None:","def port_nic_add(switch, port, node, nic):\n client.port.connect_nic(switch, port, node, nic)","def get_named_bind_string(self, name):\n\n return \":\" + name","async def _async_add_port_mapping(\n self, external_port: int, local_ip: str, internal_port: int\n ) -> None:\n entry = [external_port, local_ip, internal_port]\n self.added_port_mappings.append(entry)","def bind_view(self, resource, view, tail=()):\n if isinstance(tail, str) and tail != '*':\n tail = tuple(i for i in tail.split('/') if i)\n\n setup = self.resources.setdefault(resource, {'views': {}})\n setup.setdefault('views', {})[tail] = view","def bind(self, server_name: str, port: int) -> None:\n self.socket.bind((server_name, port))","def create_port_postcommit(self, context):\n if self.rpc_handler is None:\n return\n port = self._get_port_info(context)\n if port is not None:\n try:\n self.rpc_handler.create_port(port)\n except:\n pass","def addVarBinds(self, *varBinds):\n debug.logger & debug.flagMIB and debug.logger('additional var-binds: %r' % (varBinds,))\n if self.__state & self.stClean:\n self.__varBinds.extend(varBinds)\n else:\n self.__additionalVarBinds.extend(varBinds)\n return self","def add_binding_sites(self, binding_sites, final_conformation: str = None):\n\n if final_conformation is None:\n self.binding_sites[self.name] += binding_sites\n elif final_conformation in self.binding_sites:\n self.binding_sites[final_conformation] += binding_sites\n else:\n self.binding_sites[final_conformation] = binding_sites\n\n # also reverse_link the binding sites to this TF object\n for binding_site in binding_sites:\n binding_site.add_transcription_factor(self, final_conformation)","def bind(self, port, address=None, family=socket.AF_UNSPEC, backlog=128):\n sockets = self.bind_udp_sockets(port, address=address, family=family, backlog=backlog)\n if self._started:\n self.add_sockets(sockets)\n else:\n self._pending_sockets.extend(sockets)","def register(self):\n self.app.bind('CreateSuperUserCommand', CreateSuperUser())\n self.app.bind('InstallCommand', Install())\n self.app.bind(\n 'AdminUserMigrationDirectory',\n os.path.join(package_directory, 'migrations')\n )","def bind(self, svc, svc_ref):\n with self._lock:\n if ORDER_HANDLER in svc_ref.get_property(pelix.OBJECTCLASS):\n targets = svc_ref.get_property(ORDER_TARGETS)\n if isinstance(targets, (list, tuple)):\n for target in targets:\n self._target_handlers.setdefault(target, []).append(svc)\n\n else:\n self._target_handlers.setdefault(str(targets), []).append(svc)","def _handle_port_binding(self, context, port_id, trunk, trunk_host):\n port = self.core_plugin.update_port(\n context, port_id,\n {'port': {portbindings.HOST_ID: trunk_host,\n 'device_owner': trunk_consts.TRUNK_SUBPORT_OWNER}})\n vif_type = port.get(portbindings.VIF_TYPE)\n if vif_type == portbindings.VIF_TYPE_BINDING_FAILED:\n raise trunk_exc.SubPortBindingError(port_id=port_id,\n trunk_id=trunk.id)\n return port","def https_ports(self, https_ports):\n\n self._https_ports = https_ports","def bind(self, _target: aws_cdk.aws_lambda.IFunction) -> None:\n ...","def get_port_binding():\n import docker\n client = docker.from_env()\n return [c.attrs['NetworkSettings']['Ports']['5555/tcp'][0]\n for c in client.containers.list(\n filters={'label': 'org.label-schema.name=profemag/femag'})]","def process_create_port(self, context, port, result):\n\n # Process extension data\n port_ext = self._create_port_ext(result, port, context=context)\n switchports = self._update_switchports(result, port,\n session=context.session)\n self._find_port_dict_extensions(result, None, port_ext=port_ext,\n switchports=switchports,\n session=context.session)\n\n # Validate we can actually configure this port\n if result[\"commit\"]:\n self._validate_port_can_commit(result, None,\n session=context.session)","def addPort( self, src, dst, sport=None, dport=None ):\n # Initialize if necessary\n ports = self.ports\n ports.setdefault( src, {} )\n ports.setdefault( dst, {} )\n # New port: number of outlinks + base\n if sport is None:\n src_base = 1 if self.isSwitch( src ) else 0\n sport = len( ports[ src ] ) + src_base\n if dport is None:\n dst_base = 1 if self.isSwitch( dst ) else 0\n dport = len( ports[ dst ] ) + dst_base\n ports[ src ][ sport ] = ( dst, dport )\n ports[ dst ][ dport ] = ( src, sport )\n return sport, dport","def connect_icache(self, port: Port) -> None:\n self.port_end.req_ports = port","def filter_create_port_attributes(cls, port, context):\n cls.add_security_groups(port, context)\n # TODO(kmestery): Converting to uppercase due to ODL bug\n # https://bugs.opendaylight.org/show_bug.cgi?id=477\n port['mac_address'] = port['mac_address'].upper()\n try_del(port, ['status'])","def listen(self, port, address=\"\"):\n sockets = self.bind_udp_sockets(port, address=address)\n self.add_sockets(sockets)","def bind(self, configurator: Configurator) -> None:\n\n # Workspace\n configurator.add_route('workspace', '/workspaces/{workspace_id}', request_method='GET') # nopep8\n configurator.add_view(self.workspace, route_name='workspace')\n # Create workspace\n configurator.add_route('create_workspace', '/workspaces', request_method='POST') # nopep8\n configurator.add_view(self.create_workspace, route_name='create_workspace') # nopep8\n # Update Workspace\n configurator.add_route('update_workspace', '/workspaces/{workspace_id}', request_method='PUT') # nopep8\n configurator.add_view(self.update_workspace, route_name='update_workspace') # nopep8\n # Workspace Members (Roles)\n configurator.add_route('workspace_members', '/workspaces/{workspace_id}/members', request_method='GET') # nopep8\n configurator.add_view(self.workspaces_members, route_name='workspace_members') # nopep8\n # Update Workspace Members roles\n configurator.add_route('update_workspace_member', '/workspaces/{workspace_id}/members/{user_id}', request_method='PUT') # nopep8\n configurator.add_view(self.update_workspaces_members_role, route_name='update_workspace_member') # nopep8\n # Create Workspace Members roles\n configurator.add_route('create_workspace_member', '/workspaces/{workspace_id}/members', request_method='POST') # nopep8\n configurator.add_view(self.create_workspaces_members_role, route_name='create_workspace_member') # nopep8\n # Workspace Content\n configurator.add_route('workspace_content', '/workspaces/{workspace_id}/contents', request_method='GET') # nopep8\n configurator.add_view(self.workspace_content, route_name='workspace_content') # nopep8\n # Create Generic Content\n configurator.add_route('create_generic_content', '/workspaces/{workspace_id}/contents', request_method='POST') # nopep8\n configurator.add_view(self.create_generic_empty_content, route_name='create_generic_content') # nopep8\n # Move Content\n configurator.add_route('move_content', '/workspaces/{workspace_id}/contents/{content_id}/move', request_method='PUT') # nopep8\n configurator.add_view(self.move_content, route_name='move_content') # nopep8\n # Delete/Undelete Content\n configurator.add_route('delete_content', '/workspaces/{workspace_id}/contents/{content_id}/delete', request_method='PUT') # nopep8\n configurator.add_view(self.delete_content, route_name='delete_content') # nopep8\n configurator.add_route('undelete_content', '/workspaces/{workspace_id}/contents/{content_id}/undelete', request_method='PUT') # nopep8\n configurator.add_view(self.undelete_content, route_name='undelete_content') # nopep8\n # # Archive/Unarchive Content\n configurator.add_route('archive_content', '/workspaces/{workspace_id}/contents/{content_id}/archive', request_method='PUT') # nopep8\n configurator.add_view(self.archive_content, route_name='archive_content') # nopep8\n configurator.add_route('unarchive_content', '/workspaces/{workspace_id}/contents/{content_id}/unarchive', request_method='PUT') # nopep8\n configurator.add_view(self.unarchive_content, route_name='unarchive_content') # nopep8","def http_ports(self, http_ports):\n\n self._http_ports = http_ports","def listentcp(self, site):\n logger.warning('Setting TCP listener on port %d for HTTP requests',\n self.bind_port)\n self.reactor.listenTCP(self.bind_port, site)","def bind(self, scope: aws_cdk.core.Construct, source_log_group: \"ILogGroup\") -> \"LogSubscriptionDestinationConfig\":\n ...","def test_port_create_with_binding_information(self):\n network, segments, subnets = self._create_test_segments_with_subnets(3)\n\n # Map the host to the middle segment (by mocking host/segment mapping)\n self._setup_host_mappings([\n (segments[1]['segment']['id'], 'fakehost'),\n (segments[1]['segment']['id'], 'otherhost'),\n (segments[0]['segment']['id'], 'thirdhost')])\n\n response = self._create_port(self.fmt,\n net_id=network['network']['id'],\n tenant_id=network['network']['tenant_id'],\n is_admin=True,\n arg_list=(portbindings.HOST_ID,),\n **{portbindings.HOST_ID: 'fakehost'})\n res = self.deserialize(self.fmt, response)\n self._validate_immediate_ip_allocation(res['port']['id'])\n\n # Since host mapped to middle segment, IP must come from middle subnet\n self._assert_one_ip_in_subnet(response, subnets[1]['subnet']['cidr'])","def bind_all(self, sequence=None, func=None, add=None):\n return super().bind_all(sequence, func, add)","def addConstraint(self, constraint: Constraint, /) -> None:\n ...","def addPortToURL(url, port):\n (scheme, netloc, url, query, fragment) = urllib2.urlparse.urlsplit(url)\n netloc = netloc.split(':')[0]\n netloc = \"%s:%s\" % (netloc, port)\n url = urllib2.urlparse.urlunsplit((scheme, netloc, url, query, fragment))\n return url","def port1(self, port1):\n\n self._port1 = port1","def _addBindParam(self, name, type = DEFAULT_TYPE_STRING):\n warnings.warn(\"This function is deprecated; shader references have been replaced with shader nodes in 1.38.\", DeprecationWarning, stacklevel = 2)\n return self.addInput(name, type)","def bind_class(self, className, sequence=None, func=None, add=None):\n return super().bind_class(className, sequence, func, add)","def ports(self, ports):\n\n self._ports = ports","def bind(self, **new_ctx) -> \"LoggerAdapter\":\n extra = self._copy_dict_none_to_empty(self.extra)\n ctx = self._copy_dict_none_to_empty(extra.get(\"ctx\"))\n ctx.update(new_ctx)\n extra[\"ctx\"] = ctx\n return LoggerAdapter(self.logger, extra)"],"string":"[\n \"def add_binding(ctx, binding_name, pool_name, acl_name, nat_type, twice_nat_id):\\n\\n entryFound = False\\n table = 'NAT_BINDINGS'\\n key = binding_name\\n dataKey1 = 'access_list'\\n dataKey2 = 'nat_pool'\\n dataKey3 = 'nat_type'\\n dataKey4 = 'twice_nat_id'\\n\\n if acl_name is None:\\n acl_name = \\\"\\\"\\n\\n if len(binding_name) > 32:\\n ctx.fail(\\\"Invalid binding name. Maximum allowed binding name is 32 characters !!\\\")\\n\\n config_db = ConfigDBConnector()\\n config_db.connect()\\n\\n data = config_db.get_entry(table, key)\\n if data:\\n if data[dataKey1] == acl_name and data[dataKey2] == pool_name:\\n click.echo(\\\"Trying to add binding, which is already present.\\\")\\n entryFound = True\\n\\n binding_dict = config_db.get_table(table)\\n if len(binding_dict) == 16:\\n click.echo(\\\"Failed to add binding, as already reached maximum binding limit 16.\\\")\\n entryFound = True\\n\\n if nat_type is not None:\\n if nat_type == \\\"dnat\\\":\\n click.echo(\\\"Ignored, DNAT is not yet suported for Binding \\\")\\n entryFound = True\\n else:\\n nat_type = \\\"snat\\\"\\n\\n if twice_nat_id is None:\\n twice_nat_id = \\\"NULL\\\"\\n\\n if entryFound is False:\\n count = 0\\n if twice_nat_id is not None:\\n count = getTwiceNatIdCountWithStaticEntries(twice_nat_id, 'STATIC_NAT', count)\\n count = getTwiceNatIdCountWithStaticEntries(twice_nat_id, 'STATIC_NAPT', count)\\n count = getTwiceNatIdCountWithDynamicBinding(twice_nat_id, count, key)\\n if count > 1:\\n ctx.fail(\\\"Same Twice nat id is not allowed for more than 2 entries!!\\\")\\n\\n config_db.set_entry(table, key, {dataKey1: acl_name, dataKey2: pool_name, dataKey3: nat_type, dataKey4: twice_nat_id})\",\n \"def get_catalystport_binding(vland_id):\\n LOG.debug(\\\"get_catlystport_binding() called\\\")\\n session = db.get_session()\\n try:\\n binding = (session.query(catalyst_models.CatalystPortBinding). \\\\\\n filter_by(vland_id).all())\\n return binding\\n except exc.NoresultFound:\\n raise c_exc.CatalystPortBindingNotFound(vlan_id=vlan_id)\",\n \"def add_port(self, port):\\n self._main_model.add_port(port)\",\n \"def add_port_acl(self, port, acl):\\n raise NotImplementedError # pragma: no cover\",\n \"def configure_dcbx_app(self, ports, **kwargs):\\n pass\",\n \"def bind_acl_to_ports(self, acl_name=None, ports=None):\\n pass\",\n \"def add_port(cls, port, ser):\\n cls._open_ports[port] = ser\",\n \"def addPort(self, *args):\\n return _libsbml.CompModelPlugin_addPort(self, *args)\",\n \"def add_port(self, port):\\n self._ports.add(port)\",\n \"def add_bindings(self, configuration, bind_to, typ, bindings):\\n wanted = list(bindings.wanted(configuration[typ].values()))\\n if not self.get_current(bind_to)[0]:\\n log.info(\\\"Would bind <%s>(%s) to %s\\\", typ, ', '.join(wanted), bind_to.long_name)\\n return\\n\\n for thing in wanted:\\n bound = self.is_bound(typ, thing, bind_to.typ, bind_to.name)\\n\\n if not bound:\\n log.info(\\\"Binding <%s>(%s) to %s\\\", typ, thing, bind_to.long_name)\\n combined_typ, binding_name_str, name_str = self.combined_typ(bind_to.typ, typ)\\n payload = {binding_name_str: bind_to.name, name_str: thing}\\n payload.update(configuration[typ][thing].binding_options)\\n self.post(combined_typ, {combined_typ: payload, \\\"params\\\": {\\\"action\\\": \\\"bind\\\"}}, content_type=self.content_type(combined_typ))\\n else:\\n log.debug(\\\"<%s(%s) already bound to %s\\\", typ, thing, bind_to.long_name)\",\n \"def AddPortFlag(parser, required=False):\\n help_text = \\\"\\\"\\\"\\\\\\n Network port of the database.\\n \\\"\\\"\\\"\\n parser.add_argument('--port', help=help_text, required=required, type=int)\",\n \"def _bind(self):\\n\\n pass\",\n \"def addBoundConnection(self, connection):\\r\\n system_id = connection.system_id\\r\\n self.log.debug('Adding SMPP binding for %s' % system_id)\\r\\n if not system_id in self.bound_connections:\\r\\n self.bound_connections[system_id] = SMPPBindManager(system_id)\\r\\n self.bound_connections[system_id].addBinding(connection)\\r\\n bind_type = connection.bind_type\\r\\n self.log.info(\\\"Added %s bind for '%s'. Active binds: %s. Max binds: %s\\\" % (bind_type, system_id, self.getBoundConnectionCountsStr(system_id), self.config.systems[system_id]['max_bindings']))\",\n \"def add_port_mac(self, context, port_dict):\\n self._get_driver_for_provider(constants.l2gw\\n ).add_port_mac(context, port_dict)\",\n \"def add_binding(self, variable, value):\\n # If there's already a binding, update it rather than add a new one.\\n for binding in self.bindings:\\n if binding.variable.name == variable:\\n return self.update_binding(variable, value)\\n variable = Variable(self.canvas, self, variable)\\n binding = Binding(self.canvas, variable, value)\\n self.bindings.append(binding)\\n x, y = self.pos\\n variable.set_pos(x + 10, y + len(self.bindings) * 20)\\n if value.moves_with_binding:\\n value.set_pos(x + 140, y + len(self.bindings) * 20)\\n self.update()\",\n \"def add_port(bridge, port, external_id=None):\\n _run('ip', 'link', 'set', port, 'up')\\n _run('ovs-vsctl', 'add-port', bridge, port)\\n if external_id:\\n ports = SimpleOVSDB('ovs-vsctl', 'port')\\n for port in ports.find('name={}'.format(port)):\\n ports.set(port['_uuid'],\\n 'external_ids:{}'.format(external_id[0]),\\n external_id[1])\",\n \"def bind(self, address: Tuple[str, int]) -> None:\\n ...\",\n \"def remove_catalystport_binding(vlan_id):\\n LOG.debug(\\\"remove_catalystport_binding() called\\\")\\n session = db.get_session()\\n try:\\n binding = (session.query(catalyst_models.CatalystPortBinding).\\n filter_by(vlan_id=vlan_id).all())\\n for bind in binding:\\n session.delete(bind)\\n session.flush()\\n return binding\\n except exc.NoResultFound:\\n pass\",\n \"def add_service(torconfig, service, port=None):\\n # picks a random port until it finds one avaible.\\n while not service.tcp:\\n port = port or new_port()\\n try:\\n service.tcp = reactor.listenTCP(port, service.factory)\\n except error.CannotListenError:\\n pass\\n\\n service.hs = txtorcon.HiddenService(\\n torconfig, os.path.join(config.tor_data, service.name),\\n ['%d 127.0.0.1:%d' % (service.port, port)])\\n apaf.hiddenservices.append(service)\",\n \"def visit_AttributeBinding(self, node):\\n obj = self.stack[-1]\\n py_ast = node.binding.expr.py_ast\\n op = node.binding.op\\n op_compiler = COMPILE_OP_MAP[op]\\n code = op_compiler(py_ast, self.filename)\\n binding = {\\n 'operator': op,\\n 'code': code,\\n 'name': node.name,\\n 'lineno': node.binding.lineno,\\n 'filename': self.filename,\\n 'block': self.block,\\n }\\n obj['bindings'].append(binding)\",\n \"def extend_hosting_port_info(self, context, port_db, hosting_info):\\n pass\",\n \"def addBindingToFrame(var, val, frame):\\n set_car(frame, cons(var, frame_variables(frame)))\\n set_cdr(frame, cons(val, frame_values(frame)))\\n return\",\n \"def setup_logical_port_connectivity(self, context, port_db):\\n pass\",\n \"def add(env, identifier, **args):\\n\\n mgr = SoftLayer.LoadBalancerManager(env.client)\\n uuid, _ = mgr.get_lbaas_uuid_id(identifier)\\n\\n new_listener = {\\n 'backendPort': args.get('backport'),\\n 'backendProtocol': args.get('backprotocol') if args.get('backprotocol') else args.get('frontprotocol'),\\n 'frontendPort': args.get('frontport'),\\n 'frontendProtocol': args.get('frontprotocol'),\\n 'loadBalancingMethod': args.get('method'),\\n 'maxConn': args.get('connections', None),\\n 'sessionType': args.get('sticky'),\\n 'tlsCertificateId': args.get('sslcert')\\n }\\n\\n try:\\n mgr.add_lb_listener(uuid, new_listener)\\n click.secho(\\\"Success\\\", fg='green')\\n except SoftLayerAPIError as exception:\\n click.secho(f\\\"ERROR: {exception.faultString}\\\", fg='red')\",\n \"def add_in_port(self, m: int, content: str, **opts) -> None:\",\n \"def bind_sockets(port, address=..., family=..., backlog=..., flags=..., reuse_port=...):\\n ...\",\n \"def add_port(self, port):\\n self.ports.append(port)\\n if port.io_type not in self.port_seqs:\\n self.port_seqs[port.io_type] = 0\\n self.port_seqs[port.io_type] += 1\\n port.sequence = self.port_seqs[port.io_type]\\n return self\",\n \"def bind(self,cluster_name,ip_address='',bind_details={},project_id=''):\\n project_id = project_id if project_id != '' else self.__project_id\\n if ip_address == '':\\n headers = { 'User-Agent': 'curl/7.61.0'} # spoof for simple response\\n ip = requests.get('http://ifconfig.co', headers)\\n ip_address = ip.text.rstrip()\\n logger.info(f'bind: looked up ip address: {ip_address}')\\n #key = self.create_programatic_apikey(description=description,project_id=project_id)\\n db_user = { 'username' : 'foo'\\n ,'password' : 'changeme'\\n ,'databaseName' : 'admin'\\n ,'roles' : [ {'databaseName' : 'admin', 'roleName' : 'dbAdminAnyDatabase'} ] \\n }\\n user = self.create_database_user(db_user,project_id=project_id) \\n cluster = self.get_cluster(cluster_name)\\n cs = cluster['mongoURIWithOptions'].split('/',1)\\n #conn_str = f'{cs[0]//{key['publicKey']}:{key['privateKey']}@{cs[1]}'\\n return conn_str\",\n \"def add_reserved_port(port):\\n _free_ports.add(port)\",\n \"def column_bind(arguments):\\n return Component(\\n \\\"ColumnBind\\\",\\n arguments=arguments,\\n options={\\n \\n },\\n constraints=None)\",\n \"def bind_ports(self, ip, ports): #{\\n if isinstance(ports, int):\\n ports = [ports]\\n for p in ports:\\n try:\\n if p==0:\\n port = self.socket.bind_to_random_port(\\\"tcp://%s\\\" % ip)\\n else:\\n self.socket.bind(\\\"tcp://%s:%i\\\" % (ip, p))\\n port = p\\n except zmq.ZMQError:\\n # bind raises this if the port is not free\\n continue\\n except zmq.ZMQBindError:\\n # bind_to_random_port raises this if no port could be found\\n continue\\n else:\\n break\\n else:\\n raise zmq.ZMQBindError('Could not find an available port')\\n\\n url = 'tcp://%s:%i' % (ip, port)\\n self.bound.add(url)\\n self._ready = True\\n\\n return port\",\n \"def add_port(self, port, game):\\n if port in self.ports:\\n return False\\n self.ports[port] = Port(port, game)\\n return self.start_port(port)\",\n \"def _create_port_ext(self, res_port, req_port, context):\\n commit = self._get_port_attr(req_port, \\\"commit\\\")\\n trunked = self._get_port_attr(req_port, \\\"trunked\\\")\\n hardware_id = self._get_port_attr(req_port, \\\"switch:hardware_id\\\")\\n if commit is None:\\n commit = False\\n port_ext = db.create_port_ext(\\n port_id=res_port[\\\"id\\\"],\\n commit=commit,\\n trunked=trunked,\\n hardware_id=hardware_id,\\n session=context.session)\\n return port_ext.as_dict()\",\n \"def add_connection(self, switch_name, port1, port2, bidir=False):\\n raise NotImplementedError()\",\n \"def bind_server(self):\\n self.MAIN_CONNECTION.bind((self.HOST, self.PORT))\",\n \"def __bind(self, args = []):\\n \\n try: \\n\\n # Start the local chat server and be ready to receive incoming requests\\n localServerPort = self.__agent.startLocalServer()\\n\\n # Sleep a little bit to allow the new thread to open the listening port\\n sleep(0.3)\\n \\n serverIp, serverPort = self.__cm.getConnectionInfo()\\n\\n self.__cm.send(p.T_BIND, [serverIp, localServerPort])\\n reply = self.__cm.receive()\\n \\n if (reply.type == p.T_ERR):\\n raise Exception, \\\"Port binding was not succussful!\\\"\\n\\n except Exception,e:\\n self.__handleError('Bind', e)\",\n \"def activate(self, ext_ip, ext_port):\\n self.sql_manager.port_update(self.id, external_ip=ext_ip, external_port=ext_port)\\n self.external_port = ext_port\\n self.external_ip = ext_ip\",\n \"def _do_backend_bind(self, backend, port_id, device_owner, zone, device_id,\\n host, binding_profile):\\n\\n logger.debug('Binding port %s on backend %s: compute: %s/%s/%s location %s'\\n % (port_id, backend['name'], device_owner,\\n zone, device_id, host))\\n driver = self.backend_manager.get_backend_driver(backend)\\n # TODO these are not thoroughly documented or validated and are a\\n # part of the API. Write down what the values must be, must mean\\n # and how the backend can use them.\\n driver.bind(port_id,\\n device_owner, zone, device_id,\\n host, binding_profile)\\n\\n # TODO required? Do we trust the backend to set this?\\n ports[port_id]['zone'] = zone\",\n \"def _add_varbind(self, oid, value):\\n \\n self._encoded_oids.append(oid)\\n self._encoded_vals.append(value)\",\n \"def port_configure(self,port,**config):\\n if not port in self.ports:\\n self.ports[port] = {}\\n\\n for k,v in config.items():\\n self.ports[port][k] = v\",\n \"def add_console_setting(db, linenum, baud, flowcontrol, devicename):\\n config_db = db.cfgdb\\n\\n table = \\\"CONSOLE_PORT\\\"\\n dataKey1 = 'baud_rate'\\n dataKey2 = 'flow_control'\\n dataKey3 = 'remote_device'\\n\\n ctx = click.get_current_context()\\n data = config_db.get_entry(table, linenum)\\n if data:\\n ctx.fail(\\\"Trying to add console port setting, which is already exists.\\\")\\n else:\\n console_entry = { dataKey1: baud }\\n console_entry[dataKey2] = \\\"1\\\" if flowcontrol else \\\"0\\\"\\n\\n if devicename:\\n if isExistingSameDevice(config_db, devicename, table):\\n ctx.fail(\\\"Given device name {} has been used. Please enter a valid device name or remove the existing one !!\\\".format(devicename))\\n console_entry[dataKey3] = devicename\\n\\n config_db.set_entry(table, linenum, console_entry)\",\n \"def add_port_gnmap(self, port_string):\\n parts = port_string.split('/')\\n port = parts[0]\\n port_status = parts[1]\\n protocol = parts[2]\\n name = parts[4]\\n service = parts[6]\\n self.add_port(port, port_status, protocol, name, service)\",\n \"def with_exposed_port(\\n self,\\n port: int,\\n protocol: Optional[NetworkProtocol] = None,\\n description: Optional[str] = None,\\n ) -> \\\"Container\\\":\\n _args = [\\n Arg(\\\"port\\\", port),\\n Arg(\\\"protocol\\\", protocol, None),\\n Arg(\\\"description\\\", description, None),\\n ]\\n _ctx = self._select(\\\"withExposedPort\\\", _args)\\n return Container(_ctx)\",\n \"def _bind_to_service(self):\\n if self._service_dn:\\n # bind with the service_dn\\n self._server.simple_bind_s(self._service_dn, self._service_password)\\n else:\\n # force a connection without binding\\n self._server.whoami_s()\",\n \"def bind(self, sequence=None, func=None, add=None):\\n return self._widget_bind(sequence, func, add, internal=False)\",\n \"def modify_ports(self, ports, **kwargs):\\n pass\",\n \"def add_connection(self, ip, port, key):\\n\\n # Socket declaration\\n sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n sock.bind((ip, port))\\n\\n # Adding connection to the list\\n self.connections[key] = sock\",\n \"def pybind11(self, line, cell):\\n\\n line = line.strip().rstrip(';')\\n args = self.pybind11.parser.parse_args(shlex.split(line))\\n code = self.format_code(cell)\\n module = 'pybind11_{}'.format(self.compute_hash(code, args))\\n libfile = cache_path(module + ext_suffix())\\n need_rebuild = not os.path.isfile(libfile) or args.force\\n if need_rebuild:\\n source = self.save_source(code, module)\\n self.build_module(module, source, args)\\n self.import_module(module, libfile, import_symbols=not args.module)\",\n \"def bind_funct(self, symbol, argtypes=[], restypes=None, used_name=None):\\n if used_name is None:\\n used_name = symbol\\n funct = self.__get_symbol(symbol)\\n #print(\\\"binding function {0}: {1}\\\".format(symbol, funct))\\n self.__funct_config(funct, argtypes, restypes)\\n self.__funct_add(used_name, funct)\",\n \"def _set_port(self, ip, port_name):\\n\\n inst_args = getattr(self, ip.top_name)\\n try:\\n name = [key for key in inst_args.keys() if key[2:] == port_name][0]\\n except IndexError:\\n raise ValueError(f'port: \\\"{port_name}\\\" does not exist in ip: '\\n f'{ip.top_name}')\\n sig = inst_args[name]\\n sig.name = port_name\\n setattr(self, port_name, sig)\\n self._ports.append(sig)\",\n \"def binding_site(self, binding_site):\\n self._binding_site = binding_site\",\n \"def create_port(self, body=None):\\r\\n return self.post(self.ports_path, body=body)\",\n \"def set_bindpoint(self, bindpoint):\\n self.options['bindpoint'] = bindpoint\",\n \"def bind_port(self, port_context):\\n LOG.debug(\\\"Attempting to bind port %(port)s on \\\"\\n \\\"network %(network)s\\\",\\n {'port': port_context.current['id'],\\n 'network': port_context.network.current['id']})\\n vnic_type = port_context.current.get(portbindings.VNIC_TYPE,\\n portbindings.VNIC_NORMAL)\\n if vnic_type not in self.supported_vnic_types:\\n LOG.debug(\\\"Refusing to bind due to unsupported \\\"\\n \\\"vnic_type: %s\\\",\\n vnic_type)\\n return\\n\\n for segment in port_context.segments_to_bind:\\n if self.check_segment(segment, port_context.host):\\n vif_details = dict(self.vif_details)\\n # TODO(ijw) should be in a library that the agent uses\\n vif_type = self.get_vif_type(port_context)\\n if vif_type == 'vhostuser':\\n vif_details['vhostuser_socket'] = \\\\\\n os.path.join(cfg.CONF.ml2_vpp.vhost_user_dir,\\n port_context.current['id'])\\n vif_details['vhostuser_mode'] = 'server'\\n LOG.debug('Setting details: %s', vif_details)\\n port_context.set_binding(segment[api.ID],\\n vif_type,\\n vif_details)\\n LOG.debug(\\\"Bind selected using segment: %s\\\", segment)\\n return\",\n \"def get_all_catalystport_bindings():\\n LOG.debug(\\\"get_all_catalystport_bindings() called\\\")\\n session = db.get_session()\\n try:\\n bindings = session.query\\n (catalyst_models.CatalystPortBinding).all()\\n return bindings\\n except exc.NoResultFound:\\n return []\",\n \"def configure_dcbx_cn(self, ports, **kwargs):\\n pass\",\n \"def connect_bind(self, account=DEFAULT_ACCOUNT, trace_level=2, trace_file=sys.stdout):\\n\\n self.connect(trace_level=trace_level, trace_file=trace_file)\\n\\n # auto bind\\n self.bind(account)\",\n \"def rebind(self, newPortNum):\\n self.socket.bind((\\\"localhost\\\",newPortNum))\\n self.PORT_NUM = newPortNum\",\n \"def test_bind(self):\\n x = t.Exactly(\\\"x\\\")\\n b = t.Bind(\\\"var\\\", x)\\n self.assertEqual(writePython(b),\\n dd(\\\"\\\"\\\"\\n _G_exactly_1, lastError = self.exactly('x')\\n self.considerError(lastError, None)\\n _locals['var'] = _G_exactly_1\\n _locals['var']\\n \\\"\\\"\\\"))\",\n \"def bind(port, socket_type, socket_proto):\\n return _bind(port, socket_type, socket_proto)\",\n \"def connect(self, binding):\\n\\n # Check whether the binding setting is correct or not.\\n if self.io_owner == binding.io_owner:\\n raise RuntimeError(\\\"Can not bind itself.\\\")\\n\\n if self.io_type == \\\"param\\\" and not self.is_pipeline_executor_interface():\\n raise RuntimeError(\\n 'The \\\"param\\\" binding can only be used by a pipeline executor interface!'\\n )\\n\\n if not self.is_pipeline_executor_interface() and self.io_type == \\\"input\\\":\\n raise RuntimeError(\\\"Module can only bind from output interface!\\\")\\n\\n if self.io_type == \\\"param\\\" and binding.io_type != \\\"param\\\":\\n raise RuntimeError(\\n 'A global \\\"param\\\" interface can only be bind with a module \\\"param\\\" interface!'\\n )\\n\\n if (\\n not self.is_pipeline_executor_interface()\\n and not binding.is_pipeline_executor_interface()\\n and binding.io_type == \\\"output\\\"\\n ):\\n raise RuntimeError(\\\"Can not bind module output with another module output!\\\")\\n\\n if (\\n not self.is_pipeline_executor_interface()\\n and binding.is_pipeline_executor_interface()\\n and binding.io_type == \\\"input\\\"\\n ):\\n raise RuntimeError(\\\"Can not bind module output with pipeline input!\\\")\\n\\n if self.is_pipeline_executor_interface() and self.io_type == \\\"output\\\":\\n raise RuntimeError(\\\"Global output can not be used as binding start point.\\\")\\n\\n if (\\n self.is_pipeline_executor_interface()\\n and self.io_type == \\\"input\\\"\\n and binding.io_type != \\\"input\\\"\\n ):\\n raise RuntimeError(\\\"Global input can only bind with module input.\\\")\\n\\n self.bindings.append(binding)\\n if not self.is_pipeline_executor_interface():\\n # Check whether the data types of the source and destination are the same.\\n if (\\n isinstance(binding.io_owner, PipelineConfig.ModuleWrapper)\\n and self.data_type != binding.data_type\\n ):\\n raise RuntimeError(\\n f\\\"Illegal type (%s vs. %s): binding type is not same!\\\"\\n % (self.data_type, binding.data_type)\\n )\\n\\n binding.parents.append(self)\\n\\n # Do acyclic check after increasing the in-degree of child node by setting\\n # current interface as a parent of the child node.\\n\\n if not self.check_dag_acyclic(\\n binding.io_owner, self.io_owner.input_bindings.bindings\\n ):\\n raise RuntimeError(\\\"Illegal connection: Cause a cycle!\\\")\",\n \"def bind(self, *args):\\r\\n return self._fd.bind(*args)\",\n \"def bound(name):\",\n \"def toBind(node, fail_display=None):\\n bind_name = node.name().replace(pcfg.skeleton_namespace, pcfg.bind_namespace)\\n\\n if not pm.objExists(bind_name) and fail_display:\\n fail_display(bind_name + ' does not exist!')\\n return\\n\\n return pm.PyNode(bind_name)\",\n \"def allocate_hosting_port(self, context, router_id, port_db, network_type,\\n hosting_device_id):\\n pass\",\n \"def bind_key(self, key):\\n self.key_bindings.append(key)\",\n \"def add_out_port(self, m: int, content: str, **opts) -> None:\",\n \"def port_nic_add(switch, port, node, nic):\\n client.port.connect_nic(switch, port, node, nic)\",\n \"def get_named_bind_string(self, name):\\n\\n return \\\":\\\" + name\",\n \"async def _async_add_port_mapping(\\n self, external_port: int, local_ip: str, internal_port: int\\n ) -> None:\\n entry = [external_port, local_ip, internal_port]\\n self.added_port_mappings.append(entry)\",\n \"def bind_view(self, resource, view, tail=()):\\n if isinstance(tail, str) and tail != '*':\\n tail = tuple(i for i in tail.split('/') if i)\\n\\n setup = self.resources.setdefault(resource, {'views': {}})\\n setup.setdefault('views', {})[tail] = view\",\n \"def bind(self, server_name: str, port: int) -> None:\\n self.socket.bind((server_name, port))\",\n \"def create_port_postcommit(self, context):\\n if self.rpc_handler is None:\\n return\\n port = self._get_port_info(context)\\n if port is not None:\\n try:\\n self.rpc_handler.create_port(port)\\n except:\\n pass\",\n \"def addVarBinds(self, *varBinds):\\n debug.logger & debug.flagMIB and debug.logger('additional var-binds: %r' % (varBinds,))\\n if self.__state & self.stClean:\\n self.__varBinds.extend(varBinds)\\n else:\\n self.__additionalVarBinds.extend(varBinds)\\n return self\",\n \"def add_binding_sites(self, binding_sites, final_conformation: str = None):\\n\\n if final_conformation is None:\\n self.binding_sites[self.name] += binding_sites\\n elif final_conformation in self.binding_sites:\\n self.binding_sites[final_conformation] += binding_sites\\n else:\\n self.binding_sites[final_conformation] = binding_sites\\n\\n # also reverse_link the binding sites to this TF object\\n for binding_site in binding_sites:\\n binding_site.add_transcription_factor(self, final_conformation)\",\n \"def bind(self, port, address=None, family=socket.AF_UNSPEC, backlog=128):\\n sockets = self.bind_udp_sockets(port, address=address, family=family, backlog=backlog)\\n if self._started:\\n self.add_sockets(sockets)\\n else:\\n self._pending_sockets.extend(sockets)\",\n \"def register(self):\\n self.app.bind('CreateSuperUserCommand', CreateSuperUser())\\n self.app.bind('InstallCommand', Install())\\n self.app.bind(\\n 'AdminUserMigrationDirectory',\\n os.path.join(package_directory, 'migrations')\\n )\",\n \"def bind(self, svc, svc_ref):\\n with self._lock:\\n if ORDER_HANDLER in svc_ref.get_property(pelix.OBJECTCLASS):\\n targets = svc_ref.get_property(ORDER_TARGETS)\\n if isinstance(targets, (list, tuple)):\\n for target in targets:\\n self._target_handlers.setdefault(target, []).append(svc)\\n\\n else:\\n self._target_handlers.setdefault(str(targets), []).append(svc)\",\n \"def _handle_port_binding(self, context, port_id, trunk, trunk_host):\\n port = self.core_plugin.update_port(\\n context, port_id,\\n {'port': {portbindings.HOST_ID: trunk_host,\\n 'device_owner': trunk_consts.TRUNK_SUBPORT_OWNER}})\\n vif_type = port.get(portbindings.VIF_TYPE)\\n if vif_type == portbindings.VIF_TYPE_BINDING_FAILED:\\n raise trunk_exc.SubPortBindingError(port_id=port_id,\\n trunk_id=trunk.id)\\n return port\",\n \"def https_ports(self, https_ports):\\n\\n self._https_ports = https_ports\",\n \"def bind(self, _target: aws_cdk.aws_lambda.IFunction) -> None:\\n ...\",\n \"def get_port_binding():\\n import docker\\n client = docker.from_env()\\n return [c.attrs['NetworkSettings']['Ports']['5555/tcp'][0]\\n for c in client.containers.list(\\n filters={'label': 'org.label-schema.name=profemag/femag'})]\",\n \"def process_create_port(self, context, port, result):\\n\\n # Process extension data\\n port_ext = self._create_port_ext(result, port, context=context)\\n switchports = self._update_switchports(result, port,\\n session=context.session)\\n self._find_port_dict_extensions(result, None, port_ext=port_ext,\\n switchports=switchports,\\n session=context.session)\\n\\n # Validate we can actually configure this port\\n if result[\\\"commit\\\"]:\\n self._validate_port_can_commit(result, None,\\n session=context.session)\",\n \"def addPort( self, src, dst, sport=None, dport=None ):\\n # Initialize if necessary\\n ports = self.ports\\n ports.setdefault( src, {} )\\n ports.setdefault( dst, {} )\\n # New port: number of outlinks + base\\n if sport is None:\\n src_base = 1 if self.isSwitch( src ) else 0\\n sport = len( ports[ src ] ) + src_base\\n if dport is None:\\n dst_base = 1 if self.isSwitch( dst ) else 0\\n dport = len( ports[ dst ] ) + dst_base\\n ports[ src ][ sport ] = ( dst, dport )\\n ports[ dst ][ dport ] = ( src, sport )\\n return sport, dport\",\n \"def connect_icache(self, port: Port) -> None:\\n self.port_end.req_ports = port\",\n \"def filter_create_port_attributes(cls, port, context):\\n cls.add_security_groups(port, context)\\n # TODO(kmestery): Converting to uppercase due to ODL bug\\n # https://bugs.opendaylight.org/show_bug.cgi?id=477\\n port['mac_address'] = port['mac_address'].upper()\\n try_del(port, ['status'])\",\n \"def listen(self, port, address=\\\"\\\"):\\n sockets = self.bind_udp_sockets(port, address=address)\\n self.add_sockets(sockets)\",\n \"def bind(self, configurator: Configurator) -> None:\\n\\n # Workspace\\n configurator.add_route('workspace', '/workspaces/{workspace_id}', request_method='GET') # nopep8\\n configurator.add_view(self.workspace, route_name='workspace')\\n # Create workspace\\n configurator.add_route('create_workspace', '/workspaces', request_method='POST') # nopep8\\n configurator.add_view(self.create_workspace, route_name='create_workspace') # nopep8\\n # Update Workspace\\n configurator.add_route('update_workspace', '/workspaces/{workspace_id}', request_method='PUT') # nopep8\\n configurator.add_view(self.update_workspace, route_name='update_workspace') # nopep8\\n # Workspace Members (Roles)\\n configurator.add_route('workspace_members', '/workspaces/{workspace_id}/members', request_method='GET') # nopep8\\n configurator.add_view(self.workspaces_members, route_name='workspace_members') # nopep8\\n # Update Workspace Members roles\\n configurator.add_route('update_workspace_member', '/workspaces/{workspace_id}/members/{user_id}', request_method='PUT') # nopep8\\n configurator.add_view(self.update_workspaces_members_role, route_name='update_workspace_member') # nopep8\\n # Create Workspace Members roles\\n configurator.add_route('create_workspace_member', '/workspaces/{workspace_id}/members', request_method='POST') # nopep8\\n configurator.add_view(self.create_workspaces_members_role, route_name='create_workspace_member') # nopep8\\n # Workspace Content\\n configurator.add_route('workspace_content', '/workspaces/{workspace_id}/contents', request_method='GET') # nopep8\\n configurator.add_view(self.workspace_content, route_name='workspace_content') # nopep8\\n # Create Generic Content\\n configurator.add_route('create_generic_content', '/workspaces/{workspace_id}/contents', request_method='POST') # nopep8\\n configurator.add_view(self.create_generic_empty_content, route_name='create_generic_content') # nopep8\\n # Move Content\\n configurator.add_route('move_content', '/workspaces/{workspace_id}/contents/{content_id}/move', request_method='PUT') # nopep8\\n configurator.add_view(self.move_content, route_name='move_content') # nopep8\\n # Delete/Undelete Content\\n configurator.add_route('delete_content', '/workspaces/{workspace_id}/contents/{content_id}/delete', request_method='PUT') # nopep8\\n configurator.add_view(self.delete_content, route_name='delete_content') # nopep8\\n configurator.add_route('undelete_content', '/workspaces/{workspace_id}/contents/{content_id}/undelete', request_method='PUT') # nopep8\\n configurator.add_view(self.undelete_content, route_name='undelete_content') # nopep8\\n # # Archive/Unarchive Content\\n configurator.add_route('archive_content', '/workspaces/{workspace_id}/contents/{content_id}/archive', request_method='PUT') # nopep8\\n configurator.add_view(self.archive_content, route_name='archive_content') # nopep8\\n configurator.add_route('unarchive_content', '/workspaces/{workspace_id}/contents/{content_id}/unarchive', request_method='PUT') # nopep8\\n configurator.add_view(self.unarchive_content, route_name='unarchive_content') # nopep8\",\n \"def http_ports(self, http_ports):\\n\\n self._http_ports = http_ports\",\n \"def listentcp(self, site):\\n logger.warning('Setting TCP listener on port %d for HTTP requests',\\n self.bind_port)\\n self.reactor.listenTCP(self.bind_port, site)\",\n \"def bind(self, scope: aws_cdk.core.Construct, source_log_group: \\\"ILogGroup\\\") -> \\\"LogSubscriptionDestinationConfig\\\":\\n ...\",\n \"def test_port_create_with_binding_information(self):\\n network, segments, subnets = self._create_test_segments_with_subnets(3)\\n\\n # Map the host to the middle segment (by mocking host/segment mapping)\\n self._setup_host_mappings([\\n (segments[1]['segment']['id'], 'fakehost'),\\n (segments[1]['segment']['id'], 'otherhost'),\\n (segments[0]['segment']['id'], 'thirdhost')])\\n\\n response = self._create_port(self.fmt,\\n net_id=network['network']['id'],\\n tenant_id=network['network']['tenant_id'],\\n is_admin=True,\\n arg_list=(portbindings.HOST_ID,),\\n **{portbindings.HOST_ID: 'fakehost'})\\n res = self.deserialize(self.fmt, response)\\n self._validate_immediate_ip_allocation(res['port']['id'])\\n\\n # Since host mapped to middle segment, IP must come from middle subnet\\n self._assert_one_ip_in_subnet(response, subnets[1]['subnet']['cidr'])\",\n \"def bind_all(self, sequence=None, func=None, add=None):\\n return super().bind_all(sequence, func, add)\",\n \"def addConstraint(self, constraint: Constraint, /) -> None:\\n ...\",\n \"def addPortToURL(url, port):\\n (scheme, netloc, url, query, fragment) = urllib2.urlparse.urlsplit(url)\\n netloc = netloc.split(':')[0]\\n netloc = \\\"%s:%s\\\" % (netloc, port)\\n url = urllib2.urlparse.urlunsplit((scheme, netloc, url, query, fragment))\\n return url\",\n \"def port1(self, port1):\\n\\n self._port1 = port1\",\n \"def _addBindParam(self, name, type = DEFAULT_TYPE_STRING):\\n warnings.warn(\\\"This function is deprecated; shader references have been replaced with shader nodes in 1.38.\\\", DeprecationWarning, stacklevel = 2)\\n return self.addInput(name, type)\",\n \"def bind_class(self, className, sequence=None, func=None, add=None):\\n return super().bind_class(className, sequence, func, add)\",\n \"def ports(self, ports):\\n\\n self._ports = ports\",\n \"def bind(self, **new_ctx) -> \\\"LoggerAdapter\\\":\\n extra = self._copy_dict_none_to_empty(self.extra)\\n ctx = self._copy_dict_none_to_empty(extra.get(\\\"ctx\\\"))\\n ctx.update(new_ctx)\\n extra[\\\"ctx\\\"] = ctx\\n return LoggerAdapter(self.logger, extra)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6066342","0.6021122","0.5959869","0.5917959","0.5857227","0.5852962","0.5653113","0.56476676","0.55153096","0.5501996","0.5498605","0.5429331","0.5401681","0.53849334","0.5362603","0.53139096","0.52629244","0.5251845","0.5244616","0.5243897","0.52343386","0.520219","0.51781756","0.51657325","0.5161052","0.51316255","0.5123875","0.51179594","0.5111514","0.5097052","0.50926137","0.5092597","0.509013","0.5088287","0.5074072","0.50648063","0.50408465","0.50339854","0.5000876","0.49937397","0.49899665","0.49833614","0.49745882","0.4968702","0.49671623","0.4955954","0.495449","0.49215946","0.4914803","0.49098068","0.49083984","0.4877592","0.4875665","0.4864319","0.4853997","0.48253736","0.48222515","0.48168963","0.4806081","0.48051557","0.48025993","0.47782123","0.47774422","0.47769797","0.4773572","0.47723013","0.47721255","0.47716135","0.47457376","0.47439912","0.474331","0.47384003","0.4730204","0.47222978","0.4705377","0.4697331","0.46927154","0.46844277","0.46829024","0.46734148","0.467253","0.46660188","0.46567723","0.46544504","0.46531194","0.4650262","0.46468496","0.46368262","0.4631704","0.4630069","0.46260735","0.46247536","0.4620632","0.4617688","0.46116966","0.46071312","0.46058434","0.46034363","0.46023005","0.4594072"],"string":"[\n \"0.6066342\",\n \"0.6021122\",\n \"0.5959869\",\n \"0.5917959\",\n \"0.5857227\",\n \"0.5852962\",\n \"0.5653113\",\n \"0.56476676\",\n \"0.55153096\",\n \"0.5501996\",\n \"0.5498605\",\n \"0.5429331\",\n \"0.5401681\",\n \"0.53849334\",\n \"0.5362603\",\n \"0.53139096\",\n \"0.52629244\",\n \"0.5251845\",\n \"0.5244616\",\n \"0.5243897\",\n \"0.52343386\",\n \"0.520219\",\n \"0.51781756\",\n \"0.51657325\",\n \"0.5161052\",\n \"0.51316255\",\n \"0.5123875\",\n \"0.51179594\",\n \"0.5111514\",\n \"0.5097052\",\n \"0.50926137\",\n \"0.5092597\",\n \"0.509013\",\n \"0.5088287\",\n \"0.5074072\",\n \"0.50648063\",\n \"0.50408465\",\n \"0.50339854\",\n \"0.5000876\",\n \"0.49937397\",\n \"0.49899665\",\n \"0.49833614\",\n \"0.49745882\",\n \"0.4968702\",\n \"0.49671623\",\n \"0.4955954\",\n \"0.495449\",\n \"0.49215946\",\n \"0.4914803\",\n \"0.49098068\",\n \"0.49083984\",\n \"0.4877592\",\n \"0.4875665\",\n \"0.4864319\",\n \"0.4853997\",\n \"0.48253736\",\n \"0.48222515\",\n \"0.48168963\",\n \"0.4806081\",\n \"0.48051557\",\n \"0.48025993\",\n \"0.47782123\",\n \"0.47774422\",\n \"0.47769797\",\n \"0.4773572\",\n \"0.47723013\",\n \"0.47721255\",\n \"0.47716135\",\n \"0.47457376\",\n \"0.47439912\",\n \"0.474331\",\n \"0.47384003\",\n \"0.4730204\",\n \"0.47222978\",\n \"0.4705377\",\n \"0.4697331\",\n \"0.46927154\",\n \"0.46844277\",\n \"0.46829024\",\n \"0.46734148\",\n \"0.467253\",\n \"0.46660188\",\n \"0.46567723\",\n \"0.46544504\",\n \"0.46531194\",\n \"0.4650262\",\n \"0.46468496\",\n \"0.46368262\",\n \"0.4631704\",\n \"0.4630069\",\n \"0.46260735\",\n \"0.46247536\",\n \"0.4620632\",\n \"0.4617688\",\n \"0.46116966\",\n \"0.46071312\",\n \"0.46058434\",\n \"0.46034363\",\n \"0.46023005\",\n \"0.4594072\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7463989"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":20066,"cells":{"query":{"kind":"string","value":"Removes a catalystport binding"},"document":{"kind":"string","value":"def remove_catalystport_binding(vlan_id):\n LOG.debug(\"remove_catalystport_binding() called\")\n session = db.get_session()\n try:\n binding = (session.query(catalyst_models.CatalystPortBinding).\n filter_by(vlan_id=vlan_id).all())\n for bind in binding:\n session.delete(bind)\n session.flush()\n return binding\n except exc.NoResultFound:\n pass"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def remove_binding(ctx, binding_name):\n\n entryFound = False\n table = 'NAT_BINDINGS'\n key = binding_name\n\n if len(binding_name) > 32:\n ctx.fail(\"Invalid binding name. Maximum allowed binding name is 32 characters !!\")\n\n config_db = ConfigDBConnector()\n config_db.connect()\n\n data = config_db.get_entry(table, key)\n if not data:\n click.echo(\"Trying to delete binding, which is not present.\")\n entryFound = True\n\n if entryFound == False:\n config_db.set_entry(table, key, None)","def remove_bindings(ctx):\n\n config_db = ConfigDBConnector()\n config_db.connect()\n\n binding_table_name = 'NAT_BINDINGS'\n binding_dict = config_db.get_table(binding_table_name)\n if binding_dict:\n for binding_key_name in binding_dict:\n config_db.set_entry(binding_table_name, binding_key_name, None)","def removePort(self, *args):\n return _libsbml.CompModelPlugin_removePort(self, *args)","def remove_port(cls, port):\n if port in cls._open_ports:\n if cls._open_ports[port].is_open:\n cls._open_ports[port].close()\n del cls._open_ports[port]","def remove_port(self, port):\n if port not in self.ports:\n return False\n del self.ports[port]\n return True","def port_delete(switch, port):\n client.port.delete(switch, port)","def delete_port(self, port):\r\n return self.delete(self.port_path % (port))","def removeConnection(self, connection):\r\n if connection.system_id is None:\r\n self.log.debug(\"SMPP connection attempt failed without binding.\")\r\n else:\r\n system_id = connection.system_id\r\n bind_type = connection.bind_type\r\n self.bound_connections[system_id].removeBinding(connection)\r\n self.log.info(\"Dropped %s bind for '%s'. Active binds: %s. Max binds: %s\" % (bind_type, system_id, self.getBoundConnectionCountsStr(system_id), self.config.systems[system_id]['max_bindings']))\r\n # If this is the last binding for this service then remove the BindManager\r\n if self.bound_connections[system_id].getBindingCount() == 0:\r\n self.bound_connections.pop(system_id)","def del_port(bridge, port):\n _run('ovs-vsctl', 'del-port', bridge, port)","def remove_console_setting(db, linenum):\n config_db = db.cfgdb\n\n table = \"CONSOLE_PORT\"\n\n data = config_db.get_entry(table, linenum)\n if data:\n config_db.mod_entry(table, linenum, None)\n else:\n ctx = click.get_current_context()\n ctx.fail(\"Trying to delete console port setting, which is not present.\")","def unload_bindings(self):\n self.ignoreAll()","def delete(self, request, cluster_id, bind_id, service_id=None): # pylint: disable=arguments-differ\n bind = self.get_obj(cluster_id, service_id, bind_id)\n cm.api.unbind(bind)\n return Response(status=status.HTTP_204_NO_CONTENT)","def removeOutputBinding(self, factory, product):\n # remove the {product} monitor from my pile of observers\n self.removeObserver(observer=product.pyre_status)\n # and chain up\n return super().removeOutputBinding(factory=factory, product=product)","def teardown_logical_port_connectivity(self, context, port_db):\n pass","def delete_lag_ports(self, ports, lag):\n pass","def delete_port_acl(self, port, acl):\n raise NotImplementedError # pragma: no cover","def port_delete_end(self, payload):\n port = self.cache.get_port_by_id(payload['port_id'])\n if port:\n network = self.cache.get_network_by_id(port.network_id)\n self.cache.remove_port(port)\n self.call_driver('reload_allocations', network)","def remove_pools(ctx):\n\n config_db = ConfigDBConnector()\n config_db.connect()\n\n entryFound = False\n pool_table_name = 'NAT_POOL'\n binding_table_name = 'NAT_BINDINGS'\n binding_dict = config_db.get_table(binding_table_name)\n pool_dict = config_db.get_table(pool_table_name)\n if pool_dict:\n for pool_key_name in pool_dict:\n entryFound = False\n for binding_name, binding_values in binding_dict.items():\n if binding_values['nat_pool'] == pool_key_name:\n click.echo(\"Pool {} is not removed, as it is mapped to Binding {}, remove the pool binding first !!\".format(pool_key_name,binding_name))\n entryFound = True\n break\n\n if entryFound == False: \n config_db.set_entry(pool_table_name, pool_key_name, None)","def delete_port_postcommit(self, context):\n if self.rpc_handler is None:\n return\n port = self._get_port_info(context)\n if port is not None:\n try:\n self.rpc_handler.delete_port(port)\n except:\n pass","def test_dhcp_bind_uninstall(self):\n self._common_uninstall_delete(\n 'esg_id|bind_id', dhcp_bind.delete,\n {'bind': {}},\n delete_args=['dhcpStaticBindingID'],\n delete_kwargs={\n 'uri_parameters': {'edgeId': 'esg_id', 'bindingID': 'bind_id'}\n }\n )","def remove_app(self):\n \n pass","def unbind(cls, name: str):\n if cls.instance() is None:\n return\n\n if not name in cls.instance().m_axis_bindings and not name in cls.instance().m_button_bindings:\n print( 'Unable to unbind: {}. Name not bound to axis or button.'.format( name ) )\n return\n\n if name in cls.instance().m_axis_bindings:\n axis = cls.instance().m_axis_bindings[ name ].axis\n del cls.instance().m_axis_bindings[ name ]\n del cls.instance().m_axis_name_table[ axis ]\n if name in cls.instance().m_button_bindings:\n button = cls.instance().m_button_bindings[ name ].button\n del cls.instance().m_button_bindings[ name ]\n del cls.instance().m_button_name_table[ button ]","async def _async_delete_port_mapping(self, external_port: int) -> None:\n entry = external_port\n self.removed_port_mappings.append(entry)","def delete_port_mac(self, context, port):\n self._get_driver_for_provider(constants.l2gw\n ).delete_port_mac(context, port)","def without_exposed_port(\n self,\n port: int,\n protocol: Optional[NetworkProtocol] = None,\n ) -> \"Container\":\n _args = [\n Arg(\"port\", port),\n Arg(\"protocol\", protocol, None),\n ]\n _ctx = self._select(\"withoutExposedPort\", _args)\n return Container(_ctx)","def port_nic_remove(switch, port):\n client.port.detach_nic(switch, port)","def _do_backend_unbind(self, backend, port_id):\n\n driver = self.backend_manager.get_backend_driver(backend)\n driver.unbind(port_id)","def removeConstraint(self, constraint: Constraint, /) -> None:\n ...","def unsetPortRef(self):\n return _libsbml.SBaseRef_unsetPortRef(self)","def RemoveIamPolicyBinding(zone_ref, member, role):\n policy = GetIamPolicy(zone_ref)\n iam_util.RemoveBindingFromIamPolicy(policy, member, role)\n return SetIamPolicy(zone_ref, policy)","def remove_compiled_app():\r\n app = get_app()\r\n remove_compiled_application(apath(app, r=request))\r\n session.flash = T('compiled application removed')\r\n redirect(URL('site'))","def delModule(name):","def remove(self, *args):\n return _libsbml.ListOfOutwardBindingSites_remove(self, *args)","def unplug(self, bridge):\n ovsdb = self.bridge.ovsdb\n with ovsdb.transaction() as txn:\n txn.add(ovsdb.del_port(self.patch_port_int_name,\n bridge.br_name))","def remove_tcp(ctx, global_ip, global_port, local_ip, local_port):\n\n # Verify the ip address format \n if is_valid_ipv4_address(local_ip) is False:\n ctx.fail(\"Given local ip address {} is invalid. Please enter a valid local ip address !!\".format(local_ip))\n\n if is_valid_ipv4_address(global_ip) is False:\n ctx.fail(\"Given global ip address {} is invalid. Please enter a valid global ip address !!\".format(global_ip))\n\n config_db = ConfigDBConnector()\n config_db.connect()\n\n entryFound = False\n table = \"STATIC_NAPT\"\n key = \"{}|TCP|{}\".format(global_ip, global_port)\n\n data = config_db.get_entry(table, key)\n if data:\n if data['local_ip'] == local_ip and data['local_port'] == str(local_port):\n config_db.set_entry(table, key, None)\n entryFound = True\n\n if entryFound is False:\n click.echo(\"Trying to delete static napt entry, which is not present.\")","def delete_port(self, port):\n try:\n self.client.delete_nic(port.vm.backend_id, port.backend_id)\n except VMwareError as e:\n raise VMwareBackendError(e)","def remove_self_from_bindings(self):\n for binding in self.output_bindings.bindings.values():\n for child in binding.bindings:\n if binding in child.parents:\n child.parents.remove(binding)","def del_connection(self, switch_name, port1, port2, bidir=False):\n raise NotImplementedError()","def _delete_router_port(self, method, api, header, data):\n self._execute_api(method, api, header, data)","def RemoveIamPolicyBinding(task_ref, member, role):\n policy = GetIamPolicy(task_ref)\n iam_util.RemoveBindingFromIamPolicy(policy, member, role)\n return SetIamPolicy(task_ref, policy)","def remove(self, *args):\n return _libsbml.ListOfPorts_remove(self, *args)","def remove_pool(ctx, pool_name):\n \n entryFound = False\n table = \"NAT_POOL\"\n key = pool_name\n\n if len(pool_name) > 32:\n ctx.fail(\"Invalid pool name. Maximum allowed pool name is 32 characters !!\")\n\n config_db = ConfigDBConnector()\n config_db.connect()\n\n data = config_db.get_entry(table, key)\n if not data:\n click.echo(\"Trying to delete pool, which is not present.\")\n entryFound = True\n\n binding_dict = config_db.get_table('NAT_BINDINGS')\n if binding_dict and entryFound == False: \n for binding_name, binding_values in binding_dict.items():\n if binding_values['nat_pool'] == pool_name:\n click.echo(\"Pool is not removed, as it is mapped to Binding {}, remove the pool binding first !!\".format(binding_name))\n entryFound = True\n break\n\n if entryFound == False:\n config_db.set_entry(table, key, None)","def port_buffer_drop():","def unsetComponent(self):\n return _libsbml.OutwardBindingSite_unsetComponent(self)","def _delete_internal_port(self, port_id):\n with self.client_plugin('neutron').ignore_not_found:\n self.client('neutron').delete_port(port_id)\n\n self._data_update_ports(port_id, 'delete')","def remove_adapter(self, domain, adapter):\n pass","def removeOutwardBindingSite(self, *args):\n return _libsbml.MultiSpeciesPlugin_removeOutwardBindingSite(self, *args)","def remove(self, session: \"pwncat.manager.Session\"):","def _delete_vports(self):\n self._api._remove(self._ixn_vport, self._api.config.ports)","def remove():\n run('pew rm {0}'.format(package_name()))","def free_port():\n\n with socket.socket() as sock:\n sock.bind(('', 0))\n return sock.getsockname()[1]","def remove_proxy_config(self):\n self.external_port = 8008","def unbind(self, uid):\r\n if self.bind_uid == uid:\r\n self.bind_uid = ''\r\n if len(self.queue_applicants) > 0:\r\n self.queue_applicants.reverse()\r\n self.bind_uid = self.queue_applicants.pop()\r\n self.queue_applicants.reverse()\r\n else:\r\n if uid in self.queue_applicants:\r\n self.queue_applicants.remove(uid)\r\n return self.bind_uid","def remove_service(self, zeroconf, service_type, name):","def map_clear_to(self, src_port, dst_port, command_logger=None):\r\n\r\n #Isolate source port number from list provided by Cloudshell\r\n source = src_port[2]\r\n #Define URI to delete rules via REST\r\n uri = 'http://' + self.address + '/rest/rules?'\r\n #Create the parameters for the rule to be deleted from the Packetmaster\r\n params = {'priority': 32768,\r\n 'match[in_port]': source}\r\n #Make REST delete request for the rule to be deleted\r\n try:\r\n response = requests.delete(uri, params=params, auth=(self.username, self.password))\r\n except ConnectionError as e:\r\n raise e","def delete_dev_endpoint(self):\n self.glue_engine.delete_dev_endpoint(EndpointName=self.dev_endpoint_name)","def add_catalystport_binding(port_id, vlan_id):\n LOG.debug(\"add_catalystport_binding() called\")\n session = db.get_session()\n binding = catalyst_models.CatalystPortBinding(port_id, vlan_id)\n session.add(binding)\n session.flush()\n return binding","def unsetBindingStatus(self):\n return _libsbml.OutwardBindingSite_unsetBindingStatus(self)","def cleanup(self):\n byteblower_instance = ByteBlower.InstanceGet()\n if self.port_1:\n self.server.PortDestroy(self.port_1)\n self.port_1 = None\n\n if self.port_2:\n self.server.PortDestroy(self.port_2)\n self.port_2 = None\n\n if self.server is not None:\n byteblower_instance.ServerRemove(self.server)\n self.server = None","def _reset_bind(self):\n self.binded = False\n self._exec_group = None\n self._data_shapes = None\n self._label_shapes = None","def remove_tcp_forward(self, host_port):\n if self._ssh_connection:\n remote_port = self._ssh_connection.close_ssh_tunnel(host_port)\n if remote_port is None:\n logging.warning(\"Cannot close unknown forwarded tcp port: %d\",\n host_port)\n return\n # The actual port we need to disable via adb is on the remote host.\n host_port = remote_port\n self.forward(\"--remove tcp:%d\" % host_port)","def fusion_api_remove_switch(self, name=None, uri=None, api=None, headers=None):\n return self.switch.delete(name, uri, api, headers)","def delete(clients, context):\n port_id = context['port_id']\n logger.info(\"Taking action port.delete {}\".format(port_id))\n neutron = clients.get_neutron()\n neutron.delete_port(port_id)","def remove_descriptor(self, uuid):","def unsetName(self):\n return _libsbml.OutwardBindingSite_unsetName(self)","def removeEditVariable(self, variable: Variable, /) -> None:\n ...","def unplug(self, bridge):\n ovsdb = self.bridge.ovsdb\n with ovsdb.transaction() as txn:\n if self.bridge.exists():\n txn.add(ovsdb.del_port(self.patch_port_trunk_name,\n self.bridge.br_name))\n txn.add(ovsdb.del_port(self.patch_port_int_name,\n bridge.br_name))","def find_unused_port(family=socket.AF_INET, socktype=socket.SOCK_STREAM):\n tempsock = socket.socket(family, socktype)\n port = bind_port(tempsock)\n tempsock.close()\n del tempsock\n return port","def clear_bound_event(self, name: str):\n assert self.is_websocket_state, Exception(\"Cannot bind events in non websocket state. See is_websocket_state\")\n\n if isinstance(name, Enum):\n name = str(name)\n\n assert isinstance(name, str), ValueError(\"The event name must be a string or enum.\")\n\n if name in self._bind_events:\n del self._bind_events[name]","def remove(self, name):\n cont = getattr(self, name)\n self.disconnect(name)\n self._exprmapper.remove(name)\n if has_interface(cont, IComponent):\n self._depgraph.remove(name)\n for obj in self.__dict__.values():\n if obj is not cont and is_instance(obj, Driver):\n obj.workflow.remove(name)\n obj.remove_references(name)\n\n return super(Assembly, self).remove(name)","def pibooth_cleanup(app):","def test_delete_namespaced_role_binding(self):\n pass","def get_catalystport_binding(vland_id):\n LOG.debug(\"get_catlystport_binding() called\")\n session = db.get_session()\n try:\n binding = (session.query(catalyst_models.CatalystPortBinding). \\\n filter_by(vland_id).all())\n return binding\n except exc.NoresultFound:\n raise c_exc.CatalystPortBindingNotFound(vlan_id=vlan_id)","def unbind_class(self, className, sequence):\n return super().unbind_class(className, sequence)","def port_unbind(endpoint_id, neutron_port, **kwargs):\n\n vif_type = neutron_port.get(constants.VIF_TYPE_KEY,\n constants.FALLBACK_VIF_TYPE)\n vif_details = lib_utils.string_mappings(neutron_port.get(\n constants.VIF_DETAILS_KEY))\n unbinding_exec_path = os.path.join(cfg.CONF.bindir, vif_type)\n\n port_id = neutron_port['id']\n ifname, _ = utils.get_veth_pair_names(port_id)\n\n mac_address = neutron_port['mac_address']\n network_id = neutron_port['network_id']\n stdout, stderr = processutils.execute(\n unbinding_exec_path, constants.UNBINDING_SUBCOMMAND, port_id, ifname,\n endpoint_id, mac_address, vif_details, network_id)\n try:\n utils.remove_device(ifname)\n except pyroute2.NetlinkError:\n LOG.exception(\"Error happened during deleting the veth pair\")\n raise exceptions.VethDeletionFailure(\n 'Deleting the veth pair failed.')\n return (stdout, stderr)","def unbind(self, svc, svc_ref):\n with self._lock:\n if ORDER_HANDLER in svc_ref.get_property(pelix.OBJECTCLASS):\n targets = svc_ref.get_property(ORDER_TARGETS)\n if isinstance(targets, (list, tuple)):\n for target in targets:\n associates = self._target_handlers.get(target, None)\n if svc in associates:\n del associates[svc]\n\n else:\n associates = self._target_handlers.get(str(targets), None)\n if svc in associates:\n del associates[svc]","def update_port_precommit(self, port_context):\n # TODO(ijw): optimisation: the update port may leave the\n # binding state the same as before if someone updated\n # something other than the binding on the port, but this\n # way we always send it out and it's the far end's job to\n # ignore it. Doing less work is nevertheless good, so we\n # should in future avoid the send.\n\n # unbind port from old host, if already bound\n if port_context.original_binding_levels is not None:\n prev_bind = port_context.original_binding_levels[-1]\n\n if (prev_bind is not None and\n prev_bind.get(api.BOUND_DRIVER) == self.MECH_NAME and\n port_context.host != port_context.original_host):\n\n # Note that we skip this step if the change happens while\n # 'unbinding' and rebinding to the same host - it's probably\n # an update of extraneous detail and not really a request\n # that requires binding.\n\n self.communicator.unbind(port_context._plugin_context.session,\n port_context.original,\n port_context.original_host,\n prev_bind[api.BOUND_SEGMENT]\n )\n\n # (Re)bind port to the new host, if it needs to be bound\n if port_context.binding_levels is not None:\n current_bind = port_context.binding_levels[-1]\n\n if (current_bind is not None and\n current_bind.get(api.BOUND_DRIVER) == self.MECH_NAME):\n\n binding_type = self.get_vif_type(port_context)\n # Remove port membership from any previously associated\n # security groups for updating remote_security_group_id ACLs\n self.communicator.unbind_port_from_remote_groups(\n port_context._plugin_context.session,\n port_context.original,\n port_context.current)\n\n self.communicator.bind(port_context._plugin_context.session,\n port_context.current,\n current_bind[api.BOUND_SEGMENT],\n port_context.host,\n binding_type)\n\n # TODO(ijW): The agent driver checks for a change of\n # host, but we're oddly seeing that the orig_host is\n # always set. Should confirm if this is a problem or\n # not.\n self._insert_provisioning_block(port_context)","def clear_db_env():\n global _FACADE\n _FACADE = None","def remove(self, component) -> None:\n pass","def remove_redirect(self, expr, node_host, node_port, openflow_host, openflow_port):\n pusher = self.StaticFlowEntryPusher(openflow_host, openflow_port)\n pusher.remove({\"name\":\"request_hands_off-\" + node_host + \"-\" + node_port + \"-\" + expr})\n pusher.remove({\"name\":\"request_out-\" + node_host + \"-\" + node_port + \"-\" + expr})\n pusher.remove({\"name\":\"request_in-\" + node_host + \"-\" + node_port + \"-\" + expr})","def unbind(self, sequence, funcid=None):\n return self._widget_unbind(sequence, funcid, internal=False)","def unbind_all(cls):\n if cls.instance() is None:\n return\n\n cls.instance().m_axis_bindings.clear()\n cls.instance().m_axis_name_table.clear()\n\n cls.instance().m_button_bindings.clear()\n cls.instance().m_button_name_table.clear()","def remove_study_version_dbgap_link(apps, schema_editor):\n SourceStudyVersion = apps.get_model('trait_browser', 'SourceStudyVersion')\n for ssv in SourceStudyVersion.objects.all():\n ssv.dbgap_link = ''\n ssv.save()","def stop_app(self, name, stateless):\n raise NotImplementedError","def _remove_definition(self, definition):\n global_callback._call_library_remove_definition(self, definition)\n definition._library = None","def cancel_port_forward_request(self, address, port):\n pass","def unset(self, name, level=None):\n level = level or self.local_variables\n assert isinstance(level, EnvironmentLevel)\n\n # TODO need to remove from the expressions list as well?\n return level.bindings.pop(name, None)","def detach(openstack_resource, port_id):\n device_id = openstack_resource.resource_id\n # Check if the port is provided or not\n if not port_id:\n raise NonRecoverableError(\n 'Unable to attach port to device {0},'\n ' `port_id` is missing'.format(\n device_id)\n )\n # Unlink port connection from server\n # No need to detach floating ip from the port because when delete port\n # with floating ip assigned to port it can removed without any issue\n _update_port_association(openstack_resource.client_config,\n port_id)","def port_revert(switch, port):\n print client.port.port_revert(switch, port)","def remove(self, middleware):\n pass # pragma: no cover","def unregister_router(self, hostname):","def remove_breakpoint(self, bp: Breakpoint) -> None:\n for hwbp in self.hw_breakpoints:\n if hwbp.enabled and hwbp.addr == bp.addr:\n hwbp.enabled = False\n self.ap.write_memory(hwbp.comp_register_addr, 0)\n self.num_hw_breakpoint_used -= 1\n return","def unbind(self, binding_id):\n\n if binding_id not in self._bindings:\n return False\n\n event_props = self._bindings[binding_id][1]\n del self._bindings[binding_id]\n\n if event_props in self._active_bindings:\n self.ignore(event_props)\n\n return True","def remove_httpd_service_ipa_conf(self):\n raise NotImplementedError()","def detach(cls, factory, attrib_name):\n cls._to_attach.remove((factory, attrib_name))","def _unbind_observers(self):\n workbench = self.workbench\n point = workbench.get_extension_point(PREFS_POINT)\n point.unobserve('extensions', self._on_pref_decls_updated)","def delete_ports(module, system):\n changed = False\n\n host = system.hosts.get(name=module.params['host'])\n for wwn_port in module.params['wwns']:\n wwn = WWN(wwn_port)\n if system.hosts.get_host_by_initiator_address(wwn) == host:\n if not module.check_mode:\n host.remove_port(wwn)\n changed = True\n for iscsi_port in module.params['iqns']:\n iscsi_name = make_iscsi_name(iscsi_port)\n if system.hosts.get_host_by_initiator_address(iscsi_name) == host:\n if not module.check_mode:\n host.remove_port(iscsi_name)\n changed = True\n return changed","def _disassociate_port_id(self, context, port_id):\n\n criterion = {\n 'managed': 1,\n 'managed_resource_type': 'a:floatingip',\n 'managed_extra': 'portid:%s' % (port_id),\n 'managed_plugin_name': self.get_plugin_name(),\n 'managed_plugin_type': self.get_plugin_type(),\n }\n records = self.central_api.find_records(context, criterion=criterion)\n LOG.debug('Found %d records to delete that matched port id %s' %\n (len(records), port_id))\n for record in records:\n LOG.debug('Deleting record %s' % (record['id']))\n self.central_api.delete_record(context,\n record['domain_id'],\n record['recordset_id'],\n record['id'])\n\n LOG.info('Deleted %d records that matched port_id %s' %\n (len(records), port_id))\n\n return len(records)","def remove(self, s):\n if s in self.outputs:\n self.outputs.remove(s)\n self.inputs.remove(s)\n del self.conns[s]\n s.close()","def delete_servicech(self, conf, phone_num):\n\t\tpass"],"string":"[\n \"def remove_binding(ctx, binding_name):\\n\\n entryFound = False\\n table = 'NAT_BINDINGS'\\n key = binding_name\\n\\n if len(binding_name) > 32:\\n ctx.fail(\\\"Invalid binding name. Maximum allowed binding name is 32 characters !!\\\")\\n\\n config_db = ConfigDBConnector()\\n config_db.connect()\\n\\n data = config_db.get_entry(table, key)\\n if not data:\\n click.echo(\\\"Trying to delete binding, which is not present.\\\")\\n entryFound = True\\n\\n if entryFound == False:\\n config_db.set_entry(table, key, None)\",\n \"def remove_bindings(ctx):\\n\\n config_db = ConfigDBConnector()\\n config_db.connect()\\n\\n binding_table_name = 'NAT_BINDINGS'\\n binding_dict = config_db.get_table(binding_table_name)\\n if binding_dict:\\n for binding_key_name in binding_dict:\\n config_db.set_entry(binding_table_name, binding_key_name, None)\",\n \"def removePort(self, *args):\\n return _libsbml.CompModelPlugin_removePort(self, *args)\",\n \"def remove_port(cls, port):\\n if port in cls._open_ports:\\n if cls._open_ports[port].is_open:\\n cls._open_ports[port].close()\\n del cls._open_ports[port]\",\n \"def remove_port(self, port):\\n if port not in self.ports:\\n return False\\n del self.ports[port]\\n return True\",\n \"def port_delete(switch, port):\\n client.port.delete(switch, port)\",\n \"def delete_port(self, port):\\r\\n return self.delete(self.port_path % (port))\",\n \"def removeConnection(self, connection):\\r\\n if connection.system_id is None:\\r\\n self.log.debug(\\\"SMPP connection attempt failed without binding.\\\")\\r\\n else:\\r\\n system_id = connection.system_id\\r\\n bind_type = connection.bind_type\\r\\n self.bound_connections[system_id].removeBinding(connection)\\r\\n self.log.info(\\\"Dropped %s bind for '%s'. Active binds: %s. Max binds: %s\\\" % (bind_type, system_id, self.getBoundConnectionCountsStr(system_id), self.config.systems[system_id]['max_bindings']))\\r\\n # If this is the last binding for this service then remove the BindManager\\r\\n if self.bound_connections[system_id].getBindingCount() == 0:\\r\\n self.bound_connections.pop(system_id)\",\n \"def del_port(bridge, port):\\n _run('ovs-vsctl', 'del-port', bridge, port)\",\n \"def remove_console_setting(db, linenum):\\n config_db = db.cfgdb\\n\\n table = \\\"CONSOLE_PORT\\\"\\n\\n data = config_db.get_entry(table, linenum)\\n if data:\\n config_db.mod_entry(table, linenum, None)\\n else:\\n ctx = click.get_current_context()\\n ctx.fail(\\\"Trying to delete console port setting, which is not present.\\\")\",\n \"def unload_bindings(self):\\n self.ignoreAll()\",\n \"def delete(self, request, cluster_id, bind_id, service_id=None): # pylint: disable=arguments-differ\\n bind = self.get_obj(cluster_id, service_id, bind_id)\\n cm.api.unbind(bind)\\n return Response(status=status.HTTP_204_NO_CONTENT)\",\n \"def removeOutputBinding(self, factory, product):\\n # remove the {product} monitor from my pile of observers\\n self.removeObserver(observer=product.pyre_status)\\n # and chain up\\n return super().removeOutputBinding(factory=factory, product=product)\",\n \"def teardown_logical_port_connectivity(self, context, port_db):\\n pass\",\n \"def delete_lag_ports(self, ports, lag):\\n pass\",\n \"def delete_port_acl(self, port, acl):\\n raise NotImplementedError # pragma: no cover\",\n \"def port_delete_end(self, payload):\\n port = self.cache.get_port_by_id(payload['port_id'])\\n if port:\\n network = self.cache.get_network_by_id(port.network_id)\\n self.cache.remove_port(port)\\n self.call_driver('reload_allocations', network)\",\n \"def remove_pools(ctx):\\n\\n config_db = ConfigDBConnector()\\n config_db.connect()\\n\\n entryFound = False\\n pool_table_name = 'NAT_POOL'\\n binding_table_name = 'NAT_BINDINGS'\\n binding_dict = config_db.get_table(binding_table_name)\\n pool_dict = config_db.get_table(pool_table_name)\\n if pool_dict:\\n for pool_key_name in pool_dict:\\n entryFound = False\\n for binding_name, binding_values in binding_dict.items():\\n if binding_values['nat_pool'] == pool_key_name:\\n click.echo(\\\"Pool {} is not removed, as it is mapped to Binding {}, remove the pool binding first !!\\\".format(pool_key_name,binding_name))\\n entryFound = True\\n break\\n\\n if entryFound == False: \\n config_db.set_entry(pool_table_name, pool_key_name, None)\",\n \"def delete_port_postcommit(self, context):\\n if self.rpc_handler is None:\\n return\\n port = self._get_port_info(context)\\n if port is not None:\\n try:\\n self.rpc_handler.delete_port(port)\\n except:\\n pass\",\n \"def test_dhcp_bind_uninstall(self):\\n self._common_uninstall_delete(\\n 'esg_id|bind_id', dhcp_bind.delete,\\n {'bind': {}},\\n delete_args=['dhcpStaticBindingID'],\\n delete_kwargs={\\n 'uri_parameters': {'edgeId': 'esg_id', 'bindingID': 'bind_id'}\\n }\\n )\",\n \"def remove_app(self):\\n \\n pass\",\n \"def unbind(cls, name: str):\\n if cls.instance() is None:\\n return\\n\\n if not name in cls.instance().m_axis_bindings and not name in cls.instance().m_button_bindings:\\n print( 'Unable to unbind: {}. Name not bound to axis or button.'.format( name ) )\\n return\\n\\n if name in cls.instance().m_axis_bindings:\\n axis = cls.instance().m_axis_bindings[ name ].axis\\n del cls.instance().m_axis_bindings[ name ]\\n del cls.instance().m_axis_name_table[ axis ]\\n if name in cls.instance().m_button_bindings:\\n button = cls.instance().m_button_bindings[ name ].button\\n del cls.instance().m_button_bindings[ name ]\\n del cls.instance().m_button_name_table[ button ]\",\n \"async def _async_delete_port_mapping(self, external_port: int) -> None:\\n entry = external_port\\n self.removed_port_mappings.append(entry)\",\n \"def delete_port_mac(self, context, port):\\n self._get_driver_for_provider(constants.l2gw\\n ).delete_port_mac(context, port)\",\n \"def without_exposed_port(\\n self,\\n port: int,\\n protocol: Optional[NetworkProtocol] = None,\\n ) -> \\\"Container\\\":\\n _args = [\\n Arg(\\\"port\\\", port),\\n Arg(\\\"protocol\\\", protocol, None),\\n ]\\n _ctx = self._select(\\\"withoutExposedPort\\\", _args)\\n return Container(_ctx)\",\n \"def port_nic_remove(switch, port):\\n client.port.detach_nic(switch, port)\",\n \"def _do_backend_unbind(self, backend, port_id):\\n\\n driver = self.backend_manager.get_backend_driver(backend)\\n driver.unbind(port_id)\",\n \"def removeConstraint(self, constraint: Constraint, /) -> None:\\n ...\",\n \"def unsetPortRef(self):\\n return _libsbml.SBaseRef_unsetPortRef(self)\",\n \"def RemoveIamPolicyBinding(zone_ref, member, role):\\n policy = GetIamPolicy(zone_ref)\\n iam_util.RemoveBindingFromIamPolicy(policy, member, role)\\n return SetIamPolicy(zone_ref, policy)\",\n \"def remove_compiled_app():\\r\\n app = get_app()\\r\\n remove_compiled_application(apath(app, r=request))\\r\\n session.flash = T('compiled application removed')\\r\\n redirect(URL('site'))\",\n \"def delModule(name):\",\n \"def remove(self, *args):\\n return _libsbml.ListOfOutwardBindingSites_remove(self, *args)\",\n \"def unplug(self, bridge):\\n ovsdb = self.bridge.ovsdb\\n with ovsdb.transaction() as txn:\\n txn.add(ovsdb.del_port(self.patch_port_int_name,\\n bridge.br_name))\",\n \"def remove_tcp(ctx, global_ip, global_port, local_ip, local_port):\\n\\n # Verify the ip address format \\n if is_valid_ipv4_address(local_ip) is False:\\n ctx.fail(\\\"Given local ip address {} is invalid. Please enter a valid local ip address !!\\\".format(local_ip))\\n\\n if is_valid_ipv4_address(global_ip) is False:\\n ctx.fail(\\\"Given global ip address {} is invalid. Please enter a valid global ip address !!\\\".format(global_ip))\\n\\n config_db = ConfigDBConnector()\\n config_db.connect()\\n\\n entryFound = False\\n table = \\\"STATIC_NAPT\\\"\\n key = \\\"{}|TCP|{}\\\".format(global_ip, global_port)\\n\\n data = config_db.get_entry(table, key)\\n if data:\\n if data['local_ip'] == local_ip and data['local_port'] == str(local_port):\\n config_db.set_entry(table, key, None)\\n entryFound = True\\n\\n if entryFound is False:\\n click.echo(\\\"Trying to delete static napt entry, which is not present.\\\")\",\n \"def delete_port(self, port):\\n try:\\n self.client.delete_nic(port.vm.backend_id, port.backend_id)\\n except VMwareError as e:\\n raise VMwareBackendError(e)\",\n \"def remove_self_from_bindings(self):\\n for binding in self.output_bindings.bindings.values():\\n for child in binding.bindings:\\n if binding in child.parents:\\n child.parents.remove(binding)\",\n \"def del_connection(self, switch_name, port1, port2, bidir=False):\\n raise NotImplementedError()\",\n \"def _delete_router_port(self, method, api, header, data):\\n self._execute_api(method, api, header, data)\",\n \"def RemoveIamPolicyBinding(task_ref, member, role):\\n policy = GetIamPolicy(task_ref)\\n iam_util.RemoveBindingFromIamPolicy(policy, member, role)\\n return SetIamPolicy(task_ref, policy)\",\n \"def remove(self, *args):\\n return _libsbml.ListOfPorts_remove(self, *args)\",\n \"def remove_pool(ctx, pool_name):\\n \\n entryFound = False\\n table = \\\"NAT_POOL\\\"\\n key = pool_name\\n\\n if len(pool_name) > 32:\\n ctx.fail(\\\"Invalid pool name. Maximum allowed pool name is 32 characters !!\\\")\\n\\n config_db = ConfigDBConnector()\\n config_db.connect()\\n\\n data = config_db.get_entry(table, key)\\n if not data:\\n click.echo(\\\"Trying to delete pool, which is not present.\\\")\\n entryFound = True\\n\\n binding_dict = config_db.get_table('NAT_BINDINGS')\\n if binding_dict and entryFound == False: \\n for binding_name, binding_values in binding_dict.items():\\n if binding_values['nat_pool'] == pool_name:\\n click.echo(\\\"Pool is not removed, as it is mapped to Binding {}, remove the pool binding first !!\\\".format(binding_name))\\n entryFound = True\\n break\\n\\n if entryFound == False:\\n config_db.set_entry(table, key, None)\",\n \"def port_buffer_drop():\",\n \"def unsetComponent(self):\\n return _libsbml.OutwardBindingSite_unsetComponent(self)\",\n \"def _delete_internal_port(self, port_id):\\n with self.client_plugin('neutron').ignore_not_found:\\n self.client('neutron').delete_port(port_id)\\n\\n self._data_update_ports(port_id, 'delete')\",\n \"def remove_adapter(self, domain, adapter):\\n pass\",\n \"def removeOutwardBindingSite(self, *args):\\n return _libsbml.MultiSpeciesPlugin_removeOutwardBindingSite(self, *args)\",\n \"def remove(self, session: \\\"pwncat.manager.Session\\\"):\",\n \"def _delete_vports(self):\\n self._api._remove(self._ixn_vport, self._api.config.ports)\",\n \"def remove():\\n run('pew rm {0}'.format(package_name()))\",\n \"def free_port():\\n\\n with socket.socket() as sock:\\n sock.bind(('', 0))\\n return sock.getsockname()[1]\",\n \"def remove_proxy_config(self):\\n self.external_port = 8008\",\n \"def unbind(self, uid):\\r\\n if self.bind_uid == uid:\\r\\n self.bind_uid = ''\\r\\n if len(self.queue_applicants) > 0:\\r\\n self.queue_applicants.reverse()\\r\\n self.bind_uid = self.queue_applicants.pop()\\r\\n self.queue_applicants.reverse()\\r\\n else:\\r\\n if uid in self.queue_applicants:\\r\\n self.queue_applicants.remove(uid)\\r\\n return self.bind_uid\",\n \"def remove_service(self, zeroconf, service_type, name):\",\n \"def map_clear_to(self, src_port, dst_port, command_logger=None):\\r\\n\\r\\n #Isolate source port number from list provided by Cloudshell\\r\\n source = src_port[2]\\r\\n #Define URI to delete rules via REST\\r\\n uri = 'http://' + self.address + '/rest/rules?'\\r\\n #Create the parameters for the rule to be deleted from the Packetmaster\\r\\n params = {'priority': 32768,\\r\\n 'match[in_port]': source}\\r\\n #Make REST delete request for the rule to be deleted\\r\\n try:\\r\\n response = requests.delete(uri, params=params, auth=(self.username, self.password))\\r\\n except ConnectionError as e:\\r\\n raise e\",\n \"def delete_dev_endpoint(self):\\n self.glue_engine.delete_dev_endpoint(EndpointName=self.dev_endpoint_name)\",\n \"def add_catalystport_binding(port_id, vlan_id):\\n LOG.debug(\\\"add_catalystport_binding() called\\\")\\n session = db.get_session()\\n binding = catalyst_models.CatalystPortBinding(port_id, vlan_id)\\n session.add(binding)\\n session.flush()\\n return binding\",\n \"def unsetBindingStatus(self):\\n return _libsbml.OutwardBindingSite_unsetBindingStatus(self)\",\n \"def cleanup(self):\\n byteblower_instance = ByteBlower.InstanceGet()\\n if self.port_1:\\n self.server.PortDestroy(self.port_1)\\n self.port_1 = None\\n\\n if self.port_2:\\n self.server.PortDestroy(self.port_2)\\n self.port_2 = None\\n\\n if self.server is not None:\\n byteblower_instance.ServerRemove(self.server)\\n self.server = None\",\n \"def _reset_bind(self):\\n self.binded = False\\n self._exec_group = None\\n self._data_shapes = None\\n self._label_shapes = None\",\n \"def remove_tcp_forward(self, host_port):\\n if self._ssh_connection:\\n remote_port = self._ssh_connection.close_ssh_tunnel(host_port)\\n if remote_port is None:\\n logging.warning(\\\"Cannot close unknown forwarded tcp port: %d\\\",\\n host_port)\\n return\\n # The actual port we need to disable via adb is on the remote host.\\n host_port = remote_port\\n self.forward(\\\"--remove tcp:%d\\\" % host_port)\",\n \"def fusion_api_remove_switch(self, name=None, uri=None, api=None, headers=None):\\n return self.switch.delete(name, uri, api, headers)\",\n \"def delete(clients, context):\\n port_id = context['port_id']\\n logger.info(\\\"Taking action port.delete {}\\\".format(port_id))\\n neutron = clients.get_neutron()\\n neutron.delete_port(port_id)\",\n \"def remove_descriptor(self, uuid):\",\n \"def unsetName(self):\\n return _libsbml.OutwardBindingSite_unsetName(self)\",\n \"def removeEditVariable(self, variable: Variable, /) -> None:\\n ...\",\n \"def unplug(self, bridge):\\n ovsdb = self.bridge.ovsdb\\n with ovsdb.transaction() as txn:\\n if self.bridge.exists():\\n txn.add(ovsdb.del_port(self.patch_port_trunk_name,\\n self.bridge.br_name))\\n txn.add(ovsdb.del_port(self.patch_port_int_name,\\n bridge.br_name))\",\n \"def find_unused_port(family=socket.AF_INET, socktype=socket.SOCK_STREAM):\\n tempsock = socket.socket(family, socktype)\\n port = bind_port(tempsock)\\n tempsock.close()\\n del tempsock\\n return port\",\n \"def clear_bound_event(self, name: str):\\n assert self.is_websocket_state, Exception(\\\"Cannot bind events in non websocket state. See is_websocket_state\\\")\\n\\n if isinstance(name, Enum):\\n name = str(name)\\n\\n assert isinstance(name, str), ValueError(\\\"The event name must be a string or enum.\\\")\\n\\n if name in self._bind_events:\\n del self._bind_events[name]\",\n \"def remove(self, name):\\n cont = getattr(self, name)\\n self.disconnect(name)\\n self._exprmapper.remove(name)\\n if has_interface(cont, IComponent):\\n self._depgraph.remove(name)\\n for obj in self.__dict__.values():\\n if obj is not cont and is_instance(obj, Driver):\\n obj.workflow.remove(name)\\n obj.remove_references(name)\\n\\n return super(Assembly, self).remove(name)\",\n \"def pibooth_cleanup(app):\",\n \"def test_delete_namespaced_role_binding(self):\\n pass\",\n \"def get_catalystport_binding(vland_id):\\n LOG.debug(\\\"get_catlystport_binding() called\\\")\\n session = db.get_session()\\n try:\\n binding = (session.query(catalyst_models.CatalystPortBinding). \\\\\\n filter_by(vland_id).all())\\n return binding\\n except exc.NoresultFound:\\n raise c_exc.CatalystPortBindingNotFound(vlan_id=vlan_id)\",\n \"def unbind_class(self, className, sequence):\\n return super().unbind_class(className, sequence)\",\n \"def port_unbind(endpoint_id, neutron_port, **kwargs):\\n\\n vif_type = neutron_port.get(constants.VIF_TYPE_KEY,\\n constants.FALLBACK_VIF_TYPE)\\n vif_details = lib_utils.string_mappings(neutron_port.get(\\n constants.VIF_DETAILS_KEY))\\n unbinding_exec_path = os.path.join(cfg.CONF.bindir, vif_type)\\n\\n port_id = neutron_port['id']\\n ifname, _ = utils.get_veth_pair_names(port_id)\\n\\n mac_address = neutron_port['mac_address']\\n network_id = neutron_port['network_id']\\n stdout, stderr = processutils.execute(\\n unbinding_exec_path, constants.UNBINDING_SUBCOMMAND, port_id, ifname,\\n endpoint_id, mac_address, vif_details, network_id)\\n try:\\n utils.remove_device(ifname)\\n except pyroute2.NetlinkError:\\n LOG.exception(\\\"Error happened during deleting the veth pair\\\")\\n raise exceptions.VethDeletionFailure(\\n 'Deleting the veth pair failed.')\\n return (stdout, stderr)\",\n \"def unbind(self, svc, svc_ref):\\n with self._lock:\\n if ORDER_HANDLER in svc_ref.get_property(pelix.OBJECTCLASS):\\n targets = svc_ref.get_property(ORDER_TARGETS)\\n if isinstance(targets, (list, tuple)):\\n for target in targets:\\n associates = self._target_handlers.get(target, None)\\n if svc in associates:\\n del associates[svc]\\n\\n else:\\n associates = self._target_handlers.get(str(targets), None)\\n if svc in associates:\\n del associates[svc]\",\n \"def update_port_precommit(self, port_context):\\n # TODO(ijw): optimisation: the update port may leave the\\n # binding state the same as before if someone updated\\n # something other than the binding on the port, but this\\n # way we always send it out and it's the far end's job to\\n # ignore it. Doing less work is nevertheless good, so we\\n # should in future avoid the send.\\n\\n # unbind port from old host, if already bound\\n if port_context.original_binding_levels is not None:\\n prev_bind = port_context.original_binding_levels[-1]\\n\\n if (prev_bind is not None and\\n prev_bind.get(api.BOUND_DRIVER) == self.MECH_NAME and\\n port_context.host != port_context.original_host):\\n\\n # Note that we skip this step if the change happens while\\n # 'unbinding' and rebinding to the same host - it's probably\\n # an update of extraneous detail and not really a request\\n # that requires binding.\\n\\n self.communicator.unbind(port_context._plugin_context.session,\\n port_context.original,\\n port_context.original_host,\\n prev_bind[api.BOUND_SEGMENT]\\n )\\n\\n # (Re)bind port to the new host, if it needs to be bound\\n if port_context.binding_levels is not None:\\n current_bind = port_context.binding_levels[-1]\\n\\n if (current_bind is not None and\\n current_bind.get(api.BOUND_DRIVER) == self.MECH_NAME):\\n\\n binding_type = self.get_vif_type(port_context)\\n # Remove port membership from any previously associated\\n # security groups for updating remote_security_group_id ACLs\\n self.communicator.unbind_port_from_remote_groups(\\n port_context._plugin_context.session,\\n port_context.original,\\n port_context.current)\\n\\n self.communicator.bind(port_context._plugin_context.session,\\n port_context.current,\\n current_bind[api.BOUND_SEGMENT],\\n port_context.host,\\n binding_type)\\n\\n # TODO(ijW): The agent driver checks for a change of\\n # host, but we're oddly seeing that the orig_host is\\n # always set. Should confirm if this is a problem or\\n # not.\\n self._insert_provisioning_block(port_context)\",\n \"def clear_db_env():\\n global _FACADE\\n _FACADE = None\",\n \"def remove(self, component) -> None:\\n pass\",\n \"def remove_redirect(self, expr, node_host, node_port, openflow_host, openflow_port):\\n pusher = self.StaticFlowEntryPusher(openflow_host, openflow_port)\\n pusher.remove({\\\"name\\\":\\\"request_hands_off-\\\" + node_host + \\\"-\\\" + node_port + \\\"-\\\" + expr})\\n pusher.remove({\\\"name\\\":\\\"request_out-\\\" + node_host + \\\"-\\\" + node_port + \\\"-\\\" + expr})\\n pusher.remove({\\\"name\\\":\\\"request_in-\\\" + node_host + \\\"-\\\" + node_port + \\\"-\\\" + expr})\",\n \"def unbind(self, sequence, funcid=None):\\n return self._widget_unbind(sequence, funcid, internal=False)\",\n \"def unbind_all(cls):\\n if cls.instance() is None:\\n return\\n\\n cls.instance().m_axis_bindings.clear()\\n cls.instance().m_axis_name_table.clear()\\n\\n cls.instance().m_button_bindings.clear()\\n cls.instance().m_button_name_table.clear()\",\n \"def remove_study_version_dbgap_link(apps, schema_editor):\\n SourceStudyVersion = apps.get_model('trait_browser', 'SourceStudyVersion')\\n for ssv in SourceStudyVersion.objects.all():\\n ssv.dbgap_link = ''\\n ssv.save()\",\n \"def stop_app(self, name, stateless):\\n raise NotImplementedError\",\n \"def _remove_definition(self, definition):\\n global_callback._call_library_remove_definition(self, definition)\\n definition._library = None\",\n \"def cancel_port_forward_request(self, address, port):\\n pass\",\n \"def unset(self, name, level=None):\\n level = level or self.local_variables\\n assert isinstance(level, EnvironmentLevel)\\n\\n # TODO need to remove from the expressions list as well?\\n return level.bindings.pop(name, None)\",\n \"def detach(openstack_resource, port_id):\\n device_id = openstack_resource.resource_id\\n # Check if the port is provided or not\\n if not port_id:\\n raise NonRecoverableError(\\n 'Unable to attach port to device {0},'\\n ' `port_id` is missing'.format(\\n device_id)\\n )\\n # Unlink port connection from server\\n # No need to detach floating ip from the port because when delete port\\n # with floating ip assigned to port it can removed without any issue\\n _update_port_association(openstack_resource.client_config,\\n port_id)\",\n \"def port_revert(switch, port):\\n print client.port.port_revert(switch, port)\",\n \"def remove(self, middleware):\\n pass # pragma: no cover\",\n \"def unregister_router(self, hostname):\",\n \"def remove_breakpoint(self, bp: Breakpoint) -> None:\\n for hwbp in self.hw_breakpoints:\\n if hwbp.enabled and hwbp.addr == bp.addr:\\n hwbp.enabled = False\\n self.ap.write_memory(hwbp.comp_register_addr, 0)\\n self.num_hw_breakpoint_used -= 1\\n return\",\n \"def unbind(self, binding_id):\\n\\n if binding_id not in self._bindings:\\n return False\\n\\n event_props = self._bindings[binding_id][1]\\n del self._bindings[binding_id]\\n\\n if event_props in self._active_bindings:\\n self.ignore(event_props)\\n\\n return True\",\n \"def remove_httpd_service_ipa_conf(self):\\n raise NotImplementedError()\",\n \"def detach(cls, factory, attrib_name):\\n cls._to_attach.remove((factory, attrib_name))\",\n \"def _unbind_observers(self):\\n workbench = self.workbench\\n point = workbench.get_extension_point(PREFS_POINT)\\n point.unobserve('extensions', self._on_pref_decls_updated)\",\n \"def delete_ports(module, system):\\n changed = False\\n\\n host = system.hosts.get(name=module.params['host'])\\n for wwn_port in module.params['wwns']:\\n wwn = WWN(wwn_port)\\n if system.hosts.get_host_by_initiator_address(wwn) == host:\\n if not module.check_mode:\\n host.remove_port(wwn)\\n changed = True\\n for iscsi_port in module.params['iqns']:\\n iscsi_name = make_iscsi_name(iscsi_port)\\n if system.hosts.get_host_by_initiator_address(iscsi_name) == host:\\n if not module.check_mode:\\n host.remove_port(iscsi_name)\\n changed = True\\n return changed\",\n \"def _disassociate_port_id(self, context, port_id):\\n\\n criterion = {\\n 'managed': 1,\\n 'managed_resource_type': 'a:floatingip',\\n 'managed_extra': 'portid:%s' % (port_id),\\n 'managed_plugin_name': self.get_plugin_name(),\\n 'managed_plugin_type': self.get_plugin_type(),\\n }\\n records = self.central_api.find_records(context, criterion=criterion)\\n LOG.debug('Found %d records to delete that matched port id %s' %\\n (len(records), port_id))\\n for record in records:\\n LOG.debug('Deleting record %s' % (record['id']))\\n self.central_api.delete_record(context,\\n record['domain_id'],\\n record['recordset_id'],\\n record['id'])\\n\\n LOG.info('Deleted %d records that matched port_id %s' %\\n (len(records), port_id))\\n\\n return len(records)\",\n \"def remove(self, s):\\n if s in self.outputs:\\n self.outputs.remove(s)\\n self.inputs.remove(s)\\n del self.conns[s]\\n s.close()\",\n \"def delete_servicech(self, conf, phone_num):\\n\\t\\tpass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.73616654","0.6799897","0.6344198","0.6337611","0.617905","0.6130057","0.61269933","0.6104561","0.5963844","0.5883558","0.58721596","0.5811271","0.5778574","0.5777863","0.57622343","0.5755362","0.5706742","0.5679512","0.5677332","0.5649031","0.5630688","0.55856186","0.55844265","0.5553671","0.5516873","0.55082446","0.54849905","0.54827493","0.54577625","0.5456618","0.54338855","0.54203194","0.5406284","0.54053545","0.5393623","0.5390668","0.5387953","0.5382552","0.5379057","0.537512","0.53647923","0.53641796","0.5361277","0.5339943","0.532627","0.5316131","0.5302041","0.52724576","0.52684444","0.52611154","0.5259109","0.5250157","0.5248595","0.5247772","0.52181685","0.5215242","0.5208156","0.5204411","0.5190901","0.51756734","0.5137763","0.51287365","0.5080886","0.5077869","0.50749975","0.50710636","0.506911","0.50445825","0.504273","0.50356984","0.50350696","0.50270027","0.5025963","0.5025878","0.50201976","0.5009693","0.5004563","0.49856794","0.4985254","0.4983445","0.49753815","0.49731925","0.49655536","0.4957037","0.49521345","0.49460846","0.494298","0.49426016","0.49335283","0.49146074","0.49108824","0.49099565","0.49078357","0.49076653","0.48964882","0.4888078","0.48874012","0.48832273","0.48814726","0.48814696"],"string":"[\n \"0.73616654\",\n \"0.6799897\",\n \"0.6344198\",\n \"0.6337611\",\n \"0.617905\",\n \"0.6130057\",\n \"0.61269933\",\n \"0.6104561\",\n \"0.5963844\",\n \"0.5883558\",\n \"0.58721596\",\n \"0.5811271\",\n \"0.5778574\",\n \"0.5777863\",\n \"0.57622343\",\n \"0.5755362\",\n \"0.5706742\",\n \"0.5679512\",\n \"0.5677332\",\n \"0.5649031\",\n \"0.5630688\",\n \"0.55856186\",\n \"0.55844265\",\n \"0.5553671\",\n \"0.5516873\",\n \"0.55082446\",\n \"0.54849905\",\n \"0.54827493\",\n \"0.54577625\",\n \"0.5456618\",\n \"0.54338855\",\n \"0.54203194\",\n \"0.5406284\",\n \"0.54053545\",\n \"0.5393623\",\n \"0.5390668\",\n \"0.5387953\",\n \"0.5382552\",\n \"0.5379057\",\n \"0.537512\",\n \"0.53647923\",\n \"0.53641796\",\n \"0.5361277\",\n \"0.5339943\",\n \"0.532627\",\n \"0.5316131\",\n \"0.5302041\",\n \"0.52724576\",\n \"0.52684444\",\n \"0.52611154\",\n \"0.5259109\",\n \"0.5250157\",\n \"0.5248595\",\n \"0.5247772\",\n \"0.52181685\",\n \"0.5215242\",\n \"0.5208156\",\n \"0.5204411\",\n \"0.5190901\",\n \"0.51756734\",\n \"0.5137763\",\n \"0.51287365\",\n \"0.5080886\",\n \"0.5077869\",\n \"0.50749975\",\n \"0.50710636\",\n \"0.506911\",\n \"0.50445825\",\n \"0.504273\",\n \"0.50356984\",\n \"0.50350696\",\n \"0.50270027\",\n \"0.5025963\",\n \"0.5025878\",\n \"0.50201976\",\n \"0.5009693\",\n \"0.5004563\",\n \"0.49856794\",\n \"0.4985254\",\n \"0.4983445\",\n \"0.49753815\",\n \"0.49731925\",\n \"0.49655536\",\n \"0.4957037\",\n \"0.49521345\",\n \"0.49460846\",\n \"0.494298\",\n \"0.49426016\",\n \"0.49335283\",\n \"0.49146074\",\n \"0.49108824\",\n \"0.49099565\",\n \"0.49078357\",\n \"0.49076653\",\n \"0.48964882\",\n \"0.4888078\",\n \"0.48874012\",\n \"0.48832273\",\n \"0.48814726\",\n \"0.48814696\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7389213"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":20067,"cells":{"query":{"kind":"string","value":"Use encoder to get embedding vectors first."},"document":{"kind":"string","value":"def distances_from_obs(self, session, obs_first, obs_second, hashes_first=None, hashes_second=None, **kwargs):\n obs_encoder = self.obs_encoder\n\n if hashes_first is None:\n hashes_first = [hash_observation(obs) for obs in obs_first]\n if hashes_second is None:\n hashes_second = [hash_observation(obs) for obs in obs_second]\n\n obs_encoder.encode(session, obs_first + obs_second, hashes_first + hashes_second)\n\n obs_first_encoded = [obs_encoder.encoded_obs[h] for h in hashes_first]\n obs_second_encoded = [obs_encoder.encoded_obs[h] for h in hashes_second]\n\n d = self.distances(session, obs_first_encoded, obs_second_encoded)\n return d"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def compute_embeddings(encoder, data_batches):\n\n vectors = []\n for batch in iter(data_batches):\n X, Y = batch\n X_embedded = encoder(X)\n for vec in np.array(X_embedded):\n vectors.append(vec)\n vectors = np.array(vectors)\n\n return vectors","def set_embeddings(self):","def get_embeddings() -> tuple:\n # Initialize the model loading Universal Sentense Encoder\n # into a KerasLayer from Kaggle dataset file\n model = tf.keras.Sequential(\n [KerasLayer(encoder_path, input_shape=[], dtype=tf.string,\n output_shape=[512], trainable=False),\n # tf.keras.layers.Layer(512, dtype=tf.float16) # To reduce memory footprint\n ]\n )\n\n train_emb = model.predict(data_train['text'])\n print('Train texts converted into embeddings. Shape:', train_emb.shape)\n\n test_emb = model.predict(data_test['text'])\n print('Test texts converted into embeddings. Shape:', test_emb.shape)\n\n return train_emb, test_emb","def add_embedding(self):\n ### YOUR CODE HERE (~4-6 lines)\n embeddingTensor = tf.Variable(self.pretrained_embeddings)\n embeddings = tf.nn.embedding_lookup(embeddingTensor, self.input_placeholder)\n embeddings = tf.reshape(embeddings, [-1, self.max_length, Config.n_features * Config.embed_size])\n ### END YOUR CODE\n return embeddings","def vectorize_texts(self, encoder) -> NoReturn:\n self.encoder = encoder\n self.vectorized_texts = [self.encoder.encode(t) for t in self.tokenized_texts]","def embed(self, encoded: List[int]) -> np.ndarray:\n return np.vstack([self.embeddings[index] for index in encoded])","def _add_pre_trained_embedding(self):\n\n if self.embedding_type['type'] == 'glove':\n self.logging.info('use pre-trained glove word2vec')\n # a. load pre trained glove\n GLOVE_DIR = '../data/glove_pretrained/glove.6B'\n glove_suffix_name = 'glove.6B.' + str(self.embedding_size) + 'd.txt'\n import os\n import numpy as np\n\n embeddings_index = {}\n f = open(os.path.join(GLOVE_DIR, glove_suffix_name)) # 'glove.6B.100d.txt'))\n for line in f:\n values = line.split()\n word = values[0]\n coefs = np.asarray(values[1:], dtype='float32')\n embeddings_index[word] = coefs\n f.close()\n\n self.logging.info('')\n self.logging.info('Found %s word vectors.' % len(embeddings_index))\n\n # b. compute embedding matrix\n embedding_matrix = np.zeros((len(self.word_index) + 1, self.embedding_size))\n cnt = 0\n for word, i in self.word_index.items():\n embedding_vector = embeddings_index.get(word)\n if embedding_vector is not None:\n embedding_matrix[i] = embedding_vector # words not found in embedding index will be all-zeros.\n else:\n # self.logging.info('token in train missing in word2vec: ' + str(word))\n cnt += 1\n self.logging.info('total tokens missing: ' + str(cnt) + ' / ' + str(len(self.word_index)))\n\n # c. build embedding layer\n from keras.layers import Embedding\n embedding_layer = Embedding(len(self.word_index) + 1,\n self.embedding_size,\n weights=[embedding_matrix],\n input_length=self.maxlen,\n trainable=False)\n\n elif self.embedding_type['type'] == 'gensim':\n self.logging.info('use pre-trained gensim word2vec')\n\n import gzip\n import gensim\n from keras.layers import Embedding\n import numpy as np\n\n # fname = '../data/word2vec_pretrained/motors/d_300_k_712904_w_6_e_60_v_motors'\n # fname = '../data/word2vec_pretrained/fashion/d_300_k_1341062_w_6_e_70_v_fashion'\n\n self.logging.info('load word2vec path: ' + str(self.embedding_type['path']))\n model = gensim.models.Word2Vec.load(self.embedding_type['path'])\n pretrained_weights = model.wv.syn0\n vocab_size, vector_dim = pretrained_weights.shape\n\n method = 3\n if method == 1:\n self.logging.info('word2vec attempt to fit into embedding layer - middle complex')\n # convert the wv word vectors into a numpy matrix that is suitable for insertion\n # into our TensorFlow and Keras models\n\n embedding_matrix = np.zeros((len(model.wv.vocab), vector_dim))\n for i in range(len(model.wv.vocab)):\n embedding_vector = model.wv[model.wv.index2word[i]]\n if embedding_vector is not None:\n embedding_matrix[i] = embedding_vector\n\n embedding_layer = Embedding(input_dim=embedding_matrix.shape[0],\n output_dim=embedding_matrix.shape[1],\n # input_length=self.maxlen,\n weights=[embedding_matrix],\n trainable=False)\n elif method == 2:\n self.logging.info('word2vec simple embedding matching - simple complex')\n embedding_layer = Embedding(input_dim=vocab_size,\n output_dim=vector_dim,\n input_length=self.maxlen,\n weights=[pretrained_weights],\n trainable=False)\n elif method == 3:\n\n self.logging.info('word2vec match using word_index from keras tokenizer - as used in glove match above')\n # b. compute embedding matrix\n\n # sd = 1 / np.sqrt(len(self.word_index) + 1)\n # embedding_matrix = np.random.normal(0, scale=sd, size=(len(self.word_index) + 1, self.embedding_size))\n\n embedding_matrix = np.zeros((len(self.word_index) + 1, self.embedding_size))\n cnt = 0\n for word, i in self.word_index.items():\n if word in model.wv:\n embedding_vector = model.wv[word]\n embedding_matrix[i] = embedding_vector\n else:\n # self.logging.info('token in train missing in word2vec: ' + str(word))\n cnt += 1\n self.logging.info('total tokens missing: ' + str(cnt))\n\n\n # c. build embedding layer\n from keras.layers import Embedding\n embedding_layer = Embedding(len(self.word_index) + 1,\n self.embedding_size,\n weights=[embedding_matrix],\n input_length=self.maxlen,\n trainable=False)\n else:\n raise ValueError('unknown method value')\n\n else:\n raise ValueError('unknown embedding type')\n self.logging.info('create glove pre-trained embedding: ' + str(self.embedding_size))\n return embedding_layer","def get_embeddings(self, data):\n raise NotImplementedError()","def forward(self, input_sentence):\n sentence = self.word_embedding(input_sentence)\n embedding = self.encoder(sentence)\n return embedding","def build_encoder(tparams, options):\n\t# word embedding (source)\n\tembedding = tensor.tensor3('embedding', dtype='float32')\n\tx_mask = tensor.matrix('x_mask', dtype='float32')\n\n\t# encoder\n\tproj = get_layer(options['encoder'])[1](tparams, embedding, options,\n\t\t\t\t\t\t\t\t\t\t\tprefix='encoder',\n\t\t\t\t\t\t\t\t\t\t\tmask=x_mask)\n\tctx = proj[0][-1]\n\n\treturn embedding, x_mask, ctx","def _create_encoder(self):\n\n def _init_weights(layer):\n \"\"\"Initializes the weights of a layer based on type.\"\"\"\n if isinstance(layer, (nn.Conv2d, nn.Linear)):\n torch.nn.init.xavier_uniform_(layer.weight)\n try:\n # Some layers may not have biases, so catch the exception and pass.\n layer.bias.data.fill_(0.0)\n except AttributeError:\n pass\n\n kernel_size = 5\n pad = 2\n input_channels = 1\n first_conv_channels = 6\n second_conv_channels = 16\n max_pool_kernel = 2\n linear_size = 120\n n_pixels = 7\n\n encoder = nn.Sequential(\n nn.Conv2d(\n input_channels, first_conv_channels, kernel_size, padding=pad),\n nn.BatchNorm2d(first_conv_channels),\n nn.ReLU(),\n nn.MaxPool2d(max_pool_kernel),\n nn.Conv2d(\n first_conv_channels, second_conv_channels, kernel_size,\n padding=pad),\n nn.BatchNorm2d(second_conv_channels),\n nn.ReLU(),\n nn.MaxPool2d(max_pool_kernel),\n utils.Flatten(),\n nn.Linear(n_pixels * n_pixels * self.n_digits * second_conv_channels,\n linear_size),\n nn.BatchNorm1d(linear_size),\n nn.ReLU(),\n nn.Linear(linear_size, self.embedding_dim),\n nn.Linear(self.embedding_dim, self.n_classes, bias=False),\n )\n\n encoder.apply(_init_weights)\n\n # This is the empirical approximation for initialization the vMF\n # distributions for each class in the final layer.\n if self.use_vmf:\n utils.vmf_class_weight_init(encoder[-1].weight, self.kappa_confidence,\n self.embedding_dim)\n\n return encoder","def pretrained_embedding_layer(word_to_vec_map, word_to_index):\n \n vocab_size = len(word_to_index) + 1 # adding 1 to fit Keras embedding (requirement)\n any_word = list(word_to_vec_map.keys())[0]\n emb_dim = word_to_vec_map[any_word].shape[0] # define dimensionality of your GloVe word vectors (= 50)\n \n ### START CODE HERE ###\n # Step 1\n # Initialize the embedding matrix as a numpy array of zeros.\n # See instructions above to choose the correct shape.\n emb_matrix = np.zeros((vocab_size, emb_dim))\n \n # Step 2\n # Set each row \"idx\" of the embedding matrix to be \n # the word vector representation of the idx'th word of the vocabulary\n for word, idx in word_to_index.items():\n emb_matrix[idx, :] = word_to_vec_map[word]\n\n # Step 3\n # Define Keras embedding layer with the correct input and output sizes\n # Make it non-trainable.\n embedding_layer = tensorflow.keras.layers.Embedding(input_dim = vocab_size, output_dim = emb_dim, trainable = False)\n ### END CODE HERE ###\n\n # Step 4 (already done for you; please do not modify)\n # Build the embedding layer, it is required before setting the weights of the embedding layer. \n embedding_layer.build((None,)) # Do not modify the \"None\". This line of code is complete as-is.\n \n # Set the weights of the embedding layer to the embedding matrix. Your layer is now pretrained.\n embedding_layer.set_weights([emb_matrix])\n \n return embedding_layer","def fit_transform(self, *args, **kwargs) -> np.ndarray:\n self.fit(*args, **kwargs)\n return self.embedding_","def build_encoder_bi(tparams, options):\n\t# word embedding (source)\n\tembedding = tensor.tensor3('embedding', dtype='float32')\n\tembeddingr = embedding[::-1]\n\tx_mask = tensor.matrix('x_mask', dtype='float32')\n\txr_mask = x_mask[::-1]\n\n\t# encoder\n\tproj = get_layer(options['encoder'])[1](tparams, embedding, options,\n\t\t\t\t\t\t\t\t\t\t\tprefix='encoder',\n\t\t\t\t\t\t\t\t\t\t\tmask=x_mask)\n\tprojr = get_layer(options['encoder'])[1](tparams, embeddingr, options,\n\t\t\t\t\t\t\t\t\t\t\t prefix='encoder_r',\n\t\t\t\t\t\t\t\t\t\t\t mask=xr_mask)\n\n\tctx = tensor.concatenate([proj[0][-1], projr[0][-1]], axis=1)\n\n\treturn embedding, x_mask, ctx","def encode(self):\n with tf.name_scope(\"encode\"):\n self.encoder_inputs = tf.layers.dense(\n inputs=self.encoder_inputs,\n units=self.options['hidden_size'], activation=None, use_bias=True,\n kernel_initializer=tf.keras.initializers.he_normal(seed=None),\n bias_initializer=tf.zeros_initializer(),\n kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None,\n kernel_constraint=None, bias_constraint=None, trainable=True,\n name=None, reuse=None)\n self.encoder_inputs = tf.layers.batch_normalization(self.encoder_inputs,\n axis=-1,\n momentum=0.99,\n epsilon=0.001,\n center=True,\n scale=True,\n beta_initializer=tf.zeros_initializer(),\n gamma_initializer=tf.ones_initializer(),\n moving_mean_initializer=tf.zeros_initializer(),\n moving_variance_initializer=tf.ones_initializer(),\n training=self.is_training,\n trainable=True,\n renorm=False,\n renorm_momentum=0.99)\n # Prepare inputs to the layer stack by adding positional encodings and\n # applying dropout.\n # embedded_inputs = self.embedding_softmax_layer(inputs)\n #\n inputs_padding = transformer_model_utils.get_padding(tf.cast(\n tf.reduce_max(100*self.encoder_inputs, [-1]),\n dtype=tf.int32))\n\n with tf.name_scope(\"add_pos_encoding\"):\n length = tf.shape(self.encoder_inputs)[1]\n pos_encoding = transformer_model_utils.get_position_encoding(\n length, self.options[\"hidden_size\"])\n encoder_inputs = self.encoder_inputs + pos_encoding\n\n if self.is_training:\n encoder_inputs = tf.nn.dropout(\n encoder_inputs, 1 - self.options[\"layer_postprocess_dropout\"])\n\n return self.encoder_stack(encoder_inputs, self.attention_bias, inputs_padding)","def get_movie_embedding(self):\n raise NotImplementedError(\"has to be overwritten\")","def encode(data, encoder):\n # Get the list of hidden depths\n\thd = encoder.hidden_depths\n # Find the middle hidden layer\n\tmiddle_layer_index = (len(hd)-1)/2\n # Initialize empty container for the encoded data\n\tdata_encoded = np.zeros((data.shape[0],hd[middle_layer_index]))\n\tfor i, d_ in enumerate(data):\n # feed forward, get all the activations, and just keep\n # the middle layer, which is the encoding\n\t\tx, z_container, x_container = encoder.ff(d_,True,True)\n\t\tx_encoded = x_container[1+middle_layer_index]\n\t\tdata_encoded[i] = x_encoded\n\t#\n\treturn data_encoded","def encode_input_for_decoder(x_tensor, inp_lens_tensor, model_input_emb: EmbeddingLayer, model_enc: RNNEncoder):\n input_emb = model_input_emb.forward(x_tensor)\n (enc_output_each_word, enc_context_mask, enc_final_states) = model_enc.forward(input_emb, inp_lens_tensor)\n enc_final_states_reshaped = (enc_final_states[0].unsqueeze(0), enc_final_states[1].unsqueeze(0))\n return (enc_output_each_word, enc_context_mask, enc_final_states_reshaped)","def encode_input(self, x_tensor, inp_lens_tensor):\r\n input_emb = self.input_emb.forward(x_tensor)\r\n enc_output_each_word, enc_context_mask, enc_final_states = self.encoder(input_emb, inp_lens_tensor)\r\n enc_final_states_reshaped = (enc_final_states[0].unsqueeze(0), enc_final_states[1].unsqueeze(0))\r\n # print('lest go', enc_final_states_reshaped[1].shape)\r\n return enc_output_each_word, enc_context_mask, enc_final_states_reshaped","def _get_embedding(self, data):\n embedding_list = [super()._get_embedding(data)]\n context = data['context']\n for i in range(context.shape[1]):\n embedding_list.append(getattr(self, f'context{i}')(context[:, i:i+1]))\n return torch.cat(embedding_list, dim=1)","def embeddings(self):\n self._ensure_is_connected()\n return self._embeddings","def forward(self, src, mask):\n bs = src.shape[0]\n src = src.permute(2, 0, 1)\n m = src \n enc_embed = self.enc_embed.weight.unsqueeze(1).repeat(1, bs, 1)\n for layer in self.encoder_layers:\n m = layer(m,\n pos=enc_embed,\n src_mask = mask\n )\n return m.permute(1, 2, 0), enc_embed.permute(1, 2, 0)","def construct_embedding(self):\n i = 0\n self.load_dicts()\n embedding_shape = (max(self.word2idx.values()) + 1,\n self.embedding_size)\n self.embedding = np.zeros(embedding_shape)\n\n with open(self.config.word_vec_fi_glove, 'r') as fi:\n for line in fi:\n word_vec = line.split(\" \")[1:]\n self.embedding[i, :] = np.array(word_vec, dtype=np.float32)\n i += 1\n\n self.write_embedding()","def __init__(self, vocab_size, embedding_size, output_size):\n super(VanillaEncoder, self).__init__()\n\n self.vocab_size = vocab_size\n self.embedding = nn.Embedding(vocab_size, embedding_size)\n self.gru = nn.GRU(embedding_size, output_size)","def get_embedding(self, model):\n embedding = []\n for node in range(len(self.graph.nodes())):\n embedding.append(list(model[str(node)]))\n embedding = np.array(embedding)\n return embedding","def get_embeddings():\n # Load the raw embedding data\n X_train = np.load('./train_embeddings.npy')\n \n y_train = np.load('./train_labels.npy')\n \n X_valid = np.load('./valid_embeddings.npy')\n \n y_valid = np.load('./valid_labels.npy')\n \n X_test = np.load('./test_embeddings.npy')\n \n y_test = np.load('./test_labels.npy')\n\n #return X_train, y_train\n return X_train, y_train, X_valid, y_valid, X_test, y_test","def embedding_layer(self):\n with tf.name_scope(\"Embedding_Layer\"):\n V_size = len(self.vocab)\n embed_dim = len(self.embed[0]) \n W_embed_ = tf.get_variable(\"W_embed\",shape=[V_size, embed_dim],trainable=False).assign(np.asarray(self.embed))\n W_analogy_embed_ = tf.get_variable(\"W_analogy_embed\",shape=[V_size, embed_dim],trainable=True,initializer=tf.random_uniform_initializer(minval=-1,maxval=1))\n return W_embed_, W_analogy_embed_","def generate_embeddings(vae, dataset):\n data = ((torch.unsqueeze(img,0), label) for img, label in dataset)\n data = ((vae.encoder(tens), label) for tens, label in data)\n data = ((vae.codebook(emb),label) for emb, label in data)\n data = ((torch.flatten(img),label) for img, label in data)\n data = (torch.cat([inds,Tensor([label]).int()]) for inds, label in data)\n return data","def test_extract_embeddings():\n docs = [\"some document\"]\n model = BERTopic(embedding_model=\"distilbert-base-nli-stsb-mean-tokens\")\n bertopic_embeddings = model._extract_embeddings(docs)\n\n assert isinstance(bertopic_embeddings, np.ndarray)\n assert bertopic_embeddings.shape == (1, 768)\n\n sentence_embeddings = embedding_model.encode(docs, show_progress_bar=False)\n assert np.array_equal(bertopic_embeddings, sentence_embeddings)","def init_emb(self):\n # Initialize users and items' embeddings\n nn.init.xavier_uniform_(self.user_embedding.weight)\n nn.init.xavier_uniform_(self.item_embedding.weight)","def get_vec(self, img, tensor=False):\n image = (\n self.normalize(self.to_tensor(self.scaler(img)))\n .unsqueeze(0)\n .to(self.device)\n )\n\n if self.model_name == \"alexnet\":\n my_embedding = torch.zeros(1, self.layer_output_size)\n else:\n my_embedding = torch.zeros(1, self.layer_output_size, 1, 1)\n\n def copy_data(m, i, o):\n my_embedding.copy_(o.data)\n\n h = self.extraction_layer.register_forward_hook(copy_data)\n h_x = self.model(image)\n h.remove()\n\n if tensor:\n return my_embedding\n else:\n if self.model_name == \"alexnet\":\n return my_embedding.numpy()[0, :]\n else:\n return my_embedding.numpy()[0, :, 0, 0]","def init_embedding(self):\n self.embedding.weight.data.uniform_(-1./self.num_embeddings, 1./self.num_embeddings)","def pretrained_embedding_layer(word_to_vec_map, word_to_index):\n vocab_len = len(word_to_index) + 1 # adding 1 to fit Keras embedding (requirement)\n emb_dim = word_to_vec_map[\"cucumber\"].shape[0] # define dimensionality of your GloVe word vectors (= 50)\n emb_matrix = np.zeros((vocab_len, emb_dim)) # Initialize the embedding matrix as a numpy array of zeros of shape (vocab_len, dimensions of word vectors = emb_dim)\n for word, index in word_to_index.items(): # Set each row \"index\" of the embedding matrix to be the word vector representation of the \"index\"th word of the vocabulary\n emb_matrix[index, :] = word_to_vec_map[word]\n embedding_layer = Embedding(vocab_len, emb_dim, trainable = False) # Define Keras embedding layer with the correct output/input sizes, make it trainable. Use Embedding(...). Make sure to set trainable=False. \n embedding_layer.build((None,)) # Build the embedding layer, it is required before setting the weights of the embedding layer. Do not modify the \"None\".\n embedding_layer.set_weights([emb_matrix]) # Set the weights of the embedding layer to the embedding matrix. Your layer is now pretrained.\n return embedding_layer","def _embed(self):\n with tf.variable_scope('word_embedding'):\n self.word_embeddings = tf.get_variable(\n 'word_embeddings',\n shape=(self.term_vocab.size(), self.term_vocab.embed_dim),\n initializer=tf.constant_initializer(self.term_vocab.embeddings),\n trainable=True\n )\n self.p_word_emb = tf.nn.embedding_lookup(self.word_embeddings, self.p)\n self.q_word_emb = tf.nn.embedding_lookup(self.word_embeddings, self.q)\n\n with tf.variable_scope('char_embedding'):\n self.char_embeddings = tf.get_variable(\n 'char_embeddings',\n shape=(self.char_vocab.size(), self.char_vocab.embed_dim),\n initializer=tf.constant_initializer(self.char_vocab.embeddings),\n trainable=True\n )\n self.p_char_emb = tf.nn.embedding_lookup(self.char_embeddings, self.p_char) # [batch, seqlen, max_char_num, embedding_size]\n self.q_char_emb = tf.nn.embedding_lookup(self.char_embeddings, self.q_char)\n\n self.p_char_emb = self.cnn_emb(self.p_char_emb, \"p_emb\")\n self.q_char_emb = self.cnn_emb(self.q_char_emb, \"q_emb\")\n '''\n self.p_char_emb = tf.reshape(self.p_char_emb, [-1, self.max_char_num, self.emb_size])\n self.q_char_emb = tf.reshape(self.q_char_emb, [-1, self.max_char_num, self.emb_size])\n\n self.p_char_emb = cnn_layer.conv(self.p_char_emb, self.emb_size,\n bias=True, activation=tf.nn.relu, kernel_size=5, name=\"char_conv\", reuse=None)\n self.q_char_emb = cnn_layer.conv(self.q_char_emb, self.emb_size,\n bias=True, activation=tf.nn.relu, kernel_size=5, name=\"char_conv\", reuse=True)\n\n self.p_char_emb = tf.reduce_max(self.p_char_emb, axis=1) # [batch*seqlen, 1, emb_size]\n self.q_char_emb = tf.reduce_max(self.q_char_emb, axis=1)\n\n batch_size = tf.shape(self.p_word_emb)[0]\n self.p_char_emb = tf.reshape(self.p_char_emb, [batch_size, -1, self.emb_size])\n self.q_char_emb = tf.reshape(self.q_char_emb, [batch_size, -1, self.emb_size])\n\n self.p_char_emb = tf.nn.dropout(self.p_char_emb, 0.95)\n self.q_char_emb = tf.nn.dropout(self.q_char_emb, 0.95)\n '''\n self.p_emb = tf.concat([self.p_word_emb, self.p_char_emb], -1)\n self.q_emb = tf.concat([self.q_word_emb, self.q_char_emb], -1)","def make_embeddings(self):\n\t\tprint(\"Presetting embedding weights\")\n\t\t\t\n\t\tnp.random.seed(0)\n\t\tweights = np.random.uniform(low = -0.05, high = 0.05, size = (self.FREQCAP, self.EMB_SIZE))\n\t\t\n\t\tcounter = 0\n\n\t\twords = []\n\t\tweights_tmp = []\n\n\t\twith open(self.embeddingpath) as handle:\n\t\t\tfor i, line in enumerate(handle):\n\t\t\t\ttmp = line.strip()\n\t\t\t\tif len(tmp) > 0:\n\t\t\t\t\tsplit = tmp.split(\" \")\n\t\t\t\t\tif split[0] in self.worddict and len(split[1:]) == 300:\n\t\t\t\t\t\twords.append(split[0])\n\t\t\t\t\t\tweights_tmp.append([float(a) for a in split[1:]])\n\t\t\n\t\tweights_tmp = np.array(weights_tmp)\n\n\t\tfor word, column in zip(words, weights_tmp):\n\t\t\tif self.worddict[word] < self.FREQCAP:\n\t\t\t\tcounter += 1\n\t\t\t\tweights[self.worddict[word],:] = column\n\t\t\n\t\tprint(\"Set\", counter, \"of\", weights.shape[0], \"columns\")\n\t\t\n\t\tif self.EMB_SIZE < weights.shape[-1]:\n\t\t\tprint(\"Reducing dimensionality to\", self.EMB_SIZE)\n\t\t\tpca = PCA(self.EMB_SIZE)\n\t\t\tweights = pca.fit_transform(weights)\n\t\t\n\t\tself.embeddings = [weights]","def _get_embedding(self, data):\n # Tensor(n, c)\n cat = data['cat']\n return self.one_hot_embed(cat)","def create_embedding_matrix(self):\n self.id2word = dict([(self.vocab[word]['id'], word) for word in self.vocab])\n vocab_size = len(self.vocab)\n result = np.zeros((vocab_size, self.embed_dim))\n unknown_token_set = set()\n\n found_words = 0\n avg = np.zeros(self.embed_dim)\n for _ in range(1, vocab_size): # skip PAD embedding (initialize as zero embedding)\n try:\n result[_] = self.pretrained_embedding[self.id2word[_]]\n avg += result[_]\n found_words += 1\n except:\n unknown_token_set.add(_)\n\n avg /= found_words\n for _ in unknown_token_set:\n result[_] = avg\n self.embedding = result\n return found_words, len(self.id2word)","def forward(self, tgt, m, enc_embed, mask):\n bs = tgt.shape[0]\n enc_embed = enc_embed.permute(2, 0, 1)\n m = m.permute(2, 0, 1)\n tgt = tgt.permute(2, 0, 1)\n dec_embed = self.dec_embed.weight.unsqueeze(1).repeat(1, bs, 1)\n\n out = tgt\n for layer in self.decoder_layers:\n out = layer(out, m, \n pos=enc_embed,\n query_pos=dec_embed\n )\n \n return self.decoder_norm(out).permute(1, 2, 0), dec_embed.permute(1, 2, 0)","def _embed(self):\n with tf.variable_scope('word_embedding'):\n self.pretrained_word_mat = tf.get_variable(\"word_emb_mat\",\n [self.vocab.word_size() - 2, self.vocab.word_embed_dim],\n dtype=tf.float32,\n initializer=tf.constant_initializer(\n self.vocab.word_embeddings[2:],\n dtype=tf.float32),\n trainable=False)\n self.word_pad_unk_mat = tf.get_variable(\"word_unk_pad\",\n [2, self.pretrained_word_mat.get_shape()[1]],\n dtype=tf.float32,\n initializer=tf.constant_initializer(\n self.vocab.word_embeddings[:2],\n dtype=tf.float32),\n trainable=True)\n\n self.word_mat = tf.concat([self.word_pad_unk_mat, self.pretrained_word_mat], axis=0)\n self.p_emb = tf.nn.embedding_lookup(self.word_mat, self.p)\n self.q_emb = tf.nn.embedding_lookup(self.word_mat, self.q)","def fit_transform(self, X):\n self.fit(X)\n return self.embedding_","def pretrained_embedding_layer(model,model2,model3, word_to_index,emb_dim_max):\n words_ignored = []\n vocab_len = len(word_to_index) + 1 \n emb_matrix = np.zeros([vocab_len,emb_dim_max])\n \n print(' Total words would be processed : '+str(vocab_len))\n for word, idx in word_to_index.items():\n if word in model:\n emb_matrix[idx,:200] = model[word]\n emb_matrix[idx,200:] = 0\n if word in model2:\n emb_matrix[idx, :100] = model2[word]\n emb_matrix[idx, 100:] = 0\n if word in model3.keys():\n emb_matrix[idx,:] = model3[word]\n else:\n words_ignored.append(word)\n print(str(len(words_ignored))+\" words ignored\")\n print(emb_matrix.shape) \n \n \n embedding_layer = Embedding(vocab_len,emb_dim_max,trainable = True)\n \n # Build the embedding layer, it is required before setting the weights of the embedding layer. \n embedding_layer.build((None,)) # Do not modify the \"None\". This line of code is complete as-is.\n # Set the weights of the embedding layer to the embedding matrix. Your layer is now pretrained.\n embedding_layer.set_weights([emb_matrix])\n \n return embedding_layer,words_ignored","def get_embeddings(self, in_data):\n context, da = in_data\n if self.fixed_divide:\n da_emb = super(PersonageContextDAEmbeddingSeq2SeqExtract, self).get_embeddings(da, pad=True)\n else:\n da_emb = super(PersonageContextDAEmbeddingSeq2SeqExtract, self).get_embeddings(da, pad=False)\n\n # Shubhangi: what this step essentially does is it replaces the context words by their token, with UNK as default.\n # again , we don't need this since our context data is essentially vectors therefore commenting this out\n # similary we don't need context embedding , that's exactly what context is already .\n\n # context_emb = []\n context_emb = [float(parameter[0]) for parameter in context]\n\n # for tok in context[-max_context_len:]:\n # context_emb.append(self.dict_token.get(tok, self.UNK_TOKEN))\n\n # Shubhangi: padding is needed because each context sentence could be of different length ,\n # we don't need to include context in padding as we're going to have a fixed size\n # (max_context_len - len(context)) = 0\n\n\n # padding = [self.UNK_TOKEN] * (max_context_len - len(context))\n\n # Shubhangi: padding might be harmless for now therefore not removing ,\n # essentially what this is doing is concatenating the arrays and sending\n if self.use_div_token:\n return context_emb + [self.DIV_TOKEN] + da_emb\n # return padding + context_emb + [self.DIV_TOKEN] + da_emb\n # return padding + context_emb + da_emb\n return context_emb + da_emb","def source_embedding_init_value(self):\n if self._src_embedding is None:\n return None\n return self._src_embedding.word_vecs","def _embeddings(self, xs):\n n_feats, batch_size, seq_len = xs.size()\n\n assert n_feats == self.n_feats\n\n res = [emb(x) for emb, x in zip(self.embeddings, xs)]\n x = torch.cat(res, 2)\n\n return x","def init_embedding(size=50):\n vector = np.random.normal(0.0, 0.01, size)\n return vector","def __init__(self, vocab, embed_size=512, dropout_rate=0.1, max_len=200):\n super(DecoderEmbeddings, self).__init__()\n pad_token_idx = 0 #vocab.tokenizer.ids_to_tokens[0]\n assert vocab.tokenizer.ids_to_tokens[0] == '[PAD]'\n self.embeddings = nn.Embedding(len(vocab.tokenizer.ids_to_tokens), embed_size, padding_idx=pad_token_idx)\n self.positional_encoding = PositionalEncoding(d_model=embed_size, dropout=dropout_rate, max_len=max_len)","def add_embedding(self):\n #with tf.variable_scope(\"RNN\", reuse = tf.AUTO_REUSE):\n embeddings = tf.get_variable(\"embeddings\", initializer = self.pretrained_embeddings,trainable=True)\n inputs = self.input_placeholder\n inputs = tf.reshape(inputs, [self.config.batch_size, -1 , self.config.n_features])\n embeddings = tf.nn.embedding_lookup(embeddings, self.input_placeholder)\n embeddings = tf.reshape(embeddings, [self.config.batch_size, -1, self.config.n_features* self.config.embed_size])\n embeddings = tf.cast(embeddings, tf.float32)\n return embeddings","def _add_seq2seq(self):\n mode = self._mode\n vsize = self._vocab.size() # size of the vocabulary\n\n with tf.variable_scope('seq2seq'):\n # Some initializers\n self.rand_unif_init = tf.random_uniform_initializer(-config.rand_unif_init_mag, config.rand_unif_init_mag, seed=123)\n self.trunc_norm_init = tf.truncated_normal_initializer(stddev=config.trunc_norm_init_std)\n\n # Add embedding matrix (shared by the encoder and decoder inputs)\n with tf.variable_scope('embedding'):\n embedding = tf.get_variable('embedding', [vsize, config.emb_dim], dtype=tf.float32, initializer=self.trunc_norm_init)\n if mode==\"train\": self._add_emb_vis(embedding) # add to tensorboard\n emb_enc_inputs = tf.nn.embedding_lookup(embedding, self._enc_batch) # tensor with shape (batch_size, max_enc_steps, emb_size)\n emb_dec_inputs = tf.nn.embedding_lookup(embedding, self._dec_batch) # tensor with shape (batch_size, max_dec_steps, emb_size)\n #emb_dec_inputs = [tf.nn.embedding_lookup(embedding, x) for x in tf.unstack(self._dec_batch, axis=1)] # list length max_dec_steps containing shape (batch_size, emb_size)\n\n # Add the encoder.\n enc_fw_states, enc_bw_states, enc_fw, enc_bw = self._add_input_encoder(emb_enc_inputs, self._enc_lens)\n\n print(\"Encoder FW\", enc_fw_states.shape)\n print(\"Encoder BW\", enc_bw_states.shape)\n raise Exception(\"testing mode\")\n\n #reshape encoder states from [batch_size, input_size, hidden_dim] to [batch_size, input_size * hidden_dim]\n enc_fw_states = tf.reshape(enc_fw_states, [config.batch_size, config.hidden_dim * tf.shape(enc_fw_states)[1]])\n enc_bw_states = tf.reshape(enc_bw_states, [config.batch_size, config.hidden_dim * tf.shape(enc_bw_states)[1]])\n\n\n # python run.py --mode=decode --data_path=data/chunked/train_1/train_1_*.bin --vocab_path=data/vocab_1 --exp_name=full1isto1\n\n # Add the decoder.\n dec_fw_states, dec_bw_states = self._add_input_decoder(emb_dec_inputs, self._dec_lens, enc_fw, enc_bw)\n\n #reshape decoder states from [batch_size, input_size, hidden_dim] to [batch_size, input_size * hidden_dim]\n dec_fw_states = tf.reshape(dec_fw_states, [config.batch_size, config.hidden_dim * tf.shape(dec_fw_states)[1]])\n dec_bw_states = tf.reshape(dec_bw_states, [config.batch_size, config.hidden_dim * tf.shape(dec_bw_states)[1]])\n #print(\"Decoder FW\", dec_fw_states.shape)\n #print(\"Decoder BW\", dec_bw_states.shape)\n\n\n #enc_c = tf.concat(axis=1, values=[enc_fw.c, enc_bw.c])\n #enc_h = tf.concat(axis=1, values=[enc_fw.h, enc_bw.h])\n #dec_c = tf.concat(axis=1, values=[dec_fw.c, dec_bw.c])\n #dec_h = tf.concat(axis=1, values=[dec_fw.h, dec_bw.h])\n\n final_encoding = tf.concat(axis=1, values=[enc_fw_states, enc_bw_states, dec_fw_states, dec_bw_states])\n #print(\"Final encoding\", final_encoding.shape)\n #raise Exception(\"Test\")\n dims_final_enc = tf.shape(final_encoding)\n\n \"\"\"\n #convo_input = tf.concat(axis=1, values=[enc_c, enc_h, dec_c, dec_h])\n input_layer = tf.reshape(final_encoding, [config.batch_size, dims_final_enc[1], 1])\n print(\"Convolution input shape\", input_layer.shape)\n\n conv1 = tf.layers.conv1d(\n inputs=input_layer,\n filters=8,\n kernel_size=5,\n padding=\"same\",\n activation=tf.nn.relu)\n conv1 = tf.layers.batch_normalization(conv1)\n print(\"Convolution1 output shape\", conv1.shape)\n\n pool1 = tf.layers.max_pooling1d(inputs=conv1, pool_size=2, strides=2)\n print(\"Pool1 output shape\", pool1.shape)\n\n conv2 = tf.layers.conv1d(\n inputs=pool1,\n filters=16,\n kernel_size=5,\n padding=\"same\",\n activation=tf.nn.relu)\n\n\n conv2 = tf.layers.batch_normalization(conv2)\n print(\"Convolution2 output shape\", conv2.shape)\n\n pool2 = tf.layers.max_pooling1d(inputs=conv2, pool_size=2, strides=2)\n print(\"Pool2 output shape\", pool2.shape)\n\n dims_pool2 = tf.shape(pool2)\n\n pool2_flat = tf.reshape(pool2, [config.batch_size, dims_pool2[1] * 16])\n print(\"Pool2_flat output shape\", pool2_flat.shape)\n dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)\n \"\"\"\n #raise Exception(\"testing mode\")\n\n #dropout = tf.layers.dropout(inputs=dense, rate=0.4, training=mode==\"train\")\n #print(\"Dense output shape\", dense.shape)\n\n #raise Exception(\"Just testing\")\n # Add the output projection to obtain the vocabulary distribution\n with tf.variable_scope('output_projection'):\n w = tf.get_variable('w', [dims_final_enc[1], 2], dtype=tf.float32, initializer=self.trunc_norm_init)\n bias_output = tf.get_variable('bias_output', [2], dtype=tf.float32, initializer=self.trunc_norm_init)\n #concatenate abstract and article outputs [batch_size, hidden_dim*4]\n\n\n #get classification output [batch_size, 1] default on last axis\n self._logits = tf.matmul(final_encoding, w) + bias_output\n #self._logits = tf.layers.dense(final_encoding, 2, kernel_initializer=self.trunc_norm_init, bias_initializer=self.trunc_norm_init)\n #self._prob = tf.nn.softmax(logits, \"class_prob\")\n\n if mode in ['train', 'eval']:\n # Calculate the loss\n with tf.variable_scope('loss'):\n #self._prob = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=self._targets)\n #class_weights = tf.constant([0.1, 5.])\n self._loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=self._targets, logits=self._logits))\n #self._loss = tf.reduce_mean(tf.nn.weighted_cross_entropy_with_logits(targets=self._targets, logits=self._logits, pos_weight=class_weights))\n tf.summary.scalar('loss', self._loss)\n\n\n\n #if mode == \"decode\":","def get_word_embedding(counter, emb_file, emb_size, vocab_size, vec_size, vocab_file):\n print(\"Generating word embedding...\")\n # load word embeddings\n embedding_dict = {}\n with open(emb_file, \"r\", encoding=\"utf-8\") as fh:\n for line in tqdm(fh, total=emb_size):\n array = line.split()\n word = \"\".join(array[0:-vec_size])\n vector = list(map(float, array[-vec_size:]))\n embedding_dict[word] = vector\n\n TRANSLATE = {\n \"-lsb-\": \"[\", \"-rsb-\": \"]\", \"-lrb-\": \"(\", \"-rrb-\": \")\", \"-lcb-\": \"{\",\n \"-rcb-\": \"}\", \"-LSB-\": \"[\", \"-RSB-\": \"]\", \"-LRB-\": \"(\", \"-RRB-\": \")\",\n \"-LCB-\": \"{\", \"-RCB-\": \"}\"\n }\n SPECIAL_TOKENS = [\"<NULL>\", \"<UNK>\", \"<S>\", \"</S>\"]\n words = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x: x[1], reverse=True)))\n words = SPECIAL_TOKENS + words\n if vocab_size > 0:\n words = words[:vocab_size]\n with open(vocab_file, 'w') as f:\n f.write('\\n'.join(words[1:]))\n embedding = np.random.normal(scale=0.1, size=(len(words), vec_size))\n word2idx_dict = {}\n unknown_count = 0\n for i, word in enumerate(words):\n word2idx_dict[word] = i\n if word in TRANSLATE:\n word = TRANSLATE[word]\n done = False\n for w in (word, word.lower(), word.upper(), word.capitalize()):\n if w in embedding_dict:\n embedding[i] = embedding_dict[w]\n done = True\n break\n if not done:\n unknown_count += 1\n return embedding, word2idx_dict, unknown_count","def make_embedding(path, words, indices):\n #root = '/'.join(path.split('/')[0:-1])\n #all_paths = [root+'/'+x for x in os.listdir(root)] #'/'.join(path.split('/')[0:-1]))\n #for path in all_paths:\n vec_path = 'data/'+path.split('/')[-1]+'_'+mode\n print(vec_path)\n if os.path.exists(vec_path+'.npy'):\n np_vecs = np.load(vec_path+'.npy')\n else:\n words_len = len(words)\n vecs = []\n if mode == 'word':\n f = load_model('wiki.en.bin')\n for i, w in enumerate(words):\n if mode == 'word':\n vec = f.get_word_vector(w)\n else:\n vec = eye[indices[w]]\n vecs.append(vec) \n if i % 10000 == 0:\n print(\"{} / {}\".format(i, words_len))\n np_vecs = np.asarray(vecs, dtype=np.int8)\n np.save(vec_path, np_vecs)\n return np_vecs","def get_pretrained_embeddings(source_vocab,embed_df):\r\n \r\n num_tokens = len(source_vocab)\r\n embedding_dim = embed_df.shape[1]\r\n weights = np.zeros((num_tokens,embedding_dim),dtype=np.float32)\r\n \r\n for idx in range(num_tokens):\r\n token = source_vocab.lookup_index(idx)\r\n if token in embed_df.index:\r\n weights[idx,:] = embed_df.loc[token]\r\n else:\r\n weights[idx,:] = np.random.randn(1,embedding_dim)\r\n \r\n embed_tensor = torch.FloatTensor(weights)\r\n return embed_tensor","def _get_embedding_layer(self, input_data, doc_input_data):\n opts = self._options\n word_embedding = tf.Variable(tf.random_uniform((self.vocab_size, opts.embed_dim), -1.0, 1.0))\n embed = []\n\n temp = tf.zeros([opts.batch_size, opts.embed_dim])\n embed_d = []\n for n in range(opts.sentence_sample):\n temp = tf.add(temp, tf.nn.embedding_lookup(word_embedding, doc_input_data[:, n]))\n embed_d.append(temp)\n\n if opts.concat == 'True':\n combined_embed_vector_length = opts.embed_dim * opts.window_size + opts.embed_dim\n for j in range(opts.window_size):\n embed_w = tf.nn.embedding_lookup(word_embedding, input_data[:, j])\n embed.append(embed_w)\n embed.append(embed_d)\n else:\n combined_embed_vector_length = opts.embed_dim\n embed_w = tf.zeros([opts.batch_size, opts.embed_dim])\n for j in range(opts.window_size):\n embed_w += tf.nn.embedding_lookup(word_embedding, input_data[:, j])\n embed_w += embed_d\n embed.append(embed_w)\n\n return tf.concat(embed, 1), word_embedding, combined_embed_vector_length","def embedding(self, images):\n predict = self.model.predict(images)\n return predict","def embed(self, sequence):\n words = sequence.split(' ')\n vecs = [self._E[self._w2i[i]] if i in self._w2i else self._E[self._w2i[\"UNK\"]]\n for i in words]\n return vecs","def produce_outputs(self):\n # if self.loaded_aggregated:\n # debug(\"Skippping {} mapping due to preloading\".format(self.base_name))\n # return\n # need to calc term numeric index for aggregation\n\n\n # if self.loaded_preprocessed:\n # debug(\"Skippping {} mapping due to preloading\".format(self.base_name))\n # return\n\n bagger = Bag(vocabulary=self.term_list, weighting=self.base_name, ngram_range=self.ngram_range)\n\n self.embeddings = np.ndarray((0, len(self.term_list)), dtype=np.int32)\n for idx in self.indices.get_train_test():\n texts = Text.get_strings(self.text.data.get_slice(idx))\n vecs = bagger.map_collection(texts, fit=False, transform=True)\n self.embeddings = np.append(self.embeddings, vecs, axis=0)\n del texts\n\n # texts = Text.get_strings(self.text.data.get_slice(test_idx))\n # vec_test = bagger.map_collection(texts, fit=do_fit)\n # del texts\n\n # self.embeddings = np.vstack((vec_train, vec_test))\n\n # self.embeddings = np.append(vec_train, vec_test)\n # self.vector_indices = (np.arange(len(train)), np.arange(len(test)))\n\n # set misc required variables\n self.set_constant_elements_per_instance()","def build_embeddings(opt, word_dict, for_encoder='src'):\n if for_encoder=='src':\n embedding_dim = opt.src_word_vec_size #512\n elif for_encoder=='tgt':\n embedding_dim = opt.tgt_word_vec_size\n elif for_encoder=='structure':\n embedding_dim = 64\n\n word_padding_idx = word_dict.stoi[Constants.PAD_WORD]\n num_word_embeddings = len(word_dict)\n \n if for_encoder=='src' or for_encoder=='tgt':\n\n return Embeddings(word_vec_size=embedding_dim,\n position_encoding=opt.position_encoding,\n dropout=opt.dropout,\n word_padding_idx=word_padding_idx,\n word_vocab_size=num_word_embeddings,\n sparse=opt.optim == \"sparseadam\")\n elif for_encoder=='structure':\n return Embeddings(word_vec_size=embedding_dim,\n position_encoding=False,\n dropout=opt.dropout,\n word_padding_idx=word_padding_idx,\n word_vocab_size=num_word_embeddings,\n sparse=opt.optim == \"sparseadam\")","def convert(encoder, bert_model):\n num_layers = encoder._config[\"num_layers\"]\n num_attention_heads = encoder._config[\"num_attention_heads\"]\n hidden_size = encoder._config[\"hidden_size\"]\n head_size = hidden_size // num_attention_heads\n assert head_size * num_attention_heads == hidden_size\n encoder._embedding_layer.set_weights(\n [bert_model[\"embeddings.word_embeddings.weight\"]])\n encoder._embedding_norm_layer.set_weights([\n bert_model[\"embeddings.LayerNorm.weight\"],\n bert_model[\"embeddings.LayerNorm.bias\"]\n ])\n encoder._type_embedding_layer.set_weights(\n [bert_model[\"embeddings.token_type_embeddings.weight\"]])\n encoder._position_embedding_layer.set_weights(\n [bert_model[\"embeddings.position_embeddings.weight\"]])\n for layer_num in range(num_layers):\n encoder._transformer_layers[\n layer_num]._attention_layer._key_dense.set_weights([\n bert_model[f\"encoder.layer.{layer_num}.attention.self.key.weight\"].T\n .reshape((hidden_size, num_attention_heads, head_size)),\n bert_model[f\"encoder.layer.{layer_num}.attention.self.key.bias\"]\n .reshape((num_attention_heads, head_size))\n ])\n encoder._transformer_layers[\n layer_num]._attention_layer._query_dense.set_weights([\n bert_model[f\"encoder.layer.{layer_num}.attention.self.query.weight\"]\n .T.reshape((hidden_size, num_attention_heads, head_size)),\n bert_model[f\"encoder.layer.{layer_num}.attention.self.query.bias\"]\n .reshape((num_attention_heads, head_size))\n ])\n encoder._transformer_layers[\n layer_num]._attention_layer._value_dense.set_weights([\n bert_model[f\"encoder.layer.{layer_num}.attention.self.value.weight\"]\n .T.reshape((hidden_size, num_attention_heads, head_size)),\n bert_model[f\"encoder.layer.{layer_num}.attention.self.value.bias\"]\n .reshape((num_attention_heads, head_size))\n ])\n encoder._transformer_layers[\n layer_num]._attention_layer._output_dense.set_weights([\n bert_model[\n f\"encoder.layer.{layer_num}.attention.output.dense.weight\"].T\n .reshape((num_attention_heads, head_size, hidden_size)),\n bert_model[f\"encoder.layer.{layer_num}.attention.output.dense.bias\"]\n ])\n encoder._transformer_layers[layer_num]._attention_layer_norm.set_weights([\n bert_model[\n f\"encoder.layer.{layer_num}.attention.output.LayerNorm.weight\"],\n bert_model[f\"encoder.layer.{layer_num}.attention.output.LayerNorm.bias\"]\n ])\n encoder._transformer_layers[layer_num]._intermediate_dense.set_weights([\n bert_model[f\"encoder.layer.{layer_num}.intermediate.dense.weight\"].T,\n bert_model[f\"encoder.layer.{layer_num}.intermediate.dense.bias\"]\n ])\n encoder._transformer_layers[layer_num]._output_dense.set_weights([\n bert_model[f\"encoder.layer.{layer_num}.output.dense.weight\"].T,\n bert_model[f\"encoder.layer.{layer_num}.output.dense.bias\"]\n ])\n encoder._transformer_layers[layer_num]._output_layer_norm.set_weights([\n bert_model[f\"encoder.layer.{layer_num}.output.LayerNorm.weight\"],\n bert_model[f\"encoder.layer.{layer_num}.output.LayerNorm.bias\"]\n ])","def _define_encoder(self):\n self.encoder = nn.Sequential(View((-1, 64 * 64 * 3)),\n nn.Linear(64 * 64 * 3, 5120, bias=False), nn.SELU(),\n nn.BatchNorm1d(5120),\n nn.Linear(5120, 2560, bias=False), nn.SELU(),\n nn.BatchNorm1d(2560),\n nn.Linear(2560, 512, bias=False), nn.SELU(),\n nn.BatchNorm1d(512),\n nn.Linear(512, 128, bias=False), nn.SELU(),\n nn.BatchNorm1d(128),\n nn.Linear(128, self.encoding_shape, bias=False), nn.SELU(),\n )","def _decode_train(self, decoder, _encoder_output, _features, labels):\r\n target_embedded = tf.nn.embedding_lookup(decoder.target_embedding,\r\n labels[\"target_ids\"])\r\n\r\n return decoder(_encoder_output, labels=target_embedded[:,:-1], sequence_length=labels[\"target_len\"]-1)","def pretrain_forward(self, inp):\n return self.encoder(inp)","def extract_embedding(self, from_model):\n return from_model","def call(self, x, training, mask):\n seq_len = tf.shape(x)[1]\n # adding embedding and position encoding.\n embedding = self.embedding(x) # (batch_size, input_seq_len, d_model)\n embedding *= tf.math.sqrt(tf.cast(self.dm, tf.float32))\n embedding += self.positional_encoding[:, :seq_len, :]\n\n encoder_out = self.dropout(embedding, training=training)\n\n for i in range(self.N):\n encoder_out = self.blocks[i](encoder_out, training, mask)\n\n return encoder_out","def position_encoding(sentence_size, embedding_size):\n encoding = np.ones((embedding_size, sentence_size), dtype=np.float32)\n ls = sentence_size+1\n le = embedding_size+1\n for i in range(1, le):\n for j in range(1, ls):\n encoding[i-1, j-1] = (i - (le-1)/2) * (j - (ls-1)/2)\n encoding = 1 + 4 * encoding / embedding_size / sentence_size\n return np.transpose(encoding)","def Aut(A):\n return Embeddings(A,A)","def build_embedding(graph):\n\n nodes_list = list(graph.nodes.values())\n sorted_nodes = sorted(\n nodes_list,\n key=lambda x: hash_labels_only(labels=x.labels, node_label_hash=NODE_TYPE_HASH))\n embedding = []\n\n for i in range(MAX_NODES):\n if i < len(sorted_nodes) and \"name\" in sorted_nodes[i].properties and \\\n sorted_nodes[i].properties[\"name\"] != []:\n\n # The 'name' property on each node is a list, the current solution is to\n # take the first element.\n name = sorted_nodes[i].properties[\"name\"][0]\n encoded_name = hashing_trick(name, VOCAB_SIZE, hash_simhash)\n\n if \"cmdline\" in sorted_nodes[i].properties:\n cmdline = sorted_nodes[i].properties[\"cmdline\"]\n encoded_cmdline = hashing_trick(cmdline, VOCAB_SIZE, hash_simhash)\n else:\n encoded_cmdline = []\n\n embedding += [encoded_name, encoded_cmdline]\n else:\n embedding += [[], []]\n\n padded_embedding = pad_sequences(embedding, maxlen=EMBEDDING_LENGTH)\n combined_embedding = [num for sublist in padded_embedding for num in sublist]\n return np.asarray(combined_embedding, dtype=np.int16)","def build_sentence_encoder(self, raw_encoder_input, input_seq_len):\n with tf.variable_scope('text_encoder'):\n self.embedding = \\\n tf.get_variable(\n \"embedding\", initializer=tf.random_uniform(\n [self.config.word_voc_size,\n self.config.word_embedding_space_size],\n -self.config.TRAIN.SENCODER.none_rnn_para_initial_max,\n self.config.TRAIN.SENCODER.none_rnn_para_initial_max))\n inputs = tf.nn.embedding_lookup(self.embedding, raw_encoder_input)\n\n # now it is [MAX_SEQ_LENGTH, batch_size, embedding_length]\n input_batch_order = tf.transpose(inputs, [1, 0, 2])\n\n # now it is [MAX_SEQ_LENGTH * batch_size, embedding_length]\n input_batch_order = tf.reshape(\n input_batch_order, [-1, self.config.word_embedding_space_size])\n\n # now it is LIST OF [BATCH_SIZE, embedding_length]\n encoder_input = tf.split(0, self.config.seq_max_len,\n input_batch_order)\n\n # the encoder part\n encode_gru_cell = tf.nn.rnn_cell.GRUCell(\n self.config.encoder_dimension)\n # big news: The state is final state, output is a list of tensor.\n # We don't to do that\n _, sentence_rep = tf.nn.rnn(encode_gru_cell, encoder_input,\n dtype=tf.float32,\n sequence_length=input_seq_len)\n self.sentence_rep = sentence_rep\n self.sentence_rep = tf.nn.l2_normalize(self.sentence_rep, 1)\n return","def get_word_embeddings(t, folder, lang=\"en\"):\n vecs_url = f\"https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.{lang}.300.vec.gz\"\n vecs_gz_filename = vecs_url.rpartition(\"/\")[2]\n os.makedirs(folder, exist_ok=True)\n vecs_gz_filepath = os.path.join(folder, vecs_gz_filename)\n\n tokenizer_vocab_size = len(t.vocab)\n\n if wait_for_file_stable(vecs_gz_filepath):\n print(\"Using existing embeddings file\")\n else:\n print(\"Downloading word vectors...\")\n subprocess.run([\" \".join([\"wget\", \"-NP\", folder, vecs_url])], check=True, shell=True)\n\n print(\"Loading into memory...\")\n embeddings_index = dict()\n with gzip.open(vecs_gz_filepath, \"rt\") as zipf:\n firstline = zipf.readline()\n emb_vocab_size, emb_d = firstline.split(\" \")\n emb_vocab_size = int(emb_vocab_size)\n emb_d = int(emb_d)\n for line in zipf:\n values = line.split()\n word = values[0]\n # Only load subset of the embeddings recognised by the tokenizer:\n if word in t.vocab.stoi:\n coefs = np.asarray(values[1:], dtype=\"float32\")\n embeddings_index[word] = coefs\n print(\"Loaded {} of {} word vectors for tokenizer vocabulary length {}\".format(\n len(embeddings_index),\n emb_vocab_size,\n tokenizer_vocab_size,\n ))\n\n # create a weight matrix for words in training docs\n embedding_matrix = np.zeros((tokenizer_vocab_size, emb_d))\n for word, i in t.vocab.stoi.items():\n embedding_vector = embeddings_index.get(word)\n if embedding_vector is not None:\n embedding_matrix[i] = embedding_vector\n\n return embedding_matrix","def _apply_encoder(self, frame, prop, encoder, encoder_type=\"category\"):\n vectors = None\n if encoder_type == \"category\":\n vectors = encoder.transform(frame[prop].apply(normalize_to_set))\n elif encoder_type == \"text\":\n column = frame[prop].copy()\n column[column.isna()] = \" \"\n vectors = encoder.transform(column)\n elif encoder_type == \"numeric\" and encoder is not None:\n vectors = encoder.transform(\n np.reshape(frame[prop].to_list(), (frame[prop].shape[0], 1)))\n if vectors is not None:\n if issparse(vectors):\n vectors = vectors.todense()\n df = pd.DataFrame(columns=[prop], index=frame.index)\n df[prop] = vectors.tolist()\n return df","def get_embeddings():\n embeddings = dict(get_coefs(*o.strip().split()) for o in open(EMBEDDING_FILE))\n return embeddings","def get_embeddings(self, in_data):\n context, da = in_data\n if self.fixed_divide:\n da_emb = super(ContextDAEmbeddingSeq2SeqExtract, self).get_embeddings(da, pad=True)\n else:\n da_emb = super(ContextDAEmbeddingSeq2SeqExtract, self).get_embeddings(da, pad=False)\n max_context_len = (self.max_context_len + 3 * self.max_da_len) - len(da_emb)\n context_emb = []\n for tok in context[-max_context_len:]:\n context_emb.append(self.dict_token.get(tok, self.UNK_TOKEN))\n\n padding = [self.UNK_TOKEN] * (max_context_len - len(context))\n\n if self.use_div_token:\n return padding + context_emb + [self.DIV_TOKEN] + da_emb\n return padding + context_emb + da_emb","def embedding(self, seqs):\n batch_size, seqlen = seqs.shape\n seqs = np.reshape(seqs, (-1)) # convert to 1-d indexes [(batch_sz*seqlen)]\n embs = self.word2vec[seqs] # lookup [(batch_sz*seqlen) x emb_sz]\n embs = np.reshape(embs, (batch_size, seqlen, -1)) # recover the shape [batch_sz x seqlen x emb_sz]\n return embs","def add_embeddings(self):\n\n with tf.device('/cpu:0'):\n with tf.variable_scope('Embedding_Layer'):\n embeddings = tf.Variable(self.initial_embeddings,name = 'Embeddings')\n self.input_embeddings = tf.nn.embedding_lookup(embeddings, self.inputs_placeholder) #(N,S,D)\n self.question_embeddings = tf.nn.embedding_lookup(embeddings, self.questions_placeholder) #(N,S,D)","def null_embeddings(self, detections):\n embeddings = np.ones((len(detections), self.feature_dim))\n embeddings /= np.linalg.norm(embeddings, axis=1, keepdims=True)\n return embeddings","def forward(self, images):\n embedding = self.model(images)\n embedding = self.l2_norm(embedding)\n # Multiply by alpha = 10 as suggested in https://arxiv.org/pdf/1703.09507.pdf\n # Equation 9: number of classes in VGGFace2 dataset = 9131\n # lower bound on alpha = 5, multiply alpha by 2; alpha = 10\n alpha = 10\n embedding = embedding * alpha\n\n return embedding","def _get_words_emb(self, words):\n\n if not self._is_cased_embedding:\n return self._word_vocab[[word.lower() for word in words]]\n\n result = np.full([len(words)], fill_value=0, dtype=np.float32)\n word_emb_matrix = np.full([len(words), 4], fill_value=0, dtype=np.float32)\n\n for i, w in enumerate(words):\n word_emb_matrix[i, :] = self._word_vocab[[w, w.lower(), w.capitalize(), w.upper()]]\n\n mask = word_emb_matrix != 0\n first_non_zero_embeddings_indices = np.where(mask.any(axis=1), mask.argmax(axis=1), -1)\n\n for i, index in enumerate(first_non_zero_embeddings_indices):\n result[i] = word_emb_matrix[i, index]\n\n return result","def postprocess(self):\n if self.last_num_features == 0:\n return np.empty((0, self.feature_dim))\n\n embedding_out = self.backend.synchronize()[0][:self.last_num_features * self.feature_dim]\n self.embeddings.append(embedding_out)\n embeddings = np.concatenate(self.embeddings).reshape(-1, self.feature_dim)\n embeddings /= np.linalg.norm(embeddings, axis=1, keepdims=True)\n return embeddings","def position_encoding(sentence_size, embedding_size):\n\tencoding = np.ones((embedding_size, sentence_size), dtype=np.float32)\n\tls = sentence_size+1\n\tle = embedding_size+1\n\tfor i in range(1, le):\n\t\tfor j in range(1, ls):\n\t\t\tencoding[i-1, j-1] = (i - (le-1)/2) * (j - (ls-1)/2)\n\tencoding = 1 + 4 * encoding / embedding_size / sentence_size\n\treturn np.transpose(encoding)","def build_word_embeddings(self):\n if self.mode == \"encode\":\n # Word embeddings are fed from an external vocabulary which has possibly\n # been expanded (see vocabulary_expansion.py).\n encode_emb1 = tf.placeholder(tf.float32, (\n None, None, self.config.word_embedding_dim), \"encode_emb1\")\n # No sequences to decode.\n encode_emb2 = tf.placeholder(tf.float32, (\n None, None, self.config.word_embedding_dim), \"encode_emb2\")\n elif self.mode == \"test\":\n encode_emb1 = tf.placeholder(tf.float32, (\n None, None, self.config.word_embedding_dim), \"encode_emb1\")\n # No sequences to decode.\n encode_emb2 = tf.placeholder(tf.float32, (\n None, None, self.config.word_embedding_dim), \"encode_emb2\")\n else:\n word_emb = tf.get_variable(\n name=\"word_embedding\",\n shape=[self.config.vocab_size, self.config.word_embedding_dim],\n initializer=self.uniform_initializer)\n\n encode_emb1 = tf.nn.embedding_lookup(word_emb, self.encode_ids1)\n encode_emb2 = tf.nn.embedding_lookup(word_emb, self.encode_ids2)\n\n\n self.encode_emb1 = encode_emb1\n self.encode_emb2 = encode_emb2","def sentence_to_embedding(sent, word_to_vec, seq_len, embedding_dim):\n embedding_vec = np.zeros((seq_len,embedding_dim))\n for i in range(min(len(sent),seq_len)):\n embedding_vec[i,:] = word_to_vec.get(sent[i])\n return embedding_vec","def word_embedding_forward(x, W):\n out, cache = None, None\n ##############################################################################\n # TODO: Implement the forward pass for word embeddings. #\n # #\n # HINT: This should be very simple. #\n ##############################################################################\n out = W[x,:]\n cache = x, W\n\n ##############################################################################\n # END OF YOUR CODE #\n ##############################################################################\n return out, cache","def create_embedding(self):\n self.embedding = []\n\n for index in range(1,self.args.window_size+1):\n print(\"\\nOptimization round: \" +str(index)+\"/\"+str(self.args.window_size)+\".\")\n print(\"Creating documents.\")\n clean_documents = self.walk_extracts(index)\n print(\"Fitting model.\")\n model = Word2Vec(clean_documents,\n size = self.args.dimensions,\n window = 1,\n min_count = self.args.min_count,\n sg = 1,\n workers = self.args.workers)\n\n new_embedding = self.get_embedding(model)\n self.embedding = self.embedding +[new_embedding]\n self.embedding = np.concatenate(self.embedding, axis = 1)","def forward(\n self,\n encoder_out,\n encoded_captions: torch.Tensor,\n caption_lengths: torch.Tensor\n ):\n pass","def build_seq_embeddings(self):\n with tf.variable_scope(\"seq_embedding\"), tf.device(\"/cpu:0\"):\n embedding_map = tf.get_variable(\n name=\"map\",\n shape=[self.config.vocab_size, self.config.word_embedding_size],\n initializer=self.initializer)\n \n # We need to store the normalized lookup table for efficient mapping of embedding vectors to closest words\n self.normed_embedding_map = tf.nn.l2_normalize(embedding_map, dim=1)\n \n seq_embeddings = tf.nn.embedding_lookup(embedding_map, self.input_seqs) \n # seq_embeddings has the shape (batch_size, sequence_length, sentence_length, embedding_size)\n # meaning, for each index in input_seqs (with shape (batch_size, sequence_length, sentence_length)) it stores an embedding vector\n\n #print('Shape seq_embeddings: ' + str(seq_embeddings.get_shape()))\n\n self.seq_embeddings = seq_embeddings","def _words_to_vec(self, sentence):\n return torch.FloatTensor([self._use_embeddings(word) for word in sentence])","def embed(self, loader, model):\n print(\" ** Embedding words\")\n\n words = loader.words\n vectors = [model.get_word_vector(word) for word in words]\n\n return [(w, *v) for w, v in zip(words, vectors)]","def fit_transform(self):\n if self.enc_types == \"label\":\n return self._label_encoding()\n elif self.enc_types == \"ohe\":\n return self._one_hot_encoder()\n elif self.enc_types == \"binary\":\n return self._binarization()\n else:\n raise Exception(\"Encoding type not understood\")","def embed(self, smi_or_mol):\n if not isinstance(smi_or_mol, Chem.Mol):\n mol = Chem.MolFromSmiles(smi_or_mol)\n else:\n mol = smi_or_mol\n wv = self.model.wv\n sentence = self.substructure(mol)\n vec = np.zeros(self.model.vector_size)\n for fp in sentence:\n if fp in wv.vocab:\n vec += wv[fp]\n return vec","def _generate_embeddings(self, config): \n tr_parts = []\n te_parts = []\n all_columns = []\n for comp in self.components:\n tr_tmp, te_tmp, cols = comp.generate(config)\n if cols != None:\n print(tr_tmp.shape,te_tmp.shape)\n tr_parts.append(tr_tmp)\n te_parts.append(te_tmp)\n all_columns += cols\n X_train = np.concatenate(tr_parts, axis=1)\n X_test = np.concatenate(te_parts, axis=1)\n print(\"Concatenated size:\", X_train.shape, X_test.shape)\n self.feature_columns = all_columns\n return X_train, X_test","def load_pretrained_embeddings(self, embeddings):\r\n self.embedding.weight = nn.Parameter(embeddings)","def __init__(self, in_dimension, layer_1d, layer_2d, layer_3d,\n latent_dimension):\n super(VAEEncoder, self).__init__()\n self.latent_dimension = latent_dimension\n\n # Reduce dimension up to second last layer of Encoder\n self.encode_nn = nn.Sequential(\n nn.Linear(in_dimension, layer_1d),\n nn.ReLU(),\n nn.Linear(layer_1d, layer_2d),\n nn.ReLU(),\n nn.Linear(layer_2d, layer_3d),\n nn.ReLU()\n )\n\n # Latent space mean\n self.encode_mu = nn.Linear(layer_3d, latent_dimension)\n\n # Latent space variance\n self.encode_log_var = nn.Linear(layer_3d, latent_dimension)","def __init__(self, in_dimension, layer_1d, layer_2d, layer_3d,\n latent_dimension):\n super(VAEEncoder, self).__init__()\n self.latent_dimension = latent_dimension\n\n # Reduce dimension up to second last layer of Encoder\n self.encode_nn = nn.Sequential(\n nn.Linear(in_dimension, layer_1d),\n nn.ReLU(),\n nn.Linear(layer_1d, layer_2d),\n nn.ReLU(),\n nn.Linear(layer_2d, layer_3d),\n nn.ReLU()\n )\n\n # Latent space mean\n self.encode_mu = nn.Linear(layer_3d, latent_dimension)\n\n # Latent space variance\n self.encode_log_var = nn.Linear(layer_3d, latent_dimension)","def generate_conll2003_embeddings():\n glove_embedding = get_glove_embedding()\n\n word2index = {}\n idx2word = {}\n embed_array = []\n\n word2index[\"<pad>\"] = 1\n embed_array.append(init_embedding())\n\n word2index[\"<unk>\"] = 0\n embed_array.append(init_embedding())\n\n data = []\n with open(TRAIN_DATA_PATH, \"r\") as f:\n for line in f:\n data.append(json.loads(line))\n\n idx = 2\n\n for sample in tqdm(data, total=len(data)):\n words = sample[\"tokens\"]\n\n for w in words:\n w = w.lower()\n\n # if word is not present in dictionary, add to dictionary and append embedding vector\n if w not in word2index.keys():\n word2index[w] = idx\n idx += 1\n if w not in glove_embedding.keys():\n ev = init_embedding()\n else:\n ev = glove_embedding[w]\n\n embed_array.append(ev)\n\n else:\n continue\n\n # save embeddings\n embed_array = np.vstack(embed_array)\n np.save(EMBD_OUTPUT_PATH, embed_array)\n\n # save dictionary\n print(\"Dicitionary Size: \", len(word2index))\n with open(DICTIONARY_OUTPUT_PATH, \"w\") as f:\n json.dump(word2index, f)","def fit_transform(self, X):\n self._fit(X)\n return self.embedding","def get_align_vec(align, symbols):\n enc = OrdinalEncoder()\n enc.fit(symbols)\n\n align_vec = []\n\n for column_in in align.T:\n column_out = enc.transform(np.expand_dims(column_in, axis=1)).flatten()\n align_vec.append(column_out)\n align_vec = np.array(align_vec).T\n\n return align_vec, enc","def encode_vecs(self, vecs, lstm):\n initial_state = lstm.initial_state()\n states = initial_state.transduce(vecs)\n return states","def get_embeddings(vectors, text, generate_missing=False, k=300):\r\n embeddings = text.apply(lambda x: get_average_vec(x, vectors, generate_missing=generate_missing, k=k))\r\n return list(embeddings)","def encode(self, token_ids_tensor, attention_mask):\n with torch.no_grad():\n outputs = self.embedding(token_ids_tensor, attention_mask)\n\n hidden_states = outputs[2]\n embeddings = torch.stack(hidden_states[-4:]).sum(0)\n return embeddings","def forward(self, inputs, decode_len=None):\n\n batch_size = inputs.size(0)\n input_dim = inputs.size(1)\n assert input_dim == self.input_dim, 'input dim should be {:d} but now: {:d}'.format(self.input_dim, input_dim)\n\n sourceL = inputs.size(2)\n\n if self.embed_input:\n # repeat embeddings across batch_size\n # result is [batch_size x input_dim x embedding_dim]\n # TODO: repeat or expand?\n embedding = self.embedding.repeat(batch_size, 1, 1)\n embedded_inputs = []\n # result is [batch_size, 1, input_dim, sourceL]\n ips = inputs.unsqueeze(1)\n\n for i in range(sourceL):\n # [batch_size x 1 x input_dim] * [batch_size x input_dim x embedding_dim]\n # result is [batch_size, embedding_dim]\n embedded_inputs.append(torch.bmm(\n ips[:, :, :, i].float(),\n embedding).squeeze(1))\n\n # Result is [sourceL x batch_size x embedding_dim]\n embedded_inputs = torch.cat(embedded_inputs).view(\n sourceL,\n batch_size,\n embedding.size(2))\n else:\n embedded_inputs = inputs.permute(2, 0, 1)\n\n (encoder_hx, encoder_cx) = init_zero_hidden(self.hidden_dim, inputs.is_cuda)\n encoder_hx = encoder_hx.unsqueeze(0).repeat(embedded_inputs.size(1), 1).unsqueeze(0)\n encoder_cx = encoder_cx.unsqueeze(0).repeat(embedded_inputs.size(1), 1).unsqueeze(0)\n \n # encoder forward pass\n enc_h, (enc_h_t, enc_c_t) = self.encoder(embedded_inputs, (encoder_hx, encoder_cx))\n\n enc_h_linear = enc_h.view(-1, self.hidden_dim)\n # enc_h_linear_2d = enc_h_linear.view(self.hidden_dim, -1)\n enc_action_scores = self.EncodeScore(enc_h_linear)\n enc_action_scores = enc_action_scores.view(-1, batch_size).permute(1, 0)\n dec_init_state = (enc_h_t[-1], enc_c_t[-1])\n \n # repeat decoder_in_0 across batch\n decoder_input = self.decoder_in_0.unsqueeze(0).repeat(embedded_inputs.size(1), 1)\n\n (head_pointer_probs, head_positions, tail_pointer_probs, tail_positions, cls_scores), dec_hidden_t = self.decoder(decoder_input,\n embedded_inputs,\n dec_init_state,\n enc_h, max_len=decode_len)\n #TODO: added conversion to tensors\n head_pointer_probs = torch.stack(head_pointer_probs)\n head_pointer_probs = head_pointer_probs.permute(1, 0, 2)\n tail_pointer_probs = torch.stack(tail_pointer_probs)\n tail_pointer_probs = tail_pointer_probs.permute(1, 0, 2)\n cls_scores = torch.stack(cls_scores)\n cls_scores = cls_scores.permute(1, 0, 2)\n head_positions = torch.stack(head_positions)\n head_positions = head_positions.permute(1, 0)\n tail_positions = torch.stack(tail_positions)\n tail_positions = tail_positions.permute(1, 0)\n\n\n\n return head_pointer_probs, head_positions, tail_pointer_probs, tail_positions, cls_scores, enc_action_scores","def __init__(self, embed_size):\n super(ImgEncoder, self).__init__()\n model = models.vgg19(pretrained=True)\n in_features = model.classifier[-1].in_features # input size of feature vector\n model.classifier = nn.Sequential(\n *list(model.classifier.children())[:-1]) # remove last fc layer\n\n self.model = model # loaded model without last fc layer\n self.fc = nn.Linear(in_features, embed_size) # feature vector of image","def encode(self, embedded_sentence):\n embedding = self.encoder(embedded_sentence)\n return embedding","def _use_embeddings(self, word):\n if word == \"@PAD@\":\n return torch.zeros(self.embeddings_dim)\n else:\n return self.embeddings[word]"],"string":"[\n \"def compute_embeddings(encoder, data_batches):\\n\\n vectors = []\\n for batch in iter(data_batches):\\n X, Y = batch\\n X_embedded = encoder(X)\\n for vec in np.array(X_embedded):\\n vectors.append(vec)\\n vectors = np.array(vectors)\\n\\n return vectors\",\n \"def set_embeddings(self):\",\n \"def get_embeddings() -> tuple:\\n # Initialize the model loading Universal Sentense Encoder\\n # into a KerasLayer from Kaggle dataset file\\n model = tf.keras.Sequential(\\n [KerasLayer(encoder_path, input_shape=[], dtype=tf.string,\\n output_shape=[512], trainable=False),\\n # tf.keras.layers.Layer(512, dtype=tf.float16) # To reduce memory footprint\\n ]\\n )\\n\\n train_emb = model.predict(data_train['text'])\\n print('Train texts converted into embeddings. Shape:', train_emb.shape)\\n\\n test_emb = model.predict(data_test['text'])\\n print('Test texts converted into embeddings. Shape:', test_emb.shape)\\n\\n return train_emb, test_emb\",\n \"def add_embedding(self):\\n ### YOUR CODE HERE (~4-6 lines)\\n embeddingTensor = tf.Variable(self.pretrained_embeddings)\\n embeddings = tf.nn.embedding_lookup(embeddingTensor, self.input_placeholder)\\n embeddings = tf.reshape(embeddings, [-1, self.max_length, Config.n_features * Config.embed_size])\\n ### END YOUR CODE\\n return embeddings\",\n \"def vectorize_texts(self, encoder) -> NoReturn:\\n self.encoder = encoder\\n self.vectorized_texts = [self.encoder.encode(t) for t in self.tokenized_texts]\",\n \"def embed(self, encoded: List[int]) -> np.ndarray:\\n return np.vstack([self.embeddings[index] for index in encoded])\",\n \"def _add_pre_trained_embedding(self):\\n\\n if self.embedding_type['type'] == 'glove':\\n self.logging.info('use pre-trained glove word2vec')\\n # a. load pre trained glove\\n GLOVE_DIR = '../data/glove_pretrained/glove.6B'\\n glove_suffix_name = 'glove.6B.' + str(self.embedding_size) + 'd.txt'\\n import os\\n import numpy as np\\n\\n embeddings_index = {}\\n f = open(os.path.join(GLOVE_DIR, glove_suffix_name)) # 'glove.6B.100d.txt'))\\n for line in f:\\n values = line.split()\\n word = values[0]\\n coefs = np.asarray(values[1:], dtype='float32')\\n embeddings_index[word] = coefs\\n f.close()\\n\\n self.logging.info('')\\n self.logging.info('Found %s word vectors.' % len(embeddings_index))\\n\\n # b. compute embedding matrix\\n embedding_matrix = np.zeros((len(self.word_index) + 1, self.embedding_size))\\n cnt = 0\\n for word, i in self.word_index.items():\\n embedding_vector = embeddings_index.get(word)\\n if embedding_vector is not None:\\n embedding_matrix[i] = embedding_vector # words not found in embedding index will be all-zeros.\\n else:\\n # self.logging.info('token in train missing in word2vec: ' + str(word))\\n cnt += 1\\n self.logging.info('total tokens missing: ' + str(cnt) + ' / ' + str(len(self.word_index)))\\n\\n # c. build embedding layer\\n from keras.layers import Embedding\\n embedding_layer = Embedding(len(self.word_index) + 1,\\n self.embedding_size,\\n weights=[embedding_matrix],\\n input_length=self.maxlen,\\n trainable=False)\\n\\n elif self.embedding_type['type'] == 'gensim':\\n self.logging.info('use pre-trained gensim word2vec')\\n\\n import gzip\\n import gensim\\n from keras.layers import Embedding\\n import numpy as np\\n\\n # fname = '../data/word2vec_pretrained/motors/d_300_k_712904_w_6_e_60_v_motors'\\n # fname = '../data/word2vec_pretrained/fashion/d_300_k_1341062_w_6_e_70_v_fashion'\\n\\n self.logging.info('load word2vec path: ' + str(self.embedding_type['path']))\\n model = gensim.models.Word2Vec.load(self.embedding_type['path'])\\n pretrained_weights = model.wv.syn0\\n vocab_size, vector_dim = pretrained_weights.shape\\n\\n method = 3\\n if method == 1:\\n self.logging.info('word2vec attempt to fit into embedding layer - middle complex')\\n # convert the wv word vectors into a numpy matrix that is suitable for insertion\\n # into our TensorFlow and Keras models\\n\\n embedding_matrix = np.zeros((len(model.wv.vocab), vector_dim))\\n for i in range(len(model.wv.vocab)):\\n embedding_vector = model.wv[model.wv.index2word[i]]\\n if embedding_vector is not None:\\n embedding_matrix[i] = embedding_vector\\n\\n embedding_layer = Embedding(input_dim=embedding_matrix.shape[0],\\n output_dim=embedding_matrix.shape[1],\\n # input_length=self.maxlen,\\n weights=[embedding_matrix],\\n trainable=False)\\n elif method == 2:\\n self.logging.info('word2vec simple embedding matching - simple complex')\\n embedding_layer = Embedding(input_dim=vocab_size,\\n output_dim=vector_dim,\\n input_length=self.maxlen,\\n weights=[pretrained_weights],\\n trainable=False)\\n elif method == 3:\\n\\n self.logging.info('word2vec match using word_index from keras tokenizer - as used in glove match above')\\n # b. compute embedding matrix\\n\\n # sd = 1 / np.sqrt(len(self.word_index) + 1)\\n # embedding_matrix = np.random.normal(0, scale=sd, size=(len(self.word_index) + 1, self.embedding_size))\\n\\n embedding_matrix = np.zeros((len(self.word_index) + 1, self.embedding_size))\\n cnt = 0\\n for word, i in self.word_index.items():\\n if word in model.wv:\\n embedding_vector = model.wv[word]\\n embedding_matrix[i] = embedding_vector\\n else:\\n # self.logging.info('token in train missing in word2vec: ' + str(word))\\n cnt += 1\\n self.logging.info('total tokens missing: ' + str(cnt))\\n\\n\\n # c. build embedding layer\\n from keras.layers import Embedding\\n embedding_layer = Embedding(len(self.word_index) + 1,\\n self.embedding_size,\\n weights=[embedding_matrix],\\n input_length=self.maxlen,\\n trainable=False)\\n else:\\n raise ValueError('unknown method value')\\n\\n else:\\n raise ValueError('unknown embedding type')\\n self.logging.info('create glove pre-trained embedding: ' + str(self.embedding_size))\\n return embedding_layer\",\n \"def get_embeddings(self, data):\\n raise NotImplementedError()\",\n \"def forward(self, input_sentence):\\n sentence = self.word_embedding(input_sentence)\\n embedding = self.encoder(sentence)\\n return embedding\",\n \"def build_encoder(tparams, options):\\n\\t# word embedding (source)\\n\\tembedding = tensor.tensor3('embedding', dtype='float32')\\n\\tx_mask = tensor.matrix('x_mask', dtype='float32')\\n\\n\\t# encoder\\n\\tproj = get_layer(options['encoder'])[1](tparams, embedding, options,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tprefix='encoder',\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tmask=x_mask)\\n\\tctx = proj[0][-1]\\n\\n\\treturn embedding, x_mask, ctx\",\n \"def _create_encoder(self):\\n\\n def _init_weights(layer):\\n \\\"\\\"\\\"Initializes the weights of a layer based on type.\\\"\\\"\\\"\\n if isinstance(layer, (nn.Conv2d, nn.Linear)):\\n torch.nn.init.xavier_uniform_(layer.weight)\\n try:\\n # Some layers may not have biases, so catch the exception and pass.\\n layer.bias.data.fill_(0.0)\\n except AttributeError:\\n pass\\n\\n kernel_size = 5\\n pad = 2\\n input_channels = 1\\n first_conv_channels = 6\\n second_conv_channels = 16\\n max_pool_kernel = 2\\n linear_size = 120\\n n_pixels = 7\\n\\n encoder = nn.Sequential(\\n nn.Conv2d(\\n input_channels, first_conv_channels, kernel_size, padding=pad),\\n nn.BatchNorm2d(first_conv_channels),\\n nn.ReLU(),\\n nn.MaxPool2d(max_pool_kernel),\\n nn.Conv2d(\\n first_conv_channels, second_conv_channels, kernel_size,\\n padding=pad),\\n nn.BatchNorm2d(second_conv_channels),\\n nn.ReLU(),\\n nn.MaxPool2d(max_pool_kernel),\\n utils.Flatten(),\\n nn.Linear(n_pixels * n_pixels * self.n_digits * second_conv_channels,\\n linear_size),\\n nn.BatchNorm1d(linear_size),\\n nn.ReLU(),\\n nn.Linear(linear_size, self.embedding_dim),\\n nn.Linear(self.embedding_dim, self.n_classes, bias=False),\\n )\\n\\n encoder.apply(_init_weights)\\n\\n # This is the empirical approximation for initialization the vMF\\n # distributions for each class in the final layer.\\n if self.use_vmf:\\n utils.vmf_class_weight_init(encoder[-1].weight, self.kappa_confidence,\\n self.embedding_dim)\\n\\n return encoder\",\n \"def pretrained_embedding_layer(word_to_vec_map, word_to_index):\\n \\n vocab_size = len(word_to_index) + 1 # adding 1 to fit Keras embedding (requirement)\\n any_word = list(word_to_vec_map.keys())[0]\\n emb_dim = word_to_vec_map[any_word].shape[0] # define dimensionality of your GloVe word vectors (= 50)\\n \\n ### START CODE HERE ###\\n # Step 1\\n # Initialize the embedding matrix as a numpy array of zeros.\\n # See instructions above to choose the correct shape.\\n emb_matrix = np.zeros((vocab_size, emb_dim))\\n \\n # Step 2\\n # Set each row \\\"idx\\\" of the embedding matrix to be \\n # the word vector representation of the idx'th word of the vocabulary\\n for word, idx in word_to_index.items():\\n emb_matrix[idx, :] = word_to_vec_map[word]\\n\\n # Step 3\\n # Define Keras embedding layer with the correct input and output sizes\\n # Make it non-trainable.\\n embedding_layer = tensorflow.keras.layers.Embedding(input_dim = vocab_size, output_dim = emb_dim, trainable = False)\\n ### END CODE HERE ###\\n\\n # Step 4 (already done for you; please do not modify)\\n # Build the embedding layer, it is required before setting the weights of the embedding layer. \\n embedding_layer.build((None,)) # Do not modify the \\\"None\\\". This line of code is complete as-is.\\n \\n # Set the weights of the embedding layer to the embedding matrix. Your layer is now pretrained.\\n embedding_layer.set_weights([emb_matrix])\\n \\n return embedding_layer\",\n \"def fit_transform(self, *args, **kwargs) -> np.ndarray:\\n self.fit(*args, **kwargs)\\n return self.embedding_\",\n \"def build_encoder_bi(tparams, options):\\n\\t# word embedding (source)\\n\\tembedding = tensor.tensor3('embedding', dtype='float32')\\n\\tembeddingr = embedding[::-1]\\n\\tx_mask = tensor.matrix('x_mask', dtype='float32')\\n\\txr_mask = x_mask[::-1]\\n\\n\\t# encoder\\n\\tproj = get_layer(options['encoder'])[1](tparams, embedding, options,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tprefix='encoder',\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tmask=x_mask)\\n\\tprojr = get_layer(options['encoder'])[1](tparams, embeddingr, options,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t prefix='encoder_r',\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t mask=xr_mask)\\n\\n\\tctx = tensor.concatenate([proj[0][-1], projr[0][-1]], axis=1)\\n\\n\\treturn embedding, x_mask, ctx\",\n \"def encode(self):\\n with tf.name_scope(\\\"encode\\\"):\\n self.encoder_inputs = tf.layers.dense(\\n inputs=self.encoder_inputs,\\n units=self.options['hidden_size'], activation=None, use_bias=True,\\n kernel_initializer=tf.keras.initializers.he_normal(seed=None),\\n bias_initializer=tf.zeros_initializer(),\\n kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None,\\n kernel_constraint=None, bias_constraint=None, trainable=True,\\n name=None, reuse=None)\\n self.encoder_inputs = tf.layers.batch_normalization(self.encoder_inputs,\\n axis=-1,\\n momentum=0.99,\\n epsilon=0.001,\\n center=True,\\n scale=True,\\n beta_initializer=tf.zeros_initializer(),\\n gamma_initializer=tf.ones_initializer(),\\n moving_mean_initializer=tf.zeros_initializer(),\\n moving_variance_initializer=tf.ones_initializer(),\\n training=self.is_training,\\n trainable=True,\\n renorm=False,\\n renorm_momentum=0.99)\\n # Prepare inputs to the layer stack by adding positional encodings and\\n # applying dropout.\\n # embedded_inputs = self.embedding_softmax_layer(inputs)\\n #\\n inputs_padding = transformer_model_utils.get_padding(tf.cast(\\n tf.reduce_max(100*self.encoder_inputs, [-1]),\\n dtype=tf.int32))\\n\\n with tf.name_scope(\\\"add_pos_encoding\\\"):\\n length = tf.shape(self.encoder_inputs)[1]\\n pos_encoding = transformer_model_utils.get_position_encoding(\\n length, self.options[\\\"hidden_size\\\"])\\n encoder_inputs = self.encoder_inputs + pos_encoding\\n\\n if self.is_training:\\n encoder_inputs = tf.nn.dropout(\\n encoder_inputs, 1 - self.options[\\\"layer_postprocess_dropout\\\"])\\n\\n return self.encoder_stack(encoder_inputs, self.attention_bias, inputs_padding)\",\n \"def get_movie_embedding(self):\\n raise NotImplementedError(\\\"has to be overwritten\\\")\",\n \"def encode(data, encoder):\\n # Get the list of hidden depths\\n\\thd = encoder.hidden_depths\\n # Find the middle hidden layer\\n\\tmiddle_layer_index = (len(hd)-1)/2\\n # Initialize empty container for the encoded data\\n\\tdata_encoded = np.zeros((data.shape[0],hd[middle_layer_index]))\\n\\tfor i, d_ in enumerate(data):\\n # feed forward, get all the activations, and just keep\\n # the middle layer, which is the encoding\\n\\t\\tx, z_container, x_container = encoder.ff(d_,True,True)\\n\\t\\tx_encoded = x_container[1+middle_layer_index]\\n\\t\\tdata_encoded[i] = x_encoded\\n\\t#\\n\\treturn data_encoded\",\n \"def encode_input_for_decoder(x_tensor, inp_lens_tensor, model_input_emb: EmbeddingLayer, model_enc: RNNEncoder):\\n input_emb = model_input_emb.forward(x_tensor)\\n (enc_output_each_word, enc_context_mask, enc_final_states) = model_enc.forward(input_emb, inp_lens_tensor)\\n enc_final_states_reshaped = (enc_final_states[0].unsqueeze(0), enc_final_states[1].unsqueeze(0))\\n return (enc_output_each_word, enc_context_mask, enc_final_states_reshaped)\",\n \"def encode_input(self, x_tensor, inp_lens_tensor):\\r\\n input_emb = self.input_emb.forward(x_tensor)\\r\\n enc_output_each_word, enc_context_mask, enc_final_states = self.encoder(input_emb, inp_lens_tensor)\\r\\n enc_final_states_reshaped = (enc_final_states[0].unsqueeze(0), enc_final_states[1].unsqueeze(0))\\r\\n # print('lest go', enc_final_states_reshaped[1].shape)\\r\\n return enc_output_each_word, enc_context_mask, enc_final_states_reshaped\",\n \"def _get_embedding(self, data):\\n embedding_list = [super()._get_embedding(data)]\\n context = data['context']\\n for i in range(context.shape[1]):\\n embedding_list.append(getattr(self, f'context{i}')(context[:, i:i+1]))\\n return torch.cat(embedding_list, dim=1)\",\n \"def embeddings(self):\\n self._ensure_is_connected()\\n return self._embeddings\",\n \"def forward(self, src, mask):\\n bs = src.shape[0]\\n src = src.permute(2, 0, 1)\\n m = src \\n enc_embed = self.enc_embed.weight.unsqueeze(1).repeat(1, bs, 1)\\n for layer in self.encoder_layers:\\n m = layer(m,\\n pos=enc_embed,\\n src_mask = mask\\n )\\n return m.permute(1, 2, 0), enc_embed.permute(1, 2, 0)\",\n \"def construct_embedding(self):\\n i = 0\\n self.load_dicts()\\n embedding_shape = (max(self.word2idx.values()) + 1,\\n self.embedding_size)\\n self.embedding = np.zeros(embedding_shape)\\n\\n with open(self.config.word_vec_fi_glove, 'r') as fi:\\n for line in fi:\\n word_vec = line.split(\\\" \\\")[1:]\\n self.embedding[i, :] = np.array(word_vec, dtype=np.float32)\\n i += 1\\n\\n self.write_embedding()\",\n \"def __init__(self, vocab_size, embedding_size, output_size):\\n super(VanillaEncoder, self).__init__()\\n\\n self.vocab_size = vocab_size\\n self.embedding = nn.Embedding(vocab_size, embedding_size)\\n self.gru = nn.GRU(embedding_size, output_size)\",\n \"def get_embedding(self, model):\\n embedding = []\\n for node in range(len(self.graph.nodes())):\\n embedding.append(list(model[str(node)]))\\n embedding = np.array(embedding)\\n return embedding\",\n \"def get_embeddings():\\n # Load the raw embedding data\\n X_train = np.load('./train_embeddings.npy')\\n \\n y_train = np.load('./train_labels.npy')\\n \\n X_valid = np.load('./valid_embeddings.npy')\\n \\n y_valid = np.load('./valid_labels.npy')\\n \\n X_test = np.load('./test_embeddings.npy')\\n \\n y_test = np.load('./test_labels.npy')\\n\\n #return X_train, y_train\\n return X_train, y_train, X_valid, y_valid, X_test, y_test\",\n \"def embedding_layer(self):\\n with tf.name_scope(\\\"Embedding_Layer\\\"):\\n V_size = len(self.vocab)\\n embed_dim = len(self.embed[0]) \\n W_embed_ = tf.get_variable(\\\"W_embed\\\",shape=[V_size, embed_dim],trainable=False).assign(np.asarray(self.embed))\\n W_analogy_embed_ = tf.get_variable(\\\"W_analogy_embed\\\",shape=[V_size, embed_dim],trainable=True,initializer=tf.random_uniform_initializer(minval=-1,maxval=1))\\n return W_embed_, W_analogy_embed_\",\n \"def generate_embeddings(vae, dataset):\\n data = ((torch.unsqueeze(img,0), label) for img, label in dataset)\\n data = ((vae.encoder(tens), label) for tens, label in data)\\n data = ((vae.codebook(emb),label) for emb, label in data)\\n data = ((torch.flatten(img),label) for img, label in data)\\n data = (torch.cat([inds,Tensor([label]).int()]) for inds, label in data)\\n return data\",\n \"def test_extract_embeddings():\\n docs = [\\\"some document\\\"]\\n model = BERTopic(embedding_model=\\\"distilbert-base-nli-stsb-mean-tokens\\\")\\n bertopic_embeddings = model._extract_embeddings(docs)\\n\\n assert isinstance(bertopic_embeddings, np.ndarray)\\n assert bertopic_embeddings.shape == (1, 768)\\n\\n sentence_embeddings = embedding_model.encode(docs, show_progress_bar=False)\\n assert np.array_equal(bertopic_embeddings, sentence_embeddings)\",\n \"def init_emb(self):\\n # Initialize users and items' embeddings\\n nn.init.xavier_uniform_(self.user_embedding.weight)\\n nn.init.xavier_uniform_(self.item_embedding.weight)\",\n \"def get_vec(self, img, tensor=False):\\n image = (\\n self.normalize(self.to_tensor(self.scaler(img)))\\n .unsqueeze(0)\\n .to(self.device)\\n )\\n\\n if self.model_name == \\\"alexnet\\\":\\n my_embedding = torch.zeros(1, self.layer_output_size)\\n else:\\n my_embedding = torch.zeros(1, self.layer_output_size, 1, 1)\\n\\n def copy_data(m, i, o):\\n my_embedding.copy_(o.data)\\n\\n h = self.extraction_layer.register_forward_hook(copy_data)\\n h_x = self.model(image)\\n h.remove()\\n\\n if tensor:\\n return my_embedding\\n else:\\n if self.model_name == \\\"alexnet\\\":\\n return my_embedding.numpy()[0, :]\\n else:\\n return my_embedding.numpy()[0, :, 0, 0]\",\n \"def init_embedding(self):\\n self.embedding.weight.data.uniform_(-1./self.num_embeddings, 1./self.num_embeddings)\",\n \"def pretrained_embedding_layer(word_to_vec_map, word_to_index):\\n vocab_len = len(word_to_index) + 1 # adding 1 to fit Keras embedding (requirement)\\n emb_dim = word_to_vec_map[\\\"cucumber\\\"].shape[0] # define dimensionality of your GloVe word vectors (= 50)\\n emb_matrix = np.zeros((vocab_len, emb_dim)) # Initialize the embedding matrix as a numpy array of zeros of shape (vocab_len, dimensions of word vectors = emb_dim)\\n for word, index in word_to_index.items(): # Set each row \\\"index\\\" of the embedding matrix to be the word vector representation of the \\\"index\\\"th word of the vocabulary\\n emb_matrix[index, :] = word_to_vec_map[word]\\n embedding_layer = Embedding(vocab_len, emb_dim, trainable = False) # Define Keras embedding layer with the correct output/input sizes, make it trainable. Use Embedding(...). Make sure to set trainable=False. \\n embedding_layer.build((None,)) # Build the embedding layer, it is required before setting the weights of the embedding layer. Do not modify the \\\"None\\\".\\n embedding_layer.set_weights([emb_matrix]) # Set the weights of the embedding layer to the embedding matrix. Your layer is now pretrained.