitter) if not var: raise UserError("Supplied emitter '%s' does not appear to refer to an Environment variable" % emitter) kw['emitter'] = EmitterProxy(var) elif SCons.Util.is_Dict(emitter): kw['emitter'] = DictEmitter(emitter) elif SCons.Util.is_List(emitter): kw['emitter'] = ListEmitter(emitter) result = BuilderBase(**kw) if not composite is None: result = CompositeBuilder(result, composite) return result
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def _remove_qualifiers(obj): """ Remove all qualifiers from the input objectwhere the object may be an CIMInstance or CIMClass. Removes qualifiers from the object and from properties, methods, and parameters This is used to process the IncludeQualifier parameter for classes and instances """ assert isinstance(obj, (CIMInstance, CIMClass)) obj.qualifiers = NocaseDict() for prop in obj.properties: obj.properties[prop].qualifiers = NocaseDict() if isinstance(obj, CIMClass): for method in obj.methods: obj.methods[method].qualifiers = NocaseDict() for param in obj.methods[method].parameters: obj.methods[method].parameters[param].qualifiers = \ NocaseDict()
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def taper(self, side='leftright'): """Taper the ends of this `TimeSeries` smoothly to zero. Parameters ---------- side : `str`, optional the side of the `TimeSeries` to taper, must be one of `'left'`, `'right'`, or `'leftright'` Returns ------- out : `TimeSeries` a copy of `self` tapered at one or both ends Raises ------ ValueError if `side` is not one of `('left', 'right', 'leftright')` Examples -------- To see the effect of the Planck-taper window, we can taper a sinusoidal `TimeSeries` at both ends: >>> import numpy >>> from gwpy.timeseries import TimeSeries >>> t = numpy.linspace(0, 1, 2048) >>> series = TimeSeries(numpy.cos(10.5*numpy.pi*t), times=t) >>> tapered = series.taper() We can plot it to see how the ends now vary smoothly from 0 to 1: >>> from gwpy.plot import Plot >>> plot = Plot(series, tapered, separate=True, sharex=True) >>> plot.show() Notes ----- The :meth:`TimeSeries.taper` automatically tapers from the second stationary point (local maximum or minimum) on the specified side of the input. However, the method will never taper more than half the full width of the `TimeSeries`, and will fail if there are no stationary points. See :func:`~gwpy.signal.window.planck` for the generic Planck taper window, and see :func:`scipy.signal.get_window` for other common window formats. """ # check window properties if side not in ('left', 'right', 'leftright'): raise ValueError("side must be one of 'left', 'right', " "or 'leftright'") out = self.copy() # identify the second stationary point away from each boundary, # else default to half the TimeSeries width nleft, nright = 0, 0 mini, = signal.argrelmin(out.value) maxi, = signal.argrelmax(out.value) if 'left' in side: nleft = max(mini[0], maxi[0]) nleft = min(nleft, self.size/2) if 'right' in side: nright = out.size - min(mini[-1], maxi[-1]) nright = min(nright, self.size/2) out *= planck(out.size, nleft=nleft, nright=nright) return out
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def on_data(self, ws, message, message_type, fin): """ Callback executed when message is received from the server. :param ws: Websocket client :param message: utf-8 string which we get from the server. :param message_type: Message type which is either ABNF.OPCODE_TEXT or ABNF.OPCODE_BINARY :param fin: continue flag. If 0, the data continues. """ try: json_object = json.loads(message) except Exception: self.on_error(ws, 'Unable to parse received message.') if 'error' in json_object: # Only call on_error() if a real error occurred. The STT service sends # {"error" : "No speech detected for 5s"} for valid timeouts, configured by # options.inactivity_timeout error = json_object['error'] if error.startswith(TIMEOUT_PREFIX): self.callback.on_inactivity_timeout(error) else: self.on_error(ws, error) # if uninitialized, receive the initialization response from the server elif 'state' in json_object: if not self.isListening: self.isListening = True self.callback.on_listening() self.send_audio(ws) else: # close the connection self.callback.on_close() ws.close() # if in streaming elif 'results' in json_object or 'speaker_labels' in json_object: hypothesis = '' if 'results' in json_object: hypothesis = json_object['results'][0]['alternatives'][0][ 'transcript'] b_final = (json_object['results'][0]['final'] is True) transcripts = self.extract_transcripts( json_object['results'][0]['alternatives']) if b_final: self.callback.on_transcription(transcripts) self.callback.on_hypothesis(hypothesis) self.callback.on_data(json_object)
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def remove_wirevector(self, wirevector): """ Remove a wirevector object to the block.""" self.wirevector_set.remove(wirevector) del self.wirevector_by_name[wirevector.name]
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def get_scrim(path=None, auto_write=None, shell=None, script=None, cache={}): '''Get a :class:`Scrim` instance. Each instance is cached so if you call get_scrim again with the same arguments you get the same instance. See also: :class:`Scrim` ''' args = (path, auto_write, shell, script) if args not in cache: cache[args] = Scrim(*args) return cache[args]
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def make_choice_validator( choices, default_key=None, normalizer=None): """ Returns a callable that accepts the choices provided. Choices should be provided as a list of 2-tuples, where the first element is a string that should match user input (the key); the second being the value associated with the key. The callable by default will match, upon complete match the first value associated with the result will be returned. Partial matches are supported. If a default is provided, that value will be returned if the user provided input is empty, i.e. the value that is mapped to the empty string. Finally, a normalizer function can be passed. This normalizes all keys and validation value. """ def normalize_all(_choices): # normalize all the keys for easier comparison if normalizer: _choices = [(normalizer(key), value) for key, value in choices] return _choices choices = normalize_all(choices) def choice_validator(value): if normalizer: value = normalizer(value) if not value and default_key: value = choices[default_key][0] results = [] for choice, mapped in choices: if value == choice: return mapped if choice.startswith(value): results.append((choice, mapped)) if len(results) == 1: return results[0][1] elif not results: raise ValueError('Invalid choice.') else: raise ValueError( 'Choice ambiguous between (%s)' % ', '.join( k for k, v in normalize_all(results)) ) return choice_validator
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def default(self, obj): """If input object is an ndarray it will be converted into a dict holding dtype, shape and the data, base64 encoded. """ if isinstance(obj, np.ndarray): if obj.flags['C_CONTIGUOUS']: obj_data = obj.data else: cont_obj = np.ascontiguousarray(obj) assert(cont_obj.flags['C_CONTIGUOUS']) obj_data = cont_obj.data data_b64 = base64.b64encode(obj_data) return dict(__ndarray__=data_b64, dtype=str(obj.dtype), shape=obj.shape) elif isinstance(obj, np.generic): return np.asscalar(obj) # Let the base class default method raise the TypeError return json.JSONEncoder(self, obj)
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def UV_H(Hg,gw): """ Constructs implications and intents based on H gw = g width Hg = H(g), g is the binary coding of the attribute set UV = all non-trivial (!V⊂U) implications U->V with UuV closed; in ternary coding (1=V,2=U) K = all closed sets """ lefts = set() K = [] UV = [] p = Hwidth(gw) pp = 2**p while p: pp = pp>>1 p = p-1 if Hg&pp: y = istr(p,3,gw) yy = y.replace('1','0') if yy not in lefts: if y.find('1') == -1:#y∈{0,2}^n K.append(y) else: UV.append(y) lefts.add(yy) return (UV,K)
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def cli_check_normalize_args(args): '''Check and normalize arguments This function checks that basis set names, families, roles, etc, are valid (and raise an exception if they aren't) The original data passed to this function is not modified. A modified copy is returned. ''' args_keys = vars(args).keys() # What args we have args_copy = copy.copy(args) if 'data_dir' in args_keys: args_copy.data_dir = _cli_check_data_dir(args.data_dir) if 'basis' in args: args_copy.basis = _cli_check_basis(args.basis, args.data_dir) if 'basis1' in args_keys: args_copy.basis1 = _cli_check_basis(args.basis1, args.data_dir) if 'basis2' in args_keys: args_copy.basis2 = _cli_check_basis(args.basis2, args.data_dir) if 'fmt' in args_keys: args_copy.fmt = _cli_check_format(args.fmt) if 'reffmt' in args_keys: args_copy.reffmt = _cli_check_ref_format(args.reffmt) if 'role' in args_keys: args_copy.role = _cli_check_role(args.role) if 'family' in args_keys: args_copy.family = _cli_check_family(args.family, args.data_dir) if 'readfmt1' in args_keys: args_copy.readfmt1 = _cli_check_readfmt(args.readfmt1) if 'readfmt2' in args_keys: args_copy.readfmt2 = _cli_check_readfmt(args.readfmt2) return args_copy
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40.242424
0.000735
def to_str(obj, encoding='utf-8', **encode_args): r""" Returns a ``str`` of ``obj``, encoding using ``encoding`` if necessary. For example:: >>> some_str = b"\xff" >>> some_unicode = u"\u1234" >>> some_exception = Exception(u'Error: ' + some_unicode) >>> r(to_str(some_str)) b'\xff' >>> r(to_str(some_unicode)) b'\xe1\x88\xb4' >>> r(to_str(some_exception)) b'Error: \xe1\x88\xb4' >>> r(to_str([42])) b'[42]' See source code for detailed semantics. """ # Note: On py3, ``b'x'.__str__()`` returns ``"b'x'"``, so we need to do the # explicit check first. if isinstance(obj, binary_type): return obj # We coerce to unicode if '__unicode__' is available because there is no # way to specify encoding when calling ``str(obj)``, so, eg, # ``str(Exception(u'\u1234'))`` will explode. if isinstance(obj, text_type) or hasattr(obj, text_type_magicmethod): # Note: unicode(u'foo') is O(1) (by experimentation) return text_type(obj).encode(encoding, **encode_args) return binary_type(obj)
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34.78125
0.000874
def ranking_game(n, steps=10, random_state=None): """ Return a NormalFormGame instance of (the 2-player version of) the "ranking game" studied by Goldberg et al. (2013), where each player chooses an effort level associated with a score and a cost which are both increasing functions with randomly generated step sizes. The player with the higher score wins the first prize, whose value is 1, and the other player obtains the "second prize" of value 0; in the case of a tie, the first prize is split and each player receives a value of 0.5. The payoff of a player is given by the value of the prize minus the cost of the effort. Parameters ---------- n : scalar(int) Number of actions, i.e, number of possible effort levels. steps : scalar(int), optional(default=10) Parameter determining the upper bound for the size of the random steps for the scores and costs for each player: The step sizes for the scores are drawn from `1`, ..., `steps`, while those for the costs are multiples of `1/(n*steps)`, where the cost of effort level `0` is 0, and the maximum possible cost of effort level `n-1` is less than or equal to 1. random_state : int or np.random.RandomState, optional Random seed (integer) or np.random.RandomState instance to set the initial state of the random number generator for reproducibility. If None, a randomly initialized RandomState is used. Returns ------- g : NormalFormGame Examples -------- >>> g = ranking_game(5, random_state=1234) >>> g.players[0] Player([[ 0. , 0. , 0. , 0. , 0. ], [ 0.82, -0.18, -0.18, -0.18, -0.18], [ 0.8 , 0.8 , -0.2 , -0.2 , -0.2 ], [ 0.68, 0.68, 0.68, -0.32, -0.32], [ 0.66, 0.66, 0.66, 0.66, -0.34]]) >>> g.players[1] Player([[ 1. , 0. , 0. , 0. , 0. ], [ 0.8 , 0.8 , -0.2 , -0.2 , -0.2 ], [ 0.66, 0.66, 0.66, -0.34, -0.34], [ 0.6 , 0.6 , 0.6 , 0.6 , -0.4 ], [ 0.58, 0.58, 0.58, 0.58, 0.58]]) """ payoff_arrays = tuple(np.empty((n, n)) for i in range(2)) random_state = check_random_state(random_state) scores = random_state.randint(1, steps+1, size=(2, n)) scores.cumsum(axis=1, out=scores) costs = np.empty((2, n-1)) costs[:] = random_state.randint(1, steps+1, size=(2, n-1)) costs.cumsum(axis=1, out=costs) costs[:] /= (n * steps) _populate_ranking_payoff_arrays(payoff_arrays, scores, costs) g = NormalFormGame( [Player(payoff_array) for payoff_array in payoff_arrays] ) return g
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def _handle_read_chunk(self): """Some data can be read""" new_data = b'' buffer_length = len(self.read_buffer) try: while buffer_length < self.MAX_BUFFER_SIZE: try: piece = self.recv(4096) except OSError as e: if e.errno == errno.EAGAIN: # End of the available data break elif e.errno == errno.EIO and new_data: # Hopefully we could read an error message before the # actual termination break else: raise if not piece: # A closed connection is indicated by signaling a read # condition, and having recv() return 0. break new_data += piece buffer_length += len(piece) finally: new_data = new_data.replace(b'\r', b'\n') self.read_buffer += new_data return new_data
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34.935484
0.001797
def guess_file_name_stream_type_header(args): """ Guess filename, file stream, file type, file header from args. :param args: may be string (filepath), 2-tuples (filename, fileobj), 3-tuples (filename, fileobj, contentype) or 4-tuples (filename, fileobj, contentype, custom_headers). :return: filename, file stream, file type, file header """ ftype = None fheader = None if isinstance(args, (tuple, list)): if len(args) == 2: fname, fstream = args elif len(args) == 3: fname, fstream, ftype = args else: fname, fstream, ftype, fheader = args else: fname, fstream = guess_filename_stream(args) ftype = guess_content_type(fname) if isinstance(fstream, (str, bytes, bytearray)): fdata = fstream else: fdata = fstream.read() return fname, fdata, ftype, fheader
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33.730769
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def isNumber(self, value): """ Validate whether a value is a number or not """ try: str(value) float(value) return True except ValueError: return False
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20.909091
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def get_all_tasks(conf): """Returns a list with every task registred on Hamster. """ db = HamsterDB(conf) fact_list = db.all_facts_id security_days = int(conf.get_option('tasks.security_days')) today = datetime.today() tasks = {} for fact_id in fact_list: ht = HamsterTask(fact_id, conf, db) if ht.end_time: end_time = ht.get_object_dates()[1] if today - timedelta(security_days) <= end_time: rt = ht.get_remote_task() tasks[rt.task_id] = rt db.close_connection() print 'Obtained %d tasks' % len(tasks) return tasks
