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codesearchnet-python-pep8-v1

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def get_public_network_ip(ips, public_subnet): """ Given a public subnet, chose the one IP from the remote host that exists within the subnet range. """ for ip in ips: if net.ip_in_subnet(ip, public_subnet): return ip msg = "IPs (%s) are not valid for any of subnet specified %s" % (str(ips), str(public_subnet)) raise RuntimeError(msg)

def run(self): """ Writes data in JSON format into the task's output target. The data objects have the following attributes: * `_id` is the default Elasticsearch id field, * `text`: the text, * `date`: the day when the data was created. """ today = datetime.date.today() with self.output().open('w') as output: for i in range(5): output.write(json.dumps({'_id': i, 'text': 'Hi %s' % i, 'date': str(today)})) output.write('\n')

def encode_example(self, example_data): """See base class for details.""" np_dtype = np.dtype(self._dtype.as_numpy_dtype) # Convert to numpy if possible if not isinstance(example_data, np.ndarray): example_data = np.array(example_data, dtype=np_dtype) # Ensure the shape and dtype match if example_data.dtype != np_dtype: raise ValueError('Dtype {} do not match {}'.format( example_data.dtype, np_dtype)) utils.assert_shape_match(example_data.shape, self._shape) # For booleans, convert to integer (tf.train.Example does not support bool) if example_data.dtype == np.bool_: example_data = example_data.astype(int) return example_data

def _interpolate_stream_track_kick(self): """Build interpolations of the stream track near the kick""" if hasattr(self,'_kick_interpolatedThetasTrack'): #pragma: no cover self._store_closest() return None #Already did this # Setup the trackpoints where the kick will be computed, covering the # full length of the stream self._kick_interpolatedThetasTrack= \ numpy.linspace(self._gap_thetasTrack[0], self._gap_thetasTrack[-1], self._nKickPoints) TrackX= self._gap_ObsTrack[:,0]*numpy.cos(self._gap_ObsTrack[:,5]) TrackY= self._gap_ObsTrack[:,0]*numpy.sin(self._gap_ObsTrack[:,5]) TrackZ= self._gap_ObsTrack[:,3] TrackvX, TrackvY, TrackvZ=\ bovy_coords.cyl_to_rect_vec(self._gap_ObsTrack[:,1], self._gap_ObsTrack[:,2], self._gap_ObsTrack[:,4], self._gap_ObsTrack[:,5]) #Interpolate self._kick_interpTrackX=\ interpolate.InterpolatedUnivariateSpline(self._gap_thetasTrack, TrackX,k=3) self._kick_interpTrackY=\ interpolate.InterpolatedUnivariateSpline(self._gap_thetasTrack, TrackY,k=3) self._kick_interpTrackZ=\ interpolate.InterpolatedUnivariateSpline(self._gap_thetasTrack, TrackZ,k=3) self._kick_interpTrackvX=\ interpolate.InterpolatedUnivariateSpline(self._gap_thetasTrack, TrackvX,k=3) self._kick_interpTrackvY=\ interpolate.InterpolatedUnivariateSpline(self._gap_thetasTrack, TrackvY,k=3) self._kick_interpTrackvZ=\ interpolate.InterpolatedUnivariateSpline(self._gap_thetasTrack, TrackvZ,k=3) #Now store an interpolated version of the stream track self._kick_interpolatedObsTrackXY= numpy.empty((len(self._kick_interpolatedThetasTrack),6)) self._kick_interpolatedObsTrackXY[:,0]=\ self._kick_interpTrackX(self._kick_interpolatedThetasTrack) self._kick_interpolatedObsTrackXY[:,1]=\ self._kick_interpTrackY(self._kick_interpolatedThetasTrack) self._kick_interpolatedObsTrackXY[:,2]=\ self._kick_interpTrackZ(self._kick_interpolatedThetasTrack) self._kick_interpolatedObsTrackXY[:,3]=\ self._kick_interpTrackvX(self._kick_interpolatedThetasTrack) self._kick_interpolatedObsTrackXY[:,4]=\ self._kick_interpTrackvY(self._kick_interpolatedThetasTrack) self._kick_interpolatedObsTrackXY[:,5]=\ self._kick_interpTrackvZ(self._kick_interpolatedThetasTrack) #Also in cylindrical coordinates self._kick_interpolatedObsTrack= \ numpy.empty((len(self._kick_interpolatedThetasTrack),6)) tR,tphi,tZ= bovy_coords.rect_to_cyl(self._kick_interpolatedObsTrackXY[:,0], self._kick_interpolatedObsTrackXY[:,1], self._kick_interpolatedObsTrackXY[:,2]) tvR,tvT,tvZ=\ bovy_coords.rect_to_cyl_vec(self._kick_interpolatedObsTrackXY[:,3], self._kick_interpolatedObsTrackXY[:,4], self._kick_interpolatedObsTrackXY[:,5], tR,tphi,tZ,cyl=True) self._kick_interpolatedObsTrack[:,0]= tR self._kick_interpolatedObsTrack[:,1]= tvR self._kick_interpolatedObsTrack[:,2]= tvT self._kick_interpolatedObsTrack[:,3]= tZ self._kick_interpolatedObsTrack[:,4]= tvZ self._kick_interpolatedObsTrack[:,5]= tphi self._store_closest() return None

def get_recent_matches(self, card_type="micro_card"): """ Calling the Recent Matches API. Arg: card_type: optional, default to micro_card. Accepted values are micro_card & summary_card. Return: json data """ recent_matches_url = self.api_path + "recent_matches/" params = {} params["card_type"] = card_type response = self.get_response(recent_matches_url, params) return response

def get_title(self, lang: str=None) -> Literal: """ Get the title of the object :param lang: Lang to retrieve :return: Title string representation :rtype: Literal """ return self.metadata.get_single(key=DC.title, lang=lang) or \ self.metadata.get_single(key=DCT.title, lang=lang)

def _disconnect_hanging_devices(self): """Periodic callback that checks for devices that haven't been used and disconnects them.""" now = monotonic() for uuid, data in self._connections.items(): if (now - data['last_touch']) > self.client_timeout: self._logger.info("Disconnect inactive client %s from device 0x%X", data['client'], uuid) self._loop.add_callback(self._disconnect_from_device, uuid, data['key'], data['client'], unsolicited=True)

def restrict_chat_member(self, *args, **kwargs): """See :func:`restrict_chat_member`""" return restrict_chat_member(*args, **self._merge_overrides(**kwargs)).run()

def get_variations(self): """Gets the OpenType font variations for the font options object. See :meth:`set_variations` for details about the string format. :return: the font variations for the font options object. The returned string belongs to the ``options`` and must not be modified. It is valid until either the font options object is destroyed or the font variations in this object is modified with :meth:`set_variations`. *New in cairo 1.16.* *New in cairocffi 0.9.* """ variations = cairo.cairo_font_options_get_variations(self._pointer) if variations != ffi.NULL: return ffi.string(variations).decode('utf8', 'replace')

def get_keyboard_mapping(conn): """ Return a keyboard mapping cookie that can be used to fetch the table of keysyms in the current X environment. :rtype: xcb.xproto.GetKeyboardMappingCookie """ mn, mx = get_min_max_keycode(conn) return conn.core.GetKeyboardMapping(mn, mx - mn + 1)

def set_node_attributes(G, values, name=None): """Set node attributes from dictionary of nodes and values Parameters ---------- G : DyNetx Graph name : string Attribute name values: dict Dictionary of attribute values keyed by node. If `values` is not a dictionary, then it is treated as a single attribute value that is then applied to every node in `G`. """ # Set node attributes based on type of `values` if name is not None: # `values` must not be a dict of dict try: # `values` is a dict for n, v in values.items(): try: G.node[n][name] = values[n] except KeyError: pass except AttributeError: # `values` is a constant for n in G: G.node[n][name] = values else: # `values` must be dict of dict for n, d in values.items(): try: G.node[n].update(d) except KeyError: pass

def request_token(self) -> None: """ Requests a new Client Credentials Flow authentication token from the Spotify API and stores it in the `token` property of the object. Raises: requests.HTTPError: If an HTTP error occurred during the request. """ response: requests.Response = requests.post( self._TOKEN_URL, auth=HTTPBasicAuth(self._client_id, self._client_key), data={"grant_type": self._GRANT_TYPE}, verify=True ) response.raise_for_status() self._token = response.json() self._token_expires_at = time.time() + self._token["expires_in"]

def values(service, id, ranges): """Fetch and return spreadsheet cell values with Google sheets API.""" params = {'majorDimension': 'ROWS', 'valueRenderOption': 'UNFORMATTED_VALUE', 'dateTimeRenderOption': 'FORMATTED_STRING'} params.update(spreadsheetId=id, ranges=ranges) response = service.spreadsheets().values().batchGet(**params).execute() return response['valueRanges']

def _stop_scan(self): """Stop scanning for BLE devices """ try: response = self._send_command(6, 4, []) if response.payload[0] != 0: # Error code 129 means we just were not currently scanning if response.payload[0] != 129: self._logger.error('Error stopping scan for devices, error=%d', response.payload[0]) return False, {'reason': "Could not stop scan for ble devices"} except InternalTimeoutError: return False, {'reason': "Timeout waiting for response"} except DeviceNotConfiguredError: return True, {'reason': "Device not connected (did you disconnect the dongle?"} return True, None

def format_time_point( time_point_string): """ :param str time_point_string: String representation of a time point to format :return: Formatted time point :rtype: str :raises ValueError: If *time_point_string* is not formatted by dateutil.parser.parse See :py:meth:`datetime.datetime.isoformat` function for supported formats. """ time_point = dateutil.parser.parse(time_point_string) if not is_aware(time_point): time_point = make_aware(time_point) time_point = local_time_point(time_point) return time_point.strftime("%Y-%m-%dT%H:%M:%S")

def _inject_target(self, target_adaptor): """Inject a target, respecting all sources of dependencies.""" target_cls = self._target_types[target_adaptor.type_alias] declared_deps = target_adaptor.dependencies implicit_deps = (Address.parse(s, relative_to=target_adaptor.address.spec_path, subproject_roots=self._address_mapper.subproject_roots) for s in target_cls.compute_dependency_specs(kwargs=target_adaptor.kwargs())) for dep in declared_deps: self._dependent_address_map[dep].add(target_adaptor.address) for dep in implicit_deps: self._implicit_dependent_address_map[dep].add(target_adaptor.address)