\\n return embedding_layer\",\n \"def _embed(self):\\n with tf.variable_scope('word_embedding'):\\n self.word_embeddings = tf.get_variable(\\n 'word_embeddings',\\n shape=(self.term_vocab.size(), self.term_vocab.embed_dim),\\n initializer=tf.constant_initializer(self.term_vocab.embeddings),\\n trainable=True\\n )\\n self.p_word_emb = tf.nn.embedding_lookup(self.word_embeddings, self.p)\\n self.q_word_emb = tf.nn.embedding_lookup(self.word_embeddings, self.q)\\n\\n with tf.variable_scope('char_embedding'):\\n self.char_embeddings = tf.get_variable(\\n 'char_embeddings',\\n shape=(self.char_vocab.size(), self.char_vocab.embed_dim),\\n initializer=tf.constant_initializer(self.char_vocab.embeddings),\\n trainable=True\\n )\\n self.p_char_emb = tf.nn.embedding_lookup(self.char_embeddings, self.p_char) # [batch, seqlen, max_char_num, embedding_size]\\n self.q_char_emb = tf.nn.embedding_lookup(self.char_embeddings, self.q_char)\\n\\n self.p_char_emb = self.cnn_emb(self.p_char_emb, \\\"p_emb\\\")\\n self.q_char_emb = self.cnn_emb(self.q_char_emb, \\\"q_emb\\\")\\n '''\\n self.p_char_emb = tf.reshape(self.p_char_emb, [-1, self.max_char_num, self.emb_size])\\n self.q_char_emb = tf.reshape(self.q_char_emb, [-1, self.max_char_num, self.emb_size])\\n\\n self.p_char_emb = cnn_layer.conv(self.p_char_emb, self.emb_size,\\n bias=True, activation=tf.nn.relu, kernel_size=5, name=\\\"char_conv\\\", reuse=None)\\n self.q_char_emb = cnn_layer.conv(self.q_char_emb, self.emb_size,\\n bias=True, activation=tf.nn.relu, kernel_size=5, name=\\\"char_conv\\\", reuse=True)\\n\\n self.p_char_emb = tf.reduce_max(self.p_char_emb, axis=1) # [batch*seqlen, 1, emb_size]\\n self.q_char_emb = tf.reduce_max(self.q_char_emb, axis=1)\\n\\n batch_size = tf.shape(self.p_word_emb)[0]\\n self.p_char_emb = tf.reshape(self.p_char_emb, [batch_size, -1, self.emb_size])\\n self.q_char_emb = tf.reshape(self.q_char_emb, [batch_size, -1, self.emb_size])\\n\\n self.p_char_emb = tf.nn.dropout(self.p_char_emb, 0.95)\\n self.q_char_emb = tf.nn.dropout(self.q_char_emb, 0.95)\\n '''\\n self.p_emb = tf.concat([self.p_word_emb, self.p_char_emb], -1)\\n self.q_emb = tf.concat([self.q_word_emb, self.q_char_emb], -1)\",\n \"def make_embeddings(self):\\n\\t\\tprint(\\\"Presetting embedding weights\\\")\\n\\t\\t\\t\\n\\t\\tnp.random.seed(0)\\n\\t\\tweights = np.random.uniform(low = -0.05, high = 0.05, size = (self.FREQCAP, self.EMB_SIZE))\\n\\t\\t\\n\\t\\tcounter = 0\\n\\n\\t\\twords = []\\n\\t\\tweights_tmp = []\\n\\n\\t\\twith open(self.embeddingpath) as handle:\\n\\t\\t\\tfor i, line in enumerate(handle):\\n\\t\\t\\t\\ttmp = line.strip()\\n\\t\\t\\t\\tif len(tmp) > 0:\\n\\t\\t\\t\\t\\tsplit = tmp.split(\\\" \\\")\\n\\t\\t\\t\\t\\tif split[0] in self.worddict and len(split[1:]) == 300:\\n\\t\\t\\t\\t\\t\\twords.append(split[0])\\n\\t\\t\\t\\t\\t\\tweights_tmp.append([float(a) for a in split[1:]])\\n\\t\\t\\n\\t\\tweights_tmp = np.array(weights_tmp)\\n\\n\\t\\tfor word, column in zip(words, weights_tmp):\\n\\t\\t\\tif self.worddict[word] < self.FREQCAP:\\n\\t\\t\\t\\tcounter += 1\\n\\t\\t\\t\\tweights[self.worddict[word],:] = column\\n\\t\\t\\n\\t\\tprint(\\\"Set\\\", counter, \\\"of\\\", weights.shape[0], \\\"columns\\\")\\n\\t\\t\\n\\t\\tif self.EMB_SIZE < weights.shape[-1]:\\n\\t\\t\\tprint(\\\"Reducing dimensionality to\\\", self.EMB_SIZE)\\n\\t\\t\\tpca = PCA(self.EMB_SIZE)\\n\\t\\t\\tweights = pca.fit_transform(weights)\\n\\t\\t\\n\\t\\tself.embeddings = [weights]\",\n \"def _get_embedding(self, data):\\n # Tensor(n, c)\\n cat = data['cat']\\n return self.one_hot_embed(cat)\",\n \"def create_embedding_matrix(self):\\n self.id2word = dict([(self.vocab[word]['id'], word) for word in self.vocab])\\n vocab_size = len(self.vocab)\\n result = np.zeros((vocab_size, self.embed_dim))\\n unknown_token_set = set()\\n\\n found_words = 0\\n avg = np.zeros(self.embed_dim)\\n for _ in range(1, vocab_size): # skip PAD embedding (initialize as zero embedding)\\n try:\\n result[_] = self.pretrained_embedding[self.id2word[_]]\\n avg += result[_]\\n found_words += 1\\n except:\\n unknown_token_set.add(_)\\n\\n avg /= found_words\\n for _ in unknown_token_set:\\n result[_] = avg\\n self.embedding = result\\n return found_words, len(self.id2word)\",\n \"def forward(self, tgt, m, enc_embed, mask):\\n bs = tgt.shape[0]\\n enc_embed = enc_embed.permute(2, 0, 1)\\n m = m.permute(2, 0, 1)\\n tgt = tgt.permute(2, 0, 1)\\n dec_embed = self.dec_embed.weight.unsqueeze(1).repeat(1, bs, 1)\\n\\n out = tgt\\n for layer in self.decoder_layers:\\n out = layer(out, m, \\n pos=enc_embed,\\n query_pos=dec_embed\\n )\\n \\n return self.decoder_norm(out).permute(1, 2, 0), dec_embed.permute(1, 2, 0)\",\n \"def _embed(self):\\n with tf.variable_scope('word_embedding'):\\n self.pretrained_word_mat = tf.get_variable(\\\"word_emb_mat\\\",\\n [self.vocab.word_size() - 2, self.vocab.word_embed_dim],\\n dtype=tf.float32,\\n initializer=tf.constant_initializer(\\n self.vocab.word_embeddings[2:],\\n dtype=tf.float32),\\n trainable=False)\\n self.word_pad_unk_mat = tf.get_variable(\\\"word_unk_pad\\\",\\n [2, self.pretrained_word_mat.get_shape()[1]],\\n dtype=tf.float32,\\n initializer=tf.constant_initializer(\\n self.vocab.word_embeddings[:2],\\n dtype=tf.float32),\\n trainable=True)\\n\\n self.word_mat = tf.concat([self.word_pad_unk_mat, self.pretrained_word_mat], axis=0)\\n self.p_emb = tf.nn.embedding_lookup(self.word_mat, self.p)\\n self.q_emb = tf.nn.embedding_lookup(self.word_mat, self.q)\",\n \"def fit_transform(self, X):\\n self.fit(X)\\n return self.embedding_\",\n \"def pretrained_embedding_layer(model,model2,model3, word_to_index,emb_dim_max):\\n words_ignored = []\\n vocab_len = len(word_to_index) + 1 \\n emb_matrix = np.zeros([vocab_len,emb_dim_max])\\n \\n print(' Total words would be processed : '+str(vocab_len))\\n for word, idx in word_to_index.items():\\n if word in model:\\n emb_matrix[idx,:200] = model[word]\\n emb_matrix[idx,200:] = 0\\n if word in model2:\\n emb_matrix[idx, :100] = model2[word]\\n emb_matrix[idx, 100:] = 0\\n if word in model3.keys():\\n emb_matrix[idx,:] = model3[word]\\n else:\\n words_ignored.append(word)\\n print(str(len(words_ignored))+\\\" words ignored\\\")\\n print(emb_matrix.shape) \\n \\n \\n embedding_layer = Embedding(vocab_len,emb_dim_max,trainable = True)\\n \\n # Build the embedding layer, it is required before setting the weights of the embedding layer. \\n embedding_layer.build((None,)) # Do not modify the \\\"None\\\". This line of code is complete as-is.\\n # Set the weights of the embedding layer to the embedding matrix. Your layer is now pretrained.\\n embedding_layer.set_weights([emb_matrix])\\n \\n return embedding_layer,words_ignored\",\n \"def get_embeddings(self, in_data):\\n context, da = in_data\\n if self.fixed_divide:\\n da_emb = super(PersonageContextDAEmbeddingSeq2SeqExtract, self).get_embeddings(da, pad=True)\\n else:\\n da_emb = super(PersonageContextDAEmbeddingSeq2SeqExtract, self).get_embeddings(da, pad=False)\\n\\n # Shubhangi: what this step essentially does is it replaces the context words by their token, with UNK as default.\\n # again , we don't need this since our context data is essentially vectors therefore commenting this out\\n # similary we don't need context embedding , that's exactly what context is already .\\n\\n # context_emb = []\\n context_emb = [float(parameter[0]) for parameter in context]\\n\\n # for tok in context[-max_context_len:]:\\n # context_emb.append(self.dict_token.get(tok, self.UNK_TOKEN))\\n\\n # Shubhangi: padding is needed because each context sentence could be of different length ,\\n # we don't need to include context in padding as we're going to have a fixed size\\n # (max_context_len - len(context)) = 0\\n\\n\\n # padding = [self.UNK_TOKEN] * (max_context_len - len(context))\\n\\n # Shubhangi: padding might be harmless for now therefore not removing ,\\n # essentially what this is doing is concatenating the arrays and sending\\n if self.use_div_token:\\n return context_emb + [self.DIV_TOKEN] + da_emb\\n # return padding + context_emb + [self.DIV_TOKEN] + da_emb\\n # return padding + context_emb + da_emb\\n return context_emb + da_emb\",\n \"def source_embedding_init_value(self):\\n if self._src_embedding is None:\\n return None\\n return self._src_embedding.word_vecs\",\n \"def _embeddings(self, xs):\\n n_feats, batch_size, seq_len = xs.size()\\n\\n assert n_feats == self.n_feats\\n\\n res = [emb(x) for emb, x in zip(self.embeddings, xs)]\\n x = torch.cat(res, 2)\\n\\n return x\",\n \"def init_embedding(size=50):\\n vector = np.random.normal(0.0, 0.01, size)\\n return vector\",\n \"def __init__(self, vocab, embed_size=512, dropout_rate=0.1, max_len=200):\\n super(DecoderEmbeddings, self).__init__()\\n pad_token_idx = 0 #vocab.tokenizer.ids_to_tokens[0]\\n assert vocab.tokenizer.ids_to_tokens[0] == '[PAD]'\\n self.embeddings = nn.Embedding(len(vocab.tokenizer.ids_to_tokens), embed_size, padding_idx=pad_token_idx)\\n self.positional_encoding = PositionalEncoding(d_model=embed_size, dropout=dropout_rate, max_len=max_len)\",\n \"def add_embedding(self):\\n #with tf.variable_scope(\\\"RNN\\\", reuse = tf.AUTO_REUSE):\\n embeddings = tf.get_variable(\\\"embeddings\\\", initializer = self.pretrained_embeddings,trainable=True)\\n inputs = self.input_placeholder\\n inputs = tf.reshape(inputs, [self.config.batch_size, -1 , self.config.n_features])\\n embeddings = tf.nn.embedding_lookup(embeddings, self.input_placeholder)\\n embeddings = tf.reshape(embeddings, [self.config.batch_size, -1, self.config.n_features* self.config.embed_size])\\n embeddings = tf.cast(embeddings, tf.float32)\\n return embeddings\",\n \"def _add_seq2seq(self):\\n mode = self._mode\\n vsize = self._vocab.size() # size of the vocabulary\\n\\n with tf.variable_scope('seq2seq'):\\n # Some initializers\\n self.rand_unif_init = tf.random_uniform_initializer(-config.rand_unif_init_mag, config.rand_unif_init_mag, seed=123)\\n self.trunc_norm_init = tf.truncated_normal_initializer(stddev=config.trunc_norm_init_std)\\n\\n # Add embedding matrix (shared by the encoder and decoder inputs)\\n with tf.variable_scope('embedding'):\\n embedding = tf.get_variable('embedding', [vsize, config.emb_dim], dtype=tf.float32, initializer=self.trunc_norm_init)\\n if mode==\\\"train\\\": self._add_emb_vis(embedding) # add to tensorboard\\n emb_enc_inputs = tf.nn.embedding_lookup(embedding, self._enc_batch) # tensor with shape (batch_size, max_enc_steps, emb_size)\\n emb_dec_inputs = tf.nn.embedding_lookup(embedding, self._dec_batch) # tensor with shape (batch_size, max_dec_steps, emb_size)\\n #emb_dec_inputs = [tf.nn.embedding_lookup(embedding, x) for x in tf.unstack(self._dec_batch, axis=1)] # list length max_dec_steps containing shape (batch_size, emb_size)\\n\\n # Add the encoder.\\n enc_fw_states, enc_bw_states, enc_fw, enc_bw = self._add_input_encoder(emb_enc_inputs, self._enc_lens)\\n\\n print(\\\"Encoder FW\\\", enc_fw_states.shape)\\n print(\\\"Encoder BW\\\", enc_bw_states.shape)\\n raise Exception(\\\"testing mode\\\")\\n\\n #reshape encoder states from [batch_size, input_size, hidden_dim] to [batch_size, input_size * hidden_dim]\\n enc_fw_states = tf.reshape(enc_fw_states, [config.batch_size, config.hidden_dim * tf.shape(enc_fw_states)[1]])\\n enc_bw_states = tf.reshape(enc_bw_states, [config.batch_size, config.hidden_dim * tf.shape(enc_bw_states)[1]])\\n\\n\\n # python run.py --mode=decode --data_path=data/chunked/train_1/train_1_*.bin --vocab_path=data/vocab_1 --exp_name=full1isto1\\n\\n # Add the decoder.\\n dec_fw_states, dec_bw_states = self._add_input_decoder(emb_dec_inputs, self._dec_lens, enc_fw, enc_bw)\\n\\n #reshape decoder states from [batch_size, input_size, hidden_dim] to [batch_size, input_size * hidden_dim]\\n dec_fw_states = tf.reshape(dec_fw_states, [config.batch_size, config.hidden_dim * tf.shape(dec_fw_states)[1]])\\n dec_bw_states = tf.reshape(dec_bw_states, [config.batch_size, config.hidden_dim * tf.shape(dec_bw_states)[1]])\\n #print(\\\"Decoder FW\\\", dec_fw_states.shape)\\n #print(\\\"Decoder BW\\\", dec_bw_states.shape)\\n\\n\\n #enc_c = tf.concat(axis=1, values=[enc_fw.c, enc_bw.c])\\n #enc_h = tf.concat(axis=1, values=[enc_fw.h, enc_bw.h])\\n #dec_c = tf.concat(axis=1, values=[dec_fw.c, dec_bw.c])\\n #dec_h = tf.concat(axis=1, values=[dec_fw.h, dec_bw.h])\\n\\n final_encoding = tf.concat(axis=1, values=[enc_fw_states, enc_bw_states, dec_fw_states, dec_bw_states])\\n #print(\\\"Final encoding\\\", final_encoding.shape)\\n #raise Exception(\\\"Test\\\")\\n dims_final_enc = tf.shape(final_encoding)\\n\\n \\\"\\\"\\\"\\n #convo_input = tf.concat(axis=1, values=[enc_c, enc_h, dec_c, dec_h])\\n input_layer = tf.reshape(final_encoding, [config.batch_size, dims_final_enc[1], 1])\\n print(\\\"Convolution input shape\\\", input_layer.shape)\\n\\n conv1 = tf.layers.conv1d(\\n inputs=input_layer,\\n filters=8,\\n kernel_size=5,\\n padding=\\\"same\\\",\\n activation=tf.nn.relu)\\n conv1 = tf.layers.batch_normalization(conv1)\\n print(\\\"Convolution1 output shape\\\", conv1.shape)\\n\\n pool1 = tf.layers.max_pooling1d(inputs=conv1, pool_size=2, strides=2)\\n print(\\\"Pool1 output shape\\\", pool1.shape)\\n\\n conv2 = tf.layers.conv1d(\\n inputs=pool1,\\n filters=16,\\n kernel_size=5,\\n padding=\\\"same\\\",\\n activation=tf.nn.relu)\\n\\n\\n conv2 = tf.layers.batch_normalization(conv2)\\n print(\\\"Convolution2 output shape\\\", conv2.shape)\\n\\n pool2 = tf.layers.max_pooling1d(inputs=conv2, pool_size=2, strides=2)\\n print(\\\"Pool2 output shape\\\", pool2.shape)\\n\\n dims_pool2 = tf.shape(pool2)\\n\\n pool2_flat = tf.reshape(pool2, [config.batch_size, dims_pool2[1] * 16])\\n print(\\\"Pool2_flat output shape\\\", pool2_flat.shape)\\n dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)\\n \\\"\\\"\\\"\\n #raise Exception(\\\"testing mode\\\")\\n\\n #dropout = tf.layers.dropout(inputs=dense, rate=0.4, training=mode==\\\"train\\\")\\n #print(\\\"Dense output shape\\\", dense.shape)\\n\\n #raise Exception(\\\"Just testing\\\")\\n # Add the output projection to obtain the vocabulary distribution\\n with tf.variable_scope('output_projection'):\\n w = tf.get_variable('w', [dims_final_enc[1], 2], dtype=tf.float32, initializer=self.trunc_norm_init)\\n bias_output = tf.get_variable('bias_output', [2], dtype=tf.float32, initializer=self.trunc_norm_init)\\n #concatenate abstract and article outputs [batch_size, hidden_dim*4]\\n\\n\\n #get classification output [batch_size, 1] default on last axis\\n self._logits = tf.matmul(final_encoding, w) + bias_output\\n #self._logits = tf.layers.dense(final_encoding, 2, kernel_initializer=self.trunc_norm_init, bias_initializer=self.trunc_norm_init)\\n #self._prob = tf.nn.softmax(logits, \\\"class_prob\\\")\\n\\n if mode in ['train', 'eval']:\\n # Calculate the loss\\n with tf.variable_scope('loss'):\\n #self._prob = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=self._targets)\\n #class_weights = tf.constant([0.1, 5.])\\n self._loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=self._targets, logits=self._logits))\\n #self._loss = tf.reduce_mean(tf.nn.weighted_cross_entropy_with_logits(targets=self._targets, logits=self._logits, pos_weight=class_weights))\\n tf.summary.scalar('loss', self._loss)\\n\\n\\n\\n #if mode == \\\"decode\\\":\",\n \"def get_word_embedding(counter, emb_file, emb_size, vocab_size, vec_size, vocab_file):\\n print(\\\"Generating word embedding...\\\")\\n # load word embeddings\\n embedding_dict = {}\\n with open(emb_file, \\\"r\\\", encoding=\\\"utf-8\\\") as fh:\\n for line in tqdm(fh, total=emb_size):\\n array = line.split()\\n word = \\\"\\\".join(array[0:-vec_size])\\n vector = list(map(float, array[-vec_size:]))\\n embedding_dict[word] = vector\\n\\n TRANSLATE = {\\n \\\"-lsb-\\\": \\\"[\\\", \\\"-rsb-\\\": \\\"]\\\", \\\"-lrb-\\\": \\\"(\\\", \\\"-rrb-\\\": \\\")\\\", \\\"-lcb-\\\": \\\"{\\\",\\n \\\"-rcb-\\\": \\\"}\\\", \\\"-LSB-\\\": \\\"[\\\", \\\"-RSB-\\\": \\\"]\\\", \\\"-LRB-\\\": \\\"(\\\", \\\"-RRB-\\\": \\\")\\\",\\n \\\"-LCB-\\\": \\\"{\\\", \\\"-RCB-\\\": \\\"}\\\"\\n }\\n SPECIAL_TOKENS = [\\\"<NULL>\\\", \\\"<UNK>\\\", \\\"<S>\\\", \\\"</S>\\\"]\\n words = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x: x[1], reverse=True)))\\n words = SPECIAL_TOKENS + words\\n if vocab_size > 0:\\n words = words[:vocab_size]\\n with open(vocab_file, 'w') as f:\\n f.write('\\\\n'.join(words[1:]))\\n embedding = np.random.normal(scale=0.1, size=(len(words), vec_size))\\n word2idx_dict = {}\\n unknown_count = 0\\n for i, word in enumerate(words):\\n word2idx_dict[word] = i\\n if word in TRANSLATE:\\n word = TRANSLATE[word]\\n done = False\\n for w in (word, word.lower(), word.upper(), word.capitalize()):\\n if w in embedding_dict:\\n embedding[i] = embedding_dict[w]\\n done = True\\n break\\n if not done:\\n unknown_count += 1\\n return embedding, word2idx_dict, unknown_count\",\n \"def make_embedding(path, words, indices):\\n #root = '/'.join(path.split('/')[0:-1])\\n #all_paths = [root+'/'+x for x in os.listdir(root)] #'/'.join(path.split('/')[0:-1]))\\n #for path in all_paths:\\n vec_path = 'data/'+path.split('/')[-1]+'_'+mode\\n print(vec_path)\\n if os.path.exists(vec_path+'.npy'):\\n np_vecs = np.load(vec_path+'.npy')\\n else:\\n words_len = len(words)\\n vecs = []\\n if mode == 'word':\\n f = load_model('wiki.en.bin')\\n for i, w in enumerate(words):\\n if mode == 'word':\\n vec = f.get_word_vector(w)\\n else:\\n vec = eye[indices[w]]\\n vecs.append(vec) \\n if i % 10000 == 0:\\n print(\\\"{} / {}\\\".format(i, words_len))\\n np_vecs = np.asarray(vecs, dtype=np.int8)\\n np.save(vec_path, np_vecs)\\n return np_vecs\",\n \"def get_pretrained_embeddings(source_vocab,embed_df):\\r\\n \\r\\n num_tokens = len(source_vocab)\\r\\n embedding_dim = embed_df.shape[1]\\r\\n weights = np.zeros((num_tokens,embedding_dim),dtype=np.float32)\\r\\n \\r\\n for idx in range(num_tokens):\\r\\n token = source_vocab.lookup_index(idx)\\r\\n if token in embed_df.index:\\r\\n weights[idx,:] = embed_df.loc[token]\\r\\n else:\\r\\n weights[idx,:] = np.random.randn(1,embedding_dim)\\r\\n \\r\\n embed_tensor = torch.FloatTensor(weights)\\r\\n return embed_tensor\",\n \"def _get_embedding_layer(self, input_data, doc_input_data):\\n opts = self._options\\n word_embedding = tf.Variable(tf.random_uniform((self.vocab_size, opts.embed_dim), -1.0, 1.0))\\n embed = []\\n\\n temp = tf.zeros([opts.batch_size, opts.embed_dim])\\n embed_d = []\\n for n in range(opts.sentence_sample):\\n temp = tf.add(temp, tf.nn.embedding_lookup(word_embedding, doc_input_data[:, n]))\\n embed_d.append(temp)\\n\\n if opts.concat == 'True':\\n combined_embed_vector_length = opts.embed_dim * opts.window_size + opts.embed_dim\\n for j in range(opts.window_size):\\n embed_w = tf.nn.embedding_lookup(word_embedding, input_data[:, j])\\n embed.append(embed_w)\\n embed.append(embed_d)\\n else:\\n combined_embed_vector_length = opts.embed_dim\\n embed_w = tf.zeros([opts.batch_size, opts.embed_dim])\\n for j in range(opts.window_size):\\n embed_w += tf.nn.embedding_lookup(word_embedding, input_data[:, j])\\n embed_w += embed_d\\n embed.append(embed_w)\\n\\n return tf.concat(embed, 1), word_embedding, combined_embed_vector_length\",\n \"def embedding(self, images):\\n predict = self.model.predict(images)\\n return predict\",\n \"def embed(self, sequence):\\n words = sequence.split(' ')\\n vecs = [self._E[self._w2i[i]] if i in self._w2i else self._E[self._w2i[\\\"UNK\\\"]]\\n for i in words]\\n return vecs\",\n \"def produce_outputs(self):\\n # if self.loaded_aggregated:\\n # debug(\\\"Skippping {} mapping due to preloading\\\".format(self.base_name))\\n # return\\n # need to calc term numeric index for aggregation\\n\\n\\n # if self.loaded_preprocessed:\\n # debug(\\\"Skippping {} mapping due to preloading\\\".format(self.base_name))\\n # return\\n\\n bagger = Bag(vocabulary=self.term_list, weighting=self.base_name, ngram_range=self.ngram_range)\\n\\n self.embeddings = np.ndarray((0, len(self.term_list)), dtype=np.int32)\\n for idx in self.indices.get_train_test():\\n texts = Text.get_strings(self.text.data.get_slice(idx))\\n vecs = bagger.map_collection(texts, fit=False, transform=True)\\n self.embeddings = np.append(self.embeddings, vecs, axis=0)\\n del texts\\n\\n # texts = Text.get_strings(self.text.data.get_slice(test_idx))\\n # vec_test = bagger.map_collection(texts, fit=do_fit)\\n # del texts\\n\\n # self.embeddings = np.vstack((vec_train, vec_test))\\n\\n # self.embeddings = np.append(vec_train, vec_test)\\n # self.vector_indices = (np.arange(len(train)), np.arange(len(test)))\\n\\n # set misc required variables\\n self.set_constant_elements_per_instance()\",\n \"def build_embeddings(opt, word_dict, for_encoder='src'):\\n if for_encoder=='src':\\n embedding_dim = opt.src_word_vec_size #512\\n elif for_encoder=='tgt':\\n embedding_dim = opt.tgt_word_vec_size\\n elif for_encoder=='structure':\\n embedding_dim = 64\\n\\n word_padding_idx = word_dict.stoi[Constants.PAD_WORD]\\n num_word_embeddings = len(word_dict)\\n \\n if for_encoder=='src' or for_encoder=='tgt':\\n\\n return Embeddings(word_vec_size=embedding_dim,\\n position_encoding=opt.position_encoding,\\n dropout=opt.dropout,\\n word_padding_idx=word_padding_idx,\\n word_vocab_size=num_word_embeddings,\\n sparse=opt.optim == \\\"sparseadam\\\")\\n elif for_encoder=='structure':\\n return Embeddings(word_vec_size=embedding_dim,\\n position_encoding=False,\\n dropout=opt.dropout,\\n word_padding_idx=word_padding_idx,\\n word_vocab_size=num_word_embeddings,\\n sparse=opt.optim == \\\"sparseadam\\\")\",\n \"def convert(encoder, bert_model):\\n num_layers = encoder._config[\\\"num_layers\\\"]\\n num_attention_heads = encoder._config[\\\"num_attention_heads\\\"]\\n hidden_size = encoder._config[\\\"hidden_size\\\"]\\n head_size = hidden_size // num_attention_heads\\n assert head_size * num_attention_heads == hidden_size\\n encoder._embedding_layer.set_weights(\\n [bert_model[\\\"embeddings.word_embeddings.weight\\\"]])\\n encoder._embedding_norm_layer.set_weights([\\n bert_model[\\\"embeddings.LayerNorm.weight\\\"],\\n bert_model[\\\"embeddings.LayerNorm.bias\\\"]\\n ])\\n encoder._type_embedding_layer.set_weights(\\n [bert_model[\\\"embeddings.token_type_embeddings.weight\\\"]])\\n encoder._position_embedding_layer.set_weights(\\n [bert_model[\\\"embeddings.position_embeddings.weight\\\"]])\\n for layer_num in range(num_layers):\\n encoder._transformer_layers[\\n layer_num]._attention_layer._key_dense.set_weights([\\n bert_model[f\\\"encoder.layer.{layer_num}.attention.self.key.weight\\\"].T\\n .reshape((hidden_size, num_attention_heads, head_size)),\\n bert_model[f\\\"encoder.layer.{layer_num}.attention.self.key.bias\\\"]\\n .reshape((num_attention_heads, head_size))\\n ])\\n encoder._transformer_layers[\\n layer_num]._attention_layer._query_dense.set_weights([\\n bert_model[f\\\"encoder.layer.{layer_num}.attention.self.query.weight\\\"]\\n .T.reshape((hidden_size, num_attention_heads, head_size)),\\n bert_model[f\\\"encoder.layer.{layer_num}.attention.self.query.bias\\\"]\\n .reshape((num_attention_heads, head_size))\\n ])\\n encoder._transformer_layers[\\n layer_num]._attention_layer._value_dense.set_weights([\\n bert_model[f\\\"encoder.layer.{layer_num}.attention.self.value.weight\\\"]\\n .T.reshape((hidden_size, num_attention_heads, head_size)),\\n bert_model[f\\\"encoder.layer.{layer_num}.attention.self.value.bias\\\"]\\n .reshape((num_attention_heads, head_size))\\n ])\\n encoder._transformer_layers[\\n layer_num]._attention_layer._output_dense.set_weights([\\n bert_model[\\n f\\\"encoder.layer.{layer_num}.attention.output.dense.weight\\\"].T\\n .reshape((num_attention_heads, head_size, hidden_size)),\\n bert_model[f\\\"encoder.layer.{layer_num}.attention.output.dense.bias\\\"]\\n ])\\n encoder._transformer_layers[layer_num]._attention_layer_norm.set_weights([\\n bert_model[\\n f\\\"encoder.layer.{layer_num}.attention.output.LayerNorm.weight\\\"],\\n bert_model[f\\\"encoder.layer.{layer_num}.attention.output.LayerNorm.bias\\\"]\\n ])\\n encoder._transformer_layers[layer_num]._intermediate_dense.set_weights([\\n bert_model[f\\\"encoder.layer.{layer_num}.intermediate.dense.weight\\\"].T,\\n bert_model[f\\\"encoder.layer.{layer_num}.intermediate.dense.bias\\\"]\\n ])\\n encoder._transformer_layers[layer_num]._output_dense.set_weights([\\n bert_model[f\\\"encoder.layer.{layer_num}.output.dense.weight\\\"].T,\\n bert_model[f\\\"encoder.layer.{layer_num}.output.dense.bias\\\"]\\n ])\\n encoder._transformer_layers[layer_num]._output_layer_norm.set_weights([\\n bert_model[f\\\"encoder.layer.{layer_num}.output.LayerNorm.weight\\\"],\\n bert_model[f\\\"encoder.layer.{layer_num}.output.LayerNorm.bias\\\"]\\n ])\",\n \"def _define_encoder(self):\\n self.encoder = nn.Sequential(View((-1, 64 * 64 * 3)),\\n nn.Linear(64 * 64 * 3, 5120, bias=False), nn.SELU(),\\n nn.BatchNorm1d(5120),\\n nn.Linear(5120, 2560, bias=False), nn.SELU(),\\n nn.BatchNorm1d(2560),\\n nn.Linear(2560, 512, bias=False), nn.SELU(),\\n nn.BatchNorm1d(512),\\n nn.Linear(512, 128, bias=False), nn.SELU(),\\n nn.BatchNorm1d(128),\\n nn.Linear(128, self.encoding_shape, bias=False), nn.SELU(),\\n )\",\n \"def _decode_train(self, decoder, _encoder_output, _features, labels):\\r\\n target_embedded = tf.nn.embedding_lookup(decoder.target_embedding,\\r\\n labels[\\\"target_ids\\\"])\\r\\n\\r\\n return decoder(_encoder_output, labels=target_embedded[:,:-1], sequence_length=labels[\\\"target_len\\\"]-1)\",\n \"def pretrain_forward(self, inp):\\n return self.encoder(inp)\",\n \"def extract_embedding(self, from_model):\\n return from_model\",\n \"def call(self, x, training, mask):\\n seq_len = tf.shape(x)[1]\\n # adding embedding and position encoding.\\n embedding = self.embedding(x) # (batch_size, input_seq_len, d_model)\\n embedding *= tf.math.sqrt(tf.cast(self.dm, tf.float32))\\n embedding += self.positional_encoding[:, :seq_len, :]\\n\\n encoder_out = self.dropout(embedding, training=training)\\n\\n for i in range(self.N):\\n encoder_out = self.blocks[i](encoder_out, training, mask)\\n\\n return encoder_out\",\n \"def position_encoding(sentence_size, embedding_size):\\n encoding = np.ones((embedding_size, sentence_size), dtype=np.float32)\\n ls = sentence_size+1\\n le = embedding_size+1\\n for i in range(1, le):\\n for j in range(1, ls):\\n encoding[i-1, j-1] = (i - (le-1)/2) * (j - (ls-1)/2)\\n encoding = 1 + 4 * encoding / embedding_size / sentence_size\\n return np.transpose(encoding)\",\n \"def Aut(A):\\n return Embeddings(A,A)\",\n \"def build_embedding(graph):\\n\\n nodes_list = list(graph.nodes.values())\\n sorted_nodes = sorted(\\n nodes_list,\\n key=lambda x: hash_labels_only(labels=x.labels, node_label_hash=NODE_TYPE_HASH))\\n embedding = []\\n\\n for i in range(MAX_NODES):\\n if i < len(sorted_nodes) and \\\"name\\\" in sorted_nodes[i].properties and \\\\\\n sorted_nodes[i].properties[\\\"name\\\"] != []:\\n\\n # The 'name' property on each node is a list, the current solution is to\\n # take the first element.\\n name = sorted_nodes[i].properties[\\\"name\\\"][0]\\n encoded_name = hashing_trick(name, VOCAB_SIZE, hash_simhash)\\n\\n if \\\"cmdline\\\" in sorted_nodes[i].properties:\\n cmdline = sorted_nodes[i].properties[\\\"cmdline\\\"]\\n encoded_cmdline = hashing_trick(cmdline, VOCAB_SIZE, hash_simhash)\\n else:\\n encoded_cmdline = []\\n\\n embedding += [encoded_name, encoded_cmdline]\\n else:\\n embedding += [[], []]\\n\\n padded_embedding = pad_sequences(embedding, maxlen=EMBEDDING_LENGTH)\\n combined_embedding = [num for sublist in padded_embedding for num in sublist]\\n return np.asarray(combined_embedding, dtype=np.int16)\",\n \"def build_sentence_encoder(self, raw_encoder_input, input_seq_len):\\n with tf.variable_scope('text_encoder'):\\n self.embedding = \\\\\\n tf.get_variable(\\n \\\"embedding\\\", initializer=tf.random_uniform(\\n [self.config.word_voc_size,\\n self.config.word_embedding_space_size],\\n -self.config.TRAIN.SENCODER.none_rnn_para_initial_max,\\n self.config.TRAIN.SENCODER.none_rnn_para_initial_max))\\n inputs = tf.nn.embedding_lookup(self.embedding, raw_encoder_input)\\n\\n # now it is [MAX_SEQ_LENGTH, batch_size, embedding_length]\\n input_batch_order = tf.transpose(inputs, [1, 0, 2])\\n\\n # now it is [MAX_SEQ_LENGTH * batch_size, embedding_length]\\n input_batch_order = tf.reshape(\\n input_batch_order, [-1, self.config.word_embedding_space_size])\\n\\n # now it is LIST OF [BATCH_SIZE, embedding_length]\\n encoder_input = tf.split(0, self.config.seq_max_len,\\n input_batch_order)\\n\\n # the encoder part\\n encode_gru_cell = tf.nn.rnn_cell.GRUCell(\\n self.config.encoder_dimension)\\n # big news: The state is final state, output is a list of tensor.\\n # We don't to do that\\n _, sentence_rep = tf.nn.rnn(encode_gru_cell, encoder_input,\\n dtype=tf.float32,\\n sequence_length=input_seq_len)\\n self.sentence_rep = sentence_rep\\n self.sentence_rep = tf.nn.l2_normalize(self.sentence_rep, 1)\\n return\",\n \"def get_word_embeddings(t, folder, lang=\\\"en\\\"):\\n vecs_url = f\\\"https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.{lang}.300.vec.gz\\\"\\n vecs_gz_filename = vecs_url.rpartition(\\\"/\\\")[2]\\n os.makedirs(folder, exist_ok=True)\\n vecs_gz_filepath = os.path.join(folder, vecs_gz_filename)\\n\\n tokenizer_vocab_size = len(t.vocab)\\n\\n if wait_for_file_stable(vecs_gz_filepath):\\n print(\\\"Using existing embeddings file\\\")\\n else:\\n print(\\\"Downloading word vectors...\\\")\\n subprocess.run([\\\" \\\".join([\\\"wget\\\", \\\"-NP\\\", folder, vecs_url])], check=True, shell=True)\\n\\n print(\\\"Loading into memory...\\\")\\n embeddings_index = dict()\\n with gzip.open(vecs_gz_filepath, \\\"rt\\\") as zipf:\\n firstline = zipf.readline()\\n emb_vocab_size, emb_d = firstline.split(\\\" \\\")\\n emb_vocab_size = int(emb_vocab_size)\\n emb_d = int(emb_d)\\n for line in zipf:\\n values = line.split()\\n word = values[0]\\n # Only load subset of the embeddings recognised by the tokenizer:\\n if word in t.vocab.stoi:\\n coefs = np.asarray(values[1:], dtype=\\\"float32\\\")\\n embeddings_index[word] = coefs\\n print(\\\"Loaded {} of {} word vectors for tokenizer vocabulary length {}\\\".format(\\n len(embeddings_index),\\n emb_vocab_size,\\n tokenizer_vocab_size,\\n ))\\n\\n # create a weight matrix for words in training docs\\n embedding_matrix = np.zeros((tokenizer_vocab_size, emb_d))\\n for word, i in t.vocab.stoi.items():\\n embedding_vector = embeddings_index.get(word)\\n if embedding_vector is not None:\\n embedding_matrix[i] = embedding_vector\\n\\n return embedding_matrix\",\n \"def _apply_encoder(self, frame, prop, encoder, encoder_type=\\\"category\\\"):\\n vectors = None\\n if encoder_type == \\\"category\\\":\\n vectors = encoder.transform(frame[prop].apply(normalize_to_set))\\n elif encoder_type == \\\"text\\\":\\n column = frame[prop].copy()\\n column[column.isna()] = \\\" \\\"\\n vectors = encoder.transform(column)\\n elif encoder_type == \\\"numeric\\\" and encoder is not None:\\n vectors = encoder.transform(\\n np.reshape(frame[prop].to_list(), (frame[prop].shape[0], 1)))\\n if vectors is not None:\\n if issparse(vectors):\\n vectors = vectors.todense()\\n df = pd.DataFrame(columns=[prop], index=frame.index)\\n df[prop] = vectors.tolist()\\n return df\",\n \"def get_embeddings():\\n embeddings = dict(get_coefs(*o.strip().split()) for o in open(EMBEDDING_FILE))\\n return embeddings\",\n \"def get_embeddings(self, in_data):\\n context, da = in_data\\n if self.fixed_divide:\\n da_emb = super(ContextDAEmbeddingSeq2SeqExtract, self).get_embeddings(da, pad=True)\\n else:\\n da_emb = super(ContextDAEmbeddingSeq2SeqExtract, self).get_embeddings(da, pad=False)\\n max_context_len = (self.max_context_len + 3 * self.max_da_len) - len(da_emb)\\n context_emb = []\\n for tok in context[-max_context_len:]:\\n context_emb.append(self.dict_token.get(tok, self.UNK_TOKEN))\\n\\n padding = [self.UNK_TOKEN] * (max_context_len - len(context))\\n\\n if self.use_div_token:\\n return padding + context_emb + [self.DIV_TOKEN] + da_emb\\n return padding + context_emb + da_emb\",\n \"def embedding(self, seqs):\\n batch_size, seqlen = seqs.shape\\n seqs = np.reshape(seqs, (-1)) # convert to 1-d indexes [(batch_sz*seqlen)]\\n embs = self.word2vec[seqs] # lookup [(batch_sz*seqlen) x emb_sz]\\n embs = np.reshape(embs, (batch_size, seqlen, -1)) # recover the shape [batch_sz x seqlen x emb_sz]\\n return embs\",\n \"def add_embeddings(self):\\n\\n with tf.device('/cpu:0'):\\n with tf.variable_scope('Embedding_Layer'):\\n embeddings = tf.Variable(self.initial_embeddings,name = 'Embeddings')\\n self.input_embeddings = tf.nn.embedding_lookup(embeddings, self.inputs_placeholder) #(N,S,D)\\n self.question_embeddings = tf.nn.embedding_lookup(embeddings, self.questions_placeholder) #(N,S,D)\",\n \"def null_embeddings(self, detections):\\n embeddings = np.ones((len(detections), self.feature_dim))\\n embeddings /= np.linalg.norm(embeddings, axis=1, keepdims=True)\\n return embeddings\",\n \"def forward(self, images):\\n embedding = self.model(images)\\n embedding = self.l2_norm(embedding)\\n # Multiply by alpha = 10 as suggested in https://arxiv.org/pdf/1703.09507.pdf\\n # Equation 9: number of classes in VGGFace2 dataset = 9131\\n # lower bound on alpha = 5, multiply alpha by 2; alpha = 10\\n alpha = 10\\n embedding = embedding * alpha\\n\\n return embedding\",\n \"def _get_words_emb(self, words):\\n\\n if not self._is_cased_embedding:\\n return self._word_vocab[[word.lower() for word in words]]\\n\\n result = np.full([len(words)], fill_value=0, dtype=np.float32)\\n word_emb_matrix = np.full([len(words), 4], fill_value=0, dtype=np.float32)\\n\\n for i, w in enumerate(words):\\n word_emb_matrix[i, :] = self._word_vocab[[w, w.lower(), w.capitalize(), w.upper()]]\\n\\n mask = word_emb_matrix != 0\\n first_non_zero_embeddings_indices = np.where(mask.any(axis=1), mask.argmax(axis=1), -1)\\n\\n for i, index in enumerate(first_non_zero_embeddings_indices):\\n result[i] = word_emb_matrix[i, index]\\n\\n return result\",\n \"def postprocess(self):\\n if self.last_num_features == 0:\\n return np.empty((0, self.feature_dim))\\n\\n embedding_out = self.backend.synchronize()[0][:self.last_num_features * self.feature_dim]\\n self.embeddings.append(embedding_out)\\n embeddings = np.concatenate(self.embeddings).reshape(-1, self.feature_dim)\\n embeddings /= np.linalg.norm(embeddings, axis=1, keepdims=True)\\n return embeddings\",\n \"def position_encoding(sentence_size, embedding_size):\\n\\tencoding = np.ones((embedding_size, sentence_size), dtype=np.float32)\\n\\tls = sentence_size+1\\n\\tle = embedding_size+1\\n\\tfor i in range(1, le):\\n\\t\\tfor j in range(1, ls):\\n\\t\\t\\tencoding[i-1, j-1] = (i - (le-1)/2) * (j - (ls-1)/2)\\n\\tencoding = 1 + 4 * encoding / embedding_size / sentence_size\\n\\treturn np.transpose(encoding)\",\n \"def build_word_embeddings(self):\\n if self.mode == \\\"encode\\\":\\n # Word embeddings are fed from an external vocabulary which has possibly\\n # been expanded (see vocabulary_expansion.py).\\n encode_emb1 = tf.placeholder(tf.float32, (\\n None, None, self.config.word_embedding_dim), \\\"encode_emb1\\\")\\n # No sequences to decode.\\n encode_emb2 = tf.placeholder(tf.float32, (\\n None, None, self.config.word_embedding_dim), \\\"encode_emb2\\\")\\n elif self.mode == \\\"test\\\":\\n encode_emb1 = tf.placeholder(tf.float32, (\\n None, None, self.config.word_embedding_dim), \\\"encode_emb1\\\")\\n # No sequences to decode.\\n encode_emb2 = tf.placeholder(tf.float32, (\\n None, None, self.config.word_embedding_dim), \\\"encode_emb2\\\")\\n else:\\n word_emb = tf.get_variable(\\n name=\\\"word_embedding\\\",\\n shape=[self.config.vocab_size, self.config.word_embedding_dim],\\n initializer=self.uniform_initializer)\\n\\n encode_emb1 = tf.nn.embedding_lookup(word_emb, self.encode_ids1)\\n encode_emb2 = tf.nn.embedding_lookup(word_emb, self.encode_ids2)\\n\\n\\n self.encode_emb1 = encode_emb1\\n self.encode_emb2 = encode_emb2\",\n \"def sentence_to_embedding(sent, word_to_vec, seq_len, embedding_dim):\\n embedding_vec = np.zeros((seq_len,embedding_dim))\\n for i in range(min(len(sent),seq_len)):\\n embedding_vec[i,:] = word_to_vec.get(sent[i])\\n return embedding_vec\",\n \"def word_embedding_forward(x, W):\\n out, cache = None, None\\n ##############################################################################\\n # TODO: Implement the forward pass for word embeddings. #\\n # #\\n # HINT: This should be very simple. #\\n ##############################################################################\\n out = W[x,:]\\n cache = x, W\\n\\n ##############################################################################\\n # END OF YOUR CODE #\\n ##############################################################################\\n return out, cache\",\n \"def create_embedding(self):\\n self.embedding = []\\n\\n for index in range(1,self.args.window_size+1):\\n print(\\\"\\\\nOptimization round: \\\" +str(index)+\\\"/\\\"+str(self.args.window_size)+\\\".\\\")\\n print(\\\"Creating documents.\\\")\\n clean_documents = self.walk_extracts(index)\\n print(\\\"Fitting model.\\\")\\n model = Word2Vec(clean_documents,\\n size = self.args.dimensions,\\n window = 1,\\n min_count = self.args.min_count,\\n sg = 1,\\n workers = self.args.workers)\\n\\n new_embedding = self.get_embedding(model)\\n self.embedding = self.embedding +[new_embedding]\\n self.embedding = np.concatenate(self.embedding, axis = 1)\",\n \"def forward(\\n self,\\n encoder_out,\\n encoded_captions: torch.Tensor,\\n caption_lengths: torch.Tensor\\n ):\\n pass\",\n \"def build_seq_embeddings(self):\\n with tf.variable_scope(\\\"seq_embedding\\\"), tf.device(\\\"/cpu:0\\\"):\\n embedding_map = tf.get_variable(\\n name=\\\"map\\\",\\n shape=[self.config.vocab_size, self.config.word_embedding_size],\\n initializer=self.initializer)\\n \\n # We need to store the normalized lookup table for efficient mapping of embedding vectors to closest words\\n self.normed_embedding_map = tf.nn.l2_normalize(embedding_map, dim=1)\\n \\n seq_embeddings = tf.nn.embedding_lookup(embedding_map, self.input_seqs) \\n # seq_embeddings has the shape (batch_size, sequence_length, sentence_length, embedding_size)\\n # meaning, for each index in input_seqs (with shape (batch_size, sequence_length, sentence_length)) it stores an embedding vector\\n\\n #print('Shape seq_embeddings: ' + str(seq_embeddings.get_shape()))\\n\\n self.seq_embeddings = seq_embeddings\",\n \"def _words_to_vec(self, sentence):\\n return torch.FloatTensor([self._use_embeddings(word) for word in sentence])\",\n \"def embed(self, loader, model):\\n print(\\\" ** Embedding words\\\")\\n\\n words = loader.words\\n vectors = [model.get_word_vector(word) for word in words]\\n\\n return [(w, *v) for w, v in zip(words, vectors)]\",\n \"def fit_transform(self):\\n if self.enc_types == \\\"label\\\":\\n return self._label_encoding()\\n elif self.enc_types == \\\"ohe\\\":\\n return self._one_hot_encoder()\\n elif self.enc_types == \\\"binary\\\":\\n return self._binarization()\\n else:\\n raise Exception(\\\"Encoding type not understood\\\")\",\n \"def embed(self, smi_or_mol):\\n if not isinstance(smi_or_mol, Chem.Mol):\\n mol = Chem.MolFromSmiles(smi_or_mol)\\n else:\\n mol = smi_or_mol\\n wv = self.model.wv\\n sentence = self.substructure(mol)\\n vec = np.zeros(self.model.vector_size)\\n for fp in sentence:\\n if fp in wv.vocab:\\n vec += wv[fp]\\n return vec\",\n \"def _generate_embeddings(self, config): \\n tr_parts = []\\n te_parts = []\\n all_columns = []\\n for comp in self.components:\\n tr_tmp, te_tmp, cols = comp.generate(config)\\n if cols != None:\\n print(tr_tmp.shape,te_tmp.shape)\\n tr_parts.append(tr_tmp)\\n te_parts.append(te_tmp)\\n all_columns += cols\\n X_train = np.concatenate(tr_parts, axis=1)\\n X_test = np.concatenate(te_parts, axis=1)\\n print(\\\"Concatenated size:\\\", X_train.shape, X_test.shape)\\n self.feature_columns = all_columns\\n return X_train, X_test\",\n \"def load_pretrained_embeddings(self, embeddings):\\r\\n self.embedding.weight = nn.Parameter(embeddings)\",\n \"def __init__(self, in_dimension, layer_1d, layer_2d, layer_3d,\\n latent_dimension):\\n super(VAEEncoder, self).__init__()\\n self.latent_dimension = latent_dimension\\n\\n # Reduce dimension up to second last layer of Encoder\\n self.encode_nn = nn.Sequential(\\n nn.Linear(in_dimension, layer_1d),\\n nn.ReLU(),\\n nn.Linear(layer_1d, layer_2d),\\n nn.ReLU(),\\n nn.Linear(layer_2d, layer_3d),\\n nn.ReLU()\\n )\\n\\n # Latent space mean\\n self.encode_mu = nn.Linear(layer_3d, latent_dimension)\\n\\n # Latent space variance\\n self.encode_log_var = nn.Linear(layer_3d, latent_dimension)\",\n \"def __init__(self, in_dimension, layer_1d, layer_2d, layer_3d,\\n latent_dimension):\\n super(VAEEncoder, self).__init__()\\n self.latent_dimension = latent_dimension\\n\\n # Reduce dimension up to second last layer of Encoder\\n self.encode_nn = nn.Sequential(\\n nn.Linear(in_dimension, layer_1d),\\n nn.ReLU(),\\n nn.Linear(layer_1d, layer_2d),\\n nn.ReLU(),\\n nn.Linear(layer_2d, layer_3d),\\n nn.ReLU()\\n )\\n\\n # Latent space mean\\n self.encode_mu = nn.Linear(layer_3d, latent_dimension)\\n\\n # Latent space variance\\n self.encode_log_var = nn.Linear(layer_3d, latent_dimension)\",\n \"def generate_conll2003_embeddings():\\n glove_embedding = get_glove_embedding()\\n\\n word2index = {}\\n idx2word = {}\\n embed_array = []\\n\\n word2index[\\\"<pad>\\\"] = 1\\n embed_array.append(init_embedding())\\n\\n word2index[\\\"<unk>\\\"] = 0\\n embed_array.append(init_embedding())\\n\\n data = []\\n with open(TRAIN_DATA_PATH, \\\"r\\\") as f:\\n for line in f:\\n data.append(json.loads(line))\\n\\n idx = 2\\n\\n for sample in tqdm(data, total=len(data)):\\n words = sample[\\\"tokens\\\"]\\n\\n for w in words:\\n w = w.lower()\\n\\n # if word is not present in dictionary, add to dictionary and append embedding vector\\n if w not in word2index.keys():\\n word2index[w] = idx\\n idx += 1\\n if w not in glove_embedding.keys():\\n ev = init_embedding()\\n else:\\n ev = glove_embedding[w]\\n\\n embed_array.append(ev)\\n\\n else:\\n continue\\n\\n # save embeddings\\n embed_array = np.vstack(embed_array)\\n np.save(EMBD_OUTPUT_PATH, embed_array)\\n\\n # save dictionary\\n print(\\\"Dicitionary Size: \\\", len(word2index))\\n with open(DICTIONARY_OUTPUT_PATH, \\\"w\\\") as f:\\n json.dump(word2index, f)\",\n \"def fit_transform(self, X):\\n self._fit(X)\\n return self.embedding\",\n \"def get_align_vec(align, symbols):\\n enc = OrdinalEncoder()\\n enc.fit(symbols)\\n\\n align_vec = []\\n\\n for column_in in align.T:\\n column_out = enc.transform(np.expand_dims(column_in, axis=1)).flatten()\\n align_vec.append(column_out)\\n align_vec = np.array(align_vec).T\\n\\n return align_vec, enc\",\n \"def encode_vecs(self, vecs, lstm):\\n initial_state = lstm.initial_state()\\n states = initial_state.transduce(vecs)\\n return states\",\n \"def get_embeddings(vectors, text, generate_missing=False, k=300):\\r\\n embeddings = text.apply(lambda x: get_average_vec(x, vectors, generate_missing=generate_missing, k=k))\\r\\n return list(embeddings)\",\n \"def encode(self, token_ids_tensor, attention_mask):\\n with torch.no_grad():\\n outputs = self.embedding(token_ids_tensor, attention_mask)\\n\\n hidden_states = outputs[2]\\n embeddings = torch.stack(hidden_states[-4:]).sum(0)\\n return embeddings\",\n \"def forward(self, inputs, decode_len=None):\\n\\n batch_size = inputs.size(0)\\n input_dim = inputs.size(1)\\n assert input_dim == self.input_dim, 'input dim should be {:d} but now: {:d}'.format(self.input_dim, input_dim)\\n\\n sourceL = inputs.size(2)\\n\\n if self.embed_input:\\n # repeat embeddings across batch_size\\n # result is [batch_size x input_dim x embedding_dim]\\n # TODO: repeat or expand?\\n embedding = self.embedding.repeat(batch_size, 1, 1)\\n embedded_inputs = []\\n # result is [batch_size, 1, input_dim, sourceL]\\n ips = inputs.unsqueeze(1)\\n\\n for i in range(sourceL):\\n # [batch_size x 1 x input_dim] * [batch_size x input_dim x embedding_dim]\\n # result is [batch_size, embedding_dim]\\n embedded_inputs.append(torch.bmm(\\n ips[:, :, :, i].float(),\\n embedding).squeeze(1))\\n\\n # Result is [sourceL x batch_size x embedding_dim]\\n embedded_inputs = torch.cat(embedded_inputs).view(\\n sourceL,\\n batch_size,\\n embedding.size(2))\\n else:\\n embedded_inputs = inputs.permute(2, 0, 1)\\n\\n (encoder_hx, encoder_cx) = init_zero_hidden(self.hidden_dim, inputs.is_cuda)\\n encoder_hx = encoder_hx.unsqueeze(0).repeat(embedded_inputs.size(1), 1).unsqueeze(0)\\n encoder_cx = encoder_cx.unsqueeze(0).repeat(embedded_inputs.size(1), 1).unsqueeze(0)\\n \\n # encoder forward pass\\n enc_h, (enc_h_t, enc_c_t) = self.encoder(embedded_inputs, (encoder_hx, encoder_cx))\\n\\n enc_h_linear = enc_h.view(-1, self.hidden_dim)\\n # enc_h_linear_2d = enc_h_linear.view(self.hidden_dim, -1)\\n enc_action_scores = self.EncodeScore(enc_h_linear)\\n enc_action_scores = enc_action_scores.view(-1, batch_size).permute(1, 0)\\n dec_init_state = (enc_h_t[-1], enc_c_t[-1])\\n \\n # repeat decoder_in_0 across batch\\n decoder_input = self.decoder_in_0.unsqueeze(0).repeat(embedded_inputs.size(1), 1)\\n\\n (head_pointer_probs, head_positions, tail_pointer_probs, tail_positions, cls_scores), dec_hidden_t = self.decoder(decoder_input,\\n embedded_inputs,\\n dec_init_state,\\n enc_h, max_len=decode_len)\\n #TODO: added conversion to tensors\\n head_pointer_probs = torch.stack(head_pointer_probs)\\n head_pointer_probs = head_pointer_probs.permute(1, 0, 2)\\n tail_pointer_probs = torch.stack(tail_pointer_probs)\\n tail_pointer_probs = tail_pointer_probs.permute(1, 0, 2)\\n cls_scores = torch.stack(cls_scores)\\n cls_scores = cls_scores.permute(1, 0, 2)\\n head_positions = torch.stack(head_positions)\\n head_positions = head_positions.permute(1, 0)\\n tail_positions = torch.stack(tail_positions)\\n tail_positions = tail_positions.permute(1, 0)\\n\\n\\n\\n return head_pointer_probs, head_positions, tail_pointer_probs, tail_positions, cls_scores, enc_action_scores\",\n \"def __init__(self, embed_size):\\n super(ImgEncoder, self).__init__()\\n model = models.vgg19(pretrained=True)\\n in_features = model.classifier[-1].in_features # input size of feature vector\\n model.classifier = nn.Sequential(\\n *list(model.classifier.children())[:-1]) # remove last fc layer\\n\\n self.model = model # loaded model without last fc layer\\n self.fc = nn.Linear(in_features, embed_size) # feature vector of image\",\n \"def encode(self, embedded_sentence):\\n embedding = self.encoder(embedded_sentence)\\n return embedding\",\n \"def _use_embeddings(self, word):\\n if word == \\\"@PAD@\\\":\\n return torch.zeros(self.embeddings_dim)\\n else:\\n return self.embeddings[word]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7020101","0.6637058","0.6564788","0.6535303","0.6484196","0.6471777","0.63643616","0.6341164","0.6330869","0.62297994","0.6209872","0.62074643","0.61839","0.61733645","0.61321247","0.6101453","0.6098147","0.6074956","0.607458","0.6064943","0.60478055","0.6023664","0.5995383","0.59859437","0.59823656","0.5976915","0.59740776","0.59565634","0.5949766","0.5949163","0.59489083","0.59418994","0.59087986","0.5900172","0.58733594","0.58720547","0.586406","0.58526707","0.58378863","0.58313274","0.5818361","0.58180356","0.5817333","0.58124226","0.58080876","0.58071536","0.58062667","0.5805154","0.58001065","0.5787698","0.5782308","0.57815236","0.5767357","0.5763583","0.5762362","0.5761404","0.57595474","0.5752958","0.57521784","0.5751206","0.5747115","0.5737014","0.57246333","0.57142514","0.57130045","0.5703501","0.57020056","0.56951267","0.5687837","0.5681755","0.56800485","0.56778634","0.5673204","0.567295","0.5670143","0.56699765","0.56691194","0.5663274","0.56612754","0.5656632","0.5656409","0.56502825","0.5649768","0.5643277","0.5635563","0.56311154","0.56233954","0.5623334","0.5622328","0.5619856","0.5619856","0.56189895","0.5615491","0.5612539","0.5609476","0.5600953","0.5584514","0.55775946","0.5574098","0.5572588","0.5570366"],"string":"[\n \"0.7020101\",\n \"0.6637058\",\n \"0.6564788\",\n \"0.6535303\",\n \"0.6484196\",\n \"0.6471777\",\n \"0.63643616\",\n \"0.6341164\",\n \"0.6330869\",\n \"0.62297994\",\n \"0.6209872\",\n \"0.62074643\",\n \"0.61839\",\n \"0.61733645\",\n \"0.61321247\",\n \"0.6101453\",\n \"0.6098147\",\n \"0.6074956\",\n \"0.607458\",\n \"0.6064943\",\n \"0.60478055\",\n \"0.6023664\",\n \"0.5995383\",\n \"0.59859437\",\n \"0.59823656\",\n \"0.5976915\",\n \"0.59740776\",\n \"0.59565634\",\n \"0.5949766\",\n \"0.5949163\",\n \"0.59489083\",\n \"0.59418994\",\n \"0.59087986\",\n \"0.5900172\",\n \"0.58733594\",\n \"0.58720547\",\n \"0.586406\",\n \"0.58526707\",\n \"0.58378863\",\n \"0.58313274\",\n \"0.5818361\",\n \"0.58180356\",\n \"0.5817333\",\n \"0.58124226\",\n \"0.58080876\",\n \"0.58071536\",\n \"0.58062667\",\n \"0.5805154\",\n \"0.58001065\",\n \"0.5787698\",\n \"0.5782308\",\n \"0.57815236\",\n \"0.5767357\",\n \"0.5763583\",\n \"0.5762362\",\n \"0.5761404\",\n \"0.57595474\",\n \"0.5752958\",\n \"0.57521784\",\n \"0.5751206\",\n \"0.5747115\",\n \"0.5737014\",\n \"0.57246333\",\n \"0.57142514\",\n \"0.57130045\",\n \"0.5703501\",\n \"0.57020056\",\n \"0.56951267\",\n \"0.5687837\",\n \"0.5681755\",\n \"0.56800485\",\n \"0.56778634\",\n \"0.5673204\",\n \"0.567295\",\n \"0.5670143\",\n \"0.56699765\",\n \"0.56691194\",\n \"0.5663274\",\n \"0.56612754\",\n \"0.5656632\",\n \"0.5656409\",\n \"0.56502825\",\n \"0.5649768\",\n \"0.5643277\",\n \"0.5635563\",\n \"0.56311154\",\n \"0.56233954\",\n \"0.5623334\",\n \"0.5622328\",\n \"0.5619856\",\n \"0.5619856\",\n \"0.56189895\",\n \"0.5615491\",\n \"0.5612539\",\n \"0.5609476\",\n \"0.5600953\",\n \"0.5584514\",\n \"0.55775946\",\n \"0.5574098\",\n \"0.5572588\",\n \"0.5570366\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":20068,"cells":{"query":{"kind":"string","value":"Returns True if the server is running"},"document":{"kind":"string","value":"def is_running(self):\n return self._running"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _ServerIsRunning( self ):\n return utils.ProcessIsRunning( self._gocode_handle )","def status(self):\n # process running ?\n pid = self.get_pidfile()\n \n running = True\n \n # process is not running\n if pid is None:\n running = False\n else:\n if not self.send_signal(pid,0):\n running = False\n # abnormal state, delete the file\n self.delete_pidfile()\n \n if running:\n message = \"server is running\\n\"\n else:\n message = \"server is not running\\n\"\n sys.stdout.write(message)\n \n return running","def is_running(self):\n status = self._call_player_proxy('GetStatus', None).unpack()[0]\n if status[3] == 1:\n return True\n return False","def owserver_running():\n for proc in psutil.process_iter():\n if 'owserver' in proc.name():\n return True\n return False","def is_running(self) -> bool:\n return False","def _checkTorcsServer(self):\n isRunning = False\n if self.torcsServerProcess is not None:\n if self.torcsServerProcess.poll() is None:\n isRunning = True\n return isRunning","def is_running(self):\n\n command = ('ssh {0} -q -o StrictHostKeyChecking=no -o '\n 'UserKnownHostsFile=/dev/null \"pgrep httperf \"').format(self.client)\n\n if subprocess.call(command, stdout=subprocess.PIPE, shell=True) == 0:\n return True\n else:\n return False","def is_running(self):\n qstat = self._grep_qstat('running')\n if qstat:\n return True\n return False","def is_running(self):\n status = self.get_status_response()\n return ((status[1] & 2) == 2)\n #end is_running()","async def is_running(self, **kwargs: Any) -> bool:\n return True","def is_running(self):\n if self._process and self._process.poll() is None:\n return True\n return False","def is_running(self) -> bool:\n return self._is_running","def is_running(self):\n\t\treturn self._running","def is_running(self):\n if self._process:\n return self._process.poll() is None\n else:\n return False","def is_running(self):\n return self.running","def is_running(self):\n return self.running","def is_running(self):\n return self.running","def is_running(self) -> bool:\n return self.executor.is_alive() if self.executor else False","def is_running(self):\n return self._is_running","def is_running(self):\n return self._is_running","def status(self):\n pid = self.pid()\n if pid is None or not pid_exists(pid):\n return False\n\n process = Process(pid)\n try:\n for connection in process.connections():\n if connection.status == 'LISTEN' and \\\n connection.laddr[1] == self.port:\n return True\n except AccessDenied:\n return False\n\n return False","async def is_running(self, **kwargs: Any) -> bool:\n return self._enabled","def is_running(self) -> bool:\r\n return self.__running","def is_instance_running(self):\n try:\n self.instance.wait(timeout=1)\n except psutil.TimeoutExpired:\n pass\n return self.instance.is_running()","def _is_running(self):\n # Public interface is given by get_status instead.\n self._update()\n return True if self.running_mode else False","def is_running(self):\n if self._thread and self._thread.is_alive:\n return True\n\n return False","def is_running(self):\n return self._event_loop is not None and self._event_loop.is_running()","def is_alive(self):\n if (self._s.fileno()>0 and self._running and self._listen):\n return True\n else:\n return False","def _is_running(self):\n return self._run_state.is_running()","def is_running(self):\n\t\treturn self in _running","def running(self):\n return self.sub_process and self.sub_process.is_alive()","def is_running(self):\n\n return self._state == \"RUNNING\"","def ServerIsHealthy( self ):\n if not self._ServerIsRunning():\n return False\n\n try:\n self._ExecuteCommand( [ self._gocode_binary_path,\n '-sock', 'tcp',\n '-addr', self._gocode_host,\n 'status' ] )\n return True\n # We catch this exception type and not a more specific one because we\n # raise it in _ExecuteCommand when the command fails.\n except RuntimeError as error:\n _logger.exception( error )\n return False","def isRunning(self):\n if not self.running:\n return False\n elif self.process.poll() == 0 or self.process.returncode >= 0:\n return False\n else:\n return True","def is_running(self):\n # type: () -> bool\n return self._run_state.is_running()","def is_running(self) -> bool:\n return self._running.is_set()","def is_server_running(self, shut_off_is_down: bool = False) -> bool:\n out = self.cloud_cli.run_cloud_cmd(\n f\"compute --project={self.project} instances describe --zone={self.zone} {self.name} --format=json\")\n try:\n out = json.loads(out.strip())\n except json.JSONDecodeError:\n return False\n return True","def running(self):\n try:\n return self._thread.isAlive()\n except (AttributeError, RuntimeError, ThreadError):\n return False","async def is_running(self, **kwargs: Any) -> bool:\n ...","def is_connected(self):\n if self.server: return True\n return False","def listening(self):\n return self._server is not None","def is_running(self):\n return self._running.is_set()","def isRunning(self):\n if not self.hasBeenStarted():\n return False\n \n if not self._slave_dhcp_client_proc.poll(): # Poll our direct child (sudo)\n return False\n \n for pid in self._all_processes_pid:\n if not self._checkPid(pid):\n return False\n \n return True","def is_server_listening(self):\n self.log.info('Checking if the server is listening on TCP port {}'.format(self.upm_port))\n is_listening = connectivity.is_host_listening_tcp(host_address=self.upm_host,\n tcp_port=self.upm_port)\n if not is_listening:\n message = \"The server is not listening on TCP port {}\".format(self.upm_port)\n assert False, message\n self.log.info('The server is listening on TCP port {}'.format(self.upm_port))\n return True","def is_running(self) -> Awaitable[bool]:\n return self.instance.is_running()","def is_alive(self):\n ret = subprocess.call(\n shlex.split(\"ping -c 1 -W 2 %s\" % self.ip_address),\n stdout=open('/dev/null', 'w'),\n stderr=subprocess.STDOUT,\n )\n \n if ret == 0:\n return True\n else:\n return False","def _isrunning(self):\n return self.dp.state()==PyTango.DevState.RUNNING","def is_vnc_server_started(self):\n\t\treturn bool(call_sdk_function('PrlVmInfo_IsVncServerStarted', self.handle))","def alive(self):\n return self._process.is_alive()","def is_running(self):\n data = self._poll()\n return data.get('building', False)","def is_sm_running() -> bool:\n initd = '/etc/init.d'\n print(\"Checking SUSE Manager running...\")\n\n # Get tomcat\n tomcat = \"\"\n for cmd in os.listdir(initd):\n if cmd.startswith('tomcat'):\n tomcat = initd + \"/\" + cmd\n break\n\n return os.popen(tomcat + \" status 2>&1\").read().strip().find('dead') == -1","def check_status(self):\n try:\n self.server.ping()\n return True\n except Exception as e:\n return False","async def _async_is_addon_running(self):\n addon_info = await self._async_get_addon_info()\n return addon_info[\"state\"] == \"started\"","def IsRunning(self):\n return self.running","def is_running(self):\r\n if self._gone:\r\n return False\r\n try:\r\n # Checking if pid is alive is not enough as the pid might\r\n # have been reused by another process.\r\n # pid + creation time, on the other hand, is supposed to\r\n # identify a process univocally.\r\n return self.create_time == \\\r\n self.get_process_create_time()\r\n except NoSuchProcess:\r\n self._gone = True\r\n return False","def _is_alive(self) -> bool:\n\n if self._on:\n return True\n\n try:\n os.kill(self.proc.pid, 0)\n except (OSError, ProcessLookupError):\n return False\n\n return True","def is_running(self):\n return self.current_state == self.States.RUNNING","def is_mongod_running(self):\r\n \r\n try:\r\n _connect_to_mongo_port(int(self.port))\r\n return True\r\n except OSError:\r\n return False\r\n except Exception:\r\n return False","def get_status(self) -> bool:\n try:\n self.__driver.service.assert_process_still_running()\n return True\n except AttributeError:\n return False","def is_running(self):\n return self.type_id == STATE_RUNNING","def is_alive(self):\n if self.status == 1:\n return True\n else:\n return False","def is_running(self):\n self.__condition.acquire()\n result = self.__is_running\n self.__condition.release()\n return result","def running(self) -> bool:\n return self._running","def is_server_alive(self):\n self.log.info('Checking if the server is available via SSH')\n is_available = ssh_utilities.is_server_alive(remote_host=self.upm_host,\n remote_username=self.upm_username,\n remote_password=self.upm_password)\n if not is_available:\n message = 'The server is not available via SSH'\n assert False, message\n self.log.info('The server is available via SSH')\n return True","def _is_running(self):\n try:\n # Process is not killed, os.kill(pid, 0) does nothing but raise if process does not\n # exist.\n os.kill(self.pid, 0)\n except ProcessLookupError:\n return False\n else:\n return True","def is_running(self):\n return self.action_thread and self.action_thread.is_alive()","def IsServerRunningOnAllPorts(self):\n for mapping in self.mappings:\n if 'sslcert' in mapping:\n http_suffix = 's'\n else:\n http_suffix = ''\n\n url = 'http%s://127.0.0.1:%d/' % (http_suffix, mapping['port'])\n\n try:\n response = urllib.urlopen(url)\n logging.debug(\"Server running at %s\" % url)\n except IOError:\n logging.debug(\"Server NOT running at %s\" % url)\n return False\n\n return True","def is_alive(self) -> bool:\n return self._main_thread.is_alive()","def test_server_is_up_and_running(self):\n r = requests.get(self.get_server_url())\n self.assertEqual(r.status_code, 200)","def check_configuration_server(self) -> bool:\n return (\n self.container is not None\n and self.container.exec_run(\n \"bash -c 'curl -s --head http://localhost:19071/ApplicationStatus'\"\n )\n .output.decode(\"utf-8\")\n .split(\"\\r\\n\")[0]\n == \"HTTP/1.1 200 OK\"\n )","def running(self):\n\t\treturn self._start is not None","def is_running(ssh):\n\tif get_status(ssh) == \"running\":\n\t\treturn True\n\treturn False","def _engineServicesRunning():\n process = subprocess.Popen([\"ps\", \"aux\"], stdout=subprocess.PIPE)\n\n stdout = process.communicate()[0]\n result = process.returncode\n if result != 0:\n raise RuntimeError(\"Unable to check for running client job manager\")\n\n # See if the CJM is running\n running = False\n for line in stdout.split(\"\\n\"):\n if \"python\" in line and \"clientjobmanager.client_job_manager\" in line:\n running = True\n break\n\n return running","def _is_sshd_server_running(self, timeout=1):\n try:\n self.ssh_client.connect(timeout=timeout)\n self.ssh_client.close()\n return True\n except Exception:\n return False","def is_running(self):\n return all(p.status == 'running' for p in self.values())","def ServerIsReady( self ):\n return self.ServerIsHealthy()","def ready(self):\n\n if not self.running:\n return False\n\n try:\n response = requests.get(\n 'http://{}:{}'.format(\n self.running_host,\n self.running_port\n )\n )\n except requests.ConnectionError:\n return False\n\n if response.status_code == 404:\n return True\n elif response.status_code == 500:\n return False\n else:\n return False","def is_running(self):\n return self._task.running()","def is_running(self):\n return self._task.running()","def is_alive(self):\n return True","def _is_working():\n global _worker\n return _worker is not None and _worker.is_alive()","def is_on(self):\n return self._get_state() == ServerState.ON","def active(self):\n return self.server.is_active() or self.executing","def is_running(program):\n return program in get_running()","def alive(self):\n\n return self.subprocess.poll() is None and not self.thread_stop.is_set()","def is_alive(self) -> bool:\n self.check_is_alive()\n return self.__is_alive","def is_container_running(self):\n return self._is_container_running","def is_alive(self) -> bool:\n if self._thread is None:\n return False\n return self._thread.is_alive()","def is_working(self):\n if not self.__th:\n return False\n return self.__th.is_alive()","def running(self):\n return bool(self.proc and self._running())","def status_check(self):\n try:\n client = self.connect()\n client.sys.is_initialized() # make an actual network connection\n return True\n except:\n return False","def is_started(self):\n return bool(self._processes)","def isup(self):\n if self.cloudserver:\n # print self.cloudserver.status\n if self.cloudserver.status in (\"ACTIVE\",):\n return True\n \n return False","def is_alive():\n\n ## ---------------------------------------------------------------\n \n cmd = dict()\n cmd[\"type_\"] = \"is_alive\"\n cmd[\"name_\"] = \"\"\n\n s = socket.socket(\n socket.AF_INET,\n socket.SOCK_STREAM\n )\n try:\n s.connect((getml.host, getml.port))\n except ConnectionRefusedError:\n return False\n\n comm.send_string(s, json.dumps(cmd))\n\n s.close()\n\n return True","def is_running_manager(self) -> bool:\n return self.get_value(self._manager_running_attribute) == '1'","def is_connected(self):\n return self.hub.is_connected and self.client.is_running","def spotify_running():\n stdout = (\n subprocess.check_output([\"osascript\", \"-e\", 'application \"Spotify\" is running'])\n .decode(\"utf-8\")\n .strip()\n )\n\n return stdout == \"true\"","def running(self):\n return self._lifetime_state in {\"starting\",\"running\",\"finishing\"}","def is_heating_running(self) -> bool:\n\n return self.send(self.cmd.GET_HEATING_RUNNING)"],"string":"[\n \"def _ServerIsRunning( self ):\\n return utils.ProcessIsRunning( self._gocode_handle )\",\n \"def status(self):\\n # process running ?\\n pid = self.get_pidfile()\\n \\n running = True\\n \\n # process is not running\\n if pid is None:\\n running = False\\n else:\\n if not self.send_signal(pid,0):\\n running = False\\n # abnormal state, delete the file\\n self.delete_pidfile()\\n \\n if running:\\n message = \\\"server is running\\\\n\\\"\\n else:\\n message = \\\"server is not running\\\\n\\\"\\n sys.stdout.write(message)\\n \\n return running\",\n \"def is_running(self):\\n status = self._call_player_proxy('GetStatus', None).unpack()[0]\\n if status[3] == 1:\\n return True\\n return False\",\n \"def owserver_running():\\n for proc in psutil.process_iter():\\n if 'owserver' in proc.name():\\n return True\\n return False\",\n \"def is_running(self) -> bool:\\n return False\",\n \"def _checkTorcsServer(self):\\n isRunning = False\\n if self.torcsServerProcess is not None:\\n if self.torcsServerProcess.poll() is None:\\n isRunning = True\\n return isRunning\",\n \"def is_running(self):\\n\\n command = ('ssh {0} -q -o StrictHostKeyChecking=no -o '\\n 'UserKnownHostsFile=/dev/null \\\"pgrep httperf \\\"').format(self.client)\\n\\n if subprocess.call(command, stdout=subprocess.PIPE, shell=True) == 0:\\n return True\\n else:\\n return False\",\n \"def is_running(self):\\n qstat = self._grep_qstat('running')\\n if qstat:\\n return True\\n return False\",\n \"def is_running(self):\\n status = self.get_status_response()\\n return ((status[1] & 2) == 2)\\n #end is_running()\",\n \"async def is_running(self, **kwargs: Any) -> bool:\\n return True\",\n \"def is_running(self):\\n if self._process and self._process.poll() is None:\\n return True\\n return False\",\n \"def is_running(self) -> bool:\\n return self._is_running\",\n \"def is_running(self):\\n\\t\\treturn self._running\",\n \"def is_running(self):\\n if self._process:\\n return self._process.poll() is None\\n else:\\n return False\",\n \"def is_running(self):\\n return self.running\",\n \"def is_running(self):\\n return self.running\",\n \"def is_running(self):\\n return self.running\",\n \"def is_running(self) -> bool:\\n return self.executor.is_alive() if self.executor else False\",\n \"def is_running(self):\\n return self._is_running\",\n \"def is_running(self):\\n return self._is_running\",\n \"def status(self):\\n pid = self.pid()\\n if pid is None or not pid_exists(pid):\\n return False\\n\\n process = Process(pid)\\n try:\\n for connection in process.connections():\\n if connection.status == 'LISTEN' and \\\\\\n connection.laddr[1] == self.port:\\n return True\\n except AccessDenied:\\n return False\\n\\n return False\",\n \"async def is_running(self, **kwargs: Any) -> bool:\\n return self._enabled\",\n \"def is_running(self) -> bool:\\r\\n return self.__running\",\n \"def is_instance_running(self):\\n try:\\n self.instance.wait(timeout=1)\\n except psutil.TimeoutExpired:\\n pass\\n return self.instance.is_running()\",\n \"def _is_running(self):\\n # Public interface is given by get_status instead.\\n self._update()\\n return True if self.running_mode else False\",\n \"def is_running(self):\\n if self._thread and self._thread.is_alive:\\n return True\\n\\n return False\",\n \"def is_running(self):\\n return self._event_loop is not None and self._event_loop.is_running()\",\n \"def is_alive(self):\\n if (self._s.fileno()>0 and self._running and self._listen):\\n return True\\n else:\\n return False\",\n \"def _is_running(self):\\n return self._run_state.is_running()\",\n \"def is_running(self):\\n\\t\\treturn self in _running\",\n \"def running(self):\\n return self.sub_process and self.sub_process.is_alive()\",\n \"def is_running(self):\\n\\n return self._state == \\\"RUNNING\\\"\",\n \"def ServerIsHealthy( self ):\\n if not self._ServerIsRunning():\\n return False\\n\\n try:\\n self._ExecuteCommand( [ self._gocode_binary_path,\\n '-sock', 'tcp',\\n '-addr', self._gocode_host,\\n 'status' ] )\\n return True\\n # We catch this exception type and not a more specific one because we\\n # raise it in _ExecuteCommand when the command fails.\\n except RuntimeError as error:\\n _logger.exception( error )\\n return False\",\n \"def isRunning(self):\\n if not self.running:\\n return False\\n elif self.process.poll() == 0 or self.process.returncode >= 0:\\n return False\\n else:\\n return True\",\n \"def is_running(self):\\n # type: () -> bool\\n return self._run_state.is_running()\",\n \"def is_running(self) -> bool:\\n return self._running.is_set()\",\n \"def is_server_running(self, shut_off_is_down: bool = False) -> bool:\\n out = self.cloud_cli.run_cloud_cmd(\\n f\\\"compute --project={self.project} instances describe --zone={self.zone} {self.name} --format=json\\\")\\n try:\\n out = json.loads(out.strip())\\n except json.JSONDecodeError:\\n return False\\n return True\",\n \"def running(self):\\n try:\\n return self._thread.isAlive()\\n except (AttributeError, RuntimeError, ThreadError):\\n return False\",\n \"async def is_running(self, **kwargs: Any) -> bool:\\n ...\",\n \"def is_connected(self):\\n if self.server: return True\\n return False\",\n \"def listening(self):\\n return self._server is not None\",\n \"def is_running(self):\\n return self._running.is_set()\",\n \"def isRunning(self):\\n if not self.hasBeenStarted():\\n return False\\n \\n if not self._slave_dhcp_client_proc.poll(): # Poll our direct child (sudo)\\n return False\\n \\n for pid in self._all_processes_pid:\\n if not self._checkPid(pid):\\n return False\\n \\n return True\",\n \"def is_server_listening(self):\\n self.log.info('Checking if the server is listening on TCP port {}'.format(self.upm_port))\\n is_listening = connectivity.is_host_listening_tcp(host_address=self.upm_host,\\n tcp_port=self.upm_port)\\n if not is_listening:\\n message = \\\"The server is not listening on TCP port {}\\\".format(self.upm_port)\\n assert False, message\\n self.log.info('The server is listening on TCP port {}'.format(self.upm_port))\\n return True\",\n \"def is_running(self) -> Awaitable[bool]:\\n return self.instance.is_running()\",\n \"def is_alive(self):\\n ret = subprocess.call(\\n shlex.split(\\\"ping -c 1 -W 2 %s\\\" % self.ip_address),\\n stdout=open('/dev/null', 'w'),\\n stderr=subprocess.STDOUT,\\n )\\n \\n if ret == 0:\\n return True\\n else:\\n return False\",\n \"def _isrunning(self):\\n return self.dp.state()==PyTango.DevState.RUNNING\",\n \"def is_vnc_server_started(self):\\n\\t\\treturn bool(call_sdk_function('PrlVmInfo_IsVncServerStarted', self.handle))\",\n \"def alive(self):\\n return self._process.is_alive()\",\n \"def is_running(self):\\n data = self._poll()\\n return data.get('building', False)\",\n \"def is_sm_running() -> bool:\\n initd = '/etc/init.d'\\n print(\\\"Checking SUSE Manager running...\\\")\\n\\n # Get tomcat\\n tomcat = \\\"\\\"\\n for cmd in os.listdir(initd):\\n if cmd.startswith('tomcat'):\\n tomcat = initd + \\\"/\\\" + cmd\\n break\\n\\n return os.popen(tomcat + \\\" status 2>&1\\\").read().strip().find('dead') == -1\",\n \"def check_status(self):\\n try:\\n self.server.ping()\\n return True\\n except Exception as e:\\n return False\",\n \"async def _async_is_addon_running(self):\\n addon_info = await self._async_get_addon_info()\\n return addon_info[\\\"state\\\"] == \\\"started\\\"\",\n \"def IsRunning(self):\\n return self.running\",\n \"def is_running(self):\\r\\n if self._gone:\\r\\n return False\\r\\n try:\\r\\n # Checking if pid is alive is not enough as the pid might\\r\\n # have been reused by another process.\\r\\n # pid + creation time, on the other hand, is supposed to\\r\\n # identify a process univocally.\\r\\n return self.create_time == \\\\\\r\\n self.get_process_create_time()\\r\\n except NoSuchProcess:\\r\\n self._gone = True\\r\\n return False\",\n \"def _is_alive(self) -> bool:\\n\\n if self._on:\\n return True\\n\\n try:\\n os.kill(self.proc.pid, 0)\\n except (OSError, ProcessLookupError):\\n return False\\n\\n return True\",\n \"def is_running(self):\\n return self.current_state == self.States.RUNNING\",\n \"def is_mongod_running(self):\\r\\n \\r\\n try:\\r\\n _connect_to_mongo_port(int(self.port))\\r\\n return True\\r\\n except OSError:\\r\\n return False\\r\\n except Exception:\\r\\n return False\",\n \"def get_status(self) -> bool:\\n try:\\n self.__driver.service.assert_process_still_running()\\n return True\\n except AttributeError:\\n return False\",\n \"def is_running(self):\\n return self.type_id == STATE_RUNNING\",\n \"def is_alive(self):\\n if self.status == 1:\\n return True\\n else:\\n return False\",\n \"def is_running(self):\\n self.__condition.acquire()\\n result = self.__is_running\\n self.__condition.release()\\n return result\",\n \"def running(self) -> bool:\\n return self._running\",\n \"def is_server_alive(self):\\n self.log.info('Checking if the server is available via SSH')\\n is_available = ssh_utilities.is_server_alive(remote_host=self.upm_host,\\n remote_username=self.upm_username,\\n remote_password=self.upm_password)\\n if not is_available:\\n message = 'The server is not available via SSH'\\n assert False, message\\n self.log.info('The server is available via SSH')\\n return True\",\n \"def _is_running(self):\\n try:\\n # Process is not killed, os.kill(pid, 0) does nothing but raise if process does not\\n # exist.\\n os.kill(self.pid, 0)\\n except ProcessLookupError:\\n return False\\n else:\\n return True\",\n \"def is_running(self):\\n return self.action_thread and self.action_thread.is_alive()\",\n \"def IsServerRunningOnAllPorts(self):\\n for mapping in self.mappings:\\n if 'sslcert' in mapping:\\n http_suffix = 's'\\n else:\\n http_suffix = ''\\n\\n url = 'http%s://127.0.0.1:%d/' % (http_suffix, mapping['port'])\\n\\n try:\\n response = urllib.urlopen(url)\\n logging.debug(\\\"Server running at %s\\\" % url)\\n except IOError:\\n logging.debug(\\\"Server NOT running at %s\\\" % url)\\n return False\\n\\n return True\",\n \"def is_alive(self) -> bool:\\n return self._main_thread.is_alive()\",\n \"def test_server_is_up_and_running(self):\\n r = requests.get(self.get_server_url())\\n self.assertEqual(r.status_code, 200)\",\n \"def check_configuration_server(self) -> bool:\\n return (\\n self.container is not None\\n and self.container.exec_run(\\n \\\"bash -c 'curl -s --head http://localhost:19071/ApplicationStatus'\\\"\\n )\\n .output.decode(\\\"utf-8\\\")\\n .split(\\\"\\\\r\\\\n\\\")[0]\\n == \\\"HTTP/1.1 200 OK\\\"\\n )\",\n \"def running(self):\\n\\t\\treturn self._start is not None\",\n \"def is_running(ssh):\\n\\tif get_status(ssh) == \\\"running\\\":\\n\\t\\treturn True\\n\\treturn False\",\n \"def _engineServicesRunning():\\n process = subprocess.Popen([\\\"ps\\\", \\\"aux\\\"], stdout=subprocess.PIPE)\\n\\n stdout = process.communicate()[0]\\n result = process.returncode\\n if result != 0:\\n raise RuntimeError(\\\"Unable to check for running client job manager\\\")\\n\\n # See if the CJM is running\\n running = False\\n for line in stdout.split(\\\"\\\\n\\\"):\\n if \\\"python\\\" in line and \\\"clientjobmanager.client_job_manager\\\" in line:\\n running = True\\n break\\n\\n return running\",\n \"def _is_sshd_server_running(self, timeout=1):\\n try:\\n self.ssh_client.connect(timeout=timeout)\\n self.ssh_client.close()\\n return True\\n except Exception:\\n return False\",\n \"def is_running(self):\\n return all(p.status == 'running' for p in self.values())\",\n \"def ServerIsReady( self ):\\n return self.ServerIsHealthy()\",\n \"def ready(self):\\n\\n if not self.running:\\n return False\\n\\n try:\\n response = requests.get(\\n 'http://{}:{}'.format(\\n self.running_host,\\n self.running_port\\n )\\n )\\n except requests.ConnectionError:\\n return False\\n\\n if response.status_code == 404:\\n return True\\n elif response.status_code == 500:\\n return False\\n else:\\n return False\",\n \"def is_running(self):\\n return self._task.running()\",\n \"def is_running(self):\\n return self._task.running()\",\n \"def is_alive(self):\\n return True\",\n \"def _is_working():\\n global _worker\\n return _worker is not None and _worker.is_alive()\",\n \"def is_on(self):\\n return self._get_state() == ServerState.ON\",\n \"def active(self):\\n return self.server.is_active() or self.executing\",\n \"def is_running(program):\\n return program in get_running()\",\n \"def alive(self):\\n\\n return self.subprocess.poll() is None and not self.thread_stop.is_set()\",\n \"def is_alive(self) -> bool:\\n self.check_is_alive()\\n return self.__is_alive\",\n \"def is_container_running(self):\\n return self._is_container_running\",\n \"def is_alive(self) -> bool:\\n if self._thread is None:\\n return False\\n return self._thread.is_alive()\",\n \"def is_working(self):\\n if not self.__th:\\n return False\\n return self.__th.is_alive()\",\n \"def running(self):\\n return bool(self.proc and self._running())\",\n \"def status_check(self):\\n try:\\n client = self.connect()\\n client.sys.is_initialized() # make an actual network connection\\n return True\\n except:\\n return False\",\n \"def is_started(self):\\n return bool(self._processes)\",\n \"def isup(self):\\n if self.cloudserver:\\n # print self.cloudserver.status\\n if self.cloudserver.status in (\\\"ACTIVE\\\",):\\n return True\\n \\n return False\",\n \"def is_alive():\\n\\n ## ---------------------------------------------------------------\\n \\n cmd = dict()\\n cmd[\\\"type_\\\"] = \\\"is_alive\\\"\\n cmd[\\\"name_\\\"] = \\\"\\\"\\n\\n s = socket.socket(\\n socket.AF_INET,\\n socket.SOCK_STREAM\\n )\\n try:\\n s.connect((getml.host, getml.port))\\n except ConnectionRefusedError:\\n return False\\n\\n comm.send_string(s, json.dumps(cmd))\\n\\n s.close()\\n\\n return True\",\n \"def is_running_manager(self) -> bool:\\n return self.get_value(self._manager_running_attribute) == '1'\",\n \"def is_connected(self):\\n return self.hub.is_connected and self.client.is_running\",\n \"def spotify_running():\\n stdout = (\\n subprocess.check_output([\\\"osascript\\\", \\\"-e\\\", 'application \\\"Spotify\\\" is running'])\\n .decode(\\\"utf-8\\\")\\n .strip()\\n )\\n\\n return stdout == \\\"true\\\"\",\n \"def running(self):\\n return self._lifetime_state in {\\\"starting\\\",\\\"running\\\",\\\"finishing\\\"}\",\n \"def is_heating_running(self) -> bool:\\n\\n return self.send(self.cmd.GET_HEATING_RUNNING)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8809185","0.8009785","0.8002331","0.7958592","0.7890142","0.7879066","0.78770465","0.7851644","0.7850044","0.78008723","0.77929753","0.77749777","0.7774173","0.7757005","0.77514684","0.77514684","0.77514684","0.77452874","0.7717644","0.7717644","0.77032256","0.7666353","0.7658982","0.7643591","0.76321715","0.7628348","0.760522","0.75998974","0.7596659","0.75957316","0.7566516","0.75651634","0.74924207","0.7489293","0.7485419","0.7482086","0.74548656","0.7446599","0.74340063","0.7433904","0.7432833","0.74217254","0.7400543","0.73753077","0.7369742","0.7312428","0.72703934","0.7262206","0.7261207","0.72594607","0.7257109","0.7254183","0.7240537","0.72327113","0.7231537","0.7221987","0.7220413","0.72162247","0.7215945","0.72086686","0.72048247","0.71986353","0.7190047","0.7189822","0.7189793","0.71837413","0.71818054","0.7171479","0.7161473","0.71570015","0.71454155","0.7138612","0.7114964","0.71098876","0.7105625","0.70984983","0.70798457","0.7063878","0.7063878","0.704077","0.70377374","0.7009259","0.6996986","0.6987958","0.69741535","0.69700825","0.69696677","0.6963696","0.69610727","0.69601643","0.6942188","0.6941148","0.69404453","0.69078296","0.68909067","0.6858672","0.6843737","0.68324345","0.68290615"],"string":"[\n \"0.8809185\",\n \"0.8009785\",\n \"0.8002331\",\n \"0.7958592\",\n \"0.7890142\",\n \"0.7879066\",\n \"0.78770465\",\n \"0.7851644\",\n \"0.7850044\",\n \"0.78008723\",\n \"0.77929753\",\n \"0.77749777\",\n \"0.7774173\",\n \"0.7757005\",\n \"0.77514684\",\n \"0.77514684\",\n \"0.77514684\",\n \"0.77452874\",\n \"0.7717644\",\n \"0.7717644\",\n \"0.77032256\",\n \"0.7666353\",\n \"0.7658982\",\n \"0.7643591\",\n \"0.76321715\",\n \"0.7628348\",\n \"0.760522\",\n \"0.75998974\",\n \"0.7596659\",\n \"0.75957316\",\n \"0.7566516\",\n \"0.75651634\",\n \"0.74924207\",\n \"0.7489293\",\n \"0.7485419\",\n \"0.7482086\",\n \"0.74548656\",\n \"0.7446599\",\n \"0.74340063\",\n \"0.7433904\",\n \"0.7432833\",\n \"0.74217254\",\n \"0.7400543\",\n \"0.73753077\",\n \"0.7369742\",\n \"0.7312428\",\n \"0.72703934\",\n \"0.7262206\",\n \"0.7261207\",\n \"0.72594607\",\n \"0.7257109\",\n \"0.7254183\",\n \"0.7240537\",\n \"0.72327113\",\n \"0.7231537\",\n \"0.7221987\",\n \"0.7220413\",\n \"0.72162247\",\n \"0.7215945\",\n \"0.72086686\",\n \"0.72048247\",\n \"0.71986353\",\n \"0.7190047\",\n \"0.7189822\",\n \"0.7189793\",\n \"0.71837413\",\n \"0.71818054\",\n \"0.7171479\",\n \"0.7161473\",\n \"0.71570015\",\n \"0.71454155\",\n \"0.7138612\",\n \"0.7114964\",\n \"0.71098876\",\n \"0.7105625\",\n \"0.70984983\",\n \"0.70798457\",\n \"0.7063878\",\n \"0.7063878\",\n \"0.704077\",\n \"0.70377374\",\n \"0.7009259\",\n \"0.6996986\",\n \"0.6987958\",\n \"0.69741535\",\n \"0.69700825\",\n \"0.69696677\",\n \"0.6963696\",\n \"0.69610727\",\n \"0.69601643\",\n \"0.6942188\",\n \"0.6941148\",\n \"0.69404453\",\n \"0.69078296\",\n \"0.68909067\",\n \"0.6858672\",\n \"0.6843737\",\n \"0.68324345\",\n \"0.68290615\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7632167"},"document_rank":{"kind":"string","value":"26"}}},{"rowIdx":20069,"cells":{"query":{"kind":"string","value":"Return interface being listened on"},"document":{"kind":"string","value":"def interface(self):\n return self._interface"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _get_interface(self):\n return self.__interface","def _get_interface(self):\n return self.__interface","def _get_interface(self):\n return self.__interface","def _get_interface(self):\n return self.__interface","def _get_interface(self):\n return self.__interface","def _get_interface(self):\n return self.__interface","def _get_interface(self):\n return self.__interface","def _get_interface(self):\n return self.__interface","def _get_interface(self):\n return self.__interface","def _get_interface(self):\n return self.__interface","def _get_interface(self):\n return self.__interface","def _get_interface(self):\n return self.__interface","def _get_interface(self):\n return self.__interface","def _get_interface(self):\n return self.__interface","def _get_interface(self):\n return self.__interface","def interface(self) -> type:\n return self.get_interface()","def _get_interface_ref(self):\n return self.__interface_ref","def _get_interface_ref(self):\n return self.__interface_ref","def _get_interface_ref(self):\n return self.__interface_ref","def _get_interface_ref(self):\n return self.__interface_ref","def _get_interface_ref(self):\n return self.__interface_ref","def _get_interface_ref(self):\n return self.__interface_ref","def interface(cls):\n return get_interface(cls.connection_name)","def GetCurrentInterface(self):\n return self.current_interface","def _get_interface_type(self):\n return self.__interface_type","def network_interface(self): \n return self._network_interface","def get_interface(name=''):\n global interfaces\n return interfaces[name]","def interface(self):\n return self.broker.interface(**{\"DeviceRouteID\": self.DeviceRouteID})","def backend_getInterface(self):\n\t\treturn describeInterface(self)","def _get_interface_name(self):\n return self.__interface_name","def _get_interface_id(self):\n return self.__interface_id","def _get_interface_id(self):\n return self.__interface_id","def _get_interface_id(self):\n return self.__interface_id","def _get_interface_id(self):\n return self.__interface_id","def _get_interface_id(self):\n return self.__interface_id","def _get_interface_id(self):\n return self.__interface_id","def _get_interface_id(self):\n return self.__interface_id","def _get_interface_id(self):\n return self.__interface_id","def _get_interface_id(self):\n return self.__interface_id","def interface(self):\n if self._interface is None:\n expression = expressions.WPA_INTERFACE\n name = expressions.INTERFACE_NAME\n command = self.interface_list_command\n self._interface = self._match(expression,\n name,\n command)\n return self._interface","def GetInterface(self):\n ifname = self.interface_watcher.get_last_ifname()\n if ifname is None:\n ifname = ''\n logger.debug('Replying \"' + ifname + '\" to D-Bus request GetInterface')\n return ifname","def get_interface_info(self): # real signature unknown; restored from __doc__\n pass","def at(self) -> at.Interface:\n\n return self._interface","def listen(self):\n raise NotImplementedError()","def _get_interfaces(self):\n return self.__interfaces","def _get_interfaces(self):\n return self.__interfaces","def _get_interfaces(self):\n return self.__interfaces","def get_interface(self, ifname):\n \n return self._ifname","def getInterface(self):\n\t\tquery = ''\n\t\tconn = self.get_connection()\n\t\theaders = { 'Content-type' : 'application/json', 'Authorization' : 'A10 %s' %self.sessionid}\n\t\tconn.request('GET', self.get_path() + '/' + query, headers=headers)\n\t\tresponse = conn.getresponse()\n\t\texpected_status = 200\n\t\terrors = {500: 'An unexpected runtime exception', 404: 'Specified interface does not exist'}\n\t\tpayload = self.get_output(response, expected_status, errors)\n\t\tconn.close()\n\t\tif self.debug:\n\t\t\tprint 'payload:', payload\n\t\tif payload == '':\n\t\t\tpayload = None\n\t\tif payload is not None:\n\t\t\tdata = json.loads(payload)\n\t\t\tpayload= data.get('interface')\n\t\treturn deserialize_Interface_json(payload)","def get_tgis_message_interface():\n global message_interface\n return message_interface","def get_interface(self) -> SimulatorInterface:\n raise NotImplementedError(\"get_interface not implemented.\")","def container_interface(self):\r\n return self._container_if","def listen(self) -> None:\n raise NotImplementedError","def interfaces(self):","def interfaces(self):","def get_interfaces(self):\n raise NotImplementedError","def get_plugin_interface(self):","def protocol(self) -> Optional['ListenerProtocol']:\n return pulumi.get(self, \"protocol\")","def listen(self):\n pass","def interface(cls):\n return relationship.many_to_one(cls, 'interface')","def get_interface(cls, message):\r\n if message is not None:\r\n if isinstance(message, PlatformMessage):\r\n return message.interface\r\n assert isinstance(message, (list, tuple)), \"Message is expected to be a list or a tuple\"\r\n assert len(message) >= 4, \"Message's length expected to be at least 4\"\r\n assert message[0] == PlatformMessage._signature, \"Message's signature is incorrect\"\r\n return message[2]\r\n return None","def connected_interface(self) -> str:\n address = self.ip_address\n ip_property = getattr(self, f\"{self.ip_protocol}_addresses\")\n for interface, addresses in ip_property.items():\n addrs = {ip_address(addr.ip) for addr in addresses}\n if address in addrs:\n connected_interface = interface\n break\n\n # TODO: Raise custom exception for when connected_interface is not discovered\n log.debug(\"Host %s: Interface connected to %s is %s.\", self.host, address, connected_interface)\n return connected_interface","def getInterface(self, iTag):\r\n try:\r\n return self._interfaces[iTag]\r\n except KeyError:\r\n raise InvalidRequest('Can not get a non existent interface '\r\n \"'{0}' from the robot.\".format(iTag))","def GetWiredInterface(self):\n return str(self.wired.wired_interface)","def getInterface(self, iTag):\r\n try:\r\n return self._interfaces[iTag]\r\n except KeyError:\r\n raise InvalidRequest('Can not get a non existent interface '\r\n \"'{0}' from the container.\".format(iTag))","def dbus_interface(self):\n return self._dbus_interface","def interface(self):\n raise exceptions.NotImplementedError()","def interfaceType(self): # pylint: disable=invalid-name\n return self.interface_type","def get_graph_interface(self):\n if self._graph_interface is None:\n self._compute_graph_interface()\n return self._graph_interface","def callback_interface(self):\n raise exceptions.NotImplementedError()","def _get_tunnel_interface_index(self):\n return self.__tunnel_interface_index","def interface(self):\n\n data = ['[Interface]']\n for item in INTERFACE_KEYS:\n value = getattr(self, item, None)\n if value:\n data.append(value)\n\n return '''\n'''.join(data)","def Interface(self):\n from ixnetwork_restpy.testplatform.sessions.ixnetwork.vport.protocols.interface_21173f93b01472152dbb5ea4d71785b0 import Interface\n return Interface(self)","def get_interface(self, ifname):\n real_ifname = self.generic_to_host(ifname)\n retval = _parse_interface(self.do('addr', 'show', real_ifname))\n retval.ifname = ifname\n return retval","def subscribeToEvent(eventName,subscriber,msgInterface):","def getListener(self, sender):\n if isinstance(sender, (Player, )):\n return (sender).getHandle()\n if isinstance(sender, (BlockCommandSender, )):\n return (sender).getTileEntity()\n if isinstance(sender, (CommandMinecart, )):\n return ((sender).getHandle()).getCommandBlock()\n if isinstance(sender, (RemoteConsoleCommandSender, )):\n return (MinecraftServer.getServer()).remoteControlCommandListener\n if isinstance(sender, (ConsoleCommandSender, )):\n return (sender.getServer()).getServer()\n if isinstance(sender, (ProxiedCommandSender, )):\n return (sender).getHandle()\n raise IllegalArgumentException(\"Cannot make \" + sender + \" a vanilla command listener\")","def bridgeIF(self):\r\n return self._bridgeIF","def GetInterface(interface, dummy_interface=DummyInterface, is_dummy=False):\n return dummy_interface if is_dummy else interface","def interface_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"interface_name\")","def monitor_of(intf: Union[Interface, RtlSignal]):\n if isinstance(intf, Interface) and intf._interfaces:\n return MonitorIntf(intf)\n else:\n if not isinstance(intf, (Signal, RtlSignalBase)):\n raise NotImplementedError(intf)\n return Signal(dtype=intf._dtype)","def Interface(self) -> int:","def interface_name(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"interface_name\")","def Interface(self):\n unpublishedMethods = (\"roots\", \"Interface\",\n \"WishIdBeenAGirlie\")\n methodType = type(self.Interface)\n ifList = []\n for i in dir(self):\n if (type(getattr(self, i)) == methodType\n and not i.startswith('_')):\n ifList.append(i)\n for i in unpublishedMethods:\n ifList.remove(i)\n return LumberjackInterface(ifList)","def interface(self, **kwargs):\n\n return self.api_request(self._get_method_fullname(\"interface\"), kwargs)","def listen():\n msg = MSG()\n ctypes.windll.user32.GetMessageA(ctypes.byref(msg), 0, 0, 0)","def listen():\n msg = MSG()\n ctypes.windll.user32.GetMessageA(ctypes.byref(msg), 0, 0, 0)","def interface_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"interface_name\")","def target_interfaces(self):","def target_interfaces(self):","def __init__(self, interface):\r\n\r\n self.interface = interface","def getNodeInterface(self,node,interface):\n data = self.connect('get','nodes/%s/network/%s' % (node,interface),None)\n return data","def peer_interface(self, **kwargs):\n\n return self.api_request(self._get_method_fullname(\"peer_interface\"), kwargs)","def get_interface(self) -> object:\n return get_analysis_version_interface(self)","def getInterfaceElement(publication):\n\treturn _getInterfaceMaker(publication.render)(publication)","def DetectWiredInterface(self):\n iface = self.wired.DetectWiredInterface()\n if iface:\n print 'automatically detected wired interface ' + iface\n else:\n print \"Couldn't detect a wired interface.\"\n return str(iface)","def protocol(self):\n\n raise NotImplementedError()","def listen(self, *a, **kw):\n raise NotImplementedError()","def socket(self):\n return self.stream.socket","def _get_iface_index(self):\n E = data_element_maker()\n top = E.top(\n E.Ifmgr(\n E.Interfaces(\n E.Interface(\n E.Name(self.interface_name)\n )\n )\n )\n )\n\n nc_get_reply = self.device.get(('subtree', top))\n reply_data = find_in_data(\n self._iface_index_name, nc_get_reply.data_ele)\n\n if reply_data is None:\n return ''\n\n return reply_data.text","def recv(self):\n\n return self._default_receiver()"],"string":"[\n \"def _get_interface(self):\\n return self.__interface\",\n \"def _get_interface(self):\\n return self.__interface\",\n \"def _get_interface(self):\\n return self.__interface\",\n \"def _get_interface(self):\\n return self.__interface\",\n \"def _get_interface(self):\\n return self.__interface\",\n \"def _get_interface(self):\\n return self.__interface\",\n \"def _get_interface(self):\\n return self.__interface\",\n \"def _get_interface(self):\\n return self.__interface\",\n \"def _get_interface(self):\\n return self.__interface\",\n \"def _get_interface(self):\\n return self.__interface\",\n \"def _get_interface(self):\\n return self.__interface\",\n \"def _get_interface(self):\\n return self.__interface\",\n \"def _get_interface(self):\\n return self.__interface\",\n \"def _get_interface(self):\\n return self.__interface\",\n \"def _get_interface(self):\\n return self.__interface\",\n \"def interface(self) -> type:\\n return self.get_interface()\",\n \"def _get_interface_ref(self):\\n return self.__interface_ref\",\n \"def _get_interface_ref(self):\\n return self.__interface_ref\",\n \"def _get_interface_ref(self):\\n return self.__interface_ref\",\n \"def _get_interface_ref(self):\\n return self.__interface_ref\",\n \"def _get_interface_ref(self):\\n return self.__interface_ref\",\n \"def _get_interface_ref(self):\\n return self.__interface_ref\",\n \"def interface(cls):\\n return get_interface(cls.connection_name)\",\n \"def GetCurrentInterface(self):\\n return self.current_interface\",\n \"def _get_interface_type(self):\\n return self.__interface_type\",\n \"def network_interface(self): \\n return self._network_interface\",\n \"def get_interface(name=''):\\n global interfaces\\n return interfaces[name]\",\n \"def interface(self):\\n return self.broker.interface(**{\\\"DeviceRouteID\\\": self.DeviceRouteID})\",\n \"def backend_getInterface(self):\\n\\t\\treturn describeInterface(self)\",\n \"def _get_interface_name(self):\\n return self.__interface_name\",\n \"def _get_interface_id(self):\\n return self.__interface_id\",\n \"def _get_interface_id(self):\\n return self.__interface_id\",\n \"def _get_interface_id(self):\\n return self.__interface_id\",\n \"def _get_interface_id(self):\\n return self.__interface_id\",\n \"def _get_interface_id(self):\\n return self.__interface_id\",\n \"def _get_interface_id(self):\\n return self.__interface_id\",\n \"def _get_interface_id(self):\\n return self.__interface_id\",\n \"def _get_interface_id(self):\\n return self.__interface_id\",\n \"def _get_interface_id(self):\\n return self.__interface_id\",\n \"def interface(self):\\n if self._interface is None:\\n expression = expressions.WPA_INTERFACE\\n name = expressions.INTERFACE_NAME\\n command = self.interface_list_command\\n self._interface = self._match(expression,\\n name,\\n command)\\n return self._interface\",\n \"def GetInterface(self):\\n ifname = self.interface_watcher.get_last_ifname()\\n if ifname is None:\\n ifname = ''\\n logger.debug('Replying \\\"' + ifname + '\\\" to D-Bus request GetInterface')\\n return ifname\",\n \"def get_interface_info(self): # real signature unknown; restored from __doc__\\n pass\",\n \"def at(self) -> at.Interface:\\n\\n return self._interface\",\n \"def listen(self):\\n raise NotImplementedError()\",\n \"def _get_interfaces(self):\\n return self.__interfaces\",\n \"def _get_interfaces(self):\\n return self.__interfaces\",\n \"def _get_interfaces(self):\\n return self.__interfaces\",\n \"def get_interface(self, ifname):\\n \\n return self._ifname\",\n \"def getInterface(self):\\n\\t\\tquery = ''\\n\\t\\tconn = self.get_connection()\\n\\t\\theaders = { 'Content-type' : 'application/json', 'Authorization' : 'A10 %s' %self.sessionid}\\n\\t\\tconn.request('GET', self.get_path() + '/' + query, headers=headers)\\n\\t\\tresponse = conn.getresponse()\\n\\t\\texpected_status = 200\\n\\t\\terrors = {500: 'An unexpected runtime exception', 404: 'Specified interface does not exist'}\\n\\t\\tpayload = self.get_output(response, expected_status, errors)\\n\\t\\tconn.close()\\n\\t\\tif self.debug:\\n\\t\\t\\tprint 'payload:', payload\\n\\t\\tif payload == '':\\n\\t\\t\\tpayload = None\\n\\t\\tif payload is not None:\\n\\t\\t\\tdata = json.loads(payload)\\n\\t\\t\\tpayload= data.get('interface')\\n\\t\\treturn deserialize_Interface_json(payload)\",\n \"def get_tgis_message_interface():\\n global message_interface\\n return message_interface\",\n \"def get_interface(self) -> SimulatorInterface:\\n raise NotImplementedError(\\\"get_interface not implemented.\\\")\",\n \"def container_interface(self):\\r\\n return self._container_if\",\n \"def listen(self) -> None:\\n raise NotImplementedError\",\n \"def interfaces(self):\",\n \"def interfaces(self):\",\n \"def get_interfaces(self):\\n raise NotImplementedError\",\n \"def get_plugin_interface(self):\",\n \"def protocol(self) -> Optional['ListenerProtocol']:\\n return pulumi.get(self, \\\"protocol\\\")\",\n \"def listen(self):\\n pass\",\n \"def interface(cls):\\n return relationship.many_to_one(cls, 'interface')\",\n \"def get_interface(cls, message):\\r\\n if message is not None:\\r\\n if isinstance(message, PlatformMessage):\\r\\n return message.interface\\r\\n assert isinstance(message, (list, tuple)), \\\"Message is expected to be a list or a tuple\\\"\\r\\n assert len(message) >= 4, \\\"Message's length expected to be at least 4\\\"\\r\\n assert message[0] == PlatformMessage._signature, \\\"Message's signature is incorrect\\\"\\r\\n return message[2]\\r\\n return None\",\n \"def connected_interface(self) -> str:\\n address = self.ip_address\\n ip_property = getattr(self, f\\\"{self.ip_protocol}_addresses\\\")\\n for interface, addresses in ip_property.items():\\n addrs = {ip_address(addr.ip) for addr in addresses}\\n if address in addrs:\\n connected_interface = interface\\n break\\n\\n # TODO: Raise custom exception for when connected_interface is not discovered\\n log.debug(\\\"Host %s: Interface connected to %s is %s.\\\", self.host, address, connected_interface)\\n return connected_interface\",\n \"def getInterface(self, iTag):\\r\\n try:\\r\\n return self._interfaces[iTag]\\r\\n except KeyError:\\r\\n raise InvalidRequest('Can not get a non existent interface '\\r\\n \\\"'{0}' from the robot.\\\".format(iTag))\",\n \"def GetWiredInterface(self):\\n return str(self.wired.wired_interface)\",\n \"def getInterface(self, iTag):\\r\\n try:\\r\\n return self._interfaces[iTag]\\r\\n except KeyError:\\r\\n raise InvalidRequest('Can not get a non existent interface '\\r\\n \\\"'{0}' from the container.\\\".format(iTag))\",\n \"def dbus_interface(self):\\n return self._dbus_interface\",\n \"def interface(self):\\n raise exceptions.NotImplementedError()\",\n \"def interfaceType(self): # pylint: disable=invalid-name\\n return self.interface_type\",\n \"def get_graph_interface(self):\\n if self._graph_interface is None:\\n self._compute_graph_interface()\\n return self._graph_interface\",\n \"def callback_interface(self):\\n raise exceptions.NotImplementedError()\",\n \"def _get_tunnel_interface_index(self):\\n return self.__tunnel_interface_index\",\n \"def interface(self):\\n\\n data = ['[Interface]']\\n for item in INTERFACE_KEYS:\\n value = getattr(self, item, None)\\n if value:\\n data.append(value)\\n\\n return '''\\n'''.join(data)\",\n \"def Interface(self):\\n from ixnetwork_restpy.testplatform.sessions.ixnetwork.vport.protocols.interface_21173f93b01472152dbb5ea4d71785b0 import Interface\\n return Interface(self)\",\n \"def get_interface(self, ifname):\\n real_ifname = self.generic_to_host(ifname)\\n retval = _parse_interface(self.do('addr', 'show', real_ifname))\\n retval.ifname = ifname\\n return retval\",\n \"def subscribeToEvent(eventName,subscriber,msgInterface):\",\n \"def getListener(self, sender):\\n if isinstance(sender, (Player, )):\\n return (sender).getHandle()\\n if isinstance(sender, (BlockCommandSender, )):\\n return (sender).getTileEntity()\\n if isinstance(sender, (CommandMinecart, )):\\n return ((sender).getHandle()).getCommandBlock()\\n if isinstance(sender, (RemoteConsoleCommandSender, )):\\n return (MinecraftServer.getServer()).remoteControlCommandListener\\n if isinstance(sender, (ConsoleCommandSender, )):\\n return (sender.getServer()).getServer()\\n if isinstance(sender, (ProxiedCommandSender, )):\\n return (sender).getHandle()\\n raise IllegalArgumentException(\\\"Cannot make \\\" + sender + \\\" a vanilla command listener\\\")\",\n \"def bridgeIF(self):\\r\\n return self._bridgeIF\",\n \"def GetInterface(interface, dummy_interface=DummyInterface, is_dummy=False):\\n return dummy_interface if is_dummy else interface\",\n \"def interface_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"interface_name\\\")\",\n \"def monitor_of(intf: Union[Interface, RtlSignal]):\\n if isinstance(intf, Interface) and intf._interfaces:\\n return MonitorIntf(intf)\\n else:\\n if not isinstance(intf, (Signal, RtlSignalBase)):\\n raise NotImplementedError(intf)\\n return Signal(dtype=intf._dtype)\",\n \"def Interface(self) -> int:\",\n \"def interface_name(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"interface_name\\\")\",\n \"def Interface(self):\\n unpublishedMethods = (\\\"roots\\\", \\\"Interface\\\",\\n \\\"WishIdBeenAGirlie\\\")\\n methodType = type(self.Interface)\\n ifList = []\\n for i in dir(self):\\n if (type(getattr(self, i)) == methodType\\n and not i.startswith('_')):\\n ifList.append(i)\\n for i in unpublishedMethods:\\n ifList.remove(i)\\n return LumberjackInterface(ifList)\",\n \"def interface(self, **kwargs):\\n\\n return self.api_request(self._get_method_fullname(\\\"interface\\\"), kwargs)\",\n \"def listen():\\n msg = MSG()\\n ctypes.windll.user32.GetMessageA(ctypes.byref(msg), 0, 0, 0)\",\n \"def listen():\\n msg = MSG()\\n ctypes.windll.user32.GetMessageA(ctypes.byref(msg), 0, 0, 0)\",\n \"def interface_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"interface_name\\\")\",\n \"def target_interfaces(self):\",\n \"def target_interfaces(self):\",\n \"def __init__(self, interface):\\r\\n\\r\\n self.interface = interface\",\n \"def getNodeInterface(self,node,interface):\\n data = self.connect('get','nodes/%s/network/%s' % (node,interface),None)\\n return data\",\n \"def peer_interface(self, **kwargs):\\n\\n return self.api_request(self._get_method_fullname(\\\"peer_interface\\\"), kwargs)\",\n \"def get_interface(self) -> object:\\n return get_analysis_version_interface(self)\",\n \"def getInterfaceElement(publication):\\n\\treturn _getInterfaceMaker(publication.render)(publication)\",\n \"def DetectWiredInterface(self):\\n iface = self.wired.DetectWiredInterface()\\n if iface:\\n print 'automatically detected wired interface ' + iface\\n else:\\n print \\\"Couldn't detect a wired interface.\\\"\\n return str(iface)\",\n \"def protocol(self):\\n\\n raise NotImplementedError()\",\n \"def listen(self, *a, **kw):\\n raise NotImplementedError()\",\n \"def socket(self):\\n return self.stream.socket\",\n \"def _get_iface_index(self):\\n E = data_element_maker()\\n top = E.top(\\n E.Ifmgr(\\n E.Interfaces(\\n E.Interface(\\n E.Name(self.interface_name)\\n )\\n )\\n )\\n )\\n\\n nc_get_reply = self.device.get(('subtree', top))\\n reply_data = find_in_data(\\n self._iface_index_name, nc_get_reply.data_ele)\\n\\n if reply_data is None:\\n return ''\\n\\n return reply_data.text\",\n \"def recv(self):\\n\\n return self._default_receiver()\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.78353\",\n \"0.78353\",\n \"0.78353\",\n \"0.78353\",\n \"0.78353\",\n \"0.78353\",\n \"0.78353\",\n \"0.78353\",\n \"0.78353\",\n \"0.78353\",\n \"0.78353\",\n \"0.78353\",\n \"0.78353\",\n \"0.78353\",\n \"0.78353\",\n \"0.7473333\",\n \"0.7236905\",\n \"0.7236905\",\n \"0.7236905\",\n \"0.7236905\",\n \"0.7236905\",\n \"0.7236905\",\n \"0.70470303\",\n \"0.69276947\",\n \"0.681635\",\n \"0.67509925\",\n \"0.66738117\",\n \"0.6641843\",\n \"0.6606379\",\n \"0.6547531\",\n \"0.65379596\",\n \"0.65379596\",\n \"0.65379596\",\n \"0.65379596\",\n \"0.65379596\",\n \"0.65379596\",\n \"0.65379596\",\n \"0.65379596\",\n \"0.65379596\",\n \"0.65199995\",\n \"0.64568955\",\n \"0.64301497\",\n \"0.64141464\",\n \"0.63919497\",\n \"0.63386416\",\n \"0.63386416\",\n \"0.63386416\",\n \"0.6332624\",\n \"0.6315127\",\n \"0.6309935\",\n \"0.62232846\",\n \"0.6214691\",\n \"0.61440146\",\n \"0.60935825\",\n \"0.60935825\",\n \"0.6060821\",\n \"0.6059696\",\n \"0.6056683\",\n \"0.5983548\",\n 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port(self):\n return self._port"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_port(self) -> int:\n return int(self.socket.getsockname()[1])","def get_port(self) -> int:\n return self._port","def port(self) -> int:","def get_port(self):\n return self.port","def port(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"port\")","def port(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"port\")","def port(self) -> int:\n if hasattr(self, \"_port\"):\n return self._port\n _args: list[Arg] = []\n _ctx = self._select(\"port\", _args)\n return _ctx.execute_sync(int)","def port(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"port\")","def port(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"port\")","def port(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"port\")","def get_port(self):\n return self.__port","def port(self) -> pulumi.Output[Optional[int]]:\n return pulumi.get(self, \"port\")","def Port(self) -> int:","def port(self) -> int:\n return self.proto.port","def get_port(self):\n \n return self._port","def _get_port(self):\n return self.__port","def port(self):\n return f'ListenPort = {self._peer.port}'","def port(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"port\")","def port(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"port\")","def port(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"port\")","def port(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"port\")","def port(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"port\")","def port(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"port\")","def port(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"port\")","def port_number(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"port_number\")","def remote_getPort(self):\r\n return int(self._fwdPort)","def _get_nport(self):\n return self.__nport","def port(self) -> int:\n return self._port","def receiver_port(self):\n return self._receiver_port","def port(self) -> int:\n return pulumi.get(self, \"port\")","def port(self) -> int:\n return pulumi.get(self, \"port\")","def port(self) -> int:\n return pulumi.get(self, \"port\")","def port(self) -> int:\n return pulumi.get(self, \"port\")","def port(self) -> int:\n return pulumi.get(self, \"port\")","def port(self):\r\n _, port = self.server_address\r\n return port","def port(self):\n return self._host[CONF_PORT]","def get_irc_port(self):\n if self.get_tls():\n return 6697\n else:\n return 6667","def get_open_port():\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.bind((\"\", 0))\n s.listen(1)\n port = s.getsockname()[1]\n s.close()\n return port","def port():","def port(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"port\")","def getPort(self):\n return self._port","def port(self):\n _, port = self.server_address\n return port","def port(self) -> Optional[int]:\n return pulumi.get(self, \"port\")","def port(self) -> Optional[int]:\n return pulumi.get(self, \"port\")","def port(self) -> Optional[int]:\n return pulumi.get(self, \"port\")","def port(self) -> Optional[int]:\n return pulumi.get(self, \"port\")","def port(self) -> Optional[int]:\n return pulumi.get(self, \"port\")","def port(self) -> Optional[int]:\n return pulumi.get(self, \"port\")","def port(self) -> Optional[int]:\n return pulumi.get(self, \"port\")","def PortNumber(self):\n\t\treturn self._get_attribute('portNumber')","def getPort(self):\n return self._port","def get_available_port() -> int:\n with contextlib.closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:\n sock.bind(('', 0))\n _, port = sock.getsockname()\n return int(port)","def get_frontend_port(self):\n if self.frontend_port:\n return self.frontend_port\n return self.get_frontend().port","def find_unused_port():\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM, 0)\n sock.bind(('127.0.0.1', 0))\n sock.listen(socket.SOMAXCONN)\n ipaddr, port = sock.getsockname()\n sock.close()\n return port","def listener_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"listener_id\")","def listener_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"listener_id\")","def port(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"port\")","def port(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"port\")","def port(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"port\")","def port(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"port\")","def port(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"port\")","def port(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"port\")","def port_number(self):\n return self._props[\"persistent_identifiers\"].get(self._primary_port_prop)","def parse_server_port(self, default=None):\n\t\tcfg_server_port = self.cfg_root.find('server_ip')\n\t\tif cfg_server_port and cfg_server_port.text.isnumeric():\n\t\t\tcfg_server_port = int(cfg_server_port.text) # TODO: What if float...\n\t\telse: # ip not specified\n\t\t\tcfg_server_port = default\n\n\t\treturn cfg_server_port","def port(self, rel_id=None):\n rel = self.framework.model.get_relation(self.relation_name, rel_id)\n\n return rel.data[rel.app].get(\"port\")","def listener_id(self) -> str:\n return pulumi.get(self, \"listener_id\")","def listener_id(self) -> str:\n return pulumi.get(self, \"listener_id\")","def listener_id(self) -> str:\n return pulumi.get(self, \"listener_id\")","def get_open_port():\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n sock.bind((\"\", 0))\n o_port = sock.getsockname()[1]\n sock.close()\n return o_port","def external_port(self):\r\n return self._external_port","def port(self):\n\n return self._port","def listener_id(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"listener_id\")","def get_host_port(self) -> int:\n return self.config_dict.get(\"host_port\", 0)","def find_first_available_port():\n skt = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n skt.bind((\"0.0.0.0\", 0))\n _, port = skt.getsockname()\n skt.close()\n return port","def internal_port(self):\r\n return self._internal_port","def getCurPort(self):\n cmd_string = '?6'\n data = self.sendRcv(cmd_string)\n with self._syringeErrorHandler():\n try:\n port = int(data)\n except ValueError:\n raise SyringeError(7, self.__class__.ERROR_DICT)\n self.state['port'] = port\n return port","def port(self) -> Optional[pulumi.Input[float]]:\n return pulumi.get(self, \"port\")","def port(self) -> Optional[pulumi.Input[float]]:\n return pulumi.get(self, \"port\")","def port(self):\n # This property is not 100% needed, but is included instead of making the raw variable public to prevent people from accidentally overwriting the port and screwing up this representative value\n return self._port","def server_port(self):\n return self._server_port","def server_port(self):\n return self._server_port","def get_res_port():\n return get_port() + 1","def get_port(self):\n return _spacegrant_swig.ax25_udp_pdu_gen_get_port(self)","def port(self):\n if self._server_thread is None:\n raise RuntimeError('Server not started.')\n return self._port","def get_ofport(ifce):\n return check_output(\n split(\"sudo ovs-vsctl get Interface {} ofport\".format(ifce)))","def listener_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"listener_id\")","def listener_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"listener_id\")","def get_serverport(cobj):\n pass","def container_port(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"container_port\")","def port(self):\n\n return self.config.dict[\"fhdhr\"][\"port\"]","def _get_event_port(port: Optional[int], event_url: Optional[str]) -> Optional[int]:\n if port is not None:\n return port\n if event_url is not None:\n u = urlparse(event_url)\n return u.port\n return None","def port(self):\n self._port = self.lib.iperf_get_test_server_port(self._test)\n return self._port","def _get_tunnel_interface_index(self):\n return self.__tunnel_interface_index","def find_available_local_port():\n infos = socket.getaddrinfo(None, 0, socket.AF_UNSPEC, socket.SOCK_STREAM)\n family, proto, _, _, addr = next(iter(infos))\n sock = socket.socket(family, proto)\n sock.bind(addr)\n addr, port = sock.getsockname()[:2]\n sock.close()\n return port","def get_port(project_path):\n\n config = ConfigParser.ConfigParser()\n config_path = os.path.abspath(os.path.join(project_path, 'config.ini'))\n config.read(config_path)\n return config.get('SELENIUMSERVER', 'hub_port')"],"string":"[\n \"def get_port(self) -> int:\\n return int(self.socket.getsockname()[1])\",\n \"def get_port(self) -> int:\\n return self._port\",\n \"def port(self) -> int:\",\n \"def get_port(self):\\n return self.port\",\n \"def port(self) -> pulumi.Input[int]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> pulumi.Input[int]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> int:\\n if hasattr(self, \\\"_port\\\"):\\n return self._port\\n _args: list[Arg] = []\\n _ctx = self._select(\\\"port\\\", _args)\\n return _ctx.execute_sync(int)\",\n \"def port(self) -> pulumi.Output[int]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> pulumi.Output[int]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> pulumi.Output[int]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def get_port(self):\\n return self.__port\",\n \"def port(self) -> pulumi.Output[Optional[int]]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def Port(self) -> int:\",\n \"def port(self) -> int:\\n return self.proto.port\",\n \"def get_port(self):\\n \\n return self._port\",\n \"def _get_port(self):\\n return self.__port\",\n \"def port(self):\\n return f'ListenPort = {self._peer.port}'\",\n \"def port(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port_number(self) -> pulumi.Input[int]:\\n return pulumi.get(self, \\\"port_number\\\")\",\n \"def remote_getPort(self):\\r\\n return int(self._fwdPort)\",\n \"def _get_nport(self):\\n return self.__nport\",\n \"def port(self) -> int:\\n return self._port\",\n \"def receiver_port(self):\\n return self._receiver_port\",\n \"def port(self) -> int:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> int:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> int:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> int:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> int:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self):\\r\\n _, port = self.server_address\\r\\n return port\",\n \"def port(self):\\n return self._host[CONF_PORT]\",\n \"def get_irc_port(self):\\n if self.get_tls():\\n return 6697\\n else:\\n return 6667\",\n \"def get_open_port():\\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n s.bind((\\\"\\\", 0))\\n s.listen(1)\\n port = s.getsockname()[1]\\n s.close()\\n return port\",\n \"def port():\",\n \"def port(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def getPort(self):\\n return self._port\",\n \"def port(self):\\n _, port = self.server_address\\n return port\",\n \"def port(self) -> Optional[int]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> Optional[int]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> Optional[int]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> Optional[int]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> Optional[int]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> Optional[int]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> Optional[int]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def PortNumber(self):\\n\\t\\treturn self._get_attribute('portNumber')\",\n \"def getPort(self):\\n return self._port\",\n \"def get_available_port() -> int:\\n with contextlib.closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:\\n sock.bind(('', 0))\\n _, port = sock.getsockname()\\n return int(port)\",\n \"def get_frontend_port(self):\\n if self.frontend_port:\\n return self.frontend_port\\n return self.get_frontend().port\",\n \"def find_unused_port():\\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM, 0)\\n sock.bind(('127.0.0.1', 0))\\n sock.listen(socket.SOMAXCONN)\\n ipaddr, port = sock.getsockname()\\n sock.close()\\n return port\",\n \"def listener_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"listener_id\\\")\",\n \"def listener_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"listener_id\\\")\",\n \"def port(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port_number(self):\\n return self._props[\\\"persistent_identifiers\\\"].get(self._primary_port_prop)\",\n \"def parse_server_port(self, default=None):\\n\\t\\tcfg_server_port = self.cfg_root.find('server_ip')\\n\\t\\tif cfg_server_port and cfg_server_port.text.isnumeric():\\n\\t\\t\\tcfg_server_port = int(cfg_server_port.text) # TODO: What if float...\\n\\t\\telse: # ip not specified\\n\\t\\t\\tcfg_server_port = default\\n\\n\\t\\treturn cfg_server_port\",\n \"def port(self, rel_id=None):\\n rel = self.framework.model.get_relation(self.relation_name, rel_id)\\n\\n return rel.data[rel.app].get(\\\"port\\\")\",\n \"def listener_id(self) -> str:\\n return pulumi.get(self, \\\"listener_id\\\")\",\n \"def listener_id(self) -> str:\\n return pulumi.get(self, \\\"listener_id\\\")\",\n \"def listener_id(self) -> str:\\n return pulumi.get(self, \\\"listener_id\\\")\",\n \"def get_open_port():\\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n sock.bind((\\\"\\\", 0))\\n o_port = sock.getsockname()[1]\\n sock.close()\\n return o_port\",\n \"def external_port(self):\\r\\n return self._external_port\",\n \"def port(self):\\n\\n return self._port\",\n \"def listener_id(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"listener_id\\\")\",\n \"def get_host_port(self) -> int:\\n return self.config_dict.get(\\\"host_port\\\", 0)\",\n \"def find_first_available_port():\\n skt = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n skt.bind((\\\"0.0.0.0\\\", 0))\\n _, port = skt.getsockname()\\n skt.close()\\n return port\",\n \"def internal_port(self):\\r\\n return self._internal_port\",\n \"def getCurPort(self):\\n cmd_string = '?6'\\n data = self.sendRcv(cmd_string)\\n with self._syringeErrorHandler():\\n try:\\n port = int(data)\\n except ValueError:\\n raise SyringeError(7, self.__class__.ERROR_DICT)\\n self.state['port'] = port\\n return port\",\n \"def port(self) -> Optional[pulumi.Input[float]]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self) -> Optional[pulumi.Input[float]]:\\n return pulumi.get(self, \\\"port\\\")\",\n \"def port(self):\\n # This property is not 100% needed, but is included instead of making the raw variable public to prevent people from accidentally overwriting the port and screwing up this representative value\\n return self._port\",\n \"def server_port(self):\\n return self._server_port\",\n \"def server_port(self):\\n return self._server_port\",\n \"def get_res_port():\\n return get_port() + 1\",\n \"def get_port(self):\\n return _spacegrant_swig.ax25_udp_pdu_gen_get_port(self)\",\n \"def port(self):\\n if self._server_thread is None:\\n raise RuntimeError('Server not started.')\\n return self._port\",\n \"def get_ofport(ifce):\\n return check_output(\\n split(\\\"sudo ovs-vsctl get Interface {} ofport\\\".format(ifce)))\",\n \"def listener_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"listener_id\\\")\",\n \"def listener_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"listener_id\\\")\",\n \"def get_serverport(cobj):\\n pass\",\n \"def container_port(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"container_port\\\")\",\n \"def port(self):\\n\\n return self.config.dict[\\\"fhdhr\\\"][\\\"port\\\"]\",\n \"def _get_event_port(port: Optional[int], event_url: Optional[str]) -> Optional[int]:\\n if port is not None:\\n return port\\n if event_url is not None:\\n u = urlparse(event_url)\\n return u.port\\n return None\",\n \"def port(self):\\n self._port = self.lib.iperf_get_test_server_port(self._test)\\n return self._port\",\n \"def _get_tunnel_interface_index(self):\\n return self.__tunnel_interface_index\",\n \"def find_available_local_port():\\n infos = socket.getaddrinfo(None, 0, socket.AF_UNSPEC, socket.SOCK_STREAM)\\n family, proto, _, _, addr = next(iter(infos))\\n sock = socket.socket(family, proto)\\n sock.bind(addr)\\n addr, port = sock.getsockname()[:2]\\n sock.close()\\n return port\",\n \"def get_port(project_path):\\n\\n config = ConfigParser.ConfigParser()\\n config_path = os.path.abspath(os.path.join(project_path, 'config.ini'))\\n config.read(config_path)\\n return config.get('SELENIUMSERVER', 'hub_port')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7530533","0.7471657","0.7419851","0.73832756","0.7280439","0.7280439","0.72383","0.7234966","0.7234966","0.7234966","0.7211949","0.7210165","0.71787095","0.7175771","0.71601677","0.71456337","0.7141916","0.7101398","0.7101398","0.7101398","0.7101398","0.7101398","0.7101398","0.7101398","0.70925736","0.70649195","0.70595425","0.70090306","0.70017415","0.6961755","0.6961755","0.6961755","0.6961755","0.6961755","0.6939866","0.6930533","0.688472","0.68826985","0.6863439","0.68371665","0.6835134","0.6824522","0.6793433","0.6793433","0.6793433","0.6793433","0.6793433","0.6793433","0.6793433","0.67894757","0.6775492","0.67676365","0.6755178","0.6754794","0.67329925","0.67329925","0.6728223","0.6728223","0.67211205","0.67211205","0.67211205","0.67211205","0.6698647","0.6650441","0.6649763","0.6648638","0.6648638","0.6648638","0.66459244","0.6645902","0.66452783","0.6602981","0.65984195","0.65968204","0.65934545","0.6585645","0.65850616","0.65850616","0.6583624","0.6553061","0.6553061","0.6544866","0.65430236","0.6536105","0.6522054","0.6521349","0.6521349","0.65148556","0.6511819","0.6506516","0.65003425","0.64967924","0.6490339","0.6473058","0.6456094"],"string":"[\n \"0.7530533\",\n \"0.7471657\",\n \"0.7419851\",\n \"0.73832756\",\n \"0.7280439\",\n \"0.7280439\",\n \"0.72383\",\n \"0.7234966\",\n \"0.7234966\",\n \"0.7234966\",\n \"0.7211949\",\n \"0.7210165\",\n \"0.71787095\",\n \"0.7175771\",\n \"0.71601677\",\n \"0.71456337\",\n \"0.7141916\",\n \"0.7101398\",\n \"0.7101398\",\n \"0.7101398\",\n \"0.7101398\",\n \"0.7101398\",\n \"0.7101398\",\n \"0.7101398\",\n \"0.70925736\",\n \"0.70649195\",\n \"0.70595425\",\n \"0.70090306\",\n \"0.70017415\",\n \"0.6961755\",\n \"0.6961755\",\n \"0.6961755\",\n \"0.6961755\",\n \"0.6961755\",\n \"0.6939866\",\n \"0.6930533\",\n \"0.688472\",\n \"0.68826985\",\n \"0.6863439\",\n \"0.68371665\",\n \"0.6835134\",\n \"0.6824522\",\n \"0.6793433\",\n \"0.6793433\",\n \"0.6793433\",\n \"0.6793433\",\n \"0.6793433\",\n \"0.6793433\",\n \"0.6793433\",\n \"0.67894757\",\n \"0.6775492\",\n \"0.67676365\",\n \"0.6755178\",\n \"0.6754794\",\n \"0.67329925\",\n \"0.67329925\",\n \"0.6728223\",\n \"0.6728223\",\n \"0.67211205\",\n \"0.67211205\",\n \"0.67211205\",\n \"0.67211205\",\n \"0.6698647\",\n \"0.6650441\",\n \"0.6649763\",\n \"0.6648638\",\n \"0.6648638\",\n \"0.6648638\",\n \"0.66459244\",\n \"0.6645902\",\n \"0.66452783\",\n \"0.6602981\",\n \"0.65984195\",\n \"0.65968204\",\n \"0.65934545\",\n \"0.6585645\",\n \"0.65850616\",\n \"0.65850616\",\n \"0.6583624\",\n \"0.6553061\",\n \"0.6553061\",\n \"0.6544866\",\n \"0.65430236\",\n \"0.6536105\",\n \"0.6522054\",\n \"0.6521349\",\n \"0.6521349\",\n \"0.65148556\",\n \"0.6511819\",\n \"0.6506516\",\n \"0.65003425\",\n \"0.64967924\",\n \"0.6490339\",\n \"0.6473058\",\n \"0.6456094\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.68487716"},"document_rank":{"kind":"string","value":"44"}}},{"rowIdx":20071,"cells":{"query":{"kind":"string","value":"Default access mechanism if API does not specify it"},"document":{"kind":"string","value":"def default_access_control(self):\n return self._default_access_control"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def request_access(self):\n pass","def access():","def api_access(self):\n return self._api_access","def maya_useNewAPI():\n\tpass","def maya_useNewAPI():\n\tpass","def maya_useNewAPI():\r\n\r\n pass","def api_get(self, name):\n try:\n r = self._get(['apis', name])\n except requests.HTTPError:\n return None\n else:\n return r","def maya_useNewAPI():\n\n pass","def maya_useNewAPI():\n pass","def maya_useNewAPI():\n pass","def maya_useNewAPI():\n pass","def maya_useNewAPI():\n pass","def maya_useNewAPI():\n pass","def __init__(self, api_use=False):\n self.api_use = api_use","def authorization():\n pass","def use_http(self):\r\n use_http = self.config.get_bool(\"gox\", \"use_http_api\")\r\n if FORCE_HTTP_API:\r\n use_http = True\r\n if FORCE_NO_HTTP_API:\r\n use_http = False\r\n return use_http","def get_api(self, ranger_client, api_name):\n if api_name == \"create_policy\":\n return ranger_client.create_policy\n elif api_name == \"delete_policy_by_id\":\n return ranger_client.delete_policy_by_id\n elif api_name == \"get_policy_by_id\":\n return ranger_client.get_policy_by_id\n elif api_name == \"update_policy_by_id\":\n return ranger_client.update_policy_by_id\n else:\n raise Exception(f\"Unknown API name: {api_name}\")","def api(self) -> str:","def request(self, apiurl, caller, mode=\"get\", data=None):\n\n token = self._getToken(caller)\n # print(self._tokenstore)\n headers = {'Content-Type': 'application/json', 'Accept': 'application/json', 'Authorization': f'Bearer {token}'}\n if mode == \"get\":\n return self._oauth.getRequester().get(self._baseurl + apiurl, headers=headers)\n elif mode == \"post\":\n return self._oauth.getRequester().post(self._baseurl + apiurl, data=data, headers=headers)\n elif mode == \"delete\":\n return self._oauth.getRequester().delete(self._baseurl + apiurl, headers=headers)","def test_basic_api_with_mode(self):\n # Create an API with get and put\n self.create_and_verify_stack(\"single/basic_api_with_mode\")\n\n stack_output = self.get_stack_outputs()\n api_endpoint = stack_output.get(\"ApiEndpoint\")\n\n self.verify_get_request_response(f\"{api_endpoint}/get\", 200)\n\n # Removes get from the API\n self.update_and_verify_stack(file_path=\"single/basic_api_with_mode_update\")\n\n # API Gateway by default returns 403 if a path do not exist\n self.verify_get_request_response.retry_with(\n stop=stop_after_attempt(20),\n wait=wait_exponential(multiplier=1, min=4, max=10) + wait_random(0, 1),\n retry=retry_if_exception_type(StatusCodeError),\n after=after_log(LOG, logging.WARNING),\n reraise=True,\n )(self, f\"{api_endpoint}/get\", 403)\n\n LOG.log(msg=f\"retry times {self.verify_get_request_response.retry.statistics}\", level=logging.WARNING)","def get_api(self):\n return self.api","def protect_endpoint():\n pass","def auth(self):\n return self.api(self.token)","def api(self):\n return self.__api","def get_authorization():\n return True","def __init__(self, requests_api=None):\r\n self._requests = requests_api or requests","def test_anything_else_is_accessible(api_client):\n\n assert api_client().get(\"/anything/else\").status_code == 200","def auth():\n pass","def auth():\n pass","def api(self):\r\n return self._api","def get_api(provider):\n # TODO(b/167685797): Use thread-local.\n if provider == storage_url.ProviderPrefix.GCS:\n return gcs_api.GcsApi()\n elif provider == storage_url.ProviderPrefix.S3:\n return s3_api.S3Api()\n raise ValueError('Provider API value must be \"gs\" or \"s3\".')","def prepare_api(self):\n return None","def test_authorization_is_enforced(self):\n new_client = APIClient()\n response = new_client.get('/posts/', kwargs={'pk': 3}, format=\"json\")\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)","def __getattr__(self, name):\n def wrapper(*args, **kwargs): \n if name in (\"get\", \"post\", \"put\", \"patch\", \"delete\", \"head\", \"options\"): #These are HTTP methods\n is_http_method = True\n \n #Now proxy down to the requests object (or session) \n try:\n output = getattr(self.session, name)(*args, **kwargs) #Assume a function\n except TypeError: #Assume a property\n output = getattr(self.session, name)\n \n #Now catch any special HTTP calls \n if is_http_method:\n #Examine the status, if in error, raise it\n if output.status_code >= 400: #Some error has occurred!\n raise DownloadingError(\"Unable to download from resource: '%s'\\n%s\" % (output.url, output.content))\n return output\n return wrapper #This wrapper partial allows us to pass *args and **kwargs into a __getattr__ situation","def api():\n\treturn \"The API call\"","def _http(self):\n raise NotImplementedError(\"HTTP transport is not supported.\")","def Modifier_API(self):\n\t\tpass","def test_00_api_get(self):\r\n # GET as Anonymous\r\n url = '/api/'\r\n action = 'get'\r\n self.check_limit(url, action, 'app')","def authn_and_authz():\n authentication()\n authorization()","def api_access(self, api_access):\n\n self._api_access = api_access","def __call__(self, access_token):","def access_wrapper(func, *args):\n def try_access(*args):\n \"\"\"Function that tries the access and returns obj or None\"\"\"\n obj_or_dir = func(*args)\n if not obj_or_dir:\n logging.warning('plot server read access: ' +\n proto_err_msg.format(*args))\n return None\n return obj_or_dir\n return try_access(*args)","def test_dashboards_v2_request_access(self):\n pass","def api(self):\n return self._api","def api(self):\n return self._api","def api(self):\n return self._api","def api(self):\n return self._api","def _get_api():\n return os.environ.get(\"MAPBOX_API\", \"https://api.mapbox.com\")","def rest_test_no_auth():\n # Permission check is not applicable here\n return jsonify({\"answer\": 42})","def test_get_host_access(self):\n pass","def requires_auth(self):\n return True","def test_public_manager_call(self):\n\n # GIVEN public access method to model's manager\n\n # WHEN fetching data listing using unsupported call method\n response = self.api.GET(self.app_label, self.model_name2, params={'call': 'call2'})\n\n # THEN it should fail\n self.assertTrue(response.error)\n\n # -----\n\n # WHEN fetching data listing using supported call method\n response = self.api.GET(self.app_label, self.model_name2, params={'call': 'call'})\n\n # THEN it should succeed\n self.assertTrue(response.success)","def __init__(self):\n self.api = Api(consumer_key=credentials[\"consumer_key\"],\n consumer_secret=credentials[\"consumer_secret\"],\n access_token_key=credentials[\"access_token_key\"],\n access_token_secret=credentials[\"access_token_secret\"])","def get_auth(self, name = \"memory\", *args, **kwargs):\r\n\r\n name_f = name.title() + \"Auth\"\r\n auth_c = getattr(netius.auth, name_f)\r\n return auth_c","def protectAPI(call, istty, *args, **kwargs):\n\ttry:\n\t\treturn call(*args, **kwargs)\n\texcept HTTPException, e:\n\t\tif istty:\n\t\t\tprint 'Error while calling API function %s: Returned code %d' % (call.__name__, e.retcode)\n\t\t\tif e.response:\n\t\t\t\tif raw_input('View entire web response? (y/n) > ') == 'y':\n\t\t\t\t\tprint e.response\n\t\traise","def _wrap_api(self):\n import inspect\n args, varargs, varkw, defaults = inspect.getargspec(usb.core.Device.read)\n if (len(args) > 2) and (args[3] == 'interface'):\n usb_api = 1 # Require \"interface\" parameter\n else :\n usb_api = 2\n for m in ('write', 'read'):\n setattr(self, '_%s' % m, getattr(self, '_%s_v%d' % (m, usb_api)))","def create_api_handler(self):\n self.github = github3.login(username=GH_USER, password=GH_PASSWORD)\n if hasattr(self.github, 'set_user_agent'):\n self.github.set_user_agent('Jonathan Reeve: http://jonreeve.com')\n self.org = self.github.organization(login='Git-Lit')\n # FIXME: logging\n print(\"ratelimit: \" + str(self.org.ratelimit_remaining))","def http_access(self):\n return self._http_access","def __getattr__(self, name, default=None):\n if name in [\"config\", \"configuration\", \"conf\"]:\n return self._configuration\n\n if name == \"data\":\n return self._data\n\n if name == \"http\":\n return self._http\n\n return default","def api_auth(func):\n @wraps(func)\n def _decorator(request, *args, **kwargs):\n authentication = APIAuthentication(request)\n if authentication.authenticate():\n return func(request, *args, **kwargs)\n raise Http404\n return _decorator","def initial(self, request, *args, **kwargs):\n\n # It's checks the permissions for the third party endpoint or not. It give access if key present.\n bool_value, message = self.check_api_keys(request)\n if bool_value:\n super(ProjectRestrictedGenericViewSet, self).initial(request, *args, **kwargs)\n # Check action permissions\n self.check_action_permissions(request)\n else:\n self.app_permission_denied(request, message)","def Login(self):\n raise NotImplementedError()","def init(server_host_and_port):\n global __obj_starter_api\n __obj_starter_api = PrivateApi(api_url=server_host_and_port)","def __init__(self, base_url, client_id, client_secret, client_scope, api_json = None):\n # type: (str, str, str, str, str) -> None\n\n self.base_url = base_url\n self.client_id = client_id\n self.client_secret = client_secret\n self.client_scope = client_scope\n\n # If the user doesn't pass an alternate API file use the included one\n if not api_json:\n api_json = pkg_resources.resource_filename(__name__, 'apis.json')\n\n with open(api_json, encoding='utf-8') as api_file:\n apis = json.loads(api_file.read())\n\n if client_scope in apis: \n api = apis.get(client_scope)\n self.token_url = api.get('token_url')\n self.api_call = sleep_and_retry(limits(calls=api.get('limits_calls'), period=api.get('limits_period'))(self._api_call))\n self.access_token = self.get_access_token(self.token_url)\n else: \n raise Exception(f\"Scope {client_scope} not in known API dict\")","def test_unauthenticated_resource_allowed(self):\n raise NotImplementedError # FIXME","def _setupHttp(self):\r\n if self._http == None:\r\n http = httplib2.Http()\r\n self._http = self._credentials.authorize(http)","def http_method_get():\n return 'GET'","def Access(self):\n if datetime.now() < self.access_exp:\n pass\n elif datetime.now() > self.access_exp and datetime.now() < self.refresh_exp:\n grant = 'refresh_token'\n self._postRequest(grant=grant)\n elif datetime.now() > self.refresh_exp:\n grant = 'authorization_code'\n self._getURLcode()\n self._postRequest(grant=grant)","def test_api_user_get(self):\n pass","def get(self, request, format=None):\n an_apiview = [\n \"User HTTp methods get, put, post, delete method\",\n \"very similar to previous Django view\",\n \"gives you more control on api logic\",\n 'Is mapped to manually to urls'\n ]\n\n return Response({'message':\"hello\", \"an_apiview\": an_apiview} )","def acquire(self, access_mode=None):","def api_used(self, api_used):\n\n self._api_used = api_used","def access_url(context, url):\n context.response = requests.get(context.coreapi_url + url)","def test_read_o_auth_client(self):\n pass","def accessed(self, identifier, **kwargs):\r\n # Do the import here, instead of top-level, so that the model is\r\n # only required when using this throttling mechanism.\r\n from delicious_cake.models import ApiAccess\r\n super(CacheDBThrottle, self).accessed(identifier, **kwargs)\r\n # Write out the access to the DB for logging purposes.\r\n ApiAccess.objects.create(\r\n identifier=identifier,\r\n url=kwargs.get('url', ''),\r\n request_method=kwargs.get('request_method', ''))","def test_auto_auth_disabled(self):\r\n response = self.client.get(self.url)\r\n self.assertEqual(response.status_code, 404)","def test_read_o_auth_client_authorization(self):\n pass","def get_scope_read_default(self):\n return f\"{self.api_name}:read:default\"","def __init__(self, api_token):\r\n self.apiroot = 'https://api.pipedrive.com/v1'\r\n self.api_token = api_token\r\n self.add_filter(self.add_auth)","def __get_base_info_api(self):\r\n try:\r\n return Call_shelly_api(url=self.__api_address + \"/settings\")\r\n except ShellyException as err:\r\n _LOGGER.warning(err)","def api_endpoint():\n return 'localhost'","def __init__(self, api_access_code):\r\n self.apiroot = 'http://api.flurry.com'\r\n\r\n self.api_access_code = api_access_code\r\n\r\n self.add_filter(self.add_authorization)\r\n self.add_filter(self.add_json)","def _request(self, *args, **kwargs):\n raise NotImplementedError()","def get_conn(self) -> Auth.contextmgr:\n if self.conn.login and self.conn.password:\n return self._auth_via_password()\n if \"token_name\" in self.conn.extra_dejson and \"personal_access_token\" in self.conn.extra_dejson:\n return self._auth_via_token()\n raise NotImplementedError(\"No Authentication method found for given Credentials!\")","def test_by_accession_geo_platform_accession_get(self):\n pass","def check_access(self, name):\n if (not self.parsed and\n name not in ('parse', 'parsed', 'parse_configure_file')):\n raise AttributeError(\n 'Attempted to access uninitialized OPTIONS values. Please consider '\n 'calling OPTIONS.parse_configure_file() in the very beginning of '\n 'your script.')","def __init__(self):\n self.authurl = Config().auth\n self.baseurl = Config().api\n self.s = Session()\n self.s.headers = {'Accept': 'application/json'}\n data = {\"grant_type\": \"client_credentials\", \"scope\": \"/read-public\", \"client_id\": Config().client_id,\n \"client_secret\": Config().client_secret}\n r = self.s.request(method=\"post\", url=self.authurl, data=data)\n self.s.headers = {'Accept': 'application/json', \"Access token\": r.json()[\"access_token\"]}","def handle_request(self, request, environ, start_response,\n response_headers):\n method = environ[\"REQUEST_METHOD\"].upper()\n if method in (\"GET\", \"HEAD\"):\n return super(ReadOnlyServer, self).handle_request(\n request, environ, start_response, response_headers)\n else:\n return self.odata_error(request, environ, start_response,\n \"Unauthorised\", \"Method not allowed\", 403)","def __init__(self, apikey_or_username, password=None):\r\n self.apiroot = 'https://app.ducksboard.com/api'\r\n self.apikey_or_username = apikey_or_username\r\n\r\n self.add_filter(auth.BasicAuth(apikey_or_username, password))\r\n self.add_filter(self.use_json)","def data_access_auth_mode(self) -> Optional[str]:\n return pulumi.get(self, \"data_access_auth_mode\")","def api_call():\n\tresponse = requests.get(URL_API)\n\treturn response","def get() -> None:\n pass","def test_auth_private(self):\n self.do_visible(True, None, False, tenant='froggy')","def __init__(self, consumer_key, consumer_secret, access_token, access_token_secret):\n self.api = self.getAPI(consumer_key, consumer_secret, access_token, access_token_secret)","def __init__(self, **kwargs):\n\n session = vk_api.VkApi(**kwargs)\n try:\n session.auth(token_only=True)\n except vk_api.AuthError as error_msg:\n print(error_msg)\n raise\n self.api = session.get_api()","def __init__(self, app_id, api_key):\r\n self.apiroot = 'https://api.intercom.io/v1'\r\n\r\n self.add_filter(auth.BasicAuth(app_id, api_key))\r\n self.add_filter(self.use_json)","def test_apis_wo_auth(self):\n\n # Order list API\n url = reverse('orders-list')\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n # Order summary API\n url = reverse('order-summary-list')\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n # Order create API\n url = reverse('orders-list')\n response = self.client.post(url)\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n # Shares list/summary API\n url = reverse('shares-list', args=['summary'])\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n url = reverse('shares-list', args=['all'])\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)","def api_authentication():\r\n apikey = request.args.get('api_key', None)\r\n from flask import _request_ctx_stack\r\n if 'Authorization' in request.headers:\r\n apikey = request.headers.get('Authorization')\r\n if apikey:\r\n user = db.session.query(model.user.User).filter_by(api_key=apikey).first()\r\n ## HACK:\r\n # login_user sets a session cookie which we really don't want.\r\n # login_user(user)\r\n if user:\r\n _request_ctx_stack.top.user = user","def have_api(self, *apis):\n if not all(apis):\n logger.log('DEBUG', f'{self.source} module is not configured')\n return False\n return True","def get(self, *args, **kwargs):"],"string":"[\n \"def request_access(self):\\n pass\",\n \"def access():\",\n \"def api_access(self):\\n return self._api_access\",\n \"def maya_useNewAPI():\\n\\tpass\",\n \"def maya_useNewAPI():\\n\\tpass\",\n \"def maya_useNewAPI():\\r\\n\\r\\n pass\",\n \"def api_get(self, name):\\n try:\\n r = self._get(['apis', name])\\n except requests.HTTPError:\\n return None\\n else:\\n return r\",\n \"def maya_useNewAPI():\\n\\n pass\",\n \"def maya_useNewAPI():\\n pass\",\n \"def maya_useNewAPI():\\n pass\",\n \"def maya_useNewAPI():\\n pass\",\n \"def maya_useNewAPI():\\n pass\",\n \"def maya_useNewAPI():\\n pass\",\n \"def __init__(self, api_use=False):\\n self.api_use = api_use\",\n \"def authorization():\\n pass\",\n \"def use_http(self):\\r\\n use_http = self.config.get_bool(\\\"gox\\\", \\\"use_http_api\\\")\\r\\n if FORCE_HTTP_API:\\r\\n use_http = True\\r\\n if FORCE_NO_HTTP_API:\\r\\n use_http = False\\r\\n return use_http\",\n \"def get_api(self, ranger_client, api_name):\\n if api_name == \\\"create_policy\\\":\\n return ranger_client.create_policy\\n elif api_name == \\\"delete_policy_by_id\\\":\\n return ranger_client.delete_policy_by_id\\n elif api_name == \\\"get_policy_by_id\\\":\\n return ranger_client.get_policy_by_id\\n elif api_name == \\\"update_policy_by_id\\\":\\n return ranger_client.update_policy_by_id\\n else:\\n raise Exception(f\\\"Unknown API name: {api_name}\\\")\",\n \"def api(self) -> str:\",\n \"def request(self, apiurl, caller, mode=\\\"get\\\", data=None):\\n\\n token = self._getToken(caller)\\n # print(self._tokenstore)\\n headers = {'Content-Type': 'application/json', 'Accept': 'application/json', 'Authorization': f'Bearer {token}'}\\n if mode == \\\"get\\\":\\n return self._oauth.getRequester().get(self._baseurl + apiurl, headers=headers)\\n elif mode == \\\"post\\\":\\n return self._oauth.getRequester().post(self._baseurl + apiurl, data=data, headers=headers)\\n elif mode == \\\"delete\\\":\\n return self._oauth.getRequester().delete(self._baseurl + apiurl, headers=headers)\",\n \"def test_basic_api_with_mode(self):\\n # Create an API with get and put\\n self.create_and_verify_stack(\\\"single/basic_api_with_mode\\\")\\n\\n stack_output = self.get_stack_outputs()\\n api_endpoint = stack_output.get(\\\"ApiEndpoint\\\")\\n\\n self.verify_get_request_response(f\\\"{api_endpoint}/get\\\", 200)\\n\\n # Removes get from the API\\n self.update_and_verify_stack(file_path=\\\"single/basic_api_with_mode_update\\\")\\n\\n # API Gateway by default returns 403 if a path do not exist\\n self.verify_get_request_response.retry_with(\\n stop=stop_after_attempt(20),\\n wait=wait_exponential(multiplier=1, min=4, max=10) + wait_random(0, 1),\\n retry=retry_if_exception_type(StatusCodeError),\\n after=after_log(LOG, logging.WARNING),\\n reraise=True,\\n )(self, f\\\"{api_endpoint}/get\\\", 403)\\n\\n LOG.log(msg=f\\\"retry times {self.verify_get_request_response.retry.statistics}\\\", level=logging.WARNING)\",\n \"def get_api(self):\\n return self.api\",\n \"def protect_endpoint():\\n pass\",\n \"def auth(self):\\n return self.api(self.token)\",\n \"def api(self):\\n return self.__api\",\n \"def get_authorization():\\n return True\",\n \"def __init__(self, requests_api=None):\\r\\n self._requests = requests_api or requests\",\n \"def test_anything_else_is_accessible(api_client):\\n\\n assert api_client().get(\\\"/anything/else\\\").status_code == 200\",\n \"def auth():\\n pass\",\n \"def auth():\\n pass\",\n \"def api(self):\\r\\n return self._api\",\n \"def get_api(provider):\\n # TODO(b/167685797): Use thread-local.\\n if provider == storage_url.ProviderPrefix.GCS:\\n return gcs_api.GcsApi()\\n elif provider == storage_url.ProviderPrefix.S3:\\n return s3_api.S3Api()\\n raise ValueError('Provider API value must be \\\"gs\\\" or \\\"s3\\\".')\",\n \"def prepare_api(self):\\n return None\",\n \"def test_authorization_is_enforced(self):\\n new_client = APIClient()\\n response = new_client.get('/posts/', kwargs={'pk': 3}, format=\\\"json\\\")\\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\",\n \"def __getattr__(self, name):\\n def wrapper(*args, **kwargs): \\n if name in (\\\"get\\\", \\\"post\\\", \\\"put\\\", \\\"patch\\\", \\\"delete\\\", \\\"head\\\", \\\"options\\\"): #These are HTTP methods\\n is_http_method = True\\n \\n #Now proxy down to the requests object (or session) \\n try:\\n output = getattr(self.session, name)(*args, **kwargs) #Assume a function\\n except TypeError: #Assume a property\\n output = getattr(self.session, name)\\n \\n #Now catch any special HTTP calls \\n if is_http_method:\\n #Examine the status, if in error, raise it\\n if output.status_code >= 400: #Some error has occurred!\\n raise DownloadingError(\\\"Unable to download from resource: '%s'\\\\n%s\\\" % (output.url, output.content))\\n return output\\n return wrapper #This wrapper partial allows us to pass *args and **kwargs into a __getattr__ situation\",\n \"def api():\\n\\treturn \\\"The API call\\\"\",\n \"def _http(self):\\n raise NotImplementedError(\\\"HTTP transport is not supported.\\\")\",\n \"def Modifier_API(self):\\n\\t\\tpass\",\n \"def test_00_api_get(self):\\r\\n # GET as Anonymous\\r\\n url = '/api/'\\r\\n action = 'get'\\r\\n self.check_limit(url, action, 'app')\",\n \"def authn_and_authz():\\n authentication()\\n authorization()\",\n \"def api_access(self, api_access):\\n\\n self._api_access = api_access\",\n \"def __call__(self, access_token):\",\n \"def access_wrapper(func, *args):\\n def try_access(*args):\\n \\\"\\\"\\\"Function that tries the access and returns obj or None\\\"\\\"\\\"\\n obj_or_dir = func(*args)\\n if not obj_or_dir:\\n logging.warning('plot server read access: ' +\\n proto_err_msg.format(*args))\\n return None\\n return obj_or_dir\\n return try_access(*args)\",\n \"def test_dashboards_v2_request_access(self):\\n pass\",\n \"def api(self):\\n return self._api\",\n \"def api(self):\\n return self._api\",\n \"def api(self):\\n return self._api\",\n \"def api(self):\\n return self._api\",\n \"def _get_api():\\n return os.environ.get(\\\"MAPBOX_API\\\", \\\"https://api.mapbox.com\\\")\",\n \"def rest_test_no_auth():\\n # Permission check is not applicable here\\n return jsonify({\\\"answer\\\": 42})\",\n \"def test_get_host_access(self):\\n pass\",\n \"def requires_auth(self):\\n return True\",\n \"def test_public_manager_call(self):\\n\\n # GIVEN public access method to model's manager\\n\\n # WHEN fetching data listing using unsupported call method\\n response = self.api.GET(self.app_label, self.model_name2, params={'call': 'call2'})\\n\\n # THEN it should fail\\n self.assertTrue(response.error)\\n\\n # -----\\n\\n # WHEN fetching data listing using supported call method\\n response = self.api.GET(self.app_label, self.model_name2, params={'call': 'call'})\\n\\n # THEN it should succeed\\n self.assertTrue(response.success)\",\n \"def __init__(self):\\n self.api = Api(consumer_key=credentials[\\\"consumer_key\\\"],\\n consumer_secret=credentials[\\\"consumer_secret\\\"],\\n access_token_key=credentials[\\\"access_token_key\\\"],\\n access_token_secret=credentials[\\\"access_token_secret\\\"])\",\n \"def get_auth(self, name = \\\"memory\\\", *args, **kwargs):\\r\\n\\r\\n name_f = name.title() + \\\"Auth\\\"\\r\\n auth_c = getattr(netius.auth, name_f)\\r\\n return auth_c\",\n \"def protectAPI(call, istty, *args, **kwargs):\\n\\ttry:\\n\\t\\treturn call(*args, **kwargs)\\n\\texcept HTTPException, e:\\n\\t\\tif istty:\\n\\t\\t\\tprint 'Error while calling API function %s: Returned code %d' % (call.__name__, e.retcode)\\n\\t\\t\\tif e.response:\\n\\t\\t\\t\\tif raw_input('View entire web response? (y/n) > ') == 'y':\\n\\t\\t\\t\\t\\tprint e.response\\n\\t\\traise\",\n \"def _wrap_api(self):\\n import inspect\\n args, varargs, varkw, defaults = inspect.getargspec(usb.core.Device.read)\\n if (len(args) > 2) and (args[3] == 'interface'):\\n usb_api = 1 # Require \\\"interface\\\" parameter\\n else :\\n usb_api = 2\\n for m in ('write', 'read'):\\n setattr(self, '_%s' % m, getattr(self, '_%s_v%d' % (m, usb_api)))\",\n \"def create_api_handler(self):\\n self.github = github3.login(username=GH_USER, password=GH_PASSWORD)\\n if hasattr(self.github, 'set_user_agent'):\\n self.github.set_user_agent('Jonathan Reeve: http://jonreeve.com')\\n self.org = self.github.organization(login='Git-Lit')\\n # FIXME: logging\\n print(\\\"ratelimit: \\\" + str(self.org.ratelimit_remaining))\",\n \"def http_access(self):\\n return self._http_access\",\n \"def __getattr__(self, name, default=None):\\n if name in [\\\"config\\\", \\\"configuration\\\", \\\"conf\\\"]:\\n return self._configuration\\n\\n if name == \\\"data\\\":\\n return self._data\\n\\n if name == \\\"http\\\":\\n return self._http\\n\\n return default\",\n \"def api_auth(func):\\n @wraps(func)\\n def _decorator(request, *args, **kwargs):\\n authentication = APIAuthentication(request)\\n if authentication.authenticate():\\n return func(request, *args, **kwargs)\\n raise Http404\\n return _decorator\",\n \"def initial(self, request, *args, **kwargs):\\n\\n # It's checks the permissions for the third party endpoint or not. It give access if key present.\\n bool_value, message = self.check_api_keys(request)\\n if bool_value:\\n super(ProjectRestrictedGenericViewSet, self).initial(request, *args, **kwargs)\\n # Check action permissions\\n self.check_action_permissions(request)\\n else:\\n self.app_permission_denied(request, message)\",\n \"def Login(self):\\n raise NotImplementedError()\",\n \"def init(server_host_and_port):\\n global __obj_starter_api\\n __obj_starter_api = PrivateApi(api_url=server_host_and_port)\",\n \"def __init__(self, base_url, client_id, client_secret, client_scope, api_json = None):\\n # type: (str, str, str, str, str) -> None\\n\\n self.base_url = base_url\\n self.client_id = client_id\\n self.client_secret = client_secret\\n self.client_scope = client_scope\\n\\n # If the user doesn't pass an alternate API file use the included one\\n if not api_json:\\n api_json = pkg_resources.resource_filename(__name__, 'apis.json')\\n\\n with open(api_json, encoding='utf-8') as api_file:\\n apis = json.loads(api_file.read())\\n\\n if client_scope in apis: \\n api = apis.get(client_scope)\\n self.token_url = api.get('token_url')\\n self.api_call = sleep_and_retry(limits(calls=api.get('limits_calls'), period=api.get('limits_period'))(self._api_call))\\n self.access_token = self.get_access_token(self.token_url)\\n else: \\n raise Exception(f\\\"Scope {client_scope} not in known API dict\\\")\",\n \"def test_unauthenticated_resource_allowed(self):\\n raise NotImplementedError # FIXME\",\n \"def _setupHttp(self):\\r\\n if self._http == None:\\r\\n http = httplib2.Http()\\r\\n self._http = self._credentials.authorize(http)\",\n \"def http_method_get():\\n return 'GET'\",\n \"def Access(self):\\n if datetime.now() < self.access_exp:\\n pass\\n elif datetime.now() > self.access_exp and datetime.now() < self.refresh_exp:\\n grant = 'refresh_token'\\n self._postRequest(grant=grant)\\n elif datetime.now() > self.refresh_exp:\\n grant = 'authorization_code'\\n self._getURLcode()\\n self._postRequest(grant=grant)\",\n \"def test_api_user_get(self):\\n pass\",\n \"def get(self, request, format=None):\\n an_apiview = [\\n \\\"User HTTp methods get, put, post, delete method\\\",\\n \\\"very similar to previous Django view\\\",\\n \\\"gives you more control on api logic\\\",\\n 'Is mapped to manually to urls'\\n ]\\n\\n return Response({'message':\\\"hello\\\", \\\"an_apiview\\\": an_apiview} )\",\n \"def acquire(self, access_mode=None):\",\n \"def api_used(self, api_used):\\n\\n self._api_used = api_used\",\n \"def access_url(context, url):\\n context.response = requests.get(context.coreapi_url + url)\",\n \"def test_read_o_auth_client(self):\\n pass\",\n \"def accessed(self, identifier, **kwargs):\\r\\n # Do the import here, instead of top-level, so that the model is\\r\\n # only required when using this throttling mechanism.\\r\\n from delicious_cake.models import ApiAccess\\r\\n super(CacheDBThrottle, self).accessed(identifier, **kwargs)\\r\\n # Write out the access to the DB for logging purposes.\\r\\n ApiAccess.objects.create(\\r\\n identifier=identifier,\\r\\n url=kwargs.get('url', ''),\\r\\n request_method=kwargs.get('request_method', ''))\",\n \"def test_auto_auth_disabled(self):\\r\\n response = self.client.get(self.url)\\r\\n self.assertEqual(response.status_code, 404)\",\n \"def test_read_o_auth_client_authorization(self):\\n pass\",\n \"def get_scope_read_default(self):\\n return f\\\"{self.api_name}:read:default\\\"\",\n \"def __init__(self, api_token):\\r\\n self.apiroot = 'https://api.pipedrive.com/v1'\\r\\n self.api_token = api_token\\r\\n self.add_filter(self.add_auth)\",\n \"def __get_base_info_api(self):\\r\\n try:\\r\\n return Call_shelly_api(url=self.__api_address + \\\"/settings\\\")\\r\\n except ShellyException as err:\\r\\n _LOGGER.warning(err)\",\n \"def api_endpoint():\\n return 'localhost'\",\n \"def __init__(self, api_access_code):\\r\\n self.apiroot = 'http://api.flurry.com'\\r\\n\\r\\n self.api_access_code = api_access_code\\r\\n\\r\\n self.add_filter(self.add_authorization)\\r\\n self.add_filter(self.add_json)\",\n \"def _request(self, *args, **kwargs):\\n raise NotImplementedError()\",\n \"def get_conn(self) -> Auth.contextmgr:\\n if self.conn.login and self.conn.password:\\n return self._auth_via_password()\\n if \\\"token_name\\\" in self.conn.extra_dejson and \\\"personal_access_token\\\" in self.conn.extra_dejson:\\n return self._auth_via_token()\\n raise NotImplementedError(\\\"No Authentication method found for given Credentials!\\\")\",\n \"def test_by_accession_geo_platform_accession_get(self):\\n pass\",\n \"def check_access(self, name):\\n if (not self.parsed and\\n name not in ('parse', 'parsed', 'parse_configure_file')):\\n raise AttributeError(\\n 'Attempted to access uninitialized OPTIONS values. Please consider '\\n 'calling OPTIONS.parse_configure_file() in the very beginning of '\\n 'your script.')\",\n \"def __init__(self):\\n self.authurl = Config().auth\\n self.baseurl = Config().api\\n self.s = Session()\\n self.s.headers = {'Accept': 'application/json'}\\n data = {\\\"grant_type\\\": \\\"client_credentials\\\", \\\"scope\\\": \\\"/read-public\\\", \\\"client_id\\\": Config().client_id,\\n \\\"client_secret\\\": Config().client_secret}\\n r = self.s.request(method=\\\"post\\\", url=self.authurl, data=data)\\n self.s.headers = {'Accept': 'application/json', \\\"Access token\\\": r.json()[\\\"access_token\\\"]}\",\n \"def handle_request(self, request, environ, start_response,\\n response_headers):\\n method = environ[\\\"REQUEST_METHOD\\\"].upper()\\n if method in (\\\"GET\\\", \\\"HEAD\\\"):\\n return super(ReadOnlyServer, self).handle_request(\\n request, environ, start_response, response_headers)\\n else:\\n return self.odata_error(request, environ, start_response,\\n \\\"Unauthorised\\\", \\\"Method not allowed\\\", 403)\",\n \"def __init__(self, apikey_or_username, password=None):\\r\\n self.apiroot = 'https://app.ducksboard.com/api'\\r\\n self.apikey_or_username = apikey_or_username\\r\\n\\r\\n self.add_filter(auth.BasicAuth(apikey_or_username, password))\\r\\n self.add_filter(self.use_json)\",\n \"def data_access_auth_mode(self) -> Optional[str]:\\n return pulumi.get(self, \\\"data_access_auth_mode\\\")\",\n \"def api_call():\\n\\tresponse = requests.get(URL_API)\\n\\treturn response\",\n \"def get() -> None:\\n pass\",\n \"def test_auth_private(self):\\n self.do_visible(True, None, False, tenant='froggy')\",\n \"def __init__(self, consumer_key, consumer_secret, access_token, access_token_secret):\\n self.api = self.getAPI(consumer_key, consumer_secret, access_token, access_token_secret)\",\n \"def __init__(self, **kwargs):\\n\\n session = vk_api.VkApi(**kwargs)\\n try:\\n session.auth(token_only=True)\\n except vk_api.AuthError as error_msg:\\n print(error_msg)\\n raise\\n self.api = session.get_api()\",\n \"def __init__(self, app_id, api_key):\\r\\n self.apiroot = 'https://api.intercom.io/v1'\\r\\n\\r\\n self.add_filter(auth.BasicAuth(app_id, api_key))\\r\\n self.add_filter(self.use_json)\",\n \"def test_apis_wo_auth(self):\\n\\n # Order list API\\n url = reverse('orders-list')\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\\n\\n # Order summary API\\n url = reverse('order-summary-list')\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\\n\\n # Order create API\\n url = reverse('orders-list')\\n response = self.client.post(url)\\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\\n\\n # Shares list/summary API\\n url = reverse('shares-list', args=['summary'])\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\\n\\n url = reverse('shares-list', args=['all'])\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\",\n \"def api_authentication():\\r\\n apikey = request.args.get('api_key', None)\\r\\n from flask import _request_ctx_stack\\r\\n if 'Authorization' in request.headers:\\r\\n apikey = request.headers.get('Authorization')\\r\\n if apikey:\\r\\n user = db.session.query(model.user.User).filter_by(api_key=apikey).first()\\r\\n ## HACK:\\r\\n # login_user sets a session cookie which we really don't want.\\r\\n # login_user(user)\\r\\n if user:\\r\\n _request_ctx_stack.top.user = user\",\n \"def have_api(self, *apis):\\n if not all(apis):\\n logger.log('DEBUG', f'{self.source} module is not configured')\\n return False\\n return True\",\n \"def get(self, *args, **kwargs):\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6620378","0.62082165","0.5927204","0.58691835","0.58691835","0.5855044","0.58468324","0.57812655","0.5763495","0.5763495","0.5763495","0.5763495","0.5763495","0.57499105","0.56996876","0.564842","0.5636248","0.56194305","0.5615304","0.558719","0.5577919","0.55767775","0.5527883","0.5505098","0.5498185","0.5489407","0.5474914","0.54416156","0.54416156","0.5438878","0.54190177","0.54064107","0.539504","0.5390281","0.5367576","0.53571004","0.5349661","0.53408045","0.5316547","0.53066057","0.52999276","0.5298862","0.52983654","0.52928495","0.52928495","0.52928495","0.52928495","0.5284209","0.5282429","0.5259139","0.5237432","0.5237089","0.52149093","0.519194","0.5166422","0.51648486","0.5151975","0.5142033","0.5135367","0.51159006","0.51142657","0.5112817","0.51074","0.5099055","0.50963634","0.5095079","0.50902325","0.5087287","0.50754833","0.50753254","0.507436","0.50689524","0.50649464","0.50608915","0.5056137","0.5048441","0.50433606","0.50424707","0.50405324","0.5039402","0.5036349","0.5032879","0.5028718","0.50176114","0.5014952","0.50128376","0.50118816","0.5000602","0.4996979","0.4980588","0.49804556","0.4980083","0.49795946","0.4978473","0.49757716","0.4973875","0.49709877","0.4970543","0.49642634","0.4959024"],"string":"[\n \"0.6620378\",\n \"0.62082165\",\n \"0.5927204\",\n \"0.58691835\",\n \"0.58691835\",\n \"0.5855044\",\n \"0.58468324\",\n \"0.57812655\",\n \"0.5763495\",\n \"0.5763495\",\n \"0.5763495\",\n \"0.5763495\",\n \"0.5763495\",\n \"0.57499105\",\n \"0.56996876\",\n \"0.564842\",\n \"0.5636248\",\n \"0.56194305\",\n \"0.5615304\",\n \"0.558719\",\n \"0.5577919\",\n \"0.55767775\",\n \"0.5527883\",\n \"0.5505098\",\n \"0.5498185\",\n \"0.5489407\",\n \"0.5474914\",\n \"0.54416156\",\n \"0.54416156\",\n \"0.5438878\",\n \"0.54190177\",\n \"0.54064107\",\n \"0.539504\",\n \"0.5390281\",\n \"0.5367576\",\n \"0.53571004\",\n \"0.5349661\",\n \"0.53408045\",\n \"0.5316547\",\n \"0.53066057\",\n \"0.52999276\",\n \"0.5298862\",\n \"0.52983654\",\n \"0.52928495\",\n \"0.52928495\",\n \"0.52928495\",\n \"0.52928495\",\n \"0.5284209\",\n \"0.5282429\",\n \"0.5259139\",\n \"0.5237432\",\n \"0.5237089\",\n \"0.52149093\",\n \"0.519194\",\n \"0.5166422\",\n \"0.51648486\",\n \"0.5151975\",\n \"0.5142033\",\n \"0.5135367\",\n \"0.51159006\",\n \"0.51142657\",\n \"0.5112817\",\n \"0.51074\",\n \"0.5099055\",\n \"0.50963634\",\n \"0.5095079\",\n \"0.50902325\",\n \"0.5087287\",\n \"0.50754833\",\n \"0.50753254\",\n \"0.507436\",\n \"0.50689524\",\n \"0.50649464\",\n \"0.50608915\",\n \"0.5056137\",\n \"0.5048441\",\n \"0.50433606\",\n \"0.50424707\",\n \"0.50405324\",\n \"0.5039402\",\n \"0.5036349\",\n \"0.5032879\",\n \"0.5028718\",\n \"0.50176114\",\n \"0.5014952\",\n \"0.50128376\",\n \"0.50118816\",\n \"0.5000602\",\n \"0.4996979\",\n \"0.4980588\",\n \"0.49804556\",\n \"0.4980083\",\n \"0.49795946\",\n \"0.4978473\",\n \"0.49757716\",\n \"0.4973875\",\n \"0.49709877\",\n \"0.4970543\",\n \"0.49642634\",\n \"0.4959024\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5198949"},"document_rank":{"kind":"string","value":"53"}}},{"rowIdx":20072,"cells":{"query":{"kind":"string","value":"Get the current resource/API"},"document":{"kind":"string","value":"def api(self):\n return self._api"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_api(self):\n return self.api","def api(self):\r\n return self._api","def api(self):\n return self.__api","def getAPI(self):\n return self.api_url","def getResource(self):\n return self.serviceClass.app.resource()","def get_api(self):\n from geoffrey.utils import get_api\n return get_api(self.app.routes, prefix='/api')","def api(self) -> str:\n return self._api","def get_api(self):\n from geoffrey.utils import get_api\n return get_api(self.app.routes, prefix=\"/\")","def resource(self):\n return self._resource","def resource(self):\n return self._resource","def resource(self):\n return self._resource","def resource(self):\n return self._resource","def resource(self):\n return self._resource","def resource(self):\n return self._resource","def resource(self):\n return self._resource","def getResource(self):\n return self.__resource;","def api(self):\n if self._api is None:\n self._api = Api(self)\n return self._api","def api(self) -> str:","def api(self) -> Optional[pulumi.Input['ApplicationApiArgs']]:\n return pulumi.get(self, \"api\")","def api(self) -> Optional[pulumi.Input['ApplicationApiArgs']]:\n return pulumi.get(self, \"api\")","def api_get(self, name):\n try:\n r = self._get(['apis', name])\n except requests.HTTPError:\n return None\n else:\n return r","def getResource(self):\n pass;","def api_access(self):\n return self._api_access","def get_current_request():\n return crum.get_current_request()","def api(self) -> pulumi.Output[Optional['outputs.ApplicationApi']]:\n return pulumi.get(self, \"api\")","def get_id(self):\n return self.get_api_endpoint()","def info(self, resource, id):\n return self.request('/' + resource + '/' + str(id))","def get_blueprint():\n return REQUEST_API","def getResource(self):\n\n return self.__resource;","def get_info(self):\n url = self._url_for_op('info')\n data= None # This will be a GET request since data is None\n response = self._get_raw_response(self._get_json_headers,\n self._get_json_response, url, data)\n response = json.loads(response)\n self.api_info = response['results']\n return self.api_info","def api_endpoint(self) -> str:\n return pulumi.get(self, \"api_endpoint\")","def get_resource(self):\n raise errors.Unimplemented()","def get_api_path(self):\n return self._get_api_base() + '/' + self._get_resource_suffix()","def me(self):\n\n return self.client._get(self._url())","def get(self):\n\t\treturn self.connection.get(self.base_uri)","def __get_base_info_api(self):\r\n try:\r\n return Call_shelly_api(url=self.__api_address + \"/settings\")\r\n except ShellyException as err:\r\n _LOGGER.warning(err)","def get_api_path(self):\n return self._get_api_base() + '/object/' + self._get_resource_suffix()","def get_api_version(self):\n return self.connector.request('GET', '/app/webapiVersion')","def apiurl(self):\n return self._apiurl","def api_url(self):\n return self.get_api_url()","def get(owner, resource):\n resource = logic.resource.find(owner, resource)\n return jsonify(resource)","def get_current():\n return getattr(_request_store, 'context', None)","def api_get(self, *args, **kwargs):\n return self.api_get_with_response(*args, **kwargs)[0]","def get_api_url():\n return \"https://api.basespace.illumina.com/v1pre3\"","def get(self) -> Response:\n try:\n resource = request.args.getlist(\"resource\")[0]\n return set_response_headers(jsonify(get_fragments(resource)))\n except:\n return set_response_headers(jsonify(get_doc().generate()))","def getRequest():\n return getLocal('request')","def fusion_api_get_resource(self, uri, api=None, headers=None):\n if api:\n headers = self.fusion_client._set_req_api_version(api=api)\n elif not headers:\n headers = self.fusion_client._headers\n uri = 'https://%s%s' % (self.fusion_client._host, uri)\n return self.fusion_client.get(uri, headers)","def get(self) -> Response:\n response = {\"@context\": get_doc().entrypoint.context.generate()}\n return set_response_headers(jsonify(response))","def api_template(self):\n return self._api_template","def api(self):\n res = self.client.call('/', 'GET', data='')\n self.logger.debug('Get openstack identity api versions: %s' % truncate(res))\n return res[0]","def api():\n\treturn \"The API call\"","def get_resource(self):\n\n return self","def resource(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource\")","def resource(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource\")","def resource(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource\")","def get(self):\r\n return http.Request('GET', self.get_url()), parsers.parse_json","def get(self):\r\n return http.Request('GET', self.get_url()), parsers.parse_json","def api_url(self):\n return self._api_url","def __current_object__(self):\n return self.__lookup()","def get_me(self):\n req_str = SETTING.BASE_URL + SETTING.BOT_REQUEST[inspect.currentframe().f_code.co_name]\n\n req = requests.get(req_str)\n return req.json()","def info(self):\n resp = requests.get(\"%s/api/info\"%self.urlbase, verify=False)\n return resp.json","def resource(self):\n return self.properties.get('resource',\n Entity(self.context, ResourcePath(\"resource\", self.resource_path)))","def get():","def get():","def get_initial_resource(client, api_id):\n response = client.get_resources(\n restApiId=api_id\n )\n return response['items'][0]","def _get(self):\n return self.request(method=\"get\", path=self.router.fields)","def get_api(self, ApiId: str) -> Dict:\n pass","def current():\n\n return {\n 'page': 'current',\n }","def info(self):\n return self.client.call('GET', self.name + 'info')","def query_api_version(self):\n version_resp = self._session.get('/api/version',\n logon_required=False)\n self._api_version = version_resp\n return self._api_version","def __getattr__(self, attr):\n actual_resource = getattr(self.swagger_client, attr)\n if attr in [\"Authorization\", \"Effects\", \"Identify\", \"Info\",\n \"PanelLayout\", \"State\"]:\n return WrappedResource(actual_resource, attr)\n else:\n return actual_resource","def get_info(self) -> ApiInfo:\n\n response = self.http_client.get(\"/api/_info\")\n api_info = self._ApiInfo.parse_obj(response)\n return ApiInfo(version=api_info.version)","def info(self):\n return self._fetch_json('/api/info')","def __api(self) -> IWorkspace:\n assert self.__internal_api is not None\n return self.__internal_api","def get_version(self):\n return self.api_version","def get_self(self):\n return self.request(verb=requests.get, address=\"me\")","def getApi(self):\n return _SALOMERuntime.RuntimeSALOME_getApi(self)","def get_api_key(self):\r\n url = '{0}/{1}'.format(self.get_url(), 'api_key')\r\n\r\n return http.Request('GET', url), parsers.parse_json","def lookup(self):\r\n return resources.Lookup(self)","def overview():\n return render_template('api/api.html', title='API Overview')","def get():\n return web_page_api.get(HELP_PAGE_NAME)","def _get_api_url(self):\n return \"%s/%s/\" % (settings.API_URL, settings.API_VERSION)","def api_url(self):\n model_class = self.model_class()\n\n if model_class:\n # If a valid class has been found, see if it has registered an API URL\n try:\n return model_class.get_api_url()\n except Exception:\n pass\n\n return None","def get_current_site(cls):\n return cls.get_by_key_name(cls.the_key_name)","def root(self):\n return Resource()","def process_resource_api(self, resources, resource, api, context):\n pass","def _get(self):\n path = self.router.central_core\n response = self.request(method=\"get\", path=path)\n return response","def base_url(self):\n return 'http://%s/api.php?token=%s&path_info=' % \\\n (self.ac_url, self.api_key)","async def api():\n return {\n \"links\": {\n \"datasets\": \"/datasets\",\n \"natural_earth_feature\": \"/natural_earth_feature\",\n \"viewport\": \"/viewport\"\n }\n }","def on_get_resource(self, req, resp, **params):\n instance = self.get_object(**params)\n resp.json(**instance.as_resource)","def _http_get_current_id(self):\n return self._http_request('').json()['currentplid']","def getGlobalSelf(self):\r\n transports = self.reactor.getReaders()\r\n for transport in transports:\r\n try:\r\n resource = transport.factory.resource\r\n if isinstance(resource, self.__class__) and resource.port == self.port:\r\n return resource\r\n except AttributeError:\r\n pass\r\n return","def _get_extension_resource(api_root):\n # TODO: Cache this. We only use this resource as a link to sub-resources.\n return api_root.get_extension(\n extension_name='reviewbotext.extension.ReviewBotExtension')","def __get__(self, instance, owner):\n if instance._location is None:\n raise AttributeError('Cannot find URL of %s relative to URL-less %s' % (self.cls.__name__, owner.__name__))\n newurl = join(instance._location, self.api_name)\n obj = self.cls.get(newurl, auth=instance.auth)\n obj.auth = instance.auth\n return obj","def get(self):\n pass","def get(self):\n pass","def get(self):\n pass"],"string":"[\n \"def get_api(self):\\n return self.api\",\n \"def api(self):\\r\\n return self._api\",\n \"def api(self):\\n return self.__api\",\n \"def getAPI(self):\\n return self.api_url\",\n \"def getResource(self):\\n return self.serviceClass.app.resource()\",\n \"def get_api(self):\\n from geoffrey.utils import get_api\\n return get_api(self.app.routes, prefix='/api')\",\n \"def api(self) -> str:\\n return self._api\",\n \"def get_api(self):\\n from geoffrey.utils import get_api\\n return get_api(self.app.routes, prefix=\\\"/\\\")\",\n \"def resource(self):\\n return self._resource\",\n \"def resource(self):\\n return self._resource\",\n \"def resource(self):\\n return self._resource\",\n \"def resource(self):\\n return self._resource\",\n \"def resource(self):\\n return self._resource\",\n \"def resource(self):\\n return self._resource\",\n \"def resource(self):\\n return self._resource\",\n \"def getResource(self):\\n return self.__resource;\",\n \"def api(self):\\n if self._api is None:\\n self._api = Api(self)\\n return self._api\",\n \"def api(self) -> str:\",\n \"def api(self) -> Optional[pulumi.Input['ApplicationApiArgs']]:\\n return pulumi.get(self, \\\"api\\\")\",\n \"def api(self) -> Optional[pulumi.Input['ApplicationApiArgs']]:\\n return pulumi.get(self, \\\"api\\\")\",\n \"def api_get(self, name):\\n try:\\n r = self._get(['apis', name])\\n except requests.HTTPError:\\n return None\\n else:\\n return r\",\n \"def getResource(self):\\n pass;\",\n \"def api_access(self):\\n return self._api_access\",\n \"def get_current_request():\\n return crum.get_current_request()\",\n \"def api(self) -> pulumi.Output[Optional['outputs.ApplicationApi']]:\\n return pulumi.get(self, \\\"api\\\")\",\n \"def get_id(self):\\n return self.get_api_endpoint()\",\n \"def info(self, resource, id):\\n return self.request('/' + resource + '/' + str(id))\",\n \"def get_blueprint():\\n return REQUEST_API\",\n \"def getResource(self):\\n\\n return self.__resource;\",\n \"def get_info(self):\\n url = self._url_for_op('info')\\n data= None # This will be a GET request since data is None\\n response = self._get_raw_response(self._get_json_headers,\\n self._get_json_response, url, data)\\n response = json.loads(response)\\n self.api_info = response['results']\\n return self.api_info\",\n \"def api_endpoint(self) -> str:\\n return pulumi.get(self, \\\"api_endpoint\\\")\",\n \"def get_resource(self):\\n raise errors.Unimplemented()\",\n \"def get_api_path(self):\\n return self._get_api_base() + '/' + self._get_resource_suffix()\",\n \"def me(self):\\n\\n return self.client._get(self._url())\",\n \"def get(self):\\n\\t\\treturn self.connection.get(self.base_uri)\",\n \"def __get_base_info_api(self):\\r\\n try:\\r\\n return Call_shelly_api(url=self.__api_address + \\\"/settings\\\")\\r\\n except ShellyException as err:\\r\\n _LOGGER.warning(err)\",\n \"def get_api_path(self):\\n return self._get_api_base() + '/object/' + self._get_resource_suffix()\",\n \"def get_api_version(self):\\n return self.connector.request('GET', '/app/webapiVersion')\",\n \"def apiurl(self):\\n return self._apiurl\",\n \"def api_url(self):\\n return self.get_api_url()\",\n \"def get(owner, resource):\\n resource = logic.resource.find(owner, resource)\\n return jsonify(resource)\",\n \"def get_current():\\n return getattr(_request_store, 'context', None)\",\n \"def api_get(self, *args, **kwargs):\\n return self.api_get_with_response(*args, **kwargs)[0]\",\n \"def get_api_url():\\n return \\\"https://api.basespace.illumina.com/v1pre3\\\"\",\n \"def get(self) -> Response:\\n try:\\n resource = request.args.getlist(\\\"resource\\\")[0]\\n return set_response_headers(jsonify(get_fragments(resource)))\\n except:\\n return set_response_headers(jsonify(get_doc().generate()))\",\n \"def getRequest():\\n return getLocal('request')\",\n \"def fusion_api_get_resource(self, uri, api=None, headers=None):\\n if api:\\n headers = self.fusion_client._set_req_api_version(api=api)\\n elif not headers:\\n headers = self.fusion_client._headers\\n uri = 'https://%s%s' % (self.fusion_client._host, uri)\\n return self.fusion_client.get(uri, headers)\",\n \"def get(self) -> Response:\\n response = {\\\"@context\\\": get_doc().entrypoint.context.generate()}\\n return set_response_headers(jsonify(response))\",\n \"def api_template(self):\\n return self._api_template\",\n \"def api(self):\\n res = self.client.call('/', 'GET', data='')\\n self.logger.debug('Get openstack identity api versions: %s' % truncate(res))\\n return res[0]\",\n \"def api():\\n\\treturn \\\"The API call\\\"\",\n \"def get_resource(self):\\n\\n return self\",\n \"def resource(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource\\\")\",\n \"def resource(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource\\\")\",\n \"def resource(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource\\\")\",\n \"def get(self):\\r\\n return http.Request('GET', self.get_url()), parsers.parse_json\",\n \"def get(self):\\r\\n return http.Request('GET', self.get_url()), parsers.parse_json\",\n \"def api_url(self):\\n return self._api_url\",\n \"def __current_object__(self):\\n return self.__lookup()\",\n \"def get_me(self):\\n req_str = SETTING.BASE_URL + SETTING.BOT_REQUEST[inspect.currentframe().f_code.co_name]\\n\\n req = requests.get(req_str)\\n return req.json()\",\n \"def info(self):\\n resp = requests.get(\\\"%s/api/info\\\"%self.urlbase, verify=False)\\n return resp.json\",\n \"def resource(self):\\n return self.properties.get('resource',\\n Entity(self.context, ResourcePath(\\\"resource\\\", self.resource_path)))\",\n \"def get():\",\n \"def get():\",\n \"def get_initial_resource(client, api_id):\\n response = client.get_resources(\\n restApiId=api_id\\n )\\n return response['items'][0]\",\n \"def _get(self):\\n return self.request(method=\\\"get\\\", path=self.router.fields)\",\n \"def get_api(self, ApiId: str) -> Dict:\\n pass\",\n \"def current():\\n\\n return {\\n 'page': 'current',\\n }\",\n \"def info(self):\\n return self.client.call('GET', self.name + 'info')\",\n \"def query_api_version(self):\\n version_resp = self._session.get('/api/version',\\n logon_required=False)\\n self._api_version = version_resp\\n return self._api_version\",\n \"def __getattr__(self, attr):\\n actual_resource = getattr(self.swagger_client, attr)\\n if attr in [\\\"Authorization\\\", \\\"Effects\\\", \\\"Identify\\\", \\\"Info\\\",\\n \\\"PanelLayout\\\", \\\"State\\\"]:\\n return WrappedResource(actual_resource, attr)\\n else:\\n return actual_resource\",\n \"def get_info(self) -> ApiInfo:\\n\\n response = self.http_client.get(\\\"/api/_info\\\")\\n api_info = self._ApiInfo.parse_obj(response)\\n return ApiInfo(version=api_info.version)\",\n \"def info(self):\\n return self._fetch_json('/api/info')\",\n \"def __api(self) -> IWorkspace:\\n assert self.__internal_api is not None\\n return self.__internal_api\",\n \"def get_version(self):\\n return self.api_version\",\n \"def get_self(self):\\n return self.request(verb=requests.get, address=\\\"me\\\")\",\n \"def getApi(self):\\n return _SALOMERuntime.RuntimeSALOME_getApi(self)\",\n \"def get_api_key(self):\\r\\n url = '{0}/{1}'.format(self.get_url(), 'api_key')\\r\\n\\r\\n return http.Request('GET', url), parsers.parse_json\",\n \"def lookup(self):\\r\\n return resources.Lookup(self)\",\n \"def overview():\\n return render_template('api/api.html', title='API Overview')\",\n \"def get():\\n return web_page_api.get(HELP_PAGE_NAME)\",\n \"def _get_api_url(self):\\n return \\\"%s/%s/\\\" % (settings.API_URL, settings.API_VERSION)\",\n \"def api_url(self):\\n model_class = self.model_class()\\n\\n if model_class:\\n # If a valid class has been found, see if it has registered an API URL\\n try:\\n return model_class.get_api_url()\\n except Exception:\\n pass\\n\\n return None\",\n \"def get_current_site(cls):\\n return cls.get_by_key_name(cls.the_key_name)\",\n \"def root(self):\\n return Resource()\",\n \"def process_resource_api(self, resources, resource, api, context):\\n pass\",\n \"def _get(self):\\n path = self.router.central_core\\n response = self.request(method=\\\"get\\\", path=path)\\n return response\",\n \"def base_url(self):\\n return 'http://%s/api.php?token=%s&path_info=' % \\\\\\n (self.ac_url, self.api_key)\",\n \"async def api():\\n return {\\n \\\"links\\\": {\\n \\\"datasets\\\": \\\"/datasets\\\",\\n \\\"natural_earth_feature\\\": \\\"/natural_earth_feature\\\",\\n \\\"viewport\\\": \\\"/viewport\\\"\\n }\\n }\",\n \"def on_get_resource(self, req, resp, **params):\\n instance = self.get_object(**params)\\n resp.json(**instance.as_resource)\",\n \"def _http_get_current_id(self):\\n return self._http_request('').json()['currentplid']\",\n \"def getGlobalSelf(self):\\r\\n transports = self.reactor.getReaders()\\r\\n for transport in transports:\\r\\n try:\\r\\n resource = transport.factory.resource\\r\\n if isinstance(resource, self.__class__) and resource.port == self.port:\\r\\n return resource\\r\\n except AttributeError:\\r\\n pass\\r\\n return\",\n \"def _get_extension_resource(api_root):\\n # TODO: Cache this. We only use this resource as a link to sub-resources.\\n return api_root.get_extension(\\n extension_name='reviewbotext.extension.ReviewBotExtension')\",\n \"def __get__(self, instance, owner):\\n if instance._location is None:\\n raise AttributeError('Cannot find URL of %s relative to URL-less %s' % (self.cls.__name__, owner.__name__))\\n newurl = join(instance._location, self.api_name)\\n obj = self.cls.get(newurl, auth=instance.auth)\\n obj.auth = instance.auth\\n return obj\",\n \"def get(self):\\n pass\",\n \"def get(self):\\n pass\",\n \"def get(self):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7800824","0.7528681","0.7499402","0.73490024","0.6924","0.69182336","0.68368024","0.68043464","0.66442335","0.66442335","0.66442335","0.66442335","0.66442335","0.66442335","0.66442335","0.65007555","0.6493793","0.6491067","0.6483726","0.6483726","0.64763623","0.64747375","0.6442708","0.6432427","0.64315814","0.6352178","0.6351923","0.63421196","0.63358325","0.6292845","0.6285635","0.62835246","0.62526536","0.6246875","0.62449026","0.6210013","0.62084615","0.6196076","0.61776227","0.61123455","0.6111355","0.6108966","0.6081947","0.60447705","0.6044158","0.60347503","0.603219","0.6029739","0.602506","0.600886","0.60042584","0.60006475","0.59995943","0.59995943","0.59995943","0.59973943","0.59973943","0.59834427","0.5967784","0.59347695","0.5928825","0.592805","0.5927801","0.5927801","0.58892065","0.5885856","0.58806795","0.5879098","0.5858745","0.5857653","0.5857632","0.583855","0.58382344","0.5830725","0.5825487","0.5822515","0.5817502","0.5810169","0.58054096","0.58025587","0.58011913","0.5789537","0.5785551","0.57774174","0.5770562","0.5768766","0.5761876","0.5751322","0.57441664","0.574236","0.57391644","0.5733184","0.5730448","0.57249504","0.5723417","0.5723417","0.5723417"],"string":"[\n \"0.7800824\",\n \"0.7528681\",\n \"0.7499402\",\n \"0.73490024\",\n \"0.6924\",\n \"0.69182336\",\n \"0.68368024\",\n \"0.68043464\",\n \"0.66442335\",\n \"0.66442335\",\n \"0.66442335\",\n \"0.66442335\",\n \"0.66442335\",\n \"0.66442335\",\n \"0.66442335\",\n \"0.65007555\",\n \"0.6493793\",\n \"0.6491067\",\n \"0.6483726\",\n \"0.6483726\",\n \"0.64763623\",\n \"0.64747375\",\n \"0.6442708\",\n \"0.6432427\",\n \"0.64315814\",\n \"0.6352178\",\n \"0.6351923\",\n \"0.63421196\",\n \"0.63358325\",\n \"0.6292845\",\n \"0.6285635\",\n \"0.62835246\",\n \"0.62526536\",\n \"0.6246875\",\n \"0.62449026\",\n \"0.6210013\",\n \"0.62084615\",\n \"0.6196076\",\n \"0.61776227\",\n \"0.61123455\",\n \"0.6111355\",\n \"0.6108966\",\n \"0.6081947\",\n \"0.60447705\",\n \"0.6044158\",\n \"0.60347503\",\n \"0.603219\",\n \"0.6029739\",\n \"0.602506\",\n \"0.600886\",\n \"0.60042584\",\n \"0.60006475\",\n \"0.59995943\",\n \"0.59995943\",\n \"0.59995943\",\n \"0.59973943\",\n \"0.59973943\",\n \"0.59834427\",\n \"0.5967784\",\n \"0.59347695\",\n \"0.5928825\",\n \"0.592805\",\n \"0.5927801\",\n \"0.5927801\",\n \"0.58892065\",\n \"0.5885856\",\n \"0.58806795\",\n \"0.5879098\",\n \"0.5858745\",\n \"0.5857653\",\n \"0.5857632\",\n \"0.583855\",\n \"0.58382344\",\n \"0.5830725\",\n \"0.5825487\",\n \"0.5822515\",\n \"0.5817502\",\n \"0.5810169\",\n \"0.58054096\",\n \"0.58025587\",\n \"0.58011913\",\n \"0.5789537\",\n \"0.5785551\",\n \"0.57774174\",\n \"0.5770562\",\n \"0.5768766\",\n \"0.5761876\",\n \"0.5751322\",\n \"0.57441664\",\n \"0.574236\",\n \"0.57391644\",\n \"0.5733184\",\n \"0.5730448\",\n \"0.57249504\",\n \"0.5723417\",\n \"0.5723417\",\n \"0.5723417\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7439773"},"document_rank":{"kind":"string","value":"6"}}},{"rowIdx":20073,"cells":{"query":{"kind":"string","value":"Set the API resources"},"document":{"kind":"string","value":"def api(self, api):\n if self._running:\n raise ValueError('API cannot be modified while the server is running')\n\n self._api = api"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def resources(self, resources):\n self._resources = resources","def resources(self, resources):\n\n self._resources = resources","def resources(self, resources):\n\n self._resources = resources","def resources(self, resources):\n\n self._resources = resources","def resources(self, resources):\n\n self._resources = resources","def resources(self, value):\n self._resource_objects = value","def register_resources(self, resources):\n from tw.api import merge_resources\n merge_resources(self.request_local.resources, resources)","def resources(self):","def process_resource_api(self, resources, resource, api, context):\n pass","def test_get_api_resources(self):\n pass","def set_resources():\n global available_resources\n global EdgenodeResources\n recv_json = request.get_json()\n for resourcename, value in recv_json.items():\n available_resources[resourcename] = value\n # TODO make this better\n EdgenodeResources = [TaskResources(ram=int(available_resources['RAM']), cpu=int(\n available_resources['CPU']), hdd=int(available_resources['HDD'])), available_resources['DATA']]\n\n print 'Available resources set to', EdgenodeResources\n return 'Available resources set to ' + str(available_resources)","def get_api_resources(self, **kwargs):\n\n all_params = []\n all_params.append('callback')\n\n params = locals()\n for key, val in iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method get_api_resources\" % key\n )\n params[key] = val\n del params['kwargs']\n\n\n resource_path = '/oapi/v1'.replace('{format}', 'json')\n path_params = {}\n\n query_params = {}\n\n header_params = {}\n\n form_params = []\n local_var_files = {}\n\n body_params = None\n\n # HTTP header `Accept`\n header_params['Accept'] = self.api_client.