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25.583333
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def purge_key(surrogate_key, service_id, api_key): """Instant purge URLs with a given surrogate key from the Fastly caches. Parameters ---------- surrogate_key : `str` Surrogate key header (``x-amz-meta-surrogate-key``) value of objects to purge from the Fastly cache. service_id : `str` Fastly service ID. api_key : `str` Fastly API key. Raises ------ FastlyError Error with the Fastly API usage. Notes ----- This function uses Fastly's ``/service/{service}/purge/{key}`` endpoint. See the `Fastly Purge documentation <http://ls.st/jxg>`_ for more information. For other Fastly APIs, consider using `fastly-py <https://github.com/fastly/fastly-py>`_. """ logger = logging.getLogger(__name__) api_root = 'https://api.fastly.com' path = '/service/{service}/purge/{surrogate_key}'.format( service=service_id, surrogate_key=surrogate_key) logger.info('Fastly purge {0}'.format(path)) r = requests.post(api_root + path, headers={'Fastly-Key': api_key, 'Accept': 'application/json'}) if r.status_code != 200: raise FastlyError(r.json)
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30.871795
0.000805
def handler(self): 'Parametrized handler function' return ft.partial(self.base.handler, parameter=self.parameter)\ if self.parameter else self.base.handler
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def as_dictionary(self, is_proof=True): """ Return the DDO as a JSON dict. :param if is_proof: if False then do not include the 'proof' element. :return: dict """ if self._created is None: self._created = DDO._get_timestamp() data = { '@context': DID_DDO_CONTEXT_URL, 'id': self._did, 'created': self._created, } if self._public_keys: values = [] for public_key in self._public_keys: values.append(public_key.as_dictionary()) data['publicKey'] = values if self._authentications: values = [] for authentication in self._authentications: values.append(authentication) data['authentication'] = values if self._services: values = [] for service in self._services: values.append(service.as_dictionary()) data['service'] = values if self._proof and is_proof: data['proof'] = self._proof return data
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def reduce_by(self, package_request): """Reduce this scope wrt a package request. Returns: A (_PackageScope, [Reduction]) tuple, where the scope is a new scope copy with reductions applied, or self if there were no reductions, or None if the scope was completely reduced. """ self.solver.reduction_broad_tests_count += 1 if self.package_request.conflict: # conflict scopes don't reduce. Instead, other scopes will be # reduced against a conflict scope. return (self, []) # perform the reduction new_slice, reductions = self.variant_slice.reduce_by(package_request) # there was total reduction if new_slice is None: self.solver.reductions_count += 1 if self.pr: reqstr = _short_req_str(package_request) self.pr("%s was reduced to nothing by %s", self, reqstr) self.pr.br() return (None, reductions) # there was some reduction if new_slice is not self.variant_slice: self.solver.reductions_count += 1 scope = self._copy(new_slice) if self.pr: reqstr = _short_req_str(package_request) self.pr("%s was reduced to %s by %s", self, scope, reqstr) self.pr.br() return (scope, reductions) # there was no reduction return (self, [])
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34.452381
0.001344
def create_task_log(self, task_id, case_task_log): """ :param task_id: Task identifier :param case_task_log: TheHive log :type case_task_log: CaseTaskLog defined in models.py :return: TheHive log :rtype: json """ req = self.url + "/api/case/task/{}/log".format(task_id) data = {'_json': json.dumps({"message":case_task_log.message})} if case_task_log.file: f = {'attachment': (os.path.basename(case_task_log.file), open(case_task_log.file, 'rb'), magic.Magic(mime=True).from_file(case_task_log.file))} try: return requests.post(req, data=data,files=f, proxies=self.proxies, auth=self.auth, verify=self.cert) except requests.exceptions.RequestException as e: raise CaseTaskException("Case task log create error: {}".format(e)) else: try: return requests.post(req, headers={'Content-Type': 'application/json'}, data=json.dumps({'message':case_task_log.message}), proxies=self.proxies, auth=self.auth, verify=self.cert) except requests.exceptions.RequestException as e: raise CaseTaskException("Case task log create error: {}".format(e))
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51.083333
0.008006
def fbank(wav_path, flat=True): """ Currently grabs log Mel filterbank, deltas and double deltas.""" (rate, sig) = wav.read(wav_path) if len(sig) == 0: logger.warning("Empty wav: {}".format(wav_path)) fbank_feat = python_speech_features.logfbank(sig, rate, nfilt=40) energy = extract_energy(rate, sig) feat = np.hstack([energy, fbank_feat]) delta_feat = python_speech_features.delta(feat, 2) delta_delta_feat = python_speech_features.delta(delta_feat, 2) all_feats = [feat, delta_feat, delta_delta_feat] if not flat: all_feats = np.array(all_feats) # Make time the first dimension for easy length normalization padding # later. all_feats = np.swapaxes(all_feats, 0, 1) all_feats = np.swapaxes(all_feats, 1, 2) else: all_feats = np.concatenate(all_feats, axis=1) # Log Mel Filterbank, with delta, and double delta feat_fn = wav_path[:-3] + "fbank.npy" np.save(feat_fn, all_feats)
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def delete_record(table, sys_id): ''' Delete an existing record :param table: The table name, e.g. sys_user :type table: ``str`` :param sys_id: The unique ID of the record :type sys_id: ``str`` CLI Example: .. code-block:: bash salt myminion servicenow.delete_record sys_computer 2134566 ''' client = _get_client() client.table = table response = client.delete(sys_id) return response
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def diagonalize_collision_matrix(collision_matrices, i_sigma=None, i_temp=None, pinv_solver=0, log_level=0): """Diagonalize collision matrices. Note ---- collision_matricies is overwritten by eigenvectors. Parameters ---------- collision_matricies : ndarray, optional Collision matrix. This ndarray has to have the following size and flags. shapes: (sigmas, temperatures, prod(mesh), num_band, prod(mesh), num_band) (sigmas, temperatures, ir_grid_points, num_band, 3, ir_grid_points, num_band, 3) (size, size) dtype='double', order='C' i_sigma : int, optional Index of BZ integration methods, tetrahedron method and smearing method with widths. Default is None. i_temp : int, optional Index of temperature. Default is None. pinv_solver : int, optional Diagnalization solver choice. log_level : int, optional Verbosity level. Smaller is more quiet. Default is 0. Returns ------- w : ndarray, optional Eigenvalues. shape=(size_of_collision_matrix,), dtype='double' """ start = time.time() # Matrix size of collision matrix to be diagonalized. # The following value is expected: # ir-colmat: num_ir_grid_points * num_band * 3 # red-colmat: num_mesh_points * num_band shape = collision_matrices.shape if len(shape) == 6: size = shape[2] * shape[3] assert size == shape[4] * shape[5] elif len(shape) == 8: size = np.prod(shape[2:5]) assert size == np.prod(shape[5:8]) elif len(shape) == 2: size = shape[0] assert size == shape[1] solver = _select_solver(pinv_solver) # [1] dsyev: safer and slower than dsyevd and smallest memory usage # [2] dsyevd: faster than dsyev and largest memory usage if solver in [1, 2]: if log_level: routine = ['dsyev', 'dsyevd'][solver - 1] sys.stdout.write("Diagonalizing by lapacke %s... " % routine) sys.stdout.flush() import phono3py._phono3py as phono3c w = np.zeros(size, dtype='double') if i_sigma is None: _i_sigma = 0 else: _i_sigma = i_sigma if i_temp is None: _i_temp = 0 else: _i_temp = i_temp phono3c.diagonalize_collision_matrix(collision_matrices, w, _i_sigma, _i_temp, 0.0, (solver + 1) % 2, 0) # only diagonalization elif solver == 3: # np.linalg.eigh depends on dsyevd. if log_level: sys.stdout.write("Diagonalizing by np.linalg.eigh... ") sys.stdout.flush() col_mat = collision_matrices[i_sigma, i_temp].reshape( size, size) w, col_mat[:] = np.linalg.eigh(col_mat) elif solver == 4: # fully scipy dsyev if log_level: sys.stdout.write("Diagonalizing by " "scipy.linalg.lapack.dsyev... ") sys.stdout.flush() import scipy.linalg col_mat = collision_matrices[i_sigma, i_temp].reshape( size, size) w, _, info = scipy.linalg.lapack.dsyev(col_mat.T, overwrite_a=1) elif solver == 5: # fully scipy dsyevd if log_level: sys.stdout.write("Diagonalizing by " "scipy.linalg.lapack.dsyevd... ") sys.stdout.flush() import scipy.linalg col_mat = collision_matrices[i_sigma, i_temp].reshape( size, size) w, _, info = scipy.linalg.lapack.dsyevd(col_mat.T, overwrite_a=1) if log_level: print("[%.3fs]" % (time.time() - start)) sys.stdout.flush() return w
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34.853448
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def filter_intensity(df, label="", with_multiplicity=False): """ Filter to include only the Intensity values with optional specified label, excluding other Intensity measurements, but retaining all other columns. """ label += ".*__\d" if with_multiplicity else "" dft = df.filter(regex="^(?!Intensity).*$") dfi = df.filter(regex='^(.*Intensity.*%s.*__\d)$' % label) return pd.concat([dft,dfi], axis=1)
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38.727273
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def create_append(filename: str, layers: Union[np.ndarray, Dict[str, np.ndarray], loompy.LayerManager], row_attrs: Dict[str, np.ndarray], col_attrs: Dict[str, np.ndarray], *, file_attrs: Dict[str, str] = None, fill_values: Dict[str, np.ndarray] = None) -> None: """ **DEPRECATED** - Use `new` instead; see https://github.com/linnarsson-lab/loompy/issues/42 """ deprecated("'create_append' is deprecated. See https://github.com/linnarsson-lab/loompy/issues/42") if os.path.exists(filename): with connect(filename) as ds: ds.add_columns(layers, col_attrs, fill_values=fill_values) else: create(filename, layers, row_attrs, col_attrs, file_attrs=file_attrs)
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def cbpdnmd_xstep(k): """Do the X step of the cbpdn stage. The only parameter is the slice index `k` and there are no return values; all inputs and outputs are from and to global variables. """ YU0 = mp_Z_Y0[k] + mp_S[k] - mp_Z_U0[k] YU1 = mp_Z_Y1[k] - mp_Z_U1[k] if mp_cri.Cd == 1: b = np.conj(mp_Df) * sl.rfftn(YU0, None, mp_cri.axisN) + \ sl.rfftn(YU1, None, mp_cri.axisN) Xf = sl.solvedbi_sm(mp_Df, 1.0, b, axis=mp_cri.axisM) else: b = sl.inner(np.conj(mp_Df), sl.rfftn(YU0, None, mp_cri.axisN), axis=mp_cri.axisC) + \ sl.rfftn(YU1, None, mp_cri.axisN) Xf = sl.solvemdbi_ism(mp_Df, 1.0, b, mp_cri.axisM, mp_cri.axisC) mp_Z_X[k] = sl.irfftn(Xf, mp_cri.Nv, mp_cri.axisN) mp_DX[k] = sl.irfftn(sl.inner(mp_Df, Xf), mp_cri.Nv, mp_cri.axisN)
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def _hash(self, iv, value): """ Generate and hmac signature for this encrypted data :param key: :param iv: :param value: :return string: """ return hmac.new(self.key, msg=iv+value, digestmod=hashlib.sha256).hexdigest()
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def roles(self): """List[:class:`Role`]: A :class:`list` of roles that is allowed to use this emoji. If roles is empty, the emoji is unrestricted. """ guild = self.guild if guild is None: return [] return [role for role in guild.roles if self._roles.has(role.id)]
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31.6
0.009231
def qgis_composer_extractor(impact_report, component_metadata): """Extract composer context. This method extract necessary context for a given impact report and component metadata and save the context so it can be used in composer rendering phase :param impact_report: the impact report that acts as a proxy to fetch all the data that extractor needed :type impact_report: safe.report.impact_report.ImpactReport :param component_metadata: the component metadata. Used to obtain information about the component we want to render :type component_metadata: safe.report.report_metadata. ReportComponentsMetadata :return: context for rendering phase :rtype: dict .. versionadded:: 4.0 """ # QGIS Composer needed certain context to generate the output # - Map Settings # - Substitution maps # - Element settings, such as icon for picture file or image source # Generate map settings qgis_context = impact_report.qgis_composition_context inasafe_context = impact_report.inasafe_context provenance = impact_report.impact_function.provenance extra_args = component_metadata.extra_args context = QGISComposerContext() # Set default image elements to replace image_elements = [ { 'id': 'safe-logo', 'path': inasafe_context.inasafe_logo }, { 'id': 'black-inasafe-logo', 'path': inasafe_context.black_inasafe_logo }, { 'id': 'white-inasafe-logo', 'path': inasafe_context.white_inasafe_logo }, { 'id': 'north-arrow', 'path': inasafe_context.north_arrow }, { 'id': 'organisation-logo', 'path': inasafe_context.organisation_logo }, { 'id': 'supporters_logo', 'path': inasafe_context.supporters_logo } ] context.image_elements = image_elements # Set default HTML Frame elements to replace html_frame_elements = [ { 'id': 'impact-report', 'mode': 'text', # another mode is url 'text': '', # TODO: get impact summary table } ] context.html_frame_elements = html_frame_elements """Define the layers for the impact map.""" project = QgsProject.instance() layers = [] exposure_summary_layers = [] if impact_report.multi_exposure_impact_function: for impact_function in ( impact_report.multi_exposure_impact_function.impact_functions): impact_layer = impact_function.exposure_summary or ( impact_function.aggregate_hazard_impacted) exposure_summary_layers.append(impact_layer) # use custom ordered layer if any if impact_report.ordered_layers: for layer in impact_report.ordered_layers: layers.append(layer) # We are keeping this if we want to enable below behaviour again. # Currently realtime might have layer order without impact layer in it. # # make sure at least there is an impact layer # if impact_report.multi_exposure_impact_function: # additional_layers = [] # for exposure summary layers # impact_layer_found = False # impact_functions = ( # impact_report.multi_exposure_impact_function.impact_functions) # # check for impact layer occurrences # for analysis in impact_functions: # impact_layer = analysis.exposure_summary or ( # analysis.aggregate_hazard_impacted) # for index, layer in enumerate(layers): # if impact_layer.source() == layer.source(): # add_impact_layers_to_canvas(analysis) # layers[index] = impact_layer # impact_layer_found = True # if not impact_layer_found: # for analysis in impact_functions: # add_impact_layers_to_canvas(analysis) # impact_layer = analysis.exposure_summary or ( # analysis.aggregate_hazard_impacted) # layer_uri = full_layer_uri(impact_layer) # layer = load_layer_from_registry(layer_uri) # additional_layers.append(layer) # layers = additional_layers + layers # else: # impact_layer = ( # impact_report.impact_function.exposure_summary or ( # impact_report.impact_function.aggregate_hazard_impacted)) # if impact_layer not in layers: # layers.insert(0, impact_layer) # use default layer order if no custom ordered layer found else: if not impact_report.multi_exposure_impact_function: # single IF layers = [impact_report.impact] + impact_report.extra_layers else: # multi-exposure IF layers = [] + impact_report.extra_layers add_supplementary_layers = ( not impact_report.multi_exposure_impact_function or not ( impact_report.multi_exposure_impact_function. output_layers_ordered) ) if add_supplementary_layers: # Check show only impact. show_only_impact = setting( 'set_show_only_impact_on_report', expected_type=bool) if not show_only_impact: hazard_layer = project.mapLayers().get( provenance['hazard_layer_id'], None) aggregation_layer_id = provenance['aggregation_layer_id'] if aggregation_layer_id: aggregation_layer = project.mapLayers().get( aggregation_layer_id, None) layers.append(aggregation_layer) layers.append(hazard_layer) # check hide exposure settings hide_exposure_flag = setting( 'setHideExposureFlag', expected_type=bool) if not hide_exposure_flag: exposure_layers_id = [] if provenance.get( provenance_exposure_layer_id['provenance_key']): exposure_layers_id.append( provenance.get( provenance_exposure_layer_id['provenance_key'])) elif provenance.get( provenance_multi_exposure_layers_id['provenance_key']): exposure_layers_id = provenance.get( provenance_multi_exposure_layers_id['provenance_key']) # place exposure at the bottom for layer_id in exposure_layers_id: exposure_layer = project.mapLayers().get(layer_id) layers.append(exposure_layer) # default extent is analysis extent if not qgis_context.extent: qgis_context.extent = impact_report.impact_function.analysis_extent map_elements = [ { 'id': 'impact-map', 'extent': qgis_context.extent, 'grid_split_count': 5, 'layers': layers, } ] context.map_elements = map_elements # calculate map_legends, only show the legend for impact layer if impact_report.legend_layers: # use requested legend if any layers = impact_report.legend_layers elif impact_report.multi_exposure_impact_function: # multi-exposure IF layers = exposure_summary_layers else: # single IF layers = [impact_report.impact] symbol_count = 0 for l in layers: layer = l """:type: qgis.core.QgsMapLayer""" try: symbol_count += len(layer.legendSymbologyItems()) continue except Exception: # pylint: disable=broad-except pass try: symbol_count += len(layer.renderer().legendSymbolItems()) continue except Exception: # pylint: disable=broad-except pass symbol_count += 1 legend_title = provenance.get('map_legend_title') or '' map_legends = [ { 'id': 'impact-legend', 'title': legend_title, 'layers': layers, 'symbol_count': symbol_count, # 'column_count': 2, # the number of column in legend display } ] context.map_legends = map_legends # process substitution map start_datetime = provenance['start_datetime'] """:type: datetime.datetime""" date_format = resolve_from_dictionary(extra_args, 'date-format') time_format = resolve_from_dictionary(extra_args, 'time-format') if isinstance(start_datetime, datetime.datetime): date = start_datetime.strftime(date_format) time = start_datetime.strftime(time_format) else: date = '' time = '' long_version = get_version() tokens = long_version.split('.') version = '%s.%s.%s' % (tokens[0], tokens[1], tokens[2]) # Get title of the layer title = provenance.get('map_title') or '' # Set source unknown_source_text = resolve_from_dictionary( extra_args, ['defaults', 'unknown_source']) aggregation_not_used = resolve_from_dictionary( extra_args, ['defaults', 'aggregation_not_used']) hazard_source = ( provenance.get( 'hazard_keywords', {}).get('source') or unknown_source_text) exposure_source = ( provenance.get( 'exposure_keywords', {}).get('source') or unknown_source_text) if provenance['aggregation_layer']: aggregation_source = ( provenance['aggregation_keywords'].get('source') or unknown_source_text) else: aggregation_source = aggregation_not_used spatial_reference_format = resolve_from_dictionary( extra_args, 'spatial-reference-format') reference_name = spatial_reference_format.format( crs=impact_report.impact_function.crs.authid()) analysis_layer = impact_report.analysis analysis_name = value_from_field_name( analysis_name_field['field_name'], analysis_layer) # Prepare the substitution map version_title = resolve_from_dictionary(extra_args, 'version-title') disclaimer_title = resolve_from_dictionary(extra_args, 'disclaimer-title') date_title = resolve_from_dictionary(extra_args, 'date-title') time_title = resolve_from_dictionary(extra_args, 'time-title') caution_title = resolve_from_dictionary(extra_args, 'caution-title') caution_text = resolve_from_dictionary(extra_args, 'caution-text') version_text = resolve_from_dictionary(extra_args, 'version-text') legend_section_title = resolve_from_dictionary( extra_args, 'legend-title') information_title = resolve_from_dictionary( extra_args, 'information-title') supporters_title = resolve_from_dictionary( extra_args, 'supporters-title') source_title = resolve_from_dictionary(extra_args, 'source-title') analysis_title = resolve_from_dictionary(extra_args, 'analysis-title') reference_title = resolve_from_dictionary( extra_args, 'spatial-reference-title') substitution_map = { 'impact-title': title, 'date': date, 'time': time, 'safe-version': version, # deprecated 'disclaimer': inasafe_context.disclaimer, # These added in 3.2 'version-title': version_title, 'inasafe-version': version, 'disclaimer-title': disclaimer_title, 'date-title': date_title, 'time-title': time_title, 'caution-title': caution_title, 'caution-text': caution_text, 'version-text': version_text.format(version=version), 'legend-title': legend_section_title, 'information-title': information_title, 'supporters-title': supporters_title, 'source-title': source_title, 'analysis-title': analysis_title, 'analysis-name': analysis_name, 'reference-title': reference_title, 'reference-name': reference_name, 'hazard-source': hazard_source, 'exposure-source': exposure_source, 'aggregation-source': aggregation_source, } context.substitution_map = substitution_map return context
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".", "white_inasafe_logo", "}", ",", "{", "'id'", ":", "'north-arrow'", ",", "'path'", ":", "inasafe_context", ".", "north_arrow", "}", ",", "{", "'id'", ":", "'organisation-logo'", ",", "'path'", ":", "inasafe_context", ".", "organisation_logo", "}", ",", "{", "'id'", ":", "'supporters_logo'", ",", "'path'", ":", "inasafe_context", ".", "supporters_logo", "}", "]", "context", ".", "image_elements", "=", "image_elements", "# Set default HTML Frame elements to replace", "html_frame_elements", "=", "[", "{", "'id'", ":", "'impact-report'", ",", "'mode'", ":", "'text'", ",", "# another mode is url", "'text'", ":", "''", ",", "# TODO: get impact summary table", "}", "]", "context", ".", "html_frame_elements", "=", "html_frame_elements", "\"\"\"Define the layers for the impact map.\"\"\"", "project", "=", "QgsProject", ".", "instance", "(", ")", "layers", "=", "[", "]", "exposure_summary_layers", "=", "[", "]", "if", "impact_report", ".", "multi_exposure_impact_function", ":", "for", "impact_function", "in", "(", "impact_report", ".", "multi_exposure_impact_function", ".", "impact_functions", ")", ":", "impact_layer", "=", "impact_function", ".", "exposure_summary", "or", "(", "impact_function", ".", "aggregate_hazard_impacted", ")", "exposure_summary_layers", ".", "append", "(", "impact_layer", ")", "# use custom ordered layer if any", "if", "impact_report", ".", "ordered_layers", ":", "for", "layer", "in", "impact_report", ".", "ordered_layers", ":", "layers", ".", "append", "(", "layer", ")", "# We are keeping this if we want to enable below behaviour again.", "# Currently realtime might have layer order without impact layer in it.", "# # make sure at least there is an impact layer", "# if impact_report.multi_exposure_impact_function:", "# additional_layers = [] # for exposure summary layers", "# impact_layer_found = False", "# impact_functions = (", "# impact_report.multi_exposure_impact_function.impact_functions)", "# # check for impact layer occurrences", "# for analysis in impact_functions:", "# impact_layer = analysis.exposure_summary or (", "# analysis.aggregate_hazard_impacted)", "# for index, layer in enumerate(layers):", "# if impact_layer.source() == layer.source():", "# add_impact_layers_to_canvas(analysis)", "# layers[index] = impact_layer", "# impact_layer_found = True", "# if not impact_layer_found:", "# for analysis in impact_functions:", "# add_impact_layers_to_canvas(analysis)", "# impact_layer = analysis.exposure_summary or (", "# analysis.aggregate_hazard_impacted)", "# layer_uri = full_layer_uri(impact_layer)", "# layer = load_layer_from_registry(layer_uri)", "# additional_layers.append(layer)", "# layers = additional_layers + layers", "# else:", "# impact_layer = (", "# impact_report.impact_function.exposure_summary or (", "# impact_report.impact_function.aggregate_hazard_impacted))", "# if impact_layer not in layers:", "# layers.insert(0, impact_layer)", "# use default layer order if no custom ordered layer found", "else", ":", "if", "not", "impact_report", ".", "multi_exposure_impact_function", ":", "# single IF", "layers", "=", "[", "impact_report", ".", "impact", "]", "+", "impact_report", ".", "extra_layers", "else", ":", "# multi-exposure IF", "layers", "=", "[", "]", "+", "impact_report", ".", "extra_layers", "add_supplementary_layers", "=", "(", "not", "impact_report", ".", "multi_exposure_impact_function", "or", "not", "(", "impact_report", ".", "multi_exposure_impact_function", ".", "output_layers_ordered", ")", ")", "if", "add_supplementary_layers", ":", "# Check show only impact.", "show_only_impact", "=", "setting", "(", "'set_show_only_impact_on_report'", ",", "expected_type", "=", "bool", ")", "if", "not", "show_only_impact", ":", "hazard_layer", "=", "project", ".", "mapLayers", "(", ")", ".", "get", "(", "provenance", "[", "'hazard_layer_id'", "]", ",", "None", ")", "aggregation_layer_id", "=", "provenance", "[", "'aggregation_layer_id'", "]", "if", "aggregation_layer_id", ":", "aggregation_layer", "=", "project", ".", "mapLayers", "(", ")", ".", "get", "(", "aggregation_layer_id", ",", "None", ")", "layers", ".", "append", "(", "aggregation_layer", ")", "layers", ".", "append", "(", "hazard_layer", ")", "# check hide exposure settings", "hide_exposure_flag", "=", "setting", "(", "'setHideExposureFlag'", ",", "expected_type", "=", "bool", ")", "if", "not", "hide_exposure_flag", ":", "exposure_layers_id", "=", "[", "]", "if", "provenance", ".", "get", "(", "provenance_exposure_layer_id", "[", "'provenance_key'", "]", ")", ":", "exposure_layers_id", ".", "append", "(", "provenance", ".", "get", "(", "provenance_exposure_layer_id", "[", "'provenance_key'", "]", ")", ")", "elif", "provenance", ".", "get", "(", "provenance_multi_exposure_layers_id", "[", "'provenance_key'", "]", ")", ":", "exposure_layers_id", "=", "provenance", ".", "get", "(", "provenance_multi_exposure_layers_id", "[", "'provenance_key'", "]", ")", "# place exposure at the bottom", "for", "layer_id", "in", "exposure_layers_id", ":", "exposure_layer", "=", "project", ".", "mapLayers", "(", ")", ".", "get", "(", "layer_id", ")", "layers", ".", "append", "(", "exposure_layer", ")", "# default extent is analysis extent", "if", "not", "qgis_context", ".", "extent", ":", "qgis_context", ".", "extent", "=", "impact_report", ".", "impact_function", ".", "analysis_extent", "map_elements", "=", "[", "{", "'id'", ":", "'impact-map'", ",", "'extent'", ":", "qgis_context", ".", "extent", ",", "'grid_split_count'", ":", "5", ",", "'layers'", ":", "layers", ",", "}", "]", "context", ".", "map_elements", "=", "map_elements", "# calculate map_legends, only show the legend for impact layer", "if", "impact_report", ".", "legend_layers", ":", "# use requested legend if any", "layers", "=", "impact_report", ".", "legend_layers", "elif", "impact_report", ".", "multi_exposure_impact_function", ":", "# multi-exposure IF", "layers", "=", "exposure_summary_layers", "else", ":", "# single IF", "layers", "=", "[", "impact_report", ".", "impact", "]", "symbol_count", "=", "0", "for", "l", "in", "layers", ":", "layer", "=", "l", "\"\"\":type: qgis.core.QgsMapLayer\"\"\"", "try", ":", "symbol_count", "+=", "len", "(", "layer", ".", "legendSymbologyItems", "(", ")", ")", "continue", "except", "Exception", ":", "# pylint: disable=broad-except", "pass", "try", ":", "symbol_count", "+=", "len", "(", "layer", ".", "renderer", "(", ")", ".", "legendSymbolItems", "(", ")", ")", "continue", "except", "Exception", ":", "# pylint: disable=broad-except", "pass", "symbol_count", "+=", "1", "legend_title", "=", "provenance", ".", "get", "(", "'map_legend_title'", ")", "or", "''", "map_legends", "=", "[", "{", "'id'", ":", "'impact-legend'", ",", "'title'", ":", "legend_title", ",", "'layers'", ":", "layers", ",", "'symbol_count'", ":", "symbol_count", ",", "# 'column_count': 2, # the number of column in legend display", "}", "]", "context", ".", "map_legends", "=", "map_legends", "# process substitution map", "start_datetime", "=", "provenance", "[", "'start_datetime'", "]", "\"\"\":type: datetime.datetime\"\"\"", "date_format", "=", "resolve_from_dictionary", "(", "extra_args", ",", "'date-format'", ")", "time_format", "=", "resolve_from_dictionary", "(", "extra_args", ",", "'time-format'", ")", "if", "isinstance", "(", "start_datetime", ",", "datetime", ".", "datetime", ")", ":", "date", "=", "start_datetime", ".", "strftime", "(", "date_format", ")", "time", "=", "start_datetime", ".", "strftime", "(", "time_format", ")", "else", ":", "date", "=", "''", "time", "=", "''", "long_version", "=", "get_version", "(", ")", "tokens", "=", "long_version", ".", "split", "(", "'.'", ")", "version", "=", "'%s.%s.