def del_application(self, application, sync=True): """ delete application from this company :param application: the subnet to be deleted from this company :param sync: If sync=True(default) synchronize with Ariane server. If sync=False, add the application object on list to be removed on next save(). :return: """ LOGGER.debug("Company.del_application") if not sync: self.applications_2_rm.append(application) else: if application.id is None: application.sync() if self.id is not None and application.id is not None: params = { 'id': self.id, 'applicationID': application.id } args = {'http_operation': 'GET', 'operation_path': 'update/applications/delete', 'parameters': params} response = CompanyService.requester.call(args) if response.rc != 0: LOGGER.warning( 'Company.del_application - Problem while updating company ' + self.name + '. Reason: ' + str(response.response_content) + '-' + str(response.error_message) + " (" + str(response.rc) + ")" ) else: self.applications_ids.remove(application.id) application.sync() else: LOGGER.warning( 'Company.del_application - Problem while updating company ' + self.name + '. Reason: application ' + application.name + ' id is None or self.id is None' )

def nextparent(self, parent, depth): ''' Add lines to current line by traversing the grandparent object again and once we reach our current line counting every line that is prefixed with the parent directory. ''' if depth > 1: # can't jump to parent of root node! pdir = os.path.dirname(self.name) line = 0 for c, d in parent.traverse(): if line > parent.curline and c.name.startswith(pdir): parent.curline += 1 line += 1 else: # otherwise just skip to next directory line = -1 # skip hidden parent node for c, d in parent.traverse(): if line > parent.curline: parent.curline += 1 if os.path.isdir(c.name) and c.name in parent.children[0:]: break line += 1

def _mic_required(target_info): """ Checks the MsvAvFlags field of the supplied TargetInfo structure to determine in the MIC flags is set :param target_info: The TargetInfo structure to check :return: a boolean value indicating that the MIC flag is set """ if target_info is not None and target_info[TargetInfo.NTLMSSP_AV_FLAGS] is not None: flags = struct.unpack('<I', target_info[TargetInfo.NTLMSSP_AV_FLAGS][1])[0] return bool(flags & 0x00000002)

def __extract_model_summary_value(model, value): """ Extract a model summary field value """ field_value = None if isinstance(value, _precomputed_field): field_value = value.field else: field_value = model._get(value) if isinstance(field_value, float): try: field_value = round(field_value, 4) except: pass return field_value

def activities_list(self, since=None, **kwargs): "https://developer.zendesk.com/rest_api/docs/core/activity_stream#list-activities" api_path = "/api/v2/activities.json" api_query = {} if "query" in kwargs.keys(): api_query.update(kwargs["query"]) del kwargs["query"] if since: api_query.update({ "since": since, }) return self.call(api_path, query=api_query, **kwargs)

def plugin(cls, name): """ Retrieves the plugin based on the inputted name. :param name | <str> :return <Plugin> """ cls.loadPlugins() plugs = getattr(cls, '_%s__plugins' % cls.__name__, {}) return plugs.get(nstr(name))

def arg_type_to_string(arg_type) -> str: """ Converts the argument type to a string :param arg_type: :return: String representation of the argument type. Multiple return types are turned into a comma delimited list of type names """ union_params = ( getattr(arg_type, '__union_params__', None) or getattr(arg_type, '__args__', None) ) if union_params and isinstance(union_params, (list, tuple)): return ', '.join([arg_type_to_string(item) for item in union_params]) try: return arg_type.__name__ except AttributeError: return '{}'.format(arg_type)

def filter(filter_creator): """ Creates a decorator that can be used as a filter. .. warning:: This is currently not compatible with most other decorators, if you are using a decorator that isn't part of `hurler` you should take caution. """ filter_func = [None] def function_getter(function): if isinstance(function, Filter): function.add_filter(filter) return function else: return Filter( filter=filter_func[0], callback=function, ) def filter_decorator(*args, **kwargs): filter_function = filter_creator(*args, **kwargs) filter_func[0] = filter_function return function_getter return filter_decorator

def UNTL_to_encodedUNTL(subject): """Normalize a UNTL subject heading to be used in SOLR.""" subject = normalize_UNTL(subject) subject = subject.replace(' ', '_') subject = subject.replace('_-_', '/') return subject

def should_compile_incrementally(self, vts, ctx): """Check to see if the compile should try to re-use the existing analysis. Returns true if we should try to compile the target incrementally. """ if not vts.is_incremental: return False if not self._clear_invalid_analysis: return True return os.path.exists(ctx.analysis_file)

def check_network_role(self, public_key): """ Check the public key of a node on the network to see if they are permitted to participate. The roles being checked are the following, from first to last: "network" "default" The first role that is set will be the one used to enforce if the node is allowed. Args: public_key (string): The public key belonging to a node on the network """ state_root = self._current_root_func() if state_root == INIT_ROOT_KEY: LOGGER.debug("Chain head is not set yet. Permit all.") return True self._cache.update_view(state_root) role = self._cache.get_role("network", state_root) if role is None: policy_name = "default" else: policy_name = role.policy_name policy = self._cache.get_policy(policy_name, state_root) if policy is not None: if not self._allowed(public_key, policy): LOGGER.debug("Node is not permitted: %s.", public_key) return False return True

def do_delete(self, args): '''delete the entire contents of the current namespace''' namespace = self.config['namespace'] if not args.assume_yes: response = raw_input('Delete everything in {0!r}? Enter namespace: ' .format(namespace)) if response != namespace: self.stdout.write('not deleting anything\n') return self.stdout.write('deleting namespace {0!r}\n'.format(namespace)) self.task_master.clear()

def addVariantAnnotationSet(self, variantAnnotationSet): """ Adds the specified variantAnnotationSet to this dataset. """ id_ = variantAnnotationSet.getId() self._variantAnnotationSetIdMap[id_] = variantAnnotationSet self._variantAnnotationSetIds.append(id_)

def GetCellValueNoFail (self, column, row = None): """ get a cell, if it does not exist fail note that column at row START AT 1 same as excel """ if row == None: (row, column) = ParseCellSpec(column) cell = GetCellValue(self, column, row) if cell == None: raise ValueError("cell %d:%d does not exist" % (column, row)) return cell

def exists(path): """Determine if a Path or string is an existing path on the file system.""" try: return path.expanduser().absolute().exists() except AttributeError: return os.path.exists(os.path.abspath(os.path.expanduser(str(path))))

def containsTie(self): """ Returns True if the underlying weighted majority graph contains a tie between any pair of candidates and returns False otherwise. """ # If a value of 0 is present in the wmgMap, we assume that it represents a tie. for cand in self.wmgMap.keys(): if 0 in self.wmgMap[cand].values(): return True return False

def matrix(self): """Build matrix representation of Householder transformation. Builds the matrix representation :math:`H = I - \\beta vv^*`. **Use with care!** This routine may be helpful for testing purposes but should not be used in production codes for high dimensions since the resulting matrix is dense. """ n = self.v.shape[0] return numpy.eye(n, n) - self.beta * numpy.dot(self.v, self.v.T.conj())

def ispercolating(am, inlets, outlets, mode='site'): r""" Determines if a percolating clusters exists in the network spanning the given inlet and outlet sites Parameters ---------- am : adjacency_matrix The adjacency matrix with the ``data`` attribute indicating if a bond is occupied or not inlets : array_like An array of indices indicating which sites are part of the inlets outlets : array_like An array of indices indicating which sites are part of the outlets mode : string Indicates which type of percolation to apply, either `'site'` or `'bond'` """ if am.format is not 'coo': am = am.to_coo() ij = sp.vstack((am.col, am.row)).T if mode.startswith('site'): occupied_sites = sp.zeros(shape=am.shape[0], dtype=bool) occupied_sites[ij[am.data].flatten()] = True clusters = site_percolation(ij, occupied_sites) elif mode.startswith('bond'): occupied_bonds = am.data clusters = bond_percolation(ij, occupied_bonds) ins = sp.unique(clusters.sites[inlets]) if ins[0] == -1: ins = ins[1:] outs = sp.unique(clusters.sites[outlets]) if outs[0] == -1: outs = outs[1:] hits = sp.in1d(ins, outs) return sp.any(hits)

def to_jd(year, month, day): '''Determine Julian day count from Islamic date''' return (day + ceil(29.5 * (month - 1)) + (year - 1) * 354 + trunc((3 + (11 * year)) / 30) + EPOCH) - 1

def imread(path, grayscale=False, size=None, interpolate="bilinear", channel_first=False, as_uint16=False, num_channels=-1, **kwargs): """ Read image from ``path``. If you specify the ``size``, the output array is resized. Default output shape is (height, width, channel) for RGB image and (height, width) for gray-scale image. Args: path (String or File Object): Input image path. grayscale (bool): If True, the img is rescaled to gray-scale. Default is False. size (tuple of int): Output shape. The order is (width, height). If None, the image is not resized. Default is None. interpolate (str): Interpolation method. This argument is depend on the backend. If you want to specify this argument, you should pay much attention to which backend you use now. What you can select is below: - pil backend: ["nearest", "box", "bilinear", "hamming", "bicubic", "lanczos"]. - cv2 backend: ["nearest", "bilinear", "bicubic", "lanczos"]. Default is "bilinear" for both backends. channel_first (bool): If True, the shape of the output array is (channel, height, width) for RGB image. Default is False. as_uint16 (bool): If True, this function tries to read img as np.uint16. Default is False. num_channels (int): channel size of output array. Default is -1 which preserves raw image shape. Returns: numpy.ndarray : if as_uint16=True output dtype is np.uint16, else np.uint8 (default). """ return backend_manager.module.imread(path, grayscale=grayscale, size=size, interpolate=interpolate, channel_first=channel_first, as_uint16=as_uint16, num_channels=num_channels, **kwargs)

def script_args(f): """single decorator for adding script args""" args = [ magic_arguments.argument( '--out', type=str, help="""The variable in which to store stdout from the script. If the script is backgrounded, this will be the stdout *pipe*, instead of the stderr text itself. """ ), magic_arguments.argument( '--err', type=str, help="""The variable in which to store stderr from the script. If the script is backgrounded, this will be the stderr *pipe*, instead of the stderr text itself. """ ), magic_arguments.argument( '--bg', action="store_true", help="""Whether to run the script in the background. If given, the only way to see the output of the command is with --out/err. """ ), magic_arguments.argument( '--proc', type=str, help="""The variable in which to store Popen instance. This is used only when --bg option is given. """ ), ] for arg in args: f = arg(f) return f