\\\n select_header_accept(['application/json', 'application/yaml'])\n if not header_params['Accept']:\n del header_params['Accept']\n\n # HTTP header `Content-Type`\n header_params['Content-Type'] = self.api_client.\\\n select_header_content_type(['application/json', 'application/yaml'])\n\n # Authentication setting\n auth_settings = []\n\n response = self.api_client.call_api(resource_path, 'GET',\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=local_var_files,\n response_type=None,\n auth_settings=auth_settings,\n callback=params.get('callback'))\n return response","def resources(self, resources):\n if resources is None:\n raise ValueError(\"Invalid value for `resources`, must not be `None`\")\n\n self._resources = resources","def process_resource_listing_api(self, resources, listing_api, context):\n pass","def register_resources(self, resources):\n for resource in resources:\n self.register_resource(resource)","def resource_ids(self, resource_ids):\n\n self._resource_ids = resource_ids","def initialize_api(app, api):\n api.init_app(app=app) # Initialize api first\n _resources = getattr(app, \"api_registry\", None)\n if _resources and isinstance(_resources, (list, tuple,)):\n for cls, args, kwargs in _resources:\n api.add_resource(cls, *args, **kwargs)","def register_resources(self):\n raise NotImplementedError","def _setup_api_properties(self):\n self.implicit_api_logical_id = GeneratedLogicalId.implicit_http_api()\n self.implicit_api_condition = \"ServerlessHttpApiCondition\"\n self.api_event_type = \"HttpApi\"\n self.api_type = SamResourceType.HttpApi.value\n self.api_id_property = \"ApiId\"\n self.editor = OpenApiEditor","def rest_api_config(self):\n with self.resource_lock:\n pass","def __init__(__self__, *,\n api_groups: Optional[Sequence[str]] = None,\n api_versions: Optional[Sequence[str]] = None,\n operations: Optional[Sequence[str]] = None,\n resource_names: Optional[Sequence[str]] = None,\n resources: Optional[Sequence[str]] = None,\n scope: Optional[str] = None):\n if api_groups is not None:\n pulumi.set(__self__, \"api_groups\", api_groups)\n if api_versions is not None:\n pulumi.set(__self__, \"api_versions\", api_versions)\n if operations is not None:\n pulumi.set(__self__, \"operations\", operations)\n if resource_names is not None:\n pulumi.set(__self__, \"resource_names\", resource_names)\n if resources is not None:\n pulumi.set(__self__, \"resources\", resources)\n if scope is not None:\n pulumi.set(__self__, \"scope\", scope)","def __init__(__self__, *,\n api_groups: Optional[Sequence[str]] = None,\n api_versions: Optional[Sequence[str]] = None,\n operations: Optional[Sequence[str]] = None,\n resource_names: Optional[Sequence[str]] = None,\n resources: Optional[Sequence[str]] = None,\n scope: Optional[str] = None):\n if api_groups is not None:\n pulumi.set(__self__, \"api_groups\", api_groups)\n if api_versions is not None:\n pulumi.set(__self__, \"api_versions\", api_versions)\n if operations is not None:\n pulumi.set(__self__, \"operations\", operations)\n if resource_names is not None:\n pulumi.set(__self__, \"resource_names\", resource_names)\n if resources is not None:\n pulumi.set(__self__, \"resources\", resources)\n if scope is not None:\n pulumi.set(__self__, \"scope\", scope)","def resources(self):\n return [self]","def __init__(self, resources):\r\n self._resources = Queue()\r\n for resource in resources:\r\n self._resources.put(resource)","def __init__(self, resources={}):\n super().__init__()\n self._lock = threading.Lock()\n self._resources = {}\n for key, resource in resources.items():\n self.register(key, resource)","def __init__(__self__, *,\n api_groups: Optional[Sequence[str]] = None,\n api_versions: Optional[Sequence[str]] = None,\n operations: Optional[Sequence[str]] = None,\n resources: Optional[Sequence[str]] = None,\n scope: Optional[str] = None):\n if api_groups is not None:\n pulumi.set(__self__, \"api_groups\", api_groups)\n if api_versions is not None:\n pulumi.set(__self__, \"api_versions\", api_versions)\n if operations is not None:\n pulumi.set(__self__, \"operations\", operations)\n if resources is not None:\n pulumi.set(__self__, \"resources\", resources)\n if scope is not None:\n pulumi.set(__self__, \"scope\", scope)","def __init__(__self__, *,\n api_groups: Optional[Sequence[str]] = None,\n api_versions: Optional[Sequence[str]] = None,\n operations: Optional[Sequence[str]] = None,\n resources: Optional[Sequence[str]] = None,\n scope: Optional[str] = None):\n if api_groups is not None:\n pulumi.set(__self__, \"api_groups\", api_groups)\n if api_versions is not None:\n pulumi.set(__self__, \"api_versions\", api_versions)\n if operations is not None:\n pulumi.set(__self__, \"operations\", operations)\n if resources is not None:\n pulumi.set(__self__, \"resources\", resources)\n if scope is not None:\n pulumi.set(__self__, \"scope\", scope)","def update_resources(self, resources=all, **extra_args):\n if resources is all:\n resources = self.resources.values()\n for lrms in self.resources.itervalues():\n try:\n if not lrms.enabled:\n continue\n # auto_enable_auth = extra_args.get(\n # 'auto_enable_auth', self.auto_enable_auth)\n lrms.get_resource_status()\n lrms.updated = True\n except gc3libs.exceptions.UnrecoverableError as err:\n # disable resource -- there's no point in\n # trying it again at a later stage\n lrms.enabled = False\n lrms.updated = False\n gc3libs.log.error(\n \"Unrecoverable error updating status\"\n \" of resource '%s': %s.\"\n \" Disabling resource.\",\n lrms.name, err)\n gc3libs.log.warning(\n \"Resource %s will be ignored from now on.\",\n lrms.name)\n gc3libs.log.debug(\n \"Got error '%s' in updating resource '%s';\"\n \" printing full traceback.\",\n err.__class__.__name__, lrms.name,\n exc_info=True)\n # pylint: disable=broad-except\n except Exception as err:\n gc3libs.log.error(\n \"Ignoring error updating resource '%s': %s.\",\n lrms.name, err)\n gc3libs.log.debug(\n \"Got error '%s' in updating resource '%s';\"\n \" printing full traceback.\",\n err.__class__.__name__, lrms.name,\n exc_info=True)\n lrms.updated = False","def resources(self):\n return self.__resources","def init_rest(app_):\n\n rest_api = Api(app_)\n rest_api.add_resource(views.rest_resources.AppListResource,\n ActiveConfig.REST_URL_APPS_LIST,\n ActiveConfig.REST_URL_APPS_LIST + '/')\n rest_api.add_resource(views.rest_resources.AppResource,\n ActiveConfig.REST_URL_APPS_ITEM,\n ActiveConfig.REST_URL_APPS,\n ActiveConfig.REST_URL_APPS + '/')","def _load_resources(self):\n puts = (getattr(self, 'project', None) or self).puts\n for resource_type, resource_cls in six.iteritems(AVAILABLE_RESOURCES):\n for name in self.settings.get(resource_type, {}):\n extra = {\n 'project': getattr(self, 'project', None) or self,\n 'app': self if hasattr(self, 'project') else None,\n }\n\n with indent(4 if hasattr(self, 'project') else 2):\n puts(colored.green(u\"✓ {}:{}\".format(resource_type, name)))\n\n self._resources[resource_type].append(\n resource_cls.factory(\n name=name,\n settings=self.settings.get(resource_type, {})[name],\n **extra\n )\n )","def initialize_routes(api):\n api.add_resource(WatchlistsApi, '/api/watchlists')\n api.add_resource(WatchlistApi, '/api/watchlist/<id>')\n api.add_resource(RegisterUserApi, '/api/auth/register')\n api.add_resource(LoginUserApi, '/api/auth/login')\n api.add_resource(ResetPassword, '/api/auth/reset')\n api.add_resource(ResetFogottenPassword, '/api/auth/reset/password')\n api.add_resource(ForgotPassword, '/api/auth/forgot')\n api.add_resource(ForgotPasswordReset, '/reset/password/<token>')\n api.add_resource(Home, '/')\n api.add_resource(Logout, '/logout')\n api.add_resource(Dashboard, '/dashboard')\n api.add_resource(DashboardSearch, '/dashboard/search')\n api.add_resource(SearchMovies, '/search/movies/<title>')\n api.add_resource(SearchMovieDetails, '/search/movie/details/<id>')\n api.add_resource(SearchTvShows, '/search/shows/<title>')\n api.add_resource(SearchShowDetails, '/search/show/details/<id>')\n api.add_resource(SearchTrendingMovies, '/search/trending/movies')\n api.add_resource(Recommend, '/recommend')","def process_api_declaration(self, resources, resource, context):\n pass","def update_from_resources(self, resources):\n for resource in resources:\n cmpt_name = resource['component']\n cmpt = self.get_component(cmpt_name)\n cmpt.update_from_index(resource)","async def get_resources(self, **kwargs) -> dict:\n resources = await self.request.get(self._base_path, **kwargs)\n self._sanitize_resources(resources)\n return resources","def process_resource_listing(self, resources, context):\n pass","def set_resources_and_sync(videos, channels=[], update_kwargs={}):\n set_resources(videos, 'video')\n set_resources(channels, 'channel')\n sync.update_related_models_from_cache(**update_kwargs)\n return videos","def _load_apis(self):\n cannabis_reports = __import__('cannabis_reports.apis')\n for class_name in cannabis_reports.apis.__all__:\n if not class_name.startswith('_'):\n cls = getattr(cannabis_reports.apis, class_name)\n api = AuthProxy(self.session, cls)\n setattr(self, class_name, api)\n self.__apis__[class_name] = api","def update_resources(self, request):\n request.worker.update_resources(request.message.test_id,\n request.message.resources)\n\n return SuccessReply()","def _load_api(self):\n self.app.add_url_rule('/scheduler', 'get_scheduler_info', self._apply_auth(api.get_scheduler_info))\n self.app.add_url_rule('/scheduler/jobs', 'add_job', self._apply_auth(api.add_job), methods=['POST'])\n self.app.add_url_rule('/scheduler/jobs', 'get_jobs', self._apply_auth(api.get_jobs))\n self.app.add_url_rule('/scheduler/jobs/reload_jobs', 'reload_jobs', self._apply_auth(api.reload_jobs), methods=['POST'])\n self.app.add_url_rule('/scheduler/jobs/<job_id>', 'get_job', self._apply_auth(api.get_job))\n self.app.add_url_rule('/scheduler/jobs/<job_id>', 'delete_job', self._apply_auth(api.delete_job), methods=['DELETE'])\n self.app.add_url_rule('/scheduler/jobs/<job_id>', 'update_job', self._apply_auth(api.update_job), methods=['PATCH'])\n self.app.add_url_rule('/scheduler/jobs/<id>/reschedule', 'reschedule_job', self._apply_auth(api.reschedule_job), methods=['PATCH'])\n self.app.add_url_rule('/scheduler/jobs/<id>/reschedule_once', 'reschedule_job_once', self._apply_auth(api.reschedule_job_once), methods=['PATCH'])\n self.app.add_url_rule('/scheduler/jobs/<job_id>/pause', 'pause_job', self._apply_auth(api.pause_job), methods=['POST'])\n self.app.add_url_rule('/scheduler/jobs/<job_id>/resume', 'resume_job', self._apply_auth(api.resume_job), methods=['POST'])\n self.app.add_url_rule('/scheduler/jobs/<job_id>/run', 'run_job', self._apply_auth(api.run_job), methods=['POST'])","def put_list(self, request, **kwargs):\r\n response = super(BaseCorsResource, self).put_list(request, **kwargs)\r\n return self.add_cors_headers(response, True)","def sync_resources(self, collection_name, context):\n to_be_synced = []\n dbcontext = context._plugin_context\n obj_getter = getattr(context._plugin, 'get_%s' % collection_name)\n resources = obj_getter(dbcontext)\n for resource in resources:\n try:\n urlpath = collection_name + '/' + resource['id']\n self.sendjson('get', urlpath, None)\n except requests.exceptions.HTTPError as e:\n with excutils.save_and_reraise_exception() as ctx:\n if e.response.status_code == requests.codes.not_found:\n attr_filter = self.create_object_map[collection_name]\n attr_filter(resource, context)\n to_be_synced.append(resource)\n ctx.reraise = False\n key = collection_name[:-1] if len(to_be_synced) == 1 else (\n collection_name)\n self.sendjson('post', collection_name, {key: to_be_synced})","def resource_names(self, resource_names):\n\n self._resource_names = resource_names","def create_resources(self) -> List[ResourceDescription]:\r\n return self.resources","def load_api_keys(self):\n self.api_keys = self.config.get('keys', [])","def setUp(self):\n self.client = APIClient()\n for issue in api_response_issues:\n validate_and_store_issue(issue)","def set_resource_data(self, resource, meta):","def test_permission_resource_list(self):\n for i in API_MODELS_LOOKUP + API_MODELS:\n url = reverse('api:{}_api_resource'.format(i))\n self.client.logout()\n response = self.client.get(url) # Anonymous user\n self.assertEqual(response.status_code, 200)\n self.client.login(username='normaluser', password='pass')\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n self.client.login(username='readonlyuser', password='pass')\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)","def set_defaults(self):\n for key, constraints in self.__class__.MODEL.items():\n if key not in self.resource:\n self.resource[key] = constraints[3]","def set_paths(self, specs, resources):\n self.install = 'install.xml'\n self.specs_path = path_format(specs)\n self.root = path_format(dirname(dirname(self.specs_path)) + '/')\n self.res_path = path_format(resources)\n self.resources['BASE'] = self.res_path\n self.specs['BASE'] = self.specs_path","def _get_resources():\n return {\n 'searchPageUrl': flask.url_for('search_page'),\n 'searchJsonUrl': flask.url_for('search'),\n 'userAddIconUrl': flask.url_for('static', filename='img/add-users.svg'),\n 'logoutUrl': flask.url_for('logout'),\n 'settingsUrl': flask.url_for('setup') + '#settingsDisplayTemplate',\n 'listAdminUrl': flask.url_for('admin_list'),\n 'addAdminUrl': flask.url_for('add_admin'),\n 'changeAdminPasswordUrl': flask.url_for('change_admin_password'),\n 'removeAdminUrl': flask.url_for('delete_admin'),\n 'loginUrl': flask.url_for('login'),\n 'recaptchaKey': ufo.app.config.get('RECAPTCHA_SITE_KEY', ''),\n 'setupUrl': flask.url_for('setup'),\n 'setupAdminUrl': flask.url_for('setup_admin'),\n 'setupOauthUrl': flask.url_for('setup_oauth'),\n 'download_chrome_policy': flask.url_for('download_chrome_policy'),\n 'policy_filename': 'chrome_policy.json',\n 'proxyServerAddUrl': flask.url_for('proxyserver_add'),\n 'proxyServerAddIconUrl': flask.url_for('static',\n filename='img/add-servers.svg'),\n 'proxyServerInverseAddIconUrl': flask.url_for(\n 'static', filename='img/add-servers-inverse.svg'),\n 'proxyServerListId': 'proxyList',\n 'proxyServerListUrl': flask.url_for('proxyserver_list'),\n 'listLimit': 10,\n 'proxyServerDetailsButtonId': 'serverDetailsButton',\n 'editButtonId': 'serverEditButton',\n 'proxyServerDetailsOverlayId': 'serverDetailsOverlay',\n 'proxyServerEditUrl': flask.url_for('proxyserver_edit'),\n 'proxyServerDeleteUrl': flask.url_for('proxyserver_delete'),\n 'proxyServerIconUrl': flask.url_for('static', filename='img/server.svg'),\n 'proxyServerAddButtonId': 'addServerButton',\n 'proxyServerModalId': 'serverModal',\n 'textAreaMaxRows': 10,\n 'ipInput': 'ipInput',\n 'nameInput': 'nameInput',\n 'sshPrivateKeyInput': 'sshPrivateKeyInput',\n 'hostPublicKeyInput': 'hostPublicKeyInput',\n 'getSettingsUrl': flask.url_for('get_settings'),\n 'settingsEditUrl': flask.url_for('edit_settings'),\n 'userAddUrl': flask.url_for('add_user'),\n 'userInverseAddIconUrl': flask.url_for(\n 'static', filename='img/add-users-inverse.svg'),\n 'userListId': 'userList',\n 'userListUrl': flask.url_for('user_list'),\n 'revokeToggleUrl': flask.url_for('user_toggle_revoked'),\n 'rotateKeysUrl': flask.url_for('user_get_new_key_pair'),\n 'inviteCodeUrl': flask.url_for('user_get_invite_code'),\n 'userDeleteUrl': flask.url_for('delete_user'),\n 'userDetailsButtonId': 'userDetailsButton',\n 'userDetailsOverlayId': 'userDetailsOverlay',\n 'userIconUrl': flask.url_for('static', filename='img/user.svg'),\n 'userAddButtonId': 'addUserButton',\n 'userModalId': 'userModal',\n 'groupAddTabId': 'groupAddTab',\n 'groupAddFormId': 'groupAdd',\n 'groupAddInputName': 'group_key',\n 'userAddTabId': 'userAddTab',\n 'userAddFormId': 'userAdd',\n 'userAddInputName': 'user_key',\n 'domainAddTabId': 'domainAddTab',\n 'domainAddFormId': 'domainAdd',\n 'manualAddTabId': 'manualAddTab',\n 'manualAddFormId': 'manualAdd',\n 'regexes': regex.REGEXES_AND_ERRORS_DICTIONARY,\n 'jsonPrefix': ufo.XSSI_PREFIX,\n 'maxFailedLoginsBeforeRecaptcha': ufo.MAX_FAILED_LOGINS_BEFORE_RECAPTCHA,\n 'userAddListFlipperId': 'userAddListFlipper',\n 'proxyServerAddListFlipperId': 'proxyServerAddListFlipper',\n 'userAddTabsId': 'userAddTabs',\n 'proxyServerAddFormId': 'serverAddFormHolder',\n }","def __init__(self, resources=None): # noqa: E501\n self.openapi_types = {\n 'resources': List[WorkspaceResourceEntity]\n }\n\n self.attribute_map = {\n 'resources': 'resources'\n }\n\n self._resources = resources","def getResources(self):\n\t\treturn deepcopy(self.server.resources)","def set_assets(self, assets):\n self._assets = {}\n for asset in assets:\n self.add_asset(asset)","def resources(self):\n\n return self.FIXTURE.resources_collection(self)","def pop_resources(self):\n resources = self.request_local.resources\n self.request_local.resources = {}\n # deal with aggregated resources\n if resources and \"head\" in resources:\n # This is lazy, because we otherwise run\n # into circular import issues\n if self.aggregation_config is not None:\n self._setup_aggregation_mapping()\n\n\n if self.aggregated_js_mapping:\n self._replace_resources_with_aggregates(resources,\n self.aggregated_js_mapping,\n JSLink,\n )\n if self.aggregated_css_mapping:\n self._replace_resources_with_aggregates(resources,\n self.aggregated_css_mapping,\n CSSLink,\n )\n return resources","def gen_resources(self):\n\n print \"\\t* Adding resources to compute template\"\n\n # add all the nets and subnets\n self.gen_net_resources()\n\n # add all routers\n self.gen_router_resources()\n\n # add all servers/intances\n self.gen_server_resources()","def getAllAPI():\n list_strain = StrainAPI().get_all()\n schema = StrainSchema()\n results = schema.load(list_strain, many=True)\n return results","def get_resources():\n return Response(f\"{Resource.get_all_resources()}\", 200, mimetype='text/plain')","def __init__(self):\n super().__init__()\n self._api_url = API_BASE_URL\n self._api_params = API_BASE_PARAMS.copy()","def __init__(self, api_key, api_name):\n self.api_key = api_key\n self.api_name = api_name\n self.raven_client = get_raven_client()\n self.stats_client = get_stats_client()","def set_global_sqla_resources(sqla_resources):\n md = sqla_resources.get_metadata()\n #_bind_tables_to_metadata(md, nest_project)\n GLOBAL_SQLA_RESOURCES = sqla_resources\n return","def ResourceList(self):\n url = AddToUrl(self, 'https://api.spiget.org/v2/resources?')\n return ApiSearch(url)","def get_resources(self):\n return []","def setup_nltk_resources(resource_urls):\r\n\r\n if not list == type(resource_urls):\r\n raise ValueError(\"resource_urls must contain a list.\")\r\n\r\n for res in resource_urls:\r\n download(res)","def resources(self) -> Generator[Tuple[str, str, Dict[str, Any]], None, None]:\n for path, methods in self.data[\"paths\"].items():\n for method in methods:\n if method.lower() not in HTTP_VERBS:\n continue\n\n yield method, path, methods","def resources(self):\n return self._resources","def resources(self):\n return self._resources","def resources(self):\n return self._resources","def permission_resources(self, permission_resources):\n\n self._permission_resources = permission_resources","def permission_resources(self, permission_resources):\n\n self._permission_resources = permission_resources","def set_up():\n ResourcesManager().set_up()\n LocatorUtil().load_locators()","def get_resources(self):\n res = set()\n res.update(self.get_inputs())\n res.update(self.get_outputs())\n return res","def before_all(context):\n set_services(context)\n context.api = {}\n context.repositories = {}\n\n for service, location in context.services.items():\n url = urlparse.urlparse(location)\n api = API(url.scheme + '://' + url.netloc, async=False)\n try:\n context.api[service] = getattr(api, url.path.split('/')[2])\n except:\n context.api[service] = getattr(api, service)","def __init__(self, starting_api: API, controllers: List[Bot]):\n self._startingApi = starting_api\n self._controllers = controllers\n for controller in self._controllers:\n controller.gameState = self._startingApi.copy().game\n controller.getReady()\n self._results = self._initDict()\n self._eventsToSend = self._initDict()","def __init__(__self__, *,\n api_id: pulumi.Input[str],\n api_service: pulumi.Input[str],\n location: pulumi.Input[str],\n properties: pulumi.Input['APIMgmtAPISpecPropertiesArgs'],\n resource_group: pulumi.Input[str]):\n pulumi.set(__self__, \"api_id\", api_id)\n pulumi.set(__self__, \"api_service\", api_service)\n pulumi.set(__self__, \"location\", location)\n pulumi.set(__self__, \"properties\", properties)\n pulumi.set(__self__, \"resource_group\", resource_group)","def get_resources(self, request, **resources):\r\n\r\n if self.parent:\r\n resources = self.parent.get_resources(request, **resources)\r\n\r\n pks = (\r\n resources.get(self._meta.name) or\r\n request.REQUEST.getlist(self._meta.name) or\r\n getattr(request, 'data', None) and request.data.get(\r\n self._meta.name))\r\n\r\n if not pks or self._meta.queryset is None:\r\n return resources\r\n\r\n pks = as_tuple(pks)\r\n\r\n try:\r\n if len(pks) == 1:\r\n resources[self._meta.name] = self._meta.queryset.get(pk=pks[0])\r\n\r\n else:\r\n resources[self._meta.name] = self._meta.queryset.filter(\r\n pk__in=pks)\r\n\r\n except (ObjectDoesNotExist, ValueError, AssertionError):\r\n raise HttpError(\"Resource not found.\",\r\n status=status.HTTP_404_NOT_FOUND)\r\n\r\n except MultipleObjectsReturned:\r\n raise HttpError(\"Resources conflict.\",\r\n status=status.HTTP_409_CONFLICT)\r\n\r\n return resources","def get_apis(self, MaxResults: str = None, NextToken: str = None) -> Dict:\n pass","def setUp(self):\n\n self.client = APIClient()","def setUp(self):\n\n self.client = APIClient()","def setUp(self):\n\n self.client = APIClient()","def __init__(self,\n *,\n resources: List['InvitedUser'] = None) -> None:\n self.resources = resources","def __init__(self):\n self.uris = AsyncSet('uris')\n self.sockets = AsyncSet('sockets')","def __init__(self, api_groups=None, attribute_restrictions=None, non_resource_ur_ls=None, resource_names=None, resources=None, verbs=None):\n self.swagger_types = {\n 'api_groups': 'list[str]',\n 'attribute_restrictions': 'RuntimeRawExtension',\n 'non_resource_ur_ls': 'list[str]',\n 'resource_names': 'list[str]',\n 'resources': 'list[str]',\n 'verbs': 'list[str]'\n }\n\n self.attribute_map = {\n 'api_groups': 'apiGroups',\n 'attribute_restrictions': 'attributeRestrictions',\n 'non_resource_ur_ls': 'nonResourceURLs',\n 'resource_names': 'resourceNames',\n 'resources': 'resources',\n 'verbs': 'verbs'\n }\n\n self._api_groups = api_groups\n self._attribute_restrictions = attribute_restrictions\n self._non_resource_ur_ls = non_resource_ur_ls\n self._resource_names = resource_names\n self._resources = resources\n self._verbs = verbs","def test_permission_resource_detail(self):\n for i in API_MODELS_LOOKUP:\n url = reverse('api:{}_api_resource'.format(i))\n self.client.login(username='normaluser', password='pass')\n response = self.client.get(url)\n res_list = response.json()\n if res_list: # Object(s) exist.\n obj_id = res_list[0]['id']\n url = reverse('api:{}_api_resource'.format(i), kwargs={'pk': obj_id})\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n self.client.logout()\n self.client.login(username='readonlyuser', password='pass')\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n self.client.logout()\n response = self.client.get(url) # Anonymous user\n self.assertEqual(response.status_code, 200)\n # The API response is a bit different for these models.\n # TODO: test filtering and pagination.\n for i in API_MODELS:\n url = reverse('api:{}_api_resource'.format(i))\n self.client.login(username='normaluser', password='pass')\n response = self.client.get(url)\n res_list = response.json()\n if res_list: # Object(s) exist.\n obj_id = res_list['objects'][0]['id']\n url = reverse('api:{}_api_resource'.format(i), kwargs={'pk': obj_id})\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n self.client.logout()\n self.client.login(username='readonlyuser', password='pass')\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n self.client.logout()\n response = self.client.get(url) # Anonymous user\n self.assertEqual(response.status_code, 200)","def __init__(self, api_manager):\n self.conf_doc = api_manager.all_conf","def update_resources(urls: List[str] = None, force: bool = False, email: str = None):\n\n if urls is None:\n urls = []\n\n results = {}\n\n # Load provided url if available\n if urls:\n for url in urls:\n try:\n results[url] = load_resource(resource_url=url, force=force)\n except Exception as e:\n results[url][\"state\"] = \"Failed\"\n results[url][\"messages\"].append(f\"ERROR: Problem loading resource: {url} {str(e)}\")\n logger.exception(f\"Problem loading resource: {url} {str(e)}\")\n\n # Load using Resource URLs from bel resource metadata\n else:\n\n for resource in arangodb.resources_metadata_coll:\n if \"resource_download_url\" not in resource:\n logger.info(\"Continuing\")\n continue\n logger.info(f\"Resource {resource}\")\n url = resource[\"resource_download_url\"]\n try:\n results[url] = load_resource(resource_url=url, force=force)\n except Exception as e:\n results[url][\"state\"] = \"Failed\"\n results[url][\"messages\"].append(f\"ERROR: Problem loading resource: {url} {str(e)}\")\n logger.exception(f\"Problem loading resource: {url} {str(e)}\")\n\n if email is not None:\n subject = f\"BEL Resources Update for {settings.HOST_NAME}\"\n (body, body_html) = create_email_body_for_update_resources(results)\n bel.core.mail.send_simple_email(email, subject, body, body_html=body_html)\n\n logger.info(\"Finished updating BEL Resources\")\n\n return results","def __init__(self, conf_files):\n\n APIManager.__max_size_csv()\n\n self.all_conf = OrderedDict()\n self.base_url = []\n for conf_file in conf_files:\n conf = OrderedDict()\n tp = None\n conf_json = HashFormatHandler().read(conf_file)\n base_url = None\n addon = None\n for item in conf_json:\n if base_url is None:\n base_url = item[\"url\"]\n self.base_url.append(item[\"url\"])\n website = item[\"base\"]\n tp = item[\"endpoint\"]\n if \"addon\" in item:\n addon_abspath = abspath(\n dirname(conf_file) + sep + item[\"addon\"]\n )\n path.append(dirname(addon_abspath))\n addon = import_module(basename(addon_abspath))\n sparql_http_method = \"post\"\n if \"method\" in item:\n sparql_http_method = item[\"method\"].strip().lower()\n else:\n conf[APIManager.nor_api_url(item, base_url)] = item\n\n self.all_conf[base_url] = {\n \"conf\": conf,\n \"tp\": tp,\n \"conf_json\": conf_json,\n \"base_url\": base_url,\n \"website\": website,\n \"addon\": addon,\n \"sparql_http_method\": sparql_http_method,\n }","def get_resources(self, resource_data=None):\n if not resource_data and self.component:\n resource_data = self.component.get_resource_data()\n\n resources = []\n for resource in self.files:\n resource.update(resource_data)\n\n resource['storage_path'] = self.prefix + '/' + resource['name']\n relative_path = self.relative_path(data=resource)\n resource['relative_path'] = relative_path\n resource['url'] = resource['url'] + '/' + relative_path\n resources.append(resource)\n return resources","def augment(self, resources):\n return self.source.augment(resources)","def prepare_api(self):\n return None","def resource_map(self):","def setUp(self):\n self.client = APIClient()","def setUp(self):\n self.client = APIClient()","def setOptManagerResources(o): # pylint: disable=global-statement\n # pylint: disable=global-statement\n global OPT_MANAGER_RESOURCES_PGAAS\n OPT_MANAGER_RESOURCES_PGAAS = \"{}/pgaas\".format(o)","def resources(self) -> \"Resources\":\n return self._resources","def set_api_access_keys(**kwargs):\n API_BASE_PARAMS['key'] = kwargs['key']","def _update_api(self) -> None:\n LOG.debug(\"%sTrying to update RestAPI through client\", self.log_prefix)\n response_put = cast(\n Dict,\n self._api_client.put_rest_api(restApiId=self._api_physical_id, mode=\"overwrite\", body=self._swagger_body),\n )\n LOG.debug(\"%sPut RestApi Result: %s\", self.log_prefix, response_put)","def test_index_response_descriptor_variables_library_variable_set_library_variable_set_resource_spaces(self):\n pass","def get_resources():\n user_id = session[\"email\"]\n resources = fm.get_resources(user_id)\n returned_val = dict(resources=resources)\n return jsonify(returned_val)","def urls(self):\n \n from django.conf.urls.defaults import url, include\n from tastypie.utils.urls import trailing_slash\n from client.views import checkin, checkout, login, logout, register, create_anonymous, delete_anonymous\n \n pattern_list = [\n url(r\"^(?P<api_name>%s)%s$\" % (self.api_name, trailing_slash()), self.wrap_view('top_level'), name=\"api_%s_top_level\" % self.api_name),\n ]\n\n for name in sorted(self._registry.keys()):\n self._registry[name].api_name = self.api_name\n pattern_list.append((r\"^(?P<api_name>%s)/resources/\" % self.api_name, include(self._registry[name].urls)))\n\n ## then add the actions\n pattern_list.extend([\n url(r\"^%s/actions/create_anonymous/$\" % self.api_name, create_anonymous, name=\"create_anonymous\"),\n url(r\"^%s/actions/delete_anonymous/$\" % self.api_name, delete_anonymous, name=\"delete_anonymous\"),\n url(r\"^%s/actions/register/$\" % self.api_name, register, name=\"register\"),\n url(r\"^%s/actions/login/$\" % self.api_name, login, name=\"login\"),\n url(r\"^%s/actions/logout/$\" % self.api_name, logout, name=\"logout\"),\n url(r\"^%s/actions/checkin/$\" % self.api_name, checkin, name=\"checkin\"),\n url(r\"^%s/actions/checkout/$\" % self.api_name, checkout, name=\"checkout\")\n ])\n\n urlpatterns = self.prepend_urls()\n \n urlpatterns += patterns('',\n *pattern_list\n )\n return urlpatterns"],"string":"[\n \"def resources(self, resources):\\n self._resources = resources\",\n \"def resources(self, resources):\\n\\n self._resources = resources\",\n \"def resources(self, resources):\\n\\n self._resources = resources\",\n \"def resources(self, resources):\\n\\n self._resources = resources\",\n \"def resources(self, resources):\\n\\n self._resources = resources\",\n \"def resources(self, value):\\n self._resource_objects = value\",\n \"def register_resources(self, resources):\\n from tw.api import merge_resources\\n merge_resources(self.request_local.resources, resources)\",\n \"def resources(self):\",\n \"def process_resource_api(self, resources, resource, api, context):\\n pass\",\n \"def test_get_api_resources(self):\\n pass\",\n \"def set_resources():\\n global available_resources\\n global EdgenodeResources\\n recv_json = request.get_json()\\n for resourcename, value in recv_json.items():\\n available_resources[resourcename] = value\\n # TODO make this better\\n EdgenodeResources = [TaskResources(ram=int(available_resources['RAM']), cpu=int(\\n available_resources['CPU']), hdd=int(available_resources['HDD'])), available_resources['DATA']]\\n\\n print 'Available resources set to', EdgenodeResources\\n return 'Available resources set to ' + str(available_resources)\",\n \"def get_api_resources(self, **kwargs):\\n\\n all_params = []\\n all_params.append('callback')\\n\\n params = locals()\\n for key, val in iteritems(params['kwargs']):\\n if key not in all_params:\\n raise TypeError(\\n \\\"Got an unexpected keyword argument '%s'\\\"\\n \\\" to method get_api_resources\\\" % key\\n )\\n params[key] = val\\n del params['kwargs']\\n\\n\\n resource_path = '/oapi/v1'.replace('{format}', 'json')\\n path_params = {}\\n\\n query_params = {}\\n\\n header_params = {}\\n\\n form_params = []\\n local_var_files = {}\\n\\n body_params = None\\n\\n # HTTP header `Accept`\\n header_params['Accept'] = self.api_client.\\\\\\n select_header_accept(['application/json', 'application/yaml'])\\n if not header_params['Accept']:\\n del header_params['Accept']\\n\\n # HTTP header `Content-Type`\\n header_params['Content-Type'] = self.api_client.\\\\\\n select_header_content_type(['application/json', 'application/yaml'])\\n\\n # Authentication setting\\n auth_settings = []\\n\\n response = self.api_client.call_api(resource_path, 'GET',\\n path_params,\\n query_params,\\n header_params,\\n body=body_params,\\n post_params=form_params,\\n files=local_var_files,\\n response_type=None,\\n auth_settings=auth_settings,\\n callback=params.get('callback'))\\n return response\",\n \"def resources(self, resources):\\n if resources is None:\\n raise ValueError(\\\"Invalid value for `resources`, must not be `None`\\\")\\n\\n self._resources = resources\",\n \"def process_resource_listing_api(self, resources, listing_api, context):\\n pass\",\n \"def register_resources(self, resources):\\n for resource in resources:\\n self.register_resource(resource)\",\n \"def resource_ids(self, resource_ids):\\n\\n self._resource_ids = resource_ids\",\n \"def initialize_api(app, api):\\n api.init_app(app=app) # Initialize api first\\n _resources = getattr(app, \\\"api_registry\\\", None)\\n if _resources and isinstance(_resources, (list, tuple,)):\\n for cls, args, kwargs in _resources:\\n api.add_resource(cls, *args, **kwargs)\",\n \"def register_resources(self):\\n raise NotImplementedError\",\n \"def _setup_api_properties(self):\\n self.implicit_api_logical_id = GeneratedLogicalId.implicit_http_api()\\n self.implicit_api_condition = \\\"ServerlessHttpApiCondition\\\"\\n self.api_event_type = \\\"HttpApi\\\"\\n self.api_type = SamResourceType.HttpApi.value\\n self.api_id_property = \\\"ApiId\\\"\\n self.editor = OpenApiEditor\",\n \"def rest_api_config(self):\\n with self.resource_lock:\\n pass\",\n \"def __init__(__self__, *,\\n api_groups: Optional[Sequence[str]] = None,\\n api_versions: Optional[Sequence[str]] = None,\\n operations: Optional[Sequence[str]] = None,\\n resource_names: Optional[Sequence[str]] = None,\\n resources: Optional[Sequence[str]] = None,\\n scope: Optional[str] = None):\\n if api_groups is not None:\\n pulumi.set(__self__, \\\"api_groups\\\", api_groups)\\n if api_versions is not None:\\n pulumi.set(__self__, \\\"api_versions\\\", api_versions)\\n if operations is not None:\\n pulumi.set(__self__, \\\"operations\\\", operations)\\n if resource_names is not None:\\n pulumi.set(__self__, \\\"resource_names\\\", resource_names)\\n if resources is not None:\\n pulumi.set(__self__, \\\"resources\\\", resources)\\n if scope is not None:\\n pulumi.set(__self__, \\\"scope\\\", scope)\",\n \"def __init__(__self__, *,\\n api_groups: Optional[Sequence[str]] = None,\\n api_versions: Optional[Sequence[str]] = None,\\n operations: Optional[Sequence[str]] = None,\\n resource_names: Optional[Sequence[str]] = None,\\n resources: Optional[Sequence[str]] = None,\\n scope: Optional[str] = None):\\n if api_groups is not None:\\n pulumi.set(__self__, \\\"api_groups\\\", api_groups)\\n if api_versions is not None:\\n pulumi.set(__self__, \\\"api_versions\\\", api_versions)\\n if operations is not None:\\n pulumi.set(__self__, \\\"operations\\\", operations)\\n if resource_names is not None:\\n pulumi.set(__self__, \\\"resource_names\\\", resource_names)\\n if resources is not None:\\n pulumi.set(__self__, \\\"resources\\\", resources)\\n if scope is not None:\\n pulumi.set(__self__, \\\"scope\\\", scope)\",\n \"def resources(self):\\n return [self]\",\n \"def __init__(self, resources):\\r\\n self._resources = Queue()\\r\\n for resource in resources:\\r\\n self._resources.put(resource)\",\n \"def __init__(self, resources={}):\\n super().__init__()\\n self._lock = threading.Lock()\\n self._resources = {}\\n for key, resource in resources.items():\\n self.register(key, resource)\",\n \"def __init__(__self__, *,\\n api_groups: Optional[Sequence[str]] = None,\\n api_versions: Optional[Sequence[str]] = None,\\n operations: Optional[Sequence[str]] = None,\\n resources: Optional[Sequence[str]] = None,\\n scope: Optional[str] = None):\\n if api_groups is not None:\\n pulumi.set(__self__, \\\"api_groups\\\", api_groups)\\n if api_versions is not None:\\n pulumi.set(__self__, \\\"api_versions\\\", api_versions)\\n if operations is not None:\\n pulumi.set(__self__, \\\"operations\\\", operations)\\n if resources is not None:\\n pulumi.set(__self__, \\\"resources\\\", resources)\\n if scope is not None:\\n pulumi.set(__self__, \\\"scope\\\", scope)\",\n \"def __init__(__self__, *,\\n api_groups: Optional[Sequence[str]] = None,\\n api_versions: Optional[Sequence[str]] = None,\\n operations: Optional[Sequence[str]] = None,\\n resources: Optional[Sequence[str]] = None,\\n scope: Optional[str] = None):\\n if api_groups is not None:\\n pulumi.set(__self__, \\\"api_groups\\\", api_groups)\\n if api_versions is not None:\\n pulumi.set(__self__, \\\"api_versions\\\", api_versions)\\n if operations is not None:\\n pulumi.set(__self__, \\\"operations\\\", operations)\\n if resources is not None:\\n pulumi.set(__self__, \\\"resources\\\", resources)\\n if scope is not None:\\n pulumi.set(__self__, \\\"scope\\\", scope)\",\n \"def update_resources(self, resources=all, **extra_args):\\n if resources is all:\\n resources = self.resources.values()\\n for lrms in self.resources.itervalues():\\n try:\\n if not lrms.enabled:\\n continue\\n # auto_enable_auth = extra_args.get(\\n # 'auto_enable_auth', self.auto_enable_auth)\\n lrms.get_resource_status()\\n lrms.updated = True\\n except gc3libs.exceptions.UnrecoverableError as err:\\n # disable resource -- there's no point in\\n # trying it again at a later stage\\n lrms.enabled = False\\n lrms.updated = False\\n gc3libs.log.error(\\n \\\"Unrecoverable error updating status\\\"\\n \\\" of resource '%s': %s.\\\"\\n \\\" Disabling resource.\\\",\\n lrms.name, err)\\n gc3libs.log.warning(\\n \\\"Resource %s will be ignored from now on.\\\",\\n lrms.name)\\n gc3libs.log.debug(\\n \\\"Got error '%s' in updating resource '%s';\\\"\\n \\\" printing full traceback.\\\",\\n err.__class__.__name__, lrms.name,\\n exc_info=True)\\n # pylint: disable=broad-except\\n except Exception as err:\\n gc3libs.log.error(\\n \\\"Ignoring error updating resource '%s': %s.\\\",\\n lrms.name, err)\\n gc3libs.log.debug(\\n \\\"Got error '%s' in updating resource '%s';\\\"\\n \\\" printing full traceback.\\\",\\n err.__class__.__name__, lrms.name,\\n exc_info=True)\\n lrms.updated = False\",\n \"def resources(self):\\n return self.__resources\",\n \"def init_rest(app_):\\n\\n rest_api = Api(app_)\\n rest_api.add_resource(views.rest_resources.AppListResource,\\n ActiveConfig.REST_URL_APPS_LIST,\\n ActiveConfig.REST_URL_APPS_LIST + '/')\\n rest_api.add_resource(views.rest_resources.AppResource,\\n ActiveConfig.REST_URL_APPS_ITEM,\\n ActiveConfig.REST_URL_APPS,\\n ActiveConfig.REST_URL_APPS + '/')\",\n \"def _load_resources(self):\\n puts = (getattr(self, 'project', None) or self).puts\\n for resource_type, resource_cls in six.iteritems(AVAILABLE_RESOURCES):\\n for name in self.settings.get(resource_type, {}):\\n extra = {\\n 'project': getattr(self, 'project', None) or self,\\n 'app': self if hasattr(self, 'project') else None,\\n }\\n\\n with indent(4 if hasattr(self, 'project') else 2):\\n puts(colored.green(u\\\"✓ {}:{}\\\".format(resource_type, name)))\\n\\n self._resources[resource_type].append(\\n resource_cls.factory(\\n name=name,\\n settings=self.settings.get(resource_type, {})[name],\\n **extra\\n )\\n )\",\n \"def initialize_routes(api):\\n api.add_resource(WatchlistsApi, '/api/watchlists')\\n api.add_resource(WatchlistApi, '/api/watchlist/<id>')\\n api.add_resource(RegisterUserApi, '/api/auth/register')\\n api.add_resource(LoginUserApi, '/api/auth/login')\\n api.add_resource(ResetPassword, '/api/auth/reset')\\n api.add_resource(ResetFogottenPassword, '/api/auth/reset/password')\\n api.add_resource(ForgotPassword, '/api/auth/forgot')\\n api.add_resource(ForgotPasswordReset, '/reset/password/<token>')\\n api.add_resource(Home, '/')\\n api.add_resource(Logout, '/logout')\\n api.add_resource(Dashboard, '/dashboard')\\n api.add_resource(DashboardSearch, '/dashboard/search')\\n api.add_resource(SearchMovies, '/search/movies/<title>')\\n api.add_resource(SearchMovieDetails, '/search/movie/details/<id>')\\n api.add_resource(SearchTvShows, '/search/shows/<title>')\\n api.add_resource(SearchShowDetails, '/search/show/details/<id>')\\n api.add_resource(SearchTrendingMovies, '/search/trending/movies')\\n api.add_resource(Recommend, '/recommend')\",\n \"def process_api_declaration(self, resources, resource, context):\\n pass\",\n \"def update_from_resources(self, resources):\\n for resource in resources:\\n cmpt_name = resource['component']\\n cmpt = self.get_component(cmpt_name)\\n cmpt.update_from_index(resource)\",\n \"async def get_resources(self, **kwargs) -> dict:\\n resources = await self.request.get(self._base_path, **kwargs)\\n self._sanitize_resources(resources)\\n return resources\",\n \"def process_resource_listing(self, resources, context):\\n pass\",\n \"def set_resources_and_sync(videos, channels=[], update_kwargs={}):\\n set_resources(videos, 'video')\\n set_resources(channels, 'channel')\\n sync.update_related_models_from_cache(**update_kwargs)\\n return videos\",\n \"def _load_apis(self):\\n cannabis_reports = __import__('cannabis_reports.apis')\\n for class_name in cannabis_reports.apis.__all__:\\n if not class_name.startswith('_'):\\n cls = getattr(cannabis_reports.apis, class_name)\\n api = AuthProxy(self.session, cls)\\n setattr(self, class_name, api)\\n self.__apis__[class_name] = api\",\n \"def update_resources(self, request):\\n request.worker.update_resources(request.message.test_id,\\n request.message.resources)\\n\\n return SuccessReply()\",\n \"def _load_api(self):\\n self.app.add_url_rule('/scheduler', 'get_scheduler_info', self._apply_auth(api.get_scheduler_info))\\n self.app.add_url_rule('/scheduler/jobs', 'add_job', self._apply_auth(api.add_job), methods=['POST'])\\n self.app.add_url_rule('/scheduler/jobs', 'get_jobs', self._apply_auth(api.get_jobs))\\n self.app.add_url_rule('/scheduler/jobs/reload_jobs', 'reload_jobs', self._apply_auth(api.reload_jobs), methods=['POST'])\\n self.app.add_url_rule('/scheduler/jobs/<job_id>', 'get_job', self._apply_auth(api.get_job))\\n self.app.add_url_rule('/scheduler/jobs/<job_id>', 'delete_job', self._apply_auth(api.delete_job), methods=['DELETE'])\\n self.app.add_url_rule('/scheduler/jobs/<job_id>', 'update_job', self._apply_auth(api.update_job), methods=['PATCH'])\\n self.app.add_url_rule('/scheduler/jobs/<id>/reschedule', 'reschedule_job', self._apply_auth(api.reschedule_job), methods=['PATCH'])\\n self.app.add_url_rule('/scheduler/jobs/<id>/reschedule_once', 'reschedule_job_once', self._apply_auth(api.reschedule_job_once), methods=['PATCH'])\\n self.app.add_url_rule('/scheduler/jobs/<job_id>/pause', 'pause_job', self._apply_auth(api.pause_job), methods=['POST'])\\n self.app.add_url_rule('/scheduler/jobs/<job_id>/resume', 'resume_job', self._apply_auth(api.resume_job), methods=['POST'])\\n self.app.add_url_rule('/scheduler/jobs/<job_id>/run', 'run_job', self._apply_auth(api.run_job), methods=['POST'])\",\n \"def put_list(self, request, **kwargs):\\r\\n response = super(BaseCorsResource, self).put_list(request, **kwargs)\\r\\n return self.add_cors_headers(response, True)\",\n \"def sync_resources(self, collection_name, context):\\n to_be_synced = []\\n dbcontext = context._plugin_context\\n obj_getter = getattr(context._plugin, 'get_%s' % collection_name)\\n resources = obj_getter(dbcontext)\\n for resource in resources:\\n try:\\n urlpath = collection_name + '/' + resource['id']\\n self.sendjson('get', urlpath, None)\\n except requests.exceptions.HTTPError as e:\\n with excutils.save_and_reraise_exception() as ctx:\\n if e.response.status_code == requests.codes.not_found:\\n attr_filter = self.create_object_map[collection_name]\\n attr_filter(resource, context)\\n to_be_synced.append(resource)\\n ctx.reraise = False\\n key = collection_name[:-1] if len(to_be_synced) == 1 else (\\n collection_name)\\n self.sendjson('post', collection_name, {key: to_be_synced})\",\n \"def resource_names(self, resource_names):\\n\\n self._resource_names = resource_names\",\n \"def create_resources(self) -> List[ResourceDescription]:\\r\\n return self.resources\",\n \"def load_api_keys(self):\\n self.api_keys = self.config.get('keys', [])\",\n \"def setUp(self):\\n self.client = APIClient()\\n for issue in api_response_issues:\\n validate_and_store_issue(issue)\",\n \"def set_resource_data(self, resource, meta):\",\n \"def test_permission_resource_list(self):\\n for i in API_MODELS_LOOKUP + API_MODELS:\\n url = reverse('api:{}_api_resource'.format(i))\\n self.client.logout()\\n response = self.client.get(url) # Anonymous user\\n self.assertEqual(response.status_code, 200)\\n self.client.login(username='normaluser', password='pass')\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, 200)\\n self.client.login(username='readonlyuser', password='pass')\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, 200)\",\n \"def set_defaults(self):\\n for key, constraints in self.__class__.MODEL.items():\\n if key not in self.resource:\\n self.resource[key] = constraints[3]\",\n \"def set_paths(self, specs, resources):\\n self.install = 'install.xml'\\n self.specs_path = path_format(specs)\\n self.root = path_format(dirname(dirname(self.specs_path)) + '/')\\n self.res_path = path_format(resources)\\n self.resources['BASE'] = self.res_path\\n self.specs['BASE'] = self.specs_path\",\n \"def _get_resources():\\n return {\\n 'searchPageUrl': flask.url_for('search_page'),\\n 'searchJsonUrl': flask.url_for('search'),\\n 'userAddIconUrl': flask.url_for('static', filename='img/add-users.svg'),\\n 'logoutUrl': flask.url_for('logout'),\\n 'settingsUrl': flask.url_for('setup') + '#settingsDisplayTemplate',\\n 'listAdminUrl': flask.url_for('admin_list'),\\n 'addAdminUrl': flask.url_for('add_admin'),\\n 'changeAdminPasswordUrl': flask.url_for('change_admin_password'),\\n 'removeAdminUrl': flask.url_for('delete_admin'),\\n 'loginUrl': flask.url_for('login'),\\n 'recaptchaKey': ufo.app.config.get('RECAPTCHA_SITE_KEY', ''),\\n 'setupUrl': flask.url_for('setup'),\\n 'setupAdminUrl': flask.url_for('setup_admin'),\\n 'setupOauthUrl': flask.url_for('setup_oauth'),\\n 'download_chrome_policy': flask.url_for('download_chrome_policy'),\\n 'policy_filename': 'chrome_policy.json',\\n 'proxyServerAddUrl': flask.url_for('proxyserver_add'),\\n 'proxyServerAddIconUrl': flask.url_for('static',\\n filename='img/add-servers.svg'),\\n 'proxyServerInverseAddIconUrl': flask.url_for(\\n 'static', filename='img/add-servers-inverse.svg'),\\n 'proxyServerListId': 'proxyList',\\n 'proxyServerListUrl': flask.url_for('proxyserver_list'),\\n 'listLimit': 10,\\n 'proxyServerDetailsButtonId': 'serverDetailsButton',\\n 'editButtonId': 'serverEditButton',\\n 'proxyServerDetailsOverlayId': 'serverDetailsOverlay',\\n 'proxyServerEditUrl': flask.url_for('proxyserver_edit'),\\n 'proxyServerDeleteUrl': flask.url_for('proxyserver_delete'),\\n 'proxyServerIconUrl': flask.url_for('static', filename='img/server.svg'),\\n 'proxyServerAddButtonId': 'addServerButton',\\n 'proxyServerModalId': 'serverModal',\\n 'textAreaMaxRows': 10,\\n 'ipInput': 'ipInput',\\n 'nameInput': 'nameInput',\\n 'sshPrivateKeyInput': 'sshPrivateKeyInput',\\n 'hostPublicKeyInput': 'hostPublicKeyInput',\\n 'getSettingsUrl': flask.url_for('get_settings'),\\n 'settingsEditUrl': flask.url_for('edit_settings'),\\n 'userAddUrl': flask.url_for('add_user'),\\n 'userInverseAddIconUrl': flask.url_for(\\n 'static', filename='img/add-users-inverse.svg'),\\n 'userListId': 'userList',\\n 'userListUrl': flask.url_for('user_list'),\\n 'revokeToggleUrl': flask.url_for('user_toggle_revoked'),\\n 'rotateKeysUrl': flask.url_for('user_get_new_key_pair'),\\n 'inviteCodeUrl': flask.url_for('user_get_invite_code'),\\n 'userDeleteUrl': flask.url_for('delete_user'),\\n 'userDetailsButtonId': 'userDetailsButton',\\n 'userDetailsOverlayId': 'userDetailsOverlay',\\n 'userIconUrl': flask.url_for('static', filename='img/user.svg'),\\n 'userAddButtonId': 'addUserButton',\\n 'userModalId': 'userModal',\\n 'groupAddTabId': 'groupAddTab',\\n 'groupAddFormId': 'groupAdd',\\n 'groupAddInputName': 'group_key',\\n 'userAddTabId': 'userAddTab',\\n 'userAddFormId': 'userAdd',\\n 'userAddInputName': 'user_key',\\n 'domainAddTabId': 'domainAddTab',\\n 'domainAddFormId': 'domainAdd',\\n 'manualAddTabId': 'manualAddTab',\\n 'manualAddFormId': 'manualAdd',\\n 'regexes': regex.REGEXES_AND_ERRORS_DICTIONARY,\\n 'jsonPrefix': ufo.XSSI_PREFIX,\\n 'maxFailedLoginsBeforeRecaptcha': ufo.MAX_FAILED_LOGINS_BEFORE_RECAPTCHA,\\n 'userAddListFlipperId': 'userAddListFlipper',\\n 'proxyServerAddListFlipperId': 'proxyServerAddListFlipper',\\n 'userAddTabsId': 'userAddTabs',\\n 'proxyServerAddFormId': 'serverAddFormHolder',\\n }\",\n \"def __init__(self, resources=None): # noqa: E501\\n self.openapi_types = {\\n 'resources': List[WorkspaceResourceEntity]\\n }\\n\\n self.attribute_map = {\\n 'resources': 'resources'\\n }\\n\\n self._resources = resources\",\n \"def getResources(self):\\n\\t\\treturn deepcopy(self.server.resources)\",\n \"def set_assets(self, assets):\\n self._assets = {}\\n for asset in assets:\\n self.add_asset(asset)\",\n \"def resources(self):\\n\\n return self.FIXTURE.resources_collection(self)\",\n \"def pop_resources(self):\\n resources = self.request_local.resources\\n self.request_local.resources = {}\\n # deal with aggregated resources\\n if resources and \\\"head\\\" in resources:\\n # This is lazy, because we otherwise run\\n # into circular import issues\\n if self.aggregation_config is not None:\\n self._setup_aggregation_mapping()\\n\\n\\n if self.aggregated_js_mapping:\\n self._replace_resources_with_aggregates(resources,\\n self.aggregated_js_mapping,\\n JSLink,\\n )\\n if self.aggregated_css_mapping:\\n self._replace_resources_with_aggregates(resources,\\n self.aggregated_css_mapping,\\n CSSLink,\\n )\\n return resources\",\n \"def gen_resources(self):\\n\\n print \\\"\\\\t* Adding resources to compute template\\\"\\n\\n # add all the nets and subnets\\n self.gen_net_resources()\\n\\n # add all routers\\n self.gen_router_resources()\\n\\n # add all servers/intances\\n self.gen_server_resources()\",\n \"def getAllAPI():\\n list_strain = StrainAPI().get_all()\\n schema = StrainSchema()\\n results = schema.load(list_strain, many=True)\\n return results\",\n \"def get_resources():\\n return Response(f\\\"{Resource.get_all_resources()}\\\", 200, mimetype='text/plain')\",\n \"def __init__(self):\\n super().__init__()\\n self._api_url = API_BASE_URL\\n self._api_params = API_BASE_PARAMS.copy()\",\n \"def __init__(self, api_key, api_name):\\n self.api_key = api_key\\n self.api_name = api_name\\n self.raven_client = get_raven_client()\\n self.stats_client = get_stats_client()\",\n \"def set_global_sqla_resources(sqla_resources):\\n md = sqla_resources.get_metadata()\\n #_bind_tables_to_metadata(md, nest_project)\\n GLOBAL_SQLA_RESOURCES = sqla_resources\\n return\",\n \"def ResourceList(self):\\n url = AddToUrl(self, 'https://api.spiget.org/v2/resources?')\\n return ApiSearch(url)\",\n \"def get_resources(self):\\n return []\",\n \"def setup_nltk_resources(resource_urls):\\r\\n\\r\\n if not list == type(resource_urls):\\r\\n raise ValueError(\\\"resource_urls must contain a list.\\\")\\r\\n\\r\\n for res in resource_urls:\\r\\n download(res)\",\n \"def resources(self) -> Generator[Tuple[str, str, Dict[str, Any]], None, None]:\\n for path, methods in self.data[\\\"paths\\\"].items():\\n for method in methods:\\n if method.lower() not in HTTP_VERBS:\\n continue\\n\\n yield method, path, methods\",\n \"def resources(self):\\n return self._resources\",\n \"def resources(self):\\n return self._resources\",\n \"def resources(self):\\n return self._resources\",\n \"def permission_resources(self, permission_resources):\\n\\n self._permission_resources = permission_resources\",\n \"def permission_resources(self, permission_resources):\\n\\n self._permission_resources = permission_resources\",\n \"def set_up():\\n ResourcesManager().set_up()\\n LocatorUtil().load_locators()\",\n \"def get_resources(self):\\n res = set()\\n res.update(self.get_inputs())\\n res.update(self.get_outputs())\\n return res\",\n \"def before_all(context):\\n set_services(context)\\n context.api = {}\\n context.repositories = {}\\n\\n for service, location in context.services.items():\\n url = urlparse.urlparse(location)\\n api = API(url.scheme + '://' + url.netloc, async=False)\\n try:\\n context.api[service] = getattr(api, url.path.split('/')[2])\\n except:\\n context.api[service] = getattr(api, service)\",\n \"def __init__(self, starting_api: API, controllers: List[Bot]):\\n self._startingApi = starting_api\\n self._controllers = controllers\\n for controller in self._controllers:\\n controller.gameState = self._startingApi.copy().game\\n controller.getReady()\\n self._results = self._initDict()\\n self._eventsToSend = self._initDict()\",\n \"def __init__(__self__, *,\\n api_id: pulumi.Input[str],\\n api_service: pulumi.Input[str],\\n location: pulumi.Input[str],\\n properties: pulumi.Input['APIMgmtAPISpecPropertiesArgs'],\\n resource_group: pulumi.Input[str]):\\n pulumi.set(__self__, \\\"api_id\\\", api_id)\\n pulumi.set(__self__, \\\"api_service\\\", api_service)\\n pulumi.set(__self__, \\\"location\\\", location)\\n pulumi.set(__self__, \\\"properties\\\", properties)\\n pulumi.set(__self__, \\\"resource_group\\\", resource_group)\",\n \"def get_resources(self, request, **resources):\\r\\n\\r\\n if self.parent:\\r\\n resources = self.parent.get_resources(request, **resources)\\r\\n\\r\\n pks = (\\r\\n resources.get(self._meta.name) or\\r\\n request.REQUEST.getlist(self._meta.name) or\\r\\n getattr(request, 'data', None) and request.data.get(\\r\\n self._meta.name))\\r\\n\\r\\n if not pks or self._meta.queryset is None:\\r\\n return resources\\r\\n\\r\\n pks = as_tuple(pks)\\r\\n\\r\\n try:\\r\\n if len(pks) == 1:\\r\\n resources[self._meta.name] = self._meta.queryset.get(pk=pks[0])\\r\\n\\r\\n else:\\r\\n resources[self._meta.name] = self._meta.queryset.filter(\\r\\n pk__in=pks)\\r\\n\\r\\n except (ObjectDoesNotExist, ValueError, AssertionError):\\r\\n raise HttpError(\\\"Resource not found.\\\",\\r\\n status=status.HTTP_404_NOT_FOUND)\\r\\n\\r\\n except MultipleObjectsReturned:\\r\\n raise HttpError(\\\"Resources conflict.\\\",\\r\\n status=status.HTTP_409_CONFLICT)\\r\\n\\r\\n return resources\",\n \"def get_apis(self, MaxResults: str = None, NextToken: str = None) -> Dict:\\n pass\",\n \"def setUp(self):\\n\\n self.client = APIClient()\",\n \"def setUp(self):\\n\\n self.client = APIClient()\",\n \"def setUp(self):\\n\\n self.client = APIClient()\",\n \"def __init__(self,\\n *,\\n resources: List['InvitedUser'] = None) -> None:\\n self.resources = resources\",\n \"def __init__(self):\\n self.uris = AsyncSet('uris')\\n self.sockets = AsyncSet('sockets')\",\n \"def __init__(self, api_groups=None, attribute_restrictions=None, non_resource_ur_ls=None, resource_names=None, resources=None, verbs=None):\\n self.swagger_types = {\\n 'api_groups': 'list[str]',\\n 'attribute_restrictions': 'RuntimeRawExtension',\\n 'non_resource_ur_ls': 'list[str]',\\n 'resource_names': 'list[str]',\\n 'resources': 'list[str]',\\n 'verbs': 'list[str]'\\n }\\n\\n self.attribute_map = {\\n 'api_groups': 'apiGroups',\\n 'attribute_restrictions': 'attributeRestrictions',\\n 'non_resource_ur_ls': 'nonResourceURLs',\\n 'resource_names': 'resourceNames',\\n 'resources': 'resources',\\n 'verbs': 'verbs'\\n }\\n\\n self._api_groups = api_groups\\n self._attribute_restrictions = attribute_restrictions\\n self._non_resource_ur_ls = non_resource_ur_ls\\n self._resource_names = resource_names\\n self._resources = resources\\n self._verbs = verbs\",\n \"def test_permission_resource_detail(self):\\n for i in API_MODELS_LOOKUP:\\n url = reverse('api:{}_api_resource'.format(i))\\n self.client.login(username='normaluser', password='pass')\\n response = self.client.get(url)\\n res_list = response.json()\\n if res_list: # Object(s) exist.\\n obj_id = res_list[0]['id']\\n url = reverse('api:{}_api_resource'.format(i), kwargs={'pk': obj_id})\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, 200)\\n self.client.logout()\\n self.client.login(username='readonlyuser', password='pass')\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, 200)\\n self.client.logout()\\n response = self.client.get(url) # Anonymous user\\n self.assertEqual(response.status_code, 200)\\n # The API response is a bit different for these models.\\n # TODO: test filtering and pagination.\\n for i in API_MODELS:\\n url = reverse('api:{}_api_resource'.format(i))\\n self.client.login(username='normaluser', password='pass')\\n response = self.client.get(url)\\n res_list = response.json()\\n if res_list: # Object(s) exist.\\n obj_id = res_list['objects'][0]['id']\\n url = reverse('api:{}_api_resource'.format(i), kwargs={'pk': obj_id})\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, 200)\\n self.client.logout()\\n self.client.login(username='readonlyuser', password='pass')\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, 200)\\n self.client.logout()\\n response = self.client.get(url) # Anonymous user\\n self.assertEqual(response.status_code, 200)\",\n \"def __init__(self, api_manager):\\n self.conf_doc = api_manager.all_conf\",\n \"def update_resources(urls: List[str] = None, force: bool = False, email: str = None):\\n\\n if urls is None:\\n urls = []\\n\\n results = {}\\n\\n # Load provided url if available\\n if urls:\\n for url in urls:\\n try:\\n results[url] = load_resource(resource_url=url, force=force)\\n except Exception as e:\\n results[url][\\\"state\\\"] = \\\"Failed\\\"\\n results[url][\\\"messages\\\"].append(f\\\"ERROR: Problem loading resource: {url} {str(e)}\\\")\\n logger.exception(f\\\"Problem loading resource: {url} {str(e)}\\\")\\n\\n # Load using Resource URLs from bel resource metadata\\n else:\\n\\n for resource in arangodb.resources_metadata_coll:\\n if \\\"resource_download_url\\\" not in resource:\\n logger.info(\\\"Continuing\\\")\\n continue\\n logger.info(f\\\"Resource {resource}\\\")\\n url = resource[\\\"resource_download_url\\\"]\\n try:\\n results[url] = load_resource(resource_url=url, force=force)\\n except Exception as e:\\n results[url][\\\"state\\\"] = \\\"Failed\\\"\\n results[url][\\\"messages\\\"].append(f\\\"ERROR: Problem loading resource: {url} {str(e)}\\\")\\n logger.exception(f\\\"Problem loading resource: {url} {str(e)}\\\")\\n\\n if email is not None:\\n subject = f\\\"BEL Resources Update for {settings.HOST_NAME}\\\"\\n (body, body_html) = create_email_body_for_update_resources(results)\\n bel.core.mail.send_simple_email(email, subject, body, body_html=body_html)\\n\\n logger.info(\\\"Finished updating BEL Resources\\\")\\n\\n return results\",\n \"def __init__(self, conf_files):\\n\\n APIManager.__max_size_csv()\\n\\n self.all_conf = OrderedDict()\\n self.base_url = []\\n for conf_file in conf_files:\\n conf = OrderedDict()\\n tp = None\\n conf_json = HashFormatHandler().read(conf_file)\\n base_url = None\\n addon = None\\n for item in conf_json:\\n if base_url is None:\\n base_url = item[\\\"url\\\"]\\n self.base_url.append(item[\\\"url\\\"])\\n website = item[\\\"base\\\"]\\n tp = item[\\\"endpoint\\\"]\\n if \\\"addon\\\" in item:\\n addon_abspath = abspath(\\n dirname(conf_file) + sep + item[\\\"addon\\\"]\\n )\\n path.append(dirname(addon_abspath))\\n addon = import_module(basename(addon_abspath))\\n sparql_http_method = \\\"post\\\"\\n if \\\"method\\\" in item:\\n sparql_http_method = item[\\\"method\\\"].strip().lower()\\n else:\\n conf[APIManager.nor_api_url(item, base_url)] = item\\n\\n self.all_conf[base_url] = {\\n \\\"conf\\\": conf,\\n \\\"tp\\\": tp,\\n \\\"conf_json\\\": conf_json,\\n \\\"base_url\\\": base_url,\\n \\\"website\\\": website,\\n \\\"addon\\\": addon,\\n \\\"sparql_http_method\\\": sparql_http_method,\\n }\",\n \"def get_resources(self, resource_data=None):\\n if not resource_data and self.component:\\n resource_data = self.component.get_resource_data()\\n\\n resources = []\\n for resource in self.files:\\n resource.update(resource_data)\\n\\n resource['storage_path'] = self.prefix + '/' + resource['name']\\n relative_path = self.relative_path(data=resource)\\n resource['relative_path'] = relative_path\\n resource['url'] = resource['url'] + '/' + relative_path\\n resources.append(resource)\\n return resources\",\n \"def augment(self, resources):\\n return self.source.augment(resources)\",\n \"def prepare_api(self):\\n return None\",\n \"def resource_map(self):\",\n \"def setUp(self):\\n self.client = APIClient()\",\n \"def setUp(self):\\n self.client = APIClient()\",\n \"def setOptManagerResources(o): # pylint: disable=global-statement\\n # pylint: disable=global-statement\\n global OPT_MANAGER_RESOURCES_PGAAS\\n OPT_MANAGER_RESOURCES_PGAAS = \\\"{}/pgaas\\\".format(o)\",\n \"def resources(self) -> \\\"Resources\\\":\\n return self._resources\",\n \"def set_api_access_keys(**kwargs):\\n API_BASE_PARAMS['key'] = kwargs['key']\",\n \"def _update_api(self) -> None:\\n LOG.debug(\\\"%sTrying to update RestAPI through client\\\", self.log_prefix)\\n response_put = cast(\\n Dict,\\n self._api_client.put_rest_api(restApiId=self._api_physical_id, mode=\\\"overwrite\\\", body=self._swagger_body),\\n )\\n LOG.debug(\\\"%sPut RestApi Result: %s\\\", self.log_prefix, response_put)\",\n \"def test_index_response_descriptor_variables_library_variable_set_library_variable_set_resource_spaces(self):\\n pass\",\n \"def get_resources():\\n user_id = session[\\\"email\\\"]\\n resources = fm.get_resources(user_id)\\n returned_val = dict(resources=resources)\\n return jsonify(returned_val)\",\n \"def urls(self):\\n \\n from django.conf.urls.defaults import url, include\\n from tastypie.utils.urls import trailing_slash\\n from client.views import checkin, checkout, login, logout, register, create_anonymous, delete_anonymous\\n \\n pattern_list = [\\n url(r\\\"^(?P<api_name>%s)%s$\\\" % (self.api_name, trailing_slash()), self.wrap_view('top_level'), name=\\\"api_%s_top_level\\\" % self.api_name),\\n ]\\n\\n for name in sorted(self._registry.keys()):\\n self._registry[name].api_name = self.api_name\\n pattern_list.append((r\\\"^(?P<api_name>%s)/resources/\\\" % self.api_name, include(self._registry[name].urls)))\\n\\n ## then add the actions\\n pattern_list.extend([\\n url(r\\\"^%s/actions/create_anonymous/$\\\" % self.api_name, create_anonymous, name=\\\"create_anonymous\\\"),\\n url(r\\\"^%s/actions/delete_anonymous/$\\\" % self.api_name, delete_anonymous, name=\\\"delete_anonymous\\\"),\\n url(r\\\"^%s/actions/register/$\\\" % self.api_name, register, name=\\\"register\\\"),\\n url(r\\\"^%s/actions/login/$\\\" % self.api_name, login, name=\\\"login\\\"),\\n url(r\\\"^%s/actions/logout/$\\\" % self.api_name, logout, name=\\\"logout\\\"),\\n url(r\\\"^%s/actions/checkin/$\\\" % self.api_name, checkin, name=\\\"checkin\\\"),\\n url(r\\\"^%s/actions/checkout/$\\\" % self.api_name, checkout, name=\\\"checkout\\\")\\n ])\\n\\n urlpatterns = self.prepend_urls()\\n \\n urlpatterns += patterns('',\\n *pattern_list\\n )\\n return urlpatterns\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.74864537","0.742781","0.742781","0.742781","0.742781","0.69810915","0.6825962","0.6772192","0.66917735","0.66321164","0.64988405","0.64539236","0.6449976","0.6399484","0.63794327","0.6372078","0.63311076","0.62079686","0.6152585","0.6030097","0.6025588","0.6025588","0.59984666","0.5982669","0.59788185","0.59731394","0.59731394","0.5908156","0.5876307","0.5857608","0.58391416","0.5811761","0.57520676","0.5731664","0.5691214","0.56653273","0.56520635","0.5640066","0.56293637","0.5627029","0.55769813","0.55417186","0.5538937","0.55318725","0.552913","0.5523464","0.5518425","0.5509003","0.55019426","0.54934335","0.54860836","0.54668057","0.5461066","0.54388624","0.54368305","0.54349566","0.543206","0.54276973","0.5423331","0.5422084","0.5421754","0.5413826","0.54134464","0.54099005","0.54039395","0.54027987","0.5400421","0.5400421","0.5400421","0.5397058","0.5397058","0.5387135","0.53866524","0.5383655","0.5381689","0.5369642","0.5360441","0.5352284","0.534542","0.534542","0.534542","0.53446454","0.5342142","0.53310865","0.532573","0.5315227","0.53141356","0.5299439","0.5298957","0.52918637","0.5287465","0.5287055","0.52850854","0.52850854","0.52779484","0.5273078","0.52729475","0.52632","0.52621883","0.52606505","0.5255394"],"string":"[\n \"0.74864537\",\n \"0.742781\",\n \"0.742781\",\n \"0.742781\",\n \"0.742781\",\n \"0.69810915\",\n \"0.6825962\",\n \"0.6772192\",\n \"0.66917735\",\n \"0.66321164\",\n \"0.64988405\",\n \"0.64539236\",\n \"0.6449976\",\n \"0.6399484\",\n \"0.63794327\",\n \"0.6372078\",\n \"0.63311076\",\n \"0.62079686\",\n \"0.6152585\",\n \"0.6030097\",\n \"0.6025588\",\n \"0.6025588\",\n \"0.59984666\",\n \"0.5982669\",\n \"0.59788185\",\n \"0.59731394\",\n \"0.59731394\",\n \"0.5908156\",\n \"0.5876307\",\n \"0.5857608\",\n \"0.58391416\",\n \"0.5811761\",\n \"0.57520676\",\n \"0.5731664\",\n \"0.5691214\",\n \"0.56653273\",\n \"0.56520635\",\n \"0.5640066\",\n \"0.56293637\",\n \"0.5627029\",\n \"0.55769813\",\n \"0.55417186\",\n \"0.5538937\",\n \"0.55318725\",\n \"0.552913\",\n \"0.5523464\",\n \"0.5518425\",\n \"0.5509003\",\n \"0.55019426\",\n \"0.54934335\",\n \"0.54860836\",\n \"0.54668057\",\n \"0.5461066\",\n \"0.54388624\",\n \"0.54368305\",\n \"0.54349566\",\n \"0.543206\",\n \"0.54276973\",\n \"0.5423331\",\n \"0.5422084\",\n \"0.5421754\",\n \"0.5413826\",\n \"0.54134464\",\n \"0.54099005\",\n \"0.54039395\",\n \"0.54027987\",\n \"0.5400421\",\n \"0.5400421\",\n \"0.5400421\",\n \"0.5397058\",\n \"0.5397058\",\n \"0.5387135\",\n \"0.53866524\",\n \"0.5383655\",\n \"0.5381689\",\n \"0.5369642\",\n \"0.5360441\",\n \"0.5352284\",\n \"0.534542\",\n \"0.534542\",\n \"0.534542\",\n \"0.53446454\",\n \"0.5342142\",\n \"0.53310865\",\n \"0.532573\",\n \"0.5315227\",\n \"0.53141356\",\n \"0.5299439\",\n \"0.5298957\",\n \"0.52918637\",\n \"0.5287465\",\n \"0.5287055\",\n \"0.52850854\",\n \"0.52850854\",\n \"0.52779484\",\n \"0.5273078\",\n \"0.52729475\",\n \"0.52632\",\n \"0.52621883\",\n \"0.52606505\",\n \"0.5255394\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":20074,"cells":{"query":{"kind":"string","value":"Start the server if it is not running"},"document":{"kind":"string","value":"def start(self):\n if not self._running:\n try:\n resource = self._default_access_control.secure_resource(self._api)\n site = Site(resource=resource)\n self._listener = reactor.listenTCP(self._port, # pylint: disable=no-member\n site,\n interface=self._interface)\n self._running = True\n\n except CannotListenError as ex:\n return fail(failure.Failure(ex))\n # return failure.Failure(ex)\n\n return succeed(True)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def start_server(self):\n if not self._server:","def start():\n\n start_server()","def local_webserver_start():\n if not _is_webserver_running():\n local(_webserver_command())","def run():\n server = current_server()\n server._auto_stop = True\n return start()","def run():\r\n log.debug('Starter::run()')\r\n try:\r\n # check specified port\r\n if not conf.port:\r\n raise Exception(\"Please specify port number! (use --port)\")\r\n Server(conf.port).run()\r\n except Exception as E:\r\n log.critical(E)","def start_server(self):\n app.run(host=str(self.__constants.host),\n port=int(self.__constants.port),\n debug=bool(self.__constants.runindebug))","def start(self):\n self.serve_forever()","def start(self):\n self.serve_forever()","def start_server_if_needed(self):\n # Number of seconds required for owserver to start up.\n SECONDS_FOR_STARTUP = 4.0\n \n if not owserver_running():\n\n if len(self.known_port):\n # has been started before using port self.known_port \n if port_has_1wire_adapter(self.known_port):\n subprocess.run([\"/usr/bin/owserver\", \"-d\", self.known_port])\n time.sleep(SECONDS_FOR_STARTUP) # give server time to start\n return True\n else:\n return False\n \n else:\n # Find the port that has the adapter. The adapter uses an FTDI\n # USB-to-Serial chip, so only search those ports.\n for p_path in base_reader.Reader.available_ftdi_ports:\n if port_has_1wire_adapter(p_path):\n # Save the port in case we need to restart the server later.\n self.known_port = p_path\n \n # Remove this port from the Master list so other readers\n # don't try to use it.\n base_reader.Reader.available_ftdi_ports.remove(p_path)\n \n subprocess.run([\"/usr/bin/owserver\", \"-d\", p_path])\n time.sleep(SECONDS_FOR_STARTUP) # give server time to start\n return True\n\n return False\n\n else:\n return True","def webserver_start():\n run(_webserver_command())","def start(self):\n logging.info(\"checking for port conflicts...\")\n while self.port_is_open(self.port):\n try:\n logging.info(\"attempting to shut down an existing service \"\n \"listening on %s...\", self.port)\n self.send_shutdown_request()\n except requests.exceptions.ConnectionError as ex:\n logging.info(\"error shutting down previous service: %s\", ex)\n time.sleep(2)\n server_address = ('', self.port)\n self.httpd = http.server.HTTPServer(server_address, ChromeInfoServiceHandler)\n logging.info(\"starting http server on %s\", server_address)\n self.httpd.serve_forever()","def start_server(self, server_id):\n status, data, errors, messages = self._make_post_request(MCAPIRoutes.START, extra_params={'id': server_id})\n \n if status == 200:\n return True\n elif status == 500:\n self._check_errors(errors, messages)","def start(self):\n threading.Thread(target=self.serve_forever).start()","def start_server(self):\n if self.esp_mgr.ap:\n self.server_socket = adafruit_esp32spi_socket.socket()\n self.esp_mgr.esp.start_server(23, self.server_socket.socknum)","def _server_started(self):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n try:\n s.connect((self.host, self.port))\n s.shutdown(2)\n return True\n except:\n return False","def run_server(self, _):\n if not ENABLE_SERVER:\n logger.info('server not enabled, exit')\n return\n app.run(host=API_HOST, port=API_PORT, threaded=API_THREADED)","def start(self):\n # check for running server\n if self.running():\n return False\n\n # check for creation in the meantime\n file_name = os.path.join(self.cache_dir_, MGR_PID_FILE)\n if os.path.exists(file_name):\n return\n\n # launch child process\n f = open(file_name, 'w')\n self.server_pid_ = os.fork()\n if self.server_pid_ > 0: # parent process\n # create pid file\n f.write('%d\\n' %(self.server_pid_))\n else:\n time.sleep(MGR_SLEEP_TIME)\n if not self.running():\n logging.error('Server not started. PID file did not exist')\n raise ValueError()\n self.pid_ = self.server_pid_\n logging.info('Server started with pid %d' %(self.pid_))\n self.run()","def start(self):\n run(self.app, host=self.host, port=self.port, server=AsyncServer,\n quiet=True, debug=False)","def run(self):\n self._server = self._get_server()\n self._server.serve_forever()","def run(self):\n self.__server.serve_forever()","def __run_server(self):\n os.chdir(os.path.dirname(self.server_path))\n self.server_process = subprocess.Popen([self.server_path, \\\n \"{}:{}\".format(self.args.ip, self.args.port)])","def start(cfg: 'Settings', server: str):\n\n try:\n server_path = find_server(cfg.parent_directory, server)\n except (ParentDirMissing, ServerNotFound, NoInvocation) as e:\n e.log_this()\n return\n\n if isUp(server):\n log.info(f'{server} appears to be running already!')\n else:\n invocation = get_invocation(server_path)\n os.chdir(server_path)\n log.info(f'Starting {server}')\n run(['screen', '-h', '5000', '-dmS', server, *invocation, 'nogui'])\n sleep(5)\n if isUp(server):\n log.info(f'{server} is now running!')\n # run_startup_commands(server)\n else:\n log.warning(f'{server} does not appear to have started!')","def server_init(self):\n if not self.web_interface_thread.isAlive():\n # spawn the web interface.\n self.web_interface_thread.start()","def server(port, wsgi_app):\n try:\n httpd = wsgiref.simple_server.make_server(self._host, port, wsgi_app)\n except socket.error:\n # Try IPv6\n httpd = wsgiref.simple_server.make_server(\n self._host, port, wsgi_app, server_class=WsgiServerIpv6)\n started.set()\n httpd.timeout = 30\n while not stopping.is_set():\n httpd.handle_request()\n stopped.set()","def server_init(self):\n if not self._web_interface_thread.isAlive():\n # spawn the web interface.\n self._web_interface_thread.start()","def start(self):\n retries=0\n while retries<=self.RETRIES and self._running==False:\n try:\n print(\"Trying to start server\")\n self._s.bind((self.address, self._listenToPort))\n #self._s.listen(5) #not required for UDP\n self._running = True\n print(\"Running\")\n\n except Exception as e:\n #self.stop()\n if retries<self.RETRIES:\n print(\"starting server failed, retrying...\",e)\n sleep(1)\n else:\n print(\"Server Failed \",e)\n return False\n retries=retries+1\n\n\n self._listen=True\n self._t1.start() #_listenForDataThread\n return True","def start(self) -> None:\n pass # for pydocstyle\n\n def serve() -> None:\n \"\"\"Serve forever.\"\"\"\n prefix = f\"In {ThreadedServer.__name__}.{serve.__name__}\"\n try:\n print(\n f\"{prefix}: Starting to serve {self.scenarios_dir} forever on: \"\n f\"http://localhost:{self.port}\",\n file=self.stdout,\n )\n\n self._httpd.serve_forever()\n\n print(f\"{prefix}: Stopped serving forever.\", file=self.stdout)\n\n except Exception as error:\n print(\n f\"{prefix}: Caught an exception in the HTTPD server \"\n f\"(it will be raised at shutdown): {error}\",\n file=self.stderr,\n )\n\n with self._server_exception_lock:\n self._server_exception = error\n\n self._work_thread = threading.Thread(target=serve)\n self._work_thread.start()","def _ServerIsRunning( self ):\n return utils.ProcessIsRunning( self._gocode_handle )","def start(self):\n self._isAlive = True\n super(ThreadedServer, self).start()\n logger.debug(\"Threaded Server has been started.\")","def start(self):\n self.listener.listen(self.backlog)\n h, p = self.listener.getsockname()\n self.logger.info(\"server started on %s:%s\", h, p)\n self.active = True\n if self.auto_register:\n t = THG.Thread(target = self._bg_register)\n t.setDaemon(True)\n t.start()\n #if sys.platform == \"win32\":\n # hack so we can receive Ctrl+C on windows\n self.listener.settimeout(0.5)\n try:\n try:\n while True:\n self.accept()\n except EOFError:\n pass # server closed by another thread\n except SystemExit:\n self.logger.warn(\"System exit\")\n except KeyboardInterrupt:\n self.logger.warn(\"keyboard interrupt!\")\n finally:\n self.logger.info(\"server has terminated\")\n self.close()","async def start(self):\n server = await asyncio.start_server(\n self.handle_request, self.host, self.port)\n\n addr = server.sockets[0].getsockname()\n print(f'Serving on {addr}')\n\n async with server:\n await server.serve_forever()","def start(self):\n if self._callable:\n self._is_running = True\n self._run_client()","def start():\n server = current_server()\n logger.info('Starting Flexx event loop.')\n server.start()","def startServer(self):\n processor = ThinService.Processor(self.serverLogic)\n serverSocket = TSocket.TServerSocket(Constants.SERVER_HOST, Constants.SERVER_PORT)\n transportFactory = TTransport.TBufferedTransportFactory()\n protocolFactory = TBinaryProtocol.TBinaryProtocolFactory()\n\n server = TServer.TSimpleServer(processor, serverSocket, transportFactory, protocolFactory)\n server.serve()","def start(self):\n if self._params.arq:\n s = udpsocket.UDPSocket()\n else:\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n try:\n s.bind(('', self._params.port))\n s.listen(5)\n self.check_and_print_debug_message(\n 'HTTP Server is running on port: ' + str(self._params.port))\n while True:\n c, addr = s.accept()\n Thread(\n target=self.process_http_request,\n args=(c, addr)\n ).start()\n except Exception as e:\n self.check_and_print_debug_message(str(e))\n s.close()\n finally:\n self.check_and_print_debug_message(\n \"HTTP Server connection is closed.\")\n s.close()","def start_server():\n server.bind(constants.ADDRESS)\n server.listen()\n print(\"Server listening on: \" + constants.HOST + \" on port \" + str(constants.PORT) + \"...\")","def startup(req=None):\n global started\n if not started:\n started = True\n cherrypy.server.start(init_only=True, server_class=None)\n return 0 # apache.OK","def startservers():\n try:\n dns = subprocess.Popen(['python', FAKE_LOC, '-c', DNS_LOC])\n except IOError:\n sys.exit('>> Unable to locate FakeDns')\n\n try:\n httpd = MyTCPServer(('', 80), MyHandler)\n except socket.error:\n dns.kill()\n sys.exit('>> Port 80 already in use')\n try:\n print '>> Starting HTTP Server...'\n httpd.serve_forever()\n except KeyboardInterrupt:\n httpd.shutdown()\n httpd.server_close()\n dns.kill()\n sys.exit()","def run():\n\n # Construct a server.\n server = wsgiref.simple_server.make_server(\n _config[ 'address' ],\n _config[ 'port' ],\n application\n )\n\n # Run the server.\n server.serve_forever()\n\n # Return result.\n return 0","def MaybeStartDebugServer():\n port = os.environ.get('CROS_DEBUG_SERVER_PORT')\n if port:\n return StartDebugServer(port=int(port))\n return None","def _start(self, host):\n pass","def __start_server(self):\n self.server = Server(self.browser_mob)\n self.server.start()\n self.proxy = self.server.create_proxy()","def start() -> None:\n from app import app\n app.run(debug = True, host = HOST, port = PORT)","def start(self, wait_for_stop=False):\n\n self.ua_server.start()\n if wait_for_stop:\n self.wait_for_stop()\n self.stop()","def start(self):\n assert(self._cbs is not None)\n self._as.start() # start the server","def on_server_start(self):\n raise NotImplementedError","def start_server(self, server, script):\n self.connector.sendline(script)\n expect = \"88.Exit\"\n i = self.connector.expect([expect, TIMEOUT], timeout=2)\n if i == 0:\n info_log(\"Start server[%s] success!\",server)\n return True\n else:\n warn_log(\"Start server[%s] failed!\",server)\n return False","def start(self, host: str = '127.0.0.1', port: int = 6543) -> None:\n\n def _go(handler):\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n\n start_server = websockets.serve(handler, host, port)\n\n asyncio.get_event_loop().run_until_complete(start_server)\n asyncio.get_event_loop().run_forever()\n\n if not self.running:\n # test if other instance is already running\n with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as test_sock:\n status = test_sock.connect_ex((host, port))\n\n # check for both Windows and Linux status codes\n if status in {10061, 111}: # nothing running\n server_thread = threading.Thread(\n target=_go, args=(ProxyServer._ws_handler,))\n server_thread.start()\n\n logger.info(f'Proxy server running on ws://{host}:{port}/')\n self.running = True\n else:\n logger.warning(\n f'Connection test to {host}:{port} returned status {status}, '\n 'proxy server not started')","def start():\n app.run()","def start(self, modulename=None):\n if modulename:\n # Unhook httpserver so cherrypy.server.start() creates a new\n # one (with config from setup_server, if declared).\n cherrypy.server.httpserver = None\n\n cherrypy.engine.start()\n\n self.sync_apps()","def start_server(self):\n LOGGER.info('Starting test plugin server...')\n test_plugin_service_pb2_grpc.add_TestPluginServiceServicer_to_server(\n self.servicer, self.server)\n self.server.add_insecure_port(PLUGIN_SERVICE_ADDRESS)\n self.server.start()\n LOGGER.info('Test plugin server is running!')\n\n if self.device_proxy:\n self.device_proxy.start()\n LOGGER.info('Test plugin proxy server is running!')","def do_start(self, line):\n\n if not line:\n line = \"cortex\"\n\n # First, check that the name isn't already taken\n clients = self.registry.get_clients()\n if clients.has_key(line):\n print \"A server already exists with that name (%s)\" % line\n return False\n\n subprocess.Popen([\"python\", \"cortex.py\", line])\n # Wait for the system to init\n time.sleep(1)\n print \"Started server, connecting...\"\n return self.do_connect(line)","def _start_server(apps, port, no_browser):\n # necessary for the dashboard to work when called from a notebook\n asyncio.set_event_loop(asyncio.new_event_loop())\n\n # this is adapted from bokeh.subcommands.serve\n with report_server_init_errors(port=port):\n server = Server(apps, port=port)\n\n # On a remote server, we do not want to start the dashboard here.\n if not no_browser:\n\n def show_callback():\n server.show(\"/\")\n\n server.io_loop.add_callback(show_callback)\n\n address_string = server.address if server.address else \"localhost\"\n\n print( # noqa: T201\n \"Bokeh app running at:\",\n f\"http://{address_string}:{server.port}{server.prefix}/\",\n )\n server._loop.start()\n server.start()","def start(self):\r\n for srv in self._servers:\r\n srv.start()","def start(self):\n\n self.keep_running = True # Set running flag to true\n self.th = threading.Thread(target=self.listenSocket)\n self.th.daemon = True # Thread will terminate with the main\n self.th.start()\n self.th.join(0)","def start(self, reload_from=None):\n assert not self._process, \"server instance already started\"\n pid = Value(\"i\")\n self._process = Process(target=self._start,\n args=(pid, socket_queue),\n kwargs={\"reload_from\": reload_from})\n self._process.start()\n pid.value = self._process.pid","def start(self):\n #url = '{}://{}:{}/'.format('http',\n # self.ip,\n # self.port)\n #self.service_info = ServiceInfo(\n # '_webthing._sub._http._tcp.local.',\n # '{}._http._tcp.local.'.format(self.name),\n # address=socket.inet_aton(self.ip),\n # port=self.port,\n # properties={\n # 'url': url,\n # },\n # server='{}.local.'.format(socket.gethostname()))\n #self.zeroconf = Zeroconf()\n #self.zeroconf.register_service(self.service_info)\n\n # If WebSocketS used and NOT running in thread, and WebServer IS\n # running in thread make shure WebServer has enough stack size to\n # handle also the WebSocket requests.\n log.info('Starting Web Server')\n self.server.Start(threaded=srv_run_in_thread, stackSize=8192)","def _start_server_process():\n from gevent import monkey\n monkey.patch_all()\n\n from tellapart.frontend import gevent_profiler\n from tellapart.frontend import util\n\n # In this example, profile 100% of requests.\n # In a production server, you'd typically profile far fewer.\n\n if USE_PYWSGI:\n profiler = gevent_profiler.Profiler(\n request_profiling_pct=1.0,\n request_info_class=gevent_profiler.PyWsgiServerRequestInfo)\n\n util.launch_gevent_wsgi_server(_do_stuff, 8088, 16, 'example server',\n use_pywsgi=True)\n else:\n profiler = gevent_profiler.Profiler(request_profiling_pct=1.0)\n util.launch_gevent_wsgi_server(_do_stuff, 8088, 16, 'example server')","def started(self):\n return False","def start(self):\n if self.is_alive:\n self.logger.warning('Already started!')\n return\n self._create_tunnels()\n if not self.is_active:\n self._raise(BaseSSHTunnelForwarderError,\n reason='Could not establish session to SSH gateway')\n for _srv in self._server_list:\n thread = threading.Thread(\n target=self._serve_forever_wrapper,\n args=(_srv, ),\n name='Srv-{0}'.format(address_to_str(_srv.local_port))\n )\n thread.daemon = self.daemon_forward_servers\n thread.start()\n self._check_tunnel(_srv)\n self.is_alive = any(self.tunnel_is_up.values())\n if not self.is_alive:\n self._raise(HandlerSSHTunnelForwarderError,\n 'An error occurred while opening tunnels.')","def start_server():\n if not os.path.exists(cf.get(\"Selenium\", \"server_path\")):\n jar_name = \"selenium-server-standalone-2.25.0.jar\"\n server_jar = os.path.join(tempfile.gettempdir(), jar_name)\n if not os.path.exists(server_jar):\n r = requests.get(\"http://selenium.googlecode.com/files/%s\" % jar_name)\n jar_on_disk = open(server_jar, \"wb\")\n jar_on_disk.write(r.content)\n jar_on_disk.close()\n else:\n server_jar = cf.get(\"Selenium\", \"server_path\")\n \n s = subprocess.Popen(['java', '-jar', server_jar], \n stdout=tempfile.TemporaryFile(), \n stderr=tempfile.TemporaryFile()).pid\n pidfile = open(pid_file_path, \"w\")\n pidfile.write(str(s))\n pidfile.close()\n\n # make sure the server is actually up\n server_up = False\n waiting = 0\n while server_up == False and waiting < 60:\n try:\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((\"localhost\", 4444))\n s.close()\n server_up = True\n except socket.error:\n time.sleep(1)\n waiting = waiting + 1\n server_up = False\n\n return server_up","def start_server(self):\n self.logger.info(\"Starting WebSocket server on port %d\" % self.port)\n http_server = Thread(target=tornado.ioloop.IOLoop.instance().start)\n http_server.start()","def start_server(self):\n server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))\n\n # The two services we added in the proto. You can find these functions in\n # jellybeanrobot_pb2_grpc.py.\n jellybeanrobot_pb2_grpc.add_JellyServicer_to_server(Robot(), server)\n\n # Start listening on a port.\n server.add_insecure_port(\"localhost:%d\" % self.port)\n print \"Listening on localhost:%d!\\n\" % self.port\n server.start()\n\n try:\n while True:\n time.sleep(3600) # one hour. \n except KeyboardInterrupt:\n server.stop(0)","def start(self,force_restart=0,timeout=60):\n if not force_restart and self.is_running():\n return\n # start the worker processes.\n for worker in self.workers:\n worker.start_server()\n\n if not self.is_running():\n print ' Starting Servers'\n print ' |----|----|----15---|----|----30---|----|----45---' \\\n '|----|----60'\n print '0.',\n stop_watch = timer()\n stop_watch.start()\n minute = 0\n import sys\n while not self.is_running():\n if stop_watch.current_lap() > 1:\n sys.stdout.write('.')\n stop_watch.mark_lap()\n elapsed = stop_watch.elapsed()\n if (elapsed - minute * 60) > 60:\n minute = minute + 1\n print\n print minute,\n if elapsed > timeout:\n raise TimeoutError\n print 'servers running!'","def ensure_running_service_daemon(context):\n svc = Management()\n if svc._check_if_its_up(usutil.UBERSERVER):\n context.temp_service_daemon = None\n else:\n svc.start()\n context.temp_service_daemon = svc","def start_server(self):\n if not self.config_store:\n raise Exception(\"Error owamp: the configuration of the server has not been done\\n\")\n\n self.server = OwampServer(self.config_store)\n try:\n self.server.launch_owampd()\n except Exception as err:\n template = \"Error owamp: Exception of type {0} occurred:\\n{1!r}\"\n message = template.format(type(err).__name__, err)\n raise Exception(message)","async def _start_server(self) -> None:\n # First, figure out what address to listen on. Open a connection to\n # the Hubitat hub and see what address it used. This assumes this\n # machine and the Hubitat hub are on the same network.\n with _open_socket(socket.AF_INET, socket.SOCK_DGRAM) as s:\n s.connect((self.host, 80))\n address = s.getsockname()[0]\n\n self._server = server.create_server(\n self._process_event, address, self.port or 0, self.ssl_context\n )\n self._server.start()\n _LOGGER.debug(\n \"Listening on %s:%d with SSL %s\",\n address,\n self._server.port,\n \"disabled\" if self.ssl_context is None else \"enabled\",\n )\n\n await self.set_event_url(self.event_url)","def start(self, name=None):\n server = self.cloudman.get_server(name)['id']\n r = self.cloudman.compute.start_server(server)\n return r","def runserver():\n app.run(host=config.HOST, port=config.PORT, debug=config.DEBUG, threaded=config.THREADED)","def startserver(path):\n global urlpath\n urlpath = path\n app.run(debug=True, host='0.0.0.0', port=4444)","def start_server():\n server = WebsocketServer(9001, host='0.0.0.0')\n server.set_fn_message_received(message_received)\n server.set_fn_client_left(client_left)\n print(\"Started\")\n server.run_forever()","def startRunHook(self):\n #Verify iserver is running\n if not self.msw.isServerRunning():\n raise EnvironmentError,'Server is down'\n\n #start sdebug 3\n self.localShell.runCommand('localShelljava GetiServerConfig mymsw LoggingConfig')","def _StartServer( self ):\n with self._gocode_lock:\n _logger.info( 'Starting Gocode server' )\n\n self._gocode_port = utils.GetUnusedLocalhostPort()\n self._gocode_host = '127.0.0.1:{0}'.format( self._gocode_port )\n\n command = [ self._gocode_binary_path,\n '-s',\n '-sock', 'tcp',\n '-addr', self._gocode_host ]\n\n if _logger.isEnabledFor( logging.DEBUG ):\n command.append( '-debug' )\n\n self._gocode_stdout = utils.CreateLogfile(\n LOGFILE_FORMAT.format( port = self._gocode_port, std = 'stdout' ) )\n self._gocode_stderr = utils.CreateLogfile(\n LOGFILE_FORMAT.format( port = self._gocode_port, std = 'stderr' ) )\n\n with utils.OpenForStdHandle( self._gocode_stdout ) as stdout:\n with utils.OpenForStdHandle( self._gocode_stderr ) as stderr:\n self._gocode_handle = utils.SafePopen( command,\n stdout = stdout,\n stderr = stderr )","def startServer(serverName, serverType, serverMode, hostnameOrIpAddr = None, port = 0):\n print(\"PSHELL_INFO: STUB Server: %s Started\" % serverName)\n if (serverMode == BLOCKING):\n while (True):\n time.sleep(100000)","def _start_servers(self):\n for user, host, port in self.server_addresses:\n remoteHost = \"%s@%s\" % (user, host)\n logger.info(\"starting remote server %s:%s\", host, port)\n command = (\"cd ~/goaway;\" +\n \"find . -name '*.pyc' -delete ;\" +\n \"DEBUG=true goaway/cmdserver.py %s %s %s >> server.std.log 2>&1\" % (\n host,\n port,\n self._config.remote_path,\n ))\n logger.debug(\"Starting server:%s remoteHost with command:%s\" % (remoteHost, command))\n ## subprocess.call blocks, while subprocces.Popen doesn't block.\n sshPopen = subprocess.Popen([\"ssh\", remoteHost, command],\n shell = False, stdout= subprocess.PIPE, stderr = subprocess.PIPE)\n self._start_local_server()","def start(self):\n self.port = self.conn.evalInServer(server_code.format(key=self.key))","def run_server():\n app = init_app()\n app.run(host=app.config['HOST'], port=app.config['PORT'])","def start(self):\n try:\n try:\n logging.debug('Checking for running daemon')\n exec_cmd(['transmission-remote', '--list'])\n\n except FileNotFoundError:\n logging.error('Cannot find transmission-remote, make sure it\\'s installed')\n logging.debug('Error details', stack_info=True, exc_info=True)\n return False\n\n except subprocess.CalledProcessError:\n logging.info('Starting Transmission daemon')\n exec_cmd(['transmission-daemon'], wait_after=3)\n self._started_daemon = True\n\n self._done = False\n threading.Thread(target=self._update).start()\n return True\n\n except FileNotFoundError:\n logging.error('Cannot find transmission-daemon, make sure it\\'s installed')\n logging.debug('Error details', stack_info=True, exc_info=True)\n\n except subprocess.CalledProcessError:\n logging.error('Unable to start daemon')\n logging.debug('Error details', stack_info=True, exc_info=True)\n\n return False","async def start(self):\n try:\n # Initialize the run\n await self._initialize()\n\n # Start and manage the run\n await self._run()\n\n # Terminate the run\n await self._shutdown()\n except:\n await self._cleanup(exc=True)\n else:\n await self._cleanup()\n\n return True","def serve_forever(self):\n from socketio.server import SocketIOServer\n server = SocketIOServer((self.args.interface, int(self.args.port)),\n self.application, resource=\"socket.io\",\n policy_server=False)\n logger.info(\"Start socket.io server %r:%r\",\n self.args.interface, self.args.port)\n try:\n server.serve_forever()\n except KeyboardInterrupt:\n for socket in server.sockets.values():\n for namespace in socket.active_ns.values():\n if hasattr(namespace, 'stop_server'):\n namespace.stop_server()","def main():\n try:\n port = 8080\n ip = '0.0.0.0'\n http_server = WSGIServer((ip, port),\n app,\n log=logging,\n error_log=logging,\n )\n print(\"Server started at: {0}:{1}\".format(ip, port))\n http_server.serve_forever()\n except Exception as exc:\n logger.error(exc.message)\n logger.exception(traceback.format_exc())\n finally:\n # Do something here\n pass","def _start_server():\n args = [sys.executable] + sys.argv\n args.insert(args.index('wserver'), 'server')\n args.remove('wserver')\n pid = os.spawnv(os.P_NOWAIT, sys.executable, args)\n return pid","def start_services(self):\n ircd_result = True\n synapse_result = self.start_synapse()\n if self.charm_config.get(\"enable-ircd\"):\n ircd_result = self.start_ircd()\n return synapse_result and ircd_result","def start(self) -> None:\n try:\n self._socket.bind((self.ip, self.port))\n\n except socket.error as e:\n print(e)\n\n else:\n self._socket.listen()\n logger.info('Server is online!')\n\n run = True\n while run:\n conn_data = ConnectionData()\n self._accept_conn(conn_data)\n\n # Makes the server stoppable\n while conn_data.conn is None or conn_data.addr is None:\n try:\n time.sleep(0.1)\n except KeyboardInterrupt:\n run = False\n break\n\n conn, addr = conn_data.conn, conn_data.addr\n logger.info(f'Connection established to {addr}')\n\n if self.func is not None:\n self.func(conn, addr)","def serve(self):\n\t\timport thread\n\t\tthread.start_new_thread(self._server_thread, tuple())","def serve_forever(self):\n self._server.serve_forever()","def _start_server(codechecker_cfg, test_config, auth=False):\n def start_server_proc(event, server_cmd, checking_env):\n \"\"\"Target function for starting the CodeChecker server.\"\"\"\n proc = subprocess.Popen(server_cmd, env=checking_env)\n\n # Blocking termination until event is set.\n event.wait()\n\n # If proc is still running, stop it.\n if proc.poll() is None:\n proc.terminate()\n\n server_cmd = codechecker.serv_cmd(codechecker_cfg['workspace'],\n str(codechecker_cfg['viewer_port']),\n env.get_postgresql_cfg())\n\n server_proc = multiprocessing.Process(\n name='server',\n target=start_server_proc,\n args=(__STOP_SERVER, server_cmd, codechecker_cfg['check_env']))\n\n server_proc.start()\n\n # Wait for server to start and connect to database.\n time.sleep(20)","def start(self, start_stackless=True):\r\n self.sock_server = sock_server(self.bind_addr)\r\n self.running = True\r\n \r\n self.tasklet_class(self._accept_loop)()\r\n \r\n if start_stackless:\r\n stackless.run()","def startapp():","def run(server_class=HTTPServer, handler_class=CPUUsageHandler):\n server_address = (settings.HOST, settings.PORT)\n httpd = server_class(server_address, handler_class)\n print settings.START_MSG\n try:\n httpd.serve_forever()\n except KeyboardInterrupt:\n httpd.socket.close()\n print settings.STOP_MSG\n except Exception:\n raise","def start_tftp_server(self):\n self.install_pkgs()\n if self.shared_tftp_server:\n # perform the rest\n raise NotImplementedError\n raise NotImplementedError","def run_forever(self):\n self.app.run()","def serve(self):\n if self._server_thread is not None:\n return\n if self._port is None:\n self._port = portpicker.pick_unused_port()\n started = threading.Event()\n self._stopped = threading.Event()\n self._stopping = threading.Event()\n\n def build_server(started, stopped, stopping):\n \"\"\"Closure to build the server function to be passed to the thread.\n\n Args:\n started: Threading event to notify when started.\n stopped: Threading event to notify when stopped.\n stopping: Threading event to notify when stopping.\n Returns:\n A function that function that takes a port and WSGI app and notifies\n about its status via the threading events provided.\n \"\"\"\n\n def server(port, wsgi_app):\n \"\"\"Serve a WSGI application until stopped.\n\n Args:\n port: Port number to serve on.\n wsgi_app: WSGI application to serve.\n \"\"\"\n try:\n httpd = wsgiref.simple_server.make_server(self._host, port, wsgi_app)\n except socket.error:\n # Try IPv6\n httpd = wsgiref.simple_server.make_server(\n self._host, port, wsgi_app, server_class=WsgiServerIpv6)\n started.set()\n httpd.timeout = 30\n while not stopping.is_set():\n httpd.handle_request()\n stopped.set()\n\n return server\n\n server = build_server(started, self._stopped, self._stopping)\n server_thread = threading.Thread(\n target=server, args=(self._port, self._app))\n self._server_thread = server_thread\n\n server_thread.start()\n started.wait()","def _checkTorcsServer(self):\n isRunning = False\n if self.torcsServerProcess is not None:\n if self.torcsServerProcess.poll() is None:\n isRunning = True\n return isRunning","def start_webserver(inital_port=6543, wait_until_started=True, block=False):\n global server_process, server_process_port\n if server_process is not None and server_process.is_alive():\n return True, server_process_port\n\n queue = Queue(1)\n server_process = Process(\n target=_webserver,\n kwargs={\n 'port': inital_port,\n 'outqueue': queue,\n }\n )\n server_process.start()\n if wait_until_started:\n try:\n connected, server_process_port = queue.get(timeout=1)\n except Exception as e:\n return False, None\n atexit.register(stop_server)\n if block:\n server_process.join()\n\n return connected, server_process_port","def serve(self):\n logger.info(\"NqServer listening at port {}\".format(FLAGS.port))\n server = wsgiref.simple_server.make_server('', FLAGS.port, self.application)\n server.serve_forever()","def start(self) -> None:\n if self.bolt_app.logger.level > logging.INFO:\n print(get_boot_message())\n else:\n self.bolt_app.logger.info(get_boot_message())\n\n web.run_app(self.web_app, host=\"0.0.0.0\", port=self.port)","def start_server(self, app, **kwargs):\n\n # start server with app and pass Dash arguments\n self.server(app, **kwargs)\n\n # set the default server_url, it implicitly call wait_for_page\n self.server_url = self.server.url","def startListening(self):\n \n self.listener_thread = threading.Thread(target=self.listening, daemon=True)\n self.listener_thread.start()\n\n # stateupdate = threading.Thread(target=self.showStatus, daemon=True)\n # stateupdate.start()\n\n # Main App Loop (Keeps the Client opened)\n while self.listener_thread.is_alive():\n time.sleep(1)\n else:\n print('Shutting Main Thread-1')\n sys.exit()","def start():\n # Import any local level utilities that may be used\n # before the web-server is initialized.\n from django.core.management import call_command\n from db.models import ApplicationState\n from db.utilities import generate_models\n\n # Run the migrate command within django.\n # Making sure our models are upto date.\n call_command(command_name=\"migrate\", app=\"titandash\")\n\n # Server is being started, it is safe for us\n # to update our active flag.\n ApplicationState.objects.set(state=True)\n\n # Generate any initial models that we expect\n # to be available by default.\n generate_models()\n\n _url = EEL_DASHBOARD if User.objects.valid() else EEL_LOGIN\n\n logger.info(\"starting titandash application with options: '{options}'\".format(options={\"path\": _url, **EEL_START_OPTIONS}))\n # Start eel, providing our start url defined above, the close callback\n # to deal with cleanup functionality, and default options.\n eel.start(_url, close_callback=close_callback, **EEL_START_OPTIONS)"],"string":"[\n \"def start_server(self):\\n if not self._server:\",\n \"def start():\\n\\n start_server()\",\n \"def local_webserver_start():\\n if not _is_webserver_running():\\n local(_webserver_command())\",\n \"def run():\\n server = current_server()\\n server._auto_stop = True\\n return start()\",\n \"def run():\\r\\n log.debug('Starter::run()')\\r\\n try:\\r\\n # check specified port\\r\\n if not conf.port:\\r\\n raise Exception(\\\"Please specify port number! (use --port)\\\")\\r\\n Server(conf.port).run()\\r\\n except Exception as E:\\r\\n log.critical(E)\",\n \"def start_server(self):\\n app.run(host=str(self.__constants.host),\\n port=int(self.__constants.port),\\n debug=bool(self.__constants.runindebug))\",\n \"def start(self):\\n self.serve_forever()\",\n \"def start(self):\\n self.serve_forever()\",\n \"def start_server_if_needed(self):\\n # Number of seconds required for owserver to start up.\\n SECONDS_FOR_STARTUP = 4.0\\n \\n if not owserver_running():\\n\\n if len(self.known_port):\\n # has been started before using port self.known_port \\n if port_has_1wire_adapter(self.known_port):\\n subprocess.run([\\\"/usr/bin/owserver\\\", \\\"-d\\\", self.known_port])\\n time.sleep(SECONDS_FOR_STARTUP) # give server time to start\\n return True\\n else:\\n return False\\n \\n else:\\n # Find the port that has the adapter. The adapter uses an FTDI\\n # USB-to-Serial chip, so only search those ports.\\n for p_path in base_reader.Reader.available_ftdi_ports:\\n if port_has_1wire_adapter(p_path):\\n # Save the port in case we need to restart the server later.\\n self.known_port = p_path\\n \\n # Remove this port from the Master list so other readers\\n # don't try to use it.\\n base_reader.Reader.available_ftdi_ports.remove(p_path)\\n \\n subprocess.run([\\\"/usr/bin/owserver\\\", \\\"-d\\\", p_path])\\n time.sleep(SECONDS_FOR_STARTUP) # give server time to start\\n return True\\n\\n return False\\n\\n else:\\n return True\",\n \"def webserver_start():\\n run(_webserver_command())\",\n \"def start(self):\\n logging.info(\\\"checking for port conflicts...\\\")\\n while self.port_is_open(self.port):\\n try:\\n logging.info(\\\"attempting to shut down an existing service \\\"\\n \\\"listening on %s...\\\", self.port)\\n self.send_shutdown_request()\\n except requests.exceptions.ConnectionError as ex:\\n logging.info(\\\"error shutting down previous service: %s\\\", ex)\\n time.sleep(2)\\n server_address = ('', self.port)\\n self.httpd = http.server.HTTPServer(server_address, ChromeInfoServiceHandler)\\n logging.info(\\\"starting http server on %s\\\", server_address)\\n self.httpd.serve_forever()\",\n \"def start_server(self, server_id):\\n status, data, errors, messages = self._make_post_request(MCAPIRoutes.START, extra_params={'id': server_id})\\n \\n if status == 200:\\n return True\\n elif status == 500:\\n self._check_errors(errors, messages)\",\n \"def start(self):\\n threading.Thread(target=self.serve_forever).start()\",\n \"def start_server(self):\\n if self.esp_mgr.ap:\\n self.server_socket = adafruit_esp32spi_socket.socket()\\n self.esp_mgr.esp.start_server(23, self.server_socket.socknum)\",\n \"def _server_started(self):\\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n try:\\n s.connect((self.host, self.port))\\n s.shutdown(2)\\n return True\\n except:\\n return False\",\n \"def run_server(self, _):\\n if not ENABLE_SERVER:\\n logger.info('server not enabled, exit')\\n return\\n app.run(host=API_HOST, port=API_PORT, threaded=API_THREADED)\",\n \"def start(self):\\n # check for running server\\n if self.running():\\n return False\\n\\n # check for creation in the meantime\\n file_name = os.path.join(self.cache_dir_, MGR_PID_FILE)\\n if os.path.exists(file_name):\\n return\\n\\n # launch child process\\n f = open(file_name, 'w')\\n self.server_pid_ = os.fork()\\n if self.server_pid_ > 0: # parent process\\n # create pid file\\n f.write('%d\\\\n' %(self.server_pid_))\\n else:\\n time.sleep(MGR_SLEEP_TIME)\\n if not self.running():\\n logging.error('Server not started. PID file did not exist')\\n raise ValueError()\\n self.pid_ = self.server_pid_\\n logging.info('Server started with pid %d' %(self.pid_))\\n self.run()\",\n \"def start(self):\\n run(self.app, host=self.host, port=self.port, server=AsyncServer,\\n quiet=True, debug=False)\",\n \"def run(self):\\n self._server = self._get_server()\\n self._server.serve_forever()\",\n \"def run(self):\\n self.__server.serve_forever()\",\n \"def __run_server(self):\\n os.chdir(os.path.dirname(self.server_path))\\n self.server_process = subprocess.Popen([self.server_path, \\\\\\n \\\"{}:{}\\\".format(self.args.ip, self.args.port)])\",\n \"def start(cfg: 'Settings', server: str):\\n\\n try:\\n server_path = find_server(cfg.parent_directory, server)\\n except (ParentDirMissing, ServerNotFound, NoInvocation) as e:\\n e.log_this()\\n return\\n\\n if isUp(server):\\n log.info(f'{server} appears to be running already!')\\n else:\\n invocation = get_invocation(server_path)\\n os.chdir(server_path)\\n log.info(f'Starting {server}')\\n run(['screen', '-h', '5000', '-dmS', server, *invocation, 'nogui'])\\n sleep(5)\\n if isUp(server):\\n log.info(f'{server} is now running!')\\n # run_startup_commands(server)\\n else:\\n log.warning(f'{server} does not appear to have started!')\",\n \"def server_init(self):\\n if not self.web_interface_thread.isAlive():\\n # spawn the web interface.\\n self.web_interface_thread.start()\",\n \"def server(port, wsgi_app):\\n try:\\n httpd = wsgiref.simple_server.make_server(self._host, port, wsgi_app)\\n except socket.error:\\n # Try IPv6\\n httpd = wsgiref.simple_server.make_server(\\n self._host, port, wsgi_app, server_class=WsgiServerIpv6)\\n started.set()\\n httpd.timeout = 30\\n while not stopping.is_set():\\n httpd.handle_request()\\n stopped.set()\",\n \"def server_init(self):\\n if not self._web_interface_thread.isAlive():\\n # spawn the web interface.\\n self._web_interface_thread.start()\",\n \"def start(self):\\n retries=0\\n while retries<=self.RETRIES and self._running==False:\\n try:\\n print(\\\"Trying to start server\\\")\\n self._s.bind((self.address, self._listenToPort))\\n #self._s.listen(5) #not required for UDP\\n self._running = True\\n print(\\\"Running\\\")\\n\\n except Exception as e:\\n #self.stop()\\n if retries<self.RETRIES:\\n print(\\\"starting server failed, retrying...\\\",e)\\n sleep(1)\\n else:\\n print(\\\"Server Failed \\\",e)\\n return False\\n retries=retries+1\\n\\n\\n self._listen=True\\n self._t1.start() #_listenForDataThread\\n return True\",\n \"def start(self) -> None:\\n pass # for pydocstyle\\n\\n def serve() -> None:\\n \\\"\\\"\\\"Serve forever.\\\"\\\"\\\"\\n prefix = f\\\"In {ThreadedServer.__name__}.{serve.__name__}\\\"\\n try:\\n print(\\n f\\\"{prefix}: Starting to serve {self.scenarios_dir} forever on: \\\"\\n f\\\"http://localhost:{self.port}\\\",\\n file=self.stdout,\\n )\\n\\n self._httpd.serve_forever()\\n\\n print(f\\\"{prefix}: Stopped serving forever.\\\", file=self.stdout)\\n\\n except Exception as error:\\n print(\\n f\\\"{prefix}: Caught an exception in the HTTPD server \\\"\\n f\\\"(it will be raised at shutdown): {error}\\\",\\n file=self.stderr,\\n )\\n\\n with self._server_exception_lock:\\n self._server_exception = error\\n\\n self._work_thread = threading.Thread(target=serve)\\n self._work_thread.start()\",\n \"def _ServerIsRunning( self ):\\n return utils.ProcessIsRunning( self._gocode_handle )\",\n \"def start(self):\\n self._isAlive = True\\n super(ThreadedServer, self).start()\\n logger.debug(\\\"Threaded Server has been started.\\\")\",\n \"def start(self):\\n self.listener.listen(self.backlog)\\n h, p = self.listener.getsockname()\\n self.logger.info(\\\"server started on %s:%s\\\", h, p)\\n self.active = True\\n if self.auto_register:\\n t = THG.Thread(target = self._bg_register)\\n t.setDaemon(True)\\n t.start()\\n #if sys.platform == \\\"win32\\\":\\n # hack so we can receive Ctrl+C on windows\\n self.listener.settimeout(0.5)\\n try:\\n try:\\n while True:\\n self.accept()\\n except EOFError:\\n pass # server closed by another thread\\n except SystemExit:\\n self.logger.warn(\\\"System exit\\\")\\n except KeyboardInterrupt:\\n self.logger.warn(\\\"keyboard interrupt!\\\")\\n finally:\\n self.logger.info(\\\"server has terminated\\\")\\n self.close()\",\n \"async def start(self):\\n server = await asyncio.start_server(\\n self.handle_request, self.host, self.port)\\n\\n addr = server.sockets[0].getsockname()\\n print(f'Serving on {addr}')\\n\\n async with server:\\n await server.serve_forever()\",\n \"def start(self):\\n if self._callable:\\n self._is_running = True\\n self._run_client()\",\n \"def start():\\n server = current_server()\\n logger.info('Starting Flexx event loop.')\\n server.start()\",\n \"def startServer(self):\\n processor = ThinService.Processor(self.serverLogic)\\n serverSocket = TSocket.TServerSocket(Constants.SERVER_HOST, Constants.SERVER_PORT)\\n transportFactory = TTransport.TBufferedTransportFactory()\\n protocolFactory = TBinaryProtocol.TBinaryProtocolFactory()\\n\\n server = TServer.TSimpleServer(processor, serverSocket, transportFactory, protocolFactory)\\n server.serve()\",\n \"def start(self):\\n if self._params.arq:\\n s = udpsocket.UDPSocket()\\n else:\\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\\n try:\\n s.bind(('', self._params.port))\\n s.listen(5)\\n self.check_and_print_debug_message(\\n 'HTTP Server is running on port: ' + str(self._params.port))\\n while True:\\n c, addr = s.accept()\\n Thread(\\n target=self.process_http_request,\\n args=(c, addr)\\n ).start()\\n except Exception as e:\\n self.check_and_print_debug_message(str(e))\\n s.close()\\n finally:\\n self.check_and_print_debug_message(\\n \\\"HTTP Server connection is closed.\\\")\\n s.close()\",\n \"def start_server():\\n server.bind(constants.ADDRESS)\\n server.listen()\\n print(\\\"Server listening on: \\\" + constants.HOST + \\\" on port \\\" + str(constants.PORT) + \\\"...\\\")\",\n \"def startup(req=None):\\n global started\\n if not started:\\n started = True\\n cherrypy.server.start(init_only=True, server_class=None)\\n return 0 # apache.OK\",\n \"def startservers():\\n try:\\n dns = subprocess.Popen(['python', FAKE_LOC, '-c', DNS_LOC])\\n except IOError:\\n sys.exit('>> Unable to locate FakeDns')\\n\\n try:\\n httpd = MyTCPServer(('', 80), MyHandler)\\n except socket.error:\\n dns.kill()\\n sys.exit('>> Port 80 already in use')\\n try:\\n print '>> Starting HTTP Server...'\\n httpd.serve_forever()\\n except KeyboardInterrupt:\\n httpd.shutdown()\\n httpd.server_close()\\n dns.kill()\\n sys.exit()\",\n \"def run():\\n\\n # Construct a server.\\n server = wsgiref.simple_server.make_server(\\n _config[ 'address' ],\\n _config[ 'port' ],\\n application\\n )\\n\\n # Run the server.\\n server.serve_forever()\\n\\n # Return result.\\n return 0\",\n \"def MaybeStartDebugServer():\\n port = os.environ.get('CROS_DEBUG_SERVER_PORT')\\n if port:\\n return StartDebugServer(port=int(port))\\n return None\",\n \"def _start(self, host):\\n pass\",\n \"def __start_server(self):\\n self.server = Server(self.browser_mob)\\n self.server.start()\\n self.proxy = self.server.create_proxy()\",\n \"def start() -> None:\\n from app import app\\n app.run(debug = True, host = HOST, port = PORT)\",\n \"def start(self, wait_for_stop=False):\\n\\n self.ua_server.start()\\n if wait_for_stop:\\n self.wait_for_stop()\\n self.stop()\",\n \"def start(self):\\n assert(self._cbs is not None)\\n self._as.start() # start the server\",\n \"def on_server_start(self):\\n raise NotImplementedError\",\n \"def start_server(self, server, script):\\n self.connector.sendline(script)\\n expect = \\\"88.Exit\\\"\\n i = self.connector.expect([expect, TIMEOUT], timeout=2)\\n if i == 0:\\n info_log(\\\"Start server[%s] success!\\\",server)\\n return True\\n else:\\n warn_log(\\\"Start server[%s] failed!\\\",server)\\n return False\",\n \"def start(self, host: str = '127.0.0.1', port: int = 6543) -> None:\\n\\n def _go(handler):\\n loop = asyncio.new_event_loop()\\n asyncio.set_event_loop(loop)\\n\\n start_server = websockets.serve(handler, host, port)\\n\\n asyncio.get_event_loop().run_until_complete(start_server)\\n asyncio.get_event_loop().run_forever()\\n\\n if not self.running:\\n # test if other instance is already running\\n with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as test_sock:\\n status = test_sock.connect_ex((host, port))\\n\\n # check for both Windows and Linux status codes\\n if status in {10061, 111}: # nothing running\\n server_thread = threading.Thread(\\n target=_go, args=(ProxyServer._ws_handler,))\\n server_thread.start()\\n\\n logger.info(f'Proxy server running on ws://{host}:{port}/')\\n self.running = True\\n else:\\n logger.warning(\\n f'Connection test to {host}:{port} returned status {status}, '\\n 'proxy server not started')\",\n \"def start():\\n app.run()\",\n \"def start(self, modulename=None):\\n if modulename:\\n # Unhook httpserver so cherrypy.server.start() creates a new\\n # one (with config from setup_server, if declared).\\n cherrypy.server.httpserver = None\\n\\n cherrypy.engine.start()\\n\\n self.sync_apps()\",\n \"def start_server(self):\\n LOGGER.info('Starting test plugin server...')\\n test_plugin_service_pb2_grpc.add_TestPluginServiceServicer_to_server(\\n self.servicer, self.server)\\n self.server.add_insecure_port(PLUGIN_SERVICE_ADDRESS)\\n self.server.start()\\n LOGGER.info('Test plugin server is running!')\\n\\n if self.device_proxy:\\n self.device_proxy.start()\\n LOGGER.info('Test plugin proxy server is running!')\",\n \"def do_start(self, line):\\n\\n if not line:\\n line = \\\"cortex\\\"\\n\\n # First, check that the name isn't already taken\\n clients = self.registry.get_clients()\\n if clients.has_key(line):\\n print \\\"A server already exists with that name (%s)\\\" % line\\n return False\\n\\n subprocess.Popen([\\\"python\\\", \\\"cortex.py\\\", line])\\n # Wait for the system to init\\n time.sleep(1)\\n print \\\"Started server, connecting...\\\"\\n return self.do_connect(line)\",\n \"def _start_server(apps, port, no_browser):\\n # necessary for the dashboard to work when called from a notebook\\n asyncio.set_event_loop(asyncio.new_event_loop())\\n\\n # this is adapted from bokeh.subcommands.serve\\n with report_server_init_errors(port=port):\\n server = Server(apps, port=port)\\n\\n # On a remote server, we do not want to start the dashboard here.\\n if not no_browser:\\n\\n def show_callback():\\n server.show(\\\"/\\\")\\n\\n server.io_loop.add_callback(show_callback)\\n\\n address_string = server.address if server.address else \\\"localhost\\\"\\n\\n print( # noqa: T201\\n \\\"Bokeh app running at:\\\",\\n f\\\"http://{address_string}:{server.port}{server.prefix}/\\\",\\n )\\n server._loop.start()\\n server.start()\",\n \"def start(self):\\r\\n for srv in self._servers:\\r\\n srv.start()\",\n \"def start(self):\\n\\n self.keep_running = True # Set running flag to true\\n self.th = threading.Thread(target=self.listenSocket)\\n self.th.daemon = True # Thread will terminate with the main\\n self.th.start()\\n self.th.join(0)\",\n \"def start(self, reload_from=None):\\n assert not self._process, \\\"server instance already started\\\"\\n pid = Value(\\\"i\\\")\\n self._process = Process(target=self._start,\\n args=(pid, socket_queue),\\n kwargs={\\\"reload_from\\\": reload_from})\\n self._process.start()\\n pid.value = self._process.pid\",\n \"def start(self):\\n #url = '{}://{}:{}/'.format('http',\\n # self.ip,\\n # self.port)\\n #self.service_info = ServiceInfo(\\n # '_webthing._sub._http._tcp.local.',\\n # '{}._http._tcp.local.'.format(self.name),\\n # address=socket.inet_aton(self.ip),\\n # port=self.port,\\n # properties={\\n # 'url': url,\\n # },\\n # server='{}.local.'.format(socket.gethostname()))\\n #self.zeroconf = Zeroconf()\\n #self.zeroconf.register_service(self.service_info)\\n\\n # If WebSocketS used and NOT running in thread, and WebServer IS\\n # running in thread make shure WebServer has enough stack size to\\n # handle also the WebSocket requests.\\n log.info('Starting Web Server')\\n self.server.Start(threaded=srv_run_in_thread, stackSize=8192)\",\n \"def _start_server_process():\\n from gevent import monkey\\n monkey.patch_all()\\n\\n from tellapart.frontend import gevent_profiler\\n from tellapart.frontend import util\\n\\n # In this example, profile 100% of requests.\\n # In a production server, you'd typically profile far fewer.\\n\\n if USE_PYWSGI:\\n profiler = gevent_profiler.Profiler(\\n request_profiling_pct=1.0,\\n request_info_class=gevent_profiler.PyWsgiServerRequestInfo)\\n\\n util.launch_gevent_wsgi_server(_do_stuff, 8088, 16, 'example server',\\n use_pywsgi=True)\\n else:\\n profiler = gevent_profiler.Profiler(request_profiling_pct=1.0)\\n util.launch_gevent_wsgi_server(_do_stuff, 8088, 16, 'example server')\",\n \"def started(self):\\n return False\",\n \"def start(self):\\n if self.is_alive:\\n self.logger.warning('Already started!')\\n return\\n self._create_tunnels()\\n if not self.is_active:\\n self._raise(BaseSSHTunnelForwarderError,\\n reason='Could not establish session to SSH gateway')\\n for _srv in self._server_list:\\n thread = threading.Thread(\\n target=self._serve_forever_wrapper,\\n args=(_srv, ),\\n name='Srv-{0}'.format(address_to_str(_srv.local_port))\\n )\\n thread.daemon = self.daemon_forward_servers\\n thread.start()\\n self._check_tunnel(_srv)\\n self.is_alive = any(self.tunnel_is_up.values())\\n if not self.is_alive:\\n self._raise(HandlerSSHTunnelForwarderError,\\n 'An error occurred while opening tunnels.')\",\n \"def start_server():\\n if not os.path.exists(cf.get(\\\"Selenium\\\", \\\"server_path\\\")):\\n jar_name = \\\"selenium-server-standalone-2.25.0.jar\\\"\\n server_jar = os.path.join(tempfile.gettempdir(), jar_name)\\n if not os.path.exists(server_jar):\\n r = requests.get(\\\"http://selenium.googlecode.com/files/%s\\\" % jar_name)\\n jar_on_disk = open(server_jar, \\\"wb\\\")\\n jar_on_disk.write(r.content)\\n jar_on_disk.close()\\n else:\\n server_jar = cf.get(\\\"Selenium\\\", \\\"server_path\\\")\\n \\n s = subprocess.Popen(['java', '-jar', server_jar], \\n stdout=tempfile.TemporaryFile(), \\n stderr=tempfile.TemporaryFile()).pid\\n pidfile = open(pid_file_path, \\\"w\\\")\\n pidfile.write(str(s))\\n pidfile.close()\\n\\n # make sure the server is actually up\\n server_up = False\\n waiting = 0\\n while server_up == False and waiting < 60:\\n try:\\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n s.connect((\\\"localhost\\\", 4444))\\n s.close()\\n server_up = True\\n except socket.error:\\n time.sleep(1)\\n waiting = waiting + 1\\n server_up = False\\n\\n return server_up\",\n \"def start_server(self):\\n self.logger.info(\\\"Starting WebSocket server on port %d\\\" % self.port)\\n http_server = Thread(target=tornado.ioloop.IOLoop.instance().start)\\n http_server.start()\",\n \"def start_server(self):\\n server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))\\n\\n # The two services we added in the proto. You can find these functions in\\n # jellybeanrobot_pb2_grpc.py.\\n jellybeanrobot_pb2_grpc.add_JellyServicer_to_server(Robot(), server)\\n\\n # Start listening on a port.\\n server.add_insecure_port(\\\"localhost:%d\\\" % self.port)\\n print \\\"Listening on localhost:%d!\\\\n\\\" % self.port\\n server.start()\\n\\n try:\\n while True:\\n time.sleep(3600) # one hour. \\n except KeyboardInterrupt:\\n server.stop(0)\",\n \"def start(self,force_restart=0,timeout=60):\\n if not force_restart and self.is_running():\\n return\\n # start the worker processes.\\n for worker in self.workers:\\n worker.start_server()\\n\\n if not self.is_running():\\n print ' Starting Servers'\\n print ' |----|----|----15---|----|----30---|----|----45---' \\\\\\n '|----|----60'\\n print '0.',\\n stop_watch = timer()\\n stop_watch.start()\\n minute = 0\\n import sys\\n while not self.is_running():\\n if stop_watch.current_lap() > 1:\\n sys.stdout.write('.')\\n stop_watch.mark_lap()\\n elapsed = stop_watch.elapsed()\\n if (elapsed - minute * 60) > 60:\\n minute = minute + 1\\n print\\n print minute,\\n if elapsed > timeout:\\n raise TimeoutError\\n print 'servers running!'\",\n \"def ensure_running_service_daemon(context):\\n svc = Management()\\n if svc._check_if_its_up(usutil.UBERSERVER):\\n context.temp_service_daemon = None\\n else:\\n svc.start()\\n context.temp_service_daemon = svc\",\n \"def start_server(self):\\n if not self.config_store:\\n raise Exception(\\\"Error owamp: the configuration of the server has not been done\\\\n\\\")\\n\\n self.server = OwampServer(self.config_store)\\n try:\\n self.server.launch_owampd()\\n except Exception as err:\\n template = \\\"Error owamp: Exception of type {0} occurred:\\\\n{1!r}\\\"\\n message = template.format(type(err).__name__, err)\\n raise Exception(message)\",\n \"async def _start_server(self) -> None:\\n # First, figure out what address to listen on. Open a connection to\\n # the Hubitat hub and see what address it used. This assumes this\\n # machine and the Hubitat hub are on the same network.\\n with _open_socket(socket.AF_INET, socket.SOCK_DGRAM) as s:\\n s.connect((self.host, 80))\\n address = s.getsockname()[0]\\n\\n self._server = server.create_server(\\n self._process_event, address, self.port or 0, self.ssl_context\\n )\\n self._server.start()\\n _LOGGER.debug(\\n \\\"Listening on %s:%d with SSL %s\\\",\\n address,\\n self._server.port,\\n \\\"disabled\\\" if self.ssl_context is None else \\\"enabled\\\",\\n )\\n\\n await self.set_event_url(self.event_url)\",\n \"def start(self, name=None):\\n server = self.cloudman.get_server(name)['id']\\n r = self.cloudman.compute.start_server(server)\\n return r\",\n \"def runserver():\\n app.run(host=config.HOST, port=config.PORT, debug=config.DEBUG, threaded=config.THREADED)\",\n \"def startserver(path):\\n global urlpath\\n urlpath = path\\n app.run(debug=True, host='0.0.0.0', port=4444)\",\n \"def start_server():\\n server = WebsocketServer(9001, host='0.0.0.0')\\n server.set_fn_message_received(message_received)\\n server.set_fn_client_left(client_left)\\n print(\\\"Started\\\")\\n server.run_forever()\",\n \"def startRunHook(self):\\n #Verify iserver is running\\n if not self.msw.isServerRunning():\\n raise EnvironmentError,'Server is down'\\n\\n #start sdebug 3\\n self.localShell.runCommand('localShelljava GetiServerConfig mymsw LoggingConfig')\",\n \"def _StartServer( self ):\\n with self._gocode_lock:\\n _logger.info( 'Starting Gocode server' )\\n\\n self._gocode_port = utils.GetUnusedLocalhostPort()\\n self._gocode_host = '127.0.0.1:{0}'.format( self._gocode_port )\\n\\n command = [ self._gocode_binary_path,\\n '-s',\\n '-sock', 'tcp',\\n '-addr', self._gocode_host ]\\n\\n if _logger.isEnabledFor( logging.DEBUG ):\\n command.append( '-debug' )\\n\\n self._gocode_stdout = utils.CreateLogfile(\\n LOGFILE_FORMAT.format( port = self._gocode_port, std = 'stdout' ) )\\n self._gocode_stderr = utils.CreateLogfile(\\n LOGFILE_FORMAT.format( port = self._gocode_port, std = 'stderr' ) )\\n\\n with utils.OpenForStdHandle( self._gocode_stdout ) as stdout:\\n with utils.OpenForStdHandle( self._gocode_stderr ) as stderr:\\n self._gocode_handle = utils.SafePopen( command,\\n stdout = stdout,\\n stderr = stderr )\",\n \"def startServer(serverName, serverType, serverMode, hostnameOrIpAddr = None, port = 0):\\n print(\\\"PSHELL_INFO: STUB Server: %s Started\\\" % serverName)\\n if (serverMode == BLOCKING):\\n while (True):\\n time.sleep(100000)\",\n \"def _start_servers(self):\\n for user, host, port in self.server_addresses:\\n remoteHost = \\\"%s@%s\\\" % (user, host)\\n logger.info(\\\"starting remote server %s:%s\\\", host, port)\\n command = (\\\"cd ~/goaway;\\\" +\\n \\\"find . -name '*.pyc' -delete ;\\\" +\\n \\\"DEBUG=true goaway/cmdserver.py %s %s %s >> server.std.log 2>&1\\\" % (\\n host,\\n port,\\n self._config.remote_path,\\n ))\\n logger.debug(\\\"Starting server:%s remoteHost with command:%s\\\" % (remoteHost, command))\\n ## subprocess.call blocks, while subprocces.Popen doesn't block.\\n sshPopen = subprocess.Popen([\\\"ssh\\\", remoteHost, command],\\n shell = False, stdout= subprocess.PIPE, stderr = subprocess.PIPE)\\n self._start_local_server()\",\n \"def start(self):\\n self.port = self.conn.evalInServer(server_code.format(key=self.key))\",\n \"def run_server():\\n app = init_app()\\n app.run(host=app.config['HOST'], port=app.config['PORT'])\",\n \"def start(self):\\n try:\\n try:\\n logging.debug('Checking for running daemon')\\n exec_cmd(['transmission-remote', '--list'])\\n\\n except FileNotFoundError:\\n logging.error('Cannot find transmission-remote, make sure it\\\\'s installed')\\n logging.debug('Error details', stack_info=True, exc_info=True)\\n return False\\n\\n except subprocess.CalledProcessError:\\n logging.info('Starting Transmission daemon')\\n exec_cmd(['transmission-daemon'], wait_after=3)\\n self._started_daemon = True\\n\\n self._done = False\\n threading.Thread(target=self._update).start()\\n return True\\n\\n except FileNotFoundError:\\n logging.error('Cannot find transmission-daemon, make sure it\\\\'s installed')\\n logging.debug('Error details', stack_info=True, exc_info=True)\\n\\n except subprocess.CalledProcessError:\\n logging.error('Unable to start daemon')\\n logging.debug('Error details', stack_info=True, exc_info=True)\\n\\n return False\",\n \"async def start(self):\\n try:\\n # Initialize the run\\n await self._initialize()\\n\\n # Start and manage the run\\n await self._run()\\n\\n # Terminate the run\\n await self._shutdown()\\n except:\\n await self._cleanup(exc=True)\\n else:\\n await self._cleanup()\\n\\n return True\",\n \"def serve_forever(self):\\n from socketio.server import SocketIOServer\\n server = SocketIOServer((self.args.interface, int(self.args.port)),\\n self.application, resource=\\\"socket.io\\\",\\n policy_server=False)\\n logger.info(\\\"Start socket.io server %r:%r\\\",\\n self.args.interface, self.args.port)\\n try:\\n server.serve_forever()\\n except KeyboardInterrupt:\\n for socket in server.sockets.values():\\n for namespace in socket.active_ns.values():\\n if hasattr(namespace, 'stop_server'):\\n namespace.stop_server()\",\n \"def main():\\n try:\\n port = 8080\\n ip = '0.0.0.0'\\n http_server = WSGIServer((ip, port),\\n app,\\n log=logging,\\n error_log=logging,\\n )\\n print(\\\"Server started at: {0}:{1}\\\".format(ip, port))\\n http_server.serve_forever()\\n except Exception as exc:\\n logger.error(exc.message)\\n logger.exception(traceback.format_exc())\\n finally:\\n # Do something here\\n pass\",\n \"def _start_server():\\n args = [sys.executable] + sys.argv\\n args.insert(args.index('wserver'), 'server')\\n args.remove('wserver')\\n pid = os.spawnv(os.P_NOWAIT, sys.executable, args)\\n return pid\",\n \"def start_services(self):\\n ircd_result = True\\n synapse_result = self.start_synapse()\\n if self.charm_config.get(\\\"enable-ircd\\\"):\\n ircd_result = self.start_ircd()\\n return synapse_result and ircd_result\",\n \"def start(self) -> None:\\n try:\\n self._socket.bind((self.ip, self.port))\\n\\n except socket.error as e:\\n print(e)\\n\\n else:\\n self._socket.listen()\\n logger.info('Server is online!')\\n\\n run = True\\n while run:\\n conn_data = ConnectionData()\\n self._accept_conn(conn_data)\\n\\n # Makes the server stoppable\\n while conn_data.conn is None or conn_data.addr is None:\\n try:\\n time.sleep(0.1)\\n except KeyboardInterrupt:\\n run = False\\n break\\n\\n conn, addr = conn_data.conn, conn_data.addr\\n logger.info(f'Connection established to {addr}')\\n\\n if self.func is not None:\\n self.func(conn, addr)\",\n \"def serve(self):\\n\\t\\timport thread\\n\\t\\tthread.start_new_thread(self._server_thread, tuple())\",\n \"def serve_forever(self):\\n self._server.serve_forever()\",\n \"def _start_server(codechecker_cfg, test_config, auth=False):\\n def start_server_proc(event, server_cmd, checking_env):\\n \\\"\\\"\\\"Target function for starting the CodeChecker server.\\\"\\\"\\\"\\n proc = subprocess.Popen(server_cmd, env=checking_env)\\n\\n # Blocking termination until event is set.\\n event.wait()\\n\\n # If proc is still running, stop it.\\n if proc.poll() is None:\\n proc.terminate()\\n\\n server_cmd = codechecker.serv_cmd(codechecker_cfg['workspace'],\\n str(codechecker_cfg['viewer_port']),\\n env.get_postgresql_cfg())\\n\\n server_proc = multiprocessing.Process(\\n name='server',\\n target=start_server_proc,\\n args=(__STOP_SERVER, server_cmd, codechecker_cfg['check_env']))\\n\\n server_proc.start()\\n\\n # Wait for server to start and connect to database.\\n time.sleep(20)\",\n \"def start(self, start_stackless=True):\\r\\n self.sock_server = sock_server(self.bind_addr)\\r\\n self.running = True\\r\\n \\r\\n self.tasklet_class(self._accept_loop)()\\r\\n \\r\\n if start_stackless:\\r\\n stackless.run()\",\n \"def startapp():\",\n \"def run(server_class=HTTPServer, handler_class=CPUUsageHandler):\\n server_address = (settings.HOST, settings.PORT)\\n httpd = server_class(server_address, handler_class)\\n print settings.START_MSG\\n try:\\n httpd.serve_forever()\\n except KeyboardInterrupt:\\n httpd.socket.close()\\n print settings.STOP_MSG\\n except Exception:\\n raise\",\n \"def start_tftp_server(self):\\n self.install_pkgs()\\n if self.shared_tftp_server:\\n # perform the rest\\n raise NotImplementedError\\n raise NotImplementedError\",\n \"def run_forever(self):\\n self.app.run()\",\n \"def serve(self):\\n if self._server_thread is not None:\\n return\\n if self._port is None:\\n self._port = portpicker.pick_unused_port()\\n started = threading.Event()\\n self._stopped = threading.Event()\\n self._stopping = threading.Event()\\n\\n def build_server(started, stopped, stopping):\\n \\\"\\\"\\\"Closure to build the server function to be passed to the thread.\\n\\n Args:\\n started: Threading event to notify when started.\\n stopped: Threading event to notify when stopped.\\n stopping: Threading event to notify when stopping.\\n Returns:\\n A function that function that takes a port and WSGI app and notifies\\n about its status via the threading events provided.\\n \\\"\\\"\\\"\\n\\n def server(port, wsgi_app):\\n \\\"\\\"\\\"Serve a WSGI application until stopped.\\n\\n Args:\\n port: Port number to serve on.\\n wsgi_app: WSGI application to serve.\\n \\\"\\\"\\\"\\n try:\\n httpd = wsgiref.simple_server.make_server(self._host, port, wsgi_app)\\n except socket.error:\\n # Try IPv6\\n httpd = wsgiref.simple_server.make_server(\\n self._host, port, wsgi_app, server_class=WsgiServerIpv6)\\n started.set()\\n httpd.timeout = 30\\n while not stopping.is_set():\\n httpd.handle_request()\\n stopped.set()\\n\\n return server\\n\\n server = build_server(started, self._stopped, self._stopping)\\n server_thread = threading.Thread(\\n target=server, args=(self._port, self._app))\\n self._server_thread = server_thread\\n\\n server_thread.start()\\n started.wait()\",\n \"def _checkTorcsServer(self):\\n isRunning = False\\n if self.torcsServerProcess is not None:\\n if self.torcsServerProcess.poll() is None:\\n isRunning = True\\n return isRunning\",\n \"def start_webserver(inital_port=6543, wait_until_started=True, block=False):\\n global server_process, server_process_port\\n if server_process is not None and server_process.is_alive():\\n return True, server_process_port\\n\\n queue = Queue(1)\\n server_process = Process(\\n target=_webserver,\\n kwargs={\\n 'port': inital_port,\\n 'outqueue': queue,\\n }\\n )\\n server_process.start()\\n if wait_until_started:\\n try:\\n connected, server_process_port = queue.get(timeout=1)\\n except Exception as e:\\n return False, None\\n atexit.register(stop_server)\\n if block:\\n server_process.join()\\n\\n return connected, server_process_port\",\n \"def serve(self):\\n logger.info(\\\"NqServer listening at port {}\\\".format(FLAGS.port))\\n server = wsgiref.simple_server.make_server('', FLAGS.port, self.application)\\n server.serve_forever()\",\n \"def start(self) -> None:\\n if self.bolt_app.logger.level > logging.INFO:\\n print(get_boot_message())\\n else:\\n self.bolt_app.logger.info(get_boot_message())\\n\\n web.run_app(self.web_app, host=\\\"0.0.0.0\\\", port=self.port)\",\n \"def start_server(self, app, **kwargs):\\n\\n # start server with app and pass Dash arguments\\n self.server(app, **kwargs)\\n\\n # set the default server_url, it implicitly call wait_for_page\\n self.server_url = self.server.url\",\n \"def startListening(self):\\n \\n self.listener_thread = threading.Thread(target=self.listening, daemon=True)\\n self.listener_thread.start()\\n\\n # stateupdate = threading.Thread(target=self.showStatus, daemon=True)\\n # stateupdate.start()\\n\\n # Main App Loop (Keeps the Client opened)\\n while self.listener_thread.is_alive():\\n time.sleep(1)\\n else:\\n print('Shutting Main Thread-1')\\n sys.exit()\",\n \"def start():\\n # Import any local level utilities that may be used\\n # before the web-server is initialized.\\n from django.core.management import call_command\\n from db.models import ApplicationState\\n from db.utilities import generate_models\\n\\n # Run the migrate command within django.\\n # Making sure our models are upto date.\\n call_command(command_name=\\\"migrate\\\", app=\\\"titandash\\\")\\n\\n # Server is being started, it is safe for us\\n # to update our active flag.\\n ApplicationState.objects.set(state=True)\\n\\n # Generate any initial models that we expect\\n # to be available by default.\\n generate_models()\\n\\n _url = EEL_DASHBOARD if User.objects.valid() else EEL_LOGIN\\n\\n logger.info(\\\"starting titandash application with options: '{options}'\\\".format(options={\\\"path\\\": _url, **EEL_START_OPTIONS}))\\n # Start eel, providing our start url defined above, the close callback\\n # to deal with cleanup functionality, and default options.\\n eel.start(_url, close_callback=close_callback, **EEL_START_OPTIONS)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8494984","0.7766544","0.7548727","0.73513985","0.7320159","0.7309142","0.72342896","0.72342896","0.7074414","0.69020855","0.68957585","0.6856507","0.68551105","0.68490946","0.6826151","0.6805642","0.6805338","0.67718863","0.67642325","0.673737","0.6726076","0.67110306","0.66985947","0.66770357","0.665817","0.6616642","0.66038394","0.6592948","0.65857935","0.65494436","0.65385205","0.65308553","0.6530392","0.6520876","0.64482456","0.6412695","0.63965976","0.63917166","0.63877255","0.63801205","0.63782364","0.6350519","0.6347546","0.6341204","0.63367903","0.63357294","0.6332113","0.6331522","0.6330245","0.6328673","0.63021517","0.629013","0.6284353","0.6283826","0.62791157","0.6277087","0.62745285","0.6264265","0.62411207","0.6233573","0.6229302","0.6216581","0.6216304","0.6215733","0.6208042","0.62034947","0.619615","0.61913097","0.6185027","0.6184652","0.6182366","0.6169826","0.61649656","0.6151897","0.61423916","0.614218","0.61385083","0.6135927","0.6129594","0.61255366","0.61247236","0.61231786","0.6100513","0.6094931","0.6093852","0.6089595","0.6081493","0.6078832","0.6068353","0.60682404","0.60638857","0.6060163","0.60590714","0.6057865","0.605612","0.60516506","0.60475504","0.6045512","0.60436815","0.60419613"],"string":"[\n \"0.8494984\",\n \"0.7766544\",\n \"0.7548727\",\n \"0.73513985\",\n \"0.7320159\",\n \"0.7309142\",\n \"0.72342896\",\n \"0.72342896\",\n \"0.7074414\",\n \"0.69020855\",\n \"0.68957585\",\n \"0.6856507\",\n \"0.68551105\",\n \"0.68490946\",\n \"0.6826151\",\n \"0.6805642\",\n \"0.6805338\",\n \"0.67718863\",\n \"0.67642325\",\n \"0.673737\",\n \"0.6726076\",\n \"0.67110306\",\n \"0.66985947\",\n \"0.66770357\",\n \"0.665817\",\n \"0.6616642\",\n \"0.66038394\",\n \"0.6592948\",\n \"0.65857935\",\n \"0.65494436\",\n \"0.65385205\",\n \"0.65308553\",\n \"0.6530392\",\n \"0.6520876\",\n \"0.64482456\",\n \"0.6412695\",\n \"0.63965976\",\n \"0.63917166\",\n \"0.63877255\",\n \"0.63801205\",\n \"0.63782364\",\n \"0.6350519\",\n \"0.6347546\",\n \"0.6341204\",\n \"0.63367903\",\n \"0.63357294\",\n \"0.6332113\",\n \"0.6331522\",\n \"0.6330245\",\n \"0.6328673\",\n \"0.63021517\",\n \"0.629013\",\n \"0.6284353\",\n \"0.6283826\",\n \"0.62791157\",\n \"0.6277087\",\n \"0.62745285\",\n \"0.6264265\",\n \"0.62411207\",\n \"0.6233573\",\n \"0.6229302\",\n \"0.6216581\",\n \"0.6216304\",\n \"0.6215733\",\n \"0.6208042\",\n \"0.62034947\",\n \"0.619615\",\n \"0.61913097\",\n \"0.6185027\",\n \"0.6184652\",\n \"0.6182366\",\n \"0.6169826\",\n \"0.61649656\",\n \"0.6151897\",\n \"0.61423916\",\n \"0.614218\",\n \"0.61385083\",\n \"0.6135927\",\n \"0.6129594\",\n \"0.61255366\",\n \"0.61247236\",\n \"0.61231786\",\n \"0.6100513\",\n \"0.6094931\",\n \"0.6093852\",\n \"0.6089595\",\n \"0.6081493\",\n \"0.6078832\",\n \"0.6068353\",\n \"0.60682404\",\n \"0.60638857\",\n \"0.6060163\",\n \"0.60590714\",\n \"0.6057865\",\n \"0.605612\",\n \"0.60516506\",\n \"0.60475504\",\n \"0.6045512\",\n \"0.60436815\",\n \"0.60419613\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6707576"},"document_rank":{"kind":"string","value":"22"}}},{"rowIdx":20075,"cells":{"query":{"kind":"string","value":"Test whether the numpy data type `dt` can be safely cast to an int."},"document":{"kind":"string","value":"def _safely_castable_to_int(dt):\n int_size = np.dtype(int).itemsize\n safe = (np.issubdtype(dt, np.signedinteger) and dt.itemsize <= int_size) or (\n np.issubdtype(dt, np.unsignedinteger) and dt.itemsize < int_size\n )\n return safe"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _is_integer(x):\n return (not isinstance(x, (bool, np.bool))) and \\\n isinstance(x, (numbers.Integral, int, np.int, np.long, long)) # no long type in python 3","def is_int(x):\n # From sktime: BSD 3-Clause\n # boolean are subclasses of integers in Python, so explicitly exclude them\n return isinstance(x, (int, np.integer)) and not isinstance(x, bool)","def is_int(value):\n return isinstance(value, int)","def is_convertible_to_int(v: Any) -> bool:\n\n try:\n test = int(v)\n return True\n except:\n return False","def isInteger(data):\n\tif type(data) == list or type(data) == np.ndarray:\n\t\tcol = pd.Series(data)\n\telse:\n\t\tcol = data\n\treturn col.dtype == np.int32 or col.dtype == np.int64","def _is_int(test_val):\n try:\n int(test_val)\n return True\n except ValueError:\n return False","def is_int(self):\n return self.value_type in (int, arrow.JuArrow)","def DataIsInteger(self):\n return self.data_type in (\n definitions.REG_DWORD, definitions.REG_DWORD_BIG_ENDIAN,\n definitions.REG_QWORD)","def is_int(x):\n # boolean are subclasses of integers in Python, so explicitly exclude them\n return isinstance(x, (int, np.integer)) and not isinstance(x, bool)","def isInteger(data):\n\ttry:\n\t\tfrom types import LongType, IntType\n\t\tif type(data) == LongType or type(data) == IntType:\n\t\t\treturn True\n\texcept ImportError:\n\t\tif type(data) == type(int(0)):\n\t\t\treturn True\n\treturn False","def _check_index(idx):\n return isinstance(idx, _Int)","def check_for_int(check):","def is_integer(matrix):\n return numpy.issubdtype(matrix.dtype, numpy.integer)","def could_be_int(val):\n if val == None:\n return False\n\n if isinstance(val, int):\n return True\n\n # allow coercion from str\n if isinstance(val, (str, unicode)):\n try:\n i = int(val)\n if not isinstance(i, int):\n raise ValueError\n else:\n return True\n except:\n return False\n\n # otherwise\n return False","def is_int(x):\n return int(x) == x","def is_int(self): \n return (self._den == 1)","def check_if_input_is_int(self):\n try:\n int(self.input)\n except ValueError:\n return False\n else:\n return True","def _is_valid_target_int(self, target):\n if isinstance(target, (int, np.int, np.int8, np.int16, np.int32, np.int64)):\n return True\n else:\n return False","def is_int(n):\n try:\n int(n)\n return True\n except ValueError:\n return False","def _is_integer_like(input):\n if _is_boolean_like(input):\n return True\n if type(input) is int:\n return True\n if isinstance(input, _ScalarConstant):\n if input.dtype in _int_like_types:\n return True\n return False","def is_int(self, val):\n try:\n int(val)\n return True\n except ValueError:\n return False","def is_intscalar(x: Any) -> bool:\r\n return isinstance(x, (\r\n int,\r\n np.int8,\r\n np.int16,\r\n np.int32,\r\n np.int64,\r\n np.uint8,\r\n np.uint16,\r\n np.uint32,\r\n np.uint64,\r\n ))","def is_int(value):\n try:\n int(value)\n except ValueError:\n return False\n else:\n return True","def is_int(value):\n try:\n int(value)\n return True\n except ValueError:\n return False","def is_int(value):\n try:\n int(value)\n return True\n except ValueError:\n return False","def is_int(num):\n return int(num) == num","def isinteger(self):\n return self.den == 1","def is_int(self, size=None):\n return False","def isInteger(self):","def isInteger(self):","def try_int(data):\n try:\n return int(data)\n except (ValueError, TypeError ):\n return data","def is_int(*args): \n try:\n for i in args:\n int(i)\n return True\n except Exception:\n return False","def is_integer(x):\n try:\n int(x)\n return True\n except ValueError:\n return False","def isInteger(self):\n pass","def isInteger(self):\n return isinstance(self.value, int)","def is_32_bit_int_compatible( i ) :\r\n\r\n #\r\n # can we convert the input to an integer value ? \r\n #\r\n\r\n try : \r\n\r\n ## debug:\r\n ## print \"compat: \", i, truncate_pyint_to_i32_interval(i)\r\n\r\n if i == truncate_pyint_to_i32_interval( i ) : \r\n ## # debug \r\n ## print \"compat: TRUE\", i, truncate_pyint_to_i32_interval(i)\r\n return True\r\n\r\n except :\r\n\r\n ## # debug : \r\n ## print \"compat: FALSE\", i, truncate_pyint_to_i32_interval(i)\r\n pass\r\n \r\n # else ... \r\n return False","def __check_datatype(event, resp):\n\n def unsigned_int_check(val):\n \"\"\"checks if something is a positive (unsigned) integer\n\n Arguments:\n val (object): value to check\n\n Returns:\n (bool): True if val is an integer, otherwise False\n \n \"\"\"\n\n is_valid_number = True\n try:\n val = int(val)\n if val < 0:\n raise ValueError(\"Not an unsigned int\")\n except ValueError as e:\n is_valid_number = False\n \n return is_valid_number\n \n # for start and end base, if not None, perform the number check\n # if either is not an unsigned int, this is a BAD REQUEST\n vals = [resp.get_datum(\"start\"), resp.get_datum(\"end\")]\n for val in vals:\n if val:\n if not unsigned_int_check(val):\n resp.set_status_code(SC.BAD_REQUEST)\n resp.set_body(json.dumps({\n \"message\": \"start/end must be unsigned int\"\n }))","def is_integer(value: Union[float, np.ndarray]) -> bool:\n if type(value) == np.ndarray:\n for entry in value:\n result = Comparator.is_integer(entry)\n if not result:\n return False\n return True\n else:\n value = abs(value)\n value -= int(value)\n if value > 0.5:\n return Comparator.is_close_to_zero(1 - value)\n return Comparator.is_close_to_zero(value)","def is_integer(space, w_obj):\n return space.wrap(w_obj.tp == space.tp_int)","def able_int_type(values):\n if any([v % 1 for v in values]):\n return None\n mn = min(values)\n mx = max(values)\n if mn >= 0:\n for ityp in np.sctypes['uint']:\n if mx <= np.iinfo(ityp).max:\n return ityp\n for ityp in np.sctypes['int']:\n info = np.iinfo(ityp)\n if mn >= info.min and mx <= info.max:\n return ityp\n return None","def is_integer(i):\n import numpy as np\n if isinstance(i, (int, long)):\n return True\n if isinstance(i, float):\n return (i).is_integer()\n if issubclass(type(i), np.integer):\n return i\n else:\n return False","def is_int(self):\n return self.v & 1 != 0","def isint(s):\n try:\n x = int(s)\n return True\n except:\n return False","def is_int(symbol):\n return isa(symbol, int)","def dtype_int(dtype: DType):\n # TODO: Is there a better way of doing this?\n name = list(convert(dtype, NPDType).__name__)\n while name and name[0] not in set([str(i) for i in range(10)]):\n name.pop(0)\n return _convert_back(_name_to_numpy_dtype(\"int\" + \"\".join(name)), dtype)","def is_integer(x):\r\n if isinstance(x, float):\r\n return x == int(x)\r\n else:\r\n raise TypeError, \"Input float\"","def ensure_int(cls, data):\n if data is None: # Failed to fetch attribute. no need to convert.\n return None\n\n # For some reason bool is considered an integral type. We want to avoid True\n # to be converted to 1, and False to 0 on numeric matchers since it can be\n # misleading.\n if isinstance(data, numbers.Integral) and not isinstance(data, bool):\n return data\n\n if not isinstance(data, str):\n _LOGGER.error('Cannot convert %s to int. Failing.', type(data))\n return None\n\n _LOGGER.warning(\n 'Supplied attribute is of type %s and should have been an int. ',\n type(data)\n )\n\n try:\n return int(data)\n except ValueError:\n _LOGGER.error('Cannot convert %s to int. Failing.', type(data))\n return None","def _is_number(data):\n return len(data) and np.issubdtype(_to_ndarray(data).dtype, np.number)","def is_int(num):\n try:\n if num is None:\n return False;\n int(num)\n return True\n except ValueError:\n return False","def check_if_int(self, data):\n # Create a temporary blank list.\n temp = []\n \n # Iterate over the elements in data and check whether they are an \n # integer using the modulo logic. If the data is not an integer,\n # add the index of that element to the list called temp.\n for x in range(len(data)):\n if data[x] % 1 != 0:\n temp.append(x)\n \n # Assume that all values are integers.\n is_int = True\n \n # Check whether the count of temp list exceeds 0. If it does,\n # then it means that there is an element that is not an integer.\n # Then convert the temp list into an array.\n if len(temp) > 0:\n is_int = False\n index = np.array(temp)\n \n # This conditional statement will return only a Boolean value if\n # data is all integers. Otherwise, it will also return an array of \n # the indeces where there are non-integer values.\n if is_int == True:\n return is_int\n else:\n return [is_int, index]","def execute_cast_datetime_to_integer(op, data, type, **kwargs):\n return pd.Timestamp(data).value","def test_basic_numpy_dtypes():\n assert np.int != np.int8\n assert np.int != np.int16\n assert np.int != np.int32\n assert np.int != np.int64\n\n assert np.int == int\n assert np.int8 != int\n assert np.int16 != int\n assert np.int32 != int\n assert np.int64 != int\n\n assert np.dtype(np.int) == np.dtype('int') == np.dtype(int)\n assert np.dtype(np.int8) == np.dtype('int8') == np.int8\n assert np.dtype(np.int16) == np.dtype('int16') == np.int16\n assert np.dtype(np.int32) == np.dtype('int32') == np.int32\n assert np.dtype(np.int64) == np.dtype('int64') == np.int64","def is_int_value(int_value):\n try:\n int(int_value)\n except ValueError:\n return False\n return True","def isInteger(self):\n return self._is_int","def try_int_cast(value):\n try: \n return int(value)\n except:\n return value","def _check_data(data, dset_attrs):\n dtype = dset_attrs['dtype']\n float_to_int = (np.issubdtype(dtype, np.integer)\n and np.issubdtype(data.dtype, np.floating))\n if float_to_int:\n attrs = dset_attrs['attrs']\n scale_factor = [c for c in attrs if 'scale_factor' in c][0]\n scale_factor = attrs[scale_factor]\n\n # apply scale factor and dtype\n data = np.multiply(data, scale_factor)\n if np.issubdtype(dtype, np.integer):\n data = np.round(data)\n\n data = data.astype(dtype)\n\n return data","def test_datatype(self):\n dates = pd.date_range(start=\"2007-01-01\", end=\"2007-02-01\")\n\n ts = pd.DataFrame(\n {\n \"var1\": np.arange(len(dates), dtype=np.int8),\n \"var2\": np.arange(len(dates), dtype=np.int16),\n \"var3\": np.arange(len(dates), dtype=np.int32),\n \"var4\": np.arange(len(dates), dtype=np.int64)\n },\n index=dates)\n\n dataset_w = GriddedNcContiguousRaggedTs(self.testdatapath,\n self.grid,\n mode=\"w\")\n\n for gpi in self.gpis:\n dataset_w.write(gpi, ts)\n\n dataset_r = GriddedNcContiguousRaggedTs(self.testdatapath,\n self.grid,\n mode=\"r\")\n\n for gpi in self.gpis:\n arr = dataset_r.read(gpi)\n assert (arr[\"var1\"].dtype == np.int8)\n assert (arr[\"var2\"].dtype == np.int16)\n assert (arr[\"var3\"].dtype == np.int32)\n assert (arr[\"var4\"].dtype == np.int64)","def __is_int(self,string):\r\n try: \r\n int(string)\r\n return True\r\n except ValueError:\r\n return False","def isInt(s):\n try:\n int(s)\n return True\n except ValueError:\n return False","def is_int(self, size=None):\n return self._is_of_size_if_not_none(size)","def _isintvar(self, index):\n return 65528 <= self._typlist[index] <= 65530","def _isintvar(self, index):\n return 251 <= self._typlist[index] <= 253","def __checkInput(self, var):\n try:\n int(var)\n\n except:\n return False\n\n else:\n return True","def is_integer(s: Union[str, int, float]):\n try:\n float(s)\n except ValueError:\n return False\n else:\n return float(s).is_integer() and not np.isnan(float(s))","def is_int_like(val):\n try:\n return str(int(val)) == str(val)\n except Exception:\n return False","def is_int_like(val):\n try:\n return str(int(val)) == str(val)\n except Exception:\n return False","def _is_pos_int(number: int) -> bool:\n return type(number) == int and number >= 0","def represents_int(s):\n try:\n int(s)\n return True\n except ValueError:\n return False","def test_toint(number, expected, cond):\n assert toInt(number, cond=cond) == expected","def _is_non_negative_int(item):\n if not isinstance(item, int):\n return False\n return item >= 0","def can_insert(data):\n if not isinstance(data, np.ndarray):\n return False\n if data.dtype.char in UNSUPPORTED_NUMERIC_TYPE_CODES:\n return False\n return np.issubdtype(data.dtype, np.number)","def is_integer_type(self):\n raise exceptions.NotImplementedError()","def as_int(x, check=True):\n x = np.array(x)\n if x.dtype.kind in 'iu':\n # This works around a nasty numpy 1.4.1 bug such that:\n # >>> int(np.uint32(2**32-1)\n # -1\n return int(str(x))\n ix = int(x)\n if ix == x:\n return ix\n fx = np.floor(x)\n if check and fx != x:\n raise FloatingError('Not an integer: %s' % x)\n if not fx.dtype.type == np.longdouble:\n return int(x)\n # Subtract float64 chunks until we have all of the number. If the int is\n # too large, it will overflow\n ret = 0\n while fx != 0:\n f64 = np.float64(fx)\n fx -= f64\n ret += int(f64)\n return ret","def deconstructable(self):\n return isinstance(self.get(), int)","def can_insert(data):\n types = (float, complex, int, np.long)\n if isinstance(data, types) and not isinstance(data, bool):\n return True\n elif isinstance(data, np.number):\n return data.dtype.char not in UNSUPPORTED_NUMERIC_TYPE_CODES","def datetime_checkinput(year, month, day):\n try:\n datetime.datetime(year, month, day)\n except:\n raise Invaliddatetimeinput\n return 0","def isint(str):\n ok = 1\n if not str:\n return 0\n try:\n int(str)\n except ValueError:\n ok = 0\n except TypeError:\n ok = 0\n return ok","def execute_cast_timestamp_to_integer(op, data, type, **kwargs):\n return data.value","def tryCastToInt(number):\n try:\n return int(number)\n except:\n print(\"Error! Impossible to parse this variable\")\n return 0","def _validate_scalar(obj):\n if not isinstance(obj, int):\n raise TypeError(\"scalar must be an integer\")","def _is_all_int(df_list: List[Union[dd.DataFrame, pd.DataFrame]], col: str) -> bool:\n for df in df_list:\n if col in df.columns:\n srs = df[col]\n if isinstance(srs, (dd.DataFrame, pd.DataFrame)):\n for dtype in srs.dtypes:\n if not is_integer_dtype(dtype):\n return False\n elif isinstance(srs, (dd.Series, pd.Series)):\n if not is_integer_dtype(srs.dtype):\n return False\n else:\n raise ValueError(f\"unprocessed type of data:{type(srs)}\")\n return True","def value_is_float_not_int(value):\n # this is klugy and only needed to display deprecation warnings\n try:\n int(value)\n return False\n except ValueError:\n try:\n float(value)\n return True\n except ValueError:\n return False\n except TypeError:\n return False","def strict_integer_validator(cls, v: Any) -> int:\n if int(v) != Decimal(v):\n raise ValueError\n return int(v)","def _int_validator(arg):\n if arg is None or type(arg) != int:\n raise ValueError('Incorrect value: input should be an int')","def check_for_float_and_int(check):","def can_insert(data):\n if not issparse(data):\n return False\n if data.dtype.char in UNSUPPORTED_NUMERIC_TYPE_CODES:\n return False\n return np.issubdtype(data.dtype, np.number)","def _is_positive_int(item):\n if not isinstance(item, int):\n return False\n return item > 0","def check_pos_int(v):\n status = True\n try:\n val = int(v)\n if val <= 0:\n status = False\n except ValueError:\n status = False\n return status","def is_num(var):\n try:\n int(var)\n return True\n except ValueError:\n return False","def isNumber(x):\n return isinstance(x, (int, float))","def test_safe_array_cast(self):\n msg = '^Copying array of size \\(5, 5\\) to convert it in the ' \\\n 'right format$'\n with self.assertWarnsRegex(RuntimeWarning, msg):\n PoissonRegression._safe_array(self.X.astype(int))\n\n msg = '^Copying array of size \\(3, 5\\) to create a ' \\\n 'C-contiguous version of it$'\n with self.assertWarnsRegex(RuntimeWarning, msg):\n PoissonRegression._safe_array(self.X[::2])\n\n np.testing.assert_array_equal(self.X,\n PoissonRegression._safe_array(self.X))","def _check_dtype(input_dtype):\n\n product_version = tbe_platform.cce_conf.get_soc_spec(\"SOC_VERSION\")\n if product_version in (\"Hi3796CV300ES\"):\n if input_dtype == \"float32\":\n raise RuntimeError(\"float32 is not support in ES\")\n util.check_dtype_rule(input_dtype, (\"float16\",))\n else:\n util.check_dtype_rule(input_dtype, (\"float16\", \"float32\",))","def is_sequence_of_int(items):\n return all(isinstance(item, int) for item in items)","def _isint(string, inttype=int):\n return (\n type(string) is inttype\n or isinstance(string, (bytes, str))\n and _isconvertible(inttype, string)\n )","def is_integer(value):\n try:\n return True, int(value)\n except (ValueError, TypeError):\n return False","def isNumeric(data):\n\tif type(data) == list or type(data) == np.ndarray:\n\t\tcol = pd.Series(data)\n\telse:\n\t\tcol = data\n\treturn col.dtype == np.int32 or col.dtype == np.int64 or col.dtype == np.float32 or col.dtype == np.float64","def _can_cast(from_dtype, to_dtype):\n if cudf.utils.utils.is_na_like(from_dtype):\n return True\n if isinstance(from_dtype, type):\n from_dtype = cudf.dtype(from_dtype)\n if isinstance(to_dtype, type):\n to_dtype = cudf.dtype(to_dtype)\n\n # TODO : Add precision & scale checking for\n # decimal types in future\n\n if isinstance(from_dtype, cudf.core.dtypes.DecimalDtype):\n if isinstance(to_dtype, cudf.core.dtypes.DecimalDtype):\n return True\n elif isinstance(to_dtype, np.dtype):\n if to_dtype.kind in {\"i\", \"f\", \"u\", \"U\", \"O\"}:\n return True\n else:\n return False\n elif isinstance(from_dtype, np.dtype):\n if isinstance(to_dtype, np.dtype):\n return np.can_cast(from_dtype, to_dtype)\n elif isinstance(to_dtype, cudf.core.dtypes.DecimalDtype):\n if from_dtype.kind in {\"i\", \"f\", \"u\", \"U\", \"O\"}:\n return True\n else:\n return False\n elif isinstance(to_dtype, cudf.core.types.CategoricalDtype):\n return True\n else:\n return False\n elif isinstance(from_dtype, cudf.core.dtypes.ListDtype):\n # TODO: Add level based checks too once casting of\n # list columns is supported\n if isinstance(to_dtype, cudf.core.dtypes.ListDtype):\n return np.can_cast(from_dtype.leaf_type, to_dtype.leaf_type)\n else:\n return False\n elif isinstance(from_dtype, cudf.core.dtypes.CategoricalDtype):\n if isinstance(to_dtype, cudf.core.dtypes.CategoricalDtype):\n return True\n elif isinstance(to_dtype, np.dtype):\n return np.can_cast(from_dtype._categories.dtype, to_dtype)\n else:\n return False\n else:\n return np.can_cast(from_dtype, to_dtype)","def _check_dtype(ds_in, dset_attrs):\n dtype = dset_attrs['dtype']\n attrs = dset_attrs['attrs']\n if ds_in.dtype.name != dtype:\n msg = ('Source dtype ({}) does not match specified dtype ({}), '\n .format(ds_in.dtype, dtype))\n logger.warning(msg)\n warn(msg)\n float_to_int = (np.issubdtype(ds_in.dtype, np.floating)\n and np.issubdtype(dtype, np.integer))\n int_to_float = (np.issubdtype(ds_in.dtype, np.integer)\n and np.issubdtype(dtype, np.floating))\n if float_to_int:\n if not any(c for c in attrs if 'scale_factor' in c):\n msg = ('Cannot downscale from {} to {} without a '\n 'scale_factor!'.format(ds_in.dtype, dtype))\n logger.error(msg)\n raise RuntimeError(msg)\n else:\n msg = 'Converting {} to {}'.format(ds_in.dtype, dtype)\n logger.warning(msg)\n warn(msg)\n elif int_to_float:\n msg = ('Cannot scale up an {} to a {}'\n .format(ds_in.dtype, dtype))\n logger.error(msg)\n raise RuntimeError(msg)\n elif not np.issubdtype(dtype, ds_in.dtype):\n msg = ('Output dtype ({}) has greater precision than input '\n 'dtype ({}), using input dtype'\n .format(dtype, ds_in.dtype))\n logger.warning(msg)\n warn(msg)\n\n dset_attrs['dtype'] = ds_in.dtype\n\n return dset_attrs","def is_int(q):\n if isinstance(q, (Integer, int)):\n return True\n if isinstance(q, Rational):\n if q.denominator() == 1:\n return True\n if isinstance(q, tuple):\n return False\n try:\n if floor(q) == ceil(q):\n return True\n except TypeError:\n pass\n return False","def _check_dtype(self):\n\n # assert valid dtype\n if self.dtype not in PRIMITIVE_TYPES:\n raise ValueError(\"Type '{}' is invalid. Following types are \"\n \"allowed: {}\"\n .format(self.dtype, PRIMITIVE_TYPES.keys()))\n\n # assert valid dtypes for values\n allowed_types = PRIMITIVE_TYPES[self.dtype]\n\n for value in self.values:\n if not isinstance(value, allowed_types):\n raise TypeError(\"Column '{}' has invalud value '{}' with \"\n \"invalid type '{}'. Allowed types are: {}.\"\n .format(self.name,\n value,\n type(value),\n allowed_types))"],"string":"[\n \"def _is_integer(x):\\n return (not isinstance(x, (bool, np.bool))) and \\\\\\n isinstance(x, (numbers.Integral, int, np.int, np.long, long)) # no long type in python 3\",\n \"def is_int(x):\\n # From sktime: BSD 3-Clause\\n # boolean are subclasses of integers in Python, so explicitly exclude them\\n return isinstance(x, (int, np.integer)) and not isinstance(x, bool)\",\n \"def is_int(value):\\n return isinstance(value, int)\",\n \"def is_convertible_to_int(v: Any) -> bool:\\n\\n try:\\n test = int(v)\\n return True\\n except:\\n return False\",\n \"def isInteger(data):\\n\\tif type(data) == list or type(data) == np.ndarray:\\n\\t\\tcol = pd.Series(data)\\n\\telse:\\n\\t\\tcol = data\\n\\treturn col.dtype == np.int32 or col.dtype == np.int64\",\n \"def _is_int(test_val):\\n try:\\n int(test_val)\\n return True\\n except ValueError:\\n return False\",\n \"def is_int(self):\\n return self.value_type in (int, arrow.JuArrow)\",\n \"def DataIsInteger(self):\\n return self.data_type in (\\n definitions.REG_DWORD, definitions.REG_DWORD_BIG_ENDIAN,\\n definitions.REG_QWORD)\",\n \"def is_int(x):\\n # boolean are subclasses of integers in Python, so explicitly exclude them\\n return isinstance(x, (int, np.integer)) and not isinstance(x, bool)\",\n \"def isInteger(data):\\n\\ttry:\\n\\t\\tfrom types import LongType, IntType\\n\\t\\tif type(data) == LongType or type(data) == IntType:\\n\\t\\t\\treturn True\\n\\texcept ImportError:\\n\\t\\tif type(data) == type(int(0)):\\n\\t\\t\\treturn True\\n\\treturn False\",\n \"def _check_index(idx):\\n return isinstance(idx, _Int)\",\n \"def check_for_int(check):\",\n \"def is_integer(matrix):\\n return numpy.issubdtype(matrix.dtype, numpy.integer)\",\n \"def could_be_int(val):\\n if val == None:\\n return False\\n\\n if isinstance(val, int):\\n return True\\n\\n # allow coercion from str\\n if isinstance(val, (str, unicode)):\\n try:\\n i = int(val)\\n if not isinstance(i, int):\\n raise ValueError\\n else:\\n return True\\n except:\\n return False\\n\\n # otherwise\\n return False\",\n \"def is_int(x):\\n return int(x) == x\",\n \"def is_int(self): \\n return (self._den == 1)\",\n \"def check_if_input_is_int(self):\\n try:\\n int(self.input)\\n except ValueError:\\n return False\\n else:\\n return True\",\n \"def _is_valid_target_int(self, target):\\n if isinstance(target, (int, np.int, np.int8, np.int16, np.int32, np.int64)):\\n return True\\n else:\\n return False\",\n \"def is_int(n):\\n try:\\n int(n)\\n return True\\n except ValueError:\\n return False\",\n \"def _is_integer_like(input):\\n if _is_boolean_like(input):\\n return True\\n if type(input) is int:\\n return True\\n if isinstance(input, _ScalarConstant):\\n if input.dtype in _int_like_types:\\n return True\\n return False\",\n \"def is_int(self, val):\\n try:\\n int(val)\\n return True\\n except ValueError:\\n return False\",\n \"def is_intscalar(x: Any) -> bool:\\r\\n return isinstance(x, (\\r\\n int,\\r\\n np.int8,\\r\\n np.int16,\\r\\n np.int32,\\r\\n np.int64,\\r\\n np.uint8,\\r\\n np.uint16,\\r\\n np.uint32,\\r\\n np.uint64,\\r\\n ))\",\n \"def is_int(value):\\n try:\\n int(value)\\n except ValueError:\\n return False\\n else:\\n return True\",\n \"def is_int(value):\\n try:\\n int(value)\\n return True\\n except ValueError:\\n return False\",\n \"def is_int(value):\\n try:\\n int(value)\\n return True\\n except ValueError:\\n return False\",\n \"def is_int(num):\\n return int(num) == num\",\n \"def isinteger(self):\\n return self.den == 1\",\n \"def is_int(self, size=None):\\n return False\",\n \"def isInteger(self):\",\n \"def isInteger(self):\",\n \"def try_int(data):\\n try:\\n return int(data)\\n except (ValueError, TypeError ):\\n return data\",\n \"def is_int(*args): \\n try:\\n for i in args:\\n int(i)\\n return True\\n except Exception:\\n return False\",\n \"def is_integer(x):\\n try:\\n int(x)\\n return True\\n except ValueError:\\n return False\",\n \"def isInteger(self):\\n pass\",\n \"def isInteger(self):\\n return isinstance(self.value, int)\",\n \"def is_32_bit_int_compatible( i ) :\\r\\n\\r\\n #\\r\\n # can we convert the input to an integer value ? \\r\\n #\\r\\n\\r\\n try : \\r\\n\\r\\n ## debug:\\r\\n ## print \\\"compat: \\\", i, truncate_pyint_to_i32_interval(i)\\r\\n\\r\\n if i == truncate_pyint_to_i32_interval( i ) : \\r\\n ## # debug \\r\\n ## print \\\"compat: TRUE\\\", i, truncate_pyint_to_i32_interval(i)\\r\\n return True\\r\\n\\r\\n except :\\r\\n\\r\\n ## # debug : \\r\\n ## print \\\"compat: FALSE\\\", i, truncate_pyint_to_i32_interval(i)\\r\\n pass\\r\\n \\r\\n # else ... \\r\\n return False\",\n \"def __check_datatype(event, resp):\\n\\n def unsigned_int_check(val):\\n \\\"\\\"\\\"checks if something is a positive (unsigned) integer\\n\\n Arguments:\\n val (object): value to check\\n\\n Returns:\\n (bool): True if val is an integer, otherwise False\\n \\n \\\"\\\"\\\"\\n\\n is_valid_number = True\\n try:\\n val = int(val)\\n if val < 0:\\n raise ValueError(\\\"Not an unsigned int\\\")\\n except ValueError as e:\\n is_valid_number = False\\n \\n return is_valid_number\\n \\n # for start and end base, if not None, perform the number check\\n # if either is not an unsigned int, this is a BAD REQUEST\\n vals = [resp.get_datum(\\\"start\\\"), resp.get_datum(\\\"end\\\")]\\n for val in vals:\\n if val:\\n if not unsigned_int_check(val):\\n resp.set_status_code(SC.BAD_REQUEST)\\n resp.set_body(json.dumps({\\n \\\"message\\\": \\\"start/end must be unsigned int\\\"\\n }))\",\n \"def is_integer(value: Union[float, np.ndarray]) -> bool:\\n if type(value) == np.ndarray:\\n for entry in value:\\n result = Comparator.is_integer(entry)\\n if not result:\\n return False\\n return True\\n else:\\n value = abs(value)\\n value -= int(value)\\n if value > 0.5:\\n return Comparator.is_close_to_zero(1 - value)\\n return Comparator.is_close_to_zero(value)\",\n \"def is_integer(space, w_obj):\\n return space.wrap(w_obj.tp == space.tp_int)\",\n \"def able_int_type(values):\\n if any([v % 1 for v in values]):\\n return None\\n mn = min(values)\\n mx = max(values)\\n if mn >= 0:\\n for ityp in np.sctypes['uint']:\\n if mx <= np.iinfo(ityp).max:\\n return ityp\\n for ityp in np.sctypes['int']:\\n info = np.iinfo(ityp)\\n if mn >= info.min and mx <= info.max:\\n return ityp\\n return None\",\n \"def is_integer(i):\\n import numpy as np\\n if isinstance(i, (int, long)):\\n return True\\n if isinstance(i, float):\\n return (i).is_integer()\\n if issubclass(type(i), np.integer):\\n return i\\n else:\\n return False\",\n \"def is_int(self):\\n return self.v & 1 != 0\",\n \"def isint(s):\\n try:\\n x = int(s)\\n return True\\n except:\\n return False\",\n \"def is_int(symbol):\\n return isa(symbol, int)\",\n \"def dtype_int(dtype: DType):\\n # TODO: Is there a better way of doing this?\\n name = list(convert(dtype, NPDType).__name__)\\n while name and name[0] not in set([str(i) for i in range(10)]):\\n name.pop(0)\\n return _convert_back(_name_to_numpy_dtype(\\\"int\\\" + \\\"\\\".join(name)), dtype)\",\n \"def is_integer(x):\\r\\n if isinstance(x, float):\\r\\n return x == int(x)\\r\\n else:\\r\\n raise TypeError, \\\"Input float\\\"\",\n \"def ensure_int(cls, data):\\n if data is None: # Failed to fetch attribute. no need to convert.\\n return None\\n\\n # For some reason bool is considered an integral type. We want to avoid True\\n # to be converted to 1, and False to 0 on numeric matchers since it can be\\n # misleading.\\n if isinstance(data, numbers.Integral) and not isinstance(data, bool):\\n return data\\n\\n if not isinstance(data, str):\\n _LOGGER.error('Cannot convert %s to int. Failing.', type(data))\\n return None\\n\\n _LOGGER.warning(\\n 'Supplied attribute is of type %s and should have been an int. ',\\n type(data)\\n )\\n\\n try:\\n return int(data)\\n except ValueError:\\n _LOGGER.error('Cannot convert %s to int. Failing.', type(data))\\n return None\",\n \"def _is_number(data):\\n return len(data) and np.issubdtype(_to_ndarray(data).dtype, np.number)\",\n \"def is_int(num):\\n try:\\n if num is None:\\n return False;\\n int(num)\\n return True\\n except ValueError:\\n return False\",\n \"def check_if_int(self, data):\\n # Create a temporary blank list.\\n temp = []\\n \\n # Iterate over the elements in data and check whether they are an \\n # integer using the modulo logic. If the data is not an integer,\\n # add the index of that element to the list called temp.\\n for x in range(len(data)):\\n if data[x] % 1 != 0:\\n temp.append(x)\\n \\n # Assume that all values are integers.\\n is_int = True\\n \\n # Check whether the count of temp list exceeds 0. If it does,\\n # then it means that there is an element that is not an integer.\\n # Then convert the temp list into an array.\\n if len(temp) > 0:\\n is_int = False\\n index = np.array(temp)\\n \\n # This conditional statement will return only a Boolean value if\\n # data is all integers. Otherwise, it will also return an array of \\n # the indeces where there are non-integer values.\\n if is_int == True:\\n return is_int\\n else:\\n return [is_int, index]\",\n \"def execute_cast_datetime_to_integer(op, data, type, **kwargs):\\n return pd.Timestamp(data).value\",\n \"def test_basic_numpy_dtypes():\\n assert np.int != np.int8\\n assert np.int != np.int16\\n assert np.int != np.int32\\n assert np.int != np.int64\\n\\n assert np.int == int\\n assert np.int8 != int\\n assert np.int16 != int\\n assert np.int32 != int\\n assert np.int64 != int\\n\\n assert np.dtype(np.int) == np.dtype('int') == np.dtype(int)\\n assert np.dtype(np.int8) == np.dtype('int8') == np.int8\\n assert np.dtype(np.int16) == np.dtype('int16') == np.int16\\n assert np.dtype(np.int32) == np.dtype('int32') == np.int32\\n assert np.dtype(np.int64) == np.dtype('int64') == np.int64\",\n \"def is_int_value(int_value):\\n try:\\n int(int_value)\\n except ValueError:\\n return False\\n return True\",\n \"def isInteger(self):\\n return self._is_int\",\n \"def try_int_cast(value):\\n try: \\n return int(value)\\n except:\\n return value\",\n \"def _check_data(data, dset_attrs):\\n dtype = dset_attrs['dtype']\\n float_to_int = (np.issubdtype(dtype, np.integer)\\n and np.issubdtype(data.dtype, np.floating))\\n if float_to_int:\\n attrs = dset_attrs['attrs']\\n scale_factor = [c for c in attrs if 'scale_factor' in c][0]\\n scale_factor = attrs[scale_factor]\\n\\n # apply scale factor and dtype\\n data = np.multiply(data, scale_factor)\\n if np.issubdtype(dtype, np.integer):\\n data = np.round(data)\\n\\n data = data.astype(dtype)\\n\\n return data\",\n \"def test_datatype(self):\\n dates = pd.date_range(start=\\\"2007-01-01\\\", end=\\\"2007-02-01\\\")\\n\\n ts = pd.DataFrame(\\n {\\n \\\"var1\\\": np.arange(len(dates), dtype=np.int8),\\n \\\"var2\\\": np.arange(len(dates), dtype=np.int16),\\n \\\"var3\\\": np.arange(len(dates), dtype=np.int32),\\n \\\"var4\\\": np.arange(len(dates), dtype=np.int64)\\n },\\n index=dates)\\n\\n dataset_w = GriddedNcContiguousRaggedTs(self.testdatapath,\\n self.grid,\\n mode=\\\"w\\\")\\n\\n for gpi in self.gpis:\\n dataset_w.write(gpi, ts)\\n\\n dataset_r = GriddedNcContiguousRaggedTs(self.testdatapath,\\n self.grid,\\n mode=\\\"r\\\")\\n\\n for gpi in self.gpis:\\n arr = dataset_r.read(gpi)\\n assert (arr[\\\"var1\\\"].dtype == np.int8)\\n assert (arr[\\\"var2\\\"].dtype == np.int16)\\n assert (arr[\\\"var3\\\"].dtype == np.int32)\\n assert (arr[\\\"var4\\\"].dtype == np.int64)\",\n \"def __is_int(self,string):\\r\\n try: \\r\\n int(string)\\r\\n return True\\r\\n except ValueError:\\r\\n return False\",\n \"def isInt(s):\\n try:\\n int(s)\\n return True\\n except ValueError:\\n return False\",\n \"def is_int(self, size=None):\\n return self._is_of_size_if_not_none(size)\",\n \"def _isintvar(self, index):\\n return 65528 <= self._typlist[index] <= 65530\",\n \"def _isintvar(self, index):\\n return 251 <= self._typlist[index] <= 253\",\n \"def __checkInput(self, var):\\n try:\\n int(var)\\n\\n except:\\n return False\\n\\n else:\\n return True\",\n \"def is_integer(s: Union[str, int, float]):\\n try:\\n float(s)\\n except ValueError:\\n return False\\n else:\\n return float(s).is_integer() and not np.isnan(float(s))\",\n \"def is_int_like(val):\\n try:\\n return str(int(val)) == str(val)\\n except Exception:\\n return False\",\n \"def is_int_like(val):\\n try:\\n return str(int(val)) == str(val)\\n except Exception:\\n return False\",\n \"def _is_pos_int(number: int) -> bool:\\n return type(number) == int and number >= 0\",\n \"def represents_int(s):\\n try:\\n int(s)\\n return True\\n except ValueError:\\n return False\",\n \"def test_toint(number, expected, cond):\\n assert toInt(number, cond=cond) == expected\",\n \"def _is_non_negative_int(item):\\n if not isinstance(item, int):\\n return False\\n return item >= 0\",\n \"def can_insert(data):\\n if not isinstance(data, np.ndarray):\\n return False\\n if data.dtype.char in UNSUPPORTED_NUMERIC_TYPE_CODES:\\n return False\\n return np.issubdtype(data.dtype, np.number)\",\n \"def is_integer_type(self):\\n raise exceptions.NotImplementedError()\",\n \"def as_int(x, check=True):\\n x = np.array(x)\\n if x.dtype.kind in 'iu':\\n # This works around a nasty numpy 1.4.1 bug such that:\\n # >>> int(np.uint32(2**32-1)\\n # -1\\n return int(str(x))\\n ix = int(x)\\n if ix == x:\\n return ix\\n fx = np.floor(x)\\n if check and fx != x:\\n raise FloatingError('Not an integer: %s' % x)\\n if not fx.dtype.type == np.longdouble:\\n return int(x)\\n # Subtract float64 chunks until we have all of the number. If the int is\\n # too large, it will overflow\\n ret = 0\\n while fx != 0:\\n f64 = np.float64(fx)\\n fx -= f64\\n ret += int(f64)\\n return ret\",\n \"def deconstructable(self):\\n return isinstance(self.get(), int)\",\n \"def can_insert(data):\\n types = (float, complex, int, np.long)\\n if isinstance(data, types) and not isinstance(data, bool):\\n return True\\n elif isinstance(data, np.number):\\n return data.dtype.char not in UNSUPPORTED_NUMERIC_TYPE_CODES\",\n \"def datetime_checkinput(year, month, day):\\n try:\\n datetime.datetime(year, month, day)\\n except:\\n raise Invaliddatetimeinput\\n return 0\",\n \"def isint(str):\\n ok = 1\\n if not str:\\n return 0\\n try:\\n int(str)\\n except ValueError:\\n ok = 0\\n except TypeError:\\n ok = 0\\n return ok\",\n \"def execute_cast_timestamp_to_integer(op, data, type, **kwargs):\\n return data.value\",\n \"def tryCastToInt(number):\\n try:\\n return int(number)\\n except:\\n print(\\\"Error! Impossible to parse this variable\\\")\\n return 0\",\n \"def _validate_scalar(obj):\\n if not isinstance(obj, int):\\n raise TypeError(\\\"scalar must be an integer\\\")\",\n \"def _is_all_int(df_list: List[Union[dd.DataFrame, pd.DataFrame]], col: str) -> bool:\\n for df in df_list:\\n if col in df.columns:\\n srs = df[col]\\n if isinstance(srs, (dd.DataFrame, pd.DataFrame)):\\n for dtype in srs.dtypes:\\n if not is_integer_dtype(dtype):\\n return False\\n elif isinstance(srs, (dd.Series, pd.Series)):\\n if not is_integer_dtype(srs.dtype):\\n return False\\n else:\\n raise ValueError(f\\\"unprocessed type of data:{type(srs)}\\\")\\n return True\",\n \"def value_is_float_not_int(value):\\n # this is klugy and only needed to display deprecation warnings\\n try:\\n int(value)\\n return False\\n except ValueError:\\n try:\\n float(value)\\n return True\\n except ValueError:\\n return False\\n except TypeError:\\n return False\",\n \"def strict_integer_validator(cls, v: Any) -> int:\\n if int(v) != Decimal(v):\\n raise ValueError\\n return int(v)\",\n \"def _int_validator(arg):\\n if arg is None or type(arg) != int:\\n raise ValueError('Incorrect value: input should be an int')\",\n \"def check_for_float_and_int(check):\",\n \"def can_insert(data):\\n if not issparse(data):\\n return False\\n if data.dtype.char in UNSUPPORTED_NUMERIC_TYPE_CODES:\\n return False\\n return np.issubdtype(data.dtype, np.number)\",\n \"def _is_positive_int(item):\\n if not isinstance(item, int):\\n return False\\n return item > 0\",\n \"def check_pos_int(v):\\n status = True\\n try:\\n val = int(v)\\n if val <= 0:\\n status = False\\n except ValueError:\\n status = False\\n return status\",\n \"def is_num(var):\\n try:\\n int(var)\\n return True\\n except ValueError:\\n return False\",\n \"def isNumber(x):\\n return isinstance(x, (int, float))\",\n \"def test_safe_array_cast(self):\\n msg = '^Copying array of size \\\\(5, 5\\\\) to convert it in the ' \\\\\\n 'right format$'\\n with self.assertWarnsRegex(RuntimeWarning, msg):\\n PoissonRegression._safe_array(self.X.astype(int))\\n\\n msg = '^Copying array of size \\\\(3, 5\\\\) to create a ' \\\\\\n 'C-contiguous version of it$'\\n with self.assertWarnsRegex(RuntimeWarning, msg):\\n PoissonRegression._safe_array(self.X[::2])\\n\\n np.testing.assert_array_equal(self.X,\\n PoissonRegression._safe_array(self.X))\",\n \"def _check_dtype(input_dtype):\\n\\n product_version = tbe_platform.cce_conf.get_soc_spec(\\\"SOC_VERSION\\\")\\n if product_version in (\\\"Hi3796CV300ES\\\"):\\n if input_dtype == \\\"float32\\\":\\n raise RuntimeError(\\\"float32 is not support in ES\\\")\\n util.check_dtype_rule(input_dtype, (\\\"float16\\\",))\\n else:\\n util.check_dtype_rule(input_dtype, (\\\"float16\\\", \\\"float32\\\",))\",\n \"def is_sequence_of_int(items):\\n return all(isinstance(item, int) for item in items)\",\n \"def _isint(string, inttype=int):\\n return (\\n type(string) is inttype\\n or isinstance(string, (bytes, str))\\n and _isconvertible(inttype, string)\\n )\",\n \"def is_integer(value):\\n try:\\n return True, int(value)\\n except (ValueError, TypeError):\\n return False\",\n \"def isNumeric(data):\\n\\tif type(data) == list or type(data) == np.ndarray:\\n\\t\\tcol = pd.Series(data)\\n\\telse:\\n\\t\\tcol = data\\n\\treturn col.dtype == np.int32 or col.dtype == np.int64 or col.dtype == np.float32 or col.dtype == np.float64\",\n \"def _can_cast(from_dtype, to_dtype):\\n if cudf.utils.utils.is_na_like(from_dtype):\\n return True\\n if isinstance(from_dtype, type):\\n from_dtype = cudf.dtype(from_dtype)\\n if isinstance(to_dtype, type):\\n to_dtype = cudf.dtype(to_dtype)\\n\\n # TODO : Add precision & scale checking for\\n # decimal types in future\\n\\n if isinstance(from_dtype, cudf.core.dtypes.DecimalDtype):\\n if isinstance(to_dtype, cudf.core.dtypes.DecimalDtype):\\n return True\\n elif isinstance(to_dtype, np.dtype):\\n if to_dtype.kind in {\\\"i\\\", \\\"f\\\", \\\"u\\\", \\\"U\\\", \\\"O\\\"}:\\n return True\\n else:\\n return False\\n elif isinstance(from_dtype, np.dtype):\\n if isinstance(to_dtype, np.dtype):\\n return np.can_cast(from_dtype, to_dtype)\\n elif isinstance(to_dtype, cudf.core.dtypes.DecimalDtype):\\n if from_dtype.kind in {\\\"i\\\", \\\"f\\\", \\\"u\\\", \\\"U\\\", \\\"O\\\"}:\\n return True\\n else:\\n return False\\n elif isinstance(to_dtype, cudf.core.types.CategoricalDtype):\\n return True\\n else:\\n return False\\n elif isinstance(from_dtype, cudf.core.dtypes.ListDtype):\\n # TODO: Add level based checks too once casting of\\n # list columns is supported\\n if isinstance(to_dtype, cudf.core.dtypes.ListDtype):\\n return np.can_cast(from_dtype.leaf_type, to_dtype.leaf_type)\\n else:\\n return False\\n elif isinstance(from_dtype, cudf.core.dtypes.CategoricalDtype):\\n if isinstance(to_dtype, cudf.core.dtypes.CategoricalDtype):\\n return True\\n elif isinstance(to_dtype, np.dtype):\\n return np.can_cast(from_dtype._categories.dtype, to_dtype)\\n else:\\n return False\\n else:\\n return np.can_cast(from_dtype, to_dtype)\",\n \"def _check_dtype(ds_in, dset_attrs):\\n dtype = dset_attrs['dtype']\\n attrs = dset_attrs['attrs']\\n if ds_in.dtype.name != dtype:\\n msg = ('Source dtype ({}) does not match specified dtype ({}), '\\n .format(ds_in.dtype, dtype))\\n logger.warning(msg)\\n warn(msg)\\n float_to_int = (np.issubdtype(ds_in.dtype, np.floating)\\n and np.issubdtype(dtype, np.integer))\\n int_to_float = (np.issubdtype(ds_in.dtype, np.integer)\\n and np.issubdtype(dtype, np.floating))\\n if float_to_int:\\n if not any(c for c in attrs if 'scale_factor' in c):\\n msg = ('Cannot downscale from {} to {} without a '\\n 'scale_factor!'.format(ds_in.dtype, dtype))\\n logger.error(msg)\\n raise RuntimeError(msg)\\n else:\\n msg = 'Converting {} to {}'.format(ds_in.dtype, dtype)\\n logger.warning(msg)\\n warn(msg)\\n elif int_to_float:\\n msg = ('Cannot scale up an {} to a {}'\\n .format(ds_in.dtype, dtype))\\n logger.error(msg)\\n raise RuntimeError(msg)\\n elif not np.issubdtype(dtype, ds_in.dtype):\\n msg = ('Output dtype ({}) has greater precision than input '\\n 'dtype ({}), using input dtype'\\n .format(dtype, ds_in.dtype))\\n logger.warning(msg)\\n warn(msg)\\n\\n dset_attrs['dtype'] = ds_in.dtype\\n\\n return dset_attrs\",\n \"def is_int(q):\\n if isinstance(q, (Integer, int)):\\n return True\\n if isinstance(q, Rational):\\n if q.denominator() == 1:\\n return True\\n if isinstance(q, tuple):\\n return False\\n try:\\n if floor(q) == ceil(q):\\n return True\\n except TypeError:\\n pass\\n return False\",\n \"def _check_dtype(self):\\n\\n # assert valid dtype\\n if self.dtype not in PRIMITIVE_TYPES:\\n raise ValueError(\\\"Type '{}' is invalid. Following types are \\\"\\n \\\"allowed: {}\\\"\\n .format(self.dtype, PRIMITIVE_TYPES.keys()))\\n\\n # assert valid dtypes for values\\n allowed_types = PRIMITIVE_TYPES[self.dtype]\\n\\n for value in self.values:\\n if not isinstance(value, allowed_types):\\n raise TypeError(\\\"Column '{}' has invalud value '{}' with \\\"\\n \\\"invalid type '{}'. Allowed types are: {}.