%s'", "%", "(", "tokens", "[", "0", "]", ",", "tokens", "[", "1", "]", ",", "tokens", "[", "2", "]", ")", "# Get title of the layer", "title", "=", "provenance", ".", "get", "(", "'map_title'", ")", "or", "''", "# Set source", "unknown_source_text", "=", "resolve_from_dictionary", "(", "extra_args", ",", "[", "'defaults'", ",", "'unknown_source'", "]", ")", "aggregation_not_used", "=", "resolve_from_dictionary", "(", "extra_args", ",", "[", "'defaults'", ",", "'aggregation_not_used'", "]", ")", "hazard_source", "=", "(", "provenance", ".", "get", "(", "'hazard_keywords'", ",", "{", "}", ")", ".", "get", "(", "'source'", ")", "or", "unknown_source_text", ")", "exposure_source", "=", "(", "provenance", ".", "get", "(", "'exposure_keywords'", ",", "{", "}", ")", ".", "get", "(", "'source'", ")", "or", "unknown_source_text", ")", "if", "provenance", "[", "'aggregation_layer'", "]", ":", "aggregation_source", "=", "(", "provenance", "[", "'aggregation_keywords'", "]", ".", "get", "(", "'source'", ")", "or", "unknown_source_text", ")", "else", ":", "aggregation_source", "=", "aggregation_not_used", "spatial_reference_format", "=", "resolve_from_dictionary", "(", "extra_args", ",", "'spatial-reference-format'", ")", "reference_name", "=", "spatial_reference_format", ".", "format", "(", "crs", "=", "impact_report", ".", "impact_function", ".", "crs", ".", "authid", "(", ")", ")", "analysis_layer", "=", "impact_report", ".", "analysis", "analysis_name", "=", "value_from_field_name", "(", "analysis_name_field", "[", "'field_name'", "]", ",", "analysis_layer", ")", "# Prepare the substitution map", "version_title", "=", "resolve_from_dictionary", "(", "extra_args", ",", "'version-title'", ")", "disclaimer_title", "=", "resolve_from_dictionary", "(", "extra_args", ",", "'disclaimer-title'", ")", "date_title", "=", "resolve_from_dictionary", "(", "extra_args", ",", "'date-title'", ")", "time_title", "=", "resolve_from_dictionary", "(", "extra_args", ",", "'time-title'", ")", "caution_title", "=", "resolve_from_dictionary", "(", "extra_args", ",", "'caution-title'", ")", "caution_text", "=", "resolve_from_dictionary", "(", "extra_args", ",", "'caution-text'", ")", "version_text", "=", "resolve_from_dictionary", "(", "extra_args", ",", "'version-text'", ")", "legend_section_title", "=", "resolve_from_dictionary", "(", "extra_args", ",", "'legend-title'", ")", "information_title", "=", "resolve_from_dictionary", "(", "extra_args", ",", "'information-title'", ")", "supporters_title", "=", "resolve_from_dictionary", "(", "extra_args", ",", "'supporters-title'", ")", "source_title", "=", "resolve_from_dictionary", "(", "extra_args", ",", "'source-title'", ")", "analysis_title", "=", "resolve_from_dictionary", "(", "extra_args", ",", "'analysis-title'", ")", "reference_title", "=", "resolve_from_dictionary", "(", "extra_args", ",", "'spatial-reference-title'", ")", "substitution_map", "=", "{", "'impact-title'", ":", "title", ",", "'date'", ":", "date", ",", "'time'", ":", "time", ",", "'safe-version'", ":", "version", ",", "# deprecated", "'disclaimer'", ":", "inasafe_context", ".", "disclaimer", ",", "# These added in 3.2", "'version-title'", ":", "version_title", ",", "'inasafe-version'", ":", "version", ",", "'disclaimer-title'", ":", "disclaimer_title", ",", "'date-title'", ":", "date_title", ",", "'time-title'", ":", "time_title", ",", "'caution-title'", ":", "caution_title", ",", "'caution-text'", ":", "caution_text", ",", "'version-text'", ":", "version_text", ".", "format", "(", "version", "=", "version", ")", ",", "'legend-title'", ":", "legend_section_title", ",", "'information-title'", ":", "information_title", ",", "'supporters-title'", ":", "supporters_title", ",", "'source-title'", ":", "source_title", ",", "'analysis-title'", ":", "analysis_title", ",", "'analysis-name'", ":", "analysis_name", ",", "'reference-title'", ":", "reference_title", ",", "'reference-name'", ":", "reference_name", ",", "'hazard-source'", ":", "hazard_source", ",", "'exposure-source'", ":", "exposure_source", ",", "'aggregation-source'", ":", "aggregation_source", ",", "}", "context", ".", "substitution_map", "=", "substitution_map", "return", "context" ]
38.330159
0.000161
def namespace(self, elem=None): """return the URL, if any, for the doc root or elem, if given.""" if elem is None: elem = self.root return XML.tag_namespace(elem.tag)
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40.4
0.009709
def add_child(self, name, child): """ Add a new child to the node. :param name: Name of the child that must be used to access that child. Should not contain anything that could interfere with the operator `.` (dot). :param child: The new child, an instance of :any:`Scale` or :any:`Parameter` or :any:`ParameterNode`. """ if name in self.children: raise ValueError("{} has already a child named {}".format(self.name, name)) if not (isinstance(child, ParameterNode) or isinstance(child, Parameter) or isinstance(child, Scale)): raise TypeError("child must be of type ParameterNode, Parameter, or Scale. Instead got {}".format(type(child))) self.children[name] = child setattr(self, name, child)
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59.846154
0.008861
def binOp(op, indx, amap, bmap, fill_vec): ''' Combines the values from two map objects using the indx values using the op operator. In situations where there is a missing value it will use the callable function handle_missing ''' def op_or_missing(id): va = amap.get(id, None) vb = bmap.get(id, None) if va is None or vb is None: # This should create as many elements as the number of columns!? result = fill_vec else: try: result = op(va, vb) except Exception: result = None if result is None: result = fill_vec return result seq_arys = map(op_or_missing, indx) data = np.vstack(seq_arys) return data
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34.347826
0.001232
def read_atoms(fn, cycfn=None, pos_only=False, conv=1.0): """ Read atom information from an atoms.dat file (i.e., tblmd, MDCORE input file) """ f = paropen(fn, "r") l = f.readline().lstrip() while len(l) > 0 and ( l[0] == '#' or l[0] == '<' ): l = f.readline().lstrip() n_atoms = int(l) l = f.readline().lstrip() while len(l) > 0 and ( l[0] == '#' or l[0] == '<' ): l = f.readline().lstrip() l = f.readline().lstrip() while len(l) > 0 and ( l[0] == '#' or l[0] == '<' ): l = f.readline().lstrip() # # Read positions # forces = np.zeros( [ n_atoms, 3 ] ) groups = np.zeros( [ n_atoms ] ) gamma = np.zeros( [ n_atoms ] ) T = np.zeros( [ n_atoms ] ) ats = [ ] for i in range(n_atoms): s = l.split() # type x y z sym = None try: Z = int(s[0]) sym = ase.data.chemical_symbols[Z] except: sym = s[0] a = ase.Atom(sym, ( float(s[2])*conv, float(s[3])*conv, float(s[4])*conv ) ) groups[i] = int(s[5]) gamma[i] = float(s[6]) T[i] = float(s[7]) ats += [ a ] l = f.readline() this = ase.Atoms(ats, pbc=True) if not pos_only: while l and l == "": l = f.readline().strip() while l: key = l.strip(" <-#\r\n") if key.upper() == "VELOCITIES": for i in range(n_atoms): s = f.readline().split() m = this[i].mass if m is None: m = ase.data.atomic_masses[ase.data.chemical_symbols.index(this[i].symbol)] this[i].momentum = ( m*float(s[0]), m*float(s[1]), m*float(s[2]) ) l = None elif key.upper() == "FORCES": for i in range(n_atoms): s = f.readline().split() forces[i] = np.array( [ float(s[0]), float(s[1]), float(s[2]) ] ) l = None elif key.upper() == "CHARGES": for i in this: l = f.readline() if l and len(l.split()) == 1: i.charge = float(l) l = None elif key.upper() == "CELL" or key.upper().split()[0:2] == ("BOX", "VECTORS" ): l1 = f.readline() l2 = f.readline() l3 = f.readline() this.set_cell( [ [float(x) for x in l1.split()], [float(x) for x in l2.split()], [float(x) for x in l3.split()] ] ) l = None else: aux = [ ] l = f.readline().strip() while l and l[0] not in [ '<', '#' ]: s = l.split() aux += [ [float(x) for x in s] ] l = f.readline().strip() if len(aux) == n_atoms: this.set_array(key, np.asarray(aux)) else: print("Warning: Encountered field '%s' which does not seem to be per-atom data." % key) if l is None: l = f.readline().strip() while l and l == "": l = f.readline().strip() f.close() this.set_array("forces", forces) this.set_array("groups", groups) this.set_array("gamma", gamma) this.set_array("T", T) if cycfn: read_cyc(this, cycfn, conv=conv) return this
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28.300813
0.022481
def paste_format(self): """Pastes cell formats Pasting starts at cursor or at top left bbox corner """ row, col, tab = self.grid.actions.cursor selection = self.get_selection() if selection: # Use selection rather than cursor for top left cell if present row, col = [tl if tl is not None else 0 for tl in selection.get_bbox()[0]] cell_attributes = self.grid.code_array.cell_attributes string_data = self.grid.main_window.clipboard.get_clipboard() format_data = ast.literal_eval(string_data) ca = format_data["cell_attributes"] rh = format_data["row_heights"] cw = format_data["col_widths"] assert isinstance(ca, types.ListType) assert isinstance(rh, types.DictType) assert isinstance(cw, types.DictType) # Cell attributes for selection_params, tab, attrs in ca: base_selection = Selection(*selection_params) shifted_selection = base_selection.shifted(row, col) if "merge_area" not in attrs: # Do not paste merge areas because this may have # inintended consequences for existing merge areas new_cell_attribute = shifted_selection, tab, attrs cell_attributes.append(new_cell_attribute) # Row heights row_heights = self.grid.code_array.row_heights for __row, __tab in rh: row_heights[__row+row, tab] = rh[__row, __tab] # Column widths col_widths = self.grid.code_array.col_widths for __col, __tab in cw: col_widths[__col+col, tab] = cw[(__col, __tab)]
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34.791667
0.001165
def determine_master(port=4000): """Determine address of master so that workers can connect to it. If the environment variable SPARK_LOCAL_IP is set, that address will be used. :param port: port on which the application runs :return: Master address Example usage: SPARK_LOCAL_IP=127.0.0.1 spark-submit --master \ local[8] examples/mllib_mlp.py """ if os.environ.get('SPARK_LOCAL_IP'): return os.environ['SPARK_LOCAL_IP'] + ":" + str(port) else: return gethostbyname(gethostname()) + ":" + str(port)
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34.8125
0.001748
async def parse_response(self): """ :py:func:`asyncio.coroutine` Parsing full server response (all lines). :return: (code, lines) :rtype: (:py:class:`aioftp.Code`, :py:class:`list` of :py:class:`str`) :raises aioftp.StatusCodeError: if received code does not matches all already received codes """ code, rest = await self.parse_line() info = [rest] curr_code = code while rest.startswith("-") or not curr_code.isdigit(): curr_code, rest = await self.parse_line() if curr_code.isdigit(): info.append(rest) if curr_code != code: raise errors.StatusCodeError(code, curr_code, info) else: info.append(curr_code + rest) return code, info
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34.333333
0.002361
def add_requirement( self, install_req, # type: InstallRequirement parent_req_name=None, # type: Optional[str] extras_requested=None # type: Optional[Iterable[str]] ): # type: (...) -> Tuple[List[InstallRequirement], Optional[InstallRequirement]] # noqa: E501 """Add install_req as a requirement to install. :param parent_req_name: The name of the requirement that needed this added. The name is used because when multiple unnamed requirements resolve to the same name, we could otherwise end up with dependency links that point outside the Requirements set. parent_req must already be added. Note that None implies that this is a user supplied requirement, vs an inferred one. :param extras_requested: an iterable of extras used to evaluate the environment markers. :return: Additional requirements to scan. That is either [] if the requirement is not applicable, or [install_req] if the requirement is applicable and has just been added. """ name = install_req.name # If the markers do not match, ignore this requirement. if not install_req.match_markers(extras_requested): logger.info( "Ignoring %s: markers '%s' don't match your environment", name, install_req.markers, ) return [], None # If the wheel is not supported, raise an error. # Should check this after filtering out based on environment markers to # allow specifying different wheels based on the environment/OS, in a # single requirements file. if install_req.link and install_req.link.is_wheel: wheel = Wheel(install_req.link.filename) if self.check_supported_wheels and not wheel.supported(): raise InstallationError( "%s is not a supported wheel on this platform." % wheel.filename ) # This next bit is really a sanity check. assert install_req.is_direct == (parent_req_name is None), ( "a direct req shouldn't have a parent and also, " "a non direct req should have a parent" ) # Unnamed requirements are scanned again and the requirement won't be # added as a dependency until after scanning. if not name: # url or path requirement w/o an egg fragment self.unnamed_requirements.append(install_req) return [install_req], None try: existing_req = self.get_requirement(name) except KeyError: existing_req = None has_conflicting_requirement = ( parent_req_name is None and existing_req and not existing_req.constraint and existing_req.extras == install_req.extras and existing_req.req.specifier != install_req.req.specifier ) if has_conflicting_requirement: raise InstallationError( "Double requirement given: %s (already in %s, name=%r)" % (install_req, existing_req, name) ) # When no existing requirement exists, add the requirement as a # dependency and it will be scanned again after. if not existing_req: self.requirements[name] = install_req # FIXME: what about other normalizations? E.g., _ vs. -? if name.lower() != name: self.requirement_aliases[name.lower()] = name # We'd want to rescan this requirements later return [install_req], install_req # Assume there's no need to scan, and that we've already # encountered this for scanning. if install_req.constraint or not existing_req.constraint: return [], existing_req does_not_satisfy_constraint = ( install_req.link and not ( existing_req.link and install_req.link.path == existing_req.link.path ) ) if does_not_satisfy_constraint: self.reqs_to_cleanup.append(install_req) raise InstallationError( "Could not satisfy constraints for '%s': " "installation from path or url cannot be " "constrained to a version" % name, ) # If we're now installing a constraint, mark the existing # object for real installation. existing_req.constraint = False existing_req.extras = tuple(sorted( set(existing_req.extras) | set(install_req.extras) )) logger.debug( "Setting %s extras to: %s", existing_req, existing_req.extras, ) # Return the existing requirement for addition to the parent and # scanning again. return [existing_req], existing_req
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42.094828
0.0008
def find_item_in_path_by_type(self, si, path, obj_type): """ This function finds the first item of that type in path :param ServiceInstance si: pyvmomi ServiceInstance :param str path: the path to search in :param type obj_type: the vim type of the object :return: pyvmomi type instance object or None """ if obj_type is None: return None search_index = si.content.searchIndex sub_folder = si.content.rootFolder if path is None or not path: return sub_folder paths = path.split("/") for currPath in paths: if currPath is None or not currPath: continue manage = search_index.FindChild(sub_folder, currPath) if isinstance(manage, obj_type): return manage return None
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31.37037
0.002291