def plot_fit(self, **kwargs): """ Plots the fit of the model Returns ---------- None (plots data and the fit) """ import matplotlib.pyplot as plt import seaborn as sns figsize = kwargs.get('figsize',(10,7)) if self.latent_variables.estimated is False: raise Exception("No latent variables estimated!") else: plt.figure(figsize=figsize) date_index = self.index[max(self.p, self.q):] t_params = self.transform_z() sigma2, Y, ___ = self._model(self.latent_variables.get_z_values()) plt.plot(date_index, np.abs(Y-t_params[-1]), label=self.data_name + ' Absolute Demeaned Values') plt.plot(date_index, np.power(sigma2,0.5), label='GARCH(' + str(self.p) + ',' + str(self.q) + ') Conditional Volatility',c='black') plt.title(self.data_name + " Volatility Plot") plt.legend(loc=2) plt.show()

def indent(s, c=" ", n=4): """Indent the string 's' with the character 'c', 'n' times. Parameters ---------- s : str String to indent c : str, default space String to use as indentation n : int, default 4 Number of chars to indent """ indentation = c * n return "\n".join([indentation + l for l in s.splitlines()])

def _get_commit(self): """ :return: Commit object we point to, works for detached and non-detached SymbolicReferences. The symbolic reference will be dereferenced recursively.""" obj = self._get_object() if obj.type == 'tag': obj = obj.object # END dereference tag if obj.type != Commit.type: raise TypeError("Symbolic Reference pointed to object %r, commit was required" % obj) # END handle type return obj

def get_bandwidth(self, cache_level, read_streams, write_streams, threads_per_core, cores=None): """ Return best fitting bandwidth according to number of threads, read and write streams. :param cache_level: integer of cache (0 is L1, 1 is L2 ...) :param read_streams: number of read streams expected :param write_streams: number of write streams expected :param threads_per_core: number of threads that are run on each core :param cores: if not given, will choose maximum bandwidth for single NUMA domain """ # try to find best fitting kernel (closest to read/write ratio): # write allocate has to be handled in kernel information (all writes are also reads) # TODO support for non-write-allocate architectures try: target_ratio = read_streams/write_streams except ZeroDivisionError: target_ratio = float('inf') measurement_kernel = 'load' measurement_kernel_info = self['benchmarks']['kernels'][measurement_kernel] measurement_kernel_ratio = float('inf') for kernel_name, kernel_info in sorted(self['benchmarks']['kernels'].items()): try: kernel_ratio = ((kernel_info['read streams']['streams'] + kernel_info['write streams']['streams'] - kernel_info['read+write streams']['streams']) / kernel_info['write streams']['streams']) except ZeroDivisionError: kernel_ratio = float('inf') if abs(kernel_ratio - target_ratio) < abs(measurement_kernel_ratio - target_ratio): measurement_kernel = kernel_name measurement_kernel_info = kernel_info measurement_kernel_ratio = kernel_ratio # choose smt, and then use max/saturation bw bw_level = self['memory hierarchy'][cache_level]['level'] bw_measurements = \ self['benchmarks']['measurements'][bw_level][threads_per_core] assert threads_per_core == bw_measurements['threads per core'], \ 'malformed measurement dictionary in machine file.' if cores is not None: # Used by Roofline model run_index = bw_measurements['cores'].index(cores) bw = bw_measurements['results'][measurement_kernel][run_index] else: # Used by ECM model # Choose maximum number of cores to get bandwidth for max_cores = min(self['memory hierarchy'][cache_level]['cores per group'], self['cores per NUMA domain']) bw = max(bw_measurements['results'][measurement_kernel][:max_cores]) # Correct bandwidth due to miss-measurement of write allocation # TODO support non-temporal stores and non-write-allocate architectures if cache_level == 0: # L1 does not have write-allocate, so everything is measured correctly factor = 1.0 else: factor = (float(measurement_kernel_info['read streams']['bytes']) + 2.0*float(measurement_kernel_info['write streams']['bytes']) - float(measurement_kernel_info['read+write streams']['bytes'])) / \ (float(measurement_kernel_info['read streams']['bytes']) + float(measurement_kernel_info['write streams']['bytes'])) bw = bw * factor return bw, measurement_kernel

def _encrypt(self, data, recipients, default_key=None, passphrase=None, armor=True, encrypt=True, symmetric=False, always_trust=True, output=None, throw_keyids=False, hidden_recipients=None, cipher_algo='AES256', digest_algo='SHA512', compress_algo='ZLIB'): """Encrypt the message read from the file-like object **data**. :param str data: The file or bytestream to encrypt. :param str recipients: The recipients to encrypt to. Recipients must be specified keyID/fingerprint. .. warning:: Care should be taken in Python2 to make sure that the given fingerprints for **recipients** are in fact strings and not unicode objects. :param str default_key: The keyID/fingerprint of the key to use for signing. If given, **data** will be encrypted *and* signed. :param str passphrase: If given, and **default_key** is also given, use this passphrase to unlock the secret portion of the **default_key** to sign the encrypted **data**. Otherwise, if **default_key** is not given, but **symmetric** is ``True``, then use this passphrase as the passphrase for symmetric encryption. Signing and symmetric encryption should *not* be combined when sending the **data** to other recipients, else the passphrase to the secret key would be shared with them. :param bool armor: If True, ascii armor the output; otherwise, the output will be in binary format. (Default: True) :param bool encrypt: If True, encrypt the **data** using the **recipients** public keys. (Default: True) :param bool symmetric: If True, encrypt the **data** to **recipients** using a symmetric key. See the **passphrase** parameter. Symmetric encryption and public key encryption can be used simultaneously, and will result in a ciphertext which is decryptable with either the symmetric **passphrase** or one of the corresponding private keys. :param bool always_trust: If True, ignore trust warnings on **recipients** keys. If False, display trust warnings. (default: True) :type output: str or file-like object :param output: The output file to write to. If not specified, the encrypted output is returned, and thus should be stored as an object in Python. For example: >>> import shutil >>> import gnupg >>> if os.path.exists("doctests"): ... shutil.rmtree("doctests") >>> gpg = gnupg.GPG(homedir="doctests") >>> key_settings = gpg.gen_key_input(key_type='RSA', ... key_length=1024, ... key_usage='ESCA', ... passphrase='foo') >>> key = gpg.gen_key(key_settings) >>> message = "The crow flies at midnight." >>> encrypted = str(gpg.encrypt(message, key.fingerprint)) >>> assert encrypted != message >>> assert not encrypted.isspace() >>> decrypted = str(gpg.decrypt(encrypted, passphrase='foo')) >>> assert not decrypted.isspace() >>> decrypted 'The crow flies at midnight.' :param bool throw_keyids: If True, make all **recipients** keyids be zero'd out in packet information. This is the same as using **hidden_recipients** for all **recipients**. (Default: False). :param list hidden_recipients: A list of recipients that should have their keyids zero'd out in packet information. :param str cipher_algo: The cipher algorithm to use. To see available algorithms with your version of GnuPG, do: :command:`$ gpg --with-colons --list-config ciphername`. The default **cipher_algo**, if unspecified, is ``'AES256'``. :param str digest_algo: The hash digest to use. Again, to see which hashes your GnuPG is capable of using, do: :command:`$ gpg --with-colons --list-config digestname`. The default, if unspecified, is ``'SHA512'``. :param str compress_algo: The compression algorithm to use. Can be one of ``'ZLIB'``, ``'BZIP2'``, ``'ZIP'``, or ``'Uncompressed'``. """ args = [] ## FIXME: GnuPG appears to ignore the --output directive when being ## programmatically driven. We'll handle the IO ourselves to fix this ## for now. output_filename = None if output: if getattr(output, 'fileno', None) is not None: ## avoid overwrite confirmation message if getattr(output, 'name', None) is not None: output_filename = output.name if os.path.exists(output.name): os.remove(output.name) #args.append('--output %s' % output.name) else: output_filename = output if os.path.exists(output): os.remove(output) #args.append('--output %s' % output) if armor: args.append('--armor') if always_trust: args.append('--always-trust') if cipher_algo: args.append('--cipher-algo %s' % cipher_algo) if compress_algo: args.append('--compress-algo %s' % compress_algo) if default_key: args.append('--sign') args.append('--default-key %s' % default_key) if digest_algo: args.append('--digest-algo %s' % digest_algo) ## both can be used at the same time for an encrypted file which ## is decryptable with a passphrase or secretkey. if symmetric: args.append('--symmetric') if encrypt: args.append('--encrypt') if throw_keyids: args.append('--throw-keyids') if len(recipients) >= 1: log.debug("GPG.encrypt() called for recipients '%s' with type '%s'" % (recipients, type(recipients))) if isinstance(recipients, (list, tuple)): for recp in recipients: if not _util._py3k: if isinstance(recp, unicode): try: assert _parsers._is_hex(str(recp)) except AssertionError: log.info("Can't accept recipient string: %s" % recp) else: self._add_recipient_string(args, hidden_recipients, str(recp)) continue ## will give unicode in 2.x as '\uXXXX\uXXXX' if isinstance(hidden_recipients, (list, tuple)): if [s for s in hidden_recipients if recp in str(s)]: args.append('--hidden-recipient %r' % recp) else: args.append('--recipient %r' % recp) else: args.append('--recipient %r' % recp) continue if isinstance(recp, str): self._add_recipient_string(args, hidden_recipients, recp) elif (not _util._py3k) and isinstance(recp, basestring): for recp in recipients.split('\x20'): self._add_recipient_string(args, hidden_recipients, recp) elif _util._py3k and isinstance(recp, str): for recp in recipients.split(' '): self._add_recipient_string(args, hidden_recipients, recp) ## ...and now that we've proven py3k is better... else: log.debug("Don't know what to do with recipients: %r" % recipients) result = self._result_map['crypt'](self) log.debug("Got data '%s' with type '%s'." % (data, type(data))) self._handle_io(args, data, result, passphrase=passphrase, binary=True) # Avoid writing raw encrypted bytes to terminal loggers and breaking # them in that adorable way where they spew hieroglyphics until reset: if armor: log.debug("\n%s" % result.data) if output_filename: log.info("Writing encrypted output to file: %s" % output_filename) with open(output_filename, 'wb') as fh: fh.write(result.data) fh.flush() log.info("Encrypted output written successfully.") return result

def _all_params(arr): """ Ensures that the argument is a list that either is empty or contains only GPParamSpec's :param arr: list :return: """ if not isinstance([], list): raise TypeError("non-list value found for parameters") return all(isinstance(x, GPParamSpec) for x in arr)

def execute(self, raise_on_error=True): "Execute all the commands in the current pipeline" stack = self.command_stack if not stack: return [] execute = self._execute_pipeline conn = self.connection if not conn: conn = self.connection_pool.get_connection('batch') # assign to self.connection so reset() releases the connection # back to the pool after we're done self.connection = conn try: return execute(conn, stack, raise_on_error) except ConnectionError: conn.disconnect() return execute(conn, stack, raise_on_error) finally: self.reset()