\\\"\\n .format(self.name,\\n value,\\n type(value),\\n allowed_types))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68389726","0.67490387","0.6626902","0.6598004","0.6508628","0.64784265","0.64210194","0.6398246","0.63903487","0.6372374","0.6356024","0.6314418","0.63074124","0.6301813","0.6293644","0.62889963","0.6231258","0.62195265","0.6181786","0.6180847","0.6174092","0.61200774","0.6115712","0.6110412","0.6110412","0.61072934","0.60870695","0.6042212","0.604044","0.604044","0.6036313","0.60162836","0.59937227","0.598876","0.5963861","0.59592474","0.59521055","0.59448063","0.5936501","0.58944297","0.5893645","0.5876832","0.5871717","0.5839987","0.5795291","0.5772561","0.57688135","0.5762725","0.5760685","0.5750205","0.5747403","0.5743813","0.57292974","0.572638","0.5716742","0.5708472","0.5706346","0.569493","0.56914073","0.5656927","0.56461906","0.5637468","0.56307924","0.5605929","0.5604141","0.5604141","0.5553575","0.55419236","0.55394876","0.55346894","0.5514877","0.5513188","0.5509554","0.5493034","0.54826176","0.54759634","0.54718316","0.5468175","0.5458709","0.545498","0.54368997","0.5425614","0.54206455","0.541494","0.5404758","0.5399461","0.53985846","0.5397147","0.5390614","0.5390587","0.5389908","0.53863066","0.53648907","0.53597236","0.53531134","0.5349853","0.5348376","0.53475314","0.5337949","0.53251743"],"string":"[\n \"0.68389726\",\n \"0.67490387\",\n \"0.6626902\",\n \"0.6598004\",\n \"0.6508628\",\n \"0.64784265\",\n \"0.64210194\",\n \"0.6398246\",\n \"0.63903487\",\n \"0.6372374\",\n \"0.6356024\",\n \"0.6314418\",\n \"0.63074124\",\n \"0.6301813\",\n \"0.6293644\",\n \"0.62889963\",\n \"0.6231258\",\n \"0.62195265\",\n \"0.6181786\",\n \"0.6180847\",\n \"0.6174092\",\n \"0.61200774\",\n \"0.6115712\",\n \"0.6110412\",\n \"0.6110412\",\n \"0.61072934\",\n \"0.60870695\",\n \"0.6042212\",\n \"0.604044\",\n \"0.604044\",\n \"0.6036313\",\n \"0.60162836\",\n \"0.59937227\",\n \"0.598876\",\n \"0.5963861\",\n \"0.59592474\",\n \"0.59521055\",\n \"0.59448063\",\n \"0.5936501\",\n \"0.58944297\",\n \"0.5893645\",\n \"0.5876832\",\n \"0.5871717\",\n \"0.5839987\",\n \"0.5795291\",\n \"0.5772561\",\n \"0.57688135\",\n \"0.5762725\",\n \"0.5760685\",\n \"0.5750205\",\n \"0.5747403\",\n \"0.5743813\",\n \"0.57292974\",\n \"0.572638\",\n \"0.5716742\",\n \"0.5708472\",\n \"0.5706346\",\n \"0.569493\",\n \"0.56914073\",\n \"0.5656927\",\n \"0.56461906\",\n \"0.5637468\",\n \"0.56307924\",\n \"0.5605929\",\n \"0.5604141\",\n \"0.5604141\",\n \"0.5553575\",\n \"0.55419236\",\n \"0.55394876\",\n \"0.55346894\",\n \"0.5514877\",\n \"0.5513188\",\n \"0.5509554\",\n \"0.5493034\",\n \"0.54826176\",\n \"0.54759634\",\n \"0.54718316\",\n \"0.5468175\",\n \"0.5458709\",\n \"0.545498\",\n \"0.54368997\",\n \"0.5425614\",\n \"0.54206455\",\n \"0.541494\",\n \"0.5404758\",\n \"0.5399461\",\n \"0.53985846\",\n \"0.5397147\",\n \"0.5390614\",\n \"0.5390587\",\n \"0.5389908\",\n \"0.53863066\",\n \"0.53648907\",\n \"0.53597236\",\n \"0.53531134\",\n \"0.5349853\",\n \"0.5348376\",\n \"0.53475314\",\n \"0.5337949\",\n \"0.53251743\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8405704"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":20076,"cells":{"query":{"kind":"string","value":"Calculate a percentile of the array values over labeled regions."},"document":{"kind":"string","value":"def percentile(data, qval, labels=None, index=None):\n data = np.asanyarray(data)\n\n def single_group(vals):\n return np.percentile(vals, qval)\n\n if labels is None:\n return single_group(data)\n\n # ensure input and labels match sizes\n data, labels = np.broadcast_arrays(data, labels)\n\n if index is None:\n mask = labels > 0\n return single_group(data[mask])\n\n if np.isscalar(index):\n mask = labels == index\n return single_group(data[mask])\n\n # remap labels to unique integers if necessary, or if the largest\n # label is larger than the number of values.\n if (\n not _safely_castable_to_int(labels.dtype)\n or labels.min() < 0\n or labels.max() > labels.size\n ):\n # remap labels, and indexes\n unique_labels, labels = np.unique(labels, return_inverse=True)\n idxs = np.searchsorted(unique_labels, index)\n\n # make all of idxs valid\n idxs[idxs >= unique_labels.size] = 0\n found = unique_labels[idxs] == index\n else:\n # labels are an integer type, and there aren't too many.\n idxs = np.asanyarray(index, int).copy()\n found = (idxs >= 0) & (idxs <= labels.max())\n\n idxs[~found] = labels.max() + 1\n\n # reorder data and labels, first by labels, then by data\n order = np.lexsort((data.ravel(), labels.ravel()))\n data = data.ravel()[order]\n labels = labels.ravel()[order]\n\n locs = np.arange(len(labels))\n lo = np.zeros(labels.max() + 2, int)\n lo[labels[::-1]] = locs[::-1]\n hi = np.zeros(labels.max() + 2, int)\n hi[labels] = locs\n lo = lo[idxs]\n hi = hi[idxs]\n # lo is an index to the lowest value in input for each label,\n # hi is an index to the largest value.\n\n # here starts the part that really diverts from scipy's median finder; the\n # linear interpolation method used corresponds to the default behaviour of\n # np.percentile().\n size = hi - lo + 1 # size of the group\n frac = (size - 1) * (qval / 100) # fractional index relative to lo\n hi = lo - np.int64(-frac // 1) # ceiled absolute index to data\n lo = lo + np.int64(frac // 1) # floored absolute index to data\n part = frac % 1 # fractional part of index\n return (data[lo] + part * (data[hi] - data[lo])).tolist()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _calc_perc(arr: np.array, p: Sequence[float] = None):\n if p is None:\n p = [50]\n\n nan_count = np.isnan(arr).sum(axis=-1)\n out = np.moveaxis(np.percentile(arr, p, axis=-1), 0, -1)\n nans = (nan_count > 0) & (nan_count < arr.shape[-1])\n if np.any(nans):\n out_mask = np.stack([nans] * len(p), axis=-1)\n # arr1 = arr.reshape(int(arr.size / arr.shape[-1]), arr.shape[-1])\n # only use nanpercentile where we need it (slow performance compared to standard) :\n out[out_mask] = np.moveaxis(\n np.nanpercentile(arr[nans], p, axis=-1), 0, -1\n ).ravel()\n return out","def matlab_percentile(in_data, percentiles):\n data = np.sort(in_data)\n p_rank = 100.0 * (np.arange(data.size) + 0.5) / data.size\n perc = np.interp(percentiles, p_rank, data, left=data[0], right=data[-1])\n return perc","def percentile(scores, student_score):\n scores = np.array(sorted(scores))\n num_scores = len(scores)\n return round(sum(scores <= student_score) / float(num_scores) * 100, 2)","def percentile(self, values, percent):\r\n if not values:\r\n return None\r\n k = (len(values)-1) * percent\r\n f = math.floor(k)\r\n c = math.ceil(k)\r\n if f == c:\r\n return values[int(k)]\r\n d0 = values[int(f)] * (c-k)\r\n d1 = values[int(c)] * (k-f)\r\n return d0+d1","def get_percentile(obs, bootstrap):\n if np.isnan(obs):\n return np.nan\n else:\n return np.searchsorted(np.sort(bootstrap), obs) / len(bootstrap)","def test_lots_of_percentiles(self):\n data = np.array(\n [\n [[14.4, -46, 10.2], [-73.0, -46, -89.2], [-89.2, -93.25, -94.6]],\n [[19.2, 8.25, 10.6], [-19, 8.25, -67.6], [-67.6, -79.75, -83.8]],\n [[24.0, 8.75, 11.0], [8.33333, 8.75, -46.0], [-46.0, -66.25, -73.0]],\n [[28.8, 9.25, 11.4], [9.0, 9.25, -24.4], [-24.4, -52.75, -62.2]],\n [[33.6, 9.75, 11.8], [9.666667, 9.75, -2.8], [-2.8, -39.25, -51.4]],\n [\n [38.4, 10.333333, 16.8],\n [10.333333, 10.2, 8.5],\n [8.333333, -25.75, -40.6],\n ],\n [[43.2, 11.0, 26.4], [11.0, 10.6, 9.5], [9.0, -12.25, -29.8]],\n [\n [48.0, 11.666667, 36.0],\n [11.666667, 11.0, 10.5],\n [9.666667, 1.25, -19.0],\n ],\n [[52.8, 24, 45.6], [24, 11.4, 11.5], [10.5, 8.5, -8.2]],\n [[57.6, 48, 55.2], [48, 11.8, 36.0], [11.5, 9.5, 2.6]],\n ],\n dtype=np.float32,\n )\n\n percentiles = np.arange(5, 100, 10)\n result = Plugin()._probabilities_to_percentiles(self.cube, percentiles)\n self.assertArrayAlmostEqual(result.data, data, decimal=5)","def compute_percentile(value, cutoffs):\n\tif value < cutoffs[0]:\n\t\treturn 0.0\n\n\tfor i, cutoff in enumerate(cutoffs):\n\t\tif value < cutoff:\n\t\t\treturn math.floor(100 * (float(i)/(len(cutoffs))))\n\t\t\tbreak\n\treturn 100.0","def percentile(N, percent):\n N.sort()\n if not N:\n return None\n k = (len(N) - 1) * percent\n f = math.floor(k)\n c = math.ceil(k)\n if f == c:\n return N[int(k)]\n d0 = N[int(f)] * (c - k)\n d1 = N[int(c)] * (k - f)\n return d0 + d1","def calc_percentile(self, p):\n bounds = self.range_bins\n r = []\n v = []\n for b in bounds:\n d = self._get_data_distance(0., b)\n if len(d) < 1:\n continue\n r.append(b)\n v.append(np.percentile(d, p * 100.)) # percentile value\n\n r = np.asarray(r)\n np.asarray(v)\n\n o = {'r': np.asarray(r), 'value': np.asarray(v)}\n if 'percentiles' not in self.statistic.keys():\n self.statistic.update({'percentiles': {}})\n\n self.statistic['percentiles'].update({p: o})","def describe_reduce_nb(col, a, perc, ddof, *args):\n a = a[~np.isnan(a)]\n out = np.empty(5 + len(perc), dtype=np.float_)\n out[0] = len(a)\n if len(a) > 0:\n out[1] = np.mean(a)\n out[2] = nanstd_1d_nb(a, ddof=ddof)\n out[3] = np.min(a)\n out[4:-1] = np.percentile(a, perc * 100)\n out[4 + len(perc)] = np.max(a)\n else:\n out[1:] = np.nan\n return out","def stratify_array(array, num_percentiles=4):\n\n percentiles = [ii*100/num_percentiles for ii in list(range(0,num_percentiles+1))]\n stratif_labels = np.copy(array)\n\n for ii in range(0,len(percentiles)-1):\n lo = percentiles[ii]\n hi = percentiles[ii+1]\n plo = np.percentile(array, lo)\n phi = np.percentile(array, hi)\n stratif_labels[( plo <= array) & (array <= phi )] = ii+1\n\n return stratif_labels","def _percentile(self, data, percent):\n if not data:\n return None\n k = (len(data) - 1) * percent\n f = math.floor(k)\n c = math.ceil(k)\n if f == c:\n return data[int(k)]\n d0 = data[int(f)] * (c - k)\n d1 = data[int(c)] * (k - f)\n return d0 + d1","def percentile(N, percent):\n if not N:\n return None\n k = (len(N)-1) * percent\n f = math.floor(k)\n c = math.ceil(k)\n if f == c:\n return N[int(k)]\n d0 = N[int(f)] * (c-k)\n d1 = N[int(c)] * (k-f)\n return d0+d1","def percentile(self, percentile):\n assert(percentile >= 0 and percentile <= 100)\n assert(self.kind in [\"exponential\", \"linear\", \"enumerated\", \"boolean\"])\n\n fraction = percentile/100\n to_count = fraction*self.buckets.sum()\n percentile_bucket = 0\n\n for percentile_bucket in range(len(self.buckets)):\n freq = self.buckets.values[percentile_bucket]\n if to_count - freq <= 0:\n break\n to_count -= freq\n\n percentile_lower_boundary = self.buckets.index[percentile_bucket]\n percentile_frequency = self.buckets.values[percentile_bucket]\n\n if percentile_bucket == len(self.buckets) - 1 or percentile_frequency == 0:\n return percentile_lower_boundary\n\n width = self.buckets.index[percentile_bucket + 1] - self.buckets.index[percentile_bucket]\n return percentile_lower_boundary + width*to_count/percentile_frequency","def getPercentileAp(tpf, percentile=80, both=False):\n # Find all targets in image; those above the percentile mark\n median_image = np.nanmedian(tpf.flux, axis=0)\n above_percentile = median_image > np.nanpercentile(median_image, percentile)\n\n # Return only the contiguous region closest to `region`.\n # Method borrowed from lightkurve create_threshold_mask() function\n reference_pixel = (tpf.shape[2] / 2, tpf.shape[1] / 2)\n # First, label all the regions:\n labels = label(above_percentile)[0]\n # For all pixels above threshold, compute distance to reference pixel:\n label_args = np.argwhere(labels > 0)\n distances = [np.hypot(crd[0], crd[1]) for crd in label_args - np.array([reference_pixel[1], reference_pixel[0]])]\n # Which label corresponds to the centermost pixel?\n closest_arg = label_args[np.argmin(distances)]\n closest_label = labels[closest_arg[0], closest_arg[1]]\n percentile_ap = labels == closest_label\n\n if both:\n return percentile_ap, above_percentile\n else:\n return percentile_ap","def percentile(self, pct):\n return percentile(self.results, pct, interpolation='nearest')","def wilks(percentiles, alpha=0.10):\n percentiles = np.asarray(percentiles)\n pvals = list(percentiles.flat) # want in decimal\n pvals = sorted(2 * min(pval, 1 - pval) for pval in pvals)\n ptest = [alpha * i / len(pvals) for i in range(len(pvals))]\n ppick = max(pv for pv, pt in zip(pvals, ptest) if pv <= pt) / 2\n mask = (percentiles <= ppick) | (percentiles >= (1 - ppick))\n return percentiles[mask]","def robust_median_from_percentiles(array,percentiles=()):\n\n if percentiles:\n percentiles = np.array(percentiles)\n SigmaVector = scipy.stats.norm.ppf(percentiles/100.)\n else:\n percentiles = np.array([10.,20.,30.,40.,45.])\n SigmaVector = np.array([-1.28155157, -0.84162123, -0.52440051, -0.2533471 , -0.12566135])\n\n PercentileValues = np.percentile(array,percentiles)\n \n sig, med = fit_slope_1d(SigmaVector,PercentileValues)\n\n return med","def percentile(field, q):\n # https://gist.github.com/spezold/42a451682422beb42bc43ad0c0967a30\n # Note that ``kthvalue()`` works one-based, i.e. the first sorted value\n # indeed corresponds to k=1, not k=0! Use float(q) instead of q directly,\n # so that ``round()`` returns an integer, even if q is a np.float32.\n k = 1 + round(.01 * float(q) * (field.shape[1] - 1))\n result = field.kthvalue(k, dim=1).values\n return result","def ninetieth_percentile(y_test, y_pred) :\n return np.abs(y_test - y_pred).sort_values().iloc[int(len(y_test)*0.90)]/60","def get_percentile(before, level):\n snr = np.array(before.getColumnByName('snr')[:])\n return np.percentile(snr, level)","def percentile(histogram, percentile=0.75):\n covered = 0\n normalization = sum(histogram.values())\n for key, frequency in sorted(histogram.items(), reverse=True):\n covered += frequency\n assert covered <= normalization\n if covered > ((1.0 - percentile) * normalization):\n return key\n raise RuntimeError('Percentile computation should have terminated '\n 'mid-loop.')","def percentile(data, percentiles, weights=None):\n # check if actually weighted percentiles is needed\n if weights is None:\n return np.percentile(data, list(percentiles))\n if np.equal(weights, 1.).all():\n return np.percentile(data, list(percentiles))\n\n # make sure percentiles are fractions between 0 and 1\n if not np.greater_equal(percentiles, 0.0).all():\n raise ValueError(\"Percentiles less than 0\")\n if not np.less_equal(percentiles, 100.0).all():\n raise ValueError(\"Percentiles greater than 100\")\n\n # Make sure data is in correct shape\n shape = np.shape(data)\n n = len(data)\n if (len(shape) != 1):\n raise ValueError(\"wrong data shape, expecting 1d\")\n\n if len(weights) != n:\n print(n, len(weights))\n raise ValueError(\"weights must be the same shape as data\")\n if not np.greater_equal(weights, 0.0).all():\n raise ValueError(\"Not all weights are non-negative.\")\n\n _data = np.asarray(data, dtype=float)\n\n if hasattr(percentiles, '__iter__'):\n _p = np.asarray(percentiles, dtype=float) * 0.01\n else:\n _p = np.asarray([percentiles * 0.01], dtype=float)\n\n _wt = np.asarray(weights, dtype=float)\n\n len_p = len(_p)\n sd = np.empty(n, dtype=float)\n sw = np.empty(n, dtype=float)\n aw = np.empty(n, dtype=float)\n o = np.empty(len_p, dtype=float)\n\n i = np.argsort(_data)\n np.take(_data, i, axis=0, out=sd)\n np.take(_wt, i, axis=0, out=sw)\n np.add.accumulate(sw, out=aw)\n\n if not aw[-1] > 0:\n raise ValueError(\"Nonpositive weight sum\")\n\n w = (aw - 0.5 * sw) / aw[-1]\n\n spots = np.searchsorted(w, _p)\n for (pk, s, p) in zip(range(len_p), spots, _p):\n if s == 0:\n o[pk] = sd[0]\n elif s == n:\n o[pk] = sd[n - 1]\n else:\n f1 = (w[s] - p) / (w[s] - w[s - 1])\n f2 = (p - w[s - 1]) / (w[s] - w[s - 1])\n o[pk] = sd[s - 1] * f1 + sd[s] * f2\n return o","def prctile(data, p_vals=[0, 25, 50, 75, 100], sorted_=False):\r\n ps = [p_vals] if np.isscalar(p_vals) else p_vals\r\n\r\n if not sorted_:\r\n data = sorted(data)\r\n n = len(data)\r\n d = []\r\n for p in ps:\r\n fi = p * n / 100 - 0.5\r\n if fi <= 0: # maybe extrapolate?\r\n d.append(data[0])\r\n elif fi >= n - 1:\r\n d.append(data[-1])\r\n else:\r\n i = int(fi)\r\n d.append((i+1 - fi) * data[i] + (fi - i) * data[i+1])\r\n return d[0] if np.isscalar(p_vals) else d","def percentile(data, statfxn, niter=10000, alpha=0.05):\n\n index = _make_boot_index(data.shape[0], niter)\n boot_stats = statfxn(data[index], axis=-1)\n\n # compute the `alpha/2` and `1-alpha/2` percentiles of `boot_stats`\n CI = numpy.percentile(boot_stats, [alpha * 50, 100 - (alpha * 50)], axis=0)\n\n return CI","def _quantile_normalization(arr, mode=\"mean\"):\n n = len(arr)\n perc = percentileofscore\n arr_ = arr.copy()[~np.isnan(arr)]\n out = np.zeros(n)\n for i in range(n):\n if not np.isnan(arr[i]):\n out[i] = norm.ppf(perc(arr_, arr[i], mode) / 100.)\n else:\n out[i] = np.nan\n return out","def get_percentile(N, percent, key=lambda x:x):\r\n if not N:\r\n return 0\r\n k = (len(N)-1) * percent\r\n f = math.floor(k)\r\n c = math.ceil(k)\r\n if f == c:\r\n return key(N[int(k)])\r\n d0 = key(N[int(f)]) * (c-k)\r\n d1 = key(N[int(c)]) * (k-f)\r\n return d0+d1","def get_percentile(N, percent, key=lambda x:x):\r\n if not N:\r\n return 0\r\n k = (len(N)-1) * percent\r\n f = math.floor(k)\r\n c = math.ceil(k)\r\n if f == c:\r\n return key(N[int(k)])\r\n d0 = key(N[int(f)]) * (c-k)\r\n d1 = key(N[int(c)]) * (k-f)\r\n return d0+d1","def _sdc_pandas_format_percentiles(arr):\n\n percentiles_strs = []\n for percentile in arr:\n p_as_string = str(percentile * 100)\n\n trim_index = len(p_as_string) - 1\n while trim_index >= 0:\n if p_as_string[trim_index] == '0':\n trim_index -= 1\n continue\n elif p_as_string[trim_index] == '.':\n break\n\n trim_index += 1\n break\n\n if trim_index < 0:\n p_as_string_trimmed = '0'\n else:\n p_as_string_trimmed = p_as_string[:trim_index]\n\n percentiles_strs.append(p_as_string_trimmed + '%')\n\n return percentiles_strs","def samples_to_percentiles(samples):\n pcnts = [np.percentile(s, [16,50,84]) for s in samples.T]\n return np.array([[p[1], p[1]-p[0], p[2]-p[1]] for p in pcnts])","def test__quantile(self):\r\n # regular cases\r\n sample_data = array(range(25, 42))\r\n assert_almost_equal(_quantile(sample_data, 0.5), median(sample_data))\r\n\r\n # sorted data is assumed for this function\r\n sample_data = sorted(\r\n array([0.17483293, 0.99891939, 0.81377467, 0.8137437,\r\n 0.51990174, 0.35521497, 0.98751461]))\r\n assert_almost_equal(_quantile(sample_data, 0.10), 0.283062154)","def stack_percentile(arrs, q=50, nodata=None):\r\n a = check_stack(arrs)\r\n if nodata is not None:\r\n a = mask_stack(a, nodata=nodata)\r\n nan_per = np.nanpercentile(a, q=q, axis=0)\r\n return nan_per","def estimate_percentile(self, percentile):\n if percentile > 1.0:\n raise ValueError(\"Percentile must be between 0 and 1.\")\n\n if self.__total_count == 0:\n return None\n\n # The first step is to calculate which bucket this percentile lands in. We do this by calculating the \"index\"\n # of what that percentile's sample would have been. For example, if we are calculating the 75% and there were\n # 100 values, then the 75% would be the 75th value in sorted order.\n target_count = self.__total_count * percentile\n\n cumulative_count = 0\n\n # Now find the bucket by going over the buckets, keeping track of the cumulative counts across all buckets.\n for bucket_count, lower_bound, upper_bound in self.buckets(\n disable_last_bucket_padding=True\n ):\n cumulative_count = cumulative_count + bucket_count\n if target_count <= cumulative_count:\n # Ok, we found the bucket. To minimize error, we estimate the value of the percentile to be the\n # midpoint between the lower and upper bounds.\n return (upper_bound + lower_bound) / 2.0\n\n # We should never get here because target_count will always be <= the total counts across all buckets.","def percentile_filter(data, feats):\n\n # Determines the fraction of nonzero values in the data.\n sparsity_frac = feats['nonzeros'] / (feats['nrows'] * feats['ncols'])\n\n # NOTE: Added convenience term derived from experience.\n thresh = np.percentile(data.ravel(), q=100 * (1 - (sparsity_frac + 0.1)))\n\n # Replace p-values below threshold with zero.\n data[(data > 0) & (data < thresh)] = 0\n\n return data","def get_percentile(histogram, percentile):\n n_percentile = None\n sorted_set_of_keys = sorted(histogram.keys())\n sum_of_values = sum(histogram.values())\n percentile_index = sum_of_values * (percentile / 100)\n index_sum = 0\n if len(sorted_set_of_keys) < 1:\n n_percentile = 0\n else:\n for i in range(len(sorted_set_of_keys)):\n k = sorted_set_of_keys[i]\n index_sum += histogram.get(k)\n if index_sum > percentile_index:\n n_percentile = k\n break\n elif index_sum == percentile_index:\n n_percentile = (k + sorted_set_of_keys[i + 1])/2\n break\n return n_percentile","def p(n):\n def p_(x):\n return np.percentile(x, n)\n\n p_.__name__ = \"p_%s\" % n\n return p_","def perc(data,percentile):\n clip_value = np.percentile(data,percentile)\n data = clip(data,clip_value)\n return data","def test_check_data_specifying_percentiles(self):\n expected_data = np.array(\n [self.percentile_25, self.percentile_50, self.percentile_75]\n )\n result = Plugin().process(self.cube, percentiles=[25, 50, 75])\n self.assertArrayAlmostEqual(result.data, expected_data, decimal=5)","def Quartiles(data):\n q = np.percentile(data, [25, 50, 75])\n\n return q[0], q[1], q[2]","def quartiles(x, percentile):\n length = len(x)\n\n if percentile == 25:\n center = length // 4\n elif percentile == 75:\n center = length // 2 + length // 4\n\n x.sort()\n\n if length % 2 == 0:\n return (x[center - 1] + x[center]) / 2\n else:\n return x[center]","def _ComputePercentiles(buckets, total, percentiles):\n if not percentiles:\n return {}\n current_count = 0\n current_percentage = 0\n next_percentile_index = 0\n result = {}\n for bucket in buckets:\n if bucket['count'] > 0:\n current_count += bucket['count']\n old_percentage = current_percentage\n current_percentage = float(current_count) / total\n\n # Check whether we passed one of the percentiles we're interested in.\n while (next_percentile_index < len(percentiles) and\n current_percentage > percentiles[next_percentile_index]):\n if not 'high' in bucket:\n result[percentiles[next_percentile_index]] = bucket['low']\n else:\n result[percentiles[next_percentile_index]] = float(_BucketInterpolate(\n old_percentage, percentiles[next_percentile_index],\n current_percentage, bucket['low'], bucket['high']))\n next_percentile_index += 1\n return result","def _pctiles_from_sorted(self, values, pcs):\n if not all(0 <= pc <= 100 for pc in pcs):\n raise ValueError(\"pctiles must be between 0 and 100\")\n nvals = len(values)\n pctiles = []\n for pc in pcs:\n if pc == 0:\n new_pct = values[0]\n elif pc == 100:\n new_pct = values[nvals-1]\n else:\n n = pc * nvals / 100\n if n == int(n):\n new_pct = (values[int(n)-1] + values[int(n)]) / 2\n else:\n new_pct = values[floor(n)]\n pctiles.append(new_pct)\n return pctiles","def percentile_discrete(self, x, n=20):\n res = np.array([0] * x.shape[-1], dtype=int)\n # logging.info(\"before_counter: \" + str(Counter(x)))\n x_temp = x[x != -1.0]\n interval_list = []\n for i in range(1+n):\n if i == 0:\n x1 = x[np.where(x == -1.0)]\n mask = np.in1d(x, x1)\n res[mask] = (i + 1)\n # logging.info(\"discrete: \" + str((-1.0, -1.0)))\n point1, point2 = -1, -1\n else:\n point1, point2 = stats.scoreatpercentile(x_temp, [(i-1)*100/n, i*100/n])\n x1 = x[np.where((x >= point1) & (x <= point2))]\n mask = np.in1d(x, x1)\n res[mask] = (i + 1)\n # logging.info(\"discrete: \" + str(res) + str((point1, point2)))\n interval_list.append((point1, point2))\n # logging.info(\"mask: \" + str(mask))\n # logging.info(\"discrete_main: \" + str(res))\n # logging.info(\"discrete_counter: \" + str(Counter(res)))\n return res, interval_list","def mapPercentile(scalarMap, percentile, area = False):\n \n arr = pcr.pcr2numpy(scalarMap, -9999).flatten()\n arr = arr[arr[:] > -9999]\n score = np.percentile(arr, percentile)\n if area == False:\n return score\n elif area == True:\n percentileArea = pcr.ifthen(scalarMap < score, scalarMap)\n return score, percentileArea","def iqr(self, arr):\n a = np.asarray(arr)\n self.q1 = stats.scoreatpercentile(a, 25)\n self.q2 = stats.scoreatpercentile(a, 50)\n self.q3 = stats.scoreatpercentile(a, 75)","def keep_top_percentile(image, percentile, *args, **kwargs):\n # TODO: Implement the method\n\n imageSort = num.sort(image, axis=None)\n elem = int(len(imageSort) * (percentile/100))\n big = num.zeros(elem)\n\n k = len(imageSort)-1\n for i in range(0, elem):\n if imageSort[k] in big:\n k -= 1\n else:\n big[i] = imageSort[k]\n k -= 1\n\n thresholded = num.zeros((640, 480))\n for i in range (0, 640):\n for j in range (0, 480):\n l = image[i][j]\n if l in big:\n thresholded[i][j] = image[i][j]\n else:\n thresholded[i][j] = 0\n\n save_image_to_h5(thresholded, \"thresholded.h5\")\n \n\n return thresholded","def get_percentile(self, q):\n return None","def get_percentile(self, q):\n return None","def percentile(p):\n assert 1.0 > p >= 0.0\n\n def __p1(ls):\n k = math.floor(len(ls) * p)\n assert k >= 0\n assert k < len(ls)\n\n start = 0\n end = len(ls) - 1\n\n while start <= end:\n pivot_index = start\n pivot_val = ls[pivot_index]\n\n for i in range(pivot_index + 1, end + 1):\n if ls[i] >= pivot_val:\n continue\n else:\n ls[pivot_index] = ls[i]\n pivot_index += 1\n ls[i] = ls[pivot_index]\n\n ls[pivot_index] = pivot_val\n if pivot_index == k:\n return pivot_val\n elif pivot_index > k:\n end = pivot_index - 1\n else:\n start = pivot_index + 1\n\n return ls[start]\n\n return __p1","def test_profiled_quantiles(self):\n\n # this data has 4 bins, range of 3\n # with equal bin size, each bin has the width of 0.75\n\n data = [\"1.0\", \"2.0\", \"3.0\", \"4.0\"]\n df = pd.Series(data)\n profiler = FloatColumn(df.name)\n profiler.update(df)\n profile = profiler.profile\n\n est_quartiles = profile['quantiles']\n est_Q1 = est_quartiles[249]\n est_Q2 = est_quartiles[499]\n est_Q3 = est_quartiles[749]\n\n data_to_num = [float(item) for item in data]\n exact_Q1 = np.percentile(data_to_num, 25)\n exact_Q2 = np.percentile(data_to_num, 50)\n exact_Q3 = np.percentile(data_to_num, 75)\n\n self.assertEqual(est_Q1, exact_Q1)\n self.assertEqual(est_Q2, exact_Q2)\n self.assertEqual(est_Q3, exact_Q3)","def percentile_points(axis, pdf, percentiles):\n cdf_points = np.zeros(len(percentiles), dtype=float)\n\n for index, percentile in enumerate(percentiles):\n try:\n # oritinal\n #cdf = np.cumsum(pdf) / float(np.sum(pdf))\n #cdf = np.cumsum(pdf) / np.sum(pdf)\n #cdf_points[index] = np.nanmin(axis[cdf >= percentile])\n percentile_range = (np.cumsum(pdf) >= (percentile * np.sum(pdf)))\n cdf_points[index] = np.nanmin(axis[percentile_range])\n except FloatingPointError:\n print \"percentile_points: invalid PDF\"\n cdf = np.zeros_like(pdf)\n cdf_points[index] = -1.\n\n return cdf_points","def get_percentile(self, q):\n return self.sum_stat_sample_ratio.get_percentile(q)","def percentage_od_xy(range,count):\n percentages = []\n for i in range:\n percentages.append(n_percentage_part(i, count))\n\n percentile_list = pd.DataFrame(\n {'percentage': range,\n 'od_needed': percentages\n })\n return percentile_list","def get_percentile(data_list, score, kind='weak'):\n n = len(data_list)\n\n if kind == 'strict':\n return len([i for i in data_list if i < score]) / float(n) * 100\n elif kind == 'weak':\n return len([i for i in data_list if i <= score]) / float(n) * 100\n elif kind == 'mean':\n return (len([i for i in data_list if i < score]) + len([i for i in data_list if i <= score])) * 50 / float(n)\n else:\n raise ValueError(\"The kind kwarg must be 'strict', 'weak' or 'mean'. You can also opt to leave it out and rely on the default method.\")","def IQR(data):\n return percentile(data, 75) - percentile(data, 25)","def get_IQR(lst):\n return (float(np.percentile(lst, 75)) - float(np.percentile(lst, 25)))","def transfo_quantile(xx):\n\n # nn = np.zeros(len(xx))\n # oo = np.argsort(xx)\n # nn[oo] = np.arange(len(xx)) / len(xx) + 1 / (2 * len(xx))\n # return nn\n\n return rankdata(xx) / len(xx) - 1 / (2 * len(xx))","def quantile(a, prob):\n a=numpy.asanyarray(a)\n a=a[numpy.logical_not(numpy.isnan(a))].ravel()\n n=a.size\n\n if prob>=1-.5/n:\n return a.max()\n elif prob<=.5/n:\n return a.min()\n\n # find the two bounds we're interpreting between:\n # that is, find i such that (i+.5) / n <= prob <= (i+1.5)/n\n t=n*prob-.5\n i=int(t)\n\n # partial sort so that the ith element is at position i, with bigger ones\n # to the right and smaller to the left\n a.sort()\n\n if i==t: # did we luck out and get an integer index?\n return a[i]\n else:\n # we'll linearly interpolate between this and the next index\n smaller=a[i]\n larger=a[i+1:].min()\n if numpy.isinf(smaller):\n return smaller # avoid inf - inf\n return smaller+(larger-smaller)*(t-i)","def get_quantile_thresholds(ds, quantile, dim, lat_name='lat', lon_name='lon', lat_chunk=1, lon_chunk=1):\n ds = ds.chunk({**{lat_name: 1, lon_name: 1},\n **{d: -1 for d in dim}})\n return ds.quantile(quantile, dim)","def _percentile_factory(perc):\n def percentile_(values):\n return numpy.percentile(values, perc)\n percentile_.__name__ = f\"percentile_{perc}\"\n return percentile_","def get_percentile(self, q):\n return self.sum_stat_sample_delta.get_percentile(q)","def _calculate_percentile_cutoff(run_numbers):\n mcp_values = []\n andor_values = []\n for run_number in run_numbers:\n current_data_path = ''.join([DATA_PATH, 'run', str(run_number), 'allevts.h5'])\n f = h5py.File(current_data_path, 'r')\n current_phot = _get_photon_energy(f, run_number)\n current_mcp = np.array(f['Acqiris2']['acq'])\n current_mcp = current_mcp[(current_phot > 781) & (current_phot < 782)]\n mcp_values.extend(current_mcp)\n current_andor = np.array(f['Andor']['signal'])\n current_andor = current_andor[(current_phot > 781) & (current_phot < 782)]\n andor_values.extend(current_andor)\n #plt.figure()\n #plt.scatter(mcp_values, andor_values)\n mcp_percentile_cutoff = min([percentileofscore(andor_values, 4000), 99.9])\n return mcp_percentile_cutoff","def test_percentile_discrete(self, test_X, interval=None):\n if not interval:\n interval = self.interval\n temp = []\n for idx in range(test_X.shape[-1]):\n x = test_X[:, idx]\n res = np.array([0] * x.shape[-1], dtype=int)\n # logging.info(\"test_before_counter: \" + str(Counter(x)))\n for i, point in enumerate(interval[idx]):\n point1, point2 = point[0], point[1]\n x1 = x[np.where((x >= point1) & (x <= point2))]\n mask = np.in1d(x, x1)\n res[mask] = (i + 1)\n # logging.info(\"test_discrete: \" + str(res) + str((point1, point2)))\n # logging.info(\"test_mask: \" + str(mask))\n # logging.info(\"test_interval_point: \" + str(point))\n # logging.info(\"test_discrete_main: \" + str(res))\n # logging.info(\"test_discrete_counter: \" + str(Counter(res)))\n temp.append(res)\n return np.array(temp).T","def get_percentile(self, q):\n raise NotImplementedError(\"This is an abstract method and needs to be implemented in derived classes.\")","def lscoreatpercentile (inlist, percent):\r\n if percent > 1:\r\n print \"\\nDividing percent>1 by 100 in lscoreatpercentile().\\n\"\r\n percent = percent / 100.0\r\n targetcf = percent*len(inlist)\r\n h, lrl, binsize, extras = histogram(inlist)\r\n cumhist = cumsum(copy.deepcopy(h))\r\n for i in range(len(cumhist)):\r\n if cumhist[i] >= targetcf:\r\n break\r\n score = binsize * ((targetcf - cumhist[i-1]) / float(h[i])) + (lrl+binsize*i)\r\n return score","def imAdjQuantiles(pc, im_p, adj_p):\n\n def scale(input):\n \"\"\"Scale array between 0 and 1\"\"\"\n return (input - np.min(input)) / ((np.max(input) - np.min(input)))\n\n pc_low = pc_high = 0\n\n if np.shape(pc)[0] == 1:\n pc_low = pc[0]\n pc_high = 100 - pc[0]\n elif np.shape(pc)[0] == 2:\n pc_low = pc[0]\n pc_high = pc[1]\n else:\n print('Clipping percentile format is wrong, no clipping is performed \\non image {}'.format(im_p))\n if type(im_p) == str:\n img = scale(plt.imread(im_p))\n else:\n img = scale(im_p)\n low_in = np.percentile(img, pc_low)\n high_in = np.percentile(img, pc_high)\n adjusted = scale(np.clip(img, low_in, high_in)) * 65535\n # plt.imshow(adjusted)\n if adj_p == []:\n return adjusted\n else:\n tiff.imsave(adj_p, adjusted.astype('uint16'))","def fd_no_bins(y):\n q75, q25 = np.percentile(y, [75 ,25])\n iqr = q75 - q25\n h=2*iqr*len(y)**(-1./3) \n return int((max(y)-min(y))/h)","def test_check_data_specifying_no_of_percentiles(self):\n expected_data = np.array(\n [self.percentile_25, self.percentile_50, self.percentile_75]\n )\n result = Plugin().process(self.cube, no_of_percentiles=3)\n self.assertArrayAlmostEqual(result.data, expected_data, decimal=5)","def calcPercentile(percent, set): #TESTED\r\n\r\n # check for 100%\r\n if percent == Decimal('1.0'):\r\n return max(set)\r\n\r\n # convert percent to the appropriate index\r\n pValue = percent * len(set)\r\n\r\n set = sorted(set)\r\n\r\n # check for 0%\r\n if percent == Decimal('0'):\r\n return set[0]\r\n\r\n # check if percent is an integer\r\n if pValue % 1 == 0:\r\n\r\n # cast pValue as int so it can be used as an index\r\n pValue = int(pValue)\r\n\r\n # take average of values at indices percent and percent - 1\r\n return (set[pValue - 1] + set[pValue]) / Decimal('2')\r\n\r\n # if percentage needs to be rounded\r\n else:\r\n # round number up to nearest integer\r\n print pValue # DELETE\r\n pValue = pValue.to_integral_exact(rounding=ROUND_CEILING) # WHAT'S UP WITH THIS FUNCTION?\r\n print pValue # DELETE\r\n pValue = int(pValue)\r\n\r\n return set[pValue - 1]","def percentile(self, perc, field_path, method='linear', filter_func=None):\n return self._collection.percentile(perc, field_path, method, filter_func)","def test_percentile_kurtosis():\n f = np.asarray([\n [0.99, 1.0, 0.5, 0.52],\n [0.69, 0.6, 0.61, 1.0]])\n R = common_metrics.percentile_kurtosis(f, maximise=True)\n expected = np.asarray(\n [1.06382979, 5.0])\n assert np.allclose(R, expected)","def percentile(t: torch.tensor, q: float):\n # Note that ``kthvalue()`` works one-based, i.e. the first sorted value\n # indeed corresponds to k=1, not k=0! Use float(q) instead of q directly,\n # so that ``round()`` returns an integer, even if q is a np.float32.\n k = 1 + round(.01 * float(q) * (t.numel() - 1))\n result = t.view(-1).kthvalue(k).values.item()\n return result","def interquartile_range(xs: List[float]) -> float:\n return quantile(xs, 0.75) - quantile(xs, 0.25)","def quantile(xs: List[float], p: float) -> float:\n p_index = int(p * len(xs))\n return sorted(xs)[p_index]","def od_percentage_xy(range, count):\n percentages = []\n for i in range:\n percentages.append(n_odpairs_percentage(i, count))\n\n percentile_list = pd.DataFrame(\n {'od_needed': range,\n 'percentage': percentages\n })\n return percentile_list","def bootstrap_interval(data, percentiles=(2.5, 97.5), n_boots=100):\n # Create empty array to fill the results\n bootstrap_means = np.zeros([n_boots, data.shape[-1]])\n for ii in range(n_boots):\n # Generate random indices for data *with* replacement, then take the sample mean\n random_sample = resample(data)\n bootstrap_means[ii] = random_sample.mean(axis=0)\n\n # Compute the percentiles of choice for the bootstrapped means\n percentiles = np.percentile(bootstrap_means, percentiles, axis=0)\n return percentiles","def weighted_percentiles(data, wt, percentiles): \r\n assert np.greater_equal(percentiles, 0.0).all(), \"Percentiles less than zero\" \r\n assert np.less_equal(percentiles, 1.0).all(), \"Percentiles greater than one\" \r\n data = np.asarray(data) \r\n assert len(data.shape) == 1 \r\n if wt is None: \r\n wt = np.ones(data.shape, np.float) \r\n else: \r\n wt = np.asarray(wt, np.float) \r\n assert wt.shape == data.shape \r\n assert np.greater_equal(wt, 0.0).all(), \"Not all weights are non-negative.\" \r\n assert len(wt.shape) == 1 \r\n n = data.shape[0] \r\n assert n > 0 \r\n i = np.argsort(data) \r\n sd = np.take(data, i, axis=0) \r\n sw = np.take(wt, i, axis=0) \r\n aw = np.add.accumulate(sw) \r\n if not aw[-1] > 0: \r\n raise ValueError(\"Nonpositive weight sum\")\r\n w = (aw-0.5*sw)/aw[-1] \r\n spots = np.searchsorted(w, percentiles) \r\n o = [] \r\n for (s, p) in zip(spots, percentiles): \r\n if s == 0: \r\n o.append(sd[0]) \r\n elif s == n: \r\n o.append(sd[n-1]) \r\n else: \r\n f1 = (w[s] - p)/(w[s] - w[s-1]) \r\n f2 = (p - w[s-1])/(w[s] - w[s-1]) \r\n assert f1>=0 and f2>=0 and f1<=1 and f2<=1 \r\n assert abs(f1+f2-1.0) < 1e-6 \r\n o.append(sd[s-1]*f1 + sd[s]*f2) \r\n return o","def _pctiles_from_sorted_v2(self, values, pcs):\n if not all(0 <= pc <= 100 for pc in pcs):\n raise ValueError(\"pctiles must be between 0 and 100\")\n nvals = len(values)\n pctiles = []\n for pc in pcs:\n if pc == 0:\n new_pct = values[0]\n elif pc == 100:\n new_pct = values[nvals-1]\n else:\n loc = nvals * pc / 100\n loc_flr = floor(loc)\n t = loc - loc_flr\n new_pct = (1 - t) * values[loc_flr - 1] + t * values[loc_flr]\n pctiles.append(new_pct)\n return pctiles","def belonging(f_bar, neighborhood_avg):\n new_labels = np.argmax(neighborhood_avg - f_bar, axis=1)\n return new_labels","def median(self, name, **kwargs):\n data = self.get(name,**kwargs)\n return np.percentile(data,[50])","def tenth_percentile(y_test, y_pred) :\n return np.abs(y_test - y_pred).sort_values().iloc[int(len(y_test)*0.10)]/60","def test_percentile_regret():\n f = np.asarray([\n [0.99, 1.0, 0.5],\n [0.69, 0.6, 0.6]])\n R = common_metrics.percentile_regret(f, maximise=True, percentile=0.5)\n expected = np.asarray(\n [0.0, -0.3])\n assert np.allclose(R, expected)\n R = common_metrics.percentile_regret(f, maximise=False, percentile=0.5)\n expected = np.asarray(\n [-0.3, 0.0])\n assert np.allclose(R, expected)","def cdf_scores(values, refs, greater=True):\n if np.isnan(refs).all():\n return np.nan * np.ones(np.shape(values))\n if greater:\n return np.array([percentileofscore(refs, _v, kind='weak')/100 * len(refs) / (len(refs)+2) + 1/(len(refs)+2) \n for _v in values])\n else:\n return np.array([1 - percentileofscore(refs, _v, kind='weak')/100 * len(refs) / (len(refs)+2) - 1/(len(refs)+2) \n for _v in values])","def percent_bias(x_arr, y_arr):\n delta = np.mean(x_arr) - np.mean(y_arr)\n denom = np.sqrt((np.var(x_arr) + np.var(y_arr)) / 2.0)\n return 100.0 * delta / denom","def quantile(x,p):\n p_index = int(p*len(x))\n return sorted(x)[p_index]","def percentile_normalization(data: np.ndarray, percentile: int = 1) -> np.ndarray:\n\n min_percentile = np.percentile(data, percentile)\n max_percentile = np.percentile(data, 100 - percentile)\n\n # limit maximum intensity of data by max_percentile\n data[data >= max_percentile] = max_percentile\n\n # limit minimum intensity of data by min_percentile\n data[data <= min_percentile] = min_percentile\n\n return data","def test_run_simulations_and_get_percentile_allele_length_1():\n simulator = genotype_confidence_simulator.GenotypeConfidenceSimulator(\n 50, 300, 0.01, iterations=5\n )\n simulator.run_simulations()\n expected_confidence_scores_percentiles = {\n 193: 20.0,\n 221: 40.0,\n 271: 60.0,\n 278: 80.0,\n 303: 100.0\n }\n assert (\n simulator.confidence_scores_percentiles\n == expected_confidence_scores_percentiles\n )\n assert simulator.get_percentile(193) == 20.00\n assert simulator.get_percentile(221) == 40.00\n # Try getting number that is not in the dict and will have to be inferred\n assert simulator.get_percentile(207) == 30.0\n # Try values outside the range of what we already have\n simulator.get_percentile(192) == 0.00\n simulator.get_percentile(191) == 0.00\n simulator.get_percentile(304) == 100.00\n simulator.get_percentile(305) == 100.00","def test_check_data_specifying_single_percentile(self):\n expected_data = np.array(self.percentile_25)\n result = Plugin().process(self.cube, percentiles=[25])\n self.assertArrayAlmostEqual(result.data, expected_data, decimal=5)","def percent_blank(coadd,obj,filter_i):\n \n #Get array of pixel values\n scidata = coadd[0].data \n #Flatten n-d array to 1-d \n new = np.ravel(scidata)\n new = np.sort(new)\n try:\n #Find the range of indices where the pixel value is EXACTLY 0 since the\n #array is sorted and sometimes there are negative values which \n #we want to include\n index_0_ends = np.where(new == 0)[0][-1]\n index_0_starts = np.where(new == 0)[0][0]\n index_diff = index_0_ends - index_0_starts\n size = len(new)\n #Calculate percentage blank\n percentage = ((index_diff+1)/size)*100\n except:\n #Go here if there aren't any pixel values that are exactly 0 in which \n #case the image is 0% blank\n percentage = 0\n print('Percentage blank {0}%'.format(percentage))\n #Move back up to interim folder\n os.chdir('..')\n #Write the percentage blank along with the object name and filter \n #name to a text file\n with open('percent_blankv2.txt','a') as newfile:\n newfile.write('{0},{1},{2}%\\n'.format(obj,filter_i,percentage))\n newfile.close()","def get_statistics_percentile(self,table,field):\n dict = {}\n for x in xrange(1,11):\n dict[x] = db.session.execute(\"select statistics_viewCount as percentile from meta order by percentile asc limit 1 OFFSET 19346*\"+str(x)+\"/10-1\").first().percentile","def scale_percentile(matrix): #Function to convert the given 4 band image to png uint8 image.\n orig_shape = matrix.shape\n matrix = np.reshape(matrix, [matrix.shape[0]*matrix.shape[1], 3]).astype(float)\n # Get 2nd and 98th percentile\n mins = np.percentile(matrix, 1, axis=0)\n maxs = np.percentile(matrix, 99, axis=0) - mins\n \n matrix = (matrix - mins[None,:])/maxs[None,:]\n matrix = np.reshape(matrix, orig_shape)\n matrix = matrix.clip(0,1)\n return matrix","def default_quantile():\n return np.logspace(-5, 0, 100)","def quantile(x, p):\n p_index = int(p * len(x))\n return sorted(x)[p_index]","def quantile(x, p):\n p_index = int(p * len(x))\n return sorted(x)[p_index]","def quantile(x, p):\n p_index = int(p * len(x))\n return sorted(x)[p_index]","def quantile(agg, k=4, name='quantile'):\n\n w = 100.0 / k\n p = np.arange(w, 100 + w, w)\n\n if p[-1] > 100.0:\n p[-1] = 100.0\n\n q = np.array([stats.scoreatpercentile(agg.data, pct) for pct in p])\n q = np.unique(q)\n k_q = len(q)\n\n if k_q < k:\n print(\"Quantile Warning: Not enough unique values for k classes (using {} bins)\".format(k_q))\n\n return DataArray(_bin(agg.data, q, np.arange(k_q)),\n name=name,\n dims=agg.dims,\n coords=agg.coords,\n attrs=agg.attrs)","def plot_percentile_bounds_vs_x(sps, x, y, q=25, nbins=15, pcol='blue', scol='orange', sorted=True, line_alpha=1):\n if min(x) == max(x):\n return\n bins = np.linspace(min(x), max(x), nbins + 1)\n\n if not sorted:\n inds = np.argsort(x)\n x = x[inds]\n y = y[inds]\n\n inds = np.digitize(x, bins[1:], right=True) # assume x data is sorted\n base = []\n h1 = []\n m_new = []\n h2 = []\n cc = 0\n no_dist = 0\n for j in range(0, nbins):\n lh = np.searchsorted(inds, j, side='left')\n rh = np.searchsorted(inds, j, side='right')\n\n if lh == rh:\n cc += 0 # change this to 1 if you want limit\n else:\n vals = y[lh:rh]\n xxx = (x[lh:lh+1][0] + x[rh-1:rh][0]) / 2\n median = np.median(vals)\n p_lb = np.percentile(vals, q)\n p_ub = np.percentile(vals, 100 - q)\n\n base.append(xxx)\n h1.append(p_lb)\n m_new.append(median)\n h2.append(p_ub)\n cc = 0\n if cc >= 2:\n no_dist = 1\n if no_dist == 0:\n sps.plot(base, m_new, c=pcol, label='Mean')\n sps.fill_between(base, h1, h2, facecolor=scol, alpha=0.3)\n lab = f'{q} - {100-q} %'\n sps.plot(base, h1, c=scol, alpha=line_alpha, label=lab)\n sps.plot(base, h2, c=scol, alpha=line_alpha)","def compute_object_percentile_surface_distances(\n labelled_surface_distances_1, labelled_surface_distances_2, percentile\n):\n hausdorffs_label_1 = []\n for surface_distance in labelled_surface_distances_1:\n hausdorffs_label_1.append(np.percentile(surface_distance, percentile))\n\n hausdorffs_label_2 = []\n for surface_distance in labelled_surface_distances_2:\n hausdorffs_label_2.append(np.percentile(surface_distance, percentile))\n return np.array(hausdorffs_label_1), np.array(hausdorffs_label_2)","def mode_statistic(data, percentiles=range(10,91,10)):\n from scipy.interpolate import UnivariateSpline, LSQUnivariateSpline\n \n so = np.argsort(data)\n order = np.arange(len(data))\n #spl = UnivariateSpline(data[so], order)\n \n knots = np.percentile(data, percentiles)\n dx = np.diff(knots)\n mask = (data[so] >= knots[0]-dx[0]) & (data[so] <= knots[-1]+dx[-1])\n spl = LSQUnivariateSpline(data[so][mask], order[mask], knots, ext='zeros')\n \n mask = (data[so] >= knots[0]) & (data[so] <= knots[-1])\n ix = (spl(data[so], nu=1, ext='zeros')*mask).argmax()\n mode = data[so][ix]\n return mode","def quantile(x, p):\n sorted_x = sorted(x)\n # round p_index to base int\n p_index = int(p * len(x))\n return sorted_x[p_index]"],"string":"[\n \"def _calc_perc(arr: np.array, p: Sequence[float] = None):\\n if p is None:\\n p = [50]\\n\\n nan_count = np.isnan(arr).sum(axis=-1)\\n out = np.moveaxis(np.percentile(arr, p, axis=-1), 0, -1)\\n nans = (nan_count > 0) & (nan_count < arr.shape[-1])\\n if np.any(nans):\\n out_mask = np.stack([nans] * len(p), axis=-1)\\n # arr1 = arr.reshape(int(arr.size / arr.shape[-1]), arr.shape[-1])\\n # only use nanpercentile where we need it (slow performance compared to standard) :\\n out[out_mask] = np.moveaxis(\\n np.nanpercentile(arr[nans], p, axis=-1), 0, -1\\n ).ravel()\\n return out\",\n \"def matlab_percentile(in_data, percentiles):\\n data = np.sort(in_data)\\n p_rank = 100.0 * (np.arange(data.size) + 0.5) / data.size\\n perc = np.interp(percentiles, p_rank, data, left=data[0], right=data[-1])\\n return perc\",\n \"def percentile(scores, student_score):\\n scores = np.array(sorted(scores))\\n num_scores = len(scores)\\n return round(sum(scores <= student_score) / float(num_scores) * 100, 2)\",\n \"def percentile(self, values, percent):\\r\\n if not values:\\r\\n return None\\r\\n k = (len(values)-1) * percent\\r\\n f = math.floor(k)\\r\\n c = math.ceil(k)\\r\\n if f == c:\\r\\n return values[int(k)]\\r\\n d0 = values[int(f)] * (c-k)\\r\\n d1 = values[int(c)] * (k-f)\\r\\n return d0+d1\",\n \"def get_percentile(obs, bootstrap):\\n if np.isnan(obs):\\n return np.nan\\n else:\\n return np.searchsorted(np.sort(bootstrap), obs) / len(bootstrap)\",\n \"def test_lots_of_percentiles(self):\\n data = np.array(\\n [\\n [[14.4, -46, 10.2], [-73.0, -46, -89.2], [-89.2, -93.25, -94.6]],\\n [[19.2, 8.25, 10.6], [-19, 8.25, -67.6], [-67.6, -79.75, -83.8]],\\n [[24.0, 8.75, 11.0], [8.33333, 8.75, -46.0], [-46.0, -66.25, -73.0]],\\n [[28.8, 9.25, 11.4], [9.0, 9.25, -24.4], [-24.4, -52.75, -62.2]],\\n [[33.6, 9.75, 11.8], [9.666667, 9.75, -2.8], [-2.8, -39.25, -51.4]],\\n [\\n [38.4, 10.333333, 16.8],\\n [10.333333, 10.2, 8.5],\\n [8.333333, -25.75, -40.6],\\n ],\\n [[43.2, 11.0, 26.4], [11.0, 10.6, 9.5], [9.0, -12.25, -29.8]],\\n [\\n [48.0, 11.666667, 36.0],\\n [11.666667, 11.0, 10.5],\\n [9.666667, 1.25, -19.0],\\n ],\\n [[52.8, 24, 45.6], [24, 11.4, 11.5], [10.5, 8.5, -8.2]],\\n [[57.6, 48, 55.2], [48, 11.8, 36.0], [11.5, 9.5, 2.6]],\\n ],\\n dtype=np.float32,\\n )\\n\\n percentiles = np.arange(5, 100, 10)\\n result = Plugin()._probabilities_to_percentiles(self.cube, percentiles)\\n self.assertArrayAlmostEqual(result.data, data, decimal=5)\",\n \"def compute_percentile(value, cutoffs):\\n\\tif value < cutoffs[0]:\\n\\t\\treturn 0.0\\n\\n\\tfor i, cutoff in enumerate(cutoffs):\\n\\t\\tif value < cutoff:\\n\\t\\t\\treturn math.floor(100 * (float(i)/(len(cutoffs))))\\n\\t\\t\\tbreak\\n\\treturn 100.0\",\n \"def percentile(N, percent):\\n N.sort()\\n if not N:\\n return None\\n k = (len(N) - 1) * percent\\n f = math.floor(k)\\n c = math.ceil(k)\\n if f == c:\\n return N[int(k)]\\n d0 = N[int(f)] * (c - k)\\n d1 = N[int(c)] * (k - f)\\n return d0 + d1\",\n \"def calc_percentile(self, p):\\n bounds = self.range_bins\\n r = []\\n v = []\\n for b in bounds:\\n d = self._get_data_distance(0., b)\\n if len(d) < 1:\\n continue\\n r.append(b)\\n v.append(np.percentile(d, p * 100.)) # percentile value\\n\\n r = np.asarray(r)\\n np.asarray(v)\\n\\n o = {'r': np.asarray(r), 'value': np.asarray(v)}\\n if 'percentiles' not in self.statistic.keys():\\n self.statistic.update({'percentiles': {}})\\n\\n self.statistic['percentiles'].update({p: o})\",\n \"def describe_reduce_nb(col, a, perc, ddof, *args):\\n a = a[~np.isnan(a)]\\n out = np.empty(5 + len(perc), dtype=np.float_)\\n out[0] = len(a)\\n if len(a) > 0:\\n out[1] = np.mean(a)\\n out[2] = nanstd_1d_nb(a, ddof=ddof)\\n out[3] = np.min(a)\\n out[4:-1] = np.percentile(a, perc * 100)\\n out[4 + len(perc)] = np.max(a)\\n else:\\n out[1:] = np.nan\\n return out\",\n \"def stratify_array(array, num_percentiles=4):\\n\\n percentiles = [ii*100/num_percentiles for ii in list(range(0,num_percentiles+1))]\\n stratif_labels = np.copy(array)\\n\\n for ii in range(0,len(percentiles)-1):\\n lo = percentiles[ii]\\n hi = percentiles[ii+1]\\n plo = np.percentile(array, lo)\\n phi = np.percentile(array, hi)\\n stratif_labels[( plo <= array) & (array <= phi )] = ii+1\\n\\n return stratif_labels\",\n \"def _percentile(self, data, percent):\\n if not data:\\n return None\\n k = (len(data) - 1) * percent\\n f = math.floor(k)\\n c = math.ceil(k)\\n if f == c:\\n return data[int(k)]\\n d0 = data[int(f)] * (c - k)\\n d1 = data[int(c)] * (k - f)\\n return d0 + d1\",\n \"def percentile(N, percent):\\n if not N:\\n return None\\n k = (len(N)-1) * percent\\n f = math.floor(k)\\n c = math.ceil(k)\\n if f == c:\\n return N[int(k)]\\n d0 = N[int(f)] * (c-k)\\n d1 = N[int(c)] * (k-f)\\n return d0+d1\",\n \"def percentile(self, percentile):\\n assert(percentile >= 0 and percentile <= 100)\\n assert(self.kind in [\\\"exponential\\\", \\\"linear\\\", \\\"enumerated\\\", \\\"boolean\\\"])\\n\\n fraction = percentile/100\\n to_count = fraction*self.buckets.sum()\\n percentile_bucket = 0\\n\\n for percentile_bucket in range(len(self.buckets)):\\n freq = self.buckets.values[percentile_bucket]\\n if to_count - freq <= 0:\\n break\\n to_count -= freq\\n\\n percentile_lower_boundary = self.buckets.index[percentile_bucket]\\n percentile_frequency = self.buckets.values[percentile_bucket]\\n\\n if percentile_bucket == len(self.buckets) - 1 or percentile_frequency == 0:\\n return percentile_lower_boundary\\n\\n width = self.buckets.index[percentile_bucket + 1] - self.buckets.index[percentile_bucket]\\n return percentile_lower_boundary + width*to_count/percentile_frequency\",\n \"def getPercentileAp(tpf, percentile=80, both=False):\\n # Find all targets in image; those above the percentile mark\\n median_image = np.nanmedian(tpf.flux, axis=0)\\n above_percentile = median_image > np.nanpercentile(median_image, percentile)\\n\\n # Return only the contiguous region closest to `region`.\\n # Method borrowed from lightkurve create_threshold_mask() function\\n reference_pixel = (tpf.shape[2] / 2, tpf.shape[1] / 2)\\n # First, label all the regions:\\n labels = label(above_percentile)[0]\\n # For all pixels above threshold, compute distance to reference pixel:\\n label_args = np.argwhere(labels > 0)\\n distances = [np.hypot(crd[0], crd[1]) for crd in label_args - np.array([reference_pixel[1], reference_pixel[0]])]\\n # Which label corresponds to the centermost pixel?\\n closest_arg = label_args[np.argmin(distances)]\\n closest_label = labels[closest_arg[0], closest_arg[1]]\\n percentile_ap = labels == closest_label\\n\\n if both:\\n return percentile_ap, above_percentile\\n else:\\n return percentile_ap\",\n \"def percentile(self, pct):\\n return percentile(self.results, pct, interpolation='nearest')\",\n \"def wilks(percentiles, alpha=0.10):\\n percentiles = np.asarray(percentiles)\\n pvals = list(percentiles.flat) # want in decimal\\n pvals = sorted(2 * min(pval, 1 - pval) for pval in pvals)\\n ptest = [alpha * i / len(pvals) for i in range(len(pvals))]\\n ppick = max(pv for pv, pt in zip(pvals, ptest) if pv <= pt) / 2\\n mask = (percentiles <= ppick) | (percentiles >= (1 - ppick))\\n return percentiles[mask]\",\n \"def robust_median_from_percentiles(array,percentiles=()):\\n\\n if percentiles:\\n percentiles = np.array(percentiles)\\n SigmaVector = scipy.stats.norm.ppf(percentiles/100.)\\n else:\\n percentiles = np.array([10.,20.,30.,40.,45.])\\n SigmaVector = np.array([-1.28155157, -0.84162123, -0.52440051, -0.2533471 , -0.12566135])\\n\\n PercentileValues = np.percentile(array,percentiles)\\n \\n sig, med = fit_slope_1d(SigmaVector,PercentileValues)\\n\\n return med\",\n \"def percentile(field, q):\\n # https://gist.github.com/spezold/42a451682422beb42bc43ad0c0967a30\\n # Note that ``kthvalue()`` works one-based, i.e. the first sorted value\\n # indeed corresponds to k=1, not k=0! Use float(q) instead of q directly,\\n # so that ``round()`` returns an integer, even if q is a np.float32.\\n k = 1 + round(.01 * float(q) * (field.shape[1] - 1))\\n result = field.kthvalue(k, dim=1).values\\n return result\",\n \"def ninetieth_percentile(y_test, y_pred) :\\n return np.abs(y_test - y_pred).sort_values().iloc[int(len(y_test)*0.90)]/60\",\n \"def get_percentile(before, level):\\n snr = np.array(before.getColumnByName('snr')[:])\\n return np.percentile(snr, level)\",\n \"def percentile(histogram, percentile=0.75):\\n covered = 0\\n normalization = sum(histogram.values())\\n for key, frequency in sorted(histogram.items(), reverse=True):\\n covered += frequency\\n assert covered <= normalization\\n if covered > ((1.0 - percentile) * normalization):\\n return key\\n raise RuntimeError('Percentile computation should have terminated '\\n 'mid-loop.')\",\n \"def percentile(data, percentiles, weights=None):\\n # check if actually weighted percentiles is needed\\n if weights is None:\\n return np.percentile(data, list(percentiles))\\n if np.equal(weights, 1.).all():\\n return np.percentile(data, list(percentiles))\\n\\n # make sure percentiles are fractions between 0 and 1\\n if not np.greater_equal(percentiles, 0.0).all():\\n raise ValueError(\\\"Percentiles less than 0\\\")\\n if not np.less_equal(percentiles, 100.0).all():\\n raise ValueError(\\\"Percentiles greater than 100\\\")\\n\\n # Make sure data is in correct shape\\n shape = np.shape(data)\\n n = len(data)\\n if (len(shape) != 1):\\n raise ValueError(\\\"wrong data shape, expecting 1d\\\")\\n\\n if len(weights) != n:\\n print(n, len(weights))\\n raise ValueError(\\\"weights must be the same shape as data\\\")\\n if not np.greater_equal(weights, 0.0).all():\\n raise ValueError(\\\"Not all weights are non-negative.\\\")\\n\\n _data = np.asarray(data, dtype=float)\\n\\n if hasattr(percentiles, '__iter__'):\\n _p = np.asarray(percentiles, dtype=float) * 0.01\\n else:\\n _p = np.asarray([percentiles * 0.01], dtype=float)\\n\\n _wt = np.asarray(weights, dtype=float)\\n\\n len_p = len(_p)\\n sd = np.empty(n, dtype=float)\\n sw = np.empty(n, dtype=float)\\n aw = np.empty(n, dtype=float)\\n o = np.empty(len_p, dtype=float)\\n\\n i = np.argsort(_data)\\n np.take(_data, i, axis=0, out=sd)\\n np.take(_wt, i, axis=0, out=sw)\\n np.add.accumulate(sw, out=aw)\\n\\n if not aw[-1] > 0:\\n raise ValueError(\\\"Nonpositive weight sum\\\")\\n\\n w = (aw - 0.5 * sw) / aw[-1]\\n\\n spots = np.searchsorted(w, _p)\\n for (pk, s, p) in zip(range(len_p), spots, _p):\\n if s == 0:\\n o[pk] = sd[0]\\n elif s == n:\\n o[pk] = sd[n - 1]\\n else:\\n f1 = (w[s] - p) / (w[s] - w[s - 1])\\n f2 = (p - w[s - 1]) / (w[s] - w[s - 1])\\n o[pk] = sd[s - 1] * f1 + sd[s] * f2\\n return o\",\n \"def prctile(data, p_vals=[0, 25, 50, 75, 100], sorted_=False):\\r\\n ps = [p_vals] if np.isscalar(p_vals) else p_vals\\r\\n\\r\\n if not sorted_:\\r\\n data = sorted(data)\\r\\n n = len(data)\\r\\n d = []\\r\\n for p in ps:\\r\\n fi = p * n / 100 - 0.5\\r\\n if fi <= 0: # maybe extrapolate?\\r\\n d.append(data[0])\\r\\n elif fi >= n - 1:\\r\\n d.append(data[-1])\\r\\n else:\\r\\n i = int(fi)\\r\\n d.append((i+1 - fi) * data[i] + (fi - i) * data[i+1])\\r\\n return d[0] if np.isscalar(p_vals) else d\",\n \"def percentile(data, statfxn, niter=10000, alpha=0.05):\\n\\n index = _make_boot_index(data.shape[0], niter)\\n boot_stats = statfxn(data[index], axis=-1)\\n\\n # compute the `alpha/2` and `1-alpha/2` percentiles of `boot_stats`\\n CI = numpy.percentile(boot_stats, [alpha * 50, 100 - (alpha * 50)], axis=0)\\n\\n return CI\",\n \"def _quantile_normalization(arr, mode=\\\"mean\\\"):\\n n = len(arr)\\n perc = percentileofscore\\n arr_ = arr.copy()[~np.isnan(arr)]\\n out = np.zeros(n)\\n for i in range(n):\\n if not np.isnan(arr[i]):\\n out[i] = norm.ppf(perc(arr_, arr[i], mode) / 100.)\\n else:\\n out[i] = np.nan\\n return out\",\n \"def get_percentile(N, percent, key=lambda x:x):\\r\\n if not N:\\r\\n return 0\\r\\n k = (len(N)-1) * percent\\r\\n f = math.floor(k)\\r\\n c = math.ceil(k)\\r\\n if f == c:\\r\\n return key(N[int(k)])\\r\\n d0 = key(N[int(f)]) * (c-k)\\r\\n d1 = key(N[int(c)]) * (k-f)\\r\\n return d0+d1\",\n \"def get_percentile(N, percent, key=lambda x:x):\\r\\n if not N:\\r\\n return 0\\r\\n k = (len(N)-1) * percent\\r\\n f = math.floor(k)\\r\\n c = math.ceil(k)\\r\\n if f == c:\\r\\n return key(N[int(k)])\\r\\n d0 = key(N[int(f)]) * (c-k)\\r\\n d1 = key(N[int(c)]) * (k-f)\\r\\n return d0+d1\",\n \"def _sdc_pandas_format_percentiles(arr):\\n\\n percentiles_strs = []\\n for percentile in arr:\\n p_as_string = str(percentile * 100)\\n\\n trim_index = len(p_as_string) - 1\\n while trim_index >= 0:\\n if p_as_string[trim_index] == '0':\\n trim_index -= 1\\n continue\\n elif p_as_string[trim_index] == '.':\\n break\\n\\n trim_index += 1\\n break\\n\\n if trim_index < 0:\\n p_as_string_trimmed = '0'\\n else:\\n p_as_string_trimmed = p_as_string[:trim_index]\\n\\n percentiles_strs.append(p_as_string_trimmed + '%')\\n\\n return percentiles_strs\",\n \"def samples_to_percentiles(samples):\\n pcnts = [np.percentile(s, [16,50,84]) for s in samples.T]\\n return np.array([[p[1], p[1]-p[0], p[2]-p[1]] for p in pcnts])\",\n \"def test__quantile(self):\\r\\n # regular cases\\r\\n sample_data = array(range(25, 42))\\r\\n assert_almost_equal(_quantile(sample_data, 0.5), median(sample_data))\\r\\n\\r\\n # sorted data is assumed for this function\\r\\n sample_data = sorted(\\r\\n array([0.17483293, 0.99891939, 0.81377467, 0.8137437,\\r\\n 0.51990174, 0.35521497, 0.98751461]))\\r\\n assert_almost_equal(_quantile(sample_data, 0.10), 0.283062154)\",\n \"def stack_percentile(arrs, q=50, nodata=None):\\r\\n a = check_stack(arrs)\\r\\n if nodata is not None:\\r\\n a = mask_stack(a, nodata=nodata)\\r\\n nan_per = np.nanpercentile(a, q=q, axis=0)\\r\\n return nan_per\",\n \"def estimate_percentile(self, percentile):\\n if percentile > 1.0:\\n raise ValueError(\\\"Percentile must be between 0 and 1.\\\")\\n\\n if self.__total_count == 0:\\n return None\\n\\n # The first step is to calculate which bucket this percentile lands in. We do this by calculating the \\\"index\\\"\\n # of what that percentile's sample would have been. For example, if we are calculating the 75% and there were\\n # 100 values, then the 75% would be the 75th value in sorted order.\\n target_count = self.__total_count * percentile\\n\\n cumulative_count = 0\\n\\n # Now find the bucket by going over the buckets, keeping track of the cumulative counts across all buckets.\\n for bucket_count, lower_bound, upper_bound in self.buckets(\\n disable_last_bucket_padding=True\\n ):\\n cumulative_count = cumulative_count + bucket_count\\n if target_count <= cumulative_count:\\n # Ok, we found the bucket. To minimize error, we estimate the value of the percentile to be the\\n # midpoint between the lower and upper bounds.\\n return (upper_bound + lower_bound) / 2.0\\n\\n # We should never get here because target_count will always be <= the total counts across all buckets.\",\n \"def percentile_filter(data, feats):\\n\\n # Determines the fraction of nonzero values in the data.\\n sparsity_frac = feats['nonzeros'] / (feats['nrows'] * feats['ncols'])\\n\\n # NOTE: Added convenience term derived from experience.\\n thresh = np.percentile(data.ravel(), q=100 * (1 - (sparsity_frac + 0.1)))\\n\\n # Replace p-values below threshold with zero.\\n data[(data > 0) & (data < thresh)] = 0\\n\\n return data\",\n \"def get_percentile(histogram, percentile):\\n n_percentile = None\\n sorted_set_of_keys = sorted(histogram.keys())\\n sum_of_values = sum(histogram.values())\\n percentile_index = sum_of_values * (percentile / 100)\\n index_sum = 0\\n if len(sorted_set_of_keys) < 1:\\n n_percentile = 0\\n else:\\n for i in range(len(sorted_set_of_keys)):\\n k = sorted_set_of_keys[i]\\n index_sum += histogram.get(k)\\n if index_sum > percentile_index:\\n n_percentile = k\\n break\\n elif index_sum == percentile_index:\\n n_percentile = (k + sorted_set_of_keys[i + 1])/2\\n break\\n return n_percentile\",\n \"def p(n):\\n def p_(x):\\n return np.percentile(x, n)\\n\\n p_.__name__ = \\\"p_%s\\\" % n\\n return p_\",\n \"def perc(data,percentile):\\n clip_value = np.percentile(data,percentile)\\n data = clip(data,clip_value)\\n return data\",\n \"def test_check_data_specifying_percentiles(self):\\n expected_data = np.array(\\n [self.percentile_25, self.percentile_50, self.percentile_75]\\n )\\n result = Plugin().process(self.cube, percentiles=[25, 50, 75])\\n self.assertArrayAlmostEqual(result.data, expected_data, decimal=5)\",\n \"def Quartiles(data):\\n q = np.percentile(data, [25, 50, 75])\\n\\n return q[0], q[1], q[2]\",\n \"def quartiles(x, percentile):\\n length = len(x)\\n\\n if percentile == 25:\\n center = length // 4\\n elif percentile == 75:\\n center = length // 2 + length // 4\\n\\n x.sort()\\n\\n if length % 2 == 0:\\n return (x[center - 1] + x[center]) / 2\\n else:\\n return x[center]\",\n \"def _ComputePercentiles(buckets, total, percentiles):\\n if not percentiles:\\n return {}\\n current_count = 0\\n current_percentage = 0\\n next_percentile_index = 0\\n result = {}\\n for bucket in buckets:\\n if bucket['count'] > 0:\\n current_count += bucket['count']\\n old_percentage = current_percentage\\n current_percentage = float(current_count) / total\\n\\n # Check whether we passed one of the percentiles we're interested in.\\n while (next_percentile_index < len(percentiles) and\\n current_percentage > percentiles[next_percentile_index]):\\n if not 'high' in bucket:\\n result[percentiles[next_percentile_index]] = bucket['low']\\n else:\\n result[percentiles[next_percentile_index]] = float(_BucketInterpolate(\\n old_percentage, percentiles[next_percentile_index],\\n current_percentage, bucket['low'], bucket['high']))\\n next_percentile_index += 1\\n return result\",\n \"def _pctiles_from_sorted(self, values, pcs):\\n if not all(0 <= pc <= 100 for pc in pcs):\\n raise ValueError(\\\"pctiles must be between 0 and 100\\\")\\n nvals = len(values)\\n pctiles = []\\n for pc in pcs:\\n if pc == 0:\\n new_pct = values[0]\\n elif pc == 100:\\n new_pct = values[nvals-1]\\n else:\\n n = pc * nvals / 100\\n if n == int(n):\\n new_pct = (values[int(n)-1] + values[int(n)]) / 2\\n else:\\n new_pct = values[floor(n)]\\n pctiles.append(new_pct)\\n return pctiles\",\n \"def percentile_discrete(self, x, n=20):\\n res = np.array([0] * x.shape[-1], dtype=int)\\n # logging.info(\\\"before_counter: \\\" + str(Counter(x)))\\n x_temp = x[x != -1.0]\\n interval_list = []\\n for i in range(1+n):\\n if i == 0:\\n x1 = x[np.where(x == -1.0)]\\n mask = np.in1d(x, x1)\\n res[mask] = (i + 1)\\n # logging.info(\\\"discrete: \\\" + str((-1.0, -1.0)))\\n point1, point2 = -1, -1\\n else:\\n point1, point2 = stats.scoreatpercentile(x_temp, [(i-1)*100/n, i*100/n])\\n x1 = x[np.where((x >= point1) & (x <= point2))]\\n mask = np.in1d(x, x1)\\n res[mask] = (i + 1)\\n # logging.info(\\\"discrete: \\\" + str(res) + str((point1, point2)))\\n interval_list.append((point1, point2))\\n # logging.info(\\\"mask: \\\" + str(mask))\\n # logging.info(\\\"discrete_main: \\\" + str(res))\\n # logging.info(\\\"discrete_counter: \\\" + str(Counter(res)))\\n return res, interval_list\",\n \"def mapPercentile(scalarMap, percentile, area = False):\\n \\n arr = pcr.pcr2numpy(scalarMap, -9999).flatten()\\n arr = arr[arr[:] > -9999]\\n score = np.percentile(arr, percentile)\\n if area == False:\\n return score\\n elif area == True:\\n percentileArea = pcr.ifthen(scalarMap < score, scalarMap)\\n return score, percentileArea\",\n \"def iqr(self, arr):\\n a = np.asarray(arr)\\n self.q1 = stats.scoreatpercentile(a, 25)\\n self.q2 = stats.scoreatpercentile(a, 50)\\n self.q3 = stats.scoreatpercentile(a, 75)\",\n \"def keep_top_percentile(image, percentile, *args, **kwargs):\\n # TODO: Implement the method\\n\\n imageSort = num.sort(image, axis=None)\\n elem = int(len(imageSort) * (percentile/100))\\n big = num.zeros(elem)\\n\\n k = len(imageSort)-1\\n for i in range(0, elem):\\n if imageSort[k] in big:\\n k -= 1\\n else:\\n big[i] = imageSort[k]\\n k -= 1\\n\\n thresholded = num.zeros((640, 480))\\n for i in range (0, 640):\\n for j in range (0, 480):\\n l = image[i][j]\\n if l in big:\\n thresholded[i][j] = image[i][j]\\n else:\\n thresholded[i][j] = 0\\n\\n save_image_to_h5(thresholded, \\\"thresholded.h5\\\")\\n \\n\\n return thresholded\",\n \"def get_percentile(self, q):\\n return None\",\n \"def get_percentile(self, q):\\n return None\",\n \"def percentile(p):\\n assert 1.0 > p >= 0.0\\n\\n def __p1(ls):\\n k = math.floor(len(ls) * p)\\n assert k >= 0\\n assert k < len(ls)\\n\\n start = 0\\n end = len(ls) - 1\\n\\n while start <= end:\\n pivot_index = start\\n pivot_val = ls[pivot_index]\\n\\n for i in range(pivot_index + 1, end + 1):\\n if ls[i] >= pivot_val:\\n continue\\n else:\\n ls[pivot_index] = ls[i]\\n pivot_index += 1\\n ls[i] = ls[pivot_index]\\n\\n ls[pivot_index] = pivot_val\\n if pivot_index == k:\\n return pivot_val\\n elif pivot_index > k:\\n end = pivot_index - 1\\n else:\\n start = pivot_index + 1\\n\\n return ls[start]\\n\\n return __p1\",\n \"def test_profiled_quantiles(self):\\n\\n # this data has 4 bins, range of 3\\n # with equal bin size, each bin has the width of 0.75\\n\\n data = [\\\"1.0\\\", \\\"2.0\\\", \\\"3.0\\\", \\\"4.0\\\"]\\n df = pd.Series(data)\\n profiler = FloatColumn(df.name)\\n profiler.update(df)\\n profile = profiler.profile\\n\\n est_quartiles = profile['quantiles']\\n est_Q1 = est_quartiles[249]\\n est_Q2 = est_quartiles[499]\\n est_Q3 = est_quartiles[749]\\n\\n data_to_num = [float(item) for item in data]\\n exact_Q1 = np.percentile(data_to_num, 25)\\n exact_Q2 = np.percentile(data_to_num, 50)\\n exact_Q3 = np.percentile(data_to_num, 75)\\n\\n self.assertEqual(est_Q1, exact_Q1)\\n self.assertEqual(est_Q2, exact_Q2)\\n self.assertEqual(est_Q3, exact_Q3)\",\n \"def percentile_points(axis, pdf, percentiles):\\n cdf_points = np.zeros(len(percentiles), dtype=float)\\n\\n for index, percentile in enumerate(percentiles):\\n try:\\n # oritinal\\n #cdf = np.cumsum(pdf) / float(np.sum(pdf))\\n #cdf = np.cumsum(pdf) / np.sum(pdf)\\n #cdf_points[index] = np.nanmin(axis[cdf >= percentile])\\n percentile_range = (np.cumsum(pdf) >= (percentile * np.sum(pdf)))\\n cdf_points[index] = np.nanmin(axis[percentile_range])\\n except FloatingPointError:\\n print \\\"percentile_points: invalid PDF\\\"\\n cdf = np.zeros_like(pdf)\\n cdf_points[index] = -1.\\n\\n return cdf_points\",\n \"def get_percentile(self, q):\\n return self.sum_stat_sample_ratio.get_percentile(q)\",\n \"def percentage_od_xy(range,count):\\n percentages = []\\n for i in range:\\n percentages.append(n_percentage_part(i, count))\\n\\n percentile_list = pd.DataFrame(\\n {'percentage': range,\\n 'od_needed': percentages\\n })\\n return percentile_list\",\n \"def get_percentile(data_list, score, kind='weak'):\\n n = len(data_list)\\n\\n if kind == 'strict':\\n return len([i for i in data_list if i < score]) / float(n) * 100\\n elif kind == 'weak':\\n return len([i for i in data_list if i <= score]) / float(n) * 100\\n elif kind == 'mean':\\n return (len([i for i in data_list if i < score]) + len([i for i in data_list if i <= score])) * 50 / float(n)\\n else:\\n raise ValueError(\\\"The kind kwarg must be 'strict', 'weak' or 'mean'. You can also opt to leave it out and rely on the default method.\\\")\",\n \"def IQR(data):\\n return percentile(data, 75) - percentile(data, 25)\",\n \"def get_IQR(lst):\\n return (float(np.percentile(lst, 75)) - float(np.percentile(lst, 25)))\",\n \"def transfo_quantile(xx):\\n\\n # nn = np.zeros(len(xx))\\n # oo = np.argsort(xx)\\n # nn[oo] = np.arange(len(xx)) / len(xx) + 1 / (2 * len(xx))\\n # return nn\\n\\n return rankdata(xx) / len(xx) - 1 / (2 * len(xx))\",\n \"def quantile(a, prob):\\n a=numpy.asanyarray(a)\\n a=a[numpy.logical_not(numpy.isnan(a))].ravel()\\n n=a.size\\n\\n if prob>=1-.5/n:\\n return a.max()\\n elif prob<=.5/n:\\n return a.min()\\n\\n # find the two bounds we're interpreting between:\\n # that is, find i such that (i+.5) / n <= prob <= (i+1.5)/n\\n t=n*prob-.5\\n i=int(t)\\n\\n # partial sort so that the ith element is at position i, with bigger ones\\n # to the right and smaller to the left\\n a.sort()\\n\\n if i==t: # did we luck out and get an integer index?\\n return a[i]\\n else:\\n # we'll linearly interpolate between this and the next index\\n smaller=a[i]\\n larger=a[i+1:].min()\\n if numpy.isinf(smaller):\\n return smaller # avoid inf - inf\\n return smaller+(larger-smaller)*(t-i)\",\n \"def get_quantile_thresholds(ds, quantile, dim, lat_name='lat', lon_name='lon', lat_chunk=1, lon_chunk=1):\\n ds = ds.chunk({**{lat_name: 1, lon_name: 1},\\n **{d: -1 for d in dim}})\\n return ds.quantile(quantile, dim)\",\n \"def _percentile_factory(perc):\\n def percentile_(values):\\n return numpy.percentile(values, perc)\\n percentile_.__name__ = f\\\"percentile_{perc}\\\"\\n return percentile_\",\n \"def get_percentile(self, q):\\n return self.sum_stat_sample_delta.get_percentile(q)\",\n \"def _calculate_percentile_cutoff(run_numbers):\\n mcp_values = []\\n andor_values = []\\n for run_number in run_numbers:\\n current_data_path = ''.join([DATA_PATH, 'run', str(run_number), 'allevts.h5'])\\n f = h5py.File(current_data_path, 'r')\\n current_phot = _get_photon_energy(f, run_number)\\n current_mcp = np.array(f['Acqiris2']['acq'])\\n current_mcp = current_mcp[(current_phot > 781) & (current_phot < 782)]\\n mcp_values.extend(current_mcp)\\n current_andor = np.array(f['Andor']['signal'])\\n current_andor = current_andor[(current_phot > 781) & (current_phot < 782)]\\n andor_values.extend(current_andor)\\n #plt.figure()\\n #plt.scatter(mcp_values, andor_values)\\n mcp_percentile_cutoff = min([percentileofscore(andor_values, 4000), 99.9])\\n return mcp_percentile_cutoff\",\n \"def test_percentile_discrete(self, test_X, interval=None):\\n if not interval:\\n interval = self.interval\\n temp = []\\n for idx in range(test_X.shape[-1]):\\n x = test_X[:, idx]\\n res = np.array([0] * x.shape[-1], dtype=int)\\n # logging.info(\\\"test_before_counter: \\\" + str(Counter(x)))\\n for i, point in enumerate(interval[idx]):\\n point1, point2 = point[0], point[1]\\n x1 = x[np.where((x >= point1) & (x <= point2))]\\n mask = np.in1d(x, x1)\\n res[mask] = (i + 1)\\n # logging.info(\\\"test_discrete: \\\" + str(res) + str((point1, point2)))\\n # logging.info(\\\"test_mask: \\\" + str(mask))\\n # logging.info(\\\"test_interval_point: \\\" + str(point))\\n # logging.info(\\\"test_discrete_main: \\\" + str(res))\\n # logging.info(\\\"test_discrete_counter: \\\" + str(Counter(res)))\\n temp.append(res)\\n return np.array(temp).T\",\n \"def get_percentile(self, q):\\n raise NotImplementedError(\\\"This is an abstract method and needs to be implemented in derived classes.\\\")\",\n \"def lscoreatpercentile (inlist, percent):\\r\\n if percent > 1:\\r\\n print \\\"\\\\nDividing percent>1 by 100 in lscoreatpercentile().\\\\n\\\"\\r\\n percent = percent / 100.0\\r\\n targetcf = percent*len(inlist)\\r\\n h, lrl, binsize, extras = histogram(inlist)\\r\\n cumhist = cumsum(copy.deepcopy(h))\\r\\n for i in range(len(cumhist)):\\r\\n if cumhist[i] >= targetcf:\\r\\n break\\r\\n score = binsize * ((targetcf - cumhist[i-1]) / float(h[i])) + (lrl+binsize*i)\\r\\n return score\",\n \"def imAdjQuantiles(pc, im_p, adj_p):\\n\\n def scale(input):\\n \\\"\\\"\\\"Scale array between 0 and 1\\\"\\\"\\\"\\n return (input - np.min(input)) / ((np.max(input) - np.min(input)))\\n\\n pc_low = pc_high = 0\\n\\n if np.shape(pc)[0] == 1:\\n pc_low = pc[0]\\n pc_high = 100 - pc[0]\\n elif np.shape(pc)[0] == 2:\\n pc_low = pc[0]\\n pc_high = pc[1]\\n else:\\n print('Clipping percentile format is wrong, no clipping is performed \\\\non image {}'.format(im_p))\\n if type(im_p) == str:\\n img = scale(plt.imread(im_p))\\n else:\\n img = scale(im_p)\\n low_in = np.percentile(img, pc_low)\\n high_in = np.percentile(img, pc_high)\\n adjusted = scale(np.clip(img, low_in, high_in)) * 65535\\n # plt.imshow(adjusted)\\n if adj_p == []:\\n return adjusted\\n else:\\n tiff.imsave(adj_p, adjusted.astype('uint16'))\",\n \"def fd_no_bins(y):\\n q75, q25 = np.percentile(y, [75 ,25])\\n iqr = q75 - q25\\n h=2*iqr*len(y)**(-1./3) \\n return int((max(y)-min(y))/h)\",\n \"def test_check_data_specifying_no_of_percentiles(self):\\n expected_data = np.array(\\n [self.percentile_25, self.percentile_50, self.percentile_75]\\n )\\n result = Plugin().process(self.cube, no_of_percentiles=3)\\n self.assertArrayAlmostEqual(result.data, expected_data, decimal=5)\",\n \"def calcPercentile(percent, set): #TESTED\\r\\n\\r\\n # check for 100%\\r\\n if percent == Decimal('1.0'):\\r\\n return max(set)\\r\\n\\r\\n # convert percent to the appropriate index\\r\\n pValue = percent * len(set)\\r\\n\\r\\n set = sorted(set)\\r\\n\\r\\n # check for 0%\\r\\n if percent == Decimal('0'):\\r\\n return set[0]\\r\\n\\r\\n # check if percent is an integer\\r\\n if pValue % 1 == 0:\\r\\n\\r\\n # cast pValue as int so it can be used as an index\\r\\n pValue = int(pValue)\\r\\n\\r\\n # take average of values at indices percent and percent - 1\\r\\n return (set[pValue - 1] + set[pValue]) / Decimal('2')\\r\\n\\r\\n # if percentage needs to be rounded\\r\\n else:\\r\\n # round number up to nearest integer\\r\\n print pValue # DELETE\\r\\n pValue = pValue.to_integral_exact(rounding=ROUND_CEILING) # WHAT'S UP WITH THIS FUNCTION?\\r\\n print pValue # DELETE\\r\\n pValue = int(pValue)\\r\\n\\r\\n return set[pValue - 1]\",\n \"def percentile(self, perc, field_path, method='linear', filter_func=None):\\n return self._collection.percentile(perc, field_path, method, filter_func)\",\n \"def test_percentile_kurtosis():\\n f = np.asarray([\\n [0.99, 1.0, 0.5, 0.52],\\n [0.69, 0.6, 0.61, 1.0]])\\n R = common_metrics.percentile_kurtosis(f, maximise=True)\\n expected = np.asarray(\\n [1.06382979, 5.0])\\n assert np.allclose(R, expected)\",\n \"def percentile(t: torch.tensor, q: float):\\n # Note that ``kthvalue()`` works one-based, i.e. the first sorted value\\n # indeed corresponds to k=1, not k=0! Use float(q) instead of q directly,\\n # so that ``round()`` returns an integer, even if q is a np.float32.\\n k = 1 + round(.01 * float(q) * (t.numel() - 1))\\n result = t.view(-1).kthvalue(k).values.item()\\n return result\",\n \"def interquartile_range(xs: List[float]) -> float:\\n return quantile(xs, 0.75) - quantile(xs, 0.25)\",\n \"def quantile(xs: List[float], p: float) -> float:\\n p_index = int(p * len(xs))\\n return sorted(xs)[p_index]\",\n \"def od_percentage_xy(range, count):\\n percentages = []\\n for i in range:\\n percentages.append(n_odpairs_percentage(i, count))\\n\\n percentile_list = pd.DataFrame(\\n {'od_needed': range,\\n 'percentage': percentages\\n })\\n return percentile_list\",\n \"def bootstrap_interval(data, percentiles=(2.5, 97.5), n_boots=100):\\n # Create empty array to fill the results\\n bootstrap_means = np.zeros([n_boots, data.shape[-1]])\\n for ii in range(n_boots):\\n # Generate random indices for data *with* replacement, then take the sample mean\\n random_sample = resample(data)\\n bootstrap_means[ii] = random_sample.mean(axis=0)\\n\\n # Compute the percentiles of choice for the bootstrapped means\\n percentiles = np.percentile(bootstrap_means, percentiles, axis=0)\\n return percentiles\",\n \"def weighted_percentiles(data, wt, percentiles): \\r\\n assert np.greater_equal(percentiles, 0.0).all(), \\\"Percentiles less than zero\\\" \\r\\n assert np.less_equal(percentiles, 1.0).all(), \\\"Percentiles greater than one\\\" \\r\\n data = np.asarray(data) \\r\\n assert len(data.shape) == 1 \\r\\n if wt is None: \\r\\n wt = np.ones(data.shape, np.float) \\r\\n else: \\r\\n wt = np.asarray(wt, np.float) \\r\\n assert wt.shape == data.shape \\r\\n assert np.greater_equal(wt, 0.0).all(), \\\"Not all weights are non-negative.\\\" \\r\\n assert len(wt.shape) == 1 \\r\\n n = data.shape[0] \\r\\n assert n > 0 \\r\\n i = np.argsort(data) \\r\\n sd = np.take(data, i, axis=0) \\r\\n sw = np.take(wt, i, axis=0) \\r\\n aw = np.add.accumulate(sw) \\r\\n if not aw[-1] > 0: \\r\\n raise ValueError(\\\"Nonpositive weight sum\\\")\\r\\n w = (aw-0.5*sw)/aw[-1] \\r\\n spots = np.searchsorted(w, percentiles) \\r\\n o = [] \\r\\n for (s, p) in zip(spots, percentiles): \\r\\n if s == 0: \\r\\n o.append(sd[0]) \\r\\n elif s == n: \\r\\n o.append(sd[n-1]) \\r\\n else: \\r\\n f1 = (w[s] - p)/(w[s] - w[s-1]) \\r\\n f2 = (p - w[s-1])/(w[s] - w[s-1]) \\r\\n assert f1>=0 and f2>=0 and f1<=1 and f2<=1 \\r\\n assert abs(f1+f2-1.0) < 1e-6 \\r\\n o.append(sd[s-1]*f1 + sd[s]*f2) \\r\\n return o\",\n \"def _pctiles_from_sorted_v2(self, values, pcs):\\n if not all(0 <= pc <= 100 for pc in pcs):\\n raise ValueError(\\\"pctiles must be between 0 and 100\\\")\\n nvals = len(values)\\n pctiles = []\\n for pc in pcs:\\n if pc == 0:\\n new_pct = values[0]\\n elif pc == 100:\\n new_pct = values[nvals-1]\\n else:\\n loc = nvals * pc / 100\\n loc_flr = floor(loc)\\n t = loc - loc_flr\\n new_pct = (1 - t) * values[loc_flr - 1] + t * values[loc_flr]\\n pctiles.append(new_pct)\\n return pctiles\",\n \"def belonging(f_bar, neighborhood_avg):\\n new_labels = np.argmax(neighborhood_avg - f_bar, axis=1)\\n return new_labels\",\n \"def median(self, name, **kwargs):\\n data = self.get(name,**kwargs)\\n return np.percentile(data,[50])\",\n \"def tenth_percentile(y_test, y_pred) :\\n return np.abs(y_test - y_pred).sort_values().iloc[int(len(y_test)*0.10)]/60\",\n \"def test_percentile_regret():\\n f = np.asarray([\\n [0.99, 1.0, 0.5],\\n [0.69, 0.6, 0.6]])\\n R = common_metrics.percentile_regret(f, maximise=True, percentile=0.5)\\n expected = np.asarray(\\n [0.0, -0.3])\\n assert np.allclose(R, expected)\\n R = common_metrics.percentile_regret(f, maximise=False, percentile=0.5)\\n expected = np.asarray(\\n [-0.3, 0.0])\\n assert np.allclose(R, expected)\",\n \"def cdf_scores(values, refs, greater=True):\\n if np.isnan(refs).all():\\n return np.nan * np.ones(np.shape(values))\\n if greater:\\n return np.array([percentileofscore(refs, _v, kind='weak')/100 * len(refs) / (len(refs)+2) + 1/(len(refs)+2) \\n for _v in values])\\n else:\\n return np.array([1 - percentileofscore(refs, _v, kind='weak')/100 * len(refs) / (len(refs)+2) - 1/(len(refs)+2) \\n for _v in values])\",\n \"def percent_bias(x_arr, y_arr):\\n delta = np.mean(x_arr) - np.mean(y_arr)\\n denom = np.sqrt((np.var(x_arr) + np.var(y_arr)) / 2.0)\\n return 100.0 * delta / denom\",\n \"def quantile(x,p):\\n p_index = int(p*len(x))\\n return sorted(x)[p_index]\",\n \"def percentile_normalization(data: np.ndarray, percentile: int = 1) -> np.ndarray:\\n\\n min_percentile = np.percentile(data, percentile)\\n max_percentile = np.percentile(data, 100 - percentile)\\n\\n # limit maximum intensity of data by max_percentile\\n data[data >= max_percentile] = max_percentile\\n\\n # limit minimum intensity of data by min_percentile\\n data[data <= min_percentile] = min_percentile\\n\\n return data\",\n \"def test_run_simulations_and_get_percentile_allele_length_1():\\n simulator = genotype_confidence_simulator.GenotypeConfidenceSimulator(\\n 50, 300, 0.01, iterations=5\\n )\\n simulator.run_simulations()\\n expected_confidence_scores_percentiles = {\\n 193: 20.0,\\n 221: 40.0,\\n 271: 60.0,\\n 278: 80.0,\\n 303: 100.0\\n }\\n assert (\\n simulator.confidence_scores_percentiles\\n == expected_confidence_scores_percentiles\\n )\\n assert simulator.get_percentile(193) == 20.00\\n assert simulator.get_percentile(221) == 40.00\\n # Try getting number that is not in the dict and will have to be inferred\\n assert simulator.get_percentile(207) == 30.0\\n # Try values outside the range of what we already have\\n simulator.get_percentile(192) == 0.00\\n simulator.get_percentile(191) == 0.00\\n simulator.get_percentile(304) == 100.00\\n simulator.get_percentile(305) == 100.00\",\n \"def test_check_data_specifying_single_percentile(self):\\n expected_data = np.array(self.percentile_25)\\n result = Plugin().process(self.cube, percentiles=[25])\\n self.assertArrayAlmostEqual(result.data, expected_data, decimal=5)\",\n \"def percent_blank(coadd,obj,filter_i):\\n \\n #Get array of pixel values\\n scidata = coadd[0].data \\n #Flatten n-d array to 1-d \\n new = np.ravel(scidata)\\n new = np.sort(new)\\n try:\\n #Find the range of indices where the pixel value is EXACTLY 0 since the\\n #array is sorted and sometimes there are negative values which \\n #we want to include\\n index_0_ends = np.where(new == 0)[0][-1]\\n index_0_starts = np.where(new == 0)[0][0]\\n index_diff = index_0_ends - index_0_starts\\n size = len(new)\\n #Calculate percentage blank\\n percentage = ((index_diff+1)/size)*100\\n except:\\n #Go here if there aren't any pixel values that are exactly 0 in which \\n #case the image is 0% blank\\n percentage = 0\\n print('Percentage blank {0}%'.format(percentage))\\n #Move back up to interim folder\\n os.chdir('..')\\n #Write the percentage blank along with the object name and filter \\n #name to a text file\\n with open('percent_blankv2.txt','a') as newfile:\\n newfile.write('{0},{1},{2}%\\\\n'.format(obj,filter_i,percentage))\\n newfile.close()\",\n \"def get_statistics_percentile(self,table,field):\\n dict = {}\\n for x in xrange(1,11):\\n dict[x] = db.session.execute(\\\"select statistics_viewCount as percentile from meta order by percentile asc limit 1 OFFSET 19346*\\\"+str(x)+\\\"/10-1\\\").first().percentile\",\n \"def scale_percentile(matrix): #Function to convert the given 4 band image to png uint8 image.\\n orig_shape = matrix.shape\\n matrix = np.reshape(matrix, [matrix.shape[0]*matrix.shape[1], 3]).astype(float)\\n # Get 2nd and 98th percentile\\n mins = np.percentile(matrix, 1, axis=0)\\n maxs = np.percentile(matrix, 99, axis=0) - mins\\n \\n matrix = (matrix - mins[None,:])/maxs[None,:]\\n matrix = np.reshape(matrix, orig_shape)\\n matrix = matrix.clip(0,1)\\n return matrix\",\n \"def default_quantile():\\n return np.logspace(-5, 0, 100)\",\n \"def quantile(x, p):\\n p_index = int(p * len(x))\\n return sorted(x)[p_index]\",\n \"def quantile(x, p):\\n p_index = int(p * len(x))\\n return sorted(x)[p_index]\",\n \"def quantile(x, p):\\n p_index = int(p * len(x))\\n return sorted(x)[p_index]\",\n \"def quantile(agg, k=4, name='quantile'):\\n\\n w = 100.0 / k\\n p = np.arange(w, 100 + w, w)\\n\\n if p[-1] > 100.0:\\n p[-1] = 100.0\\n\\n q = np.array([stats.scoreatpercentile(agg.data, pct) for pct in p])\\n q = np.unique(q)\\n k_q = len(q)\\n\\n if k_q < k:\\n print(\\\"Quantile Warning: Not enough unique values for k classes (using {} bins)\\\".format(k_q))\\n\\n return DataArray(_bin(agg.data, q, np.arange(k_q)),\\n name=name,\\n dims=agg.dims,\\n coords=agg.coords,\\n attrs=agg.attrs)\",\n \"def plot_percentile_bounds_vs_x(sps, x, y, q=25, nbins=15, pcol='blue', scol='orange', sorted=True, line_alpha=1):\\n if min(x) == max(x):\\n return\\n bins = np.linspace(min(x), max(x), nbins + 1)\\n\\n if not sorted:\\n inds = np.argsort(x)\\n x = x[inds]\\n y = y[inds]\\n\\n inds = np.digitize(x, bins[1:], right=True) # assume x data is sorted\\n base = []\\n h1 = []\\n m_new = []\\n h2 = []\\n cc = 0\\n no_dist = 0\\n for j in range(0, nbins):\\n lh = np.searchsorted(inds, j, side='left')\\n rh = np.searchsorted(inds, j, side='right')\\n\\n if lh == rh:\\n cc += 0 # change this to 1 if you want limit\\n else:\\n vals = y[lh:rh]\\n xxx = (x[lh:lh+1][0] + x[rh-1:rh][0]) / 2\\n median = np.median(vals)\\n p_lb = np.percentile(vals, q)\\n p_ub = np.percentile(vals, 100 - q)\\n\\n base.append(xxx)\\n h1.append(p_lb)\\n m_new.append(median)\\n h2.append(p_ub)\\n cc = 0\\n if cc >= 2:\\n no_dist = 1\\n if no_dist == 0:\\n sps.plot(base, m_new, c=pcol, label='Mean')\\n sps.fill_between(base, h1, h2, facecolor=scol, alpha=0.3)\\n lab = f'{q} - {100-q} %'\\n sps.plot(base, h1, c=scol, alpha=line_alpha, label=lab)\\n sps.plot(base, h2, c=scol, alpha=line_alpha)\",\n \"def compute_object_percentile_surface_distances(\\n labelled_surface_distances_1, labelled_surface_distances_2, percentile\\n):\\n hausdorffs_label_1 = []\\n for surface_distance in labelled_surface_distances_1:\\n hausdorffs_label_1.append(np.percentile(surface_distance, percentile))\\n\\n hausdorffs_label_2 = []\\n for surface_distance in labelled_surface_distances_2:\\n hausdorffs_label_2.append(np.percentile(surface_distance, percentile))\\n return np.array(hausdorffs_label_1), np.array(hausdorffs_label_2)\",\n \"def mode_statistic(data, percentiles=range(10,91,10)):\\n from scipy.interpolate import UnivariateSpline, LSQUnivariateSpline\\n \\n so = np.argsort(data)\\n order = np.arange(len(data))\\n #spl = UnivariateSpline(data[so], order)\\n \\n knots = np.percentile(data, percentiles)\\n dx = np.diff(knots)\\n mask = (data[so] >= knots[0]-dx[0]) & (data[so] <= knots[-1]+dx[-1])\\n spl = LSQUnivariateSpline(data[so][mask], order[mask], knots, ext='zeros')\\n \\n mask = (data[so] >= knots[0]) & (data[so] <= knots[-1])\\n ix = (spl(data[so], nu=1, ext='zeros')*mask).argmax()\\n mode = data[so][ix]\\n return mode\",\n \"def quantile(x, p):\\n sorted_x = sorted(x)\\n # round p_index to base int\\n p_index = int(p * len(x))\\n return sorted_x[p_index]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70549434","0.6741164","0.6662035","0.66016424","0.6500401","0.6459244","0.6303143","0.6247118","0.6219375","0.61808074","0.6145661","0.6143983","0.6136194","0.61153173","0.60878253","0.60767406","0.60635877","0.6059681","0.5997567","0.59776396","0.5942029","0.59344673","0.5873005","0.5865658","0.5829489","0.5827651","0.5822596","0.5822596","0.58040696","0.57874906","0.5774456","0.5773354","0.5764051","0.5759549","0.5741488","0.5711268","0.56983465","0.568413","0.566613","0.56654173","0.5659489","0.5642492","0.56422776","0.56284356","0.5610526","0.5599532","0.5599437","0.5599437","0.559532","0.5586044","0.5584627","0.55835295","0.557736","0.55665714","0.5564324","0.55604416","0.55520725","0.55511713","0.55502677","0.5545582","0.5536987","0.55348593","0.5534311","0.55326855","0.5529773","0.55296665","0.55269915","0.55002743","0.5496716","0.5491528","0.5489257","0.5476127","0.54750615","0.54726076","0.54700094","0.54686594","0.54495454","0.54435384","0.5424379","0.5400664","0.53734034","0.53689665","0.5367661","0.53587407","0.5356035","0.53477466","0.53213876","0.5313152","0.5289263","0.528651","0.5284353","0.5281142","0.5277545","0.5277545","0.5277545","0.52661103","0.52541894","0.52503","0.5246017","0.5234167"],"string":"[\n \"0.70549434\",\n \"0.6741164\",\n \"0.6662035\",\n \"0.66016424\",\n \"0.6500401\",\n \"0.6459244\",\n \"0.6303143\",\n \"0.6247118\",\n \"0.6219375\",\n \"0.61808074\",\n \"0.6145661\",\n \"0.6143983\",\n \"0.6136194\",\n \"0.61153173\",\n \"0.60878253\",\n \"0.60767406\",\n \"0.60635877\",\n \"0.6059681\",\n \"0.5997567\",\n \"0.59776396\",\n \"0.5942029\",\n \"0.59344673\",\n \"0.5873005\",\n \"0.5865658\",\n \"0.5829489\",\n \"0.5827651\",\n \"0.5822596\",\n \"0.5822596\",\n \"0.58040696\",\n \"0.57874906\",\n \"0.5774456\",\n \"0.5773354\",\n \"0.5764051\",\n \"0.5759549\",\n \"0.5741488\",\n \"0.5711268\",\n \"0.56983465\",\n \"0.568413\",\n \"0.566613\",\n \"0.56654173\",\n \"0.5659489\",\n \"0.5642492\",\n \"0.56422776\",\n \"0.56284356\",\n \"0.5610526\",\n \"0.5599532\",\n \"0.5599437\",\n \"0.5599437\",\n \"0.559532\",\n \"0.5586044\",\n \"0.5584627\",\n \"0.55835295\",\n \"0.557736\",\n \"0.55665714\",\n \"0.5564324\",\n \"0.55604416\",\n \"0.55520725\",\n \"0.55511713\",\n \"0.55502677\",\n \"0.5545582\",\n \"0.5536987\",\n \"0.55348593\",\n \"0.5534311\",\n \"0.55326855\",\n \"0.5529773\",\n \"0.55296665\",\n \"0.55269915\",\n \"0.55002743\",\n \"0.5496716\",\n \"0.5491528\",\n \"0.5489257\",\n \"0.5476127\",\n \"0.54750615\",\n \"0.54726076\",\n \"0.54700094\",\n \"0.54686594\",\n \"0.54495454\",\n \"0.54435384\",\n \"0.5424379\",\n \"0.5400664\",\n \"0.53734034\",\n \"0.53689665\",\n \"0.5367661\",\n \"0.53587407\",\n \"0.5356035\",\n \"0.53477466\",\n \"0.53213876\",\n \"0.5313152\",\n \"0.5289263\",\n \"0.528651\",\n \"0.5284353\",\n \"0.5281142\",\n \"0.5277545\",\n \"0.5277545\",\n \"0.5277545\",\n \"0.52661103\",\n \"0.52541894\",\n \"0.52503\",\n \"0.5246017\",\n \"0.5234167\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.65258336"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":20077,"cells":{"query":{"kind":"string","value":"Initializes all the element of the GUI, supported by Tkinter"},"document":{"kind":"string","value":"def __init__(self, tello):\n self.tello = tello # videostream device\n self.thread = None # thread of the Tkinter mainloop\n self.stopEvent = None \n \n # control variables\n self.distance = 0.1 # default distance for 'move' cmd\n self.degree = 30 # default degree for 'cw' or 'ccw' cmd\n\n # if the flag is TRUE,the auto-takeoff thread will stop waiting\n # for the response from tello\n self.quit_waiting_flag = False\n \n # initialize the root window and image panel\n self.root = tki.Tk()\n self.panel = None\n\n # create buttons\n self.btn_landing = tki.Button(\n self.root, text='Open Command Panel', relief='raised', command=self.openCmdWindow)\n self.btn_landing.pack(side='bottom', fill='both',\n expand='yes', padx=10, pady=5)\n \n # start a thread that constantly pools the video sensor for\n # the most recently read frame\n self.stopEvent = threading.Event()\n \n # set a callback to handle when the window is closed\n self.root.wm_title('TELLO Controller')\n self.root.wm_protocol('WM_DELETE_WINDOW', self.on_close)\n\n # the sending_command will send command to tello every 5 seconds\n self.sending_command_thread = threading.Thread(target = self._sendingCommand)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __init__(self):\n self.window = Tk() # The main window\n self.__initialize_variables__() # Initialize the variables\n self.__initialize_menu__() # Initialize the Menu\n self.__initialize_status_bar__()\n self.__initialize_gui__() # Initialize the GUI widgets","def __init__(self):\n tkinter.Tk.__init__(self)\n #Variables de la fenêtre\n self.nombreDeChars = tkinter.IntVar()\n\n #Caractéristiques de la fenêtre\n self.geometry(constantes.geometry)\n self.title(constantes.title)\n self.resizable(False, False)\n\n #Appel des widgets\n self.widgets()","def initGUI(self):\n\n\t\t# Set main frame's location \n\t\tself.grid(row=0, column=0, sticky=\"nsew\")\n\n\t\t# Set path entry frame and its location\n\t\tself.entryFrame = Frame(self, relief = RAISED, borderwidth = 1)\n\t\tself.entryFrame.pack(fill = BOTH, expand = False)\n\t\t# Make label\n\t\tif self.message:\n\t\t\tmessageLabel = Label(self.entryFrame, text = self.message, font=(\"Bradley\", 10))\n\t\t\tmessageLabel.pack(anchor=W, padx=0, pady=0)\n\n\t\t# Set path entry and its location\n\t\tself.filePathEntry = Entry(self.entryFrame, bd = 4, width = 50)\n\t\tself.filePathEntry.pack(side = LEFT, padx=2, pady=1)","def init_widget(self):","def initUI(self):\n self.logger.debug('Setting up the Measurement GUI')\n self.setWindowTitle(self.title)\n\n self.show()\n\n self.make_combobox_scanner()\n self.make_combobox_movements()\n self.make_combobox_configurate()\n self.make_combobox_basic()","def __init__(self):\n\n self.ai_list = []\n self.master = Tk()\n self.ui_draw = IntVar()\n\n self.populate_ai_list(self.ai_list)\n self.frame = Frame(self.master)\n self.build_ui()\n\n self.master.mainloop()","def _initialize(self):\n self._frame = ttk.Frame(master=self._root)\n self._ingredients_frame = ttk.Frame(master=self._frame)\n\n self._create_header()\n self._show_ingredient_list()\n self._create_footer()\n\n self._ingredients_frame.grid(row=1, column=1, columnspan=2)\n self._frame.grid_columnconfigure(1, weight=1, minsize=250)","def set_GUI(\r\n self\r\n ):\r\n self.top = tk.Tk()\r\n self.top.title(\"Data Collection Interface\")\r\n \r\n self.get_label(\r\n self.top,\r\n text = \"Folder name\",\r\n width = None, # in characters\r\n height = 1, # in lines\r\n font = None,\r\n stick = tk.W,\r\n row = 0,\r\n column = 0,\r\n return_lbl = False\r\n )\r\n\r\n self.e_path = self.get_entry(\r\n self.top,\r\n default_txt = \"Collected_data\",\r\n enable = True,\r\n width = 30,\r\n row = 0,\r\n column = 1)\r\n\r\n self.get_label(\r\n self.top,\r\n text = \"Number of Images\",\r\n width = None, # in characters\r\n height = 1, # in lines\r\n font = None,\r\n stick = tk.W,\r\n row = 1,\r\n column = 0,\r\n return_lbl = False\r\n )\r\n\r\n self.e_num_images = self.get_entry(\r\n self.top,\r\n default_txt = \"1800\",\r\n enable = True,\r\n width = 30,\r\n row = 1,\r\n column = 1)\r\n\r\n self.get_label(\r\n self.top,\r\n text = \"Total Time\",\r\n width = None, # in characters\r\n height = 1, # in lines\r\n font = None,\r\n stick = tk.W,\r\n row = 2,\r\n column = 0,\r\n return_lbl = False\r\n )\r\n\r\n self.e_tot_time = self.get_entry(\r\n self.top,\r\n default_txt = \"15\",\r\n enable = True,\r\n width = 30,\r\n row = 2,\r\n column = 1)\r\n\r\n self.get_label(\r\n self.top,\r\n text = \"Minutes\",\r\n width = None, # in characters\r\n height = 1, # in lines\r\n font = None,\r\n stick = tk.W,\r\n row = 2,\r\n column = 2,\r\n return_lbl = False\r\n )\r\n\r\n self.get_label(\r\n self.top,\r\n text = \"Time between photos\",\r\n width = None, # in characters\r\n height = 1, # in lines\r\n font = None,\r\n stick = tk.W,\r\n row = 3,\r\n column = 0,\r\n return_lbl = False\r\n )\r\n\r\n self.e_interval = self.get_entry(\r\n self.top,\r\n default_txt = \"0.001\",\r\n enable = False,\r\n width = 30,\r\n row = 3,\r\n column = 1)\r\n\r\n self.get_label(\r\n self.top,\r\n text = \"Seconds\",\r\n width = None, # in characters\r\n height = 1, # in lines\r\n font = None,\r\n stick = tk.W,\r\n row = 3,\r\n column = 2,\r\n return_lbl = False\r\n )\r\n\r\n self.get_label(\r\n self.top,\r\n text = \"Images Per Folder\",\r\n width = None, # in characters\r\n height = 1, # in lines\r\n font = None,\r\n stick = tk.W,\r\n row = 4,\r\n column = 0,\r\n return_lbl = False\r\n )\r\n\r\n self.e_images_per_folder = self.get_entry(\r\n self.top,\r\n default_txt = \"500\",\r\n enable = True,\r\n width = 30,\r\n row = 4,\r\n column = 1)\r\n\r\n self.get_label(\r\n self.top,\r\n text = \"Progress Display Frequency\",\r\n width = None, # in characters\r\n height = 1, # in lines\r\n font = None,\r\n stick = tk.W,\r\n row = 5,\r\n column = 0,\r\n return_lbl = False\r\n )\r\n\r\n self.e_prog_display_freq = self.get_entry(\r\n self.top,\r\n default_txt = \"5\",\r\n enable = True,\r\n width = 30,\r\n row = 5,\r\n column = 1)\r\n\r\n self.get_label(\r\n self.top,\r\n text = \"Preview Display Frequency\",\r\n width = None, # in characters\r\n height = 1, # in lines\r\n font = None,\r\n stick = tk.W,\r\n row = 6,\r\n column = 0,\r\n return_lbl = False\r\n )\r\n\r\n self.e_prew_display_freq = self.get_entry(\r\n self.top,\r\n default_txt = \"10\",\r\n enable = True,\r\n width = 30,\r\n row = 6,\r\n column = 1)\r\n\r\n self.get_label(\r\n self.top,\r\n text = \"\",\r\n width = None, # in characters\r\n height = 2, # in lines\r\n font = None,\r\n stick = tk.W,\r\n row = 7,\r\n column = 2,\r\n return_lbl = False\r\n )\r\n \"\"\"\r\n self.get_label(\r\n self.top,\r\n text = \"\",\r\n width = None, # in characters\r\n height = 2, # in lines\r\n font = None,\r\n stick = tk.W,\r\n row = 8,\r\n column = 2,\r\n return_lbl = False\r\n )\"\"\"\r\n\r\n self.r_radio_button_variable = tk.IntVar(self.top,1)\r\n\r\n self.r_images_time = self.get_radio_button(\r\n self.top,\r\n control_variable =self.r_radio_button_variable ,\r\n returned_value = 1,\r\n text = \"Images + Total time\",\r\n enable = True,\r\n default_state = True,\r\n #width = 30,\r\n row = 9,\r\n column = 0,\r\n align = tk.W,\r\n command = self.block_entry)\r\n\r\n self.r_images_interval = self.get_radio_button(\r\n self.top,\r\n control_variable =self.r_radio_button_variable ,\r\n returned_value = 2,\r\n text = \"Images + Time interval\",\r\n enable = True,\r\n default_state = False,\r\n #width = 30,\r\n row = 10,\r\n column = 0,\r\n align = tk.W,\r\n command = self.block_entry)\r\n\r\n self.r_time_interval = self.get_radio_button(\r\n self.top,\r\n control_variable =self.r_radio_button_variable ,\r\n returned_value = 3,\r\n text = \"Total time + Time interval\",\r\n enable = True,\r\n default_state = False,\r\n #width = 30,\r\n row = 11,\r\n column = 0,\r\n align = tk.W,\r\n command = self.block_entry)\r\n\r\n self.get_label(\r\n self.top,\r\n text = \"\",\r\n width = None, # in characters\r\n height = 2, # in lines\r\n font = None,\r\n stick = tk.W,\r\n row = 12,\r\n column = 0,\r\n return_lbl = False\r\n )\r\n\r\n self.get_label(\r\n self.top,\r\n text = \"\",\r\n width = None, # in characters\r\n height = 1, # in lines\r\n font = None,\r\n stick = tk.W,\r\n row = 14,\r\n column = 0,\r\n return_lbl = False\r\n )\r\n\r\n self.r_quality_variable = tk.StringVar(self.top,\"Low\")\r\n\r\n self.r_HQuality = self.get_radio_button(\r\n self.top,\r\n control_variable =self.r_quality_variable ,\r\n returned_value = \"High\",\r\n text = \"High Quality\",\r\n enable = True,\r\n default_state = False,\r\n #width = 30,\r\n row = 16,\r\n column = 0,\r\n align = tk.W,\r\n command = self.quality_change)\r\n\r\n self.r_LQuality = self.get_radio_button(\r\n self.top,\r\n control_variable =self.r_quality_variable ,\r\n returned_value = \"Low\",\r\n text = \"Low Quality\",\r\n enable = True,\r\n default_state = True,\r\n #width = 30,\r\n row = 15,\r\n column = 0,\r\n align = tk.W,\r\n command = self.quality_change)\r\n\r\n self.r_Day_Night_variable = tk.StringVar(self.top,\"Day\")\r\n\r\n self.r_Day = self.get_radio_button(\r\n self.top,\r\n control_variable =self.r_Day_Night_variable ,\r\n returned_value = \"Day\",\r\n text = \"Day\",\r\n enable = True,\r\n default_state = True,\r\n #width = 30,\r\n row = 15,\r\n column = 1,\r\n align = tk.W,\r\n command = self.day_change)\r\n\r\n self.r_Night = self.get_radio_button(\r\n self.top,\r\n control_variable =self.r_Day_Night_variable ,\r\n returned_value = \"Night\",\r\n text = \"Night\",\r\n enable = True,\r\n default_state = False,\r\n #width = 30,\r\n row = 16,\r\n column = 1,\r\n align = tk.W,\r\n command = self.day_change)\r\n\r\n self.c_auto_zip_variable = tk.IntVar(self.top,0)\r\n\r\n self.c_auto_zip = tk.Checkbutton(\r\n self.top,\r\n text = \"Auto Zip\",\r\n variable = self.c_auto_zip_variable)\r\n self.c_auto_zip.grid(row = 17,column = 0, sticky = tk.W)\r\n self.c_auto_zip.deselect()\r\n\r\n self.get_label(\r\n self.top,\r\n text = \"\",\r\n width = None, # in characters\r\n height = 1, # in lines\r\n font = None,\r\n stick = tk.W,\r\n row = 18,\r\n column = 0,\r\n return_lbl = False\r\n )\r\n\r\n\r\n self.l_image = self.get_label(\r\n self.top,\r\n text = None,\r\n width = None, # in characters\r\n height = None, # in lines\r\n font = None,\r\n stick = None,\r\n row = 114,\r\n column = 3,#0,\r\n return_lbl = True,\r\n ctr_var = None\r\n )\r\n\r\n self.get_label(\r\n self.top,\r\n text = \"Progress :\",\r\n width = None, # in characters\r\n height = 1, # in lines\r\n font = None,\r\n stick = tk.W,\r\n row = 113,\r\n column = 0,\r\n return_lbl = False\r\n )\r\n\r\n self.progress_var = tk.StringVar(self.top)\r\n \r\n self.get_label(\r\n self.top,\r\n text = \"\",\r\n width = 40, # in characters\r\n height = 2, # in lines\r\n font = None,\r\n stick = tk.W,\r\n row = 113,\r\n column = 1,\r\n return_lbl = False,\r\n ctr_var = self.progress_var\r\n )\r\n \"\"\"\r\n self.lab = self.get_label(\r\n self.top,\r\n text = \"\",\r\n width = 40, # in characters\r\n height = 1, # in lines\r\n font = None,\r\n stick = tk.W,\r\n row = 10,\r\n column = 1,\r\n return_lbl = True,\r\n #ctr_var = self.progress_var\r\n )\"\"\"\r\n\r\n self.b_start = self.get_button(\r\n root = self.top,\r\n button_text = \"Start\",\r\n row = 5,\r\n column = 2,\r\n enable = True,\r\n width = 10,\r\n height =1,\r\n command = self.start_collecting\r\n )\r\n\r\n self.b_pause = self.get_button(\r\n root = self.top,\r\n button_text = \"Zip Folder\",\r\n row = 6,\r\n column = 2,\r\n enable = True,\r\n width = 10,\r\n height =1,\r\n command = self.zip_folder\r\n )\r\n\r\n self.b_stop = self.get_button(\r\n root = self.top,\r\n button_text = \"Stop\",\r\n row = 7,\r\n column = 2,\r\n enable = True,\r\n width = 10,\r\n height =1,\r\n command = self.stop_collecting\r\n )\r\n\r\n self.b_red = self.get_button(\r\n root = self.top,\r\n button_text = \"Red\",\r\n row = 10,\r\n column = 2,\r\n enable = True,\r\n width = 10,\r\n height =1,\r\n command = self.red\r\n )\r\n self.b_red.config(bg='red',activebackground = 'red')\r\n\r\n self.b_yellow = self.get_button(\r\n root = self.top,\r\n button_text = \"Yellow\",\r\n row = 11,\r\n column = 2,\r\n enable = True,\r\n width = 10,\r\n height =1,\r\n command = self.yellow\r\n )\r\n self.b_yellow.config(bg='yellow', activebackground = 'yellow')\r\n\r\n self.b_green = self.get_button(\r\n root = self.top,\r\n button_text = \"Green\",\r\n row = 12,\r\n column = 2,\r\n enable = True,\r\n width = 10,\r\n height =1,\r\n command = self.green\r\n )\r\n self.b_green.config(bg='green', activebackground = 'green')\r\n\r\n self.b_normal = self.get_button(\r\n root = self.top,\r\n button_text = \"No light\",\r\n row = 13,\r\n column = 2,\r\n enable = True,\r\n width = 10,\r\n height =1,\r\n command = self.normal\r\n )\r\n\r\n self.b_load_orientation = self.get_button(\r\n root = self.top,\r\n button_text = \"Load Orientation\",\r\n row = 15,\r\n column = 2,\r\n enable = True,\r\n width = 10,\r\n height =1,\r\n command = self.load_orientation\r\n )\r\n\r\n \r\n\r\n \"\"\"\r\n self.get_label(\r\n self.top,\r\n text = \"tesing\",\r\n width = 10, # in characters\r\n height = 2, # in lines\r\n font = ('Times', '12', 'normal'),\r\n row = 0,\r\n column = 0,\r\n return_lbl = False\r\n )\r\n \r\n self.button1 = self.get_button(\r\n root = self.top,\r\n button_text = \"test\",\r\n row = 1,\r\n column = 3,\r\n enable = True,\r\n width = 10,\r\n height =1,\r\n command = self.pt\r\n )\r\n\r\n self.entry = self.get_entry(\r\n self.top,\r\n default_txt = \"Test\",\r\n enable = True,\r\n width = 30,\r\n row = 3,\r\n column = 0)\r\n\r\n self.contrl = tk.IntVar(self.top)\r\n self.radio = self.get_radio_button(\r\n self.top,\r\n control_variable =self.contrl ,\r\n returned_value = 5,\r\n text = \"radio\",\r\n enable = True,\r\n default_state = False,\r\n #width = 30,\r\n row = 0,\r\n column = 0,\r\n align = tk.W,\r\n command = self.pt)\r\n\r\n self.radio2 = self.get_radio_button(\r\n self.top,\r\n control_variable =self.contrl ,\r\n returned_value = 6,\r\n text = \"radio2\",\r\n enable = True,\r\n default_state = False,\r\n width = None,\r\n row = 1,\r\n column = 0,\r\n align = tk.W,\r\n command = self.pt)\"\"\"","def init_ui(self):\n self.master.title(\"Backbone\")\n self.master.geometry(\"300x150\")\n\n self.pack(fill=BOTH, expand=1)\n\n self.btn_upload_file = Button(self, text=\"Upload file\", command=self.upload_file)\n self.btn_upload_file.place(x=90, y=10)\n\n self.btn_create_training_file = Button(self, text=\"Create & upload training file\",\n command=self.create_training_file)\n self.btn_create_training_file.place(x=30, y=40)\n\n self.btn_run_algorithm = Button(self, text=\"Run algorithm\", command=self.run_algorithm)\n self.btn_run_algorithm.place(x=80, y=70)\n\n self.btn_view_results = Button(self, text=\"View Results\", command=self.view_results)\n self.btn_view_results.place(x=85, y=100)","def _initialize(self):\n\n cancel_button = ttk.Button(\n master=self._frame,\n text=\"Cancel\",\n command=self._hide_confirmation_window\n )\n\n delete_button = ttk.Button(\n master=self._frame,\n text=\"Delete\",\n command=self._handle_delete\n )\n\n self._initilize_message()\n\n cancel_button.grid(row=1, column=0, padx=5, pady=5)\n delete_button.grid(row=1, column=1, padx=5, pady=5)","def _init_ui(self):\r\n\t\t\r\n\t\tself.input_frame = Input(self)\r\n\t\tself.input_frame.pack()\r\n\t\t\r\n\t\tbutton_ok = Button(self, text = \"Ping\", command = self._go)\r\n\t\tbutton_ok.pack()\r\n\t\t\r\n\t\tself.result_frame = Result(self)\r\n\t\tself.result_frame.pack()","def initialize():\n global root\n\n if root is not None:\n print \"param.tk already initialized; ignoring call to param.tk.initialize()\"\n return\n\n if T._default_root is not None:\n root = T._default_root\n else:\n root = T.Tk()\n root.withdraw()\n\n # Until tklib, tcllib, and scrodget become more commonly\n # available, we include them in tkgui.\n externaltcl_path = os.path.join(os.path.split(__file__)[0],\"tcl\")\n root.tk.call(\"lappend\",\"auto_path\",externaltcl_path)\n\n T.BooleanVar.get = _BooleanVar_get","def set_ui(self):\r\n\r\n self.canvas = tk.Canvas(self)\r\n self.canvas.pack()\r\n\r\n self.entry = ttk.Entry(self.canvas, justify=\"center\", font=(\"Calibri\", 12))\r\n\r\n self.grid = Grid(self.canvas)","def InitUI(self):\n\t\tself._initMenuBar()\n\t\tself._initLayout()\n\t\t\n\t\t# Bindings\n\t\tself.Bind(wx.EVT_BUTTON, self.OnButtonClicked)\n\t\t\n\t\t# We can't even start without an input file\n\t\tself.OnOpen(None)","def _initUI(self) -> None:\n self._createActions()\n self._addActionsToMoveButtons()\n self._createToolBar()\n self._createStatusBar()\n self._createMainContextMenu()","def initUI(self):\n\n self.wid = RosGenWidget()\n self.setCentralWidget(self.wid)\n menubar = self.menuBar()\n fileMenu = menubar.addMenu('&Файл')\n editMenu = menubar.addMenu('&Редактирование')\n self.create_menu_par('Менеджер подписчиков и издателей', self.wid.show_manager, fileMenu, 'Ctrl+M')\n self.create_menu_par('Очистить', self.wid.clear_all_lines, editMenu, 'Ctrl+D')\n self.create_menu_par('Загрузить данные из...', self.wid.open_fileDialog, fileMenu, 'Ctrl+F')\n self.create_menu_par('Сохранить как...', self.wid.save_fileDialog, fileMenu, 'Ctrl+S')\n self.create_menu_par('Выход', self.exit_app, fileMenu, 'Esc')\n self.statusbar = self.statusBar()\n self.statusbar.showMessage('Ожидание данных')\n self.wid.msg2Statusbar[str].connect(self.statusbar.showMessage)\n self.setGeometry(600, 200, 700, 400)\n self.setWindowTitle('Генератор шаблонов ROS-приложения')\n self.show()","def init_ui():\n\t# Use wrapper to set reasonable defaults and reset the terminal on exit\n\t#curses.wrapper(_main_loop)\n\tcurses.wrapper(_init_func)","def init_gui(self):\n # Choose a layout.\n main_vb = QtGui.QVBoxLayout(self)\n\n # Add a list or tree view.\n self.list_view = QtGui.QListWidget()\n\n # Add the buttons.\n load_btn = QtGui.QPushButton('Load Selected')\n cancel_btn = QtGui.QPushButton('Cancel')\n load_btn.clicked.connect(self.update_list_view)\n cancel_btn.clicked.connect(self.close)\n\n # Connect the list/tree view with a method appropriate for user interaction.\n self.list_view.currentItemChanged['QListWidgetItem*', 'QListWidgetItem*'].connect(self.set_current_name)\n self.list_view.itemChanged['QListWidgetItem*'].connect(self.change_name)\n\n # Add the widgets to the layout.\n btn_hb = QtGui.QHBoxLayout()\n btn_hb.addWidget(load_btn)\n btn_hb.addWidget(cancel_btn)\n main_vb.addWidget(self.list_view)\n main_vb.addLayout(btn_hb)\n\n # Show the GUI.\n self.setGeometry(300, 300, 450, 300)\n self.setWindowTitle('Hello World')\n img_icon = 'C:/Users/caj150430/code/so_much_win.png'\n self.setWindowIcon(QtGui.QIcon(img_icon))\n self.show()","def initWidgets(self):\r\n if self.autoExampleWidgets:\r\n self.initExampleWidgets()","def initGUI(self):\n\n\t\t# Set window's title\n\t\tself.parent.title(\"Error Message\")\n\t\t# Creat frames that contain messages and buttons \n\t\tself.buttonFrame = Frame(self.parent)\n\t\tself.buttonFrame.pack(fill = BOTH, expand = True)\n\t\tmessageFrame = Frame(self.buttonFrame, borderwidth = 1)\n\t\tmessageFrame.pack(fill = BOTH, expand = True)\n\t\t# Creat buttons\n\t\tself.makeButtons()\n\t\t# Create and show an error message as an label\n\t\tvar = StringVar()\n\t\tlabel = Message(messageFrame, textvariable=var, relief=RAISED, width = 1000)\n\t\tvar.set(self.message)\n\t\tlabel.pack(fill = BOTH, expand = True)","def init_ui(self):\n raise NotImplementedError","def init_ui(self):\n raise NotImplementedError","def initializeUI(self):\n self.setStyleSheet(abstyle)\n self.setGeometry(140, 100, 860, 484)\n self.setWindowTitle('Emotions Data View')\n self.setupModelView()","def gui_init(self):\n GUI_element.gui_init(self)\n\n self.hover_sound = False\n \n if not self.image is None:\n self.generic_button = False\n self.width = self.image.width if self.width == 0 else self.width\n self.height = self.image.height if self.height == 0 else self.height\n else:\n # Set up a generic button\n self.generic_button = True\n self.image = self.game.core.media.gfx['gui_button_generic_background']\n self.draw_strategy = \"gui_button\"\n\n # fixed height\n self.height = 30\n \n # Create the text\n self.generic_button_text_object = Text(self.game.core.media.fonts[\"generic_buttons\"], self.x, self.y + (self.height / 2), TEXT_ALIGN_CENTER, self.generic_button_text)\n self.generic_button_text_object.z = self.z - 1\n self.generic_button_text_object.colour = (1.0,1.0,1.0)\n \n # Set up the width, if we have a larger than normal width then we want to centre the text.\n if self.width < self.generic_button_text_object.text_width + 20:\n self.width = self.generic_button_text_object.text_width + 20\n self.generic_button_text_object.x += (self.width / 2)\n \n self.sequence_count = self.image.num_of_frames\n self.draw_strategy_call_parent = False","def __init__(self):\n # Root window\n self.root = tk.Tk()\n self.root.title(\"Crossword\")\n # Padding frame\n self.frame = tk.Frame(self.root)\n self.frame.pack(fill=\"both\", padx=PAD, pady=PAD)\n # Initialize widget groups\n self.header = HeaderView(self)\n self.puzzle = PuzzleView(self)\n self.clues = CluesView(self)\n # Show widgets\n self.header.show()\n self.puzzle.show()\n self.clues.show()","def initialize_user_interface(self):\n self.parent.title(\"DB operations\")\n self.parent.grid_rowconfigure(0,weight=1)\n self.parent.grid_columnconfigure(0,weight=1)\n self.parent.config(background=\"lavender\")\n\n self.label_user=Tkinter.Label(self.parent,text=\"DB User: \",anchor=Tkinter.W,background=\"dark slate gray\",foreground=\"white\", font=\"Helvetica 8 bold\")\n self.label_password=Tkinter.Label(self.parent,text=\"DB Password:\", anchor=Tkinter.W,background=\"dark slate gray\",foreground=\"white\", font=\"Helvetica 8 bold\")\n\n self.label_user.grid(row=0,column=0,sticky=Tkinter.E+Tkinter.W)\n self.label_password.grid(row=1,column=0, sticky=Tkinter.E+Tkinter.W)\n\n self.dbuser=Tkinter.Entry(self.parent)\n self.dbpassword=Tkinter.Entry(self.parent,show=\"*\")\n\n self.dbuser.grid(row=0,column=1,sticky=Tkinter.E+Tkinter.W)\n self.dbpassword.grid(row=1,column=1,sticky=Tkinter.E+Tkinter.W)\n\n self.connectb=Tkinter.Button(self.parent,text=\"Log in\",font=\"Helvetica 10 bold\",command=self.dbconnexion)\n self.cancelb=Tkinter.Button(self.parent,text=\"Cancel\",command=self.parent.quit,font=\"Helvetica 10 bold\")\n\n self.connectb.grid(row=2,column=1,sticky=Tkinter.W)\n self.cancelb.grid(row=2,column=2)","def init_UI(self):\n\n self.master.title(\"Search for different companies\")\n self.master.geometry(\"400x400\")\n\n self.label_combobox = Label(self, text=\"Search by\")\n self.label_combobox.pack()\n\n self.combo_searching_options = Combobox(self, state=\"readonly\")\n self.combo_searching_options['values'] = self.combobox_values\n self.combo_searching_options.pack()\n\n self.label_input = Label(self, text=\"Entry the value\")\n self.label_input.pack()\n\n self.user_input = Entry(self, width=40)\n self.user_input.pack()\n\n self.btn_submit = Button(self, text=\"Submit\", command=self.submit)\n self.btn_submit.pack()\n\n self.text_area = scrolledtext.ScrolledText(self)\n self.text_area.pack()\n\n sys.stdout = RedirectOutputText(self.text_area)\n\n self.btn_back = Button(self, text=\"Back\", command=self.go_back)\n self.btn_back.pack()","def __init__(self,root,app,row=0,column=0):\r\n # tkinter info\r\n self.root = root\r\n self.app = app\r\n # Frame for Layout\r\n self.frame = tk.Frame(self.root,borderwidth=1,relief=tk.GROOVE)\r\n self.frame.grid(row=row,column=column,sticky=tk.NW)\r\n # Checkbutton Widgets\r\n self.checks = []# tk.Checkbutton instances\r\n self.intvars = []# tk.IntVar instances\r","def _init_widgets(self):\n # Container frame\n self.container = Frame(self)\n # Workspace block\n self.main_container = Frame(self.container)\n\n self.text = Label(self.main_container)\n self.text.config(text=\"PyEventLogViewer is a timeline-based tool used to simplify the way\\n\"\n \"a user can view and explore Windows EVTX files. To begin using this\\n\"\n \"software you must do the following:\\n\\n\"\n \"\\t1) File → New → 'Create a new project'\\n\"\n \"\\t2) Tools → Import Log File → 'Open a specified EVTX file'\\n\"\n \"\\t3) Explore the presented timeline.\\n\"\n \"\\t4) Double-click a specific record to view the XML data for that record.\\n\"\n \"\\t5) File → Export → 'Generate a CSV or HTML file for timeline presentation.'\\n\\n\"\n \"At this point, only System and Security EVTX files are parsable with this software.\")\n\n self.show_var = BooleanVar()\n self.show_check = Checkbutton(self.main_container, text=\"Don't Show on Startup\", variable=self.show_var)\n\n # Action block\n self.button_ok = Button(self.main_container, text='Ok', underline=0, command=self.callback_close)\n self.bind('<Return>', self.callback_close)\n self.bind('<Escape>', self.callback_close)\n\n # Focus on window - required for binds to work.\n self.focus_set()","def __init__(self,*args, **kwargs):\n tk.Tk.__init__(self, *args, **kwargs)\n self.winfo_toplevel().title(\"ElogQP\")\n self.container = tk.Frame(self)\n self.container.pack(side=\"top\", fill=\"both\", expand=True)\n self.container.grid_rowconfigure(0, weight=1)\n self.container.grid_columnconfigure(0, weight=1)\n self.activeFrames = []\n for F in (Frames.frame_start.frame_start, Frames.frame_modules.frame_modules, Frames.frame_showError.frame_showError):\n self.createFrame(F, F.__name__)\n \n self.showFrame(\"frame_start\")","def _initialize_widgets(self):\n self.outer_board = [[Frame(self.root, bd = self.FRAME_BORDER_WIDTH, \n relief = self.FRAME_RELIEF) \n for _ in range(self.BOARD_DIM)] \n for _ in range(self.BOARD_DIM)]\n self.inner_boards = [[self._generate_inner_board(r, c) \n for c in range(self.BOARD_DIM)]\n for r in range(self.BOARD_DIM)]","def initialize(self):\n self._ui.img_name.setText('No files selected')\n self._ui.bt_next.setEnabled(False)\n self._ui.bt_prev.setEnabled(False)\n self._ui.bt_right.setEnabled(False)\n self._ui.bt_left.setEnabled(False)\n self._ui.gps_button.setEnabled(False)\n\n self._open_btn = QPushButton('Open File', self._ui.img_label)\n self.adjustSize()","def setUpFrame(self):\n #adds labels to the Board\n self.mineLabel = tk.Label(self, text=\"Mines: \"+str(self.numMines))\n self.mineLabel.grid(row=0, column=0, sticky=\"W\", columnspan=int((self.cols-2)/2))\n self.smileButton = tk.Label(self, image=self.images[1])\n self.smileButton.grid(row=0, column=int((self.cols-2)/2), sticky=\"WE\", columnspan=2)\n self.flagLabel = tk.Label(self, text=\"Flags: \"+str(self.numFlags))\n self.flagLabel.grid(row=0, column=int((self.cols-2)/2)+2, sticky=\"E\", columnspan=int((self.cols-1)/2))\n\n #left click listeners on smileButton\n self.smileButton.bind('<ButtonPress-1>', lambda event, num=0: self.changeSmile(num))\n self.smileButton.bind('<ButtonRelease-1>', self.replay)","def init_tkvars(self):\n\n for key in self.defaultprefs:\n value = self.defaultprefs[key]\n if type(value) is types.IntType:\n var = self.__dict__[key] = IntVar()\n elif type(value) is types.StringType:\n var = self.__dict__[key] = StringVar()\n var.set(value)\n\n self.resnum = IntVar()\n self.resnum.set(1)\n # Method for calculating Tm of primers\n self.Tm_method = StringVar()\n self.Tm_method.set('Stratagene')\n if 'Darwin' in self.currplatform:\n self.seqfontsize.set(16)\n else:\n self.seqfontsize.set(14)\n return","def initGUI(self):\r\n\r\n self.pack(fill=tk.BOTH, expand=True)\r\n\r\n # Figure out sizing.\r\n width = 200\r\n height = 200\r\n pad = 5\r\n fontWidth = 8\r\n bigWidth = int((width*3 + pad*6) / fontWidth)\r\n \r\n # Create option frames.\r\n self.frameOptions = tk.LabelFrame(self, text=\"Options:\",\r\n width=width, height=height)\r\n self.frameSegment = tk.LabelFrame(self, text=\"Segmentation Method:\",\r\n width=width, height=height)\r\n self.frameMeasure = tk.LabelFrame(self, text=\"Measurements:\",\r\n width=width, height=height)\r\n\r\n # Create text boxes and labels.\r\n self.labelStatus = tk.LabelFrame(self, text=\"Status:\", bd=0)\r\n self.labelResults = tk.LabelFrame(self, text=\"Results:\", bd=0)\r\n self.textStatus = ScrolledText(self.labelStatus, height=5,\r\n width=bigWidth)\r\n self.textResults = ScrolledText(self.labelResults, height=10,\r\n width=bigWidth)\r\n\r\n # Create buttons.\r\n self.buttonCalculate = tk.Button(self, text='Calculate',\r\n width=20, height=1, font=12, bd=3,\r\n command=lambda:self.prepare())\r\n self.buttonSaveAll = tk.Button(self, text='Save Session Summary',\r\n command=self.saveAll)\r\n self.buttonSelectOutFold = tk.Button(self, text='Set Output Folder',\r\n command=self.setOutputFolder)\r\n self.buttonAbout = tk.Button(self, text='About', command=self.about)\r\n\r\n # Arrange toplevel widgets.\r\n self.frameOptions.grid(row=0, column=2, padx=pad, pady=pad,\r\n sticky='NESW')\r\n self.frameSegment.grid(row=0, column=1, padx=pad, pady=pad,\r\n sticky='NESW')\r\n self.frameMeasure.grid(row=0, column=0, padx=pad, pady=pad,\r\n sticky='NESW')\r\n\r\n self.buttonCalculate.grid(row=1, column=1, \r\n padx=pad, pady=pad*3)\r\n self.buttonSelectOutFold.grid(row=1, column=0, \r\n padx=pad, pady=pad*3)\r\n self.buttonAbout.grid(row=6, column=2, sticky='e', padx=20, pady=10)\r\n\r\n self.labelStatus.grid(row=2, column=0, columnspan=3, sticky='w',\r\n padx=pad, pady=pad)\r\n self.textStatus.grid(row=3, column=0, columnspan=3)\r\n self.labelResults.grid(row=4, column=0, columnspan=3, sticky='w',\r\n padx=pad, pady=pad)\r\n self.textResults.grid(row=5, column=0, columnspan=3)\r\n self.buttonSaveAll.grid(row=6, column=1, padx=pad, pady=pad)\r\n\r\n # Variables\r\n self.outFold = None\r\n columns = [[\"\",\"\",\"\",\"\",\r\n \"Bright phase diameter\",\r\n \"\",\"\",\r\n \"Dark phase diameter\",\r\n \"\",\"\",\r\n \"Bright length\",\r\n \"\",\"\",\r\n \"Dark length\",\r\n \"\",\"\",\r\n \"Bright area\",\r\n \"\",\"\",\r\n \"Dark area\",\r\n \"\",\"\",\r\n \"Bright connected length\",\r\n \"\",\"\",\r\n \"Dark connected length\",\r\n \"\",\"\"], \r\n [\"image\",\r\n \"pixel size\",\r\n \"area frac\",\r\n \"est diam\",\r\n \"Average\",\r\n \"SD\",\r\n \"Measured\",\r\n \"Average\",\r\n \"SD\",\r\n \"Measured\",\r\n \"Average\",\r\n \"SD\",\r\n \"Measured\",\r\n \"Average\",\r\n \"SD\",\r\n \"Measured\",\r\n \"Average\",\r\n \"SD\",\r\n \"Measured\",\r\n \"Average\",\r\n \"SD\",\r\n \"Measured\",\r\n \"Average\",\r\n \"SD\",\r\n \"Measured\",\r\n \"Average\",\r\n \"SD\",\r\n \"Measured\"]]\r\n \r\n self.saveAll = np.array(columns)\r\n\r\n # Measurement options.\r\n # Variables.\r\n self.varDiameter = tk.BooleanVar()\r\n self.varLength = tk.BooleanVar()\r\n self.varArea = tk.BooleanVar()\r\n self.varSumConnectedLength = tk.BooleanVar()\r\n self.varAreaFraction = tk.BooleanVar()\r\n # Create widgets.\r\n self.checkDiameter = tk.Checkbutton(self.frameMeasure,\r\n text=\"Diameter\", variable=self.varDiameter)\r\n self.checkLength = tk.Checkbutton(self.frameMeasure,\r\n text=\"Length\", variable=self.varLength)\r\n self.checkArea = tk.Checkbutton(self.frameMeasure,\r\n text=\"Area\", variable=self.varArea)\r\n self.checkSumConnectedLength = tk.Checkbutton(self.frameMeasure,\r\n text=\"Connected length\", variable=self.varSumConnectedLength)\r\n self.checkAreaFraction = tk.Checkbutton(self.frameMeasure,\r\n text=\"Area fraction\", variable=self.varAreaFraction)\r\n # Pack widgets.\r\n self.checkDiameter.grid(row=0, column=0, sticky='w')\r\n self.checkLength.grid(row=1, column=0, sticky='w')\r\n self.checkArea.grid(row=2, column=0, sticky='w')\r\n self.checkSumConnectedLength.grid(row=3, column=0, sticky='w')\r\n self.checkAreaFraction.grid(row=4, column=0, sticky='w')\r\n # Check appropriate boxes.\r\n self.checkDiameter.select()\r\n self.checkLength.select()\r\n self.checkArea.select()\r\n self.checkSumConnectedLength.select()\r\n self.checkAreaFraction.select()\r\n \r\n # Segment options.\r\n # Variables.\r\n self.varSegment = tk.StringVar()\r\n # Create widgets.\r\n self.radAccurate = tk.Radiobutton(self.frameSegment,\r\n text=\"Accurate\", variable=self.varSegment, value=\"accurate\",\r\n command=self.updateOptions)\r\n self.radFast = tk.Radiobutton(self.frameSegment,\r\n text=\"Fast\", variable=self.varSegment, value=\"fast\",\r\n command=self.updateOptions)\r\n self.radManual= tk.Radiobutton(self.frameSegment,\r\n text=\"Manual\", variable=self.varSegment, value=\"manual\",\r\n command=self.updateOptions)\r\n self.radFromBinary = tk.Radiobutton(self.frameSegment,\r\n text=\"From binary\", variable=self.varSegment, value=\"binary\",\r\n command=self.updateOptions)\r\n # Pack widgets.\r\n self.radAccurate.grid(row=0, column=0, sticky='w')\r\n self.radFast.grid(row=1, column=0, sticky='w')\r\n self.radManual.grid(row=2, column=0, sticky='w')\r\n self.radFromBinary.grid(row=3, column=0, sticky='w')\r\n # Check appropriate boxes.\r\n self.radAccurate.select()\r\n\r\n # Option options.\r\n # Profiles\r\n profiles = autoSelect.profiles()\r\n # Variables.\r\n self.varShowSteps = tk.BooleanVar()\r\n self.varOutputExcel = tk.BooleanVar()\r\n self.varSavePDF = tk.BooleanVar()\r\n self.varSaveMovie = tk.BooleanVar()\r\n self.varSaveBinary = tk.BooleanVar()\r\n self.varAutoParse = tk.BooleanVar()\r\n self.varProfile = tk.StringVar()\r\n self.varProfile.set(profiles[0])\r\n # Create widgets.\r\n self.checkShowSteps = tk.Checkbutton(self.frameOptions,\r\n text=\"Show steps\", variable=self.varShowSteps)\r\n self.checkOutputExcel = tk.Checkbutton(self.frameOptions,\r\n text=\"Output to Excel\", variable=self.varOutputExcel)\r\n self.checkSavePDF = tk.Checkbutton(self.frameOptions,\r\n text=\"Save PDF\", variable=self.varSavePDF)\r\n self.checkSaveMovie = tk.Checkbutton(self.frameOptions,\r\n text=\"Save movie\", variable=self.varSaveMovie)\r\n self.checkSaveBinary = tk.Checkbutton(self.frameOptions,\r\n text=\"Save binary\", variable=self.varSaveBinary)\r\n self.checkAutoParse = tk.Checkbutton(self.frameOptions,\r\n text=\"Auto parse raw image\", variable=self.varAutoParse,\r\n command=self.updateAuto)\r\n self.optionProfile = tk.OptionMenu(self.frameOptions, self.varProfile,\r\n *profiles)\r\n self.optionProfile.config(state=tk.DISABLED)\r\n\r\n # Pack widgets.\r\n self.checkShowSteps.grid(row=0, column=0, sticky='w')\r\n self.checkOutputExcel.grid(row=1, column=0, sticky='w')\r\n self.checkSavePDF.grid(row=2, column=0, sticky='w')\r\n #self.checkSaveMovie.grid(row=3, column=0, sticky='w')\r\n self.checkSaveBinary.grid(row=4, column=0, sticky='w')\r\n self.checkAutoParse.grid(row=5, column=0, sticky='w')\r\n self.optionProfile.grid(row=6, column=0, sticky='w', padx=15)\r\n \r\n # Check appropriate boxes.\r\n self.checkOutputExcel.select()\r\n\r\n self.createToolTips()","def controls_setup(self):\n pass","def initWidgets(self):\n self.lambdtext.setText(str(self.lambd))\n self.ptext.setText(str(self.p))","def initUI(self):\n \n self.setWindowTitle(\"Intecol Flir camera\")\n self.setGeometry(300, 100, 1012, 622)","def initialize(self):\n self.setWindowTitle(\"Playlist Maker\")\n self.setGeometry(0,0, 800, 494)\n self.mbox = QVBoxLayout()\n self.hbox = QHBoxLayout()\n self.hbtnbox = QHBoxLayout()","def __init__(self):\n self.window = Tk()\n self.window.title(\"Brick Breaker\")\n self.window.attributes(\"-fullscreen\", True)\n self.window.iconbitmap(\"data/wall.ico\")\n self.window.config(background=\"light blue\")\n\n # initialization des composants\n self.frame = Frame(self.window, bg='light blue')\n self.littleFrame = Frame(self.frame, bg='light blue')\n self.littleFrame_bis = LabelFrame(self.frame, bg='light blue', text=\"USER NAME\")\n\n # creation des composants\n self.create_title()\n self.create_subtitle()\n self.create_play_button()\n self.create_quit_button()\n\n # empaquetage\n self.littleFrame_bis.pack(expand=YES, pady=30)\n self.littleFrame.pack(expand=YES, pady=50)\n self.frame.pack(expand=YES, fill=BOTH, pady=200)","def widgetSetup(self):\n self.master.resizable(0, 0)\n self.master.iconbitmap('logo.ico')\n self.master.title(\"Ejercicio POO\")\n\n self.master.bind(\"<Return>\", lambda e: self.create())\n self.master.bind(\"<Delete>\", lambda e: self.delete())","def init_frame(self):\n self._exit_button.grid(row=0, column=2, sticky=tk.W)\n self._clear_button.grid(row=0, column=0, sticky=tk.E)\n # self._copy_button.grid(row=0, column=1, sticky=(tk.W, tk.W))\n return None","def __init__(self):\n\t\tself.root = Tk()\n\t\tself.root.geometry(\"1600x1600\")\n\t\ttitle_arabic=u'\\uFEB3\\uFEE8\\uFECC\\uFEEE\\uFEA9'\n\t\tself.root.title(title_arabic)\n\t\tself.root.geometry(\"1600x900\")\t\t\n\t\tself.root.resizable(width=False, height=False)\n\t\tmylabel=Label(text=\"Loading . . .\",fg=\"#ff1a75\",bg=\"black\",font=(\"Times New Roman\",18))\n\t\tmylabel.place(x=665,y=300)\n\t\tmylabel3=Text()\n\t\tmylabel3.place(x=550,y=395)\n\t\tmylabel3['width']='50'\n\t\tmylabel3['height']=0.6\n\t\tmylabel3.configure(state='disabled')\n\t\tself.root.configure(background='black')\n\t\tself.load()\n\t\tself.root.mainloop()","def init_main(self):\n self.start_game = tk.Button(self.view.frame_2, text=\"Start Game\",\n command=lambda: self.draw_game_table())\n\n self.start_game.pack(side=tk.LEFT)\n self.see_log = tk.Button(self.view.frame_2, text=\"See Log\",\n command=lambda: self.look_log())\n self.see_log.pack(side=tk.LEFT)\n\n self.clean_log = tk.Button(self.view.frame_2, text=\"Clean Log\",\n command=lambda: self.model.clean_log_file())\n self.clean_log.pack(side=tk.LEFT)\n self.close_game = tk.Button(self.view.frame_2, text=\"Close Game\",\n command=lambda: self.view.root.destroy())\n self.close_game.pack(side=tk.LEFT)","def initGui(self):\r\n\r\n # Create help action \r\n self.helpAction = QAction( QIcon(\":/plugins/layercombinations/about.png\"), u\"Help\", self.iface.mainWindow())\r\n # connect the action \r\n self.helpAction.triggered.connect( self.showHelp )\r\n # Add menu item\r\n self.iface.addPluginToMenu(u\"&Layer Combinations\", self.helpAction)\r\n\r\n # Create the action that allows to change the widget type\r\n self.changeWidgetAction = QAction(\"Change widget type\", self.iface.mainWindow())\r\n self.changeWidgetAction.triggered.connect( self.changeWidget )\r\n self.iface.addPluginToMenu(u\"&Layer Combinations\", self.changeWidgetAction)\r\n\r\n # Create the action that will toggle the plugin panel\r\n self.action = QAction(QIcon(\":/plugins/layercombinations/icon.png\"), \"Show/hide the Layer Combinations widgets\", self.iface.mainWindow())\r\n self.action.triggered.connect( self.widget.toggle )\r\n # Add toolbar button and menu item\r\n self.iface.addToolBarIcon(self.action)\r\n self.iface.addPluginToMenu(u\"&Layer Combinations\", self.action)\r\n\r\n\r\n # Add the widget to the mainWindow\r\n self.widget.addToiFace(self.iface)","def __init__(self, master=None, *args, **kw):\n Tix.Frame.__init__(self, master, *args, **kw)\n self._init_widgets()\n self._init_bindings()\n self._root().title(\"KaeChat\")\n self._page_serial = 0","def prepare_UI(self):","def _initialize(self):\n \n self.view.lineEdit_3.setText(\"C,H,N,O,P,S\")\n self.view.spin_hit.setValue(20)\n self.view.lineEdit_2.setValue(10.)\n self.view.checkBox_8.setChecked(True)","def initializeUI(self):\n self.setGeometry(100, 100, 300, 200)\n self.setWindowTitle('Event Handling Example')\n\n self.show()","def initialize_buttons(self):\r\n self.start_button = tk.Button(self.master, text='Start', command = self.startRunning)\r\n self.start_button.grid(row=0, column=0)\r\n\r\n self.pause_button = tk.Button(self.master, text='Pause', command = self.pauseRunning)\r\n self.pause_button.grid(row=0, column=1)\r\n\r\n self.graph_button = tk.Button(self.master, text='Graph', command = self.showGraph)\r\n self.graph_button.grid(row=0, column=2)\r\n \r\n self.plot_button = tk.Button(self.master, text='Plot', command = self.showPlot)\r\n self.plot_button.grid(row=0, column=3)\r\n \r\n self.draw_button = tk.Button(self.master, text='Draw', command = self.drawCells)\r\n self.draw_button.grid(row=0, column=4)\r\n \r\n # Initialize Button States and Actions\r\n self.pause_button['state'] = 'disabled'\r\n # Boolean switch to control flow of placement process\r\n self.running = False\r\n # Boolean switch to plot placement connections and tags, turn off for faster processing\r\n self.plot = False\r\n self.drawing = False\r\n self.graph = False\r\n # Boolean switch to specify first run and allow stop/continue behavior that doesn't initialize program\r\n self.firstRun = True","def init_UI(self):\n\n self.master.title(\"Create and upload training file\")\n self.master.geometry('400x400')\n\n self.text_area = scrolledtext.ScrolledText(self)\n self.text_area.pack()\n\n self.user_input = Entry(self, width=10)\n self.user_input.pack()\n\n sys.stdout = RedirectOutputText(self.text_area)\n\n self.create_uncertain_pairs_file()\n\n self.console_label = ConsoleLabel(self.get_uncertain_pairs_file())\n self.current_record_pair = self.console_label.get_uncertain_pair()\n\n self.btn_next = Button(self, text=\"Next\", bg=\"green\", command=self.get_input)\n self.btn_next.pack()\n\n self.back = Button(self, text=\"Back\", command=self.go_back)\n self.back.pack()","def gui_init(self):\n if not self.parent is None:\n self.parent.children.append(self)\n self.children = []\n self.priority = PRIORITY_GUI_ELEMENTS","def _initialize(self):\n self._entry = tk.Entry(master = self._window)\n self._entry.focus_set()\n \n self._confirm_button = tk.Button(master = self._window,\n text = \"Ok\",\n font = DEFAULT_FONT,\n command = self._confirm_color)\n\n self._entry.grid(row = 0, column = 0,\n padx = 10, pady = 10)\n self._confirm_button.grid(row = 1, column = 0,\n padx = 10, pady = 10)\n\n self._window.bind(\"<Return>\", self._confirm_color)","def __init__ (self,c,title,label):\n \n leoTkinterDialog.__init__(self,title,resizeable=True) # Initialize the base class.\n self.createTopFrame()\n self.top.protocol(\"WM_DELETE_WINDOW\", self.destroy)\n \n # Initialize common ivars.\n self.c = c\n self.label = label\n self.vnodeList = []\n self.vnodeList = []\n self.buttonFrame = None\n \n # Fill in the frame.\n self.createFrame()\n self.fillbox()\n \n # Make the common bindings after creating self.box.\n \n self.box.bind(\"<Double-Button-1>\",self.go)","def create_widgets(self):\n # self.var_spherical = IntVar()\n # self.var_3d = IntVar()\n # self.var_spatial_audio = IntVar()\n # self.button_open[\"command\"] = self.action_open\n # self.button_inject[\"command\"] = self.action_inject\n pass","def build_initial() :\r\n titleframe = T.Frame(ROOT)\r\n TITLE = T.Label(titleframe, text = \"Welcome to Microgp!\")\r\n var = T.StringVar()\r\n INSTRUCTIONS = T.Message(titleframe, textvariable = var, width = 100)\r\n var.set(\"By Erik and Sam\")\r\n instruct_b = T.Button(titleframe, text = \"Instructions\",\r\n command = get_instructions)\r\n instruct_b.pack(side = T.BOTTOM)\r\n TITLE.pack(side = T.TOP)\r\n INSTRUCTIONS.pack(side = T.BOTTOM)\r\n titleframe.pack()","def _init_widgets(self):\n comps = self.ui.component_list\n comps.addItems(sorted(self._labels.keys()))\n data = self.ui.data_list\n data.addItems(sorted(self._data.keys()))","def __init__(self):\n self.defaultTheme = \"DarkAmber\"\n self.version = 1.4\n self.versionName = \"class update\"\n self.title = \"Lms GUI default window\"\n self.layout = [[sg.Text(\"This is the base window class layout.\")]]\n self.elementJustification = 'c'\n self.location=(500, 300)\n self.running = True\n self.window = None\n self.event = \"\"\n self.values = []\n self.nextAction = None","def createWidgets(self):\r\n top = self.winfo_toplevel()\r\n top.rowconfigure(0, weight=1)\r\n top.columnconfigure(0, weight=1)\r\n self.rowconfigure(0, weight=1)\r\n self.columnconfigure(0, weight=1) \r\n\r\n self.button_quit = tk.Button(self, text='Quit', command=self.quit)\r\n self.button_quit.grid(row=0, column=0, sticky=tk.N+tk.S+tk.E+tk.W)","def init_objects(self):\n self.start_button_local = box(pos=vector(0, 1.2, 0), size=vector(8, 2, 1))\n # if no IP address was defined, the button \"remote game\" will not appear\n self.start_button_remote = None if not self.remote_enabled else box(pos=vector(0, -1.2, 0), size=vector(8, 2, 1))\n # a label is a text associated to a box\n self.start_label_local = label(pos=self.start_button_local.pos, xoffset=0, yoffset=0, text='Local Game', color=color.black, opacity=0, line=False, height=20, box=False)\n self.start_label_remote = None if not self.remote_enabled else label(pos=self.start_button_remote.pos, xoffset=0, yoffset=0, text='Remote Game', color=color.black, opacity=0, line=False, height=20, box=False)\n self.title = text(pos=vector(0, 5, 0), text='Morpion 3D', align='center', color=color.green, billboard=True, depth=0.5)\n # return the list of elements that actually appear\n return filter(lambda x: x is not None, [self.start_button_local, self.start_button_remote, self.start_label_local, self.start_label_remote, self.title])","def __init__(self, target=None):\n\t\ttkinter.Frame.__init__(self, target)\n\t\tself.create_widgets()","def __init__(self):\n # clears the console window\n if sys.platform in ('linux-i386','linux2'):\n os.system(\"clear\")\n elif sys.platform in ('win32','dos','ms-dos'):\n os.system(\"cls\")\n\n # print scripts info\n print self.WELCOME_MESSAGE\n\n # initialize all instance variables\n self.guiElements = {} # dictionary of gui elements (buttons, strings, sliders, ...)\n self.gui_events = [] # list of events\n self.gui_event_ids = {} # dictionary of event ids\n self.config = {} # configuration dictionary\n self.target = None # import or export\n self.callback = None # function to call when config gui is done\n self.texpathIndex = 0\n self.texpathCurrent = ''\n\n # reset GUI coordinates\n self.xPos = self.XORIGIN\n self.yPos = self.YORIGIN + Blender.Window.GetAreaSize()[1]\n\n # load configuration\n self.load()","def __init__(self, master, **kwargs):\n GenericWindow.__init__(self, master, **kwargs)\n self.states = None\n self.master = master\n self.display()","def init_ui(self):\n raise NotImplementedError(\"This is an abstract method.\")","def initialize(self):\n # Notebook holds all the tabs\n n = ttk.Notebook(self)\n f1 = RectTab(self)\n f2 = LTab(self)\n f3 = CircTab(self)\n f4 = BuminTab(self)\n f5 = LorentzTab(self)\n n.add(f1, text='Rectangle')\n n.add(f2, text='L')\n n.add(f3, text='Circle ')\n n.add(f4, text='Buminovich')\n n.add(f5, text='Lorentz')\n # need to pack for the Notebook to display\n n.pack()","def initGui(self):\n from p4_view import Gui\n self.updateStatus(\"Launching GUI...\")\n self.gui = Gui(self, self.lmap)\n self.gui.setStart(self.cfg[\"START\"])\n self.gui.setGoal(self.cfg[\"GOAL\"])\n self.gui.setPossGoals(self.cfg[\"POSS_GOALS\"])\n #GHD\n self.gui.setMapName(self.cfg[\"MAP_FILE\"])\n self.updateStatus(\"OK\")\n self.gui.mainloop()","def create_widgets(self):\n\n # tk.Button(win, text=\"Update\", command=self.update).grid(row=1, column=1)\n tkvar = tk.StringVar(win)\n # Dictionary with options\n choices = ('Clear', 'Small Glider', 'Glider', 'Exploder', '10 Cell Row', 'Light Weight Spaceship', 'Tumbler',\n 'Gosper Glider Gu')\n self.combo_input = ttk.Combobox(self.control_area, width=25, values=choices, state='readonly')\n self.combo_input.pack(side=tk.LEFT)\n self.combo_input.current(0)\n self.combo_input.bind(\"<<ComboboxSelected>>\", self.combo_callback)\n\n self.next = tk.Button(self.control_area, text=\"Next\", command=self.next_generation)\n self.next.pack(side=tk.LEFT, padx=3, pady=2)\n self.start = tk.Button(self.control_area, text=\"Start\", command=self.start_game)\n self.start.pack(side=tk.LEFT, padx=3, pady=2)\n\n self.stop = tk.Button(self.control_area, text=\"Stop\", fg=\"red\", command=self.stop_game)\n self.stop.pack(side=tk.LEFT, padx=3, pady=2)\n\n self.stop = tk.Button(self.control_area, text=\"Fast\", fg=\"red\", command=self.stop_game)\n self.stop.pack(side=tk.LEFT, padx=3, pady=2)\n self.gen_label = tk.Label(win, text=\"label\", bg=\"#808080\")\n self.gen_label.grid(row=0, column=1)","def setup(self):\n self.ui.setup_window()","def Init(self):\r\n\r\n AuiDefaultDockArt.Init(self)\r\n \r\n self._active_caption_colour = self._inactive_caption_colour\r\n self._active_caption_text_colour = wx.SystemSettings.GetColour(wx.SYS_COLOUR_CAPTIONTEXT)\r\n self._inactive_caption_text_colour = self._active_caption_text_colour","def configure_widgets(self):\r\n\r\n # 'command' - callback function executed when button is pressed\r\n # since we can't pass it a function with arguments, we use the partial \r\n # function from the functools module\r\n self.btn_tl['command'] = partial(self.play, \"x\", (0,0))\r\n self.btn_tm['command'] = partial(self.play, \"x\", (0,1))\r\n self.btn_tr['command'] = partial(self.play, \"x\", (0,2))\r\n self.btn_ml['command'] = partial(self.play, \"x\", (1,0))\r\n self.btn_mm['command'] = partial(self.play, \"x\", (1,1))\r\n self.btn_mr['command'] = partial(self.play, \"x\", (1,2))\r\n self.btn_bl['command'] = partial(self.play, \"x\", (2,0))\r\n self.btn_bm['command'] = partial(self.play, \"x\", (2,1))\r\n self.btn_br['command'] = partial(self.play, \"x\", (2,2))\r\n\r\n self.btn_reset['text'] = \"Reset\"\r\n self.btn_reset['command'] = self.reset","def create_widgets(self):\n #create first button\n self.button1 = Button(self, text = \"Start\")\n self.button1.bind\n self.button1.grid()","def init ( self, parent ):\n # Create a panel to hold all of the buttons:\n self.control = panel = wx.Panel( parent, -1 )\n sizer = wx.BoxSizer( wx.VERTICAL )\n \n # Add the standard font control:\n font = self._font = wx.TextCtrl( panel, -1, self.str_value )\n wx.EVT_KILL_FOCUS( font, self.update_object )\n wx.EVT_TEXT_ENTER( panel, font.GetId(), self.update_object )\n sizer.Add( font, 0, wx.EXPAND | wx.BOTTOM, 3 )\n \n # Add all of the font choice controls:\n sizer2 = wx.BoxSizer( wx.HORIZONTAL )\n facenames = all_facenames()\n control = self._facename = wx.Choice( panel, -1, wx.Point( 0, 0 ), \n wx.Size( choice_width( facenames ), 20 ), \n facenames )\n \n sizer2.Add( control, 2, wx.EXPAND )\n wx.EVT_CHOICE( panel, control.GetId(), self.update_object_parts )\n \n control = self._point_size = wx.Choice( panel, -1, \n wx.Point( 0, 0 ), wx.Size( 30, 20 ), \n PointSizes )\n sizer2.Add( control, 1, wx.EXPAND | wx.RIGHT, 3 )\n wx.EVT_CHOICE( panel, control.GetId(), self.update_object_parts ) \n \n sizer.Add( sizer2, 0, wx.EXPAND )\n \n # Set-up the layout:\n panel.SetAutoLayout( True )\n panel.SetSizer( sizer )\n sizer.Fit( panel )","def initUI(self):\n startbtn = QPushButton(\"Start Recroding\", self)\n startbtn.move(30, 50)\n\n stopbtn = QPushButton(\"Stop Recording\", self)\n stopbtn.move(150, 50)\n\n initbtn = QPushButton(\"Initilize\", self)\n initbtn.move(30, 100)\n\n plotbtn = QPushButton(\"Plot\", self)\n plotbtn.move(150, 100)\n\n startbtn.clicked.connect(self.start_recording)\n stopbtn.clicked.connect(self.stop_recording)\n initbtn.clicked.connect(self.init_recording)\n plotbtn.clicked.connect(self.plot_signals)\n\n self.statusBar()\n self.statusBar().showMessage('Click Init')\n\n self.setGeometry(300, 300, 290, 150)\n self.setWindowTitle('Recorder 1.0')\n self.setWindowIcon(QIcon(\"./Static/Images/icon.jpg\"))\n self.show()","def init_gui(self):\r\n self.root.title('Video Resizer')\r\n self.root.option_add('*tearOff', 'FALSE')\r\n self.grid(column=0, row=0, sticky='nsew')\r\n\r\n # Buttons getvideos, save videos, start resize\r\n self.openButton = ttk.Button(self, width=8, text=\"Browse\", command=self.getVideosPath)\r\n self.openButton.grid(column=1, row=2)\r\n self.saveButton = ttk.Button(self, width=8, text=\"Browse\", command=self.getCapturePath)\r\n self.saveButton.grid(column=3, row=2)\r\n self.startButton = ttk.Button(self, text='Start to Resize', command=self.startResizing)\r\n self.startButton.grid(column=0, row=5)\r\n self.multiStartButton = ttk.Button(self, text='Start to multi Resize', command=self.startMultiResizing)\r\n self.multiStartButton.grid(column=2, row=5)\r\n\r\n # listbox to choose what video type to save\r\n # add a label for the combobox\r\n ttk.Label(self, text=\"Select Video Type to Save\").grid(column=0, row=4)\r\n\r\n def clickMe():\r\n \"\"\"\r\n button clicked to select video type\r\n called when action is clicked\r\n :return:\r\n \"\"\"\r\n global Format\r\n Format = typeToChoose.get()\r\n print(Format)\r\n action.configure(text='selected ' + Format) # show the selected item after clicked\r\n action.configure(state='disabled') # button disabled after clicked\r\n\r\n # Button\r\n action = ttk.Button(self, text=\"Select \", command=clickMe)\r\n action.grid(column=2, row=4)\r\n\r\n # Combobox\r\n typeToChoose = StringVar()\r\n # value in combobox is formatType\r\n numberChosen = ttk.Combobox(self, width=12, textvariable=typeToChoose, values=self.formatTypes)\r\n numberChosen.grid(column=1, row=4)\r\n numberChosen.current(0)\r\n\r\n # Frame show info related to the resizing process\r\n self.resultFrame = ttk.LabelFrame(self, text='Result', height=100)\r\n self.resultFrame.grid(column=0, row=6, columnspan=4, sticky='nesw')\r\n self.resultLabel = ttk.Label(self.resultFrame, text='')\r\n self.resultLabel.grid(column=0, row=0)\r\n\r\n # Labels that remain constant throughout execution.\r\n ttk.Label(self, text='Video Resizer').grid(column=0, row=0, columnspan=4)\r\n ttk.Label(self, text='Select videos').grid(column=0, row=2, sticky='w')\r\n ttk.Label(self, text='Saving folder').grid(column=2, row=2, sticky='w')\r\n ttk.Separator(self, orient='horizontal').grid(column=0, row=1, columnspan=4, sticky='ew')\r\n\r\n # configure for the window grid\r\n for child in self.winfo_children():\r\n child.grid_configure(padx=5, pady=5)","def vp_start_gui():\n global val, w, root\n root = tk.Tk()\n plot_support.set_Tk_var()\n top = Toplevel1(root)\n plot_support.init(root, top)\n root.mainloop()","def widgets(self):\r\n self.setWindowTitle(\"PyCrypt\")\r\n self.setMinimumSize(QSize(500, 500))\r\n self.setMaximumSize(QSize(500, 500))\r\n# Adding the sub def for widgets etc\r\n self.add_menus_and_status()\r\n self.add_buttons()","def __init__(self, parent):\r\n Frame.__init__(self, parent) \r\n \r\n self.parent = parent\r\n self.initUI()","def initGui(self):\n\n icon_path = ':/plugins/AreaPrinter/mountainIcon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'AreaPrinter'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n\tself.add_action(\n icon_path,\n text=self.tr(u'Grid Convergence'),\n callback=self.runTools,\n parent=self.iface.mainWindow())","def __init__(self):\n super().__init__()\n self.root = tk.Tk() # different Tk than this class.\n self.root.title(\"Python Pyui!\")\n self.top_frame = tk.Frame(self.root, bg=\"yellow\")\n self.top_frame.pack(fill=c.BOTH, expand=1)\n self.bottom_frame = tk.Frame(self.root, bg=\"blue\",\n borderwidth=1, relief=c.RAISED)\n self.bottom_frame.pack(fill=c.X)\n okButton = tk.Button(self.bottom_frame, text=\"OK\",\n command=self.cb_ok)\n okButton.pack(fill='none', padx=5, pady=5, side=c.RIGHT)\n cancelButton = tk.Button(self.bottom_frame, text=\"Cancel\",\n command=self.cb_cancel)\n cancelButton.pack(fill='none', padx=5, pady=5, side=c.RIGHT)\n\n self.not_cancelled = False\n self.current_parent = self.top_frame\n self.parent_is_grid= False\n self.entries = {}","def __init__(self):\n self.master = Tk()\n self.master.title(\"Brick Breaker\")\n self.master.geometry(\"800x600\")\n self.master.minsize(800, 600)\n self.master.iconbitmap(\"data/wall.ico\")\n self.master.config(background=\"lightblue\")\n self.frame = Frame(self.master, bg='lightblue')\n self.littleFrame = Frame(self.frame, bg='lightblue')\n\n # creation des composants\n self.create_title()\n self.create_play_button()\n self.create_quit_button()\n\n # empaquetage\n self.littleFrame.pack(expand=YES, pady=100)\n self.frame.pack(expand=YES)","def onInit(self):\n self.list_control = self.getControl(6)\n self.getControl(1).setLabel(self.window_title)\n self.getControl(3).setVisible(False)\n try:\n self.getControl(7).setLabel(xbmc.getLocalizedString(222))\n except Exception:\n pass\n\n self.getControl(5).setVisible(False)\n\n # add our items to the listing and focus the control\n self.list_control.addItems(self.listing)\n self.setFocus(self.list_control)","def __init__(self, master):\n self.window = tk.Canvas(master, width=500, height=300)\n self.reset_button = tk.Button(master, text=\"Reset\", command=self.reset_window)\n self.start_button = tk.Button(master, text=\"Start\", command=self.start_sorting)\n self.window.pack()\n self.reset_button.pack()\n self.start_button.pack()\n self.reset_window()","def _init_tkvars(self,PO):\n for name,param in PO.params().items():\n self._create_tkvar(PO,name,param)","def Define_Frame(self):\n self.frame=Frame(self.master, relief=GROOVE, bd=4)\n self.frame.grid(row=0,column=1,rowspan=2,columnspan=2)\n frame_title = Label(self.frame,text=\"Stage Control\",relief=RAISED,bd=2,width=24, bg=\"light yellow\",font=(\"Times\", 16))\n frame_title.grid(row=0, column=1)\n self.encoder_text = [] # These hold the stage position as read by the encoders\n self.coo_ent = [] # These hold the coordinate entry values\n but = []\n encoder_display = []\n for i in range(3):\n self.coo_ent.append(Entry(self.frame, justify=\"center\", width=12))\n but.append(Button(self.frame, text=\"Move %s (relative)\"%self.POS_NAME[i], width=12,command=lambda axis=i:self.GUI_move(axis)))\n self.encoder_text.append(StringVar())\n encoder_display.append(Label(self.frame,textvariable=self.encoder_text[i],relief=SUNKEN,bd=1, width=20))\n self.coo_ent[i].grid(row=i+1,column=0)\n self.coo_ent[i].focus_set()\n but[i].grid(row=i+1,column=1)\n encoder_display[i].grid(row=i+1,column=2)\n self.encoder_text[i].set(\"%8s microns\"%str(self.read_pos[i]))\n zero_encoders_button = Button(self.frame, text=\"Re-Initialize Encoders\", width=20, command=self.GUI_ReInitialize_Encoders)\n zero_encoders_button.grid(row=5,column=1)\n return","def init_ui(self):\n\n # Display default values until first update\n self.window.pcBatteryDisplay.setValue(100)\n self.window.pcCpuDisplay.setValue(0)","def __init__(self):\n self.handlers = {}\n self.start_state = None\n self.end_states = []\n self.btn = Button()","def initWidgets(self):\n self.loctext.setText(\"{0:g}\".format(self.loc))\n self.scaletext.setText(\"{0:g}\".format(self.scale))","def __init__(self):\n self.computer_first = 0 # randint(0, 1)\n self.app = Tk()\n self.app.attributes(\"-toolwindow\", 1)\n self.app.title('Tic Tac Toe')\n self.app.resizable(width=False, height=False)\n self.board = Board()\n self.font = Font(family=\"Helvetica\", size=32)\n self.buttons = {}\n for x, y in self.board.fields:\n handler = lambda x=x, y=y: self.move(x, y)\n button = Button(self.app, command=handler, font=self.font,\n width=2, height=1)\n button.grid(row=y, column=x)\n self.buttons[x, y] = button\n handler = lambda: self.reset()\n button = Button(self.app, text='reset', command=handler)\n button.grid(row=self.board.size + 1, column=0,\n columnspan=self.board.size, stick='WE')\n self.update()\n if self.computer_first:\n self.move(randint(0, self.board.size - 1),\n randint(0, self.board.size - 1))","def __setup_ui_controls(self):\n self.scene.append_to_caption('\\n')\n\n # Button to reset camera\n btn_reset = button(\n bind=self.__reset_camera, text=\"Reset Camera\")\n self.__ui_controls.btn_reset = btn_reset\n self.scene.append_to_caption('\\t')\n\n chkbox_cam = checkbox(\n bind=self.__camera_lock_checkbox,\n text=\"Camera Lock\", checked=self.__camera_lock)\n self.__ui_controls.chkbox_cam = chkbox_cam\n self.scene.append_to_caption('\\t')\n\n chkbox_rel = checkbox(\n bind=self.__grid_relative_checkbox,\n text=\"Grid Relative\", checked=self.__grid_relative)\n self.__ui_controls.chkbox_rel = chkbox_rel\n self.scene.append_to_caption('\\n\\n')\n\n # Button to clear the screen\n btn_clr = button(bind=self.clear_scene, text=\"Clear Scene\")\n self.__ui_controls.btn_clear = btn_clr\n self.scene.append_to_caption('\\n\\n')\n\n # Checkbox for grid visibility\n chkbox_grid = checkbox(\n bind=self.__grid_visibility_checkbox, text=\"Grid Visibility\",\n checked=self.__grid_visibility)\n self.__ui_controls.chkbox_grid = chkbox_grid\n self.scene.append_to_caption('\\t')\n\n # Prevent the space bar from toggling the active checkbox/button/etc\n # (default browser behaviour)\n self.scene.append_to_caption('''\n <script type=\"text/javascript\">\n $(document).keyup(function(event) {\n if(event.which === 32) {\n event.preventDefault();\n }\n });\n </script>''')\n # https://stackoverflow.com/questions/22280139/prevent-space-button-from-triggering-any-other-button-click-in-jquery\n\n # Control manual\n controls_str = '<br><b>Controls</b><br>' \\\n '<b>PAN</b><br>' \\\n 'SHFT + LMB | <i>free pan</i><br>' \\\n 'W , S | <i>up / down</i><br>' \\\n 'A , D | <i>left / right</i><br>' \\\n '<b>ROTATE</b><br>' \\\n 'ARROWS KEYS | <i>rotate direction</i><br>' \\\n 'Q , E | <i>roll left / right</i><br>' \\\n '<b>ZOOM</b></br>' \\\n 'MOUSEWHEEL | <i>zoom in / out</i><br>' \\\n '<script type=\"text/javascript\">var arrow_keys_handler = function(e) {switch(e.keyCode){ case 37: case 39: case 38: case 40: case 32: e.preventDefault(); break; default: break;}};window.addEventListener(\"keydown\", arrow_keys_handler, false);</script>' # noqa\n # Disable the arrow keys from scrolling in the browser\n # https://stackoverflow.com/questions/8916620/disable-arrow-key-scrolling-in-users-browser\n self.scene.append_to_caption(controls_str)","def init_ui(self):\n\n self.master.title(\"Upload file\")\n self.master.geometry(\"300x200\")\n\n self.pack(fill=BOTH, expand=1)\n\n self.btn_select_file = Button(self, text=\"Select file\", command=self.on_open)\n self.btn_select_file.place(x=80, y=50)\n\n self.selected_file_name = Label(self, text=\"<Selected file name>\")\n self.selected_file_name.place(x=60, y=90)\n\n self.btn_upload_file = Button(self, text=\"Upload file\", command=self.upload_file)\n self.btn_upload_file.place(x=80, y=130)\n\n self.btn_back = Button(self, text=\"Back\", command=self.go_back)\n self.btn_back.place(x=10, y=10)","def textbox_init(self):\n if not self.textbox:\n self.textbox = Textbox()\n self.textarea = Textarea()","def set_controls(self):\n # Image control\n image = pyxbmct.Image(addonfolder+artsfolder+'/osc.png')\n self.placeControl(image, 0, 0, rowspan=7, columnspan=16)\n\n\t\t# Label information\n image = pyxbmct.Image(addonfolder+artsfolder+'/readers.png')\n self.placeControl(image, 7, 1, rowspan=1, columnspan=14)\n\t\t\n\t\t# Hostname input\n image = pyxbmct.Image(addonfolder+artsfolder+'/hostname.png')\n self.placeControl(image, 9, 0, rowspan=1, columnspan=4)\n self.hostname_input = pyxbmct.Edit('')\n self.placeControl(self.hostname_input, 9, 4, rowspan=1, columnspan=5)\n\n\t\t# Port input\n image = pyxbmct.Image(addonfolder+artsfolder+'/port.png')\n self.placeControl(image, 12, 1, rowspan=1, columnspan=3)\n self.port_input = pyxbmct.Edit('')\n self.placeControl(self.port_input, 12, 4, rowspan=1, columnspan=2)\n\n\t\t# Username input\n image = pyxbmct.Image(addonfolder+artsfolder+'/username.png')\n self.placeControl(image, 10, 1, rowspan=1, columnspan=3)\n self.username_input = pyxbmct.Edit('')\n self.placeControl(self.username_input, 10, 4, rowspan=1, columnspan=4)\n\t\t\n\t\t# Password input\n image = pyxbmct.Image(addonfolder+artsfolder+'/password.png')\n self.placeControl(image, 11, 1, rowspan=1, columnspan=3)\n self.password_input = pyxbmct.Edit('', isPassword=True)\n self.placeControl(self.password_input, 11, 4, rowspan=1, columnspan=4)\n\n\t\t# Close button\n self.close_button = pyxbmct.Button('Exit')\n self.placeControl(self.close_button, 13, 15, rowspan=1, columnspan=1)\n self.connect(self.close_button, lambda: self.page(Readers))","def __init__(self):\n self.rows = [18, 23, 24, 25]\n self.cols = [17, 27, 22]\n self.keypad = [\n [\"1\", \"2\", \"3\"],\n [\"4\", \"5\", \"6\"],\n [\"7\", \"8\", \"9\"],\n [\"*\", \"0\", \"#\"]\n ]\n self.setup()","def create_widgets(self):\n root.title(\"Universal Converter\")\n root.resizable(False, False)\n root.config(bg=\"white\")\n head_frame = Frame(root, bd=3, bg=\"Navy\", relief=GROOVE)\n Label(head_frame, text=\"UNIVERSAL CONVERTER\", font=(\"Trebuchet MS\", 24, \"bold\"), fg=\"White\", bg=\"Navy\").pack(pady=50)\n head_frame.grid(row=0, column=0, columnspan=4, ipadx=20, sticky=\"ew\")\n Label(root, text=\" Choose the Converter \", font=(\"Trebuchet MS\", 16, \"bold\"), fg=\"Navy\", bg=\"White\").grid(row=2, column=0, columnspan=4, ipadx=20, ipady=20)\n button_frame = Frame(root, bd=5, bg=\"Navy\", relief=FLAT)\n self.measurements_list = [\"Angle\", \"Area\", \"Bit Byte\", \"Density\", \"Electric Current\", \"Energy\", \"Force\", \"Fuel Consumption\", \"Length\", \"Mass\", \"Power\", \"Pressure\", \"Speed\", \"Temperature\", \"Time\", \"Volume\"]\n self.measurements_dict = {\"Angle\": self.angle, \"Area\": self.area, \"Bit Byte\": self.bitbyte, \"Density\": self.density, \"Electric Current\": self.electriccurrent, \"Energy\": self.energy, \"Force\": self.force, \"Fuel Consumption\": self.fuelconsumption, \"Length\": self.length, \"Mass\": self.mass, \"Power\": self.power, \"Pressure\": self.pressure, \"Speed\": self.speed, \"Temperature\": self.temperature, \"Time\": self.time, \"Volume\": self.volume}\n for i in range(16):\n self.button = Button(button_frame, text=self.measurements_list[i], font=(\"Trebuchet MS\", 12), width=13, fg=\"Navy\", bg=\"White\", relief=FLAT, overrelief=SOLID, bd=5, activebackground=\"Navy\", activeforeground=\"White\", command=self.measurements_dict[self.measurements_list[i]])\n self.button.grid(row=i/4+4, column=i%4, ipady=15, ipadx=15, padx=2, pady=2)\n button_frame.grid(row=3, column=0, columnspan=4, sticky=\"we\", padx=5, pady=5)\n root.protocol(\"WM_DELETE_WINDOW\", self.callback) #When user will quit, program will show you the messagebox","def initUI(self):\n # Setting the main layout as Vertical.\n self.mainLayout = QHBoxLayout()\n\n # Create title.\n self.title = QLabel(self.__name + \" : \")\n\n # Add description as tooltip.\n self.title.setToolTip(self.__description)\n\n # Add title to main layout.\n self.mainLayout.addWidget(self.title)\n\n # Create ComboBox.\n self.dropDown = QComboBox()\n\n # Add datas to drop down.\n self.dropDown.addItems(self.__datas)\n\n # Set default index to dropdown.\n self.dropDown.setCurrentIndex(self.__currentValue)\n\n # Connect dropdown with update method.\n self.dropDown.currentIndexChanged.connect(self.changeCurrentValue)\n\n # Add ComboBox to main layout.\n self.mainLayout.addWidget(self.dropDown)\n\n # Add the main layout to the window.\n self.setLayout(self.mainLayout)","def initialize(self):\n super(QtWidgetComponent, self).initialize()\n shell = self.shell_obj\n self.set_enabled(shell.enabled)\n if shell.bgcolor:\n self.set_bgcolor(shell.bgcolor)\n if shell.fgcolor:\n self.set_fgcolor(shell.fgcolor)\n if shell.font:\n self.set_font(shell.font)","def setup(self):\n self.ui_manager.purge_ui_elements()\n\n button = FlatButton(\n 'Lockdown',\n center_x=1000,\n center_y=20,\n width=250\n )\n button.balls(self.ball_list)\n self.ui_manager.add_ui_element(button)","def __init__(self, min_height=600, min_width=600):\n self.window = Tk()\n # set minimum size to which the window can be reduced\n self.window.minsize(min_width, min_height)\n self.canvas = None\n self.frames = {\n \"parameters\": None,\n \"canvas\": None\n }\n self.menubar = {\n \"menubar\": None,\n \"helpmenu\": None,\n \"filemenu\": None,\n \"editmenu\": None\n }\n self.combo_box = {\n \"class\": None,\n \"variable\": None\n }\n self.init_canvas_frame()\n self.init_parameters_frame()\n # self.init_menu_bar()\n self.classes = {\n \"parameters\": Parameters(self),\n \"fractal\": FastFractal(self)\n }\n self.init_parameter_combobox()","def buildmainframe(self):\n self.mainframewidgets=[]\n for x in range(3):\n thislabel = Label(self.mainframe, text=str(x))\n thislabel.grid()\n self.mainframewidgets.append(thislabel)","def init_layout(self):\n\t\tself.pack_start(self.edit, expand=True)\n\t\tself.pack_start(self.button, expand=False)\n\t\tself.show_all()","def run_init(self):\n InitEditor(self.root, self)"],"string":"[\n \"def __init__(self):\\n self.window = Tk() # The main window\\n self.__initialize_variables__() # Initialize the variables\\n self.__initialize_menu__() # Initialize the Menu\\n self.__initialize_status_bar__()\\n self.__initialize_gui__() # Initialize the GUI widgets\",\n \"def __init__(self):\\n tkinter.Tk.__init__(self)\\n #Variables de la fenêtre\\n self.nombreDeChars = tkinter.IntVar()\\n\\n #Caractéristiques de la fenêtre\\n self.geometry(constantes.geometry)\\n self.title(constantes.title)\\n self.resizable(False, False)\\n\\n #Appel des widgets\\n self.widgets()\",\n \"def initGUI(self):\\n\\n\\t\\t# Set main frame's location \\n\\t\\tself.grid(row=0, column=0, sticky=\\\"nsew\\\")\\n\\n\\t\\t# Set path entry frame and its location\\n\\t\\tself.entryFrame = Frame(self, relief = RAISED, borderwidth = 1)\\n\\t\\tself.entryFrame.pack(fill = BOTH, expand = False)\\n\\t\\t# Make label\\n\\t\\tif self.message:\\n\\t\\t\\tmessageLabel = Label(self.entryFrame, text = self.message, font=(\\\"Bradley\\\", 10))\\n\\t\\t\\tmessageLabel.pack(anchor=W, padx=0, pady=0)\\n\\n\\t\\t# Set path entry and its location\\n\\t\\tself.filePathEntry = Entry(self.entryFrame, bd = 4, width = 50)\\n\\t\\tself.filePathEntry.pack(side = LEFT, padx=2, pady=1)\",\n \"def init_widget(self):\",\n \"def initUI(self):\\n self.logger.debug('Setting up the Measurement GUI')\\n self.setWindowTitle(self.title)\\n\\n self.show()\\n\\n self.make_combobox_scanner()\\n self.make_combobox_movements()\\n self.make_combobox_configurate()\\n self.make_combobox_basic()\",\n \"def __init__(self):\\n\\n self.ai_list = []\\n self.master = Tk()\\n self.ui_draw = IntVar()\\n\\n self.populate_ai_list(self.ai_list)\\n self.frame = Frame(self.master)\\n self.build_ui()\\n\\n self.master.mainloop()\",\n \"def _initialize(self):\\n self._frame = ttk.Frame(master=self._root)\\n self._ingredients_frame = ttk.Frame(master=self._frame)\\n\\n self._create_header()\\n self._show_ingredient_list()\\n self._create_footer()\\n\\n self._ingredients_frame.grid(row=1, column=1, columnspan=2)\\n self._frame.grid_columnconfigure(1, weight=1, minsize=250)\",\n \"def set_GUI(\\r\\n self\\r\\n ):\\r\\n self.top = tk.Tk()\\r\\n self.top.title(\\\"Data Collection Interface\\\")\\r\\n \\r\\n self.get_label(\\r\\n self.top,\\r\\n text = \\\"Folder name\\\",\\r\\n width = None, # in characters\\r\\n height = 1, # in lines\\r\\n font = None,\\r\\n stick = tk.W,\\r\\n row = 0,\\r\\n column = 0,\\r\\n return_lbl = False\\r\\n )\\r\\n\\r\\n self.e_path = self.get_entry(\\r\\n self.top,\\r\\n default_txt = \\\"Collected_data\\\",\\r\\n enable = True,\\r\\n width = 30,\\r\\n row = 0,\\r\\n column = 1)\\r\\n\\r\\n self.get_label(\\r\\n self.top,\\r\\n text = \\\"Number of Images\\\",\\r\\n width = None, # in characters\\r\\n height = 1, # in lines\\r\\n font = None,\\r\\n stick = tk.W,\\r\\n row = 1,\\r\\n column = 0,\\r\\n return_lbl = False\\r\\n )\\r\\n\\r\\n self.e_num_images = self.get_entry(\\r\\n self.top,\\r\\n default_txt = \\\"1800\\\",\\r\\n enable = True,\\r\\n width = 30,\\r\\n row = 1,\\r\\n column = 1)\\r\\n\\r\\n self.get_label(\\r\\n self.top,\\r\\n text = \\\"Total Time\\\",\\r\\n width = None, # in characters\\r\\n height = 1, # in lines\\r\\n font = None,\\r\\n stick = tk.W,\\r\\n row = 2,\\r\\n column = 0,\\r\\n return_lbl = False\\r\\n )\\r\\n\\r\\n self.e_tot_time = self.get_entry(\\r\\n self.top,\\r\\n default_txt = \\\"15\\\",\\r\\n enable = True,\\r\\n width = 30,\\r\\n row = 2,\\r\\n column = 1)\\r\\n\\r\\n self.get_label(\\r\\n self.top,\\r\\n text = \\\"Minutes\\\",\\r\\n width = None, # in characters\\r\\n height = 1, # in lines\\r\\n font = None,\\r\\n stick = tk.W,\\r\\n row = 2,\\r\\n column = 2,\\r\\n return_lbl = False\\r\\n )\\r\\n\\r\\n self.get_label(\\r\\n self.top,\\r\\n text = \\\"Time between photos\\\",\\r\\n width = None, # in characters\\r\\n height = 1, # in lines\\r\\n font = None,\\r\\n stick = tk.W,\\r\\n row = 3,\\r\\n column = 0,\\r\\n return_lbl = False\\r\\n )\\r\\n\\r\\n self.e_interval = self.get_entry(\\r\\n self.top,\\r\\n default_txt = \\\"0.001\\\",\\r\\n enable = False,\\r\\n width = 30,\\r\\n row = 3,\\r\\n column = 1)\\r\\n\\r\\n self.get_label(\\r\\n self.top,\\r\\n text = \\\"Seconds\\\",\\r\\n width = None, # in characters\\r\\n height = 1, # in lines\\r\\n font = None,\\r\\n stick = tk.W,\\r\\n row = 3,\\r\\n column = 2,\\r\\n return_lbl = False\\r\\n )\\r\\n\\r\\n self.get_label(\\r\\n self.top,\\r\\n text = \\\"Images Per Folder\\\",\\r\\n width = None, # in characters\\r\\n height = 1, # in lines\\r\\n font = None,\\r\\n stick = tk.W,\\r\\n row = 4,\\r\\n column = 0,\\r\\n return_lbl = False\\r\\n )\\r\\n\\r\\n self.e_images_per_folder = self.get_entry(\\r\\n self.top,\\r\\n default_txt = \\\"500\\\",\\r\\n enable = True,\\r\\n width = 30,\\r\\n row = 4,\\r\\n column = 1)\\r\\n\\r\\n self.get_label(\\r\\n self.top,\\r\\n text = \\\"Progress Display Frequency\\\",\\r\\n width = None, # in characters\\r\\n height = 1, # in lines\\r\\n font = None,\\r\\n stick = tk.W,\\r\\n row = 5,\\r\\n column = 0,\\r\\n return_lbl = False\\r\\n )\\r\\n\\r\\n self.e_prog_display_freq = self.get_entry(\\r\\n self.top,\\r\\n default_txt = \\\"5\\\",\\r\\n enable = True,\\r\\n width = 30,\\r\\n row = 5,\\r\\n column = 1)\\r\\n\\r\\n self.get_label(\\r\\n self.top,\\r\\n text = \\\"Preview Display Frequency\\\",\\r\\n width = None, # in characters\\r\\n height = 1, # in lines\\r\\n font = None,\\r\\n stick = tk.W,\\r\\n row = 6,\\r\\n column = 0,\\r\\n return_lbl = False\\r\\n )\\r\\n\\r\\n self.e_prew_display_freq = self.get_entry(\\r\\n self.top,\\r\\n default_txt = \\\"10\\\",\\r\\n enable = True,\\r\\n width = 30,\\r\\n row = 6,\\r\\n column = 1)\\r\\n\\r\\n self.get_label(\\r\\n self.top,\\r\\n text = \\\"\\\",\\r\\n width = None, # in characters\\r\\n height = 2, # in lines\\r\\n font = None,\\r\\n stick = tk.W,\\r\\n row = 7,\\r\\n column = 2,\\r\\n return_lbl = False\\r\\n )\\r\\n \\\"\\\"\\\"\\r\\n self.get_label(\\r\\n self.top,\\r\\n text = \\\"\\\",\\r\\n width = None, # in characters\\r\\n height = 2, # in lines\\r\\n font = None,\\r\\n stick = tk.W,\\r\\n row = 8,\\r\\n column = 2,\\r\\n return_lbl = False\\r\\n )\\\"\\\"\\\"\\r\\n\\r\\n self.r_radio_button_variable = tk.IntVar(self.top,1)\\r\\n\\r\\n self.r_images_time = self.get_radio_button(\\r\\n self.top,\\r\\n control_variable =self.r_radio_button_variable ,\\r\\n returned_value = 1,\\r\\n text = \\\"Images + Total time\\\",\\r\\n enable = True,\\r\\n default_state = True,\\r\\n #width = 30,\\r\\n row = 9,\\r\\n column = 0,\\r\\n align = tk.W,\\r\\n command = self.block_entry)\\r\\n\\r\\n self.r_images_interval = self.get_radio_button(\\r\\n self.top,\\r\\n control_variable =self.r_radio_button_variable ,\\r\\n returned_value = 2,\\r\\n text = \\\"Images + Time interval\\\",\\r\\n enable = True,\\r\\n default_state = False,\\r\\n #width = 30,\\r\\n row = 10,\\r\\n column = 0,\\r\\n align = tk.W,\\r\\n command = self.block_entry)\\r\\n\\r\\n self.r_time_interval = self.get_radio_button(\\r\\n self.top,\\r\\n control_variable =self.r_radio_button_variable ,\\r\\n returned_value = 3,\\r\\n text = \\\"Total time + Time interval\\\",\\r\\n enable = True,\\r\\n default_state = False,\\r\\n #width = 30,\\r\\n row = 11,\\r\\n column = 0,\\r\\n align = tk.W,\\r\\n command = self.block_entry)\\r\\n\\r\\n self.get_label(\\r\\n self.top,\\r\\n text = \\\"\\\",\\r\\n width = None, # in characters\\r\\n height = 2, # in lines\\r\\n font = None,\\r\\n stick = tk.W,\\r\\n row = 12,\\r\\n column = 0,\\r\\n return_lbl = False\\r\\n )\\r\\n\\r\\n self.get_label(\\r\\n self.top,\\r\\n text = \\\"\\\",\\r\\n width = None, # in characters\\r\\n height = 1, # in lines\\r\\n font = None,\\r\\n stick = tk.W,\\r\\n row = 14,\\r\\n column = 0,\\r\\n return_lbl = False\\r\\n )\\r\\n\\r\\n self.r_quality_variable = tk.StringVar(self.top,\\\"Low\\\")\\r\\n\\r\\n self.r_HQuality = self.get_radio_button(\\r\\n self.top,\\r\\n control_variable =self.r_quality_variable ,\\r\\n returned_value = \\\"High\\\",\\r\\n text = \\\"High Quality\\\",\\r\\n enable = True,\\r\\n default_state = False,\\r\\n #width = 30,\\r\\n row = 16,\\r\\n column = 0,\\r\\n align = tk.W,\\r\\n command = self.quality_change)\\r\\n\\r\\n self.r_LQuality = self.get_radio_button(\\r\\n self.top,\\r\\n control_variable =self.r_quality_variable ,\\r\\n returned_value = \\\"Low\\\",\\r\\n text = \\\"Low Quality\\\",\\r\\n enable = True,\\r\\n default_state = True,\\r\\n #width = 30,\\r\\n row = 15,\\r\\n column = 0,\\r\\n align = tk.W,\\r\\n command = self.quality_change)\\r\\n\\r\\n self.r_Day_Night_variable = tk.StringVar(self.top,\\\"Day\\\")\\r\\n\\r\\n self.r_Day = self.get_radio_button(\\r\\n self.top,\\r\\n control_variable =self.r_Day_Night_variable ,\\r\\n returned_value = \\\"Day\\\",\\r\\n text = \\\"Day\\\",\\r\\n enable = True,\\r\\n default_state = True,\\r\\n #width = 30,\\r\\n row = 15,\\r\\n column = 1,\\r\\n align = tk.W,\\r\\n command = self.day_change)\\r\\n\\r\\n self.r_Night = self.get_radio_button(\\r\\n self.top,\\r\\n control_variable =self.r_Day_Night_variable ,\\r\\n returned_value = \\\"Night\\\",\\r\\n text = \\\"Night\\\",\\r\\n enable = True,\\r\\n default_state = False,\\r\\n #width = 30,\\r\\n row = 16,\\r\\n column = 1,\\r\\n align = tk.W,\\r\\n command = self.day_change)\\r\\n\\r\\n self.c_auto_zip_variable = tk.IntVar(self.top,0)\\r\\n\\r\\n self.c_auto_zip = tk.Checkbutton(\\r\\n self.top,\\r\\n text = \\\"Auto Zip\\\",\\r\\n variable = self.c_auto_zip_variable)\\r\\n self.c_auto_zip.grid(row = 17,column = 0, sticky = tk.W)\\r\\n self.c_auto_zip.deselect()\\r\\n\\r\\n self.get_label(\\r\\n self.top,\\r\\n text = \\\"\\\",\\r\\n width = None, # in characters\\r\\n height = 1, # in lines\\r\\n font = None,\\r\\n stick = tk.W,\\r\\n row = 18,\\r\\n column = 0,\\r\\n return_lbl = False\\r\\n )\\r\\n\\r\\n\\r\\n self.l_image = self.get_label(\\r\\n self.top,\\r\\n text = None,\\r\\n width = None, # in characters\\r\\n height = None, # in lines\\r\\n font = None,\\r\\n stick = None,\\r\\n row = 114,\\r\\n column = 3,#0,\\r\\n return_lbl = True,\\r\\n ctr_var = None\\r\\n )\\r\\n\\r\\n self.get_label(\\r\\n self.top,\\r\\n text = \\\"Progress :\\\",\\r\\n width = None, # in characters\\r\\n height = 1, # in lines\\r\\n font = None,\\r\\n stick = tk.W,\\r\\n row = 113,\\r\\n column = 0,\\r\\n return_lbl = False\\r\\n )\\r\\n\\r\\n self.progress_var = tk.StringVar(self.top)\\r\\n \\r\\n self.get_label(\\r\\n self.top,\\r\\n text = \\\"\\\",\\r\\n width = 40, # in characters\\r\\n height = 2, # in lines\\r\\n font = None,\\r\\n stick = tk.W,\\r\\n row = 113,\\r\\n column = 1,\\r\\n return_lbl = False,\\r\\n ctr_var = self.progress_var\\r\\n )\\r\\n \\\"\\\"\\\"\\r\\n self.lab = self.get_label(\\r\\n self.top,\\r\\n text = \\\"\\\",\\r\\n width = 40, # in characters\\r\\n height = 1, # in lines\\r\\n font = None,\\r\\n stick = tk.W,\\r\\n row = 10,\\r\\n column = 1,\\r\\n return_lbl = True,\\r\\n #ctr_var = self.progress_var\\r\\n )\\\"\\\"\\\"\\r\\n\\r\\n self.b_start = self.get_button(\\r\\n root = self.top,\\r\\n button_text = \\\"Start\\\",\\r\\n row = 5,\\r\\n column = 2,\\r\\n enable = True,\\r\\n width = 10,\\r\\n height =1,\\r\\n command = self.start_collecting\\r\\n )\\r\\n\\r\\n self.b_pause = self.get_button(\\r\\n root = self.top,\\r\\n button_text = \\\"Zip Folder\\\",\\r\\n row = 6,\\r\\n column = 2,\\r\\n enable = True,\\r\\n width = 10,\\r\\n height =1,\\r\\n command = self.zip_folder\\r\\n )\\r\\n\\r\\n self.b_stop = self.get_button(\\r\\n root = self.top,\\r\\n button_text = \\\"Stop\\\",\\r\\n row = 7,\\r\\n column = 2,\\r\\n enable = True,\\r\\n width = 10,\\r\\n height =1,\\r\\n command = self.stop_collecting\\r\\n )\\r\\n\\r\\n self.b_red = self.get_button(\\r\\n root = self.top,\\r\\n button_text = \\\"Red\\\",\\r\\n row = 10,\\r\\n column = 2,\\r\\n enable = True,\\r\\n width = 10,\\r\\n height =1,\\r\\n command = self.red\\r\\n )\\r\\n self.b_red.config(bg='red',activebackground = 'red')\\r\\n\\r\\n self.b_yellow = self.get_button(\\r\\n root = self.top,\\r\\n button_text = \\\"Yellow\\\",\\r\\n row = 11,\\r\\n column = 2,\\r\\n enable = True,\\r\\n width = 10,\\r\\n height =1,\\r\\n command = self.yellow\\r\\n )\\r\\n self.b_yellow.config(bg='yellow', activebackground = 'yellow')\\r\\n\\r\\n self.b_green = self.get_button(\\r\\n root = self.top,\\r\\n button_text = \\\"Green\\\",\\r\\n row = 12,\\r\\n column = 2,\\r\\n enable = True,\\r\\n width = 10,\\r\\n height =1,\\r\\n command = self.green\\r\\n )\\r\\n self.b_green.config(bg='green', activebackground = 'green')\\r\\n\\r\\n self.b_normal = self.get_button(\\r\\n root = self.top,\\r\\n button_text = \\\"No light\\\",\\r\\n row = 13,\\r\\n column = 2,\\r\\n enable = True,\\r\\n width = 10,\\r\\n height =1,\\r\\n command = self.normal\\r\\n )\\r\\n\\r\\n self.b_load_orientation = self.get_button(\\r\\n root = self.top,\\r\\n button_text = \\\"Load Orientation\\\",\\r\\n row = 15,\\r\\n column = 2,\\r\\n enable = True,\\r\\n width = 10,\\r\\n height =1,\\r\\n command = self.load_orientation\\r\\n )\\r\\n\\r\\n \\r\\n\\r\\n \\\"\\\"\\\"\\r\\n self.get_label(\\r\\n self.top,\\r\\n text = \\\"tesing\\\",\\r\\n width = 10, # in characters\\r\\n height = 2, # in lines\\r\\n font = ('Times', '12', 'normal'),\\r\\n row = 0,\\r\\n column = 0,\\r\\n return_lbl = False\\r\\n )\\r\\n \\r\\n self.button1 = self.get_button(\\r\\n root = self.top,\\r\\n button_text = \\\"test\\\",\\r\\n row = 1,\\r\\n column = 3,\\r\\n enable = True,\\r\\n width = 10,\\r\\n height =1,\\r\\n command = self.pt\\r\\n )\\r\\n\\r\\n self.entry = self.get_entry(\\r\\n self.top,\\r\\n default_txt = \\\"Test\\\",\\r\\n enable = True,\\r\\n width = 30,\\r\\n row = 3,\\r\\n column = 0)\\r\\n\\r\\n self.contrl = tk.IntVar(self.top)\\r\\n self.radio = self.get_radio_button(\\r\\n self.top,\\r\\n control_variable =self.contrl ,\\r\\n returned_value = 5,\\r\\n text = \\\"radio\\\",\\r\\n enable = True,\\r\\n default_state = False,\\r\\n #width = 30,\\r\\n row = 0,\\r\\n column = 0,\\r\\n align = tk.W,\\r\\n command = self.pt)\\r\\n\\r\\n self.radio2 = self.get_radio_button(\\r\\n self.top,\\r\\n control_variable =self.contrl ,\\r\\n returned_value = 6,\\r\\n text = \\\"radio2\\\",\\r\\n enable = True,\\r\\n default_state = False,\\r\\n width = None,\\r\\n row = 1,\\r\\n column = 0,\\r\\n align = tk.W,\\r\\n command = self.pt)\\\"\\\"\\\"\",\n \"def init_ui(self):\\n self.master.title(\\\"Backbone\\\")\\n self.master.geometry(\\\"300x150\\\")\\n\\n self.pack(fill=BOTH, expand=1)\\n\\n self.btn_upload_file = Button(self, text=\\\"Upload file\\\", command=self.upload_file)\\n self.btn_upload_file.place(x=90, y=10)\\n\\n self.btn_create_training_file = Button(self, text=\\\"Create & upload training file\\\",\\n command=self.create_training_file)\\n self.btn_create_training_file.place(x=30, y=40)\\n\\n self.btn_run_algorithm = Button(self, text=\\\"Run algorithm\\\", command=self.run_algorithm)\\n self.btn_run_algorithm.place(x=80, y=70)\\n\\n self.btn_view_results = Button(self, text=\\\"View Results\\\", command=self.view_results)\\n self.btn_view_results.place(x=85, y=100)\",\n \"def _initialize(self):\\n\\n cancel_button = ttk.Button(\\n master=self._frame,\\n text=\\\"Cancel\\\",\\n command=self._hide_confirmation_window\\n )\\n\\n delete_button = ttk.Button(\\n master=self._frame,\\n text=\\\"Delete\\\",\\n command=self._handle_delete\\n )\\n\\n self._initilize_message()\\n\\n cancel_button.grid(row=1, column=0, padx=5, pady=5)\\n delete_button.grid(row=1, column=1, padx=5, pady=5)\",\n \"def _init_ui(self):\\r\\n\\t\\t\\r\\n\\t\\tself.input_frame = Input(self)\\r\\n\\t\\tself.input_frame.pack()\\r\\n\\t\\t\\r\\n\\t\\tbutton_ok = Button(self, text = \\\"Ping\\\", command = self._go)\\r\\n\\t\\tbutton_ok.pack()\\r\\n\\t\\t\\r\\n\\t\\tself.result_frame = Result(self)\\r\\n\\t\\tself.result_frame.pack()\",\n \"def initialize():\\n global root\\n\\n if root is not None:\\n print \\\"param.tk already initialized; ignoring call to param.tk.initialize()\\\"\\n return\\n\\n if T._default_root is not None:\\n root = T._default_root\\n else:\\n root = T.Tk()\\n root.withdraw()\\n\\n # Until tklib, tcllib, and scrodget become more commonly\\n # available, we include them in tkgui.\\n externaltcl_path = os.path.join(os.path.split(__file__)[0],\\\"tcl\\\")\\n root.tk.call(\\\"lappend\\\",\\\"auto_path\\\",externaltcl_path)\\n\\n T.BooleanVar.get = _BooleanVar_get\",\n \"def set_ui(self):\\r\\n\\r\\n self.canvas = tk.Canvas(self)\\r\\n self.canvas.pack()\\r\\n\\r\\n self.entry = ttk.Entry(self.canvas, justify=\\\"center\\\", font=(\\\"Calibri\\\", 12))\\r\\n\\r\\n self.grid = Grid(self.canvas)\",\n \"def InitUI(self):\\n\\t\\tself._initMenuBar()\\n\\t\\tself._initLayout()\\n\\t\\t\\n\\t\\t# Bindings\\n\\t\\tself.Bind(wx.EVT_BUTTON, self.OnButtonClicked)\\n\\t\\t\\n\\t\\t# We can't even start without an input file\\n\\t\\tself.OnOpen(None)\",\n \"def _initUI(self) -> None:\\n self._createActions()\\n self._addActionsToMoveButtons()\\n self._createToolBar()\\n self._createStatusBar()\\n self._createMainContextMenu()\",\n \"def initUI(self):\\n\\n self.wid = RosGenWidget()\\n self.setCentralWidget(self.wid)\\n menubar = self.menuBar()\\n fileMenu = menubar.addMenu('&Файл')\\n editMenu = menubar.addMenu('&Редактирование')\\n self.create_menu_par('Менеджер подписчиков и издателей', self.wid.show_manager, fileMenu, 'Ctrl+M')\\n self.create_menu_par('Очистить', self.wid.clear_all_lines, editMenu, 'Ctrl+D')\\n self.create_menu_par('Загрузить данные из...', self.wid.open_fileDialog, fileMenu, 'Ctrl+F')\\n self.create_menu_par('Сохранить как...', self.wid.save_fileDialog, fileMenu, 'Ctrl+S')\\n self.create_menu_par('Выход', self.exit_app, fileMenu, 'Esc')\\n self.statusbar = self.statusBar()\\n self.statusbar.showMessage('Ожидание данных')\\n self.wid.msg2Statusbar[str].connect(self.statusbar.showMessage)\\n self.setGeometry(600, 200, 700, 400)\\n self.setWindowTitle('Генератор шаблонов ROS-приложения')\\n self.show()\",\n \"def init_ui():\\n\\t# Use wrapper to set reasonable defaults and reset the terminal on exit\\n\\t#curses.wrapper(_main_loop)\\n\\tcurses.wrapper(_init_func)\",\n \"def init_gui(self):\\n # Choose a layout.\\n main_vb = QtGui.QVBoxLayout(self)\\n\\n # Add a list or tree view.\\n self.list_view = QtGui.QListWidget()\\n\\n # Add the buttons.\\n load_btn = QtGui.QPushButton('Load Selected')\\n cancel_btn = QtGui.QPushButton('Cancel')\\n load_btn.clicked.connect(self.update_list_view)\\n cancel_btn.clicked.connect(self.close)\\n\\n # Connect the list/tree view with a method appropriate for user interaction.\\n self.list_view.currentItemChanged['QListWidgetItem*', 'QListWidgetItem*'].connect(self.set_current_name)\\n self.list_view.itemChanged['QListWidgetItem*'].connect(self.change_name)\\n\\n # Add the widgets to the layout.\\n btn_hb = QtGui.QHBoxLayout()\\n btn_hb.addWidget(load_btn)\\n btn_hb.addWidget(cancel_btn)\\n main_vb.addWidget(self.list_view)\\n main_vb.addLayout(btn_hb)\\n\\n # Show the GUI.\\n self.setGeometry(300, 300, 450, 300)\\n self.setWindowTitle('Hello World')\\n img_icon = 'C:/Users/caj150430/code/so_much_win.png'\\n self.setWindowIcon(QtGui.QIcon(img_icon))\\n self.show()\",\n \"def initWidgets(self):\\r\\n if self.autoExampleWidgets:\\r\\n self.initExampleWidgets()\",\n \"def initGUI(self):\\n\\n\\t\\t# Set window's title\\n\\t\\tself.parent.title(\\\"Error Message\\\")\\n\\t\\t# Creat frames that contain messages and buttons \\n\\t\\tself.buttonFrame = Frame(self.parent)\\n\\t\\tself.buttonFrame.pack(fill = BOTH, expand = True)\\n\\t\\tmessageFrame = Frame(self.buttonFrame, borderwidth = 1)\\n\\t\\tmessageFrame.pack(fill = BOTH, expand = True)\\n\\t\\t# Creat buttons\\n\\t\\tself.makeButtons()\\n\\t\\t# Create and show an error message as an label\\n\\t\\tvar = StringVar()\\n\\t\\tlabel = Message(messageFrame, textvariable=var, relief=RAISED, width = 1000)\\n\\t\\tvar.set(self.message)\\n\\t\\tlabel.pack(fill = BOTH, expand = True)\",\n \"def init_ui(self):\\n raise NotImplementedError\",\n \"def init_ui(self):\\n raise NotImplementedError\",\n \"def initializeUI(self):\\n self.setStyleSheet(abstyle)\\n self.setGeometry(140, 100, 860, 484)\\n self.setWindowTitle('Emotions Data View')\\n self.setupModelView()\",\n \"def gui_init(self):\\n GUI_element.gui_init(self)\\n\\n self.hover_sound = False\\n \\n if not self.image is None:\\n self.generic_button = False\\n self.width = self.image.width if self.width == 0 else self.width\\n self.height = self.image.height if self.height == 0 else self.height\\n else:\\n # Set up a generic button\\n self.generic_button = True\\n self.image = self.game.core.media.gfx['gui_button_generic_background']\\n self.draw_strategy = \\\"gui_button\\\"\\n\\n # fixed height\\n self.height = 30\\n \\n # Create the text\\n self.generic_button_text_object = Text(self.game.core.media.fonts[\\\"generic_buttons\\\"], self.x, self.y + (self.height / 2), TEXT_ALIGN_CENTER, self.generic_button_text)\\n self.generic_button_text_object.z = self.z - 1\\n self.generic_button_text_object.colour = (1.0,1.0,1.0)\\n \\n # Set up the width, if we have a larger than normal width then we want to centre the text.