def align_two_alignments(aln1, aln2, moltype, params=None): """Returns an Alignment object from two existing Alignments. aln1, aln2: cogent.core.alignment.Alignment objects, or data that can be used to build them. params: dict of parameters to pass in to the Clustal app controller. """ #create SequenceCollection object from seqs aln1 = Alignment(aln1,MolType=moltype) #Create mapping between abbreviated IDs and full IDs aln1_int_map, aln1_int_keys = aln1.getIntMap() #Create SequenceCollection from int_map. aln1_int_map = Alignment(aln1_int_map,MolType=moltype) #create Alignment object from aln aln2 = Alignment(aln2,MolType=moltype) #Create mapping between abbreviated IDs and full IDs aln2_int_map, aln2_int_keys = aln2.getIntMap(prefix='seqn_') #Create SequenceCollection from int_map. aln2_int_map = Alignment(aln2_int_map,MolType=moltype) #Update aln1_int_keys with aln2_int_keys aln1_int_keys.update(aln2_int_keys) #Create Mafft app. app = Clustalw(InputHandler='_input_as_multiline_string',\ params=params, SuppressStderr=True) app.Parameters['-align'].off() app.Parameters['-infile'].off() app.Parameters['-profile'].on() #Add aln_int_map as profile1 app.Parameters['-profile1'].on(\ app._tempfile_as_multiline_string(aln1_int_map.toFasta())) #Add seq_int_map as profile2 app.Parameters['-profile2'].on(\ app._tempfile_as_multiline_string(aln2_int_map.toFasta())) #Get results using int_map as input to app res = app() #Get alignment as dict out of results alignment = dict(ClustalParser(res['Align'].readlines())) #Make new dict mapping original IDs new_alignment = {} for k,v in alignment.items(): new_alignment[aln1_int_keys[k]]=v #Create an Alignment object from alignment dict new_alignment = Alignment(new_alignment,MolType=moltype) #Clean up res.cleanUp() remove(app.Parameters['-profile1'].Value) remove(app.Parameters['-profile2'].Value) del(aln1,aln1_int_map,aln1_int_keys,\ aln2,aln2_int_map,aln2_int_keys,app,res,alignment) return new_alignment
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35.666667
0.016371
def run(connection): """ Parse arguments and start upload/download """ parser = argparse.ArgumentParser(description=""" Process database dumps. Either download of upload a dump file to the objectstore. downloads the latest dump and uploads with envronment and date into given container destination """) parser.add_argument( 'location', nargs=1, default=f'{DUMPFOLDER}/database.{ENV}.dump', help="Dump file location") parser.add_argument( 'objectstore', nargs=1, default=f'{DUMPFOLDER}/database.{ENV}.dump', help="Dump file objectstore location") parser.add_argument( '--download-db', action='store_true', dest='download', default=False, help='Download db') parser.add_argument( '--upload-db', action='store_true', dest='upload', default=False, help='Upload db') parser.add_argument( '--container', action='store_true', dest='container', default=False, help='Upload db') parser.add_argument( '--days', type=int, nargs=1, dest='days', default=0, help='Days to keep database dumps') args = parser.parse_args() if args.days: LOG.debug('Cleanup old dumps') remove_old_dumps( connection, args.objectstore[0], args.days[0]) elif args.download: download_database( connection, args.objectstore[0], args.location[0]) elif args.upload: upload_database( connection, args.objectstore[0], args.location[0]) else: parser.print_help()
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23.898551
0.000582
def url_for(context, __route_name, **parts): """Filter for generating urls. Usage: {{ url('the-view-name') }} might become "/path/to/view" or {{ url('item-details', id=123, query={'active': 'true'}) }} might become "/items/1?active=true". """ app = context['app'] query = None if 'query_' in parts: query = parts.pop('query_') for key in parts: val = parts[key] if isinstance(val, str): # if type is inherited from str expilict cast to str makes sense # if type is exactly str the operation is very fast val = str(val) elif type(val) is int: # int inherited classes like bool are forbidden val = str(val) else: raise TypeError("argument value should be str or int, " "got {} -> [{}] {!r}".format(key, type(val), val)) parts[key] = val url = app.router[__route_name].url_for(**parts) if query: url = url.with_query(query) return url
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32.548387
0.000962
def complete(text, state): """On tab press, return the next possible completion""" global completion_results if state == 0: line = readline.get_line_buffer() if line.startswith(':'): # Control command completion completion_results = complete_control_command(line, text) else: if line.startswith('!') and text and line.startswith(text): dropped_exclam = True text = text[1:] else: dropped_exclam = False completion_results = [] # Complete local paths completion_results += complete_local_path(text) # Complete from history l = len(text) completion_results += [w + ' ' for w in history_words if len(w) > l and w.startswith(text)] if readline.get_begidx() == 0: # Completing first word from $PATH completion_results += [w + ' ' for w in user_commands_in_path if len(w) > l and w.startswith(text)] completion_results = remove_dupes(completion_results) if dropped_exclam: completion_results = ['!' + r for r in completion_results] if state < len(completion_results): return completion_results[state] completion_results = None return None
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42.030303
0.002114
def _get_rev(self, fpath): """ Get an SCM version number. Try svn and git. """ rev = None try: cmd = ["git", "log", "-n1", "--pretty=format:\"%h\"", fpath] rev = Popen(cmd, stdout=PIPE, stderr=PIPE).communicate()[0] except: pass if not rev: try: cmd = ["svn", "info", fpath] svninfo = Popen(cmd, stdout=PIPE, stderr=PIPE).stdout.readlines() for info in svninfo: tokens = info.split(":") if tokens[0].strip() == "Last Changed Rev": rev = tokens[1].strip() except: pass return rev
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30.615385
0.008526
def newline(self, copy_margin=True): """ Insert a line ending at the current position. """ if copy_margin: self.insert_text('\n' + self.document.leading_whitespace_in_current_line) else: self.insert_text('\n')
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33.25
0.010989
def Produce_Predictions(FileName,train,test): """ Produces predictions for testing set, based off of training set. :param FileName: This is the csv file name we wish to have our predictions exported to. :param train: This is the file name of a csv file that will be the training set. :param test: This is the file name of the testing set that predictions will be made for. :returns: Returns nothing, creates csv file containing predictions for testing set. """ TestFileName=test TrainFileName=train trainDF=pd.read_csv(train) train=Feature_Engineering(train,trainDF) test=Feature_Engineering(test,trainDF) MLA=Create_Random_Forest(TrainFileName) predictions = MLA.predict(test) predictions = pd.DataFrame(predictions, columns=['Survived']) test = pd.read_csv(TestFileName) predictions = pd.concat((test.iloc[:, 0], predictions), axis = 1) predictions.to_csv(FileName, sep=",", index = False)
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47.45
0.020661
def get_dir_walker(recursive, topdown=True, followlinks=False): """ Returns a recursive or a non-recursive directory walker. :param recursive: ``True`` produces a recursive walker; ``False`` produces a non-recursive walker. :returns: A walker function. """ if recursive: walk = partial(os.walk, topdown=topdown, followlinks=followlinks) else: def walk(path, topdown=topdown, followlinks=followlinks): try: yield next(os.walk(path, topdown=topdown, followlinks=followlinks)) except NameError: yield os.walk(path, topdown=topdown, followlinks=followlinks).next() #IGNORE:E1101 return walk
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36.684211
0.008392
def close(self): """ Finalize the GDSII stream library. """ self._outfile.write(struct.pack('>2h', 4, 0x0400)) if self._close: self._outfile.close()
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27.714286
0.01
def preferred_height(self, cli, width, max_available_height, wrap_lines): """ Preferred height: as much as needed in order to display all the completions. """ complete_state = cli.current_buffer.complete_state column_width = self._get_column_width(complete_state) column_count = max(1, (width - self._required_margin) // column_width) return int(math.ceil(len(complete_state.current_completions) / float(column_count)))
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52
0.008403
def cli(): """Run the command line interface.""" args = docopt.docopt(__doc__, version=__VERSION__) secure = args['--secure'] numberofwords = int(args['<numberofwords>']) dictpath = args['--dict'] if dictpath is not None: dictfile = open(dictpath) else: dictfile = load_stream('words.txt') with dictfile: wordlist = read_wordlist(dictfile) words = generate_words(numberofwords, wordlist, secure=secure) print(' '.join(words))
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29.75
0.002037
def infer_returned_object(pyfunction, args): """Infer the `PyObject` this `PyFunction` returns after calling""" object_info = pyfunction.pycore.object_info result = object_info.get_exact_returned(pyfunction, args) if result is not None: return result result = _infer_returned(pyfunction, args) if result is not None: if args and pyfunction.get_module().get_resource() is not None: params = args.get_arguments( pyfunction.get_param_names(special_args=False)) object_info.function_called(pyfunction, params, result) return result result = object_info.get_returned(pyfunction, args) if result is not None: return result hint_return = get_type_hinting_factory(pyfunction.pycore.project).make_return_provider() type_ = hint_return(pyfunction) if type_ is not None: return rope.base.pyobjects.PyObject(type_)
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45.35
0.00216
def _parsed_pkg_info(self): """Parse and cache metadata""" try: return self._pkg_info except AttributeError: from email.parser import Parser self._pkg_info = Parser().parsestr(self.get_metadata(self.PKG_INFO)) return self._pkg_info
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37
0.009901
def delete(self, accountId): """Delete an account""" acct = BaseAccount.get(accountId) if not acct: raise Exception('No such account found') acct.delete() auditlog(event='account.delete', actor=session['user'].username, data={'accountId': accountId}) return self.make_response('Account deleted')
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34.8
0.008403
def recipe(recipe): """Apply the given recipe to a node Sets the run_list to the given recipe If no nodes/hostname.json file exists, it creates one """ env.host_string = lib.get_env_host_string() lib.print_header( "Applying recipe '{0}' on node {1}".format(recipe, env.host_string)) # Create configuration and sync node data = lib.get_node(env.host_string) data["run_list"] = ["recipe[{0}]".format(recipe)] if not __testing__: if env.autodeploy_chef and not chef.chef_test(): deploy_chef(ask="no") chef.sync_node(data)
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34.117647
0.001678
def get_stakes(self): """List all your stakes. Returns: list of dicts: stakes Each stake is a dict with the following fields: * confidence (`decimal.Decimal`) * roundNumber (`int`) * tournamentId (`int`) * soc (`decimal.Decimal`) * insertedAt (`datetime`) * staker (`str`): NMR adress used for staking * status (`str`) * txHash (`str`) * value (`decimal.Decimal`) Example: >>> api = NumerAPI(secret_key="..", public_id="..") >>> api.get_stakes() [{'confidence': Decimal('0.053'), 'insertedAt': datetime.datetime(2017, 9, 26, 8, 18, 36, 709000, tzinfo=tzutc()), 'roundNumber': 74, 'soc': Decimal('56.60'), 'staker': '0x0000000000000000000000000000000000003f9e', 'status': 'confirmed', 'tournamentId': 1, 'txHash': '0x1cbb985629552a0f57b98a1e30a5e7f101a992121db318cef02e02aaf0e91c95', 'value': Decimal('3.00')}, .. ] """ query = """ query { user { stakeTxs { confidence insertedAt roundNumber tournamentId soc staker status txHash value } } } """ data = self.raw_query(query, authorization=True)['data'] stakes = data['user']['stakeTxs'] # convert strings to python objects for s in stakes: utils.replace(s, "insertedAt", utils.parse_datetime_string) utils.replace(s, "soc", utils.parse_float_string) utils.replace(s, "confidence", utils.parse_float_string) utils.replace(s, "value", utils.parse_float_string) return stakes
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32.7
0.001979
def time(self): """! @brief (list) Returns sampling times when dynamic is measured during simulation. """ if ( (self._ccore_sync_dynamic_pointer is not None) and ( (self._time is None) or (len(self._time) == 0) ) ): self._time = wrapper.sync_dynamic_get_time(self._ccore_sync_dynamic_pointer); return self._time;
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42.444444
0.033333
def xyplot(points, title="", c="b", corner=1, lines=False): """ Return a ``vtkXYPlotActor`` that is a plot of `x` versus `y`, where `points` is a list of `(x,y)` points. :param int corner: assign position: - 1, topleft, - 2, topright, - 3, bottomleft, - 4, bottomright. .. hint:: Example: |fitspheres1.py|_ """ c = vc.getColor(c) # allow different codings array_x = vtk.vtkFloatArray() array_y = vtk.vtkFloatArray() array_x.SetNumberOfTuples(len(points)) array_y.SetNumberOfTuples(len(points)) for i, p in enumerate(points): array_x.InsertValue(i, p[0]) array_y.InsertValue(i, p[1]) field = vtk.vtkFieldData() field.AddArray(array_x) field.AddArray(array_y) data = vtk.vtkDataObject() data.SetFieldData(field) plot = vtk.vtkXYPlotActor() plot.AddDataObjectInput(data) plot.SetDataObjectXComponent(0, 0) plot.SetDataObjectYComponent(0, 1) plot.SetXValuesToValue() plot.SetXTitle(title) plot.SetYTitle("") plot.ExchangeAxesOff() plot.PlotPointsOn() if not lines: plot.PlotLinesOff() plot.GetProperty().SetPointSize(5) plot.GetProperty().SetLineWidth(2) plot.SetNumberOfXLabels(3) # not working plot.GetProperty().SetColor(0, 0, 0) plot.GetProperty().SetOpacity(0.7) plot.SetPlotColor(0, c[0], c[1], c[2]) tprop = plot.GetAxisLabelTextProperty() tprop.SetColor(0, 0, 0) tprop.SetOpacity(0.7) tprop.SetFontFamily(0) tprop.BoldOff() tprop.ItalicOff() tprop.ShadowOff() tprop.SetFontSize(3) # not working plot.SetAxisTitleTextProperty(tprop) plot.SetAxisLabelTextProperty(tprop) plot.SetTitleTextProperty(tprop) if corner == 1: plot.GetPositionCoordinate().SetValue(0.0, 0.8, 0) if corner == 2: plot.GetPositionCoordinate().SetValue(0.7, 0.8, 0) if corner == 3: plot.GetPositionCoordinate().SetValue(0.0, 0.0, 0) if corner == 4: plot.GetPositionCoordinate().SetValue(0.7, 0.0, 0) plot.GetPosition2Coordinate().SetValue(0.3, 0.2, 0) return plot
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30.397059
0.000469