def get_response_code(url, timeout=10): ''' Visit the URL and return the HTTP response code in 'int' ''' try: req = urllib2.urlopen(url, timeout=timeout) except HTTPError, e: return e.getcode() except Exception, _: fail("Couldn't reach the URL '%s'" % url) else: return req.getcode()

def value(self): """ A tuple of values. This attribute can be set with any iterable; the iterable is then evaluated into a tuple and stored at the bound field. Whenever values are written to this attribute, they are passed through the :meth:`~.AbstractCDataType.coerce` method of the :attr:`~.AbstractField.type_` of the field. To revert the :attr:`value` to its default, use the ``del`` operator. """ try: return self._value except AttributeError: self.value = self._field.default() return self._value

def getmembers(object, predicate=None): """Return all members of an object as (name, value) pairs sorted by name. Optionally, only return members that satisfy a given predicate.""" results = [] for key in dir(object): value = getattr(object, key) if not predicate or predicate(value): results.append((key, value)) results.sort() return results

def matrix_and_line_shell(figsize=(5, 12), strip=False): """ Helper function to construct an empty figure that has space for a matrix, a summary line plot directly below it, a colorbar axis, and an optional "strip" axis that parallels the matrix (and shares its y-axis) where data can be added to create callbacks. Returns a tuple of (fig, matrix_ax, line_ax, strip_ax, colorbar_ax) that can then be used to plot upon. :param figsize: Tuple of (width, height), in inches, for the figure to be :param strip: If `strip` is False, then the returned `strip_ax` will be None and no strip axes will be created. """ fig = plt.figure(figsize=figsize) # Constants to keep track if strip: STRIP_COLS = 1 else: STRIP_COLS = 0 ROWS = 4 COLS = 8 + STRIP_COLS MAT_COLS = 7 MAT_ROWS = 3 LINE_ROWS = ROWS - MAT_ROWS mat_ax = plt.subplot2grid( shape=(ROWS, COLS), loc=(0, STRIP_COLS), rowspan=MAT_ROWS, colspan=MAT_COLS, ) line_ax = plt.subplot2grid( shape=(ROWS, COLS), loc=(MAT_ROWS, STRIP_COLS), rowspan=LINE_ROWS, colspan=MAT_COLS, sharex=mat_ax) if strip: strip_ax = plt.subplot2grid( shape=(ROWS, COLS), loc=(0, 0), rowspan=MAT_ROWS, colspan=STRIP_COLS, sharey=mat_ax, ) else: strip_ax = None cax = plt.subplot2grid( shape=(ROWS, COLS), loc=(ROWS - MAT_ROWS, MAT_COLS + STRIP_COLS), rowspan=1, colspan=1, ) fig.subplots_adjust(hspace=0.1, wspace=0.2, right=0.88, left=0.23) return fig, mat_ax, line_ax, strip_ax, cax

def prime_gen() -> int: # credit to David Eppstein, Wolfgang Beneicke, Paul Hofstra """ A generator for prime numbers starting from 2. """ D = {} yield 2 for q in itertools.islice(itertools.count(3), 0, None, 2): p = D.pop(q, None) if p is None: D[q * q] = 2 * q yield q else: x = p + q while x in D: x += p D[x] = p

def get_language_pack(locale): """Get/cache a language pack Returns the langugage pack from cache if it exists, caches otherwise >>> get_language_pack('fr')['Dashboards'] "Tableaux de bords" """ pack = ALL_LANGUAGE_PACKS.get(locale) if not pack: filename = DIR + '/{}/LC_MESSAGES/messages.json'.format(locale) try: with open(filename) as f: pack = json.load(f) ALL_LANGUAGE_PACKS[locale] = pack except Exception: # Assuming english, client side falls back on english pass return pack

def retrieveVals(self): """Retrieve values for graphs.""" if self._stats is None: serverInfo = MemcachedInfo(self._host, self._port, self._socket_file) stats = serverInfo.getStats() else: stats = self._stats if stats is None: raise Exception("Undetermined error accesing stats.") stats['set_hits'] = stats.get('total_items') if stats.has_key('cmd_set') and stats.has_key('total_items'): stats['set_misses'] = stats['cmd_set'] - stats['total_items'] self.saveState(stats) if self.hasGraph('memcached_connections'): self.setGraphVal('memcached_connections', 'conn', stats.get('curr_connections')) if self.hasGraph('memcached_items'): self.setGraphVal('memcached_items', 'items', stats.get('curr_items')) if self.hasGraph('memcached_memory'): self.setGraphVal('memcached_memory', 'bytes', stats.get('bytes')) if self.hasGraph('memcached_connrate'): self.setGraphVal('memcached_connrate', 'conn', stats.get('total_connections')) if self.hasGraph('memcached_traffic'): self.setGraphVal('memcached_traffic', 'rxbytes', stats.get('bytes_read')) self.setGraphVal('memcached_traffic', 'txbytes', stats.get('bytes_written')) if self.hasGraph('memcached_reqrate'): self.setGraphVal('memcached_reqrate', 'set', stats.get('cmd_set')) self.setGraphVal('memcached_reqrate', 'get', stats.get('cmd_get')) if self.graphHasField('memcached_reqrate', 'del'): self.setGraphVal('memcached_reqrate', 'del', safe_sum([stats.get('delete_hits'), stats.get('delete_misses')])) if self.graphHasField('memcached_reqrate', 'cas'): self.setGraphVal('memcached_reqrate', 'cas', safe_sum([stats.get('cas_hits'), stats.get('cas_misses'), stats.get('cas_badval')])) if self.graphHasField('memcached_reqrate', 'incr'): self.setGraphVal('memcached_reqrate', 'incr', safe_sum([stats.get('incr_hits'), stats.get('incr_misses')])) if self.graphHasField('memcached_reqrate', 'decr'): self.setGraphVal('memcached_reqrate', 'decr', safe_sum([stats.get('decr_hits'), stats.get('decr_misses')])) if self.hasGraph('memcached_statget'): self.setGraphVal('memcached_statget', 'hit', stats.get('get_hits')) self.setGraphVal('memcached_statget', 'miss', stats.get('get_misses')) self.setGraphVal('memcached_statget', 'total', safe_sum([stats.get('get_hits'), stats.get('get_misses')])) if self.hasGraph('memcached_statset'): self.setGraphVal('memcached_statset', 'hit', stats.get('set_hits')) self.setGraphVal('memcached_statset', 'miss', stats.get('set_misses')) self.setGraphVal('memcached_statset', 'total', safe_sum([stats.get('set_hits'), stats.get('set_misses')])) if self.hasGraph('memcached_statdel'): self.setGraphVal('memcached_statdel', 'hit', stats.get('delete_hits')) self.setGraphVal('memcached_statdel', 'miss', stats.get('delete_misses')) self.setGraphVal('memcached_statdel', 'total', safe_sum([stats.get('delete_hits'), stats.get('delete_misses')])) if self.hasGraph('memcached_statcas'): self.setGraphVal('memcached_statcas', 'hit', stats.get('cas_hits')) self.setGraphVal('memcached_statcas', 'miss', stats.get('cas_misses')) self.setGraphVal('memcached_statcas', 'badval', stats.get('cas_badval')) self.setGraphVal('memcached_statcas', 'total', safe_sum([stats.get('cas_hits'), stats.get('cas_misses'), stats.get('cas_badval')])) if self.hasGraph('memcached_statincrdecr'): self.setGraphVal('memcached_statincrdecr', 'incr_hit', stats.get('incr_hits')) self.setGraphVal('memcached_statincrdecr', 'decr_hit', stats.get('decr_hits')) self.setGraphVal('memcached_statincrdecr', 'incr_miss', stats.get('incr_misses')) self.setGraphVal('memcached_statincrdecr', 'decr_miss', stats.get('decr_misses')) self.setGraphVal('memcached_statincrdecr', 'total', safe_sum([stats.get('incr_hits'), stats.get('decr_hits'), stats.get('incr_misses'), stats.get('decr_misses')])) if self.hasGraph('memcached_statevict'): self.setGraphVal('memcached_statevict', 'evict', stats.get('evictions')) if self.graphHasField('memcached_statevict', 'reclaim'): self.setGraphVal('memcached_statevict', 'reclaim', stats.get('reclaimed')) if self.hasGraph('memcached_statauth'): self.setGraphVal('memcached_statauth', 'reqs', stats.get('auth_cmds')) self.setGraphVal('memcached_statauth', 'errors', stats.get('auth_errors')) if self.hasGraph('memcached_hitpct'): prev_stats = self._prev_stats for (field_name, field_hits, field_misses) in ( ('set', 'set_hits', 'set_misses'), ('get', 'get_hits', 'get_misses'), ('del', 'delete_hits', 'delete_misses'), ('cas', 'cas_hits', 'cas_misses'), ('incr', 'incr_hits', 'incr_misses'), ('decr', 'decr_hits', 'decr_misses') ): if prev_stats: if (stats.has_key(field_hits) and prev_stats.has_key(field_hits) and stats.has_key(field_misses) and prev_stats.has_key(field_misses)): hits = stats[field_hits] - prev_stats[field_hits] misses = stats[field_misses] - prev_stats[field_misses] total = hits + misses if total > 0: val = 100.0 * hits / total else: val = 0 self.setGraphVal('memcached_hitpct', field_name, round(val, 2))

def fix_bam_header(job, bamfile, sample_type, univ_options): """ This module modified the header in BAMFILE ARGUMENTS 1. bamfile: <JSid for a bam file> 2. sample_type: string of 'tumor_dna' or 'normal_dna' 3. univ_options: Dict of universal arguments used by almost all tools univ_options +- 'dockerhub': <dockerhub to use> RETURN VALUES 1. output_files: REFER output_files in run_bwa() """ job.fileStore.logToMaster('Running reheader on %s:%s' % (univ_options['patient'], sample_type)) work_dir = job.fileStore.getLocalTempDir() input_files = { 'aligned.bam': bamfile} input_files = get_files_from_filestore(job, input_files, work_dir, docker=True) parameters = ['view', '-H', input_files['aligned.bam']] with open('/'.join([work_dir, 'aligned_bam.header']), 'w') as headerfile: docker_call(tool='samtools', tool_parameters=parameters, work_dir=work_dir, dockerhub=univ_options['dockerhub'], outfile=headerfile) with open(headerfile.name, 'r') as headerfile, \ open('/'.join([work_dir, 'output_bam.header']), 'w') as outheaderfile: for line in headerfile: if line.startswith('@PG'): line = '\t'.join([x for x in line.strip().split('\t') if not x.startswith('CL')]) print(line.strip(), file=outheaderfile) parameters = ['reheader', docker_path(outheaderfile.name), input_files['aligned.bam']] with open('/'.join([work_dir, 'aligned_fixPG.bam']), 'w') as fixpg_bamfile: docker_call(tool='samtools', tool_parameters=parameters, work_dir=work_dir, dockerhub=univ_options['dockerhub'], outfile=fixpg_bamfile) output_file = job.fileStore.writeGlobalFile(fixpg_bamfile.name) job.fileStore.deleteGlobalFile(bamfile) add_rg = job.wrapJobFn(add_readgroups, output_file, sample_type, univ_options, disk='60G') job.addChild(add_rg) return add_rg.rv()