\\n if self.width < self.generic_button_text_object.text_width + 20:\\n self.width = self.generic_button_text_object.text_width + 20\\n self.generic_button_text_object.x += (self.width / 2)\\n \\n self.sequence_count = self.image.num_of_frames\\n self.draw_strategy_call_parent = False\",\n \"def __init__(self):\\n # Root window\\n self.root = tk.Tk()\\n self.root.title(\\\"Crossword\\\")\\n # Padding frame\\n self.frame = tk.Frame(self.root)\\n self.frame.pack(fill=\\\"both\\\", padx=PAD, pady=PAD)\\n # Initialize widget groups\\n self.header = HeaderView(self)\\n self.puzzle = PuzzleView(self)\\n self.clues = CluesView(self)\\n # Show widgets\\n self.header.show()\\n self.puzzle.show()\\n self.clues.show()\",\n \"def initialize_user_interface(self):\\n self.parent.title(\\\"DB operations\\\")\\n self.parent.grid_rowconfigure(0,weight=1)\\n self.parent.grid_columnconfigure(0,weight=1)\\n self.parent.config(background=\\\"lavender\\\")\\n\\n self.label_user=Tkinter.Label(self.parent,text=\\\"DB User: \\\",anchor=Tkinter.W,background=\\\"dark slate gray\\\",foreground=\\\"white\\\", font=\\\"Helvetica 8 bold\\\")\\n self.label_password=Tkinter.Label(self.parent,text=\\\"DB Password:\\\", anchor=Tkinter.W,background=\\\"dark slate gray\\\",foreground=\\\"white\\\", font=\\\"Helvetica 8 bold\\\")\\n\\n self.label_user.grid(row=0,column=0,sticky=Tkinter.E+Tkinter.W)\\n self.label_password.grid(row=1,column=0, sticky=Tkinter.E+Tkinter.W)\\n\\n self.dbuser=Tkinter.Entry(self.parent)\\n self.dbpassword=Tkinter.Entry(self.parent,show=\\\"*\\\")\\n\\n self.dbuser.grid(row=0,column=1,sticky=Tkinter.E+Tkinter.W)\\n self.dbpassword.grid(row=1,column=1,sticky=Tkinter.E+Tkinter.W)\\n\\n self.connectb=Tkinter.Button(self.parent,text=\\\"Log in\\\",font=\\\"Helvetica 10 bold\\\",command=self.dbconnexion)\\n self.cancelb=Tkinter.Button(self.parent,text=\\\"Cancel\\\",command=self.parent.quit,font=\\\"Helvetica 10 bold\\\")\\n\\n self.connectb.grid(row=2,column=1,sticky=Tkinter.W)\\n self.cancelb.grid(row=2,column=2)\",\n \"def init_UI(self):\\n\\n self.master.title(\\\"Search for different companies\\\")\\n self.master.geometry(\\\"400x400\\\")\\n\\n self.label_combobox = Label(self, text=\\\"Search by\\\")\\n self.label_combobox.pack()\\n\\n self.combo_searching_options = Combobox(self, state=\\\"readonly\\\")\\n self.combo_searching_options['values'] = self.combobox_values\\n self.combo_searching_options.pack()\\n\\n self.label_input = Label(self, text=\\\"Entry the value\\\")\\n self.label_input.pack()\\n\\n self.user_input = Entry(self, width=40)\\n self.user_input.pack()\\n\\n self.btn_submit = Button(self, text=\\\"Submit\\\", command=self.submit)\\n self.btn_submit.pack()\\n\\n self.text_area = scrolledtext.ScrolledText(self)\\n self.text_area.pack()\\n\\n sys.stdout = RedirectOutputText(self.text_area)\\n\\n self.btn_back = Button(self, text=\\\"Back\\\", command=self.go_back)\\n self.btn_back.pack()\",\n \"def __init__(self,root,app,row=0,column=0):\\r\\n # tkinter info\\r\\n self.root = root\\r\\n self.app = app\\r\\n # Frame for Layout\\r\\n self.frame = tk.Frame(self.root,borderwidth=1,relief=tk.GROOVE)\\r\\n self.frame.grid(row=row,column=column,sticky=tk.NW)\\r\\n # Checkbutton Widgets\\r\\n self.checks = []# tk.Checkbutton instances\\r\\n self.intvars = []# tk.IntVar instances\\r\",\n \"def _init_widgets(self):\\n # Container frame\\n self.container = Frame(self)\\n # Workspace block\\n self.main_container = Frame(self.container)\\n\\n self.text = Label(self.main_container)\\n self.text.config(text=\\\"PyEventLogViewer is a timeline-based tool used to simplify the way\\\\n\\\"\\n \\\"a user can view and explore Windows EVTX files. To begin using this\\\\n\\\"\\n \\\"software you must do the following:\\\\n\\\\n\\\"\\n \\\"\\\\t1) File → New → 'Create a new project'\\\\n\\\"\\n \\\"\\\\t2) Tools → Import Log File → 'Open a specified EVTX file'\\\\n\\\"\\n \\\"\\\\t3) Explore the presented timeline.\\\\n\\\"\\n \\\"\\\\t4) Double-click a specific record to view the XML data for that record.\\\\n\\\"\\n \\\"\\\\t5) File → Export → 'Generate a CSV or HTML file for timeline presentation.'\\\\n\\\\n\\\"\\n \\\"At this point, only System and Security EVTX files are parsable with this software.\\\")\\n\\n self.show_var = BooleanVar()\\n self.show_check = Checkbutton(self.main_container, text=\\\"Don't Show on Startup\\\", variable=self.show_var)\\n\\n # Action block\\n self.button_ok = Button(self.main_container, text='Ok', underline=0, command=self.callback_close)\\n self.bind('<Return>', self.callback_close)\\n self.bind('<Escape>', self.callback_close)\\n\\n # Focus on window - required for binds to work.\\n self.focus_set()\",\n \"def __init__(self,*args, **kwargs):\\n tk.Tk.__init__(self, *args, **kwargs)\\n self.winfo_toplevel().title(\\\"ElogQP\\\")\\n self.container = tk.Frame(self)\\n self.container.pack(side=\\\"top\\\", fill=\\\"both\\\", expand=True)\\n self.container.grid_rowconfigure(0, weight=1)\\n self.container.grid_columnconfigure(0, weight=1)\\n self.activeFrames = []\\n for F in (Frames.frame_start.frame_start, Frames.frame_modules.frame_modules, Frames.frame_showError.frame_showError):\\n self.createFrame(F, F.__name__)\\n \\n self.showFrame(\\\"frame_start\\\")\",\n \"def _initialize_widgets(self):\\n self.outer_board = [[Frame(self.root, bd = self.FRAME_BORDER_WIDTH, \\n relief = self.FRAME_RELIEF) \\n for _ in range(self.BOARD_DIM)] \\n for _ in range(self.BOARD_DIM)]\\n self.inner_boards = [[self._generate_inner_board(r, c) \\n for c in range(self.BOARD_DIM)]\\n for r in range(self.BOARD_DIM)]\",\n \"def initialize(self):\\n self._ui.img_name.setText('No files selected')\\n self._ui.bt_next.setEnabled(False)\\n self._ui.bt_prev.setEnabled(False)\\n self._ui.bt_right.setEnabled(False)\\n self._ui.bt_left.setEnabled(False)\\n self._ui.gps_button.setEnabled(False)\\n\\n self._open_btn = QPushButton('Open File', self._ui.img_label)\\n self.adjustSize()\",\n \"def setUpFrame(self):\\n #adds labels to the Board\\n self.mineLabel = tk.Label(self, text=\\\"Mines: \\\"+str(self.numMines))\\n self.mineLabel.grid(row=0, column=0, sticky=\\\"W\\\", columnspan=int((self.cols-2)/2))\\n self.smileButton = tk.Label(self, image=self.images[1])\\n self.smileButton.grid(row=0, column=int((self.cols-2)/2), sticky=\\\"WE\\\", columnspan=2)\\n self.flagLabel = tk.Label(self, text=\\\"Flags: \\\"+str(self.numFlags))\\n self.flagLabel.grid(row=0, column=int((self.cols-2)/2)+2, sticky=\\\"E\\\", columnspan=int((self.cols-1)/2))\\n\\n #left click listeners on smileButton\\n self.smileButton.bind('<ButtonPress-1>', lambda event, num=0: self.changeSmile(num))\\n self.smileButton.bind('<ButtonRelease-1>', self.replay)\",\n \"def init_tkvars(self):\\n\\n for key in self.defaultprefs:\\n value = self.defaultprefs[key]\\n if type(value) is types.IntType:\\n var = self.__dict__[key] = IntVar()\\n elif type(value) is types.StringType:\\n var = self.__dict__[key] = StringVar()\\n var.set(value)\\n\\n self.resnum = IntVar()\\n self.resnum.set(1)\\n # Method for calculating Tm of primers\\n self.Tm_method = StringVar()\\n self.Tm_method.set('Stratagene')\\n if 'Darwin' in self.currplatform:\\n self.seqfontsize.set(16)\\n else:\\n self.seqfontsize.set(14)\\n return\",\n \"def initGUI(self):\\r\\n\\r\\n self.pack(fill=tk.BOTH, expand=True)\\r\\n\\r\\n # Figure out sizing.\\r\\n width = 200\\r\\n height = 200\\r\\n pad = 5\\r\\n fontWidth = 8\\r\\n bigWidth = int((width*3 + pad*6) / fontWidth)\\r\\n \\r\\n # Create option frames.\\r\\n self.frameOptions = tk.LabelFrame(self, text=\\\"Options:\\\",\\r\\n width=width, height=height)\\r\\n self.frameSegment = tk.LabelFrame(self, text=\\\"Segmentation Method:\\\",\\r\\n width=width, height=height)\\r\\n self.frameMeasure = tk.LabelFrame(self, text=\\\"Measurements:\\\",\\r\\n width=width, height=height)\\r\\n\\r\\n # Create text boxes and labels.\\r\\n self.labelStatus = tk.LabelFrame(self, text=\\\"Status:\\\", bd=0)\\r\\n self.labelResults = tk.LabelFrame(self, text=\\\"Results:\\\", bd=0)\\r\\n self.textStatus = ScrolledText(self.labelStatus, height=5,\\r\\n width=bigWidth)\\r\\n self.textResults = ScrolledText(self.labelResults, height=10,\\r\\n width=bigWidth)\\r\\n\\r\\n # Create buttons.\\r\\n self.buttonCalculate = tk.Button(self, text='Calculate',\\r\\n width=20, height=1, font=12, bd=3,\\r\\n command=lambda:self.prepare())\\r\\n self.buttonSaveAll = tk.Button(self, text='Save Session Summary',\\r\\n command=self.saveAll)\\r\\n self.buttonSelectOutFold = tk.Button(self, text='Set Output Folder',\\r\\n command=self.setOutputFolder)\\r\\n self.buttonAbout = tk.Button(self, text='About', command=self.about)\\r\\n\\r\\n # Arrange toplevel widgets.\\r\\n self.frameOptions.grid(row=0, column=2, padx=pad, pady=pad,\\r\\n sticky='NESW')\\r\\n self.frameSegment.grid(row=0, column=1, padx=pad, pady=pad,\\r\\n sticky='NESW')\\r\\n self.frameMeasure.grid(row=0, column=0, padx=pad, pady=pad,\\r\\n sticky='NESW')\\r\\n\\r\\n self.buttonCalculate.grid(row=1, column=1, \\r\\n padx=pad, pady=pad*3)\\r\\n self.buttonSelectOutFold.grid(row=1, column=0, \\r\\n padx=pad, pady=pad*3)\\r\\n self.buttonAbout.grid(row=6, column=2, sticky='e', padx=20, pady=10)\\r\\n\\r\\n self.labelStatus.grid(row=2, column=0, columnspan=3, sticky='w',\\r\\n padx=pad, pady=pad)\\r\\n self.textStatus.grid(row=3, column=0, columnspan=3)\\r\\n self.labelResults.grid(row=4, column=0, columnspan=3, sticky='w',\\r\\n padx=pad, pady=pad)\\r\\n self.textResults.grid(row=5, column=0, columnspan=3)\\r\\n self.buttonSaveAll.grid(row=6, column=1, padx=pad, pady=pad)\\r\\n\\r\\n # Variables\\r\\n self.outFold = None\\r\\n columns = [[\\\"\\\",\\\"\\\",\\\"\\\",\\\"\\\",\\r\\n \\\"Bright phase diameter\\\",\\r\\n \\\"\\\",\\\"\\\",\\r\\n \\\"Dark phase diameter\\\",\\r\\n \\\"\\\",\\\"\\\",\\r\\n \\\"Bright length\\\",\\r\\n \\\"\\\",\\\"\\\",\\r\\n \\\"Dark length\\\",\\r\\n \\\"\\\",\\\"\\\",\\r\\n \\\"Bright area\\\",\\r\\n \\\"\\\",\\\"\\\",\\r\\n \\\"Dark area\\\",\\r\\n \\\"\\\",\\\"\\\",\\r\\n \\\"Bright connected length\\\",\\r\\n \\\"\\\",\\\"\\\",\\r\\n \\\"Dark connected length\\\",\\r\\n \\\"\\\",\\\"\\\"], \\r\\n [\\\"image\\\",\\r\\n \\\"pixel size\\\",\\r\\n \\\"area frac\\\",\\r\\n \\\"est diam\\\",\\r\\n \\\"Average\\\",\\r\\n \\\"SD\\\",\\r\\n \\\"Measured\\\",\\r\\n \\\"Average\\\",\\r\\n \\\"SD\\\",\\r\\n \\\"Measured\\\",\\r\\n \\\"Average\\\",\\r\\n \\\"SD\\\",\\r\\n \\\"Measured\\\",\\r\\n \\\"Average\\\",\\r\\n \\\"SD\\\",\\r\\n \\\"Measured\\\",\\r\\n \\\"Average\\\",\\r\\n \\\"SD\\\",\\r\\n \\\"Measured\\\",\\r\\n \\\"Average\\\",\\r\\n \\\"SD\\\",\\r\\n \\\"Measured\\\",\\r\\n \\\"Average\\\",\\r\\n \\\"SD\\\",\\r\\n \\\"Measured\\\",\\r\\n \\\"Average\\\",\\r\\n \\\"SD\\\",\\r\\n \\\"Measured\\\"]]\\r\\n \\r\\n self.saveAll = np.array(columns)\\r\\n\\r\\n # Measurement options.\\r\\n # Variables.\\r\\n self.varDiameter = tk.BooleanVar()\\r\\n self.varLength = tk.BooleanVar()\\r\\n self.varArea = tk.BooleanVar()\\r\\n self.varSumConnectedLength = tk.BooleanVar()\\r\\n self.varAreaFraction = tk.BooleanVar()\\r\\n # Create widgets.\\r\\n self.checkDiameter = tk.Checkbutton(self.frameMeasure,\\r\\n text=\\\"Diameter\\\", variable=self.varDiameter)\\r\\n self.checkLength = tk.Checkbutton(self.frameMeasure,\\r\\n text=\\\"Length\\\", variable=self.varLength)\\r\\n self.checkArea = tk.Checkbutton(self.frameMeasure,\\r\\n text=\\\"Area\\\", variable=self.varArea)\\r\\n self.checkSumConnectedLength = tk.Checkbutton(self.frameMeasure,\\r\\n text=\\\"Connected length\\\", variable=self.varSumConnectedLength)\\r\\n self.checkAreaFraction = tk.Checkbutton(self.frameMeasure,\\r\\n text=\\\"Area fraction\\\", variable=self.varAreaFraction)\\r\\n # Pack widgets.\\r\\n self.checkDiameter.grid(row=0, column=0, sticky='w')\\r\\n self.checkLength.grid(row=1, column=0, sticky='w')\\r\\n self.checkArea.grid(row=2, column=0, sticky='w')\\r\\n self.checkSumConnectedLength.grid(row=3, column=0, sticky='w')\\r\\n self.checkAreaFraction.grid(row=4, column=0, sticky='w')\\r\\n # Check appropriate boxes.\\r\\n self.checkDiameter.select()\\r\\n self.checkLength.select()\\r\\n self.checkArea.select()\\r\\n self.checkSumConnectedLength.select()\\r\\n self.checkAreaFraction.select()\\r\\n \\r\\n # Segment options.\\r\\n # Variables.\\r\\n self.varSegment = tk.StringVar()\\r\\n # Create widgets.\\r\\n self.radAccurate = tk.Radiobutton(self.frameSegment,\\r\\n text=\\\"Accurate\\\", variable=self.varSegment, value=\\\"accurate\\\",\\r\\n command=self.updateOptions)\\r\\n self.radFast = tk.Radiobutton(self.frameSegment,\\r\\n text=\\\"Fast\\\", variable=self.varSegment, value=\\\"fast\\\",\\r\\n command=self.updateOptions)\\r\\n self.radManual= tk.Radiobutton(self.frameSegment,\\r\\n text=\\\"Manual\\\", variable=self.varSegment, value=\\\"manual\\\",\\r\\n command=self.updateOptions)\\r\\n self.radFromBinary = tk.Radiobutton(self.frameSegment,\\r\\n text=\\\"From binary\\\", variable=self.varSegment, value=\\\"binary\\\",\\r\\n command=self.updateOptions)\\r\\n # Pack widgets.\\r\\n self.radAccurate.grid(row=0, column=0, sticky='w')\\r\\n self.radFast.grid(row=1, column=0, sticky='w')\\r\\n self.radManual.grid(row=2, column=0, sticky='w')\\r\\n self.radFromBinary.grid(row=3, column=0, sticky='w')\\r\\n # Check appropriate boxes.\\r\\n self.radAccurate.select()\\r\\n\\r\\n # Option options.\\r\\n # Profiles\\r\\n profiles = autoSelect.profiles()\\r\\n # Variables.\\r\\n self.varShowSteps = tk.BooleanVar()\\r\\n self.varOutputExcel = tk.BooleanVar()\\r\\n self.varSavePDF = tk.BooleanVar()\\r\\n self.varSaveMovie = tk.BooleanVar()\\r\\n self.varSaveBinary = tk.BooleanVar()\\r\\n self.varAutoParse = tk.BooleanVar()\\r\\n self.varProfile = tk.StringVar()\\r\\n self.varProfile.set(profiles[0])\\r\\n # Create widgets.\\r\\n self.checkShowSteps = tk.Checkbutton(self.frameOptions,\\r\\n text=\\\"Show steps\\\", variable=self.varShowSteps)\\r\\n self.checkOutputExcel = tk.Checkbutton(self.frameOptions,\\r\\n text=\\\"Output to Excel\\\", variable=self.varOutputExcel)\\r\\n self.checkSavePDF = tk.Checkbutton(self.frameOptions,\\r\\n text=\\\"Save PDF\\\", variable=self.varSavePDF)\\r\\n self.checkSaveMovie = tk.Checkbutton(self.frameOptions,\\r\\n text=\\\"Save movie\\\", variable=self.varSaveMovie)\\r\\n self.checkSaveBinary = tk.Checkbutton(self.frameOptions,\\r\\n text=\\\"Save binary\\\", variable=self.varSaveBinary)\\r\\n self.checkAutoParse = tk.Checkbutton(self.frameOptions,\\r\\n text=\\\"Auto parse raw image\\\", variable=self.varAutoParse,\\r\\n command=self.updateAuto)\\r\\n self.optionProfile = tk.OptionMenu(self.frameOptions, self.varProfile,\\r\\n *profiles)\\r\\n self.optionProfile.config(state=tk.DISABLED)\\r\\n\\r\\n # Pack widgets.\\r\\n self.checkShowSteps.grid(row=0, column=0, sticky='w')\\r\\n self.checkOutputExcel.grid(row=1, column=0, sticky='w')\\r\\n self.checkSavePDF.grid(row=2, column=0, sticky='w')\\r\\n #self.checkSaveMovie.grid(row=3, column=0, sticky='w')\\r\\n self.checkSaveBinary.grid(row=4, column=0, sticky='w')\\r\\n self.checkAutoParse.grid(row=5, column=0, sticky='w')\\r\\n self.optionProfile.grid(row=6, column=0, sticky='w', padx=15)\\r\\n \\r\\n # Check appropriate boxes.\\r\\n self.checkOutputExcel.select()\\r\\n\\r\\n self.createToolTips()\",\n \"def controls_setup(self):\\n pass\",\n \"def initWidgets(self):\\n self.lambdtext.setText(str(self.lambd))\\n self.ptext.setText(str(self.p))\",\n \"def initUI(self):\\n \\n self.setWindowTitle(\\\"Intecol Flir camera\\\")\\n self.setGeometry(300, 100, 1012, 622)\",\n \"def initialize(self):\\n self.setWindowTitle(\\\"Playlist Maker\\\")\\n self.setGeometry(0,0, 800, 494)\\n self.mbox = QVBoxLayout()\\n self.hbox = QHBoxLayout()\\n self.hbtnbox = QHBoxLayout()\",\n \"def __init__(self):\\n self.window = Tk()\\n self.window.title(\\\"Brick Breaker\\\")\\n self.window.attributes(\\\"-fullscreen\\\", True)\\n self.window.iconbitmap(\\\"data/wall.ico\\\")\\n self.window.config(background=\\\"light blue\\\")\\n\\n # initialization des composants\\n self.frame = Frame(self.window, bg='light blue')\\n self.littleFrame = Frame(self.frame, bg='light blue')\\n self.littleFrame_bis = LabelFrame(self.frame, bg='light blue', text=\\\"USER NAME\\\")\\n\\n # creation des composants\\n self.create_title()\\n self.create_subtitle()\\n self.create_play_button()\\n self.create_quit_button()\\n\\n # empaquetage\\n self.littleFrame_bis.pack(expand=YES, pady=30)\\n self.littleFrame.pack(expand=YES, pady=50)\\n self.frame.pack(expand=YES, fill=BOTH, pady=200)\",\n \"def widgetSetup(self):\\n self.master.resizable(0, 0)\\n self.master.iconbitmap('logo.ico')\\n self.master.title(\\\"Ejercicio POO\\\")\\n\\n self.master.bind(\\\"<Return>\\\", lambda e: self.create())\\n self.master.bind(\\\"<Delete>\\\", lambda e: self.delete())\",\n \"def init_frame(self):\\n self._exit_button.grid(row=0, column=2, sticky=tk.W)\\n self._clear_button.grid(row=0, column=0, sticky=tk.E)\\n # self._copy_button.grid(row=0, column=1, sticky=(tk.W, tk.W))\\n return None\",\n \"def __init__(self):\\n\\t\\tself.root = Tk()\\n\\t\\tself.root.geometry(\\\"1600x1600\\\")\\n\\t\\ttitle_arabic=u'\\\\uFEB3\\\\uFEE8\\\\uFECC\\\\uFEEE\\\\uFEA9'\\n\\t\\tself.root.title(title_arabic)\\n\\t\\tself.root.geometry(\\\"1600x900\\\")\\t\\t\\n\\t\\tself.root.resizable(width=False, height=False)\\n\\t\\tmylabel=Label(text=\\\"Loading . . .\\\",fg=\\\"#ff1a75\\\",bg=\\\"black\\\",font=(\\\"Times New Roman\\\",18))\\n\\t\\tmylabel.place(x=665,y=300)\\n\\t\\tmylabel3=Text()\\n\\t\\tmylabel3.place(x=550,y=395)\\n\\t\\tmylabel3['width']='50'\\n\\t\\tmylabel3['height']=0.6\\n\\t\\tmylabel3.configure(state='disabled')\\n\\t\\tself.root.configure(background='black')\\n\\t\\tself.load()\\n\\t\\tself.root.mainloop()\",\n \"def init_main(self):\\n self.start_game = tk.Button(self.view.frame_2, text=\\\"Start Game\\\",\\n command=lambda: self.draw_game_table())\\n\\n self.start_game.pack(side=tk.LEFT)\\n self.see_log = tk.Button(self.view.frame_2, text=\\\"See Log\\\",\\n command=lambda: self.look_log())\\n self.see_log.pack(side=tk.LEFT)\\n\\n self.clean_log = tk.Button(self.view.frame_2, text=\\\"Clean Log\\\",\\n command=lambda: self.model.clean_log_file())\\n self.clean_log.pack(side=tk.LEFT)\\n self.close_game = tk.Button(self.view.frame_2, text=\\\"Close Game\\\",\\n command=lambda: self.view.root.destroy())\\n self.close_game.pack(side=tk.LEFT)\",\n \"def initGui(self):\\r\\n\\r\\n # Create help action \\r\\n self.helpAction = QAction( QIcon(\\\":/plugins/layercombinations/about.png\\\"), u\\\"Help\\\", self.iface.mainWindow())\\r\\n # connect the action \\r\\n self.helpAction.triggered.connect( self.showHelp )\\r\\n # Add menu item\\r\\n self.iface.addPluginToMenu(u\\\"&Layer Combinations\\\", self.helpAction)\\r\\n\\r\\n # Create the action that allows to change the widget type\\r\\n self.changeWidgetAction = QAction(\\\"Change widget type\\\", self.iface.mainWindow())\\r\\n self.changeWidgetAction.triggered.connect( self.changeWidget )\\r\\n self.iface.addPluginToMenu(u\\\"&Layer Combinations\\\", self.changeWidgetAction)\\r\\n\\r\\n # Create the action that will toggle the plugin panel\\r\\n self.action = QAction(QIcon(\\\":/plugins/layercombinations/icon.png\\\"), \\\"Show/hide the Layer Combinations widgets\\\", self.iface.mainWindow())\\r\\n self.action.triggered.connect( self.widget.toggle )\\r\\n # Add toolbar button and menu item\\r\\n self.iface.addToolBarIcon(self.action)\\r\\n self.iface.addPluginToMenu(u\\\"&Layer Combinations\\\", self.action)\\r\\n\\r\\n\\r\\n # Add the widget to the mainWindow\\r\\n self.widget.addToiFace(self.iface)\",\n \"def __init__(self, master=None, *args, **kw):\\n Tix.Frame.__init__(self, master, *args, **kw)\\n self._init_widgets()\\n self._init_bindings()\\n self._root().title(\\\"KaeChat\\\")\\n self._page_serial = 0\",\n \"def prepare_UI(self):\",\n \"def _initialize(self):\\n \\n self.view.lineEdit_3.setText(\\\"C,H,N,O,P,S\\\")\\n self.view.spin_hit.setValue(20)\\n self.view.lineEdit_2.setValue(10.)\\n self.view.checkBox_8.setChecked(True)\",\n \"def initializeUI(self):\\n self.setGeometry(100, 100, 300, 200)\\n self.setWindowTitle('Event Handling Example')\\n\\n self.show()\",\n \"def initialize_buttons(self):\\r\\n self.start_button = tk.Button(self.master, text='Start', command = self.startRunning)\\r\\n self.start_button.grid(row=0, column=0)\\r\\n\\r\\n self.pause_button = tk.Button(self.master, text='Pause', command = self.pauseRunning)\\r\\n self.pause_button.grid(row=0, column=1)\\r\\n\\r\\n self.graph_button = tk.Button(self.master, text='Graph', command = self.showGraph)\\r\\n self.graph_button.grid(row=0, column=2)\\r\\n \\r\\n self.plot_button = tk.Button(self.master, text='Plot', command = self.showPlot)\\r\\n self.plot_button.grid(row=0, column=3)\\r\\n \\r\\n self.draw_button = tk.Button(self.master, text='Draw', command = self.drawCells)\\r\\n self.draw_button.grid(row=0, column=4)\\r\\n \\r\\n # Initialize Button States and Actions\\r\\n self.pause_button['state'] = 'disabled'\\r\\n # Boolean switch to control flow of placement process\\r\\n self.running = False\\r\\n # Boolean switch to plot placement connections and tags, turn off for faster processing\\r\\n self.plot = False\\r\\n self.drawing = False\\r\\n self.graph = False\\r\\n # Boolean switch to specify first run and allow stop/continue behavior that doesn't initialize program\\r\\n self.firstRun = True\",\n \"def init_UI(self):\\n\\n self.master.title(\\\"Create and upload training file\\\")\\n self.master.geometry('400x400')\\n\\n self.text_area = scrolledtext.ScrolledText(self)\\n self.text_area.pack()\\n\\n self.user_input = Entry(self, width=10)\\n self.user_input.pack()\\n\\n sys.stdout = RedirectOutputText(self.text_area)\\n\\n self.create_uncertain_pairs_file()\\n\\n self.console_label = ConsoleLabel(self.get_uncertain_pairs_file())\\n self.current_record_pair = self.console_label.get_uncertain_pair()\\n\\n self.btn_next = Button(self, text=\\\"Next\\\", bg=\\\"green\\\", command=self.get_input)\\n self.btn_next.pack()\\n\\n self.back = Button(self, text=\\\"Back\\\", command=self.go_back)\\n self.back.pack()\",\n \"def gui_init(self):\\n if not self.parent is None:\\n self.parent.children.append(self)\\n self.children = []\\n self.priority = PRIORITY_GUI_ELEMENTS\",\n \"def _initialize(self):\\n self._entry = tk.Entry(master = self._window)\\n self._entry.focus_set()\\n \\n self._confirm_button = tk.Button(master = self._window,\\n text = \\\"Ok\\\",\\n font = DEFAULT_FONT,\\n command = self._confirm_color)\\n\\n self._entry.grid(row = 0, column = 0,\\n padx = 10, pady = 10)\\n self._confirm_button.grid(row = 1, column = 0,\\n padx = 10, pady = 10)\\n\\n self._window.bind(\\\"<Return>\\\", self._confirm_color)\",\n \"def __init__ (self,c,title,label):\\n \\n leoTkinterDialog.__init__(self,title,resizeable=True) # Initialize the base class.\\n self.createTopFrame()\\n self.top.protocol(\\\"WM_DELETE_WINDOW\\\", self.destroy)\\n \\n # Initialize common ivars.\\n self.c = c\\n self.label = label\\n self.vnodeList = []\\n self.vnodeList = []\\n self.buttonFrame = None\\n \\n # Fill in the frame.\\n self.createFrame()\\n self.fillbox()\\n \\n # Make the common bindings after creating self.box.\\n \\n self.box.bind(\\\"<Double-Button-1>\\\",self.go)\",\n \"def create_widgets(self):\\n # self.var_spherical = IntVar()\\n # self.var_3d = IntVar()\\n # self.var_spatial_audio = IntVar()\\n # self.button_open[\\\"command\\\"] = self.action_open\\n # self.button_inject[\\\"command\\\"] = self.action_inject\\n pass\",\n \"def build_initial() :\\r\\n titleframe = T.Frame(ROOT)\\r\\n TITLE = T.Label(titleframe, text = \\\"Welcome to Microgp!\\\")\\r\\n var = T.StringVar()\\r\\n INSTRUCTIONS = T.Message(titleframe, textvariable = var, width = 100)\\r\\n var.set(\\\"By Erik and Sam\\\")\\r\\n instruct_b = T.Button(titleframe, text = \\\"Instructions\\\",\\r\\n command = get_instructions)\\r\\n instruct_b.pack(side = T.BOTTOM)\\r\\n TITLE.pack(side = T.TOP)\\r\\n INSTRUCTIONS.pack(side = T.BOTTOM)\\r\\n titleframe.pack()\",\n \"def _init_widgets(self):\\n comps = self.ui.component_list\\n comps.addItems(sorted(self._labels.keys()))\\n data = self.ui.data_list\\n data.addItems(sorted(self._data.keys()))\",\n \"def __init__(self):\\n self.defaultTheme = \\\"DarkAmber\\\"\\n self.version = 1.4\\n self.versionName = \\\"class update\\\"\\n self.title = \\\"Lms GUI default window\\\"\\n self.layout = [[sg.Text(\\\"This is the base window class layout.\\\")]]\\n self.elementJustification = 'c'\\n self.location=(500, 300)\\n self.running = True\\n self.window = None\\n self.event = \\\"\\\"\\n self.values = []\\n self.nextAction = None\",\n \"def createWidgets(self):\\r\\n top = self.winfo_toplevel()\\r\\n top.rowconfigure(0, weight=1)\\r\\n top.columnconfigure(0, weight=1)\\r\\n self.rowconfigure(0, weight=1)\\r\\n self.columnconfigure(0, weight=1) \\r\\n\\r\\n self.button_quit = tk.Button(self, text='Quit', command=self.quit)\\r\\n self.button_quit.grid(row=0, column=0, sticky=tk.N+tk.S+tk.E+tk.W)\",\n \"def init_objects(self):\\n self.start_button_local = box(pos=vector(0, 1.2, 0), size=vector(8, 2, 1))\\n # if no IP address was defined, the button \\\"remote game\\\" will not appear\\n self.start_button_remote = None if not self.remote_enabled else box(pos=vector(0, -1.2, 0), size=vector(8, 2, 1))\\n # a label is a text associated to a box\\n self.start_label_local = label(pos=self.start_button_local.pos, xoffset=0, yoffset=0, text='Local Game', color=color.black, opacity=0, line=False, height=20, box=False)\\n self.start_label_remote = None if not self.remote_enabled else label(pos=self.start_button_remote.pos, xoffset=0, yoffset=0, text='Remote Game', color=color.black, opacity=0, line=False, height=20, box=False)\\n self.title = text(pos=vector(0, 5, 0), text='Morpion 3D', align='center', color=color.green, billboard=True, depth=0.5)\\n # return the list of elements that actually appear\\n return filter(lambda x: x is not None, [self.start_button_local, self.start_button_remote, self.start_label_local, self.start_label_remote, self.title])\",\n \"def __init__(self, target=None):\\n\\t\\ttkinter.Frame.__init__(self, target)\\n\\t\\tself.create_widgets()\",\n \"def __init__(self):\\n # clears the console window\\n if sys.platform in ('linux-i386','linux2'):\\n os.system(\\\"clear\\\")\\n elif sys.platform in ('win32','dos','ms-dos'):\\n os.system(\\\"cls\\\")\\n\\n # print scripts info\\n print self.WELCOME_MESSAGE\\n\\n # initialize all instance variables\\n self.guiElements = {} # dictionary of gui elements (buttons, strings, sliders, ...)\\n self.gui_events = [] # list of events\\n self.gui_event_ids = {} # dictionary of event ids\\n self.config = {} # configuration dictionary\\n self.target = None # import or export\\n self.callback = None # function to call when config gui is done\\n self.texpathIndex = 0\\n self.texpathCurrent = ''\\n\\n # reset GUI coordinates\\n self.xPos = self.XORIGIN\\n self.yPos = self.YORIGIN + Blender.Window.GetAreaSize()[1]\\n\\n # load configuration\\n self.load()\",\n \"def __init__(self, master, **kwargs):\\n GenericWindow.__init__(self, master, **kwargs)\\n self.states = None\\n self.master = master\\n self.display()\",\n \"def init_ui(self):\\n raise NotImplementedError(\\\"This is an abstract method.\\\")\",\n \"def initialize(self):\\n # Notebook holds all the tabs\\n n = ttk.Notebook(self)\\n f1 = RectTab(self)\\n f2 = LTab(self)\\n f3 = CircTab(self)\\n f4 = BuminTab(self)\\n f5 = LorentzTab(self)\\n n.add(f1, text='Rectangle')\\n n.add(f2, text='L')\\n n.add(f3, text='Circle ')\\n n.add(f4, text='Buminovich')\\n n.add(f5, text='Lorentz')\\n # need to pack for the Notebook to display\\n n.pack()\",\n \"def initGui(self):\\n from p4_view import Gui\\n self.updateStatus(\\\"Launching GUI...\\\")\\n self.gui = Gui(self, self.lmap)\\n self.gui.setStart(self.cfg[\\\"START\\\"])\\n self.gui.setGoal(self.cfg[\\\"GOAL\\\"])\\n self.gui.setPossGoals(self.cfg[\\\"POSS_GOALS\\\"])\\n #GHD\\n self.gui.setMapName(self.cfg[\\\"MAP_FILE\\\"])\\n self.updateStatus(\\\"OK\\\")\\n self.gui.mainloop()\",\n \"def create_widgets(self):\\n\\n # tk.Button(win, text=\\\"Update\\\", command=self.update).grid(row=1, column=1)\\n tkvar = tk.StringVar(win)\\n # Dictionary with options\\n choices = ('Clear', 'Small Glider', 'Glider', 'Exploder', '10 Cell Row', 'Light Weight Spaceship', 'Tumbler',\\n 'Gosper Glider Gu')\\n self.combo_input = ttk.Combobox(self.control_area, width=25, values=choices, state='readonly')\\n self.combo_input.pack(side=tk.LEFT)\\n self.combo_input.current(0)\\n self.combo_input.bind(\\\"<<ComboboxSelected>>\\\", self.combo_callback)\\n\\n self.next = tk.Button(self.control_area, text=\\\"Next\\\", command=self.next_generation)\\n self.next.pack(side=tk.LEFT, padx=3, pady=2)\\n self.start = tk.Button(self.control_area, text=\\\"Start\\\", command=self.start_game)\\n self.start.pack(side=tk.LEFT, padx=3, pady=2)\\n\\n self.stop = tk.Button(self.control_area, text=\\\"Stop\\\", fg=\\\"red\\\", command=self.stop_game)\\n self.stop.pack(side=tk.LEFT, padx=3, pady=2)\\n\\n self.stop = tk.Button(self.control_area, text=\\\"Fast\\\", fg=\\\"red\\\", command=self.stop_game)\\n self.stop.pack(side=tk.LEFT, padx=3, pady=2)\\n self.gen_label = tk.Label(win, text=\\\"label\\\", bg=\\\"#808080\\\")\\n self.gen_label.grid(row=0, column=1)\",\n \"def setup(self):\\n self.ui.setup_window()\",\n \"def Init(self):\\r\\n\\r\\n AuiDefaultDockArt.Init(self)\\r\\n \\r\\n self._active_caption_colour = self._inactive_caption_colour\\r\\n self._active_caption_text_colour = wx.SystemSettings.GetColour(wx.SYS_COLOUR_CAPTIONTEXT)\\r\\n self._inactive_caption_text_colour = self._active_caption_text_colour\",\n \"def configure_widgets(self):\\r\\n\\r\\n # 'command' - callback function executed when button is pressed\\r\\n # since we can't pass it a function with arguments, we use the partial \\r\\n # function from the functools module\\r\\n self.btn_tl['command'] = partial(self.play, \\\"x\\\", (0,0))\\r\\n self.btn_tm['command'] = partial(self.play, \\\"x\\\", (0,1))\\r\\n self.btn_tr['command'] = partial(self.play, \\\"x\\\", (0,2))\\r\\n self.btn_ml['command'] = partial(self.play, \\\"x\\\", (1,0))\\r\\n self.btn_mm['command'] = partial(self.play, \\\"x\\\", (1,1))\\r\\n self.btn_mr['command'] = partial(self.play, \\\"x\\\", (1,2))\\r\\n self.btn_bl['command'] = partial(self.play, \\\"x\\\", (2,0))\\r\\n self.btn_bm['command'] = partial(self.play, \\\"x\\\", (2,1))\\r\\n self.btn_br['command'] = partial(self.play, \\\"x\\\", (2,2))\\r\\n\\r\\n self.btn_reset['text'] = \\\"Reset\\\"\\r\\n self.btn_reset['command'] = self.reset\",\n \"def create_widgets(self):\\n #create first button\\n self.button1 = Button(self, text = \\\"Start\\\")\\n self.button1.bind\\n self.button1.grid()\",\n \"def init ( self, parent ):\\n # Create a panel to hold all of the buttons:\\n self.control = panel = wx.Panel( parent, -1 )\\n sizer = wx.BoxSizer( wx.VERTICAL )\\n \\n # Add the standard font control:\\n font = self._font = wx.TextCtrl( panel, -1, self.str_value )\\n wx.EVT_KILL_FOCUS( font, self.update_object )\\n wx.EVT_TEXT_ENTER( panel, font.GetId(), self.update_object )\\n sizer.Add( font, 0, wx.EXPAND | wx.BOTTOM, 3 )\\n \\n # Add all of the font choice controls:\\n sizer2 = wx.BoxSizer( wx.HORIZONTAL )\\n facenames = all_facenames()\\n control = self._facename = wx.Choice( panel, -1, wx.Point( 0, 0 ), \\n wx.Size( choice_width( facenames ), 20 ), \\n facenames )\\n \\n sizer2.Add( control, 2, wx.EXPAND )\\n wx.EVT_CHOICE( panel, control.GetId(), self.update_object_parts )\\n \\n control = self._point_size = wx.Choice( panel, -1, \\n wx.Point( 0, 0 ), wx.Size( 30, 20 ), \\n PointSizes )\\n sizer2.Add( control, 1, wx.EXPAND | wx.RIGHT, 3 )\\n wx.EVT_CHOICE( panel, control.GetId(), self.update_object_parts ) \\n \\n sizer.Add( sizer2, 0, wx.EXPAND )\\n \\n # Set-up the layout:\\n panel.SetAutoLayout( True )\\n panel.SetSizer( sizer )\\n sizer.Fit( panel )\",\n \"def initUI(self):\\n startbtn = QPushButton(\\\"Start Recroding\\\", self)\\n startbtn.move(30, 50)\\n\\n stopbtn = QPushButton(\\\"Stop Recording\\\", self)\\n stopbtn.move(150, 50)\\n\\n initbtn = QPushButton(\\\"Initilize\\\", self)\\n initbtn.move(30, 100)\\n\\n plotbtn = QPushButton(\\\"Plot\\\", self)\\n plotbtn.move(150, 100)\\n\\n startbtn.clicked.connect(self.start_recording)\\n stopbtn.clicked.connect(self.stop_recording)\\n initbtn.clicked.connect(self.init_recording)\\n plotbtn.clicked.connect(self.plot_signals)\\n\\n self.statusBar()\\n self.statusBar().showMessage('Click Init')\\n\\n self.setGeometry(300, 300, 290, 150)\\n self.setWindowTitle('Recorder 1.0')\\n self.setWindowIcon(QIcon(\\\"./Static/Images/icon.jpg\\\"))\\n self.show()\",\n \"def init_gui(self):\\r\\n self.root.title('Video Resizer')\\r\\n self.root.option_add('*tearOff', 'FALSE')\\r\\n self.grid(column=0, row=0, sticky='nsew')\\r\\n\\r\\n # Buttons getvideos, save videos, start resize\\r\\n self.openButton = ttk.Button(self, width=8, text=\\\"Browse\\\", command=self.getVideosPath)\\r\\n self.openButton.grid(column=1, row=2)\\r\\n self.saveButton = ttk.Button(self, width=8, text=\\\"Browse\\\", command=self.getCapturePath)\\r\\n self.saveButton.grid(column=3, row=2)\\r\\n self.startButton = ttk.Button(self, text='Start to Resize', command=self.startResizing)\\r\\n self.startButton.grid(column=0, row=5)\\r\\n self.multiStartButton = ttk.Button(self, text='Start to multi Resize', command=self.startMultiResizing)\\r\\n self.multiStartButton.grid(column=2, row=5)\\r\\n\\r\\n # listbox to choose what video type to save\\r\\n # add a label for the combobox\\r\\n ttk.Label(self, text=\\\"Select Video Type to Save\\\").grid(column=0, row=4)\\r\\n\\r\\n def clickMe():\\r\\n \\\"\\\"\\\"\\r\\n button clicked to select video type\\r\\n called when action is clicked\\r\\n :return:\\r\\n \\\"\\\"\\\"\\r\\n global Format\\r\\n Format = typeToChoose.get()\\r\\n print(Format)\\r\\n action.configure(text='selected ' + Format) # show the selected item after clicked\\r\\n action.configure(state='disabled') # button disabled after clicked\\r\\n\\r\\n # Button\\r\\n action = ttk.Button(self, text=\\\"Select \\\", command=clickMe)\\r\\n action.grid(column=2, row=4)\\r\\n\\r\\n # Combobox\\r\\n typeToChoose = StringVar()\\r\\n # value in combobox is formatType\\r\\n numberChosen = ttk.Combobox(self, width=12, textvariable=typeToChoose, values=self.formatTypes)\\r\\n numberChosen.grid(column=1, row=4)\\r\\n numberChosen.current(0)\\r\\n\\r\\n # Frame show info related to the resizing process\\r\\n self.resultFrame = ttk.LabelFrame(self, text='Result', height=100)\\r\\n self.resultFrame.grid(column=0, row=6, columnspan=4, sticky='nesw')\\r\\n self.resultLabel = ttk.Label(self.resultFrame, text='')\\r\\n self.resultLabel.grid(column=0, row=0)\\r\\n\\r\\n # Labels that remain constant throughout execution.\\r\\n ttk.Label(self, text='Video Resizer').grid(column=0, row=0, columnspan=4)\\r\\n ttk.Label(self, text='Select videos').grid(column=0, row=2, sticky='w')\\r\\n ttk.Label(self, text='Saving folder').grid(column=2, row=2, sticky='w')\\r\\n ttk.Separator(self, orient='horizontal').grid(column=0, row=1, columnspan=4, sticky='ew')\\r\\n\\r\\n # configure for the window grid\\r\\n for child in self.winfo_children():\\r\\n child.grid_configure(padx=5, pady=5)\",\n \"def vp_start_gui():\\n global val, w, root\\n root = tk.Tk()\\n plot_support.set_Tk_var()\\n top = Toplevel1(root)\\n plot_support.init(root, top)\\n root.mainloop()\",\n \"def widgets(self):\\r\\n self.setWindowTitle(\\\"PyCrypt\\\")\\r\\n self.setMinimumSize(QSize(500, 500))\\r\\n self.setMaximumSize(QSize(500, 500))\\r\\n# Adding the sub def for widgets etc\\r\\n self.add_menus_and_status()\\r\\n self.add_buttons()\",\n \"def __init__(self, parent):\\r\\n Frame.__init__(self, parent) \\r\\n \\r\\n self.parent = parent\\r\\n self.initUI()\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/AreaPrinter/mountainIcon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'AreaPrinter'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n\\tself.add_action(\\n icon_path,\\n text=self.tr(u'Grid Convergence'),\\n callback=self.runTools,\\n parent=self.iface.mainWindow())\",\n \"def __init__(self):\\n super().__init__()\\n self.root = tk.Tk() # different Tk than this class.\\n self.root.title(\\\"Python Pyui!\\\")\\n self.top_frame = tk.Frame(self.root, bg=\\\"yellow\\\")\\n self.top_frame.pack(fill=c.BOTH, expand=1)\\n self.bottom_frame = tk.Frame(self.root, bg=\\\"blue\\\",\\n borderwidth=1, relief=c.RAISED)\\n self.bottom_frame.pack(fill=c.X)\\n okButton = tk.Button(self.bottom_frame, text=\\\"OK\\\",\\n command=self.cb_ok)\\n okButton.pack(fill='none', padx=5, pady=5, side=c.RIGHT)\\n cancelButton = tk.Button(self.bottom_frame, text=\\\"Cancel\\\",\\n command=self.cb_cancel)\\n cancelButton.pack(fill='none', padx=5, pady=5, side=c.RIGHT)\\n\\n self.not_cancelled = False\\n self.current_parent = self.top_frame\\n self.parent_is_grid= False\\n self.entries = {}\",\n \"def __init__(self):\\n self.master = Tk()\\n self.master.title(\\\"Brick Breaker\\\")\\n self.master.geometry(\\\"800x600\\\")\\n self.master.minsize(800, 600)\\n self.master.iconbitmap(\\\"data/wall.ico\\\")\\n self.master.config(background=\\\"lightblue\\\")\\n self.frame = Frame(self.master, bg='lightblue')\\n self.littleFrame = Frame(self.frame, bg='lightblue')\\n\\n # creation des composants\\n self.create_title()\\n self.create_play_button()\\n self.create_quit_button()\\n\\n # empaquetage\\n self.littleFrame.pack(expand=YES, pady=100)\\n self.frame.pack(expand=YES)\",\n \"def onInit(self):\\n self.list_control = self.getControl(6)\\n self.getControl(1).setLabel(self.window_title)\\n self.getControl(3).setVisible(False)\\n try:\\n self.getControl(7).setLabel(xbmc.getLocalizedString(222))\\n except Exception:\\n pass\\n\\n self.getControl(5).setVisible(False)\\n\\n # add our items to the listing and focus the control\\n self.list_control.addItems(self.listing)\\n self.setFocus(self.list_control)\",\n \"def __init__(self, master):\\n self.window = tk.Canvas(master, width=500, height=300)\\n self.reset_button = tk.Button(master, text=\\\"Reset\\\", command=self.reset_window)\\n self.start_button = tk.Button(master, text=\\\"Start\\\", command=self.start_sorting)\\n self.window.pack()\\n self.reset_button.pack()\\n self.start_button.pack()\\n self.reset_window()\",\n \"def _init_tkvars(self,PO):\\n for name,param in PO.params().items():\\n self._create_tkvar(PO,name,param)\",\n \"def Define_Frame(self):\\n self.frame=Frame(self.master, relief=GROOVE, bd=4)\\n self.frame.grid(row=0,column=1,rowspan=2,columnspan=2)\\n frame_title = Label(self.frame,text=\\\"Stage Control\\\",relief=RAISED,bd=2,width=24, bg=\\\"light yellow\\\",font=(\\\"Times\\\", 16))\\n frame_title.grid(row=0, column=1)\\n self.encoder_text = [] # These hold the stage position as read by the encoders\\n self.coo_ent = [] # These hold the coordinate entry values\\n but = []\\n encoder_display = []\\n for i in range(3):\\n self.coo_ent.append(Entry(self.frame, justify=\\\"center\\\", width=12))\\n but.append(Button(self.frame, text=\\\"Move %s (relative)\\\"%self.POS_NAME[i], width=12,command=lambda axis=i:self.GUI_move(axis)))\\n self.encoder_text.append(StringVar())\\n encoder_display.append(Label(self.frame,textvariable=self.encoder_text[i],relief=SUNKEN,bd=1, width=20))\\n self.coo_ent[i].grid(row=i+1,column=0)\\n self.coo_ent[i].focus_set()\\n but[i].grid(row=i+1,column=1)\\n encoder_display[i].grid(row=i+1,column=2)\\n self.encoder_text[i].set(\\\"%8s microns\\\"%str(self.read_pos[i]))\\n zero_encoders_button = Button(self.frame, text=\\\"Re-Initialize Encoders\\\", width=20, command=self.GUI_ReInitialize_Encoders)\\n zero_encoders_button.grid(row=5,column=1)\\n return\",\n \"def init_ui(self):\\n\\n # Display default values until first update\\n self.window.pcBatteryDisplay.setValue(100)\\n self.window.pcCpuDisplay.setValue(0)\",\n \"def __init__(self):\\n self.handlers = {}\\n self.start_state = None\\n self.end_states = []\\n self.btn = Button()\",\n \"def initWidgets(self):\\n self.loctext.setText(\\\"{0:g}\\\".format(self.loc))\\n self.scaletext.setText(\\\"{0:g}\\\".format(self.scale))\",\n \"def __init__(self):\\n self.computer_first = 0 # randint(0, 1)\\n self.app = Tk()\\n self.app.attributes(\\\"-toolwindow\\\", 1)\\n self.app.title('Tic Tac Toe')\\n self.app.resizable(width=False, height=False)\\n self.board = Board()\\n self.font = Font(family=\\\"Helvetica\\\", size=32)\\n self.buttons = {}\\n for x, y in self.board.fields:\\n handler = lambda x=x, y=y: self.move(x, y)\\n button = Button(self.app, command=handler, font=self.font,\\n width=2, height=1)\\n button.grid(row=y, column=x)\\n self.buttons[x, y] = button\\n handler = lambda: self.reset()\\n button = Button(self.app, text='reset', command=handler)\\n button.grid(row=self.board.size + 1, column=0,\\n columnspan=self.board.size, stick='WE')\\n self.update()\\n if self.computer_first:\\n self.move(randint(0, self.board.size - 1),\\n randint(0, self.board.size - 1))\",\n \"def __setup_ui_controls(self):\\n self.scene.append_to_caption('\\\\n')\\n\\n # Button to reset camera\\n btn_reset = button(\\n bind=self.__reset_camera, text=\\\"Reset Camera\\\")\\n self.__ui_controls.btn_reset = btn_reset\\n self.scene.append_to_caption('\\\\t')\\n\\n chkbox_cam = checkbox(\\n bind=self.__camera_lock_checkbox,\\n text=\\\"Camera Lock\\\", checked=self.__camera_lock)\\n self.__ui_controls.chkbox_cam = chkbox_cam\\n self.scene.append_to_caption('\\\\t')\\n\\n chkbox_rel = checkbox(\\n bind=self.__grid_relative_checkbox,\\n text=\\\"Grid Relative\\\", checked=self.__grid_relative)\\n self.__ui_controls.chkbox_rel = chkbox_rel\\n self.scene.append_to_caption('\\\\n\\\\n')\\n\\n # Button to clear the screen\\n btn_clr = button(bind=self.clear_scene, text=\\\"Clear Scene\\\")\\n self.__ui_controls.btn_clear = btn_clr\\n self.scene.append_to_caption('\\\\n\\\\n')\\n\\n # Checkbox for grid visibility\\n chkbox_grid = checkbox(\\n bind=self.__grid_visibility_checkbox, text=\\\"Grid Visibility\\\",\\n checked=self.__grid_visibility)\\n self.__ui_controls.chkbox_grid = chkbox_grid\\n self.scene.append_to_caption('\\\\t')\\n\\n # Prevent the space bar from toggling the active checkbox/button/etc\\n # (default browser behaviour)\\n self.scene.append_to_caption('''\\n <script type=\\\"text/javascript\\\">\\n $(document).keyup(function(event) {\\n if(event.which === 32) {\\n event.preventDefault();\\n }\\n });\\n </script>''')\\n # https://stackoverflow.com/questions/22280139/prevent-space-button-from-triggering-any-other-button-click-in-jquery\\n\\n # Control manual\\n controls_str = '<br><b>Controls</b><br>' \\\\\\n '<b>PAN</b><br>' \\\\\\n 'SHFT + LMB | <i>free pan</i><br>' \\\\\\n 'W , S | <i>up / down</i><br>' \\\\\\n 'A , D | <i>left / right</i><br>' \\\\\\n '<b>ROTATE</b><br>' \\\\\\n 'ARROWS KEYS | <i>rotate direction</i><br>' \\\\\\n 'Q , E | <i>roll left / right</i><br>' \\\\\\n '<b>ZOOM</b></br>' \\\\\\n 'MOUSEWHEEL | <i>zoom in / out</i><br>' \\\\\\n '<script type=\\\"text/javascript\\\">var arrow_keys_handler = function(e) {switch(e.keyCode){ case 37: case 39: case 38: case 40: case 32: e.preventDefault(); break; default: break;}};window.addEventListener(\\\"keydown\\\", arrow_keys_handler, false);</script>' # noqa\\n # Disable the arrow keys from scrolling in the browser\\n # https://stackoverflow.com/questions/8916620/disable-arrow-key-scrolling-in-users-browser\\n self.scene.append_to_caption(controls_str)\",\n \"def init_ui(self):\\n\\n self.master.title(\\\"Upload file\\\")\\n self.master.geometry(\\\"300x200\\\")\\n\\n self.pack(fill=BOTH, expand=1)\\n\\n self.btn_select_file = Button(self, text=\\\"Select file\\\", command=self.on_open)\\n self.btn_select_file.place(x=80, y=50)\\n\\n self.selected_file_name = Label(self, text=\\\"<Selected file name>\\\")\\n self.selected_file_name.place(x=60, y=90)\\n\\n self.btn_upload_file = Button(self, text=\\\"Upload file\\\", command=self.upload_file)\\n self.btn_upload_file.place(x=80, y=130)\\n\\n self.btn_back = Button(self, text=\\\"Back\\\", command=self.go_back)\\n self.btn_back.place(x=10, y=10)\",\n \"def textbox_init(self):\\n if not self.textbox:\\n self.textbox = Textbox()\\n self.textarea = Textarea()\",\n \"def set_controls(self):\\n # Image control\\n image = pyxbmct.Image(addonfolder+artsfolder+'/osc.png')\\n self.placeControl(image, 0, 0, rowspan=7, columnspan=16)\\n\\n\\t\\t# Label information\\n image = pyxbmct.Image(addonfolder+artsfolder+'/readers.png')\\n self.placeControl(image, 7, 1, rowspan=1, columnspan=14)\\n\\t\\t\\n\\t\\t# Hostname input\\n image = pyxbmct.Image(addonfolder+artsfolder+'/hostname.png')\\n self.placeControl(image, 9, 0, rowspan=1, columnspan=4)\\n self.hostname_input = pyxbmct.Edit('')\\n self.placeControl(self.hostname_input, 9, 4, rowspan=1, columnspan=5)\\n\\n\\t\\t# Port input\\n image = pyxbmct.Image(addonfolder+artsfolder+'/port.png')\\n self.placeControl(image, 12, 1, rowspan=1, columnspan=3)\\n self.port_input = pyxbmct.Edit('')\\n self.placeControl(self.port_input, 12, 4, rowspan=1, columnspan=2)\\n\\n\\t\\t# Username input\\n image = pyxbmct.Image(addonfolder+artsfolder+'/username.png')\\n self.placeControl(image, 10, 1, rowspan=1, columnspan=3)\\n self.username_input = pyxbmct.Edit('')\\n self.placeControl(self.username_input, 10, 4, rowspan=1, columnspan=4)\\n\\t\\t\\n\\t\\t# Password input\\n image = pyxbmct.Image(addonfolder+artsfolder+'/password.png')\\n self.placeControl(image, 11, 1, rowspan=1, columnspan=3)\\n self.password_input = pyxbmct.Edit('', isPassword=True)\\n self.placeControl(self.password_input, 11, 4, rowspan=1, columnspan=4)\\n\\n\\t\\t# Close button\\n self.close_button = pyxbmct.Button('Exit')\\n self.placeControl(self.close_button, 13, 15, rowspan=1, columnspan=1)\\n self.connect(self.close_button, lambda: self.page(Readers))\",\n \"def __init__(self):\\n self.rows = [18, 23, 24, 25]\\n self.cols = [17, 27, 22]\\n self.keypad = [\\n [\\\"1\\\", \\\"2\\\", \\\"3\\\"],\\n [\\\"4\\\", \\\"5\\\", \\\"6\\\"],\\n [\\\"7\\\", \\\"8\\\", \\\"9\\\"],\\n [\\\"*\\\", \\\"0\\\", \\\"#\\\"]\\n ]\\n self.setup()\",\n \"def create_widgets(self):\\n root.title(\\\"Universal Converter\\\")\\n root.resizable(False, False)\\n root.config(bg=\\\"white\\\")\\n head_frame = Frame(root, bd=3, bg=\\\"Navy\\\", relief=GROOVE)\\n Label(head_frame, text=\\\"UNIVERSAL CONVERTER\\\", font=(\\\"Trebuchet MS\\\", 24, \\\"bold\\\"), fg=\\\"White\\\", bg=\\\"Navy\\\").pack(pady=50)\\n head_frame.grid(row=0, column=0, columnspan=4, ipadx=20, sticky=\\\"ew\\\")\\n Label(root, text=\\\" Choose the Converter \\\", font=(\\\"Trebuchet MS\\\", 16, \\\"bold\\\"), fg=\\\"Navy\\\", bg=\\\"White\\\").grid(row=2, column=0, columnspan=4, ipadx=20, ipady=20)\\n button_frame = Frame(root, bd=5, bg=\\\"Navy\\\", relief=FLAT)\\n self.measurements_list = [\\\"Angle\\\", \\\"Area\\\", \\\"Bit Byte\\\", \\\"Density\\\", \\\"Electric Current\\\", \\\"Energy\\\", \\\"Force\\\", \\\"Fuel Consumption\\\", \\\"Length\\\", \\\"Mass\\\", \\\"Power\\\", \\\"Pressure\\\", \\\"Speed\\\", \\\"Temperature\\\", \\\"Time\\\", \\\"Volume\\\"]\\n self.measurements_dict = {\\\"Angle\\\": self.angle, \\\"Area\\\": self.area, \\\"Bit Byte\\\": self.bitbyte, \\\"Density\\\": self.density, \\\"Electric Current\\\": self.electriccurrent, \\\"Energy\\\": self.energy, \\\"Force\\\": self.force, \\\"Fuel Consumption\\\": self.fuelconsumption, \\\"Length\\\": self.length, \\\"Mass\\\": self.mass, \\\"Power\\\": self.power, \\\"Pressure\\\": self.pressure, \\\"Speed\\\": self.speed, \\\"Temperature\\\": self.temperature, \\\"Time\\\": self.time, \\\"Volume\\\": self.volume}\\n for i in range(16):\\n self.button = Button(button_frame, text=self.measurements_list[i], font=(\\\"Trebuchet MS\\\", 12), width=13, fg=\\\"Navy\\\", bg=\\\"White\\\", relief=FLAT, overrelief=SOLID, bd=5, activebackground=\\\"Navy\\\", activeforeground=\\\"White\\\", command=self.measurements_dict[self.measurements_list[i]])\\n self.button.grid(row=i/4+4, column=i%4, ipady=15, ipadx=15, padx=2, pady=2)\\n button_frame.grid(row=3, column=0, columnspan=4, sticky=\\\"we\\\", padx=5, pady=5)\\n root.protocol(\\\"WM_DELETE_WINDOW\\\", self.callback) #When user will quit, program will show you the messagebox\",\n \"def initUI(self):\\n # Setting the main layout as Vertical.\\n self.mainLayout = QHBoxLayout()\\n\\n # Create title.\\n self.title = QLabel(self.__name + \\\" : \\\")\\n\\n # Add description as tooltip.\\n self.title.setToolTip(self.__description)\\n\\n # Add title to main layout.\\n self.mainLayout.addWidget(self.title)\\n\\n # Create ComboBox.\\n self.dropDown = QComboBox()\\n\\n # Add datas to drop down.\\n self.dropDown.addItems(self.__datas)\\n\\n # Set default index to dropdown.\\n self.dropDown.setCurrentIndex(self.__currentValue)\\n\\n # Connect dropdown with update method.\\n self.dropDown.currentIndexChanged.connect(self.changeCurrentValue)\\n\\n # Add ComboBox to main layout.\\n self.mainLayout.addWidget(self.dropDown)\\n\\n # Add the main layout to the window.\\n self.setLayout(self.mainLayout)\",\n \"def initialize(self):\\n super(QtWidgetComponent, self).initialize()\\n shell = self.shell_obj\\n self.set_enabled(shell.enabled)\\n if shell.bgcolor:\\n self.set_bgcolor(shell.bgcolor)\\n if shell.fgcolor:\\n self.set_fgcolor(shell.fgcolor)\\n if shell.font:\\n self.set_font(shell.font)\",\n \"def setup(self):\\n self.ui_manager.purge_ui_elements()\\n\\n button = FlatButton(\\n 'Lockdown',\\n center_x=1000,\\n center_y=20,\\n width=250\\n )\\n button.balls(self.ball_list)\\n self.ui_manager.add_ui_element(button)\",\n \"def __init__(self, min_height=600, min_width=600):\\n self.window = Tk()\\n # set minimum size to which the window can be reduced\\n self.window.minsize(min_width, min_height)\\n self.canvas = None\\n self.frames = {\\n \\\"parameters\\\": None,\\n \\\"canvas\\\": None\\n }\\n self.menubar = {\\n \\\"menubar\\\": None,\\n \\\"helpmenu\\\": None,\\n \\\"filemenu\\\": None,\\n \\\"editmenu\\\": None\\n }\\n self.combo_box = {\\n \\\"class\\\": None,\\n \\\"variable\\\": None\\n }\\n self.init_canvas_frame()\\n self.init_parameters_frame()\\n # self.init_menu_bar()\\n self.classes = {\\n \\\"parameters\\\": Parameters(self),\\n \\\"fractal\\\": FastFractal(self)\\n }\\n self.init_parameter_combobox()\",\n \"def buildmainframe(self):\\n self.mainframewidgets=[]\\n for x in range(3):\\n thislabel = Label(self.mainframe, text=str(x))\\n thislabel.grid()\\n self.mainframewidgets.append(thislabel)\",\n \"def init_layout(self):\\n\\t\\tself.pack_start(self.edit, expand=True)\\n\\t\\tself.pack_start(self.button, expand=False)\\n\\t\\tself.show_all()\",\n \"def run_init(self):\\n InitEditor(self.root, self)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77829045","0.7319447","0.7121587","0.71154493","0.7044531","0.70197904","0.7015567","0.696209","0.68833053","0.6865509","0.6856705","0.6856659","0.68557364","0.6814217","0.6804366","0.67954916","0.6790265","0.67835164","0.67754626","0.6773747","0.67669374","0.67669374","0.6733773","0.67196304","0.6711201","0.67089576","0.6698033","0.669739","0.66848624","0.6668839","0.6668221","0.66550744","0.6651097","0.6642811","0.6636429","0.6622687","0.66152066","0.66050833","0.6602694","0.659605","0.6595507","0.65869147","0.65728575","0.65653366","0.65645283","0.6557212","0.65303516","0.6527516","0.6525461","0.65180206","0.65050155","0.6504938","0.64946294","0.64906687","0.6488394","0.6470244","0.64589876","0.64435863","0.64434344","0.643631","0.64154804","0.6411661","0.63885665","0.63876575","0.6386988","0.63851","0.6379997","0.63773394","0.63728315","0.6366945","0.63665706","0.63616735","0.63615036","0.6357116","0.6354143","0.63506144","0.6342037","0.63407063","0.6338828","0.63387734","0.6336553","0.63339853","0.6333156","0.6331395","0.6323894","0.63212246","0.63162863","0.6309738","0.6305401","0.6295285","0.6291731","0.6283306","0.6279753","0.6275898","0.6265302","0.624682","0.62394994","0.6236699","0.6230597","0.62275887","0.6215836"],"string":"[\n \"0.77829045\",\n \"0.7319447\",\n \"0.7121587\",\n \"0.71154493\",\n \"0.7044531\",\n \"0.70197904\",\n \"0.7015567\",\n \"0.696209\",\n \"0.68833053\",\n \"0.6865509\",\n \"0.6856705\",\n \"0.6856659\",\n \"0.68557364\",\n \"0.6814217\",\n \"0.6804366\",\n \"0.67954916\",\n \"0.6790265\",\n \"0.67835164\",\n \"0.67754626\",\n \"0.6773747\",\n \"0.67669374\",\n \"0.67669374\",\n \"0.6733773\",\n \"0.67196304\",\n \"0.6711201\",\n \"0.67089576\",\n \"0.6698033\",\n \"0.669739\",\n \"0.66848624\",\n \"0.6668839\",\n \"0.6668221\",\n \"0.66550744\",\n \"0.6651097\",\n \"0.6642811\",\n \"0.6636429\",\n \"0.6622687\",\n \"0.66152066\",\n \"0.66050833\",\n \"0.6602694\",\n \"0.659605\",\n \"0.6595507\",\n \"0.65869147\",\n \"0.65728575\",\n \"0.65653366\",\n \"0.65645283\",\n \"0.6557212\",\n \"0.65303516\",\n \"0.6527516\",\n \"0.6525461\",\n \"0.65180206\",\n \"0.65050155\",\n \"0.6504938\",\n \"0.64946294\",\n \"0.64906687\",\n \"0.6488394\",\n \"0.6470244\",\n \"0.64589876\",\n \"0.64435863\",\n \"0.64434344\",\n \"0.643631\",\n \"0.64154804\",\n \"0.6411661\",\n \"0.63885665\",\n \"0.63876575\",\n \"0.6386988\",\n \"0.63851\",\n \"0.6379997\",\n \"0.63773394\",\n \"0.63728315\",\n \"0.6366945\",\n \"0.63665706\",\n \"0.63616735\",\n \"0.63615036\",\n \"0.6357116\",\n \"0.6354143\",\n \"0.63506144\",\n \"0.6342037\",\n \"0.63407063\",\n \"0.6338828\",\n \"0.63387734\",\n \"0.6336553\",\n \"0.63339853\",\n \"0.6333156\",\n \"0.6331395\",\n \"0.6323894\",\n \"0.63212246\",\n \"0.63162863\",\n \"0.6309738\",\n \"0.6305401\",\n \"0.6295285\",\n \"0.6291731\",\n \"0.6283306\",\n \"0.6279753\",\n \"0.6275898\",\n \"0.6265302\",\n \"0.624682\",\n \"0.62394994\",\n \"0.6236699\",\n \"0.6230597\",\n \"0.62275887\",\n \"0.6215836\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":20078,"cells":{"query":{"kind":"string","value":"Starts a while loop that sends 'command' to tello every 5 second."},"document":{"kind":"string","value":"def _sendingCommand(self): \n\n while True:\n self.tello.send_command('command') \n time.sleep(5)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def Listen(self):\n while True:\n time.sleep(1)","def run(self):\n while True:\n time.sleep(RTM_READ_DELAY)\n for event in self._slack_client.rtm_read():\n self.handle_event(event)","def run():\n # 1 sec delay to allow DHT22 sensor to start as per datasheet\n sleep_ms(1000)\n last_run = ticks_ms()\n _read()\n\n while True:\n if ticks_diff(ticks_ms(), last_run) > _READING_DELAY_MS:\n last_run = ticks_ms()\n _read()\n\n _signal_alive()\n sleep_ms(1000)","def run(self):\n self.cmdloop()","def init_trig(motion):\n for x in range(40):\n # Loop for 40 iterations.\n time.sleep(0.1)\n # Delay for 10ms.\n if (GPIO.input(motion) == 1):\n # Determines if corresponding motion sensor is triggered.\n TCP.client(str(motion))\n # Sends direction of motion as message to the connected server.\n break","async def do(ctx, times : int, *, command):\n msg = copy.copy(ctx.message)\n msg.content = command\n for i in range(times):\n await bot.process_commands(msg)","def loop(self):\n while True:\n delay_until_next = self.tick()\n if not delay_until_next:\n break\n self._sleep(delay_until_next)","def run(self):\n while self._running:\n temp = self._sense.get_temperature()\n self.display_temperature(temp)\n sleep(10)\n self._sense.clear()","def run(self):\n while True:\n print(\"I'm running in the background\")\n time.sleep(self.interval)","def feed(self, amount=network.default_listen_time):\n asyncore.loop(timeout=amount, count=1)","def loop_forever(self):\n self.client.loop_forever()","def run(self):\n run1=0\n while (run1==0):\n Publisher().sendMessage(\"updatetext\", \"\")\n time.sleep(3)","def loop_forever(self):\n while self.running:\n time.sleep(0.01)","def loop():\n\n load_config_project()\n\n L.debug(\"running with version: %s\", sys.version)\n is_version_2 = sys.version.startswith(\"2\")\n while True:\n response = ''\n if num_version == 2:\n response = raw_input(\"Enter command:\")\n if num_version == 3:\n response = input(\"Enter command:\")\n\n if response != '':\n commander.parse(response)\n sleep(0.5)","def run(self):\n while True:\n # Do something\n print('Doing something imporant in the background')\n\n self.loadData()\n time.sleep(self.interval)","def loop_forever(self):\n self.running = True\n while self.running:\n time.sleep(0.1)","def run(self):\n while True:\n self.sm.run()\n time.sleep(0.05)","def run(self):\n time_to_quit = False\n while True:\n time_to_quit = self.run_to_yield_or_quit()\n if time_to_quit:\n print(self, 'quitting')\n break\n else:\n time.sleep(self.polling_interval)","async def check():\r\n while True:\r\n if rss.check_new():\r\n item = rss.most_recent()\r\n queue = format_message.format_notes(item)\r\n for message in queue:\r\n await client.send_message(client.get_channel(\"350634825516056577\"), message)\r\n await asyncio.sleep(28800) # Check every 8 hours\r","async def loop():\n # ArmDevice.storage.joints_pos = await get_positions() # Use this if encoders are wired up.\n # ArmDevice.storage.joints_pos = simulate_positions() # Use this for testing without position feedback.\n log.debug(\"command: {}\".format(ArmDevice.storage.command))\n ArmDevice.storage.controller.user_command(ArmDevice.storage.mode, *ArmDevice.storage.command)\n ArmDevice.storage.speeds = ArmDevice.storage.controller.update_duties(ArmDevice.storage.joints_pos)\n\n # publish speeds/duty cycles here\n log.debug(\"joints_pos: {}\".format(ArmDevice.storage.joints_pos))\n log.debug(\"speeds: {}\".format(ArmDevice.storage.speeds))\n await send_duties()","def ping_moonrat():\n threading.Timer(3600, ping_moonrat).start()\n text = \"Moonrat is still active\\n\"\n slack_client.api_call(\n \"chat.postMessage\",\n channel='G9P7X8Q0H',\n text=text,\n )","def loop(self,timeout=1):\n self.stream.loop(timeout)","def run(self):\n r = rospy.Rate(100)\n while not rospy.is_shutdown():\n r.sleep()","def handle_chat(self):\n while True:\n if self.chat_breakout:\n return\n\n time.sleep(1)\n messages = \"\"\n for i in range(5):\n try:\n messages += f\"{self.queue.popleft()}\\n\"\n except IndexError:\n # Queue is empty but no worries\n continue\n\n if messages != \"\":\n self.loop.create_task(\n self.ingame_cog.send_chat_to_discord(\n self.bot, self.channel, messages\n )\n )","def run_aqi(self):\r\n while True:\r\n self.get_aqi()\r\n time.sleep(30 - time.time() % 30)","def run_forever(self):\n while True:\n self.run_once()\n\n self.logger.info(f\"Sleeping for {self.config.sleep()} seconds\")\n time.sleep(self.config.sleep())","def run(self):\n while not rospy.is_shutdown():\n if self.state is not None:\n # get robot state\n x = self.state.pose.position.x\n y = self.state.pose.position.y\n quat = [\n self.state.pose.orientation.x,\n self.state.pose.orientation.y,\n self.state.pose.orientation.z,\n self.state.pose.orientation.w\n ]\n eulers = tf.transformations.euler_from_quaternion(quat, 'sxyz')\n # generate ping and send\n ping, heading = self.sim.gen_ping((x, y), eulers[2])\n self.__socket.send(self.__create_msg(ping))\n rospy.logdebug(\n \"Sent ping with heading: \" + str(np.rad2deg(heading)))\n self.rate.sleep()","def run_chat_client():\r\n while must_run:\r\n print_menu()\r\n action = select_user_action()\r\n perform_user_action(action)\r\n print(\"Thanks for watching. Like and subscribe! 👍\")","def __call__(self):\n hub.sleep(random.randint(1, self.interval))\n while True:\n self.send_req()\n self.reply_pending = True\n hub.sleep(self.interval)\n if self.reply_pending:\n self.no_response()","def auto_connect(address):\r\n _connected=False\r\n _timeout=100\r\n _count = 0\r\n\r\n while not _connected:\r\n try:\r\n _c = telnetlib.Telnet(address)\r\n if _c.sock is not None:\r\n _connected = True\r\n\r\n except socket.error:\r\n _count = _count + 1\r\n print \"Trima socket not ready, waiting to retry, attempt #\"+str(_count)\r\n time.sleep(5)\r\n\r\n print(\"Trima Telnet Connection Ready\")","def loop(self):\n keys.mode = 'main'\n for line in client.readlines('/event'):\n if not self.alive:\n break\n self.dispatch(*line.split(' ', 1))\n self.alive = False","def run(self):\n while True:\n if self.timeout - time() <= 0:\n if self.state in [State.CANDIDATE, State.FOLLOWER]:\n self.start_election()\n\n elif self.state is State.LEADER:\n self.send_appends()\n self.commit_entries()\n\n else:\n self.unknown_state()\n\n self.handle_message()","def run(self):\r\n while True:\r\n if self.camera_device.is_detecting():\r\n self.alarm_device.switch_alarm()","def loop(self):\n while True:\n self._print_field()\n try:\n cmd = input(PROMPT)\n self._invoke_cmd(cmd)\n except EOFError: # Allows to exit by pressing ⌃D without error\n break","def loop_start( self ):\n self.client.loop_start()","def run(self):\n \n \n # pyautogui.click(x=1310, y=210)\n # pyautogui.click(x=1310, y=210)\n # pyautogui.click(x=1310, y=210)\n # pyautogui.typewrite(self.t_amount, interval=0.05)\n while True : \n read_email_from_gmail(self.tp_val,self.sl_val,self.t_amount,self.get_iq_balance(),self.file_path) \n\n time.sleep(self.interval)","def run(self):\n while True:\n msg = self.recv()","def run(self):\n while self.running:\n self.__update_battery()\n self.__update_signal()\n time.sleep(5)","def run(self):\n self.cncLock.acquire()\n self.running = True\n\n # Initialize\n try:\n self.cnc = serial.Serial(self.deviceFile,BAUD_RATE)\n\n self.updaterThread = threading.Thread(target=self.periodic_timer)\n self.updaterThread.start()\n\n # Wake up grbl\n log.info(\"Initializing Grbl...\")\n cmd = \"\\r\\n\\r\\n\"\n self.cnc.write(cmd.encode())\n\n # Wait for grbl to initialize and flush startup text in serial input\n time.sleep(2)\n self.cnc.flushInput()\n self.cncLock.release()\n\n while self.running :\n cmd = self.commandQueue.get().strip() + EOLStr\n if self.running == False:\n break\n self.cncLock.acquire()\n self.cnc.write(cmd.encode())\n\n out = str(self.cnc.readline().strip()) # Wait for grbl response\n if out.find('ok') >= 0 :\n log.debug(f'MSG: {out}') # Debug response\n elif out.find('error') >= 0 :\n log.error(f'ERROR: {out}')\n else:\n log.info(out)\n self.cncLock.release()\n except:\n raise\n finally:\n log.debug(\"CNC main loop left\")\n self.cnc.close()","def loop(self,runhours, runmin, runsec, repmin, repsec):\n c =\"/cli:python /app:matrix /cmd:loop /runh:\"+str(runhours)+ \" /runm:\"+str(runmin)\n c += \" /runs:\"+str(runsec) + \" /repm:\"+ str(repmin) + \" /reps:\"+str(reps)\n self.addtoCMDlist(c)\n self.sendCMDlist()","def run(self):\n self.connect()\n self.run_forever()","def run_for_mins(bot, nr_mins):\n for i in range(1, nr_mins+1):\n time.sleep(60)\n bot.send_msg('It has been {} minutes.'.format(i))","async def __loop(self):\n await self.bot.wait_until_ready()\n while not self.bot.is_closed():\n await self.post_data()\n await asyncio.sleep(3600)","def console():\r\n while True:\r\n interpret_command(input(\"POM> \"))","def firethelights():\n for i in enumerate(PROTOCOL):\n elem = []\n elem = PROTOCOL[i]\n colsend = elem[0]\n timer = elem[1]\n commandstring = \"sudo python /home/pi/rpi_ws281x/python/examples/neopixel_args.py \"\n commandstring = commandstring+str(colsend[1])+\" \"+str(colsend[0])+\" \"+str(colsend[2])+\" \"\n os.system(commandstring+str(timer))\n os.system(commandstring+\"0 0 0 1\")\n print \"End of Protocol\"","def loop(self):\n pass","def control_loop():\n global rate, regions, speed\n rospy.init_node('ebot_controller')\n rate = rospy.Rate(10)\n rospy.Subscriber('/ebot/laser/scan', LaserScan, laser_callback)\n rospy.Subscriber('/odom', Odometry, odom_callback)\n speed = Twist()\n speed.linear.x = 0\n speed.angular.z = 0\n pub.publish(speed)\n while not rospy.is_shutdown():\n if x <= 6.28:\n user_x, user_y = waypoints(15)\n go_to_x_y(user_x, user_y, 1)\n go_to_x_y(user_x, user_y, 0.25)\n else:\n go_to_x_y(12.5, 0, 3)\n go_to_x_y(12.5, 0, 1)\n go_to_x_y(12.5, 0, 0.2)\n exit()\n speed.linear.x = 0\n speed.angular.z = 0\n pub.publish(speed)\n rate.sleep()","def connect(self):\n\n self.tello.connect()\n self.tello.wait_for_connection(60.0)","def run(self):\n while True:\n self.current_wifi_clients()\n self._eval_is_someone_home()\n time.sleep(self._interval)","def run(self):\n self.startSerial()\n # Wait about five seconds before doing anything\n time.sleep(5)\n while True:\n # Check setpoints against all controllers\n self.check_setpoints()\n\n # Issue any new commands as necessary\n self.check_pins()\n\n # Receive the latest Arduino data and process into dictionary\n self.read_arduino_data_and_format_dictionary()\n\n # Clean all of the arduino stuff to avoid incorrect inputs\n with self.lock:\n self.ser.reset_output_buffer()\n with self.lock:\n self.ser.reset_input_buffer()","def main_loop(self) -> None:\n while True:\n # Log a message to say that Wheatley is waiting for 'Look To!'\n self.logger.info(\"Waiting for 'Look To!'...\")\n # Sit in an infinite loop whilst we're not ringing, and exit Wheatley if enough time\n # has passed\n self._last_activity_time = time.time()\n while not self._is_ringing:\n time.sleep(0.01)\n if self._server_mode and time.time() > self._last_activity_time + INACTIVITY_EXIT_TIME:\n self.logger.info(f\"Timed out - no activity for {INACTIVITY_EXIT_TIME}s. Exiting.\")\n return\n\n self.logger.info(f\"Starting to ring {self.row_generator.summary_string()}\")\n if self._server_mode:\n self._tower.set_is_ringing(True)\n\n while self._is_ringing:\n self.tick()\n time.sleep(0.01)\n\n self.logger.info(\"Stopping ringing!\")\n if self._server_mode:\n self._tower.set_is_ringing(False)","async def esay(self, string):\r\n said = False\r\n while not said:\r\n if not self.ended:\r\n for x in range(5):\r\n try:\r\n msg = await bot.send_message(self.channel, string)\r\n self.end()\r\n return\r\n except (discord.HTTPException, OSError, aiohttp.ClientResponseError) as e:\r\n print(\"Suffered\", type(e), \"error in botcommand.esay().\")\r\n print(\"info: \", string, self.channel.name, self.player.id)\r\n await asyncio.sleep(x ** x)\r\n self.end()\r\n else:\r\n raise CommandEndedError","def ping_daemon(self):\n s = self.ping_interval\n while True:\n p = domintell.messages.Ping()\n self.send(p)\n time.sleep(s)","def loop_tcp(imu, poll_interval):\n config = configparser.ConfigParser()\n config.read('config.ini')\n host = str(config['SERVERTCP']['HOST'])\n port = int(config['SERVERTCP']['PORT'])\n\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.bind((host, port))\n s.listen(1)\n\n printCounter = 0\n while True:\n print 'waiting connection'\n conn, addr = s.accept()\n\n print 'Client connection accepted ', addr\n while True:\n if imu.IMURead():\n try:\n data = imu.getIMUData()\n selected_data = [data.get(key) for key in fields]\n conn.send(str(selected_data))\n\n if printCounter % 100 == 0:\n print selected_data\n\n time.sleep(poll_interval*1.0/1000.0)\n printCounter += 1\n except socket.error, msg:\n print 'Client connection closed', addr\n break\n conn.close()","def start(self):\n self.running = True\n while self.running:\n self.update_prompt()\n with exception_handler(self.engine):\n self.cmdloop()\n self.engine.reset()","async def _ping_loop(self, interval: float):\n while True:\n await asyncio.sleep(interval)\n self._send(\n PacketHeader(\n client_id=u32(self._cid), flags=PacketFlags.PING\n ).serialize()\n )","def _readloop(self):\r\n\r\n while self._ll_alive:\r\n with self._rx_lock:\r\n data = self._Random(1)\r\n if len(data) != 0: # check for timeout\r\n self._uart_rx_queue.put(data)","def run(self):\n\n self._daemon_thread.start()\n\n while True:\n time.sleep(5)","def run(self):\n terminal.open()\n self.terminal_init()\n terminal.refresh()\n\n try:\n self.loop_until_terminal_exits()\n except KeyboardInterrupt:\n pass\n finally:\n terminal.close()","def Loop(self):\n self.coshell.SetModesCallback(self.SetModes)\n while True:\n try:\n text = self.Prompt()\n if text is None:\n break\n self.Run(text) # paradoxically ignored - coshell maintains $?\n except EOFError:\n # ctrl-d\n if not self.coshell.ignore_eof:\n break\n except KeyboardInterrupt:\n # ignore ctrl-c\n pass\n except interactive_coshell.CoshellExitError:\n break","def main_loop(self):\n # run for only the allotted time (lifetime)\n for _ in range(self.lifetime * self.ticks_per_second):\n start_time = time()\n new_message, queue_len = self.communicator.get_message()\n if new_message is None: # no incoming messages\n self.do_random_task()\n else:\n # Convert string message back into tuple of ints\n new_message = list(map(int, new_message.split('@@@')))\n self.handle_incoming_message(new_message, queue_len)\n\n # this accounts for the time already taken in test_communication\n # and other activities from the total time allotted for the loop iteration\n already_taken = time() - start_time\n sleep_time = max(1/self.ticks_per_second - already_taken, 0)\n sleep(sleep_time)","async def async_send_command(self, command: Iterable[str], **kwargs: Any) -> None:\n num_repeats = kwargs[ATTR_NUM_REPEATS]\n\n for _ in range(num_repeats):\n for single_command in command:\n await self.coordinator.roku.remote(single_command)\n\n await self.coordinator.async_request_refresh()","def thread_function(client):\n threading.Timer(30.0, thread_function).start()\n client.publish(\"serverCommand/keepalive\", \"0\")\n print(\"Message Sent. (keepalive)\")","def run(self):\n while True:\n self.command = input(\"> cmd >>> \")\n self.invoker.run(self.command)","def slackbot(self, *args, **kwargs):\n while True:\n self.slack.api_connect()","def start(self):\n while True:\n self.pull_accounts_rolls()\n sleep(PULL_FREQUENCY_SECONDS)","def wait_forever(self):\r\n while True:\r\n time.sleep(0.5)","def run(self):\n last_time = time.time()\n while self.running:\n now_time = time.time()\n interval = now_time - last_time\n last_time = now_time\n self.update(interval)\n time.sleep(Options['update interval'])","def run(self):\n while True:\n try:\n sleep(influx_settings.write_freq)\n self._redis2influx()\n except InterruptExceptions as ex:\n raise ex","def run(self):\n while self.time <= self.deadline:\n self.setup_period()\n self.execute_process(self.select_process())\n\n self.time += 1","def do_command(command):\n send_command(command)\n # time.sleep(0.1) # may be required on slow machines\n response = get_response()\n print(\"Rcvd: <<< \" + response)\n return response","def _loop(self):\n while True:\n if GameLoop.getInstance()._cancelation_token==True:\n break\n self._update_signal.notify_all()\n sleep(1/60)","def run(self):\n rate = rospy.Rate(self._run_rate)\n while not rospy.is_shutdown():\n try:\n rate.sleep()\n except:\n break","def background_catch_up():\n while True:\n time.sleep(interval)\n s = 'http://{0}:{1}'.format(args.host, port)\n req = urllib2.Request(s)\n try:\n response = urllib2.urlopen(req)\n response.read()\n except Exception as e:\n pass","def run(self):\n threadPID = os.getpid()\n index = 0\n while True:\n index += 1\n if ((index * self.m_to) > 1):\n #self.check4kill(threadPID)\n index = 1\n if(self.status == Modem.Status.IDLE or self.status == Modem.Status.BUSY2REQ):\n r, e = self.conn.dataAvailable()\n if(e):\n break\n if(r):\n rx = self.recvCommand()\n if (len(rx) == 0):\n break\n elif(self.status == Modem.Status.KILL):\n break\n sleep(self.m_to)\n self.close()\n print >>sys.stderr, 'Closing'","def run(self):\n self.read_from_serial()","async def repeat(times : int, content='repeating...'):\n for i in range(times):\n await bot.say(content)","def _thread_loop(self):\n while not self.stop_thread.is_set():\n # First, read a line\n try:\n line = self._read_line()\n except EvseTimeoutError:\n continue\n # Then if the line is a status change, execute the callback\n if line[:3] in ('ST ', '$ST'):\n self.callback(states[int(line.split()[1], 16)])\n # write_allowed is only cleared if the board has been reset ;\n # in this case, we should wait 1 more second before executing\n # commands in order for the board to finish booting.\n if not self.write_allowed.is_set():\n threading.Timer(1, self.write_allowed.set).start()\n continue\n # Do not write a new line if\n # the previous one isn't read and is not old enough\n previous_newline_age = 0\n while (self.newline_available.is_set() and\n previous_newline_age <= NEWLINE_MAX_AGE):\n time.sleep(SYNC_SERIAL_TIMEOUT)\n previous_newline_age += SYNC_SERIAL_TIMEOUT\n # Write the new received line\n self.newline = line\n self.newline_available.set()","def loop(self):\n\t\twhile (self.quit == 0):\n\t\t\ttry:\n\t\t\t\tuserInput = raw_input(\"> \")\n\t\t\t\tself.processCommand(userInput)\n\t\t\texcept EOFError:\n\t\t\t\tsys.exit()\n\t\t\texcept KeyboardInterrupt:\n\t\t\t\tsys.exit()","def run(self):\n # Start the thread to receive commands\n self.command_input.start()\n\n # The next time to execute a state\n self.next_time = time.time() + self.control_period\n\n # Run forever!\n logger.debug(\"Starting\")\n while True:\n old_state = self.current_state\n\n # Wait for the next control cycle\n if time.time() < self.next_time:\n time.sleep(0.001)\n continue\n else: # Set the next execution time\n self.next_time += self.control_period\n\n # Execute the state!\n if self.current_state == LightSystem.IDLE_STATE:\n self.idle_state()\n elif self.current_state == LightSystem.MANUAL_STATE:\n self.manual_state()\n elif self.current_state == LightSystem.EXIT_STATE:\n self.pi.stop()\n return\n\n if old_state != self.current_state:\n logger.debug(\"%s -> %s\"%(old_state, self.current_state))","async def _main(self):\n while True:\n time.sleep(1)","def run(self):\n running = True\n while running:\n ################################### ARDUINO POLLING ###################################\n try:\n print(\"DEBUG: running\")\n if self.debug == 'n':\n # Arduino is connected\n inp = self.arduino.read()\n \n else: \n inp = str(input(\"Enter Packet, Enter Key to skip. Ex: (DATA&BUZZ)\\n\")) \n\n if inp:\n self.socket.setblocking(True)\n arduino_input_header, arduino_input = inp.split('&')\n \n if arduino_input_header == \"DATA\": \n if arduino_input == \"BUZZ\":\n # buzz the owner\n self.buzz_subroutine()\n continue \n \n else:\n # a PIN was sent by the Arduino\n self.pin_check_subroutine(arduino_input)\n continue\n \n \n ########################## LISTEN FOR COMMAND FROM SERVER #########################\n else:\n self.socket.settimeout(1) \n \n # check if a command was received\n try:\n cmd = self.socket.recv(4096)\n\n except:\n print(\"DEBUG: receive timed out\")\n continue\n\n else: \n cmd_hdr, cmd_msg, cmd_sdr = self.parse_packet(cmd)\n \n if cmd_hdr == \"CMD\":\n if cmd_msg == \"LOCK DOOR\": \n # a remote lock was issued\n \n if self.debug == 'n':\n # tell Arduino to lock the door\n self.arduino.write(\"LD\")\n\n print(\"DEBUG: locking override finished\")\n\n elif cmd_msg == \"UNLOCK DOOR\":\n # a remote unlock was issued\n \n if self.debug == 'n':\n # tell Arduino to unlock the door\n self.arduino.write(\"UD\")\n\n print(\"DEBUG: unlocking override finished\") \n \n except (KeyboardInterrupt, SystemExit):\n self.socket.sendall(self.make_packet(\"CMD\", \"SHUTTING DOWN\"))\n raise \n \n except Exception as e:\n self.socket.sendall(self.create_log(sys.exc_info()))","def run_command(self):\r\n self.update_settings()\r\n self.run = True\r\n self.pause = False\r\n if self.run_call is not None:\r\n self.wm.after(1, self.run_call)","def run():\n board = GoBoard(7)\n con = GtpConnection(Gomoku(), board)\n con.start_connection()","async def repeat(ctx, times : int, content='repeating...'):\n for i in range(times):\n await bot.say(content)","def run(self):\n threading.Thread(target=self._get_output, daemon=True).start() # Run selected command\n threading.Thread(target=self._cycle_loop, daemon=True).start() # Cycle between commands\n\n self._display_loop()","async def repeat(self,ctx, times: int, content='repeating...'):\n for i in range(times):\n await ctx.send(content)","def run(self):\n self.running = True\n dynamic_reconfigure.client.Client(\"dynamic_reconfigure_server\", timeout=5, config_callback=self.dynamic_reconfigure_callback)\n rate = rospy.Rate(20)\n while not rospy.is_shutdown() and self.running:\n self.pub.publish(self.get_cmd_vel())\n rate.sleep()","def runloop(self, prompt=''):\n if not self.connected(): return\n\n try:\n self.start_anti_idle_timer()\n\n use_rawinput = True\n try:\n import readline\n except ImportError:\n use_rawinput = False\n\n while True:\n if use_rawinput:\n try:\n ln = raw_input(prompt)\n except EOFError:\n break\n else:\n if prompt:\n sys.stdout.write(prompt)\n sys.stdout.flush()\n ln = sys.stdin.readline()\n if not len(ln):\n break\n\n ln = ln.rstrip('\\r\\n')\n if not ln:\n continue\n rc, res = self.run(ln)\n if not rc or not self.connected():\n break\n for l in res:\n sys.stdout.write(l + '\\n')\n finally:\n self.cancel_anti_idle_timer()","def start(self):\n while True:\n import traceback\n try:\n command, args = self.parse_line(input(self.prompt))\n if not command:\n self.refresh()\n if command == \"exit\" or command == \"quit\":\n break\n command_handler = self.get_command_handler(command)\n command_handler(args)\n self.refresh()\n except KeyboardInterrupt:\n print(\"\")\n except AttributeError:\n pass\n except:\n traceback.print_exc()\n pass","async def loop(self):\n\t\twhile self.active:\n\t\t\ttry:\n\t\t\t\tawait self.process_data(await self.websocket.recv())\n\t\t\texcept exceptions.ClientError as e:\n\t\t\t\tawait self.send(0, str(e))\n\t\t\texcept KeyboardInterrupt:\n\t\t\t\tawait self.unregister()","async def repeat(ctx, times: int, content='repeating...'):\n for i in range(times):\n await ctx.send(content)","def loop_run(self):\n super(TimerLoop, self).loop_run()\n self.timer = self.cothread.Timer(self.timeout,\n self.callback,\n retrigger=True)","def run():\n board = SimpleGoBoard(7)\n con = GtpConnection(Gomoku4(), board)\n con.start_connection()","def start(self):\n\n while True:\n measurement = self.generate_message()\n measurement.save()\n print(\"Storing new measurement\")\n time.sleep(10)","def run_checks():\n while True:\n if datetime.now() > core.misc_data.check_date+timedelta(minutes=45):\n for stuff in stuff_to_do:\n threading.Thread(target=stuff).start()\n core.misc_data.check_date = datetime.now() + config.utils.tasks.repeat_every\n time.sleep(5*60*60)","def run(self):\n while True:\n self.connect_lock.acquire()\n if self.stopped():\n return\n self.__connect()\n self.connect_lock.release()\n self.ws.run_forever()","def run(self):\n while True:\n try:\n command_requests = self._read_request_commands()\n responses = self._command_executor.execute_commands(command_requests)\n self._send_response(\n CommandResponsesBuilder.to_string(CommandResponsesBuilder.build_xml_result(responses)))\n except ConnectionClosedException:\n self._logger.debug('Connection closed')\n break\n except Exception as ex:\n self._send_response(\n CommandResponsesBuilder.to_string(CommandResponsesBuilder.build_xml_error(0, ex.message)))\n tb = traceback.format_exc()\n self._logger.critical(tb)\n self._connection_socket.close()\n raise","async def tick(self):\n room = self.bot.Room.load('19961884194@chatroom')\n await room.ready()\n await room.say(f'i love you -> {datetime.now()}')","def run():\n adapter = pygatt.GATTToolBackend()\n ADDRESS_TYPE = pygatt.BLEAddressType.random\n\n # TODO if a thread is killed then this will never reestablish a new one since connections never has elements removed\n while True:\n try:\n for device in adapter.scan():\n if device[\"name\"] == \"DYP Hydrometer\":\n print(\"NEW HYDROMETER FOUND\")\n t = threading.Thread(target=handle_connection, args=(device[\"address\"],))\n t.start()\n except:\n pass\n ptime.sleep(5)"],"string":"[\n \"def Listen(self):\\n while True:\\n time.sleep(1)\",\n \"def run(self):\\n while True:\\n time.sleep(RTM_READ_DELAY)\\n for event in self._slack_client.rtm_read():\\n self.handle_event(event)\",\n \"def run():\\n # 1 sec delay to allow DHT22 sensor to start as per datasheet\\n sleep_ms(1000)\\n last_run = ticks_ms()\\n _read()\\n\\n while True:\\n if ticks_diff(ticks_ms(), last_run) > _READING_DELAY_MS:\\n last_run = ticks_ms()\\n _read()\\n\\n _signal_alive()\\n sleep_ms(1000)\",\n \"def run(self):\\n self.cmdloop()\",\n \"def init_trig(motion):\\n for x in range(40):\\n # Loop for 40 iterations.\\n time.sleep(0.1)\\n # Delay for 10ms.\\n if (GPIO.input(motion) == 1):\\n # Determines if corresponding motion sensor is triggered.\\n TCP.client(str(motion))\\n # Sends direction of motion as message to the connected server.\\n break\",\n \"async def do(ctx, times : int, *, command):\\n msg = copy.copy(ctx.message)\\n msg.content = command\\n for i in range(times):\\n await bot.process_commands(msg)\",\n \"def loop(self):\\n while True:\\n delay_until_next = self.tick()\\n if not delay_until_next:\\n break\\n self._sleep(delay_until_next)\",\n \"def run(self):\\n while self._running:\\n temp = self._sense.get_temperature()\\n self.display_temperature(temp)\\n sleep(10)\\n self._sense.clear()\",\n \"def run(self):\\n while True:\\n print(\\\"I'm running in the background\\\")\\n time.sleep(self.interval)\",\n \"def feed(self, amount=network.default_listen_time):\\n asyncore.loop(timeout=amount, count=1)\",\n \"def loop_forever(self):\\n self.client.loop_forever()\",\n \"def run(self):\\n run1=0\\n while (run1==0):\\n Publisher().sendMessage(\\\"updatetext\\\", \\\"\\\")\\n time.sleep(3)\",\n \"def loop_forever(self):\\n while self.running:\\n time.sleep(0.01)\",\n \"def loop():\\n\\n load_config_project()\\n\\n L.debug(\\\"running with version: %s\\\", sys.version)\\n is_version_2 = sys.version.startswith(\\\"2\\\")\\n while True:\\n response = ''\\n if num_version == 2:\\n response = raw_input(\\\"Enter command:\\\")\\n if num_version == 3:\\n response = input(\\\"Enter command:\\\")\\n\\n if response != '':\\n commander.parse(response)\\n sleep(0.5)\",\n \"def run(self):\\n while True:\\n # Do something\\n print('Doing something imporant in the background')\\n\\n self.loadData()\\n time.sleep(self.interval)\",\n \"def loop_forever(self):\\n self.running = True\\n while self.running:\\n time.sleep(0.1)\",\n \"def run(self):\\n while True:\\n self.sm.run()\\n time.sleep(0.05)\",\n \"def run(self):\\n time_to_quit = False\\n while True:\\n time_to_quit = self.run_to_yield_or_quit()\\n if time_to_quit:\\n print(self, 'quitting')\\n break\\n else:\\n time.sleep(self.polling_interval)\",\n \"async def check():\\r\\n while True:\\r\\n if rss.check_new():\\r\\n item = rss.most_recent()\\r\\n queue = format_message.format_notes(item)\\r\\n for message in queue:\\r\\n await client.send_message(client.get_channel(\\\"350634825516056577\\\"), message)\\r\\n await asyncio.sleep(28800) # Check every 8 hours\\r\",\n \"async def loop():\\n # ArmDevice.storage.joints_pos = await get_positions() # Use this if encoders are wired up.\\n # ArmDevice.storage.joints_pos = simulate_positions() # Use this for testing without position feedback.\\n log.debug(\\\"command: {}\\\".format(ArmDevice.storage.command))\\n ArmDevice.storage.controller.user_command(ArmDevice.storage.mode, *ArmDevice.storage.command)\\n ArmDevice.storage.speeds = ArmDevice.storage.controller.update_duties(ArmDevice.storage.joints_pos)\\n\\n # publish speeds/duty cycles here\\n log.debug(\\\"joints_pos: {}\\\".format(ArmDevice.storage.joints_pos))\\n log.debug(\\\"speeds: {}\\\".format(ArmDevice.storage.speeds))\\n await send_duties()\",\n \"def ping_moonrat():\\n threading.Timer(3600, ping_moonrat).start()\\n text = \\\"Moonrat is still active\\\\n\\\"\\n slack_client.api_call(\\n \\\"chat.postMessage\\\",\\n channel='G9P7X8Q0H',\\n text=text,\\n )\",\n \"def loop(self,timeout=1):\\n self.stream.loop(timeout)\",\n \"def run(self):\\n r = rospy.Rate(100)\\n while not rospy.is_shutdown():\\n r.sleep()\",\n \"def handle_chat(self):\\n while True:\\n if self.chat_breakout:\\n return\\n\\n time.sleep(1)\\n messages = \\\"\\\"\\n for i in range(5):\\n try:\\n messages += f\\\"{self.queue.popleft()}\\\\n\\\"\\n except IndexError:\\n # Queue is empty but no worries\\n continue\\n\\n if messages != \\\"\\\":\\n self.loop.create_task(\\n self.ingame_cog.send_chat_to_discord(\\n self.bot, self.channel, messages\\n )\\n )\",\n \"def run_aqi(self):\\r\\n while True:\\r\\n self.get_aqi()\\r\\n time.sleep(30 - time.time() % 30)\",\n \"def run_forever(self):\\n while True:\\n self.run_once()\\n\\n self.logger.info(f\\\"Sleeping for {self.config.sleep()} seconds\\\")\\n time.sleep(self.config.sleep())\",\n \"def run(self):\\n while not rospy.is_shutdown():\\n if self.state is not None:\\n # get robot state\\n x = self.state.pose.position.x\\n y = self.state.pose.position.y\\n quat = [\\n self.state.pose.orientation.x,\\n self.state.pose.orientation.y,\\n self.state.pose.orientation.z,\\n self.state.pose.orientation.w\\n ]\\n eulers = tf.transformations.euler_from_quaternion(quat, 'sxyz')\\n # generate ping and send\\n ping, heading = self.sim.gen_ping((x, y), eulers[2])\\n self.__socket.send(self.__create_msg(ping))\\n rospy.logdebug(\\n \\\"Sent ping with heading: \\\" + str(np.rad2deg(heading)))\\n self.rate.sleep()\",\n \"def run_chat_client():\\r\\n while must_run:\\r\\n print_menu()\\r\\n action = select_user_action()\\r\\n perform_user_action(action)\\r\\n print(\\\"Thanks for watching. Like and subscribe! 👍\\\")\",\n \"def __call__(self):\\n hub.sleep(random.randint(1, self.interval))\\n while True:\\n self.send_req()\\n self.reply_pending = True\\n hub.sleep(self.interval)\\n if self.reply_pending:\\n self.no_response()\",\n \"def auto_connect(address):\\r\\n _connected=False\\r\\n _timeout=100\\r\\n _count = 0\\r\\n\\r\\n while not _connected:\\r\\n try:\\r\\n _c = telnetlib.Telnet(address)\\r\\n if _c.sock is not None:\\r\\n _connected = True\\r\\n\\r\\n except socket.error:\\r\\n _count = _count + 1\\r\\n print \\\"Trima socket not ready, waiting to retry, attempt #\\\"+str(_count)\\r\\n time.sleep(5)\\r\\n\\r\\n print(\\\"Trima Telnet Connection Ready\\\")\",\n \"def loop(self):\\n keys.mode = 'main'\\n for line in client.readlines('/event'):\\n if not self.alive:\\n break\\n self.dispatch(*line.split(' ', 1))\\n self.alive = False\",\n \"def run(self):\\n while True:\\n if self.timeout - time() <= 0:\\n if self.state in [State.CANDIDATE, State.FOLLOWER]:\\n self.start_election()\\n\\n elif self.state is State.LEADER:\\n self.send_appends()\\n self.commit_entries()\\n\\n else:\\n self.unknown_state()\\n\\n self.handle_message()\",\n \"def run(self):\\r\\n while True:\\r\\n if self.camera_device.is_detecting():\\r\\n self.alarm_device.switch_alarm()\",\n \"def loop(self):\\n while True:\\n self._print_field()\\n try:\\n cmd = input(PROMPT)\\n self._invoke_cmd(cmd)\\n except EOFError: # Allows to exit by pressing ⌃D without error\\n break\",\n \"def loop_start( self ):\\n self.client.loop_start()\",\n \"def run(self):\\n \\n \\n # pyautogui.click(x=1310, y=210)\\n # pyautogui.click(x=1310, y=210)\\n # pyautogui.click(x=1310, y=210)\\n # pyautogui.typewrite(self.t_amount, interval=0.05)\\n while True : \\n read_email_from_gmail(self.tp_val,self.sl_val,self.t_amount,self.get_iq_balance(),self.file_path) \\n\\n time.sleep(self.interval)\",\n \"def run(self):\\n while True:\\n msg = self.recv()\",\n \"def run(self):\\n while self.running:\\n self.__update_battery()\\n self.__update_signal()\\n time.sleep(5)\",\n \"def run(self):\\n self.cncLock.acquire()\\n self.running = True\\n\\n # Initialize\\n try:\\n self.cnc = serial.Serial(self.deviceFile,BAUD_RATE)\\n\\n self.updaterThread = threading.Thread(target=self.periodic_timer)\\n self.updaterThread.start()\\n\\n # Wake up grbl\\n log.info(\\\"Initializing Grbl...\\\")\\n cmd = \\\"\\\\r\\\\n\\\\r\\\\n\\\"\\n self.cnc.write(cmd.encode())\\n\\n # Wait for grbl to initialize and flush startup text in serial input\\n time.sleep(2)\\n self.cnc.flushInput()\\n self.cncLock.release()\\n\\n while self.running :\\n cmd = self.commandQueue.get().strip() + EOLStr\\n if self.running == False:\\n break\\n self.cncLock.acquire()\\n self.cnc.write(cmd.encode())\\n\\n out = str(self.cnc.readline().strip()) # Wait for grbl response\\n if out.find('ok') >= 0 :\\n log.debug(f'MSG: {out}') # Debug response\\n elif out.find('error') >= 0 :\\n log.error(f'ERROR: {out}')\\n else:\\n log.info(out)\\n self.cncLock.release()\\n except:\\n raise\\n finally:\\n log.debug(\\\"CNC main loop left\\\")\\n self.cnc.close()\",\n \"def loop(self,runhours, runmin, runsec, repmin, repsec):\\n c =\\\"/cli:python /app:matrix /cmd:loop /runh:\\\"+str(runhours)+ \\\" /runm:\\\"+str(runmin)\\n c += \\\" /runs:\\\"+str(runsec) + \\\" /repm:\\\"+ str(repmin) + \\\" /reps:\\\"+str(reps)\\n self.addtoCMDlist(c)\\n self.sendCMDlist()\",\n \"def run(self):\\n self.connect()\\n self.run_forever()\",\n \"def run_for_mins(bot, nr_mins):\\n for i in range(1, nr_mins+1):\\n time.sleep(60)\\n bot.send_msg('It has been {} minutes.'.format(i))\",\n \"async def __loop(self):\\n await self.bot.wait_until_ready()\\n while not self.bot.is_closed():\\n await self.post_data()\\n await asyncio.sleep(3600)\",\n \"def console():\\r\\n while True:\\r\\n interpret_command(input(\\\"POM> \\\"))\",\n \"def firethelights():\\n for i in enumerate(PROTOCOL):\\n elem = []\\n elem = PROTOCOL[i]\\n colsend = elem[0]\\n timer = elem[1]\\n commandstring = \\\"sudo python /home/pi/rpi_ws281x/python/examples/neopixel_args.py \\\"\\n commandstring = commandstring+str(colsend[1])+\\\" \\\"+str(colsend[0])+\\\" \\\"+str(colsend[2])+\\\" \\\"\\n os.system(commandstring+str(timer))\\n os.system(commandstring+\\\"0 0 0 1\\\")\\n print \\\"End of Protocol\\\"\",\n \"def loop(self):\\n pass\",\n \"def control_loop():\\n global rate, regions, speed\\n rospy.init_node('ebot_controller')\\n rate = rospy.Rate(10)\\n rospy.Subscriber('/ebot/laser/scan', LaserScan, laser_callback)\\n rospy.Subscriber('/odom', Odometry, odom_callback)\\n speed = Twist()\\n speed.linear.x = 0\\n speed.angular.z = 0\\n pub.publish(speed)\\n while not rospy.is_shutdown():\\n if x <= 6.28:\\n user_x, user_y = waypoints(15)\\n go_to_x_y(user_x, user_y, 1)\\n go_to_x_y(user_x, user_y, 0.25)\\n else:\\n go_to_x_y(12.5, 0, 3)\\n go_to_x_y(12.5, 0, 1)\\n go_to_x_y(12.5, 0, 0.2)\\n exit()\\n speed.linear.x = 0\\n speed.angular.z = 0\\n pub.publish(speed)\\n rate.sleep()\",\n \"def connect(self):\\n\\n self.tello.connect()\\n self.tello.wait_for_connection(60.0)\",\n \"def run(self):\\n while True:\\n self.current_wifi_clients()\\n self._eval_is_someone_home()\\n time.sleep(self._interval)\",\n \"def run(self):\\n self.startSerial()\\n # Wait about five seconds before doing anything\\n time.sleep(5)\\n while True:\\n # Check setpoints against all controllers\\n self.check_setpoints()\\n\\n # Issue any new commands as necessary\\n self.check_pins()\\n\\n # Receive the latest Arduino data and process into dictionary\\n self.read_arduino_data_and_format_dictionary()\\n\\n # Clean all of the arduino stuff to avoid incorrect inputs\\n with self.lock:\\n self.ser.reset_output_buffer()\\n with self.lock:\\n self.ser.reset_input_buffer()\",\n \"def main_loop(self) -> None:\\n while True:\\n # Log a message to say that Wheatley is waiting for 'Look To!'\\n self.logger.info(\\\"Waiting for 'Look To!'...\\\")\\n # Sit in an infinite loop whilst we're not ringing, and exit Wheatley if enough time\\n # has passed\\n self._last_activity_time = time.time()\\n while not self._is_ringing:\\n time.sleep(0.01)\\n if self._server_mode and time.time() > self._last_activity_time + INACTIVITY_EXIT_TIME:\\n self.logger.info(f\\\"Timed out - no activity for {INACTIVITY_EXIT_TIME}s. Exiting.\\\")\\n return\\n\\n self.logger.info(f\\\"Starting to ring {self.row_generator.summary_string()}\\\")\\n if self._server_mode:\\n self._tower.set_is_ringing(True)\\n\\n while self._is_ringing:\\n self.tick()\\n time.sleep(0.01)\\n\\n self.logger.info(\\\"Stopping ringing!\\\")\\n if self._server_mode:\\n self._tower.set_is_ringing(False)\",\n \"async def esay(self, string):\\r\\n said = False\\r\\n while not said:\\r\\n if not self.ended:\\r\\n for x in range(5):\\r\\n try:\\r\\n msg = await bot.send_message(self.channel, string)\\r\\n self.end()\\r\\n return\\r\\n except (discord.HTTPException, OSError, aiohttp.ClientResponseError) as e:\\r\\n print(\\\"Suffered\\\", type(e), \\\"error in botcommand.esay().\\\")\\r\\n print(\\\"info: \\\", string, self.channel.name, self.player.id)\\r\\n await asyncio.sleep(x ** x)\\r\\n self.end()\\r\\n else:\\r\\n raise CommandEndedError\",\n \"def ping_daemon(self):\\n s = self.ping_interval\\n while True:\\n p = domintell.messages.Ping()\\n self.send(p)\\n time.sleep(s)\",\n \"def loop_tcp(imu, poll_interval):\\n config = configparser.ConfigParser()\\n config.read('config.ini')\\n host = str(config['SERVERTCP']['HOST'])\\n port = int(config['SERVERTCP']['PORT'])\\n\\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n s.bind((host, port))\\n s.listen(1)\\n\\n printCounter = 0\\n while True:\\n print 'waiting connection'\\n conn, addr = s.accept()\\n\\n print 'Client connection accepted ', addr\\n while True:\\n if imu.IMURead():\\n try:\\n data = imu.getIMUData()\\n selected_data = [data.get(key) for key in fields]\\n conn.send(str(selected_data))\\n\\n if printCounter % 100 == 0:\\n print selected_data\\n\\n time.sleep(poll_interval*1.0/1000.0)\\n printCounter += 1\\n except socket.error, msg:\\n print 'Client connection closed', addr\\n break\\n conn.close()\",\n \"def start(self):\\n self.running = True\\n while self.running:\\n self.update_prompt()\\n with exception_handler(self.engine):\\n self.cmdloop()\\n self.engine.reset()\",\n \"async def _ping_loop(self, interval: float):\\n while True:\\n await asyncio.sleep(interval)\\n self._send(\\n PacketHeader(\\n client_id=u32(self._cid), flags=PacketFlags.PING\\n ).serialize()\\n )\",\n \"def _readloop(self):\\r\\n\\r\\n while self._ll_alive:\\r\\n with self._rx_lock:\\r\\n data = self._Random(1)\\r\\n if len(data) != 0: # check for timeout\\r\\n self._uart_rx_queue.put(data)\",\n \"def run(self):\\n\\n self._daemon_thread.start()\\n\\n while True:\\n time.sleep(5)\",\n \"def run(self):\\n terminal.open()\\n self.terminal_init()\\n terminal.refresh()\\n\\n try:\\n self.loop_until_terminal_exits()\\n except KeyboardInterrupt:\\n pass\\n finally:\\n terminal.close()\",\n \"def Loop(self):\\n self.coshell.SetModesCallback(self.SetModes)\\n while True:\\n try:\\n text = self.Prompt()\\n if text is None:\\n break\\n self.Run(text) # paradoxically ignored - coshell maintains $?\\n except EOFError:\\n # ctrl-d\\n if not self.coshell.ignore_eof:\\n break\\n except KeyboardInterrupt:\\n # ignore ctrl-c\\n pass\\n except interactive_coshell.CoshellExitError:\\n break\",\n \"def main_loop(self):\\n # run for only the allotted time (lifetime)\\n for _ in range(self.lifetime * self.ticks_per_second):\\n start_time = time()\\n new_message, queue_len = self.communicator.get_message()\\n if new_message is None: # no incoming messages\\n self.do_random_task()\\n else:\\n # Convert string message back into tuple of ints\\n new_message = list(map(int, new_message.split('@@@')))\\n self.handle_incoming_message(new_message, queue_len)\\n\\n # this accounts for the time already taken in test_communication\\n # and other activities from the total time allotted for the loop iteration\\n already_taken = time() - start_time\\n sleep_time = max(1/self.ticks_per_second - already_taken, 0)\\n sleep(sleep_time)\",\n \"async def async_send_command(self, command: Iterable[str], **kwargs: Any) -> None:\\n num_repeats = kwargs[ATTR_NUM_REPEATS]\\n\\n for _ in range(num_repeats):\\n for single_command in command:\\n await self.coordinator.roku.remote(single_command)\\n\\n await self.coordinator.async_request_refresh()\",\n \"def thread_function(client):\\n threading.Timer(30.0, thread_function).start()\\n client.publish(\\\"serverCommand/keepalive\\\", \\\"0\\\")\\n print(\\\"Message Sent. (keepalive)\\\")\",\n \"def run(self):\\n while True:\\n self.command = input(\\\"> cmd >>> \\\")\\n self.invoker.run(self.command)\",\n \"def slackbot(self, *args, **kwargs):\\n while True:\\n self.slack.api_connect()\",\n \"def start(self):\\n while True:\\n self.pull_accounts_rolls()\\n sleep(PULL_FREQUENCY_SECONDS)\",\n \"def wait_forever(self):\\r\\n while True:\\r\\n time.sleep(0.5)\",\n \"def run(self):\\n last_time = time.time()\\n while self.running:\\n now_time = time.time()\\n interval = now_time - last_time\\n last_time = now_time\\n self.update(interval)\\n time.sleep(Options['update interval'])\",\n \"def run(self):\\n while True:\\n try:\\n sleep(influx_settings.write_freq)\\n self._redis2influx()\\n except InterruptExceptions as ex:\\n raise ex\",\n \"def run(self):\\n while self.time <= self.deadline:\\n self.setup_period()\\n self.execute_process(self.select_process())\\n\\n self.time += 1\",\n \"def do_command(command):\\n send_command(command)\\n # time.sleep(0.1) # may be required on slow machines\\n response = get_response()\\n print(\\\"Rcvd: <<< \\\" + response)\\n return response\",\n \"def _loop(self):\\n while True:\\n if GameLoop.getInstance()._cancelation_token==True:\\n break\\n self._update_signal.notify_all()\\n sleep(1/60)\",\n \"def run(self):\\n rate = rospy.Rate(self._run_rate)\\n while not rospy.is_shutdown():\\n try:\\n rate.sleep()\\n except:\\n break\",\n \"def background_catch_up():\\n while True:\\n time.sleep(interval)\\n s = 'http://{0}:{1}'.format(args.host, port)\\n req = urllib2.Request(s)\\n try:\\n response = urllib2.urlopen(req)\\n response.read()\\n except Exception as e:\\n pass\",\n \"def run(self):\\n threadPID = os.getpid()\\n index = 0\\n while True:\\n index += 1\\n if ((index * self.m_to) > 1):\\n #self.check4kill(threadPID)\\n index = 1\\n if(self.status == Modem.Status.IDLE or self.status == Modem.Status.BUSY2REQ):\\n r, e = self.conn.dataAvailable()\\n if(e):\\n break\\n if(r):\\n rx = self.recvCommand()\\n if (len(rx) == 0):\\n break\\n elif(self.status == Modem.Status.KILL):\\n break\\n sleep(self.m_to)\\n self.close()\\n print >>sys.stderr, 'Closing'\",\n \"def run(self):\\n self.read_from_serial()\",\n \"async def repeat(times : int, content='repeating...'):\\n for i in range(times):\\n await bot.say(content)\",\n \"def _thread_loop(self):\\n while not self.stop_thread.is_set():\\n # First, read a line\\n try:\\n line = self._read_line()\\n except EvseTimeoutError:\\n continue\\n # Then if the line is a status change, execute the callback\\n if line[:3] in ('ST ', '$ST'):\\n self.callback(states[int(line.split()[1], 16)])\\n # write_allowed is only cleared if the board has been reset ;\\n # in this case, we should wait 1 more second before executing\\n # commands in order for the board to finish booting.\\n if not self.write_allowed.is_set():\\n threading.Timer(1, self.write_allowed.set).start()\\n continue\\n # Do not write a new line if\\n # the previous one isn't read and is not old enough\\n previous_newline_age = 0\\n while (self.newline_available.is_set() and\\n previous_newline_age <= NEWLINE_MAX_AGE):\\n time.sleep(SYNC_SERIAL_TIMEOUT)\\n previous_newline_age += SYNC_SERIAL_TIMEOUT\\n # Write the new received line\\n self.newline = line\\n self.newline_available.set()\",\n \"def loop(self):\\n\\t\\twhile (self.quit == 0):\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tuserInput = raw_input(\\\"> \\\")\\n\\t\\t\\t\\tself.processCommand(userInput)\\n\\t\\t\\texcept EOFError:\\n\\t\\t\\t\\tsys.exit()\\n\\t\\t\\texcept KeyboardInterrupt:\\n\\t\\t\\t\\tsys.exit()\",\n \"def run(self):\\n # Start the thread to receive commands\\n self.command_input.start()\\n\\n # The next time to execute a state\\n self.next_time = time.time() + self.control_period\\n\\n # Run forever!\\n logger.debug(\\\"Starting\\\")\\n while True:\\n old_state = self.current_state\\n\\n # Wait for the next control cycle\\n if time.time() < self.next_time:\\n time.sleep(0.001)\\n continue\\n else: # Set the next execution time\\n self.next_time += self.control_period\\n\\n # Execute the state!\\n if self.current_state == LightSystem.IDLE_STATE:\\n self.idle_state()\\n elif self.current_state == LightSystem.MANUAL_STATE:\\n self.manual_state()\\n elif self.current_state == LightSystem.EXIT_STATE:\\n self.pi.stop()\\n return\\n\\n if old_state != self.current_state:\\n logger.debug(\\\"%s -> %s\\\"%(old_state, self.current_state))\",\n \"async def _main(self):\\n while True:\\n time.sleep(1)\",\n \"def run(self):\\n running = True\\n while running:\\n ################################### ARDUINO POLLING ###################################\\n try:\\n print(\\\"DEBUG: running\\\")\\n if self.debug == 'n':\\n # Arduino is connected\\n inp = self.arduino.read()\\n \\n else: \\n inp = str(input(\\\"Enter Packet, Enter Key to skip. Ex: (DATA&BUZZ)\\\\n\\\")) \\n\\n if inp:\\n self.socket.setblocking(True)\\n arduino_input_header, arduino_input = inp.split('&')\\n \\n if arduino_input_header == \\\"DATA\\\": \\n if arduino_input == \\\"BUZZ\\\":\\n # buzz the owner\\n self.buzz_subroutine()\\n continue \\n \\n else:\\n # a PIN was sent by the Arduino\\n self.pin_check_subroutine(arduino_input)\\n continue\\n \\n \\n ########################## LISTEN FOR COMMAND FROM SERVER #########################\\n else:\\n self.socket.settimeout(1) \\n \\n # check if a command was received\\n try:\\n cmd = self.socket.recv(4096)\\n\\n except:\\n print(\\\"DEBUG: receive timed out\\\")\\n continue\\n\\n else: \\n cmd_hdr, cmd_msg, cmd_sdr = self.parse_packet(cmd)\\n \\n if cmd_hdr == \\\"CMD\\\":\\n if cmd_msg == \\\"LOCK DOOR\\\": \\n # a remote lock was issued\\n \\n if self.debug == 'n':\\n # tell Arduino to lock the door\\n self.arduino.write(\\\"LD\\\")\\n\\n print(\\\"DEBUG: locking override finished\\\")\\n\\n elif cmd_msg == \\\"UNLOCK DOOR\\\":\\n # a remote unlock was issued\\n \\n if self.debug == 'n':\\n # tell Arduino to unlock the door\\n self.arduino.write(\\\"UD\\\")\\n\\n print(\\\"DEBUG: unlocking override finished\\\") \\n \\n except (KeyboardInterrupt, SystemExit):\\n self.socket.sendall(self.make_packet(\\\"CMD\\\", \\\"SHUTTING DOWN\\\"))\\n raise \\n \\n except Exception as e:\\n self.socket.sendall(self.create_log(sys.exc_info()))\",\n \"def run_command(self):\\r\\n self.update_settings()\\r\\n self.run = True\\r\\n self.pause = False\\r\\n if self.run_call is not None:\\r\\n self.wm.after(1, self.run_call)\",\n \"def run():\\n board = GoBoard(7)\\n con = GtpConnection(Gomoku(), board)\\n con.start_connection()\",\n \"async def repeat(ctx, times : int, content='repeating...'):\\n for i in range(times):\\n await bot.say(content)\",\n \"def run(self):\\n threading.Thread(target=self._get_output, daemon=True).start() # Run selected command\\n threading.Thread(target=self._cycle_loop, daemon=True).start() # Cycle between commands\\n\\n self._display_loop()\",\n \"async def repeat(self,ctx, times: int, content='repeating...'):\\n for i in range(times):\\n await ctx.send(content)\",\n \"def run(self):\\n self.running = True\\n dynamic_reconfigure.client.Client(\\\"dynamic_reconfigure_server\\\", timeout=5, config_callback=self.dynamic_reconfigure_callback)\\n rate = rospy.Rate(20)\\n while not rospy.is_shutdown() and self.running:\\n self.pub.publish(self.get_cmd_vel())\\n rate.sleep()\",\n \"def runloop(self, prompt=''):\\n if not self.connected(): return\\n\\n try:\\n self.start_anti_idle_timer()\\n\\n use_rawinput = True\\n try:\\n import readline\\n except ImportError:\\n use_rawinput = False\\n\\n while True:\\n if use_rawinput:\\n try:\\n ln = raw_input(prompt)\\n except EOFError:\\n break\\n else:\\n if prompt:\\n sys.stdout.write(prompt)\\n sys.stdout.flush()\\n ln = sys.stdin.readline()\\n if not len(ln):\\n break\\n\\n ln = ln.rstrip('\\\\r\\\\n')\\n if not ln:\\n continue\\n rc, res = self.run(ln)\\n if not rc or not self.connected():\\n break\\n for l in res:\\n sys.stdout.write(l + '\\\\n')\\n finally:\\n self.cancel_anti_idle_timer()\",\n \"def start(self):\\n while True:\\n import traceback\\n try:\\n command, args = self.parse_line(input(self.prompt))\\n if not command:\\n self.refresh()\\n if command == \\\"exit\\\" or command == \\\"quit\\\":\\n break\\n command_handler = self.get_command_handler(command)\\n command_handler(args)\\n self.refresh()\\n except KeyboardInterrupt:\\n print(\\\"\\\")\\n except AttributeError:\\n pass\\n except:\\n traceback.print_exc()\\n pass\",\n \"async def loop(self):\\n\\t\\twhile self.active:\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tawait self.process_data(await self.websocket.recv())\\n\\t\\t\\texcept exceptions.ClientError as e:\\n\\t\\t\\t\\tawait self.send(0, str(e))\\n\\t\\t\\texcept KeyboardInterrupt:\\n\\t\\t\\t\\tawait self.unregister()\",\n \"async def repeat(ctx, times: int, content='repeating...'):\\n for i in range(times):\\n await ctx.send(content)\",\n \"def loop_run(self):\\n super(TimerLoop, self).loop_run()\\n self.timer = self.cothread.Timer(self.timeout,\\n self.callback,\\n retrigger=True)\",\n \"def run():\\n board = SimpleGoBoard(7)\\n con = GtpConnection(Gomoku4(), board)\\n con.start_connection()\",\n \"def start(self):\\n\\n while True:\\n measurement = self.generate_message()\\n measurement.save()\\n print(\\\"Storing new measurement\\\")\\n time.sleep(10)\",\n \"def run_checks():\\n while True:\\n if datetime.now() > core.misc_data.check_date+timedelta(minutes=45):\\n for stuff in stuff_to_do:\\n threading.Thread(target=stuff).start()\\n core.misc_data.check_date = datetime.now() + config.utils.tasks.repeat_every\\n time.sleep(5*60*60)\",\n \"def run(self):\\n while True:\\n self.connect_lock.acquire()\\n if self.stopped():\\n return\\n self.__connect()\\n self.connect_lock.release()\\n self.ws.run_forever()\",\n \"def run(self):\\n while True:\\n try:\\n command_requests = self._read_request_commands()\\n responses = self._command_executor.execute_commands(command_requests)\\n self._send_response(\\n CommandResponsesBuilder.to_string(CommandResponsesBuilder.build_xml_result(responses)))\\n except ConnectionClosedException:\\n self._logger.debug('Connection closed')\\n break\\n except Exception as ex:\\n self._send_response(\\n CommandResponsesBuilder.to_string(CommandResponsesBuilder.build_xml_error(0, ex.message)))\\n tb = traceback.format_exc()\\n self._logger.critical(tb)\\n self._connection_socket.close()\\n raise\",\n \"async def tick(self):\\n room = self.bot.Room.load('19961884194@chatroom')\\n await room.ready()\\n await room.say(f'i love you -> {datetime.now()}')\",\n \"def run():\\n adapter = pygatt.GATTToolBackend()\\n ADDRESS_TYPE = pygatt.BLEAddressType.random\\n\\n # TODO if a thread is killed then this will never reestablish a new one since connections never has elements removed\\n while True:\\n try:\\n for device in adapter.scan():\\n if device[\\\"name\\\"] == \\\"DYP Hydrometer\\\":\\n print(\\\"NEW HYDROMETER FOUND\\\")\\n t = threading.Thread(target=handle_connection, args=(device[\\\"address\\\"],))\\n t.start()\\n except:\\n pass\\n ptime.sleep(5)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.63576066","0.6166633","0.60652816","0.60415244","0.60114336","0.59595996","0.595747","0.5923307","0.5898725","0.58431983","0.58387035","0.58356047","0.5832241","0.56968737","0.5675272","0.56747895","0.56568396","0.56439304","0.562048","0.56196946","0.5619503","0.5618823","0.5618439","0.56009734","0.55994874","0.5590761","0.5589073","0.5580813","0.55764973","0.55510527","0.55410105","0.5537417","0.5534441","0.55318326","0.5520385","0.55189586","0.55167633","0.55055875","0.5504661","0.5503521","0.55016637","0.54987454","0.5498296","0.5497735","0.5483031","0.54753137","0.5474792","0.5460013","0.54559624","0.5449734","0.5448087","0.54392046","0.54333055","0.5425251","0.5422185","0.5418387","0.5417563","0.5417067","0.54101205","0.54085135","0.5407744","0.540514","0.54013395","0.53972286","0.5396542","0.53921473","0.53885585","0.53819895","0.5371945","0.53697544","0.53673047","0.53655785","0.53643703","0.53518146","0.5349242","0.53479844","0.53441477","0.5341585","0.53413504","0.533235","0.5329263","0.53263354","0.53260946","0.5321478","0.5319075","0.5310604","0.5305241","0.5304787","0.5300405","0.5295624","0.5295274","0.5290032","0.52883416","0.5283662","0.5280951","0.5272954","0.5264495","0.52637637","0.5263557","0.5252159"],"string":"[\n \"0.63576066\",\n \"0.6166633\",\n \"0.60652816\",\n \"0.60415244\",\n \"0.60114336\",\n \"0.59595996\",\n \"0.595747\",\n \"0.5923307\",\n \"0.5898725\",\n \"0.58431983\",\n \"0.58387035\",\n \"0.58356047\",\n \"0.5832241\",\n \"0.56968737\",\n \"0.5675272\",\n \"0.56747895\",\n \"0.56568396\",\n \"0.56439304\",\n \"0.562048\",\n \"0.56196946\",\n \"0.5619503\",\n \"0.5618823\",\n \"0.5618439\",\n \"0.56009734\",\n \"0.55994874\",\n \"0.5590761\",\n \"0.5589073\",\n \"0.5580813\",\n \"0.55764973\",\n \"0.55510527\",\n \"0.55410105\",\n \"0.5537417\",\n \"0.5534441\",\n \"0.55318326\",\n \"0.5520385\",\n \"0.55189586\",\n \"0.55167633\",\n \"0.55055875\",\n \"0.5504661\",\n \"0.5503521\",\n \"0.55016637\",\n \"0.54987454\",\n \"0.5498296\",\n \"0.5497735\",\n \"0.5483031\",\n \"0.54753137\",\n \"0.5474792\",\n \"0.5460013\",\n \"0.54559624\",\n \"0.5449734\",\n \"0.5448087\",\n \"0.54392046\",\n \"0.54333055\",\n \"0.5425251\",\n \"0.5422185\",\n \"0.5418387\",\n \"0.5417563\",\n \"0.5417067\",\n \"0.54101205\",\n \"0.54085135\",\n \"0.5407744\",\n \"0.540514\",\n \"0.54013395\",\n \"0.53972286\",\n \"0.5396542\",\n \"0.53921473\",\n \"0.53885585\",\n \"0.53819895\",\n \"0.5371945\",\n \"0.53697544\",\n \"0.53673047\",\n \"0.53655785\",\n \"0.53643703\",\n \"0.53518146\",\n \"0.5349242\",\n \"0.53479844\",\n \"0.53441477\",\n \"0.5341585\",\n \"0.53413504\",\n \"0.533235\",\n \"0.5329263\",\n \"0.53263354\",\n \"0.53260946\",\n \"0.5321478\",\n \"0.5319075\",\n \"0.5310604\",\n \"0.5305241\",\n \"0.5304787\",\n \"0.5300405\",\n \"0.5295624\",\n \"0.5295274\",\n \"0.5290032\",\n \"0.52883416\",\n \"0.5283662\",\n \"0.5280951\",\n \"0.5272954\",\n \"0.5264495\",\n \"0.52637637\",\n \"0.5263557\",\n \"0.5252159\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7657067"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":20079,"cells":{"query":{"kind":"string","value":"Set the variable as TRUE; it will stop computer waiting for response from tello."},"document":{"kind":"string","value":"def _setQuitWaitingFlag(self): \n self.quit_waiting_flag = True"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __bool__(self):\n return self.wait(0)","def _stop(self):\n return True","def kinbot(self):\n self.success = False","def stopCond(self):\n\t\treturn False","def stop(self):\n command = input(\"Enter anything to finish (or 'exit' to cancel)>>>\")\n return command != 'exit'","def poll(self):\n return False","def aeroLoopOff():\n return RoboCaller().call(\"aeroLoopOff\", \"void\")","def interrupt(self):\n return True","def check_response(ser_if): # TODO:timeout handling\n if str(ser_if.read()) == 'y':\n return True\n else:\n return False","def _checkTorcsServer(self):\n isRunning = False\n if self.torcsServerProcess is not None:\n if self.torcsServerProcess.poll() is None:\n isRunning = True\n return isRunning","def ignore_if_busy(self):\r\n if self.is_waiting_for_message():\r\n self.beep()\r\n return True\r\n return False","def __bool__(self):\n return not self._stopped","def request() -> None:\n\t_flag.set()","def ask_stop_game(self):\n return self.stop_game","def _keep_running():\n return True","def stop_locmut_download(self) -> None:\n self.wcstatus.set_busy(False)","def stop(self):\r\n self.running = False","def stop(self):\r\n self.running = False","def continue_or_kill_server(self):\n try:\n will_shut_down = self.service.shutdown_service_flag\n except AttributeError:\n print('did not define service shutdown flag in your service. Server cannot be closed with this flag')\n else:\n if will_shut_down:\n self.active = False\n self.close()","def send(self):\n return False","def _wait_lift_cmd(xbee):\n shared.status['command'] = 'STDBY'\n util.log_info(\"%s Standby, awaiting 'LIFT'.\" % shared.AGENT_ID)\n \n wait_count = 0\n while True:\n time.sleep(.1)\n wait_count = wait_count + 1\n \n if shared.status['command'] == 'LIFT':\n comm.xbee_broadcast(xbee, \"IFO,%s cleared for takeoff.\" % shared.AGENT_ID)\n util.log_info(\"'LIFT' received! Taking off!\")\n return True\n \n elif shared.status['command'] == 'EXIT':\n comm.xbee_broadcast(xbee, \"IFO,%s abort takeoff.\" % shared.AGENT_ID)\n util.log_info(\"'EXIT' received. Abort takeoff.\")\n return False\n \n elif wait_count >= 100:\n wait_count = 0\n comm.xbee_broadcast(xbee,\"IFO,%s standby. Alt: %.2f m.\" % (shared.AGENT_ID, shared.des_alt))","def stop() -> None:","def stop(self):\r\n self.terminating = True","def Shutdown(self):\n self.conn.send(False)\n self.process.join()","def stop(self):\n self.halt = True","def __remote_control_mode(self):\n while True:\n while True:\n if self.android_api.is_connect():\n break\n self.android_api.init_bluetooth()\n time.sleep(0.05)\n\n self.android_api.read_for_remote_control()\n time.sleep(0.05)","def wait_for_readability(self):\n return False","def send_stopping(self):\n self.stopped = True","def set_tty(self):\n try:\n # Increase the width of tty to enable long line grep in check_server()\n self.connector.setwinsize(24, 256)\n # echo default is False\n self.connector.setecho(False)\n return True\n except:\n error_log(\"set tty failed: exception type[%s], value[%s]\",\n sys.exc_info()[0], sys.exc_info()[1])\n return False","def nanny(self): \n while not self.started and not self.failed:\n eventlet.sleep(.1)\n return not self.failed","def stop_searching(self):\n self.searching = False\n self.receive_msg(\"No one is online :(\", keyboard=True)","def stop():\n global running\n global reading\n global zeroed\n if zeroed == False:\n time.sleep(1)\n xy_stage.reset_input_buffer();\n changeStatus('Zeroing')\n\n sendSerial(xy_stage, \"0lo0;0or;\\r\\n\");\n time.sleep(com_sleep);\n print(recSerial(xy_stage));\n time.sleep(com_sleep);\n sendSerial(xy_stage,\"0pr\"+str(start_x)+\";1pr\"+str(start_y)+\";\\r\\n\");\n time.sleep(com_sleep);\n print(recSerial(xy_stage));\n\n running = False\n reading = False\n zeroed = True\n changeStatus('Ready')","def exit(self):\n self._status = \"\"\n self._sock.settimeout(1.0)\n self._sock.sendto(bytes(\"bla\", \"utf-8\"), (self._cfg.host, self._cfg.port))","def lysis(self) :\n self.kill()\n return True","def halt(self):\n res = int(self._dll.JLINKARM_Halt())\n if res == 0:\n time.sleep(1)\n return True\n return False","def is_alive(self):\n self.ssh.sendline(\"clear\")\n return self.ssh.prompt()","def detect():\n try:\n s = serial.Serial(port = 0, baudrate = 19200, parity = 'O', timeout=1)\n except Exception, e:\n log = logging.getLogger('root')\n log.exception(e)\n return False\n else:\n return True\n finally:\n s.close()","def wait(self):\n\t\twhile True:\n\t\t\tr1 = self.zaberSend(self.translation[\"hor\"], self.cmd[\"returnStatus\"], data=0)\n\t\t\tr2 = self.zaberSend(self.translation[\"ver\"], self.cmd[\"returnStatus\"], data=0)\n\t\t\tif r1[2] == 0 and r2[2] == 0:\n\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\ttime.sleep(.01)","def stop(self):\n self.running = False","def stop(self):\n self.running = False","def stop(self):\n self.running = False","def stop(self):\n self.running = False","def stop(self):\n self.running = False","def do_stop(self):\n debug(\"CBA4.do_stop()\")\n if (self.__thread and self.__thread.isAlive()):\n self.__thread.stop()\n self.__thread.join(None)\n self.__thread = None\n\n if (self.is_valid()):\n tx = bytearray(16)\n tx[0] = 0x53\n tx[1] = 1\n self.get_status_response(tx)\n #end do_stop()","def _continue_running(self):\n if self._signal_recieved == signal.SIGINT:\n return False\n\n return True","def KeepAlive(self) -> bool:","def stopDetection(self):\n self.statusWrite(\"stop\")\n self.p.sleep()\n self.birdHere = 0","def on_timeout_cb(self):\n returncode = self.process.poll()\n if returncode is None:\n self.progress_bar.pulse()\n return True\n\n self.response(gtk.RESPONSE_ACCEPT)\n return False","def is_dialing(self) -> bool:","def unreliability(flag):\n test_str = \"unr3l14b13 p4ck3t!!!!! !!!~\\n\"\n server = start_server()\n client = start_client(flags=[flag, \"100\"])\n\n write_to(client, test_str)\n time.sleep(TEST_TIMEOUT)\n return read_from(server) == test_str","def ping_zybo(self, port_select):\n try:\n ctrl_comm_obj.open(port_select)\n except ValueError as e:\n return False\n if ctrl_comm_obj.isOpen() is True:\n ctrl_comm_obj.write(sig_serial.START_BYTE.value.encode('ascii'))\n ctrl_comm_obj.write(struct.pack('!B', enum_commands.ping.value))\n ctrl_comm_obj.write(sig_serial.END_BYTE.value.encode('ascii'))\n cnt = 0\n while True:\n if self.recieve_acknowlege_zybo(port_select):\n return True\n elif cnt > 10:\n return False\n else:\n t.sleep(0.1)\n cnt += 1\n # if ctrl_comm_obj.read_byte() == sig_serial.START_BYTE.value:\n # buffer = \"\"\n # byte_value = \"\"\n # if len(buffer) < 20:\n # while byte_value != sig_serial.END_BYTE.value:\n # byte_value = ctrl_comm_obj.read_byte()\n # if byte_value != sig_serial.END_BYTE.value:\n # buffer += byte_value\n # else:\n # print(\"Acknowledge was incorrect\")\n # return False\n # if buffer == 'ACK':\n # print(buffer)\n # return True\n # else:\n # print(\"Acknowledge was incorrect\")\n # return False\n # else:\n # pass\n else:\n return False","def should_terminate(self):\n return False","def sleep(self):\n for i in range(10):\n if cancelled: return False\n time.sleep(1)\n return True","def stop_speaking(self):\n self.ts.send_message(\"Okay, I'll shut up for a bit. !start_speaking when you want me to speak again.\")\n self.allowed_to_chat = False","def stop():","def stop():","def stop():","def stop():","def NoPrompt(self) -> bool:","def waiting(self) -> bool: # pylint: disable=W0221\n return True","def screen_terminate(self):\n self._channel.send('\\x04')\n # make sure we didn't kill our connection\n if self._channel.closed:\n self.connected = False\n print(\"!! Connection closed by remote host\")\n return False\n else:\n rdata = '\\n'.join(self.recv_to_prompt())\n return bool(re.search(r'screen is terminating', rdata, re.I|re.M))","def stop(self):\n self.stopped = True","def stop(self) -> bool:\n return self._sendcommand(STOP_KEY, self._retry_count)","def stop(self):\r\n self.stopped = True","def go_again(self):\n return False","def abort(self):\n if self.process:\n self.process.kill()\n return True\n else:\n return False","def check_stop_flag(con):\n k, v = con.kv.get(\"service/rebootmgr/stop\")\n if v:\n return True\n return False","def get_no_wait(self) -> bool:\n # read the original value passed by the command\n no_wait = self.raw_param.get(\"no_wait\")\n\n # this parameter does not need dynamic completion\n # this parameter does not need validation\n return no_wait","def status_callback():\n if args['retire_idle']:\n return False\n\n return True","def no():\r\n global RESPONSEOPTIONS\r\n if RESPONSEOPTIONS != []:\r\n RESPONSEOPTIONS.pop(0)\r\n else:\r\n print(\"RESPONSEOPTIONS - empty. connect a new state or reset\")","def ready_bool(ready):\r\n if ready.lower() == 'y':\r\n return True\r\n else:\r\n return False","def waitForCompletion(self):\n\n while(json.loads(self.robot.device())['state']!=0):\n time.sleep(0.1)\n continue\n\n return","def stop(self):\n self.idle = True\n # pass","def do(self):\n this_server = TangoServerHelper.get_instance()\n try:\n sdp_master_ln_fqdn = \"\"\n property_val = this_server.read_property(\"SdpMasterFQDN\")[0]\n sdp_master_ln_fqdn = sdp_master_ln_fqdn.join(property_val)\n sdp_mln_client_obj = TangoClient(sdp_master_ln_fqdn)\n sdp_mln_client_obj.send_command_async(\n const.CMD_Disable, None, self.disable_cmd_ended_cb\n )\n self.logger.debug(const.STR_DISABLE_CMS_SUCCESS)\n this_server.write_attr(\n \"activityMessage\", const.STR_DISABLE_CMS_SUCCESS, False\n )\n\n except DevFailed as dev_failed:\n self.logger.exception(dev_failed)\n log_msg = f\"{const.ERR_DISABLE_CMD_FAIL}{dev_failed}\"\n tango.Except.re_throw_exception(\n dev_failed,\n const.ERR_INVOKING_CMD,\n log_msg,\n \"SdpMasterLeafNode.DisableCommand()\",\n tango.ErrSeverity.ERR,\n )","def stop(self):\n self.running = False\n self.hop_channel(\"auto\")","def aeroLoopOn():\n return RoboCaller().call(\"aeroLoopOn\", \"void\")","def stop(self):\n \r\n self.__stopevent.set()\r\n\n return True","def poll(self):\r\n if self.channel.is_available():\r\n self.serve()\r\n return True\r\n else:\r\n return False","def stop_running(self):\n self.running = False","def terminate(self):\n self._running = False","def should_stop(self):\n return self._cmaes.should_stop()","def foreceStop(self):\n self.__success = False\n self.stop()","def stop(self):\r\n self.stopped = True\r\n time.sleep(1)","def abort(self):\n\n if self.process:\n self.process.kill()\n return True\n else:\n return False","def stop_telescope_move():\n\ttarget = send_command('stoptel')","def busy(self, flag, message=\"\"): \n return None","def should_poll(self):\r\n return False","def stop(self):\n self.alive = False\n if self.serial and hasattr(self.serial, 'cancel_read'):\n try:\n self.serial.cancel_read()\n except Exception as e:\n logger.exception(\"Error while stopping the serial thread\")","def stop(self):\n self.alive = False","def stop(self):\n self.alive = False","def is_done():\n return False","def _abort_if_false(ctx, param, value):\n if not value:\n ctx.abort()","def stop(self):\n if self.thread_state is True:\n os.system('qq stop')\n self.thread_state = False","def infinite_loop():\n return True","def socket_thread_stopped(self):\n self.done = True","def ShutDown(self):\n self.stop = True","def stop(self):\n self.ctrl_obj.finish = True","def stop(self):\n self.ctrl_obj.finish = True","def command_done(self):\n return self.read(\"*OPC?\") == \"1\"","def set_answer(self) -> None:\n if self.check_ans:\n self.check_ans = False\n else:\n self.check_ans = True"],"string":"[\n \"def __bool__(self):\\n return self.wait(0)\",\n \"def _stop(self):\\n return True\",\n \"def kinbot(self):\\n self.success = False\",\n \"def stopCond(self):\\n\\t\\treturn False\",\n \"def stop(self):\\n command = input(\\\"Enter anything to finish (or 'exit' to cancel)>>>\\\")\\n return command != 'exit'\",\n \"def poll(self):\\n return False\",\n \"def aeroLoopOff():\\n return RoboCaller().call(\\\"aeroLoopOff\\\", \\\"void\\\")\",\n \"def interrupt(self):\\n return True\",\n \"def check_response(ser_if): # TODO:timeout handling\\n if str(ser_if.read()) == 'y':\\n return True\\n else:\\n return False\",\n \"def _checkTorcsServer(self):\\n isRunning = False\\n if self.torcsServerProcess is not None:\\n if self.torcsServerProcess.poll() is None:\\n isRunning = True\\n return isRunning\",\n \"def ignore_if_busy(self):\\r\\n if self.is_waiting_for_message():\\r\\n self.beep()\\r\\n return True\\r\\n return False\",\n \"def __bool__(self):\\n return not self._stopped\",\n \"def request() -> None:\\n\\t_flag.set()\",\n \"def ask_stop_game(self):\\n return self.stop_game\",\n \"def _keep_running():\\n return True\",\n \"def stop_locmut_download(self) -> None:\\n self.wcstatus.set_busy(False)\",\n \"def stop(self):\\r\\n self.running = False\",\n \"def stop(self):\\r\\n self.running = False\",\n \"def continue_or_kill_server(self):\\n try:\\n will_shut_down = self.service.shutdown_service_flag\\n except AttributeError:\\n print('did not define service shutdown flag in your service. Server cannot be closed with this flag')\\n else:\\n if will_shut_down:\\n self.active = False\\n self.close()\",\n \"def send(self):\\n return False\",\n \"def _wait_lift_cmd(xbee):\\n shared.status['command'] = 'STDBY'\\n util.log_info(\\\"%s Standby, awaiting 'LIFT'.\\\" % shared.AGENT_ID)\\n \\n wait_count = 0\\n while True:\\n time.sleep(.1)\\n wait_count = wait_count + 1\\n \\n if shared.status['command'] == 'LIFT':\\n comm.xbee_broadcast(xbee, \\\"IFO,%s cleared for takeoff.\\\" % shared.AGENT_ID)\\n util.log_info(\\\"'LIFT' received! Taking off!\\\")\\n return True\\n \\n elif shared.status['command'] == 'EXIT':\\n comm.xbee_broadcast(xbee, \\\"IFO,%s abort takeoff.\\\" % shared.AGENT_ID)\\n util.log_info(\\\"'EXIT' received. Abort takeoff.\\\")\\n return False\\n \\n elif wait_count >= 100:\\n wait_count = 0\\n comm.xbee_broadcast(xbee,\\\"IFO,%s standby. Alt: %.2f m.\\\" % (shared.AGENT_ID, shared.des_alt))\",\n \"def stop() -> None:\",\n \"def stop(self):\\r\\n self.terminating = True\",\n \"def Shutdown(self):\\n self.conn.send(False)\\n self.process.join()\",\n \"def stop(self):\\n self.halt = True\",\n \"def __remote_control_mode(self):\\n while True:\\n while True:\\n if self.android_api.is_connect():\\n break\\n self.android_api.init_bluetooth()\\n time.sleep(0.05)\\n\\n self.android_api.read_for_remote_control()\\n time.sleep(0.05)\",\n \"def wait_for_readability(self):\\n return False\",\n \"def send_stopping(self):\\n self.stopped = True\",\n \"def set_tty(self):\\n try:\\n # Increase the width of tty to enable long line grep in check_server()\\n self.connector.setwinsize(24, 256)\\n # echo default is False\\n self.connector.setecho(False)\\n return True\\n except:\\n error_log(\\\"set tty failed: exception type[%s], value[%s]\\\",\\n sys.exc_info()[0], sys.exc_info()[1])\\n return False\",\n \"def nanny(self): \\n while not self.started and not self.failed:\\n eventlet.sleep(.1)\\n return not self.failed\",\n \"def stop_searching(self):\\n self.searching = False\\n self.receive_msg(\\\"No one is online :(\\\", keyboard=True)\",\n \"def stop():\\n global running\\n global reading\\n global zeroed\\n if zeroed == False:\\n time.sleep(1)\\n xy_stage.reset_input_buffer();\\n changeStatus('Zeroing')\\n\\n sendSerial(xy_stage, \\\"0lo0;0or;\\\\r\\\\n\\\");\\n time.sleep(com_sleep);\\n print(recSerial(xy_stage));\\n time.sleep(com_sleep);\\n sendSerial(xy_stage,\\\"0pr\\\"+str(start_x)+\\\";1pr\\\"+str(start_y)+\\\";\\\\r\\\\n\\\");\\n time.sleep(com_sleep);\\n print(recSerial(xy_stage));\\n\\n running = False\\n reading = False\\n zeroed = True\\n changeStatus('Ready')\",\n \"def exit(self):\\n self._status = \\\"\\\"\\n self._sock.settimeout(1.0)\\n self._sock.sendto(bytes(\\\"bla\\\", \\\"utf-8\\\"), (self._cfg.host, self._cfg.port))\",\n \"def lysis(self) :\\n self.kill()\\n return True\",\n \"def halt(self):\\n res = int(self._dll.JLINKARM_Halt())\\n if res == 0:\\n time.sleep(1)\\n return True\\n return False\",\n \"def is_alive(self):\\n self.ssh.sendline(\\\"clear\\\")\\n return self.ssh.prompt()\",\n \"def detect():\\n try:\\n s = serial.Serial(port = 0, baudrate = 19200, parity = 'O', timeout=1)\\n except Exception, e:\\n log = logging.getLogger('root')\\n log.exception(e)\\n return False\\n else:\\n return True\\n finally:\\n s.close()\",\n \"def wait(self):\\n\\t\\twhile True:\\n\\t\\t\\tr1 = self.zaberSend(self.translation[\\\"hor\\\"], self.cmd[\\\"returnStatus\\\"], data=0)\\n\\t\\t\\tr2 = self.zaberSend(self.translation[\\\"ver\\\"], self.cmd[\\\"returnStatus\\\"], data=0)\\n\\t\\t\\tif r1[2] == 0 and r2[2] == 0:\\n\\t\\t\\t\\tbreak\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttime.sleep(.01)\",\n \"def stop(self):\\n self.running = False\",\n \"def stop(self):\\n self.running = False\",\n \"def stop(self):\\n self.running = False\",\n \"def stop(self):\\n self.running = False\",\n \"def stop(self):\\n self.running = False\",\n \"def do_stop(self):\\n debug(\\\"CBA4.do_stop()\\\")\\n if (self.__thread and self.__thread.isAlive()):\\n self.__thread.stop()\\n self.__thread.join(None)\\n self.__thread = None\\n\\n if (self.is_valid()):\\n tx = bytearray(16)\\n tx[0] = 0x53\\n tx[1] = 1\\n self.get_status_response(tx)\\n #end do_stop()\",\n \"def _continue_running(self):\\n if self._signal_recieved == signal.SIGINT:\\n return False\\n\\n return True\",\n \"def KeepAlive(self) -> bool:\",\n \"def stopDetection(self):\\n self.statusWrite(\\\"stop\\\")\\n self.p.sleep()\\n self.birdHere = 0\",\n \"def on_timeout_cb(self):\\n returncode = self.process.poll()\\n if returncode is None:\\n self.progress_bar.pulse()\\n return True\\n\\n self.response(gtk.RESPONSE_ACCEPT)\\n return False\",\n \"def is_dialing(self) -> bool:\",\n \"def unreliability(flag):\\n test_str = \\\"unr3l14b13 p4ck3t!!!!! !!!~\\\\n\\\"\\n server = start_server()\\n client = start_client(flags=[flag, \\\"100\\\"])\\n\\n write_to(client, test_str)\\n time.sleep(TEST_TIMEOUT)\\n return read_from(server) == test_str\",\n \"def ping_zybo(self, port_select):\\n try:\\n ctrl_comm_obj.open(port_select)\\n except ValueError as e:\\n return False\\n if ctrl_comm_obj.isOpen() is True:\\n ctrl_comm_obj.write(sig_serial.START_BYTE.value.encode('ascii'))\\n ctrl_comm_obj.write(struct.pack('!B', enum_commands.ping.value))\\n ctrl_comm_obj.write(sig_serial.END_BYTE.value.encode('ascii'))\\n cnt = 0\\n while True:\\n if self.recieve_acknowlege_zybo(port_select):\\n return True\\n elif cnt > 10:\\n return False\\n else:\\n t.sleep(0.1)\\n cnt += 1\\n # if ctrl_comm_obj.read_byte() == sig_serial.START_BYTE.value:\\n # buffer = \\\"\\\"\\n # byte_value = \\\"\\\"\\n # if len(buffer) < 20:\\n # while byte_value != sig_serial.END_BYTE.value:\\n # byte_value = ctrl_comm_obj.read_byte()\\n # if byte_value != sig_serial.END_BYTE.value:\\n # buffer += byte_value\\n # else:\\n # print(\\\"Acknowledge was incorrect\\\")\\n # return False\\n # if buffer == 'ACK':\\n # print(buffer)\\n # return True\\n # else:\\n # print(\\\"Acknowledge was incorrect\\\")\\n # return False\\n # else:\\n # pass\\n else:\\n return False\",\n \"def should_terminate(self):\\n return False\",\n \"def sleep(self):\\n for i in range(10):\\n if cancelled: return False\\n time.sleep(1)\\n return True\",\n \"def stop_speaking(self):\\n self.ts.send_message(\\\"Okay, I'll shut up for a bit. !start_speaking when you want me to speak again.\\\")\\n self.allowed_to_chat = False\",\n \"def stop():\",\n \"def stop():\",\n \"def stop():\",\n \"def stop():\",\n \"def NoPrompt(self) -> bool:\",\n \"def waiting(self) -> bool: # pylint: disable=W0221\\n return True\",\n \"def screen_terminate(self):\\n self._channel.send('\\\\x04')\\n # make sure we didn't kill our connection\\n if self._channel.closed:\\n self.connected = False\\n print(\\\"!! Connection closed by remote host\\\")\\n return False\\n else:\\n rdata = '\\\\n'.join(self.recv_to_prompt())\\n return bool(re.search(r'screen is terminating', rdata, re.I|re.M))\",\n \"def stop(self):\\n self.stopped = True\",\n \"def stop(self) -> bool:\\n return self._sendcommand(STOP_KEY, self._retry_count)\",\n \"def stop(self):\\r\\n self.stopped = True\",\n \"def go_again(self):\\n return False\",\n \"def abort(self):\\n if self.process:\\n self.process.kill()\\n return True\\n else:\\n return False\",\n \"def check_stop_flag(con):\\n k, v = con.kv.get(\\\"service/rebootmgr/stop\\\")\\n if v:\\n return True\\n return False\",\n \"def get_no_wait(self) -> bool:\\n # read the original value passed by the command\\n no_wait = self.raw_param.get(\\\"no_wait\\\")\\n\\n # this parameter does not need dynamic completion\\n # this parameter does not need validation\\n return no_wait\",\n \"def status_callback():\\n if args['retire_idle']:\\n return False\\n\\n return True\",\n \"def no():\\r\\n global RESPONSEOPTIONS\\r\\n if RESPONSEOPTIONS != []:\\r\\n RESPONSEOPTIONS.pop(0)\\r\\n else:\\r\\n print(\\\"RESPONSEOPTIONS - empty. connect a new state or reset\\\")\",\n \"def ready_bool(ready):\\r\\n if ready.lower() == 'y':\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"def waitForCompletion(self):\\n\\n while(json.loads(self.robot.device())['state']!=0):\\n time.sleep(0.1)\\n continue\\n\\n return\",\n \"def stop(self):\\n self.idle = True\\n # pass\",\n \"def do(self):\\n this_server = TangoServerHelper.get_instance()\\n try:\\n sdp_master_ln_fqdn = \\\"\\\"\\n property_val = this_server.read_property(\\\"SdpMasterFQDN\\\")[0]\\n sdp_master_ln_fqdn = sdp_master_ln_fqdn.join(property_val)\\n sdp_mln_client_obj = TangoClient(sdp_master_ln_fqdn)\\n sdp_mln_client_obj.send_command_async(\\n const.CMD_Disable, None, self.disable_cmd_ended_cb\\n )\\n self.logger.debug(const.STR_DISABLE_CMS_SUCCESS)\\n this_server.write_attr(\\n \\\"activityMessage\\\", const.STR_DISABLE_CMS_SUCCESS, False\\n )\\n\\n except DevFailed as dev_failed:\\n self.logger.exception(dev_failed)\\n log_msg = f\\\"{const.ERR_DISABLE_CMD_FAIL}{dev_failed}\\\"\\n tango.Except.re_throw_exception(\\n dev_failed,\\n const.ERR_INVOKING_CMD,\\n log_msg,\\n \\\"SdpMasterLeafNode.DisableCommand()\\\",\\n tango.ErrSeverity.ERR,\\n )\",\n \"def stop(self):\\n self.running = False\\n self.hop_channel(\\\"auto\\\")\",\n \"def aeroLoopOn():\\n return RoboCaller().call(\\\"aeroLoopOn\\\", \\\"void\\\")\",\n \"def stop(self):\\n \\r\\n self.__stopevent.set()\\r\\n\\n return True\",\n \"def poll(self):\\r\\n if self.channel.is_available():\\r\\n self.serve()\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"def stop_running(self):\\n self.running = False\",\n \"def terminate(self):\\n self._running = False\",\n \"def should_stop(self):\\n return self._cmaes.should_stop()\",\n \"def foreceStop(self):\\n self.__success = False\\n self.stop()\",\n \"def stop(self):\\r\\n self.stopped = True\\r\\n time.sleep(1)\",\n \"def abort(self):\\n\\n if self.process:\\n self.process.kill()\\n return True\\n else:\\n return False\",\n \"def stop_telescope_move():\\n\\ttarget = send_command('stoptel')\",\n \"def busy(self, flag, message=\\\"\\\"): \\n return None\",\n \"def should_poll(self):\\r\\n return False\",\n \"def stop(self):\\n self.alive = False\\n if self.serial and hasattr(self.serial, 'cancel_read'):\\n try:\\n self.serial.cancel_read()\\n except Exception as e:\\n logger.exception(\\\"Error while stopping the serial thread\\\")\",\n \"def stop(self):\\n self.alive = False\",\n \"def stop(self):\\n self.alive = False\",\n \"def is_done():\\n return False\",\n \"def _abort_if_false(ctx, param, value):\\n if not value:\\n ctx.abort()\",\n \"def stop(self):\\n if self.thread_state is True:\\n os.system('qq stop')\\n self.thread_state = False\",\n \"def infinite_loop():\\n return True\",\n \"def socket_thread_stopped(self):\\n self.done = True\",\n \"def ShutDown(self):\\n self.stop = True\",\n \"def stop(self):\\n self.ctrl_obj.finish = True\",\n \"def stop(self):\\n self.ctrl_obj.finish = True\",\n \"def command_done(self):\\n return self.read(\\\"*OPC?\\\") == \\\"1\\\"\",\n \"def set_answer(self) -> None:\\n if self.check_ans:\\n self.check_ans = False\\n else:\\n self.check_ans = True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6232066","0.60531396","0.5993092","0.5933566","0.58755565","0.5861535","0.580857","0.5768193","0.57589114","0.57057345","0.5682822","0.5668132","0.56636673","0.5655856","0.56449413","0.56356215","0.5628359","0.5628359","0.56276935","0.56122077","0.5600596","0.5586086","0.5572357","0.55712974","0.55651027","0.5559199","0.55507493","0.5548737","0.5544946","0.55446947","0.55414057","0.5540812","0.55307436","0.55261046","0.551765","0.55126685","0.5505657","0.54997265","0.5484159","0.5484159","0.5484159","0.5484159","0.5484159","0.5477492","0.5467837","0.5465197","0.5458991","0.5454129","0.5452428","0.54486865","0.5446033","0.54378825","0.54215205","0.5419458","0.54192275","0.54192275","0.54192275","0.54192275","0.54106176","0.540196","0.5398427","0.53978413","0.5393279","0.5393106","0.5387398","0.5384241","0.5382286","0.5380377","0.5378866","0.53772104","0.5375574","0.53725624","0.5369863","0.53684264","0.53645235","0.53607386","0.5356032","0.5354199","0.53493845","0.53399634","0.5338569","0.53371114","0.5333607","0.5325106","0.53244025","0.532412","0.5318809","0.5315708","0.53139865","0.53139865","0.5308651","0.5307651","0.53062373","0.5304513","0.5302571","0.5301847","0.529771","0.529771","0.5296194","0.52917945"],"string":"[\n \"0.6232066\",\n \"0.60531396\",\n \"0.5993092\",\n \"0.5933566\",\n \"0.58755565\",\n \"0.5861535\",\n \"0.580857\",\n \"0.5768193\",\n \"0.57589114\",\n \"0.57057345\",\n \"0.5682822\",\n \"0.5668132\",\n \"0.56636673\",\n \"0.5655856\",\n \"0.56449413\",\n \"0.56356215\",\n \"0.5628359\",\n \"0.5628359\",\n \"0.56276935\",\n \"0.56122077\",\n \"0.5600596\",\n \"0.5586086\",\n \"0.5572357\",\n \"0.55712974\",\n \"0.55651027\",\n \"0.5559199\",\n \"0.55507493\",\n \"0.5548737\",\n \"0.5544946\",\n \"0.55446947\",\n \"0.55414057\",\n \"0.5540812\",\n \"0.55307436\",\n \"0.55261046\",\n \"0.551765\",\n \"0.55126685\",\n \"0.5505657\",\n \"0.54997265\",\n \"0.5484159\",\n \"0.5484159\",\n \"0.5484159\",\n \"0.5484159\",\n \"0.5484159\",\n \"0.5477492\",\n \"0.5467837\",\n \"0.5465197\",\n \"0.5458991\",\n \"0.5454129\",\n \"0.5452428\",\n \"0.54486865\",\n \"0.5446033\",\n \"0.54378825\",\n \"0.54215205\",\n \"0.5419458\",\n \"0.54192275\",\n \"0.54192275\",\n \"0.54192275\",\n \"0.54192275\",\n \"0.54106176\",\n \"0.540196\",\n \"0.5398427\",\n \"0.53978413\",\n \"0.5393279\",\n \"0.5393106\",\n \"0.5387398\",\n \"0.5384241\",\n \"0.5382286\",\n \"0.5380377\",\n \"0.5378866\",\n \"0.53772104\",\n \"0.5375574\",\n \"0.53725624\",\n \"0.5369863\",\n \"0.53684264\",\n \"0.53645235\",\n \"0.53607386\",\n \"0.5356032\",\n \"0.5354199\",\n \"0.53493845\",\n \"0.53399634\",\n \"0.5338569\",\n \"0.53371114\",\n \"0.5333607\",\n \"0.5325106\",\n \"0.53244025\",\n \"0.532412\",\n \"0.5318809\",\n \"0.5315708\",\n \"0.53139865\",\n \"0.53139865\",\n \"0.5308651\",\n \"0.5307651\",\n \"0.53062373\",\n \"0.5304513\",\n \"0.5302571\",\n \"0.5301847\",\n \"0.529771\",\n \"0.529771\",\n \"0.5296194\",\n \"0.52917945\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5679991"},"document_rank":{"kind":"string","value":"11"}}},{"rowIdx":20080,"cells":{"query":{"kind":"string","value":"Open the cmd window and initial all the button and text."},"document":{"kind":"string","value":"def openCmdWindow(self): \n panel = Toplevel(self.root)\n panel.wm_title('Command Panel')\n\n # create text input entry\n text0 = tki.Label(panel,\n text='This Controller map keyboard inputs to Tello control commands\\n'\n 'Adjust the trackbar to reset distance and degree parameter',\n font='Helvetica 10 bold'\n )\n text0.pack(side='top')\n\n text1 = tki.Label(panel, text=\n 'W - Move Tello Up\\t\\t\\tArrow Up - Move Tello Forward\\n'\n 'S - Move Tello Down\\t\\t\\tArrow Down - Move Tello Backward\\n'\n 'A - Rotate Tello Counter-Clockwise\\tArrow Left - Move Tello Left\\n'\n 'D - Rotate Tello Clockwise\\t\\tArrow Right - Move Tello Right',\n justify='left')\n text1.pack(side='top')\n\n self.btn_landing = tki.Button(\n panel, text='Land', relief='raised', command=self.telloLanding)\n self.btn_landing.pack(side='bottom', fill='both',\n expand='yes', padx=10, pady=5)\n\n self.btn_takeoff = tki.Button(\n panel, text='Takeoff', relief='raised', command=self.telloTakeOff)\n self.btn_takeoff.pack(side='bottom', fill='both',\n expand='yes', padx=10, pady=5)\n\n # binding arrow keys to drone control\n self.tmp_f = tki.Frame(panel, width=100, height=2)\n self.tmp_f.bind('<KeyPress-w>', self.on_keypress_w)\n self.tmp_f.bind('<KeyPress-s>', self.on_keypress_s)\n self.tmp_f.bind('<KeyPress-a>', self.on_keypress_a)\n self.tmp_f.bind('<KeyPress-d>', self.on_keypress_d)\n self.tmp_f.bind('<KeyPress-Up>', self.on_keypress_up)\n self.tmp_f.bind('<KeyPress-Down>', self.on_keypress_down)\n self.tmp_f.bind('<KeyPress-Left>', self.on_keypress_left)\n self.tmp_f.bind('<KeyPress-Right>', self.on_keypress_right)\n self.tmp_f.pack(side='bottom')\n self.tmp_f.focus_set()\n\n self.btn_landing = tki.Button(\n panel, text='Flip', relief='raised', command=self.openFlipWindow)\n self.btn_landing.pack(side='bottom', fill='both',\n expand='yes', padx=10, pady=5)\n\n self.distance_bar = Scale(panel, from_=0.02, to=5, tickinterval=0.01, \n digits=3, label='Distance(m)',\n resolution=0.01)\n self.distance_bar.set(0.2)\n self.distance_bar.pack(side='left')\n\n self.btn_distance = tki.Button(panel, text='Reset Distance', relief='raised',\n command=self.updateDistancebar,\n )\n self.btn_distance.pack(side='left', fill='both',\n expand='yes', padx=10, pady=5)\n\n self.degree_bar = Scale(panel, from_=1, to=360, tickinterval=10, label='Degree')\n self.degree_bar.set(30)\n self.degree_bar.pack(side='right')\n\n self.btn_distance = tki.Button(panel, text='Reset Degree', relief='raised', \n command=self.updateDegreebar)\n self.btn_distance.pack(side='right', fill='both',\n expand='yes', padx=10, pady=5)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def open(self):\n self.state = True\n self.mainwindow.sendMessage('a')\n print(\"opening \" + self.name)","def build_initial() :\r\n titleframe = T.Frame(ROOT)\r\n TITLE = T.Label(titleframe, text = \"Welcome to Microgp!\")\r\n var = T.StringVar()\r\n INSTRUCTIONS = T.Message(titleframe, textvariable = var, width = 100)\r\n var.set(\"By Erik and Sam\")\r\n instruct_b = T.Button(titleframe, text = \"Instructions\",\r\n command = get_instructions)\r\n instruct_b.pack(side = T.BOTTOM)\r\n TITLE.pack(side = T.TOP)\r\n INSTRUCTIONS.pack(side = T.BOTTOM)\r\n titleframe.pack()","def helpButton():\n nuke.tcl(\"start\", HELP_PAGE)","def open_launcher(self):\n vim.command('silent! botright split {0}'.format(self.name))\n self.setup_buffer()","def buttonPress(self, argv):\n self.entry.insert(END, argv)","def exec_init_cmd(self):\n\n sys.argv = ['-c']\n self.push(self.rc.c)","def __init__(self, master, text, command=None):\r\n Button.__init__(self, master=master, text=text, height=1, command=command)","def openTB1Settings(self):\n self.TB1_Window = QtWidgets.QDialog()\n self.TB1_ui = Ui_robotOneConfig()\n self.TB1_ui.setupUi(self.TB1_Window)\n self.TB1_Window.show()","def run_button(self):\r\n self.step = False # Clear step command\r\n self.is_pause = False\r\n self.run_command()","def open(self):\n self._command = \"open\"","def initGui(self):\n\n icon_path = ':/plugins/MCDM/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'MCDM'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def start(self):\n self.menu()","def display(self):\n\t\tprint('The button in the window was clicked!')","def focus_on(window):\n return Cmd(\"{}wincmd w\", window)","def start(self):\n print(\"*\"*20)\n print(\"*\" + \" \"*18 + \"*\")\n print(\"*\" + \" \"*4 + \"Connect 4X\" + \" \"*4 + \"*\")\n print(\"*\" + \" \" * 18 + \"*\")\n print(\"*\" * 20)\n print(\"\\nConsole Version 1.0.0\\n\")\n self.print_menu()\n self.get_input()","def startSelectedMode(self):\n selectedBtn = self.buttons_group.checkedButton()\n if selectedBtn is None:\n QMessageBox.information(self, 'Attention', 'Aucun mode selectionné.\\n'\n 'Vous devez choisir un mode avant de le lancer.')\n return\n\n user = self.mainwindow.currentUser\n try:\n widget = selectedBtn.constructor(user.get_mode(selectedBtn.id))\n except:\n widget = selectedBtn.constructor(None)\n widget.closeRequested.connect(self.closeWidget)\n\n self.mainwindow.setWindowTitle(\"Consmaster\" +\n ' [' + selectedBtn.text().replace('\\n', '') + ']')\n\n self.mainwindow.central_widget.addWidget(widget)\n self.mainwindow.central_widget.setCurrentWidget(widget)","def display_main(self):\n self.clear_terminal()\n self.main_menu()\n self.handle_selection_main()","def showGUI(self,**kwargs):\n self.baxter.menu.select(self.modes[0])","def startapp(self, command):\n e = self.emu\n e.alt(\"F2\")\n e.shortwait()\n e.clickat(self.screen.center)\n e.shortwait()\n e.type(command + \"\\n\")\n e.longwait()","def _doOpenTool(self):\n self._cmdOpenTool()","def __init__(self, win):\n Menu.__init__(self, ['START', 'QUIT'], FONT_LA_CASA, win)","def main_menu_for_testing():\n print(PROMPT_TEXT)","def do_activate(self):\n\n Gtk.Application.do_activate(self)\n self.initiate_plugins()\n self.other[\"menu_button\"].set_menu_model(self.prepare_menu())\n self.output_window.show_all()\n self.window.show_all()","def show(self):\n\n self.serial = self.parent.board.serial\n self.deiconify() # Show window\n self.visible = True\n\n self.input_entry.focus()\n\n self.start_repl()","def _open_window(self):\r\n\t\t# Creating the window\r\n\t\tself._window = Window(self, Locations.RESTAL)","def initGui(self):\n\n icon_path = ':/plugins/TMD/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Weather Today'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def initGui(self):\n\n icon_path = ':/plugins/save_attributes/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u''),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def open(self, wait=True):\n self.gripper.actuate(1, 1)","def launchHelpWindow(self):\r\n self.popup(\"Help\",HELP,geom=\"350x200\")","def start(self):\n self.print_separator()\n self.stdscr.box()\n\n box = curses.newwin(4, self.maxx-8, self.pad, self.pad)\n box.addstr(1,1,\"hello\")\n while self.running:\n # Enter the main program loop\n key = self.stdscr.getkey()\n for fn in [self.stdscr.clear,\n lambda: self.handle_key(key),\n self.update_xy,\n self.print_pattern,\n self.print_separator,\n self.stdscr.box,\n self.generate_menu_items,\n self.print_menu_items,\n self.print_current_selection,\n self.stdscr.refresh]:\n fn()","def CreateConsole(self):\n lc = launcher.TextFrame('title')\n return lc","def create_widgets(self):\n #create first button\n self.button1 = Button(self, text = \"Start\")\n self.button1.bind\n self.button1.grid()","def _open_editor(self, editcmd):\n subprocess.Popen(editcmd, shell=True).communicate()","def at_cmdset_creation(self):\n self.add(default_cmds.CmdLook())\n self.add(default_cmds.CmdSay())","def start(self):\n self.__main_window = Tk()\n self.__start_loading_window()","def initGui(self):\n\n icon_path = ':/plugins/Hybriddekning/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Hybriddekning'),\n callback=self.run,\n parent=self.iface.mainWindow())","def initGui(self):\n\n icon_path = ':/plugins/cctv_mapper/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'CCTV Mapper'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def initGui(self):\n\n icon_path = ':/plugins/OD/icon.png'\n self.add_action(\n icon_path,\n text='OD',\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def run_command(self):\r\n self.update_settings()\r\n self.run = True\r\n self.pause = False\r\n if self.run_call is not None:\r\n self.wm.after(1, self.run_call)","def runCLI(self):\n\t\tself.available_cmds['help'].__call__()\n\n\t\twhile True:\n\t\t\tcmd = input('--> Enter Cmd: ')\n\t\t\tprint(\"\\n\")\n\t\t\tcmd = cmd.split()\n\n\t\t\tif len(cmd) > 0 and cmd[0] in self.available_cmds:\n\t\t\t\tif len(cmd) >= 1:\n\t\t\t\t\targs = cmd[1:]\n\t\t\t\telse:\n\t\t\t\t\targs = []\n\n\t\t\t\tself.available_cmds[cmd[0]].__call__(args)","def prepare_command(self, arg=None):\n\n self.textwin.print_blank(0)\n self.textwin.win.addch(0, 0, ':')\n self.textwin.win.chgat(0, 1, 1, curses.A_STANDOUT)\n\n self.inp = True\n self.command_event.set()","def launch(**kwargs):\n logger.info('launch dream command')\n launch_gui()","def initGui(self):\n\n icon_path = ':/plugins/new_raptor/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Add New Raptor nest'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def quick_test():\n do_command('Help: Command=Help')\n do_command('Help: Command=\"GetInfo\"')\n #do_command('SetPreference: Name=GUI/Theme Value=classic Reload=1')","def start():\r\n window = loop_menuDolar()\r\n window.close()","def insert_start_button(note):\n txt_start_button = open(PATH_UTILS + 'run_selected_cells.py', 'r', encoding='utf-8').read()\n note['cells'] += [nb.v4.new_code_cell(txt_start_button)]\n note.cells[-1].metadata = {\"init_cell\": True}","def initGui(self):\n\n icon_path = ':/plugins/roadsalt_mapbuilder/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'&Road Salt Map Builder'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def initGui(self):\n\n icon_path = ':/plugins/Integracion/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u''),\n callback=self.run,\n parent=self.iface.mainWindow())","def initGui(self):\n\n\t\ticon_path = ':/plugins/main_index_extractor/icon.png'\n\t\tself.add_action(\n\t\t\ticon_path,\n\t\t\ttext=self.tr(u'Extract VI from Sentinel-2 data'),\n\t\t\tcallback=self.run,\n\t\t\tparent=self.iface.mainWindow())","def activateButtonClicked(self):\n print(\"trying to start process...\")\n subprocess.Popen(\"/usr/local/bin/g13d --config /usr/local/bin/defaults.bind\", shell=True)\n self.checkProcess()","def InitUI(self):\n\t\tself._initMenuBar()\n\t\tself._initLayout()\n\t\t\n\t\t# Bindings\n\t\tself.Bind(wx.EVT_BUTTON, self.OnButtonClicked)\n\t\t\n\t\t# We can't even start without an input file\n\t\tself.OnOpen(None)","def openTB3Settings(self):\n self.TB3_Window = QtWidgets.QDialog()\n self.TB3_ui = Ui_robotThreeConfig()\n self.TB3_ui.setupUi(self.TB3_Window)\n self.TB3_Window.show()","def __init__(self, content, start, space, size, selected=None):\n self.command = None\n self.selected = selected\n self.buttons = self.create_buttons(content, start, space, size)","def main():\n run_it = tools.Control(prepare.ORIGINAL_CAPTION)\n state_dict = {\"SPLASH\" : splash.Splash(),\n \"MENU\" : menu.Menu(),\n \"DEMO\" : demo.Demo(),\n \"GAME\" : game.Game()}\n run_it.setup_states(state_dict, \"SPLASH\")\n run_it.main()","def start(self):\n op = self.menu()\n self.opcoes(op)\n if op != \"q\" and op != \"w\":\n self.start()","def exec(self):\n self._root.after(100, self.change_state, States.INITIAL) # enter the state once gui is setup\n super().exec()","def start_preparation(self):\n from managers.buttons_manager import ButtonsManager\n ButtonsManager(self).begin()","def execPushButton(self):\n\t\t# verbose.detail(\"%s %s\" %(self.sender().objectName(), self.sender().property('exec')))\n\t\tprint(\"%s %s\" %(self.sender().objectName(), self.sender().property('exec')))","def open_gui():\n guiController.main()","def initGui(self):\n\n icon_path = ':/plugins/AreaPrinter/mountainIcon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'AreaPrinter'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n\tself.add_action(\n icon_path,\n text=self.tr(u'Grid Convergence'),\n callback=self.runTools,\n parent=self.iface.mainWindow())","def start_command_handler(update, context):\n add_typing(update, context)\n buttons = MultiItems(\"What would you like to receive?\", [\"Text\", \"File\", \"GoogleDoc\", \"Gallery\"])\n add_suggested_actions(update, context, buttons)","def show_gui():\n pass","def initDefaultCommand(self):\n pass","def init():\n unicornhathd.rotation(270)\n unicornhathd.brightness(1.0)\n\n stdscr = curses.initscr()\n curses.cbreak()\n curses.noecho()\n stdscr.nodelay(1)\n stdscr.keypad(1)\n\n return stdscr","def ask_custom_command(self):\n\n shell='Bash'\n if platform == 'win32':\n shell='Batch'\n self.manager.root.show_text_box_popup('Please Enter A {} Command:'.format(shell), self.handle_user_command)","def initGui(self):\n\n icon_path = ':/plugins/LidarProcessor/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'LiDAR Operations'),\n callback=self.run,\n parent=self.iface.mainWindow())","def main():\r\n root = tk.Tk()\r\n root.config(bg='gray40')\r\n root.geometry('800x400')\r\n # OpenUser('Christof', lambda: print('Hallo'), 'german')\r\n inter = User_Interface(root, 'Christof')\r\n inter.UserInterface.place(x=10, y=10)\r\n\r\n root.mainloop()","def menu_screen(win):\n\tpass","def setup(self):\n self.ui.setup_window()","def run_cmd(self):\r\n self.run = True","def set_edits(self):\n self._window.input_line.setPlaceholderText('Input item to import')\n self._window.output_text.setPlaceholderText('Import Item')","def openTB4Settings(self):\n self.TB4_Window = QtWidgets.QDialog()\n self.TB4_ui = Ui_robotFourConfig()\n self.TB4_ui.setupUi(self.TB4_Window)\n self.TB4_Window.show()","def browse( self ):\n Tk.Tk().withdraw()\n dirname = askdirectory()\n\n self.set_text( dirname )\n #rint( f\"get_text = {self.get_text()}\", flush = True )","def showUI(cls):\r\n win = cls()\r\n win.create()\r\n return win","def init_ui():\n\t# Use wrapper to set reasonable defaults and reset the terminal on exit\n\t#curses.wrapper(_main_loop)\n\tcurses.wrapper(_init_func)","def vp_start_gui():\n global val, w, root\n root = tk.Tk()\n plot_support.set_Tk_var()\n top = Toplevel1(root)\n plot_support.init(root, top)\n root.mainloop()","def opm_popup(opmvers, text, nrow):\n\n layout1 = [[sg.Multiline(text, size=(80, nrow), background_color='white', text_color='darkgreen')],\n [sg.CloseButton('OK')]]\n window1 = sg.Window('OPMRUN - Flow Job Scheduler ' + opmvers, layout=layout1)\n window1.Read()\n return ()","def __init__(self, root):\r\n self.root = root\r\n self.root.geometry(\"400x300\")\r\n \r\n self.button_rename = tk.Button(self.root, text = \"New window\",\r\n command= self.new_winF).pack()","def edit_command(self):\n porter = self._get_porter()\n command = porter.command(True)\n edit_command_dialog = EditCommandDialog(self)\n edit_command_dialog.command_edit.setPlainText(command)\n if edit_command_dialog.exec_():\n edited_command = edit_command_dialog.command_edit.toPlainText()\n self.run(edited_command)","def printMenu():\n # tWelc = PrettyTable(['Welcome to the CLI-of the repository classifier'])\n print('Welcome to the CLI of the repository classifier')\n print(strStopper1)\n t = PrettyTable(['Action', ' Shortcut '])\n t.add_row(['Show Menu', '- m -'])\n t.add_row([' Predict repositories form txt-file ', '- i -'])\n t.add_row(['Input URL', '- u -'])\n t.add_row(['Show Info', '- f -'])\n t.add_row(['Train Model', '- t -'])\n t.add_row(['set GitHub-Token', '- g -'])\n t.add_row(['Help', '- h -'])\n t.add_row(['Quit', '- q -'])\n print(t)\n print('')","def switch_state():\n\tDmg.OpenWindow()","def vp_start_gui():\n global top, app\n app = tk.Tk()\n ui_support.set_Tk_var()\n top = Window(app)\n top.price_panel.old_matrix_checkbox.select()\n top.price_panel.select_old()\n top.generic_panel.dynamic_stop_checkbox.select()\n top.generic_panel.select_dynamic()\n ui_support.init(app, top)\n app.mainloop()","def Run_command_window(argument):\n if os.name == 'posix':\n argument = argument.replace(\".exe\",\"\")\n os.system(argument)\n\n else:\n startupinfo = subprocess.STARTUPINFO()\n startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW\n\n process = subprocess.Popen(argument, startupinfo=startupinfo, stderr=subprocess.PIPE, stdout=subprocess.PIPE)\n process.wait()\n\n return()","def exec_script_and_interact(self):\r\n self.exec_script(set_focus=True)","def start_ui(self):\n\t\tself.start_animation()\n\t\tself.app.exec()","def openTB2Settings(self):\n self.TB2_Window = QtWidgets.QDialog()\n self.TB2_ui = Ui_robotTwoConfig()\n self.TB2_ui.setupUi(self.TB2_Window)\n self.TB2_Window.show()","def help_menu_about_activate(self, widget, data=None):\n print \"cmon\"\n button1 = gtk.Button(\"Press Me!\")\n self.fixed1.put(button1, 0, 0)\n button1.window.raise_()\n button1.show()\n button2 = gtk.Button(\"Prease Press Me!\")\n self.fixed1.put(button2, 380, 380)\n button2.show()\n button2.window.raise_()","def __init__(self):\n self.stdscr = curses.initscr()\n self.client = None\n self.max_y, self.max_x = self.stdscr.getmaxyx()\n self.chat_container = curses.newwin(self.max_y - 2, self.max_x, 1, 0)\n self.chat_win = self.chat_container.subwin(self.max_y - 3, self.max_x - 4, 2, 2)\n self.prompt_win = curses.newwin(1, self.max_x, self.max_y - 1, 0)\n self.setup()","def show(self, window):\r\n\r\n return","def tool_open_clicked(self, widget, data=None):\n self.open_chooser.show()","def start_execution(self):\n self.send_message(\"control.start\",None)","def onClick(self):\n self.app.setActiveMode(\"start\")","def _start_gui():\n main_window = MainWindow()\n main_window.mainloop()","def _init_ui(self):\r\n\t\t\r\n\t\tself.input_frame = Input(self)\r\n\t\tself.input_frame.pack()\r\n\t\t\r\n\t\tbutton_ok = Button(self, text = \"Ping\", command = self._go)\r\n\t\tbutton_ok.pack()\r\n\t\t\r\n\t\tself.result_frame = Result(self)\r\n\t\tself.result_frame.pack()","def start(self) -> None:\n self.execute_startup_menu()\n self.execute_main_menu()","def setup(self):\n curses.curs_set(1)\n curses.noecho()\n curses.cbreak()\n # Keypad disabled until scrolling properly implemented\n # self.stdscr.keypad(True)\n self.stdscr.clear()\n self.stdscr.addstr(\"SecureChat v{}\".format(__version__))\n self.chat_container.box()\n self.chat_win.addstr(\"Welcome to SecureChat!\")\n self.chat_win.scrollok(True)\n self.chat_win.setscrreg(0, self.max_y - 5)\n self.prompt_win.addstr(\"> \")\n self.refresh_all()","def open_win(e) -> None:\n widget_sfx()\n r, g, b = parse_color(var.get())\n new_color, tk_color = askcolor(\n color=f\"#{r:02x}{g:02x}{b:02x}\",\n parent=parent.winfo_toplevel(),\n title=str(TRANS_SELECT_TITLE),\n )\n if new_color is not None:\n # On 3.8, these are floats.\n rf, gf, bf = new_color\n var.set(f'{int(rf)} {int(gf)} {int(bf)}')","def __init__(self, win):\n Menu.__init__(self, None, FONT_ADV, win)","def show_main_buttons(self):\n pass","def setBtns(self):\n w = Button(self.root, text=\"Login\", width=10, command=self.loginCallback, default=ACTIVE)\n w.pack(side=LEFT, padx=5, pady=5)\n w = Button(self.root, text=\"New User\", width=10, command=self.newUserCallback)\n w.pack(side=LEFT, padx=5, pady=5)\n self.root.wait_window()"],"string":"[\n \"def open(self):\\n self.state = True\\n self.mainwindow.sendMessage('a')\\n print(\\\"opening \\\" + self.name)\",\n \"def build_initial() :\\r\\n titleframe = T.Frame(ROOT)\\r\\n TITLE = T.Label(titleframe, text = \\\"Welcome to Microgp!\\\")\\r\\n var = T.StringVar()\\r\\n INSTRUCTIONS = T.Message(titleframe, textvariable = var, width = 100)\\r\\n var.set(\\\"By Erik and Sam\\\")\\r\\n instruct_b = T.Button(titleframe, text = \\\"Instructions\\\",\\r\\n command = get_instructions)\\r\\n instruct_b.pack(side = T.BOTTOM)\\r\\n TITLE.pack(side = T.TOP)\\r\\n INSTRUCTIONS.pack(side = T.BOTTOM)\\r\\n titleframe.pack()\",\n \"def helpButton():\\n nuke.tcl(\\\"start\\\", HELP_PAGE)\",\n \"def open_launcher(self):\\n vim.command('silent! botright split {0}'.format(self.name))\\n self.setup_buffer()\",\n \"def buttonPress(self, argv):\\n self.entry.insert(END, argv)\",\n \"def exec_init_cmd(self):\\n\\n sys.argv = ['-c']\\n self.push(self.rc.c)\",\n \"def __init__(self, master, text, command=None):\\r\\n Button.__init__(self, master=master, text=text, height=1, command=command)\",\n \"def openTB1Settings(self):\\n self.TB1_Window = QtWidgets.QDialog()\\n self.TB1_ui = Ui_robotOneConfig()\\n self.TB1_ui.setupUi(self.TB1_Window)\\n self.TB1_Window.show()\",\n \"def run_button(self):\\r\\n self.step = False # Clear step command\\r\\n self.is_pause = False\\r\\n self.run_command()\",\n \"def open(self):\\n self._command = \\\"open\\\"\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/MCDM/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'MCDM'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def start(self):\\n self.menu()\",\n \"def display(self):\\n\\t\\tprint('The button in the window was clicked!')\",\n \"def focus_on(window):\\n return Cmd(\\\"{}wincmd w\\\", window)\",\n \"def start(self):\\n print(\\\"*\\\"*20)\\n print(\\\"*\\\" + \\\" \\\"*18 + \\\"*\\\")\\n print(\\\"*\\\" + \\\" \\\"*4 + \\\"Connect 4X\\\" + \\\" \\\"*4 + \\\"*\\\")\\n print(\\\"*\\\" + \\\" \\\" * 18 + \\\"*\\\")\\n print(\\\"*\\\" * 20)\\n print(\\\"\\\\nConsole Version 1.0.0\\\\n\\\")\\n self.print_menu()\\n self.get_input()\",\n \"def startSelectedMode(self):\\n selectedBtn = self.buttons_group.checkedButton()\\n if selectedBtn is None:\\n QMessageBox.information(self, 'Attention', 'Aucun mode selectionné.\\\\n'\\n 'Vous devez choisir un mode avant de le lancer.')\\n return\\n\\n user = self.mainwindow.currentUser\\n try:\\n widget = selectedBtn.constructor(user.get_mode(selectedBtn.id))\\n except:\\n widget = selectedBtn.constructor(None)\\n widget.closeRequested.connect(self.closeWidget)\\n\\n self.mainwindow.setWindowTitle(\\\"Consmaster\\\" +\\n ' [' + selectedBtn.text().replace('\\\\n', '') + ']')\\n\\n self.mainwindow.central_widget.addWidget(widget)\\n self.mainwindow.central_widget.setCurrentWidget(widget)\",\n \"def display_main(self):\\n self.clear_terminal()\\n self.main_menu()\\n self.handle_selection_main()\",\n \"def showGUI(self,**kwargs):\\n self.baxter.menu.select(self.modes[0])\",\n \"def startapp(self, command):\\n e = self.emu\\n e.alt(\\\"F2\\\")\\n e.shortwait()\\n e.clickat(self.screen.center)\\n e.shortwait()\\n e.type(command + \\\"\\\\n\\\")\\n e.longwait()\",\n \"def _doOpenTool(self):\\n self._cmdOpenTool()\",\n \"def __init__(self, win):\\n Menu.__init__(self, ['START', 'QUIT'], FONT_LA_CASA, win)\",\n \"def main_menu_for_testing():\\n print(PROMPT_TEXT)\",\n \"def do_activate(self):\\n\\n Gtk.Application.do_activate(self)\\n self.initiate_plugins()\\n self.other[\\\"menu_button\\\"].set_menu_model(self.prepare_menu())\\n self.output_window.show_all()\\n self.window.show_all()\",\n \"def show(self):\\n\\n self.serial = self.parent.board.serial\\n self.deiconify() # Show window\\n self.visible = True\\n\\n self.input_entry.focus()\\n\\n self.start_repl()\",\n \"def _open_window(self):\\r\\n\\t\\t# Creating the window\\r\\n\\t\\tself._window = Window(self, Locations.RESTAL)\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/TMD/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Weather Today'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/save_attributes/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u''),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def open(self, wait=True):\\n self.gripper.actuate(1, 1)\",\n \"def launchHelpWindow(self):\\r\\n self.popup(\\\"Help\\\",HELP,geom=\\\"350x200\\\")\",\n \"def start(self):\\n self.print_separator()\\n self.stdscr.box()\\n\\n box = curses.newwin(4, self.maxx-8, self.pad, self.pad)\\n box.addstr(1,1,\\\"hello\\\")\\n while self.running:\\n # Enter the main program loop\\n key = self.stdscr.getkey()\\n for fn in [self.stdscr.clear,\\n lambda: self.handle_key(key),\\n self.update_xy,\\n self.print_pattern,\\n self.print_separator,\\n self.stdscr.box,\\n self.generate_menu_items,\\n self.print_menu_items,\\n self.print_current_selection,\\n self.stdscr.refresh]:\\n fn()\",\n \"def CreateConsole(self):\\n lc = launcher.TextFrame('title')\\n return lc\",\n \"def create_widgets(self):\\n #create first button\\n self.button1 = Button(self, text = \\\"Start\\\")\\n self.button1.bind\\n self.button1.grid()\",\n \"def _open_editor(self, editcmd):\\n subprocess.Popen(editcmd, shell=True).communicate()\",\n \"def at_cmdset_creation(self):\\n self.add(default_cmds.CmdLook())\\n self.add(default_cmds.CmdSay())\",\n \"def start(self):\\n self.__main_window = Tk()\\n self.__start_loading_window()\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/Hybriddekning/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Hybriddekning'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/cctv_mapper/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'CCTV Mapper'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/OD/icon.png'\\n self.add_action(\\n icon_path,\\n text='OD',\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def run_command(self):\\r\\n self.update_settings()\\r\\n self.run = True\\r\\n self.pause = False\\r\\n if self.run_call is not None:\\r\\n self.wm.after(1, self.run_call)\",\n \"def runCLI(self):\\n\\t\\tself.available_cmds['help'].__call__()\\n\\n\\t\\twhile True:\\n\\t\\t\\tcmd = input('--> Enter Cmd: ')\\n\\t\\t\\tprint(\\\"\\\\n\\\")\\n\\t\\t\\tcmd = cmd.split()\\n\\n\\t\\t\\tif len(cmd) > 0 and cmd[0] in self.available_cmds:\\n\\t\\t\\t\\tif len(cmd) >= 1:\\n\\t\\t\\t\\t\\targs = cmd[1:]\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\targs = []\\n\\n\\t\\t\\t\\tself.available_cmds[cmd[0]].__call__(args)\",\n \"def prepare_command(self, arg=None):\\n\\n self.textwin.print_blank(0)\\n self.textwin.win.addch(0, 0, ':')\\n self.textwin.win.chgat(0, 1, 1, curses.A_STANDOUT)\\n\\n self.inp = True\\n self.command_event.set()\",\n \"def launch(**kwargs):\\n logger.info('launch dream command')\\n launch_gui()\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/new_raptor/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Add New Raptor nest'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def quick_test():\\n do_command('Help: Command=Help')\\n do_command('Help: Command=\\\"GetInfo\\\"')\\n #do_command('SetPreference: Name=GUI/Theme Value=classic Reload=1')\",\n \"def start():\\r\\n window = loop_menuDolar()\\r\\n window.close()\",\n \"def insert_start_button(note):\\n txt_start_button = open(PATH_UTILS + 'run_selected_cells.py', 'r', encoding='utf-8').read()\\n note['cells'] += [nb.v4.new_code_cell(txt_start_button)]\\n note.cells[-1].metadata = {\\\"init_cell\\\": True}\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/roadsalt_mapbuilder/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'&Road Salt Map Builder'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/Integracion/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u''),\\n callback=self.run,\\n parent=self.iface.mainWindow())\",\n \"def initGui(self):\\n\\n\\t\\ticon_path = ':/plugins/main_index_extractor/icon.png'\\n\\t\\tself.add_action(\\n\\t\\t\\ticon_path,\\n\\t\\t\\ttext=self.tr(u'Extract VI from Sentinel-2 data'),\\n\\t\\t\\tcallback=self.run,\\n\\t\\t\\tparent=self.iface.mainWindow())\",\n \"def activateButtonClicked(self):\\n print(\\\"trying to start process...\\\")\\n subprocess.Popen(\\\"/usr/local/bin/g13d --config /usr/local/bin/defaults.bind\\\", shell=True)\\n self.checkProcess()\",\n \"def InitUI(self):\\n\\t\\tself._initMenuBar()\\n\\t\\tself._initLayout()\\n\\t\\t\\n\\t\\t# Bindings\\n\\t\\tself.Bind(wx.EVT_BUTTON, self.OnButtonClicked)\\n\\t\\t\\n\\t\\t# We can't even start without an input file\\n\\t\\tself.OnOpen(None)\",\n \"def openTB3Settings(self):\\n self.TB3_Window = QtWidgets.QDialog()\\n self.TB3_ui = Ui_robotThreeConfig()\\n self.TB3_ui.setupUi(self.TB3_Window)\\n self.TB3_Window.show()\",\n \"def __init__(self, content, start, space, size, selected=None):\\n self.command = None\\n self.selected = selected\\n self.buttons = self.create_buttons(content, start, space, size)\",\n \"def main():\\n run_it = tools.Control(prepare.ORIGINAL_CAPTION)\\n state_dict = {\\\"SPLASH\\\" : splash.Splash(),\\n \\\"MENU\\\" : menu.Menu(),\\n \\\"DEMO\\\" : demo.Demo(),\\n \\\"GAME\\\" : game.Game()}\\n run_it.setup_states(state_dict, \\\"SPLASH\\\")\\n run_it.main()\",\n \"def start(self):\\n op = self.menu()\\n self.opcoes(op)\\n if op != \\\"q\\\" and op != \\\"w\\\":\\n self.start()\",\n \"def exec(self):\\n self._root.after(100, self.change_state, States.INITIAL) # enter the state once gui is setup\\n super().exec()\",\n \"def start_preparation(self):\\n from managers.buttons_manager import ButtonsManager\\n ButtonsManager(self).begin()\",\n \"def execPushButton(self):\\n\\t\\t# verbose.detail(\\\"%s %s\\\" %(self.sender().objectName(), self.sender().property('exec')))\\n\\t\\tprint(\\\"%s %s\\\" %(self.sender().objectName(), self.sender().property('exec')))\",\n \"def open_gui():\\n guiController.main()\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/AreaPrinter/mountainIcon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'AreaPrinter'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n\\tself.add_action(\\n icon_path,\\n text=self.tr(u'Grid Convergence'),\\n callback=self.runTools,\\n parent=self.iface.mainWindow())\",\n \"def start_command_handler(update, context):\\n add_typing(update, context)\\n buttons = MultiItems(\\\"What would you like to receive?\\\", [\\\"Text\\\", \\\"File\\\", \\\"GoogleDoc\\\", \\\"Gallery\\\"])\\n add_suggested_actions(update, context, buttons)\",\n \"def show_gui():\\n pass\",\n \"def initDefaultCommand(self):\\n pass\",\n \"def init():\\n unicornhathd.rotation(270)\\n unicornhathd.brightness(1.0)\\n\\n stdscr = curses.initscr()\\n curses.cbreak()\\n curses.noecho()\\n stdscr.nodelay(1)\\n stdscr.keypad(1)\\n\\n return stdscr\",\n \"def ask_custom_command(self):\\n\\n shell='Bash'\\n if platform == 'win32':\\n shell='Batch'\\n self.manager.root.show_text_box_popup('Please Enter A {} Command:'.format(shell), self.handle_user_command)\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/LidarProcessor/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'LiDAR Operations'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\",\n \"def main():\\r\\n root = tk.Tk()\\r\\n root.config(bg='gray40')\\r\\n root.geometry('800x400')\\r\\n # OpenUser('Christof', lambda: print('Hallo'), 'german')\\r\\n inter = User_Interface(root, 'Christof')\\r\\n inter.UserInterface.place(x=10, y=10)\\r\\n\\r\\n root.mainloop()\",\n \"def menu_screen(win):\\n\\tpass\",\n \"def setup(self):\\n self.ui.setup_window()\",\n \"def run_cmd(self):\\r\\n self.run = True\",\n \"def set_edits(self):\\n self._window.input_line.setPlaceholderText('Input item to import')\\n self._window.output_text.setPlaceholderText('Import Item')\",\n \"def openTB4Settings(self):\\n self.TB4_Window = QtWidgets.QDialog()\\n self.TB4_ui = Ui_robotFourConfig()\\n self.TB4_ui.setupUi(self.TB4_Window)\\n self.TB4_Window.show()\",\n \"def browse( self ):\\n Tk.Tk().withdraw()\\n dirname = askdirectory()\\n\\n self.set_text( dirname )\\n #rint( f\\\"get_text = {self.get_text()}\\\", flush = True )\",\n \"def showUI(cls):\\r\\n win = cls()\\r\\n win.create()\\r\\n return win\",\n \"def init_ui():\\n\\t# Use wrapper to set reasonable defaults and reset the terminal on exit\\n\\t#curses.wrapper(_main_loop)\\n\\tcurses.wrapper(_init_func)\",\n \"def vp_start_gui():\\n global val, w, root\\n root = tk.Tk()\\n plot_support.set_Tk_var()\\n top = Toplevel1(root)\\n plot_support.init(root, top)\\n root.mainloop()\",\n \"def opm_popup(opmvers, text, nrow):\\n\\n layout1 = [[sg.Multiline(text, size=(80, nrow), background_color='white', text_color='darkgreen')],\\n [sg.CloseButton('OK')]]\\n window1 = sg.Window('OPMRUN - Flow Job Scheduler ' + opmvers, layout=layout1)\\n window1.Read()\\n return ()\",\n \"def __init__(self, root):\\r\\n self.root = root\\r\\n self.root.geometry(\\\"400x300\\\")\\r\\n \\r\\n self.button_rename = tk.Button(self.root, text = \\\"New window\\\",\\r\\n command= self.new_winF).pack()\",\n \"def edit_command(self):\\n porter = self._get_porter()\\n command = porter.command(True)\\n edit_command_dialog = EditCommandDialog(self)\\n edit_command_dialog.command_edit.setPlainText(command)\\n if edit_command_dialog.exec_():\\n edited_command = edit_command_dialog.command_edit.toPlainText()\\n self.run(edited_command)\",\n \"def printMenu():\\n # tWelc = PrettyTable(['Welcome to the CLI-of the repository classifier'])\\n print('Welcome to the CLI of the repository classifier')\\n print(strStopper1)\\n t = PrettyTable(['Action', ' Shortcut '])\\n t.add_row(['Show Menu', '- m -'])\\n t.add_row([' Predict repositories form txt-file ', '- i -'])\\n t.add_row(['Input URL', '- u -'])\\n t.add_row(['Show Info', '- f -'])\\n t.add_row(['Train Model', '- t -'])\\n t.add_row(['set GitHub-Token', '- g -'])\\n t.add_row(['Help', '- h -'])\\n t.add_row(['Quit', '- q -'])\\n print(t)\\n print('')\",\n \"def switch_state():\\n\\tDmg.OpenWindow()\",\n \"def vp_start_gui():\\n global top, app\\n app = tk.Tk()\\n ui_support.set_Tk_var()\\n top = Window(app)\\n top.price_panel.old_matrix_checkbox.select()\\n top.price_panel.select_old()\\n top.generic_panel.dynamic_stop_checkbox.select()\\n top.generic_panel.select_dynamic()\\n ui_support.init(app, top)\\n app.mainloop()\",\n \"def Run_command_window(argument):\\n if os.name == 'posix':\\n argument = argument.replace(\\\".exe\\\",\\\"\\\")\\n os.system(argument)\\n\\n else:\\n startupinfo = subprocess.STARTUPINFO()\\n startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW\\n\\n process = subprocess.Popen(argument, startupinfo=startupinfo, stderr=subprocess.PIPE, stdout=subprocess.PIPE)\\n process.wait()\\n\\n return()\",\n \"def exec_script_and_interact(self):\\r\\n self.exec_script(set_focus=True)\",\n \"def start_ui(self):\\n\\t\\tself.start_animation()\\n\\t\\tself.app.exec()\",\n \"def openTB2Settings(self):\\n self.TB2_Window = QtWidgets.QDialog()\\n self.TB2_ui = Ui_robotTwoConfig()\\n self.TB2_ui.setupUi(self.TB2_Window)\\n self.TB2_Window.show()\",\n \"def help_menu_about_activate(self, widget, data=None):\\n print \\\"cmon\\\"\\n button1 = gtk.Button(\\\"Press Me!\\\")\\n self.fixed1.put(button1, 0, 0)\\n button1.window.raise_()\\n button1.show()\\n button2 = gtk.Button(\\\"Prease Press Me!\\\")\\n self.fixed1.put(button2, 380, 380)\\n button2.show()\\n button2.window.raise_()\",\n \"def __init__(self):\\n self.stdscr = curses.initscr()\\n self.client = None\\n self.max_y, self.max_x = self.stdscr.getmaxyx()\\n self.chat_container = curses.newwin(self.max_y - 2, self.max_x, 1, 0)\\n self.chat_win = self.chat_container.subwin(self.max_y - 3, self.max_x - 4, 2, 2)\\n self.prompt_win = curses.newwin(1, self.max_x, self.max_y - 1, 0)\\n self.setup()\",\n \"def show(self, window):\\r\\n\\r\\n return\",\n \"def tool_open_clicked(self, widget, data=None):\\n self.open_chooser.show()\",\n \"def start_execution(self):\\n self.send_message(\\\"control.start\\\",None)\",\n \"def onClick(self):\\n self.app.setActiveMode(\\\"start\\\")\",\n \"def _start_gui():\\n main_window = MainWindow()\\n main_window.mainloop()\",\n \"def _init_ui(self):\\r\\n\\t\\t\\r\\n\\t\\tself.input_frame = Input(self)\\r\\n\\t\\tself.input_frame.pack()\\r\\n\\t\\t\\r\\n\\t\\tbutton_ok = Button(self, text = \\\"Ping\\\", command = self._go)\\r\\n\\t\\tbutton_ok.pack()\\r\\n\\t\\t\\r\\n\\t\\tself.result_frame = Result(self)\\r\\n\\t\\tself.result_frame.pack()\",\n \"def start(self) -> None:\\n self.execute_startup_menu()\\n self.execute_main_menu()\",\n \"def setup(self):\\n curses.curs_set(1)\\n curses.noecho()\\n curses.cbreak()\\n # Keypad disabled until scrolling properly implemented\\n # self.stdscr.keypad(True)\\n self.stdscr.clear()\\n self.stdscr.addstr(\\\"SecureChat v{}\\\".format(__version__))\\n self.chat_container.box()\\n self.chat_win.addstr(\\\"Welcome to SecureChat!\\\")\\n self.chat_win.scrollok(True)\\n self.chat_win.setscrreg(0, self.max_y - 5)\\n self.prompt_win.addstr(\\\"> \\\")\\n self.refresh_all()\",\n \"def open_win(e) -> None:\\n widget_sfx()\\n r, g, b = parse_color(var.get())\\n new_color, tk_color = askcolor(\\n color=f\\\"#{r:02x}{g:02x}{b:02x}\\\",\\n parent=parent.winfo_toplevel(),\\n title=str(TRANS_SELECT_TITLE),\\n )\\n if new_color is not None:\\n # On 3.8, these are floats.\\n rf, gf, bf = new_color\\n var.set(f'{int(rf)} {int(gf)} {int(bf)}')\",\n \"def __init__(self, win):\\n Menu.__init__(self, None, FONT_ADV, win)\",\n \"def show_main_buttons(self):\\n pass\",\n \"def setBtns(self):\\n w = Button(self.root, text=\\\"Login\\\", width=10, command=self.loginCallback, default=ACTIVE)\\n w.pack(side=LEFT, padx=5, pady=5)\\n w = Button(self.root, text=\\\"New User\\\", width=10, command=self.newUserCallback)\\n w.pack(side=LEFT, padx=5, pady=5)\\n self.root.wait_window()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.61820495","0.61781216","0.6119948","0.60934174","0.60702914","0.5959592","0.5948371","0.5943641","0.593772","0.5910269","0.59019053","0.5892788","0.58925205","0.5891495","0.5889927","0.58737874","0.5858974","0.5835644","0.5834646","0.58337766","0.58289456","0.5808443","0.5792496","0.57748127","0.5762847","0.57525045","0.57489944","0.57435477","0.5724686","0.57101214","0.57068443","0.57057935","0.5705649","0.5701938","0.5691835","0.5682384","0.56727064","0.56725955","0.5662729","0.56608737","0.5655763","0.56466776","0.5635799","0.5621457","0.5616277","0.5615801","0.5614837","0.5613637","0.5610994","0.56104654","0.5610139","0.5600305","0.5594723","0.5592397","0.55913067","0.5584206","0.55824846","0.5574146","0.55646175","0.55608356","0.555829","0.5555443","0.5547986","0.554791","0.5546854","0.55384094","0.553713","0.55354005","0.55335575","0.5530968","0.5530633","0.5530479","0.5526426","0.55186456","0.5518426","0.5513076","0.55072856","0.55033576","0.5500829","0.54987687","0.54971147","0.5494537","0.54938287","0.54904735","0.5490092","0.5484306","0.54805845","0.5480164","0.547592","0.5473877","0.5467252","0.5465718","0.54646885","0.5464455","0.5454377","0.5443537","0.5440493","0.5440203","0.5437964","0.5437727"],"string":"[\n \"0.61820495\",\n \"0.61781216\",\n \"0.6119948\",\n \"0.60934174\",\n \"0.60702914\",\n \"0.5959592\",\n \"0.5948371\",\n \"0.5943641\",\n \"0.593772\",\n \"0.5910269\",\n \"0.59019053\",\n \"0.5892788\",\n \"0.58925205\",\n \"0.5891495\",\n \"0.5889927\",\n \"0.58737874\",\n \"0.5858974\",\n \"0.5835644\",\n \"0.5834646\",\n \"0.58337766\",\n \"0.58289456\",\n \"0.5808443\",\n \"0.5792496\",\n \"0.57748127\",\n \"0.5762847\",\n \"0.57525045\",\n \"0.57489944\",\n \"0.57435477\",\n \"0.5724686\",\n \"0.57101214\",\n \"0.57068443\",\n \"0.57057935\",\n \"0.5705649\",\n \"0.5701938\",\n \"0.5691835\",\n \"0.5682384\",\n \"0.56727064\",\n \"0.56725955\",\n \"0.5662729\",\n \"0.56608737\",\n \"0.5655763\",\n \"0.56466776\",\n \"0.5635799\",\n \"0.5621457\",\n \"0.5616277\",\n \"0.5615801\",\n \"0.5614837\",\n \"0.5613637\",\n \"0.5610994\",\n \"0.56104654\",\n \"0.5610139\",\n \"0.5600305\",\n \"0.5594723\",\n \"0.5592397\",\n \"0.55913067\",\n \"0.5584206\",\n \"0.55824846\",\n \"0.5574146\",\n \"0.55646175\",\n \"0.55608356\",\n \"0.555829\",\n \"0.5555443\",\n \"0.5547986\",\n \"0.554791\",\n \"0.5546854\",\n \"0.55384094\",\n \"0.553713\",\n \"0.55354005\",\n \"0.55335575\",\n \"0.5530968\",\n \"0.5530633\",\n \"0.5530479\",\n \"0.5526426\",\n \"0.55186456\",\n \"0.5518426\",\n \"0.5513076\",\n \"0.55072856\",\n \"0.55033576\",\n \"0.5500829\",\n \"0.54987687\",\n \"0.54971147\",\n \"0.5494537\",\n \"0.54938287\",\n \"0.54904735\",\n \"0.5490092\",\n \"0.5484306\",\n \"0.54805845\",\n \"0.5480164\",\n \"0.547592\",\n \"0.5473877\",\n \"0.5467252\",\n \"0.5465718\",\n \"0.54646885\",\n \"0.5464455\",\n \"0.5454377\",\n \"0.5443537\",\n \"0.5440493\",\n \"0.5440203\",\n \"0.5437964\",\n \"0.5437727\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.68487906"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":20081,"cells":{"query":{"kind":"string","value":"Open the flip window and initial all the button and text."},"document":{"kind":"string","value":"def openFlipWindow(self):\n panel = Toplevel(self.root)\n panel.wm_title('Gesture Recognition')\n\n self.btn_flipl = tki.Button(\n panel, text='Flip Left', relief='raised', command=self.telloFlip_l)\n self.btn_flipl.pack(side='bottom', fill='both',\n expand='yes', padx=10, pady=5)\n\n self.btn_flipr = tki.Button(\n panel, text='Flip Right', relief='raised', command=self.telloFlip_r)\n self.btn_flipr.pack(side='bottom', fill='both',\n expand='yes', padx=10, pady=5)\n\n self.btn_flipf = tki.Button(\n panel, text='Flip Forward', relief='raised', command=self.telloFlip_f)\n self.btn_flipf.pack(side='bottom', fill='both',\n expand='yes', padx=10, pady=5)\n\n self.btn_flipb = tki.Button(\n panel, text='Flip Backward', relief='raised', command=self.telloFlip_b)\n self.btn_flipb.pack(side='bottom', fill='both',\n expand='yes', padx=10, pady=5)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def switch_state():\n\tDmg.OpenWindow()","def show(self):\r\n self.wf.Show()","def show(self, window):\r\n\r\n return","def finish_render():\n get_window().static_display = True\n get_window().flip_count = 0\n get_window().flip()","def cb_main_window(self, event):\n self.main_frame.Show()","def show_window(self):\n self.show()","def show(self):\n self.Show()","def flip(self):","def debwt_window(self: object) -> None:\n if self.file:\n debwt_window = Toplevel(self)\n debwt_window.title(\"Reversing Burros-Wheeler Transform\")\n debwt_window.geometry(\"1000x1000\")\n debwt_window.configure(bg='#ebebeb')\n controller = BWDecoder(self.file)\n controller.decode()\n protocol = self.DeBW_output(controller)\n prot= list(protocol)\n\n reconstructed = [\"Step 2: Creating the Burros-Wheeler Matrix\" for n in range(len(prot) - 2)]\n\n names = (step for step in [\"Step 1 : Visualizing the sequence\",\n *reconstructed,\n \"Step 3: The original sequence is the one that has a $ sign as a last column\",\n \"Please refer to the main menu to select another sequence\"])\n\n self.step_by_step(debwt_window, iter(prot), names)\n self.program_output(debwt_window, controller.debwt_output)\n\n else:\n self.no_file_error()","def show(self, initial = 0):\n self.Show(1)\n# self.update_title()\n# print 'showing'\n if initial:\n self.initial_show()","def flip(self):\n \n if self.faceup:\n self.faceup = False\n else:\n self.faceup = True","def open(self):\n self.state = True\n self.mainwindow.sendMessage('a')\n print(\"opening \" + self.name)","def window(main):\r\n main.title(\"BinCryptor 1.0\")\r\n main.update_idletasks()\r\n width = main.winfo_width() #Width of the current screen\r\n height = main.winfo_height() #Height of the current screen\r\n x = (main.winfo_screenwidth() // 2) - (width // 2)\r\n y = (main.winfo_screenheight() // 2) - (height // 2)\r\n main.geometry(f'{width}x{height}+{x}+{y}') #Adjusts the height and width\r","def open_preferences(self, event):\n editPrefFrame = Single_pref(parent=self, ID=997)\n editPrefFrame.Centre()\n editPrefFrame.Show()\n editPrefFrame.ShowModal()\n editPrefFrame.Destroy()","def show(self):\n # * displays the window, after using either the iconify or the withdraw methods\n self.wm_deiconify()\n # * this method can be called after the event which needs to happen before the window event\n self.wait_window()","def start(self):\n self.delegate.start_preview(fullscreen=False, window = (350, 10, self.size[0] - 350, self.size[1] - 10))","def flip_faceup(self):\r\n self.faceup = True","def openPremade(self):\n dialog = PremadeFrame(self)\n if dialog.ShowModal() == wx.ID_OK:\n newClass = dialog.selectedClass\n newFrame = newClass(self)\n self.frames.append(newFrame)\n self.names.append(newFrame.experimentName)\n newFrame.Show()\n newFrame.Maximize()\n self.Show(False)","def buttonPress(self):\n if self.inPlay and not self.shown:\n self.configure(image = Tile.images[0])","def on_activate(self, caller):\n self.window = GameWindow()\n self.add_window(self.window)","def _open_window(self):\r\n\t\t# Creating the window\r\n\t\tself._window = Window(self, Locations.RESTAL)","def onClick(self):\n self.app.setActiveMode(\"start\")","def do_activate(self):\n\n Gtk.Application.do_activate(self)\n self.initiate_plugins()\n self.other[\"menu_button\"].set_menu_model(self.prepare_menu())\n self.output_window.show_all()\n self.window.show_all()","def open_generatorWindow(self):\n self.window = generatorWindow(self)\n self.hide()","def setHorizontalFlip(self, flag):\n\t\tself.flipHorizontally = flag","def selection_form():\n sg.theme('DarkBlue') \n layout = [\n [\n sg.Button(\"Encrypt Files\"),\n sg.Button(\"View Images\")\n ]\n ]\n \n window = sg.Window('Encrypted Image Viewer', layout)\n while True:\n event, values = window.read()\n if event == \"Exit\" or event == sg.WIN_CLOSED:\n sys.exit()\n if event == \"Encrypt Files\":\n window.close()\n encryption_form()\n \n if event == \"View Images\":\n window.close()\n image_viewer()","def setupWindow(self):\n\n\t\tself.main_menu_window = MenuFrame.MainMenuFrame(self.uiCoordinator)\n\t\tself.menu_window = self.main_menu_window._mf\n\t\tself.score_window = self.main_menu_window._hf\n\t\tself.instructions_window = self.main_menu_window._if\n\t\tself.menu_window.playButton.focus_set()","def display(self):\n\t\tprint('The button in the window was clicked!')","def start():\r\n window = loop_menuDolar()\r\n window.close()","def flip(self):\n if self.is_face_up:\n arcade.load_texture(self.back_file)\n self.is_face_up = False\n else:\n arcade.load_texture(self.face_file)\n self.is_face_up = True","def flip(self, xflip=True, yflip=False):\n self.drawer.flush()\n img = self.img\n if xflip: img = img.transpose(PIL.Image.FLIP_LEFT_RIGHT)\n if yflip: img = img.transpose(PIL.Image.FLIP_TOP_BOTTOM)\n self.img = img\n self.update_drawer_img()\n return self","def show(self):\n self.root.update()\n self.root.deiconify()","def initialize(window):\n MY.restart_button.location = window / 2","def show(self):\r\n\t\tself.frame.Show(True)","def open(self, wait=True):\n self.gripper.actuate(1, 1)","def open(self):\n windowFlags = self.getWindowFlags(self.settings)\n self.surface = pygame.display.set_mode(self._resolution, windowFlags)\n self._printVideoInfo(pygame.display.Info())\n logger.info(\"Initialized display with driver: \" + pygame.display.get_driver())\n\n self.surface.fill(self._skin.guiColor(\"Background\"))\n self._initializePanels(self._resolution, self._skin)\n pygame.display.flip()\n\n self._statusLoop.statusProvider = self.getStatusProvider(self.settings)","def open_transitWindow(self):\n self.window = surveyWindow(self, imaging=False)\n self.hide()","def MacReopenApp(self):\n self.BringWindowToFront()","def main():\n run_it = tools.Control(prepare.ORIGINAL_CAPTION)\n state_dict = {\"SPLASH\" : splash.Splash(),\n \"MENU\" : menu.Menu(),\n \"DEMO\" : demo.Demo(),\n \"GAME\" : game.Game()}\n run_it.setup_states(state_dict, \"SPLASH\")\n run_it.main()","def flip(self, mode='h'):\n # TODO: Implement the flip function. Remember to record the boolean values is_horizontal_flip and\n # is_vertical_flip.