def all_nonperiodic_features(times, mags, errs, magsarefluxes=False, stetson_weightbytimediff=True): '''This rolls up the feature functions above and returns a single dict. NOTE: this doesn't calculate the CDPP to save time since binning and smoothing takes a while for dense light curves. Parameters ---------- times,mags,errs : np.array The input mag/flux time-series to calculate CDPP for. magsarefluxes : bool If True, indicates `mags` is actually an array of flux values. stetson_weightbytimediff : bool If this is True, the Stetson index for any pair of mags will be reweighted by the difference in times between them using the scheme in Fruth+ 2012 and Zhange+ 2003 (as seen in Sokolovsky+ 2017):: w_i = exp(- (t_i+1 - t_i)/ delta_t ) Returns ------- dict Returns a dict with all of the variability features. ''' # remove nans first finiteind = npisfinite(times) & npisfinite(mags) & npisfinite(errs) ftimes, fmags, ferrs = times[finiteind], mags[finiteind], errs[finiteind] # remove zero errors nzind = npnonzero(ferrs) ftimes, fmags, ferrs = ftimes[nzind], fmags[nzind], ferrs[nzind] xfeatures = nonperiodic_lightcurve_features(times, mags, errs, magsarefluxes=magsarefluxes) stetj = stetson_jindex(ftimes, fmags, ferrs, weightbytimediff=stetson_weightbytimediff) stetk = stetson_kindex(fmags, ferrs) xfeatures.update({'stetsonj':stetj, 'stetsonk':stetk}) return xfeatures
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33.06
0.001763
def dumps(obj, protocol=None): """Serialize obj as a string of bytes allocated in memory protocol defaults to cloudpickle.DEFAULT_PROTOCOL which is an alias to pickle.HIGHEST_PROTOCOL. This setting favors maximum communication speed between processes running the same Python version. Set protocol=pickle.DEFAULT_PROTOCOL instead if you need to ensure compatibility with older versions of Python. """ file = StringIO() try: cp = CloudPickler(file, protocol=protocol) cp.dump(obj) return file.getvalue() finally: file.close()
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34.176471
0.001675
def __process_node(self, node: yaml.Node, expected_type: Type) -> yaml.Node: """Processes a node. This is the main function that implements yatiml's \ functionality. It figures out how to interpret this node \ (recognition), then applies syntactic sugar, and finally \ recurses to the subnodes, if any. Args: node: The node to process. expected_type: The type we expect this node to be. Returns: The transformed node, or a transformed copy. """ logger.info('Processing node {} expecting type {}'.format( node, expected_type)) # figure out how to interpret this node recognized_types, message = self.__recognizer.recognize( node, expected_type) if len(recognized_types) != 1: raise RecognitionError(message) recognized_type = recognized_types[0] # remove syntactic sugar logger.debug('Savorizing node {}'.format(node)) if recognized_type in self._registered_classes.values(): node = self.__savorize(node, recognized_type) logger.debug('Savorized, now {}'.format(node)) # process subnodes logger.debug('Recursing into subnodes') if is_generic_list(recognized_type): if node.tag != 'tag:yaml.org,2002:seq': raise RecognitionError('{}{}Expected a {} here'.format( node.start_mark, os.linesep, type_to_desc(expected_type))) for item in node.value: self.__process_node(item, generic_type_args(recognized_type)[0]) elif is_generic_dict(recognized_type): if node.tag != 'tag:yaml.org,2002:map': raise RecognitionError('{}{}Expected a {} here'.format( node.start_mark, os.linesep, type_to_desc(expected_type))) for _, value_node in node.value: self.__process_node(value_node, generic_type_args(recognized_type)[1]) elif recognized_type in self._registered_classes.values(): if (not issubclass(recognized_type, enum.Enum) and not issubclass(recognized_type, str) and not issubclass(recognized_type, UserString)): for attr_name, type_, _ in class_subobjects(recognized_type): cnode = Node(node) if cnode.has_attribute(attr_name): subnode = cnode.get_attribute(attr_name) new_subnode = self.__process_node( subnode.yaml_node, type_) cnode.set_attribute(attr_name, new_subnode) else: logger.debug('Not a generic class or a user-defined class, not' ' recursing') node.tag = self.__type_to_tag(recognized_type) logger.debug('Finished processing node {}'.format(node)) return node
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42.647887
0.000968
def kallisto_table(kallisto_dir, index): """ convert kallisto output to a count table where the rows are equivalence classes and the columns are cells """ quant_dir = os.path.join(kallisto_dir, "quant") out_file = os.path.join(quant_dir, "matrix.csv") if file_exists(out_file): return out_file tsvfile = os.path.join(quant_dir, "matrix.tsv") ecfile = os.path.join(quant_dir, "matrix.ec") cellsfile = os.path.join(quant_dir, "matrix.cells") fastafile = os.path.splitext(index)[0] + ".fa" fasta_names = fasta.sequence_names(fastafile) ec_names = get_ec_names(ecfile, fasta_names) df = pd.read_table(tsvfile, header=None, names=["ec", "cell", "count"]) df["ec"] = [ec_names[x] for x in df["ec"]] df = df.pivot(index='ec', columns='cell', values='count') cellnames = get_cell_names(cellsfile) colnames = [cellnames[x] for x in df.columns] df.columns = colnames df.to_csv(out_file) return out_file
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41.826087
0.001016
def _parse_00(ofile): """ return 00 outfile as a pandas DataFrame """ with open(ofile) as infile: ## read in the results summary from the end of the outfile arr = np.array( [" "] + infile.read().split("Summary of MCMC results\n\n\n")[1:][0]\ .strip().split()) ## reshape array rows = 12 cols = (arr.shape[0] + 1) / rows arr = arr.reshape(rows, cols) ## make into labeled data frame df = pd.DataFrame( data=arr[1:, 1:], columns=arr[0, 1:], index=arr[1:, 0], ).T return df
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28.272727
0.015552
def query_cached_package_list(self): """Return list of pickled package names from PYPI""" if self.debug: self.logger.debug("DEBUG: reading pickled cache file") return cPickle.load(open(self.pkg_cache_file, "r"))
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48.6
0.008097
def network_del_notif(self, tenant_id, tenant_name, net_id): """Network delete notification. """ if not self.fw_init: return self.network_delete_notif(tenant_id, tenant_name, net_id)
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42.8
0.009174
def _printTraceback(self, test, err): """Print a nicely formatted traceback. :arg err: exc_info()-style traceback triple :arg test: the test that precipitated this call """ # Don't bind third item to a local var; that can create # circular refs which are expensive to collect. See the # sys.exc_info() docs. exception_type, exception_value = err[:2] # TODO: In Python 3, the traceback is attached to the exception # instance through the __traceback__ attribute. If the instance # is saved in a local variable that persists outside the except # block, the traceback will create a reference cycle with the # current frame and its dictionary of local variables. This will # delay reclaiming dead resources until the next cyclic garbage # collection pass. extracted_tb = extract_relevant_tb( err[2], exception_type, exception_type is test.failureException) test_frame_index = index_of_test_frame( extracted_tb, exception_type, exception_value, test) if test_frame_index: # We have a good guess at which frame is the test, so # trim everything until that. We don't care to see test # framework frames. extracted_tb = extracted_tb[test_frame_index:] with self.bar.dodging(): self.stream.write(''.join( format_traceback( extracted_tb, exception_type, exception_value, self._cwd, self._term, self._options.function_color, self._options.dim_color, self._options.editor, self._options.editor_shortcut_template)))
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40.282609
0.001054
def _advapi32_encrypt(cipher, key, data, iv, padding): """ Encrypts plaintext via CryptoAPI :param cipher: A unicode string of "aes", "des", "tripledes_2key", "tripledes_3key", "rc2", "rc4" :param key: The encryption key - a byte string 5-16 bytes long :param data: The plaintext - a byte string :param iv: The initialization vector - a byte string - unused for RC4 :param padding: Boolean, if padding should be used - unused for RC4 :raises: ValueError - when any of the parameters contain an invalid value TypeError - when any of the parameters are of the wrong type OSError - when an error is returned by the OS crypto library :return: A byte string of the ciphertext """ context_handle = None key_handle = None try: context_handle, key_handle = _advapi32_create_handles(cipher, key, iv) out_len = new(advapi32, 'DWORD *', len(data)) res = advapi32.CryptEncrypt( key_handle, null(), True, 0, null(), out_len, 0 ) handle_error(res) buffer_len = deref(out_len) buffer = buffer_from_bytes(buffer_len) write_to_buffer(buffer, data) pointer_set(out_len, len(data)) res = advapi32.CryptEncrypt( key_handle, null(), True, 0, buffer, out_len, buffer_len ) handle_error(res) output = bytes_from_buffer(buffer, deref(out_len)) # Remove padding when not required. CryptoAPI doesn't support this, so # we just manually remove it. if cipher == 'aes' and not padding: if output[-16:] != (b'\x10' * 16): raise ValueError('Invalid padding generated by OS crypto library') output = output[:-16] return output finally: if key_handle: advapi32.CryptDestroyKey(key_handle) if context_handle: close_context_handle(context_handle)
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26.189873
0.000932
def add(self, bounds1, label1, bounds2, label2, bin3, label3, data_label): """ Combines signals from multiple instruments within given bounds. Parameters ---------- bounds1 : (min, max) Bounds for selecting data on the axis of label1 Data points with label1 in [min, max) will be considered. label1 : string Data label for bounds1 to act on. bounds2 : (min, max) Bounds for selecting data on the axis of label2 Data points with label1 in [min, max) will be considered. label2 : string Data label for bounds2 to act on. bin3 : (min, max, #bins) Min and max bounds and number of bins for third axis. label3 : string Data label for third axis. data_label : array of strings Data label(s) for data product(s) to be averaged. Returns ------- median : dictionary Dictionary indexed by data label, each value of which is a dictionary with keys 'median', 'count', 'avg_abs_dev', and 'bin' (the values of the bin edges.) """ # TODO Update for 2.7 compatability. if isinstance(data_label, str): data_label = [data_label, ] elif not isinstance(data_label, collections.Sequence): raise ValueError("Please pass data_label as a string or " "collection of strings.") # Modeled after pysat.ssnl.median2D # Make bin boundaries. # y: values at label3 # z: *data_labels biny = np.linspace(bin3[0], bin3[1], bin3[2]+1) numy = len(biny)-1 numz = len(data_label) # Ranges yarr, zarr = map(np.arange, (numy, numz)) # Store data here. ans = [[[collections.deque()] for j in yarr] for k in zarr] # Filter data by bounds and bin it. # Idiom for loading all of the data in an instrument's bounds. for inst in self: for inst in inst: if len(inst.data) != 0: # Select indicies for each piece of data we're interest in. # Not all of this data is in bounds on label3 but we'll # sort this later. min1, max1 = bounds1 min2, max2 = bounds2 data1 = inst.data[label1] data2 = inst.data[label2] in_bounds, = np.where((min1 <= data1) & (data1 < max1) & (min2 <= data2) & (data2 < max2)) # Grab the data in bounds on data1, data2. data_considered = inst.data.iloc[in_bounds] y_indexes = np.digitize(data_considered[label3], biny) - 1 # Iterate over the bins along y for yj in yarr: # Indicies of data in this bin yindex, = np.where(y_indexes == yj) # If there's data in this bin if len(yindex) > 0: # For each data label, add the points. for zk in zarr: ans[zk][yj][0].extend( data_considered.ix[yindex, data_label[zk]].tolist()) # Now for the averaging. # Let's, try .. packing the answers for the 2d function. numx = 1 xarr = np.arange(numx) binx = None # TODO modify output out_2d = _calc_2d_median(ans, data_label, binx, biny, xarr, yarr, zarr, numx, numy, numz) # Transform output output = {} for i, label in enumerate(data_label): median = [r[0] for r in out_2d[label]['median']] count = [r[0] for r in out_2d[label]['count']] dev = [r[0] for r in out_2d[label]['avg_abs_dev']] output[label] = {'median': median, 'count': count, 'avg_abs_dev': dev, 'bin': out_2d[label]['bin_y']} return output
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38.25
0.00236
def _analyze(self): '''Run-once function to generate analysis over all series, considering both full and partial data. Initializes the self.analysis dict which maps: (non-reference) column/series -> 'full' and/or 'partial' -> stats dict returned by get_xy_dataset_statistics ''' if not self.analysis: for dseries in self.data_series: # Count number of non-NaN rows dseries_count = self.df[dseries].count() assert(len(self.df_pruned) <= dseries_count <= len(self.df) or dseries_count) self.analysis[dseries] = dict( partial = None, full = None, ) # Compute the statistics for the common records stats = get_xy_dataset_statistics_pandas(self.df_pruned, self.reference_series, dseries, fcorrect_x_cutoff = 1.0, fcorrect_y_cutoff = 1.0, bootstrap_data = False, x_fuzzy_range = 0.1, y_scalar = 1.0, ignore_null_values = True) if (len(self.df_pruned) == len(self.df)): # There are no pruned records so these are actually the full stats self.analysis[dseries]['full'] = dict(data = stats, description = format_stats(stats, floating_point_format = '%0.3f', sci_notation_format = '%.2E', return_string = True)) else: # Store the results for the partial dataset self.analysis[dseries]['partial'] = dict(data = stats, description = format_stats(stats, floating_point_format = '%0.3f', sci_notation_format = '%.2E', return_string = True)) if dseries_count > len(self.df_pruned): # This dataset has records which are not in the pruned dataset stats = get_xy_dataset_statistics_pandas(self.df, self.reference_series, dseries, fcorrect_x_cutoff = 1.0, fcorrect_y_cutoff = 1.0, bootstrap_data = False, x_fuzzy_range = 0.1, y_scalar = 1.0, ignore_null_values = True) self.analysis[dseries]['full'] = dict(data = stats, description = format_stats(stats, floating_point_format = '%0.3f', sci_notation_format = '%.2E', return_string = True)) return self.analysis
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68.05
0.027888
def to_dict(self, **kw): u"""Converts the lxml object to a dict. possible kwargs: without_comments: bool """ _, value = helpers.etree_to_dict(self._xml, **kw).popitem() return value
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28.375
0.008547