def lifetimes(self, dates, include_start_date, country_codes): """ Compute a DataFrame representing asset lifetimes for the specified date range. Parameters ---------- dates : pd.DatetimeIndex The dates for which to compute lifetimes. include_start_date : bool Whether or not to count the asset as alive on its start_date. This is useful in a backtesting context where `lifetimes` is being used to signify "do I have data for this asset as of the morning of this date?" For many financial metrics, (e.g. daily close), data isn't available for an asset until the end of the asset's first day. country_codes : iterable[str] The country codes to get lifetimes for. Returns ------- lifetimes : pd.DataFrame A frame of dtype bool with `dates` as index and an Int64Index of assets as columns. The value at `lifetimes.loc[date, asset]` will be True iff `asset` existed on `date`. If `include_start_date` is False, then lifetimes.loc[date, asset] will be false when date == asset.start_date. See Also -------- numpy.putmask zipline.pipeline.engine.SimplePipelineEngine._compute_root_mask """ if isinstance(country_codes, string_types): raise TypeError( "Got string {!r} instead of an iterable of strings in " "AssetFinder.lifetimes.".format(country_codes), ) # normalize to a cache-key so that we can memoize results. country_codes = frozenset(country_codes) lifetimes = self._asset_lifetimes.get(country_codes) if lifetimes is None: self._asset_lifetimes[country_codes] = lifetimes = ( self._compute_asset_lifetimes(country_codes) ) raw_dates = as_column(dates.asi8) if include_start_date: mask = lifetimes.start <= raw_dates else: mask = lifetimes.start < raw_dates mask &= (raw_dates <= lifetimes.end) return pd.DataFrame(mask, index=dates, columns=lifetimes.sid)

def get_empty_results(self): """ Because the base result type is different depending on the return structure (e.g. list for flat, dict for object), `get_result_type` initials the `results` variable to the proper type """ assert self.result_type is not None, ( '{} must specify a `result_type` value or overwrite the ' '`get_empty_result` method.'.format(self.__class__.__name__) ) return self.result_type()

def get_polygon(self, polygon_id): """ Retrieves a named polygon registered on the Agro API. :param id: the ID of the polygon :type id: str :returns: a `pyowm.agro10.polygon.Polygon` object """ status, data = self.http_client.get_json( NAMED_POLYGON_URI % str(polygon_id), params={'appid': self.API_key}, headers={'Content-Type': 'application/json'}) return Polygon.from_dict(data)

def pprint_path(path): """ print information of a pathlib / os.DirEntry() instance with all "is_*" functions. """ print("\n*** %s" % path) for attrname in sorted(dir(path)): if attrname.startswith("is_"): value = getattr(path, attrname) print("%20s: %s" % (attrname, value)) print()

def _chunks(self, iterable, chunk_size): """Chunks data into chunk with size<=chunk_size.""" iterator = iter(iterable) chunk = list(itertools.islice(iterator, 0, chunk_size)) while chunk: yield chunk chunk = list(itertools.islice(iterator, 0, chunk_size))

def blank_stim(self,type=None,fill=0): '''Makes a blank version of stim. If a type is not given, returned as same type as current stim. If a column stim, will fill in blanks with ``fill``''' blank = copy.copy(self) blank.name = 'Blank' if type==None: type = self.type() if type=="column": num_reps = self.reps if num_reps==None: if self.type()=="column": self.read_file() num_reps = len(self.column) else: nl.notify('Error: requested to return a blank column, but I can\'t figure out how many reps to make it!',level=nl.level.error) blank.column = [fill]*num_reps return blank if type=="times": blank.times = [] return blank

def send_many(kwargs_list): """ Similar to mail.send(), but this function accepts a list of kwargs. Internally, it uses Django's bulk_create command for efficiency reasons. Currently send_many() can't be used to send emails with priority = 'now'. """ emails = [] for kwargs in kwargs_list: emails.append(send(commit=False, **kwargs)) Email.objects.bulk_create(emails)

def setEmissionClass(self, typeID, clazz): """setEmissionClass(string, string) -> None Sets the emission class of vehicles of this type. """ self._connection._sendStringCmd( tc.CMD_SET_VEHICLETYPE_VARIABLE, tc.VAR_EMISSIONCLASS, typeID, clazz)

def drag_and_drop(self, source, target): """ Holds down the left mouse button on the source element, then moves to the target element and releases the mouse button. :Args: - source: The element to mouse down. - target: The element to mouse up. """ self.click_and_hold(source) self.release(target) return self

def dump_frames(self, frame): """ dumps frames chain in a representation suitable for serialization and remote (debugger) client usage. """ current_thread = threading.currentThread() frames = [] frame_browser = frame # Browse the frame chain as far as we can _logger.f_debug("dump_frames(), frame analysis:") spacer = "" while hasattr(frame_browser, 'f_back') and frame_browser.f_back != self.frame_beginning: spacer += "=" _logger.f_debug("%s>frame = %s, frame.f_code = %s, frame.f_back = %s, " "self.frame_beginning = %s", spacer, hex(id(frame_browser)), frame_browser.f_code, hex(id(frame_browser.f_back)), hex(id(self.frame_beginning))) # At root frame, globals == locals so we dump only globals if hasattr(frame_browser.f_back, 'f_back')\ and frame_browser.f_back.f_back != self.frame_beginning: locals_vars_list = self.extract_object_properties(frame_browser.f_locals, limit_size=True) else: locals_vars_list = [] globals_vars_list = self.extract_object_properties(frame_browser.f_globals, limit_size=True) # normalize path sent to debugging client file_path = self.normalize_path_out(frame_browser.f_code.co_filename) frame_name = "%s() [%s]" % (frame_browser.f_code.co_name, current_thread.name,) remote_frame = { 'id': id(frame_browser), 'name': frame_name, 'line_number': frame_browser.f_lineno, # Warning 1 based 'file_path': file_path, 'f_locals': locals_vars_list + globals_vars_list, 'thread': current_thread.ident, 'thread_name': current_thread.name } frames.append(remote_frame) frame_browser = frame_browser.f_back return frames