\n if mode == 'h':\n self.is_horizontal_flip = True\n self.x = np.flipud(self.x)\n elif mode == 'v':\n self.is_vertical_flip = True\n self.x = np.fliplr(self.x)\n else:\n self.is_vertical_flip = True\n self.is_horizontal_flip = True\n self.x = np.fliplr(self.x)\n self.x = np.flipud(self.x)\n # raise NotImplementedError\n #######################################################################\n # #\n # #\n # TODO: YOUR CODE HERE #\n # #\n # #\n #######################################################################","def __call__(self):\n self.show()","def openInstructions(self, e):\n\n\t\tif (not self.open_window):\n\t\t\tself.open_window = True\n\t\t\tself.instructions_window.close.focus_set()\n\t\t\tself.main_menu_window.liftFrame(self.instructions_window.instructions_frame)\n\t\telif (self.open_window):\n\t\t\tself.open_window = False\n\t\t\tself.menu_window.playButton.focus_set()\n\t\t\tself.main_menu_window.lowerFrame(self.instructions_window.instructions_frame)","def back_window(self):\n\n self.controller.set_new_model_running(False)\n self.controller.show_frame(\"MainWindow\")","def execute_app(self):\n\n self.root = Tk()\n self.root.geometry(f\"{vk_common.MAX_IMAGE_WIDTH}x{vk_common.MAX_IMAGE_HEIGHT}\")\n self.root.title(\"Лайкер\")\n\n self.f_source = Frame(self.root)\n self.f_source.pack(side=TOP)\n\n self.f_control = Frame(self.root)\n self.f_control.pack(side=TOP)\n\n self.f_image = Frame(self.root)\n self.f_image.pack(side=BOTTOM)\n\n vk_common.dprint(2, vk_common.func_name(), \"Список пользователей при входе: \", self.users)\n for number, user in enumerate(self.users, 1):\n set_button(self.f_source, str(number), self.select, number)\n\n set_button(self.f_control, \"Лайкнуть\", self.like, 1)\n set_button(self.f_control, \"ДизЛайкнуть\", self.dislike, 2)\n set_button(self.f_control, \"<<\", self.left, 3)\n set_button(self.f_control, \">>\", self.right, 4)\n\n self.top_id = 1\n self.select_user(1)\n\n self.root.focus_set()\n self.root.wm_state('zoomed')\n self.root.call('wm', 'attributes', '.', '-topmost', '1')\n self.root.mainloop()\n sleep(1/3)\n self.root.quit()","def show(self):\n self._window.show()","def flip_player(cls):\n cls.current_player = 'X' if cls.current_player == 'O' else 'O'\n\n cls.display_board()\n cls.prompt_player()","def ShowMe(self, event):\n self.Show(True)","def openTB3Settings(self):\n self.TB3_Window = QtWidgets.QDialog()\n self.TB3_ui = Ui_robotThreeConfig()\n self.TB3_ui.setupUi(self.TB3_Window)\n self.TB3_Window.show()","def huffcode_window(self: object) -> None:\n if self.file:\n huff_code_window = Toplevel(self)\n huff_code_window.title(\"Huffman coding\")\n huff_code_window.geometry(\"1000x1000\")\n huff_code_window.configure(bg='#ebebeb')\n names = (step for step in [\"Step 1 : Visualizing the sequence\",\n \"Step 2: Creating Huffman tree from sequence and calculating paths\",\n \"Step 3: Generating the binary sequence from paths and adding a padding\",\n \"Step 4: Coding the binary in 8-bits to unicode\",\n \"Step 5: Writing paths and unicode to an output file\",\n \"Please refer to the main menu to select another sequence\"])\n controller = HuffEncoder(self.file)\n controller.encode()\n protocol = self.Huff_output(controller)\n self.step_by_step(huff_code_window, protocol, names)\n self.program_output(huff_code_window, controller.huff_output)\n\n else:\n self.no_file_error()","def music():\n main_window.withdraw()\n window = Toplevel()\n music_gui = MusicGUI(window, main_window)","def run(self):\n self.window.show()\n Gtk.main()","def help_menu_about_activate(self, widget, data=None):\n print \"cmon\"\n button1 = gtk.Button(\"Press Me!\")\n self.fixed1.put(button1, 0, 0)\n button1.window.raise_()\n button1.show()\n button2 = gtk.Button(\"Prease Press Me!\")\n self.fixed1.put(button2, 380, 380)\n button2.show()\n button2.window.raise_()","def main(self):\n\n self.window.show_all()\n gtk.main()","def bwt_window(self: object) -> None:\n if self.file:\n bwt_window = Toplevel(self)\n bwt_window.title(\"Burros-Wheeler Transform\")\n bwt_window.geometry(\"1000x1000\")\n bwt_window.configure(bg='#ebebeb')\n controller = BWEncoder(self.file)\n controller.encode()\n protocol = self.BW_output(controller)\n prot= list(protocol)\n\n rots = [\"Step 2: Generating all rotations of the sequence\" for n in range(len(prot) - 3)]\n \n names = (step for step in [\"Step 1 : Visualizing the sequence\",\n *rots,\n \"Step 3: Creating the Burros-Wheeler matrix by sorting all rotations\",\n \"Step 4: The Burros-Wheeler transform is the last column of the matrix\",\n \"Please refer to the main menu to select another sequence\"])\n \n self.step_by_step(bwt_window, iter(prot), names)\n self.program_output(bwt_window, controller.bwt_output)\n\n else:\n self.no_file_error()","def show(self):\n self.wid.show()","def file_menu_open_activate(self, widget, data=None):\n self.open_chooser.show()","def comprar(self):\n self.new_window = tk.Toplevel(self.menu)\n Comprar(self.new_window)","def OpenMethod(self):\r\n stringName = self.saveEdit.text()\r\n ListFiles = os.listdir(\"Save\")\r\n realName = (\r\n (\"PVP_mode_\" if self.PVP else \"AI_mode_\")\r\n + stringName\r\n + (\"\" if stringName[-4:] == \".txt\" else \".txt\")\r\n )\r\n if realName in ListFiles:\r\n f = open(\"Save/\" + realName, \"r\")\r\n fen = f.readlines()[0]\r\n f.close()\r\n colorIA = 1 if self.PVP else (1 if stringName[0] == \"W\" else 2)\r\n self.WindowSave.close()\r\n MainWindow = QtWidgets.QMainWindow()\r\n if self.PVP:\r\n indFinName1 = 9\r\n while realName[indFinName1] != \"_\":\r\n indFinName1 += 1\r\n indFinName2 = indFinName1 + 1\r\n while realName[indFinName2] != \"_\":\r\n indFinName2 += 1\r\n self.windowAssoc.textW = realName[9:indFinName1]\r\n self.windowAssoc.textB = realName[indFinName1 + 1 : indFinName2]\r\n\r\n else:\r\n indFinNamePlayer = 10\r\n while realName[indFinNamePlayer] != \"_\":\r\n indFinNamePlayer += 1\r\n self.windowAssoc.textW = (\r\n \"AI\" if stringName[0] == \"B\" else realName[10:indFinNamePlayer]\r\n )\r\n self.windowAssoc.textB = (\r\n \"AI\" if stringName[0] == \"W\" else realName[10:indFinNamePlayer]\r\n )\r\n\r\n self.windowAssoc.InitialisationPerFEN(fen, colorIA)\r\n if self.IwAssoc != None:\r\n self.IwAssoc.MainWindow.close()\r\n self.windowAssoc.MainWindow.show()\r\n\r\n else:\r\n self.textlabel = \"Error - \" + (\r\n \" Save as :\" if self.FEN != None else \" Open : \"\r\n )\r\n self.saveEdit.clear()","def help_main():\n global help_window, my_iterator, iterable, canvas, forward_button, picture_lst, image\n my_iterator = iter(picture_lst)\n pill_image = Image.open(image_base)\n image = ImageTk.PhotoImage(pill_image)\n\n canvas = Canvas(help_window, width=700 + 15, height=490 + 15)\n canvas.create_image(10, 10, anchor=NW, image=image)\n\n canvas.place(x=170, y=10)\n\n forward_button = ttk.Button(help_window, text=\" Вперед \", command=forward)\n forward_button.place(x=910, y=250)\n help_window.mainloop()","def do_activate(self, *args, **kwargs):\n self.register_signals()\n self.perform_setup()\n assert self.main_window\n self.main_window.show()\n self.hold()","def showBasic(self):\n self.setWindowIcon(QIcon(self.icon))\n self.setWindowTitle(self.title)\n self.setGeometry(*self.posXY, *self.windowSize)\n self.show()","def start(self):\n # asserts preconditions are met\n #assert self.validGameSettings()\n\n #draws initial welcome screen\n #self._text = GLabel(text=\"Press 'S' to Play\")\n #self._text.draw(self.view)\n\n # initializing instance variables\n self.setState(STATE_INACTIVE)\n self.setWave(None)\n self.setText(None)\n self.lastkeys = 0 #ADD MORE ATTRIBUTES\n\n # draws iniital welcome screen\n self.welcomeScreen()","def show_window(self):\n self._window.grab_set()\n self._window.wait_window()","def build_initial() :\r\n titleframe = T.Frame(ROOT)\r\n TITLE = T.Label(titleframe, text = \"Welcome to Microgp!\")\r\n var = T.StringVar()\r\n INSTRUCTIONS = T.Message(titleframe, textvariable = var, width = 100)\r\n var.set(\"By Erik and Sam\")\r\n instruct_b = T.Button(titleframe, text = \"Instructions\",\r\n command = get_instructions)\r\n instruct_b.pack(side = T.BOTTOM)\r\n TITLE.pack(side = T.TOP)\r\n INSTRUCTIONS.pack(side = T.BOTTOM)\r\n titleframe.pack()","def file_menu_new_activate(self, widget, data=None):\n self.new_chooser.show()","def huffdecode_window(self: object) -> None:\n if self.file:\n huff_decode_window = Toplevel(self)\n huff_decode_window.title(\"Huffman decoding\")\n huff_decode_window.geometry(\"1000x1000\")\n huff_decode_window.configure(bg='#ebebeb')\n names = (step for step in [\"Step 1 : Visualizing the compressed sequence\",\n \"Step 2: Reading the header of the file that corresponds to Huffman codes that where created with the tree during compression\",\n \"Step 3: Separating the unicode sequence\",\n \"Step 4: Transforming the unicode sequence to binary using huffman codes in the header and stripping padding\",\n \"Step 5: The decompressed sequence : \",\n \"Please refer to the main menu to select another sequence\"])\n controller = HuffDecoder(self.file)\n controller.decode()\n protocol = self.deHuff_output(controller)\n self.step_by_step(huff_decode_window, protocol, names)\n self.program_output(huff_decode_window, controller.dehuffman_output)\n\n else:\n self.no_file_error()","def startSelectedMode(self):\n selectedBtn = self.buttons_group.checkedButton()\n if selectedBtn is None:\n QMessageBox.information(self, 'Attention', 'Aucun mode selectionné.\\n'\n 'Vous devez choisir un mode avant de le lancer.')\n return\n\n user = self.mainwindow.currentUser\n try:\n widget = selectedBtn.constructor(user.get_mode(selectedBtn.id))\n except:\n widget = selectedBtn.constructor(None)\n widget.closeRequested.connect(self.closeWidget)\n\n self.mainwindow.setWindowTitle(\"Consmaster\" +\n ' [' + selectedBtn.text().replace('\\n', '') + ']')\n\n self.mainwindow.central_widget.addWidget(widget)\n self.mainwindow.central_widget.setCurrentWidget(widget)","def state_preview_do(cfg, app, win, events):","def show_main_buttons(self):\n pass","def open(self):\r\n ATOM3Type.show(self, parent, topWindowParent)\r\n return ATOM3(topWindowParent, None , 0, 1, self)","def __init_window(self) -> pygame.Surface:\n pygame.display.set_caption(CAPTION)\n win = pygame.display.set_mode((WIDTH, HEIGHT))\n \n return win","def start(self):\n self.__main_window = Tk()\n self.__start_loading_window()","def launchSyncToolWindow(self):\r\n self.unbind()\r\n self.videoPlayer.pause()\r\n self.w_synctool = SyncToolWindow(self)","def run(self):\n self.ui['main_window'].widgets['main'].show_all()\n gtk.main()","def books():\n main_window.withdraw()\n window = Toplevel()\n books_gui = BookGUI(window, main_window)","def showSettings(self):\n self.c.show()","def createPrefWindow(self):\n if (not hasattr(self, \"pref_window\")):\n self.pref_window = PrefWindow(self, self.settings)\n self.pref_window.show()\n self.pref_window.button_apply.setEnabled(True)","def build_second_window():\r\n\r\n new_window = tk.Tk()\r\n windows.append(new_window)\r\n new_window.protocol(\"WM_DELETE_WINDOW\", new_round(new_window))\r\n\r\n ask = tk.Label(new_window, text='Would You Like To Play Again?', bg='Cyan')\r\n ask.pack(fill=tk.X)\r\n\r\n frame = tk.Frame(new_window)\r\n frame.pack()\r\n\r\n yes_button = tk.Button(frame, text='Yes', bg='green',\r\n command=new_round(new_window))\r\n yes_button.pack(side=tk.LEFT)\r\n\r\n no_button = tk.Button(frame, text='No', bg='red',\r\n command=close)\r\n no_button.pack(side=tk.LEFT)","def show(self,window):\n self.showFunctions(window)","def frame(self):\n self.run_command('frame')","def showUI(cls):\r\n win = cls()\r\n win.create()\r\n return win","def show():\n dialog = SkinIODialog(getMayaWindow())\n dialog.show()","def pass_click(self):\n son = tk.Toplevel(self)\n son.wm_title('Generator parolă')\n son.wm_resizable(width=False, height=False)\n\n if platform.system() != 'Windows':\n icon = tk.PhotoImage(file=utils.get_resource_path('images', 'icon.gif'))\n son.tk.call('wm', 'iconphoto', son._w, icon)\n else:\n son.wm_iconbitmap(utils.get_resource_path('images', 'icon.ico'))\n\n pop_up_icon_path = utils.get_resource_path('images', 'icon.gif')\n pop_up_icon = tk.PhotoImage(file=pop_up_icon_path)\n son.tk.call('wm', 'iconphoto', son._w, pop_up_icon)\n\n gen = passgenerator.PassGeneratorFrame(son)\n gen.grid()","def initialize(self):\n self._ui.img_name.setText('No files selected')\n self._ui.bt_next.setEnabled(False)\n self._ui.bt_prev.setEnabled(False)\n self._ui.bt_right.setEnabled(False)\n self._ui.bt_left.setEnabled(False)\n self._ui.gps_button.setEnabled(False)\n\n self._open_btn = QPushButton('Open File', self._ui.img_label)\n self.adjustSize()","def fullzip_window(self: object) -> None:\n if self.file:\n fullzip_window = Toplevel(self)\n fullzip_window.title(\"Burrow-Wheeler Transform + Huffman coding\")\n fullzip_window.geometry(\"1000x1000\")\n fullzip_window.configure(bg='#ebebeb')\n controller = FullEncoder(self.file)\n controller.full_zip()\n protocol = self.fullzip_output(controller)\n prot= list(protocol)\n\n rots = [\"Step 2: Generating all rotations of the sequence\" for n in range(len(prot) - 7)]\n names = (step for step in [\"Step 1 : Visualizing the sequence\",\n *rots,\n \"Step 3: Creating the Burros-Wheeler matrix by sorting all rotations\",\n \"Step 4: The Burros-Wheeler transform is the last column of the matrix\",\n \"Step 5: Creating Huffman tree from Burros-Wheeler transform and calculating paths\",\n \"Step 6: Generating the binary sequence from paths and adding a padding\",\n \"Step 7: Coding the binary in 8-bits to unicode\",\n \"Step 8: Writing paths and unicode to an output file\",\n \"Please refer to the main menu to select another sequence\"])\n\n self.step_by_step(fullzip_window, iter(prot), names)\n outputs = controller.bw_encoder.bwt_output + \\\n '\\n' + controller.huff_encoder.huff_output\n self.program_output(fullzip_window, outputs)\n\n else:\n self.no_file_error()","def window_info_toggle():\n window_info.hide() if window_info.showing else window_info.show()","def show(self):\n\n self.serial = self.parent.board.serial\n self.deiconify() # Show window\n self.visible = True\n\n self.input_entry.focus()\n\n self.start_repl()","def menu():\n ferme_fenetre()\n Menu()","def show(self):\n self.present(orientations=ORIENTATIONS)\n # launch a background thread\n # we can not use ui.in_background here\n # because some dialogs would not open anymoe\n thr = threading.Thread(target=self.show_messages)\n thr.daemon = True\n thr.start()","def toggle_display_mode(self):\n if self.main_window.windowState() == Qt.WindowFullScreen:\n self.main_window.showNormal()\n else:\n self.main_window.showFullScreen()\n # Keep the settings window active to prevent main window from\n # burying it\n self.settings_window.activateWindow()","def on_stateico_clicked(self, *a):\n\t\tself.window1.set_property('visible', True)\n\t\tself.stateico.set_visible(False)\n\t\tself.window1.present()","def window_front(self):\n if platform() == \"Darwin\":\n system(\"\"\"/usr/bin/osascript -e 'tell app \"Finder\" to set \\\n frontmost of process \"Python\" to true' \"\"\")","def __init__(self, master, corr_string, pygame_win=False):\n self.top = tk.Toplevel(master)\n self.top.attributes('-topmost', True)\n self.top.attributes('-topmost', False)\n self.top.geometry(\"+600+100\")\n self.corr_string = corr_string\n if pygame_win:\n pygame.display.update()","def main(self): \n self.window.hide_all()\n self.window.isHide = True\n \n gtk.main()","def show_gui():\n pass","def first_window_attempt():\n\n class InitialState(BaseState):\n \"\"\"\n Initial state for the SimpleGUI.\n \"\"\"\n\n def _on_enter(self, gui):\n \"\"\"\n Construct the buttons upon entering the state.\n\n :return:\n \"\"\"\n print(\"In initial state.\")\n\n '''Create label'''\n self.label = tk.Label(gui.root, text=\"First GUI\")\n self.label.pack()\n\n '''Create buttons'''\n gui.pack_button = tk.Button(gui.root,\n text=\"Buttons\",\n command=self.adjust_buttons(gui),\n font=Font(size=50))\n gui.pack_button.pack()\n gui.greet_button = tk.Button(gui.root,\n text=\"Greet\",\n command=self._greet,\n font=Font(size=50))\n gui.close_button = tk.Button(gui.root,\n text=\"Close\",\n command=gui.root.quit,\n font=Font(size=50))\n gui.update()\n\n\n def adjust_buttons(self, gui):\n \"\"\"\n Adjust the buttons.\n\n :return:\n \"\"\"\n def _adjust_buttons():\n print(\"\\tButton clicked.\")\n if gui.buttons_on.get():\n print(\"\\t\\tDetected buttons are on.\")\n self._remove_buttons(gui)\n else:\n print(\"\\t\\tDetected buttons are off.\")\n self._add_buttons(gui)\n return _adjust_buttons\n\n def _add_buttons(self, gui):\n \"\"\"\n Add buttons to the view.\n\n :return:\n \"\"\"\n gui.greet_button.pack()\n gui.close_button.pack()\n gui.buttons_on.set(True)\n\n def _remove_buttons(self, gui):\n \"\"\"\n Remove buttons from the view.\n\n :return:\n \"\"\"\n gui.greet_button.pack_forget()\n gui.close_button.pack_forget()\n gui.buttons_on.set(False)\n\n def _greet(self, gui):\n \"\"\"\n\n :param gui:\n :return:\n \"\"\"\n\n def _on_exit(self, gui):\n \"\"\"\n Return the next state.\n\n :param gui:\n :return:\n \"\"\"\n gui.update()\n return ButtonsOff()\n\n class ButtonsOn(BaseState):\n \"\"\"\n State for having buttons on.\n \"\"\"\n def _on_enter(self, gui):\n \"\"\"\n\n :param gui:\n :return:\n \"\"\"\n print(\"In buttons on state.\")\n\n def _state_main(self, gui):\n \"\"\"\n The main code for the ButtonsOn state.\n\n :param gui:\n :return:\n \"\"\"\n gui.pack_button.wait_variable(gui.buttons_on)\n\n def _on_exit(self, gui):\n if gui.program_running:\n gui.update()\n return ButtonsOff()\n else:\n return None\n\n class ButtonsOff(BaseState):\n \"\"\"\n State for having buttons off.\n \"\"\"\n\n def _on_enter(self, gui):\n \"\"\"\n\n :param gui:\n :return:\n \"\"\"\n print(\"In buttons off state.\")\n\n def _state_main(self, gui):\n \"\"\"\n The main code for the ButtonsOn state.\n\n :param gui:\n :return:\n \"\"\"\n gui.pack_button.wait_variable(gui.buttons_on)\n\n def _on_exit(self, gui):\n if gui.program_running:\n gui.update()\n return ButtonsOn()\n else:\n return None\n\n class SimpleGUI:\n \"\"\"\n Object for a simple gui.\n \"\"\"\n\n def __init__(self, root):\n \"\"\"\n Initializing the SimpleGUI object.\n \"\"\"\n self.root = root\n w, h = root.winfo_screenwidth(), self.root.winfo_screenheight()\n self.root.geometry(\"%dx%d+0+0\" % (w, h))\n self.root.protocol(\"WM_DELETE_WINDOW\", self.end_program)\n self.buttons_on = tk.BooleanVar()\n self.buttons_on.set(False)\n self.program_running = True\n\n def update(self):\n \"\"\"\n Update the GUI.\n\n :return:\n \"\"\"\n self.root.update_idletasks()\n self.root.update()\n return self.root\n\n def end_program(self):\n \"\"\"\n Ends the program.\n\n :return:\n \"\"\"\n self.buttons_on.set(not self.buttons_on.get())\n self.root.destroy()\n self.program_running = False\n\n\n '''Initialize and run GUI object'''\n root = tk.Tk()\n # Maximize window while maintaining title bar\n gui = SimpleGUI(root)\n state_machine = StateMachine(initial_state=InitialState())\n state_machine.run(gui)","def open_win(e) -> None:\n widget_sfx()\n r, g, b = parse_color(var.get())\n new_color, tk_color = askcolor(\n color=f\"#{r:02x}{g:02x}{b:02x}\",\n parent=parent.winfo_toplevel(),\n title=str(TRANS_SELECT_TITLE),\n )\n if new_color is not None:\n # On 3.8, these are floats.\n rf, gf, bf = new_color\n var.set(f'{int(rf)} {int(gf)} {int(bf)}')","def on_open_button(self, event):\n wildcard = \"All files (*.*)|*.*|\"\\\n \"Preprocessed _iso_res.csv file (*_iso_res.csv)|*_iso_res.csv|\"\\\n \"Massacre iso_csv file (*_iso.csv)|*_iso.csv|\"\n dlg = wx.FileDialog(\n self, message=\"Choose a file\",\n defaultDir=self.currentDirectory, \n defaultFile=\"\",\n wildcard=wildcard,\n style=wx.OPEN | wx.CHANGE_DIR\n )\n \n if dlg.ShowModal() == wx.ID_OK:\n fullname = dlg.GetPaths()[0].split('/')\n dpa = '/'.join(fullname[:-1]) + '/'\n self.currentDirectory = dpa\n fna = fullname[-1]\n [dfr, pul, vlab] = openFile(dpa+fna)\n startApp(dfr, dpa, fna, pul, vlab, fsize=self.fsize, size=self.size)\n\n dlg.Destroy()","def showFrontView(self):\r\n if(self.dataController.fileLoaded == True):\r\n self.dataController.showFrontView()","def toggle(self):\n if bool(self.show.get()):\n self.sub_frame.pack(fill=\"x\", expand=1)\n self.toggle_button.configure(text='-')\n else:\n self.sub_frame.forget()\n self.toggle_button.configure(text='+')"],"string":"[\n \"def switch_state():\\n\\tDmg.OpenWindow()\",\n \"def show(self):\\r\\n self.wf.Show()\",\n \"def show(self, window):\\r\\n\\r\\n return\",\n \"def finish_render():\\n get_window().static_display = True\\n get_window().flip_count = 0\\n get_window().flip()\",\n \"def cb_main_window(self, event):\\n self.main_frame.Show()\",\n \"def show_window(self):\\n self.show()\",\n \"def show(self):\\n self.Show()\",\n \"def flip(self):\",\n \"def debwt_window(self: object) -> None:\\n if self.file:\\n debwt_window = Toplevel(self)\\n debwt_window.title(\\\"Reversing Burros-Wheeler Transform\\\")\\n debwt_window.geometry(\\\"1000x1000\\\")\\n debwt_window.configure(bg='#ebebeb')\\n controller = BWDecoder(self.file)\\n controller.decode()\\n protocol = self.DeBW_output(controller)\\n prot= list(protocol)\\n\\n reconstructed = [\\\"Step 2: Creating the Burros-Wheeler Matrix\\\" for n in range(len(prot) - 2)]\\n\\n names = (step for step in [\\\"Step 1 : Visualizing the sequence\\\",\\n *reconstructed,\\n \\\"Step 3: The original sequence is the one that has a $ sign as a last column\\\",\\n \\\"Please refer to the main menu to select another sequence\\\"])\\n\\n self.step_by_step(debwt_window, iter(prot), names)\\n self.program_output(debwt_window, controller.debwt_output)\\n\\n else:\\n self.no_file_error()\",\n \"def show(self, initial = 0):\\n self.Show(1)\\n# self.update_title()\\n# print 'showing'\\n if initial:\\n self.initial_show()\",\n \"def flip(self):\\n \\n if self.faceup:\\n self.faceup = False\\n else:\\n self.faceup = True\",\n \"def open(self):\\n self.state = True\\n self.mainwindow.sendMessage('a')\\n print(\\\"opening \\\" + self.name)\",\n \"def window(main):\\r\\n main.title(\\\"BinCryptor 1.0\\\")\\r\\n main.update_idletasks()\\r\\n width = main.winfo_width() #Width of the current screen\\r\\n height = main.winfo_height() #Height of the current screen\\r\\n x = (main.winfo_screenwidth() // 2) - (width // 2)\\r\\n y = (main.winfo_screenheight() // 2) - (height // 2)\\r\\n main.geometry(f'{width}x{height}+{x}+{y}') #Adjusts the height and width\\r\",\n \"def open_preferences(self, event):\\n editPrefFrame = Single_pref(parent=self, ID=997)\\n editPrefFrame.Centre()\\n editPrefFrame.Show()\\n editPrefFrame.ShowModal()\\n editPrefFrame.Destroy()\",\n \"def show(self):\\n # * displays the window, after using either the iconify or the withdraw methods\\n self.wm_deiconify()\\n # * this method can be called after the event which needs to happen before the window event\\n self.wait_window()\",\n \"def start(self):\\n self.delegate.start_preview(fullscreen=False, window = (350, 10, self.size[0] - 350, self.size[1] - 10))\",\n \"def flip_faceup(self):\\r\\n self.faceup = True\",\n \"def openPremade(self):\\n dialog = PremadeFrame(self)\\n if dialog.ShowModal() == wx.ID_OK:\\n newClass = dialog.selectedClass\\n newFrame = newClass(self)\\n self.frames.append(newFrame)\\n self.names.append(newFrame.experimentName)\\n newFrame.Show()\\n newFrame.Maximize()\\n self.Show(False)\",\n \"def buttonPress(self):\\n if self.inPlay and not self.shown:\\n self.configure(image = Tile.images[0])\",\n \"def on_activate(self, caller):\\n self.window = GameWindow()\\n self.add_window(self.window)\",\n \"def _open_window(self):\\r\\n\\t\\t# Creating the window\\r\\n\\t\\tself._window = Window(self, Locations.RESTAL)\",\n \"def onClick(self):\\n self.app.setActiveMode(\\\"start\\\")\",\n \"def do_activate(self):\\n\\n Gtk.Application.do_activate(self)\\n self.initiate_plugins()\\n self.other[\\\"menu_button\\\"].set_menu_model(self.prepare_menu())\\n self.output_window.show_all()\\n self.window.show_all()\",\n \"def open_generatorWindow(self):\\n self.window = generatorWindow(self)\\n self.hide()\",\n \"def setHorizontalFlip(self, flag):\\n\\t\\tself.flipHorizontally = flag\",\n \"def selection_form():\\n sg.theme('DarkBlue') \\n layout = [\\n [\\n sg.Button(\\\"Encrypt Files\\\"),\\n sg.Button(\\\"View Images\\\")\\n ]\\n ]\\n \\n window = sg.Window('Encrypted Image Viewer', layout)\\n while True:\\n event, values = window.read()\\n if event == \\\"Exit\\\" or event == sg.WIN_CLOSED:\\n sys.exit()\\n if event == \\\"Encrypt Files\\\":\\n window.close()\\n encryption_form()\\n \\n if event == \\\"View Images\\\":\\n window.close()\\n image_viewer()\",\n \"def setupWindow(self):\\n\\n\\t\\tself.main_menu_window = MenuFrame.MainMenuFrame(self.uiCoordinator)\\n\\t\\tself.menu_window = self.main_menu_window._mf\\n\\t\\tself.score_window = self.main_menu_window._hf\\n\\t\\tself.instructions_window = self.main_menu_window._if\\n\\t\\tself.menu_window.playButton.focus_set()\",\n \"def display(self):\\n\\t\\tprint('The button in the window was clicked!')\",\n \"def start():\\r\\n window = loop_menuDolar()\\r\\n window.close()\",\n \"def flip(self):\\n if self.is_face_up:\\n arcade.load_texture(self.back_file)\\n self.is_face_up = False\\n else:\\n arcade.load_texture(self.face_file)\\n self.is_face_up = True\",\n \"def flip(self, xflip=True, yflip=False):\\n self.drawer.flush()\\n img = self.img\\n if xflip: img = img.transpose(PIL.Image.FLIP_LEFT_RIGHT)\\n if yflip: img = img.transpose(PIL.Image.FLIP_TOP_BOTTOM)\\n self.img = img\\n self.update_drawer_img()\\n return self\",\n \"def show(self):\\n self.root.update()\\n self.root.deiconify()\",\n \"def initialize(window):\\n MY.restart_button.location = window / 2\",\n \"def show(self):\\r\\n\\t\\tself.frame.Show(True)\",\n \"def open(self, wait=True):\\n self.gripper.actuate(1, 1)\",\n \"def open(self):\\n windowFlags = self.getWindowFlags(self.settings)\\n self.surface = pygame.display.set_mode(self._resolution, windowFlags)\\n self._printVideoInfo(pygame.display.Info())\\n logger.info(\\\"Initialized display with driver: \\\" + pygame.display.get_driver())\\n\\n self.surface.fill(self._skin.guiColor(\\\"Background\\\"))\\n self._initializePanels(self._resolution, self._skin)\\n pygame.display.flip()\\n\\n self._statusLoop.statusProvider = self.getStatusProvider(self.settings)\",\n \"def open_transitWindow(self):\\n self.window = surveyWindow(self, imaging=False)\\n self.hide()\",\n \"def MacReopenApp(self):\\n self.BringWindowToFront()\",\n \"def main():\\n run_it = tools.Control(prepare.ORIGINAL_CAPTION)\\n state_dict = {\\\"SPLASH\\\" : splash.Splash(),\\n \\\"MENU\\\" : menu.Menu(),\\n \\\"DEMO\\\" : demo.Demo(),\\n \\\"GAME\\\" : game.Game()}\\n run_it.setup_states(state_dict, \\\"SPLASH\\\")\\n run_it.main()\",\n \"def flip(self, mode='h'):\\n # TODO: Implement the flip function. Remember to record the boolean values is_horizontal_flip and\\n # is_vertical_flip.\\n if mode == 'h':\\n self.is_horizontal_flip = True\\n self.x = np.flipud(self.x)\\n elif mode == 'v':\\n self.is_vertical_flip = True\\n self.x = np.fliplr(self.x)\\n else:\\n self.is_vertical_flip = True\\n self.is_horizontal_flip = True\\n self.x = np.fliplr(self.x)\\n self.x = np.flipud(self.x)\\n # raise NotImplementedError\\n #######################################################################\\n # #\\n # #\\n # TODO: YOUR CODE HERE #\\n # #\\n # #\\n #######################################################################\",\n \"def __call__(self):\\n self.show()\",\n \"def openInstructions(self, e):\\n\\n\\t\\tif (not self.open_window):\\n\\t\\t\\tself.open_window = True\\n\\t\\t\\tself.instructions_window.close.focus_set()\\n\\t\\t\\tself.main_menu_window.liftFrame(self.instructions_window.instructions_frame)\\n\\t\\telif (self.open_window):\\n\\t\\t\\tself.open_window = False\\n\\t\\t\\tself.menu_window.playButton.focus_set()\\n\\t\\t\\tself.main_menu_window.lowerFrame(self.instructions_window.instructions_frame)\",\n \"def back_window(self):\\n\\n self.controller.set_new_model_running(False)\\n self.controller.show_frame(\\\"MainWindow\\\")\",\n \"def execute_app(self):\\n\\n self.root = Tk()\\n self.root.geometry(f\\\"{vk_common.MAX_IMAGE_WIDTH}x{vk_common.MAX_IMAGE_HEIGHT}\\\")\\n self.root.title(\\\"Лайкер\\\")\\n\\n self.f_source = Frame(self.root)\\n self.f_source.pack(side=TOP)\\n\\n self.f_control = Frame(self.root)\\n self.f_control.pack(side=TOP)\\n\\n self.f_image = Frame(self.root)\\n self.f_image.pack(side=BOTTOM)\\n\\n vk_common.dprint(2, vk_common.func_name(), \\\"Список пользователей при входе: \\\", self.users)\\n for number, user in enumerate(self.users, 1):\\n set_button(self.f_source, str(number), self.select, number)\\n\\n set_button(self.f_control, \\\"Лайкнуть\\\", self.like, 1)\\n set_button(self.f_control, \\\"ДизЛайкнуть\\\", self.dislike, 2)\\n set_button(self.f_control, \\\"<<\\\", self.left, 3)\\n set_button(self.f_control, \\\">>\\\", self.right, 4)\\n\\n self.top_id = 1\\n self.select_user(1)\\n\\n self.root.focus_set()\\n self.root.wm_state('zoomed')\\n self.root.call('wm', 'attributes', '.', '-topmost', '1')\\n self.root.mainloop()\\n sleep(1/3)\\n self.root.quit()\",\n \"def show(self):\\n self._window.show()\",\n \"def flip_player(cls):\\n cls.current_player = 'X' if cls.current_player == 'O' else 'O'\\n\\n cls.display_board()\\n cls.prompt_player()\",\n \"def ShowMe(self, event):\\n self.Show(True)\",\n \"def openTB3Settings(self):\\n self.TB3_Window = QtWidgets.QDialog()\\n self.TB3_ui = Ui_robotThreeConfig()\\n self.TB3_ui.setupUi(self.TB3_Window)\\n self.TB3_Window.show()\",\n \"def huffcode_window(self: object) -> None:\\n if self.file:\\n huff_code_window = Toplevel(self)\\n huff_code_window.title(\\\"Huffman coding\\\")\\n huff_code_window.geometry(\\\"1000x1000\\\")\\n huff_code_window.configure(bg='#ebebeb')\\n names = (step for step in [\\\"Step 1 : Visualizing the sequence\\\",\\n \\\"Step 2: Creating Huffman tree from sequence and calculating paths\\\",\\n \\\"Step 3: Generating the binary sequence from paths and adding a padding\\\",\\n \\\"Step 4: Coding the binary in 8-bits to unicode\\\",\\n \\\"Step 5: Writing paths and unicode to an output file\\\",\\n \\\"Please refer to the main menu to select another sequence\\\"])\\n controller = HuffEncoder(self.file)\\n controller.encode()\\n protocol = self.Huff_output(controller)\\n self.step_by_step(huff_code_window, protocol, names)\\n self.program_output(huff_code_window, controller.huff_output)\\n\\n else:\\n self.no_file_error()\",\n \"def music():\\n main_window.withdraw()\\n window = Toplevel()\\n music_gui = MusicGUI(window, main_window)\",\n \"def run(self):\\n self.window.show()\\n Gtk.main()\",\n \"def help_menu_about_activate(self, widget, data=None):\\n print \\\"cmon\\\"\\n button1 = gtk.Button(\\\"Press Me!\\\")\\n self.fixed1.put(button1, 0, 0)\\n button1.window.raise_()\\n button1.show()\\n button2 = gtk.Button(\\\"Prease Press Me!\\\")\\n self.fixed1.put(button2, 380, 380)\\n button2.show()\\n button2.window.raise_()\",\n \"def main(self):\\n\\n self.window.show_all()\\n gtk.main()\",\n \"def bwt_window(self: object) -> None:\\n if self.file:\\n bwt_window = Toplevel(self)\\n bwt_window.title(\\\"Burros-Wheeler Transform\\\")\\n bwt_window.geometry(\\\"1000x1000\\\")\\n bwt_window.configure(bg='#ebebeb')\\n controller = BWEncoder(self.file)\\n controller.encode()\\n protocol = self.BW_output(controller)\\n prot= list(protocol)\\n\\n rots = [\\\"Step 2: Generating all rotations of the sequence\\\" for n in range(len(prot) - 3)]\\n \\n names = (step for step in [\\\"Step 1 : Visualizing the sequence\\\",\\n *rots,\\n \\\"Step 3: Creating the Burros-Wheeler matrix by sorting all rotations\\\",\\n \\\"Step 4: The Burros-Wheeler transform is the last column of the matrix\\\",\\n \\\"Please refer to the main menu to select another sequence\\\"])\\n \\n self.step_by_step(bwt_window, iter(prot), names)\\n self.program_output(bwt_window, controller.bwt_output)\\n\\n else:\\n self.no_file_error()\",\n \"def show(self):\\n self.wid.show()\",\n \"def file_menu_open_activate(self, widget, data=None):\\n self.open_chooser.show()\",\n \"def comprar(self):\\n self.new_window = tk.Toplevel(self.menu)\\n Comprar(self.new_window)\",\n \"def OpenMethod(self):\\r\\n stringName = self.saveEdit.text()\\r\\n ListFiles = os.listdir(\\\"Save\\\")\\r\\n realName = (\\r\\n (\\\"PVP_mode_\\\" if self.PVP else \\\"AI_mode_\\\")\\r\\n + stringName\\r\\n + (\\\"\\\" if stringName[-4:] == \\\".txt\\\" else \\\".txt\\\")\\r\\n )\\r\\n if realName in ListFiles:\\r\\n f = open(\\\"Save/\\\" + realName, \\\"r\\\")\\r\\n fen = f.readlines()[0]\\r\\n f.close()\\r\\n colorIA = 1 if self.PVP else (1 if stringName[0] == \\\"W\\\" else 2)\\r\\n self.WindowSave.close()\\r\\n MainWindow = QtWidgets.QMainWindow()\\r\\n if self.PVP:\\r\\n indFinName1 = 9\\r\\n while realName[indFinName1] != \\\"_\\\":\\r\\n indFinName1 += 1\\r\\n indFinName2 = indFinName1 + 1\\r\\n while realName[indFinName2] != \\\"_\\\":\\r\\n indFinName2 += 1\\r\\n self.windowAssoc.textW = realName[9:indFinName1]\\r\\n self.windowAssoc.textB = realName[indFinName1 + 1 : indFinName2]\\r\\n\\r\\n else:\\r\\n indFinNamePlayer = 10\\r\\n while realName[indFinNamePlayer] != \\\"_\\\":\\r\\n indFinNamePlayer += 1\\r\\n self.windowAssoc.textW = (\\r\\n \\\"AI\\\" if stringName[0] == \\\"B\\\" else realName[10:indFinNamePlayer]\\r\\n )\\r\\n self.windowAssoc.textB = (\\r\\n \\\"AI\\\" if stringName[0] == \\\"W\\\" else realName[10:indFinNamePlayer]\\r\\n )\\r\\n\\r\\n self.windowAssoc.InitialisationPerFEN(fen, colorIA)\\r\\n if self.IwAssoc != None:\\r\\n self.IwAssoc.MainWindow.close()\\r\\n self.windowAssoc.MainWindow.show()\\r\\n\\r\\n else:\\r\\n self.textlabel = \\\"Error - \\\" + (\\r\\n \\\" Save as :\\\" if self.FEN != None else \\\" Open : \\\"\\r\\n )\\r\\n self.saveEdit.clear()\",\n \"def help_main():\\n global help_window, my_iterator, iterable, canvas, forward_button, picture_lst, image\\n my_iterator = iter(picture_lst)\\n pill_image = Image.open(image_base)\\n image = ImageTk.PhotoImage(pill_image)\\n\\n canvas = Canvas(help_window, width=700 + 15, height=490 + 15)\\n canvas.create_image(10, 10, anchor=NW, image=image)\\n\\n canvas.place(x=170, y=10)\\n\\n forward_button = ttk.Button(help_window, text=\\\" Вперед \\\", command=forward)\\n forward_button.place(x=910, y=250)\\n help_window.mainloop()\",\n \"def do_activate(self, *args, **kwargs):\\n self.register_signals()\\n self.perform_setup()\\n assert self.main_window\\n self.main_window.show()\\n self.hold()\",\n \"def showBasic(self):\\n self.setWindowIcon(QIcon(self.icon))\\n self.setWindowTitle(self.title)\\n self.setGeometry(*self.posXY, *self.windowSize)\\n self.show()\",\n \"def start(self):\\n # asserts preconditions are met\\n #assert self.validGameSettings()\\n\\n #draws initial welcome screen\\n #self._text = GLabel(text=\\\"Press 'S' to Play\\\")\\n #self._text.draw(self.view)\\n\\n # initializing instance variables\\n self.setState(STATE_INACTIVE)\\n self.setWave(None)\\n self.setText(None)\\n self.lastkeys = 0 #ADD MORE ATTRIBUTES\\n\\n # draws iniital welcome screen\\n self.welcomeScreen()\",\n \"def show_window(self):\\n self._window.grab_set()\\n self._window.wait_window()\",\n \"def build_initial() :\\r\\n titleframe = T.Frame(ROOT)\\r\\n TITLE = T.Label(titleframe, text = \\\"Welcome to Microgp!\\\")\\r\\n var = T.StringVar()\\r\\n INSTRUCTIONS = T.Message(titleframe, textvariable = var, width = 100)\\r\\n var.set(\\\"By Erik and Sam\\\")\\r\\n instruct_b = T.Button(titleframe, text = \\\"Instructions\\\",\\r\\n command = get_instructions)\\r\\n instruct_b.pack(side = T.BOTTOM)\\r\\n TITLE.pack(side = T.TOP)\\r\\n INSTRUCTIONS.pack(side = T.BOTTOM)\\r\\n titleframe.pack()\",\n \"def file_menu_new_activate(self, widget, data=None):\\n self.new_chooser.show()\",\n \"def huffdecode_window(self: object) -> None:\\n if self.file:\\n huff_decode_window = Toplevel(self)\\n huff_decode_window.title(\\\"Huffman decoding\\\")\\n huff_decode_window.geometry(\\\"1000x1000\\\")\\n huff_decode_window.configure(bg='#ebebeb')\\n names = (step for step in [\\\"Step 1 : Visualizing the compressed sequence\\\",\\n \\\"Step 2: Reading the header of the file that corresponds to Huffman codes that where created with the tree during compression\\\",\\n \\\"Step 3: Separating the unicode sequence\\\",\\n \\\"Step 4: Transforming the unicode sequence to binary using huffman codes in the header and stripping padding\\\",\\n \\\"Step 5: The decompressed sequence : \\\",\\n \\\"Please refer to the main menu to select another sequence\\\"])\\n controller = HuffDecoder(self.file)\\n controller.decode()\\n protocol = self.deHuff_output(controller)\\n self.step_by_step(huff_decode_window, protocol, names)\\n self.program_output(huff_decode_window, controller.dehuffman_output)\\n\\n else:\\n self.no_file_error()\",\n \"def startSelectedMode(self):\\n selectedBtn = self.buttons_group.checkedButton()\\n if selectedBtn is None:\\n QMessageBox.information(self, 'Attention', 'Aucun mode selectionné.\\\\n'\\n 'Vous devez choisir un mode avant de le lancer.')\\n return\\n\\n user = self.mainwindow.currentUser\\n try:\\n widget = selectedBtn.constructor(user.get_mode(selectedBtn.id))\\n except:\\n widget = selectedBtn.constructor(None)\\n widget.closeRequested.connect(self.closeWidget)\\n\\n self.mainwindow.setWindowTitle(\\\"Consmaster\\\" +\\n ' [' + selectedBtn.text().replace('\\\\n', '') + ']')\\n\\n self.mainwindow.central_widget.addWidget(widget)\\n self.mainwindow.central_widget.setCurrentWidget(widget)\",\n \"def state_preview_do(cfg, app, win, events):\",\n \"def show_main_buttons(self):\\n pass\",\n \"def open(self):\\r\\n ATOM3Type.show(self, parent, topWindowParent)\\r\\n return ATOM3(topWindowParent, None , 0, 1, self)\",\n \"def __init_window(self) -> pygame.Surface:\\n pygame.display.set_caption(CAPTION)\\n win = pygame.display.set_mode((WIDTH, HEIGHT))\\n \\n return win\",\n \"def start(self):\\n self.__main_window = Tk()\\n self.__start_loading_window()\",\n \"def launchSyncToolWindow(self):\\r\\n self.unbind()\\r\\n self.videoPlayer.pause()\\r\\n self.w_synctool = SyncToolWindow(self)\",\n \"def run(self):\\n self.ui['main_window'].widgets['main'].show_all()\\n gtk.main()\",\n \"def books():\\n main_window.withdraw()\\n window = Toplevel()\\n books_gui = BookGUI(window, main_window)\",\n \"def showSettings(self):\\n self.c.show()\",\n \"def createPrefWindow(self):\\n if (not hasattr(self, \\\"pref_window\\\")):\\n self.pref_window = PrefWindow(self, self.settings)\\n self.pref_window.show()\\n self.pref_window.button_apply.setEnabled(True)\",\n \"def build_second_window():\\r\\n\\r\\n new_window = tk.Tk()\\r\\n windows.append(new_window)\\r\\n new_window.protocol(\\\"WM_DELETE_WINDOW\\\", new_round(new_window))\\r\\n\\r\\n ask = tk.Label(new_window, text='Would You Like To Play Again?', bg='Cyan')\\r\\n ask.pack(fill=tk.X)\\r\\n\\r\\n frame = tk.Frame(new_window)\\r\\n frame.pack()\\r\\n\\r\\n yes_button = tk.Button(frame, text='Yes', bg='green',\\r\\n command=new_round(new_window))\\r\\n yes_button.pack(side=tk.LEFT)\\r\\n\\r\\n no_button = tk.Button(frame, text='No', bg='red',\\r\\n command=close)\\r\\n no_button.pack(side=tk.LEFT)\",\n \"def show(self,window):\\n self.showFunctions(window)\",\n \"def frame(self):\\n self.run_command('frame')\",\n \"def showUI(cls):\\r\\n win = cls()\\r\\n win.create()\\r\\n return win\",\n \"def show():\\n dialog = SkinIODialog(getMayaWindow())\\n dialog.show()\",\n \"def pass_click(self):\\n son = tk.Toplevel(self)\\n son.wm_title('Generator parolă')\\n son.wm_resizable(width=False, height=False)\\n\\n if platform.system() != 'Windows':\\n icon = tk.PhotoImage(file=utils.get_resource_path('images', 'icon.gif'))\\n son.tk.call('wm', 'iconphoto', son._w, icon)\\n else:\\n son.wm_iconbitmap(utils.get_resource_path('images', 'icon.ico'))\\n\\n pop_up_icon_path = utils.get_resource_path('images', 'icon.gif')\\n pop_up_icon = tk.PhotoImage(file=pop_up_icon_path)\\n son.tk.call('wm', 'iconphoto', son._w, pop_up_icon)\\n\\n gen = passgenerator.PassGeneratorFrame(son)\\n gen.grid()\",\n \"def initialize(self):\\n self._ui.img_name.setText('No files selected')\\n self._ui.bt_next.setEnabled(False)\\n self._ui.bt_prev.setEnabled(False)\\n self._ui.bt_right.setEnabled(False)\\n self._ui.bt_left.setEnabled(False)\\n self._ui.gps_button.setEnabled(False)\\n\\n self._open_btn = QPushButton('Open File', self._ui.img_label)\\n self.adjustSize()\",\n \"def fullzip_window(self: object) -> None:\\n if self.file:\\n fullzip_window = Toplevel(self)\\n fullzip_window.title(\\\"Burrow-Wheeler Transform + Huffman coding\\\")\\n fullzip_window.geometry(\\\"1000x1000\\\")\\n fullzip_window.configure(bg='#ebebeb')\\n controller = FullEncoder(self.file)\\n controller.full_zip()\\n protocol = self.fullzip_output(controller)\\n prot= list(protocol)\\n\\n rots = [\\\"Step 2: Generating all rotations of the sequence\\\" for n in range(len(prot) - 7)]\\n names = (step for step in [\\\"Step 1 : Visualizing the sequence\\\",\\n *rots,\\n \\\"Step 3: Creating the Burros-Wheeler matrix by sorting all rotations\\\",\\n \\\"Step 4: The Burros-Wheeler transform is the last column of the matrix\\\",\\n \\\"Step 5: Creating Huffman tree from Burros-Wheeler transform and calculating paths\\\",\\n \\\"Step 6: Generating the binary sequence from paths and adding a padding\\\",\\n \\\"Step 7: Coding the binary in 8-bits to unicode\\\",\\n \\\"Step 8: Writing paths and unicode to an output file\\\",\\n \\\"Please refer to the main menu to select another sequence\\\"])\\n\\n self.step_by_step(fullzip_window, iter(prot), names)\\n outputs = controller.bw_encoder.bwt_output + \\\\\\n '\\\\n' + controller.huff_encoder.huff_output\\n self.program_output(fullzip_window, outputs)\\n\\n else:\\n self.no_file_error()\",\n \"def window_info_toggle():\\n window_info.hide() if window_info.showing else window_info.show()\",\n \"def show(self):\\n\\n self.serial = self.parent.board.serial\\n self.deiconify() # Show window\\n self.visible = True\\n\\n self.input_entry.focus()\\n\\n self.start_repl()\",\n \"def menu():\\n ferme_fenetre()\\n Menu()\",\n \"def show(self):\\n self.present(orientations=ORIENTATIONS)\\n # launch a background thread\\n # we can not use ui.in_background here\\n # because some dialogs would not open anymoe\\n thr = threading.Thread(target=self.show_messages)\\n thr.daemon = True\\n thr.start()\",\n \"def toggle_display_mode(self):\\n if self.main_window.windowState() == Qt.WindowFullScreen:\\n self.main_window.showNormal()\\n else:\\n self.main_window.showFullScreen()\\n # Keep the settings window active to prevent main window from\\n # burying it\\n self.settings_window.activateWindow()\",\n \"def on_stateico_clicked(self, *a):\\n\\t\\tself.window1.set_property('visible', True)\\n\\t\\tself.stateico.set_visible(False)\\n\\t\\tself.window1.present()\",\n \"def window_front(self):\\n if platform() == \\\"Darwin\\\":\\n system(\\\"\\\"\\\"/usr/bin/osascript -e 'tell app \\\"Finder\\\" to set \\\\\\n frontmost of process \\\"Python\\\" to true' \\\"\\\"\\\")\",\n \"def __init__(self, master, corr_string, pygame_win=False):\\n self.top = tk.Toplevel(master)\\n self.top.attributes('-topmost', True)\\n self.top.attributes('-topmost', False)\\n self.top.geometry(\\\"+600+100\\\")\\n self.corr_string = corr_string\\n if pygame_win:\\n pygame.display.update()\",\n \"def main(self): \\n self.window.hide_all()\\n self.window.isHide = True\\n \\n gtk.main()\",\n \"def show_gui():\\n pass\",\n \"def first_window_attempt():\\n\\n class InitialState(BaseState):\\n \\\"\\\"\\\"\\n Initial state for the SimpleGUI.\\n \\\"\\\"\\\"\\n\\n def _on_enter(self, gui):\\n \\\"\\\"\\\"\\n Construct the buttons upon entering the state.\\n\\n :return:\\n \\\"\\\"\\\"\\n print(\\\"In initial state.\\\")\\n\\n '''Create label'''\\n self.label = tk.Label(gui.root, text=\\\"First GUI\\\")\\n self.label.pack()\\n\\n '''Create buttons'''\\n gui.pack_button = tk.Button(gui.root,\\n text=\\\"Buttons\\\",\\n command=self.adjust_buttons(gui),\\n font=Font(size=50))\\n gui.pack_button.pack()\\n gui.greet_button = tk.Button(gui.root,\\n text=\\\"Greet\\\",\\n command=self._greet,\\n font=Font(size=50))\\n gui.close_button = tk.Button(gui.root,\\n text=\\\"Close\\\",\\n command=gui.root.quit,\\n font=Font(size=50))\\n gui.update()\\n\\n\\n def adjust_buttons(self, gui):\\n \\\"\\\"\\\"\\n Adjust the buttons.\\n\\n :return:\\n \\\"\\\"\\\"\\n def _adjust_buttons():\\n print(\\\"\\\\tButton clicked.\\\")\\n if gui.buttons_on.get():\\n print(\\\"\\\\t\\\\tDetected buttons are on.\\\")\\n self._remove_buttons(gui)\\n else:\\n print(\\\"\\\\t\\\\tDetected buttons are off.\\\")\\n self._add_buttons(gui)\\n return _adjust_buttons\\n\\n def _add_buttons(self, gui):\\n \\\"\\\"\\\"\\n Add buttons to the view.\\n\\n :return:\\n \\\"\\\"\\\"\\n gui.greet_button.pack()\\n gui.close_button.pack()\\n gui.buttons_on.set(True)\\n\\n def _remove_buttons(self, gui):\\n \\\"\\\"\\\"\\n Remove buttons from the view.\\n\\n :return:\\n \\\"\\\"\\\"\\n gui.greet_button.pack_forget()\\n gui.close_button.pack_forget()\\n gui.buttons_on.set(False)\\n\\n def _greet(self, gui):\\n \\\"\\\"\\\"\\n\\n :param gui:\\n :return:\\n \\\"\\\"\\\"\\n\\n def _on_exit(self, gui):\\n \\\"\\\"\\\"\\n Return the next state.\\n\\n :param gui:\\n :return:\\n \\\"\\\"\\\"\\n gui.update()\\n return ButtonsOff()\\n\\n class ButtonsOn(BaseState):\\n \\\"\\\"\\\"\\n State for having buttons on.\\n \\\"\\\"\\\"\\n def _on_enter(self, gui):\\n \\\"\\\"\\\"\\n\\n :param gui:\\n :return:\\n \\\"\\\"\\\"\\n print(\\\"In buttons on state.\\\")\\n\\n def _state_main(self, gui):\\n \\\"\\\"\\\"\\n The main code for the ButtonsOn state.\\n\\n :param gui:\\n :return:\\n \\\"\\\"\\\"\\n gui.pack_button.wait_variable(gui.buttons_on)\\n\\n def _on_exit(self, gui):\\n if gui.program_running:\\n gui.update()\\n return ButtonsOff()\\n else:\\n return None\\n\\n class ButtonsOff(BaseState):\\n \\\"\\\"\\\"\\n State for having buttons off.\\n \\\"\\\"\\\"\\n\\n def _on_enter(self, gui):\\n \\\"\\\"\\\"\\n\\n :param gui:\\n :return:\\n \\\"\\\"\\\"\\n print(\\\"In buttons off state.\\\")\\n\\n def _state_main(self, gui):\\n \\\"\\\"\\\"\\n The main code for the ButtonsOn state.\\n\\n :param gui:\\n :return:\\n \\\"\\\"\\\"\\n gui.pack_button.wait_variable(gui.buttons_on)\\n\\n def _on_exit(self, gui):\\n if gui.program_running:\\n gui.update()\\n return ButtonsOn()\\n else:\\n return None\\n\\n class SimpleGUI:\\n \\\"\\\"\\\"\\n Object for a simple gui.\\n \\\"\\\"\\\"\\n\\n def __init__(self, root):\\n \\\"\\\"\\\"\\n Initializing the SimpleGUI object.\\n \\\"\\\"\\\"\\n self.root = root\\n w, h = root.winfo_screenwidth(), self.root.winfo_screenheight()\\n self.root.geometry(\\\"%dx%d+0+0\\\" % (w, h))\\n self.root.protocol(\\\"WM_DELETE_WINDOW\\\", self.end_program)\\n self.buttons_on = tk.BooleanVar()\\n self.buttons_on.set(False)\\n self.program_running = True\\n\\n def update(self):\\n \\\"\\\"\\\"\\n Update the GUI.\\n\\n :return:\\n \\\"\\\"\\\"\\n self.root.update_idletasks()\\n self.root.update()\\n return self.root\\n\\n def end_program(self):\\n \\\"\\\"\\\"\\n Ends the program.\\n\\n :return:\\n \\\"\\\"\\\"\\n self.buttons_on.set(not self.buttons_on.get())\\n self.root.destroy()\\n self.program_running = False\\n\\n\\n '''Initialize and run GUI object'''\\n root = tk.Tk()\\n # Maximize window while maintaining title bar\\n gui = SimpleGUI(root)\\n state_machine = StateMachine(initial_state=InitialState())\\n state_machine.run(gui)\",\n \"def open_win(e) -> None:\\n widget_sfx()\\n r, g, b = parse_color(var.get())\\n new_color, tk_color = askcolor(\\n color=f\\\"#{r:02x}{g:02x}{b:02x}\\\",\\n parent=parent.winfo_toplevel(),\\n title=str(TRANS_SELECT_TITLE),\\n )\\n if new_color is not None:\\n # On 3.8, these are floats.\\n rf, gf, bf = new_color\\n var.set(f'{int(rf)} {int(gf)} {int(bf)}')\",\n \"def on_open_button(self, event):\\n wildcard = \\\"All files (*.*)|*.*|\\\"\\\\\\n \\\"Preprocessed _iso_res.csv file (*_iso_res.csv)|*_iso_res.csv|\\\"\\\\\\n \\\"Massacre iso_csv file (*_iso.csv)|*_iso.csv|\\\"\\n dlg = wx.FileDialog(\\n self, message=\\\"Choose a file\\\",\\n defaultDir=self.currentDirectory, \\n defaultFile=\\\"\\\",\\n wildcard=wildcard,\\n style=wx.OPEN | wx.CHANGE_DIR\\n )\\n \\n if dlg.ShowModal() == wx.ID_OK:\\n fullname = dlg.GetPaths()[0].split('/')\\n dpa = '/'.join(fullname[:-1]) + '/'\\n self.currentDirectory = dpa\\n fna = fullname[-1]\\n [dfr, pul, vlab] = openFile(dpa+fna)\\n startApp(dfr, dpa, fna, pul, vlab, fsize=self.fsize, size=self.size)\\n\\n dlg.Destroy()\",\n \"def showFrontView(self):\\r\\n if(self.dataController.fileLoaded == True):\\r\\n self.dataController.showFrontView()\",\n \"def toggle(self):\\n if bool(self.show.get()):\\n self.sub_frame.pack(fill=\\\"x\\\", expand=1)\\n self.toggle_button.configure(text='-')\\n else:\\n self.sub_frame.forget()\\n self.toggle_button.configure(text='+')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6244811","0.61130655","0.60361177","0.58716136","0.5835317","0.5802721","0.58015156","0.57977974","0.57581085","0.5736185","0.5727664","0.56942797","0.5663911","0.5637016","0.5626047","0.5598497","0.55893236","0.5587088","0.55717754","0.55463487","0.5534851","0.5523327","0.55169094","0.55123574","0.55033803","0.5497679","0.5496618","0.54909104","0.54730654","0.54510957","0.5430896","0.5427933","0.542706","0.54179907","0.54131734","0.54078674","0.5405026","0.53933954","0.53898156","0.5386416","0.538228","0.5370848","0.53656393","0.53622115","0.5357925","0.53559726","0.5340768","0.53317094","0.533042","0.532116","0.531845","0.53140783","0.5302952","0.5296226","0.52923244","0.5288068","0.5277223","0.5275759","0.5273876","0.52591753","0.5258694","0.525622","0.5253377","0.5252865","0.5252176","0.52516013","0.5245494","0.5233133","0.5232801","0.52319765","0.52316064","0.5229773","0.52293855","0.5222881","0.5220395","0.5219907","0.5215819","0.5214963","0.5207801","0.5207659","0.5205322","0.52014655","0.51966363","0.51947254","0.5190547","0.5184114","0.51826537","0.5180596","0.517975","0.5178944","0.5172733","0.5170953","0.5168469","0.5166528","0.5166516","0.5163974","0.51591456","0.51573336","0.5151005","0.5149707"],"string":"[\n \"0.6244811\",\n \"0.61130655\",\n \"0.60361177\",\n \"0.58716136\",\n \"0.5835317\",\n \"0.5802721\",\n \"0.58015156\",\n \"0.57977974\",\n \"0.57581085\",\n \"0.5736185\",\n \"0.5727664\",\n \"0.56942797\",\n \"0.5663911\",\n \"0.5637016\",\n \"0.5626047\",\n \"0.5598497\",\n \"0.55893236\",\n \"0.5587088\",\n \"0.55717754\",\n \"0.55463487\",\n \"0.5534851\",\n \"0.5523327\",\n \"0.55169094\",\n \"0.55123574\",\n \"0.55033803\",\n \"0.5497679\",\n \"0.5496618\",\n \"0.54909104\",\n \"0.54730654\",\n \"0.54510957\",\n \"0.5430896\",\n \"0.5427933\",\n \"0.542706\",\n \"0.54179907\",\n \"0.54131734\",\n \"0.54078674\",\n \"0.5405026\",\n \"0.53933954\",\n \"0.53898156\",\n \"0.5386416\",\n \"0.538228\",\n \"0.5370848\",\n \"0.53656393\",\n \"0.53622115\",\n \"0.5357925\",\n \"0.53559726\",\n \"0.5340768\",\n \"0.53317094\",\n \"0.533042\",\n \"0.532116\",\n \"0.531845\",\n \"0.53140783\",\n \"0.5302952\",\n \"0.5296226\",\n \"0.52923244\",\n \"0.5288068\",\n \"0.5277223\",\n \"0.5275759\",\n \"0.5273876\",\n \"0.52591753\",\n \"0.5258694\",\n \"0.525622\",\n \"0.5253377\",\n \"0.5252865\",\n \"0.5252176\",\n \"0.52516013\",\n \"0.5245494\",\n \"0.5233133\",\n \"0.5232801\",\n \"0.52319765\",\n \"0.52316064\",\n \"0.5229773\",\n \"0.52293855\",\n \"0.5222881\",\n \"0.5220395\",\n \"0.5219907\",\n \"0.5215819\",\n \"0.5214963\",\n \"0.5207801\",\n \"0.5207659\",\n \"0.5205322\",\n \"0.52014655\",\n \"0.51966363\",\n \"0.51947254\",\n \"0.5190547\",\n \"0.5184114\",\n \"0.51826537\",\n \"0.5180596\",\n \"0.517975\",\n \"0.5178944\",\n \"0.5172733\",\n \"0.5170953\",\n \"0.5168469\",\n \"0.5166528\",\n \"0.5166516\",\n \"0.5163974\",\n \"0.51591456\",\n \"0.51573336\",\n \"0.5151005\",\n \"0.5149707\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8400445"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":20082,"cells":{"query":{"kind":"string","value":"Sets the stop event, cleanup the camera, and allow the rest of the quit process to continue."},"document":{"kind":"string","value":"def on_close(self):\n print('[INFO] closing...')\n self.stopEvent.set()\n del self.tello\n self.root.quit()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def stop(self):\n self.running = False\n self.cam.stop()\n self.amplifier.stop()\n pass","def stop():\n global running\n running = False\n messagebox.showinfo(\"Camera mode\",\"Stop image grab\")\n camera.stop_preview()","def stop(self):\n self.stop_aperture()","def stop(self):\n self.stop_event.set()","def stop(self):\n self.stop_event.set()","def stop(self):\n self._stop_event.set()","def stop(self):\r\n self.stopped = True","def stop(self):\n self._stop_event.set()","def stop(self):\n self._stop_event.set()","def stop(self):\n\n self.keep_running = False","def stop(self):\n self.stopped = True","def stop(self):\n self.stopped = True","def stop(self):\n self.ids.camera.opacity = 0\n self.ids.camera.play = False\n self.ids.start.text = 'Start Camera'\n self.ids.camera.texture = None","def stopit(self):\n\n self.stop.stop()\n self.stream.close()\n self.p.terminate()\n self.p = None\n\n print(\"Recording terminated!\")","def stop(self):\n self.exit.set()","def stop(self):\n self.exit.set()","def stop(self):\n self._kill_event.set()","def stop(self):\r\n self.running = False","def stop(self):\r\n self.running = False","def stop(self):\n self.stop_recognising.set()\n self.thread.join()","def stop(self):\n self.running = False","def stop(self):\n self.running = False","def stop(self):\n self.running = False","def stop(self):\n self.running = False","def stop(self):\n self.running = False","def stop(self):\n try:\n self.logger.debug('Halting VideoSave thread')\n cv2.destroyAllWindows()\n except:\n self.logger.exception('Exception while halting VideoSave')\n super().stop()","def stop(self):\n self.on_stop()","def stop() -> None:","def exit_engine(self):\n self.stop_flag = True","def on_Exit_button_clicked(self):\n self.cam.release()\n self.close()","def stop(self):\n self.ctrl_obj.finish = True","def stop(self):\n self.ctrl_obj.finish = True","def stop(self):\n self.stopped = True","def quit(self):\r\n self.unbind()\r\n self.videoPlayer.stop()\r\n self.saveConfig()\r\n self.root.destroy()","def stop(self):\n self._stop_flag = True","def _stop(self):\n self._pi.stop()","def stop(self):\n self.ids.camera.opacity = 0\n self.ids.camera.play = False\n self.ids.camera_button.text = \"Start Camera\"\n self.ids.camera.texture=None","def stop(self):\n self._stop_event.set()\n super().stop()","def stop(self):\n self._stop_signal = True","def shutdown(signal=None, frame=None):\n\n print(\"Shutting down\")\n\n # Close the camera\n camera.shutdown()\n\n time.sleep(0.25)\n sys.exit(0)","def stop():","def stop():","def stop():","def stop():","def stop(self) -> None:\n ...","def stop(self):\n\n self.stop_thread = True","def stop(self):\n self.__running = False","def trigger_stop(self):\n self.terminate = True\n self.stop_event.set()","def stop_hik(self, event):\n self.camdata.disconnect()","def stop(self):\n self._running = False","def stop(self):\n self._running = False","def stop(self):\r\n self._stop.set()","def stop(self):\n with self.stop_event_lock:\n self.stop_event.set()\n self.clean_up()","def _stop(self) -> None:\n self._stopped.set()","def stop(self) -> None:\n self._running = False","def stop(self):\n GameEngine().stop()\n self.on_stop()","def stop(self):\n\n self.detach()\n self._pi.stop()\n self._stopped = True","def stop(self) -> None:\n self.system.notify(\"Jarvis::Stop\")\n self.media.stop()","def stop_recording(self):\n self.timer.stop()\n self.camera.release()","def stop(self):\n self._stop = True\n self.wake_up()","def stop(self):\n self.recording = False","def stop(self) -> None:","def stop(self) -> None:","def stop (self):\n pass","def stop (self):\n pass","def stop(self):\n self._run = False","def stop(self):\n self._run_flag = False\n self.wait()","def stop(self):\n self._run_flag = False\n self.wait()","def stop(self):\n self._run_flag = False\n self.wait()","def stop(self):\n self._run_flag = False\n self.wait()","def stop(self):\n self._run_flag = False\n self.wait()","def stop(self):\n self._run_flag = False\n self.wait()","def stop(self):\r\n self.terminating = True","def stop(self):\r\n pass","def stop(self):\n\t\tself._run_flag = False\n\t\tself.wait()","def stop(self):\r\n self.stopped = True\r\n time.sleep(1)","def stop(self):\n self._stop.set()","def stop(self):\n pass","def stop(self):\n pass","def stop(self):\n pass","def stop(self):\n pass","def stop(self):\n pass","def stop(self):\n pass","def stop(self):\n pass","def stop(self):\n pass","def stop(self):\n pass","def stop(self):\n pass","def stop(self):\n pass","def quit(self):\n self.stop()\n self.handler = None","def event_stop(self, **kwargs):\n del kwargs\n self.stop()","def stop(self):\r\n self.stopped = True\r\n time.sleep(3)","def stop(self):\n self.config(image=None)\n self.frames = None","def stop(self):\n if not self.__started:\n return\n\n self.__started = False\n self.__gi_loop.quit()","def stop_recording(self):\n self.flag_event.clear()\n self.statusBar().showMessage('Recording Stopped')\n print('boo ya')","def foreceStop(self):\n self.__success = False\n self.stop()","def stop(self):\n\t\tpass","def stop(self):","def stop(self):","def stop(self):\n with self.stop_lock:\n self._stop_event.set()","def stop_live(self):\n self._toolbox.disable_streaming_flag()\n self._toolbox.disable_webcam(self._index)\n self._prompt.kill()","def _device_stop_capture(self):\n\n # TODO: we may want to provide an option to flush the SDRam buffer here before capture stops?\n self._stop_capture_to_ram()\n self._stop_streaming_ram_to_host()"],"string":"[\n \"def stop(self):\\n self.running = False\\n self.cam.stop()\\n self.amplifier.stop()\\n pass\",\n \"def stop():\\n global running\\n running = False\\n messagebox.showinfo(\\\"Camera mode\\\",\\\"Stop image grab\\\")\\n camera.stop_preview()\",\n \"def stop(self):\\n self.stop_aperture()\",\n \"def stop(self):\\n self.stop_event.set()\",\n \"def stop(self):\\n self.stop_event.set()\",\n \"def stop(self):\\n self._stop_event.set()\",\n \"def stop(self):\\r\\n self.stopped = True\",\n \"def stop(self):\\n self._stop_event.set()\",\n \"def stop(self):\\n self._stop_event.set()\",\n \"def stop(self):\\n\\n self.keep_running = False\",\n \"def stop(self):\\n self.stopped = True\",\n \"def stop(self):\\n self.stopped = True\",\n \"def stop(self):\\n self.ids.camera.opacity = 0\\n self.ids.camera.play = False\\n self.ids.start.text = 'Start Camera'\\n self.ids.camera.texture = None\",\n \"def stopit(self):\\n\\n self.stop.stop()\\n self.stream.close()\\n self.p.terminate()\\n self.p = None\\n\\n print(\\\"Recording terminated!\\\")\",\n \"def stop(self):\\n self.exit.set()\",\n \"def stop(self):\\n self.exit.set()\",\n \"def stop(self):\\n self._kill_event.set()\",\n \"def stop(self):\\r\\n self.running = False\",\n \"def stop(self):\\r\\n self.running = False\",\n \"def stop(self):\\n self.stop_recognising.set()\\n self.thread.join()\",\n \"def stop(self):\\n self.running = False\",\n \"def stop(self):\\n self.running = False\",\n \"def stop(self):\\n self.running = False\",\n \"def stop(self):\\n self.running = False\",\n \"def stop(self):\\n self.running = False\",\n \"def stop(self):\\n try:\\n self.logger.debug('Halting VideoSave thread')\\n cv2.destroyAllWindows()\\n except:\\n self.logger.exception('Exception while halting VideoSave')\\n super().stop()\",\n \"def stop(self):\\n self.on_stop()\",\n \"def stop() -> None:\",\n \"def exit_engine(self):\\n self.stop_flag = True\",\n \"def on_Exit_button_clicked(self):\\n self.cam.release()\\n self.close()\",\n \"def stop(self):\\n self.ctrl_obj.finish = True\",\n \"def stop(self):\\n self.ctrl_obj.finish = True\",\n \"def stop(self):\\n self.stopped = True\",\n \"def quit(self):\\r\\n self.unbind()\\r\\n self.videoPlayer.stop()\\r\\n self.saveConfig()\\r\\n self.root.destroy()\",\n \"def stop(self):\\n self._stop_flag = True\",\n \"def _stop(self):\\n self._pi.stop()\",\n \"def stop(self):\\n self.ids.camera.opacity = 0\\n self.ids.camera.play = False\\n self.ids.camera_button.text = \\\"Start Camera\\\"\\n self.ids.camera.texture=None\",\n \"def stop(self):\\n self._stop_event.set()\\n super().stop()\",\n \"def stop(self):\\n self._stop_signal = True\",\n \"def shutdown(signal=None, frame=None):\\n\\n print(\\\"Shutting down\\\")\\n\\n # Close the camera\\n camera.shutdown()\\n\\n time.sleep(0.25)\\n sys.exit(0)\",\n \"def stop():\",\n \"def stop():\",\n \"def stop():\",\n \"def stop():\",\n \"def stop(self) -> None:\\n ...\",\n \"def stop(self):\\n\\n self.stop_thread = True\",\n \"def stop(self):\\n self.__running = False\",\n \"def trigger_stop(self):\\n self.terminate = True\\n self.stop_event.set()\",\n \"def stop_hik(self, event):\\n self.camdata.disconnect()\",\n \"def stop(self):\\n self._running = False\",\n \"def stop(self):\\n self._running = False\",\n \"def stop(self):\\r\\n self._stop.set()\",\n \"def stop(self):\\n with self.stop_event_lock:\\n self.stop_event.set()\\n self.clean_up()\",\n \"def _stop(self) -> None:\\n self._stopped.set()\",\n \"def stop(self) -> None:\\n self._running = False\",\n \"def stop(self):\\n GameEngine().stop()\\n self.on_stop()\",\n \"def stop(self):\\n\\n self.detach()\\n self._pi.stop()\\n self._stopped = True\",\n \"def stop(self) -> None:\\n self.system.notify(\\\"Jarvis::Stop\\\")\\n self.media.stop()\",\n \"def stop_recording(self):\\n self.timer.stop()\\n self.camera.release()\",\n \"def stop(self):\\n self._stop = True\\n self.wake_up()\",\n \"def stop(self):\\n self.recording = False\",\n \"def stop(self) -> None:\",\n \"def stop(self) -> None:\",\n \"def stop (self):\\n pass\",\n \"def stop (self):\\n pass\",\n \"def stop(self):\\n self._run = False\",\n \"def stop(self):\\n self._run_flag = False\\n self.wait()\",\n \"def stop(self):\\n self._run_flag = False\\n self.wait()\",\n \"def stop(self):\\n self._run_flag = False\\n self.wait()\",\n \"def stop(self):\\n self._run_flag = False\\n self.wait()\",\n \"def stop(self):\\n self._run_flag = False\\n self.wait()\",\n \"def stop(self):\\n self._run_flag = False\\n self.wait()\",\n \"def stop(self):\\r\\n self.terminating = True\",\n \"def stop(self):\\r\\n pass\",\n \"def stop(self):\\n\\t\\tself._run_flag = False\\n\\t\\tself.wait()\",\n \"def stop(self):\\r\\n self.stopped = True\\r\\n time.sleep(1)\",\n \"def stop(self):\\n self._stop.set()\",\n \"def stop(self):\\n pass\",\n \"def stop(self):\\n pass\",\n \"def stop(self):\\n pass\",\n \"def stop(self):\\n pass\",\n \"def stop(self):\\n pass\",\n \"def stop(self):\\n pass\",\n \"def stop(self):\\n pass\",\n \"def stop(self):\\n pass\",\n \"def stop(self):\\n pass\",\n \"def stop(self):\\n pass\",\n \"def stop(self):\\n pass\",\n \"def quit(self):\\n self.stop()\\n self.handler = None\",\n \"def event_stop(self, **kwargs):\\n del kwargs\\n self.stop()\",\n \"def stop(self):\\r\\n self.stopped = True\\r\\n time.sleep(3)\",\n \"def stop(self):\\n self.config(image=None)\\n self.frames = None\",\n \"def stop(self):\\n if not self.__started:\\n return\\n\\n self.__started = False\\n self.__gi_loop.quit()\",\n \"def stop_recording(self):\\n self.flag_event.clear()\\n self.statusBar().showMessage('Recording Stopped')\\n print('boo ya')\",\n \"def foreceStop(self):\\n self.__success = False\\n self.stop()\",\n \"def stop(self):\\n\\t\\tpass\",\n \"def stop(self):\",\n \"def stop(self):\",\n \"def stop(self):\\n with self.stop_lock:\\n self._stop_event.set()\",\n \"def stop_live(self):\\n self._toolbox.disable_streaming_flag()\\n self._toolbox.disable_webcam(self._index)\\n self._prompt.kill()\",\n \"def _device_stop_capture(self):\\n\\n # TODO: we may want to provide an option to flush the SDRam buffer here before capture stops?\\n self._stop_capture_to_ram()\\n self._stop_streaming_ram_to_host()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7624942","0.76044846","0.7556506","0.744567","0.744567","0.7432174","0.7239621","0.72074336","0.72074336","0.72029865","0.71938837","0.71938837","0.7178905","0.7176315","0.71751827","0.71751827","0.71670294","0.7162203","0.7162203","0.7145708","0.71312815","0.71312815","0.71312815","0.71312815","0.71312815","0.70994264","0.70865154","0.7080489","0.70803946","0.70728993","0.70708185","0.70708185","0.707058","0.7064293","0.7045672","0.7041951","0.70373625","0.7035896","0.70307446","0.7028414","0.70254904","0.70254904","0.70254904","0.70254904","0.7024721","0.7012498","0.7010504","0.7008933","0.699593","0.6989294","0.6989294","0.69786173","0.69695574","0.69587225","0.6941365","0.69248265","0.69232655","0.6910736","0.6905434","0.69043523","0.6896815","0.68850344","0.68850344","0.6877282","0.6877282","0.68734246","0.68684316","0.68684316","0.68684316","0.68684316","0.68684316","0.68684316","0.68663067","0.68619823","0.6861425","0.6857583","0.6857394","0.68517077","0.68517077","0.68517077","0.68517077","0.68517077","0.68517077","0.68517077","0.68517077","0.68517077","0.68517077","0.68517077","0.68343776","0.6833359","0.6830253","0.6819945","0.68044025","0.67971945","0.6795798","0.6792212","0.6791999","0.6791999","0.6784606","0.67763513","0.6773937"],"string":"[\n \"0.7624942\",\n \"0.76044846\",\n \"0.7556506\",\n \"0.744567\",\n \"0.744567\",\n \"0.7432174\",\n \"0.7239621\",\n \"0.72074336\",\n \"0.72074336\",\n \"0.72029865\",\n \"0.71938837\",\n \"0.71938837\",\n \"0.7178905\",\n \"0.7176315\",\n \"0.71751827\",\n \"0.71751827\",\n \"0.71670294\",\n \"0.7162203\",\n \"0.7162203\",\n \"0.7145708\",\n \"0.71312815\",\n \"0.71312815\",\n \"0.71312815\",\n \"0.71312815\",\n \"0.71312815\",\n \"0.70994264\",\n \"0.70865154\",\n \"0.7080489\",\n \"0.70803946\",\n \"0.70728993\",\n \"0.70708185\",\n \"0.70708185\",\n \"0.707058\",\n \"0.7064293\",\n \"0.7045672\",\n \"0.7041951\",\n \"0.70373625\",\n \"0.7035896\",\n \"0.70307446\",\n \"0.7028414\",\n \"0.70254904\",\n \"0.70254904\",\n \"0.70254904\",\n \"0.70254904\",\n \"0.7024721\",\n \"0.7012498\",\n \"0.7010504\",\n \"0.7008933\",\n \"0.699593\",\n \"0.6989294\",\n \"0.6989294\",\n \"0.69786173\",\n \"0.69695574\",\n \"0.69587225\",\n \"0.6941365\",\n \"0.69248265\",\n \"0.69232655\",\n \"0.6910736\",\n \"0.6905434\",\n \"0.69043523\",\n \"0.6896815\",\n \"0.68850344\",\n \"0.68850344\",\n \"0.6877282\",\n \"0.6877282\",\n \"0.68734246\",\n \"0.68684316\",\n \"0.68684316\",\n \"0.68684316\",\n \"0.68684316\",\n \"0.68684316\",\n \"0.68684316\",\n \"0.68663067\",\n \"0.68619823\",\n \"0.6861425\",\n \"0.6857583\",\n \"0.6857394\",\n \"0.68517077\",\n \"0.68517077\",\n \"0.68517077\",\n \"0.68517077\",\n \"0.68517077\",\n \"0.68517077\",\n \"0.68517077\",\n \"0.68517077\",\n \"0.68517077\",\n \"0.68517077\",\n \"0.68517077\",\n \"0.68343776\",\n \"0.6833359\",\n \"0.6830253\",\n \"0.6819945\",\n \"0.68044025\",\n \"0.67971945\",\n \"0.6795798\",\n \"0.6792212\",\n \"0.6791999\",\n \"0.6791999\",\n \"0.6784606\",\n \"0.67763513\",\n \"0.6773937\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":20083,"cells":{"query":{"kind":"string","value":"example action using the internationalization operator T and flash rendered by views/default/index.html or views/generic.html"},"document":{"kind":"string","value":"def index():\n \n return dict(message=T('Welcome to Audi Volkswagon Porsche'))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def index(request, *args, **kwargs):\n return Response({\"message\":\"Nobody expects the spanish inquisition!\"})","def index():\n return dict(message=T('Hello World'))","def index():\n response.flash = \"Welcome to Myapp!\"\n return dict(message=T('Hello World'))","def index_en(request):\n context = {'is_logged_in': request.user.is_authenticated}\n return render(request, 'sacms/index_en.html', context)","def test(done = None):\n print(inspect.stack()[1][3])\n if(done):\n flash('New Entry Done!')\n return render_template('webpage/index1.html')","def index():\n \n return dict(message=T('Welcome to Learn2Cook!'))","def trialpage(request):\n template = 'PhysioWat/index.html'\n context = {'l':['a','b','c']}\n return render(request, template, context)","def notifications():\n flash(\"primary\", \"primary\")\n flash(\"secondary\", \"secondary\")\n flash(\"success\", \"success\")\n flash(\"danger\", \"danger\")\n flash(\"warning\", \"warning\")\n flash(\"info\", \"info\")\n return render_template(\"notifications.html\")","def notice_en(request, notice_id):\n notice = get_object_or_404(Notice, pk=notice_id)\n context = {'notice': notice}\n return render(request, 'sacms/notice_en.html', context)","def render(request, *args, **kw):","def flash(text, type=INFO):\n flash_message['text'] = text\n flash_message['type'] = type","def interested_act(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/interested.html',\n context_instance=RequestContext(request,\n {\n 'title': 'Which Act are you interested in?',\n 'year': datetime.now().year,\n })\n )","def test_i18n11(self):\n output = self.engine.render_to_string('i18n11', {'bool': True})\n self.assertEqual(output, 'ja')","def pagemainred():\n return render_template('do_action.html')","def show_message(request):\n return render_to_response('woodstock/messages/view.html', {},\n context_instance = RequestContext(request))","def index(request):\n\n logger.debug(\"index\")\n\n context = {\n 'text': \"Sir!\",\n }\n\n return render(request, 'general_website/index.html', context=context)","def _test (self) :\n print (_T (ckw.title or \"Baz\"))\n print (_T (\"Foo\"))\n foo = _(\"Markup %d\")\n print (_T(foo) % 42)\n print (_Tn (\"Singular\", \"Plural\", 4))","def entry_page() -> 'html':\n return render_template('entry.html',\n enry_language_list = l_list,\n\t\t\t\t\t\t enry_language_list_01 = l_list_01,\n the_title='欢迎来到翻译吧')","def about_view(request):\n return {'message': 'Info about us.'}","def say_thanks():\n return render_template(\"completion.html\")","def message_page(request,page_name):\n return HttpResponse(\"This will be the {0} page.\".format(page_name))","def index():\n response.flash = T(\"Hello World\")\n return dict(message=T('Welcome to web2py!'))","def index():\n response.flash = T(\"Hello World\")\n return dict(message=T('Welcome to web2py!'))","def test():\n return render_template(\n 'test.html',\n title='Test',\n time=datetime.now(),\n message='test your model',\n )","def __display_form(self, template, message=None):\r\n # page = memcache.get(str(language_code) + key, namespace='Pages')\r\n # if page is None:\r\n template_values = {\r\n 'user_email': self.user_email,\r\n constants.VAR_NAME_ERRORMESSAGE: message\r\n }\r\n template = self.jinja2_env.get_template(template)\r\n page = template.render(template_values)\r\n # memcache.add(str(language_code) + key, page, namespace='Pages')\r\n self.response.out.write(page)","def index():\n return 'There is nothing here.'","def t(message):\n\n tpl = string.Template(message)\n return tpl.substitute(country=settings.COUNTRY_NAME, language=settings.LANGUAGE_NAME)","def index(request, err_msg=None):\n template = loader.get_template(\"aimodel/index.html\")\n context = {}\n\n context[\"err_msg\"] = err_msg\n\n return HttpResponse(template.render(context, request))","def test_i18n23(self):\n with translation.override('de'):\n output = self.engine.render_to_string('i18n23')\n self.assertEqual(output, 'nicht gefunden')","def test_i18n28(self):\n output = self.engine.render_to_string('i18n28', {'anton': 'α', 'berta': 'β'})\n self.assertEqual(output, 'α + β')","def test_i18n28(self):\n output = self.engine.render_to_string('i18n28', {'anton': 'α', 'berta': 'β'})\n self.assertEqual(output, 'α + β')","def test_i18n26(self):\n output = self.engine.render_to_string('i18n26', {'myextra_field': 'test', 'number': 1})\n self.assertEqual(output, 'singular test')","def test_i18n26(self):\n output = self.engine.render_to_string('i18n26', {'myextra_field': 'test', 'number': 1})\n self.assertEqual(output, 'singular test')","def index():\n #response.flash = \"Test\"\n #return auth.wiki()\n redirect(URL('bot_admin'))\n return dict()","def translate():\n pass","def handle_request():\n # Fetch and return a translated string.\n # This is the interesting bit, from a client's point-of-view.\n print _('bli'), _('bla'), _('blo')\n\n # Do something else.\n time.sleep(random.random())","def index(request):\n return HttpResponse(\"Testing\")","def test_i18n26(self):\n output = self.engine.render_to_string(\n \"i18n26\", {\"myextra_field\": \"test\", \"number\": 1}\n )\n self.assertEqual(output, \"singular test\")","def index(request):\n form = textForm()\n return render(request, 'index.html', {'form': form})","def to(request):\n cart = Cart(request)\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/to.html',\n {\n 'title':'ТО автомобиля',\n 'cart': cart,\n 'message':'Техническое обслуживание автомобиля (ТО авто) – совокупность действий, предпринимаемых в целях создания безопасных условий использования транспортного средства. ',\n 'year':datetime.now().year,\n }\n )","def render( context, *args, **kwargs ):","def t(eng, chinese):\n return chinese if 'zh' in get_info().user_language else eng","def status(request):\n return HttpResponse('Hi, I am healty!')","def how_to(request):\n return render(request, 'metro_app/how_to.html')","def test_i18n10(self):\n with translation.override('de'):\n output = self.engine.render_to_string('i18n10', {'bool': True})\n self.assertEqual(output, 'Ja')","def torm(request):\n cart = Cart(request)\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/torm.html',\n {\n 'title':'Тормозная система',\n 'cart': cart,\n 'message':'Важность своевременного обслуживания тормозной системы.',\n 'year':datetime.now().year,\n }\n )","def test_i18n28(self):\n output = self.engine.render_to_string(\"i18n28\", {\"anton\": \"α\", \"berta\": \"β\"})\n self.assertEqual(output, \"α + β\")","def help(request):\n\tlang = request.GET['language']\n\ttry:\n\t\thlp = Help.objects.get(language=lang)\n\texcept Help.DoesNotExist:\n\t\traise Http404\n\treturn HttpResponse(hlp.text)","def test_i18n09(self):\n with translation.override('de'):\n output = self.engine.render_to_string('i18n09')\n self.assertEqual(output, 'Page not found')","def get(self, request, *args, **kwargs):\n return HttpResponse('It\\'s working ✨')","def say(*args):\n try:\n ttsEng.say(args[0])\n except Exception, e:\n logging.error(e)","def vote_act(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/vote.html',\n context_instance=RequestContext(request,\n {\n 'title': 'Vote the act',\n 'year': datetime.now().year,\n })\n )","def new(self):\n flash_message(_(\"success message\"), \"success\")\n flash_message(_(\"warning message\"), \"warning\")\n flash_message(_(\"error message\"), \"error\")\n flash_message(_(\"notice message\"), \"notice\")\n return render('/derived/rock/new.mako')","def test_i18n01(self):\n output = self.engine.render_to_string('i18n01')\n self.assertEqual(output, 'xxxyyyxxx')","def callForTutorials(request):\n # Test Comment\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'callForTutorials.html',\n context_instance=RequestContext(request, {})\n )","def help(request):\n return render(request, 'collab_user_views/help.html', {})","def get(self, request, *args, **kwargs):\n return render(request, self.template_name)","def get(self, request, *args, **kwargs):\n return render(request, self.template_name)","def get(self, request, *args, **kwargs):\n return render(request, self.template_name)","def get(self, request, *args, **kwargs):\n return render(request, self.template_name)","def get(self, request, *args, **kwargs):\n return render(request, self.template_name)","def say_hello():\n\n return render_template(\"hello.html\")","def say_hello():\n\n return render_template(\"hello.html\")","def say_hello():\n\n return render_template(\"hello.html\")","def say_hello():\n\n return render_template(\"hello.html\")","def say_hello():\n\n return render_template(\"hello.html\")","def get(self, request, *args, **kwargs):\n return render(request, 'tweets/index.html', {'form': self.form_class(user=request.user)})","def render( request, etype, value, tb ):","def index():\n response.flash = T(\"Welcome to web2py!\")\n return dict(message=T('Hello World'))","def thanks():\n return render_template('submitted.html')","def instruction():\n return render_template(\n 'instruction.html',\n title='说明',\n year=datetime.now().year,\n message='Instruction'\n )","def ui_view(request):\n\treturn render(request, 'sett_ui_view.html', {})","def index(request):\n\treturn render(request, 'Toeic/index.html')","def test_single_locale_activation(self):\n with translation.override('fr'):\n self.assertEqual(\n Template(\"{% load i18n %}{% blocktrans %}Yes{% endblocktrans %}\").render(Context({})),\n 'Oui'\n )","def welcome(request):\n return render(request, 'code_challenge/welcome.html', {})","def index(request):\r\n\treturn render(request, 'templates/index.html')","def translate(self, action):\r\n self.current_language = str(action.data().toString()).strip(\"tr_\").rstrip(\".qm\")\r\n\r\n log.info(\"Switching language to: %s\" % action.text())\r\n self.uiTranslator.load(\":/languages/tr_%s.qm\" % self.current_language)\r\n self.app.installTranslator(self.uiTranslator)\r\n\r\n self.retranslateFreeseerApp()\r\n self.aboutDialog.aboutWidget.retranslate(self.current_language)\r\n self.retranslate()","def get(self, request) -> render:\n context = self.get_menu_context('music', 'Загрузка музыки')\n context['music_pages'] = 'upload'\n\n context['form'] = UploadMusicForm()\n\n return render(request, self.template_name, context)","def about_us():\r\n return render_template(\"about_us.html\")","def test_bad_placeholder_1(self):\n with translation.override('fr'):\n t = Template('{% load i18n %}{% blocktrans %}My name is {{ person }}.{% endblocktrans %}')\n rendered = t.render(Context({'person': 'James'}))\n self.assertEqual(rendered, 'My name is James.')","def info_pollu(request):\r\n return render(request, 'info_pollu.html')","def jaamasTrack(request):\n # Test Comment\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'jaamas.html',\n context_instance=RequestContext(request, {})\n )","def index():\n if request.method == 'POST':\n phrase = request.form\n if phrase['form_type'] == 'get_sentiment':\n sent = get_sentiment(phrase['phrase'])\n if sent == 1:\n return render_template('index_neutral.html')\n elif sent == 2:\n return render_template('index_positive.html')\n elif sent == 0:\n return render_template('index_negative.html')\n return render_template('index.html')","def test_single_locale_activation(self):\n with translation.override(\"fr\"):\n self.assertEqual(\n self.get_template(\n \"{% load i18n %}{% blocktranslate %}Yes{% endblocktranslate %}\"\n ).render(Context({})),\n \"Oui\",\n )","def about_us(request):\n\treturn render(request, 'about_us.html')","def test_bad_placeholder_2(self):\n with translation.override('fr'):\n t = Template('{% load i18n %}{% blocktrans %}My other name is {{ person }}.{% endblocktrans %}')\n rendered = t.render(Context({'person': 'James'}))\n self.assertEqual(rendered, 'My other name is James.')","def post(self, request, *args, **kwargs):\n return render(request, self.template_name, self.get_context_data(**kwargs))","def add_song():\n return render_template('pong!')","def tourism(request):\n # Test Comment\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'tourism.html',\n context_instance=RequestContext(request, {})\n )","def index(request):\n template = loader.get_template('index.html')\n context = {\n\n }\n return HttpResponse(template.render(context, request))","def test_i18n05(self):\n output = self.engine.render_to_string('i18n05', {'anton': 'yyy'})\n self.assertEqual(output, 'xxxyyyxxx')","def test_i18n05(self):\n output = self.engine.render_to_string('i18n05', {'anton': 'yyy'})\n self.assertEqual(output, 'xxxyyyxxx')","def index(request):\n return render(request, 'std/index.html')","def thanks(request):\n return render(request, 'SNP_Feature_View/thanks.html')","def index():\n print(\"Inside index()\")\n if \"display_name\" not in session:\n return render_template(\"create_account.html\")\n\n return f\"Hello, {session['display_name']}\"","def index(request):\n return render_to_response('todo/index.html')","def index():\n return 'Welcome to the resource manager!'","def feedback():\n return render_template(\"feedback.html\")","def wyniki():\n return render_template('results.html', result=request.args.get('result'))","def about() -> Any:\n return render_template(\"about.html\")"],"string":"[\n \"def index(request, *args, **kwargs):\\n return Response({\\\"message\\\":\\\"Nobody expects the spanish inquisition!\\\"})\",\n \"def index():\\n return dict(message=T('Hello World'))\",\n \"def index():\\n response.flash = \\\"Welcome to Myapp!\\\"\\n return dict(message=T('Hello World'))\",\n \"def index_en(request):\\n context = {'is_logged_in': request.user.is_authenticated}\\n return render(request, 'sacms/index_en.html', context)\",\n \"def test(done = None):\\n print(inspect.stack()[1][3])\\n if(done):\\n flash('New Entry Done!')\\n return render_template('webpage/index1.html')\",\n \"def index():\\n \\n return dict(message=T('Welcome to Learn2Cook!'))\",\n \"def trialpage(request):\\n template = 'PhysioWat/index.html'\\n context = {'l':['a','b','c']}\\n return render(request, template, context)\",\n \"def notifications():\\n flash(\\\"primary\\\", \\\"primary\\\")\\n flash(\\\"secondary\\\", \\\"secondary\\\")\\n flash(\\\"success\\\", \\\"success\\\")\\n flash(\\\"danger\\\", \\\"danger\\\")\\n flash(\\\"warning\\\", \\\"warning\\\")\\n flash(\\\"info\\\", \\\"info\\\")\\n return render_template(\\\"notifications.html\\\")\",\n \"def notice_en(request, notice_id):\\n notice = get_object_or_404(Notice, pk=notice_id)\\n context = {'notice': notice}\\n return render(request, 'sacms/notice_en.html', context)\",\n \"def render(request, *args, **kw):\",\n \"def flash(text, type=INFO):\\n flash_message['text'] = text\\n flash_message['type'] = type\",\n \"def interested_act(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/interested.html',\\n context_instance=RequestContext(request,\\n {\\n 'title': 'Which Act are you interested in?',\\n 'year': datetime.now().year,\\n })\\n )\",\n \"def test_i18n11(self):\\n output = self.engine.render_to_string('i18n11', {'bool': True})\\n self.assertEqual(output, 'ja')\",\n \"def pagemainred():\\n return render_template('do_action.html')\",\n \"def show_message(request):\\n return render_to_response('woodstock/messages/view.html', {},\\n context_instance = RequestContext(request))\",\n \"def index(request):\\n\\n logger.debug(\\\"index\\\")\\n\\n context = {\\n 'text': \\\"Sir!\\\",\\n }\\n\\n return render(request, 'general_website/index.html', context=context)\",\n \"def _test (self) :\\n print (_T (ckw.title or \\\"Baz\\\"))\\n print (_T (\\\"Foo\\\"))\\n foo = _(\\\"Markup %d\\\")\\n print (_T(foo) % 42)\\n print (_Tn (\\\"Singular\\\", \\\"Plural\\\", 4))\",\n \"def entry_page() -> 'html':\\n return render_template('entry.html',\\n enry_language_list = l_list,\\n\\t\\t\\t\\t\\t\\t enry_language_list_01 = l_list_01,\\n the_title='欢迎来到翻译吧')\",\n \"def about_view(request):\\n return {'message': 'Info about us.'}\",\n \"def say_thanks():\\n return render_template(\\\"completion.html\\\")\",\n \"def message_page(request,page_name):\\n return HttpResponse(\\\"This will be the {0} page.\\\".format(page_name))\",\n \"def index():\\n response.flash = T(\\\"Hello World\\\")\\n return dict(message=T('Welcome to web2py!'))\",\n \"def index():\\n response.flash = T(\\\"Hello World\\\")\\n return dict(message=T('Welcome to web2py!'))\",\n \"def test():\\n return render_template(\\n 'test.html',\\n title='Test',\\n time=datetime.now(),\\n message='test your model',\\n )\",\n \"def __display_form(self, template, message=None):\\r\\n # page = memcache.get(str(language_code) + key, namespace='Pages')\\r\\n # if page is None:\\r\\n template_values = {\\r\\n 'user_email': self.user_email,\\r\\n constants.VAR_NAME_ERRORMESSAGE: message\\r\\n }\\r\\n template = self.jinja2_env.get_template(template)\\r\\n page = template.render(template_values)\\r\\n # memcache.add(str(language_code) + key, page, namespace='Pages')\\r\\n self.response.out.write(page)\",\n \"def index():\\n return 'There is nothing here.'\",\n \"def t(message):\\n\\n tpl = string.Template(message)\\n return tpl.substitute(country=settings.COUNTRY_NAME, language=settings.LANGUAGE_NAME)\",\n \"def index(request, err_msg=None):\\n template = loader.get_template(\\\"aimodel/index.html\\\")\\n context = {}\\n\\n context[\\\"err_msg\\\"] = err_msg\\n\\n return HttpResponse(template.render(context, request))\",\n \"def test_i18n23(self):\\n with translation.override('de'):\\n output = self.engine.render_to_string('i18n23')\\n self.assertEqual(output, 'nicht gefunden')\",\n \"def test_i18n28(self):\\n output = self.engine.render_to_string('i18n28', {'anton': 'α', 'berta': 'β'})\\n self.assertEqual(output, 'α + β')\",\n \"def test_i18n28(self):\\n output = self.engine.render_to_string('i18n28', {'anton': 'α', 'berta': 'β'})\\n self.assertEqual(output, 'α + β')\",\n \"def test_i18n26(self):\\n output = self.engine.render_to_string('i18n26', {'myextra_field': 'test', 'number': 1})\\n self.assertEqual(output, 'singular test')\",\n \"def test_i18n26(self):\\n output = self.engine.render_to_string('i18n26', {'myextra_field': 'test', 'number': 1})\\n self.assertEqual(output, 'singular test')\",\n \"def index():\\n #response.flash = \\\"Test\\\"\\n #return auth.wiki()\\n redirect(URL('bot_admin'))\\n return dict()\",\n \"def translate():\\n pass\",\n \"def handle_request():\\n # Fetch and return a translated string.\\n # This is the interesting bit, from a client's point-of-view.\\n print _('bli'), _('bla'), _('blo')\\n\\n # Do something else.\\n time.sleep(random.random())\",\n \"def index(request):\\n return HttpResponse(\\\"Testing\\\")\",\n \"def test_i18n26(self):\\n output = self.engine.render_to_string(\\n \\\"i18n26\\\", {\\\"myextra_field\\\": \\\"test\\\", \\\"number\\\": 1}\\n )\\n self.assertEqual(output, \\\"singular test\\\")\",\n \"def index(request):\\n form = textForm()\\n return render(request, 'index.html', {'form': form})\",\n \"def to(request):\\n cart = Cart(request)\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/to.html',\\n {\\n 'title':'ТО автомобиля',\\n 'cart': cart,\\n 'message':'Техническое обслуживание автомобиля (ТО авто) – совокупность действий, предпринимаемых в целях создания безопасных условий использования транспортного средства. ',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def render( context, *args, **kwargs ):\",\n \"def t(eng, chinese):\\n return chinese if 'zh' in get_info().user_language else eng\",\n \"def status(request):\\n return HttpResponse('Hi, I am healty!')\",\n \"def how_to(request):\\n return render(request, 'metro_app/how_to.html')\",\n \"def test_i18n10(self):\\n with translation.override('de'):\\n output = self.engine.render_to_string('i18n10', {'bool': True})\\n self.assertEqual(output, 'Ja')\",\n \"def torm(request):\\n cart = Cart(request)\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/torm.html',\\n {\\n 'title':'Тормозная система',\\n 'cart': cart,\\n 'message':'Важность своевременного обслуживания тормозной системы.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def test_i18n28(self):\\n output = self.engine.render_to_string(\\\"i18n28\\\", {\\\"anton\\\": \\\"α\\\", \\\"berta\\\": \\\"β\\\"})\\n self.assertEqual(output, \\\"α + β\\\")\",\n \"def help(request):\\n\\tlang = request.GET['language']\\n\\ttry:\\n\\t\\thlp = Help.objects.get(language=lang)\\n\\texcept Help.DoesNotExist:\\n\\t\\traise Http404\\n\\treturn HttpResponse(hlp.text)\",\n \"def test_i18n09(self):\\n with translation.override('de'):\\n output = self.engine.render_to_string('i18n09')\\n self.assertEqual(output, 'Page not found')\",\n \"def get(self, request, *args, **kwargs):\\n return HttpResponse('It\\\\'s working ✨')\",\n \"def say(*args):\\n try:\\n ttsEng.say(args[0])\\n except Exception, e:\\n logging.error(e)\",\n \"def vote_act(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/vote.html',\\n context_instance=RequestContext(request,\\n {\\n 'title': 'Vote the act',\\n 'year': datetime.now().year,\\n })\\n )\",\n \"def new(self):\\n flash_message(_(\\\"success message\\\"), \\\"success\\\")\\n flash_message(_(\\\"warning message\\\"), \\\"warning\\\")\\n flash_message(_(\\\"error message\\\"), \\\"error\\\")\\n flash_message(_(\\\"notice message\\\"), \\\"notice\\\")\\n return render('/derived/rock/new.mako')\",\n \"def test_i18n01(self):\\n output = self.engine.render_to_string('i18n01')\\n self.assertEqual(output, 'xxxyyyxxx')\",\n \"def callForTutorials(request):\\n # Test Comment\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'callForTutorials.html',\\n context_instance=RequestContext(request, {})\\n )\",\n \"def help(request):\\n return render(request, 'collab_user_views/help.html', {})\",\n \"def get(self, request, *args, **kwargs):\\n return render(request, self.template_name)\",\n \"def get(self, request, *args, **kwargs):\\n return render(request, self.template_name)\",\n \"def get(self, request, *args, **kwargs):\\n return render(request, self.template_name)\",\n \"def get(self, request, *args, **kwargs):\\n return render(request, self.template_name)\",\n \"def get(self, request, *args, **kwargs):\\n return render(request, self.template_name)\",\n \"def say_hello():\\n\\n return render_template(\\\"hello.html\\\")\",\n \"def say_hello():\\n\\n return render_template(\\\"hello.html\\\")\",\n \"def say_hello():\\n\\n return render_template(\\\"hello.html\\\")\",\n \"def say_hello():\\n\\n return render_template(\\\"hello.html\\\")\",\n \"def say_hello():\\n\\n return render_template(\\\"hello.html\\\")\",\n \"def get(self, request, *args, **kwargs):\\n return render(request, 'tweets/index.html', {'form': self.form_class(user=request.user)})\",\n \"def render( request, etype, value, tb ):\",\n \"def index():\\n response.flash = T(\\\"Welcome to web2py!\\\")\\n return dict(message=T('Hello World'))\",\n \"def thanks():\\n return render_template('submitted.html')\",\n \"def instruction():\\n return render_template(\\n 'instruction.html',\\n title='说明',\\n year=datetime.now().year,\\n message='Instruction'\\n )\",\n \"def ui_view(request):\\n\\treturn render(request, 'sett_ui_view.html', {})\",\n \"def index(request):\\n\\treturn render(request, 'Toeic/index.html')\",\n \"def test_single_locale_activation(self):\\n with translation.override('fr'):\\n self.assertEqual(\\n Template(\\\"{% load i18n %}{% blocktrans %}Yes{% endblocktrans %}\\\").render(Context({})),\\n 'Oui'\\n )\",\n \"def welcome(request):\\n return render(request, 'code_challenge/welcome.html', {})\",\n \"def index(request):\\r\\n\\treturn render(request, 'templates/index.html')\",\n \"def translate(self, action):\\r\\n self.current_language = str(action.data().toString()).strip(\\\"tr_\\\").rstrip(\\\".qm\\\")\\r\\n\\r\\n log.info(\\\"Switching language to: %s\\\" % action.text())\\r\\n self.uiTranslator.load(\\\":/languages/tr_%s.qm\\\" % self.current_language)\\r\\n self.app.installTranslator(self.uiTranslator)\\r\\n\\r\\n self.retranslateFreeseerApp()\\r\\n self.aboutDialog.aboutWidget.retranslate(self.current_language)\\r\\n self.retranslate()\",\n \"def get(self, request) -> render:\\n context = self.get_menu_context('music', 'Загрузка музыки')\\n context['music_pages'] = 'upload'\\n\\n context['form'] = UploadMusicForm()\\n\\n return render(request, self.template_name, context)\",\n \"def about_us():\\r\\n return render_template(\\\"about_us.html\\\")\",\n \"def test_bad_placeholder_1(self):\\n with translation.override('fr'):\\n t = Template('{% load i18n %}{% blocktrans %}My name is {{ person }}.{% endblocktrans %}')\\n rendered = t.render(Context({'person': 'James'}))\\n self.assertEqual(rendered, 'My name is James.')\",\n \"def info_pollu(request):\\r\\n return render(request, 'info_pollu.html')\",\n \"def jaamasTrack(request):\\n # Test Comment\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'jaamas.html',\\n context_instance=RequestContext(request, {})\\n )\",\n \"def index():\\n if request.method == 'POST':\\n phrase = request.form\\n if phrase['form_type'] == 'get_sentiment':\\n sent = get_sentiment(phrase['phrase'])\\n if sent == 1:\\n return render_template('index_neutral.html')\\n elif sent == 2:\\n return render_template('index_positive.html')\\n elif sent == 0:\\n return render_template('index_negative.html')\\n return render_template('index.html')\",\n \"def test_single_locale_activation(self):\\n with translation.override(\\\"fr\\\"):\\n self.assertEqual(\\n self.get_template(\\n \\\"{% load i18n %}{% blocktranslate %}Yes{% endblocktranslate %}\\\"\\n ).render(Context({})),\\n \\\"Oui\\\",\\n )\",\n \"def about_us(request):\\n\\treturn render(request, 'about_us.html')\",\n \"def test_bad_placeholder_2(self):\\n with translation.override('fr'):\\n t = Template('{% load i18n %}{% blocktrans %}My other name is {{ person }}.{% endblocktrans %}')\\n rendered = t.render(Context({'person': 'James'}))\\n self.assertEqual(rendered, 'My other name is James.')\",\n \"def post(self, request, *args, **kwargs):\\n return render(request, self.template_name, self.get_context_data(**kwargs))\",\n \"def add_song():\\n return render_template('pong!')\",\n \"def tourism(request):\\n # Test Comment\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'tourism.html',\\n context_instance=RequestContext(request, {})\\n )\",\n \"def index(request):\\n template = loader.get_template('index.html')\\n context = {\\n\\n }\\n return HttpResponse(template.render(context, request))\",\n \"def test_i18n05(self):\\n output = self.engine.render_to_string('i18n05', {'anton': 'yyy'})\\n self.assertEqual(output, 'xxxyyyxxx')\",\n \"def test_i18n05(self):\\n output = self.engine.render_to_string('i18n05', {'anton': 'yyy'})\\n self.assertEqual(output, 'xxxyyyxxx')\",\n \"def index(request):\\n return render(request, 'std/index.html')\",\n \"def thanks(request):\\n return render(request, 'SNP_Feature_View/thanks.html')\",\n \"def index():\\n print(\\\"Inside index()\\\")\\n if \\\"display_name\\\" not in session:\\n return render_template(\\\"create_account.html\\\")\\n\\n return f\\\"Hello, {session['display_name']}\\\"\",\n \"def index(request):\\n return render_to_response('todo/index.html')\",\n \"def index():\\n return 'Welcome to the resource manager!'\",\n \"def feedback():\\n return render_template(\\\"feedback.html\\\")\",\n \"def wyniki():\\n return render_template('results.html', result=request.args.get('result'))\",\n \"def about() -> Any:\\n return render_template(\\\"about.html\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66694236","0.6106484","0.60288805","0.58857584","0.5849951","0.5769532","0.5752256","0.5746499","0.5689333","0.5651591","0.56413424","0.563141","0.5626008","0.5618876","0.5612004","0.5580761","0.55667937","0.55658025","0.5562467","0.55529743","0.5483404","0.546413","0.546413","0.5452314","0.5449884","0.5434667","0.5433252","0.54295826","0.5425607","0.54248834","0.54248834","0.54226387","0.54226387","0.54220325","0.54191464","0.5417689","0.5416365","0.54146427","0.54113865","0.54004204","0.53943384","0.53923005","0.53878576","0.53875244","0.53525156","0.53517133","0.53157055","0.53120416","0.53097385","0.52982074","0.5296194","0.5295036","0.5282195","0.5280402","0.5274446","0.5253095","0.5249882","0.5249882","0.5249882","0.5249882","0.5249882","0.52409405","0.52409405","0.52409405","0.52409405","0.52409405","0.52374446","0.52362245","0.5233827","0.5233436","0.5230111","0.52252233","0.52213633","0.52184135","0.52131516","0.52128476","0.5211992","0.5210964","0.52074075","0.52047294","0.52045965","0.5194153","0.5186914","0.51842666","0.51801556","0.5179334","0.51773924","0.51674503","0.51666516","0.51661307","0.5156746","0.5156746","0.514961","0.5149046","0.5149038","0.51406735","0.5134298","0.51330644","0.51261646","0.5124033"],"string":"[\n \"0.66694236\",\n \"0.6106484\",\n \"0.60288805\",\n \"0.58857584\",\n \"0.5849951\",\n \"0.5769532\",\n \"0.5752256\",\n \"0.5746499\",\n \"0.5689333\",\n \"0.5651591\",\n \"0.56413424\",\n \"0.563141\",\n \"0.5626008\",\n \"0.5618876\",\n \"0.5612004\",\n \"0.5580761\",\n \"0.55667937\",\n \"0.55658025\",\n \"0.5562467\",\n \"0.55529743\",\n \"0.5483404\",\n \"0.546413\",\n \"0.546413\",\n \"0.5452314\",\n \"0.5449884\",\n \"0.5434667\",\n \"0.5433252\",\n \"0.54295826\",\n \"0.5425607\",\n \"0.54248834\",\n \"0.54248834\",\n \"0.54226387\",\n \"0.54226387\",\n \"0.54220325\",\n \"0.54191464\",\n \"0.5417689\",\n \"0.5416365\",\n \"0.54146427\",\n \"0.54113865\",\n \"0.54004204\",\n \"0.53943384\",\n \"0.53923005\",\n \"0.53878576\",\n \"0.53875244\",\n \"0.53525156\",\n \"0.53517133\",\n \"0.53157055\",\n \"0.53120416\",\n \"0.53097385\",\n \"0.52982074\",\n \"0.5296194\",\n \"0.5295036\",\n \"0.5282195\",\n \"0.5280402\",\n \"0.5274446\",\n \"0.5253095\",\n \"0.5249882\",\n \"0.5249882\",\n \"0.5249882\",\n \"0.5249882\",\n \"0.5249882\",\n \"0.52409405\",\n \"0.52409405\",\n \"0.52409405\",\n \"0.52409405\",\n \"0.52409405\",\n \"0.52374446\",\n \"0.52362245\",\n \"0.5233827\",\n \"0.5233436\",\n \"0.5230111\",\n \"0.52252233\",\n \"0.52213633\",\n \"0.52184135\",\n \"0.52131516\",\n \"0.52128476\",\n \"0.5211992\",\n \"0.5210964\",\n \"0.52074075\",\n \"0.52047294\",\n \"0.52045965\",\n \"0.5194153\",\n \"0.5186914\",\n \"0.51842666\",\n \"0.51801556\",\n \"0.5179334\",\n \"0.51773924\",\n \"0.51674503\",\n \"0.51666516\",\n \"0.51661307\",\n \"0.5156746\",\n \"0.5156746\",\n \"0.514961\",\n \"0.5149046\",\n \"0.5149038\",\n \"0.51406735\",\n \"0.5134298\",\n \"0.51330644\",\n \"0.51261646\",\n \"0.5124033\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.53856117"},"document_rank":{"kind":"string","value":"44"}}},{"rowIdx":20084,"cells":{"query":{"kind":"string","value":"allows downloading of uploaded files"},"document":{"kind":"string","value":"def download():\n return response.download(request, db)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def download_files(self):","def post_download(self, remote_files):\n pass","def download_file(self, parsed_event, input_dir_path):","def download(self,fn):\n\t\treturn False #TODO: implement meme download","def download(self):\n pass","def download(self):\n pass","def pre_download(self, remote_files):\n pass","def download(self, download_request):\n raise NotImplementedError","def download_file(filename):\n return send_from_directory('uploads', filename, as_attachment=True)","def download(self, download_path):\n return","def download():\n raise NotImplementedError","def download_file():\n\n if 'POST' == request.method:\n file_id = request.form['file_id']\n else:\n file_id = request.args.get('file_id')\n\n # 1 ==> example_1.tgz\n file_path = file_manager.get_file_path_from_id(file_id)\n print \"serving file: \" + file_path\n return send_file(file_path, as_attachment=True)","def test_file_download(self):\n\n # Downloading without auth = unauthorized error (401)\n with self.assertRaises(requests.exceptions.HTTPError):\n self.assertFalse(self.api.downloadFile('/media/part/files/1/test.pdf', 'test.pdf'))","def download_finish(self, cloud_file):","def download_file(cls, uri, fobj):\n msg = \"Backend doesn't implement download_file()\"\n raise NotImplementedError(msg)","def download_file(file_ext, file_name):\n\n log_app.debug(\"file_name : %s \", \tfile_name)\n log_app.debug(\"file_ext : %s \", \tfile_ext)\n log_app.info(\"file_ext in AUTHORIZED_FILETYPES_LIST: %s\", (file_ext in AUTHORIZED_FILETYPES_LIST) )\n\n\n if file_ext in AUTHORIZED_FILETYPES_LIST :\n\n file_mimetype \t\t= AUTHORIZED_FILETYPES_DICT[file_ext][\"mimetype\"]\n file_foldername \t= AUTHORIZED_FILETYPES_DICT[file_ext][\"folder\"]\n file_folder \t\t= \"static/{}/\".format(file_foldername)\n file_name_ext \t\t= \"{}.{}\".format(file_name, file_ext)\n full_filepath \t\t= file_folder + file_name_ext\n\n try :\n\n return send_file(\tfull_filepath,\n mimetype\t\t\t= file_mimetype,\n attachment_filename\t= file_name_ext,\n as_attachment\t\t= True\n )\n except :\n\n log_app.error(\"downloading this file is not working: %s.%s \", file_name, file_ext )\n\n return redirect(url_for('home'))\n\n else :\n\n log_app.error(\"downloading this file is not authorized: %s.%s \", file_name, file_ext )\n\n return redirect(url_for('home'))","def is_downloadable(self):\n return False","def download_file(self, net_id, request_id, file_name):\n current_user_roles = get_user_roles()\n if current_user_roles[\"STFADM\"] or net_id == current_user.net_id:\n try:\n return send_from_directory(\"{0}/user_uploads/{1}/{2}/\".format(self.__APP_PATH__, net_id, request_id),\n \"{0}\".format(secure_filename(file_name)), mimetype=\"blob\")\n except Exception as e:\n print(e)\n return abort(404)\n return abort(403)","def is_downloadable(self):\n return True","def download_data(self):\r\n \r\n for file in self.__files:\r\n file_to_download = os.path.join(self.__folder, os.path.basename(file))\r\n if not os.path.isfile(file_to_download):\r\n self.__download_file(file)","def download(self,**attrs):\n\t\treturn super().download(**attrs)","def filedownload(source, destination):\n\n # Initiate the download\n urllib.request.urlretrieve(source, destination)","def download(self, url: str, dest: PathLike, force: bool = False):","def __getFile_httplib(self, _src, _dst):\n\n #-------------------- \n # Pre-download callbacks\n #-------------------- \n self.runEventCallbacks('downloadStarted', _src, -1)\n self.runEventCallbacks('downloading', _src, 0)\n\n\n\n #-------------------- \n # Download\n #-------------------- \n response = self.__httpsRequest('GET', _src)\n data = response.read() \n with open(_dst, 'wb') as f:\n f.write(data) \n\n\n\n #-------------------- \n # Post-download callbacks\n #-------------------- \n self.removeFromDownloadQueue(_src)\n self.runEventCallbacks('downloadFinished', _src)","def test_download(self):\n pass","def download_po(app, request, filename):\n \n filename = secure_filename(filename)\n file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)\n \n if file_exists(file_path):\n return send_from_directory(\n app.config['UPLOAD_FOLDER'],\n filename,\n as_attachment=True)\n \n flash('You\\'re trying to download file that are not exists.', 'error')\n return redirect(url_for('home'))","def download(request, ef_id):\n ef = get_object_or_404(ExamFile, id=ef_id)\n path = os.path.join(settings.MEDIA_ROOT, ef.path.path)\n response= HttpResponse(content=file(path, 'rb').read(), \n mimetype='application/pdf')\n # fn = os.path.split(ef.path.path)[1]\n # response['Content-Disposition'] = \"attachment; filename=%s\" % (fn)\n return response","async def download_file(\n location_id: LocationID,\n file_id: StorageFileID,\n user_id: UserID,\n link_type: LinkType = LinkType.PRESIGNED,\n):","def download_file(self, filename: str, save_dir: str) -> None:\n raise NotImplementedError()","def download_progress(self, cloud_file, size, downloaded):","def _download_file(self, video_objects):\n downloaded_video = []\n path=\"media/\"\n for video_object in video_objects:\n if 'contentUrl' in video_object.keys() and video_object['contentUrl']!='':\n \n url = video_object['contentUrl']\n filename = url.split('/')[-1]\n r = requests.get(url, stream=True)\n \n with open(filename, 'wb') as f:\n for chunk in r.iter_content(chunk_size=1024): \n if chunk:\n f.write(chunk)\n\n path+=filename\n return path","def t_getfile(self, link, filename, session):\n\n self.sema.acquire()\n\n filepath = os.path.join(os.getcwd() + '/Downloads/' + str(filename))\n os.makedirs(os.path.dirname(filepath), exist_ok=True)\n\n if not os.path.isfile(filepath):\n self.download_new_file(link, filepath, session)\n else:\n\n current_bytes = os.stat(filepath).st_size\n\n headers = requests.head(link).headers\n\n print(headers)\n if 'content-length' not in headers:\n print(f\"server doesn't support content-length for {link}\")\n self.sema.release()\n return\n\n total_bytes = int(requests.head(link).headers['content-length'])\n\n print(total_bytes)\n\n if current_bytes < total_bytes:\n #\n self.continue_file_download(link, filepath, session, current_bytes, total_bytes)\n print(f\"Current byte < total - remaining {total_bytes - current_bytes}\")\n else:\n print(f\"already done: {filename}\")\n\n self.sema.release()","def download_tmp_file(path, filename):\n try:\n _file = file(_path)\n wrapper = FileWrapper(_file)\n\n # use the same type for all files\n response = HttpResponse(wrapper, content_type='text/plain') \n response['Content-Disposition'] = \\\n 'attachment; filename=%s' % unicode(filename)\n response['Content-Length'] = os.path.getsize(_path)\n return response\n except Exception,e:\n logger.exception('could not find attached file object for id: %r', id)\n raise e","def download_file(self):\r\n file_path = filedialog.askopenfilename(initialdir=\"/\",\r\n title=\"Select a File\",\r\n filetypes=((\"Torrent files\", \"*.torrent\"), (\"all files\", \"*.*\")))\r\n\r\n if file_path:\r\n download_bar = DownloadProgressBar(self.scroll_frame.interior, file_path)\r\n download_bar.pack()\r\n download_bar.start()","def media_file_download(request, media_file_id):\n media_file = get_object_or_404(MediaFile, id=media_file_id)\n full_path = os.path.join(settings.MEDIA_ROOT, str(media_file.file))\n return sendfile(request, full_path, attachment=True, attachment_filename=media_file.filename)","def getFile(self, _src, _dst):\n\n #--------------------\n # Reset total size of downloads for all files\n #-------------------------\n self.downloadTracker['totalDownloadSize']['bytes'] = 0\n self.downloadTracker['downloadedSize']['bytes'] = 0\n downloadFolders = []\n\n #-------------------------\n # Remove existing dst files from their local URI\n #-------------------------\n if os.path.exists(_dst):\n os.remove(_dst)\n self.__getFile_requests(_src, _dst)","def download(self, filename, localfolder=None):\n raise NotImplementedError(\"Implement this method in child class\")","def download_file(self, remote_file):\n remote_file.download()","def is_downloadable(self):\n raise NotImplementedError('Implement this method.')","def download(self, download) -> None:\n path_cifarh = path.join(self.root, self.filename_cifarh)\n path_cifar = path.join(self.root, self.filename_cifar)\n is_there = path.isfile(path_cifarh) and path.isfile(path_cifar)\n if is_there:\n print(\"Files already exist.\")\n if download == \"force\" or not is_there:\n download_and_extract_archive(\n self.url_cifar, self.root, filename=self.filename_cifar\n )\n download_and_extract_archive(\n self.url_cifarh, self.root, filename=self.filename_cifarh\n )","def DownloadFile(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def __getFile_requests(self, _src, _dst):\n\n #-------------------- \n # Get the content size from scan json\n #-------------------- \n self.downloadTracker['downloadedSize']['bytes'] = 0 \n self.downloadTracker['totalDownloadSize'] = self.getFileSize(_src)\n\n #-------------------- \n # Pre-download callbacks\n #-------------------- \n size = self.downloadTracker['totalDownloadSize']['bytes'] \\\n if self.downloadTracker['totalDownloadSize']['bytes'] else -1\n self.runEventCallbacks('downloadStarted', _src, size)\n self.runEventCallbacks('downloading', _src, 0)\n\n #-------------------- \n # Open the local destination file \n # so that it can start reading in the buffers.\n #-------------------- \n try:\n dstFile = _dst\n dstDir = os.path.dirname(_dst) \n if not os.path.exists(dstDir):\n os.makedirs(dstDir)\n # print(\"dstFile: {}\".format(dstFile))\n except Exception as e:\n print(e)\n self.__downloadFailed(_src, _dst, dstFile, str(e))\n self.exceptionPopup.setText(str(e))\n return\n\n #-------------------- \n # Construct the request\n #-------------------- \n url = Xnat.path.makeXnatUrl(self.host, _src)\n r = self.__httpsRequest('GET', url, stream=True)\n f = open(dstFile, 'wb')\n\n for chunk in r.iter_content(chunk_size=1024*1024):\n # Check for cancel event\n if not self.inDownloadQueue(_src):\n f.close()\n os.remove(f.name)\n self.runEventCallbacks('downloadCancelled', _src)\n break\n\n f.write(chunk)\n\n self.downloadTracker['downloadedSize']['bytes'] += len(chunk)\n self.runEventCallbacks('downloading', _src, \n self.downloadTracker['downloadedSize']['bytes'])\n\n r.close()\n f.close()\n\n #-------------------- \n # Post-download callbacks\n #-------------------- \n self.removeFromDownloadQueue(_src)\n self.runEventCallbacks('downloadFinished', _src)","def upload_downloaded(self, release: CrossrefEventsRelease, **kwargs):\n upload_files_from_list(release.download_files, release.download_bucket)","def get_feedback_file(filename):\n \n if 'username'in session:\n \n s3_resource = boto3.resource('s3')\n bucket = s3_resource.Bucket(S3_BUCKET)\n \n file_obj = bucket.Object(filename).get()\n \n return Response(file_obj['Body'].read(),\n mimetype='text/plain',\n headers={'Content-Disposition': f'attachment;filename={filename}'}\n )\n \n flash('You need to be logged in to download files.', 'info')\n return redirect(url_for('login'))","def download_file():\n for lines in urls:\n try:\n req.urlretrieve(lines, '{0}/{1}'.format(folder_path, lines.split('/')[-1]))\n time.sleep(1)\n print ('File - {} - downloaded successfully'.format(lines.split('/')[-1]))\n except urllib.error.HTTPError:\n print('File is missing or not reachable')\n print('Download Complete & Successful!')","def downloadFile(self, path, out):\n\t\ttry:\n\t\t\tlogger.info(\"downloadFile('%s', ...)\" % (path))\n\n\t\t\t# Downloads from dropbox\n\t\t\t# Manually :( update the metadata cache\n\t\t\tf, metadata = self.client.get_file_and_metadata(path)\n\t\t\tf = f.read()\n\t\t\tlogger.info('* file downloaded')\n\t\t\tself.cache_metadata.setNewValue(path, metadata)\n\t\t\tlogger.info('* metadata updated')\n\t\t\t# Write to tmp file and close\n\t\t\tos.write(out, f)\n\t\t\tlogger.info(\"* file written\")\n\t\t\tos.close(out)\n\t\t\tlogger.info('* file closed')\n\t\t\n\t\t\treturn True\n\t\texcept Exception, e:\n\t\t\tlogger.error(\"Exception %s at downloadFile(%s)\" % (sys.exc_info()[0], path))\n\t\t\tlogger.debug(pformat(sys.exc_info()))\n\t\t\treturn False","def __handleDownload(self,block):\n self.file.write(block)","def download(self, url_match):\n pass","def uploaded_file(filename):\n return send_from_directory('/static/images/uploads/', filename)","def download(urls, dest_folder):\n pass","def _maybe_download(self, url):\n filename = os.path.basename(url)\n download_path = os.path.join(self._model_dir, filename)\n if os.path.exists(download_path):\n return download_path\n\n def _progress(count, block_size, total_size):\n sys.stdout.write(\n '\\r>> Downloading %s %.1f%%' %\n (filename, float(count * block_size) / float(total_size) * 100.0))\n sys.stdout.flush()\n\n urllib.request.urlretrieve(url, download_path, _progress)\n statinfo = os.stat(download_path)\n print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')\n return download_path","def get_piece_file(filename):\n \n if 'username'in session:\n \n s3_resource = boto3.resource('s3')\n bucket = s3_resource.Bucket(S3_BUCKET)\n \n file_obj = bucket.Object(filename).get()\n \n return Response(file_obj['Body'].read(),\n mimetype='text/plain',\n headers={'Content-Disposition': f'attachment;filename={filename}'}\n )\n \n flash('You need to be logged in to download files.', 'info')\n return redirect(url_for('login'))","def download_with_callback(self, url, path=None, filename=None, headers=None, force=False, func=None):","def download(request, file):\n print(file)\n try:\n mimetypes.init()\n file_path = settings.MEDIA_ROOT + '/' + file\n fsock=None\n with open(file_path, \"rb\") as f:\n fsock = io.BytesIO(f.read())\n file_name = os.path.basename(file_path)\n mime_type_guess = mimetypes.guess_type(file_name)\n response = HttpResponse(fsock, content_type=mime_type_guess[0])\n response['Content-Disposition'] = 'attachment; filename=' + file_name\n return response\n except:\n return not_found(request)","def send_file(self, src: PathLike, dest: PathLike, force: bool = False):","def unavoidable_download_method(self, target, name):\n # Get path to file\n file_path = os.path.join(self.work_dir, name)\n\n # Create necessary directories if not present\n self.mkdir_p(self.work_dir)\n\n # Check if file exists, download if not presente\n if not os.path.exists(file_path):\n try:\n subprocess.check_call(['curl', '-fs', self.input_urls[name], '-o', file_path])\n except subprocess.CalledProcessError:\n raise RuntimeError('\\nNecessary file could not be acquired: {}. Check input URL')\n except OSError:\n raise RuntimeError('Failed to find \"curl\". Install via \"apt-get install curl\"')\n\n assert os.path.exists(file_path)\n\n # Update FileStoreID\n target.updateGlobalFile(self.ids[name], file_path)\n\n return file_path","def __download(self, downloadType):\n\n downloadAudit = {}\n downloadAudit['download_type'] = downloadType\n downloadAudit['remote_url'] = self.remoteUrl#get the url that is being requested\n downloadAudit['download_start_time'] = datetime.now()#capture the date when the url was accessed\n #first make an entry into the db stating that a download operation is to be attempted\n downloadAudit['comment'] = 'Starting download operation'\n newRecordId = self.__auditFileRequest(downloadAudit)\n\n downloadAudit = {}\n try:\n req = urllib2.Request(self.remoteUrl)\n r = urllib2.urlopen(req)\n except URLError, urle:\n if hasattr(urle, 'reason'):\n downloadAudit['comment'] = urle.reason\n else:\n downloadAudit['comment'] = urle.__str__()\n \n self.__auditFileRequest(downloadAudit, newRecordId)\n \n return None #just return since there has been an error in connecting with the remote server\n\n try:\n downloadAudit['local_file_path'] = '/' + self.localRepoDir + '/' + downloadType + '/' + downloadType + '-' + datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '.xls'\n\n file = open(settings.APPLICATION_SETTINGS['MCH_HOME'] + downloadAudit['local_file_path'], 'wb')\n file.write(r.read())\n\n #headerInfo = r.info()\n\n isFileToBeProcessed = False #default is not to process file\n\n #before downloading, check to see if the remote file is more recent than the last file that was downloaded, whose\n #information is in the db\n latestFetch = self.__recentDownload(downloadType)\n\n if latestFetch:\n downloadAudit['file_size'] = latestFetch[0]\n file.flush()#make sure all the content is written to file \n os.fsync(file.fileno())\n if latestFetch[0] != os.path.getsize(file.name):\n isFileToBeProcessed = True\n else:\n isFileToBeProcessed = True\n\n if isFileToBeProcessed:\n downloadAudit['file_size'] = os.path.getsize(file.name)\n downloadAudit['file_modification_time'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n downloadAudit['download_completion_time'] = datetime.now()\n #now make an audit entry into the database\n downloadAudit['comment'] = 'New file to be processed.'\n else:\n os.remove(file.name)#remove the file since it looks like it has already been downloaded and processed\n #now make an audit entry into the database\n downloadAudit['comment'] = 'File already downloaded, purged it from the file system.'\n\n self.__auditFileRequest(downloadAudit, newRecordId)\n file.close()\n except Exception as e:\n self.logger.exception('\\n Unknown fatal error occured during the downloading of the raw files. %s' % str(e))\n\n return newRecordId","def test_download(api):\n # upload file prior to download\n # with pytest.raises(APIConnectionError):\n uploaded_file = api.upload(\n tag='test_upload',\n expiry='1w',\n path='tests/test_file.txt'\n )\n\n # check that instance of FileIO has these fields\n assert uploaded_file.link\n assert uploaded_file.key\n assert uploaded_file.tag\n assert uploaded_file.path\n\n # remove the uploaded file from the os\n remove('tests/test_file.txt')\n\n # download and save the file\n api.download(tag='test_upload')\n\n # check that file was saved in a filesystem\n assert path.isfile('tests/test_file.txt')","def download():\n return response.download(request,db)","def download():\n return response.download(request,db)","def download():\n return response.download(request,db)","def download():\n return response.download(request,db)","def download():\n return response.download(request,db)","def download():\n return response.download(request,db)","def _download_file(self, file_id, file_name, path):\n request = self.service.files().get_media(fileId=file_id)\n fh = io.FileIO(path + file_name, 'wb')\n downloader = MediaIoBaseDownload(fh, request)\n done = False\n print('Start download ' + file_name)\n while not done:\n status, done = downloader.next_chunk()\n print(\"Download %d%%.\" % int(status.progress() * 100))","def __download_file(self, filename):\r\n \r\n respons = requests.get(self.__url + filename, stream=True)\r\n save_filename = os.path.join(self.__folder, os.path.basename(filename))\r\n with open(save_filename, 'wb') as output_file:\r\n for chunk in respons.iter_content(chunk_size=128):\r\n output_file.write(chunk)","def test_download2(self):\n pass","def downloadFile(self, base_url, file_name):\n url = os.path.join(base_url, file_name)\n req = urllib2.Request(url)\n try:\n f = urllib2.urlopen(req, timeout=self.timeout)\n local_file = open(os.path.join(self.config.get('PATHS', 'pdfdir'), file_name), \"w\")\n local_file.write(f.read())\n local_file.close()\n except Exception, err:\n print \"[ Failed ]\"\n print \"\\n***ERROR in downloadFile: %s\" % err\n sys.exit(0)","def get_download_url(self):\n # return self.file.url # This returns the path where file is stored\n return reverse('products:download', kwargs={\n 'slug': self.product.slug, 'pk': self.pk\n }) # This returns the endpoint where file download is handled"],"string":"[\n \"def download_files(self):\",\n \"def post_download(self, remote_files):\\n pass\",\n \"def download_file(self, parsed_event, input_dir_path):\",\n \"def download(self,fn):\\n\\t\\treturn False #TODO: implement meme download\",\n \"def download(self):\\n pass\",\n \"def download(self):\\n pass\",\n \"def pre_download(self, remote_files):\\n pass\",\n \"def download(self, download_request):\\n raise NotImplementedError\",\n \"def download_file(filename):\\n return send_from_directory('uploads', filename, as_attachment=True)\",\n \"def download(self, download_path):\\n return\",\n \"def download():\\n raise NotImplementedError\",\n \"def download_file():\\n\\n if 'POST' == request.method:\\n file_id = request.form['file_id']\\n else:\\n file_id = request.args.get('file_id')\\n\\n # 1 ==> example_1.tgz\\n file_path = file_manager.get_file_path_from_id(file_id)\\n print \\\"serving file: \\\" + file_path\\n return send_file(file_path, as_attachment=True)\",\n \"def test_file_download(self):\\n\\n # Downloading without auth = unauthorized error (401)\\n with self.assertRaises(requests.exceptions.HTTPError):\\n self.assertFalse(self.api.downloadFile('/media/part/files/1/test.pdf', 'test.pdf'))\",\n \"def download_finish(self, cloud_file):\",\n \"def download_file(cls, uri, fobj):\\n msg = \\\"Backend doesn't implement download_file()\\\"\\n raise NotImplementedError(msg)\",\n \"def download_file(file_ext, file_name):\\n\\n log_app.debug(\\\"file_name : %s \\\", \\tfile_name)\\n log_app.debug(\\\"file_ext : %s \\\", \\tfile_ext)\\n log_app.info(\\\"file_ext in AUTHORIZED_FILETYPES_LIST: %s\\\", (file_ext in AUTHORIZED_FILETYPES_LIST) )\\n\\n\\n if file_ext in AUTHORIZED_FILETYPES_LIST :\\n\\n file_mimetype \\t\\t= AUTHORIZED_FILETYPES_DICT[file_ext][\\\"mimetype\\\"]\\n file_foldername \\t= AUTHORIZED_FILETYPES_DICT[file_ext][\\\"folder\\\"]\\n file_folder \\t\\t= \\\"static/{}/\\\".format(file_foldername)\\n file_name_ext \\t\\t= \\\"{}.{}\\\".format(file_name, file_ext)\\n full_filepath \\t\\t= file_folder + file_name_ext\\n\\n try :\\n\\n return send_file(\\tfull_filepath,\\n mimetype\\t\\t\\t= file_mimetype,\\n attachment_filename\\t= file_name_ext,\\n as_attachment\\t\\t= True\\n )\\n except :\\n\\n log_app.error(\\\"downloading this file is not working: %s.%s \\\", file_name, file_ext )\\n\\n return redirect(url_for('home'))\\n\\n else :\\n\\n log_app.error(\\\"downloading this file is not authorized: %s.%s \\\", file_name, file_ext )\\n\\n return redirect(url_for('home'))\",\n \"def is_downloadable(self):\\n return False\",\n \"def download_file(self, net_id, request_id, file_name):\\n current_user_roles = get_user_roles()\\n if current_user_roles[\\\"STFADM\\\"] or net_id == current_user.net_id:\\n try:\\n return send_from_directory(\\\"{0}/user_uploads/{1}/{2}/\\\".format(self.__APP_PATH__, net_id, request_id),\\n \\\"{0}\\\".format(secure_filename(file_name)), mimetype=\\\"blob\\\")\\n except Exception as e:\\n print(e)\\n return abort(404)\\n return abort(403)\",\n \"def is_downloadable(self):\\n return True\",\n \"def download_data(self):\\r\\n \\r\\n for file in self.__files:\\r\\n file_to_download = os.path.join(self.__folder, os.path.basename(file))\\r\\n if not os.path.isfile(file_to_download):\\r\\n self.__download_file(file)\",\n \"def download(self,**attrs):\\n\\t\\treturn super().download(**attrs)\",\n \"def filedownload(source, destination):\\n\\n # Initiate the download\\n urllib.request.urlretrieve(source, destination)\",\n \"def download(self, url: str, dest: PathLike, force: bool = False):\",\n \"def __getFile_httplib(self, _src, _dst):\\n\\n #-------------------- \\n # Pre-download callbacks\\n #-------------------- \\n self.runEventCallbacks('downloadStarted', _src, -1)\\n self.runEventCallbacks('downloading', _src, 0)\\n\\n\\n\\n #-------------------- \\n # Download\\n #-------------------- \\n response = self.__httpsRequest('GET', _src)\\n data = response.read() \\n with open(_dst, 'wb') as f:\\n f.write(data) \\n\\n\\n\\n #-------------------- \\n # Post-download callbacks\\n #-------------------- \\n self.removeFromDownloadQueue(_src)\\n self.runEventCallbacks('downloadFinished', _src)\",\n \"def test_download(self):\\n pass\",\n \"def download_po(app, request, filename):\\n \\n filename = secure_filename(filename)\\n file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)\\n \\n if file_exists(file_path):\\n return send_from_directory(\\n app.config['UPLOAD_FOLDER'],\\n filename,\\n as_attachment=True)\\n \\n flash('You\\\\'re trying to download file that are not exists.', 'error')\\n return redirect(url_for('home'))\",\n \"def download(request, ef_id):\\n ef = get_object_or_404(ExamFile, id=ef_id)\\n path = os.path.join(settings.MEDIA_ROOT, ef.path.path)\\n response= HttpResponse(content=file(path, 'rb').read(), \\n mimetype='application/pdf')\\n # fn = os.path.split(ef.path.path)[1]\\n # response['Content-Disposition'] = \\\"attachment; filename=%s\\\" % (fn)\\n return response\",\n \"async def download_file(\\n location_id: LocationID,\\n file_id: StorageFileID,\\n user_id: UserID,\\n link_type: LinkType = LinkType.PRESIGNED,\\n):\",\n \"def download_file(self, filename: str, save_dir: str) -> None:\\n raise NotImplementedError()\",\n \"def download_progress(self, cloud_file, size, downloaded):\",\n \"def _download_file(self, video_objects):\\n downloaded_video = []\\n path=\\\"media/\\\"\\n for video_object in video_objects:\\n if 'contentUrl' in video_object.keys() and video_object['contentUrl']!='':\\n \\n url = video_object['contentUrl']\\n filename = url.split('/')[-1]\\n r = requests.get(url, stream=True)\\n \\n with open(filename, 'wb') as f:\\n for chunk in r.iter_content(chunk_size=1024): \\n if chunk:\\n f.write(chunk)\\n\\n path+=filename\\n return path\",\n \"def t_getfile(self, link, filename, session):\\n\\n self.sema.acquire()\\n\\n filepath = os.path.join(os.getcwd() + '/Downloads/' + str(filename))\\n os.makedirs(os.path.dirname(filepath), exist_ok=True)\\n\\n if not os.path.isfile(filepath):\\n self.download_new_file(link, filepath, session)\\n else:\\n\\n current_bytes = os.stat(filepath).st_size\\n\\n headers = requests.head(link).headers\\n\\n print(headers)\\n if 'content-length' not in headers:\\n print(f\\\"server doesn't support content-length for {link}\\\")\\n self.sema.release()\\n return\\n\\n total_bytes = int(requests.head(link).headers['content-length'])\\n\\n print(total_bytes)\\n\\n if current_bytes < total_bytes:\\n #\\n self.continue_file_download(link, filepath, session, current_bytes, total_bytes)\\n print(f\\\"Current byte < total - remaining {total_bytes - current_bytes}\\\")\\n else:\\n print(f\\\"already done: {filename}\\\")\\n\\n self.sema.release()\",\n \"def download_tmp_file(path, filename):\\n try:\\n _file = file(_path)\\n wrapper = FileWrapper(_file)\\n\\n # use the same type for all files\\n response = HttpResponse(wrapper, content_type='text/plain') \\n response['Content-Disposition'] = \\\\\\n 'attachment; filename=%s' % unicode(filename)\\n response['Content-Length'] = os.path.getsize(_path)\\n return response\\n except Exception,e:\\n logger.exception('could not find attached file object for id: %r', id)\\n raise e\",\n \"def download_file(self):\\r\\n file_path = filedialog.askopenfilename(initialdir=\\\"/\\\",\\r\\n title=\\\"Select a File\\\",\\r\\n filetypes=((\\\"Torrent files\\\", \\\"*.torrent\\\"), (\\\"all files\\\", \\\"*.*\\\")))\\r\\n\\r\\n if file_path:\\r\\n download_bar = DownloadProgressBar(self.scroll_frame.interior, file_path)\\r\\n download_bar.pack()\\r\\n download_bar.start()\",\n \"def media_file_download(request, media_file_id):\\n media_file = get_object_or_404(MediaFile, id=media_file_id)\\n full_path = os.path.join(settings.MEDIA_ROOT, str(media_file.file))\\n return sendfile(request, full_path, attachment=True, attachment_filename=media_file.filename)\",\n \"def getFile(self, _src, _dst):\\n\\n #--------------------\\n # Reset total size of downloads for all files\\n #-------------------------\\n self.downloadTracker['totalDownloadSize']['bytes'] = 0\\n self.downloadTracker['downloadedSize']['bytes'] = 0\\n downloadFolders = []\\n\\n #-------------------------\\n # Remove existing dst files from their local URI\\n #-------------------------\\n if os.path.exists(_dst):\\n os.remove(_dst)\\n self.__getFile_requests(_src, _dst)\",\n \"def download(self, filename, localfolder=None):\\n raise NotImplementedError(\\\"Implement this method in child class\\\")\",\n \"def download_file(self, remote_file):\\n remote_file.download()\",\n \"def is_downloadable(self):\\n raise NotImplementedError('Implement this method.')\",\n \"def download(self, download) -> None:\\n path_cifarh = path.join(self.root, self.filename_cifarh)\\n path_cifar = path.join(self.root, self.filename_cifar)\\n is_there = path.isfile(path_cifarh) and path.isfile(path_cifar)\\n if is_there:\\n print(\\\"Files already exist.\\\")\\n if download == \\\"force\\\" or not is_there:\\n download_and_extract_archive(\\n self.url_cifar, self.root, filename=self.filename_cifar\\n )\\n download_and_extract_archive(\\n self.url_cifarh, self.root, filename=self.filename_cifarh\\n )\",\n \"def DownloadFile(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def __getFile_requests(self, _src, _dst):\\n\\n #-------------------- \\n # Get the content size from scan json\\n #-------------------- \\n self.downloadTracker['downloadedSize']['bytes'] = 0 \\n self.downloadTracker['totalDownloadSize'] = self.getFileSize(_src)\\n\\n #-------------------- \\n # Pre-download callbacks\\n #-------------------- \\n size = self.downloadTracker['totalDownloadSize']['bytes'] \\\\\\n if self.downloadTracker['totalDownloadSize']['bytes'] else -1\\n self.runEventCallbacks('downloadStarted', _src, size)\\n self.runEventCallbacks('downloading', _src, 0)\\n\\n #-------------------- \\n # Open the local destination file \\n # so that it can start reading in the buffers.\\n #-------------------- \\n try:\\n dstFile = _dst\\n dstDir = os.path.dirname(_dst) \\n if not os.path.exists(dstDir):\\n os.makedirs(dstDir)\\n # print(\\\"dstFile: {}\\\".format(dstFile))\\n except Exception as e:\\n print(e)\\n self.__downloadFailed(_src, _dst, dstFile, str(e))\\n self.exceptionPopup.setText(str(e))\\n return\\n\\n #-------------------- \\n # Construct the request\\n #-------------------- \\n url = Xnat.path.makeXnatUrl(self.host, _src)\\n r = self.__httpsRequest('GET', url, stream=True)\\n f = open(dstFile, 'wb')\\n\\n for chunk in r.iter_content(chunk_size=1024*1024):\\n # Check for cancel event\\n if not self.inDownloadQueue(_src):\\n f.close()\\n os.remove(f.name)\\n self.runEventCallbacks('downloadCancelled', _src)\\n break\\n\\n f.write(chunk)\\n\\n self.downloadTracker['downloadedSize']['bytes'] += len(chunk)\\n self.runEventCallbacks('downloading', _src, \\n self.downloadTracker['downloadedSize']['bytes'])\\n\\n r.close()\\n f.close()\\n\\n #-------------------- \\n # Post-download callbacks\\n #-------------------- \\n self.removeFromDownloadQueue(_src)\\n self.runEventCallbacks('downloadFinished', _src)\",\n \"def upload_downloaded(self, release: CrossrefEventsRelease, **kwargs):\\n upload_files_from_list(release.download_files, release.download_bucket)\",\n \"def get_feedback_file(filename):\\n \\n if 'username'in session:\\n \\n s3_resource = boto3.resource('s3')\\n bucket = s3_resource.Bucket(S3_BUCKET)\\n \\n file_obj = bucket.Object(filename).get()\\n \\n return Response(file_obj['Body'].read(),\\n mimetype='text/plain',\\n headers={'Content-Disposition': f'attachment;filename={filename}'}\\n )\\n \\n flash('You need to be logged in to download files.', 'info')\\n return redirect(url_for('login'))\",\n \"def download_file():\\n for lines in urls:\\n try:\\n req.urlretrieve(lines, '{0}/{1}'.format(folder_path, lines.split('/')[-1]))\\n time.sleep(1)\\n print ('File - {} - downloaded successfully'.format(lines.split('/')[-1]))\\n except urllib.error.HTTPError:\\n print('File is missing or not reachable')\\n print('Download Complete & Successful!')\",\n \"def downloadFile(self, path, out):\\n\\t\\ttry:\\n\\t\\t\\tlogger.info(\\\"downloadFile('%s', ...)\\\" % (path))\\n\\n\\t\\t\\t# Downloads from dropbox\\n\\t\\t\\t# Manually :( update the metadata cache\\n\\t\\t\\tf, metadata = self.client.get_file_and_metadata(path)\\n\\t\\t\\tf = f.read()\\n\\t\\t\\tlogger.info('* file downloaded')\\n\\t\\t\\tself.cache_metadata.setNewValue(path, metadata)\\n\\t\\t\\tlogger.info('* metadata updated')\\n\\t\\t\\t# Write to tmp file and close\\n\\t\\t\\tos.write(out, f)\\n\\t\\t\\tlogger.info(\\\"* file written\\\")\\n\\t\\t\\tos.close(out)\\n\\t\\t\\tlogger.info('* file closed')\\n\\t\\t\\n\\t\\t\\treturn True\\n\\t\\texcept Exception, e:\\n\\t\\t\\tlogger.error(\\\"Exception %s at downloadFile(%s)\\\" % (sys.exc_info()[0], path))\\n\\t\\t\\tlogger.debug(pformat(sys.exc_info()))\\n\\t\\t\\treturn False\",\n \"def __handleDownload(self,block):\\n self.file.write(block)\",\n \"def download(self, url_match):\\n pass\",\n \"def uploaded_file(filename):\\n return send_from_directory('/static/images/uploads/', filename)\",\n \"def download(urls, dest_folder):\\n pass\",\n \"def _maybe_download(self, url):\\n filename = os.path.basename(url)\\n download_path = os.path.join(self._model_dir, filename)\\n if os.path.exists(download_path):\\n return download_path\\n\\n def _progress(count, block_size, total_size):\\n sys.stdout.write(\\n '\\\\r>> Downloading %s %.1f%%' %\\n (filename, float(count * block_size) / float(total_size) * 100.0))\\n sys.stdout.flush()\\n\\n urllib.request.urlretrieve(url, download_path, _progress)\\n statinfo = os.stat(download_path)\\n print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')\\n return download_path\",\n \"def get_piece_file(filename):\\n \\n if 'username'in session:\\n \\n s3_resource = boto3.resource('s3')\\n bucket = s3_resource.Bucket(S3_BUCKET)\\n \\n file_obj = bucket.Object(filename).get()\\n \\n return Response(file_obj['Body'].read(),\\n mimetype='text/plain',\\n headers={'Content-Disposition': f'attachment;filename={filename}'}\\n )\\n \\n flash('You need to be logged in to download files.', 'info')\\n return redirect(url_for('login'))\",\n \"def download_with_callback(self, url, path=None, filename=None, headers=None, force=False, func=None):\",\n \"def download(request, file):\\n print(file)\\n try:\\n mimetypes.init()\\n file_path = settings.MEDIA_ROOT + '/' + file\\n fsock=None\\n with open(file_path, \\\"rb\\\") as f:\\n fsock = io.BytesIO(f.read())\\n file_name = os.path.basename(file_path)\\n mime_type_guess = mimetypes.guess_type(file_name)\\n response = HttpResponse(fsock, content_type=mime_type_guess[0])\\n response['Content-Disposition'] = 'attachment; filename=' + file_name\\n return response\\n except:\\n return not_found(request)\",\n \"def send_file(self, src: PathLike, dest: PathLike, force: bool = False):\",\n \"def unavoidable_download_method(self, target, name):\\n # Get path to file\\n file_path = os.path.join(self.work_dir, name)\\n\\n # Create necessary directories if not present\\n self.mkdir_p(self.work_dir)\\n\\n # Check if file exists, download if not presente\\n if not os.path.exists(file_path):\\n try:\\n subprocess.check_call(['curl', '-fs', self.input_urls[name], '-o', file_path])\\n except subprocess.CalledProcessError:\\n raise RuntimeError('\\\\nNecessary file could not be acquired: {}. Check input URL')\\n except OSError:\\n raise RuntimeError('Failed to find \\\"curl\\\". Install via \\\"apt-get install curl\\\"')\\n\\n assert os.path.exists(file_path)\\n\\n # Update FileStoreID\\n target.updateGlobalFile(self.ids[name], file_path)\\n\\n return file_path\",\n \"def __download(self, downloadType):\\n\\n downloadAudit = {}\\n downloadAudit['download_type'] = downloadType\\n downloadAudit['remote_url'] = self.remoteUrl#get the url that is being requested\\n downloadAudit['download_start_time'] = datetime.now()#capture the date when the url was accessed\\n #first make an entry into the db stating that a download operation is to be attempted\\n downloadAudit['comment'] = 'Starting download operation'\\n newRecordId = self.__auditFileRequest(downloadAudit)\\n\\n downloadAudit = {}\\n try:\\n req = urllib2.Request(self.remoteUrl)\\n r = urllib2.urlopen(req)\\n except URLError, urle:\\n if hasattr(urle, 'reason'):\\n downloadAudit['comment'] = urle.reason\\n else:\\n downloadAudit['comment'] = urle.__str__()\\n \\n self.__auditFileRequest(downloadAudit, newRecordId)\\n \\n return None #just return since there has been an error in connecting with the remote server\\n\\n try:\\n downloadAudit['local_file_path'] = '/' + self.localRepoDir + '/' + downloadType + '/' + downloadType + '-' + datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '.xls'\\n\\n file = open(settings.APPLICATION_SETTINGS['MCH_HOME'] + downloadAudit['local_file_path'], 'wb')\\n file.write(r.read())\\n\\n #headerInfo = r.info()\\n\\n isFileToBeProcessed = False #default is not to process file\\n\\n #before downloading, check to see if the remote file is more recent than the last file that was downloaded, whose\\n #information is in the db\\n latestFetch = self.__recentDownload(downloadType)\\n\\n if latestFetch:\\n downloadAudit['file_size'] = latestFetch[0]\\n file.flush()#make sure all the content is written to file \\n os.fsync(file.fileno())\\n if latestFetch[0] != os.path.getsize(file.name):\\n isFileToBeProcessed = True\\n else:\\n isFileToBeProcessed = True\\n\\n if isFileToBeProcessed:\\n downloadAudit['file_size'] = os.path.getsize(file.name)\\n downloadAudit['file_modification_time'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\\n downloadAudit['download_completion_time'] = datetime.now()\\n #now make an audit entry into the database\\n downloadAudit['comment'] = 'New file to be processed.'\\n else:\\n os.remove(file.name)#remove the file since it looks like it has already been downloaded and processed\\n #now make an audit entry into the database\\n downloadAudit['comment'] = 'File already downloaded, purged it from the file system.'\\n\\n self.__auditFileRequest(downloadAudit, newRecordId)\\n file.close()\\n except Exception as e:\\n self.logger.exception('\\\\n Unknown fatal error occured during the downloading of the raw files. %s' % str(e))\\n\\n return newRecordId\",\n \"def test_download(api):\\n # upload file prior to download\\n # with pytest.raises(APIConnectionError):\\n uploaded_file = api.upload(\\n tag='test_upload',\\n expiry='1w',\\n path='tests/test_file.txt'\\n )\\n\\n # check that instance of FileIO has these fields\\n assert uploaded_file.link\\n assert uploaded_file.key\\n assert uploaded_file.tag\\n assert uploaded_file.path\\n\\n # remove the uploaded file from the os\\n remove('tests/test_file.txt')\\n\\n # download and save the file\\n api.download(tag='test_upload')\\n\\n # check that file was saved in a filesystem\\n assert path.isfile('tests/test_file.txt')\",\n \"def download():\\n return response.download(request,db)\",\n \"def download():\\n return response.download(request,db)\",\n \"def download():\\n return response.download(request,db)\",\n \"def download():\\n return response.download(request,db)\",\n \"def download():\\n return response.download(request,db)\",\n \"def download():\\n return response.download(request,db)\",\n \"def _download_file(self, file_id, file_name, path):\\n request = self.service.files().get_media(fileId=file_id)\\n fh = io.FileIO(path + file_name, 'wb')\\n downloader = MediaIoBaseDownload(fh, request)\\n done = False\\n print('Start download ' + file_name)\\n while not done:\\n status, done = downloader.next_chunk()\\n print(\\\"Download %d%%.\\\" % int(status.progress() * 100))\",\n \"def __download_file(self, filename):\\r\\n \\r\\n respons = requests.get(self.__url + filename, stream=True)\\r\\n save_filename = os.path.join(self.__folder, os.path.basename(filename))\\r\\n with open(save_filename, 'wb') as output_file:\\r\\n for chunk in respons.iter_content(chunk_size=128):\\r\\n output_file.write(chunk)\",\n \"def test_download2(self):\\n pass\",\n \"def downloadFile(self, base_url, file_name):\\n url = os.path.join(base_url, file_name)\\n req = urllib2.Request(url)\\n try:\\n f = urllib2.urlopen(req, timeout=self.timeout)\\n local_file = open(os.path.join(self.config.get('PATHS', 'pdfdir'), file_name), \\\"w\\\")\\n local_file.write(f.read())\\n local_file.close()\\n except Exception, err:\\n print \\\"[ Failed ]\\\"\\n print \\\"\\\\n***ERROR in downloadFile: %s\\\" % err\\n sys.exit(0)\",\n \"def get_download_url(self):\\n # return self.file.url # This returns the path where file is stored\\n return reverse('products:download', kwargs={\\n 'slug': self.product.slug, 'pk': self.pk\\n }) # This returns the endpoint where file download is handled\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7924571","0.7199257","0.6978304","0.6841495","0.6764734","0.6764734","0.66671187","0.6631285","0.6588692","0.6555494","0.65398693","0.65012735","0.6458808","0.6451737","0.64442515","0.6402176","0.63864595","0.63758063","0.63659835","0.6345104","0.63313895","0.62894195","0.6285235","0.62445545","0.6241005","0.61878794","0.61681086","0.6156231","0.61406726","0.61201674","0.6108218","0.60767394","0.60732776","0.6069381","0.605985","0.605102","0.6039634","0.60326767","0.6030083","0.6026038","0.60254806","0.60215086","0.6004399","0.5992663","0.5986912","0.5984624","0.5975399","0.59732544","0.59695816","0.596551","0.5963737","0.59349173","0.5932319","0.5930642","0.59078044","0.5903928","0.5900831","0.58927655","0.5885908","0.5885908","0.5885908","0.5885908","0.5885908","0.5885908","0.58855337","0.58841926","0.58808327","0.5879412","0.58717287"],"string":"[\n \"0.7924571\",\n \"0.7199257\",\n \"0.6978304\",\n \"0.6841495\",\n \"0.6764734\",\n \"0.6764734\",\n \"0.66671187\",\n \"0.6631285\",\n \"0.6588692\",\n \"0.6555494\",\n \"0.65398693\",\n \"0.65012735\",\n \"0.6458808\",\n \"0.6451737\",\n \"0.64442515\",\n \"0.6402176\",\n \"0.63864595\",\n \"0.63758063\",\n \"0.63659835\",\n \"0.6345104\",\n \"0.63313895\",\n \"0.62894195\",\n \"0.6285235\",\n \"0.62445545\",\n \"0.6241005\",\n \"0.61878794\",\n \"0.61681086\",\n \"0.6156231\",\n \"0.61406726\",\n \"0.61201674\",\n \"0.6108218\",\n \"0.60767394\",\n \"0.60732776\",\n \"0.6069381\",\n \"0.605985\",\n \"0.605102\",\n \"0.6039634\",\n \"0.60326767\",\n \"0.6030083\",\n \"0.6026038\",\n \"0.60254806\",\n \"0.60215086\",\n \"0.6004399\",\n \"0.5992663\",\n \"0.5986912\",\n \"0.5984624\",\n \"0.5975399\",\n \"0.59732544\",\n \"0.59695816\",\n \"0.596551\",\n \"0.5963737\",\n \"0.59349173\",\n \"0.5932319\",\n \"0.5930642\",\n \"0.59078044\",\n \"0.5903928\",\n \"0.5900831\",\n \"0.58927655\",\n \"0.5885908\",\n \"0.5885908\",\n \"0.5885908\",\n \"0.5885908\",\n \"0.5885908\",\n \"0.5885908\",\n \"0.58855337\",\n \"0.58841926\",\n \"0.58808327\",\n \"0.5879412\",\n \"0.58717287\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":20085,"cells":{"query":{"kind":"string","value":"View callable parameters are either context, request or just request. There is also request.context. In mako templates, request is accessible as request, context as _context. Values contained in the returned dictionary can be accessed within the template under variables named by the dictionary keys."},"document":{"kind":"string","value":"def home(context, request):\n return {}"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_context(self):\n return {\"request\": self.request, \"format\": self.format_kwarg, \"view\": self}","def get_renderer_context(self):\n # Note: Additionally 'response' will also be added to the context,\n # by the Response object.\n return {\n 'view': self,\n 'args': getattr(self, 'args', ()),\n 'kwargs': getattr(self, 'kwargs', {}),\n 'request': getattr(self, 'request', None)\n }","def context():\n return dict()","def _wrapped_view(request, *args, **kwargs):\n return view_func(request, *args, **kwargs)","def template_context(request):\n context = {\n 'application_version': settings.APPLICATION_VERSION,\n }\n context.update(settings.STATIC_CONTEXT_VARS)\n return context","def get_context_data(self, **kwargs):\n\n return {'params': kwargs}","def context(self) -> RequestContext:\n return self._context","def request_info(self, request):\n\n\t\t# We have to re-resolve the request path here, because the information\n\t\t# is not stored on the request.\n\t\tview, args, kwargs = resolve(request.path)\n\t\tfor i, arg in enumerate(args):\n\t\t\tkwargs[i] = arg\n\n\t\tparameters = {}\n\t\tparameters.update(kwargs)\n\t\tparameters.update(request.POST.items())\n\n\t\treturn {\n\t\t\t\t\"request\": {\n\t\t\t\t\t\"session\": dict(request.session),\n\t\t\t\t\t\"remote_ip\": request.META[\"REMOTE_ADDR\"],\n\t\t\t\t\t\"parameters\": parameters,\n\t\t\t\t\t\"action\": view.__name__,\n\t\t\t\t\t\"application\": view.__module__,\n\t\t\t\t\t\"request_method\": request.method,\n\t\t\t\t\t}\n\t\t\t\t}","def render(request, *args, **kw):","def render(self, context=None):\n if context is None:\n context = self.request.context\n return ICacheKeyValue(context)","def get_context(self):\n return {}","def __call__(request):","def get_info(request):\n return {'Hello': 'World'}","def get_info(request):\n return {'Hello': 'World'}","def request_context(request):\n\n d = {'user': request.user}\n d.update(csrf(request))\n\n return d","def my_view(request):\n return {'project': 'nlp'}","def get_context_data(self, **kwargs):\n # Making an object of 'HomeView class'\n # 'context' variable is an object of Parent class 'HomeView'.\n # 'super()' identifies that, 'get_context_data' is a parent class's function.\n # Here, we are using Recursion\n context = super().get_context_data(**kwargs)\n # '['injection']' is a key and 'Basic Injection!' is the value which will be shown on Template.\n context['injection'] = \"Basic Injection!\"\n return context","def template_context(**kwrds):\n usr = User.get_user()\n\n default = {\n 'usr': usr\n }\n default.update(kwrds)\n return default","def get_serializer_context(self):\n context = {}\n context[\"request\"] = self.request\n context[\"ip_address\"] = self.get_request_ip_address()\n context[\"endpoint_url\"] = self.get_request_endpoint_url()\n context[\"endpoint_name\"] = self.get_view_name()\n\n return context","def get(self):\n submission_data = self.context.serialize(\"view\")\n return {\"data\": submission_data}","def get_context_dict(context):\n if isinstance(context, RequestContext):\n ctx = {}\n list(map(ctx.update, context.dicts))\n else:\n ctx = context\n return ctx","def template_context_render(template_name, request, context) -> Response:\n context[\"request\"] = request\n context[\"messages\"] = get_messages(request)\n context[\"irrd_internal_migration_enabled\"] = get_setting(\"auth.irrd_internal_migration_enabled\")\n\n context[\"auth_sources\"] = [\n name for name, settings in get_setting(\"sources\", {}).items() if settings.get(\"authoritative\")\n ]\n\n if \"user\" not in context:\n context[\"user\"] = request.auth.user if request.auth.is_authenticated else None\n\n return templates.TemplateResponse(template_name, context)","def get_context():\n return dict(app=app, db=db, models=models, forms=forms)","def get_current():\n return getattr(_request_store, 'context', None)","def user_context(request): # pragma: no cover\n # Disabled; this is bad practice\n raise NotImplementedError","def get_context(self, *args, **kwargs):\n ctx = {}\n for k, v in kwargs.iteritems():\n ctx[k] = v\n if args:\n for idx, arg in enumerate(args):\n ctx['arg_%d' % idx] = arg\n return ctx","def r(request):\n return request.param","def _view(self, request, **kwargs):\n return self._dispatch(request, **kwargs)","def render( context, *args, **kwargs ):","def _create_request_context(self, *args, **kwargs) -> Context:\n return Context()","def base_context(request):\n if not hasattr(request, 'user'):\n return {}\n return {\n 'is_project_admin': is_project_admin(request.user)\n }","def decorator(request, *dargs, **dkwargs):\n _set_language_by_user(request)\n return view_func(request, *dargs, **dkwargs)","def get_extra_context(\n self,\n *,\n request: HttpRequest,\n context: Context,\n ) -> dict:\n data = super().get_extra_context(request=request, context=context)\n\n if self.template_name != 'actions/action.html':\n data['action'] = self.copy_to_dict(context)\n\n return data","def conti(request):\n print request\n pass","def get_context_data(self, **kwargs): # pylint: disable=R0201\n return {}","def view( self, REQUEST=None ):\n REQUEST = REQUEST or self.REQUEST\n return self.storage(REQUEST)","def setup_view(self, view, request, *args, **kwargs):\n view.request = request\n view.args = args\n view.kwargs = kwargs\n return view","def get_context(self):\n return self.context.generate()","def get_dict_from_request(request):\n if request.method == 'GET':\n return request.GET\n elif request.method == 'POST':\n return request.POST\n else:\n raise NotImplemented","def _get_render_context(self):\r\n context = {\r\n 'id': self.input_id,\r\n 'value': self.value,\r\n 'status': Status(self.status, self.capa_system.i18n.ugettext),\r\n 'msg': self.msg,\r\n 'STATIC_URL': self.capa_system.STATIC_URL,\r\n }\r\n context.update(\r\n (a, v) for (a, v) in self.loaded_attributes.iteritems() if a in self.to_render\r\n )\r\n context.update(self._extra_context())\r\n return context","def get_context(self):\r\n return {\r\n 'module': self,\r\n 'editable_metadata_fields': self.editable_metadata_fields\r\n }","def get_context_data(self) -> dict:\n return {\n \"site\": getattr(self, \"site\")\n }","def process_view(self, request, view_func, view_args, view_kwargs):\n if not scout_config.value(\"monitor\"):\n return\n tracked_request = TrackedRequest.instance()\n tracked_request.is_real_request = True\n\n track_request_view_data(request, tracked_request)\n\n span = tracked_request.current_span()\n if span is not None:\n span.operation = get_operation_name(request)","def each_context(self, request):\n script_name = request.META['SCRIPT_NAME']\n site_url = script_name if self.site_url == '/' and script_name else self.site_url\n # 把用户的groupID传给template\n # if Group.objects.filter(id = request.user.id):\n # group_context = Group.objects.get(id = request.user.id).id\n # else:\n # group_context = 0\n # group_context = [0, ]\n # if not isinstance(request.user,AnonymousUser):\n try:\n groups = Group.objects.filter(user=request.user)\n if groups:\n group_context = [i.name for i in groups]\n else:\n if request.user.is_superuser:\n group_context = [1, ]\n else:\n # 此类用户没有分组\n group_context = [0, ]\n except:\n group_context = [0, ]\n\n return {\n 'site_title': self.site_title,\n 'site_header': self.site_header,\n 'site_url': site_url,\n 'has_permission': self.has_permission(request),\n 'group_id': group_context,\n 'available_apps': self.get_app_list(request),\n }","def get_serializer_context(self, *args, **kwargs):\n return {'request': self.request}","def get_serializer_context(self, *args, **kwargs):\n return {'request': self.request}","def view(path):\n def _decorator(func):\n @functools.wraps(func)\n def _wrapper(*args, **kwargs):\n result = func(*args, **kwargs)\n if isinstance(result, dict):\n logging.info('Return template.')\n return Template(path, **result)\n raise ValueError('Expect return a dict when using @view() decorator.')\n return _wrapper\n return _decorator","def get_template_render_context(self):\n return {\n \"distribution\": self,\n \"distribution_numbers\": self.numbers,\n \"distribution_times\": self.times.all(),\n }","def get(self, request, *args, **kwargs):\n context = self.get_context_data(request)\n return render(\n request,\n self.template_name,\n context\n )","def make_template_values(request):\n user = users.get_current_user()\n values = {'request': request}\n if user:\n values['logged_in'] = True\n values['loginout_url'] = users.create_logout_url(request.uri)\n values['loginout_url_linktext'] = 'Logout ({})'.format(user)\n else:\n values['logged_in'] = False\n values['loginout_url'] = users.create_login_url(request.uri)\n values['loginout_url_linktext'] = 'Login'\n return values","def get_serializer_context(self):\n return {\n 'request': self.request,\n 'format': self.format_kwarg,\n 'view': self\n }","def meta_est(request):\n return request.param","def context(template):\n\n return {\n v.key: v.read()\n for v in [Variable(name) for name in extract_variables(template)]\n }","def T(request):\n\treturn all_templates[request.param]","def get_context_data(\n self,\n **kwargs,\n ) -> Dict[str, Any]:\n review_request = self.review_request\n draft = review_request.get_draft(self.request.user)\n\n # We only want to show one label. If there's a draft, then that's\n # the most important information, so we'll only show that. Otherwise,\n # we'll show the submitted/discarded state.\n label = None\n\n if draft:\n label = ('review-request-infobox-label-draft', _('Draft'))\n elif review_request.status == ReviewRequest.SUBMITTED:\n label = ('review-request-infobox-label-submitted', _('Submitted'))\n elif review_request.status == ReviewRequest.DISCARDED:\n label = ('review-request-infobox-label-discarded', _('Discarded'))\n\n if label:\n label = format_html('<label class=\"{0}\">{1}</label>', *label)\n\n # Fetch information on the reviews for this review request.\n review_count = (\n review_request.reviews\n .filter(public=True, base_reply_to__isnull=True)\n .count()\n )\n\n # Fetch information on the draft for this review request.\n diffset = None\n\n if draft and draft.diffset_id:\n diffset = draft.diffset\n\n if not diffset and review_request.diffset_history_id:\n try:\n diffset = (\n DiffSet.objects\n .filter(history__pk=review_request.diffset_history_id)\n .latest()\n )\n except DiffSet.DoesNotExist:\n pass\n\n if diffset:\n diff_url = '%s#index_header' % local_site_reverse(\n 'view-diff-revision',\n args=[review_request.display_id, diffset.revision],\n local_site=review_request.local_site)\n else:\n diff_url = None\n\n return {\n 'review_request': review_request,\n 'review_request_label': label or '',\n 'review_request_details': draft or review_request,\n 'issue_total_count': (review_request.issue_open_count +\n review_request.issue_resolved_count +\n review_request.issue_dropped_count +\n review_request.issue_verifying_count),\n 'review_count': review_count,\n 'diffset': diffset,\n 'diff_url': diff_url,\n }","def post(self, request, *args, **kwargs):\n return render(request, self.template_name, self.get_context_data(**kwargs))","def get_jinja_context(include_dict=None):\n context = {\n \"app_name\": app_config[\"APP\"][\"app_name\"],\n \"app_version\": app_config[\"APP\"][\"app_version\"],\n \"app_description\": app_config[\"APP\"][\"app_description\"],\n \"app_author\": app_config[\"APP\"][\"app_author\"],\n \"app_author_website\": app_config[\"APP\"][\"app_author_website\"],\n \"is_user_logged_in\": True if get_user_id() else False\n }\n if include_dict:\n context = {**context, **include_dict} # merge dictionaries\n return context","def basis(request: Any) -> Any:\n return request.param","def request_vars(self):","def partial_helper(request, template_name):\n model = {\n 'user': request.user,\n 'authenticated': request.user.is_authenticated(),\n }\n return render_to_response(template_name, model)","def get_context():\n return RequestContext(user_id=None,\n project_id=None,\n roles=[],\n is_admin=False,\n overwrite=False)","def default_context_processor(request):\n try:\n user = request.user\n except AttributeError:\n user = None\n context = {\n 'current_user': user,\n 'navbar_active': get_navbar_active(request),\n 'logged_in': user.pk is not None,\n }\n return context","def getContext(self, form):\n context = {\n 'form': form,\n 'projectList': self.projectList,\n 'subnav_location': self.subnav_location,\n 'curr_project': self.curr_project\n }\n return context","def profile_view(self, view):\n def profiled(request, *args, **kwargs):\n t1 = time.clock()\n response = view(request, *args, **kwargs)\n t2 = time.clock()\n log = lambda *args: logger.debug(u\"\".join(map(unicode, args)))\n log(\"profiled view:\\t\\t\", view.__name__)\n log(\"url:\\t\\t\\t\", request.get_full_path())\n log(\"subdomain:\\t\\t\", request.subdomain)\n log(\"get:\\t\\t\\t\", u\"\\n\\t\\t\\t\".join(\n u\"{0} => {1}\".format(k, request.GET.getlist(k))\n for k in request.GET))\n log(\"post:\\t\\t\\t\", u\"\\n\\t\\t\\t\".join(\n u\"{0} => {1}\".format(k, request.POST.getlist(k))\n for k in request.POST))\n log(\"arguments:\\t\\t\", args)\n log(\"named arguments:\\t\", kwargs)\n log(\"execution time:\\t\\t\", t2 - t1)\n log(\"query number:\\t\\t\", len(connection.queries))\n return response\n return wraps(view)(profiled)","def setup_view(view, request, *args, **kwargs):\n\n view.request = request\n view.args = args\n view.kwargs = kwargs\n return view","def get_context(self, data, accepted_media_type, renderer_context):\n view = renderer_context['view']\n request = renderer_context['request']\n response = renderer_context['response']\n\n renderer = self.get_default_renderer(view)\n\n raw_data_post_form = self.get_raw_data_form(data, view, 'POST', request)\n raw_data_put_form = self.get_raw_data_form(data, view, 'PUT', request)\n raw_data_patch_form = self.get_raw_data_form(data, view, 'PATCH', request)\n raw_data_put_or_patch_form = raw_data_put_form or raw_data_patch_form\n\n response_headers = OrderedDict(sorted(response.items()))\n renderer_content_type = ''\n if renderer:\n renderer_content_type = '%s' % renderer.media_type\n if renderer.charset:\n renderer_content_type += ' ;%s' % renderer.charset\n response_headers['Content-Type'] = renderer_content_type\n\n if getattr(view, 'paginator', None) and view.paginator.display_page_controls:\n paginator = view.paginator\n else:\n paginator = None\n\n csrf_cookie_name = settings.CSRF_COOKIE_NAME\n csrf_header_name = settings.CSRF_HEADER_NAME\n if csrf_header_name.startswith('HTTP_'):\n csrf_header_name = csrf_header_name[5:]\n csrf_header_name = csrf_header_name.replace('_', '-')\n\n return {\n 'content': self.get_content(renderer, data, accepted_media_type, renderer_context),\n 'code_style': pygments_css(self.code_style),\n 'view': view,\n 'request': request,\n 'response': response,\n 'user': request.user,\n 'description': self.get_description(view, response.status_code),\n 'name': self.get_name(view),\n 'version': VERSION,\n 'paginator': paginator,\n 'breadcrumblist': self.get_breadcrumbs(request),\n 'allowed_methods': view.allowed_methods,\n 'available_formats': [renderer_cls.format for renderer_cls in view.renderer_classes],\n 'response_headers': response_headers,\n\n 'put_form': self.get_rendered_html_form(data, view, 'PUT', request),\n 'post_form': self.get_rendered_html_form(data, view, 'POST', request),\n 'delete_form': self.get_rendered_html_form(data, view, 'DELETE', request),\n 'options_form': self.get_rendered_html_form(data, view, 'OPTIONS', request),\n\n 'extra_actions': self.get_extra_actions(view, response.status_code),\n\n 'filter_form': self.get_filter_form(data, view, request),\n\n 'raw_data_put_form': raw_data_put_form,\n 'raw_data_post_form': raw_data_post_form,\n 'raw_data_patch_form': raw_data_patch_form,\n 'raw_data_put_or_patch_form': raw_data_put_or_patch_form,\n\n 'display_edit_forms': bool(response.status_code != 403),\n\n 'api_settings': api_settings,\n 'csrf_cookie_name': csrf_cookie_name,\n 'csrf_header_name': csrf_header_name\n }","def context(self):\n return self.app.app_context()","def _get_context_for_user(request):\n get_param = request.GET.get('context') or None\n if 'context' in request.GET and get_param is None:\n # User wants to see whole file. No further processing is needed.\n return get_param\n if request.user:\n account = models.Account.current_user_account\n default_context = account.default_context\n else:\n default_context = django_settings.DEFAULT_CONTEXT\n context = _clean_int(get_param, default_context)\n if context is not None and context not in models.CONTEXT_CHOICES:\n context = django_settings.DEFAULT_CONTEXT\n return context","def _get_context_for_user(request):\n get_param = request.GET.get('context') or None\n if 'context' in request.GET and get_param is None:\n # User wants to see whole file. No further processing is needed.\n return get_param\n if request.user:\n account = models.Account.current_user_account\n default_context = account.default_context\n else:\n default_context = django_settings.DEFAULT_CONTEXT\n context = _clean_int(get_param, default_context)\n if context is not None and context not in models.CONTEXT_CHOICES:\n context = django_settings.DEFAULT_CONTEXT\n return context","def _build_requestContext(self, startTime=datetime(1970, 1, 1, 0, 0, 0, 0, pytz.timezone(settings.TIME_ZONE)), endTime=datetime(1970, 1, 1, 0, 59, 0, 0, pytz.timezone(settings.TIME_ZONE)), data=[], tzinfo=pytz.utc):\n return {\n 'template': {},\n 'args': ({}, {}),\n 'startTime': startTime,\n 'endTime': endTime,\n 'localOnly': False,\n 'data': data,\n 'tzinfo': tzinfo\n }","def meta_params(request):\n return request.param","def get_context_data(self):\n return {\"form\": self.get_form()}","def get(self, request):\n return render(request, self.template, self.context)","def setup_view(view, request=None, *args, **kwargs):\n view.request = request\n view.args = args\n view.kwargs = kwargs\n return view","def _context(self):\n domain = Site.objects.get_current()\n scheme = 'http' if settings.DEBUG else 'https'\n return {\n 'event': self.event,\n 'documents': self.documents.all(),\n 'mchp_base_url': '{}://{}'.format(scheme, domain)\n }","def get_permissions_context(view):\n context = {}\n for name in dir(permissions):\n fn = getattr(permissions, name)\n if callable(fn):\n context[name] = partial(fn, view.request.user, **view.kwargs)\n return context","def debug_context(request):\n\n return {\n 'DEBUG': settings.DEBUG,\n }","def context(self) -> CONTEXT:","def common_context(request):\n c = {\n 'lessons': get_lesson_numbers(),\n }\n return c","def get(self, request, *args, **kwargs):\n context = {'db_data': 'Hello World'}\n return render(request, self.template_name, context)","def _get_django_request():\n return execution_context.get_opencensus_attr(REQUEST_THREAD_LOCAL_KEY)","def render_index(request, *args, **kwargs):\n # add context_instance keyword\n kwargs.update(\n {'context_instance': RequestContext(request, processors=[custom_proc])})\n\n return render(request, *args, **kwargs)","def __init__(self,context,request):\n self.context = context\n self.request = request","def get(self, request, *args, **kwargs):\n return render(request, self.template_name)","def get(self, request, *args, **kwargs):\n return render(request, self.template_name)","def get(self, request, *args, **kwargs):\n return render(request, self.template_name)","def get(self, request, *args, **kwargs):\n return render(request, self.template_name)","def get(self, request, *args, **kwargs):\n return render(request, self.template_name)","def render_to(template_name):\n def renderer(func):\n def wrapper(request, *args, **kw):\n output = func(request, *args, **kw)\n if not isinstance(output, dict):\n return output\n return render_to_response(template_name, output,\n context_instance=RequestContext(request))\n return wrapper\n return renderer","def kern(request):\n return request.param","def provide_context(self) -> Optional[Dict[Text, Any]]:","def index(request): # pylint: disable=unused-argument\n return {}","def _exec(self, bound_names, args, kw):\n if not kw.has_key('args'):\n kw['args'] = args\n bound_names['options'] = kw\n\n try:\n response = self.REQUEST.RESPONSE\n if not response.headers.has_key('content-type'):\n response.setHeader('content-type', self.content_type)\n except AttributeError:\n pass\n \n security = getSecurityManager()\n bound_names['user'] = security.getUser()\n \n # Retrieve the value from the cache.\n keyset = None\n if self.ZCacheable_isCachingEnabled():\n # Prepare a cache key.\n keyset = {'here': self._getContext(),\n 'bound_names': bound_names}\n result = self.ZCacheable_get(keywords=keyset)\n if result is not None:\n # Got a cached value.\n return result\n\n # Execute the template in a new security context.\n security.addContext(self)\n try:\n result = self._render_as(extra_context=bound_names,\n RESPONSE=response)\n if keyset is not None:\n # Store the result in the cache.\n self.ZCacheable_set(result, keywords=keyset)\n return result\n finally:\n security.removeContext(self)","def as_frame(request: Any) -> Any:\n return request.param","def as_frame(request: Any) -> Any:\n return request.param","def render(self, context):\n #Turn our resolvers into actual values:\n try:\n object_obj = self.object_name_resolver.resolve(context)\n except AttributeError: #Happens if a string was passed in as the object name. Try to rescue this and treat as a var:\n object_obj = context.get(self.object_name_resolver, None)\n method_name = self.method_name_resolver.resolve(context) or str(self.method_name_resolver) #Can resolve as variable, but will also resolve as a string. Put in \"inverted commas\" to force string resolution\n if not object_obj or not method_name:\n raise TemplateSyntaxError(\"{{%% callmethod object_name.method_name %%}} cannot make sense of the resolved values for object_name.method_name '{object_name}.{method_name}'\".format(object_name=self.object_name_resolver, method_name=self.method_name_resolver))\n #Resolve the args\n args = []\n for arg_resolver in self.args_resolvers:\n arg = arg_resolver.resolve(context)\n args.append(arg)\n #Resolve the kwargs\n kwargs = {}\n for k_raw, v_resolver in self.kwargs_resolvers.items():\n k = smart_text(k_raw,'ascii')\n v = v_resolver.resolve(context)\n kwargs[k]=v\n \n #Now try to call the method on the object\n try:\n output = getattr(object_obj, method_name)(*args, **kwargs)\n except Exception as e: #Fail silently, but tell the console:\n print(\"\\033[91m{err_type} from {{%% callmethod <{obj_name}>.{method_name}() %%}}: {err_msg}\\033[0m\".format(err_type=e.__class__.__name__, obj_name=object_obj, method_name=method_name, err_msg=e))\n output = \"\"\n \n #Set to context variable if a context variable:\n if self.asvar:\n context[self.asvar] = output #NB: context is a dict, which is mutable :-)\n return \"\"\n return output #Otherwise return output (i.e. render this string into the page) ","def context(self):\n return self.parent.context","def get_serializer_context(self):\n context = super().get_serializer_context()\n\n context[\"user\"] = self.request.user\n context[\"ip_address\"] = self.get_request_ip_address()\n context[\"endpoint_url\"] = self.get_request_endpoint_url()\n context[\"endpoint_name\"] = self.get_view_name()\n\n return context","def _extra_context(self):\r\n return {}","def get(self):\n return self.context.as_dict(self.user)","def get_context_data(self, request, **kwargs):\n for piece_name in self.pieces.keys():\n piece = getattr(self, piece_name)\n self.context = piece.get_context_data(self.context, **kwargs)\n return self.context"],"string":"[\n \"def get_context(self):\\n return {\\\"request\\\": self.request, \\\"format\\\": self.format_kwarg, \\\"view\\\": self}\",\n \"def get_renderer_context(self):\\n # Note: Additionally 'response' will also be added to the context,\\n # by the Response object.\\n return {\\n 'view': self,\\n 'args': getattr(self, 'args', ()),\\n 'kwargs': getattr(self, 'kwargs', {}),\\n 'request': getattr(self, 'request', None)\\n }\",\n \"def context():\\n return dict()\",\n \"def _wrapped_view(request, *args, **kwargs):\\n return view_func(request, *args, **kwargs)\",\n \"def template_context(request):\\n context = {\\n 'application_version': settings.APPLICATION_VERSION,\\n }\\n context.update(settings.STATIC_CONTEXT_VARS)\\n return context\",\n \"def get_context_data(self, **kwargs):\\n\\n return {'params': kwargs}\",\n \"def context(self) -> RequestContext:\\n return self._context\",\n \"def request_info(self, request):\\n\\n\\t\\t# We have to re-resolve the request path here, because the information\\n\\t\\t# is not stored on the request.\\n\\t\\tview, args, kwargs = resolve(request.path)\\n\\t\\tfor i, arg in enumerate(args):\\n\\t\\t\\tkwargs[i] = arg\\n\\n\\t\\tparameters = {}\\n\\t\\tparameters.update(kwargs)\\n\\t\\tparameters.update(request.POST.items())\\n\\n\\t\\treturn {\\n\\t\\t\\t\\t\\\"request\\\": {\\n\\t\\t\\t\\t\\t\\\"session\\\": dict(request.session),\\n\\t\\t\\t\\t\\t\\\"remote_ip\\\": request.META[\\\"REMOTE_ADDR\\\"],\\n\\t\\t\\t\\t\\t\\\"parameters\\\": parameters,\\n\\t\\t\\t\\t\\t\\\"action\\\": view.__name__,\\n\\t\\t\\t\\t\\t\\\"application\\\": view.__module__,\\n\\t\\t\\t\\t\\t\\\"request_method\\\": request.method,\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t}\",\n \"def render(request, *args, **kw):\",\n \"def render(self, context=None):\\n if context is None:\\n context = self.request.context\\n return ICacheKeyValue(context)\",\n \"def get_context(self):\\n return {}\",\n \"def __call__(request):\",\n \"def get_info(request):\\n return {'Hello': 'World'}\",\n \"def get_info(request):\\n return {'Hello': 'World'}\",\n \"def request_context(request):\\n\\n d = {'user': request.user}\\n d.update(csrf(request))\\n\\n return d\",\n \"def my_view(request):\\n return {'project': 'nlp'}\",\n \"def get_context_data(self, **kwargs):\\n # Making an object of 'HomeView class'\\n # 'context' variable is an object of Parent class 'HomeView'.\\n # 'super()' identifies that, 'get_context_data' is a parent class's function.\\n # Here, we are using Recursion\\n context = super().get_context_data(**kwargs)\\n # '['injection']' is a key and 'Basic Injection!' is the value which will be shown on Template.\\n context['injection'] = \\\"Basic Injection!\\\"\\n return context\",\n \"def template_context(**kwrds):\\n usr = User.get_user()\\n\\n default = {\\n 'usr': usr\\n }\\n default.update(kwrds)\\n return default\",\n \"def get_serializer_context(self):\\n context = {}\\n context[\\\"request\\\"] = self.request\\n context[\\\"ip_address\\\"] = self.get_request_ip_address()\\n context[\\\"endpoint_url\\\"] = self.get_request_endpoint_url()\\n context[\\\"endpoint_name\\\"] = self.get_view_name()\\n\\n return context\",\n \"def get(self):\\n submission_data = self.context.serialize(\\\"view\\\")\\n return {\\\"data\\\": submission_data}\",\n \"def get_context_dict(context):\\n if isinstance(context, RequestContext):\\n ctx = {}\\n list(map(ctx.update, context.dicts))\\n else:\\n ctx = context\\n return ctx\",\n \"def template_context_render(template_name, request, context) -> Response:\\n context[\\\"request\\\"] = request\\n context[\\\"messages\\\"] = get_messages(request)\\n context[\\\"irrd_internal_migration_enabled\\\"] = get_setting(\\\"auth.irrd_internal_migration_enabled\\\")\\n\\n context[\\\"auth_sources\\\"] = [\\n name for name, settings in get_setting(\\\"sources\\\", {}).items() if settings.get(\\\"authoritative\\\")\\n ]\\n\\n if \\\"user\\\" not in context:\\n context[\\\"user\\\"] = request.auth.user if request.auth.is_authenticated else None\\n\\n return templates.TemplateResponse(template_name, context)\",\n \"def get_context():\\n return dict(app=app, db=db, models=models, forms=forms)\",\n \"def get_current():\\n return getattr(_request_store, 'context', None)\",\n \"def user_context(request): # pragma: no cover\\n # Disabled; this is bad practice\\n raise NotImplementedError\",\n \"def get_context(self, *args, **kwargs):\\n ctx = {}\\n for k, v in kwargs.iteritems():\\n ctx[k] = v\\n if args:\\n for idx, arg in enumerate(args):\\n ctx['arg_%d' % idx] = arg\\n return ctx\",\n \"def r(request):\\n return request.param\",\n \"def _view(self, request, **kwargs):\\n return self._dispatch(request, **kwargs)\",\n \"def render( context, *args, **kwargs ):\",\n \"def _create_request_context(self, *args, **kwargs) -> Context:\\n return Context()\",\n \"def base_context(request):\\n if not hasattr(request, 'user'):\\n return {}\\n return {\\n 'is_project_admin': is_project_admin(request.user)\\n }\",\n \"def decorator(request, *dargs, **dkwargs):\\n _set_language_by_user(request)\\n return view_func(request, *dargs, **dkwargs)\",\n \"def get_extra_context(\\n self,\\n *,\\n request: HttpRequest,\\n context: Context,\\n ) -> dict:\\n data = super().get_extra_context(request=request, context=context)\\n\\n if self.template_name != 'actions/action.html':\\n data['action'] = self.copy_to_dict(context)\\n\\n return data\",\n \"def conti(request):\\n print request\\n pass\",\n \"def get_context_data(self, **kwargs): # pylint: disable=R0201\\n return {}\",\n \"def view( self, REQUEST=None ):\\n REQUEST = REQUEST or self.REQUEST\\n return self.storage(REQUEST)\",\n \"def setup_view(self, view, request, *args, **kwargs):\\n view.request = request\\n view.args = args\\n view.kwargs = kwargs\\n return view\",\n \"def get_context(self):\\n return self.context.generate()\",\n \"def get_dict_from_request(request):\\n if request.method == 'GET':\\n return request.GET\\n elif request.method == 'POST':\\n return request.POST\\n else:\\n raise NotImplemented\",\n \"def _get_render_context(self):\\r\\n context = {\\r\\n 'id': self.input_id,\\r\\n 'value': self.value,\\r\\n 'status': Status(self.status, self.capa_system.i18n.ugettext),\\r\\n 'msg': self.msg,\\r\\n 'STATIC_URL': self.capa_system.STATIC_URL,\\r\\n }\\r\\n context.update(\\r\\n (a, v) for (a, v) in self.loaded_attributes.iteritems() if a in self.to_render\\r\\n )\\r\\n context.update(self._extra_context())\\r\\n return context\",\n \"def get_context(self):\\r\\n return {\\r\\n 'module': self,\\r\\n 'editable_metadata_fields': self.editable_metadata_fields\\r\\n }\",\n \"def get_context_data(self) -> dict:\\n return {\\n \\\"site\\\": getattr(self, \\\"site\\\")\\n }\",\n \"def process_view(self, request, view_func, view_args, view_kwargs):\\n if not scout_config.value(\\\"monitor\\\"):\\n return\\n tracked_request = TrackedRequest.instance()\\n tracked_request.is_real_request = True\\n\\n track_request_view_data(request, tracked_request)\\n\\n span = tracked_request.current_span()\\n if span is not None:\\n span.operation = get_operation_name(request)\",\n \"def each_context(self, request):\\n script_name = request.META['SCRIPT_NAME']\\n site_url = script_name if self.site_url == '/' and script_name else self.site_url\\n # 把用户的groupID传给template\\n # if Group.objects.filter(id = request.user.id):\\n # group_context = Group.objects.get(id = request.user.id).id\\n # else:\\n # group_context = 0\\n # group_context = [0, ]\\n # if not isinstance(request.user,AnonymousUser):\\n try:\\n groups = Group.objects.filter(user=request.user)\\n if groups:\\n group_context = [i.name for i in groups]\\n else:\\n if request.user.is_superuser:\\n group_context = [1, ]\\n else:\\n # 此类用户没有分组\\n group_context = [0, ]\\n except:\\n group_context = [0, ]\\n\\n return {\\n 'site_title': self.site_title,\\n 'site_header': self.site_header,\\n 'site_url': site_url,\\n 'has_permission': self.has_permission(request),\\n 'group_id': group_context,\\n 'available_apps': self.get_app_list(request),\\n }\",\n \"def get_serializer_context(self, *args, **kwargs):\\n return {'request': self.request}\",\n \"def get_serializer_context(self, *args, **kwargs):\\n return {'request': self.request}\",\n \"def view(path):\\n def _decorator(func):\\n @functools.wraps(func)\\n def _wrapper(*args, **kwargs):\\n result = func(*args, **kwargs)\\n if isinstance(result, dict):\\n logging.info('Return template.')\\n return Template(path, **result)\\n raise ValueError('Expect return a dict when using @view() decorator.')\\n return _wrapper\\n return _decorator\",\n \"def get_template_render_context(self):\\n return {\\n \\\"distribution\\\": self,\\n \\\"distribution_numbers\\\": self.numbers,\\n \\\"distribution_times\\\": self.times.all(),\\n }\",\n \"def get(self, request, *args, **kwargs):\\n context = self.get_context_data(request)\\n return render(\\n request,\\n self.template_name,\\n context\\n )\",\n \"def make_template_values(request):\\n user = users.get_current_user()\\n values = {'request': request}\\n if user:\\n values['logged_in'] = True\\n values['loginout_url'] = users.create_logout_url(request.uri)\\n values['loginout_url_linktext'] = 'Logout ({})'.format(user)\\n else:\\n values['logged_in'] = False\\n values['loginout_url'] = users.create_login_url(request.uri)\\n values['loginout_url_linktext'] = 'Login'\\n return values\",\n \"def get_serializer_context(self):\\n return {\\n 'request': self.request,\\n 'format': self.format_kwarg,\\n 'view': self\\n }\",\n \"def meta_est(request):\\n return request.param\",\n \"def context(template):\\n\\n return {\\n v.key: v.read()\\n for v in [Variable(name) for name in extract_variables(template)]\\n }\",\n \"def T(request):\\n\\treturn all_templates[request.param]\",\n \"def get_context_data(\\n self,\\n **kwargs,\\n ) -> Dict[str, Any]:\\n review_request = self.review_request\\n draft = review_request.get_draft(self.request.user)\\n\\n # We only want to show one label. If there's a draft, then that's\\n # the most important information, so we'll only show that. Otherwise,\\n # we'll show the submitted/discarded state.\\n label = None\\n\\n if draft:\\n label = ('review-request-infobox-label-draft', _('Draft'))\\n elif review_request.status == ReviewRequest.SUBMITTED:\\n label = ('review-request-infobox-label-submitted', _('Submitted'))\\n elif review_request.status == ReviewRequest.DISCARDED:\\n label = ('review-request-infobox-label-discarded', _('Discarded'))\\n\\n if label:\\n label = format_html('<label class=\\\"{0}\\\">{1}</label>', *label)\\n\\n # Fetch information on the reviews for this review request.\\n review_count = (\\n review_request.reviews\\n .filter(public=True, base_reply_to__isnull=True)\\n .count()\\n )\\n\\n # Fetch information on the draft for this review request.