def networkBibCoupling(self, weighted = True, fullInfo = False, addCR = False): """Creates a bibliographic coupling network based on citations for the RecordCollection. # Parameters _weighted_ : `optional bool` > Default `True`, if `True` the weight of the edges will be added to the network _fullInfo_ : `optional bool` > Default `False`, if `True` the full citation string will be added to each of the nodes of the network. # Returns `Networkx Graph` > A graph of the bibliographic coupling """ progArgs = (0, "Make a citation network for coupling") if metaknowledge.VERBOSE_MODE: progKwargs = {'dummy' : False} else: progKwargs = {'dummy' : True} with _ProgressBar(*progArgs, **progKwargs) as PBar: citeGrph = self.networkCitation(weighted = False, directed = True, detailedCore = True, fullInfo = fullInfo, count = False, nodeInfo = True, addCR = addCR, _quiet = True) pcount = 0 pmax = len(citeGrph) PBar.updateVal(.2, "Starting to classify nodes") workingGrph = nx.Graph() couplingSet = set() for n, d in citeGrph.nodes(data = True): pcount += 1 PBar.updateVal(.2 + .4 * (pcount / pmax), "Classifying: {}".format(n)) if d['inCore']: workingGrph.add_node(n, **d) if citeGrph.in_degree(n) > 0: couplingSet.add(n) pcount = 0 pmax = len(couplingSet) for n in couplingSet: PBar.updateVal(.6 + .4 * (pcount / pmax), "Coupling: {}".format(n)) citesLst = list(citeGrph.in_edges(n)) for i, edgeOuter in enumerate(citesLst): outerNode = edgeOuter[0] for edgeInner in citesLst[i + 1:]: innerNode = edgeInner[0] if weighted and workingGrph.has_edge(outerNode, innerNode): workingGrph.edges[outerNode, innerNode]['weight'] += 1 elif weighted: workingGrph.add_edge(outerNode, innerNode, weight = 1) else: workingGrph.add_edge(outerNode, innerNode) PBar.finish("Done making a bib-coupling network from {}".format(self)) return workingGrph
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44.218182
0.016492
def put(self): """Update a credential by file path""" cred_payload = utils.uni_to_str(json.loads(request.get_data())) return self.manager.update_credential(cred_payload)
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47.5
0.010363
def publish_collated_document(cursor, model, parent_model): """Publish a given `module`'s collated content in the context of the `parent_model`. Note, the model's content is expected to already have the collated content. This will just persist that content to the archive. """ html = bytes(cnxepub.DocumentContentFormatter(model)) sha1 = hashlib.new('sha1', html).hexdigest() cursor.execute("SELECT fileid FROM files WHERE sha1 = %s", (sha1,)) try: fileid = cursor.fetchone()[0] except TypeError: file_args = { 'media_type': 'text/html', 'data': psycopg2.Binary(html), } cursor.execute("""\ INSERT INTO files (file, media_type) VALUES (%(data)s, %(media_type)s) RETURNING fileid""", file_args) fileid = cursor.fetchone()[0] args = { 'module_ident_hash': model.ident_hash, 'parent_ident_hash': parent_model.ident_hash, 'fileid': fileid, } stmt = """\ INSERT INTO collated_file_associations (context, item, fileid) VALUES ((SELECT module_ident FROM modules WHERE ident_hash(uuid, major_version, minor_version) = %(parent_ident_hash)s), (SELECT module_ident FROM modules WHERE ident_hash(uuid, major_version, minor_version) = %(module_ident_hash)s), %(fileid)s)""" cursor.execute(stmt, args)
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35.315789
0.000725
def get_config(self, force=False): """ Returns a dictionary of all config.xml properties If `force = True` then ignore any cached state and read config.xml if possible setup_omero_cli() must be called before this method to import the correct omero module to minimise the possibility of version conflicts """ if not force and not self.has_config(): raise Exception('No config file') configxml = os.path.join(self.dir, 'etc', 'grid', 'config.xml') if not os.path.exists(configxml): raise Exception('No config file') try: # Attempt to open config.xml read-only, though this flag is not # present in early versions of OMERO 5.0 c = self._omero.config.ConfigXml( configxml, exclusive=False, read_only=True) except TypeError: c = self._omero.config.ConfigXml(configxml, exclusive=False) try: return c.as_map() finally: c.close()
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35.275862
0.001903
def chop(self, bits=1): """ Chops a BV into consecutive sub-slices. Obviously, the length of this BV must be a multiple of bits. :returns: A list of smaller bitvectors, each ``bits`` in length. The first one will be the left-most (i.e. most significant) bits. """ s = len(self) if s % bits != 0: raise ValueError("expression length (%d) should be a multiple of 'bits' (%d)" % (len(self), bits)) elif s == bits: return [ self ] else: return list(reversed([ self[(n+1)*bits - 1:n*bits] for n in range(0, s // bits) ]))
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44.714286
0.015649
def getCall(self, n): #pylint: disable=invalid-name """ Args: n: integer (index of function call) Return: SpyCall object (or None if the index is not valid) """ call_list = super(SinonSpy, self)._get_wrapper().call_list if n >= 0 and n < len(call_list): call = call_list[n] call.proxy = weakref.proxy(self) return call else: return None
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32.214286
0.008621
def get_access_token(self): ''' Returns an access token for the specified subscription. This method uses a cache to limit the number of requests to the token service. A fresh token can be re-used during its lifetime of 10 minutes. After a successful request to the token service, this method caches the access token. Subsequent invocations of the method return the cached token for the next 5 minutes. After 5 minutes, a new token is fetched from the token service and the cache is updated. ''' if (self.token is None) or (datetime.utcnow() > self.reuse_token_until): headers = {'Ocp-Apim-Subscription-Key': self.client_secret} response = requests.post(self.base_url, headers=headers) response.raise_for_status() self.token = response.content self.reuse_token_until = datetime.utcnow() + timedelta(minutes=5) return self.token.decode('utf-8')
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48.35
0.008114
def _code2xls(self, worksheets): """Writes code to xls file Format: <row>\t<col>\t<tab>\t<code>\n """ code_array = self.code_array xls_max_shape = self.xls_max_rows, self.xls_max_cols, self.xls_max_tabs for key in code_array: if all(kele < mele for kele, mele in zip(key, xls_max_shape)): # Cell lies within Excel boundaries row, col, tab = key code_str = code_array(key) if code_str is not None: style = self._get_xfstyle(worksheets, key) worksheets[tab].write(row, col, label=code_str, style=style) # Handle cell formatting in cells without code # Get bboxes for all cell_attributes max_shape = [min(xls_max_shape[0], code_array.shape[0]), min(xls_max_shape[1], code_array.shape[1])] # Prevent systems from blocking if max_shape[0] * max_shape[1] > 1024000: # Ignore all cell attributes below row 3999 max_shape[0] = 4000 cell_attributes = code_array.dict_grid.cell_attributes bboxes = [] for s, __tab, __ in cell_attributes: if s: bboxes.append((s.get_grid_bbox(code_array.shape), __tab)) # Get bbox_cell_set from bboxes cells = [] for ((bb_top, bb_left), (bb_bottom, bb_right)), __tab in bboxes: __bb_bottom = min(bb_bottom, max_shape[0]) __bb_right = min(bb_right, max_shape[1]) for __row, __col in product(xrange(bb_top, __bb_bottom + 1), xrange(bb_left, __bb_right + 1)): cells.append((__row, __col, __tab)) cell_set = set(cells) # Loop over those with non-standard attributes for key in cell_set: if key not in code_array and all(ele >= 0 for ele in key): row, col, tab = key style = self._get_xfstyle(worksheets, key) worksheets[tab].write(row, col, label="", style=style)
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38.555556
0.000937
def stop(self, signum=None, frame=None): """ handel's a termination signal """ BackgroundProcess.objects.filter(pk=self.process_id ).update(pid=0, last_update=now(), message='stopping..') # run the cleanup self.cleanup() BackgroundProcess.objects.filter(pk=self.process_id).update(pid=0, last_update=now(), message='stopped')
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49.818182
0.008961
def on_draw(self, e): """Draw all visuals.""" gloo.clear() for visual in self.visuals: logger.log(5, "Draw visual `%s`.", visual) visual.on_draw()
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31.5
0.010309
def get_init_container(self, init_command, init_args, env_vars, context_mounts, persistence_outputs, persistence_data): """Pod init container for setting outputs path.""" env_vars = to_list(env_vars, check_none=True) env_vars += [ get_env_var(name=constants.CONFIG_MAP_JOB_INFO_KEY_NAME, value=json.dumps(self.labels)), ] return client.V1Container( name=self.init_container_name, image=self.init_docker_image, image_pull_policy=self.init_docker_image_pull_policy, command=init_command, env=env_vars, args=init_args, volume_mounts=context_mounts)
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40.333333
0.009227
def list(self, search_opts=None, limit=None, marker=None, sort_by=None, reverse=None): """Get a list of Jobs.""" query = base.get_query_string(search_opts, limit=limit, marker=marker, sort_by=sort_by, reverse=reverse) url = "/jobs%s" % query return self._page(url, 'jobs', limit)
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50.857143
0.008287
def next(self, start): """ Return a (marker_code, segment_offset) 2-tuple identifying and locating the first marker in *stream* occuring after offset *start*. The returned *segment_offset* points to the position immediately following the 2-byte marker code, the start of the marker segment, for those markers that have a segment. """ position = start while True: # skip over any non-\xFF bytes position = self._offset_of_next_ff_byte(start=position) # skip over any \xFF padding bytes position, byte_ = self._next_non_ff_byte(start=position+1) # 'FF 00' sequence is not a marker, start over if found if byte_ == b'\x00': continue # this is a marker, gather return values and break out of scan marker_code, segment_offset = byte_, position+1 break return marker_code, segment_offset
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45.952381
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def minimal_residual(A, b, x0=None, tol=1e-5, maxiter=None, xtype=None, M=None, callback=None, residuals=None): """Minimal residual (MR) algorithm. Solves the linear system Ax = b. Left preconditioning is supported. Parameters ---------- A : array, matrix, sparse matrix, LinearOperator n x n, linear system to solve b : array, matrix right hand side, shape is (n,) or (n,1) x0 : array, matrix initial guess, default is a vector of zeros tol : float relative convergence tolerance, i.e. tol is scaled by the preconditioner norm of r_0, or ||r_0||_M. maxiter : int maximum number of allowed iterations xtype : type dtype for the solution, default is automatic type detection M : array, matrix, sparse matrix, LinearOperator n x n, inverted preconditioner, i.e. solve M A x = M b. callback : function User-supplied function is called after each iteration as callback(xk), where xk is the current solution vector residuals : list residuals contains the residual norm history, including the initial residual. The preconditioner norm is used, instead of the Euclidean norm. Returns ------- (xNew, info) xNew : an updated guess to the solution of Ax = b info : halting status of cg == ======================================= 0 successful exit >0 convergence to tolerance not achieved, return iteration count instead. <0 numerical breakdown, or illegal input == ======================================= Notes ----- The LinearOperator class is in scipy.sparse.linalg.interface. Use this class if you prefer to define A or M as a mat-vec routine as opposed to explicitly constructing the matrix. A.psolve(..) is still supported as a legacy. The residual in the preconditioner norm is both used for halting and returned in the residuals list. Examples -------- >>> from pyamg.krylov import minimal_residual >>> from pyamg.util.linalg import norm >>> import numpy as np >>> from pyamg.gallery import poisson >>> A = poisson((10,10)) >>> b = np.ones((A.shape[0],)) >>> (x,flag) = minimal_residual(A,b, maxiter=2, tol=1e-8) >>> print norm(b - A*x) 7.26369350856 References ---------- .. [1] Yousef Saad, "Iterative Methods for Sparse Linear Systems, Second Edition", SIAM, pp. 137--142, 2003 http://www-users.cs.umn.edu/~saad/books.html """ A, M, x, b, postprocess = make_system(A, M, x0, b) # Ensure that warnings are always reissued from this function import warnings warnings.filterwarnings('always', module='pyamg\.krylov\._minimal_residual') # determine maxiter if maxiter is None: maxiter = int(len(b)) elif maxiter < 1: raise ValueError('Number of iterations must be positive') # setup method r = M*(b - A*x) normr = norm(r) # store initial residual if residuals is not None: residuals[:] = [normr] # Check initial guess ( scaling by b, if b != 0, # must account for case when norm(b) is very small) normb = norm(b) if normb == 0.0: normb = 1.0 if normr < tol*normb: return (postprocess(x), 0) # Scale tol by ||r_0||_M if normr != 0.0: tol = tol*normr # How often should r be recomputed recompute_r = 50 iter = 0 while True: iter = iter+1 p = M*(A*r) rMAr = np.inner(p.conjugate(), r) # check curvature of M^-1 A if rMAr < 0.0: warn("\nIndefinite matrix detected in minimal residual,\ aborting\n") return (postprocess(x), -1) alpha = rMAr / np.inner(p.conjugate(), p) x = x + alpha*r if np.mod(iter, recompute_r) and iter > 0: r = M*(b - A*x) else: r = r - alpha*p normr = norm(r) if residuals is not None: residuals.append(normr) if callback is not None: callback(x) if normr < tol: return (postprocess(x), 0) if iter == maxiter: return (postprocess(x), iter)
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def autodiscover(path=None, plugin_prefix='intake_'): """Scan for Intake plugin packages and return a dict of plugins. This function searches path (or sys.path) for packages with names that start with plugin_prefix. Those modules will be imported and scanned for subclasses of intake.source.base.Plugin. Any subclasses found will be instantiated and returned in a dictionary, with the plugin's name attribute as the key. """ plugins = {} for importer, name, ispkg in pkgutil.iter_modules(path=path): if name.startswith(plugin_prefix): t = time.time() new_plugins = load_plugins_from_module(name) for plugin_name, plugin in new_plugins.items(): if plugin_name in plugins: orig_path = inspect.getfile(plugins[plugin_name]) new_path = inspect.getfile(plugin) warnings.warn('Plugin name collision for "%s" from' '\n %s' '\nand' '\n %s' '\nKeeping plugin from first location.' % (plugin_name, orig_path, new_path)) else: plugins[plugin_name] = plugin logger.debug("Import %s took: %7.2f s" % (name, time.time() - t)) return plugins
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def _compile_control_flow_expression(self, expr: Expression, scope: Dict[str, TensorFluent], batch_size: Optional[int] = None, noise: Optional[List[tf.Tensor]] = None) -> TensorFluent: '''Compile a control flow expression `expr` into a TensorFluent in the given `scope` with optional batch size. Args: expr (:obj:`rddl2tf.expr.Expression`): A RDDL control flow expression. scope (Dict[str, :obj:`rddl2tf.fluent.TensorFluent`]): A fluent scope. batch_size (Optional[size]): The batch size. Returns: :obj:`rddl2tf.fluent.TensorFluent`: The compiled expression as a TensorFluent. ''' etype = expr.etype args = expr.args if etype[1] == 'if': condition = self._compile_expression(args[0], scope, batch_size, noise) true_case = self._compile_expression(args[1], scope, batch_size, noise) false_case = self._compile_expression(args[2], scope, batch_size, noise) fluent = TensorFluent.if_then_else(condition, true_case, false_case) else: raise ValueError('Invalid control flow expression:\n{}'.format(expr)) return fluent