def energy_prolongation_smoother(A, T, Atilde, B, Bf, Cpt_params, krylov='cg', maxiter=4, tol=1e-8, degree=1, weighting='local', prefilter={}, postfilter={}): """Minimize the energy of the coarse basis functions (columns of T). Both root-node and non-root-node style prolongation smoothing is available, see Cpt_params description below. Parameters ---------- A : csr_matrix, bsr_matrix Sparse NxN matrix T : bsr_matrix Tentative prolongator, a NxM sparse matrix (M < N) Atilde : csr_matrix Strength of connection matrix B : array Near-nullspace modes for coarse grid. Has shape (M,k) where k is the number of coarse candidate vectors. Bf : array Near-nullspace modes for fine grid. Has shape (N,k) where k is the number of coarse candidate vectors. Cpt_params : tuple Tuple of the form (bool, dict). If the Cpt_params[0] = False, then the standard SA prolongation smoothing is carried out. If True, then root-node style prolongation smoothing is carried out. The dict must be a dictionary of parameters containing, (1) for P_I, P_I.T is the injection matrix for the Cpts, (2) I_F is an identity matrix for only the F-points (i.e. I, but with zero rows and columns for C-points) and I_C is the C-point analogue to I_F. See Notes below for more information. krylov : string 'cg' for SPD systems. Solve A T = 0 in a constraint space with CG 'cgnr' for nonsymmetric and/or indefinite systems. Solve A T = 0 in a constraint space with CGNR 'gmres' for nonsymmetric and/or indefinite systems. Solve A T = 0 in a constraint space with GMRES maxiter : integer Number of energy minimization steps to apply to the prolongator tol : scalar Minimization tolerance degree : int Generate sparsity pattern for P based on (Atilde^degree T) weighting : string 'block', 'diagonal' or 'local' construction of the diagonal preconditioning 'local' Uses a local row-wise weight based on the Gershgorin estimate. Avoids any potential under-damping due to inaccurate spectral radius estimates. 'block' Uses a block diagonal inverse of A if A is BSR. 'diagonal' Uses the inverse of the diagonal of A prefilter : dictionary Filter elements by row in sparsity pattern for P to reduce operator and setup complexity. If None or an empty dictionary, then no dropping in P is done. If postfilter has key 'k', then the largest 'k' entries are kept in each row. If postfilter has key 'theta', all entries such that :math:`P[i,j] < kwargs['theta']*max(abs(P[i,:]))` are dropped. If postfilter['k'] and postfiler['theta'] are present, then they are used with the union of their patterns. postfilter : dictionary Filters elements by row in smoothed P to reduce operator complexity. Only supported if using the rootnode_solver. If None or an empty dictionary, no dropping in P is done. If postfilter has key 'k', then the largest 'k' entries are kept in each row. If postfilter has key 'theta', all entries such that :math::`P[i,j] < kwargs['theta']*max(abs(P[i,:]))` are dropped. If postfilter['k'] and postfiler['theta'] are present, then they are used with the union of their patterns. Returns ------- T : bsr_matrix Smoothed prolongator Notes ----- Only 'diagonal' weighting is supported for the CGNR method, because we are working with A^* A and not A. When Cpt_params[0] == True, root-node style prolongation smoothing is used to minimize the energy of columns of T. Essentially, an identity block is maintained in T, corresponding to injection from the coarse-grid to the fine-grid root-nodes. See [2011OlScTu]_ for more details, and see util.utils.get_Cpt_params for the helper function to generate Cpt_params. If Cpt_params[0] == False, the energy of columns of T are still minimized, but without maintaining the identity block. See [1999cMaBrVa]_ for more details on smoothed aggregation. Examples -------- >>> from pyamg.aggregation import energy_prolongation_smoother >>> from pyamg.gallery import poisson >>> from scipy.sparse import coo_matrix >>> import numpy as np >>> data = np.ones((6,)) >>> row = np.arange(0,6) >>> col = np.kron([0,1],np.ones((3,))) >>> T = coo_matrix((data,(row,col)),shape=(6,2)).tocsr() >>> print T.todense() [[ 1. 0.] [ 1. 0.] [ 1. 0.] [ 0. 1.] [ 0. 1.] [ 0. 1.]] >>> A = poisson((6,),format='csr') >>> B = np.ones((2,1),dtype=float) >>> P = energy_prolongation_smoother(A,T,A,B, None, (False,{})) >>> print P.todense() [[ 1. 0. ] [ 1. 0. ] [ 0.66666667 0.33333333] [ 0.33333333 0.66666667] [ 0. 1. ] [ 0. 1. ]] References ---------- .. [1999cMaBrVa] Jan Mandel, Marian Brezina, and Petr Vanek "Energy Optimization of Algebraic Multigrid Bases" Computing 62, 205-228, 1999 http://dx.doi.org/10.1007/s006070050022 .. [2011OlScTu] Olson, L. and Schroder, J. and Tuminaro, R., "A general interpolation strategy for algebraic multigrid using energy minimization", SIAM Journal on Scientific Computing (SISC), vol. 33, pp. 966--991, 2011. """ # Test Inputs if maxiter < 0: raise ValueError('maxiter must be > 0') if tol > 1: raise ValueError('tol must be <= 1') if sparse.isspmatrix_csr(A): A = A.tobsr(blocksize=(1, 1), copy=False) elif sparse.isspmatrix_bsr(A): pass else: raise TypeError("A must be csr_matrix or bsr_matrix") if sparse.isspmatrix_csr(T): T = T.tobsr(blocksize=(1, 1), copy=False) elif sparse.isspmatrix_bsr(T): pass else: raise TypeError("T must be csr_matrix or bsr_matrix") if T.blocksize[0] != A.blocksize[0]: raise ValueError("T row-blocksize should be the same as A blocksize") if B.shape[0] != T.shape[1]: raise ValueError("B is the candidates for the coarse grid. \ num_rows(b) = num_cols(T)") if min(T.nnz, A.nnz) == 0: return T if not sparse.isspmatrix_csr(Atilde): raise TypeError("Atilde must be csr_matrix") if ('theta' in prefilter) and (prefilter['theta'] == 0): prefilter.pop('theta', None) if ('theta' in postfilter) and (postfilter['theta'] == 0): postfilter.pop('theta', None) # Prepocess Atilde, the strength matrix if Atilde is None: Atilde = sparse.csr_matrix((np.ones(len(A.indices)), A.indices.copy(), A.indptr.copy()), shape=(A.shape[0]/A.blocksize[0], A.shape[1]/A.blocksize[1])) # If Atilde has no nonzeros, then return T if min(T.nnz, A.nnz) == 0: return T # Expand allowed sparsity pattern for P through multiplication by Atilde if degree > 0: # Construct Sparsity_Pattern by multiplying with Atilde T.sort_indices() shape = (int(T.shape[0]/T.blocksize[0]), int(T.shape[1]/T.blocksize[1])) Sparsity_Pattern = sparse.csr_matrix((np.ones(T.indices.shape), T.indices, T.indptr), shape=shape) AtildeCopy = Atilde.copy() for i in range(degree): Sparsity_Pattern = AtildeCopy*Sparsity_Pattern # Optional filtering of sparsity pattern before smoothing if 'theta' in prefilter and 'k' in prefilter: Sparsity_theta = filter_matrix_rows(Sparsity_Pattern, prefilter['theta']) Sparsity_Pattern = truncate_rows(Sparsity_Pattern, prefilter['k']) # Union two sparsity patterns Sparsity_Pattern += Sparsity_theta elif 'k' in prefilter: Sparsity_Pattern = truncate_rows(Sparsity_Pattern, prefilter['k']) elif 'theta' in prefilter: Sparsity_Pattern = filter_matrix_rows(Sparsity_Pattern, prefilter['theta']) elif len(prefilter) > 0: raise ValueError("Unrecognized prefilter option") # UnAmal returns a BSR matrix with 1's in the nonzero locations Sparsity_Pattern = UnAmal(Sparsity_Pattern, T.blocksize[0], T.blocksize[1]) Sparsity_Pattern.sort_indices() else: # If degree is 0, just copy T for the sparsity pattern Sparsity_Pattern = T.copy() if 'theta' in prefilter and 'k' in prefilter: Sparsity_theta = filter_matrix_rows(Sparsity_Pattern, prefilter['theta']) Sparsity_Pattern = truncate_rows(Sparsity_Pattern, prefilter['k']) # Union two sparsity patterns Sparsity_Pattern += Sparsity_theta elif 'k' in prefilter: Sparsity_Pattern = truncate_rows(Sparsity_Pattern, prefilter['k']) elif 'theta' in prefilter: Sparsity_Pattern = filter_matrix_rows(Sparsity_Pattern, prefilter['theta']) elif len(prefilter) > 0: raise ValueError("Unrecognized prefilter option") Sparsity_Pattern.data[:] = 1.0 Sparsity_Pattern.sort_indices() # If using root nodes, enforce identity at C-points if Cpt_params[0]: Sparsity_Pattern = Cpt_params[1]['I_F'] * Sparsity_Pattern Sparsity_Pattern = Cpt_params[1]['P_I'] + Sparsity_Pattern # Construct array of inv(Bi'Bi), where Bi is B restricted to row i's # sparsity pattern in Sparsity Pattern. This array is used multiple times # in Satisfy_Constraints(...). BtBinv = compute_BtBinv(B, Sparsity_Pattern) # If using root nodes and B has more columns that A's blocksize, then # T must be updated so that T*B = Bfine. Note, if this is a 'secondpass' # after dropping entries in P, then we must re-enforce the constraints if ((Cpt_params[0] and (B.shape[1] > A.blocksize[0])) or ('secondpass' in postfilter)): T = filter_operator(T, Sparsity_Pattern, B, Bf, BtBinv) # Ensure identity at C-pts if Cpt_params[0]: T = Cpt_params[1]['I_F']*T + Cpt_params[1]['P_I'] # Iteratively minimize the energy of T subject to the constraints of # Sparsity_Pattern and maintaining T's effect on B, i.e. T*B = # (T+Update)*B, i.e. Update*B = 0 if krylov == 'cg': T = cg_prolongation_smoothing(A, T, B, BtBinv, Sparsity_Pattern, maxiter, tol, weighting, Cpt_params) elif krylov == 'cgnr': T = cgnr_prolongation_smoothing(A, T, B, BtBinv, Sparsity_Pattern, maxiter, tol, weighting, Cpt_params) elif krylov == 'gmres': T = gmres_prolongation_smoothing(A, T, B, BtBinv, Sparsity_Pattern, maxiter, tol, weighting, Cpt_params) T.eliminate_zeros() # Filter entries in P, only in the rootnode case, # i.e., Cpt_params[0] == True if ((len(postfilter) == 0) or ('secondpass' in postfilter) or (Cpt_params[0] is False)): return T else: if 'theta' in postfilter and 'k' in postfilter: T_theta = filter_matrix_rows(T, postfilter['theta']) T_k = truncate_rows(T, postfilter['k']) # Union two sparsity patterns T_theta.data[:] = 1.0 T_k.data[:] = 1.0 T_filter = T_theta + T_k T_filter.data[:] = 1.0 T_filter = T.multiply(T_filter) elif 'k' in postfilter: T_filter = truncate_rows(T, postfilter['k']) elif 'theta' in postfilter: T_filter = filter_matrix_rows(T, postfilter['theta']) else: raise ValueError("Unrecognized postfilter option") # Re-smooth T_filter and re-fit the modes B into the span. # Note, we set 'secondpass', because this is the second # filtering pass T = energy_prolongation_smoother(A, T_filter, Atilde, B, Bf, Cpt_params, krylov=krylov, maxiter=1, tol=1e-8, degree=0, weighting=weighting, prefilter={}, postfilter={'secondpass': True}) return T

def p_user_add_link(self): ''' user add link. ''' if self.check_post_role()['ADD']: pass else: return False post_data = self.get_post_data() post_data['user_name'] = self.get_current_user() cur_uid = tools.get_uudd(2) while MLink.get_by_uid(cur_uid): cur_uid = tools.get_uudd(2) if MLink.create_link(cur_uid, post_data): output = { 'addinfo ': 1, } else: output = { 'addinfo ': 0, } return json.dump(output, self)

def restore_model(self, directory=None, file=None): """ Restore TensorFlow model. If no checkpoint file is given, the latest checkpoint is restored. If no checkpoint directory is given, the model's default saver directory is used (unless file specifies the entire path). Args: directory: Optional checkpoint directory. file: Optional checkpoint file, or path if directory not given. """ self.model.restore(directory=directory, file=file)

def process_orders(self, orderbook): ''' Default and costant orders processor. Overwrite it for more sophisticated strategies ''' for stock, alloc in orderbook.iteritems(): self.logger.info('{}: Ordered {} {} stocks'.format( self.datetime, stock, alloc)) if isinstance(alloc, int): self.order(stock, alloc) elif isinstance(alloc, float) and \ alloc >= -1 and alloc <= 1: self.order_percent(stock, alloc) else: self.logger.warning( '{}: invalid order for {}: {})' .format(self.datetime, stock, alloc))

def _init_file(self): """ Initialize a new password file and set the reference password. """ self.keyring_key = self._get_new_password() # set a reference password, used to check that the password provided # matches for subsequent checks. self.set_password('keyring-setting', 'password reference', 'password reference value') self._write_config_value('keyring-setting', 'scheme', self.scheme) self._write_config_value('keyring-setting', 'version', self.version)

def handle_special_cases(request, data, baseURI, meta): """Handle sepcial cases for returned values by the doAction function""" if request.method == 'OPTIONS': r = HttpResponse('') return r if data is None: return gen404(request, baseURI, 'data') if data.__class__.__name__ == 'PlugIt500': return gen500(request, baseURI) if data.__class__.__name__ == 'PlugItSpecialCode': r = HttpResponse('') r.status_code = data.code return r if data.__class__.__name__ == 'PlugItRedirect': url = data.url if not data.no_prefix: url = baseURI + url return HttpResponseRedirect(url) if data.__class__.__name__ == 'PlugItFile': response = HttpResponse(data.content, content_type=data.content_type) response['Content-Disposition'] = data.content_disposition return response if data.__class__.__name__ == 'PlugItNoTemplate': response = HttpResponse(data.content) return response if meta.get('json_only', None): # Just send the json back # Return application/json if requested if 'HTTP_ACCEPT' in request.META and request.META['HTTP_ACCEPT'].find('json') != -1: return JsonResponse(data) # Return json data without html content type, since json was not # requiered result = json.dumps(data) return HttpResponse(result) if meta.get('xml_only', None): # Just send the xml back return HttpResponse(data['xml'], content_type='application/xml')