\\n diffset = None\\n\\n if draft and draft.diffset_id:\\n diffset = draft.diffset\\n\\n if not diffset and review_request.diffset_history_id:\\n try:\\n diffset = (\\n DiffSet.objects\\n .filter(history__pk=review_request.diffset_history_id)\\n .latest()\\n )\\n except DiffSet.DoesNotExist:\\n pass\\n\\n if diffset:\\n diff_url = '%s#index_header' % local_site_reverse(\\n 'view-diff-revision',\\n args=[review_request.display_id, diffset.revision],\\n local_site=review_request.local_site)\\n else:\\n diff_url = None\\n\\n return {\\n 'review_request': review_request,\\n 'review_request_label': label or '',\\n 'review_request_details': draft or review_request,\\n 'issue_total_count': (review_request.issue_open_count +\\n review_request.issue_resolved_count +\\n review_request.issue_dropped_count +\\n review_request.issue_verifying_count),\\n 'review_count': review_count,\\n 'diffset': diffset,\\n 'diff_url': diff_url,\\n }\",\n \"def post(self, request, *args, **kwargs):\\n return render(request, self.template_name, self.get_context_data(**kwargs))\",\n \"def get_jinja_context(include_dict=None):\\n context = {\\n \\\"app_name\\\": app_config[\\\"APP\\\"][\\\"app_name\\\"],\\n \\\"app_version\\\": app_config[\\\"APP\\\"][\\\"app_version\\\"],\\n \\\"app_description\\\": app_config[\\\"APP\\\"][\\\"app_description\\\"],\\n \\\"app_author\\\": app_config[\\\"APP\\\"][\\\"app_author\\\"],\\n \\\"app_author_website\\\": app_config[\\\"APP\\\"][\\\"app_author_website\\\"],\\n \\\"is_user_logged_in\\\": True if get_user_id() else False\\n }\\n if include_dict:\\n context = {**context, **include_dict} # merge dictionaries\\n return context\",\n \"def basis(request: Any) -> Any:\\n return request.param\",\n \"def request_vars(self):\",\n \"def partial_helper(request, template_name):\\n model = {\\n 'user': request.user,\\n 'authenticated': request.user.is_authenticated(),\\n }\\n return render_to_response(template_name, model)\",\n \"def get_context():\\n return RequestContext(user_id=None,\\n project_id=None,\\n roles=[],\\n is_admin=False,\\n overwrite=False)\",\n \"def default_context_processor(request):\\n try:\\n user = request.user\\n except AttributeError:\\n user = None\\n context = {\\n 'current_user': user,\\n 'navbar_active': get_navbar_active(request),\\n 'logged_in': user.pk is not None,\\n }\\n return context\",\n \"def getContext(self, form):\\n context = {\\n 'form': form,\\n 'projectList': self.projectList,\\n 'subnav_location': self.subnav_location,\\n 'curr_project': self.curr_project\\n }\\n return context\",\n \"def profile_view(self, view):\\n def profiled(request, *args, **kwargs):\\n t1 = time.clock()\\n response = view(request, *args, **kwargs)\\n t2 = time.clock()\\n log = lambda *args: logger.debug(u\\\"\\\".join(map(unicode, args)))\\n log(\\\"profiled view:\\\\t\\\\t\\\", view.__name__)\\n log(\\\"url:\\\\t\\\\t\\\\t\\\", request.get_full_path())\\n log(\\\"subdomain:\\\\t\\\\t\\\", request.subdomain)\\n log(\\\"get:\\\\t\\\\t\\\\t\\\", u\\\"\\\\n\\\\t\\\\t\\\\t\\\".join(\\n u\\\"{0} => {1}\\\".format(k, request.GET.getlist(k))\\n for k in request.GET))\\n log(\\\"post:\\\\t\\\\t\\\\t\\\", u\\\"\\\\n\\\\t\\\\t\\\\t\\\".join(\\n u\\\"{0} => {1}\\\".format(k, request.POST.getlist(k))\\n for k in request.POST))\\n log(\\\"arguments:\\\\t\\\\t\\\", args)\\n log(\\\"named arguments:\\\\t\\\", kwargs)\\n log(\\\"execution time:\\\\t\\\\t\\\", t2 - t1)\\n log(\\\"query number:\\\\t\\\\t\\\", len(connection.queries))\\n return response\\n return wraps(view)(profiled)\",\n \"def setup_view(view, request, *args, **kwargs):\\n\\n view.request = request\\n view.args = args\\n view.kwargs = kwargs\\n return view\",\n \"def get_context(self, data, accepted_media_type, renderer_context):\\n view = renderer_context['view']\\n request = renderer_context['request']\\n response = renderer_context['response']\\n\\n renderer = self.get_default_renderer(view)\\n\\n raw_data_post_form = self.get_raw_data_form(data, view, 'POST', request)\\n raw_data_put_form = self.get_raw_data_form(data, view, 'PUT', request)\\n raw_data_patch_form = self.get_raw_data_form(data, view, 'PATCH', request)\\n raw_data_put_or_patch_form = raw_data_put_form or raw_data_patch_form\\n\\n response_headers = OrderedDict(sorted(response.items()))\\n renderer_content_type = ''\\n if renderer:\\n renderer_content_type = '%s' % renderer.media_type\\n if renderer.charset:\\n renderer_content_type += ' ;%s' % renderer.charset\\n response_headers['Content-Type'] = renderer_content_type\\n\\n if getattr(view, 'paginator', None) and view.paginator.display_page_controls:\\n paginator = view.paginator\\n else:\\n paginator = None\\n\\n csrf_cookie_name = settings.CSRF_COOKIE_NAME\\n csrf_header_name = settings.CSRF_HEADER_NAME\\n if csrf_header_name.startswith('HTTP_'):\\n csrf_header_name = csrf_header_name[5:]\\n csrf_header_name = csrf_header_name.replace('_', '-')\\n\\n return {\\n 'content': self.get_content(renderer, data, accepted_media_type, renderer_context),\\n 'code_style': pygments_css(self.code_style),\\n 'view': view,\\n 'request': request,\\n 'response': response,\\n 'user': request.user,\\n 'description': self.get_description(view, response.status_code),\\n 'name': self.get_name(view),\\n 'version': VERSION,\\n 'paginator': paginator,\\n 'breadcrumblist': self.get_breadcrumbs(request),\\n 'allowed_methods': view.allowed_methods,\\n 'available_formats': [renderer_cls.format for renderer_cls in view.renderer_classes],\\n 'response_headers': response_headers,\\n\\n 'put_form': self.get_rendered_html_form(data, view, 'PUT', request),\\n 'post_form': self.get_rendered_html_form(data, view, 'POST', request),\\n 'delete_form': self.get_rendered_html_form(data, view, 'DELETE', request),\\n 'options_form': self.get_rendered_html_form(data, view, 'OPTIONS', request),\\n\\n 'extra_actions': self.get_extra_actions(view, response.status_code),\\n\\n 'filter_form': self.get_filter_form(data, view, request),\\n\\n 'raw_data_put_form': raw_data_put_form,\\n 'raw_data_post_form': raw_data_post_form,\\n 'raw_data_patch_form': raw_data_patch_form,\\n 'raw_data_put_or_patch_form': raw_data_put_or_patch_form,\\n\\n 'display_edit_forms': bool(response.status_code != 403),\\n\\n 'api_settings': api_settings,\\n 'csrf_cookie_name': csrf_cookie_name,\\n 'csrf_header_name': csrf_header_name\\n }\",\n \"def context(self):\\n return self.app.app_context()\",\n \"def _get_context_for_user(request):\\n get_param = request.GET.get('context') or None\\n if 'context' in request.GET and get_param is None:\\n # User wants to see whole file. No further processing is needed.\\n return get_param\\n if request.user:\\n account = models.Account.current_user_account\\n default_context = account.default_context\\n else:\\n default_context = django_settings.DEFAULT_CONTEXT\\n context = _clean_int(get_param, default_context)\\n if context is not None and context not in models.CONTEXT_CHOICES:\\n context = django_settings.DEFAULT_CONTEXT\\n return context\",\n \"def _get_context_for_user(request):\\n get_param = request.GET.get('context') or None\\n if 'context' in request.GET and get_param is None:\\n # User wants to see whole file. No further processing is needed.\\n return get_param\\n if request.user:\\n account = models.Account.current_user_account\\n default_context = account.default_context\\n else:\\n default_context = django_settings.DEFAULT_CONTEXT\\n context = _clean_int(get_param, default_context)\\n if context is not None and context not in models.CONTEXT_CHOICES:\\n context = django_settings.DEFAULT_CONTEXT\\n return context\",\n \"def _build_requestContext(self, startTime=datetime(1970, 1, 1, 0, 0, 0, 0, pytz.timezone(settings.TIME_ZONE)), endTime=datetime(1970, 1, 1, 0, 59, 0, 0, pytz.timezone(settings.TIME_ZONE)), data=[], tzinfo=pytz.utc):\\n return {\\n 'template': {},\\n 'args': ({}, {}),\\n 'startTime': startTime,\\n 'endTime': endTime,\\n 'localOnly': False,\\n 'data': data,\\n 'tzinfo': tzinfo\\n }\",\n \"def meta_params(request):\\n return request.param\",\n \"def get_context_data(self):\\n return {\\\"form\\\": self.get_form()}\",\n \"def get(self, request):\\n return render(request, self.template, self.context)\",\n \"def setup_view(view, request=None, *args, **kwargs):\\n view.request = request\\n view.args = args\\n view.kwargs = kwargs\\n return view\",\n \"def _context(self):\\n domain = Site.objects.get_current()\\n scheme = 'http' if settings.DEBUG else 'https'\\n return {\\n 'event': self.event,\\n 'documents': self.documents.all(),\\n 'mchp_base_url': '{}://{}'.format(scheme, domain)\\n }\",\n \"def get_permissions_context(view):\\n context = {}\\n for name in dir(permissions):\\n fn = getattr(permissions, name)\\n if callable(fn):\\n context[name] = partial(fn, view.request.user, **view.kwargs)\\n return context\",\n \"def debug_context(request):\\n\\n return {\\n 'DEBUG': settings.DEBUG,\\n }\",\n \"def context(self) -> CONTEXT:\",\n \"def common_context(request):\\n c = {\\n 'lessons': get_lesson_numbers(),\\n }\\n return c\",\n \"def get(self, request, *args, **kwargs):\\n context = {'db_data': 'Hello World'}\\n return render(request, self.template_name, context)\",\n \"def _get_django_request():\\n return execution_context.get_opencensus_attr(REQUEST_THREAD_LOCAL_KEY)\",\n \"def render_index(request, *args, **kwargs):\\n # add context_instance keyword\\n kwargs.update(\\n {'context_instance': RequestContext(request, processors=[custom_proc])})\\n\\n return render(request, *args, **kwargs)\",\n \"def __init__(self,context,request):\\n self.context = context\\n self.request = request\",\n \"def get(self, request, *args, **kwargs):\\n return render(request, self.template_name)\",\n \"def get(self, request, *args, **kwargs):\\n return render(request, self.template_name)\",\n \"def get(self, request, *args, **kwargs):\\n return render(request, self.template_name)\",\n \"def get(self, request, *args, **kwargs):\\n return render(request, self.template_name)\",\n \"def get(self, request, *args, **kwargs):\\n return render(request, self.template_name)\",\n \"def render_to(template_name):\\n def renderer(func):\\n def wrapper(request, *args, **kw):\\n output = func(request, *args, **kw)\\n if not isinstance(output, dict):\\n return output\\n return render_to_response(template_name, output,\\n context_instance=RequestContext(request))\\n return wrapper\\n return renderer\",\n \"def kern(request):\\n return request.param\",\n \"def provide_context(self) -> Optional[Dict[Text, Any]]:\",\n \"def index(request): # pylint: disable=unused-argument\\n return {}\",\n \"def _exec(self, bound_names, args, kw):\\n if not kw.has_key('args'):\\n kw['args'] = args\\n bound_names['options'] = kw\\n\\n try:\\n response = self.REQUEST.RESPONSE\\n if not response.headers.has_key('content-type'):\\n response.setHeader('content-type', self.content_type)\\n except AttributeError:\\n pass\\n \\n security = getSecurityManager()\\n bound_names['user'] = security.getUser()\\n \\n # Retrieve the value from the cache.\\n keyset = None\\n if self.ZCacheable_isCachingEnabled():\\n # Prepare a cache key.\\n keyset = {'here': self._getContext(),\\n 'bound_names': bound_names}\\n result = self.ZCacheable_get(keywords=keyset)\\n if result is not None:\\n # Got a cached value.\\n return result\\n\\n # Execute the template in a new security context.\\n security.addContext(self)\\n try:\\n result = self._render_as(extra_context=bound_names,\\n RESPONSE=response)\\n if keyset is not None:\\n # Store the result in the cache.\\n self.ZCacheable_set(result, keywords=keyset)\\n return result\\n finally:\\n security.removeContext(self)\",\n \"def as_frame(request: Any) -> Any:\\n return request.param\",\n \"def as_frame(request: Any) -> Any:\\n return request.param\",\n \"def render(self, context):\\n #Turn our resolvers into actual values:\\n try:\\n object_obj = self.object_name_resolver.resolve(context)\\n except AttributeError: #Happens if a string was passed in as the object name. Try to rescue this and treat as a var:\\n object_obj = context.get(self.object_name_resolver, None)\\n method_name = self.method_name_resolver.resolve(context) or str(self.method_name_resolver) #Can resolve as variable, but will also resolve as a string. Put in \\\"inverted commas\\\" to force string resolution\\n if not object_obj or not method_name:\\n raise TemplateSyntaxError(\\\"{{%% callmethod object_name.method_name %%}} cannot make sense of the resolved values for object_name.method_name '{object_name}.{method_name}'\\\".format(object_name=self.object_name_resolver, method_name=self.method_name_resolver))\\n #Resolve the args\\n args = []\\n for arg_resolver in self.args_resolvers:\\n arg = arg_resolver.resolve(context)\\n args.append(arg)\\n #Resolve the kwargs\\n kwargs = {}\\n for k_raw, v_resolver in self.kwargs_resolvers.items():\\n k = smart_text(k_raw,'ascii')\\n v = v_resolver.resolve(context)\\n kwargs[k]=v\\n \\n #Now try to call the method on the object\\n try:\\n output = getattr(object_obj, method_name)(*args, **kwargs)\\n except Exception as e: #Fail silently, but tell the console:\\n print(\\\"\\\\033[91m{err_type} from {{%% callmethod <{obj_name}>.{method_name}() %%}}: {err_msg}\\\\033[0m\\\".format(err_type=e.__class__.__name__, obj_name=object_obj, method_name=method_name, err_msg=e))\\n output = \\\"\\\"\\n \\n #Set to context variable if a context variable:\\n if self.asvar:\\n context[self.asvar] = output #NB: context is a dict, which is mutable :-)\\n return \\\"\\\"\\n return output #Otherwise return output (i.e. render this string into the page) \",\n \"def context(self):\\n return self.parent.context\",\n \"def get_serializer_context(self):\\n context = super().get_serializer_context()\\n\\n context[\\\"user\\\"] = self.request.user\\n context[\\\"ip_address\\\"] = self.get_request_ip_address()\\n context[\\\"endpoint_url\\\"] = self.get_request_endpoint_url()\\n context[\\\"endpoint_name\\\"] = self.get_view_name()\\n\\n return context\",\n \"def _extra_context(self):\\r\\n return {}\",\n \"def get(self):\\n return self.context.as_dict(self.user)\",\n \"def get_context_data(self, request, **kwargs):\\n for piece_name in self.pieces.keys():\\n piece = getattr(self, piece_name)\\n self.context = piece.get_context_data(self.context, **kwargs)\\n return self.context\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7701646","0.6560682","0.63035905","0.61721003","0.6156719","0.60182035","0.60104954","0.59970176","0.597518","0.5969505","0.5959369","0.595519","0.5925658","0.5925658","0.5907715","0.58984065","0.5887859","0.58788943","0.58776265","0.58464235","0.58325964","0.58294475","0.58103937","0.580006","0.5799959","0.57613724","0.5737244","0.5734798","0.5719283","0.57130533","0.57006013","0.56967694","0.5695515","0.56924653","0.56922495","0.5684378","0.5683692","0.56689036","0.5666996","0.5664109","0.5660029","0.56485766","0.5644393","0.5639718","0.56315476","0.56315476","0.5628081","0.5627851","0.56225353","0.5610017","0.56059146","0.56048286","0.560077","0.559711","0.55930614","0.5585951","0.5577554","0.5572256","0.55661756","0.5557813","0.5557493","0.5552508","0.55490816","0.55471706","0.55461293","0.55400187","0.5539142","0.5530793","0.5530793","0.5526509","0.5516915","0.54923135","0.5473681","0.54719067","0.54598814","0.5451492","0.54490674","0.5448528","0.5444315","0.54327106","0.54320514","0.54299384","0.5426218","0.5418508","0.5418508","0.5418508","0.5418508","0.5418508","0.5416621","0.540059","0.5375807","0.5370873","0.5370385","0.53701884","0.53701884","0.536761","0.53638375","0.5359644","0.53537667","0.53516513","0.5349631"],"string":"[\n \"0.7701646\",\n \"0.6560682\",\n \"0.63035905\",\n \"0.61721003\",\n \"0.6156719\",\n \"0.60182035\",\n \"0.60104954\",\n \"0.59970176\",\n \"0.597518\",\n \"0.5969505\",\n \"0.5959369\",\n \"0.595519\",\n \"0.5925658\",\n \"0.5925658\",\n \"0.5907715\",\n \"0.58984065\",\n \"0.5887859\",\n \"0.58788943\",\n \"0.58776265\",\n \"0.58464235\",\n \"0.58325964\",\n \"0.58294475\",\n \"0.58103937\",\n \"0.580006\",\n \"0.5799959\",\n \"0.57613724\",\n \"0.5737244\",\n \"0.5734798\",\n \"0.5719283\",\n \"0.57130533\",\n \"0.57006013\",\n \"0.56967694\",\n \"0.5695515\",\n \"0.56924653\",\n \"0.56922495\",\n \"0.5684378\",\n \"0.5683692\",\n \"0.56689036\",\n \"0.5666996\",\n \"0.5664109\",\n \"0.5660029\",\n \"0.56485766\",\n \"0.5644393\",\n \"0.5639718\",\n \"0.56315476\",\n \"0.56315476\",\n \"0.5628081\",\n \"0.5627851\",\n \"0.56225353\",\n \"0.5610017\",\n \"0.56059146\",\n \"0.56048286\",\n \"0.560077\",\n \"0.559711\",\n \"0.55930614\",\n \"0.5585951\",\n \"0.5577554\",\n \"0.5572256\",\n \"0.55661756\",\n \"0.5557813\",\n \"0.5557493\",\n \"0.5552508\",\n \"0.55490816\",\n \"0.55471706\",\n \"0.55461293\",\n \"0.55400187\",\n \"0.5539142\",\n \"0.5530793\",\n \"0.5530793\",\n \"0.5526509\",\n \"0.5516915\",\n \"0.54923135\",\n \"0.5473681\",\n \"0.54719067\",\n \"0.54598814\",\n \"0.5451492\",\n \"0.54490674\",\n \"0.5448528\",\n \"0.5444315\",\n \"0.54327106\",\n \"0.54320514\",\n \"0.54299384\",\n \"0.5426218\",\n \"0.5418508\",\n \"0.5418508\",\n \"0.5418508\",\n \"0.5418508\",\n \"0.5418508\",\n \"0.5416621\",\n \"0.540059\",\n \"0.5375807\",\n \"0.5370873\",\n \"0.5370385\",\n \"0.53701884\",\n \"0.53701884\",\n \"0.536761\",\n \"0.53638375\",\n \"0.5359644\",\n \"0.53537667\",\n \"0.53516513\",\n \"0.5349631\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":20086,"cells":{"query":{"kind":"string","value":"Returns the quantization config for transformerbased models."},"document":{"kind":"string","value":"def _get_transformer_quantization_config(subset_size: int) -> Dict[str, Any]:\n return {\n \"algorithm\": \"quantization\",\n \"preset\": \"mixed\",\n \"initializer\": {\n \"range\": {\"num_init_samples\": subset_size, \"type\": DEFAULT_RANGE_TYPE},\n \"batchnorm_adaptation\": {\"num_bn_adaptation_samples\": 0},\n },\n \"scope_overrides\": {\"activations\": {\"{re}.*matmul_0\": {\"mode\": \"symmetric\"}}},\n \"ignored_scopes\": [\n \"{re}.*Embeddings.*\",\n \"{re}.*__add___[0-1]\",\n \"{re}.*layer_norm_0\",\n \"{re}.*matmul_1\",\n \"{re}.*__truediv__*\",\n ],\n \"overflow_fix\": \"first_layer_only\",\n }"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _get_default_quantization_config(preset: QuantizationPreset, subset_size: int) -> Dict[str, Any]:\n return {\n \"algorithm\": \"quantization\",\n \"preset\": preset.value,\n \"initializer\": {\n \"range\": {\"num_init_samples\": subset_size, \"type\": DEFAULT_RANGE_TYPE},\n \"batchnorm_adaptation\": {\"num_bn_adaptation_samples\": subset_size},\n },\n \"overflow_fix\": \"first_layer_only\",\n }","def get_config_object() -> \"BaseConfig\":\n assert (\n len(G_CONFIG_OBJECT) == 1\n ), \"Have you created quantize config object before calling `quantize_model`?\"\n if G_CONFIG_OBJECT:\n return G_CONFIG_OBJECT[0]","def get_config(self):\n return {'reduction': self.reduction, 'name': self.name}","def param_quantizers(self):\n return self._param_quantizers","def get_quantization_capability(self):\n return self.cur_config['capabilities']","def input_quantizers(self):\n return self._input_quantizers","def quantize(self, tune_cfg, model, data_loader, q_func=None):\n assert q_func is None, \"quantization aware training mode is not support on tensorflow\"\n logger.info('Start to run model quantization...')\n quantized_model = os.path.join(os.getcwd(), \"tf_quantized.pb\")\n self.tuning_cfg_to_fw(tune_cfg)\n logger.debug('Dump quantization configurations:')\n logger.debug(self.quantize_config)\n from .tf_utils.graph_converter import GraphConverter\n converter = GraphConverter(self.pre_optimized_graph if self.pre_optimized_graph else model,\n quantized_model,\n inputs=self.inputs,\n outputs=self.outputs,\n qt_config=self.quantize_config,\n fp32_ops=self.fp32_ops,\n bf16_ops=self.bf16_ops,\n data_loader=data_loader)\n return converter.convert()","def get_config(self):\n config = super(EpsGreedyQPolicy, self).get_config()\n config['eps'] = self.eps\n return config","def quantize(self, tune_cfg, model, data_loader, q_func=None):\n assert q_func is None, \"quantization aware training has not been supported on ONNXRUNTIME\"\n model = self.pre_optimized_model if self.pre_optimized_model else model\n ort_version = StrictVersion(ort.__version__)\n if ort_version < ONNXRT152_VERSION: # pragma: no cover\n logger.warning(\"Quantize input needs onnxruntime 1.5.2 or newer.\")\n return model\n if model.model.opset_import[0].version < 11: # pragma: no cover\n logger.warning(\"Quantize input needs model opset 11 or newer.\")\n from neural_compressor.adaptor.ox_utils.onnx_quantizer import ONNXQuantizer\n from onnxruntime.quantization.quant_utils import QuantizationMode\n backend = QuantizationMode.QLinearOps if self.backend == \\\n \"qlinearops\" else QuantizationMode.IntegerOps\n\n self.quantizable_ops = self._query_quantizable_ops(model.model)\n tmp_model = copy.deepcopy(model)\n \n quantize_config = self._cfg_to_quantize_config(tune_cfg)\n iterations = tune_cfg.get('calib_iteration', 1)\n if self.static:\n quantize_params = self._get_quantize_params(tmp_model.model, data_loader, \\\n quantize_config, iterations)\n else:\n quantize_params = None\n self.quantize_params = quantize_params\n quantizer = ONNXQuantizer(tmp_model.model,\n quantize_config,\n backend,\n self.static,\n quantize_params,\n self.quantizable_op_types)\n quantizer.quantize_model()\n tmp_model.q_config = self._generate_qconfig(model.model, tune_cfg, quantize_params)\n tmp_model.model = quantizer.model.model\n self.quantize_config = quantize_config # update so other methods can know current configs\n \n self._dump_model_op_stastics(tmp_model)\n return tmp_model","def use_config_qty_increments(self):\n return self._use_config_qty_increments","def _quantize(self, model: nn.Module) -> nn.Module:\n if self.mask:\n model_utils.remove_pruning_reparameterization(self.params_all)\n\n # check the accuracy after each epoch\n quantized_model = torch.quantization.convert(model.eval(), inplace=False)\n quantized_model.eval()\n\n # set masks again\n if self.mask:\n self._load_masks()\n\n return quantized_model","def get_config(self):\n config = {\n \"units\": self.units,\n \"activation\": activations.serialize(self.activation),\n \"recurrent_activation\": activations.serialize(\n self.recurrent_activation\n ),\n \"attention_activation\": activations.serialize(\n self.attention_activation\n ),\n \"use_bias\": self.use_bias,\n \"kernel_initializer\": initializers.serialize(self.kernel_initializer),\n \"recurrent_initializer\": initializers.serialize(\n self.recurrent_initializer\n ),\n \"bias_initializer\": initializers.serialize(self.bias_initializer),\n \"attention_initializer\": initializers.serialize(\n self.attention_initializer\n ),\n \"use_chrono_initialization\": self.unit_forget_bias,\n \"kernel_regularizer\": regularizers.serialize(self.kernel_regularizer),\n \"recurrent_regularizer\": regularizers.serialize(\n self.recurrent_regularizer\n ),\n \"bias_regularizer\": regularizers.serialize(self.bias_regularizer),\n \"activity_regularizer\": regularizers.serialize(\n self.activity_regularizer\n ),\n \"attention_regularizer\": regularizers.serialize(\n self.attention_regularizer\n ),\n \"kernel_constraint\": constraints.serialize(self.kernel_constraint),\n \"recurrent_constraint\": constraints.serialize(\n self.recurrent_constraint\n ),\n \"bias_constraint\": constraints.serialize(self.bias_constraint),\n \"attention_constraint\": constraints.serialize(\n self.attention_constraint\n ),\n \"dropout\": self.dropout,\n \"recurrent_dropout\": self.recurrent_dropout,\n \"return_attention\": self.return_attention,\n }\n base_config = super().get_config()\n del base_config[\"cell\"]\n return dict(list(base_config.items()) + list(config.items()))","def get_config(self):\n return {'name': self.name, 'dtype': self.dtype}","def use_config_min_qty(self):\n return self._use_config_min_qty","def output_quantizers(self):\n return self._output_quantizers","def get_config(self):\n config = {\n 'membership_transform': self.membership_transform,\n 'predictions_transform': self.predictions_transform,\n 'membership_kernel': self.membership_kernel,\n 'predictions_kernel': self.predictions_kernel,\n 'name': self.name,\n }\n config = {k: v for k, v in config.items() if v is not None}\n return self._serialize_config(config)","def get_config(self):\n config = {'epsilon':self.eps}\n base_config = super(PowTransform, self).get_config()\n return dict(list(base_config.items()) + list(config.items()))","def get_config(self) -> Dict[str, Any]:\n return {\n 'num_classes': self.num_classes,\n 'name': self.name,\n 'dtype': self.dtype,\n 'sparse_y_true': self.sparse_y_true,\n 'sparse_y_pred': self.sparse_y_pred,\n 'axis': self.axis,\n }","def get_config(self):\n config = {'name': self.name, 'trainable': self.trainable}\n if hasattr(self, '_batch_input_shape'):\n config['batch_input_shape'] = self._batch_input_shape\n if hasattr(self, 'dtype'):\n config['dtype'] = self.dtype\n return config","def base_model_config():\n return {\n # TFRecord file pattern containing Example protos.\n \"input_file_pattern\": \"\",\n\n # Number of examples to keep in the input queue.\n \"input_queue_capacity\": 5 * 640000, # 5 shards of the BookCorpus.\n\n # Number of threads for prefetching TFRecord values.\n \"num_input_reader_threads\": 1,\n\n # Whether to shuffle the input data.\n \"shuffle_input_data\": True,\n\n # Scale of the random uniform initializer.\n \"uniform_init_scale\": 0.1,\n\n # Number of unique words in the vocab.\n \"vocab_size\": 20000,\n\n # Batch size (training and evaluation only).\n \"batch_size\": 128,\n\n # Word embedding dimension.\n \"word_embedding_dim\": 620,\n\n # Whether to use a bidirectional or unidirectional encoder RNN.\n \"bidirectional_encoder\": False,\n\n # Number of output dimensions of the sentence encoder.\n \"encoder_dim\": 2400,\n\n # Operation for combining the final states of the encoder GRU\n \"pooling_operation\": \"last\",\n }","def config(self) -> dict:\n if self.__class__.__name__ == 'MyFunctionTransformer':\n pass\n else:\n check_is_fitted(self)\n\n _config = {}\n for attr in self.config_paras:\n _config[attr] = getattr(self, attr)\n\n return {\"params\": self.get_params(),\n \"config\": _config}","def get_config(self):\n config = super(Sc2Policy, self).get_config()\n config['eps'] = self.eps\n config['testing'] = self.testing\n return config","def get_config(self):\n config = {\n }\n base_config = super(MatrixConcat, self).get_config()\n return dict(list(base_config.items()) + list(config.items()))","def recover(self, model, q_config):\n self._pre_optimize(model)\n model = self.pre_optimized_model\n ort_version = StrictVersion(ort.__version__)\n if ort_version < ONNXRT152_VERSION: # pragma: no cover\n logger.warning(\"Quantize input needs onnxruntime 1.5.2 or newer.\")\n return model\n if model.model.opset_import[0].version < 11: # pragma: no cover\n logger.warning(\"Quantize input needs model opset 11 or newer.\")\n\n from neural_compressor.adaptor.ox_utils.onnx_quantizer import ONNXQuantizer\n from onnxruntime.quantization.quant_utils import QuantizationMode\n backend = QuantizationMode.QLinearOps if self.backend == \\\n \"qlinearops\" else QuantizationMode.IntegerOps\n \n self.quantizable_ops = self._query_quantizable_ops(model.model)\n quantize_params, tune_cfg = self._parse_qconfig(q_config)\n quantize_config = self._cfg_to_quantize_config(tune_cfg)\n quantizer = ONNXQuantizer(model.model,\n quantize_config,\n backend,\n self.static,\n quantize_params,\n self.quantizable_op_types)\n\n quantizer.quantize_model()\n model.model = quantizer.model.model\n return model","def override_config(args):\n args.transformer_enc_config = (\n \"((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 3\"\n )","def override_config(args):\n args.transformer_enc_config = (\n \"((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 3\"\n )","def get_config(self):\n config = self._kwargs.copy()\n config.update({\n 'metric': self.__class__.__name__,\n 'name': self.name,\n 'output_names': self.output_names,\n 'label_names': self.label_names})\n return config","def get_config(self):\n config = {'epsilon':self.eps}\n base_config = super(LogTransform, self).get_config()\n return dict(list(base_config.items()) + list(config.items()))","def use_config_enable_qty_inc(self):\n return self._use_config_enable_qty_inc","def override_config(args):\n args.transformer_enc_config = (\n \"((512, 8, 2048, True, 0.15, 0.15, 0.15),) * 3\"\n )","def construct_and_initialize_trainable_quantizers(self, quant_scheme):\n # Copy user settable attributes for outputs\n\n def _create_trainable_quantizer(bw, name, quantizer):\n \"\"\" create trainable quantizer from static grid quantizer. \"\"\"\n # Initialize trainable parameters to None\n self.register_parameter(f'{name}_encoding_min', None)\n self.register_parameter(f'{name}_encoding_max', None)\n # Pass name of tensor quantizer and reference of Wrapper to tensor quantizer\n # Input quantizer\n new_quantizer = tensor_quantizer_factory(bw, self._round_mode,\n quant_scheme,\n self._is_symmetric,\n enabled_by_default=True,\n data_type=self._data_type)\n new_quantizer.name = name\n new_quantizer.wrapper_ref = self\n new_quantizer.device = self.device\n initialize_learned_grid_quantizer_attributes(new_quantizer, quantizer)\n return new_quantizer\n\n new_grouped_quantizers = {name: _create_trainable_quantizer(self._activation_bw, name, quantizer)\n for name, quantizer in self._grouped_quantizers.items()}\n\n def create_trainable_quantizer(bw, name, quantizer):\n \"\"\" create trainable quantizer if not part of a group else reuse the group quantizer. \"\"\"\n if quantizer in self._grouped_quantizers.values():\n group_names = [n for n, q in self._grouped_quantizers.items() if q == quantizer]\n assert len(group_names) == 1\n # creating a param min/max references to the shared group min/max parameters.\n setattr(self, f'{name}_encoding_min', getattr(self, f'{group_names[0]}_encoding_min'))\n setattr(self, f'{name}_encoding_max', getattr(self, f'{group_names[0]}_encoding_max'))\n return new_grouped_quantizers[group_names[0]]\n\n return _create_trainable_quantizer(bw, name, quantizer)\n\n\n self._input_quantizers = {name: create_trainable_quantizer(self._activation_bw, name, quantizer)\n for name, quantizer in self.input_quantizers.items()}\n self._output_quantizers = {name: create_trainable_quantizer(self._activation_bw, name, quantizer)\n for name, quantizer in self.output_quantizers.items()}\n self._param_quantizers = {name: create_trainable_quantizer(self._weight_bw, name, quantizer)\n for name, quantizer in self._param_quantizers.items()}\n\n self._grouped_quantizers = new_grouped_quantizers\n self._mode = QcQuantizeOpMode.LEARN_ENCODINGS","def get_config(self):\n config = {\n 'F_': self.F_,\n 'attn_heads': self.attn_heads,\n 'attn_heads_reduction': self.attn_heads_reduction,\n 'edge_type_reduction': self.edge_type_reduction,\n 'attention_type': self.attention_type,\n 'attn_dropout': self.attn_dropout,\n 'feature_dropout': self.feature_dropout,\n 'activation': self.activation,\n 'use_value_bias': self.use_value_bias,\n 'use_key_bias': self.use_key_bias,\n 'kernel_initializer': self.kernel_initializer,\n 'bias_initializer': self.bias_initializer,\n 'attn_kernel_initializer': self.attn_kernel_initializer,\n 'attn_bias_initalizer': self.attn_bias_initializer,\n 'kernel_regularizer': self.kernel_regularizer,\n 'bias_regularizer': self.bias_regularizer,\n 'attn_kernel_regularizer': self.attn_kernel_regularizer,\n 'attn_bias_regularizer': self.attn_bias_regularizer,\n 'activity_regularizer': self.activity_regularizer,\n 'kernel_constraint': self.kernel_constraint,\n 'bias_constraint': self.bias_constraint,\n 'attn_kernel_constraint': self.attn_kernel_constraint,\n 'attn_bias_constraint': self.attn_bias_constraint\n }\n base_config = super(BatchShawMultigraphAttention, self).get_config()\n return dict(list(base_config.items())) + list(config.items())","def default_config(self):\n return {\n \"xtype\": \"scalar\",\n \"ytype\": \"scalar\",\n \"xtick-delta\": 50, \n \"ytick-delta\": 20, \n \"xtick-format\": '{:g}', \n \"ytick-format\": '{:g}',\n }","def get_config(self):\n config = {'epsilon':self.eps}\n base_config = super(MatrixReLU, self).get_config()\n return dict(list(base_config.items()) + list(config.items()))","def get_config(self):\n layer_config = {\n \"anchors\": self._anchors, \n \"classes\": self._classes,\n \"ignore_thresh\": self._ignore_thresh, \n \"truth_thresh\": self._truth_thresh, \n \"iou_thresh\": self._iou_thresh, \n \"loss_type\": self._loss_type, \n \"iou_normalizer\": self._iou_normalizer,\n \"cls_normalizer\": self._cls_normalizer, \n \"scale_x_y\": self._scale_x_y, \n }\n layer_config.update(super().get_config())\n return layer_config","def get_config(self): # To support serialization\n return {\"min value\": self.minval, \"max value\": self.maxval}","def config(self):\n\n train_dataset = RandomClassificationDataset()\n eval_dataset = RandomClassificationDataset()\n\n return {\n 'model':\n SimpleModel(),\n 'train_dataloader':\n DataLoader(\n dataset=train_dataset,\n batch_size=4,\n sampler=dist.get_sampler(train_dataset),\n ),\n 'eval_dataloader':\n DataLoader(\n dataset=eval_dataset,\n sampler=dist.get_sampler(eval_dataset),\n ),\n 'max_duration':\n '2ep',\n 'autoresume':\n True,\n 'loggers': [],\n }","def _get_model_config(self, **kwargs):\n kwargs[\"example_type\"] = QUERY_EXAMPLE_TYPE\n kwargs[\"label_type\"] = CLASS_LABEL_TYPE\n loaded_config = get_classifier_config(\n self.CLF_TYPE, self._resource_loader.app_path, domain=self.domain\n )\n return super()._get_model_config(loaded_config, **kwargs)","def get_config(self):\n return super().get_config()","def settings(self) -> Dict[str, Any]:\n if self.num_pixels is not None:\n return dict(num_pixels=self.num_pixels)\n return dict(quantile=self.quantile)","def serialize(self):\n cfg = SeqLib.serialize(self)\n\n cfg[\"variants\"] = dict()\n cfg[\"variants\"][\"wild type\"] = self.wt.serialize()\n cfg[\"variants\"][\"use aligner\"] = self.aligner is not None\n if self.max_mutations != DEFAULT_MAX_MUTATIONS:\n cfg[\"variants\"][\"max mutations\"] = self.max_mutations\n if self.variant_min_count > 0:\n cfg[\"variants\"][\"min count\"] = self.variant_min_count\n\n return cfg","def _est_config(self):\n return self._est_method.config","def get_config():\n config = ml_collections.ConfigDict()\n\n # Which model to use -- see ./models.py\n config.model_name = 'ViT-B_32'\n # Where to store training logs.\n config.log_dir = '.'\n\n # Number of steps to measure.\n config.steps = 30\n # Number of steps before measuring.\n config.initial_steps = 10\n\n # Batch size\n config.batch = 0\n # Number of output classes.\n config.num_classes = 0\n # Image size (width=height).\n config.image_size = 0\n\n config.train = 'inference_time'\n\n return config","def optimizer_config(self):\r\n return {\r\n \"lr\": self.args.lr[0],\r\n \"momentum\": self.args.momentum,\r\n \"weight_decay\": self.args.weight_decay,\r\n }","def transaction_cost_model(self) -> str:\n return self.__transaction_cost_model","def update_configuration(self, config):\n\n config[\"data_transformation\"][\"n_classification_bins\"] = config[\"n_classification_bins\"]\n config[\"data_transformation\"][\"nassets\"] = config[\"nassets\"]\n config[\"data_transformation\"][\"classify_per_series\"] = config[\"classify_per_series\"]\n config[\"data_transformation\"][\"normalise_per_series\"] = config[\"normalise_per_series\"]\n\n return config","def get_base_config():\n return dict(\n dim=768,\n ff_dim=3072,\n num_heads=12,\n num_layers=12,\n attention_dropout_rate=0.0,\n dropout_rate=0.1,\n representation_size=768,\n classifier='token'\n )","def requested_config_vals():\n return {'transfer_stats_per_file':'opt'}","def get_config():\n name = 'dynamic_pricing'\n num_products = 5\n scale = 1\n noise_var = 10\n p_max = 1\n\n agents = collections.OrderedDict(\n [('bsPricing',\n functools.partial(BootstrapDynamicPricing,\n num_products, scale, noise_var, p_max))]\n )\n\n environments = collections.OrderedDict(\n [('env',\n functools.partial(DynamicPricing,\n num_products, scale, noise_var, p_max))]\n )\n experiments = collections.OrderedDict(\n [(name, ExperimentNoAction)]\n )\n n_steps = 80\n n_seeds = 2000\n config = Config(name, agents, environments, experiments, n_steps, n_seeds)\n return config","def _quantize_activation(self, tensor_quantizer: Union[StaticGridPerTensorQuantizer, LearnedGridTensorQuantizer],\n tensors_to_quantize: Union[List[torch.Tensor], torch.Tensor]) -> \\\n Union[List[torch.Tensor], torch.Tensor]:\n\n if not tensor_quantizer.enabled:\n return tensors_to_quantize\n\n outputs = []\n if not isinstance(tensors_to_quantize, list):\n tensors_to_quantize = [tensors_to_quantize]\n for input_tensor in tensors_to_quantize:\n\n if self._mode is QcQuantizeOpMode.ANALYSIS:\n\n if isinstance(input_tensor, tuple):\n for tensor in input_tensor:\n tensor_quantizer.update_encoding_stats(tensor)\n else:\n tensor_quantizer.update_encoding_stats(input_tensor)\n output = input_tensor\n\n elif self._mode is QcQuantizeOpMode.ACTIVE:\n # if we are not in training, then only nearest rounding should be used\n if self.training:\n round_mode = tensor_quantizer.round_mode\n else:\n round_mode = libpymo.RoundingMode.ROUND_NEAREST\n output = tensor_quantizer.quantize_dequantize(input_tensor, round_mode)\n\n elif self._mode is QcQuantizeOpMode.LEARN_ENCODINGS:\n encoding_min = getattr(self, tensor_quantizer.name + '_encoding_min')\n encoding_max = getattr(self, tensor_quantizer.name + '_encoding_max')\n output = tensor_quantizer.quantize_dequantize(input_tensor, encoding_min, encoding_max)\n\n else:\n output = input_tensor\n\n outputs.append(output)\n\n # Flatten if there is only one output\n return outputs[0] if len(outputs) == 1 else tuple(outputs)","def get_config(self):\n config = {\n 'multichannel': self._multichannel,\n 'complex_part': self._complex_part\n }\n base_config = super().get_config()\n return {**base_config, **config}","def use_config_notify_stock_qty(self):\n return self._use_config_notify_stock_qty","def _quantize_dequantize_params(self, inputs=None) -> Dict[str, torch.Tensor]:\n quantized_params = dict()\n shadow_params = {name: param.detach().clone() for name, param in self.named_parameters(recurse=False)}\n\n if self._mode != QcQuantizeOpMode.LEARN_ENCODINGS:\n quantizer_param_map = defaultdict(list)\n for name, param in self.named_parameters(recurse=False):\n quantizer_param_map[self._param_quantizers[name]].append((name, param))\n\n for param_quantizer, param_list in quantizer_param_map.items():\n # If we are in training mode with quant-sim nodes,\n # then we want to calculate encodings for the parameters in every pass\n if self.training or param_quantizer.encoding is None:\n param_quantizer.reset_encoding_stats()\n for _, param in param_list:\n param_quantizer.update_encoding_stats(param.data)\n param_quantizer.compute_encoding()\n\n for name, param in param_list:\n quantized_params[name] = param.data = self._param_quantize_dequantize(param.clone(), param_quantizer)\n else:\n encoding_list_for_params = []\n for name, _ in self.get_named_parameters():\n # Create a list of encoding parameters for params\n quantizer = self.param_quantizers[name]\n if quantizer.enabled:\n # if param uses a group quantizer remap to group quantizer min/max encoding params.\n if quantizer in self._grouped_quantizers.values():\n name, *_ = [n for n, q in self._grouped_quantizers.items() if q == quantizer]\n encoding_list_for_params.append(getattr(self, name + '_encoding_min'))\n encoding_list_for_params.append(getattr(self, name + '_encoding_max'))\n\n # Quantize the parameters\n inputs = ParameterQuantizer.apply(inputs, self, *encoding_list_for_params)\n\n # clone() the outputs of Custom function to avoid incorrect gradient calculation for in-place modification\n # of view (view is created since Custom function's forward return input as-is)\n inputs = inputs.clone()\n quantized_params = {name: param.clone() for name, param in self.named_parameters(recurse=False)\n if '_encoding_' not in name}\n\n yield quantized_params, inputs\n\n for name, param in self.named_parameters(recurse=False):\n if name in shadow_params:\n param.data.copy_(shadow_params[name].data)","def get_config(self):\n return self.cat_feats_cfg","def get_config(self) -> dict:\n config = {}\n\n args = ['out_dim', 'bond_dim', 'use_bias']\n for arg in args:\n config[arg] = getattr(self, arg)\n\n config['activation'] = activations.serialize(getattr(self, 'activation'))\n\n custom_initializers = ['kernel_initializer', 'bias_initializer']\n for initializer_arg in custom_initializers:\n config[initializer_arg] = initializers.serialize(\n getattr(self, initializer_arg))\n\n base_config = super().get_config()\n return dict(list(base_config.items()) + list(config.items()))","def grouped_quantizers(self):\n return self._grouped_quantizers","def prepare_fx_with_backend(model, qconfig_dict, backend, is_qat, **kwargs):\n model = prepare_qat_fx(model, qconfig_dict, **kwargs)\n\n if not is_qat:\n model.eval()\n model = fuse_prepared_model(model)\n\n if backend in export_onnx_backend:\n print(f\"Prepare to insert the FakeQuantize model based on {backend}\")\n node_name_not_quantize = get_not_quantize_node_name(model)\n model = BACKEND_QUANTIZER_FUNCTION[backend](model, node_name_not_quantize)\n\n return model","def get_model_config(self, model_num=0):\n return [], resources.get_file(\n \"config/tests/methods/unsupervised/train_test.gin\")","def get_config(self):\n config = {'kernel_initializer': initializers.serialize(self.kernel_initializer),\n 'activation': self.activation.__name__,\n 'dim_ordering': self.dim_ordering,\n 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),\n 'eps': self.eps,\n 'cov_mode': self.cov_mode\n }\n base_config = super(SecondaryStatistic, self).get_config()\n return dict(list(base_config.items()) + list(config.items()))","def use_config_min_sale_qty(self):\n return self._use_config_min_sale_qty","def config(self) -> ModelConfigDict:\n return self.config_obj.to_dict()","def create_trainable_quantizer(bw, name, quantizer):\n if quantizer in self._grouped_quantizers.values():\n group_names = [n for n, q in self._grouped_quantizers.items() if q == quantizer]\n assert len(group_names) == 1\n # creating a param min/max references to the shared group min/max parameters.\n setattr(self, f'{name}_encoding_min', getattr(self, f'{group_names[0]}_encoding_min'))\n setattr(self, f'{name}_encoding_max', getattr(self, f'{group_names[0]}_encoding_max'))\n return new_grouped_quantizers[group_names[0]]\n\n return _create_trainable_quantizer(bw, name, quantizer)","def winsorization_config_to_dict(config: WinsorizationConfig) -> Dict[str, Any]:\n return {\n \"__type\": config.__class__.__name__,\n \"lower_quantile_margin\": config.lower_quantile_margin,\n \"upper_quantile_margin\": config.upper_quantile_margin,\n \"lower_boundary\": config.lower_boundary,\n \"upper_boundary\": config.upper_boundary,\n }","def post_training_quantize(\n model: onnx.ModelProto,\n dataset: List[Dict[str, np.ndarray]],\n per_channel: bool = True,\n) -> onnx.ModelProto:\n model = optimize_model(model)\n ranges = calibrate(model, dataset)\n return quantize(model, per_channel, True, quantizer.QuantizationMode.dfg, ranges)","def _config_column(self):\n return self.totals_column","def use_config_max_sale_qty(self):\n return self._use_config_max_sale_qty","def default_tune(self):\n return self._default_tune","def config(self):\n return {\"input_dims\": self.dims, \"output_dims\": self.output_dims, \"mapping\": self.mapping}","def _getDefaultSettings(cls):\n return {'minimumROIDimensions': 1,\n 'minimumROISize': None, # Skip testing the ROI size by default\n 'normalize': False,\n 'normalizeScale': 1,\n 'removeOutliers': None,\n 'resampledPixelSpacing': None, # No resampling by default\n 'interpolator': 'sitkBSpline', # Alternative: sitk.sitkBSpline,\n 'padDistance': 5,\n 'distances': [1],\n 'force2D': False,\n 'force2Ddimension': 0,\n 'label': 1,\n 'enableCExtensions': True,\n 'additionalInfo': True}","def _create_trainable_quantizer(bw, name, quantizer):\n # Initialize trainable parameters to None\n self.register_parameter(f'{name}_encoding_min', None)\n self.register_parameter(f'{name}_encoding_max', None)\n # Pass name of tensor quantizer and reference of Wrapper to tensor quantizer\n # Input quantizer\n new_quantizer = tensor_quantizer_factory(bw, self._round_mode,\n quant_scheme,\n self._is_symmetric,\n enabled_by_default=True,\n data_type=self._data_type)\n new_quantizer.name = name\n new_quantizer.wrapper_ref = self\n new_quantizer.device = self.device\n initialize_learned_grid_quantizer_attributes(new_quantizer, quantizer)\n return new_quantizer","def get_config(self):\n if self.allow_reco():\n return self.chs_config()\n else:\n return self.get_config_j(self.id)","def get_config(self):\n\n # these are all that is needed to rebuild this class\n config = dict(hidden_size=self.hidden_size,\n word_embedding=self.word_embedding,\n detection_embedding=self.detection_embedding,\n mode=self.mode,\n decoder_pos_emb=self.decoder_pos_emb,\n ** self.kwargs)\n\n base_config = super(RegionFeature, self).get_config()\n return dict(list(base_config.items()) +\n list(config.items()))","def _create_nncf_config(\n preset: QuantizationPreset,\n target_device: TargetDevice,\n subset_size: int,\n model_type: Optional[ModelType],\n ignored_scope: Optional[IgnoredScope],\n advanced_parameters: Optional[AdvancedQuantizationParameters],\n) -> NNCFConfig:\n if model_type is None:\n compression_config = _get_default_quantization_config(preset, subset_size)\n elif model_type == ModelType.TRANSFORMER:\n compression_config = _get_transformer_quantization_config(subset_size)\n\n if ignored_scope is not None:\n _ignored_scope = convert_ignored_scope_to_list(ignored_scope)\n if \"ignored_scopes\" in compression_config:\n compression_config[\"ignored_scopes\"].extend(_ignored_scope)\n else:\n compression_config[\"ignored_scopes\"] = _ignored_scope\n compression_config[\"validate_scopes\"] = ignored_scope.validate\n\n if advanced_parameters is not None:\n compression_config = apply_advanced_parameters_to_config(compression_config, advanced_parameters)\n\n if model_type == ModelType.TRANSFORMER:\n compression_config[\"validate_scopes\"] = False\n\n return NNCFConfig({\"target_device\": target_device.value, \"compression\": compression_config})","def get_config(self): # To support serialization\n return {\"mean\": self.mean, \"stddev\": self.stddev}","def get_config(self):\n return {\"name\": self.name, \"tunable\": self.tunable}","def config(self) -> InstrumentConfig:\n ...","def quantize_impl(\n model: ov.Model,\n calibration_dataset: Dataset,\n preset: QuantizationPreset = QuantizationPreset.PERFORMANCE,\n target_device: TargetDevice = TargetDevice.ANY,\n subset_size: int = 300,\n fast_bias_correction: bool = True,\n model_type: Optional[ModelType] = None,\n ignored_scope: Optional[IgnoredScope] = None,\n advanced_parameters: Optional[AdvancedQuantizationParameters] = None,\n) -> ov.Model:\n if should_use_pot(advanced_parameters):\n from nncf.openvino.pot.quantization.quantize_model import quantize_impl as pot_quantize_impl\n\n quantize_fn = pot_quantize_impl\n else:\n quantize_fn = native_quantize_impl\n\n return quantize_fn(\n model,\n calibration_dataset,\n preset,\n target_device,\n subset_size,\n fast_bias_correction,\n model_type,\n ignored_scope,\n advanced_parameters,\n )","def units(self):\n return self.__class__.get_setting_units(self.key, **self.get_kwargs())","def _config_column(self):\n return self.rates_column","def tuner_quantity(self):\n \n return self.__tuner_quantity","def config(self) -> dict:\n\n params = self.get_params()\n _config = dict()\n _config['func'] = self.serialize_func(self.func)\n _config['inverse_func'] = self.inverse_func_ser\n _config['kw_args'] = jsonize(self.kw_args)\n _config['inv_kw_args'] = jsonize(self.inv_kw_args)\n\n for k, v in params.items():\n if k not in _config:\n _config.update({k: v})\n\n return _config","def apply(self, model, layer_quantize_map, quantize_registry, mode):\n\n transforms = [\n vitis_8bit_quantize_transforms.InputLayerQuantize(\n quantize_registry.get_input_quantizer(), mode),\n vitis_8bit_quantize_transforms.ConvActivationQuantize(),\n vitis_8bit_quantize_transforms.AddActivationQuantize(),\n ]\n return model_transformer.ModelTransformer(model, transforms,\n set(layer_quantize_map.keys()),\n layer_quantize_map).transform()","def _config(self):\r\n return (\r\n self.destructive,\r\n self.output_type,\r\n self.seed,\r\n )","def _config_table(self):\n return self.growth_rates","def cook_config(ext_config_filename):\n mc = base_model_config()\n with open(ext_config_filename, \"r\") as fp:\n ext_mc = edict(json.load(fp, encoding=\"utf8\"))\n for s in ext_mc.keys():\n mc[s] = ext_mc[s]\n # mc.ANCHOR_BOX = set_anchors(mc)\n # print(np.max(np.square(np.array(set_anchors_testing(mc)) - np.array(set_anchors(mc)))))\n # mc.ANCHORS = len(mc.ANCHOR_BOX)\n # H, W, C = _get_output_shape(mc)\n # mc.MODEL_OUTPUT_SHAPE = [H, W, mc.ANCHOR_PER_GRID]\n return mc","def get_bert_config(config):\n if config.model_size == \"large\":\n args = {\"hidden_size\": 1024, \"num_hidden_layers\": 24}\n elif config.model_size == \"base\":\n args = {\"hidden_size\": 768, \"num_hidden_layers\": 12}\n elif config.model_size == \"small\":\n args = {\"hidden_size\": 256, \"num_hidden_layers\": 12}\n else:\n raise ValueError(\"Unknown model size\", config.model_size)\n args[\"vocab_size\"] = config.vocab_size\n args.update(**config.model_hparam_overrides)\n # by default the ff size and num attn heads are determined by the hidden size\n args[\"num_attention_heads\"] = max(1, args[\"hidden_size\"] // 64)\n args[\"intermediate_size\"] = 4 * args[\"hidden_size\"]\n args.update(**config.model_hparam_overrides)\n return modeling.BertConfig.from_dict(args)","def get_qat_model(self,\n init_quant=False,\n calib_dataset=None,\n calib_batch_size=None,\n calib_steps=None,\n configs={},\n **kwargs):\n if self._model_format == 'pb':\n logger.warning('This function does not support pb format model.')\n return None\n\n with self._custom_object_scope:\n self._parse_configs(configs, kwargs)\n configs = self._quantize_strategy.get_configs()\n\n if not self._target and type(\n self._quantize_strategy\n ) == vitis_pof2s_quantize_strategy.VitisPof2SQuantizeStrategy:\n configs['quantize_pipeline_config']['quantize_with_xcompiler'] = False\n logger.info('Quantizing without specific `target`.')\n\n # Handle user-defined partition\n if not self._candidate_layers:\n input_layers = configs[\"quantize_registry_config\"][\n 'user_quantize_config']['input_layers']\n output_layers = configs[\"quantize_registry_config\"][\n 'user_quantize_config']['output_layers']\n ignore_layers = configs[\"quantize_registry_config\"][\n 'user_quantize_config']['ignore_layers']\n if input_layers or output_layers or ignore_layers:\n input_quantize_config = configs[\"quantize_registry_config\"][\n 'input_quantize_config']\n input_quantize_config[\"input_layers\"] = input_layers\n self._quantize_strategy.update(\n {\"input_quantize_config\": input_quantize_config})\n self._candidate_layers = model_utils.get_candidate_layers(\n self._float_model, input_layers, output_layers, ignore_layers)\n if configs[\"optimize_pipeline_config\"][\"remove_dropout\"]:\n self._check_near_dropout = model_utils.check_near_dropout(\n self._float_model, ignore_layers)\n\n self.optimize_model()\n\n logger.debug('Quantize Pipeline Configurations:')\n self._quantize_strategy.get_quantize_registry().print_configs()\n self._quantize_strategy.get_quantize_pipeline().print_configs()\n\n logger.info('Start Generation of Quantize-aware Training Model.')\n if not self._qat_model:\n self._create_qat_model(calib_dataset)\n\n # Do post training quantization to initialize the quantize-aware training model\n if init_quant:\n logger.info('Start Initialization with Quantize Calibration...')\n\n new_kwargs = {}\n\n for key, value in kwargs.items():\n new_kwargs[key] = value\n\n if isinstance(self._quantize_strategy,\n vitis_tqt_quantize_strategy.VitisTQTQuantizeStrategy):\n new_kwargs['convert_to_pof2s_quantize_strategy'] = False\n elif isinstance(\n self._quantize_strategy,\n vitis_pof2s_quantize_strategy.VitisPof2SQuantizeStrategy):\n new_kwargs['convert_to_fs_quantize_strategy'] = False\n\n self.quantize_model(\n loss=None,\n metrics=None,\n calib_dataset=calib_dataset,\n calib_batch_size=calib_batch_size,\n calib_steps=calib_steps,\n eval_dataset=None,\n verbose=0,\n add_shape_info=False,\n **new_kwargs)\n\n init_weights = self._qcbev_model.get_weights()\n self._qat_model.set_weights(init_weights)\n\n logger.info('Initialization with Quantize Calibration Done.')\n\n logger.info('Generation of Quantize-aware Training Model Done.')\n return self._qat_model","def default_options(cls) -> Dict:\n options = super().default_options()\n # scaling factor for temperature adaptation\n options['eta'] = 100\n # controls the adaptation degeneration velocity of the temperature\n # adaption.\n options['nu'] = 1e3\n\n return options","def _set_default_eai_quantizer_state(self):\n\n default_group_quantizer_state = {\n 'hidden_l{}': True,\n 'cell_l{}': False,\n 'bias_l{}': False,\n 'W_l{}': True,\n 'R_l{}': True\n }\n\n default_group_quantizer_state = {name.format(layer): state\n for layer in range(self.num_layers)\n for name, state in default_group_quantizer_state.items()\n }\n\n tensor_names_with_grouped_quantizers = [name.format(layer)\n for layer in range(self.num_layers)\n for tensor_names in grouped_quantizer_map.values()\n for name in tensor_names\n ]\n\n for name, quantizer in self._grouped_quantizers.items():\n quantizer.enabled = default_group_quantizer_state[name]\n\n for name, quantizer in self._output_quantizers.items():\n if name not in tensor_names_with_grouped_quantizers:\n quantizer.enabled = False\n\n for name, quantizer in self._param_quantizers.items():\n if name not in tensor_names_with_grouped_quantizers:\n quantizer.enabled = True\n\n for name, quantizer in self._input_quantizers.items():\n if name not in tensor_names_with_grouped_quantizers:\n quantizer.enabled = True","def get_default_visualizer_config(self):\n\n default_config = {\n\n \"format\": \"png\",\n\n \"fontsize\": 16,\n \"fontname\": 'Roboto',\n \"rankdir\": \"TB\",\n\n # Specific offsets can be specified here\n # for different shades. Change the values\n # below 0 and 1. For best results we recommend\n # to keep the range between 0.1 - 0.3\n \"layer_color_dict\": {\n \"InputLayer\": 0.1,\n \"Reshape\": 0.12,\n \"Conv1D\": 0.13,\n \"Conv2D\": 0.17,\n \"MaxPooling1D\": 0.19,\n \"MaxPooling2D\": 0.20,\n \"ZeroPadding3D\": 0.22,\n \"Flatten\": 0.25,\n \"AveragePooling2D\": 0.27,\n \"Dropout\": 0.29,\n \"Dense\": 0.3,\n \"Concatenate\": 0.32,\n \"Model\": 0.34,\n \"RepeatVector\": 0.36,\n \"Multiply\": 0.38,\n \"Add\": 0.39,\n \"Lambda\": 0.4,\n \"SpatialDropout1D\": 0.41,\n \"SpatialDropout2D\": 0.44\n },\n # Please provide as many colors\n # as many models you expect.\n # This package will\n # generate random colors incase\n # colors fall short, but there\n # is no guarantee that they will be\n # pretty\n 'default_color_package': [\n [0.586, 1.000, 1.000],\n [0.513, 0.141, 0.725],\n [0.094, 1.000, 1.000],\n [0.375, 0.739, 0.780],\n [0.967, 0.816, 0.961],\n [0.286, 1.000, 1.000],\n [0.750, 0.416, 0.961],\n [0.778, 0.631, 0.871],\n [0.613, 0.141, 0.725],\n [0.850, 0.539, 0.780],\n [0.186, 1.000, 1.000]\n ],\n \"class_names\": True,\n \"graph_label\": \"Nested SeeNN\",\n \"node_seperation_distance\": 0.4,\n\n 'module_connection_color': 'black',\n 'collapse_inputs': False,\n 'layer_height': 0.5,\n 'layer_width': 2,\n # 'condense_dropout_layer': False,\n\n # Specify if to use multiple color layers,\n # rather than shade\n 'use_multiple_colors_layers': False,\n\n # If use_multiple_colors_layers is Fa,se,\n # provide the colors\n 'multiple_colors_layer_package': {\n \"InputLayer\": \"grey\",\n \"Reshape\": \"#F5A286\",\n \"Conv1D\": \"#F7D7A8\",\n \"Conv2D\": \"#F7D7A8\",\n \"MaxPooling1D\": \"#AADFA2\",\n \"MaxPooling2D\": \"#AADFA2\",\n \"ZeroPadding3D\": \"grey\",\n \"Flatten\": \"grey\",\n \"AveragePooling2D\": \"#A8CFE7\",\n \"Dropout\": \"#9896C8\",\n \"Dense\": \"#C66AA7\",\n \"Concatenate\": \"#F5A286\",\n \"Model\": \"#292D30\",\n \"RepeatVector\": \"grey\",\n \"Multiply\": \"grey\",\n \"Add\": \"grey\",\n \"Lambda\": \"#CAAFE7\",\n \"SpatialDropout1D\": \"#FFAAEE\",\n \"SpatialDropout2D\": \"#CAAFE7\"\n },\n\n 'show_activation': False,\n 'rotate': 90,\n 'show_constant_input': False\n\n }\n\n return default_config","def config(self):\n raise NotImplementedError","def getConfiguration(self):\n raise NotImplementedError","def _create_param_quantizers(self, weight_bw: int, round_mode: libpymo.RoundingMode,\n quant_scheme: QuantScheme, is_symmetric: bool,\n data_type: QuantizationDataType):\n tensor_grouped_quantizer_map = {}\n for layer in range(self.num_layers):\n for group_name, tensor_names in grouped_quantizer_map.items():\n name = group_name.format(layer)\n if name not in self._grouped_quantizers:\n self._grouped_quantizers[name] = \\\n tensor_quantizer_factory(weight_bw,\n round_mode,\n quant_scheme,\n use_symmetric_encodings=is_symmetric,\n enabled_by_default=False,\n data_type=data_type)\n tensor_names = [tensor_name.format(layer) for tensor_name in tensor_names]\n for tensor_name in tensor_names:\n assert tensor_name not in tensor_grouped_quantizer_map\n tensor_grouped_quantizer_map[tensor_name] = \\\n self._grouped_quantizers[name]\n\n for name, _ in self.module_to_quantize.named_parameters():\n _logger.debug(\"Adding quantizer for parameter: %s\", name)\n if name in tensor_grouped_quantizer_map:\n self._param_quantizers[name] = tensor_grouped_quantizer_map[name]\n self._grouped_param_quantizers.add(self._param_quantizers[name])\n else:\n self._param_quantizers[name] = tensor_quantizer_factory(\n weight_bw,\n round_mode,\n quant_scheme,\n use_symmetric_encodings=is_symmetric,\n enabled_by_default=False,\n data_type=data_type)","def config(self):\n return {\n 'chat_id':\n self.chat_id,\n 'lat':\n self.lat,\n 'lon':\n self.lon,\n 'radius':\n self.radius,\n 'subscriptions':\n list(\n map(lambda sub: {\n 'query': sub.query,\n 'price': sub.price\n }, self.cart))\n }","def storage_config(self) -> 'outputs.PreventionJobTriggerInspectJobStorageConfig':\n return pulumi.get(self, \"storage_config\")","def config(self) -> Dict[str, Any]:\r\n attr_conf = {attr: getattr(self._qda, attr, None) for attr in self.attributes}\r\n return {\"params\": self._qda.get_params(), \"attributes\": attr_conf}","def getConfig(self):\n return self.cp","def quantize_impl(\n model: torch.nn.Module,\n calibration_dataset: Dataset,\n preset: QuantizationPreset,\n target_device: TargetDevice,\n subset_size: int,\n fast_bias_correction: bool,\n model_type: Optional[ModelType] = None,\n ignored_scope: Optional[IgnoredScope] = None,\n advanced_parameters: Optional[AdvancedQuantizationParameters] = None,\n) -> torch.nn.Module:\n if fast_bias_correction is False:\n raise ValueError(f\"fast_bias_correction={fast_bias_correction} is not \" \"supported\")\n if ignored_scope is not None and ignored_scope.types:\n raise RuntimeError(\n \"Quantization algorithm from the PyTorch backend \"\n \"does not support operation types in the ignored \"\n \"scopes yet\"\n )\n if target_device == TargetDevice.CPU_SPR:\n raise RuntimeError(\"target_device == CPU_SPR is not supported\")\n\n nncf_config = _create_nncf_config(\n preset, target_device, subset_size, model_type, ignored_scope, advanced_parameters\n )\n\n calibration_data_loader = CalibrationDataLoader(calibration_dataset)\n nncf_config.register_extra_structs(\n [\n QuantizationRangeInitArgs(data_loader=calibration_data_loader),\n BNAdaptationInitArgs(data_loader=calibration_data_loader),\n ]\n )\n\n def wrap_inputs(args, kwargs):\n return wrap_nncf_model_inputs_with_objwalk(args, kwargs)\n\n def wrap_outputs(retval):\n return wrap_nncf_model_outputs_with_objwalk(retval)\n\n def create_dummy_forward_fn(data_loader, device):\n def dummy_forward(model):\n with no_nncf_trace():\n data_item = next(iter(data_loader))\n args, kwargs = data_loader.get_inputs(data_item)\n\n def send_to_device(tensor):\n return tensor.to(device)\n\n args = objwalk(args, is_tensor, send_to_device)\n kwargs = objwalk(kwargs, is_tensor, send_to_device)\n\n args, kwargs = wrap_inputs(args, kwargs)\n retval = model(*args, **kwargs)\n retval = replicate_same_tensors(retval)\n return wrap_outputs(retval)\n\n return dummy_forward\n\n dummy_forward_fn = create_dummy_forward_fn(calibration_data_loader, get_model_device(model))\n\n clone_model = deepcopy(model)\n compression_ctrl, compressed_model = create_compressed_model(\n model=clone_model,\n config=nncf_config,\n dummy_forward_fn=dummy_forward_fn,\n wrap_inputs_fn=wrap_inputs,\n wrap_outputs_fn=wrap_outputs,\n )\n compression_ctrl.prepare_for_export()\n compressed_model.nncf.disable_dynamic_graph_building()\n\n return compressed_model","def _get_metric_config(self, config):\n metric_config = dict()\n metric_config['include_metrics'] = config.get('include_metrics', {})\n metric_config['exclude_metrics'] = config.get('exclude_metrics', {})\n return metric_config","def consumption_metering_config(self) -> Optional[pulumi.Input['ConsumptionMeteringConfigArgs']]:\n return pulumi.get(self, \"consumption_metering_config\")"],"string":"[\n \"def _get_default_quantization_config(preset: QuantizationPreset, subset_size: int) -> Dict[str, Any]:\\n return {\\n \\\"algorithm\\\": \\\"quantization\\\",\\n \\\"preset\\\": preset.value,\\n \\\"initializer\\\": {\\n \\\"range\\\": {\\\"num_init_samples\\\": subset_size, \\\"type\\\": DEFAULT_RANGE_TYPE},\\n \\\"batchnorm_adaptation\\\": {\\\"num_bn_adaptation_samples\\\": subset_size},\\n },\\n \\\"overflow_fix\\\": \\\"first_layer_only\\\",\\n }\",\n \"def get_config_object() -> \\\"BaseConfig\\\":\\n assert (\\n len(G_CONFIG_OBJECT) == 1\\n ), \\\"Have you created quantize config object before calling `quantize_model`?\\\"\\n if G_CONFIG_OBJECT:\\n return G_CONFIG_OBJECT[0]\",\n \"def get_config(self):\\n return {'reduction': self.reduction, 'name': self.name}\",\n \"def param_quantizers(self):\\n return self._param_quantizers\",\n \"def get_quantization_capability(self):\\n return self.cur_config['capabilities']\",\n \"def input_quantizers(self):\\n return self._input_quantizers\",\n \"def quantize(self, tune_cfg, model, data_loader, q_func=None):\\n assert q_func is None, \\\"quantization aware training mode is not support on tensorflow\\\"\\n logger.info('Start to run model quantization...')\\n quantized_model = os.path.join(os.getcwd(), \\\"tf_quantized.pb\\\")\\n self.tuning_cfg_to_fw(tune_cfg)\\n logger.debug('Dump quantization configurations:')\\n logger.debug(self.quantize_config)\\n from .tf_utils.graph_converter import GraphConverter\\n converter = GraphConverter(self.pre_optimized_graph if self.pre_optimized_graph else model,\\n quantized_model,\\n inputs=self.inputs,\\n outputs=self.outputs,\\n qt_config=self.quantize_config,\\n fp32_ops=self.fp32_ops,\\n bf16_ops=self.bf16_ops,\\n data_loader=data_loader)\\n return converter.convert()\",\n \"def get_config(self):\\n config = super(EpsGreedyQPolicy, self).get_config()\\n config['eps'] = self.eps\\n return config\",\n \"def quantize(self, tune_cfg, model, data_loader, q_func=None):\\n assert q_func is None, \\\"quantization aware training has not been supported on ONNXRUNTIME\\\"\\n model = self.pre_optimized_model if self.pre_optimized_model else model\\n ort_version = StrictVersion(ort.__version__)\\n if ort_version < ONNXRT152_VERSION: # pragma: no cover\\n logger.warning(\\\"Quantize input needs onnxruntime 1.5.2 or newer.\\\")\\n return model\\n if model.model.opset_import[0].version < 11: # pragma: no cover\\n logger.warning(\\\"Quantize input needs model opset 11 or newer.\\\")\\n from neural_compressor.adaptor.ox_utils.onnx_quantizer import ONNXQuantizer\\n from onnxruntime.quantization.quant_utils import QuantizationMode\\n backend = QuantizationMode.QLinearOps if self.backend == \\\\\\n \\\"qlinearops\\\" else QuantizationMode.IntegerOps\\n\\n self.quantizable_ops = self._query_quantizable_ops(model.model)\\n tmp_model = copy.deepcopy(model)\\n \\n quantize_config = self._cfg_to_quantize_config(tune_cfg)\\n iterations = tune_cfg.get('calib_iteration', 1)\\n if self.static:\\n quantize_params = self._get_quantize_params(tmp_model.model, data_loader, \\\\\\n quantize_config, iterations)\\n else:\\n quantize_params = None\\n self.quantize_params = quantize_params\\n quantizer = ONNXQuantizer(tmp_model.model,\\n quantize_config,\\n backend,\\n self.static,\\n quantize_params,\\n self.quantizable_op_types)\\n quantizer.quantize_model()\\n tmp_model.q_config = self._generate_qconfig(model.model, tune_cfg, quantize_params)\\n tmp_model.model = quantizer.model.model\\n self.quantize_config = quantize_config # update so other methods can know current configs\\n \\n self._dump_model_op_stastics(tmp_model)\\n return tmp_model\",\n \"def use_config_qty_increments(self):\\n return self._use_config_qty_increments\",\n \"def _quantize(self, model: nn.Module) -> nn.Module:\\n if self.mask:\\n model_utils.remove_pruning_reparameterization(self.params_all)\\n\\n # check the accuracy after each epoch\\n quantized_model = torch.quantization.convert(model.eval(), inplace=False)\\n quantized_model.eval()\\n\\n # set masks again\\n if self.mask:\\n self._load_masks()\\n\\n return quantized_model\",\n \"def get_config(self):\\n config = {\\n \\\"units\\\": self.units,\\n \\\"activation\\\": activations.serialize(self.activation),\\n \\\"recurrent_activation\\\": activations.serialize(\\n self.recurrent_activation\\n ),\\n \\\"attention_activation\\\": activations.serialize(\\n self.attention_activation\\n ),\\n \\\"use_bias\\\": self.use_bias,\\n \\\"kernel_initializer\\\": initializers.serialize(self.kernel_initializer),\\n \\\"recurrent_initializer\\\": initializers.serialize(\\n self.recurrent_initializer\\n ),\\n \\\"bias_initializer\\\": initializers.serialize(self.bias_initializer),\\n \\\"attention_initializer\\\": initializers.serialize(\\n self.attention_initializer\\n ),\\n \\\"use_chrono_initialization\\\": self.unit_forget_bias,\\n \\\"kernel_regularizer\\\": regularizers.serialize(self.kernel_regularizer),\\n \\\"recurrent_regularizer\\\": regularizers.serialize(\\n self.recurrent_regularizer\\n ),\\n \\\"bias_regularizer\\\": regularizers.serialize(self.bias_regularizer),\\n \\\"activity_regularizer\\\": regularizers.serialize(\\n self.activity_regularizer\\n ),\\n \\\"attention_regularizer\\\": regularizers.serialize(\\n self.attention_regularizer\\n ),\\n \\\"kernel_constraint\\\": constraints.serialize(self.kernel_constraint),\\n \\\"recurrent_constraint\\\": constraints.serialize(\\n self.recurrent_constraint\\n ),\\n \\\"bias_constraint\\\": constraints.serialize(self.bias_constraint),\\n \\\"attention_constraint\\\": constraints.serialize(\\n self.attention_constraint\\n ),\\n \\\"dropout\\\": self.dropout,\\n \\\"recurrent_dropout\\\": self.recurrent_dropout,\\n \\\"return_attention\\\": self.return_attention,\\n }\\n base_config = super().get_config()\\n del base_config[\\\"cell\\\"]\\n return dict(list(base_config.items()) + list(config.items()))\",\n \"def get_config(self):\\n return {'name': self.name, 'dtype': self.dtype}\",\n \"def use_config_min_qty(self):\\n return self._use_config_min_qty\",\n \"def output_quantizers(self):\\n return self._output_quantizers\",\n \"def get_config(self):\\n config = {\\n 'membership_transform': self.membership_transform,\\n 'predictions_transform': self.predictions_transform,\\n 'membership_kernel': self.membership_kernel,\\n 'predictions_kernel': self.predictions_kernel,\\n 'name': self.name,\\n }\\n config = {k: v for k, v in config.items() if v is not None}\\n return self._serialize_config(config)\",\n \"def get_config(self):\\n config = {'epsilon':self.eps}\\n base_config = super(PowTransform, self).get_config()\\n return dict(list(base_config.items()) + list(config.items()))\",\n \"def get_config(self) -> Dict[str, Any]:\\n return {\\n 'num_classes': self.num_classes,\\n 'name': self.name,\\n 'dtype': self.dtype,\\n 'sparse_y_true': self.sparse_y_true,\\n 'sparse_y_pred': self.sparse_y_pred,\\n 'axis': self.axis,\\n }\",\n \"def get_config(self):\\n config = {'name': self.name, 'trainable': self.trainable}\\n if hasattr(self, '_batch_input_shape'):\\n config['batch_input_shape'] = self._batch_input_shape\\n if hasattr(self, 'dtype'):\\n config['dtype'] = self.dtype\\n return config\",\n \"def base_model_config():\\n return {\\n # TFRecord file pattern containing Example protos.\\n \\\"input_file_pattern\\\": \\\"\\\",\\n\\n # Number of examples to keep in the input queue.\\n \\\"input_queue_capacity\\\": 5 * 640000, # 5 shards of the BookCorpus.\\n\\n # Number of threads for prefetching TFRecord values.\\n \\\"num_input_reader_threads\\\": 1,\\n\\n # Whether to shuffle the input data.\\n \\\"shuffle_input_data\\\": True,\\n\\n # Scale of the random uniform initializer.\\n \\\"uniform_init_scale\\\": 0.1,\\n\\n # Number of unique words in the vocab.\\n \\\"vocab_size\\\": 20000,\\n\\n # Batch size (training and evaluation only).\\n \\\"batch_size\\\": 128,\\n\\n # Word embedding dimension.\\n \\\"word_embedding_dim\\\": 620,\\n\\n # Whether to use a bidirectional or unidirectional encoder RNN.\\n \\\"bidirectional_encoder\\\": False,\\n\\n # Number of output dimensions of the sentence encoder.\\n \\\"encoder_dim\\\": 2400,\\n\\n # Operation for combining the final states of the encoder GRU\\n \\\"pooling_operation\\\": \\\"last\\\",\\n }\",\n \"def config(self) -> dict:\\n if self.__class__.__name__ == 'MyFunctionTransformer':\\n pass\\n else:\\n check_is_fitted(self)\\n\\n _config = {}\\n for attr in self.config_paras:\\n _config[attr] = getattr(self, attr)\\n\\n return {\\\"params\\\": self.get_params(),\\n \\\"config\\\": _config}\",\n \"def get_config(self):\\n config = super(Sc2Policy, self).get_config()\\n config['eps'] = self.eps\\n config['testing'] = self.testing\\n return config\",\n \"def get_config(self):\\n config = {\\n }\\n base_config = super(MatrixConcat, self).get_config()\\n return dict(list(base_config.items()) + list(config.items()))\",\n \"def recover(self, model, q_config):\\n self._pre_optimize(model)\\n model = self.pre_optimized_model\\n ort_version = StrictVersion(ort.__version__)\\n if ort_version < ONNXRT152_VERSION: # pragma: no cover\\n logger.warning(\\\"Quantize input needs onnxruntime 1.5.2 or newer.\\\")\\n return model\\n if model.model.opset_import[0].version < 11: # pragma: no cover\\n logger.warning(\\\"Quantize input needs model opset 11 or newer.\\\")\\n\\n from neural_compressor.adaptor.ox_utils.onnx_quantizer import ONNXQuantizer\\n from onnxruntime.quantization.quant_utils import QuantizationMode\\n backend = QuantizationMode.QLinearOps if self.backend == \\\\\\n \\\"qlinearops\\\" else QuantizationMode.IntegerOps\\n \\n self.quantizable_ops = self._query_quantizable_ops(model.model)\\n quantize_params, tune_cfg = self._parse_qconfig(q_config)\\n quantize_config = self._cfg_to_quantize_config(tune_cfg)\\n quantizer = ONNXQuantizer(model.model,\\n quantize_config,\\n backend,\\n self.static,\\n quantize_params,\\n self.quantizable_op_types)\\n\\n quantizer.quantize_model()\\n model.model = quantizer.model.model\\n return model\",\n \"def override_config(args):\\n args.transformer_enc_config = (\\n \\\"((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 3\\\"\\n )\",\n \"def override_config(args):\\n args.transformer_enc_config = (\\n \\\"((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 3\\\"\\n )\",\n \"def get_config(self):\\n config = self._kwargs.copy()\\n config.update({\\n 'metric': self.__class__.__name__,\\n 'name': self.name,\\n 'output_names': self.output_names,\\n 'label_names': self.label_names})\\n return config\",\n \"def get_config(self):\\n config = {'epsilon':self.eps}\\n base_config = super(LogTransform, self).get_config()\\n return dict(list(base_config.items()) + list(config.items()))\",\n \"def use_config_enable_qty_inc(self):\\n return self._use_config_enable_qty_inc\",\n \"def override_config(args):\\n args.transformer_enc_config = (\\n \\\"((512, 8, 2048, True, 0.15, 0.15, 0.15),) * 3\\\"\\n )\",\n \"def construct_and_initialize_trainable_quantizers(self, quant_scheme):\\n # Copy user settable attributes for outputs\\n\\n def _create_trainable_quantizer(bw, name, quantizer):\\n \\\"\\\"\\\" create trainable quantizer from static grid quantizer. \\\"\\\"\\\"\\n # Initialize trainable parameters to None\\n self.register_parameter(f'{name}_encoding_min', None)\\n self.register_parameter(f'{name}_encoding_max', None)\\n # Pass name of tensor quantizer and reference of Wrapper to tensor quantizer\\n # Input quantizer\\n new_quantizer = tensor_quantizer_factory(bw, self._round_mode,\\n quant_scheme,\\n self._is_symmetric,\\n enabled_by_default=True,\\n data_type=self._data_type)\\n new_quantizer.name = name\\n new_quantizer.wrapper_ref = self\\n new_quantizer.device = self.device\\n initialize_learned_grid_quantizer_attributes(new_quantizer, quantizer)\\n return new_quantizer\\n\\n new_grouped_quantizers = {name: _create_trainable_quantizer(self._activation_bw, name, quantizer)\\n for name, quantizer in self._grouped_quantizers.items()}\\n\\n def create_trainable_quantizer(bw, name, quantizer):\\n \\\"\\\"\\\" create trainable quantizer if not part of a group else reuse the group quantizer. \\\"\\\"\\\"\\n if quantizer in self._grouped_quantizers.values():\\n group_names = [n for n, q in self._grouped_quantizers.items() if q == quantizer]\\n assert len(group_names) == 1\\n # creating a param min/max references to the shared group min/max parameters.\\n setattr(self, f'{name}_encoding_min', getattr(self, f'{group_names[0]}_encoding_min'))\\n setattr(self, f'{name}_encoding_max', getattr(self, f'{group_names[0]}_encoding_max'))\\n return new_grouped_quantizers[group_names[0]]\\n\\n return _create_trainable_quantizer(bw, name, quantizer)\\n\\n\\n self._input_quantizers = {name: create_trainable_quantizer(self._activation_bw, name, quantizer)\\n for name, quantizer in self.input_quantizers.items()}\\n self._output_quantizers = {name: create_trainable_quantizer(self._activation_bw, name, quantizer)\\n for name, quantizer in self.output_quantizers.items()}\\n self._param_quantizers = {name: create_trainable_quantizer(self._weight_bw, name, quantizer)\\n for name, quantizer in self._param_quantizers.items()}\\n\\n self._grouped_quantizers = new_grouped_quantizers\\n self._mode = QcQuantizeOpMode.LEARN_ENCODINGS\",\n \"def get_config(self):\\n config = {\\n 'F_': self.F_,\\n 'attn_heads': self.attn_heads,\\n 'attn_heads_reduction': self.attn_heads_reduction,\\n 'edge_type_reduction': self.edge_type_reduction,\\n 'attention_type': self.attention_type,\\n 'attn_dropout': self.attn_dropout,\\n 'feature_dropout': self.feature_dropout,\\n 'activation': self.activation,\\n 'use_value_bias': self.use_value_bias,\\n 'use_key_bias': self.use_key_bias,\\n 'kernel_initializer': self.kernel_initializer,\\n 'bias_initializer': self.bias_initializer,\\n 'attn_kernel_initializer': self.attn_kernel_initializer,\\n 'attn_bias_initalizer': self.attn_bias_initializer,\\n 'kernel_regularizer': self.kernel_regularizer,\\n 'bias_regularizer': self.bias_regularizer,\\n 'attn_kernel_regularizer': self.attn_kernel_regularizer,\\n 'attn_bias_regularizer': self.attn_bias_regularizer,\\n 'activity_regularizer': self.activity_regularizer,\\n 'kernel_constraint': self.kernel_constraint,\\n 'bias_constraint': self.bias_constraint,\\n 'attn_kernel_constraint': self.attn_kernel_constraint,\\n 'attn_bias_constraint': self.attn_bias_constraint\\n }\\n base_config = super(BatchShawMultigraphAttention, self).get_config()\\n return dict(list(base_config.items())) + list(config.items())\",\n \"def default_config(self):\\n return {\\n \\\"xtype\\\": \\\"scalar\\\",\\n \\\"ytype\\\": \\\"scalar\\\",\\n \\\"xtick-delta\\\": 50, \\n \\\"ytick-delta\\\": 20, \\n \\\"xtick-format\\\": '{:g}', \\n \\\"ytick-format\\\": '{:g}',\\n }\",\n \"def get_config(self):\\n config = {'epsilon':self.eps}\\n base_config = super(MatrixReLU, self).get_config()\\n return dict(list(base_config.items()) + list(config.items()))\",\n \"def get_config(self):\\n layer_config = {\\n \\\"anchors\\\": self._anchors, \\n \\\"classes\\\": self._classes,\\n \\\"ignore_thresh\\\": self._ignore_thresh, \\n \\\"truth_thresh\\\": self._truth_thresh, \\n \\\"iou_thresh\\\": self._iou_thresh, \\n \\\"loss_type\\\": self._loss_type, \\n \\\"iou_normalizer\\\": self._iou_normalizer,\\n \\\"cls_normalizer\\\": self._cls_normalizer, \\n \\\"scale_x_y\\\": self._scale_x_y, \\n }\\n layer_config.update(super().get_config())\\n return layer_config\",\n \"def get_config(self): # To support serialization\\n return {\\\"min value\\\": self.minval, \\\"max value\\\": self.maxval}\",\n \"def config(self):\\n\\n train_dataset = RandomClassificationDataset()\\n eval_dataset = RandomClassificationDataset()\\n\\n return {\\n 'model':\\n SimpleModel(),\\n 'train_dataloader':\\n DataLoader(\\n dataset=train_dataset,\\n batch_size=4,\\n sampler=dist.get_sampler(train_dataset),\\n ),\\n 'eval_dataloader':\\n DataLoader(\\n dataset=eval_dataset,\\n sampler=dist.get_sampler(eval_dataset),\\n ),\\n 'max_duration':\\n '2ep',\\n 'autoresume':\\n True,\\n 'loggers': [],\\n }\",\n \"def _get_model_config(self, **kwargs):\\n kwargs[\\\"example_type\\\"] = QUERY_EXAMPLE_TYPE\\n kwargs[\\\"label_type\\\"] = CLASS_LABEL_TYPE\\n loaded_config = get_classifier_config(\\n self.CLF_TYPE, self._resource_loader.app_path, domain=self.domain\\n )\\n return super()._get_model_config(loaded_config, **kwargs)\",\n \"def get_config(self):\\n return super().get_config()\",\n \"def settings(self) -> Dict[str, Any]:\\n if self.num_pixels is not None:\\n return dict(num_pixels=self.num_pixels)\\n return dict(quantile=self.quantile)\",\n \"def serialize(self):\\n cfg = SeqLib.serialize(self)\\n\\n cfg[\\\"variants\\\"] = dict()\\n cfg[\\\"variants\\\"][\\\"wild type\\\"] = self.wt.serialize()\\n cfg[\\\"variants\\\"][\\\"use aligner\\\"] = self.aligner is not None\\n if self.max_mutations != DEFAULT_MAX_MUTATIONS:\\n cfg[\\\"variants\\\"][\\\"max mutations\\\"] = self.max_mutations\\n if self.variant_min_count > 0:\\n cfg[\\\"variants\\\"][\\\"min count\\\"] = self.variant_min_count\\n\\n return cfg\",\n \"def _est_config(self):\\n return self._est_method.config\",\n \"def get_config():\\n config = ml_collections.ConfigDict()\\n\\n # Which model to use -- see ./models.py\\n config.model_name = 'ViT-B_32'\\n # Where to store training logs.\\n config.log_dir = '.'\\n\\n # Number of steps to measure.\\n config.steps = 30\\n # Number of steps before measuring.\\n config.initial_steps = 10\\n\\n # Batch size\\n config.batch = 0\\n # Number of output classes.\\n config.num_classes = 0\\n # Image size (width=height).\\n config.image_size = 0\\n\\n config.train = 'inference_time'\\n\\n return config\",\n \"def optimizer_config(self):\\r\\n return {\\r\\n \\\"lr\\\": self.args.lr[0],\\r\\n \\\"momentum\\\": self.args.momentum,\\r\\n \\\"weight_decay\\\": self.args.weight_decay,\\r\\n }\",\n \"def transaction_cost_model(self) -> str:\\n return self.__transaction_cost_model\",\n \"def update_configuration(self, config):\\n\\n config[\\\"data_transformation\\\"][\\\"n_classification_bins\\\"] = config[\\\"n_classification_bins\\\"]\\n config[\\\"data_transformation\\\"][\\\"nassets\\\"] = config[\\\"nassets\\\"]\\n config[\\\"data_transformation\\\"][\\\"classify_per_series\\\"] = config[\\\"classify_per_series\\\"]\\n config[\\\"data_transformation\\\"][\\\"normalise_per_series\\\"] = config[\\\"normalise_per_series\\\"]\\n\\n return config\",\n \"def get_base_config():\\n return dict(\\n dim=768,\\n ff_dim=3072,\\n num_heads=12,\\n num_layers=12,\\n attention_dropout_rate=0.0,\\n dropout_rate=0.1,\\n representation_size=768,\\n classifier='token'\\n )\",\n \"def requested_config_vals():\\n return {'transfer_stats_per_file':'opt'}\",\n \"def get_config():\\n name = 'dynamic_pricing'\\n num_products = 5\\n scale = 1\\n noise_var = 10\\n p_max = 1\\n\\n agents = collections.OrderedDict(\\n [('bsPricing',\\n functools.partial(BootstrapDynamicPricing,\\n num_products, scale, noise_var, p_max))]\\n )\\n\\n environments = collections.OrderedDict(\\n [('env',\\n functools.partial(DynamicPricing,\\n num_products, scale, noise_var, p_max))]\\n )\\n experiments = collections.OrderedDict(\\n [(name, ExperimentNoAction)]\\n )\\n n_steps = 80\\n n_seeds = 2000\\n config = Config(name, agents, environments, experiments, n_steps, n_seeds)\\n return config\",\n \"def _quantize_activation(self, tensor_quantizer: Union[StaticGridPerTensorQuantizer, LearnedGridTensorQuantizer],\\n tensors_to_quantize: Union[List[torch.Tensor], torch.Tensor]) -> \\\\\\n Union[List[torch.Tensor], torch.Tensor]:\\n\\n if not tensor_quantizer.enabled:\\n return tensors_to_quantize\\n\\n outputs = []\\n if not isinstance(tensors_to_quantize, list):\\n tensors_to_quantize = [tensors_to_quantize]\\n for input_tensor in tensors_to_quantize:\\n\\n if self._mode is QcQuantizeOpMode.ANALYSIS:\\n\\n if isinstance(input_tensor, tuple):\\n for tensor in input_tensor:\\n tensor_quantizer.update_encoding_stats(tensor)\\n else:\\n tensor_quantizer.update_encoding_stats(input_tensor)\\n output = input_tensor\\n\\n elif self._mode is QcQuantizeOpMode.ACTIVE:\\n # if we are not in training, then only nearest rounding should be used\\n if self.training:\\n round_mode = tensor_quantizer.round_mode\\n else:\\n round_mode = libpymo.RoundingMode.ROUND_NEAREST\\n output = tensor_quantizer.quantize_dequantize(input_tensor, round_mode)\\n\\n elif self._mode is QcQuantizeOpMode.LEARN_ENCODINGS:\\n encoding_min = getattr(self, tensor_quantizer.name + '_encoding_min')\\n encoding_max = getattr(self, tensor_quantizer.name + '_encoding_max')\\n output = tensor_quantizer.quantize_dequantize(input_tensor, encoding_min, encoding_max)\\n\\n else:\\n output = input_tensor\\n\\n outputs.append(output)\\n\\n # Flatten if there is only one output\\n return outputs[0] if len(outputs) == 1 else tuple(outputs)\",\n \"def get_config(self):\\n config = {\\n 'multichannel': self._multichannel,\\n 'complex_part': self._complex_part\\n }\\n base_config = super().get_config()\\n return {**base_config, **config}\",\n \"def use_config_notify_stock_qty(self):\\n return self._use_config_notify_stock_qty\",\n \"def _quantize_dequantize_params(self, inputs=None) -> Dict[str, torch.Tensor]:\\n quantized_params = dict()\\n shadow_params = {name: param.detach().clone() for name, param in self.named_parameters(recurse=False)}\\n\\n if self._mode != QcQuantizeOpMode.LEARN_ENCODINGS:\\n quantizer_param_map = defaultdict(list)\\n for name, param in self.named_parameters(recurse=False):\\n quantizer_param_map[self._param_quantizers[name]].append((name, param))\\n\\n for param_quantizer, param_list in quantizer_param_map.items():\\n # If we are in training mode with quant-sim nodes,\\n # then we want to calculate encodings for the parameters in every pass\\n if self.training or param_quantizer.encoding is None:\\n param_quantizer.reset_encoding_stats()\\n for _, param in param_list:\\n param_quantizer.update_encoding_stats(param.data)\\n param_quantizer.compute_encoding()\\n\\n for name, param in param_list:\\n quantized_params[name] = param.data = self._param_quantize_dequantize(param.clone(), param_quantizer)\\n else:\\n encoding_list_for_params = []\\n for name, _ in self.get_named_parameters():\\n # Create a list of encoding parameters for params\\n quantizer = self.param_quantizers[name]\\n if quantizer.enabled:\\n # if param uses a group quantizer remap to group quantizer min/max encoding params.\\n if quantizer in self._grouped_quantizers.values():\\n name, *_ = [n for n, q in self._grouped_quantizers.items() if q == quantizer]\\n encoding_list_for_params.append(getattr(self, name + '_encoding_min'))\\n encoding_list_for_params.append(getattr(self, name + '_encoding_max'))\\n\\n # Quantize the parameters\\n inputs = ParameterQuantizer.apply(inputs, self, *encoding_list_for_params)\\n\\n # clone() the outputs of Custom function to avoid incorrect gradient calculation for in-place modification\\n # of view (view is created since Custom function's forward return input as-is)\\n inputs = inputs.clone()\\n quantized_params = {name: param.clone() for name, param in self.named_parameters(recurse=False)\\n if '_encoding_' not in name}\\n\\n yield quantized_params, inputs\\n\\n for name, param in self.named_parameters(recurse=False):\\n if name in shadow_params:\\n param.data.copy_(shadow_params[name].data)\",\n \"def get_config(self):\\n return self.cat_feats_cfg\",\n \"def get_config(self) -> dict:\\n config = {}\\n\\n args = ['out_dim', 'bond_dim', 'use_bias']\\n for arg in args:\\n config[arg] = getattr(self, arg)\\n\\n config['activation'] = activations.serialize(getattr(self, 'activation'))\\n\\n custom_initializers = ['kernel_initializer', 'bias_initializer']\\n for initializer_arg in custom_initializers:\\n config[initializer_arg] = initializers.serialize(\\n getattr(self, initializer_arg))\\n\\n base_config = super().get_config()\\n return dict(list(base_config.items()) + list(config.items()))\",\n \"def grouped_quantizers(self):\\n return self._grouped_quantizers\",\n \"def prepare_fx_with_backend(model, qconfig_dict, backend, is_qat, **kwargs):\\n model = prepare_qat_fx(model, qconfig_dict, **kwargs)\\n\\n if not is_qat:\\n model.eval()\\n model = fuse_prepared_model(model)\\n\\n if backend in export_onnx_backend:\\n print(f\\\"Prepare to insert the FakeQuantize model based on {backend}\\\")\\n node_name_not_quantize = get_not_quantize_node_name(model)\\n model = BACKEND_QUANTIZER_FUNCTION[backend](model, node_name_not_quantize)\\n\\n return model\",\n \"def get_model_config(self, model_num=0):\\n return [], resources.get_file(\\n \\\"config/tests/methods/unsupervised/train_test.gin\\\")\",\n \"def get_config(self):\\n config = {'kernel_initializer': initializers.serialize(self.kernel_initializer),\\n 'activation': self.activation.__name__,\\n 'dim_ordering': self.dim_ordering,\\n 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),\\n 'eps': self.eps,\\n 'cov_mode': self.cov_mode\\n }\\n base_config = super(SecondaryStatistic, self).get_config()\\n return dict(list(base_config.items()) + list(config.items()))\",\n \"def use_config_min_sale_qty(self):\\n return self._use_config_min_sale_qty\",\n \"def config(self) -> ModelConfigDict:\\n return self.config_obj.to_dict()\",\n \"def create_trainable_quantizer(bw, name, quantizer):\\n if quantizer in self._grouped_quantizers.values():\\n group_names = [n for n, q in self._grouped_quantizers.items() if q == quantizer]\\n assert len(group_names) == 1\\n # creating a param min/max references to the shared group min/max parameters.\\n setattr(self, f'{name}_encoding_min', getattr(self, f'{group_names[0]}_encoding_min'))\\n setattr(self, f'{name}_encoding_max', getattr(self, f'{group_names[0]}_encoding_max'))\\n return new_grouped_quantizers[group_names[0]]\\n\\n return _create_trainable_quantizer(bw, name, quantizer)\",\n \"def winsorization_config_to_dict(config: WinsorizationConfig) -> Dict[str, Any]:\\n return {\\n \\\"__type\\\": config.__class__.__name__,\\n \\\"lower_quantile_margin\\\": config.lower_quantile_margin,\\n \\\"upper_quantile_margin\\\": config.upper_quantile_margin,\\n \\\"lower_boundary\\\": config.lower_boundary,\\n \\\"upper_boundary\\\": config.upper_boundary,\\n }\",\n \"def post_training_quantize(\\n model: onnx.ModelProto,\\n dataset: List[Dict[str, np.ndarray]],\\n per_channel: bool = True,\\n) -> onnx.ModelProto:\\n model = optimize_model(model)\\n ranges = calibrate(model, dataset)\\n return quantize(model, per_channel, True, quantizer.QuantizationMode.dfg, ranges)\",\n \"def _config_column(self):\\n return self.totals_column\",\n \"def use_config_max_sale_qty(self):\\n return self._use_config_max_sale_qty\",\n \"def default_tune(self):\\n return self._default_tune\",\n \"def config(self):\\n return {\\\"input_dims\\\": self.dims, \\\"output_dims\\\": self.output_dims, \\\"mapping\\\": self.mapping}\",\n \"def _getDefaultSettings(cls):\\n return {'minimumROIDimensions': 1,\\n 'minimumROISize': None, # Skip testing the ROI size by default\\n 'normalize': False,\\n 'normalizeScale': 1,\\n 'removeOutliers': None,\\n 'resampledPixelSpacing': None, # No resampling by default\\n 'interpolator': 'sitkBSpline', # Alternative: sitk.sitkBSpline,\\n 'padDistance': 5,\\n 'distances': [1],\\n 'force2D': False,\\n 'force2Ddimension': 0,\\n 'label': 1,\\n 'enableCExtensions': True,\\n 'additionalInfo': True}\",\n \"def _create_trainable_quantizer(bw, name, quantizer):\\n # Initialize trainable parameters to None\\n self.register_parameter(f'{name}_encoding_min', None)\\n self.register_parameter(f'{name}_encoding_max', None)\\n # Pass name of tensor quantizer and reference of Wrapper to tensor quantizer\\n # Input quantizer\\n new_quantizer = tensor_quantizer_factory(bw, self._round_mode,\\n quant_scheme,\\n self._is_symmetric,\\n enabled_by_default=True,\\n data_type=self._data_type)\\n new_quantizer.name = name\\n new_quantizer.wrapper_ref = self\\n new_quantizer.device = self.device\\n initialize_learned_grid_quantizer_attributes(new_quantizer, quantizer)\\n return new_quantizer\",\n \"def get_config(self):\\n if self.allow_reco():\\n return self.chs_config()\\n else:\\n return self.get_config_j(self.id)\",\n \"def get_config(self):\\n\\n # these are all that is needed to rebuild this class\\n config = dict(hidden_size=self.hidden_size,\\n word_embedding=self.word_embedding,\\n detection_embedding=self.detection_embedding,\\n mode=self.mode,\\n decoder_pos_emb=self.decoder_pos_emb,\\n ** self.kwargs)\\n\\n base_config = super(RegionFeature, self).get_config()\\n return dict(list(base_config.items()) +\\n list(config.items()))\",\n \"def _create_nncf_config(\\n preset: QuantizationPreset,\\n target_device: TargetDevice,\\n subset_size: int,\\n model_type: Optional[ModelType],\\n ignored_scope: Optional[IgnoredScope],\\n advanced_parameters: Optional[AdvancedQuantizationParameters],\\n) -> NNCFConfig:\\n if model_type is None:\\n compression_config = _get_default_quantization_config(preset, subset_size)\\n elif model_type == ModelType.TRANSFORMER:\\n compression_config = _get_transformer_quantization_config(subset_size)\\n\\n if ignored_scope is not None:\\n _ignored_scope = convert_ignored_scope_to_list(ignored_scope)\\n if \\\"ignored_scopes\\\" in compression_config:\\n compression_config[\\\"ignored_scopes\\\"].extend(_ignored_scope)\\n else:\\n compression_config[\\\"ignored_scopes\\\"] = _ignored_scope\\n compression_config[\\\"validate_scopes\\\"] = ignored_scope.validate\\n\\n if advanced_parameters is not None:\\n compression_config = apply_advanced_parameters_to_config(compression_config, advanced_parameters)\\n\\n if model_type == ModelType.TRANSFORMER:\\n compression_config[\\\"validate_scopes\\\"] = False\\n\\n return NNCFConfig({\\\"target_device\\\": target_device.value, \\\"compression\\\": compression_config})\",\n \"def get_config(self): # To support serialization\\n return {\\\"mean\\\": self.mean, \\\"stddev\\\": self.stddev}\",\n \"def get_config(self):\\n return {\\\"name\\\": self.name, \\\"tunable\\\": self.tunable}\",\n \"def config(self) -> InstrumentConfig:\\n ...\",\n \"def quantize_impl(\\n model: ov.Model,\\n calibration_dataset: Dataset,\\n preset: QuantizationPreset = QuantizationPreset.PERFORMANCE,\\n target_device: TargetDevice = TargetDevice.ANY,\\n subset_size: int = 300,\\n fast_bias_correction: bool = True,\\n model_type: Optional[ModelType] = None,\\n ignored_scope: Optional[IgnoredScope] = None,\\n advanced_parameters: Optional[AdvancedQuantizationParameters] = None,\\n) -> ov.Model:\\n if should_use_pot(advanced_parameters):\\n from nncf.openvino.pot.quantization.quantize_model import quantize_impl as pot_quantize_impl\\n\\n quantize_fn = pot_quantize_impl\\n else:\\n quantize_fn = native_quantize_impl\\n\\n return quantize_fn(\\n model,\\n calibration_dataset,\\n preset,\\n target_device,\\n subset_size,\\n fast_bias_correction,\\n model_type,\\n ignored_scope,\\n advanced_parameters,\\n )\",\n \"def units(self):\\n return self.__class__.get_setting_units(self.key, **self.get_kwargs())\",\n \"def _config_column(self):\\n return self.rates_column\",\n \"def tuner_quantity(self):\\n \\n return self.__tuner_quantity\",\n \"def config(self) -> dict:\\n\\n params = self.get_params()\\n _config = dict()\\n _config['func'] = self.serialize_func(self.func)\\n _config['inverse_func'] = self.inverse_func_ser\\n _config['kw_args'] = jsonize(self.kw_args)\\n _config['inv_kw_args'] = jsonize(self.inv_kw_args)\\n\\n for k, v in params.items():\\n if k not in _config:\\n _config.update({k: v})\\n\\n return _config\",\n \"def apply(self, model, layer_quantize_map, quantize_registry, mode):\\n\\n transforms = [\\n vitis_8bit_quantize_transforms.InputLayerQuantize(\\n quantize_registry.get_input_quantizer(), mode),\\n vitis_8bit_quantize_transforms.ConvActivationQuantize(),\\n vitis_8bit_quantize_transforms.AddActivationQuantize(),\\n ]\\n return model_transformer.ModelTransformer(model, transforms,\\n set(layer_quantize_map.keys()),\\n layer_quantize_map).transform()\",\n \"def _config(self):\\r\\n return (\\r\\n self.destructive,\\r\\n self.output_type,\\r\\n self.seed,\\r\\n )\",\n \"def _config_table(self):\\n return self.growth_rates\",\n \"def cook_config(ext_config_filename):\\n mc = base_model_config()\\n with open(ext_config_filename, \\\"r\\\") as fp:\\n ext_mc = edict(json.load(fp, encoding=\\\"utf8\\\"))\\n for s in ext_mc.keys():\\n mc[s] = ext_mc[s]\\n # mc.ANCHOR_BOX = set_anchors(mc)\\n # print(np.max(np.square(np.array(set_anchors_testing(mc)) - np.array(set_anchors(mc)))))\\n # mc.ANCHORS = len(mc.ANCHOR_BOX)\\n # H, W, C = _get_output_shape(mc)\\n # mc.MODEL_OUTPUT_SHAPE = [H, W, mc.ANCHOR_PER_GRID]\\n return mc\",\n \"def get_bert_config(config):\\n if config.model_size == \\\"large\\\":\\n args = {\\\"hidden_size\\\": 1024, \\\"num_hidden_layers\\\": 24}\\n elif config.model_size == \\\"base\\\":\\n args = {\\\"hidden_size\\\": 768, \\\"num_hidden_layers\\\": 12}\\n elif config.model_size == \\\"small\\\":\\n args = {\\\"hidden_size\\\": 256, \\\"num_hidden_layers\\\": 12}\\n else:\\n raise ValueError(\\\"Unknown model size\\\", config.model_size)\\n args[\\\"vocab_size\\\"] = config.vocab_size\\n args.update(**config.model_hparam_overrides)\\n # by default the ff size and num attn heads are determined by the hidden size\\n args[\\\"num_attention_heads\\\"] = max(1, args[\\\"hidden_size\\\"] // 64)\\n args[\\\"intermediate_size\\\"] = 4 * args[\\\"hidden_size\\\"]\\n args.update(**config.model_hparam_overrides)\\n return modeling.BertConfig.from_dict(args)\",\n \"def get_qat_model(self,\\n init_quant=False,\\n calib_dataset=None,\\n calib_batch_size=None,\\n calib_steps=None,\\n configs={},\\n **kwargs):\\n if self._model_format == 'pb':\\n logger.warning('This function does not support pb format model.')\\n return None\\n\\n with self._custom_object_scope:\\n self._parse_configs(configs, kwargs)\\n configs = self._quantize_strategy.get_configs()\\n\\n if not self._target and type(\\n self._quantize_strategy\\n ) == vitis_pof2s_quantize_strategy.VitisPof2SQuantizeStrategy:\\n configs['quantize_pipeline_config']['quantize_with_xcompiler'] = False\\n logger.info('Quantizing without specific `target`.')\\n\\n # Handle user-defined partition\\n if not self._candidate_layers:\\n input_layers = configs[\\\"quantize_registry_config\\\"][\\n 'user_quantize_config']['input_layers']\\n output_layers = configs[\\\"quantize_registry_config\\\"][\\n 'user_quantize_config']['output_layers']\\n ignore_layers = configs[\\\"quantize_registry_config\\\"][\\n 'user_quantize_config']['ignore_layers']\\n if input_layers or output_layers or ignore_layers:\\n input_quantize_config = configs[\\\"quantize_registry_config\\\"][\\n 'input_quantize_config']\\n input_quantize_config[\\\"input_layers\\\"] = input_layers\\n self._quantize_strategy.update(\\n {\\\"input_quantize_config\\\": input_quantize_config})\\n self._candidate_layers = model_utils.get_candidate_layers(\\n self._float_model, input_layers, output_layers, ignore_layers)\\n if configs[\\\"optimize_pipeline_config\\\"][\\\"remove_dropout\\\"]:\\n self._check_near_dropout = model_utils.check_near_dropout(\\n self._float_model, ignore_layers)\\n\\n self.optimize_model()\\n\\n logger.debug('Quantize Pipeline Configurations:')\\n self._quantize_strategy.get_quantize_registry().print_configs()\\n self._quantize_strategy.get_quantize_pipeline().print_configs()\\n\\n logger.info('Start Generation of Quantize-aware Training Model.')\\n if not self._qat_model:\\n self._create_qat_model(calib_dataset)\\n\\n # Do post training quantization to initialize the quantize-aware training model\\n if init_quant:\\n logger.info('Start Initialization with Quantize Calibration...')\\n\\n new_kwargs = {}\\n\\n for key, value in kwargs.items():\\n new_kwargs[key] = value\\n\\n if isinstance(self._quantize_strategy,\\n vitis_tqt_quantize_strategy.VitisTQTQuantizeStrategy):\\n new_kwargs['convert_to_pof2s_quantize_strategy'] = False\\n elif isinstance(\\n self._quantize_strategy,\\n vitis_pof2s_quantize_strategy.VitisPof2SQuantizeStrategy):\\n new_kwargs['convert_to_fs_quantize_strategy'] = False\\n\\n self.quantize_model(\\n loss=None,\\n metrics=None,\\n calib_dataset=calib_dataset,\\n calib_batch_size=calib_batch_size,\\n calib_steps=calib_steps,\\n eval_dataset=None,\\n verbose=0,\\n add_shape_info=False,\\n **new_kwargs)\\n\\n init_weights = self._qcbev_model.get_weights()\\n self._qat_model.set_weights(init_weights)\\n\\n logger.info('Initialization with Quantize Calibration Done.')\\n\\n logger.info('Generation of Quantize-aware Training Model Done.')\\n return self._qat_model\",\n \"def default_options(cls) -> Dict:\\n options = super().default_options()\\n # scaling factor for temperature adaptation\\n options['eta'] = 100\\n # controls the adaptation degeneration velocity of the temperature\\n # adaption.\\n options['nu'] = 1e3\\n\\n return options\",\n \"def _set_default_eai_quantizer_state(self):\\n\\n default_group_quantizer_state = {\\n 'hidden_l{}': True,\\n 'cell_l{}': False,\\n 'bias_l{}': False,\\n 'W_l{}': True,\\n 'R_l{}': True\\n }\\n\\n default_group_quantizer_state = {name.format(layer): state\\n for layer in range(self.num_layers)\\n for name, state in default_group_quantizer_state.items()\\n }\\n\\n tensor_names_with_grouped_quantizers = [name.format(layer)\\n for layer in range(self.num_layers)\\n for tensor_names in grouped_quantizer_map.values()\\n for name in tensor_names\\n ]\\n\\n for name, quantizer in self._grouped_quantizers.items():\\n quantizer.enabled = default_group_quantizer_state[name]\\n\\n for name, quantizer in self._output_quantizers.items():\\n if name not in tensor_names_with_grouped_quantizers:\\n quantizer.enabled = False\\n\\n for name, quantizer in self._param_quantizers.items():\\n if name not in tensor_names_with_grouped_quantizers:\\n quantizer.enabled = True\\n\\n for name, quantizer in self._input_quantizers.items():\\n if name not in tensor_names_with_grouped_quantizers:\\n quantizer.enabled = True\",\n \"def get_default_visualizer_config(self):\\n\\n default_config = {\\n\\n \\\"format\\\": \\\"png\\\",\\n\\n \\\"fontsize\\\": 16,\\n \\\"fontname\\\": 'Roboto',\\n \\\"rankdir\\\": \\\"TB\\\",\\n\\n # Specific offsets can be specified here\\n # for different shades. Change the values\\n # below 0 and 1. For best results we recommend\\n # to keep the range between 0.1 - 0.3\\n \\\"layer_color_dict\\\": {\\n \\\"InputLayer\\\": 0.1,\\n \\\"Reshape\\\": 0.12,\\n \\\"Conv1D\\\": 0.13,\\n \\\"Conv2D\\\": 0.17,\\n \\\"MaxPooling1D\\\": 0.19,\\n \\\"MaxPooling2D\\\": 0.20,\\n \\\"ZeroPadding3D\\\": 0.22,\\n \\\"Flatten\\\": 0.25,\\n \\\"AveragePooling2D\\\": 0.27,\\n \\\"Dropout\\\": 0.29,\\n \\\"Dense\\\": 0.3,\\n \\\"Concatenate\\\": 0.32,\\n \\\"Model\\\": 0.34,\\n \\\"RepeatVector\\\": 0.36,\\n \\\"Multiply\\\": 0.38,\\n \\\"Add\\\": 0.39,\\n \\\"Lambda\\\": 0.4,\\n \\\"SpatialDropout1D\\\": 0.41,\\n \\\"SpatialDropout2D\\\": 0.44\\n },\\n # Please provide as many colors\\n # as many models you expect.\\n # This package will\\n # generate random colors incase\\n # colors fall short, but there\\n # is no guarantee that they will be\\n # pretty\\n 'default_color_package': [\\n [0.586, 1.000, 1.000],\\n [0.513, 0.141, 0.725],\\n [0.094, 1.000, 1.000],\\n [0.375, 0.739, 0.780],\\n [0.967, 0.816, 0.961],\\n [0.286, 1.000, 1.000],\\n [0.750, 0.416, 0.961],\\n [0.778, 0.631, 0.871],\\n [0.613, 0.141, 0.725],\\n [0.850, 0.539, 0.780],\\n [0.186, 1.000, 1.000]\\n ],\\n \\\"class_names\\\": True,\\n \\\"graph_label\\\": \\\"Nested SeeNN\\\",\\n \\\"node_seperation_distance\\\": 0.4,\\n\\n 'module_connection_color': 'black',\\n 'collapse_inputs': False,\\n 'layer_height': 0.5,\\n 'layer_width': 2,\\n # 'condense_dropout_layer': False,\\n\\n # Specify if to use multiple color layers,\\n # rather than shade\\n 'use_multiple_colors_layers': False,\\n\\n # If use_multiple_colors_layers is Fa,se,\\n # provide the colors\\n 'multiple_colors_layer_package': {\\n \\\"InputLayer\\\": \\\"grey\\\",\\n \\\"Reshape\\\": \\\"#F5A286\\\",\\n \\\"Conv1D\\\": \\\"#F7D7A8\\\",\\n \\\"Conv2D\\\": \\\"#F7D7A8\\\",\\n \\\"MaxPooling1D\\\": \\\"#AADFA2\\\",\\n \\\"MaxPooling2D\\\": \\\"#AADFA2\\\",\\n \\\"ZeroPadding3D\\\": \\\"grey\\\",\\n \\\"Flatten\\\": \\\"grey\\\",\\n \\\"AveragePooling2D\\\": \\\"#A8CFE7\\\",\\n \\\"Dropout\\\": \\\"#9896C8\\\",\\n \\\"Dense\\\": \\\"#C66AA7\\\",\\n \\\"Concatenate\\\": \\\"#F5A286\\\",\\n \\\"Model\\\": \\\"#292D30\\\",\\n \\\"RepeatVector\\\": \\\"grey\\\",\\n \\\"Multiply\\\": \\\"grey\\\",\\n \\\"Add\\\": \\\"grey\\\",\\n \\\"Lambda\\\": \\\"#CAAFE7\\\",\\n \\\"SpatialDropout1D\\\": \\\"#FFAAEE\\\",\\n \\\"SpatialDropout2D\\\": \\\"#CAAFE7\\\"\\n },\\n\\n 'show_activation': False,\\n 'rotate': 90,\\n 'show_constant_input': False\\n\\n }\\n\\n return default_config\",\n \"def config(self):\\n raise NotImplementedError\",\n \"def getConfiguration(self):\\n raise NotImplementedError\",\n \"def _create_param_quantizers(self, weight_bw: int, round_mode: libpymo.RoundingMode,\\n quant_scheme: QuantScheme, is_symmetric: bool,\\n data_type: QuantizationDataType):\\n tensor_grouped_quantizer_map = {}\\n for layer in range(self.num_layers):\\n for group_name, tensor_names in grouped_quantizer_map.items():\\n name = group_name.format(layer)\\n if name not in self._grouped_quantizers:\\n self._grouped_quantizers[name] = \\\\\\n tensor_quantizer_factory(weight_bw,\\n round_mode,\\n quant_scheme,\\n use_symmetric_encodings=is_symmetric,\\n enabled_by_default=False,\\n data_type=data_type)\\n tensor_names = [tensor_name.format(layer) for tensor_name in tensor_names]\\n for tensor_name in tensor_names:\\n assert tensor_name not in tensor_grouped_quantizer_map\\n tensor_grouped_quantizer_map[tensor_name] = \\\\\\n self._grouped_quantizers[name]\\n\\n for name, _ in self.module_to_quantize.named_parameters():\\n _logger.debug(\\\"Adding quantizer for parameter: %s\\\", name)\\n if name in tensor_grouped_quantizer_map:\\n self._param_quantizers[name] = tensor_grouped_quantizer_map[name]\\n self._grouped_param_quantizers.add(self._param_quantizers[name])\\n else:\\n self._param_quantizers[name] = tensor_quantizer_factory(\\n weight_bw,\\n round_mode,\\n quant_scheme,\\n use_symmetric_encodings=is_symmetric,\\n enabled_by_default=False,\\n data_type=data_type)\",\n \"def config(self):\\n return {\\n 'chat_id':\\n self.chat_id,\\n 'lat':\\n self.lat,\\n 'lon':\\n self.lon,\\n 'radius':\\n self.radius,\\n 'subscriptions':\\n list(\\n map(lambda sub: {\\n 'query': sub.query,\\n 'price': sub.price\\n }, self.cart))\\n }\",\n \"def storage_config(self) -> 'outputs.PreventionJobTriggerInspectJobStorageConfig':\\n return pulumi.get(self, \\\"storage_config\\\")\",\n \"def config(self) -> Dict[str, Any]:\\r\\n attr_conf = {attr: getattr(self._qda, attr, None) for attr in self.attributes}\\r\\n return {\\\"params\\\": self._qda.get_params(), \\\"attributes\\\": attr_conf}\",\n \"def getConfig(self):\\n return self.cp\",\n \"def quantize_impl(\\n model: torch.nn.Module,\\n calibration_dataset: Dataset,\\n preset: QuantizationPreset,\\n target_device: TargetDevice,\\n subset_size: int,\\n fast_bias_correction: bool,\\n model_type: Optional[ModelType] = None,\\n ignored_scope: Optional[IgnoredScope] = None,\\n advanced_parameters: Optional[AdvancedQuantizationParameters] = None,\\n) -> torch.nn.Module:\\n if fast_bias_correction is False:\\n raise ValueError(f\\\"fast_bias_correction={fast_bias_correction} is not \\\" \\\"supported\\\")\\n if ignored_scope is not None and ignored_scope.types:\\n raise RuntimeError(\\n \\\"Quantization algorithm from the PyTorch backend \\\"\\n \\\"does not support operation types in the ignored \\\"\\n \\\"scopes yet\\\"\\n )\\n if target_device == TargetDevice.CPU_SPR:\\n raise RuntimeError(\\\"target_device == CPU_SPR is not supported\\\")\\n\\n nncf_config = _create_nncf_config(\\n preset, target_device, subset_size, model_type, ignored_scope, advanced_parameters\\n )\\n\\n calibration_data_loader = CalibrationDataLoader(calibration_dataset)\\n nncf_config.register_extra_structs(\\n [\\n QuantizationRangeInitArgs(data_loader=calibration_data_loader),\\n BNAdaptationInitArgs(data_loader=calibration_data_loader),\\n ]\\n )\\n\\n def wrap_inputs(args, kwargs):\\n return wrap_nncf_model_inputs_with_objwalk(args, kwargs)\\n\\n def wrap_outputs(retval):\\n return wrap_nncf_model_outputs_with_objwalk(retval)\\n\\n def create_dummy_forward_fn(data_loader, device):\\n def dummy_forward(model):\\n with no_nncf_trace():\\n data_item = next(iter(data_loader))\\n args, kwargs = data_loader.get_inputs(data_item)\\n\\n def send_to_device(tensor):\\n return tensor.to(device)\\n\\n args = objwalk(args, is_tensor, send_to_device)\\n kwargs = objwalk(kwargs, is_tensor, send_to_device)\\n\\n args, kwargs = wrap_inputs(args, kwargs)\\n retval = model(*args, **kwargs)\\n retval = replicate_same_tensors(retval)\\n return wrap_outputs(retval)\\n\\n return dummy_forward\\n\\n dummy_forward_fn = create_dummy_forward_fn(calibration_data_loader, get_model_device(model))\\n\\n clone_model = deepcopy(model)\\n compression_ctrl, compressed_model = create_compressed_model(\\n model=clone_model,\\n config=nncf_config,\\n dummy_forward_fn=dummy_forward_fn,\\n wrap_inputs_fn=wrap_inputs,\\n wrap_outputs_fn=wrap_outputs,\\n )\\n compression_ctrl.prepare_for_export()\\n compressed_model.nncf.disable_dynamic_graph_building()\\n\\n return compressed_model\",\n \"def _get_metric_config(self, config):\\n metric_config = dict()\\n metric_config['include_metrics'] = config.get('include_metrics', {})\\n metric_config['exclude_metrics'] = config.get('exclude_metrics', {})\\n return metric_config\",\n \"def consumption_metering_config(self) -> Optional[pulumi.Input['ConsumptionMeteringConfigArgs']]:\\n return pulumi.get(self, \\\"consumption_metering_config\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66286147","0.5942934","0.5907931","0.5872018","0.5804783","0.57093644","0.56935316","0.5644522","0.56273764","0.5596549","0.5582015","0.55781955","0.5568165","0.5547753","0.5484123","0.54839206","0.54510504","0.5421256","0.54176253","0.5367854","0.5312555","0.53048676","0.53018904","0.52835107","0.5274694","0.5274694","0.52663773","0.52303475","0.52228343","0.5207249","0.51670146","0.51597637","0.5151957","0.5138265","0.51272345","0.5101482","0.5099452","0.50929856","0.507529","0.50713646","0.5049313","0.5047742","0.5040155","0.50318646","0.5027338","0.5019814","0.5007105","0.5006379","0.50048596","0.50028986","0.49974412","0.49935225","0.4983863","0.4979592","0.49763507","0.497045","0.49683282","0.49561647","0.495033","0.49430114","0.49421677","0.49270946","0.48897365","0.48705226","0.48701057","0.48658815","0.4853294","0.48513636","0.48464733","0.48375258","0.48359862","0.48209077","0.48082885","0.48011038","0.47989723","0.4788358","0.47877598","0.47840637","0.4770129","0.4767391","0.47585475","0.4757347","0.47555488","0.4753844","0.4747603","0.4745372","0.4743448","0.47302425","0.4729622","0.471859","0.47070003","0.47029686","0.46809852","0.4680432","0.4678304","0.46753046","0.4665646","0.46631628","0.46626192","0.46579665"],"string":"[\n \"0.66286147\",\n \"0.5942934\",\n \"0.5907931\",\n \"0.5872018\",\n \"0.5804783\",\n \"0.57093644\",\n \"0.56935316\",\n \"0.5644522\",\n \"0.56273764\",\n \"0.5596549\",\n \"0.5582015\",\n \"0.55781955\",\n \"0.5568165\",\n \"0.5547753\",\n \"0.5484123\",\n \"0.54839206\",\n \"0.54510504\",\n \"0.5421256\",\n \"0.54176253\",\n \"0.5367854\",\n \"0.5312555\",\n \"0.53048676\",\n \"0.53018904\",\n \"0.52835107\",\n \"0.5274694\",\n \"0.5274694\",\n \"0.52663773\",\n \"0.52303475\",\n \"0.52228343\",\n \"0.5207249\",\n \"0.51670146\",\n \"0.51597637\",\n \"0.5151957\",\n \"0.5138265\",\n \"0.51272345\",\n \"0.5101482\",\n \"0.5099452\",\n \"0.50929856\",\n \"0.507529\",\n \"0.50713646\",\n \"0.5049313\",\n \"0.5047742\",\n \"0.5040155\",\n \"0.50318646\",\n \"0.5027338\",\n \"0.5019814\",\n \"0.5007105\",\n \"0.5006379\",\n \"0.50048596\",\n \"0.50028986\",\n \"0.49974412\",\n \"0.49935225\",\n \"0.4983863\",\n \"0.4979592\",\n \"0.49763507\",\n \"0.497045\",\n \"0.49683282\",\n \"0.49561647\",\n \"0.495033\",\n \"0.49430114\",\n \"0.49421677\",\n \"0.49270946\",\n \"0.48897365\",\n \"0.48705226\",\n \"0.48701057\",\n \"0.48658815\",\n \"0.4853294\",\n \"0.48513636\",\n \"0.48464733\",\n \"0.48375258\",\n \"0.48359862\",\n \"0.48209077\",\n \"0.48082885\",\n \"0.48011038\",\n \"0.47989723\",\n \"0.4788358\",\n \"0.47877598\",\n \"0.47840637\",\n \"0.4770129\",\n \"0.4767391\",\n \"0.47585475\",\n \"0.4757347\",\n \"0.47555488\",\n \"0.4753844\",\n \"0.4747603\",\n \"0.4745372\",\n \"0.4743448\",\n \"0.47302425\",\n \"0.4729622\",\n \"0.471859\",\n \"0.47070003\",\n \"0.47029686\",\n \"0.46809852\",\n \"0.4680432\",\n \"0.4678304\",\n \"0.46753046\",\n \"0.4665646\",\n \"0.46631628\",\n \"0.46626192\",\n \"0.46579665\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.780377"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":20087,"cells":{"query":{"kind":"string","value":"Returns the default quantization config"},"document":{"kind":"string","value":"def _get_default_quantization_config(preset: QuantizationPreset, subset_size: int) -> Dict[str, Any]:\n return {\n \"algorithm\": \"quantization\",\n \"preset\": preset.value,\n \"initializer\": {\n \"range\": {\"num_init_samples\": subset_size, \"type\": DEFAULT_RANGE_TYPE},\n \"batchnorm_adaptation\": {\"num_bn_adaptation_samples\": subset_size},\n },\n \"overflow_fix\": \"first_layer_only\",\n }"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _get_transformer_quantization_config(subset_size: int) -> Dict[str, Any]:\n return {\n \"algorithm\": \"quantization\",\n \"preset\": \"mixed\",\n \"initializer\": {\n \"range\": {\"num_init_samples\": subset_size, \"type\": DEFAULT_RANGE_TYPE},\n \"batchnorm_adaptation\": {\"num_bn_adaptation_samples\": 0},\n },\n \"scope_overrides\": {\"activations\": {\"{re}.*matmul_0\": {\"mode\": \"symmetric\"}}},\n \"ignored_scopes\": [\n \"{re}.*Embeddings.*\",\n \"{re}.*__add___[0-1]\",\n \"{re}.*layer_norm_0\",\n \"{re}.*matmul_1\",\n \"{re}.*__truediv__*\",\n ],\n \"overflow_fix\": \"first_layer_only\",\n }","def default_config(self):\n return {\n \"xtype\": \"scalar\",\n \"ytype\": \"scalar\",\n \"xtick-delta\": 50, \n \"ytick-delta\": 20, \n \"xtick-format\": '{:g}', \n \"ytick-format\": '{:g}',\n }","def get_default_configuration():\n # Pre-configured default values for various parameters:\n default_config = {\n \"name\":\"Transient\",\n \"auto\":True,\n \"ra\":0.0,\n \"dec\":0.0,\n \"radius\":10.0,\n \"resolution\":1.8,\n \"energy\":70.0,\n \"pixsize\": 16,\n \"respcode\":\"czti_Aepix.out\",\n \"txycode\":\"radec2txty.out\",\n \"resppath\":\"pixarea\",\n \"plotfile\":\"plots/localize.pdf\",\n\t \"lc_bin\":5.0,\n\t \"typ\":\"band\",\n\t \"comp_bin\":20,\t\n \"verbose\":True,\n \"do_fit\":True\n }\n required_config = {\n 'l2file':\"_level2.evt\",\n 'infile':\"file.evt\",\n 'mkffile':\"file.mkf\",\n 'trigtime':0.00,\n 'transtart':0.00,\n 'tranend':0.00,\n 'bkg1start':0.00,\n 'bkg1end':0.00,\n 'bkg2start':0.00,\n 'bkg2end':0.00,\n\t 'alpha':0.00,\n\t 'beta':0.00,\n\t 'E0':0.00,\n\t 'A':0.00\n }\n return default_config, required_config","def get_config(self):\n config = super(EpsGreedyQPolicy, self).get_config()\n config['eps'] = self.eps\n return config","def getDefaultConfig():\n config = {\n \"samples\": _DEFAULT_SAMPLE_COUNT,\n \"channel\": \"all\",\n \"rate\": _DEFAULT_SAMPLE_RATE,\n \"update\": 1,\n \"output\": \"data.rld\",\n \"format\": \"rld\",\n \"size\": _DEFAULT_FILE_SIZE,\n \"comment\": _DEFAULT_FILE_COMMENT,\n \"digital\": True,\n \"ambient\": False,\n \"aggregate\": \"downsample\",\n \"high-range\": [],\n \"web\": False,\n }\n return config","def get_config_object() -> \"BaseConfig\":\n assert (\n len(G_CONFIG_OBJECT) == 1\n ), \"Have you created quantize config object before calling `quantize_model`?\"\n if G_CONFIG_OBJECT:\n return G_CONFIG_OBJECT[0]","def get_default_config(self):\n \n config = {}\n \n # default z_0_hat, zeros, flexible\n config['z_0_hat_option'] = 'flexible'\n config['initial_z_0_hat'] = np.zeros(self.dimension)\n \n # default P_0_hat, identity times a small scalar, flexible\n config['P_0_hat_option'] = 'flexible'\n config['initial_P_0_hat'] = 0.1 * np.eye(self.dimension)\n \n # default A, identity, flexible\n config['AB_option'] = 'flexible'\n config['initial_A'] = np.eye(self.dimension)\n config['initial_B'] = np.zeros((self.dimension, self.control_dimension))\n \n # default Q, identity times a small scalar, flexible\n config['Q_option'] = 'flexible'\n config['initial_Q'] = 0.1 * np.eye(self.dimension)\n \n # default R, identity times a small scalar, flexible\n config['R_option'] = 'flexible'\n config['initial_R'] = 0.1 * np.eye(self.dimension)\n \n # default stopping criteria, threshold 1e-5, num_iterations 1000\n # stop whenever either of the two critieria is reached\n config['threshold'] = 1e-5\n config['num_iterations'] = 1000\n\n return config","def default_options(cls) -> Dict:\n options = super().default_options()\n # scaling factor for temperature adaptation\n options['eta'] = 100\n # controls the adaptation degeneration velocity of the temperature\n # adaption.\n options['nu'] = 1e3\n\n return options","def get_default_visualizer_config(self):\n\n default_config = {\n\n \"format\": \"png\",\n\n \"fontsize\": 16,\n \"fontname\": 'Roboto',\n \"rankdir\": \"TB\",\n\n # Specific offsets can be specified here\n # for different shades. Change the values\n # below 0 and 1. For best results we recommend\n # to keep the range between 0.1 - 0.3\n \"layer_color_dict\": {\n \"InputLayer\": 0.1,\n \"Reshape\": 0.12,\n \"Conv1D\": 0.13,\n \"Conv2D\": 0.17,\n \"MaxPooling1D\": 0.19,\n \"MaxPooling2D\": 0.20,\n \"ZeroPadding3D\": 0.22,\n \"Flatten\": 0.25,\n \"AveragePooling2D\": 0.27,\n \"Dropout\": 0.29,\n \"Dense\": 0.3,\n \"Concatenate\": 0.32,\n \"Model\": 0.34,\n \"RepeatVector\": 0.36,\n \"Multiply\": 0.38,\n \"Add\": 0.39,\n \"Lambda\": 0.4,\n \"SpatialDropout1D\": 0.41,\n \"SpatialDropout2D\": 0.44\n },\n # Please provide as many colors\n # as many models you expect.\n # This package will\n # generate random colors incase\n # colors fall short, but there\n # is no guarantee that they will be\n # pretty\n 'default_color_package': [\n [0.586, 1.000, 1.000],\n [0.513, 0.141, 0.725],\n [0.094, 1.000, 1.000],\n [0.375, 0.739, 0.780],\n [0.967, 0.816, 0.961],\n [0.286, 1.000, 1.000],\n [0.750, 0.416, 0.961],\n [0.778, 0.631, 0.871],\n [0.613, 0.141, 0.725],\n [0.850, 0.539, 0.780],\n [0.186, 1.000, 1.000]\n ],\n \"class_names\": True,\n \"graph_label\": \"Nested SeeNN\",\n \"node_seperation_distance\": 0.4,\n\n 'module_connection_color': 'black',\n 'collapse_inputs': False,\n 'layer_height': 0.5,\n 'layer_width': 2,\n # 'condense_dropout_layer': False,\n\n # Specify if to use multiple color layers,\n # rather than shade\n 'use_multiple_colors_layers': False,\n\n # If use_multiple_colors_layers is Fa,se,\n # provide the colors\n 'multiple_colors_layer_package': {\n \"InputLayer\": \"grey\",\n \"Reshape\": \"#F5A286\",\n \"Conv1D\": \"#F7D7A8\",\n \"Conv2D\": \"#F7D7A8\",\n \"MaxPooling1D\": \"#AADFA2\",\n \"MaxPooling2D\": \"#AADFA2\",\n \"ZeroPadding3D\": \"grey\",\n \"Flatten\": \"grey\",\n \"AveragePooling2D\": \"#A8CFE7\",\n \"Dropout\": \"#9896C8\",\n \"Dense\": \"#C66AA7\",\n \"Concatenate\": \"#F5A286\",\n \"Model\": \"#292D30\",\n \"RepeatVector\": \"grey\",\n \"Multiply\": \"grey\",\n \"Add\": \"grey\",\n \"Lambda\": \"#CAAFE7\",\n \"SpatialDropout1D\": \"#FFAAEE\",\n \"SpatialDropout2D\": \"#CAAFE7\"\n },\n\n 'show_activation': False,\n 'rotate': 90,\n 'show_constant_input': False\n\n }\n\n return default_config","def default_config():\n return {'grid': {'regular': {'width': 0.05,\n 'wake': {'width': 0.1, 'progression': None},\n 'layers': 50,\n 'thickness': 5,\n 'boundary_layer': { 'initial_thickness': 4.2e-5 }}}}","def _default_config(self):\n return {\n 'penalty': 'l1',\n 'solver': 'liblinear'\n }","def getDefaultCurrency():","def _getDefaultSettings(cls):\n return {'minimumROIDimensions': 1,\n 'minimumROISize': None, # Skip testing the ROI size by default\n 'normalize': False,\n 'normalizeScale': 1,\n 'removeOutliers': None,\n 'resampledPixelSpacing': None, # No resampling by default\n 'interpolator': 'sitkBSpline', # Alternative: sitk.sitkBSpline,\n 'padDistance': 5,\n 'distances': [1],\n 'force2D': False,\n 'force2Ddimension': 0,\n 'label': 1,\n 'enableCExtensions': True,\n 'additionalInfo': True}","def get_config():\n config = dict(\n name=\"defaults\",\n # Either use geometric, zipf, or uniform i.e., data variable\n # can take one of \"geometric\", \"zipf\", \"uniform\".\n distribution=\"zipf\",\n lbd_geometric=0.8,\n degree_zipf=1.0,\n # Flags to indicate which methods to compare.\n run_approx_miracle=False,\n run_miracle=False,\n run_modified_miracle=True,\n run_ss=True,\n run_rhr=True,\n encoding_type=\"fast\", # Can take either fast or normal\n # Common parameters.\n num_itr=1,\n coding_cost=14,\n coding_cost_multiplier=1,\n approx_coding_cost_multiplier=3,\n approx_t=6,\n # Specific parameters (leave them as they are for now).\n delta=10**(-6),\n alpha=1.0,\n # Variation.\n vary=\"eps\", # Can take one of \"cc\", \"k\", \"n\", \"eps\".\n cc_space=[6, 8, 10, 12, 14],\n k_space=[200, 400, 600, 800, 1000],\n n_space=[2000, 4000, 6000, 8000, 10000],\n eps_space=list(range(1, 9)),\n # Defaults.\n n=5000,\n k=500,\n t=3,\n epsilon_target=6,\n )\n config = config_dict.ConfigDict(config)\n config.lock() # Prevent addition of new fields.\n return config","def default_tune(self):\n return self._default_tune","def _GetDefaultConfig(self) -> str:\n try:\n region = util.GetRegionFromZone(\n FLAGS.zones[0] if FLAGS.zones else FLAGS.zone[0])\n except IndexError:\n region = _DEFAULT_REGION\n return f'regional-{region}'","def get_default_config(self):\n default_config = super(SNMPInterfacePollCollector,\n self).get_default_config()\n default_config['path'] = 'snmp.interface'\n default_config['byte_unit'] = ['bit', 'byte']\n return default_config","def get_default_config(self):\n config = super(NumaCollector, self).get_default_config()\n config.update(\n {\n \"path\": \"numa\",\n \"bin\": self.find_binary(\"numactl\"),\n }\n )\n\n return config","def get_default_config(self):\n config = super(SignalfxHandler, self).get_default_config()\n\n config.update({\n 'url': 'https://ingest.signalfx.com/v2/datapoint',\n 'batch': 300,\n # Don't wait more than 10 sec between pushes\n 'batch_max_interval': 10,\n 'auth_token': '',\n })\n\n return config","def get_config(self):\n config = {'epsilon':self.eps}\n base_config = super(PowTransform, self).get_config()\n return dict(list(base_config.items()) + list(config.items()))","def get_config(self):\n config = super(Sc2Policy, self).get_config()\n config['eps'] = self.eps\n config['testing'] = self.testing\n return config","def get_config(self):\n return {'reduction': self.reduction, 'name': self.name}","def _default_config(cls):\n return dict()","def default_metric():\n return ProductRiemannianMetric","def get_config(self):\n config = {'epsilon':self.eps}\n base_config = super(MatrixReLU, self).get_config()\n return dict(list(base_config.items()) + list(config.items()))","def default_parameters():\n return BackendNSParameters()","def use_config_min_qty(self):\n return self._use_config_min_qty","def get_default_settings(cfg):\n cfg = deepcopy(cfg)\n cfg.setdefault('metric', 'regression_slope')\n cfg.setdefault('n_jobs', 1)\n cfg.setdefault('savefig_kwargs', {\n 'bbox_inches': 'tight',\n 'dpi': 600,\n 'orientation': 'landscape',\n })\n logger.info(\"Using at most %i processes\", cfg['n_jobs'])\n return cfg","def _default_options(cls):\n default_options = super()._default_options()\n default_options.data_processor = dp.DataProcessor(\n input_key=\"counts\",\n data_actions=[dp.Probability(\"1\"), dp.BasisExpectationValue()],\n )\n default_options.plotter.set_figure_options(\n xlabel=\"Flat top width\",\n ylabel=\"Pauli expectation values\",\n xval_unit=\"s\",\n ylim=(-1, 1),\n )\n default_options.data_subfit_map = {\n \"x\": {\"meas_basis\": \"x\"},\n \"y\": {\"meas_basis\": \"y\"},\n \"z\": {\"meas_basis\": \"z\"},\n }\n\n return default_options","def default_config(cls):\n\n config = {\n \"checkpoint_path\": \"\", # path to model checkpoint\n \"separated_audio_folder\": \"\" # path to folder where to save the separated audio tracks.\n }\n return config","def getDefaults():\n return {\n 'minsize': 10, # minimum size in MB\n 'pattern': [], # file name patterns\n }","def get_default_config(self):\r\n config = super(CMDCollector, self).get_default_config()\r\n config.update({\r\n 'enabled': 'True',\r\n 'fs': ',',\r\n 'timeout': 300,\r\n })\r\n return config","def get_cfg_defaults():\n return _C.clone()","def get_config():\n name = 'dynamic_pricing'\n num_products = 5\n scale = 1\n noise_var = 10\n p_max = 1\n\n agents = collections.OrderedDict(\n [('bsPricing',\n functools.partial(BootstrapDynamicPricing,\n num_products, scale, noise_var, p_max))]\n )\n\n environments = collections.OrderedDict(\n [('env',\n functools.partial(DynamicPricing,\n num_products, scale, noise_var, p_max))]\n )\n experiments = collections.OrderedDict(\n [(name, ExperimentNoAction)]\n )\n n_steps = 80\n n_seeds = 2000\n config = Config(name, agents, environments, experiments, n_steps, n_seeds)\n return config","def config(self, key, default='', astype='auto'):\n if hasattr(self, '_config') and self._config.length() > 0:\n out = self._config.curve(0).getAttribute(key, default=default)\n if astype in [str, 'str']:\n return str(out)\n else:\n return strToVar(out)\n return default","def initDefaults(self):\n return _libsbml.Curve_initDefaults(self)","def get_chart_configuration(self):\n return \"Noo\"","def get_default_config(self):\n config = super(DiskSpaceCollector, self).get_default_config()\n config.update({\n 'path': 'diskspace',\n # filesystems to examine\n 'filesystems': 'ext2, ext3, ext4, xfs, glusterfs, nfs, nfs4, ' +\n ' ntfs, hfs, fat32, fat16, btrfs',\n\n # exclude_filters\n # A list of regex patterns\n # A filesystem matching any of these patterns will be excluded\n # from disk space metrics collection.\n #\n # Examples:\n # exclude_filters =,\n # no exclude filters at all\n # exclude_filters = ^/boot, ^/mnt\n # exclude everything that begins /boot or /mnt\n # exclude_filters = m,\n # exclude everything that includes the letter \"m\"\n 'exclude_filters': ['^/export/home'],\n\n # Default numeric output\n 'byte_unit': ['byte']\n })\n return config","def get_default_config(self):\n config = super(NvidiaGPUCollector, self).get_default_config()\n config.update({\n 'path': 'nvidia',\n 'bin': '/usr/bin/nvidia-smi',\n 'stats': [\n 'index',\n 'memory.total',\n 'memory.used',\n 'memory.free',\n 'utilization.gpu',\n 'utilization.memory',\n 'temperature.gpu'\n ]\n })\n return config","def default_setting(self):\n\t\tunknown_as_zero = False\n\t\tbinary_rele = False # using the original values\n\t\tpresort = False # a default setting\n\n\t\tscale_data, scaler_id, scaler_level = get_default_scaler_setting(data_id=self.data_id)\n\n\t\t# more data settings that are rarely changed\n\t\tself.data_dict = dict(data_id=self.data_id, dir_data=self.dir_data, min_docs=10, min_rele=1,\n\t\t\t\t\t\t sample_rankings_per_q=1, unknown_as_zero=unknown_as_zero, binary_rele=binary_rele,\n\t\t\t\t\t\t presort=presort, scale_data=scale_data, scaler_id=scaler_id, scaler_level=scaler_level)\n\n\t\tdata_meta = get_data_meta(data_id=self.data_id) # add meta-information\n\t\tself.data_dict.update(data_meta)\n\n\t\treturn self.data_dict","def get_config(self):\n return {'name': self.name, 'dtype': self.dtype}","def get_config(self):\n return super().get_config()","def _set_default_eai_quantizer_state(self):\n\n default_group_quantizer_state = {\n 'hidden_l{}': True,\n 'cell_l{}': False,\n 'bias_l{}': False,\n 'W_l{}': True,\n 'R_l{}': True\n }\n\n default_group_quantizer_state = {name.format(layer): state\n for layer in range(self.num_layers)\n for name, state in default_group_quantizer_state.items()\n }\n\n tensor_names_with_grouped_quantizers = [name.format(layer)\n for layer in range(self.num_layers)\n for tensor_names in grouped_quantizer_map.values()\n for name in tensor_names\n ]\n\n for name, quantizer in self._grouped_quantizers.items():\n quantizer.enabled = default_group_quantizer_state[name]\n\n for name, quantizer in self._output_quantizers.items():\n if name not in tensor_names_with_grouped_quantizers:\n quantizer.enabled = False\n\n for name, quantizer in self._param_quantizers.items():\n if name not in tensor_names_with_grouped_quantizers:\n quantizer.enabled = True\n\n for name, quantizer in self._input_quantizers.items():\n if name not in tensor_names_with_grouped_quantizers:\n quantizer.enabled = True","def getDefaultSettings():\n return {}","def get_quantization_capability(self):\n return self.cur_config['capabilities']","def get_config(self):\n config = {'epsilon':self.eps}\n base_config = super(LogTransform, self).get_config()\n return dict(list(base_config.items()) + list(config.items()))","def default_kernel_config(defn):\n return [('beam', {}),\n ('hypers',\n {\n 'alpha_a': 4.0,\n 'alpha_b': 2.0,\n 'gamma_a': 3.0, \n 'gamma_b': 6.0\n }\n )]","def cg_config():\n return {}","def get_cfg_defaults():\r\n # Return a clone so that the defaults will not be altered\r\n # This is for the \"local variable\" use pattern\r\n return _C.clone()","def get_default_config():\r\n config = {}\r\n\r\n config[\"kl_coeff\"] = 1.0\r\n config[\"_num_workers_tf\"] = 4\r\n config[\"use_gae\"] = True\r\n config[\"num_gpus\"] = 0\r\n\r\n config[\"_env_name_rllib\"] = \"multicomp\"\r\n config[\"_env_fcn\"] = create_env\r\n config['_policies'] = [None, \"from_scratch\", \"pretrained\"]\r\n config[\"_env\"] = {'with_video': False,\r\n \"SingleAgentToMultiAgent\": False,\r\n \"env_name\": \"multicomp/YouShallNotPassHumans-v0\"}\r\n config['framework'] = 'tfe'\r\n\r\n config['_train_policies'] = ['player_1']\r\n config['_call'] = {}\r\n config['_trainer'] = \"PPO\"\r\n config['_policy'] = \"PPO\"\r\n config['_call']['checkpoint_freq'] = 0\r\n config['_train_steps'] = 99999999\r\n config['_update_config'] = None\r\n config['_run_inline'] = False\r\n config['_postprocess'] = None\r\n\r\n config['num_envs_per_worker'] = 4\r\n config['_log_error'] = True\r\n config['_model_params'] = {\r\n \"use_lstm\": False,\r\n \"fcnet_hiddens\": [64, 64],\r\n # \"custom_action_dist\": \"DiagGaussian\",\r\n \"fcnet_activation\": \"tanh\",\r\n \"free_log_std\": True,\r\n }\r\n\r\n config['_select_policy'] = select_policy_default\r\n config['_get_policies'] = get_policies_default\r\n config['_do_not_train_policies'] = []\r\n config['_update_withpolicies'] = None\r\n config['callbacks'] = InfoCallbacks\r\n\r\n return config","def get_default_config(self):\n return config.read(pathlib.Path(__file__).parent / \"ext.conf\")","def get_cfg_defaults():\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern\n return C.clone()","def get_default_config(self):\n config = super(DropwizardCollector, self).get_default_config()\n config.update({\n 'url': DEFAULT_METRICS_URL,\n 'path': 'dropwizard'\n })\n return config","def get_config(self, budget):\n\t\traise NotImplementedError('This function needs to be overwritten in %s.'%(self.__class__.__name__))","def default_config(cls) -> dict:\n return {\n \"observation\": {\n \"type\": \"Kinematics\"\n },\n \"action\": {\n \"type\": \"DiscreteMetaAction\"\n },\n \"simulation_frequency\": 15, # [Hz]\n \"policy_frequency\": 1, # [Hz]\n \"other_vehicles_type\": \"highway_env.vehicle.behavior.IDMVehicle\",\n \"screen_width\": 600, # [px]\n \"screen_height\": 150, # [px]\n \"centering_position\": [0.3, 0.5],\n \"scaling\": 5.5,\n \"show_trajectories\": False,\n \"render_agent\": True,\n \"offscreen_rendering\": os.environ.get(\"OFFSCREEN_RENDERING\", \"0\") == \"1\",\n \"manual_control\": False,\n \"real_time_rendering\": False\n }","def _get_default_options():\n return {\n \"library_folders\": [],\n \"verbose\": False,\n \"check_balanced\": True,\n \"mtime_check\": True,\n \"cache\": False,\n \"codegen\": False,\n \"expand_mx\": False,\n \"unroll_loops\": True,\n \"inline_functions\": True,\n \"expand_vectors\": False,\n \"resolve_parameter_values\": False,\n \"replace_parameter_expressions\": False,\n \"replace_constant_expressions\": False,\n \"eliminate_constant_assignments\": False,\n \"replace_parameter_values\": False,\n \"replace_constant_values\": False,\n \"eliminable_variable_expression\": None,\n \"factor_and_simplify_equations\": False,\n \"detect_aliases\": False,\n \"allow_derivative_aliases\": True,\n \"reduce_affine_expression\": False,\n }","def get_default_evaluator(self) -> EvaluatorConfig:\n raise NotImplementedError()","def get_cfg_defaults():\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern\n return _C.clone()","def get_cfg_defaults():\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern\n return _C.clone()","def get_cfg_defaults():\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern\n return _C.clone()","def get_cfg_defaults():\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern\n return _C.clone()","def get_default_config(self):\n config = super(DiskHealthCollector, self).get_default_config()\n config.update({\n 'enabled': 'True',\n 'devices': ('PhysicalDrive[0-9]+$'\n + '|md[0-9]+$'\n + '|sd[a-z]+[0-9]*$'\n + '|x?vd[a-z]+[0-9]*$'\n + '|disk[0-9]+$'\n + '|dm\\-[0-9]+$'),\n 'fs_types': ','.join(self.SUPPORTED_FS_TYPES),\n 'raw_stats_only': False,\n 'test_file_name': self.TEST_FILE_NAME,\n 'test_file_size': self.TEST_FILE_SIZE\n })\n return config","def default_setting():\n total_count.set(default_len)\n training_rate_clicked.set('50%')\n dimension_clicked.set('100 x 100')","def initDefaults(self):\n return _libsbml.CubicBezier_initDefaults(self)","def get_base_config():\n return dict(\n dim=768,\n ff_dim=3072,\n num_heads=12,\n num_layers=12,\n attention_dropout_rate=0.0,\n dropout_rate=0.1,\n representation_size=768,\n classifier='token'\n )","def getDefault():","def use_config_notify_stock_qty(self):\n return self._use_config_notify_stock_qty","def get_config(self):\n config = {\n \"units\": self.units,\n \"activation\": activations.serialize(self.activation),\n \"recurrent_activation\": activations.serialize(\n self.recurrent_activation\n ),\n \"attention_activation\": activations.serialize(\n self.attention_activation\n ),\n \"use_bias\": self.use_bias,\n \"kernel_initializer\": initializers.serialize(self.kernel_initializer),\n \"recurrent_initializer\": initializers.serialize(\n self.recurrent_initializer\n ),\n \"bias_initializer\": initializers.serialize(self.bias_initializer),\n \"attention_initializer\": initializers.serialize(\n self.attention_initializer\n ),\n \"use_chrono_initialization\": self.unit_forget_bias,\n \"kernel_regularizer\": regularizers.serialize(self.kernel_regularizer),\n \"recurrent_regularizer\": regularizers.serialize(\n self.recurrent_regularizer\n ),\n \"bias_regularizer\": regularizers.serialize(self.bias_regularizer),\n \"activity_regularizer\": regularizers.serialize(\n self.activity_regularizer\n ),\n \"attention_regularizer\": regularizers.serialize(\n self.attention_regularizer\n ),\n \"kernel_constraint\": constraints.serialize(self.kernel_constraint),\n \"recurrent_constraint\": constraints.serialize(\n self.recurrent_constraint\n ),\n \"bias_constraint\": constraints.serialize(self.bias_constraint),\n \"attention_constraint\": constraints.serialize(\n self.attention_constraint\n ),\n \"dropout\": self.dropout,\n \"recurrent_dropout\": self.recurrent_dropout,\n \"return_attention\": self.return_attention,\n }\n base_config = super().get_config()\n del base_config[\"cell\"]\n return dict(list(base_config.items()) + list(config.items()))","def default_config(self) -> Optional['outputs.FeatureSpecFleetobservabilityLoggingConfigDefaultConfig']:\n return pulumi.get(self, \"default_config\")","def GetDefaultGTSimulationConfigs():\n # Simulation configurations\n simulation = {}\n simulation['epochs'] = 100\n simulation['alpha_mu_interval'] = 200\n simulation['resultfolder'] = './results/' # With trailing slash\n simulation['rand_ic'] = False\n simulation['alpha_mu_chunk_size'] = 16\n simulation['epochs_chunk_size'] = 100\n simulation['integer_sensitivity'] = 10000\n\n # If True, this parameter defines homogeneous initial condition or not.\n # Default is True with c0=300, g0=40, p0=10\n simulation['using_c0_g0'] = True\n simulation['c0'] = 300\n simulation['g0'] = 40\n simulation['p0'] = 10\n\n # If True, this parameter saves only in hdf5 file some chunk id\n # Default is False. If True, define saved_chunkids\n simulation['selectchunk'] = False\n #simulation['saved_chunkids'] = {1, 2}\n\n # Define the number of processors on multicore processor\n simulation['n_processors'] = 1\n \n return simulation","def default_parameters():\n prm = Parameters('windkessel_model')\n\n prm.add('total_volume', float())\n\n prm.add('venous_compliance', float())\n prm.add('arterial_compliance', float())\n\n prm.add('venous_resistance', float())\n prm.add('arterial_resistance', float())\n prm.add('peripheral_resistance', float())\n\n prm.add('venous_resting_volume', float())\n prm.add('arterial_resting_volume', float())\n\n return prm","def _config_min(self):\n self.cntrl[\"imin\"] = 1\n self.cntrl[\"ntx\"] = 1\n self.cntrl[\"irest\"] = 0\n self.cntrl[\"maxcyc\"] = 5000\n self.cntrl[\"ncyc\"] = 1000\n self.cntrl[\"dt\"] = 0.0\n self.cntrl[\"nstlim\"] = 0\n self.cntrl[\"ntpr\"] = 100\n self.cntrl[\"ntwr\"] = 5000\n self.cntrl[\"ntwx\"] = 0\n self.cntrl[\"ntwe\"] = 0\n self.cntrl[\"ntxo\"] = 1\n self.cntrl[\"ntf\"] = 1\n self.cntrl[\"ntc\"] = 1\n self.cntrl[\"ntt\"] = 0\n self.cntrl[\"gamma_ln\"] = 0.0\n self.cntrl[\"ig\"] = 0\n self.cntrl[\"ntp\"] = 0\n self.cntrl[\"barostat\"] = 0\n self.mdcrd = None\n self.mden = None","def settings(self) -> Dict[str, Any]:\n if self.num_pixels is not None:\n return dict(num_pixels=self.num_pixels)\n return dict(quantile=self.quantile)","def get_default_config(cls):\n default = super(LSHNearestNeighborIndex, cls).get_default_config()\n\n lf_default = plugin.make_config(get_lsh_functor_impls())\n default['lsh_functor'] = lf_default\n\n di_default = plugin.make_config(get_descriptor_index_impls())\n default['descriptor_index'] = di_default\n\n hi_default = plugin.make_config(get_hash_index_impls())\n default['hash_index'] = hi_default\n default['hash_index_comment'] = \"'hash_index' may also be null to \" \\\n \"default to a linear index built at \" \\\n \"query time.\"\n\n h2u_default = plugin.make_config(get_key_value_store_impls())\n default['hash2uuids_kvstore'] = h2u_default\n\n return default","def get_default_config(self):\n config = super(SlurmJobWasteCollector, self).get_default_config()\n config.update({\n 'path': 'waste'\n })\n return config","def requested_config_vals():\n return {} # no extra values needed","def default_params():\n params = {}\n params['dataset'] = 'adult'\n params['engines'] = ['MD','RDA']\n params['iters'] = 10000\n params['epsilon'] = 1.0\n params['delta'] = 0.0\n params['bounded'] = True\n params['frequency'] = 1\n params['seed'] = 0\n params['save'] = None\n params['load'] = None\n params['plot'] = None\n\n return params","def get_default_config():\n return _config_schema_to_namespace(_CONFIG_SCHEMA)","def default_options(cls) -> Dict:\n return {}","def test_getfloat_with_default(self):\n self.assertEqual(self.config.getfloat('advanced','p'),None)\n self.assertEqual(self.config.getfloat('advanced','p',5.0),5.0)","def requested_config_vals():\n return {'transfer_stats_per_file':'opt'}","def _default_parameters():\n\n return {\n 'opt': 'adadelta',\n 'activation_function': 'softmax',\n 'lr': 0.0001,\n 'decay': 1e-6,\n 'loss': 'categorical_crossentropy',\n 'batch_size': 32,\n 'nb_epoch': 20,\n 'shuffle': True,\n 'momentum': 0.9,\n 'nesterov': True,\n 'rho': 0.95,\n 'epsilon': 1e-08,\n 'beta_1': 0.9,\n 'beta_2': 0.999,\n 'horizontal_flip': False,\n 'im_size': 240,#256,\n 'dense_layer': 1024,\n 'nb_classes': 10,\n 'nb_channels': 3,\n 'dropout': 0.5,\n 'metrics': ['accuracy'],\n 'volume': None,\n 'input_size': 25,\n 'temporal': False,\n 'input_dim': 512,\n 'nb_frames': 60,\n 'stride': 16,\n 'nb_hidden':512,\n 'lstm': False\n\n }","def __init__(self):\n super(ASYMMETRIC, self).__init__(quant_type=Constants.QZ_ASYMMETRIC)","def get_default_config(self):\n config = super(EndecaDgraphCollector, self).get_default_config()\n config.update({\n 'path': 'endeca.dgraph',\n 'host': 'localhost',\n 'port': 8080,\n 'timeout': 1,\n })\n return config","def default_units(x, axis):\n return getattr(x, 'units', None)","def op_config(self) -> Any:\n return self.solid_config","def get_defaults(cls, mode):\n mode_defaults = getattr(cls, f\"{mode.upper()}_DEFAULTS\")\n defaults = PlotConfig({**cls.COMMON_DEFAULTS, **mode_defaults})\n return defaults","def get_defaults(cls, mode):\n mode_defaults = getattr(cls, f\"{mode.upper()}_DEFAULTS\")\n defaults = PlotConfig({**cls.COMMON_DEFAULTS, **mode_defaults})\n return defaults","def getInitDefault(self):\n return self.initDefault","def get_config(self):\n config = {\n }\n base_config = super(MatrixConcat, self).get_config()\n return dict(list(base_config.items()) + list(config.items()))","def default_per_ocpu_in_gbps(self):\n return self._default_per_ocpu_in_gbps","def bootstrap_default():\n\treturn default_configuration","def get_default_config() -> Dict[Text, Any]:\n # please make sure to update the docs when changing a default parameter\n return {\n # ## Architecture of the used neural network\n # Hidden layer sizes for layers before the embedding layers for user message\n # and labels.\n # The number of hidden layers is equal to the length of the corresponding\n # list.\n HIDDEN_LAYERS_SIZES: {\n TEXT: [],\n ACTION_TEXT: [],\n f\"{LABEL}_{ACTION_TEXT}\": [],\n },\n # Dense dimension to use for sparse features.\n DENSE_DIMENSION: {\n TEXT: 128,\n ACTION_TEXT: 128,\n f\"{LABEL}_{ACTION_TEXT}\": 128,\n INTENT: 20,\n ACTION_NAME: 20,\n f\"{LABEL}_{ACTION_NAME}\": 20,\n ENTITIES: 20,\n SLOTS: 20,\n ACTIVE_LOOP: 20,\n },\n # Default dimension to use for concatenating sequence and sentence features.\n CONCAT_DIMENSION: {\n TEXT: 128,\n ACTION_TEXT: 128,\n f\"{LABEL}_{ACTION_TEXT}\": 128,\n },\n # Dimension size of embedding vectors before the dialogue transformer\n # encoder.\n ENCODING_DIMENSION: 50,\n # Number of units in transformer encoders\n TRANSFORMER_SIZE: {\n TEXT: 128,\n ACTION_TEXT: 128,\n f\"{LABEL}_{ACTION_TEXT}\": 128,\n DIALOGUE: 128,\n },\n # Number of layers in transformer encoders\n NUM_TRANSFORMER_LAYERS: {\n TEXT: 1,\n ACTION_TEXT: 1,\n f\"{LABEL}_{ACTION_TEXT}\": 1,\n DIALOGUE: 1,\n },\n # Number of attention heads in transformer\n NUM_HEADS: 4,\n # If 'True' use key relative embeddings in attention\n KEY_RELATIVE_ATTENTION: False,\n # If 'True' use value relative embeddings in attention\n VALUE_RELATIVE_ATTENTION: False,\n # Max position for relative embeddings. Only in effect if key- or value\n # relative\n # attention are turned on\n MAX_RELATIVE_POSITION: 5,\n # Use a unidirectional or bidirectional encoder\n # for `text`, `action_text`, and `label_action_text`.\n UNIDIRECTIONAL_ENCODER: False,\n # ## Training parameters\n # Initial and final batch sizes:\n # Batch size will be linearly increased for each epoch.\n BATCH_SIZES: [64, 256],\n # Strategy used whenc creating batches.\n # Can be either 'sequence' or 'balanced'.\n BATCH_STRATEGY: BALANCED,\n # Number of epochs to train\n EPOCHS: 1,\n # Set random seed to any 'int' to get reproducible results\n RANDOM_SEED: None,\n # Initial learning rate for the optimizer\n LEARNING_RATE: 0.001,\n # ## Parameters for embeddings\n # Dimension size of embedding vectors\n EMBEDDING_DIMENSION: 20,\n # The number of incorrect labels. The algorithm will minimize\n # their similarity to the user input during training.\n NUM_NEG: 20,\n # Type of similarity measure to use, either 'auto' or 'cosine' or 'inner'.\n SIMILARITY_TYPE: AUTO,\n # The type of the loss function, either 'cross_entropy' or 'margin'.\n LOSS_TYPE: CROSS_ENTROPY,\n # Number of top actions for which confidences should be predicted.\n # The number of Set to `0` if confidences for all actions should be\n # predicted. The confidences for all other actions will be set to 0.\n RANKING_LENGTH: 0,\n # Determines wether the confidences of the chosen top actions should be\n # renormalized so that they sum up to 1. By default, we do not renormalize\n # and return the confidences for the top actions as is.\n # Note that renormalization only makes sense if confidences are generated\n # via `softmax`.\n RENORMALIZE_CONFIDENCES: False,\n # Indicates how similar the algorithm should try to make embedding vectors\n # for correct labels.\n # Should be 0.0 < ... < 1.0 for 'cosine' similarity type.\n MAX_POS_SIM: 0.8,\n # Maximum negative similarity for incorrect labels.\n # Should be -1.0 < ... < 1.0 for 'cosine' similarity type.\n MAX_NEG_SIM: -0.2,\n # If 'True' the algorithm only minimizes maximum similarity over\n # incorrect intent labels, used only if 'loss_type' is set to 'margin'.\n USE_MAX_NEG_SIM: True,\n # If 'True' scale loss inverse proportionally to the confidence\n # of the correct prediction\n SCALE_LOSS: True,\n # ## Regularization parameters\n # The scale of regularization\n REGULARIZATION_CONSTANT: 0.001,\n # The scale of how important is to minimize the maximum similarity\n # between embeddings of different labels,\n # used only if 'loss_type' is set to 'margin'.\n NEGATIVE_MARGIN_SCALE: 0.8,\n # Dropout rate for embedding layers of dialogue features.\n DROP_RATE_DIALOGUE: 0.1,\n # Dropout rate for embedding layers of utterance level features.\n DROP_RATE: 0.0,\n # Dropout rate for embedding layers of label, e.g. action, features.\n DROP_RATE_LABEL: 0.0,\n # Dropout rate for attention.\n DROP_RATE_ATTENTION: 0.0,\n # Fraction of trainable weights in internal layers.\n CONNECTION_DENSITY: 0.2,\n # If 'True' apply dropout to sparse input tensors\n SPARSE_INPUT_DROPOUT: True,\n # If 'True' apply dropout to dense input tensors\n DENSE_INPUT_DROPOUT: True,\n # If 'True' random tokens of the input message will be masked. Since there\n # is no related loss term used inside TED, the masking effectively becomes\n # just input dropout applied to the text of user utterances.\n MASKED_LM: False,\n # ## Evaluation parameters\n # How often calculate validation accuracy.\n # Small values may hurt performance.\n EVAL_NUM_EPOCHS: 20,\n # How many examples to use for hold out validation set\n # Large values may hurt performance, e.g. model accuracy.\n # Set to 0 for no validation.\n EVAL_NUM_EXAMPLES: 0,\n # If you want to use tensorboard to visualize training and validation\n # metrics, set this option to a valid output directory.\n TENSORBOARD_LOG_DIR: None,\n # Define when training metrics for tensorboard should be logged.\n # Either after every epoch or for every training step.\n # Valid values: 'epoch' and 'batch'\n TENSORBOARD_LOG_LEVEL: \"epoch\",\n # Perform model checkpointing\n CHECKPOINT_MODEL: False,\n # Only pick e2e prediction if the policy is confident enough\n E2E_CONFIDENCE_THRESHOLD: 0.5,\n # Specify what features to use as sequence and sentence features.\n # By default all features in the pipeline are used.\n FEATURIZERS: [],\n # If set to true, entities are predicted in user utterances.\n ENTITY_RECOGNITION: True,\n # if 'True' applies sigmoid on all similarity terms and adds\n # it to the loss function to ensure that similarity values are\n # approximately bounded. Used inside cross-entropy loss only.\n CONSTRAIN_SIMILARITIES: False,\n # Model confidence to be returned during inference. Currently, the only\n # possible value is `softmax`.\n MODEL_CONFIDENCE: SOFTMAX,\n # 'BILOU_flag' determines whether to use BILOU tagging or not.\n # If set to 'True' labelling is more rigorous, however more\n # examples per entity are required.\n # Rule of thumb: you should have more than 100 examples per entity.\n BILOU_FLAG: True,\n # Split entities by comma, this makes sense e.g. for a list of\n # ingredients in a recipe, but it doesn't make sense for the parts of\n # an address\n SPLIT_ENTITIES_BY_COMMA: SPLIT_ENTITIES_BY_COMMA_DEFAULT_VALUE,\n # Max history of the policy, unbounded by default\n POLICY_MAX_HISTORY: DEFAULT_MAX_HISTORY,\n # Determines the importance of policies, higher values take precedence\n POLICY_PRIORITY: DEFAULT_POLICY_PRIORITY,\n USE_GPU: True,\n }","def get_config_defaults(self): # pylint: disable=R0201\n return {}","def get_default_dataset_config():\n code_path = utils.get_code_path()\n default_config = {\n 'GT_FOLDER': os.path.join(code_path, 'data/gt/kitti/kitti_mots_val'), # Location of GT data\n 'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/kitti/kitti_mots_val'), # Trackers location\n 'OUTPUT_FOLDER': None, # Where to save eval results (if None, same as TRACKERS_FOLDER)\n 'TRACKERS_TO_EVAL': None, # Filenames of trackers to eval (if None, all in folder)\n 'CLASSES_TO_EVAL': ['car', 'pedestrian'], # Valid: ['car', 'pedestrian']\n 'SPLIT_TO_EVAL': 'val', # Valid: 'training', 'val'\n 'INPUT_AS_ZIP': False, # Whether tracker input files are zipped\n 'PRINT_CONFIG': True, # Whether to print current config\n 'TRACKER_SUB_FOLDER': 'data', # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n 'OUTPUT_SUB_FOLDER': '', # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n 'TRACKER_DISPLAY_NAMES': None, # Names of trackers to display, if None: TRACKERS_TO_EVAL\n 'SEQMAP_FOLDER': None, # Where seqmaps are found (if None, GT_FOLDER)\n 'SEQMAP_FILE': None, # Directly specify seqmap file (if none use seqmap_folder/split_to_eval.seqmap)\n 'SEQ_INFO': None, # If not None, directly specify sequences to eval and their number of timesteps\n 'GT_LOC_FORMAT': '{gt_folder}/label_02/{seq}.txt', # format of gt localization\n }\n return default_config","def _create_defaults(self):\n return DefaultCommandOptionValues(\n min_confidence=3, output_format='vs7')","def default(self):\n return self._configs[0] if len(self._configs) else None","def getSetting():\n return \"### dataPreparation Setting:\\n\\n\" + \"\\n\".join([\"%s : %s\"%(k,str(v)) for k,v in\n [(\"**DATASET_MIN_MAX_NORMALIZE**\",DATASET_MIN_MAX_NORMALIZE)]])","def default_metric_value(self) -> float:","def get_default(cls):\n return cls.ALL"],"string":"[\n \"def _get_transformer_quantization_config(subset_size: int) -> Dict[str, Any]:\\n return {\\n \\\"algorithm\\\": \\\"quantization\\\",\\n \\\"preset\\\": \\\"mixed\\\",\\n \\\"initializer\\\": {\\n \\\"range\\\": {\\\"num_init_samples\\\": subset_size, \\\"type\\\": DEFAULT_RANGE_TYPE},\\n \\\"batchnorm_adaptation\\\": {\\\"num_bn_adaptation_samples\\\": 0},\\n },\\n \\\"scope_overrides\\\": {\\\"activations\\\": {\\\"{re}.*matmul_0\\\": {\\\"mode\\\": \\\"symmetric\\\"}}},\\n \\\"ignored_scopes\\\": [\\n \\\"{re}.*Embeddings.*\\\",\\n \\\"{re}.*__add___[0-1]\\\",\\n \\\"{re}.*layer_norm_0\\\",\\n \\\"{re}.*matmul_1\\\",\\n \\\"{re}.*__truediv__*\\\",\\n ],\\n \\\"overflow_fix\\\": \\\"first_layer_only\\\",\\n }\",\n \"def default_config(self):\\n return {\\n \\\"xtype\\\": \\\"scalar\\\",\\n \\\"ytype\\\": \\\"scalar\\\",\\n \\\"xtick-delta\\\": 50, \\n \\\"ytick-delta\\\": 20, \\n \\\"xtick-format\\\": '{:g}', \\n \\\"ytick-format\\\": '{:g}',\\n }\",\n \"def get_default_configuration():\\n # Pre-configured default values for various parameters:\\n default_config = {\\n \\\"name\\\":\\\"Transient\\\",\\n \\\"auto\\\":True,\\n \\\"ra\\\":0.0,\\n \\\"dec\\\":0.0,\\n \\\"radius\\\":10.0,\\n \\\"resolution\\\":1.8,\\n \\\"energy\\\":70.0,\\n \\\"pixsize\\\": 16,\\n \\\"respcode\\\":\\\"czti_Aepix.out\\\",\\n \\\"txycode\\\":\\\"radec2txty.out\\\",\\n \\\"resppath\\\":\\\"pixarea\\\",\\n \\\"plotfile\\\":\\\"plots/localize.pdf\\\",\\n\\t \\\"lc_bin\\\":5.0,\\n\\t \\\"typ\\\":\\\"band\\\",\\n\\t \\\"comp_bin\\\":20,\\t\\n \\\"verbose\\\":True,\\n \\\"do_fit\\\":True\\n }\\n required_config = {\\n 'l2file':\\\"_level2.evt\\\",\\n 'infile':\\\"file.evt\\\",\\n 'mkffile':\\\"file.mkf\\\",\\n 'trigtime':0.00,\\n 'transtart':0.00,\\n 'tranend':0.00,\\n 'bkg1start':0.00,\\n 'bkg1end':0.00,\\n 'bkg2start':0.00,\\n 'bkg2end':0.00,\\n\\t 'alpha':0.00,\\n\\t 'beta':0.00,\\n\\t 'E0':0.00,\\n\\t 'A':0.00\\n }\\n return default_config, required_config\",\n \"def get_config(self):\\n config = super(EpsGreedyQPolicy, self).get_config()\\n config['eps'] = self.eps\\n return config\",\n \"def getDefaultConfig():\\n config = {\\n \\\"samples\\\": _DEFAULT_SAMPLE_COUNT,\\n \\\"channel\\\": \\\"all\\\",\\n \\\"rate\\\": _DEFAULT_SAMPLE_RATE,\\n \\\"update\\\": 1,\\n \\\"output\\\": \\\"data.rld\\\",\\n \\\"format\\\": \\\"rld\\\",\\n \\\"size\\\": _DEFAULT_FILE_SIZE,\\n \\\"comment\\\": _DEFAULT_FILE_COMMENT,\\n \\\"digital\\\": True,\\n \\\"ambient\\\": False,\\n \\\"aggregate\\\": \\\"downsample\\\",\\n \\\"high-range\\\": [],\\n \\\"web\\\": False,\\n }\\n return config\",\n \"def get_config_object() -> \\\"BaseConfig\\\":\\n assert (\\n len(G_CONFIG_OBJECT) == 1\\n ), \\\"Have you created quantize config object before calling `quantize_model`?\\\"\\n if G_CONFIG_OBJECT:\\n return G_CONFIG_OBJECT[0]\",\n \"def get_default_config(self):\\n \\n config = {}\\n \\n # default z_0_hat, zeros, flexible\\n config['z_0_hat_option'] = 'flexible'\\n config['initial_z_0_hat'] = np.zeros(self.dimension)\\n \\n # default P_0_hat, identity times a small scalar, flexible\\n config['P_0_hat_option'] = 'flexible'\\n config['initial_P_0_hat'] = 0.1 * np.eye(self.dimension)\\n \\n # default A, identity, flexible\\n config['AB_option'] = 'flexible'\\n config['initial_A'] = np.eye(self.dimension)\\n config['initial_B'] = np.zeros((self.dimension, self.control_dimension))\\n \\n # default Q, identity times a small scalar, flexible\\n config['Q_option'] = 'flexible'\\n config['initial_Q'] = 0.1 * np.eye(self.dimension)\\n \\n # default R, identity times a small scalar, flexible\\n config['R_option'] = 'flexible'\\n config['initial_R'] = 0.1 * np.eye(self.dimension)\\n \\n # default stopping criteria, threshold 1e-5, num_iterations 1000\\n # stop whenever either of the two critieria is reached\\n config['threshold'] = 1e-5\\n config['num_iterations'] = 1000\\n\\n return config\",\n \"def default_options(cls) -> Dict:\\n options = super().default_options()\\n # scaling factor for temperature adaptation\\n options['eta'] = 100\\n # controls the adaptation degeneration velocity of the temperature\\n # adaption.\\n options['nu'] = 1e3\\n\\n return options\",\n \"def get_default_visualizer_config(self):\\n\\n default_config = {\\n\\n \\\"format\\\": \\\"png\\\",\\n\\n \\\"fontsize\\\": 16,\\n \\\"fontname\\\": 'Roboto',\\n \\\"rankdir\\\": \\\"TB\\\",\\n\\n # Specific offsets can be specified here\\n # for different shades. Change the values\\n # below 0 and 1. For best results we recommend\\n # to keep the range between 0.1 - 0.3\\n \\\"layer_color_dict\\\": {\\n \\\"InputLayer\\\": 0.1,\\n \\\"Reshape\\\": 0.12,\\n \\\"Conv1D\\\": 0.13,\\n \\\"Conv2D\\\": 0.17,\\n \\\"MaxPooling1D\\\": 0.19,\\n \\\"MaxPooling2D\\\": 0.20,\\n \\\"ZeroPadding3D\\\": 0.22,\\n \\\"Flatten\\\": 0.25,\\n \\\"AveragePooling2D\\\": 0.27,\\n \\\"Dropout\\\": 0.29,\\n \\\"Dense\\\": 0.3,\\n \\\"Concatenate\\\": 0.32,\\n \\\"Model\\\": 0.34,\\n \\\"RepeatVector\\\": 0.36,\\n \\\"Multiply\\\": 0.38,\\n \\\"Add\\\": 0.39,\\n \\\"Lambda\\\": 0.4,\\n \\\"SpatialDropout1D\\\": 0.41,\\n \\\"SpatialDropout2D\\\": 0.44\\n },\\n # Please provide as many colors\\n # as many models you expect.\\n # This package will\\n # generate random colors incase\\n # colors fall short, but there\\n # is no guarantee that they will be\\n # pretty\\n 'default_color_package': [\\n [0.586, 1.000, 1.000],\\n [0.513, 0.141, 0.725],\\n [0.094, 1.000, 1.000],\\n [0.375, 0.739, 0.780],\\n [0.967, 0.816, 0.961],\\n [0.286, 1.000, 1.000],\\n [0.750, 0.416, 0.961],\\n [0.778, 0.631, 0.871],\\n [0.613, 0.141, 0.725],\\n [0.850, 0.539, 0.780],\\n [0.186, 1.000, 1.000]\\n ],\\n \\\"class_names\\\": True,\\n \\\"graph_label\\\": \\\"Nested SeeNN\\\",\\n \\\"node_seperation_distance\\\": 0.4,\\n\\n 'module_connection_color': 'black',\\n 'collapse_inputs': False,\\n 'layer_height': 0.5,\\n 'layer_width': 2,\\n # 'condense_dropout_layer': False,\\n\\n # Specify if to use multiple color layers,\\n # rather than shade\\n 'use_multiple_colors_layers': False,\\n\\n # If use_multiple_colors_layers is Fa,se,\\n # provide the colors\\n 'multiple_colors_layer_package': {\\n \\\"InputLayer\\\": \\\"grey\\\",\\n \\\"Reshape\\\": \\\"#F5A286\\\",\\n \\\"Conv1D\\\": \\\"#F7D7A8\\\",\\n \\\"Conv2D\\\": \\\"#F7D7A8\\\",\\n \\\"MaxPooling1D\\\": \\\"#AADFA2\\\",\\n \\\"MaxPooling2D\\\": \\\"#AADFA2\\\",\\n \\\"ZeroPadding3D\\\": \\\"grey\\\",\\n \\\"Flatten\\\": \\\"grey\\\",\\n \\\"AveragePooling2D\\\": \\\"#A8CFE7\\\",\\n \\\"Dropout\\\": \\\"#9896C8\\\",\\n \\\"Dense\\\": \\\"#C66AA7\\\",\\n \\\"Concatenate\\\": \\\"#F5A286\\\",\\n \\\"Model\\\": \\\"#292D30\\\",\\n \\\"RepeatVector\\\": \\\"grey\\\",\\n \\\"Multiply\\\": \\\"grey\\\",\\n \\\"Add\\\": \\\"grey\\\",\\n \\\"Lambda\\\": \\\"#CAAFE7\\\",\\n \\\"SpatialDropout1D\\\": \\\"#FFAAEE\\\",\\n \\\"SpatialDropout2D\\\": \\\"#CAAFE7\\\"\\n },\\n\\n 'show_activation': False,\\n 'rotate': 90,\\n 'show_constant_input': False\\n\\n }\\n\\n return default_config\",\n \"def default_config():\\n return {'grid': {'regular': {'width': 0.05,\\n 'wake': {'width': 0.1, 'progression': None},\\n 'layers': 50,\\n 'thickness': 5,\\n 'boundary_layer': { 'initial_thickness': 4.2e-5 }}}}\",\n \"def _default_config(self):\\n return {\\n 'penalty': 'l1',\\n 'solver': 'liblinear'\\n }\",\n \"def getDefaultCurrency():\",\n \"def _getDefaultSettings(cls):\\n return {'minimumROIDimensions': 1,\\n 'minimumROISize': None, # Skip testing the ROI size by default\\n 'normalize': False,\\n 'normalizeScale': 1,\\n 'removeOutliers': None,\\n 'resampledPixelSpacing': None, # No resampling by default\\n 'interpolator': 'sitkBSpline', # Alternative: sitk.sitkBSpline,\\n 'padDistance': 5,\\n 'distances': [1],\\n 'force2D': False,\\n 'force2Ddimension': 0,\\n 'label': 1,\\n 'enableCExtensions': True,\\n 'additionalInfo': True}\",\n \"def get_config():\\n config = dict(\\n name=\\\"defaults\\\",\\n # Either use geometric, zipf, or uniform i.e., data variable\\n # can take one of \\\"geometric\\\", \\\"zipf\\\", \\\"uniform\\\".\\n distribution=\\\"zipf\\\",\\n lbd_geometric=0.8,\\n degree_zipf=1.0,\\n # Flags to indicate which methods to compare.\\n run_approx_miracle=False,\\n run_miracle=False,\\n run_modified_miracle=True,\\n run_ss=True,\\n run_rhr=True,\\n encoding_type=\\\"fast\\\", # Can take either fast or normal\\n # Common parameters.\\n num_itr=1,\\n coding_cost=14,\\n coding_cost_multiplier=1,\\n approx_coding_cost_multiplier=3,\\n approx_t=6,\\n # Specific parameters (leave them as they are for now).\\n delta=10**(-6),\\n alpha=1.0,\\n # Variation.\\n vary=\\\"eps\\\", # Can take one of \\\"cc\\\", \\\"k\\\", \\\"n\\\", \\\"eps\\\".\\n cc_space=[6, 8, 10, 12, 14],\\n k_space=[200, 400, 600, 800, 1000],\\n n_space=[2000, 4000, 6000, 8000, 10000],\\n eps_space=list(range(1, 9)),\\n # Defaults.\\n n=5000,\\n k=500,\\n t=3,\\n epsilon_target=6,\\n )\\n config = config_dict.ConfigDict(config)\\n config.lock() # Prevent addition of new fields.\\n return config\",\n \"def default_tune(self):\\n return self._default_tune\",\n \"def _GetDefaultConfig(self) -> str:\\n try:\\n region = util.GetRegionFromZone(\\n FLAGS.zones[0] if FLAGS.zones else FLAGS.zone[0])\\n except IndexError:\\n region = _DEFAULT_REGION\\n return f'regional-{region}'\",\n \"def get_default_config(self):\\n default_config = super(SNMPInterfacePollCollector,\\n self).get_default_config()\\n default_config['path'] = 'snmp.interface'\\n default_config['byte_unit'] = ['bit', 'byte']\\n return default_config\",\n \"def get_default_config(self):\\n config = super(NumaCollector, self).get_default_config()\\n config.update(\\n {\\n \\\"path\\\": \\\"numa\\\",\\n \\\"bin\\\": self.find_binary(\\\"numactl\\\"),\\n }\\n )\\n\\n return config\",\n \"def get_default_config(self):\\n config = super(SignalfxHandler, self).get_default_config()\\n\\n config.update({\\n 'url': 'https://ingest.signalfx.com/v2/datapoint',\\n 'batch': 300,\\n # Don't wait more than 10 sec between pushes\\n 'batch_max_interval': 10,\\n 'auth_token': '',\\n })\\n\\n return config\",\n \"def get_config(self):\\n config = {'epsilon':self.eps}\\n base_config = super(PowTransform, self).get_config()\\n return dict(list(base_config.items()) + list(config.items()))\",\n \"def get_config(self):\\n config = super(Sc2Policy, self).get_config()\\n config['eps'] = self.eps\\n config['testing'] = self.testing\\n return config\",\n \"def get_config(self):\\n return {'reduction': self.reduction, 'name': self.name}\",\n \"def _default_config(cls):\\n return dict()\",\n \"def default_metric():\\n return ProductRiemannianMetric\",\n \"def get_config(self):\\n config = {'epsilon':self.eps}\\n base_config = super(MatrixReLU, self).get_config()\\n return dict(list(base_config.items()) + list(config.items()))\",\n \"def default_parameters():\\n return BackendNSParameters()\",\n \"def use_config_min_qty(self):\\n return self._use_config_min_qty\",\n \"def get_default_settings(cfg):\\n cfg = deepcopy(cfg)\\n cfg.setdefault('metric', 'regression_slope')\\n cfg.setdefault('n_jobs', 1)\\n cfg.setdefault('savefig_kwargs', {\\n 'bbox_inches': 'tight',\\n 'dpi': 600,\\n 'orientation': 'landscape',\\n })\\n logger.info(\\\"Using at most %i processes\\\", cfg['n_jobs'])\\n return cfg\",\n \"def _default_options(cls):\\n default_options = super()._default_options()\\n default_options.data_processor = dp.DataProcessor(\\n input_key=\\\"counts\\\",\\n data_actions=[dp.Probability(\\\"1\\\"), dp.BasisExpectationValue()],\\n )\\n default_options.plotter.set_figure_options(\\n xlabel=\\\"Flat top width\\\",\\n ylabel=\\\"Pauli expectation values\\\",\\n xval_unit=\\\"s\\\",\\n ylim=(-1, 1),\\n )\\n default_options.data_subfit_map = {\\n \\\"x\\\": {\\\"meas_basis\\\": \\\"x\\\"},\\n \\\"y\\\": {\\\"meas_basis\\\": \\\"y\\\"},\\n \\\"z\\\": {\\\"meas_basis\\\": \\\"z\\\"},\\n }\\n\\n return default_options\",\n \"def default_config(cls):\\n\\n config = {\\n \\\"checkpoint_path\\\": \\\"\\\", # path to model checkpoint\\n \\\"separated_audio_folder\\\": \\\"\\\" # path to folder where to save the separated audio tracks.\\n }\\n return config\",\n \"def getDefaults():\\n return {\\n 'minsize': 10, # minimum size in MB\\n 'pattern': [], # file name patterns\\n }\",\n \"def get_default_config(self):\\r\\n config = super(CMDCollector, self).get_default_config()\\r\\n config.update({\\r\\n 'enabled': 'True',\\r\\n 'fs': ',',\\r\\n 'timeout': 300,\\r\\n })\\r\\n return config\",\n \"def get_cfg_defaults():\\n return _C.clone()\",\n \"def get_config():\\n name = 'dynamic_pricing'\\n num_products = 5\\n scale = 1\\n noise_var = 10\\n p_max = 1\\n\\n agents = collections.OrderedDict(\\n [('bsPricing',\\n functools.partial(BootstrapDynamicPricing,\\n num_products, scale, noise_var, p_max))]\\n )\\n\\n environments = collections.OrderedDict(\\n [('env',\\n functools.partial(DynamicPricing,\\n num_products, scale, noise_var, p_max))]\\n )\\n experiments = collections.OrderedDict(\\n [(name, ExperimentNoAction)]\\n )\\n n_steps = 80\\n n_seeds = 2000\\n config = Config(name, agents, environments, experiments, n_steps, n_seeds)\\n return config\",\n \"def config(self, key, default='', astype='auto'):\\n if hasattr(self, '_config') and self._config.length() > 0:\\n out = self._config.curve(0).getAttribute(key, default=default)\\n if astype in [str, 'str']:\\n return str(out)\\n else:\\n return strToVar(out)\\n return default\",\n \"def initDefaults(self):\\n return _libsbml.Curve_initDefaults(self)\",\n \"def get_chart_configuration(self):\\n return \\\"Noo\\\"\",\n \"def get_default_config(self):\\n config = super(DiskSpaceCollector, self).get_default_config()\\n config.update({\\n 'path': 'diskspace',\\n # filesystems to examine\\n 'filesystems': 'ext2, ext3, ext4, xfs, glusterfs, nfs, nfs4, ' +\\n ' ntfs, hfs, fat32, fat16, btrfs',\\n\\n # exclude_filters\\n # A list of regex patterns\\n # A filesystem matching any of these patterns will be excluded\\n # from disk space metrics collection.\\n #\\n # Examples:\\n # exclude_filters =,\\n # no exclude filters at all\\n # exclude_filters = ^/boot, ^/mnt\\n # exclude everything that begins /boot or /mnt\\n # exclude_filters = m,\\n # exclude everything that includes the letter \\\"m\\\"\\n 'exclude_filters': ['^/export/home'],\\n\\n # Default numeric output\\n 'byte_unit': ['byte']\\n })\\n return config\",\n \"def get_default_config(self):\\n config = super(NvidiaGPUCollector, self).get_default_config()\\n config.update({\\n 'path': 'nvidia',\\n 'bin': '/usr/bin/nvidia-smi',\\n 'stats': [\\n 'index',\\n 'memory.total',\\n 'memory.used',\\n 'memory.free',\\n 'utilization.gpu',\\n 'utilization.memory',\\n 'temperature.gpu'\\n ]\\n })\\n return config\",\n \"def default_setting(self):\\n\\t\\tunknown_as_zero = False\\n\\t\\tbinary_rele = False # using the original values\\n\\t\\tpresort = False # a default setting\\n\\n\\t\\tscale_data, scaler_id, scaler_level = get_default_scaler_setting(data_id=self.data_id)\\n\\n\\t\\t# more data settings that are rarely changed\\n\\t\\tself.data_dict = dict(data_id=self.data_id, dir_data=self.dir_data, min_docs=10, min_rele=1,\\n\\t\\t\\t\\t\\t\\t sample_rankings_per_q=1, unknown_as_zero=unknown_as_zero, binary_rele=binary_rele,\\n\\t\\t\\t\\t\\t\\t presort=presort, scale_data=scale_data, scaler_id=scaler_id, scaler_level=scaler_level)\\n\\n\\t\\tdata_meta = get_data_meta(data_id=self.data_id) # add meta-information\\n\\t\\tself.data_dict.update(data_meta)\\n\\n\\t\\treturn self.data_dict\",\n \"def get_config(self):\\n return {'name': self.name, 'dtype': self.dtype}\",\n \"def get_config(self):\\n return super().get_config()\",\n \"def _set_default_eai_quantizer_state(self):\\n\\n default_group_quantizer_state = {\\n 'hidden_l{}': True,\\n 'cell_l{}': False,\\n 'bias_l{}': False,\\n 'W_l{}': True,\\n 'R_l{}': True\\n }\\n\\n default_group_quantizer_state = {name.format(layer): state\\n for layer in range(self.num_layers)\\n for name, state in default_group_quantizer_state.items()\\n }\\n\\n tensor_names_with_grouped_quantizers = [name.format(layer)\\n for layer in range(self.num_layers)\\n for tensor_names in grouped_quantizer_map.values()\\n for name in tensor_names\\n ]\\n\\n for name, quantizer in self._grouped_quantizers.items():\\n quantizer.enabled = default_group_quantizer_state[name]\\n\\n for name, quantizer in self._output_quantizers.items():\\n if name not in tensor_names_with_grouped_quantizers:\\n quantizer.enabled = False\\n\\n for name, quantizer in self._param_quantizers.items():\\n if name not in tensor_names_with_grouped_quantizers:\\n quantizer.enabled = True\\n\\n for name, quantizer in self._input_quantizers.items():\\n if name not in tensor_names_with_grouped_quantizers:\\n quantizer.enabled = True\",\n \"def getDefaultSettings():\\n return {}\",\n \"def get_quantization_capability(self):\\n return self.cur_config['capabilities']\",\n \"def get_config(self):\\n config = {'epsilon':self.eps}\\n base_config = super(LogTransform, self).get_config()\\n return dict(list(base_config.items()) + list(config.items()))\",\n \"def default_kernel_config(defn):\\n return [('beam', {}),\\n ('hypers',\\n {\\n 'alpha_a': 4.0,\\n 'alpha_b': 2.0,\\n 'gamma_a': 3.0, \\n 'gamma_b': 6.0\\n }\\n )]\",\n \"def cg_config():\\n return {}\",\n \"def get_cfg_defaults():\\r\\n # Return a clone so that the defaults will not be altered\\r\\n # This is for the \\\"local variable\\\" use pattern\\r\\n return _C.clone()\",\n \"def get_default_config():\\r\\n config = {}\\r\\n\\r\\n config[\\\"kl_coeff\\\"] = 1.0\\r\\n config[\\\"_num_workers_tf\\\"] = 4\\r\\n config[\\\"use_gae\\\"] = True\\r\\n config[\\\"num_gpus\\\"] = 0\\r\\n\\r\\n config[\\\"_env_name_rllib\\\"] = \\\"multicomp\\\"\\r\\n config[\\\"_env_fcn\\\"] = create_env\\r\\n config['_policies'] = [None, \\\"from_scratch\\\", \\\"pretrained\\\"]\\r\\n config[\\\"_env\\\"] = {'with_video': False,\\r\\n \\\"SingleAgentToMultiAgent\\\": False,\\r\\n \\\"env_name\\\": \\\"multicomp/YouShallNotPassHumans-v0\\\"}\\r\\n config['framework'] = 'tfe'\\r\\n\\r\\n config['_train_policies'] = ['player_1']\\r\\n config['_call'] = {}\\r\\n config['_trainer'] = \\\"PPO\\\"\\r\\n config['_policy'] = \\\"PPO\\\"\\r\\n config['_call']['checkpoint_freq'] = 0\\r\\n config['_train_steps'] = 99999999\\r\\n config['_update_config'] = None\\r\\n config['_run_inline'] = False\\r\\n config['_postprocess'] = None\\r\\n\\r\\n config['num_envs_per_worker'] = 4\\r\\n config['_log_error'] = True\\r\\n config['_model_params'] = {\\r\\n \\\"use_lstm\\\": False,\\r\\n \\\"fcnet_hiddens\\\": [64, 64],\\r\\n # \\\"custom_action_dist\\\": \\\"DiagGaussian\\\",\\r\\n \\\"fcnet_activation\\\": \\\"tanh\\\",\\r\\n \\\"free_log_std\\\": True,\\r\\n }\\r\\n\\r\\n config['_select_policy'] = select_policy_default\\r\\n config['_get_policies'] = get_policies_default\\r\\n config['_do_not_train_policies'] = []\\r\\n config['_update_withpolicies'] = None\\r\\n config['callbacks'] = InfoCallbacks\\r\\n\\r\\n return config\",\n \"def get_default_config(self):\\n return config.read(pathlib.Path(__file__).parent / \\\"ext.conf\\\")\",\n \"def get_cfg_defaults():\\n # Return a clone so that the defaults will not be altered\\n # This is for the \\\"local variable\\\" use pattern\\n return C.clone()\",\n \"def get_default_config(self):\\n config = super(DropwizardCollector, self).get_default_config()\\n config.update({\\n 'url': DEFAULT_METRICS_URL,\\n 'path': 'dropwizard'\\n })\\n return config\",\n \"def get_config(self, budget):\\n\\t\\traise NotImplementedError('This function needs to be overwritten in %s.'%(self.__class__.__name__))\",\n \"def default_config(cls) -> dict:\\n return {\\n \\\"observation\\\": {\\n \\\"type\\\": \\\"Kinematics\\\"\\n },\\n \\\"action\\\": {\\n \\\"type\\\": \\\"DiscreteMetaAction\\\"\\n },\\n \\\"simulation_frequency\\\": 15, # [Hz]\\n \\\"policy_frequency\\\": 1, # [Hz]\\n \\\"other_vehicles_type\\\": \\\"highway_env.vehicle.behavior.IDMVehicle\\\",\\n \\\"screen_width\\\": 600, # [px]\\n \\\"screen_height\\\": 150, # [px]\\n \\\"centering_position\\\": [0.3, 0.5],\\n \\\"scaling\\\": 5.5,\\n \\\"show_trajectories\\\": False,\\n \\\"render_agent\\\": True,\\n \\\"offscreen_rendering\\\": os.environ.get(\\\"OFFSCREEN_RENDERING\\\", \\\"0\\\") == \\\"1\\\",\\n \\\"manual_control\\\": False,\\n \\\"real_time_rendering\\\": False\\n }\",\n \"def _get_default_options():\\n return {\\n \\\"library_folders\\\": [],\\n \\\"verbose\\\": False,\\n \\\"check_balanced\\\": True,\\n \\\"mtime_check\\\": True,\\n \\\"cache\\\": False,\\n \\\"codegen\\\": False,\\n \\\"expand_mx\\\": False,\\n \\\"unroll_loops\\\": True,\\n \\\"inline_functions\\\": True,\\n \\\"expand_vectors\\\": False,\\n \\\"resolve_parameter_values\\\": False,\\n \\\"replace_parameter_expressions\\\": False,\\n \\\"replace_constant_expressions\\\": False,\\n \\\"eliminate_constant_assignments\\\": False,\\n \\\"replace_parameter_values\\\": False,\\n \\\"replace_constant_values\\\": False,\\n \\\"eliminable_variable_expression\\\": None,\\n \\\"factor_and_simplify_equations\\\": False,\\n \\\"detect_aliases\\\": False,\\n \\\"allow_derivative_aliases\\\": True,\\n \\\"reduce_affine_expression\\\": False,\\n }\",\n \"def get_default_evaluator(self) -> EvaluatorConfig:\\n raise NotImplementedError()\",\n \"def get_cfg_defaults():\\n # Return a clone so that the defaults will not be altered\\n # This is for the \\\"local variable\\\" use pattern\\n return _C.clone()\",\n \"def get_cfg_defaults():\\n # Return a clone so that the defaults will not be altered\\n # This is for the \\\"local variable\\\" use pattern\\n return _C.clone()\",\n \"def get_cfg_defaults():\\n # Return a clone so that the defaults will not be altered\\n # This is for the \\\"local variable\\\" use pattern\\n return _C.clone()\",\n \"def get_cfg_defaults():\\n # Return a clone so that the defaults will not be altered\\n # This is for the \\\"local variable\\\" use pattern\\n return _C.clone()\",\n \"def get_default_config(self):\\n config = super(DiskHealthCollector, self).get_default_config()\\n config.update({\\n 'enabled': 'True',\\n 'devices': ('PhysicalDrive[0-9]+$'\\n + '|md[0-9]+$'\\n + '|sd[a-z]+[0-9]*$'\\n + '|x?vd[a-z]+[0-9]*$'\\n + '|disk[0-9]+$'\\n + '|dm\\\\-[0-9]+$'),\\n 'fs_types': ','.join(self.SUPPORTED_FS_TYPES),\\n 'raw_stats_only': False,\\n 'test_file_name': self.TEST_FILE_NAME,\\n 'test_file_size': self.TEST_FILE_SIZE\\n })\\n return config\",\n \"def default_setting():\\n total_count.set(default_len)\\n training_rate_clicked.set('50%')\\n dimension_clicked.set('100 x 100')\",\n \"def initDefaults(self):\\n return _libsbml.CubicBezier_initDefaults(self)\",\n \"def get_base_config():\\n return dict(\\n dim=768,\\n ff_dim=3072,\\n num_heads=12,\\n num_layers=12,\\n attention_dropout_rate=0.0,\\n dropout_rate=0.1,\\n representation_size=768,\\n classifier='token'\\n )\",\n \"def getDefault():\",\n \"def use_config_notify_stock_qty(self):\\n return self._use_config_notify_stock_qty\",\n \"def get_config(self):\\n config = {\\n \\\"units\\\": self.units,\\n \\\"activation\\\": activations.serialize(self.activation),\\n \\\"recurrent_activation\\\": activations.serialize(\\n self.recurrent_activation\\n ),\\n \\\"attention_activation\\\": activations.serialize(\\n self.attention_activation\\n ),\\n \\\"use_bias\\\": self.use_bias,\\n \\\"kernel_initializer\\\": initializers.serialize(self.kernel_initializer),\\n \\\"recurrent_initializer\\\": initializers.serialize(\\n self.recurrent_initializer\\n ),\\n \\\"bias_initializer\\\": initializers.serialize(self.bias_initializer),\\n \\\"attention_initializer\\\": initializers.serialize(\\n self.attention_initializer\\n ),\\n \\\"use_chrono_initialization\\\": self.unit_forget_bias,\\n \\\"kernel_regularizer\\\": regularizers.serialize(self.kernel_regularizer),\\n \\\"recurrent_regularizer\\\": regularizers.serialize(\\n self.recurrent_regularizer\\n ),\\n \\\"bias_regularizer\\\": regularizers.serialize(self.bias_regularizer),\\n \\\"activity_regularizer\\\": regularizers.serialize(\\n self.activity_regularizer\\n ),\\n \\\"attention_regularizer\\\": regularizers.serialize(\\n self.attention_regularizer\\n ),\\n \\\"kernel_constraint\\\": constraints.serialize(self.kernel_constraint),\\n \\\"recurrent_constraint\\\": constraints.serialize(\\n self.recurrent_constraint\\n ),\\n \\\"bias_constraint\\\": constraints.serialize(self.bias_constraint),\\n \\\"attention_constraint\\\": constraints.serialize(\\n self.attention_constraint\\n ),\\n \\\"dropout\\\": self.dropout,\\n \\\"recurrent_dropout\\\": self.recurrent_dropout,\\n \\\"return_attention\\\": self.return_attention,\\n }\\n base_config = super().get_config()\\n del base_config[\\\"cell\\\"]\\n return dict(list(base_config.items()) + list(config.items()))\",\n \"def default_config(self) -> Optional['outputs.FeatureSpecFleetobservabilityLoggingConfigDefaultConfig']:\\n return pulumi.get(self, \\\"default_config\\\")\",\n \"def GetDefaultGTSimulationConfigs():\\n # Simulation configurations\\n simulation = {}\\n simulation['epochs'] = 100\\n simulation['alpha_mu_interval'] = 200\\n simulation['resultfolder'] = './results/' # With trailing slash\\n simulation['rand_ic'] = False\\n simulation['alpha_mu_chunk_size'] = 16\\n simulation['epochs_chunk_size'] = 100\\n simulation['integer_sensitivity'] = 10000\\n\\n # If True, this parameter defines homogeneous initial condition or not.\\n # Default is True with c0=300, g0=40, p0=10\\n simulation['using_c0_g0'] = True\\n simulation['c0'] = 300\\n simulation['g0'] = 40\\n simulation['p0'] = 10\\n\\n # If True, this parameter saves only in hdf5 file some chunk id\\n # Default is False. If True, define saved_chunkids\\n simulation['selectchunk'] = False\\n #simulation['saved_chunkids'] = {1, 2}\\n\\n # Define the number of processors on multicore processor\\n simulation['n_processors'] = 1\\n \\n return simulation\",\n \"def default_parameters():\\n prm = Parameters('windkessel_model')\\n\\n prm.add('total_volume', float())\\n\\n prm.add('venous_compliance', float())\\n prm.add('arterial_compliance', float())\\n\\n prm.add('venous_resistance', float())\\n prm.add('arterial_resistance', float())\\n prm.add('peripheral_resistance', float())\\n\\n prm.add('venous_resting_volume', float())\\n prm.add('arterial_resting_volume', float())\\n\\n return prm\",\n \"def _config_min(self):\\n self.cntrl[\\\"imin\\\"] = 1\\n self.cntrl[\\\"ntx\\\"] = 1\\n self.cntrl[\\\"irest\\\"] = 0\\n self.cntrl[\\\"maxcyc\\\"] = 5000\\n self.cntrl[\\\"ncyc\\\"] = 1000\\n self.cntrl[\\\"dt\\\"] = 0.0\\n self.cntrl[\\\"nstlim\\\"] = 0\\n self.cntrl[\\\"ntpr\\\"] = 100\\n self.cntrl[\\\"ntwr\\\"] = 5000\\n self.cntrl[\\\"ntwx\\\"] = 0\\n self.cntrl[\\\"ntwe\\\"] = 0\\n self.cntrl[\\\"ntxo\\\"] = 1\\n self.cntrl[\\\"ntf\\\"] = 1\\n self.cntrl[\\\"ntc\\\"] = 1\\n self.cntrl[\\\"ntt\\\"] = 0\\n self.cntrl[\\\"gamma_ln\\\"] = 0.0\\n self.cntrl[\\\"ig\\\"] = 0\\n self.cntrl[\\\"ntp\\\"] = 0\\n self.cntrl[\\\"barostat\\\"] = 0\\n self.mdcrd = None\\n self.mden = None\",\n \"def settings(self) -> Dict[str, Any]:\\n if self.num_pixels is not None:\\n return dict(num_pixels=self.num_pixels)\\n return dict(quantile=self.quantile)\",\n \"def get_default_config(cls):\\n default = super(LSHNearestNeighborIndex, cls).get_default_config()\\n\\n lf_default = plugin.make_config(get_lsh_functor_impls())\\n default['lsh_functor'] = lf_default\\n\\n di_default = plugin.make_config(get_descriptor_index_impls())\\n default['descriptor_index'] = di_default\\n\\n hi_default = plugin.make_config(get_hash_index_impls())\\n default['hash_index'] = hi_default\\n default['hash_index_comment'] = \\\"'hash_index' may also be null to \\\" \\\\\\n \\\"default to a linear index built at \\\" \\\\\\n \\\"query time.\\\"\\n\\n h2u_default = plugin.make_config(get_key_value_store_impls())\\n default['hash2uuids_kvstore'] = h2u_default\\n\\n return default\",\n \"def get_default_config(self):\\n config = super(SlurmJobWasteCollector, self).get_default_config()\\n config.update({\\n 'path': 'waste'\\n })\\n return config\",\n \"def requested_config_vals():\\n return {} # no extra values needed\",\n \"def default_params():\\n params = {}\\n params['dataset'] = 'adult'\\n params['engines'] = ['MD','RDA']\\n params['iters'] = 10000\\n params['epsilon'] = 1.0\\n params['delta'] = 0.0\\n params['bounded'] = True\\n params['frequency'] = 1\\n params['seed'] = 0\\n params['save'] = None\\n params['load'] = None\\n params['plot'] = None\\n\\n return params\",\n \"def get_default_config():\\n return _config_schema_to_namespace(_CONFIG_SCHEMA)\",\n \"def default_options(cls) -> Dict:\\n return {}\",\n \"def test_getfloat_with_default(self):\\n self.assertEqual(self.config.getfloat('advanced','p'),None)\\n self.assertEqual(self.config.getfloat('advanced','p',5.0),5.0)\",\n \"def requested_config_vals():\\n return {'transfer_stats_per_file':'opt'}\",\n \"def _default_parameters():\\n\\n return {\\n 'opt': 'adadelta',\\n 'activation_function': 'softmax',\\n 'lr': 0.0001,\\n 'decay': 1e-6,\\n 'loss': 'categorical_crossentropy',\\n 'batch_size': 32,\\n 'nb_epoch': 20,\\n 'shuffle': True,\\n 'momentum': 0.9,\\n 'nesterov': True,\\n 'rho': 0.95,\\n 'epsilon': 1e-08,\\n 'beta_1': 0.9,\\n 'beta_2': 0.999,\\n 'horizontal_flip': False,\\n 'im_size': 240,#256,\\n 'dense_layer': 1024,\\n 'nb_classes': 10,\\n 'nb_channels': 3,\\n 'dropout': 0.5,\\n 'metrics': ['accuracy'],\\n 'volume': None,\\n 'input_size': 25,\\n 'temporal': False,\\n 'input_dim': 512,\\n 'nb_frames': 60,\\n 'stride': 16,\\n 'nb_hidden':512,\\n 'lstm': False\\n\\n }\",\n \"def __init__(self):\\n super(ASYMMETRIC, self).__init__(quant_type=Constants.QZ_ASYMMETRIC)\",\n \"def get_default_config(self):\\n config = super(EndecaDgraphCollector, self).get_default_config()\\n config.update({\\n 'path': 'endeca.dgraph',\\n 'host': 'localhost',\\n 'port': 8080,\\n 'timeout': 1,\\n })\\n return config\",\n \"def default_units(x, axis):\\n return getattr(x, 'units', None)\",\n \"def op_config(self) -> Any:\\n return self.solid_config\",\n \"def get_defaults(cls, mode):\\n mode_defaults = getattr(cls, f\\\"{mode.upper()}_DEFAULTS\\\")\\n defaults = PlotConfig({**cls.COMMON_DEFAULTS, **mode_defaults})\\n return defaults\",\n \"def get_defaults(cls, mode):\\n mode_defaults = getattr(cls, f\\\"{mode.upper()}_DEFAULTS\\\")\\n defaults = PlotConfig({**cls.COMMON_DEFAULTS, **mode_defaults})\\n return defaults\",\n \"def getInitDefault(self):\\n return self.initDefault\",\n \"def get_config(self):\\n config = {\\n }\\n base_config = super(MatrixConcat, self).get_config()\\n return dict(list(base_config.items()) + list(config.items()))\",\n \"def default_per_ocpu_in_gbps(self):\\n return self._default_per_ocpu_in_gbps\",\n \"def bootstrap_default():\\n\\treturn default_configuration\",\n \"def get_default_config() -> Dict[Text, Any]:\\n # please make sure to update the docs when changing a default parameter\\n return {\\n # ## Architecture of the used neural network\\n # Hidden layer sizes for layers before the embedding layers for user message\\n # and labels.\\n # The number of hidden layers is equal to the length of the corresponding\\n # list.\\n HIDDEN_LAYERS_SIZES: {\\n TEXT: [],\\n ACTION_TEXT: [],\\n f\\\"{LABEL}_{ACTION_TEXT}\\\": [],\\n },\\n # Dense dimension to use for sparse features.\\n DENSE_DIMENSION: {\\n TEXT: 128,\\n ACTION_TEXT: 128,\\n f\\\"{LABEL}_{ACTION_TEXT}\\\": 128,\\n INTENT: 20,\\n ACTION_NAME: 20,\\n f\\\"{LABEL}_{ACTION_NAME}\\\": 20,\\n ENTITIES: 20,\\n SLOTS: 20,\\n ACTIVE_LOOP: 20,\\n },\\n # Default dimension to use for concatenating sequence and sentence features.\\n CONCAT_DIMENSION: {\\n TEXT: 128,\\n ACTION_TEXT: 128,\\n f\\\"{LABEL}_{ACTION_TEXT}\\\": 128,\\n },\\n # Dimension size of embedding vectors before the dialogue transformer\\n # encoder.\\n ENCODING_DIMENSION: 50,\\n # Number of units in transformer encoders\\n TRANSFORMER_SIZE: {\\n TEXT: 128,\\n ACTION_TEXT: 128,\\n f\\\"{LABEL}_{ACTION_TEXT}\\\": 128,\\n DIALOGUE: 128,\\n },\\n # Number of layers in transformer encoders\\n NUM_TRANSFORMER_LAYERS: {\\n TEXT: 1,\\n ACTION_TEXT: 1,\\n f\\\"{LABEL}_{ACTION_TEXT}\\\": 1,\\n DIALOGUE: 1,\\n },\\n # Number of attention heads in transformer\\n NUM_HEADS: 4,\\n # If 'True' use key relative embeddings in attention\\n KEY_RELATIVE_ATTENTION: False,\\n # If 'True' use value relative embeddings in attention\\n VALUE_RELATIVE_ATTENTION: False,\\n # Max position for relative embeddings. Only in effect if key- or value\\n # relative\\n # attention are turned on\\n MAX_RELATIVE_POSITION: 5,\\n # Use a unidirectional or bidirectional encoder\\n # for `text`, `action_text`, and `label_action_text`.\\n UNIDIRECTIONAL_ENCODER: False,\\n # ## Training parameters\\n # Initial and final batch sizes:\\n # Batch size will be linearly increased for each epoch.\\n BATCH_SIZES: [64, 256],\\n # Strategy used whenc creating batches.\\n # Can be either 'sequence' or 'balanced'.