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def objwalk(obj, path=(), memo=None): """ Walks an arbitrary python pbject. :param mixed obj: Any python object :param tuple path: A tuple of the set attributes representing the path to the value :param set memo: The list of attributes traversed thus far :rtype <tuple<tuple>, <mixed>>: The path to the value on the object, the value. """ if len( path ) > MAX_DEPTH + 1: yield path, obj # Truncate it! if memo is None: memo = set() iterator = None if isinstance(obj, Mapping): iterator = iteritems elif isinstance(obj, (Sequence, Set)) and not isinstance(obj, string_types): iterator = enumerate elif hasattr( obj, '__class__' ) and hasattr( obj, '__dict__' ) and type(obj) not in primitives: # If type(obj) == <instance> iterator = class_iterator elif hasattr(obj, '__iter__') or isinstance(obj, types.GeneratorType): obj = [o for o in obj] else: pass if iterator: if id(obj) not in memo: memo.add(id(obj)) for path_component, value in iterator(obj): for result in objwalk(value, path + (path_component,), memo): yield result memo.remove(id(obj)) else: yield path, obj
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def open_file(orig_file_path): """ Taking in a file path, attempt to open mock data files with it. """ unquoted = unquote(orig_file_path) paths = [ convert_to_platform_safe(orig_file_path), "%s/index.html" % (convert_to_platform_safe(orig_file_path)), orig_file_path, "%s/index.html" % orig_file_path, convert_to_platform_safe(unquoted), "%s/index.html" % (convert_to_platform_safe(unquoted)), unquoted, "%s/index.html" % unquoted, ] file_path = None handle = None for path in paths: try: file_path = path handle = open(path, "rb") break except IOError: pass return handle
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def connect_callbacks(self, callbacks_bag): """Connect callbacks specified in callbacks_bag with callbacks defined in the ui definition. Return a list with the name of the callbacks not connected. """ notconnected = [] for wname, builderobj in self.objects.items(): missing = builderobj.connect_commands(callbacks_bag) if missing is not None: notconnected.extend(missing) missing = builderobj.connect_bindings(callbacks_bag) if missing is not None: notconnected.extend(missing) if notconnected: notconnected = list(set(notconnected)) msg = 'Missing callbacks for commands: {}'.format(notconnected) logger.warning(msg) return notconnected else: return None
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def validate_intervals(intervals): """Checks that an (n, 2) interval ndarray is well-formed, and raises errors if not. Parameters ---------- intervals : np.ndarray, shape=(n, 2) Array of interval start/end locations. """ # Validate interval shape if intervals.ndim != 2 or intervals.shape[1] != 2: raise ValueError('Intervals should be n-by-2 numpy ndarray, ' 'but shape={}'.format(intervals.shape)) # Make sure no times are negative if (intervals < 0).any(): raise ValueError('Negative interval times found') # Make sure all intervals have strictly positive duration if (intervals[:, 1] <= intervals[:, 0]).any(): raise ValueError('All interval durations must be strictly positive')
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def poke_array(self, store, name, elemtype, elements, container, visited, _stack): """abstract method""" raise NotImplementedError
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def controller_event(self, channel, contr_nr, contr_val): """Return the bytes for a MIDI controller event.""" return self.midi_event(CONTROLLER, channel, contr_nr, contr_val)
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def add(data, id, medium, credentials): """Adds the [medium] with the given id and data to the user's [medium]List. :param data The data for the [medium] to add. :param id The id of the data to add. :param medium Anime or manga (tokens.Medium.ANIME or tokens.Medium.MANGA). :raise ValueError For bad arguments. """ _op(data, id, medium, tokens.Operations.ADD, credentials)
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def _bind_key(self, key, func): u"""setup the mapping from key to call the function.""" if not callable(func): print u"Trying to bind non method to keystroke:%s,%s"%(key,func) raise ReadlineError(u"Trying to bind non method to keystroke:%s,%s,%s,%s"%(key,func,type(func),type(self._bind_key))) keyinfo = make_KeyPress_from_keydescr(key.lower()).tuple() log(u">>>%s -> %s<<<"%(keyinfo,func.__name__)) self.key_dispatch[keyinfo] = func
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def mesh_other(mesh, other, samples=500, scale=False, icp_first=10, icp_final=50): """ Align a mesh with another mesh or a PointCloud using the principal axes of inertia as a starting point which is refined by iterative closest point. Parameters ------------ mesh : trimesh.Trimesh object Mesh to align with other other : trimesh.Trimesh or (n, 3) float Mesh or points in space samples : int Number of samples from mesh surface to align scale : bool Allow scaling in transform icp_first : int How many ICP iterations for the 9 possible combinations of sign flippage icp_final : int How many ICP iterations for the closest candidate from the wider search Returns ----------- mesh_to_other : (4, 4) float Transform to align mesh to the other object cost : float Average squared distance per point """ def key_points(m, count): """ Return a combination of mesh vertices and surface samples with vertices chosen by likelihood to be important to registation. """ if len(m.vertices) < (count / 2): return np.vstack(( m.vertices, m.sample(count - len(m.vertices)))) else: return m.sample(count) if not util.is_instance_named(mesh, 'Trimesh'): raise ValueError('mesh must be Trimesh object!') inverse = True search = mesh # if both are meshes use the smaller one for searching if util.is_instance_named(other, 'Trimesh'): if len(mesh.vertices) > len(other.vertices): # do the expensive tree construction on the # smaller mesh and query the others points search = other inverse = False points = key_points(m=mesh, count=samples) points_mesh = mesh else: points_mesh = other points = key_points(m=other, count=samples) if points_mesh.is_volume: points_PIT = points_mesh.principal_inertia_transform else: points_PIT = points_mesh.bounding_box_oriented.principal_inertia_transform elif util.is_shape(other, (-1, 3)): # case where other is just points points = other points_PIT = bounds.oriented_bounds(points)[0] else: raise ValueError('other must be mesh or (n, 3) points!') # get the transform that aligns the search mesh principal # axes of inertia with the XYZ axis at the origin if search.is_volume: search_PIT = search.principal_inertia_transform else: search_PIT = search.bounding_box_oriented.principal_inertia_transform # transform that moves the principal axes of inertia # of the search mesh to be aligned with the best- guess # principal axes of the points search_to_points = np.dot(np.linalg.inv(points_PIT), search_PIT) # permutations of cube rotations # the principal inertia transform has arbitrary sign # along the 3 major axis so try all combinations of # 180 degree rotations with a quick first ICP pass cubes = np.array([np.eye(4) * np.append(diag, 1) for diag in [[1, 1, 1], [1, 1, -1], [1, -1, 1], [-1, 1, 1], [-1, -1, 1], [-1, 1, -1], [1, -1, -1], [-1, -1, -1]]]) # loop through permutations and run iterative closest point costs = np.ones(len(cubes)) * np.inf transforms = [None] * len(cubes) centroid = search.centroid for i, flip in enumerate(cubes): # transform from points to search mesh # flipped around the centroid of search a_to_b = np.dot( transformations.transform_around(flip, centroid), np.linalg.inv(search_to_points)) # run first pass ICP matrix, junk, cost = icp(a=points, b=search, initial=a_to_b, max_iterations=int(icp_first), scale=scale) # save transform and costs from ICP transforms[i] = matrix costs[i] = cost # run a final ICP refinement step matrix, junk, cost = icp(a=points, b=search, initial=transforms[np.argmin(costs)], max_iterations=int(icp_final), scale=scale) # convert to per- point distance average cost /= len(points) # we picked the smaller mesh to construct the tree # on so we may have calculated a transform backwards # to save computation, so just invert matrix here if inverse: mesh_to_other = np.linalg.inv(matrix) else: mesh_to_other = matrix return mesh_to_other, cost
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33.871622
0.000388
def csv(self, path, mode=None, compression=None, sep=None, quote=None, escape=None, header=None, nullValue=None, escapeQuotes=None, quoteAll=None, dateFormat=None, timestampFormat=None, ignoreLeadingWhiteSpace=None, ignoreTrailingWhiteSpace=None, charToEscapeQuoteEscaping=None, encoding=None, emptyValue=None, lineSep=None): r"""Saves the content of the :class:`DataFrame` in CSV format at the specified path. :param path: the path in any Hadoop supported file system :param mode: specifies the behavior of the save operation when data already exists. * ``append``: Append contents of this :class:`DataFrame` to existing data. * ``overwrite``: Overwrite existing data. * ``ignore``: Silently ignore this operation if data already exists. * ``error`` or ``errorifexists`` (default case): Throw an exception if data already \ exists. :param compression: compression codec to use when saving to file. This can be one of the known case-insensitive shorten names (none, bzip2, gzip, lz4, snappy and deflate). :param sep: sets a single character as a separator for each field and value. If None is set, it uses the default value, ``,``. :param quote: sets a single character used for escaping quoted values where the separator can be part of the value. If None is set, it uses the default value, ``"``. If an empty string is set, it uses ``u0000`` (null character). :param escape: sets a single character used for escaping quotes inside an already quoted value. If None is set, it uses the default value, ``\`` :param escapeQuotes: a flag indicating whether values containing quotes should always be enclosed in quotes. If None is set, it uses the default value ``true``, escaping all values containing a quote character. :param quoteAll: a flag indicating whether all values should always be enclosed in quotes. If None is set, it uses the default value ``false``, only escaping values containing a quote character. :param header: writes the names of columns as the first line. If None is set, it uses the default value, ``false``. :param nullValue: sets the string representation of a null value. If None is set, it uses the default value, empty string. :param dateFormat: sets the string that indicates a date format. Custom date formats follow the formats at ``java.time.format.DateTimeFormatter``. This applies to date type. If None is set, it uses the default value, ``yyyy-MM-dd``. :param timestampFormat: sets the string that indicates a timestamp format. Custom date formats follow the formats at ``java.time.format.DateTimeFormatter``. This applies to timestamp type. If None is set, it uses the default value, ``yyyy-MM-dd'T'HH:mm:ss.SSSXXX``. :param ignoreLeadingWhiteSpace: a flag indicating whether or not leading whitespaces from values being written should be skipped. If None is set, it uses the default value, ``true``. :param ignoreTrailingWhiteSpace: a flag indicating whether or not trailing whitespaces from values being written should be skipped. If None is set, it uses the default value, ``true``. :param charToEscapeQuoteEscaping: sets a single character used for escaping the escape for the quote character. If None is set, the default value is escape character when escape and quote characters are different, ``\0`` otherwise.. :param encoding: sets the encoding (charset) of saved csv files. If None is set, the default UTF-8 charset will be used. :param emptyValue: sets the string representation of an empty value. If None is set, it uses the default value, ``""``. :param lineSep: defines the line separator that should be used for writing. If None is set, it uses the default value, ``\\n``. Maximum length is 1 character. >>> df.write.csv(os.path.join(tempfile.mkdtemp(), 'data')) """ self.mode(mode) self._set_opts(compression=compression, sep=sep, quote=quote, escape=escape, header=header, nullValue=nullValue, escapeQuotes=escapeQuotes, quoteAll=quoteAll, dateFormat=dateFormat, timestampFormat=timestampFormat, ignoreLeadingWhiteSpace=ignoreLeadingWhiteSpace, ignoreTrailingWhiteSpace=ignoreTrailingWhiteSpace, charToEscapeQuoteEscaping=charToEscapeQuoteEscaping, encoding=encoding, emptyValue=emptyValue, lineSep=lineSep) self._jwrite.csv(path)
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75.236111
0.008746
def _interval_to_seconds(interval, valid_units='smhdw'): """Convert the timeout duration to seconds. The value must be of the form "<integer><unit>" where supported units are s, m, h, d, w (seconds, minutes, hours, days, weeks). Args: interval: A "<integer><unit>" string. valid_units: A list of supported units. Returns: A string of the form "<integer>s" or None if timeout is empty. """ if not interval: return None try: last_char = interval[-1] if last_char == 's' and 's' in valid_units: return str(float(interval[:-1])) + 's' elif last_char == 'm' and 'm' in valid_units: return str(float(interval[:-1]) * 60) + 's' elif last_char == 'h' and 'h' in valid_units: return str(float(interval[:-1]) * 60 * 60) + 's' elif last_char == 'd' and 'd' in valid_units: return str(float(interval[:-1]) * 60 * 60 * 24) + 's' elif last_char == 'w' and 'w' in valid_units: return str(float(interval[:-1]) * 60 * 60 * 24 * 7) + 's' else: raise ValueError( 'Unsupported units in interval string %s: %s' % (interval, last_char)) except (ValueError, OverflowError) as e: raise ValueError('Unable to parse interval string %s: %s' % (interval, e))
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34.685714
0.009615
def to_unicode(s): """Return the object as unicode (only matters for Python 2.x). If s is already Unicode, return s as is. Otherwise, assume that s is UTF-8 encoded, and convert to Unicode. :param (basestring) s: a str, unicode or other basestring object :return (unicode): the object as unicode """ if not isinstance(s, six.string_types): raise ValueError("{} must be str or unicode.".format(s)) if not isinstance(s, six.text_type): s = six.text_type(s, 'utf-8') return s
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36.642857
0.001901
def list(region, profile): """ List all the CloudFormation stacks in the given region. """ ini_data = {} environment = {} if region: environment['region'] = region else: environment['region'] = find_myself() if profile: environment['profile'] = profile ini_data['environment'] = environment if start_list(ini_data): sys.exit(0) else: sys.exit(1)
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20.65
0.002315