def remove_locations(node): """ Removes locations from the given AST tree completely """ def fix(node): if 'lineno' in node._attributes and hasattr(node, 'lineno'): del node.lineno if 'col_offset' in node._attributes and hasattr(node, 'col_offset'): del node.col_offset for child in iter_child_nodes(node): fix(child) fix(node)

def _apply_concretization_strategies(self, idx, strategies, action): # pylint: disable=unused-argument """ Applies concretization strategies on the index, until one of them succeeds. """ for s in strategies: try: idxes = s.concretize(self, idx) except SimUnsatError: idxes = None if idxes: return idxes raise SimMemoryAddressError("Unable to concretize index %s" % idx)

def clear_published(self): """Removes the published status. :raise: ``NoAccess`` -- ``Metadata.isRequired()`` is ``true`` or ``Metadata.isReadOnly()`` is ``true`` *compliance: mandatory -- This method must be implemented.* """ metadata = Metadata(**settings.METADATA['published']) if metadata.is_read_only() or metadata.is_required(): raise NoAccess() self._my_map['published'] = False

def patches(destination, settings=None, traverse_bases=True, filter=default_filter, recursive=True, use_decorators=True): """Decorator to create a patch for each member of a module or a class. Parameters ---------- destination : object Patch destination. settings : gorilla.Settings Settings. traverse_bases : bool If the object is a class, the base classes are also traversed. filter : function Attributes for which the function returns ``False`` are skipped. The function needs to define two parameters: ``name``, the attribute name, and ``obj``, the attribute value. If ``None``, no attribute is skipped. recursive : bool If ``True``, and a hit occurs due to an attribute at the destination already existing with the given name, and both the member and the target attributes are classes, then instead of creating a patch directly with the member attribute value as is, a patch for each of its own members is created with the target as new destination. use_decorators : bool Allows to take any modifier decorator into consideration to allow for more granular customizations. Returns ------- object The decorated object. Note ---- A 'target' differs from a 'destination' in that a target represents an existing attribute at the destination about to be hit by a patch. See Also -------- :class:`Patch`, :func:`create_patches`. """ def decorator(wrapped): settings_ = copy.deepcopy(settings) patches = create_patches( destination, wrapped, settings=settings_, traverse_bases=traverse_bases, filter=filter, recursive=recursive, use_decorators=use_decorators) data = get_decorator_data(_get_base(wrapped), set_default=True) data.patches.extend(patches) return wrapped return decorator

def dump_stream(self, iterator, stream): """ Override because Pandas UDFs require a START_ARROW_STREAM before the Arrow stream is sent. This should be sent after creating the first record batch so in case of an error, it can be sent back to the JVM before the Arrow stream starts. """ def init_stream_yield_batches(): should_write_start_length = True for series in iterator: batch = self._create_batch(series) if should_write_start_length: write_int(SpecialLengths.START_ARROW_STREAM, stream) should_write_start_length = False yield batch return ArrowStreamSerializer.dump_stream(self, init_stream_yield_batches(), stream)

def local_self_attention_layer(hparams, prefix): """Create self-attention layer based on hyperparameters.""" return transformer_layers.LocalSelfAttention( num_heads=hparams.get(prefix + "num_heads"), num_memory_heads=hparams.get(prefix + "num_memory_heads"), radius=hparams.local_attention_radius, key_value_size=hparams.d_kv, shared_kv=hparams.get(prefix + "shared_kv", False), attention_kwargs=attention_kwargs_from_hparams(hparams))

def _encrypt(self, archive): """Encrypts the compressed archive using GPG. If encryption fails for any reason, it should be logged by sos but not cause execution to stop. The assumption is that the unencrypted archive would still be of use to the user, and/or that the end user has another means of securing the archive. Returns the name of the encrypted archive, or raises an exception to signal that encryption failed and the unencrypted archive name should be used. """ arc_name = archive.replace("sosreport-", "secured-sosreport-") arc_name += ".gpg" enc_cmd = "gpg --batch -o %s " % arc_name env = None if self.enc_opts["key"]: # need to assume a trusted key here to be able to encrypt the # archive non-interactively enc_cmd += "--trust-model always -e -r %s " % self.enc_opts["key"] enc_cmd += archive if self.enc_opts["password"]: # prevent change of gpg options using a long password, but also # prevent the addition of quote characters to the passphrase passwd = "%s" % self.enc_opts["password"].replace('\'"', '') env = {"sos_gpg": passwd} enc_cmd += "-c --passphrase-fd 0 " enc_cmd = "/bin/bash -c \"echo $sos_gpg | %s\"" % enc_cmd enc_cmd += archive r = sos_get_command_output(enc_cmd, timeout=0, env=env) if r["status"] == 0: return arc_name elif r["status"] == 2: if self.enc_opts["key"]: msg = "Specified key not in keyring" else: msg = "Could not read passphrase" else: # TODO: report the actual error from gpg. Currently, we cannot as # sos_get_command_output() does not capture stderr msg = "gpg exited with code %s" % r["status"] raise Exception(msg)

def copy_keys(source, destination, keys=None): """ Add keys in source to destination Parameters ---------- source : dict destination: dict keys : None | iterable The keys in source to be copied into destination. If None, then `keys = destination.keys()` """ if keys is None: keys = destination.keys() for k in set(source) & set(keys): destination[k] = source[k] return destination

def from_email(self, value): """The email address of the sender :param value: The email address of the sender :type value: From, str, tuple """ if isinstance(value, str): value = From(value, None) if isinstance(value, tuple): value = From(value[0], value[1]) self._from_email = value

def get_wrapped_stream(stream): """ Given a stream, wrap it in a `StreamWrapper` instance and return the wrapped stream. :param stream: A stream instance to wrap :returns: A new, wrapped stream :rtype: :class:`StreamWrapper` """ if stream is None: raise TypeError("must provide a stream to wrap") encoding = getattr(stream, "encoding", None) encoding = get_output_encoding(encoding) return StreamWrapper(stream, encoding, "replace", line_buffering=True)

def num_buttons(self): """The number of buttons on a device with the :attr:`~libinput.constant.DeviceCapability.TABLET_PAD` capability. Buttons on a pad device are numbered sequentially, see `Tablet pad button numbers`_ for details. Returns: int: The number of buttons supported by the device. Raises: AttributeError """ num = self._libinput.libinput_device_tablet_pad_get_num_buttons( self._handle) if num < 0: raise AttributeError('This device is not a tablet pad device') return num

def present(name, user='root', minute='*', hour='*', daymonth='*', month='*', dayweek='*', comment=None, commented=False, identifier=False, special=None): ''' Verifies that the specified cron job is present for the specified user. It is recommended to use `identifier`. Otherwise the cron job is installed twice if you change the name. For more advanced information about what exactly can be set in the cron timing parameters, check your cron system's documentation. Most Unix-like systems' cron documentation can be found via the crontab man page: ``man 5 crontab``. name The command that should be executed by the cron job. user The name of the user whose crontab needs to be modified, defaults to the root user minute The information to be set into the minute section, this can be any string supported by your cron system's the minute field. Default is ``*`` hour The information to be set in the hour section. Default is ``*`` daymonth The information to be set in the day of month section. Default is ``*`` month The information to be set in the month section. Default is ``*`` dayweek The information to be set in the day of week section. Default is ``*`` comment User comment to be added on line previous the cron job commented The cron job is set commented (prefixed with ``#DISABLED#``). Defaults to False. .. versionadded:: 2016.3.0 identifier Custom-defined identifier for tracking the cron line for future crontab edits. This defaults to the state name special A special keyword to specify periodicity (eg. @reboot, @hourly...). Quotes must be used, otherwise PyYAML will strip the '@' sign. .. versionadded:: 2016.3.0 ''' name = name.strip() if identifier is False: identifier = name ret = {'changes': {}, 'comment': '', 'name': name, 'result': True} if __opts__['test']: status = _check_cron(user, cmd=name, minute=minute, hour=hour, daymonth=daymonth, month=month, dayweek=dayweek, comment=comment, commented=commented, identifier=identifier, special=special) ret['result'] = None if status == 'absent': ret['comment'] = 'Cron {0} is set to be added'.format(name) elif status == 'present': ret['result'] = True ret['comment'] = 'Cron {0} already present'.format(name) elif status == 'update': ret['comment'] = 'Cron {0} is set to be updated'.format(name) return ret if special is None: data = __salt__['cron.set_job'](user=user, minute=minute, hour=hour, daymonth=daymonth, month=month, dayweek=dayweek, cmd=name, comment=comment, commented=commented, identifier=identifier) else: data = __salt__['cron.set_special'](user=user, special=special, cmd=name, comment=comment, commented=commented, identifier=identifier) if data == 'present': ret['comment'] = 'Cron {0} already present'.format(name) return ret if data == 'new': ret['comment'] = 'Cron {0} added to {1}\'s crontab'.format(name, user) ret['changes'] = {user: name} return ret if data == 'updated': ret['comment'] = 'Cron {0} updated'.format(name) ret['changes'] = {user: name} return ret ret['comment'] = ('Cron {0} for user {1} failed to commit with error \n{2}' .format(name, user, data)) ret['result'] = False return ret

def upload_read(infile, table): """ Reads a table from a MagIC upload (or downloaded) txt file, puts data in a list of dictionaries """ delim = 'tab' hold, magic_data, magic_record, magic_keys = [], [], {}, [] f = open(infile, "r") # # look for right table # line = f.readline()[:-1] file_type = line.split('\t')[1] if file_type == 'delimited': file_type = line.split('\t')[2] if delim == 'tab': line = f.readline()[:-1].split('\t') else: f.close() print("only tab delimitted files are supported now") return while file_type != table: while line[0][0:5] in f.readlines() != ">>>>>": pass line = f.readline()[:-1] file_type = line.split('\t')[1] if file_type == 'delimited': file_type = line.split('\t')[2] ine = f.readline()[:-1].split('\t') while line[0][0:5] in f.readlines() != ">>>>>": for key in line: magic_keys.append(key) for line in f.readlines(): rec = line[:-1].split('\t') hold.append(rec) for rec in hold: magic_record = {} if len(magic_keys) != len(rec): print("Uneven record lengths detected: ", rec) input("Return to continue.... ") for k in range(len(magic_keys)): magic_record[magic_keys[k]] = rec[k] magic_data.append(magic_record) f.close() return magic_data

def unsubscribe(self, destination=None, id=None, headers=None, **keyword_headers): """ Unsubscribe from a destination by either id or the destination name. :param str destination: the name of the topic or queue to unsubscribe from :param str id: the unique identifier of the topic or queue to unsubscribe from :param dict headers: a map of any additional headers the broker requires :param keyword_headers: any additional headers the broker requires """ assert id is not None or destination is not None, "'id' or 'destination' is required" headers = utils.merge_headers([headers, keyword_headers]) if id: headers[HDR_ID] = id if destination: headers[HDR_DESTINATION] = destination self.send_frame(CMD_UNSUBSCRIBE, headers)

def normalizeURL(url): """Normalize a URL, converting normalization failures to DiscoveryFailure""" try: normalized = urinorm.urinorm(url) except ValueError as why: raise DiscoveryFailure('Normalizing identifier: %s' % (why, ), None) else: return urllib.parse.urldefrag(normalized)[0]

def _get_by_index(self, index, path=None): """Returns the 2-tuple (node, idx) where node is either a terminal leaf node containing the value of that index in either it's left or right slot, or is the most specific node on the path which would contain the index if it were not pruned.""" left_size = 2 ** ((self.size-1).bit_length() - 1) if index < 0 or self.size <= index: return (None, self, index) if index < left_size: if self.prune is self.LEFT_NODE: return (None, self, index) if isinstance(self.left, numbers.Integral): return (self.left, self, index) if path is not None: path.append((node, index, self.LEFT_NODE)) return self.left._get_by_index(index, path) else: if self.prune is self.RIGHT_NODE: return (None, self, index) if isinstance(self.right, numbers.Integral): return (self.right, self, index) if path is not None: path.append((node, index, self.RIGHT_NODE)) return self.right._get_by_index(index-left_size, path)

def dict(self): """ the python object for rendering json. It is called dict to be coherent with the other modules but it actually returns a list :return: the python object for rendering json :rtype: list """ json_list = [] for step in self.steps: json_list.append(step.dict) return json_list

def _values(self): """Getter for series values (flattened)""" if self.interpolate: return [ val[0] for serie in self.series for val in serie.interpolated ] else: return super(Line, self)._values

def fit(self): """Fit each distribution to `data` and calculate an SSE. WARNING: significant runtime. (~1min) """ # Defaults/anchors best_sse = np.inf best_param = (0.0, 1.0) best_dist = scs.norm # Compute the histogram of `x`. density=True gives a probability # density function at each bin, normalized such that the integral over # the range is 1.0 hist, bin_edges = np.histogram(self.x, bins=self.bins, density=True) # The results of np.histogram will have len(bin_edges) = len(hist) + 1 # Find the midpoint at each bin to reduce the size of bin_edges by 1 bin_edges = (bin_edges + np.roll(bin_edges, -1))[:-1] / 2.0 with warnings.catch_warnings(): warnings.filterwarnings("ignore") sses = [] params = [] for dist in self.distributions: dist = getattr(scs, dist) try: # The generic rv_continuous.fit() returns `mle_tuple`: # 'MLEs for any shape parameters (if applicable), # followed by those for location and scale.' param = *shape, loc, scale = dist.fit(self.x) pdf = dist.pdf(bin_edges, loc=loc, scale=scale, *shape) sse = np.sum(np.power(hist - pdf, 2.0)) sses.append(sse) params.append(param) if best_sse > sse > 0.0: best_dist = dist best_param = param best_sse = sse best_pdf = pdf except (NotImplementedError, AttributeError): sses.append(np.nan) params.append(np.nan) self.best_dist = best_dist self.best_param = best_param self.best_sse = best_sse self.best_pdf = best_pdf self.sses = sses self.params = params self.hist = hist self.bin_edges = bin_edges return self

def get_shelveset(self, shelveset_id, request_data=None): """GetShelveset. Get a single deep shelveset. :param str shelveset_id: Shelveset's unique ID :param :class:`<TfvcShelvesetRequestData> <azure.devops.v5_0.tfvc.models.TfvcShelvesetRequestData>` request_data: includeDetails, includeWorkItems, maxChangeCount, and maxCommentLength :rtype: :class:`<TfvcShelveset> <azure.devops.v5_0.tfvc.models.TfvcShelveset>` """ query_parameters = {} if shelveset_id is not None: query_parameters['shelvesetId'] = self._serialize.query('shelveset_id', shelveset_id, 'str') if request_data is not None: if request_data.name is not None: query_parameters['requestData.name'] = request_data.name if request_data.owner is not None: query_parameters['requestData.owner'] = request_data.owner if request_data.max_comment_length is not None: query_parameters['requestData.maxCommentLength'] = request_data.max_comment_length if request_data.max_change_count is not None: query_parameters['requestData.maxChangeCount'] = request_data.max_change_count if request_data.include_details is not None: query_parameters['requestData.includeDetails'] = request_data.include_details if request_data.include_work_items is not None: query_parameters['requestData.includeWorkItems'] = request_data.include_work_items if request_data.include_links is not None: query_parameters['requestData.includeLinks'] = request_data.include_links response = self._send(http_method='GET', location_id='e36d44fb-e907-4b0a-b194-f83f1ed32ad3', version='5.0', query_parameters=query_parameters) return self._deserialize('TfvcShelveset', response)

def match(tgt, functions=None, opts=None): ''' Match based on the local data store on the minion ''' if not opts: opts = __opts__ if functions is None: utils = salt.loader.utils(opts) functions = salt.loader.minion_mods(opts, utils=utils) comps = tgt.split(':') if len(comps) < 2: return False val = functions['data.getval'](comps[0]) if val is None: # The value is not defined return False if isinstance(val, list): # We are matching a single component to a single list member for member in val: if fnmatch.fnmatch(six.text_type(member).lower(), comps[1].lower()): return True return False if isinstance(val, dict): if comps[1] in val: return True return False return bool(fnmatch.fnmatch( val, comps[1], ))

def get_port_channel_detail_input_request_type_get_next_request_last_aggregator_id(self, **kwargs): """Auto Generated Code """ config = ET.Element("config") get_port_channel_detail = ET.Element("get_port_channel_detail") config = get_port_channel_detail input = ET.SubElement(get_port_channel_detail, "input") request_type = ET.SubElement(input, "request-type") get_next_request = ET.SubElement(request_type, "get-next-request") last_aggregator_id = ET.SubElement(get_next_request, "last-aggregator-id") last_aggregator_id.text = kwargs.pop('last_aggregator_id') callback = kwargs.pop('callback', self._callback) return callback(config)

def _phiforce(self,R,phi=0.,t=0.): """ NAME: _phiforce PURPOSE: evaluate the azimuthal force for this potential INPUT: R - Galactocentric cylindrical radius phi - azimuth t - time OUTPUT: the azimuthal force HISTORY: 2011-10-19 - Written - Bovy (IAS) """ if R < self._rb: return self._mphio*math.sin(self._m*phi-self._mphib)\ *self._rbp*(2.*self._r1p-self._rbp/R**self._p) else: return self._mphio*R**self._p*math.sin(self._m*phi-self._mphib)

def zset_score_pairs(response, **options): """ If ``withscores`` is specified in the options, return the response as a list of (value, score) pairs """ if not response or not options['withscores']: return response score_cast_func = options.get('score_cast_func', float) it = iter(response) return list(zip(it, map(score_cast_func, it)))

def _init_auth(self, config): """ Init authentication @dict: configuration of ldapcherry """ self.auth_mode = self._get_param('auth', 'auth.mode', config) if self.auth_mode in ['and', 'or', 'none']: pass elif self.auth_mode == 'custom': # load custom auth module auth_module = self._get_param('auth', 'auth.module', config) auth = __import__(auth_module, globals(), locals(), ['Auth'], 0) self.auth = auth.Auth(config['auth'], cherrypy.log) else: raise WrongParamValue( 'auth.mode', 'auth', ['and', 'or', 'none', 'custom'], ) self.roles_file = self._get_param('roles', 'roles.file', config) cherrypy.log.error( msg="loading roles file '%(file)s'" % {'file': self.roles_file}, severity=logging.DEBUG ) self.roles = Roles(self.roles_file)

def stop(logfile, time_format): "stop tracking for the active project" def save_and_output(records): records = server.stop(records) write(records, logfile, time_format) def output(r): print "worked on %s" % colored(r[0], attrs=['bold']) print " from %s" % colored( server.date_to_txt(r[1][0], time_format), 'green') print " to now, %s" % colored( server.date_to_txt(r[1][1], time_format), 'green') print " => %s elapsed" % colored( time_elapsed(r[1][0], r[1][1]), 'red') output(records[-1]) save_and_output(read(logfile, time_format))

async def get(self): """Printing runtime statistics in JSON""" context_data = self.get_context_data() context_data.update(getattr(self.request.app, "stats", {})) response = self.json_response(context_data) return response

def _aggregate_func(self, aggregate): """ Return a suitable aggregate score function. """ funcs = {"sum": add, "min": min, "max": max} func_name = aggregate.lower() if aggregate else 'sum' try: return funcs[func_name] except KeyError: raise TypeError("Unsupported aggregate: {}".format(aggregate))

def freq_from_str(freq_str): """Obtain frequency ranges from input string, either as list or dynamic notation. Parameters ---------- freq_str : str String with frequency ranges, either as a list: e.g. [[1-3], [3-5], [5-8]]; or with a dynamic definition: (start, stop, width, step). Returns ------- list of tuple of float or None Every tuple of float represents a frequency band. If input is invalid, returns None. """ freq = [] as_list = freq_str[1:-1].replace(' ', '').split(',') try: if freq_str[0] == '[' and freq_str[-1] == ']': for i in as_list: one_band = i[1:-1].split('-') one_band = float(one_band[0]), float(one_band[1]) freq.append(one_band) elif freq_str[0] == '(' and freq_str[-1] == ')': if len(as_list) == 4: start = float(as_list[0]) stop = float(as_list[1]) halfwidth = float(as_list[2]) / 2 step = float(as_list[3]) centres = arange(start, stop, step) for i in centres: freq.append((i - halfwidth, i + halfwidth)) else: return None else: return None except: return None return freq

def select_regexes(strings, regexes): """ select subset of strings matching a regex treats strings as a set """ strings = set(strings) select = set() if isinstance(strings, collections.Iterable): for r in regexes: s = set(filter(re.compile('^' + r + '$').search, strings)) strings -= s select |= s return select else: raise ValueError("exclude should be iterable")

def reverseCommit(self): """ Reverse the document to the original state. """ print(self.after == self.before) pos = self.qteWidget.textCursor().position() self.qteWidget.setHtml(self.before) self.placeCursor(pos)

def is_throttled(e): """ Determines whether the exception is due to API throttling. :param e: Exception raised :return: True if it's a throttling exception else False """ return True if (hasattr(e, 'response') and e.response is not None and 'Error' in e.response and e.response['Error']['Code'] in ['Throttling', 'RequestLimitExceeded', 'ThrottlingException']) else \ False