\\n BATCH_STRATEGY: BALANCED,\\n # Number of epochs to train\\n EPOCHS: 1,\\n # Set random seed to any 'int' to get reproducible results\\n RANDOM_SEED: None,\\n # Initial learning rate for the optimizer\\n LEARNING_RATE: 0.001,\\n # ## Parameters for embeddings\\n # Dimension size of embedding vectors\\n EMBEDDING_DIMENSION: 20,\\n # The number of incorrect labels. The algorithm will minimize\\n # their similarity to the user input during training.\\n NUM_NEG: 20,\\n # Type of similarity measure to use, either 'auto' or 'cosine' or 'inner'.\\n SIMILARITY_TYPE: AUTO,\\n # The type of the loss function, either 'cross_entropy' or 'margin'.\\n LOSS_TYPE: CROSS_ENTROPY,\\n # Number of top actions for which confidences should be predicted.\\n # The number of Set to `0` if confidences for all actions should be\\n # predicted. The confidences for all other actions will be set to 0.\\n RANKING_LENGTH: 0,\\n # Determines wether the confidences of the chosen top actions should be\\n # renormalized so that they sum up to 1. By default, we do not renormalize\\n # and return the confidences for the top actions as is.\\n # Note that renormalization only makes sense if confidences are generated\\n # via `softmax`.\\n RENORMALIZE_CONFIDENCES: False,\\n # Indicates how similar the algorithm should try to make embedding vectors\\n # for correct labels.\\n # Should be 0.0 < ... < 1.0 for 'cosine' similarity type.\\n MAX_POS_SIM: 0.8,\\n # Maximum negative similarity for incorrect labels.\\n # Should be -1.0 < ... < 1.0 for 'cosine' similarity type.\\n MAX_NEG_SIM: -0.2,\\n # If 'True' the algorithm only minimizes maximum similarity over\\n # incorrect intent labels, used only if 'loss_type' is set to 'margin'.\\n USE_MAX_NEG_SIM: True,\\n # If 'True' scale loss inverse proportionally to the confidence\\n # of the correct prediction\\n SCALE_LOSS: True,\\n # ## Regularization parameters\\n # The scale of regularization\\n REGULARIZATION_CONSTANT: 0.001,\\n # The scale of how important is to minimize the maximum similarity\\n # between embeddings of different labels,\\n # used only if 'loss_type' is set to 'margin'.\\n NEGATIVE_MARGIN_SCALE: 0.8,\\n # Dropout rate for embedding layers of dialogue features.\\n DROP_RATE_DIALOGUE: 0.1,\\n # Dropout rate for embedding layers of utterance level features.\\n DROP_RATE: 0.0,\\n # Dropout rate for embedding layers of label, e.g. action, features.\\n DROP_RATE_LABEL: 0.0,\\n # Dropout rate for attention.\\n DROP_RATE_ATTENTION: 0.0,\\n # Fraction of trainable weights in internal layers.\\n CONNECTION_DENSITY: 0.2,\\n # If 'True' apply dropout to sparse input tensors\\n SPARSE_INPUT_DROPOUT: True,\\n # If 'True' apply dropout to dense input tensors\\n DENSE_INPUT_DROPOUT: True,\\n # If 'True' random tokens of the input message will be masked. Since there\\n # is no related loss term used inside TED, the masking effectively becomes\\n # just input dropout applied to the text of user utterances.\\n MASKED_LM: False,\\n # ## Evaluation parameters\\n # How often calculate validation accuracy.\\n # Small values may hurt performance.\\n EVAL_NUM_EPOCHS: 20,\\n # How many examples to use for hold out validation set\\n # Large values may hurt performance, e.g. model accuracy.\\n # Set to 0 for no validation.\\n EVAL_NUM_EXAMPLES: 0,\\n # If you want to use tensorboard to visualize training and validation\\n # metrics, set this option to a valid output directory.\\n TENSORBOARD_LOG_DIR: None,\\n # Define when training metrics for tensorboard should be logged.\\n # Either after every epoch or for every training step.\\n # Valid values: 'epoch' and 'batch'\\n TENSORBOARD_LOG_LEVEL: \\\"epoch\\\",\\n # Perform model checkpointing\\n CHECKPOINT_MODEL: False,\\n # Only pick e2e prediction if the policy is confident enough\\n E2E_CONFIDENCE_THRESHOLD: 0.5,\\n # Specify what features to use as sequence and sentence features.\\n # By default all features in the pipeline are used.\\n FEATURIZERS: [],\\n # If set to true, entities are predicted in user utterances.\\n ENTITY_RECOGNITION: True,\\n # if 'True' applies sigmoid on all similarity terms and adds\\n # it to the loss function to ensure that similarity values are\\n # approximately bounded. Used inside cross-entropy loss only.\\n CONSTRAIN_SIMILARITIES: False,\\n # Model confidence to be returned during inference. Currently, the only\\n # possible value is `softmax`.\\n MODEL_CONFIDENCE: SOFTMAX,\\n # 'BILOU_flag' determines whether to use BILOU tagging or not.\\n # If set to 'True' labelling is more rigorous, however more\\n # examples per entity are required.\\n # Rule of thumb: you should have more than 100 examples per entity.\\n BILOU_FLAG: True,\\n # Split entities by comma, this makes sense e.g. for a list of\\n # ingredients in a recipe, but it doesn't make sense for the parts of\\n # an address\\n SPLIT_ENTITIES_BY_COMMA: SPLIT_ENTITIES_BY_COMMA_DEFAULT_VALUE,\\n # Max history of the policy, unbounded by default\\n POLICY_MAX_HISTORY: DEFAULT_MAX_HISTORY,\\n # Determines the importance of policies, higher values take precedence\\n POLICY_PRIORITY: DEFAULT_POLICY_PRIORITY,\\n USE_GPU: True,\\n }\",\n \"def get_config_defaults(self): # pylint: disable=R0201\\n return {}\",\n \"def get_default_dataset_config():\\n code_path = utils.get_code_path()\\n default_config = {\\n 'GT_FOLDER': os.path.join(code_path, 'data/gt/kitti/kitti_mots_val'), # Location of GT data\\n 'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/kitti/kitti_mots_val'), # Trackers location\\n 'OUTPUT_FOLDER': None, # Where to save eval results (if None, same as TRACKERS_FOLDER)\\n 'TRACKERS_TO_EVAL': None, # Filenames of trackers to eval (if None, all in folder)\\n 'CLASSES_TO_EVAL': ['car', 'pedestrian'], # Valid: ['car', 'pedestrian']\\n 'SPLIT_TO_EVAL': 'val', # Valid: 'training', 'val'\\n 'INPUT_AS_ZIP': False, # Whether tracker input files are zipped\\n 'PRINT_CONFIG': True, # Whether to print current config\\n 'TRACKER_SUB_FOLDER': 'data', # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\\n 'OUTPUT_SUB_FOLDER': '', # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\\n 'TRACKER_DISPLAY_NAMES': None, # Names of trackers to display, if None: TRACKERS_TO_EVAL\\n 'SEQMAP_FOLDER': None, # Where seqmaps are found (if None, GT_FOLDER)\\n 'SEQMAP_FILE': None, # Directly specify seqmap file (if none use seqmap_folder/split_to_eval.seqmap)\\n 'SEQ_INFO': None, # If not None, directly specify sequences to eval and their number of timesteps\\n 'GT_LOC_FORMAT': '{gt_folder}/label_02/{seq}.txt', # format of gt localization\\n }\\n return default_config\",\n \"def _create_defaults(self):\\n return DefaultCommandOptionValues(\\n min_confidence=3, output_format='vs7')\",\n \"def default(self):\\n return self._configs[0] if len(self._configs) else None\",\n \"def getSetting():\\n return \\\"### dataPreparation Setting:\\\\n\\\\n\\\" + \\\"\\\\n\\\".join([\\\"%s : %s\\\"%(k,str(v)) for k,v in\\n [(\\\"**DATASET_MIN_MAX_NORMALIZE**\\\",DATASET_MIN_MAX_NORMALIZE)]])\",\n \"def default_metric_value(self) -> float:\",\n \"def get_default(cls):\\n return cls.ALL\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7033103","0.6777367","0.6720709","0.65748835","0.6455274","0.642663","0.63270366","0.63183445","0.63130504","0.6306793","0.62361944","0.6232875","0.621485","0.6185362","0.6169694","0.6101184","0.6091094","0.6084804","0.60460657","0.59970856","0.5981526","0.5975689","0.5931584","0.5931163","0.5893432","0.5892437","0.58827627","0.58786976","0.5866956","0.5846194","0.58451027","0.5823841","0.58093894","0.5808784","0.5797929","0.5795595","0.57879925","0.57715654","0.57600653","0.5757205","0.57344544","0.5709438","0.5703094","0.5694869","0.569431","0.5685212","0.5681103","0.56778055","0.566895","0.564938","0.56327766","0.5627428","0.5625745","0.5625473","0.5623148","0.5604648","0.5602504","0.5600988","0.5600988","0.5600431","0.5600431","0.5577911","0.55765986","0.55687517","0.5567695","0.55606437","0.55558634","0.55530334","0.5551925","0.5548127","0.55425036","0.5533408","0.5531301","0.5527983","0.55035233","0.5500908","0.5500246","0.5495725","0.5495375","0.5479055","0.54756945","0.5470229","0.54657924","0.5461519","0.5460041","0.54595685","0.54564446","0.54564446","0.54465103","0.5441641","0.54405546","0.5439014","0.5438638","0.5431992","0.5424737","0.54208964","0.5414686","0.54135007","0.5412363","0.54094344"],"string":"[\n \"0.7033103\",\n \"0.6777367\",\n \"0.6720709\",\n \"0.65748835\",\n \"0.6455274\",\n \"0.642663\",\n \"0.63270366\",\n \"0.63183445\",\n \"0.63130504\",\n \"0.6306793\",\n \"0.62361944\",\n \"0.6232875\",\n \"0.621485\",\n \"0.6185362\",\n \"0.6169694\",\n \"0.6101184\",\n \"0.6091094\",\n \"0.6084804\",\n \"0.60460657\",\n \"0.59970856\",\n \"0.5981526\",\n \"0.5975689\",\n \"0.5931584\",\n \"0.5931163\",\n \"0.5893432\",\n \"0.5892437\",\n \"0.58827627\",\n \"0.58786976\",\n \"0.5866956\",\n \"0.5846194\",\n \"0.58451027\",\n \"0.5823841\",\n \"0.58093894\",\n \"0.5808784\",\n \"0.5797929\",\n \"0.5795595\",\n \"0.57879925\",\n \"0.57715654\",\n \"0.57600653\",\n \"0.5757205\",\n \"0.57344544\",\n \"0.5709438\",\n \"0.5703094\",\n \"0.5694869\",\n \"0.569431\",\n \"0.5685212\",\n \"0.5681103\",\n \"0.56778055\",\n \"0.566895\",\n \"0.564938\",\n \"0.56327766\",\n \"0.5627428\",\n \"0.5625745\",\n \"0.5625473\",\n \"0.5623148\",\n \"0.5604648\",\n \"0.5602504\",\n \"0.5600988\",\n \"0.5600988\",\n \"0.5600431\",\n \"0.5600431\",\n \"0.5577911\",\n \"0.55765986\",\n \"0.55687517\",\n \"0.5567695\",\n \"0.55606437\",\n \"0.55558634\",\n \"0.55530334\",\n \"0.5551925\",\n \"0.5548127\",\n \"0.55425036\",\n \"0.5533408\",\n \"0.5531301\",\n \"0.5527983\",\n \"0.55035233\",\n \"0.5500908\",\n \"0.5500246\",\n \"0.5495725\",\n \"0.5495375\",\n \"0.5479055\",\n \"0.54756945\",\n \"0.5470229\",\n \"0.54657924\",\n \"0.5461519\",\n \"0.5460041\",\n \"0.54595685\",\n \"0.54564446\",\n \"0.54564446\",\n \"0.54465103\",\n \"0.5441641\",\n \"0.54405546\",\n \"0.5439014\",\n \"0.5438638\",\n \"0.5431992\",\n \"0.5424737\",\n \"0.54208964\",\n \"0.5414686\",\n \"0.54135007\",\n \"0.5412363\",\n \"0.54094344\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8306506"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":20088,"cells":{"query":{"kind":"string","value":"Creates the NNCFConfig for the quantization algorithm."},"document":{"kind":"string","value":"def _create_nncf_config(\n preset: QuantizationPreset,\n target_device: TargetDevice,\n subset_size: int,\n model_type: Optional[ModelType],\n ignored_scope: Optional[IgnoredScope],\n advanced_parameters: Optional[AdvancedQuantizationParameters],\n) -> NNCFConfig:\n if model_type is None:\n compression_config = _get_default_quantization_config(preset, subset_size)\n elif model_type == ModelType.TRANSFORMER:\n compression_config = _get_transformer_quantization_config(subset_size)\n\n if ignored_scope is not None:\n _ignored_scope = convert_ignored_scope_to_list(ignored_scope)\n if \"ignored_scopes\" in compression_config:\n compression_config[\"ignored_scopes\"].extend(_ignored_scope)\n else:\n compression_config[\"ignored_scopes\"] = _ignored_scope\n compression_config[\"validate_scopes\"] = ignored_scope.validate\n\n if advanced_parameters is not None:\n compression_config = apply_advanced_parameters_to_config(compression_config, advanced_parameters)\n\n if model_type == ModelType.TRANSFORMER:\n compression_config[\"validate_scopes\"] = False\n\n return NNCFConfig({\"target_device\": target_device.value, \"compression\": compression_config})"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __init__(self, quantized_edges_in_cfg: int, total_edges_in_cfg: int):\n self.quantized_edges_in_cfg = quantized_edges_in_cfg\n self.total_edges_in_cfg = total_edges_in_cfg","def _add_fp_configs(CONFIG):\n CONFIG.declare(\n 'fp_cutoffdecr',\n ConfigValue(\n default=1e-1,\n domain=PositiveFloat,\n description='Additional relative decrement of cutoff value for the original objective function.',\n ),\n )\n CONFIG.declare(\n 'fp_iteration_limit',\n ConfigValue(\n default=20,\n domain=PositiveInt,\n description='Feasibility pump iteration limit',\n doc='Number of maximum iterations in the feasibility pump methods.',\n ),\n )\n # TODO: integrate this option\n CONFIG.declare(\n 'fp_projcuts',\n ConfigValue(\n default=True,\n description='Whether to add cut derived from regularization of MIP solution onto NLP feasible set.',\n domain=bool,\n ),\n )\n CONFIG.declare(\n 'fp_transfercuts',\n ConfigValue(\n default=True,\n description='Whether to transfer cuts from the Feasibility Pump MIP to main MIP in selected strategy (all except from the round in which the FP MIP became infeasible).',\n domain=bool,\n ),\n )\n CONFIG.declare(\n 'fp_projzerotol',\n ConfigValue(\n default=1e-4,\n domain=PositiveFloat,\n description='Tolerance on when to consider optimal value of regularization problem as zero, which may trigger the solution of a Sub-NLP.',\n ),\n )\n CONFIG.declare(\n 'fp_mipgap',\n ConfigValue(\n default=1e-2,\n domain=PositiveFloat,\n description='Optimality tolerance (relative gap) to use for solving MIP regularization problem.',\n ),\n )\n CONFIG.declare(\n 'fp_discrete_only',\n ConfigValue(\n default=True,\n description='Only calculate the distance among discrete variables in regularization problems.',\n domain=bool,\n ),\n )\n CONFIG.declare(\n 'fp_main_norm',\n ConfigValue(\n default='L1',\n domain=In(['L1', 'L2', 'L_infinity']),\n description='Different forms of objective function MIP regularization problem.',\n ),\n )\n CONFIG.declare(\n 'fp_norm_constraint',\n ConfigValue(\n default=True,\n description='Whether to add the norm constraint to FP-NLP',\n domain=bool,\n ),\n )\n CONFIG.declare(\n 'fp_norm_constraint_coef',\n ConfigValue(\n default=1,\n domain=PositiveFloat,\n description='The coefficient in the norm constraint, correspond to the Beta in the paper.',\n ),\n )","def _get_default_quantization_config(preset: QuantizationPreset, subset_size: int) -> Dict[str, Any]:\n return {\n \"algorithm\": \"quantization\",\n \"preset\": preset.value,\n \"initializer\": {\n \"range\": {\"num_init_samples\": subset_size, \"type\": DEFAULT_RANGE_TYPE},\n \"batchnorm_adaptation\": {\"num_bn_adaptation_samples\": subset_size},\n },\n \"overflow_fix\": \"first_layer_only\",\n }","def _get_transformer_quantization_config(subset_size: int) -> Dict[str, Any]:\n return {\n \"algorithm\": \"quantization\",\n \"preset\": \"mixed\",\n \"initializer\": {\n \"range\": {\"num_init_samples\": subset_size, \"type\": DEFAULT_RANGE_TYPE},\n \"batchnorm_adaptation\": {\"num_bn_adaptation_samples\": 0},\n },\n \"scope_overrides\": {\"activations\": {\"{re}.*matmul_0\": {\"mode\": \"symmetric\"}}},\n \"ignored_scopes\": [\n \"{re}.*Embeddings.*\",\n \"{re}.*__add___[0-1]\",\n \"{re}.*layer_norm_0\",\n \"{re}.*matmul_1\",\n \"{re}.*__truediv__*\",\n ],\n \"overflow_fix\": \"first_layer_only\",\n }","def create_config(self, context, mgmtport):\n pass","def create_config(self) -> None:\n pass","def create_config(self) -> None:\n pass","def get_configspace():\n cs = CS.ConfigurationSpace()\n\n \n\n # Learning rate hyperparameter\n lr = CSH.UniformFloatHyperparameter('lr', lower=1e-6, upper=1e-1, default_value='1e-2', log=True)\n\n \n\n # Stochastic gradient descent momentum as parameter.\n sgd_momentum = CSH.UniformFloatHyperparameter('sgd_momentum', lower=0.0, upper=0.99, default_value=0.9, log=False)\n\n cs.add_hyperparameters([lr, sgd_momentum])\n \n # Optimizer hyperparameters.\n #optimizer = CSH.CategoricalHyperparameter('optimizer', ['Adam', 'SGD'])\n #cs.add_hyperparameters([optimizer])\n \n # Only add the sgd_momentum hyperparameter if the optimizer is stochastic gradient descent. Otherwise, it doesn't make sense.\n #cond = CS.EqualsCondition(sgd_momentum, optimizer, 'SGD')\n #cs.add_condition(cond)\n\n ''' The below is commented out because we're not fiddling with architecture in this optimization.'''\n #num_new_fc_layers = CSH.UniformIntegerHyperparameter('num_new_fc_layers', lower=0, upper=3, default_value=0, log=False)\n #num_els_new_1 = CSH.UniformIntegerHyperparameter('num_els_new_1', lower=128, upper=4096, default_value = 1000, log=True)\n #num_els_new_2 = CSH.UniformIntegerHyperparameter('num_els_new_2', lower=128, upper=4096, default_value = 1000, log=True)\n #num_els_new_3 = CSH.UniformIntegerHyperparameter('num_els_new_3', lower=128, upper=4096, default_value = 1000, log=True)\n\n #freeze0_old = CSH.UniformIntegerHyperparameter('freeze0_cat', lower = 0, upper = 1, default_value = 1, log=False)\n #freeze1_old = CSH.UniformIntegerHyperparameter('freeze1_cat', lower=0, upper=1, default_value=1, log=False)\n\n #cs.add_hyperparameters([num_new_fc_layers, num_els_new_1, num_els_new_2, num_els_new_3, freeze0_old, freeze1_old, batchsize])\n\n dropout_rate = CSH.UniformFloatHyperparameter('dropout_rate', lower=0.0, upper=0.9, default_value=0.5, log=False)\n\n cs.add_hyperparameters([dropout_rate])\n\n return cs","def __init__(self, mode, cfg):\n super(DMCM, self).__init__()\n\n self.conv_net = cfg.get_image_net(mode)\n self.sparse_net = cfg.get_genes_net(mode)\n\n # Matrix network does not need weight initialization because there can\n # be no vanishing gradients.\n self.conv_net.apply(_init_weights_xavier)","def get_config():\n name = 'dynamic_pricing'\n num_products = 5\n scale = 1\n noise_var = 10\n p_max = 1\n\n agents = collections.OrderedDict(\n [('bsPricing',\n functools.partial(BootstrapDynamicPricing,\n num_products, scale, noise_var, p_max))]\n )\n\n environments = collections.OrderedDict(\n [('env',\n functools.partial(DynamicPricing,\n num_products, scale, noise_var, p_max))]\n )\n experiments = collections.OrderedDict(\n [(name, ExperimentNoAction)]\n )\n n_steps = 80\n n_seeds = 2000\n config = Config(name, agents, environments, experiments, n_steps, n_seeds)\n return config","def __set_special_config_values(cfg: __Config, config: dict) -> \"__Config\":\n cfg.file_name_plane_masks = lambda i: str(i) + config['file_name_plane_mask_suf']\n cfg.file_name_planercnn_image = lambda i: str(i) + config['file_name_planercnn_image_suf']\n cfg.dir_results = f\"{cfg.edge_detection_type}\" # will be the output folder, create in data dir\n cfg.image_size = tuple(int(x) for x in config['image_size'].split(\" \"))\n return cfg","def __init__(self, cfg_file,\n ckpt: str,\n output_path: str = None,\n logger: Logger = None) -> None:\n\n if logger is None:\n logger = make_logger()\n\n cfg = load_config(cfg_file)\n\n if \"test\" not in cfg[\"data\"].keys():\n raise ValueError(\"Test data must be specified in config.\")\n\n #print(cfg.keys())\n if \"dqn\" not in cfg.keys():\n raise ValueError(\"dqn data must be specified in config.\")\n self.model_dir = cfg[\"training\"][\"model_dir\"]\n # when checkpoint is not specified, take latest (best) from model dir\n if ckpt is None:\n model_dir = cfg[\"training\"][\"model_dir\"]\n ckpt = get_latest_checkpoint(model_dir)\n if ckpt is None:\n raise FileNotFoundError(\"No checkpoint found in directory {}.\"\n .format(model_dir))\n try:\n step = ckpt.split(model_dir+\"/\")[1].split(\".ckpt\")[0]\n except IndexError:\n step = \"best\"\n\n\n self.batch_size = 1 #**\n self.batch_type = cfg[\"training\"].get(\n \"eval_batch_type\", cfg[\"training\"].get(\"batch_type\", \"sentence\"))\n self.use_cuda = cfg[\"training\"].get(\"use_cuda\", False)\n self.level = cfg[\"data\"][\"level\"]\n self.eval_metric = cfg[\"training\"][\"eval_metric\"]\n self.max_output_length = cfg[\"training\"].get(\"max_output_length\", None)\n\n \n\n # load the data\n train_data, dev_data, test_data, src_vocab, trg_vocab = load_data(\n data_cfg=cfg[\"data\"])\n #Loading the DQN parameters:\n self.sample_size = cfg[\"dqn\"][\"sample_size\"]\n self.lr = cfg[\"dqn\"].get(\"lr\", 0.01)\n self.egreed_max = cfg[\"dqn\"].get(\"egreed_max\", 0.9)\n self.egreed_min = cfg[\"dqn\"].get(\"egreed_min\", 0.01)\n self.gamma_max = cfg[\"dqn\"].get(\"gamma_max\", 0.9)\n self.gamma_min = cfg[\"dqn\"].get(\"gamma_min\", 0.5)\n self.nu_iter = cfg[\"dqn\"][\"nu_iter\"]\n self.mem_cap = cfg[\"dqn\"][\"mem_cap\"]\n self.beam_min = cfg[\"dqn\"][\"beam_min\"]\n self.beam_max = cfg[\"dqn\"][\"beam_max\"]\n self.state_type = cfg[\"dqn\"][\"state_type\"]\n \n if self.state_type == 'hidden':\n self.state_size = cfg[\"model\"][\"encoder\"][\"hidden_size\"]*2\n else:\n self.state_size = cfg[\"model\"][\"encoder\"][\"hidden_size\"]\n\n self.actions_size = len(src_vocab)\n self.gamma = None\n \n print(\"Sample size: \", self.sample_size )\n print(\"State size: \", self.state_size)\n print(\"Action size: \", self.actions_size)\n self.epochs = cfg[\"dqn\"][\"epochs\"]\n\n # Inii the Qnet and Qnet2\n self.eval_net = Net(self.state_size, self.actions_size)\n self.target_net = Net(self.state_size, self.actions_size)\n\n #Following the algorithm\n self.target_net.load_state_dict(self.eval_net.state_dict())\n\n self.learn_step_counter = 0\n self.memory_counter = 0\n self.size_memory1 = self.state_size * 2 + 2 + 1\n self.memory = np.zeros((self.mem_cap, self.size_memory1 ))\n self.optimizer = torch.optim.Adam(self.eval_net.parameters()\n , lr=self.lr )\n self.loss_func = nn.MSELoss()\n\n #others parameters\n self.bos_index = trg_vocab.stoi[BOS_TOKEN]\n self.eos_index = trg_vocab.stoi[EOS_TOKEN]\n self.pad_index = trg_vocab.stoi[PAD_TOKEN]\n\n self.data_to_train_dqn = {\"train\": train_data}\n \n #self.data_to_train_dqn = {\"test\": test_data}\n #self.data_to_dev = {\"dev\": dev_data}\n self.data_to_dev = {\"dev\": dev_data}\n #self.data_to_train_dqn = {\"train\": train_data\n # ,\"dev\": dev_data, \"test\": test_data}\n # load model state from disk\n model_checkpoint = load_checkpoint(ckpt, use_cuda=self.use_cuda)\n\n # build model and load parameters into it\n self.model = build_model(cfg[\"model\"], src_vocab=src_vocab, trg_vocab=trg_vocab)\n self.model.load_state_dict(model_checkpoint[\"model_state\"])\n\n if self.use_cuda:\n self.model.cuda()\n\n # whether to use beam search for decoding, 0: greedy decoding\n beam_size = 1\n beam_alpha = -1\n\n #others not important parameters\n self.index_fin = None\n path_tensroboard = self.model_dir + \"/tensorboard_DQN/\"\n self.tb_writer = SummaryWriter( log_dir=path_tensroboard , purge_step=0)\n self.dev_network_count = 0\n print(cfg[\"dqn\"][\"reward_type\"])\n #Reward funtion related:\n if cfg[\"dqn\"][\"reward_type\"] == \"bleu_diff\" : \n print(\"You select the reward based on the Bleu score differences\")\n self.Reward = self.Reward_bleu_diff\n elif cfg[\"dqn\"][\"reward_type\"] == \"bleu_lin\" : \n print(\"You select the reward based on the linear Bleu socres, and several punishments\")\n self.Reward = self.Reward_lin\n else:\n print(\"You select the reward based on the final score on the last state \")\n self.Reward = self.Reward_bleu_fin","def generate_nnie_config(nnie_cfg, config, nnie_out_path='./config.json', tensor_type='float'):\n u8_start = False if tensor_type == 'float' else False\n default_config = {\n \"default_net_type_token\": \"nnie\",\n \"rand_input\": False,\n \"data_num\": 100,\n \"input_path_map\": {\n \"data\": \"./image_bins\",\n },\n \"nnie\": {\n \"max_batch\": 1,\n \"output_names\": [],\n \"mapper_version\": 11,\n \"u8_start\": u8_start,\n \"device\": \"gpu\",\n \"verbose\": False,\n \"image_path_list\": [\"./image_list.txt\"],\n \"mean\": [128, 128, 128],\n \"std\": [1, 1, 1]\n }\n }\n image_path_list = nnie_cfg['image_path_list']\n assert os.path.exists(image_path_list)\n with open(image_path_list, 'r') as f:\n image_list = [item.strip() for item in f.readlines()]\n\n mean = config.to_kestrel.get('pixel_means', [123.675, 116.28, 103.53])\n std = config.to_kestrel.get('pixel_stds', [58.395, 57.12, 57.375])\n resize_hw = config.to_kestrel.get('resize_hw', (224, 224))\n resize_hw = tuple(resize_hw)\n data_num = len(image_list)\n image_bin_path = generate_image_bins(image_list, mean, std, resize_hw)\n default_config['data_num'] = data_num\n default_config['input_path_map']['data'] = image_bin_path\n default_config['nnie']['max_batch'] = nnie_cfg.get('max_batch', 1)\n default_config['nnie']['mapper_version'] = nnie_cfg.get('mapper_version', 11)\n default_config['nnie']['image_path_list'] = [image_path_list]\n default_config['nnie']['mean'] = [128] * len(std)\n default_config['nnie']['std'] = [1] * len(std)\n with open(nnie_out_path, \"w\") as f:\n json.dump(default_config, f, indent=2)\n\n return nnie_out_path","def construct_and_initialize_trainable_quantizers(self, quant_scheme):\n # Copy user settable attributes for outputs\n\n def _create_trainable_quantizer(bw, name, quantizer):\n \"\"\" create trainable quantizer from static grid quantizer. \"\"\"\n # Initialize trainable parameters to None\n self.register_parameter(f'{name}_encoding_min', None)\n self.register_parameter(f'{name}_encoding_max', None)\n # Pass name of tensor quantizer and reference of Wrapper to tensor quantizer\n # Input quantizer\n new_quantizer = tensor_quantizer_factory(bw, self._round_mode,\n quant_scheme,\n self._is_symmetric,\n enabled_by_default=True,\n data_type=self._data_type)\n new_quantizer.name = name\n new_quantizer.wrapper_ref = self\n new_quantizer.device = self.device\n initialize_learned_grid_quantizer_attributes(new_quantizer, quantizer)\n return new_quantizer\n\n new_grouped_quantizers = {name: _create_trainable_quantizer(self._activation_bw, name, quantizer)\n for name, quantizer in self._grouped_quantizers.items()}\n\n def create_trainable_quantizer(bw, name, quantizer):\n \"\"\" create trainable quantizer if not part of a group else reuse the group quantizer. \"\"\"\n if quantizer in self._grouped_quantizers.values():\n group_names = [n for n, q in self._grouped_quantizers.items() if q == quantizer]\n assert len(group_names) == 1\n # creating a param min/max references to the shared group min/max parameters.\n setattr(self, f'{name}_encoding_min', getattr(self, f'{group_names[0]}_encoding_min'))\n setattr(self, f'{name}_encoding_max', getattr(self, f'{group_names[0]}_encoding_max'))\n return new_grouped_quantizers[group_names[0]]\n\n return _create_trainable_quantizer(bw, name, quantizer)\n\n\n self._input_quantizers = {name: create_trainable_quantizer(self._activation_bw, name, quantizer)\n for name, quantizer in self.input_quantizers.items()}\n self._output_quantizers = {name: create_trainable_quantizer(self._activation_bw, name, quantizer)\n for name, quantizer in self.output_quantizers.items()}\n self._param_quantizers = {name: create_trainable_quantizer(self._weight_bw, name, quantizer)\n for name, quantizer in self._param_quantizers.items()}\n\n self._grouped_quantizers = new_grouped_quantizers\n self._mode = QcQuantizeOpMode.LEARN_ENCODINGS","def get_config(self):\n config = {\n 'F_': self.F_,\n 'attn_heads': self.attn_heads,\n 'attn_heads_reduction': self.attn_heads_reduction,\n 'edge_type_reduction': self.edge_type_reduction,\n 'attention_type': self.attention_type,\n 'attn_dropout': self.attn_dropout,\n 'feature_dropout': self.feature_dropout,\n 'activation': self.activation,\n 'use_value_bias': self.use_value_bias,\n 'use_key_bias': self.use_key_bias,\n 'kernel_initializer': self.kernel_initializer,\n 'bias_initializer': self.bias_initializer,\n 'attn_kernel_initializer': self.attn_kernel_initializer,\n 'attn_bias_initalizer': self.attn_bias_initializer,\n 'kernel_regularizer': self.kernel_regularizer,\n 'bias_regularizer': self.bias_regularizer,\n 'attn_kernel_regularizer': self.attn_kernel_regularizer,\n 'attn_bias_regularizer': self.attn_bias_regularizer,\n 'activity_regularizer': self.activity_regularizer,\n 'kernel_constraint': self.kernel_constraint,\n 'bias_constraint': self.bias_constraint,\n 'attn_kernel_constraint': self.attn_kernel_constraint,\n 'attn_bias_constraint': self.attn_bias_constraint\n }\n base_config = super(BatchShawMultigraphAttention, self).get_config()\n return dict(list(base_config.items())) + list(config.items())","def __init__(self, network_controller: NetworkQuantizationController, in_channels, out_channels, kernel_size,\n stride=1,\n padding=0, dilation=1,\n group=1, disable_bn=False,\n batch_norm_epsilon=1e-5, batch_norm_momentum=0.1, tf_padding=False):\n super(ConvBN, self).__init__()\n self.stride = stride\n self.padding = padding\n self.dilation = dilation\n self.group = group\n self.network_controller = network_controller\n self.tf_padding = tf_padding\n\n if not tf_padding:\n self.pad_tensor = Identity()\n self.padding_conv = self.padding\n\n else:\n padding = 0\n self.padding_conv = padding\n pad_h = self.padding\n pad_w = self.padding\n self.pad_tensor = nn.ZeroPad2d((pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2))\n\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride,\n padding=padding, dilation=dilation, groups=group,\n bias=disable_bn)\n if disable_bn:\n self.bn = Identity()\n else:\n self.bn = nn.BatchNorm2d(out_channels, eps=batch_norm_epsilon, momentum=batch_norm_momentum)\n self.q = Quantization(network_controller, is_signed=True,\n weights_values=self.conv.weight.detach())","def __init__(self, cfg):\n\n super().__init__()\n self.K = cfg.queue_size\n self.output_size = cfg.ent_mention_output_size\n self.qid = 0\n self.span_pair_queue = []\n self.context_pair_queue = []\n self.span_tmp_queue = []\n self.context_tmp_queue = []\n self.temperature = cfg.temperature\n self.gama = 0.999\n self.device = cfg.device\n\n self.span_pair_encoder = PretrainedSpanEncoder(cfg, momentum=False)\n self.momentum_span_pair_encoder = PretrainedSpanEncoder(cfg, momentum=True)\n\n self.contrastive_loss = nn.CrossEntropyLoss()","def get_configspace():\r\n cs = CS.ConfigurationSpace()\r\n\r\n lr = CSH.UniformFloatHyperparameter('lr', lower=1e-6, upper=1e-1, default_value='1e-2', log=True)\r\n\r\n # For demonstration purposes, we add different optimizers as categorical hyperparameters.\r\n # To show how to use conditional hyperparameters with ConfigSpace, we'll add the optimizers 'Adam' and 'SGD'.\r\n # SGD has a different parameter 'momentum'.\r\n optimizer = CSH.CategoricalHyperparameter('optimizer', ['Adam', 'SGD'])\r\n\r\n sgd_momentum = CSH.UniformFloatHyperparameter('sgd_momentum', lower=0.0, upper=0.99, default_value=0.9, log=False)\r\n\r\n cs.add_hyperparameters([lr, optimizer, sgd_momentum])\r\n\r\n\r\n\r\n num_conv_layers = CSH.UniformIntegerHyperparameter('num_conv_layers', lower=1, upper=3, default_value=2)\r\n\r\n num_filters_1 = CSH.UniformIntegerHyperparameter('num_filters_1', lower=4, upper=64, default_value=16, log=True)\r\n num_filters_2 = CSH.UniformIntegerHyperparameter('num_filters_2', lower=4, upper=64, default_value=16, log=True)\r\n num_filters_3 = CSH.UniformIntegerHyperparameter('num_filters_3', lower=4, upper=64, default_value=16, log=True)\r\n\r\n cs.add_hyperparameters([num_conv_layers, num_filters_1, num_filters_2, num_filters_3])\r\n\r\n\r\n dropout_rate = CSH.UniformFloatHyperparameter('dropout_rate', lower=0.0, upper=0.9, default_value=0.5, log=False)\r\n num_fc_units = CSH.UniformIntegerHyperparameter('num_fc_units', lower=8, upper=256, default_value=32, log=True)\r\n\r\n cs.add_hyperparameters([dropout_rate, num_fc_units])\r\n\r\n\r\n # The hyperparameter sgd_momentum will be used,if the configuration\r\n # contains 'SGD' as optimizer.\r\n cond = CS.EqualsCondition(sgd_momentum, optimizer, 'SGD')\r\n cs.add_condition(cond)\r\n\r\n # You can also use inequality conditions:\r\n cond = CS.GreaterThanCondition(num_filters_2, num_conv_layers, 1)\r\n cs.add_condition(cond)\r\n\r\n cond = CS.GreaterThanCondition(num_filters_3, num_conv_layers, 2)\r\n cs.add_condition(cond)\r\n\r\n return cs","def make_config(config, out_dir=None, pism_root=pism_root):\n\n # ensure that config is a list\n if type(config) is str:\n config = [config]\n\n # initialize netCDF dataset\n nc_path = os.path.join(out_dir, 'config.nc')\n nc = Dataset(nc_path, 'w')\n var = nc.createVariable('pism_overrides', 'i1')\n\n # loop on config files\n for c in config:\n c_path = '%s/config/%s.txt' % (pism_root, c)\n\n # fill in pism overrides\n with open(c_path) as f:\n for line in f:\n\n # ignore what follows '//'\n line = line.split('//', 1)[0].strip()\n\n # parse non-empty lines and overwrite existing values\n if line:\n k, v = line.split(':', 1)\n k = k.strip()\n v = v.strip().strip('\"')\n try:\n v = float(v)\n except ValueError:\n pass\n var.setncattr(k, v)\n\n # close and return path to output file\n nc.close()\n return nc_path","def _get_MindtPy_FP_config():\n CONFIG = ConfigBlock('MindtPy-GOA')\n CONFIG.declare(\n 'init_strategy',\n ConfigValue(\n default='FP',\n domain=In(['FP']),\n description='Initialization strategy',\n doc='Initialization strategy used by any method. Currently the '\n 'continuous relaxation of the MINLP (rNLP), solve a maximal '\n 'covering problem (max_binary), and fix the initial value for '\n 'the integer variables (initial_binary).',\n ),\n )\n\n _add_common_configs(CONFIG)\n _add_fp_configs(CONFIG)\n _add_oa_cuts_configs(CONFIG)\n _add_subsolver_configs(CONFIG)\n _add_tolerance_configs(CONFIG)\n _add_bound_configs(CONFIG)\n return CONFIG","def test_quantize_training():\n config = EasyDict()\n\n config.NETWORK_CLASS = FlowNetSV3Quantized\n config.DATASET_CLASS = FlyingChairs\n\n config.IS_DEBUG = False\n config.IMAGE_SIZE = [384, 512]\n config.BATCH_SIZE = 8\n config.TEST_STEPS = 200\n config.MAX_STEPS = 5000\n config.SAVE_CHECKPOINT_STEPS = 100\n config.KEEP_CHECKPOINT_MAX = 5\n config.SUMMARISE_STEPS = 100\n config.IS_PRETRAIN = False\n config.IS_DISTRIBUTION = False\n config.TASK = Tasks.OPTICAL_FLOW_ESTIMATION\n\n # network model config\n config.NETWORK = EasyDict()\n config.NETWORK.OPTIMIZER_CLASS = tf.train.AdamOptimizer\n config.NETWORK.OPTIMIZER_KWARGS = {\"beta1\": 0.9, \"beta2\": 0.999}\n config.NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant\n config.NETWORK.LEARNING_RATE_KWARGS = {\n \"values\": [0.0000125, 0.00005],\n \"boundaries\": [5000]\n }\n\n config.NETWORK.IMAGE_SIZE = config.IMAGE_SIZE\n config.NETWORK.BATCH_SIZE = config.BATCH_SIZE\n config.NETWORK.DATA_FORMAT = \"NHWC\"\n config.NETWORK.ACTIVATION_QUANTIZER = linear_mid_tread_half_quantizer\n config.NETWORK.ACTIVATION_QUANTIZER_KWARGS = {\n 'bit': 2,\n 'max_value': 2.0\n }\n config.NETWORK.WEIGHT_QUANTIZER = binary_channel_wise_mean_scaling_quantizer\n config.NETWORK.WEIGHT_QUANTIZER_KWARGS = {}\n\n # dataset config\n config.DATASET = EasyDict()\n config.DATASET.PRE_PROCESSOR = None\n config.DATASET.BATCH_SIZE = config.BATCH_SIZE\n config.DATASET.DATA_FORMAT = \"NHWC\"\n config.DATASET.VALIDATION_RATE = 0.2\n config.DATASET.VALIDATION_SEED = 2019\n config.DATASET.AUGMENTOR = Sequence([\n # Geometric transformation\n FlipLeftRight(0.5),\n FlipTopBottom(0.5),\n Translate(-0.2, 0.2),\n Rotate(-17, +17),\n Scale(1.0, 2.0),\n # Pixel-wise augmentation\n Brightness(0.8, 1.2),\n Contrast(0.2, 1.4),\n Color(0.5, 2.0),\n Gamma(0.7, 1.5),\n # Hue(-128.0, 128.0),\n GaussianNoise(0.0, 10.0)\n ])\n config.DATASET.PRE_PROCESSOR = Sequence([\n DevideBy255(),\n ])\n environment.init(\"test_flownet_s_v3_quantize\")\n prepare_dirs(recreate=True)\n start_training(config)","def get_config():\n config = ml_collections.ConfigDict()\n config.seed = 42\n\n config.eval_num = 30000\n config.eval_avg_num = 3\n config.num_train_steps = -1\n config.log_loss_every_steps = 1000\n config.eval_every_steps = 1000\n config.checkpoint_every_steps = 5000\n\n config.dataset = \"mscoco\"\n config.coco_version = \"2014\"\n config.data_dir = \"data/\"\n config.return_text = False\n config.return_filename = False\n\n config.trial = 0 # dummy for repeated runs.\n config.beta1 = 0.5\n config.beta2 = 0.999\n config.d_lr = 0.0004\n config.g_lr = 0.0001\n config.polyak_decay = 0.999\n config.show_num = 64\n config.shuffle_buffer_size = 1000\n config.batch_norm_group_size = -1\n config.dtype = \"bfloat16\"\n config.train_shuffle = True\n\n config.image_size = 128\n config.batch_size = 56\n config.eval_batch_size = 7\n\n config.df_dim = 96\n config.gf_dim = 96\n config.z_dim = 128\n config.num_epochs = 500\n config.model_name = \"xmc\"\n config.d_step_per_g_step = 2\n config.g_spectral_norm = False\n config.d_spectral_norm = True\n config.architecture = \"xmc_net\"\n config.gamma_for_g = 15\n config.word_contrastive = True\n config.sentence_contrastive = True\n config.image_contrastive = True\n config.pretrained_image_contrastive = True\n config.cond_size = 16\n\n return config","def setUp(self):\n # The short NSC used in this example\n self.net_nsc = [\n (1, 4, 0, 0, 0), # Layer 1: Identity(input)\n (2, 1, 3, 1, 0), # Layer 2: Convolution(Layer1)\n (3, 1, 3, 2, 0), # Layer 3: Convolution(Layer2)\n (4, 5, 0, 1, 3), # Layer 4: Convolution(Layer1)\n (5, 7, 0, 0, 0), # Layer 5: Convolution(Layer4)\n ]","def createNCDF(self):\n\n rootgrp = Dataset(self.filename_out, 'w', format=self.format, clobber=True)\n\n # Create dimensions.\n if 'dimensions' in self.input_dict:\n for k, v in self.input_dict['dimensions'].items():\n rootgrp.createDimension(k, v)\n else:\n if not self.Quiet:\n print('No netCDF created:')\n print(' No dimension key found (!! has to be \\\"dimensions\\\"!!!)')\n return()\n\n # Create global attributes.\n if 'global attributes' in self.input_dict:\n for k, v in self.input_dict['global attributes'].items():\n rootgrp.setncattr(k, v)\n else:\n if not self.Quiet:\n print(' No global attribute key found (!! has to be \\\"global attributes\\\"!!!)')\n\n # Create variables.\n for k, v in self.input_dict['variables'].items():\n dims = self.input_dict['variables'][k]['dimensions']\n data = v['data']\n # Create correct data type if provided\n if 'data_type' in self.input_dict['variables'][k]:\n data_type = self.input_dict['variables'][k]['data_type']\n else:\n data_type = 'f4'\n # Check whether we've been given a fill value.\n if 'fill_value' in self.input_dict['variables'][k]:\n fill_value = self.input_dict['variables'][k]['fill_value']\n else:\n fill_value = None\n # Create ncdf variable\n if not self.Quiet:\n print(' Creating variable: {} {} {}'.format(k, data_type, dims))\n var = rootgrp.createVariable(k, data_type, dims, fill_value=fill_value)\n if len(dims) > np.ndim(data):\n # If number of dimensions given to netCDF is greater than the\n # number of dimension of the data, then fill the netCDF\n # variable accordingly.\n if 'time' in dims:\n # Check for presence of time dimension (which can be\n # unlimited variable: defined by None).\n try:\n var[:] = data\n except IndexError:\n raise(IndexError(('Supplied data shape {} does not match the specified'\n ' dimensions {}, for variable \\'{}\\'.'.format(data.shape, var.shape, k))))\n else:\n if not self.Quiet:\n print('Problem in the number of dimensions')\n else:\n try:\n var[:] = data\n except IndexError:\n raise(IndexError(('Supplied data shape {} does not match the specified'\n ' dimensions {}, for variable \\'{}\\'.'.format(data.shape, var.shape, k))))\n\n # Create attributes for variables\n if 'attributes' in self.input_dict['variables'][k]:\n for ka, va in self.input_dict['variables'][k]['attributes'].items():\n var.setncattr(ka, va)\n\n rootgrp.close()","def __init__(self, encut, name=\"scf_settings\"):\n InputParameters.__init__(self, name=name)\n self.update_electronic_settings(\"ENCUT\", encut)","def get_config(self):\n config = {\n \"units\": self.units,\n \"activation\": activations.serialize(self.activation),\n \"recurrent_activation\": activations.serialize(\n self.recurrent_activation\n ),\n \"attention_activation\": activations.serialize(\n self.attention_activation\n ),\n \"use_bias\": self.use_bias,\n \"kernel_initializer\": initializers.serialize(self.kernel_initializer),\n \"recurrent_initializer\": initializers.serialize(\n self.recurrent_initializer\n ),\n \"bias_initializer\": initializers.serialize(self.bias_initializer),\n \"attention_initializer\": initializers.serialize(\n self.attention_initializer\n ),\n \"use_chrono_initialization\": self.unit_forget_bias,\n \"kernel_regularizer\": regularizers.serialize(self.kernel_regularizer),\n \"recurrent_regularizer\": regularizers.serialize(\n self.recurrent_regularizer\n ),\n \"bias_regularizer\": regularizers.serialize(self.bias_regularizer),\n \"activity_regularizer\": regularizers.serialize(\n self.activity_regularizer\n ),\n \"attention_regularizer\": regularizers.serialize(\n self.attention_regularizer\n ),\n \"kernel_constraint\": constraints.serialize(self.kernel_constraint),\n \"recurrent_constraint\": constraints.serialize(\n self.recurrent_constraint\n ),\n \"bias_constraint\": constraints.serialize(self.bias_constraint),\n \"attention_constraint\": constraints.serialize(\n self.attention_constraint\n ),\n \"dropout\": self.dropout,\n \"recurrent_dropout\": self.recurrent_dropout,\n \"return_attention\": self.return_attention,\n }\n base_config = super().get_config()\n del base_config[\"cell\"]\n return dict(list(base_config.items()) + list(config.items()))","def setup(args):\n cfg = get_cfg()\n cfg.merge_from_file(args.config_file)\n cfg.merge_from_list(args.opts)\n # customize reszied parameters\n # cfg['INPUT']['MIN_SIZE_TRAIN'] = (20,)\n # cfg['INPUT']['MAX_SIZE_TRAIN'] = 50\n cfg.freeze()\n default_setup(\n cfg, args\n ) # if you don't like any of the default setup, write your own setup code\n return cfg","def createCfg_comp_jetToTauFakeRate(self, jobOptions):\n for charge_selection in self.charge_selections:\n lines = []\n lines.append(\"process.fwliteInput.fileNames = cms.vstring('%s')\" % jobOptions['inputFile'])\n lines.append(\"process.fwliteOutput.fileName = cms.string('%s')\" % os.path.basename(jobOptions['outputFile']))\n lines.append(\"process.comp_jetToTauFakeRate.looseRegion = cms.string('%s')\" % jobOptions['looseRegion'])\n lines.append(\"process.comp_jetToTauFakeRate.tightRegion = cms.string('%s')\" % jobOptions['tightRegion'])\n lines.append(\"process.comp_jetToTauFakeRate.processData = cms.string('data_obs')\")\n lines.append(\"process.comp_jetToTauFakeRate.processesToSubtract = cms.vstring(\")\n lines.append(\" 'TTt', 'TTl',\")\n lines.append(\" 'EWKt', 'EWKl',\")\n lines.append(\" 'Rarest', 'Raresl',\") \n lines.append(\" 'TTWt', 'TTWl', \")\n lines.append(\" 'TTZt', 'TTZl', \")\n lines.append(\" 'signalt', 'signall'\")\n lines.append(\")\")\n lines.append(\"process.comp_jetToTauFakeRate.processMC = cms.string('TTj')\")\n lines.append(\"process.comp_jetToTauFakeRate.absEtaBins = cms.vdouble(%s)\" % jobOptions['absEtaBins'])\n lines.append(\"process.comp_jetToTauFakeRate.ptBins = cms.vdouble(%s)\" % jobOptions['ptBins'])\n create_cfg(self.cfgFile_comp_jetToTauFakeRate, jobOptions['cfgFile_modified'], lines)","def get_base_config():\n return dict(\n dim=768,\n ff_dim=3072,\n num_heads=12,\n num_layers=12,\n attention_dropout_rate=0.0,\n dropout_rate=0.1,\n representation_size=768,\n classifier='token'\n )","def prepare_config(device='npu'):\n if device == 'npu':\n # config for Ascend processor\n config = tf.ConfigProto()\n custom_op = config.graph_options.rewrite_options.custom_optimizers.add()\n custom_op.name = \"NpuOptimizer\"\n custom_op.parameter_map[\"use_off_line\"].b = True\n custom_op.parameter_map[\"precision_mode\"].s = tf.compat.as_bytes(\"force_fp16\")\n custom_op.parameter_map[\"graph_run_mode\"].i = 0\n config.graph_options.rewrite_options.remapping = RewriterConfig.OFF\n custom_op.parameter_map[\"debug_dir\"].s = tf.compat.as_bytes(str(TMP))\n else:\n config = tf.ConfigProto()\n return config","def create_compression_algorithm_builder(config: NNCFConfig, should_init=True) -> PTCompressionAlgorithmBuilder:\n algo_names = extract_algorithm_names(config)\n return create_compression_algorithm_builder_from_algo_names(algo_names, config, should_init)","def __init__(self, config):\n super().__init__()\n\n self.pt_encoder = config['encoder_name']\n self.nu = config['nu']\n\n self.encoder = TransformerEncoder(name=self.pt_encoder,\n nu=self.nu)\n\n self.hidden_dims = config['hidden_dims']\n self.act_fn = nn.ReLU if config['act_fn'] == 'ReLU' else nn.Tanh\n\n self.mlp = MLP(encoder_output_size=self.encoder.model.config.hidden_size,\n hidden_dims=self.hidden_dims,\n act_fn=self.act_fn)\n\n self.n_classes = config['n_classes']\n self.clf = SF_CLF(n_classes=self.n_classes,\n hidden_dims=self.hidden_dims)","def __init__(self, config, mode=None):\n super(FacenetEvaluation, self).__init__(config, mode)\n\n print('Create {}'.format(self.evaluation_name))\n\n # Preprocess Configurations and check legal\n self._must_have_config = [\n eval_fields.distance_measure,\n eval_fields.sampling\n ]\n\n self._default_values = {\n eval_fields.distance_measure: {\n eval_fields.threshold: {\n eval_fields.start: 0.01,\n eval_fields.end: 0.7,\n eval_fields.step: 0.01\n }\n },\n eval_fields.sampling: {\n facenet_fields.sample_ratio: 0.2,\n facenet_fields.class_sample_method: facenet_fields.random_sample\n }\n }\n # metrics with condition\n self._metric_with_threshold = [\n metric_fields.accuracy,\n metric_fields.validation_rate,\n metric_fields.false_accept_rate,\n metric_fields.true_positive_rate,\n metric_fields.false_positive_rate,\n ]\n # metrics without condition\n self._metric_without_threshold = [\n metric_fields.mean_accuracy,\n metric_fields.mean_validation_rate,\n metric_fields.area_under_curve,\n ]\n\n # Set default values for must-have configs\n for _config in self._must_have_config:\n if _config not in self.metrics:\n if _config in self._default_values:\n pass\n else:\n print('WARNING: {} should be assigned'.format(_config))\n else:\n print('Use assigned {}: {}'.format(_config, self.metrics[_config]))\n\n # Set distance thresholds by config\n distance_config = self.distance_measure\n distance_thres = distance_config[eval_fields.threshold]\n dist_start = distance_thres[eval_fields.start]\n dist_end = distance_thres[eval_fields.end]\n dist_step = distance_thres[eval_fields.step]\n # TODO @kv: Do we need sanity check for start < end?\n if dist_start > dist_end:\n raise ValueError('FaceEvaluation: distance threshold start > end')\n self._distance_thresholds = np.arange(dist_start, dist_end, dist_step)\n\n # Attributes\n if len(self.attributes) == 0:\n self._has_attribute = False\n elif len(self.attributes) == 1:\n if attribute_fields.all_classes in self.attributes:\n self._has_attribute = False\n elif attribute_fields.all_attributes in self.attributes:\n self._has_attribute = True\n else:\n self._has_attribute = True\n self.show_configs()","def get_configspace() -> CS.Configuration:\n cs = CS.ConfigurationSpace(seed=0)\n # START TODO ################\n lr_hp = CS.UniformFloatHyperparameter('lr', lower=1e-6, upper=1e-1, default_value=1e-2, log=True)\n optimizer_hp = CSH.CategoricalHyperparameter(name='optimizer', choices=['Adam', 'SGD', 'RMSprop'])\n sgd_momentum_hp = CS.UniformFloatHyperparameter('sgd_momentum', lower=0.00, upper=0.99, default_value=0.9)\n\n rms_momentum_hp = CS.UniformFloatHyperparameter('rms_momentum', lower=0.00, upper=0.99, default_value=0.9)\n rms_alpha_hp = CS.UniformFloatHyperparameter('rms_alpha', lower=0.00, upper=0.99, default_value=0.99)\n\n scheduler_hp = CSH.CategoricalHyperparameter(name='scheduler',\n choices=['CosineAnnealingLR', 'CosineAnnealingWarmRestarts'])\n cosine_max_t_hp = CS.UniformIntegerHyperparameter(name='cosine_max_t', lower=50, upper=300, default_value=150)\n cosine_warm_hp = CS.UniformIntegerHyperparameter(name='warm_t_0', lower=50, upper=300, default_value=150)\n\n sgd_cond = CS.EqualsCondition(sgd_momentum_hp, optimizer_hp, 'SGD')\n rms_cond1 = CS.EqualsCondition(rms_momentum_hp, optimizer_hp, 'RMSprop')\n rms_cond2 = CS.EqualsCondition(rms_alpha_hp, optimizer_hp, 'RMSprop')\n cosine_warm_cond = CS.EqualsCondition(cosine_warm_hp, scheduler_hp, 'CosineAnnealingWarmRestarts')\n cosine_cond = CS.EqualsCondition(cosine_max_t_hp, scheduler_hp, 'CosineAnnealingLR')\n cs.add_hyperparameters([lr_hp, optimizer_hp, sgd_momentum_hp, rms_momentum_hp,\n rms_alpha_hp, scheduler_hp, cosine_max_t_hp, cosine_warm_hp])\n cs.add_conditions([sgd_cond, rms_cond1, rms_cond2, cosine_cond, cosine_warm_cond])\n # END TODO ################\n return cs","def get_config(self):\n config = super(EpsGreedyQPolicy, self).get_config()\n config['eps'] = self.eps\n return config","def _configure(self, config):\n self.friction_coef = config['friction_coef']\n self.num_cone_faces = config['num_cone_faces']\n self.num_samples = config['grasp_samples_per_surface_point']\n self.dir_prior = config['dir_prior']\n self.target_num_grasps = config['target_num_grasps']\n if self.target_num_grasps is None:\n self.target_num_grasps = config['min_num_grasps']\n\n self.min_contact_dist = config['min_contact_dist']\n self.num_grasp_rots = config['coll_check_num_grasp_rots']\n if 'max_num_surface_points' in config.keys():\n self.max_num_surface_points_ = config['max_num_surface_points']\n else:\n self.max_num_surface_points_ = 100","def update_configuration(self, config):\n\n config[\"data_transformation\"][\"n_classification_bins\"] = config[\"n_classification_bins\"]\n config[\"data_transformation\"][\"nassets\"] = config[\"nassets\"]\n config[\"data_transformation\"][\"classify_per_series\"] = config[\"classify_per_series\"]\n config[\"data_transformation\"][\"normalise_per_series\"] = config[\"normalise_per_series\"]\n\n return config","def build(self, mode, config):\n assert mode in ['training', 'inference']\n\n # Image size must be dividable by 2 multiple times\n h, w = config.IMAGE_SHAPE[:2]\n if h / 2**6 != int(h / 2**6) or w / 2**6 != int(w / 2**6):\n raise Exception(\"Image size must be dividable by 2 at least 6 times \"\n \"to avoid fractions when downscaling and upscaling.\"\n \"For example, use 256, 320, 384, 448, 512, ... etc. \")\n\n # Inputs\n input_image = KL.Input(\n shape=[None, None, 3], name=\"input_image\")\n input_image_meta = KL.Input(shape=[config.IMAGE_META_SIZE],\n name=\"input_image_meta\")\n if mode == \"training\":\n pass\n elif mode == \"inference\":\n # Anchors in normalized coordinates\n input_anchors = KL.Input(shape=[None, 4], name=\"input_anchors\")\n\n # Build the shared convolutional layers.\n # Bottom-up Layers\n # Returns a list of the last layers of each stage, 5 in total.\n # Don't create the thead (stage 5), so we pick the 4th item in the list.\n _, C2, C3, C4, C5 = resnet_graph(input_image, config.BACKBONE,\n stage5=True, train_bn=config.TRAIN_BN)\n # Top-down Layers\n # TODO: add assert to varify feature map sizes match what's in config\n P5 = KL.Conv2D(256, (1, 1), name='fpn_c5p5')(C5)\n P4 = KL.Add(name=\"fpn_p4add\")([\n KL.UpSampling2D(size=(2, 2), name=\"fpn_p5upsampled\")(P5),\n KL.Conv2D(256, (1, 1), name='fpn_c4p4')(C4)])\n P3 = KL.Add(name=\"fpn_p3add\")([\n KL.UpSampling2D(size=(2, 2), name=\"fpn_p4upsampled\")(P4),\n KL.Conv2D(256, (1, 1), name='fpn_c3p3')(C3)])\n P2 = KL.Add(name=\"fpn_p2add\")([\n KL.UpSampling2D(size=(2, 2), name=\"fpn_p3upsampled\")(P3),\n KL.Conv2D(256, (1, 1), name='fpn_c2p2')(C2)])\n # Attach 3x3 conv to all P layers to get the final feature maps.\n P2 = KL.Conv2D(256, (3, 3), padding=\"SAME\", name=\"fpn_p2\")(P2)\n P3 = KL.Conv2D(256, (3, 3), padding=\"SAME\", name=\"fpn_p3\")(P3)\n P4 = KL.Conv2D(256, (3, 3), padding=\"SAME\", name=\"fpn_p4\")(P4)\n P5 = KL.Conv2D(256, (3, 3), padding=\"SAME\", name=\"fpn_p5\")(P5)\n # P6 is used for the 5th anchor scale in RPN. Generated by\n # subsampling from P5 with stride of 2.\n P6 = KL.MaxPooling2D(pool_size=(1, 1), strides=2, name=\"fpn_p6\")(P5)\n\n # Note that P6 is used in RPN, but not in the classifier heads.\n rpn_feature_maps = [P2, P3, P4, P5, P6]\n mrcnn_feature_maps = [P2, P3, P4, P5]\n\n # Anchors\n if mode == \"training\":\n anchors = self.get_anchors(config.IMAGE_SHAPE)\n # Duplicate across the batch dimension because Keras requires it\n # TODO: can this be optimized to avoid duplicating the anchors?\n anchors = np.broadcast_to(anchors, (config.BATCH_SIZE,) + anchors.shape)\n # A hack to get around Keras's bad support for constants\n anchors = KL.Lambda(lambda x: tf.Variable(anchors), name=\"anchors\")(input_image)\n else:\n anchors = input_anchors\n\n # RPN Model\n rpn = build_rpn_model(config.RPN_ANCHOR_STRIDE,\n len(config.RPN_ANCHOR_RATIOS), 256)\n # Loop through pyramid layers\n layer_outputs = [] # list of lists\n for p in rpn_feature_maps:\n layer_outputs.append(rpn([p]))\n # Concatenate layer outputs\n # Convert from list of lists of level outputs to list of lists\n # of outputs across levels.\n # e.g. [[a1, b1, c1], [a2, b2, c2]] => [[a1, a2], [b1, b2], [c1, c2]]\n output_names = [\"rpn_class_logits\", \"rpn_class\", \"rpn_bbox\"]\n outputs = list(zip(*layer_outputs))\n outputs = [KL.Concatenate(axis=1, name=n)(list(o))\n for o, n in zip(outputs, output_names)]\n\n rpn_class_logits, rpn_class, rpn_bbox = outputs\n\n # Generate proposals\n # Proposals are [batch, N, (y1, x1, y2, x2)] in normalized coordinates\n # and zero padded.\n proposal_count = config.POST_NMS_ROIS_TRAINING if mode == \"training\"\\\n else config.POST_NMS_ROIS_INFERENCE\n rpn_rois = ProposalLayer(\n proposal_count=proposal_count,\n nms_threshold=config.RPN_NMS_THRESHOLD,\n name=\"ROI\",\n config=config)([rpn_class, rpn_bbox, anchors])\n\n if mode == \"training\":\n pass\n else:\n # Network Heads\n # Proposal classifier and BBox regressor heads\n mrcnn_class_logits, mrcnn_class, mrcnn_bbox =\\\n fpn_classifier_graph(rpn_rois, mrcnn_feature_maps, input_image_meta,\n config.POOL_SIZE, config.NUM_CLASSES,\n train_bn=config.TRAIN_BN)\n\n # Detections\n # output is [batch, num_detections, (y1, x1, y2, x2, class_id, score)] in \n # normalized coordinates\n detections = DetectionLayer(config, name=\"mrcnn_detection\")(\n [rpn_rois, mrcnn_class, mrcnn_bbox, input_image_meta])\n\n # Create masks for detections\n detection_boxes = KL.Lambda(lambda x: x[..., :4])(detections)\n mrcnn_mask = build_fpn_mask_graph(detection_boxes, mrcnn_feature_maps,\n input_image_meta,\n config.MASK_POOL_SIZE,\n config.NUM_CLASSES,\n train_bn=config.TRAIN_BN)\n\n model = KM.Model([input_image, input_image_meta, input_anchors],\n [detections, mrcnn_class, mrcnn_bbox,\n mrcnn_mask, rpn_rois, rpn_class, rpn_bbox],\n name='mask_rcnn')\n\n return model","def _build_config_sis3305_subgroup(self, config_name: str, slot: int, index: int):\n # create group\n gname = f\"SIS crate 3305 configurations[{index}]\"\n gpath = f\"{config_name}/{gname}\"\n self.create_group(gpath)\n\n # get channel array\n brd = self.slot_info[slot][0]\n sis_arr = self._active_brdch[\"SIS 3305\"][brd - 1]\n\n # populate attributes\n self[gpath].attrs.update(\n {\n \"Bandwidth\": np.uint32(1),\n \"Channel mode\": np.uint32(self._sis3305_mode),\n \"Clock rate\": np.uint32(0),\n \"Samples\": np.uint32(self.knobs.nt),\n \"Shot averaging (software)\": np.int32(1),\n }\n )\n for ii in range(1, 9):\n # setup\n if 1 <= ii <= 4:\n fpga_str = \"FPGA 1 \"\n ch = ii\n else:\n fpga_str = \"FPGA 2 \"\n ch = ii - 4\n\n # 'FPGA # Avail #' fields\n if self._sis3305_mode == 2 and ch != 1:\n mode = \"FALSE\"\n elif self._sis3305_mode == 1 and ch not in (1, 3):\n mode = \"FALSE\"\n else:\n mode = \"TRUE\"\n field = f\"{fpga_str}Avail {ch}\"\n self[gpath].attrs[field] = np.bytes_(mode)\n\n # 'FPGA # Ch #' fields\n field = f\"{fpga_str}Ch {ch}\"\n self[gpath].attrs[field] = np.int32(ii)\n\n # 'FPGA # Comment #' fields\n field = f\"{fpga_str}Comment {ch}\"\n self[gpath].attrs[field] = np.bytes_(\"\")\n\n # 'FPGA # Data type #' fields\n field = f\"{fpga_str}Data type {ch}\"\n self[gpath].attrs[field] = np.bytes_(f\"probe name {ii}\")\n\n # 'FPGA # Enabled #' fields\n field = f\"{fpga_str}Enabled {ch}\"\n self[gpath].attrs[field] = np.bytes_(\"TRUE\" if sis_arr[ii - 1] else \"FALSE\")","def default_assign_kernel_config(defn):\n # XXX(stephentu): model_descriptors should implement\n # is_conjugate()\n\n def is_nonconj(x):\n return x.name() == 'bbnc'\n\n nonconj_indices = [\n idx for idx, x in enumerate(defn.models()) if is_nonconj(x)\n ]\n\n defn = _validate_definition(defn)\n\n # assignment\n if nonconj_indices:\n # XXX(stephentu): 0.1 is arbitrary\n # XXX(stephentu): don't assume bbnc\n theta_config = {\n 'tparams': {i: {'p': 0.1} for i in nonconj_indices}\n }\n kernels = [\n ('assign_resample', {'m': 10}),\n ('theta', theta_config),\n ]\n else:\n kernels = ['assign']\n\n return kernels","def input_config():\n run_dir = 'runs/ODEMnistClassification/8'\n epoch = 'latest'\n device = 'cpu'\n min_end_time = 10\n max_end_time = 100\n tol = 1e-3","def __init__(self, config):\n self.cfg = config\n self.var_combinations = [\"tas:tas\", \"pr:pr\", \"pr:tas\"]\n self.seasons = [\"jja\", \"djf\", \"annual\"]\n self.projects = [\"cmip5\", \"cmip6\"]\n self.variables = [\"tas\", \"pr\"]\n self.scenarios = [\"26\", \"45\", \"85\"]\n\n # generate list of candidate bound limits\n small = np.arange(0.1, 1, 0.1)\n medium = np.arange(1, 11)\n high = np.arange(20, 100, 10)\n v_high = np.arange(150, 400, 50)\n self.bound_candidates = np.concatenate(\n (small, medium, high, v_high)) * 5 / 4","def QCNN_layer_gen(self):\n pixels = self.filter_size**2\n # filter size: 2^n only for this version!\n if np.log2(pixels) % 1 != 0:\n raise NotImplementedError(\"filter size: 2^n only available\")\n cirq_qubits = cirq.GridQubit.rect(self.filter_size, self.filter_size)\n # mapping input data to circuit\n input_circuit = cirq.Circuit()\n input_params = [sympy.symbols('a%d' %i) for i in range(pixels)]\n for i, qubit in enumerate(cirq_qubits):\n input_circuit.append(cirq.rx(np.pi*input_params[i])(qubit))\n # apply learnable gate set to QCNN circuit\n QCNN_circuit = cirq.Circuit()\n step_size = [2**i for i in range(np.log2(pixels).astype(np.int32))]\n for step in step_size:\n for target in range(0, pixels, 2*step):\n QCNN_circuit.append(self._QConv(step, target, cirq_qubits))\n # merge the circuits\n full_circuit = cirq.Circuit()\n full_circuit.append(input_circuit)\n full_circuit.append(QCNN_circuit)\n self.circuit = full_circuit # save circuit to the QCNN layer obj.\n self.params = input_params + self.learning_params\n self.op = cirq.Z(cirq_qubits[0])","def get_config(self):\n layer_config = {\n \"anchors\": self._anchors, \n \"classes\": self._classes,\n \"ignore_thresh\": self._ignore_thresh, \n \"truth_thresh\": self._truth_thresh, \n \"iou_thresh\": self._iou_thresh, \n \"loss_type\": self._loss_type, \n \"iou_normalizer\": self._iou_normalizer,\n \"cls_normalizer\": self._cls_normalizer, \n \"scale_x_y\": self._scale_x_y, \n }\n layer_config.update(super().get_config())\n return layer_config","def create_config(self):\n\n #FIXME: Try to do it over loops ie. self[attr].set_config()\n for attr, value in self.__dict__.items():\n if attr == \"connection\":\n self.connection.set_config(self.cfg)\n if attr == \"ipv4\":\n self.ipv4.set_config(self.cfg)\n if attr == \"ipv6\":\n self.ipv6.set_config(self.cfg)\n if attr == \"_802_3_ethernet\" and not value == \"none\":\n self._802_3_ethernet.set_config(self.cfg)\n if attr == \"_802_11_wireless\" and not value == \"none\":\n self._802_11_wireless.set_config(self.cfg)\n if attr == \"_802_11_wireless_security\" and not value == \"none\":\n self._802_11_wireless_security.set_config(self.cfg)","def generateConfig(run,subrun,conditions):\n \n configname = (conditions.numcdir + \"/\" + str(run) + \"/\" + str(subrun)\n + \"/numc_config_\" + str(run) + \"_\" + str(subrun) + \".cfg\")\n \n configContents = \"\"\n \n configContents += \"[software]\\n\"\n if conditions.oldneut:\n configContents += \"neut_setup_script = /project/t/tanaka/T2K/neut/branches/5.1.4.2_nd280_ROOTv5r34p09n01/src/neutgeom/setup.sh\\n\"\n elif conditions.newoldneut:\n configContents += \"neut_setup_script = /project/t/tanaka/T2K/neut/branches/5.1.4.3_nd280/src/neutgeom/setup.sh\\n\"\n else:\n #configContents += \"neut_setup_script = /project/t/tanaka/T2K/neut/branches/5.3.1_nd280/src/neutgeom/setup.sh\\n\"\n #configContents += \"neut_setup_script = /project/t/tanaka/T2K/neut/branches/5.3.1_nd280_wBBBA05/src/neutgeom/setup.sh\\n\"\n configContents += \"neut_setup_script = /project/t/tanaka/T2K/neut/branches/5.3.2_nd280/src/neutgeom/setup.sh\\n\"\n \n configContents += \"[geometry]\\n\"\n\n configContents += \"baseline = \" + conditions.geometry +\"\\n\"\n if conditions.waterair == \"water\":\n configContents += \"p0d_water_fill = 1\\n\"\n else:\n configContents += \"p0d_water_fill = 0\\n\"\n \n configContents += \"\"\"\n \n[configuration]\nmodule_list = neutMC\n\n[filenaming]\n\"\"\"\n configContents += \"comment = \" + conditions.comment + \"\\n\"\n configContents += \"run_number = \" + str(run) +\"\\n\"\n configContents += \"subrun = \" + str(subrun) + \"\\n\"\n\n if conditions.oldneut:\n configContents += \"\"\" \n\n[neutrino]\nneut_card = /project/t/tanaka/T2K/neut/branches/5.1.4.2_nd280_ROOTv5r34p09n01/src/neutgeom/neut.card\n\"\"\"\n elif conditions.newoldneut:\n configContents += \"\"\" \n\n[neutrino]\nneut_card = /project/t/tanaka/T2K/neut/branches/5.1.4.3_nd280/src/neutgeom/neut.card\n\"\"\"\n else:\n configContents += \"\"\" \n\n[neutrino]\nneut_card = /project/t/tanaka/T2K/neut/branches/5.3.2_nd280/src/neutgeom/neut.card\n\"\"\"\n\n configContents += \"flux_file = \" + conditions.ram_disk + \"/\" + conditions.flux_base + \"\\n\"\n\n#flux_file = flux_file\n#\"\"\"\n\n# configContents += \"flux_file_path = \" + conditions.ram_disk + \"/\" + conditions.flux_base\n\n# configContents += \"\"\" \n#flux_file_start = 1\n#flux_file_stop = 300\n#\"\"\"\n\n configContents += \"maxint_file = \" + conditions.maxint_file_local + \"\\n\"\n\n# default: 5e17 but for basket MC special production higher\n configContents += \"\"\" \npot = 5.0e17\nneutrino_type = beam\n\"\"\"\n if conditions.baskmagn == \"basket\":\n configContents += \"\"\" \nflux_region = basket\nmaster_volume = Basket \nrandom_start = 1\n\"\"\"\n elif conditions.baskmagn == \"magnet\":\n configContents += \"\"\" \nflux_region = magnet\nmaster_volume = Magnet \nrandom_start = 1\n\"\"\"\n else:\n print \"Unknown basket/magnet condition\"\n \n\n configContents += \"random_seed = \" + str(getRandom()) +\"\\n\"\n configContents += \"neut_seed1 = \" + str(getRandom())+\"\\n\" \n configContents += \"neut_seed2 = \" + str(getRandom())+\"\\n\" \n configContents += \"neut_seed3 = \" + str(getRandom())+\"\\n\" \n\n configContents += \"\\n\"\n configContents += \"[nd280mc]\\n\"\n configContents += \"mc_type=Neut_RooTracker \\n\"\n\n #print configContents\n\n try:\n macFile = open(configname,\"w\")\n macFile.write(configContents)\n \n except:\n print \"can't write config file\" \n \n\n return configname","def __init__(self):\n super(enc_clf, self).__init__()\n\n self.fc1 = nn.Linear(784, 1024)\n self.fc2 = nn.Linear(1024, 1024)\n self.fc3 = nn.Linear(1024, 512)\n self.fc4 = nn.Linear(512, 10)","def get_test_config():\n config = get_config()\n config.batch_size = 2\n config.eval_batch_size = 2\n config.eval_num = 2\n config.eval_avg_num = 1\n config.num_train_steps = 2\n config.log_loss_every_steps = 1\n config.eval_every_steps = 1\n config.checkpoint_every_steps = 1\n config.df_dim = 16\n config.gf_dim = 16\n config.z_dim = 8\n config.show_num = 4\n config.num_epochs = 1\n config.shuffle_buffer_size = 10\n return config","def get_config(self):\n config = {'name': self.name, 'trainable': self.trainable}\n if hasattr(self, '_batch_input_shape'):\n config['batch_input_shape'] = self._batch_input_shape\n if hasattr(self, 'dtype'):\n config['dtype'] = self.dtype\n return config","def make_config(outfile, sample_rate, numof_channels, mode, server = 'localhost', shape = 'None',\n security_mode = False, saving_mode = False, data_file = 'Nofile', format = 'binary',\n resolution = 0.1, returning_speed = 8, channels = 0,\n color_bg = 'white', color_trigger = 'black',\n size_window = (1000, 800)):\n\n\n config = ConfigParser.RawConfigParser()\n\n config.add_section('technics')\n config.add_section('visualization')\n config.add_section('security')\n config.add_section('data')\n\n config.set('technics', 'sample_rate', sample_rate)\n config.set('technics', 'numof_channels', numof_channels)\n config.set('technics', 'server', server)\n config.set('technics', 'resolution', resolution)\n config.set('technics', 'speed', returning_speed)\n if channels == 0:\n channels = range(numof_channels+1)[1:numof_channels+1]\n config.set('technics', 'channels', channels)\n\n config.set('visualization', 'mode', mode)\n config.set('visualization', 'color_bg', color_bg)\n config.set('visualization', 'color_trigger', color_trigger)\n config.set('visualization', 'size_window', size_window)\n\n config.set('security', 'security_mode', security_mode)\n\n config.set('data', 'saving_mode', saving_mode)\n config.set('data', 'file', data_file)\n config.set('data', 'format', format)\n\n config_file = open(outfile, 'w+')\n\n config.write(config_file)","def __init__(\n self, CONFIG, numActionList, dimList, mode=\"RA\", terminalType=\"g\",\n verbose=True\n ):\n super(DDQNPursuitEvasion, self).__init__(CONFIG)\n\n self.mode = mode # only supports 'RA'\n self.terminalType = terminalType\n\n # == ENV PARAM ==\n self.numJoinAction = int(numActionList[0] * numActionList[1])\n self.numActionList = numActionList\n\n # == Build neural network for (D)DQN ==\n assert dimList is not None, \"Define the architecture\"\n assert dimList[-1] == self.numJoinAction, (\n \"We expect the dim of the last layer to be \"\n + \"{:d}, but get {:d}\".format(self.numJoinAction, dimList[-1])\n )\n self.dimList = dimList\n self.actType = CONFIG.ACTIVATION\n self.build_network(dimList, self.actType, verbose)\n print(\n \"DDQN: mode-{}; terminalType-{}\".format(self.mode, self.terminalType)\n )","def get_config():\n config = dict(\n name=\"defaults\",\n # Either use geometric, zipf, or uniform i.e., data variable\n # can take one of \"geometric\", \"zipf\", \"uniform\".\n distribution=\"zipf\",\n lbd_geometric=0.8,\n degree_zipf=1.0,\n # Flags to indicate which methods to compare.\n run_approx_miracle=False,\n run_miracle=False,\n run_modified_miracle=True,\n run_ss=True,\n run_rhr=True,\n encoding_type=\"fast\", # Can take either fast or normal\n # Common parameters.\n num_itr=1,\n coding_cost=14,\n coding_cost_multiplier=1,\n approx_coding_cost_multiplier=3,\n approx_t=6,\n # Specific parameters (leave them as they are for now).\n delta=10**(-6),\n alpha=1.0,\n # Variation.\n vary=\"eps\", # Can take one of \"cc\", \"k\", \"n\", \"eps\".\n cc_space=[6, 8, 10, 12, 14],\n k_space=[200, 400, 600, 800, 1000],\n n_space=[2000, 4000, 6000, 8000, 10000],\n eps_space=list(range(1, 9)),\n # Defaults.\n n=5000,\n k=500,\n t=3,\n epsilon_target=6,\n )\n config = config_dict.ConfigDict(config)\n config.lock() # Prevent addition of new fields.\n return config","def __init__(self, config, fe_space, scheme_type=SchemeType.EXPLICIT_ORDERONE, init_cond_type=InitialConditionType.NONE):\n self.config = config\n self.fe_space = fe_space\n self.init_cond_type = init_cond_type\n self.scheme_type = scheme_type\n\n self.u0 = None\n self.u1 = None\n self.u2 = None\n self.ustar = None\n self.operator1 = None\n self.operator2 = None\n self.rhs_operator = None\n self.inv_operator = None\n self.timestep = None\n self.time = 0.0","def __init__(self, classCount):\n self.NUM_CLASSES = 1+classCount\n self.STEPS_PER_EPOCH = self.STEPS_PER_EPOCH / self.IMAGES_PER_GPU\n self.VALIDATION_STEPS = self.VALIDATION_STEPS / self.IMAGES_PER_GPU\n super(ModelConfig, self).__init__()","def create_configuration(self):\r\n self.current_config = np.zeros((self.n, self.d))\r\n for point_charge in range(self.n):\r\n angle = 2 * PI * np.random.rand()\r\n radius = self.r * np.random.rand()\r\n x = radius * np.cos(angle)\r\n y = radius * np.sin(angle)\r\n self.current_config[point_charge][0] = x\r\n self.current_config[point_charge][1] = y\r\n\r\n self.E1 = calculate_total_energy(self.current_config)","def __init__(self, cfg):\r\n\r\n\t\tself.image_size = cfg.MODEL.INPUT.IMAGE_SIZE\r\n\t\tanchor_config = cfg.MODEL.ANCHORS\r\n\t\tself.feature_maps = anchor_config.FEATURE_MAPS\r\n\t\tself.min_sizes = anchor_config.MIN_SIZES\r\n\t\tself.max_sizes = anchor_config.MAX_SIZES \r\n\t\tself.aspect_ratios = anchor_config.ASPECT_RATIOS\r\n\t\tself.clip = anchor_config.CLIP","def __init__(self, config={}):\n\n _, fileprefix = mkstemp()\n idx = fileprefix.rfind('/')\n fileprefix = fileprefix[idx + 1:]\n self.network = NetworkCompression(prefix = fileprefix)\n self.num_actions = self.network.get_action_space()\n self.input_shape = self.network.get_observation_space()\n self.reward = self.network.get_reward()\n self.done = False\n\n self.action_space = Box(low=0, high=1, shape=(self.num_actions,), dtype=np.uint8)\n self.observation_space = Box(low=0, high=10000,\n shape=(self.input_shape[0]*self.input_shape[1],),\n dtype=np.uint8)\n\n self.cur_pos = self._get_current_pos_in_1d()\n\n return","def createCfg_project(self, jobOptions):\n last_line = '%s %s %s %s' % (jobOptions['projection_module'], self.era, jobOptions['histName'], jobOptions['outputFile'])\n if self.projection_module != 'puHist':\n last_line += ' %.6e' % jobOptions['ref_genWeight']\n lines = jobOptions['inputFiles'] + [ '', last_line ]\n assert(len(lines) >= 3)\n createFile(jobOptions['cfgFile_path'], lines, nofNewLines = 1)","def ncwrt_retrieval_config( retr_setup, outname=None ):\n\n #-- set name of file to be generated\n act_outname = outname if outname!=None else 'retrconfig.nc'\n msg = \"Start writing configuration file ***{}***...\".format(act_outname)\n FileLogger.info(msg)\n\n\n #-- compression settings\n zlev = retr_setup.zlev\n use_zlib = retr_setup.use_zlib\n\n #--\n schedule_dct = retr_setup.schedule_dct\n statevector = retr_setup.prstate\n #-- turn list into array\n sim_typ = np.array(schedule_dct['sim_typ'], dtype=np.int32)\n timepts = schedule_dct['date_utc']\n nstvar,npts = statevector.shape\n #-- overpass geometries SZA,SAA,VZA,VAA\n ivgeom = np.empty((npts,4), dtype=np.float64)\n ivgeom[:,0] = schedule_dct['sza']\n ivgeom[:,1] = schedule_dct['saa']\n ivgeom[:,2] = schedule_dct['vza']\n ivgeom[:,3] = schedule_dct['vaa']\n\n #-- temporal settings, create time-values (time-since)\n time_start, time_end = datelst_get_month_aligned_bounds(timepts)\n time_coverage_start = time_start.strftime('%Y-%m-%dT%H:%M:%S')\n time_coverage_end = time_end.strftime('%Y-%m-%dT%H:%M:%S')\n ref_time = dt.datetime(timepts[0].year,1,1) #January1st in year of first point in time\n time_unit = 'seconds since {}'.format(ref_time.strftime('%Y-%m-%dT%H:%M:%S'))\n time_values = nc4.date2num(timepts, time_unit)\n\n #-- ensure directory exists\n mkdirp_smart(os.path.dirname(act_outname))\n\n #-- open file pointer\n ncfp = nc4.Dataset(act_outname, 'w')\n #-- add dimensions\n d1 = ncfp.createDimension('npoints',npts)\n d2 = ncfp.createDimension('ngeo',4)\n\n #-- time-value\n ncvar = ncfp.createVariable( 'time', statevector.dtype, ('npoints',),\n zlib=use_zlib, complevel=zlev )\n ncvar.setncattr('standard_name','time')\n ncvar.setncattr('long_name','time')\n ncvar.setncattr('units', time_unit)\n ncvar[:] = time_values[:]\n\n #-- simulation type\n ncvar = ncfp.createVariable( 'sim_typ', sim_typ.dtype, ('npoints',),\n zlib=use_zlib, complevel=zlev )\n ncvar[:] = sim_typ[:]\n ncvar.setncattr('long_name','simulation_type')\n ncvar.setncattr('comment', 'integer value which is to be bit-interpreted')\n ncvar.setncattr('nobits_set', 'time-point with other state')\n ncvar.setncattr('bit0_is_set', 'time-point for S1 simulation')\n ncvar.setncattr('bit1_is_set', 'time-point for S2 simulation')\n ncvar.setncattr('bit2_is_set', 'time-point for S1A simulation')\n ncvar.setncattr('bit3_is_set', 'time-point for S1B simulation')\n ncvar.setncattr('bit4_is_set', 'time-point for S2A simulation')\n ncvar.setncattr('bit5_is_set', 'time-point for S2B simulation')\n \n #-- illumination-view geometry\n ncvar = ncfp.createVariable( 'ivgeom', ivgeom.dtype, ('npoints','ngeo'),\n zlib=use_zlib, complevel=zlev )\n ncvar.setncattr('sza','igeo: 0')\n ncvar.setncattr('saa','igeo: 1')\n ncvar.setncattr('vza','igeo: 2')\n ncvar.setncattr('vaa','igeo: 3')\n ncvar[:,:] = ivgeom[:,:]\n \n #-- global attributes\n ncfp.setncattr('creator_name',\"The Inversion Lab, Hamburg, Germany\")\n ncfp.setncattr('creator_email', \"Michael.Vossbeck(at)Inversion-Lab.com\")\n ncfp.setncattr('netcdf_libversion',\"{}\".format(nc4.__netcdf4libversion__))\n ncfp.setncattr('date_created',\"{}\".format(dt.datetime.utcnow().isoformat()))\n ncfp.setncattr('time_coverage_start',time_coverage_start)\n ncfp.setncattr('time_coverage_end',time_coverage_end)\n\n #-- close file pointer\n ncfp.close()\n\n # logging\n msg = \"...writing ***{}*** DONE\".format(act_outname)\n FileLogger.info(msg)","def createCfg_prep_dcard(self, jobOptions):\n category_output = self.channel\n if jobOptions['label']:\n category_output += \"_%s\" % jobOptions['label']\n lines = []\n lines.append(\"process.fwliteInput.fileNames = cms.vstring('%s')\" % jobOptions['inputFile'])\n lines.append(\"process.fwliteOutput.fileName = cms.string('%s')\" % jobOptions['datacardFile'])\n lines.append(\"process.prepareDatacards.processesToCopy = cms.vstring(%s)\" % self.prep_dcard_processesToCopy)\n lines.append(\"process.prepareDatacards.signals = cms.vstring(%s)\" % self.prep_dcard_signals)\n lines.append(\"process.prepareDatacards.makeSubDir = cms.bool(True)\")\n lines.append(\"process.prepareDatacards.categories = cms.VPSet(\")\n for charge in [\"OS\", \"SS\"]:\n for ptEtaBin in [\n \"BB_LL\", \"BB_ML\", \"BB_MM\", \"BB_HL\", \"BB_HM\", \"BB_HH\",\n \"EE_LL\", \"EE_ML\", \"EE_MM\", \"EE_HL\", \"EE_HM\", \"EE_HH\",\n \"BE_LL\", \"BE_ML\", \"EB_ML\",\"BE_MM\", \"BE_HL\", \"EB_HL\",\n \"BE_HM\", \"EB_HM\", \"BE_HH\", \"total\",\n ]:\n lines.append(\" cms.PSet(\")\n lines.append(\" input = cms.string('%s/%s'),\" % (charge, ptEtaBin))\n lines.append(\" output = cms.string('ttH_%s_%s_%s')\" % (self.channel, charge, ptEtaBin))\n lines.append(\" ),\")\n lines.append(\")\")\n lines.append(\"process.prepareDatacards.histogramToFit = cms.string('%s')\" % jobOptions['histogramToFit'])\n lines.append(\"process.prepareDatacards.sysShifts = cms.vstring(%s)\" % systematics.muon_E)\n create_cfg(self.cfgFile_prep_dcard, jobOptions['cfgFile_modified'], lines)","def configure(self):\n self.data_batch_file = self.get_value_from_config('data_batch_file')\n self.batch_meta_file = self.get_value_from_config('batch_meta_file')\n self.has_background = self.get_value_from_config('has_background')\n self.num_classes = self.get_value_from_config('num_classes')\n self.converted_images_dir = self.get_value_from_config('converted_images_dir')\n if not self.converted_images_dir:\n self.converted_images_dir = self.data_batch_file.parent / 'converted_images'\n self.convert_images = self.get_value_from_config('convert_images')\n # create directory for storing images if it is necessary\n if self.convert_images and not self.converted_images_dir.exists():\n self.converted_images_dir.mkdir(parents=True)\n self.dataset_meta = self.get_value_from_config('dataset_meta_file')","def __init__(self, **config):\n super(CNN, self).__init__()\n in_channel = [26] + config['cnn_target_filters']\n kernels = config['cnn_target_kernels']\n self.layer_size = len(config['cnn_target_filters'])\n self.visual_attention=config['visual_attention']\n self.concatenation=config['concatenation']\n self.convs = nn.ModuleList([nn.Conv1d(in_channels=in_channel[i],\n out_channels=in_channel[i + 1],\n kernel_size=kernels[i]) for i in range(self.layer_size)])\n self.convs = self.convs.float()\n self.attention = config['attention']\n protein_size = self.simulate_output((26, 1000))\n self.fc = nn.Linear(protein_size, config['hidden_dim_protein'])\n self.Attention=Attention(**config)","def __init__(self, model_config):\n # Training Parameters\n self.__learning_rate = model_config[\"cnnLearningRate\"]\n\n # Network Parameters\n self.__num_classes = model_config[\"numClasses\"]\n self.__weight_decay = 1e-4\n self.__num_gpus = model_config[\"numGpus\"]\n self.__use_csnn = model_config[\"useCsnn\"]\n\n self.__csnn = Csnn(model_config)","def __init__(self, config, osc=None):\n super(BasicConsolidation, self).__init__(config, osc)\n\n # set default value for the number of released nodes\n self.number_of_released_nodes = 0\n # set default value for the number of migrations\n self.number_of_migrations = 0\n # set default value for number of allowed migration attempts\n self.migration_attempts = 0\n\n # set default value for the efficacy\n self.efficacy = 100\n\n self._ceilometer = None\n\n # TODO(jed): improve threshold overbooking?\n self.threshold_mem = 1\n self.threshold_disk = 1\n self.threshold_cores = 1\n\n # TODO(jed): target efficacy\n self.target_efficacy = 60\n\n # TODO(jed): weight\n self.weight_cpu = 1\n self.weight_mem = 1\n self.weight_disk = 1\n\n # TODO(jed): bound migration attempts (80 %)\n self.bound_migration = 0.80","def reproduct_config(config):\r\n\r\n config.dim_emb = 256\r\n config.dim_phone = 128\r\n config.dim_phone_emb = 128\r\n config.dim_pre = 512\r\n config.use_drop = True\r\n config.num_emo_classes = 4\r\n \r\n config.len_crop = 96\r\n config.num_mels = 80\r\n config.wav2vec_feat_len = 1024\r\n\r\n config.batch_size = 2\r\n config.num_iters = 1000000\r\n config.checkpoint_step = 1000001 # do not save checkpoints\r\n\r\n ## save checkpoints every 50k iterations\r\n # config.num_iters = 500000\r\n # config.checkpoint_step = 50000\r\n \r\n config.speech_input = \"wav2vec\"\r\n\r\n if config.reproduct_mode == \"small\":\r\n config.dim_neck = 8\r\n config.freq = 48\r\n elif config.reproduct_mode == \"large\":\r\n config.dim_neck = 128\r\n config.freq = 2\r\n elif config.reproduct_mode == \"spec\":\r\n config.speech_input = \"spec\"\r\n config.dim_neck = 8\r\n config.freq = 48\r\n\r\n return config","def create_nncf_network(\n model: torch.nn.Module,\n config: NNCFConfig,\n dummy_forward_fn: Callable[[Module], Any] = None,\n wrap_inputs_fn: Callable = None,\n wrap_outputs_fn: Callable = None,\n) -> NNCFNetwork:\n\n if dummy_forward_fn is not None and wrap_inputs_fn is None:\n raise ValueError(\n \"A custom dummy forward function was specified, but the corresponding input wrapping function \"\n \"was not. In case a custom dummy forward function is specified for purposes of NNCF graph \"\n \"building, then the wrap_inputs_fn parameter MUST also be specified and be consistent with \"\n \"the input wrapping done in dummy_forward_fn.\"\n )\n\n # Preserve `.training`/`.requires_grad` state since we will be building NNCFNetwork in `.eval` mode\n with training_mode_switcher(model, is_training=False):\n # Compress model that will be deployed for the inference on target device. No need to compress parts of the\n # model that are used on training stage only (e.g. AuxLogits of Inception-v3 model) or unused modules with\n # weights. As a consequence, no need to care about spoiling BN statistics, as they're disabled in eval mode.\n\n input_info_list = create_input_infos(config)\n scopes_without_shape_matching = config.get(\"scopes_without_shape_matching\", [])\n ignored_scopes = config.get(\"ignored_scopes\")\n target_scopes = config.get(\"target_scopes\")\n\n nncf_network = NNCFNetwork(\n model,\n input_infos=input_info_list,\n dummy_forward_fn=dummy_forward_fn,\n wrap_inputs_fn=wrap_inputs_fn,\n wrap_outputs_fn=wrap_outputs_fn,\n ignored_scopes=ignored_scopes,\n target_scopes=target_scopes,\n scopes_without_shape_matching=scopes_without_shape_matching,\n )\n\n nncf_network.nncf.get_tracing_context().disable_trace_dynamic_graph()\n\n synchronize_all_processes_in_distributed_mode()\n return nncf_network","def setup(args):\n cfg = get_cfg()\n\n cfg.merge_from_file(model_zoo.get_config_file(\"COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\"))\n cfg.merge_from_list(args.opts)\n\n # configs for training\n if args.small_vidor: # cfg.DATASETS.VIDOR.SIZE == 'small':\n cfg.DATASETS.TRAIN = (\"vidor_small_train\",)\n elif args.small_vidor_10imgs: # cfg.DATASETS.VIDOR.SIZE == 'small-10imgs':\n cfg.DATASETS.TRAIN = (\"vidor_small_10imgs_train\",)\n else:\n cfg.DATASETS.TRAIN = (\"vidor_large_train\",)\n # cfg.DATALOADER.NUM_WORKERS = 2\n if not args.eval_only:\n cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(\"COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\") # Let training initialize from model zoo\n factor = 4\n cfg.SOLVER.IMS_PER_BATCH = 16 * factor\n cfg.SOLVER.BASE_LR = 0.0001 * factor # finetune using 10x smaller base_lr\n cfg.SOLVER.MAX_ITER = 270000 // factor \n cfg.SOLVER.STEPS = [210000 // factor, 250000 // factor]\n # cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128 # default: 512\n cfg.MODEL.ROI_HEADS.NUM_CLASSES = 78\n\n # configs for testing\n # cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, \"model_final.pth\")\n if args.small_vidor: # cfg.DATASETS.VIDOR.SIZE == 'small':\n cfg.DATASETS.TEST = (\"vidor_small_val\",)\n elif args.small_vidor_10imgs: # cfg.DATASETS.VIDOR.SIZE == 'small-10imgs':\n cfg.DATASETS.TEST = (\"vidor_small_10imgs_val\",)\n else:\n cfg.DATASETS.TEST = (\"vidor_large_val\",)\n # cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5\n\n # cfg.OUTPUT_DIR = './output/train_vidor_with_pseudo_labels'\n \n \n if not args.eval_only:\n os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)\n cfg.freeze()\n default_setup(cfg, args)\n return cfg","def __init__(self,\n env_spec,\n conv_filters,\n conv_filter_sizes,\n conv_strides,\n conv_pads,\n hidden_sizes=(32, 32),\n pool_size=2,\n name=\"ContinuousConvQFunc\",\n hidden_nonlinearity=tf.nn.relu,\n output_nonlinearity=None,\n action_merge_layer=-2,\n input_include_goal=False,\n weight_normalization=False,\n pooling=False,\n bn=False):\n Serializable.quick_init(self, locals())\n\n self.name = name\n self._env_spec = env_spec\n if input_include_goal:\n self._obs_dim = env_spec.observation_space.flat_dim_with_keys(\n [\"observation\", \"desired_goal\"])\n else:\n self._obs_dim = env_spec.observation_space.flat_dim\n self._action_dim = env_spec.action_space.flat_dim\n self._conv_filters = conv_filters\n self._conv_filter_sizes = conv_filter_sizes\n self._conv_strides = conv_strides\n self._conv_pads = conv_pads\n self._hidden_sizes = hidden_sizes\n self._hidden_nonlinearity = hidden_nonlinearity\n self._action_merge_layer = action_merge_layer\n self._output_nonlinearity = output_nonlinearity\n self._batch_norm = bn\n self._weight_normalization = weight_normalization\n self._pooling = pooling\n self._pool_size = pool_size\n self._f_qval, self._output_layer, self._obs_layer, self._action_layer = self.build_net( # noqa: E501\n name=self.name)\n LayersPowered.__init__(self, [self._output_layer])","def get_config(self) -> Dict[str, Any]:\n return {\n 'num_classes': self.num_classes,\n 'name': self.name,\n 'dtype': self.dtype,\n 'sparse_y_true': self.sparse_y_true,\n 'sparse_y_pred': self.sparse_y_pred,\n 'axis': self.axis,\n }","def configure(self, cnf=None, **kw):\n return self.config(cnf, **kw)","def fpp_config(koi, **kwargs):\n folder = os.path.join(KOI_FPPDIR, ku.koiname(koi))\n if not os.path.exists(folder):\n os.makedirs(folder)\n config = ConfigObj(os.path.join(folder,'fpp.ini'))\n\n koi = ku.koiname(koi)\n\n rowefit = jrowe_fit(koi)\n\n config['name'] = koi\n ra,dec = ku.radec(koi)\n config['ra'] = ra\n config['dec'] = dec\n config['rprs'] = rowefit.ix['RD1','val']\n config['period'] = rowefit.ix['PE1', 'val']\n\n config['starfield'] = kepler_starfield_file(koi)\n\n for kw,val in kwargs.items():\n config[kw] = val\n\n config['constraints'] = {}\n config['constraints']['maxrad'] = default_r_exclusion(koi)\n try:\n config['constraints']['secthresh'] = pipeline_weaksec(koi)\n except NoWeakSecondaryError:\n pass\n\n return config","def configure_nccl():\n os.environ[\"NCCL_DEBUG\"] = \"INFO\"\n os.environ[\"NCCL_SOCKET_IFNAME\"] = \"ib0\"\n os.environ[\"NCCL_IB_DISABLE\"] = \"1\"\n\n os.environ[\"NCCL_LAUNCH_MODE\"] = \"PARALLEL\"\n os.environ[\"NCCL_IB_HCA\"] = subprocess.getoutput(\n \"cd /sys/class/infiniband/ > /dev/null; for i in mlx5_*; \"\n \"do cat $i/ports/1/gid_attrs/types/* 2>/dev/null \"\n \"| grep v >/dev/null && echo $i ; done; > /dev/null\"\n )\n os.environ[\"NCCL_IB_GID_INDEX\"] = \"3\"\n os.environ[\"NCCL_IB_TC\"] = \"106\"","def __init__(self, n, **kwargs):\n super(Quay, self).__init__(QC)\n\n for i in range(n):\n super(Quay, self).append(QC())\n\n for p in QC.PROPERTY:\n if p in kwargs.keys():\n if isinstance(kwargs[p], (float, int)):\n for q in self.qcs:\n setattr(q, p, kwargs[p])\n elif len(kwargs[p]) == self.size:\n for i, q in enumerate(self.qcs):\n setattr(q, p, kwargs[p][i])\n else:\n raise QCSPGenException(\"- {0}=n or =[{0}_1,...{0}_n] where n is the number of QCs!\".format(p))","def get_default_config(self):\n \n config = {}\n \n # default z_0_hat, zeros, flexible\n config['z_0_hat_option'] = 'flexible'\n config['initial_z_0_hat'] = np.zeros(self.dimension)\n \n # default P_0_hat, identity times a small scalar, flexible\n config['P_0_hat_option'] = 'flexible'\n config['initial_P_0_hat'] = 0.1 * np.eye(self.dimension)\n \n # default A, identity, flexible\n config['AB_option'] = 'flexible'\n config['initial_A'] = np.eye(self.dimension)\n config['initial_B'] = np.zeros((self.dimension, self.control_dimension))\n \n # default Q, identity times a small scalar, flexible\n config['Q_option'] = 'flexible'\n config['initial_Q'] = 0.1 * np.eye(self.dimension)\n \n # default R, identity times a small scalar, flexible\n config['R_option'] = 'flexible'\n config['initial_R'] = 0.1 * np.eye(self.dimension)\n \n # default stopping criteria, threshold 1e-5, num_iterations 1000\n # stop whenever either of the two critieria is reached\n config['threshold'] = 1e-5\n config['num_iterations'] = 1000\n\n return config","def configure(config_file):\n Config.config_file = config_file\n config = ConfigParser()\n config.optionxform = str\n config.read(config_file)\n dct = {}\n for item in list(config.sections()):\n dct[item] = dict(config.items(item))\n\n for item in dct.keys():\n if item == 'config':\n for key in dct[item].keys():\n dct[item][key] = tf(dct[item][key])\n\n if item == 'continuum':\n for key, val in dct[item].items():\n val = val.split(',')\n if len(val) == 2:\n dct[item][key] = {'xlim': float(val[0]), 'ylim': float(val[1])}\n dct[item][key] = {'ylim': float(val[0])}\n else:\n for k in [\"N\", \"b\", \"z\"]:\n try:\n dct[item][k] = dct[item][k].replace(\" \", \"\")\n dct[item][k] = list(map(float, dct[item][k].strip().split(',')))\n except KeyError:\n pass\n\n cond = []\n if \"N\" in dct[item].keys():\n cond += [dct[item][\"N\"][0] < 10., dct[item][\"N\"][-1] > 23.,\n dct[item][\"N\"][-1] < dct[item][\"N\"][0]]\n\n if \"b\" in dct[item].keys():\n cond += [dct[item][\"b\"][0] < 0., dct[item][\"b\"][-1] < dct[item][\"b\"][0]]\n\n if \"z\" in dct[item].keys():\n cond += [dct[item][\"z\"][-1] < dct[item][\"z\"][0]]\n\n if any(cond):\n raise Exception(\"check your random sampling inputs\")\n\n set_config_defaults(dct)\n _set_values(dct)","def __init__(self):\n parameters_list = []\n self.config_dict = self.open_config(parameters_list)\n\n # Define defaults\n self.disc_gt = 0.0\n self.disc_out = 0.0","def __init__(\n self,\n f,\n lb,\n ub,\n n_init,\n max_evals,\n batch_size=1,\n jitter=1e-4,\n n_discrete_points=5000,\n max_preconditioner_size=50,\n num_ciq_samples=15,\n max_minres_iterations=200,\n minres_tolerance=1e-3,\n use_ciq=True,\n use_keops=False,\n dtype=None,\n device=None,\n verbose=False,\n ):\n self.f = f\n self.lb = lb.to(dtype=dtype, device=device)\n self.ub = ub.to(dtype=dtype, device=device)\n self.n_init = n_init\n self.max_evals = max_evals\n self.batch_size = batch_size\n self.jitter = jitter\n self.n_discrete_points = n_discrete_points\n self.max_preconditioner_size = max_preconditioner_size\n self.num_ciq_samples = num_ciq_samples\n self.max_minres_iterations = max_minres_iterations\n self.minres_tolerance = minres_tolerance\n self.use_ciq = use_ciq\n self.use_keops = use_keops\n self.dtype = dtype\n self.device = device\n self.verbose = verbose\n\n self.n_evals = 0\n self.dim = len(lb)","def createCfg_comp_jetToTauFakeRate(self, jobOptions):\n lines = []\n lines.append(\"process.fwliteInput.fileNames = cms.vstring('%s')\" % jobOptions['inputFile'])\n lines.append(\"process.fwliteOutput.fileName = cms.string('%s')\" % os.path.basename(jobOptions['outputFile']))\n lines.append(\"process.comp_jetToTauFakeRate.looseRegion = cms.string('%s')\" % jobOptions['looseRegion'])\n lines.append(\"process.comp_jetToTauFakeRate.tightRegion = cms.string('%s')\" % jobOptions['tightRegion'])\n lines.append(\"process.comp_jetToTauFakeRate.processData = cms.string('data_obs')\")\n lines.append(\"process.comp_jetToTauFakeRate.processesToSubtract = cms.vstring(\")\n for process in self.processes_to_comp:\n lines.append(\" '{process}t', '{process}l',\".format(process = process))\n lines.append(\")\")\n if self.event_selection == \"TTemu\":\n lines.append(\"process.comp_jetToTauFakeRate.processMC = cms.string('TTj')\")\n elif self.event_selection == \"DYmumu\":\n lines.append(\"process.comp_jetToTauFakeRate.processMC = cms.string('EWKj')\")\n else:\n raise ValueError(\"Invalid event selection: %s\" % self.event_selection)\n lines.append(\"process.comp_jetToTauFakeRate.absEtaBins = cms.vdouble(%s)\" % jobOptions['absEtaBins'])\n lines.append(\"process.comp_jetToTauFakeRate.ptBins = cms.vdouble(%s)\" % jobOptions['ptBins'])\n lines.append(\"process.comp_jetToTauFakeRate.decayModes = cms.vint32(%s)\" % jobOptions['decayModes'])\n lines.append(\"process.comp_jetToTauFakeRate.hadTauSelections = cms.vstring(%s)\" % jobOptions['hadTauSelections'])\n lines.append(\"process.comp_jetToTauFakeRate.trigMatchingOption = cms.string('%s')\" % jobOptions['trigMatchingOption']) \n lines.append(\"process.comp_jetToTauFakeRate.outputFileName = cms.string('%s')\" % jobOptions['plots_outputFileName'])\n create_cfg(self.cfgFile_comp_jetToTauFakeRate, jobOptions['cfgFile_modified'], lines)","def cook_config(ext_config_filename):\n mc = base_model_config()\n with open(ext_config_filename, \"r\") as fp:\n ext_mc = edict(json.load(fp, encoding=\"utf8\"))\n for s in ext_mc.keys():\n mc[s] = ext_mc[s]\n # mc.ANCHOR_BOX = set_anchors(mc)\n # print(np.max(np.square(np.array(set_anchors_testing(mc)) - np.array(set_anchors(mc)))))\n # mc.ANCHORS = len(mc.ANCHOR_BOX)\n # H, W, C = _get_output_shape(mc)\n # mc.MODEL_OUTPUT_SHAPE = [H, W, mc.ANCHOR_PER_GRID]\n return mc","def add_conl_config(cfg):\n # We retry random cropping until no single category in semantic segmentation GT occupies more\n cfg.MODEL.CONL = CN()\n\n cfg.MODEL.CONL.STAGES=['res4']\n cfg.MODEL.CONL.BLOCKS=[[-1,],]\n\n cfg.MODEL.CONL.RATIO = 1.0/4.0\n cfg.MODEL.CONL.DOWNSAMPLE=True\n cfg.MODEL.CONL.USE_GN=False\n cfg.MODEL.CONL.LR_MULT=0\n cfg.MODEL.CONL.USE_OUT=False\n cfg.MODEL.CONL.OUT_BN=False\n cfg.MODEL.CONL.WHITEN_TYPE=['channel']\n cfg.MODEL.CONL.TEMP = 1.0\n cfg.MODEL.CONL.WITH_GC=False\n cfg.MODEL.CONL.WITH_2FC=False\n cfg.MODEL.CONL.DOUBLE_CONV=False\n\n cfg.MODEL.CONL.WITH_STATE=False\n cfg.MODEL.CONL.NCLS=32","def configure_nccl():\n os.environ[\"NCCL_SOCKET_IFNAME\"] = \"ib0\"\n os.environ[\"NCCL_IB_DISABLE\"] = \"1\"\n\n os.environ[\"NCCL_LAUNCH_MODE\"] = \"PARALLEL\"\n os.environ[\"NCCL_IB_HCA\"] = subprocess.getoutput(\n \"cd /sys/class/infiniband/ > /dev/null; for i in mlx5_*; \"\n \"do cat $i/ports/1/gid_attrs/types/* 2>/dev/null \"\n \"| grep v >/dev/null && echo $i ; done; > /dev/null\"\n )\n os.environ[\"NCCL_IB_GID_INDEX\"] = \"3\"\n os.environ[\"NCCL_IB_TC\"] = \"106\"","def get_configspace():\n configspace = cs.ConfigurationSpace()\n\n memory = cs.hyperparameters.UniformIntegerHyperparameter(name='memory', lower=2, upper=25)\n configspace.add_hyperparameter(hyperparameter=memory)\n\n batch_size = cs.hyperparameters.UniformIntegerHyperparameter(\n name='batch_size', lower=32, upper=8192, log=True\n )\n configspace.add_hyperparameter(hyperparameter=batch_size)\n\n frequency = cs.hyperparameters.UniformFloatHyperparameter(\n name='frequency', lower=3e-2, upper=1.0, log=True\n )\n configspace.add_hyperparameter(hyperparameter=frequency)\n\n learning_rate = cs.hyperparameters.UniformFloatHyperparameter(\n name='learning_rate', lower=1e-5, upper=3e-2, log=True\n )\n configspace.add_hyperparameter(hyperparameter=learning_rate)\n\n horizon = cs.hyperparameters.UniformIntegerHyperparameter(\n name='horizon', lower=1, upper=50\n )\n configspace.add_hyperparameter(hyperparameter=horizon)\n\n discount = cs.hyperparameters.UniformFloatHyperparameter(\n name='discount', lower=0.8, upper=1.0, log=True\n )\n configspace.add_hyperparameter(hyperparameter=discount)\n\n ratio_based = cs.hyperparameters.CategoricalHyperparameter(\n name='ratio_based', choices=('no', 'yes')\n )\n configspace.add_hyperparameter(hyperparameter=ratio_based)\n\n clipping_value = cs.hyperparameters.UniformFloatHyperparameter(\n name='clipping_value', lower=0.05, upper=0.5\n )\n configspace.add_hyperparameter(hyperparameter=clipping_value)\n\n baseline = cs.hyperparameters.CategoricalHyperparameter(\n name='baseline',\n choices=('no', 'auto', 'same-network', 'same-policy', 'same-policy-noopt')\n )\n configspace.add_hyperparameter(hyperparameter=baseline)\n\n baseline_learning_rate = cs.hyperparameters.UniformFloatHyperparameter(\n name='baseline_learning_rate', lower=1e-5, upper=3e-2, log=True\n )\n configspace.add_hyperparameter(hyperparameter=baseline_learning_rate)\n\n estimate_advantage = cs.hyperparameters.CategoricalHyperparameter(\n name='estimate_advantage', choices=('no', 'yes')\n )\n configspace.add_hyperparameter(hyperparameter=estimate_advantage)\n\n entropy_regularization = cs.hyperparameters.UniformFloatHyperparameter(\n name='entropy_regularization', lower=1e-5, upper=1.0, log=True\n )\n configspace.add_hyperparameter(hyperparameter=entropy_regularization)\n\n configspace.add_condition(\n condition=cs.EqualsCondition(child=clipping_value, parent=ratio_based, value='yes')\n )\n\n configspace.add_condition(\n condition=cs.NotEqualsCondition(\n child=baseline_learning_rate, parent=baseline, value='no'\n )\n )\n\n configspace.add_condition(\n condition=cs.NotEqualsCondition(\n child=estimate_advantage, parent=baseline, value='no'\n )\n )\n\n return configspace","def get_default_config(self):\n config = super(SignalfxHandler, self).get_default_config()\n\n config.update({\n 'url': 'https://ingest.signalfx.com/v2/datapoint',\n 'batch': 300,\n # Don't wait more than 10 sec between pushes\n 'batch_max_interval': 10,\n 'auth_token': '',\n })\n\n return config","def setup(self, num_qubit, fusion_enable, use_cu1):","def get_config():\n config = ml_collections.ConfigDict()\n\n # Which model to use -- see ./models.py\n config.model_name = 'ViT-B_32'\n # Where to store training logs.\n config.log_dir = '.'\n\n # Number of steps to measure.\n config.steps = 30\n # Number of steps before measuring.\n config.initial_steps = 10\n\n # Batch size\n config.batch = 0\n # Number of output classes.\n config.num_classes = 0\n # Image size (width=height).\n config.image_size = 0\n\n config.train = 'inference_time'\n\n return config","def __init__(self, config):\n super().__init__(config)\n\n # For NaMaster you need to pass the masks\n self.mask_files = self.config[\"tjpcov\"].get(\"mask_file\")\n self.mask_names = self.config[\"tjpcov\"].get(\"mask_names\")\n\n # Binning info is only needed if workspaces are not passed\n self.binning_info = self.config[\"tjpcov\"].get(\"binning_info\", None)\n\n # nside is needed if mask_files is a hdf5 file\n self.nside = self.config[\"tjpcov\"].get(\"nside\", None)\n\n # Read NaMaster specific options\n self.nmt_conf = self.config.get(\"NaMaster\", {})\n for k in [\"f\", \"w\", \"cw\"]:\n if k not in self.nmt_conf:\n self.nmt_conf[k] = {}\n\n # Read cache from input file. It will update the cache passed as an\n # argument of the different methods\n self.cache = self.config.get(\"cache\", {})","def config():\n\n compared_algorithms_type: AlgorithmsType = AlgorithmsType.LinearRegression\n compared_methods: List = [] # Leave empty for using all solvers.\n numpy_distribution: NumpyDistribution = NumpyDistribution.IntelDistribution\n used_database: DatabaseType = DatabaseType.Synthetic\n experiment_type: ExperimentType = ExperimentType.RunTimeExperiment\n cross_validation_folds: int = 1\n n_alphas: int = 100\n reduction_factor: int = 1\n\n run_time_experiments_config: Dict[str, range] = {\n \"run_time_compared_data_sizes\": range(int(5000 / reduction_factor), int(15000 / reduction_factor),\n int(5000 / reduction_factor)),\n \"calc_transpose_dot_residuals\": compared_algorithms_type == AlgorithmsType.LinearRegression\n }\n number_of_alphas_experiments_config: Dict[str, range] = {\n \"alphas_range\": range(1, 221, 20)\n }\n\n synthetic_data_config: Dict[str, int] = {\n \"data_size\": int(15000 / reduction_factor),\n \"features_num\": 7\n }\n\n sketch_preconditioned_config: Dict[str, float] = {\n \"sampled_rows\": 0.005,\n \"switch_sign_probability\": 0.5,\n \"min_sampled_rows\": 100.0\n }\n resources_path: str = r'Resources'\n results_path: str = r'Results'\n clusters_count: int = _choose_clusters_num(used_database, synthetic_data_config[\"features_num\"])\n elastic_net_factor: float = 0.5 # Rho factor in Elastic-Net regularization.\n is_positive_definite: bool = True","def from_config(cls, config):\n if 'np_initval' in config:\n config['np_initval'] = {\n rel: NeuralQueryContext._dict_to_sparse(matrix_dict)\n for (rel, matrix_dict) in config['np_initval'].items()\n }\n if 'symtab' in config:\n symtab = dict()\n for (k, v) in config['symtab']:\n symtab[k] = symbol.create_from_dict(v)\n config['symtab'] = symtab\n return cls(**config)","def get_config_object() -> \"BaseConfig\":\n assert (\n len(G_CONFIG_OBJECT) == 1\n ), \"Have you created quantize config object before calling `quantize_model`?\"\n if G_CONFIG_OBJECT:\n return G_CONFIG_OBJECT[0]","def configure(self, config_json):\n log.info(\"Configuring EDD backend for processing\")\n log.debug(\"Configuration string: '{}'\".format(config_json))\n\n yield self.set(config_json)\n\n cfs = json.dumps(self._config, indent=4)\n log.info(\"Final configuration:\\n\" + cfs)\n\n\n\n self.__numa_node_pool = []\n # remove numa nodes with missing capabilities\n for node in numa.getInfo():\n if len(numa.getInfo()[node]['gpus']) < 1:\n log.debug(\"Not enough gpus on numa node {} - removing from pool.\".format(node))\n continue\n elif len(numa.getInfo()[node]['net_devices']) < 1:\n log.debug(\"Not enough nics on numa node {} - removing from pool.\".format(node))\n continue\n else:\n self.__numa_node_pool.append(node)\n\n log.debug(\"{} numa nodes remaining in pool after cosntraints.\".format(len(self.__numa_node_pool)))\n\n if len(self._config['input_data_streams']) > len(self.__numa_node_pool):\n raise FailReply(\"Not enough numa nodes to process {} polarizations!\".format(len(self._config['input_data_streams'])))\n\n self._subprocessMonitor = SubprocessMonitor()\n #ToDo: Check that all input data streams have the same format, or allow different formats\n for i, streamid in enumerate(self._config['input_data_streams']):\n # calculate input buffer parameters\n stream_description = self._config['input_data_streams'][streamid]\n stream_description[\"dada_key\"] = DADABUFFERS[i]\n self.add_input_stream_sensor(streamid)\n self.input_heapSize = stream_description[\"samples_per_heap\"] * stream_description['bit_depth'] / 8\n\n nHeaps = self._config[\"samples_per_block\"] / stream_description[\"samples_per_heap\"]\n input_bufferSize = nHeaps * (self.input_heapSize)\n log.info('Input dada parameters created from configuration:\\n\\\n heap size: {} byte\\n\\\n heaps per block: {}\\n\\\n buffer size: {} byte'.format(self.input_heapSize, nHeaps, input_bufferSize))\n\n\n final_payloads, final_fpss, final_framens = EDD_VDIF_Frame_Size(stream_description['sample_rate'])\n\n if self._config['payload_size'] == 'auto':\n payload_size = final_payloads[-1]\n else:\n payload_size = int(self._config['payload_size'])\n\n log.info('Possible frame payload sizes (add 32 for framesize):')\n for k in range(final_payloads.size):\n if payload_size == final_payloads[k]:\n M = \"*\"\n else:\n M = \" \"\n log.info(' {}{:5.0f} byte {:8.0f} frames per sec {:6.3f} nsec/frame'.format(M, final_payloads[k], final_fpss[k], final_framens[k]))\n\n if payload_size not in final_payloads:\n log.warning(\"Payload size {} possibly not conform with VDIF format!\".format(payload_size))\n\n # calculate output buffer parameters\n size_of_samples = ceil(1. * self._config[\"samples_per_block\"] * 2 / 8.) # byte for two bit mode\n number_of_packages = ceil(size_of_samples / float(payload_size))\n\n output_buffer_size = number_of_packages * (payload_size + self._config['vdif_header_size'])\n\n integration_time = self._config[\"samples_per_block\"] / float(stream_description[\"sample_rate\"])\n self._integration_time_status.set_value(integration_time)\n\n rate = output_buffer_size/ integration_time # in spead documentation BYTE per second and not bit!\n rate *= self._config[\"output_rate_factor\"] # set rate to (100+X)% of expected rate\n self._output_rate_status.set_value(rate / 1E9)\n\n log.info('Output parameters calculated from configuration:\\n\\\n total size of data samples: {} byte\\n\\\n number_of_packages: {}\\n\\\n size of output buffer: {} byte\\n\\\n rate ({:.0f}%): {} Gbps'.format(size_of_samples,\n number_of_packages, output_buffer_size,\n self._config[\"output_rate_factor\"]*100, rate / 1E9))\n\n numa_node = self.__numa_node_pool[i]\n log.debug(\"Associating {} with numa node {}\".format(streamid, numa_node))\n\n # configure dada buffer\n bufferName = stream_description['dada_key']\n yield self._create_ring_buffer(input_bufferSize, 64, bufferName, numa_node)\n\n ofname = bufferName[::-1]\n # we write nSlice blocks on each go\n yield self._create_ring_buffer(output_buffer_size, 8, ofname, numa_node)\n\n # Configure + launch \n physcpu = numa.getInfo()[numa_node]['cores'][0]\n thread_id = self._config['thread_id'][streamid]\n station_id = self._config['thread_id'][streamid]\n cmd = \"taskset -c {physcpu} VLBI --input_key={dada_key} --speadheap_size={heapSize} --thread_id={thread_id} --station_id={station_id} --payload_size={payload_size} --sample_rate={sample_rate} --nbits={bit_depth} -o {ofname} --log_level={log_level} --output_type=dada\".format(ofname=ofname, heapSize=self.input_heapSize, numa_node=numa_node, physcpu=physcpu, thread_id=thread_id, station_id=station_id, payload_size=payload_size, log_level=self._config['log_level'], **stream_description)\n log.debug(\"Command to run: {}\".format(cmd))\n\n cudaDevice = numa.getInfo()[numa_node]['gpus'][0]\n cli = ManagedProcess(cmd, env={\"CUDA_VISIBLE_DEVICES\": cudaDevice})\n self._subprocessMonitor.add(cli, self._subprocess_error)\n self._subprocesses.append(cli)\n\n cfg = self._config.copy()\n cfg.update(stream_description)\n\n ip_range = []\n port = set()\n for key in self._config[\"output_data_streams\"]:\n if streamid in key:\n ip_range.append(self._config[\"output_data_streams\"][key]['ip'])\n port.add(self._config[\"output_data_streams\"][key]['port'])\n if len(port)!=1:\n raise FailReply(\"Output data for one plarization has to be on the same port! \")\n\n if self._config[\"output_type\"] == 'network':\n physcpu = \",\".join(numa.getInfo()[numa_node]['cores'][1:2])\n fastest_nic, nic_params = numa.getFastestNic(numa_node)\n log.info(\"Sending data for {} on NIC {} [ {} ] @ {} Mbit/s\".format(streamid, fastest_nic, nic_params['ip'], nic_params['speed']))\n\n cmd = \"taskset -c {physcpu} vdif_send --input_key {ofname} --if_ip {ibv_if} --dest_ip {mcast_dest} --port {port_tx} --max_rate {rate}\".format(ofname=ofname, \n physcpu=physcpu, ibv_if=nic_params['ip'], mcast_dest=\" \".join(ip_range), port_tx=port.pop(), rate=rate)\n log.debug(\"Command to run: {}\".format(cmd))\n\n elif self._config[\"output_type\"] == 'disk':\n ofpath = os.path.join(cfg[\"output_directory\"], ofname)\n log.debug(\"Writing output to {}\".format(ofpath))\n if not os.path.isdir(ofpath):\n os.makedirs(ofpath)\n cmd = \"dada_dbdisk -k {ofname} -D {ofpath} -W\".format(ofname=ofname, ofpath=ofpath, **cfg)\n else:\n log.warning(\"Selected null output. Not sending data!\")\n cmd = \"dada_dbnull -z -k {}\".format(ofname)\n\n log.debug(\"Command to run: {}\".format(cmd))\n mks = ManagedProcess(cmd, env={\"CUDA_VISIBLE_DEVICES\": cudaDevice})\n self._subprocessMonitor.add(mks, self._subprocess_error)\n self._subprocesses.append(mks)\n\n self._subprocessMonitor.start()","def __init__(self, \n n_neurons = \"micro\", # else: \"brunel\" or arrays\n C_ab = \"micro\", # else: \"brunel\" or arrays\n area = net.area, # simulation size\n neuron_model = net.neuron_model, # \"iaf_psc_delta\" or \"iaf_psc_exp\"\n connection_rule = net.connection_rule, # \"fixed_total_number\" or \"fixed_indegree\"\n j02 = net.j02, \n weight_rel_sd = net.weight_rel_sd, \n delay_rel_sd = net.delay_rel_sd, \n g = net.g, \n rate_ext = net.rate_ext):\n ###################################################\n ### \tNetwork parameters\t\t### \n ###################################################\n\n # area of network in mm^2; scales numbers of neurons\n # use 1 for the full-size network (77,169 neurons)\n self.area = area\n \n self.layers = net.layers #np.array([\"L23\", \"L4\", \"L5\", \"L6\"])\n self.types = net.types #np.array([\"e\", \"i\"]) \n self.populations = np.array([layer + typus for layer in self.layers for typus in self.types])\n self.n_populations = len(self.populations)\n self.n_layers = len(self.layers)\n self.n_types = len(self.types)\n \n # Neuron numbers\n if n_neurons == \"micro\":\n self.n_neurons = np.int_(net.full_scale_n_neurons * self.area)\n elif n_neurons == \"brunel\":\n # Provide an array of equal number of neurons in each exc./inh. population\n gamma = 0.25\n inh_factor = 1. / (gamma + 1.)\n exc_factor = 1. - inh_factor \n n_total_micro = np.sum(net.full_scale_n_neurons * self.area)\n N_exc = n_total_micro/self.n_populations * exc_factor\n N_inh = n_total_micro/self.n_populations * inh_factor\n self.n_neurons = np.tile([N_exc, N_inh], self.n_layers).astype(int)\n else:\n if type(n_neurons) == np.ndarray:\n if n_neurons.shape == (self.n_populations, ):\n self.n_neurons = np.int_(n_neurons)\n else:\n raise Exception(\"'n_neurons' has wrong shape. \"+\n \"Expects (%i,)\"%self.n_populations)\n else: \n raise Exception(\"'n_neurons' expects either numpy.ndarray or string \"+\n \"in {'micro', 'brunel'}\")\n self.n_total = np.sum(self.n_neurons)\n\n \n # Synapse numbers\n # Connection probabilities: conn_probs[post, pre] = conn_probs[target, source]\n conn_probs = net.conn_probs\n # Scale synapse numbers of the C_ab\n if net.scale_C_linearly:\n n_outer_full = np.outer(net.full_scale_n_neurons, net.full_scale_n_neurons)\n C_full_scale = np.log(1. - conn_probs) / np.log(1. - 1. / n_outer_full)\n C_scaled = np.int_(C_full_scale * self.area)\n else:\n n_outer = np.outer(self.n_neurons, self.n_neurons)\n C_scaled = np.int_(np.log(1. - conn_probs) / np.log(1. - 1. / n_outer))\n\n self.connection_rule = connection_rule\n if self.connection_rule == \"fixed_total_number\":\n C_ab_micro = C_scaled # total number, do not divide! \n elif self.connection_rule == \"fixed_indegree\":\n C_ab_micro = (C_scaled.T / (net.full_scale_n_neurons * self.area)).T\n else:\n raise Exception(\"Unexpected connection type. Use 'fixed_total_number' for microcircuit \" + \n \"model or 'fixed_indegree' for Brunel's model!\")\n\n if C_ab == \"micro\":\n self.C_ab = C_ab_micro # shall not be integer at this point!\n elif C_ab == \"brunel\":\n C_e = np.mean(C_ab_micro) # mean for microcircuit (= 501 in full scale)\n C_i = gamma * C_e\n self.C_ab = np.tile([C_e, C_i], (self.n_populations, self.n_layers)).astype(int) \n else:\n if type(C_ab) == np.ndarray:\n if C_ab.shape == (self.n_populations, self.n_populations):\n self.C_ab = np.int_(C_ab)\n else:\n raise Exception(\"'C_ab' has wrong shape. \"+\n \"Expects (%i, %i)\"%(self.n_populations, self.n_populations))\n else: \n raise Exception(\"'C_ab' expects either numpy.ndarray or string \"+\n \"in {'micro', 'brunel'}\")\n\n\n ###################################################\n ### Single-neuron parameters\t\t### \n ###################################################\n self.neuron_model = neuron_model\n self.Vm0_mean = net.Vm0_mean # mean of initial membrane potential (mV)\n self.Vm0_std = net.Vm0_std # std of initial membrane potential (mV)\n self.model_params = net.model_params\n if not self.neuron_model==\"iaf_psc_delta\":\n self.model_params[\"tau_syn_ex\"] = net.tau_syn_ex # excitatory synaptic time constant (ms)\n self.model_params[\"tau_syn_in\"] = net.tau_syn_in # inhibitory synaptic time constant (ms)\n self.tau_syn_ex = net.tau_syn_ex # ms\n self.tau_syn_in = net.tau_syn_in # ms\n self.tau_syn = np.tile([self.tau_syn_ex, self.tau_syn_in], (self.n_populations, self.n_layers))\n # Rescaling for model calculations: these values are not used in the simulation!\n self.tau_m = self.model_params[\"tau_m\"] # ms\n self.t_ref = self.model_params[\"t_ref\"] # ms\n self.E_L = self.model_params[\"E_L\"] # mV\n self.V_r = self.model_params[\"V_reset\"] - self.E_L # mV\n self.theta = self.model_params[\"V_th\"] - self.E_L # mV\n self.C_m = self.model_params[\"C_m\"] # pF\n\n\n ######################################################\n # Synaptic weights. Depend on neuron_model! ##\n ######################################################\n self.g = g\n self.j02 = j02\n\n g_all = np.tile([1., -self.g], (self.n_populations, self.n_layers))\n L23e_index = np.where(self.populations == \"L23e\")[0][0]\n L4e_index = np.where(self.populations == \"L4e\")[0][0]\n g_all[L23e_index, L4e_index] *= self.j02\n \n self.J = net.PSP_e # mv; mean PSP, used as reference PSP\n self.J_ab = self.J * g_all\n self.weight_rel_sd = weight_rel_sd # Standard deviation of weight relative to mean weight\n # Transformation from peak PSP to PSC\n delta_tau = self.tau_syn - self.tau_m\n ratio_tau = self.tau_m / self.tau_syn\n PSC_over_PSP = self.C_m * delta_tau / (self.tau_m * self.tau_syn * \\\n (ratio_tau**(self.tau_m / delta_tau) - ratio_tau**(self.tau_syn / delta_tau))) \n # Actual weights have to be adapted: from peak PSP to PSC (and back...)\n if self.neuron_model==\"iaf_psc_exp\": # PSCs calculated from PSP amplitudes\n self.weights = self.J_ab * PSC_over_PSP # neuron populations\n elif self.neuron_model==\"iaf_psc_delta\":\n self.weights = self.J_ab * PSC_over_PSP * (self.tau_syn_ex) / self.C_m\n # This might be an overkill / doing things twice...\n elif self.neuron_model==\"iaf_psc_alpha\": # PSCs calculated from PSP amplitudes\n self.weights = self.J_ab * np.exp(1) / (self.tau_syn_ex) / self.C_m\n else:\n raise Exception(\"Neuron model should be iaf_psc_ - {delta, exp, alpha}!\")\n\n\n ###################################################\n ### Delays and dicts ### \n ###################################################\n # mean dendritic delays for excitatory and inhibitory transmission (ms)\n self.delay_e = net.delay_e # ms, excitatory synapses\n self.delay_i = net.delay_i # ms, inhibitory synapses\n\n self.delays = np.tile([self.delay_e, self.delay_i], (self.n_populations, self.n_layers)) # adapt...\n self.delay_rel_sd = delay_rel_sd \n \n # Synapse dictionaries\n # default connection dictionary\n self.conn_dict = {\"rule\": connection_rule}\n # weight distribution of connections between populations\n self.weight_dict_exc = net.weight_dict_exc\n self.weight_dict_inh = net.weight_dict_inh\n # delay distribution of connections between populations\n self.delay_dict = net.delay_dict\n # default synapse dictionary\n self.syn_dict = net.syn_dict\n \n \n ###################################################\n ### External stimuli ## \n ###################################################\n # rate of background Poisson input at each external input synapse (spikes/s) \n self.rate_ext = rate_ext # Hz \n self.J_ext = net.PSP_ext # external synaptic weight\n self.delay_ext = self.delay_e # ms; mean delay of external input\n self.dc_amplitude = net.dc_amplitude # constant bg amplitude\n self.C_aext = net.C_aext # in-degrees for background input\n # Adapt weights\n if self.neuron_model==\"iaf_psc_exp\": # PSCs calculated from PSP amplitudes\n self.weight_ext = self.J_ext * PSC_over_PSP[0, 0] \n elif self.neuron_model==\"iaf_psc_delta\":\n self.weight_ext = self.J_ext * PSC_over_PSP[0, 0] * self.tau_syn_ex / self.C_m\n elif self.neuron_model==\"iaf_psc_alpha\": # PSCs calculated from PSP amplitudes\n self.weight_ext = self.J_ext * np.exp(1) / self.tau_syn_ex / self.C_m\n\n # optional additional thalamic input (Poisson)\n self.n_th = net.n_th # size of thalamic population\n self.th_start = net.th_start # onset of thalamic input (ms)\n self.th_duration = net.th_duration # duration of thalamic input (ms)\n self.th_rate = net.th_rate # rate of thalamic neurons (spikes/s)\n self.J_th = net.PSP_th # mean EPSP amplitude (mV) for thalamic input\n # Adapt weights\n if self.neuron_model==\"iaf_psc_exp\": # PSCs calculated from PSP amplitudes\n self.weight_th = self.J_th * PSC_over_PSP[0, 0] \n elif self.neuron_model==\"iaf_psc_delta\":\n self.weight_th = self.J_th * PSC_over_PSP[0, 0] * self.tau_syn_ex / self.C_m\n elif self.neuron_model==\"iaf_psc_alpha\": # PSCs calculated from PSP amplitudes\n self.weight_th = self.J_th * np.exp(1) / self.tau_syn_ex / self.C_m\n\n \n # connection probabilities for thalamic input\n conn_probs_th = net.conn_probs_th\n if net.scale_C_linearly:\n if not self.n_th == 0:\n C_th_full_scale = np.log(1. - conn_probs_th) / \\\n np.log(1. - 1. / (self.n_th * net.full_scale_n_neurons))\n self.C_th_scaled = np.int_(C_th_full_scale * self.area)\n else:\n if not self.n_th == 0:\n self.C_th_scaled = np.int_(np.log(1. - conn_probs_th) / \\\n np.log(1. - 1. / (self.n_th * self.n_neurons_micro)))\n if self.n_th == 0:\n self.C_th_scaled = None\n \n # mean delay of thalamic input (ms)\n self.delay_th = net.delay_th\n # standard deviation relative to mean delay of thalamic input\n self.delay_th_rel_sd = net.delay_th_rel_sd\n\n\n ######################################################\n # Predefine matrices for mean field ##\n ######################################################\n if self.neuron_model==\"iaf_psc_delta\":\n self.J_mu = self.weights\n self.J_sd = self.weights\n self.J_mu_ext = self.weight_ext \n self.J_sd_ext = self.weight_ext\n elif self.neuron_model==\"iaf_psc_exp\":\n self.J_mu = self.weights * self.tau_syn / self.C_m\n self.J_sd = self.weights * np.sqrt(self.tau_syn / 2.) / self.C_m\n self.J_mu_ext = self.weight_ext * self.tau_syn_ex / self.C_m\n self.J_sd_ext = self.weight_ext * np.sqrt(self.tau_syn_ex / 2.) / self.C_m\n elif self.neuron_model==\"iaf_psc_alpha\":\n self.J_mu = self.weights * self.tau_syn**2 / self.C_m\n self.J_sd = self.weights * self.tau_syn**(3./2.) / (self.C_m * 2.)\n self.J_mu_ext = self.weight_ext * self.tau_syn_ex**2 / self.C_m\n self.J_sd_ext = self.weight_ext * self.tau_syn_ex**(3./2.) / (self.C_m * 2.)\n self.mat_mu = self.tau_m * 1e-3 * self.J_mu * self.C_ab\n self.mu_ext = self.tau_m * 1e-3 * self.J_mu_ext * self.C_aext * self.rate_ext\n self.mat_var = self.tau_m * 1e-3 * (1 + self.weight_rel_sd ** 2) * self.J_sd**2 * self.C_ab\n self.var_ext = self.tau_m * 1e-3 * (1 + self.weight_rel_sd ** 2) * self.J_sd_ext**2 * self.C_aext * self.rate_ext","def make_config():\n # find date of data obtained\n current_pathname = os.path.basename(os.getcwd())\n guess_date = extract_date(current_pathname)\n\n while(True):\n if guess_date is None:\n prompt = 'YYYYMMDD'\n else:\n prompt = guess_date\n\n string = input('Date of observation [{}]: '.format(prompt))\n input_date = extract_date(string)\n if input_date is None:\n if guess_date is None:\n continue\n else:\n input_date = guess_date\n break\n else:\n break\n \n input_datetime = datetime.datetime.strptime(input_date, '%Y-%m-%d')\n\n # create config object\n config = configparser.ConfigParser()\n\n config.add_section('data')\n\n config.set('data', 'telescope', 'Keck-I')\n config.set('data', 'instrument', 'HIRES')\n config.set('data', 'rawpath', 'rawdata')\n #config.set('data', 'statime_key', statime_key)\n #config.set('data', 'exptime_key', exptime_key)\n\n config.add_section('reduce')\n config.set('reduce', 'midpath', 'midproc')\n config.set('reduce', 'figpath', 'images')\n config.set('reduce', 'odspath', 'onedspec')\n config.set('reduce', 'mode', 'normal')\n config.set('reduce', 'oned_suffix', 'ods')\n config.set('reduce', 'fig_format', 'png')\n \n config.add_section('reduce.bias')\n config.set('reduce.bias', 'bias_file', '${reduce:midpath}/bias.fits')\n config.set('reduce.bias', 'cosmic_clip', str(10))\n config.set('reduce.bias', 'maxiter', str(5))\n config.set('reduce.bias', 'smooth', 'yes')\n config.set('reduce.bias', 'smooth_method', 'gaussian')\n config.set('reduce.bias', 'smooth_sigma', str(3))\n config.set('reduce.bias', 'smooth_mode', 'nearest')\n\n config.add_section('reduce.trace')\n config.set('reduce.trace', 'minimum', str(1e-3))\n config.set('reduce.trace', 'scan_step', str(100))\n config.set('reduce.trace', 'separation', '100:84, 1500:45, 3000:14')\n config.set('reduce.trace', 'filling', str(0.2))\n config.set('reduce.trace', 'align_deg', str(2))\n config.set('reduce.trace', 'display', 'no')\n config.set('reduce.trace', 'degree', str(4))\n config.set('reduce.trace', 'file', '${reduce:midpath}/trace.fits')\n\n config.add_section('reduce.flat')\n config.set('reduce.flat', 'file', '${reduce:midpath}/flat.fits')\n\n # write to config file\n filename = 'HIRES.{}.cfg'.format(input_date)\n outfile = open(filename, 'w')\n for section in config.sections():\n maxkeylen = max([len(key) for key in config[section].keys()])\n outfile.write('[{}]'.format(section)+os.linesep)\n fmt = '{{:{}s}} = {{}}'.format(maxkeylen)\n for key, value in config[section].items():\n outfile.write(fmt.format(key, value)+os.linesep)\n outfile.write(os.linesep)\n outfile.close()\n\n print('Config file written to {}'.format(filename))","def __init__(self):\n self.label = \"Partition NNInput Files\"\n self.description = \"Partitions Neural Network class.dta of more than 200,000 records into files of 200,000 or less.\"\n self.canRunInBackground = False\n self.category = \"Neural network\"","def __init__(self,\n cfg: CfgNode,\n max_iters: int = 10,\n ftol: float = 1e-9,\n gtol: float = 1e-9,\n device: torch.device = torch.device('cuda')):\n\n # Store options\n self.cfg = cfg\n self.max_iters = max_iters\n self.ftol = ftol\n self.gtol = gtol\n self.device = device\n\n # Load SMPL model\n smpl_cfg = {k.lower(): v for k,v in dict(cfg.SMPL).items()}\n self.smpl = SMPL(**smpl_cfg).to(device)","def __init__(self, workdir, encut, struct_path, name=\"relax_bwmn\"): \n potcar_path = \"../pseudos/BWO_Mn_POTCAR\" \n kgrid = [2, 2, 2] \n input_param = DefaultOptimizationParameters(encut) \n relax_calc = SCFCalculation(workdir, pseudo_par=None, kgrid=kgrid, name=\"BWO_Mn_relax\", encut=encut, input_parameters=input_param) \n relax_calc.make_calculation(struct_path, potcar_path=potcar_path)","def build_resnet_test_config(self, test_id, test_args, batch_size=32, gpus=1,\n total_batches=300, repeat=3):\n config = {}\n config['total_batches'] = total_batches\n # Relative path in the repo to the test folder.\n config['cmd_path'] = 'imagenet'\n\n # PyTorch Automatically uses all GPUs it can see.\n gpu_list = ','.join(str(x) for x in range(gpus))\n visible_devices = 'CUDA_VISIBLE_DEVICES={}'.format(gpu_list)\n if gpus > 1:\n multi_gpu = (\" --dist-url 'tcp://127.0.0.1:6001' --dist-backend 'nccl'\"\n \"--multiprocessing-distributed --world-size 1 --rank 0 \")\n config['pycmd'] = 'python3 main.py {} {} {}'.format('{}',\n multi_gpu,\n self.imagenet_dir)\n else:\n config['pycmd'] = '{} python3 main.py {} {}'.format(visible_devices,\n '{}',\n self.imagenet_dir)\n config['test_id'] = test_id\n config['repeat'] = self.auto_test_config.get('repeat', repeat)\n # Normalized name of model being tested\n config['model'] = 'resnet50_v1.5'\n config['gpus'] = gpus\n config['batch_size'] = batch_size\n args = {}\n config['args'] = args\n # print stats every epoch.\n args['print-freq'] = self.auto_test_config.get('print_freq', 1)\n args['epochs'] = 1\n args['arch'] = 'resnet50'\n args['batch-size'] = batch_size * gpus\n args['workers'] = self.auto_test_config.get('input_threads', 5)\n\n # Override any args with the tests args\n args.update(test_args)\n\n return config","def config(self, cnf=None, **kw):\n if cnf:\n kw.update(cnf)\n for k, v in kw.items():\n if k in self.ttlabel.keys():\n self.ttlabel.config(**{k: v})\n elif k in self.custom_defs:\n setattr(self, k, v)\n else:\n raise Exception(\n \"Unexpected Key/Val Pair {k}:{v}\".format(k=k, v=v)\n )\n if not cnf and not kw:\n return _flesh_config(\n self,\n self.ttlabel.config(),\n defaults=self.defaults_d,\n base=(\"\", self.base),\n delay=(500, self.delay),\n ipadx=(2, self.ipadx),\n ipady=(1, self.ipady),\n offsetx=(2, self.offsetx),\n offsety=(2, self.offsety),\n )\n return None","def subconfig(self) -> \"Config\":\n config = Config()\n config.func = self.func\n config.targets = self.targets.copy()\n config.blacklisted_targets = self.blacklisted_targets.copy()\n config.variables = self.variables.copy()\n config.unit_iterator = self.unit_iterator.copy()\n config.ran_units = self.ran_units.copy()\n config.active_units = self.active_units.copy()\n config.entry_point = self.unit_iterator.lst[-1]\n config.signals = self.signals.copy()\n return config","def __init__(self, fsmt_layer, config):\n super().__init__(config)\n self.in_proj_weight = nn.Parameter(torch.cat([fsmt_layer.self_attn.q_proj.weight, fsmt_layer.self_attn.k_proj.weight, fsmt_layer.self_attn.v_proj.weight]))\n self.in_proj_bias = nn.Parameter(torch.cat([fsmt_layer.self_attn.q_proj.bias, fsmt_layer.self_attn.k_proj.bias, fsmt_layer.self_attn.v_proj.bias]))\n self.out_proj_weight = fsmt_layer.self_attn.out_proj.weight\n self.out_proj_bias = fsmt_layer.self_attn.out_proj.bias\n self.linear1_weight = fsmt_layer.fc1.weight\n self.linear1_bias = fsmt_layer.fc1.bias\n self.linear2_weight = fsmt_layer.fc2.weight\n self.linear2_bias = fsmt_layer.fc2.bias\n self.norm1_eps = fsmt_layer.self_attn_layer_norm.eps\n self.norm1_weight = fsmt_layer.self_attn_layer_norm.weight\n self.norm1_bias = fsmt_layer.self_attn_layer_norm.bias\n self.norm2_eps = fsmt_layer.final_layer_norm.eps\n self.norm2_weight = fsmt_layer.final_layer_norm.weight\n self.norm2_bias = fsmt_layer.final_layer_norm.bias\n self.num_heads = fsmt_layer.self_attn.num_heads\n self.embed_dim = fsmt_layer.self_attn.embed_dim\n self.is_last_layer = False\n self.validate_bettertransformer()","def get_default_config():\r\n config = {}\r\n\r\n config[\"kl_coeff\"] = 1.0\r\n config[\"_num_workers_tf\"] = 4\r\n config[\"use_gae\"] = True\r\n config[\"num_gpus\"] = 0\r\n\r\n config[\"_env_name_rllib\"] = \"multicomp\"\r\n config[\"_env_fcn\"] = create_env\r\n config['_policies'] = [None, \"from_scratch\", \"pretrained\"]\r\n config[\"_env\"] = {'with_video': False,\r\n \"SingleAgentToMultiAgent\": False,\r\n \"env_name\": \"multicomp/YouShallNotPassHumans-v0\"}\r\n config['framework'] = 'tfe'\r\n\r\n config['_train_policies'] = ['player_1']\r\n config['_call'] = {}\r\n config['_trainer'] = \"PPO\"\r\n config['_policy'] = \"PPO\"\r\n config['_call']['checkpoint_freq'] = 0\r\n config['_train_steps'] = 99999999\r\n config['_update_config'] = None\r\n config['_run_inline'] = False\r\n config['_postprocess'] = None\r\n\r\n config['num_envs_per_worker'] = 4\r\n config['_log_error'] = True\r\n config['_model_params'] = {\r\n \"use_lstm\": False,\r\n \"fcnet_hiddens\": [64, 64],\r\n # \"custom_action_dist\": \"DiagGaussian\",\r\n \"fcnet_activation\": \"tanh\",\r\n \"free_log_std\": True,\r\n }\r\n\r\n config['_select_policy'] = select_policy_default\r\n config['_get_policies'] = get_policies_default\r\n config['_do_not_train_policies'] = []\r\n config['_update_withpolicies'] = None\r\n config['callbacks'] = InfoCallbacks\r\n\r\n return config"],"string":"[\n \"def __init__(self, quantized_edges_in_cfg: int, total_edges_in_cfg: int):\\n self.quantized_edges_in_cfg = quantized_edges_in_cfg\\n self.total_edges_in_cfg = total_edges_in_cfg\",\n \"def _add_fp_configs(CONFIG):\\n CONFIG.declare(\\n 'fp_cutoffdecr',\\n ConfigValue(\\n default=1e-1,\\n domain=PositiveFloat,\\n description='Additional relative decrement of cutoff value for the original objective function.',\\n ),\\n )\\n CONFIG.declare(\\n 'fp_iteration_limit',\\n ConfigValue(\\n default=20,\\n domain=PositiveInt,\\n description='Feasibility pump iteration limit',\\n doc='Number of maximum iterations in the feasibility pump methods.',\\n ),\\n )\\n # TODO: integrate this option\\n CONFIG.declare(\\n 'fp_projcuts',\\n ConfigValue(\\n default=True,\\n description='Whether to add cut derived from regularization of MIP solution onto NLP feasible set.',\\n domain=bool,\\n ),\\n )\\n CONFIG.declare(\\n 'fp_transfercuts',\\n ConfigValue(\\n default=True,\\n description='Whether to transfer cuts from the Feasibility Pump MIP to main MIP in selected strategy (all except from the round in which the FP MIP became infeasible).',\\n domain=bool,\\n ),\\n )\\n CONFIG.declare(\\n 'fp_projzerotol',\\n ConfigValue(\\n default=1e-4,\\n domain=PositiveFloat,\\n description='Tolerance on when to consider optimal value of regularization problem as zero, which may trigger the solution of a Sub-NLP.',\\n ),\\n )\\n CONFIG.declare(\\n 'fp_mipgap',\\n ConfigValue(\\n default=1e-2,\\n domain=PositiveFloat,\\n description='Optimality tolerance (relative gap) to use for solving MIP regularization problem.',\\n ),\\n )\\n CONFIG.declare(\\n 'fp_discrete_only',\\n ConfigValue(\\n default=True,\\n description='Only calculate the distance among discrete variables in regularization problems.',\\n domain=bool,\\n ),\\n )\\n CONFIG.declare(\\n 'fp_main_norm',\\n ConfigValue(\\n default='L1',\\n domain=In(['L1', 'L2', 'L_infinity']),\\n description='Different forms of objective function MIP regularization problem.',\\n ),\\n )\\n CONFIG.declare(\\n 'fp_norm_constraint',\\n ConfigValue(\\n default=True,\\n description='Whether to add the norm constraint to FP-NLP',\\n domain=bool,\\n ),\\n )\\n CONFIG.declare(\\n 'fp_norm_constraint_coef',\\n ConfigValue(\\n default=1,\\n domain=PositiveFloat,\\n description='The coefficient in the norm constraint, correspond to the Beta in the paper.',\\n ),\\n )\",\n \"def _get_default_quantization_config(preset: QuantizationPreset, subset_size: int) -> Dict[str, Any]:\\n return {\\n \\\"algorithm\\\": \\\"quantization\\\",\\n \\\"preset\\\": preset.value,\\n \\\"initializer\\\": {\\n \\\"range\\\": {\\\"num_init_samples\\\": subset_size, \\\"type\\\": DEFAULT_RANGE_TYPE},\\n \\\"batchnorm_adaptation\\\": {\\\"num_bn_adaptation_samples\\\": subset_size},\\n },\\n \\\"overflow_fix\\\": \\\"first_layer_only\\\",\\n }\",\n \"def _get_transformer_quantization_config(subset_size: int) -> Dict[str, Any]:\\n return {\\n \\\"algorithm\\\": \\\"quantization\\\",\\n \\\"preset\\\": \\\"mixed\\\",\\n \\\"initializer\\\": {\\n \\\"range\\\": {\\\"num_init_samples\\\": subset_size, \\\"type\\\": DEFAULT_RANGE_TYPE},\\n \\\"batchnorm_adaptation\\\": {\\\"num_bn_adaptation_samples\\\": 0},\\n },\\n \\\"scope_overrides\\\": {\\\"activations\\\": {\\\"{re}.*matmul_0\\\": {\\\"mode\\\": \\\"symmetric\\\"}}},\\n \\\"ignored_scopes\\\": [\\n \\\"{re}.*Embeddings.*\\\",\\n \\\"{re}.*__add___[0-1]\\\",\\n \\\"{re}.*layer_norm_0\\\",\\n \\\"{re}.*matmul_1\\\",\\n \\\"{re}.*__truediv__*\\\",\\n ],\\n \\\"overflow_fix\\\": \\\"first_layer_only\\\",\\n }\",\n \"def create_config(self, context, mgmtport):\\n pass\",\n \"def create_config(self) -> None:\\n pass\",\n \"def create_config(self) -> None:\\n pass\",\n \"def get_configspace():\\n cs = CS.ConfigurationSpace()\\n\\n \\n\\n # Learning rate hyperparameter\\n lr = CSH.UniformFloatHyperparameter('lr', lower=1e-6, upper=1e-1, default_value='1e-2', log=True)\\n\\n \\n\\n # Stochastic gradient descent momentum as parameter.\\n sgd_momentum = CSH.UniformFloatHyperparameter('sgd_momentum', lower=0.0, upper=0.99, default_value=0.9, log=False)\\n\\n cs.add_hyperparameters([lr, sgd_momentum])\\n \\n # Optimizer hyperparameters.\\n #optimizer = CSH.CategoricalHyperparameter('optimizer', ['Adam', 'SGD'])\\n #cs.add_hyperparameters([optimizer])\\n \\n # Only add the sgd_momentum hyperparameter if the optimizer is stochastic gradient descent. Otherwise, it doesn't make sense.\\n #cond = CS.EqualsCondition(sgd_momentum, optimizer, 'SGD')\\n #cs.add_condition(cond)\\n\\n ''' The below is commented out because we're not fiddling with architecture in this optimization.'''\\n #num_new_fc_layers = CSH.UniformIntegerHyperparameter('num_new_fc_layers', lower=0, upper=3, default_value=0, log=False)\\n #num_els_new_1 = CSH.UniformIntegerHyperparameter('num_els_new_1', lower=128, upper=4096, default_value = 1000, log=True)\\n #num_els_new_2 = CSH.UniformIntegerHyperparameter('num_els_new_2', lower=128, upper=4096, default_value = 1000, log=True)\\n #num_els_new_3 = CSH.UniformIntegerHyperparameter('num_els_new_3', lower=128, upper=4096, default_value = 1000, log=True)\\n\\n #freeze0_old = CSH.UniformIntegerHyperparameter('freeze0_cat', lower = 0, upper = 1, default_value = 1, log=False)\\n #freeze1_old = CSH.UniformIntegerHyperparameter('freeze1_cat', lower=0, upper=1, default_value=1, log=False)\\n\\n #cs.add_hyperparameters([num_new_fc_layers, num_els_new_1, num_els_new_2, num_els_new_3, freeze0_old, freeze1_old, batchsize])\\n\\n dropout_rate = CSH.UniformFloatHyperparameter('dropout_rate', lower=0.0, upper=0.9, default_value=0.5, log=False)\\n\\n cs.add_hyperparameters([dropout_rate])\\n\\n return cs\",\n \"def __init__(self, mode, cfg):\\n super(DMCM, self).__init__()\\n\\n self.conv_net = cfg.get_image_net(mode)\\n self.sparse_net = cfg.get_genes_net(mode)\\n\\n # Matrix network does not need weight initialization because there can\\n # be no vanishing gradients.\\n self.conv_net.apply(_init_weights_xavier)\",\n \"def get_config():\\n name = 'dynamic_pricing'\\n num_products = 5\\n scale = 1\\n noise_var = 10\\n p_max = 1\\n\\n agents = collections.OrderedDict(\\n [('bsPricing',\\n functools.partial(BootstrapDynamicPricing,\\n num_products, scale, noise_var, p_max))]\\n )\\n\\n environments = collections.OrderedDict(\\n [('env',\\n functools.partial(DynamicPricing,\\n num_products, scale, noise_var, p_max))]\\n )\\n experiments = collections.OrderedDict(\\n [(name, ExperimentNoAction)]\\n )\\n n_steps = 80\\n n_seeds = 2000\\n config = Config(name, agents, environments, experiments, n_steps, n_seeds)\\n return config\",\n \"def __set_special_config_values(cfg: __Config, config: dict) -> \\\"__Config\\\":\\n cfg.file_name_plane_masks = lambda i: str(i) + config['file_name_plane_mask_suf']\\n cfg.file_name_planercnn_image = lambda i: str(i) + config['file_name_planercnn_image_suf']\\n cfg.dir_results = f\\\"{cfg.edge_detection_type}\\\" # will be the output folder, create in data dir\\n cfg.image_size = tuple(int(x) for x in config['image_size'].split(\\\" \\\"))\\n return cfg\",\n \"def __init__(self, cfg_file,\\n ckpt: str,\\n output_path: str = None,\\n logger: Logger = None) -> None:\\n\\n if logger is None:\\n logger = make_logger()\\n\\n cfg = load_config(cfg_file)\\n\\n if \\\"test\\\" not in cfg[\\\"data\\\"].keys():\\n raise ValueError(\\\"Test data must be specified in config.\\\")\\n\\n #print(cfg.keys())\\n if \\\"dqn\\\" not in cfg.keys():\\n raise ValueError(\\\"dqn data must be specified in config.\\\")\\n self.model_dir = cfg[\\\"training\\\"][\\\"model_dir\\\"]\\n # when checkpoint is not specified, take latest (best) from model dir\\n if ckpt is None:\\n model_dir = cfg[\\\"training\\\"][\\\"model_dir\\\"]\\n ckpt = get_latest_checkpoint(model_dir)\\n if ckpt is None:\\n raise FileNotFoundError(\\\"No checkpoint found in directory {}.\\\"\\n .format(model_dir))\\n try:\\n step = ckpt.split(model_dir+\\\"/\\\")[1].split(\\\".ckpt\\\")[0]\\n except IndexError:\\n step = \\\"best\\\"\\n\\n\\n self.batch_size = 1 #**\\n self.batch_type = cfg[\\\"training\\\"].get(\\n \\\"eval_batch_type\\\", cfg[\\\"training\\\"].get(\\\"batch_type\\\", \\\"sentence\\\"))\\n self.use_cuda = cfg[\\\"training\\\"].get(\\\"use_cuda\\\", False)\\n self.level = cfg[\\\"data\\\"][\\\"level\\\"]\\n self.eval_metric = cfg[\\\"training\\\"][\\\"eval_metric\\\"]\\n self.max_output_length = cfg[\\\"training\\\"].get(\\\"max_output_length\\\", None)\\n\\n \\n\\n # load the data\\n train_data, dev_data, test_data, src_vocab, trg_vocab = load_data(\\n data_cfg=cfg[\\\"data\\\"])\\n #Loading the DQN parameters:\\n self.sample_size = cfg[\\\"dqn\\\"][\\\"sample_size\\\"]\\n self.lr = cfg[\\\"dqn\\\"].get(\\\"lr\\\", 0.01)\\n self.egreed_max = cfg[\\\"dqn\\\"].get(\\\"egreed_max\\\", 0.9)\\n self.egreed_min = cfg[\\\"dqn\\\"].get(\\\"egreed_min\\\", 0.01)\\n self.gamma_max = cfg[\\\"dqn\\\"].get(\\\"gamma_max\\\", 0.9)\\n self.gamma_min = cfg[\\\"dqn\\\"].get(\\\"gamma_min\\\", 0.5)\\n self.nu_iter = cfg[\\\"dqn\\\"][\\\"nu_iter\\\"]\\n self.mem_cap = cfg[\\\"dqn\\\"][\\\"mem_cap\\\"]\\n self.beam_min = cfg[\\\"dqn\\\"][\\\"beam_min\\\"]\\n self.beam_max = cfg[\\\"dqn\\\"][\\\"beam_max\\\"]\\n self.state_type = cfg[\\\"dqn\\\"][\\\"state_type\\\"]\\n \\n if self.state_type == 'hidden':\\n self.state_size = cfg[\\\"model\\\"][\\\"encoder\\\"][\\\"hidden_size\\\"]*2\\n else:\\n self.state_size = cfg[\\\"model\\\"][\\\"encoder\\\"][\\\"hidden_size\\\"]\\n\\n self.actions_size = len(src_vocab)\\n self.gamma = None\\n \\n print(\\\"Sample size: \\\", self.sample_size )\\n print(\\\"State size: \\\", self.state_size)\\n print(\\\"Action size: \\\", self.actions_size)\\n self.epochs = cfg[\\\"dqn\\\"][\\\"epochs\\\"]\\n\\n # Inii the Qnet and Qnet2\\n self.eval_net = Net(self.state_size, self.actions_size)\\n self.target_net = Net(self.state_size, self.actions_size)\\n\\n #Following the algorithm\\n self.target_net.load_state_dict(self.eval_net.state_dict())\\n\\n self.learn_step_counter = 0\\n self.memory_counter = 0\\n self.size_memory1 = self.state_size * 2 + 2 + 1\\n self.memory = np.zeros((self.mem_cap, self.size_memory1 ))\\n self.optimizer = torch.optim.Adam(self.eval_net.parameters()\\n , lr=self.lr )\\n self.loss_func = nn.MSELoss()\\n\\n #others parameters\\n self.bos_index = trg_vocab.stoi[BOS_TOKEN]\\n self.eos_index = trg_vocab.stoi[EOS_TOKEN]\\n self.pad_index = trg_vocab.stoi[PAD_TOKEN]\\n\\n self.data_to_train_dqn = {\\\"train\\\": train_data}\\n \\n #self.data_to_train_dqn = {\\\"test\\\": test_data}\\n #self.data_to_dev = {\\\"dev\\\": dev_data}\\n self.data_to_dev = {\\\"dev\\\": dev_data}\\n #self.data_to_train_dqn = {\\\"train\\\": train_data\\n # ,\\\"dev\\\": dev_data, \\\"test\\\": test_data}\\n # load model state from disk\\n model_checkpoint = load_checkpoint(ckpt, use_cuda=self.use_cuda)\\n\\n # build model and load parameters into it\\n self.model = build_model(cfg[\\\"model\\\"], src_vocab=src_vocab, trg_vocab=trg_vocab)\\n self.model.load_state_dict(model_checkpoint[\\\"model_state\\\"])\\n\\n if self.use_cuda:\\n self.model.cuda()\\n\\n # whether to use beam search for decoding, 0: greedy decoding\\n beam_size = 1\\n beam_alpha = -1\\n\\n #others not important parameters\\n self.index_fin = None\\n path_tensroboard = self.model_dir + \\\"/tensorboard_DQN/\\\"\\n self.tb_writer = SummaryWriter( log_dir=path_tensroboard , purge_step=0)\\n self.dev_network_count = 0\\n print(cfg[\\\"dqn\\\"][\\\"reward_type\\\"])\\n #Reward funtion related:\\n if cfg[\\\"dqn\\\"][\\\"reward_type\\\"] == \\\"bleu_diff\\\" : \\n print(\\\"You select the reward based on the Bleu score differences\\\")\\n self.Reward = self.Reward_bleu_diff\\n elif cfg[\\\"dqn\\\"][\\\"reward_type\\\"] == \\\"bleu_lin\\\" : \\n print(\\\"You select the reward based on the linear Bleu socres, and several punishments\\\")\\n self.Reward = self.Reward_lin\\n else:\\n print(\\\"You select the reward based on the final score on the last state \\\")\\n self.Reward = self.Reward_bleu_fin\",\n \"def generate_nnie_config(nnie_cfg, config, nnie_out_path='./config.json', tensor_type='float'):\\n u8_start = False if tensor_type == 'float' else False\\n default_config = {\\n \\\"default_net_type_token\\\": \\\"nnie\\\",\\n \\\"rand_input\\\": False,\\n \\\"data_num\\\": 100,\\n \\\"input_path_map\\\": {\\n \\\"data\\\": \\\"./image_bins\\\",\\n },\\n \\\"nnie\\\": {\\n \\\"max_batch\\\": 1,\\n \\\"output_names\\\": [],\\n \\\"mapper_version\\\": 11,\\n \\\"u8_start\\\": u8_start,\\n \\\"device\\\": \\\"gpu\\\",\\n \\\"verbose\\\": False,\\n \\\"image_path_list\\\": [\\\"./image_list.txt\\\"],\\n \\\"mean\\\": [128, 128, 128],\\n \\\"std\\\": [1, 1, 1]\\n }\\n }\\n image_path_list = nnie_cfg['image_path_list']\\n assert os.path.exists(image_path_list)\\n with open(image_path_list, 'r') as f:\\n image_list = [item.strip() for item in f.readlines()]\\n\\n mean = config.to_kestrel.get('pixel_means', [123.675, 116.28, 103.53])\\n std = config.to_kestrel.get('pixel_stds', [58.395, 57.12, 57.375])\\n resize_hw = config.to_kestrel.get('resize_hw', (224, 224))\\n resize_hw = tuple(resize_hw)\\n data_num = len(image_list)\\n image_bin_path = generate_image_bins(image_list, mean, std, resize_hw)\\n default_config['data_num'] = data_num\\n default_config['input_path_map']['data'] = image_bin_path\\n default_config['nnie']['max_batch'] = nnie_cfg.get('max_batch', 1)\\n default_config['nnie']['mapper_version'] = nnie_cfg.get('mapper_version', 11)\\n default_config['nnie']['image_path_list'] = [image_path_list]\\n default_config['nnie']['mean'] = [128] * len(std)\\n default_config['nnie']['std'] = [1] * len(std)\\n with open(nnie_out_path, \\\"w\\\") as f:\\n json.dump(default_config, f, indent=2)\\n\\n return nnie_out_path\",\n \"def construct_and_initialize_trainable_quantizers(self, quant_scheme):\\n # Copy user settable attributes for outputs\\n\\n def _create_trainable_quantizer(bw, name, quantizer):\\n \\\"\\\"\\\" create trainable quantizer from static grid quantizer. \\\"\\\"\\\"\\n # Initialize trainable parameters to None\\n self.register_parameter(f'{name}_encoding_min', None)\\n self.register_parameter(f'{name}_encoding_max', None)\\n # Pass name of tensor quantizer and reference of Wrapper to tensor quantizer\\n # Input quantizer\\n new_quantizer = tensor_quantizer_factory(bw, self._round_mode,\\n quant_scheme,\\n self._is_symmetric,\\n enabled_by_default=True,\\n data_type=self._data_type)\\n new_quantizer.name = name\\n new_quantizer.wrapper_ref = self\\n new_quantizer.device = self.device\\n initialize_learned_grid_quantizer_attributes(new_quantizer, quantizer)\\n return new_quantizer\\n\\n new_grouped_quantizers = {name: _create_trainable_quantizer(self._activation_bw, name, quantizer)\\n for name, quantizer in self._grouped_quantizers.items()}\\n\\n def create_trainable_quantizer(bw, name, quantizer):\\n \\\"\\\"\\\" create trainable quantizer if not part of a group else reuse the group quantizer. \\\"\\\"\\\"\\n if quantizer in self._grouped_quantizers.values():\\n group_names = [n for n, q in self._grouped_quantizers.items() if q == quantizer]\\n assert len(group_names) == 1\\n # creating a param min/max references to the shared group min/max parameters.\\n setattr(self, f'{name}_encoding_min', getattr(self, f'{group_names[0]}_encoding_min'))\\n setattr(self, f'{name}_encoding_max', getattr(self, f'{group_names[0]}_encoding_max'))\\n return new_grouped_quantizers[group_names[0]]\\n\\n return _create_trainable_quantizer(bw, name, quantizer)\\n\\n\\n self._input_quantizers = {name: create_trainable_quantizer(self._activation_bw, name, quantizer)\\n for name, quantizer in self.input_quantizers.items()}\\n self._output_quantizers = {name: create_trainable_quantizer(self._activation_bw, name, quantizer)\\n for name, quantizer in self.output_quantizers.items()}\\n self._param_quantizers = {name: create_trainable_quantizer(self._weight_bw, name, quantizer)\\n for name, quantizer in self._param_quantizers.items()}\\n\\n self._grouped_quantizers = new_grouped_quantizers\\n self._mode = QcQuantizeOpMode.LEARN_ENCODINGS\",\n \"def get_config(self):\\n config = {\\n 'F_': self.F_,\\n 'attn_heads': self.attn_heads,\\n 'attn_heads_reduction': self.attn_heads_reduction,\\n 'edge_type_reduction': self.edge_type_reduction,\\n 'attention_type': self.attention_type,\\n 'attn_dropout': self.attn_dropout,\\n 'feature_dropout': self.feature_dropout,\\n 'activation': self.activation,\\n 'use_value_bias': self.use_value_bias,\\n 'use_key_bias': self.use_key_bias,\\n 'kernel_initializer': self.kernel_initializer,\\n 'bias_initializer': self.bias_initializer,\\n 'attn_kernel_initializer': self.attn_kernel_initializer,\\n 'attn_bias_initalizer': self.attn_bias_initializer,\\n 'kernel_regularizer': self.kernel_regularizer,\\n 'bias_regularizer': self.bias_regularizer,\\n 'attn_kernel_regularizer': self.attn_kernel_regularizer,\\n 'attn_bias_regularizer': self.attn_bias_regularizer,\\n 'activity_regularizer': self.activity_regularizer,\\n 'kernel_constraint': self.kernel_constraint,\\n 'bias_constraint': self.bias_constraint,\\n 'attn_kernel_constraint': self.attn_kernel_constraint,\\n 'attn_bias_constraint': self.attn_bias_constraint\\n }\\n base_config = super(BatchShawMultigraphAttention, self).get_config()\\n return dict(list(base_config.items())) + list(config.items())\",\n \"def __init__(self, network_controller: NetworkQuantizationController, in_channels, out_channels, kernel_size,\\n stride=1,\\n padding=0, dilation=1,\\n group=1, disable_bn=False,\\n batch_norm_epsilon=1e-5, batch_norm_momentum=0.1, tf_padding=False):\\n super(ConvBN, self).__init__()\\n self.stride = stride\\n self.padding = padding\\n self.dilation = dilation\\n self.group = group\\n self.network_controller = network_controller\\n self.tf_padding = tf_padding\\n\\n if not tf_padding:\\n self.pad_tensor = Identity()\\n self.padding_conv = self.padding\\n\\n else:\\n padding = 0\\n self.padding_conv = padding\\n pad_h = self.padding\\n pad_w = self.padding\\n self.pad_tensor = nn.ZeroPad2d((pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2))\\n\\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride,\\n padding=padding, dilation=dilation, groups=group,\\n bias=disable_bn)\\n if disable_bn:\\n self.bn = Identity()\\n else:\\n self.bn = nn.BatchNorm2d(out_channels, eps=batch_norm_epsilon, momentum=batch_norm_momentum)\\n self.q = Quantization(network_controller, is_signed=True,\\n weights_values=self.conv.weight.detach())\",\n \"def __init__(self, cfg):\\n\\n super().__init__()\\n self.K = cfg.queue_size\\n self.output_size = cfg.ent_mention_output_size\\n self.qid = 0\\n self.span_pair_queue = []\\n self.context_pair_queue = []\\n self.span_tmp_queue = []\\n self.context_tmp_queue = []\\n self.temperature = cfg.temperature\\n self.gama = 0.999\\n self.device = cfg.device\\n\\n self.span_pair_encoder = PretrainedSpanEncoder(cfg, momentum=False)\\n self.momentum_span_pair_encoder = PretrainedSpanEncoder(cfg, momentum=True)\\n\\n self.contrastive_loss = nn.CrossEntropyLoss()\",\n \"def get_configspace():\\r\\n cs = CS.ConfigurationSpace()\\r\\n\\r\\n lr = CSH.UniformFloatHyperparameter('lr', lower=1e-6, upper=1e-1, default_value='1e-2', log=True)\\r\\n\\r\\n # For demonstration purposes, we add different optimizers as categorical hyperparameters.\\r\\n # To show how to use conditional hyperparameters with ConfigSpace, we'll add the optimizers 'Adam' and 'SGD'.\\r\\n # SGD has a different parameter 'momentum'.\\r\\n optimizer = CSH.CategoricalHyperparameter('optimizer', ['Adam', 'SGD'])\\r\\n\\r\\n sgd_momentum = CSH.UniformFloatHyperparameter('sgd_momentum', lower=0.0, upper=0.99, default_value=0.9, log=False)\\r\\n\\r\\n cs.add_hyperparameters([lr, optimizer, sgd_momentum])\\r\\n\\r\\n\\r\\n\\r\\n num_conv_layers = CSH.UniformIntegerHyperparameter('num_conv_layers', lower=1, upper=3, default_value=2)\\r\\n\\r\\n num_filters_1 = CSH.UniformIntegerHyperparameter('num_filters_1', lower=4, upper=64, default_value=16, log=True)\\r\\n num_filters_2 = CSH.UniformIntegerHyperparameter('num_filters_2', lower=4, upper=64, default_value=16, log=True)\\r\\n num_filters_3 = CSH.UniformIntegerHyperparameter('num_filters_3', lower=4, upper=64, default_value=16, log=True)\\r\\n\\r\\n cs.add_hyperparameters([num_conv_layers, num_filters_1, num_filters_2, num_filters_3])\\r\\n\\r\\n\\r\\n dropout_rate = CSH.UniformFloatHyperparameter('dropout_rate', lower=0.0, upper=0.9, default_value=0.5, log=False)\\r\\n num_fc_units = CSH.UniformIntegerHyperparameter('num_fc_units', lower=8, upper=256, default_value=32, log=True)\\r\\n\\r\\n cs.add_hyperparameters([dropout_rate, num_fc_units])\\r\\n\\r\\n\\r\\n # The hyperparameter sgd_momentum will be used,if the configuration\\r\\n # contains 'SGD' as optimizer.\\r\\n cond = CS.EqualsCondition(sgd_momentum, optimizer, 'SGD')\\r\\n cs.add_condition(cond)\\r\\n\\r\\n # You can also use inequality conditions:\\r\\n cond = CS.GreaterThanCondition(num_filters_2, num_conv_layers, 1)\\r\\n cs.add_condition(cond)\\r\\n\\r\\n cond = CS.GreaterThanCondition(num_filters_3, num_conv_layers, 2)\\r\\n cs.add_condition(cond)\\r\\n\\r\\n return cs\",\n \"def make_config(config, out_dir=None, pism_root=pism_root):\\n\\n # ensure that config is a list\\n if type(config) is str:\\n config = [config]\\n\\n # initialize netCDF dataset\\n nc_path = os.path.join(out_dir, 'config.nc')\\n nc = Dataset(nc_path, 'w')\\n var = nc.createVariable('pism_overrides', 'i1')\\n\\n # loop on config files\\n for c in config:\\n c_path = '%s/config/%s.txt' % (pism_root, c)\\n\\n # fill in pism overrides\\n with open(c_path) as f:\\n for line in f:\\n\\n # ignore what follows '//'\\n line = line.split('//', 1)[0].strip()\\n\\n # parse non-empty lines and overwrite existing values\\n if line:\\n k, v = line.split(':', 1)\\n k = k.strip()\\n v = v.strip().strip('\\\"')\\n try:\\n v = float(v)\\n except ValueError:\\n pass\\n var.setncattr(k, v)\\n\\n # close and return path to output file\\n nc.close()\\n return nc_path\",\n \"def _get_MindtPy_FP_config():\\n CONFIG = ConfigBlock('MindtPy-GOA')\\n CONFIG.declare(\\n 'init_strategy',\\n ConfigValue(\\n default='FP',\\n domain=In(['FP']),\\n description='Initialization strategy',\\n doc='Initialization strategy used by any method. Currently the '\\n 'continuous relaxation of the MINLP (rNLP), solve a maximal '\\n 'covering problem (max_binary), and fix the initial value for '\\n 'the integer variables (initial_binary).',\\n ),\\n )\\n\\n _add_common_configs(CONFIG)\\n _add_fp_configs(CONFIG)\\n _add_oa_cuts_configs(CONFIG)\\n _add_subsolver_configs(CONFIG)\\n _add_tolerance_configs(CONFIG)\\n _add_bound_configs(CONFIG)\\n return CONFIG\",\n \"def test_quantize_training():\\n config = EasyDict()\\n\\n config.NETWORK_CLASS = FlowNetSV3Quantized\\n config.DATASET_CLASS = FlyingChairs\\n\\n config.IS_DEBUG = False\\n config.IMAGE_SIZE = [384, 512]\\n config.BATCH_SIZE = 8\\n config.TEST_STEPS = 200\\n config.MAX_STEPS = 5000\\n config.SAVE_CHECKPOINT_STEPS = 100\\n config.KEEP_CHECKPOINT_MAX = 5\\n config.SUMMARISE_STEPS = 100\\n config.IS_PRETRAIN = False\\n config.IS_DISTRIBUTION = False\\n config.TASK = Tasks.OPTICAL_FLOW_ESTIMATION\\n\\n # network model config\\n config.NETWORK = EasyDict()\\n config.NETWORK.OPTIMIZER_CLASS = tf.train.AdamOptimizer\\n config.NETWORK.OPTIMIZER_KWARGS = {\\\"beta1\\\": 0.9, \\\"beta2\\\": 0.999}\\n config.NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant\\n config.NETWORK.LEARNING_RATE_KWARGS = {\\n \\\"values\\\": [0.0000125, 0.00005],\\n \\\"boundaries\\\": [5000]\\n }\\n\\n config.NETWORK.IMAGE_SIZE = config.IMAGE_SIZE\\n config.NETWORK.BATCH_SIZE = config.BATCH_SIZE\\n config.NETWORK.DATA_FORMAT = \\\"NHWC\\\"\\n config.NETWORK.ACTIVATION_QUANTIZER = linear_mid_tread_half_quantizer\\n config.NETWORK.ACTIVATION_QUANTIZER_KWARGS = {\\n 'bit': 2,\\n 'max_value': 2.0\\n }\\n config.NETWORK.WEIGHT_QUANTIZER = binary_channel_wise_mean_scaling_quantizer\\n config.NETWORK.WEIGHT_QUANTIZER_KWARGS = {}\\n\\n # dataset config\\n config.DATASET = EasyDict()\\n config.DATASET.PRE_PROCESSOR = None\\n config.DATASET.BATCH_SIZE = config.BATCH_SIZE\\n config.DATASET.DATA_FORMAT = \\\"NHWC\\\"\\n config.DATASET.VALIDATION_RATE = 0.2\\n config.DATASET.VALIDATION_SEED = 2019\\n config.DATASET.AUGMENTOR = Sequence([\\n # Geometric transformation\\n FlipLeftRight(0.5),\\n FlipTopBottom(0.5),\\n Translate(-0.2, 0.2),\\n Rotate(-17, +17),\\n Scale(1.0, 2.0),\\n # Pixel-wise augmentation\\n Brightness(0.8, 1.2),\\n Contrast(0.2, 1.4),\\n Color(0.5, 2.0),\\n Gamma(0.7, 1.5),\\n # Hue(-128.0, 128.0),\\n GaussianNoise(0.0, 10.0)\\n ])\\n config.DATASET.PRE_PROCESSOR = Sequence([\\n DevideBy255(),\\n ])\\n environment.init(\\\"test_flownet_s_v3_quantize\\\")\\n prepare_dirs(recreate=True)\\n start_training(config)\",\n \"def get_config():\\n config = ml_collections.ConfigDict()\\n config.seed = 42\\n\\n config.eval_num = 30000\\n config.eval_avg_num = 3\\n config.num_train_steps = -1\\n config.log_loss_every_steps = 1000\\n config.eval_every_steps = 1000\\n config.checkpoint_every_steps = 5000\\n\\n config.dataset = \\\"mscoco\\\"\\n config.coco_version = \\\"2014\\\"\\n config.data_dir = \\\"data/\\\"\\n config.return_text = False\\n config.return_filename = False\\n\\n config.trial = 0 # dummy for repeated runs.\\n config.beta1 = 0.5\\n config.beta2 = 0.999\\n config.d_lr = 0.0004\\n config.g_lr = 0.0001\\n config.polyak_decay = 0.999\\n config.show_num = 64\\n config.shuffle_buffer_size = 1000\\n config.batch_norm_group_size = -1\\n config.dtype = \\\"bfloat16\\\"\\n config.train_shuffle = True\\n\\n config.image_size = 128\\n config.batch_size = 56\\n config.eval_batch_size = 7\\n\\n config.df_dim = 96\\n config.gf_dim = 96\\n config.z_dim = 128\\n config.num_epochs = 500\\n config.model_name = \\\"xmc\\\"\\n config.d_step_per_g_step = 2\\n config.g_spectral_norm = False\\n config.d_spectral_norm = True\\n config.architecture = \\\"xmc_net\\\"\\n config.gamma_for_g = 15\\n config.word_contrastive = True\\n config.sentence_contrastive = True\\n config.image_contrastive = True\\n config.pretrained_image_contrastive = True\\n config.cond_size = 16\\n\\n return config\",\n \"def setUp(self):\\n # The short NSC used in this example\\n self.net_nsc = [\\n (1, 4, 0, 0, 0), # Layer 1: Identity(input)\\n (2, 1, 3, 1, 0), # Layer 2: Convolution(Layer1)\\n (3, 1, 3, 2, 0), # Layer 3: Convolution(Layer2)\\n (4, 5, 0, 1, 3), # Layer 4: Convolution(Layer1)\\n (5, 7, 0, 0, 0), # Layer 5: Convolution(Layer4)\\n ]\",\n \"def createNCDF(self):\\n\\n rootgrp = Dataset(self.filename_out, 'w', format=self.format, clobber=True)\\n\\n # Create dimensions.\\n if 'dimensions' in self.input_dict:\\n for k, v in self.input_dict['dimensions'].items():\\n rootgrp.createDimension(k, v)\\n else:\\n if not self.Quiet:\\n print('No netCDF created:')\\n print(' No dimension key found (!! has to be \\\\\\\"dimensions\\\\\\\"!!!)')\\n return()\\n\\n # Create global attributes.\\n if 'global attributes' in self.input_dict:\\n for k, v in self.input_dict['global attributes'].items():\\n rootgrp.setncattr(k, v)\\n else:\\n if not self.Quiet:\\n print(' No global attribute key found (!! has to be \\\\\\\"global attributes\\\\\\\"!!!)')\\n\\n # Create variables.\\n for k, v in self.input_dict['variables'].items():\\n dims = self.input_dict['variables'][k]['dimensions']\\n data = v['data']\\n # Create correct data type if provided\\n if 'data_type' in self.input_dict['variables'][k]:\\n data_type = self.input_dict['variables'][k]['data_type']\\n else:\\n data_type = 'f4'\\n # Check whether we've been given a fill value.\\n if 'fill_value' in self.input_dict['variables'][k]:\\n fill_value = self.input_dict['variables'][k]['fill_value']\\n else:\\n fill_value = None\\n # Create ncdf variable\\n if not self.Quiet:\\n print(' Creating variable: {} {} {}'.format(k, data_type, dims))\\n var = rootgrp.createVariable(k, data_type, dims, fill_value=fill_value)\\n if len(dims) > np.ndim(data):\\n # If number of dimensions given to netCDF is greater than the\\n # number of dimension of the data, then fill the netCDF\\n # variable accordingly.\\n if 'time' in dims:\\n # Check for presence of time dimension (which can be\\n # unlimited variable: defined by None).\\n try:\\n var[:] = data\\n except IndexError:\\n raise(IndexError(('Supplied data shape {} does not match the specified'\\n ' dimensions {}, for variable \\\\'{}\\\\'.'.format(data.shape, var.shape, k))))\\n else:\\n if not self.Quiet:\\n print('Problem in the number of dimensions')\\n else:\\n try:\\n var[:] = data\\n except IndexError:\\n raise(IndexError(('Supplied data shape {} does not match the specified'\\n ' dimensions {}, for variable \\\\'{}\\\\'.'.format(data.shape, var.shape, k))))\\n\\n # Create attributes for variables\\n if 'attributes' in self.input_dict['variables'][k]:\\n for ka, va in self.input_dict['variables'][k]['attributes'].items():\\n var.setncattr(ka, va)\\n\\n rootgrp.close()\",\n \"def __init__(self, encut, name=\\\"scf_settings\\\"):\\n InputParameters.__init__(self, name=name)\\n self.update_electronic_settings(\\\"ENCUT\\\", encut)\",\n \"def get_config(self):\\n config = {\\n \\\"units\\\": self.units,\\n \\\"activation\\\": activations.serialize(self.activation),\\n \\\"recurrent_activation\\\": activations.serialize(\\n self.recurrent_activation\\n ),\\n \\\"attention_activation\\\": activations.serialize(\\n self.attention_activation\\n ),\\n \\\"use_bias\\\": self.use_bias,\\n \\\"kernel_initializer\\\": initializers.serialize(self.kernel_initializer),\\n \\\"recurrent_initializer\\\": initializers.serialize(\\n self.recurrent_initializer\\n ),\\n \\\"bias_initializer\\\": initializers.serialize(self.bias_initializer),\\n \\\"attention_initializer\\\": initializers.serialize(\\n self.attention_initializer\\n ),\\n \\\"use_chrono_initialization\\\": self.unit_forget_bias,\\n \\\"kernel_regularizer\\\": regularizers.serialize(self.kernel_regularizer),\\n \\\"recurrent_regularizer\\\": regularizers.serialize(\\n self.recurrent_regularizer\\n ),\\n \\\"bias_regularizer\\\": regularizers.serialize(self.bias_regularizer),\\n \\\"activity_regularizer\\\": regularizers.serialize(\\n self.activity_regularizer\\n ),\\n \\\"attention_regularizer\\\": regularizers.serialize(\\n self.attention_regularizer\\n ),\\n \\\"kernel_constraint\\\": constraints.serialize(self.kernel_constraint),\\n \\\"recurrent_constraint\\\": constraints.serialize(\\n self.recurrent_constraint\\n ),\\n \\\"bias_constraint\\\": constraints.serialize(self.bias_constraint),\\n \\\"attention_constraint\\\": constraints.serialize(\\n self.attention_constraint\\n ),\\n \\\"dropout\\\": self.dropout,\\n \\\"recurrent_dropout\\\": self.recurrent_dropout,\\n \\\"return_attention\\\": self.return_attention,\\n }\\n base_config = super().get_config()\\n del base_config[\\\"cell\\\"]\\n return dict(list(base_config.items()) + list(config.items()))\",\n \"def setup(args):\\n cfg = get_cfg()\\n cfg.merge_from_file(args.config_file)\\n cfg.merge_from_list(args.opts)\\n # customize reszied parameters\\n # cfg['INPUT']['MIN_SIZE_TRAIN'] = (20,)\\n # cfg['INPUT']['MAX_SIZE_TRAIN'] = 50\\n cfg.freeze()\\n default_setup(\\n cfg, args\\n ) # if you don't like any of the default setup, write your own setup code\\n return cfg\",\n \"def createCfg_comp_jetToTauFakeRate(self, jobOptions):\\n for charge_selection in self.charge_selections:\\n lines = []\\n lines.append(\\\"process.fwliteInput.fileNames = cms.vstring('%s')\\\" % jobOptions['inputFile'])\\n lines.append(\\\"process.fwliteOutput.fileName = cms.string('%s')\\\" % os.path.basename(jobOptions['outputFile']))\\n lines.append(\\\"process.comp_jetToTauFakeRate.looseRegion = cms.string('%s')\\\" % jobOptions['looseRegion'])\\n lines.append(\\\"process.comp_jetToTauFakeRate.tightRegion = cms.string('%s')\\\" % jobOptions['tightRegion'])\\n lines.append(\\\"process.comp_jetToTauFakeRate.processData = cms.string('data_obs')\\\")\\n lines.append(\\\"process.comp_jetToTauFakeRate.processesToSubtract = cms.vstring(\\\")\\n lines.append(\\\" 'TTt', 'TTl',\\\")\\n lines.append(\\\" 'EWKt', 'EWKl',\\\")\\n lines.append(\\\" 'Rarest', 'Raresl',\\\") \\n lines.append(\\\" 'TTWt', 'TTWl', \\\")\\n lines.append(\\\" 'TTZt', 'TTZl', \\\")\\n lines.append(\\\" 'signalt', 'signall'\\\")\\n lines.append(\\\")\\\")\\n lines.append(\\\"process.comp_jetToTauFakeRate.processMC = cms.string('TTj')\\\")\\n lines.append(\\\"process.comp_jetToTauFakeRate.absEtaBins = cms.vdouble(%s)\\\" % jobOptions['absEtaBins'])\\n lines.append(\\\"process.comp_jetToTauFakeRate.ptBins = cms.vdouble(%s)\\\" % jobOptions['ptBins'])\\n create_cfg(self.cfgFile_comp_jetToTauFakeRate, jobOptions['cfgFile_modified'], lines)\",\n \"def get_base_config():\\n return dict(\\n dim=768,\\n ff_dim=3072,\\n num_heads=12,\\n num_layers=12,\\n attention_dropout_rate=0.0,\\n dropout_rate=0.1,\\n representation_size=768,\\n classifier='token'\\n )\",\n \"def prepare_config(device='npu'):\\n if device == 'npu':\\n # config for Ascend processor\\n config = tf.ConfigProto()\\n custom_op = config.graph_options.rewrite_options.custom_optimizers.add()\\n custom_op.name = \\\"NpuOptimizer\\\"\\n custom_op.parameter_map[\\\"use_off_line\\\"].b = True\\n custom_op.parameter_map[\\\"precision_mode\\\"].s = tf.compat.as_bytes(\\\"force_fp16\\\")\\n custom_op.parameter_map[\\\"graph_run_mode\\\"].i = 0\\n config.graph_options.rewrite_options.remapping = RewriterConfig.OFF\\n custom_op.parameter_map[\\\"debug_dir\\\"].s = tf.compat.as_bytes(str(TMP))\\n else:\\n config = tf.ConfigProto()\\n return config\",\n \"def create_compression_algorithm_builder(config: NNCFConfig, should_init=True) -> PTCompressionAlgorithmBuilder:\\n algo_names = extract_algorithm_names(config)\\n return create_compression_algorithm_builder_from_algo_names(algo_names, config, should_init)\",\n \"def __init__(self, config):\\n super().__init__()\\n\\n self.pt_encoder = config['encoder_name']\\n self.nu = config['nu']\\n\\n self.encoder = TransformerEncoder(name=self.pt_encoder,\\n nu=self.nu)\\n\\n self.hidden_dims = config['hidden_dims']\\n self.act_fn = nn.ReLU if config['act_fn'] == 'ReLU' else nn.Tanh\\n\\n self.mlp = MLP(encoder_output_size=self.encoder.model.config.hidden_size,\\n hidden_dims=self.hidden_dims,\\n act_fn=self.act_fn)\\n\\n self.n_classes = config['n_classes']\\n self.clf = SF_CLF(n_classes=self.n_classes,\\n hidden_dims=self.hidden_dims)\",\n \"def __init__(self, config, mode=None):\\n super(FacenetEvaluation, self).__init__(config, mode)\\n\\n print('Create {}'.format(self.evaluation_name))\\n\\n # Preprocess Configurations and check legal\\n self._must_have_config = [\\n eval_fields.distance_measure,\\n eval_fields.sampling\\n ]\\n\\n self._default_values = {\\n eval_fields.distance_measure: {\\n eval_fields.threshold: {\\n eval_fields.start: 0.01,\\n eval_fields.end: 0.7,\\n eval_fields.step: 0.01\\n }\\n },\\n eval_fields.sampling: {\\n facenet_fields.sample_ratio: 0.2,\\n facenet_fields.class_sample_method: facenet_fields.random_sample\\n }\\n }\\n # metrics with condition\\n self._metric_with_threshold = [\\n metric_fields.accuracy,\\n metric_fields.validation_rate,\\n metric_fields.false_accept_rate,\\n metric_fields.true_positive_rate,\\n metric_fields.false_positive_rate,\\n ]\\n # metrics without condition\\n self._metric_without_threshold = [\\n metric_fields.mean_accuracy,\\n metric_fields.mean_validation_rate,\\n metric_fields.area_under_curve,\\n ]\\n\\n # Set default values for must-have configs\\n for _config in self._must_have_config:\\n if _config not in self.metrics:\\n if _config in self._default_values:\\n pass\\n else:\\n print('WARNING: {} should be assigned'.format(_config))\\n else:\\n print('Use assigned {}: {}'.format(_config, self.metrics[_config]))\\n\\n # Set distance thresholds by config\\n distance_config = self.distance_measure\\n distance_thres = distance_config[eval_fields.threshold]\\n dist_start = distance_thres[eval_fields.start]\\n dist_end = distance_thres[eval_fields.end]\\n dist_step = distance_thres[eval_fields.step]\\n # TODO @kv: Do we need sanity check for start < end?\\n if dist_start > dist_end:\\n raise ValueError('FaceEvaluation: distance threshold start > end')\\n self._distance_thresholds = np.arange(dist_start, dist_end, dist_step)\\n\\n # Attributes\\n if len(self.attributes) == 0:\\n self._has_attribute = False\\n elif len(self.attributes) == 1:\\n if attribute_fields.all_classes in self.attributes:\\n self._has_attribute = False\\n elif attribute_fields.all_attributes in self.attributes:\\n self._has_attribute = True\\n else:\\n self._has_attribute = True\\n self.show_configs()\",\n \"def get_configspace() -> CS.Configuration:\\n cs = CS.ConfigurationSpace(seed=0)\\n # START TODO ################\\n lr_hp = CS.UniformFloatHyperparameter('lr', lower=1e-6, upper=1e-1, default_value=1e-2, log=True)\\n optimizer_hp = CSH.CategoricalHyperparameter(name='optimizer', choices=['Adam', 'SGD', 'RMSprop'])\\n sgd_momentum_hp = CS.UniformFloatHyperparameter('sgd_momentum', lower=0.00, upper=0.99, default_value=0.9)\\n\\n rms_momentum_hp = CS.UniformFloatHyperparameter('rms_momentum', lower=0.00, upper=0.99, default_value=0.9)\\n rms_alpha_hp = CS.UniformFloatHyperparameter('rms_alpha', lower=0.00, upper=0.99, default_value=0.99)\\n\\n scheduler_hp = CSH.CategoricalHyperparameter(name='scheduler',\\n choices=['CosineAnnealingLR', 'CosineAnnealingWarmRestarts'])\\n cosine_max_t_hp = CS.UniformIntegerHyperparameter(name='cosine_max_t', lower=50, upper=300, default_value=150)\\n cosine_warm_hp = CS.UniformIntegerHyperparameter(name='warm_t_0', lower=50, upper=300, default_value=150)\\n\\n sgd_cond = CS.EqualsCondition(sgd_momentum_hp, optimizer_hp, 'SGD')\\n rms_cond1 = CS.EqualsCondition(rms_momentum_hp, optimizer_hp, 'RMSprop')\\n rms_cond2 = CS.EqualsCondition(rms_alpha_hp, optimizer_hp, 'RMSprop')\\n cosine_warm_cond = CS.EqualsCondition(cosine_warm_hp, scheduler_hp, 'CosineAnnealingWarmRestarts')\\n cosine_cond = CS.EqualsCondition(cosine_max_t_hp, scheduler_hp, 'CosineAnnealingLR')\\n cs.add_hyperparameters([lr_hp, optimizer_hp, sgd_momentum_hp, rms_momentum_hp,\\n rms_alpha_hp, scheduler_hp, cosine_max_t_hp, cosine_warm_hp])\\n cs.add_conditions([sgd_cond, rms_cond1, rms_cond2, cosine_cond, cosine_warm_cond])\\n # END TODO ################\\n return cs\",\n \"def get_config(self):\\n config = super(EpsGreedyQPolicy, self).get_config()\\n config['eps'] = self.eps\\n return config\",\n \"def _configure(self, config):\\n self.friction_coef = config['friction_coef']\\n self.num_cone_faces = config['num_cone_faces']\\n self.num_samples = config['grasp_samples_per_surface_point']\\n self.dir_prior = config['dir_prior']\\n self.target_num_grasps = config['target_num_grasps']\\n if self.target_num_grasps is None:\\n self.target_num_grasps = config['min_num_grasps']\\n\\n self.min_contact_dist = config['min_contact_dist']\\n self.num_grasp_rots = config['coll_check_num_grasp_rots']\\n if 'max_num_surface_points' in config.keys():\\n self.max_num_surface_points_ = config['max_num_surface_points']\\n else:\\n self.max_num_surface_points_ = 100\",\n \"def update_configuration(self, config):\\n\\n config[\\\"data_transformation\\\"][\\\"n_classification_bins\\\"] = config[\\\"n_classification_bins\\\"]\\n config[\\\"data_transformation\\\"][\\\"nassets\\\"] = config[\\\"nassets\\\"]\\n config[\\\"data_transformation\\\"][\\\"classify_per_series\\\"] = config[\\\"classify_per_series\\\"]\\n config[\\\"data_transformation\\\"][\\\"normalise_per_series\\\"] = config[\\\"normalise_per_series\\\"]\\n\\n return config\",\n \"def build(self, mode, config):\\n assert mode in ['training', 'inference']\\n\\n # Image size must be dividable by 2 multiple times\\n h, w = config.IMAGE_SHAPE[:2]\\n if h / 2**6 != int(h / 2**6) or w / 2**6 != int(w / 2**6):\\n raise Exception(\\\"Image size must be dividable by 2 at least 6 times \\\"\\n \\\"to avoid fractions when downscaling and upscaling.\\\"\\n \\\"For example, use 256, 320, 384, 448, 512, ... etc. \\\")\\n\\n # Inputs\\n input_image = KL.Input(\\n shape=[None, None, 3], name=\\\"input_image\\\")\\n input_image_meta = KL.Input(shape=[config.IMAGE_META_SIZE],\\n name=\\\"input_image_meta\\\")\\n if mode == \\\"training\\\":\\n pass\\n elif mode == \\\"inference\\\":\\n # Anchors in normalized coordinates\\n input_anchors = KL.Input(shape=[None, 4], name=\\\"input_anchors\\\")\\n\\n # Build the shared convolutional layers.\\n # Bottom-up Layers\\n # Returns a list of the last layers of each stage, 5 in total.\\n # Don't create the thead (stage 5), so we pick the 4th item in the list.\\n _, C2, C3, C4, C5 = resnet_graph(input_image, config.BACKBONE,\\n stage5=True, train_bn=config.TRAIN_BN)\\n # Top-down Layers\\n # TODO: add assert to varify feature map sizes match what's in config\\n P5 = KL.Conv2D(256, (1, 1), name='fpn_c5p5')(C5)\\n P4 = KL.Add(name=\\\"fpn_p4add\\\")([\\n KL.UpSampling2D(size=(2, 2), name=\\\"fpn_p5upsampled\\\")(P5),\\n KL.Conv2D(256, (1, 1), name='fpn_c4p4')(C4)])\\n P3 = KL.Add(name=\\\"fpn_p3add\\\")([\\n KL.UpSampling2D(size=(2, 2), name=\\\"fpn_p4upsampled\\\")(P4),\\n KL.Conv2D(256, (1, 1), name='fpn_c3p3')(C3)])\\n P2 = KL.Add(name=\\\"fpn_p2add\\\")([\\n KL.UpSampling2D(size=(2, 2), name=\\\"fpn_p3upsampled\\\")(P3),\\n KL.Conv2D(256, (1, 1), name='fpn_c2p2')(C2)])\\n # Attach 3x3 conv to all P layers to get the final feature maps.\\n P2 = KL.Conv2D(256, (3, 3), padding=\\\"SAME\\\", name=\\\"fpn_p2\\\")(P2)\\n P3 = KL.Conv2D(256, (3, 3), padding=\\\"SAME\\\", name=\\\"fpn_p3\\\")(P3)\\n P4 = KL.Conv2D(256, (3, 3), padding=\\\"SAME\\\", name=\\\"fpn_p4\\\")(P4)\\n P5 = KL.Conv2D(256, (3, 3), padding=\\\"SAME\\\", name=\\\"fpn_p5\\\")(P5)\\n # P6 is used for the 5th anchor scale in RPN. Generated by\\n # subsampling from P5 with stride of 2.\\n P6 = KL.MaxPooling2D(pool_size=(1, 1), strides=2, name=\\\"fpn_p6\\\")(P5)\\n\\n # Note that P6 is used in RPN, but not in the classifier heads.\\n rpn_feature_maps = [P2, P3, P4, P5, P6]\\n mrcnn_feature_maps = [P2, P3, P4, P5]\\n\\n # Anchors\\n if mode == \\\"training\\\":\\n anchors = self.get_anchors(config.IMAGE_SHAPE)\\n # Duplicate across the batch dimension because Keras requires it\\n # TODO: can this be optimized to avoid duplicating the anchors?\\n anchors = np.broadcast_to(anchors, (config.BATCH_SIZE,) + anchors.shape)\\n # A hack to get around Keras's bad support for constants\\n anchors = KL.Lambda(lambda x: tf.Variable(anchors), name=\\\"anchors\\\")(input_image)\\n else:\\n anchors = input_anchors\\n\\n # RPN Model\\n rpn = build_rpn_model(config.RPN_ANCHOR_STRIDE,\\n len(config.RPN_ANCHOR_RATIOS), 256)\\n # Loop through pyramid layers\\n layer_outputs = [] # list of lists\\n for p in rpn_feature_maps:\\n layer_outputs.append(rpn([p]))\\n # Concatenate layer outputs\\n # Convert from list of lists of level outputs to list of lists\\n # of outputs across levels.\\n # e.g. [[a1, b1, c1], [a2, b2, c2]] => [[a1, a2], [b1, b2], [c1, c2]]\\n output_names = [\\\"rpn_class_logits\\\", \\\"rpn_class\\\", \\\"rpn_bbox\\\"]\\n outputs = list(zip(*layer_outputs))\\n outputs = [KL.Concatenate(axis=1, name=n)(list(o))\\n for o, n in zip(outputs, output_names)]\\n\\n rpn_class_logits, rpn_class, rpn_bbox = outputs\\n\\n # Generate proposals\\n # Proposals are [batch, N, (y1, x1, y2, x2)] in normalized coordinates\\n # and zero padded.\\n proposal_count = config.POST_NMS_ROIS_TRAINING if mode == \\\"training\\\"\\\\\\n else config.POST_NMS_ROIS_INFERENCE\\n rpn_rois = ProposalLayer(\\n proposal_count=proposal_count,\\n nms_threshold=config.RPN_NMS_THRESHOLD,\\n name=\\\"ROI\\\",\\n config=config)([rpn_class, rpn_bbox, anchors])\\n\\n if mode == \\\"training\\\":\\n pass\\n else:\\n # Network Heads\\n # Proposal classifier and BBox regressor heads\\n mrcnn_class_logits, mrcnn_class, mrcnn_bbox =\\\\\\n fpn_classifier_graph(rpn_rois, mrcnn_feature_maps, input_image_meta,\\n config.POOL_SIZE, config.NUM_CLASSES,\\n train_bn=config.TRAIN_BN)\\n\\n # Detections\\n # output is [batch, num_detections, (y1, x1, y2, x2, class_id, score)] in \\n # normalized coordinates\\n detections = DetectionLayer(config, name=\\\"mrcnn_detection\\\")(\\n [rpn_rois, mrcnn_class, mrcnn_bbox, input_image_meta])\\n\\n # Create masks for detections\\n detection_boxes = KL.Lambda(lambda x: x[..., :4])(detections)\\n mrcnn_mask = build_fpn_mask_graph(detection_boxes, mrcnn_feature_maps,\\n input_image_meta,\\n config.MASK_POOL_SIZE,\\n config.NUM_CLASSES,\\n train_bn=config.TRAIN_BN)\\n\\n model = KM.Model([input_image, input_image_meta, input_anchors],\\n [detections, mrcnn_class, mrcnn_bbox,\\n mrcnn_mask, rpn_rois, rpn_class, rpn_bbox],\\n name='mask_rcnn')\\n\\n return model\",\n \"def _build_config_sis3305_subgroup(self, config_name: str, slot: int, index: int):\\n # create group\\n gname = f\\\"SIS crate 3305 configurations[{index}]\\\"\\n gpath = f\\\"{config_name}/{gname}\\\"\\n self.create_group(gpath)\\n\\n # get channel array\\n brd = self.slot_info[slot][0]\\n sis_arr = self._active_brdch[\\\"SIS 3305\\\"][brd - 1]\\n\\n # populate attributes\\n self[gpath].attrs.update(\\n {\\n \\\"Bandwidth\\\": np.uint32(1),\\n \\\"Channel mode\\\": np.uint32(self._sis3305_mode),\\n \\\"Clock rate\\\": np.uint32(0),\\n \\\"Samples\\\": np.uint32(self.knobs.nt),\\n \\\"Shot averaging (software)\\\": np.int32(1),\\n }\\n )\\n for ii in range(1, 9):\\n # setup\\n if 1 <= ii <= 4:\\n fpga_str = \\\"FPGA 1 \\\"\\n ch = ii\\n else:\\n fpga_str = \\\"FPGA 2 \\\"\\n ch = ii - 4\\n\\n # 'FPGA # Avail #' fields\\n if self._sis3305_mode == 2 and ch != 1:\\n mode = \\\"FALSE\\\"\\n elif self._sis3305_mode == 1 and ch not in (1, 3):\\n mode = \\\"FALSE\\\"\\n else:\\n mode = \\\"TRUE\\\"\\n field = f\\\"{fpga_str}Avail {ch}\\\"\\n self[gpath].attrs[field] = np.bytes_(mode)\\n\\n # 'FPGA # Ch #' fields\\n field = f\\\"{fpga_str}Ch {ch}\\\"\\n self[gpath].attrs[field] = np.int32(ii)\\n\\n # 'FPGA # Comment #' fields\\n field = f\\\"{fpga_str}Comment {ch}\\\"\\n self[gpath].attrs[field] = np.bytes_(\\\"\\\")\\n\\n # 'FPGA # Data type #' fields\\n field = f\\\"{fpga_str}Data type {ch}\\\"\\n self[gpath].attrs[field] = np.bytes_(f\\\"probe name {ii}\\\")\\n\\n # 'FPGA # Enabled #' fields\\n field = f\\\"{fpga_str}Enabled {ch}\\\"\\n self[gpath].attrs[field] = np.bytes_(\\\"TRUE\\\" if sis_arr[ii - 1] else \\\"FALSE\\\")\",\n \"def default_assign_kernel_config(defn):\\n # XXX(stephentu): model_descriptors should implement\\n # is_conjugate()\\n\\n def is_nonconj(x):\\n return x.name() == 'bbnc'\\n\\n nonconj_indices = [\\n idx for idx, x in enumerate(defn.models()) if is_nonconj(x)\\n ]\\n\\n defn = _validate_definition(defn)\\n\\n # assignment\\n if nonconj_indices:\\n # XXX(stephentu): 0.1 is arbitrary\\n # XXX(stephentu): don't assume bbnc\\n theta_config = {\\n 'tparams': {i: {'p': 0.1} for i in nonconj_indices}\\n }\\n kernels = [\\n ('assign_resample', {'m': 10}),\\n ('theta', theta_config),\\n ]\\n else:\\n kernels = ['assign']\\n\\n return kernels\",\n \"def input_config():\\n run_dir = 'runs/ODEMnistClassification/8'\\n epoch = 'latest'\\n device = 'cpu'\\n min_end_time = 10\\n max_end_time = 100\\n tol = 1e-3\",\n \"def __init__(self, config):\\n self.cfg = config\\n self.var_combinations = [\\\"tas:tas\\\", \\\"pr:pr\\\", \\\"pr:tas\\\"]\\n self.seasons = [\\\"jja\\\", \\\"djf\\\", \\\"annual\\\"]\\n self.projects = [\\\"cmip5\\\", \\\"cmip6\\\"]\\n self.variables = [\\\"tas\\\", \\\"pr\\\"]\\n self.scenarios = [\\\"26\\\", \\\"45\\\", \\\"85\\\"]\\n\\n # generate list of candidate bound limits\\n small = np.arange(0.1, 1, 0.1)\\n medium = np.arange(1, 11)\\n high = np.arange(20, 100, 10)\\n v_high = np.arange(150, 400, 50)\\n self.bound_candidates = np.concatenate(\\n (small, medium, high, v_high)) * 5 / 4\",\n \"def QCNN_layer_gen(self):\\n pixels = self.filter_size**2\\n # filter size: 2^n only for this version!\\n if np.log2(pixels) % 1 != 0:\\n raise NotImplementedError(\\\"filter size: 2^n only available\\\")\\n cirq_qubits = cirq.GridQubit.rect(self.filter_size, self.filter_size)\\n # mapping input data to circuit\\n input_circuit = cirq.Circuit()\\n input_params = [sympy.symbols('a%d' %i) for i in range(pixels)]\\n for i, qubit in enumerate(cirq_qubits):\\n input_circuit.append(cirq.rx(np.pi*input_params[i])(qubit))\\n # apply learnable gate set to QCNN circuit\\n QCNN_circuit = cirq.Circuit()\\n step_size = [2**i for i in range(np.log2(pixels).astype(np.int32))]\\n for step in step_size:\\n for target in range(0, pixels, 2*step):\\n QCNN_circuit.append(self._QConv(step, target, cirq_qubits))\\n # merge the circuits\\n full_circuit = cirq.Circuit()\\n full_circuit.append(input_circuit)\\n full_circuit.append(QCNN_circuit)\\n self.circuit = full_circuit # save circuit to the QCNN layer obj.\\n self.params = input_params + self.learning_params\\n self.op = cirq.Z(cirq_qubits[0])\",\n \"def get_config(self):\\n layer_config = {\\n \\\"anchors\\\": self._anchors, \\n \\\"classes\\\": self._classes,\\n \\\"ignore_thresh\\\": self._ignore_thresh, \\n \\\"truth_thresh\\\": self._truth_thresh, \\n \\\"iou_thresh\\\": self._iou_thresh, \\n \\\"loss_type\\\": self._loss_type, \\n \\\"iou_normalizer\\\": self._iou_normalizer,\\n \\\"cls_normalizer\\\": self._cls_normalizer, \\n \\\"scale_x_y\\\": self._scale_x_y, \\n }\\n layer_config.update(super().get_config())\\n return layer_config\",\n \"def create_config(self):\\n\\n #FIXME: Try to do it over loops ie. self[attr].set_config()\\n for attr, value in self.__dict__.items():\\n if attr == \\\"connection\\\":\\n self.connection.set_config(self.cfg)\\n if attr == \\\"ipv4\\\":\\n self.ipv4.set_config(self.cfg)\\n if attr == \\\"ipv6\\\":\\n self.ipv6.set_config(self.cfg)\\n if attr == \\\"_802_3_ethernet\\\" and not value == \\\"none\\\":\\n self._802_3_ethernet.set_config(self.cfg)\\n if attr == \\\"_802_11_wireless\\\" and not value == \\\"none\\\":\\n self._802_11_wireless.set_config(self.cfg)\\n if attr == \\\"_802_11_wireless_security\\\" and not value == \\\"none\\\":\\n self._802_11_wireless_security.set_config(self.cfg)\",\n \"def generateConfig(run,subrun,conditions):\\n \\n configname = (conditions.numcdir + \\\"/\\\" + str(run) + \\\"/\\\" + str(subrun)\\n + \\\"/numc_config_\\\" + str(run) + \\\"_\\\" + str(subrun) + \\\".cfg\\\")\\n \\n configContents = \\\"\\\"\\n \\n configContents += \\\"[software]\\\\n\\\"\\n if conditions.oldneut:\\n configContents += \\\"neut_setup_script = /project/t/tanaka/T2K/neut/branches/5.1.4.2_nd280_ROOTv5r34p09n01/src/neutgeom/setup.sh\\\\n\\\"\\n elif conditions.newoldneut:\\n configContents += \\\"neut_setup_script = /project/t/tanaka/T2K/neut/branches/5.1.4.3_nd280/src/neutgeom/setup.sh\\\\n\\\"\\n else:\\n #configContents += \\\"neut_setup_script = /project/t/tanaka/T2K/neut/branches/5.3.1_nd280/src/neutgeom/setup.sh\\\\n\\\"\\n #configContents += \\\"neut_setup_script = /project/t/tanaka/T2K/neut/branches/5.3.1_nd280_wBBBA05/src/neutgeom/setup.sh\\\\n\\\"\\n configContents += \\\"neut_setup_script = /project/t/tanaka/T2K/neut/branches/5.3.2_nd280/src/neutgeom/setup.sh\\\\n\\\"\\n \\n configContents += \\\"[geometry]\\\\n\\\"\\n\\n configContents += \\\"baseline = \\\" + conditions.geometry +\\\"\\\\n\\\"\\n if conditions.waterair == \\\"water\\\":\\n configContents += \\\"p0d_water_fill = 1\\\\n\\\"\\n else:\\n configContents += \\\"p0d_water_fill = 0\\\\n\\\"\\n \\n configContents += \\\"\\\"\\\"\\n \\n[configuration]\\nmodule_list = neutMC\\n\\n[filenaming]\\n\\\"\\\"\\\"\\n configContents += \\\"comment = \\\" + conditions.comment + \\\"\\\\n\\\"\\n configContents += \\\"run_number = \\\" + str(run) +\\\"\\\\n\\\"\\n configContents += \\\"subrun = \\\" + str(subrun) + \\\"\\\\n\\\"\\n\\n if conditions.oldneut:\\n configContents += \\\"\\\"\\\" \\n\\n[neutrino]\\nneut_card = /project/t/tanaka/T2K/neut/branches/5.1.4.2_nd280_ROOTv5r34p09n01/src/neutgeom/neut.card\\n\\\"\\\"\\\"\\n elif conditions.newoldneut:\\n configContents += \\\"\\\"\\\" \\n\\n[neutrino]\\nneut_card = /project/t/tanaka/T2K/neut/branches/5.1.4.3_nd280/src/neutgeom/neut.card\\n\\\"\\\"\\\"\\n else:\\n configContents += \\\"\\\"\\\" \\n\\n[neutrino]\\nneut_card = /project/t/tanaka/T2K/neut/branches/5.3.2_nd280/src/neutgeom/neut.card\\n\\\"\\\"\\\"\\n\\n configContents += \\\"flux_file = \\\" + conditions.ram_disk + \\\"/\\\" + conditions.flux_base + \\\"\\\\n\\\"\\n\\n#flux_file = flux_file\\n#\\\"\\\"\\\"\\n\\n# configContents += \\\"flux_file_path = \\\" + conditions.ram_disk + \\\"/\\\" + conditions.flux_base\\n\\n# configContents += \\\"\\\"\\\" \\n#flux_file_start = 1\\n#flux_file_stop = 300\\n#\\\"\\\"\\\"\\n\\n configContents += \\\"maxint_file = \\\" + conditions.maxint_file_local + \\\"\\\\n\\\"\\n\\n# default: 5e17 but for basket MC special production higher\\n configContents += \\\"\\\"\\\" \\npot = 5.0e17\\nneutrino_type = beam\\n\\\"\\\"\\\"\\n if conditions.baskmagn == \\\"basket\\\":\\n configContents += \\\"\\\"\\\" \\nflux_region = basket\\nmaster_volume = Basket \\nrandom_start = 1\\n\\\"\\\"\\\"\\n elif conditions.baskmagn == \\\"magnet\\\":\\n configContents += \\\"\\\"\\\" \\nflux_region = magnet\\nmaster_volume = Magnet \\nrandom_start = 1\\n\\\"\\\"\\\"\\n else:\\n print \\\"Unknown basket/magnet condition\\\"\\n \\n\\n configContents += \\\"random_seed = \\\" + str(getRandom()) +\\\"\\\\n\\\"\\n configContents += \\\"neut_seed1 = \\\" + str(getRandom())+\\\"\\\\n\\\" \\n configContents += \\\"neut_seed2 = \\\" + str(getRandom())+\\\"\\\\n\\\" \\n configContents += \\\"neut_seed3 = \\\" + str(getRandom())+\\\"\\\\n\\\" \\n\\n configContents += \\\"\\\\n\\\"\\n configContents += \\\"[nd280mc]\\\\n\\\"\\n configContents += \\\"mc_type=Neut_RooTracker \\\\n\\\"\\n\\n #print configContents\\n\\n try:\\n macFile = open(configname,\\\"w\\\")\\n macFile.write(configContents)\\n \\n except:\\n print \\\"can't write config file\\\" \\n \\n\\n return configname\",\n \"def __init__(self):\\n super(enc_clf, self).__init__()\\n\\n self.fc1 = nn.Linear(784, 1024)\\n self.fc2 = nn.Linear(1024, 1024)\\n self.fc3 = nn.Linear(1024, 512)\\n self.fc4 = nn.Linear(512, 10)\",\n \"def get_test_config():\\n config = get_config()\\n config.batch_size = 2\\n config.eval_batch_size = 2\\n config.eval_num = 2\\n config.eval_avg_num = 1\\n config.num_train_steps = 2\\n config.log_loss_every_steps = 1\\n config.eval_every_steps = 1\\n config.checkpoint_every_steps = 1\\n config.df_dim = 16\\n config.gf_dim = 16\\n config.z_dim = 8\\n config.show_num = 4\\n config.num_epochs = 1\\n config.shuffle_buffer_size = 10\\n return config\",\n \"def get_config(self):\\n config = {'name': self.name, 'trainable': self.trainable}\\n if hasattr(self, '_batch_input_shape'):\\n config['batch_input_shape'] = self._batch_input_shape\\n if hasattr(self, 'dtype'):\\n config['dtype'] = self.dtype\\n return config\",\n \"def make_config(outfile, sample_rate, numof_channels, mode, server = 'localhost', shape = 'None',\\n security_mode = False, saving_mode = False, data_file = 'Nofile', format = 'binary',\\n resolution = 0.1, returning_speed = 8, channels = 0,\\n color_bg = 'white', color_trigger = 'black',\\n size_window = (1000, 800)):\\n\\n\\n config = ConfigParser.RawConfigParser()\\n\\n config.add_section('technics')\\n config.add_section('visualization')\\n config.add_section('security')\\n config.add_section('data')\\n\\n config.set('technics', 'sample_rate', sample_rate)\\n config.set('technics', 'numof_channels', numof_channels)\\n config.set('technics', 'server', server)\\n config.set('technics', 'resolution', resolution)\\n config.set('technics', 'speed', returning_speed)\\n if channels == 0:\\n channels = range(numof_channels+1)[1:numof_channels+1]\\n config.set('technics', 'channels', channels)\\n\\n config.set('visualization', 'mode', mode)\\n config.set('visualization', 'color_bg', color_bg)\\n config.set('visualization', 'color_trigger', color_trigger)\\n config.set('visualization', 'size_window', size_window)\\n\\n config.set('security', 'security_mode', security_mode)\\n\\n config.set('data', 'saving_mode', saving_mode)\\n config.set('data', 'file', data_file)\\n config.set('data', 'format', format)\\n\\n config_file = open(outfile, 'w+')\\n\\n config.write(config_file)\",\n \"def __init__(\\n self, CONFIG, numActionList, dimList, mode=\\\"RA\\\", terminalType=\\\"g\\\",\\n verbose=True\\n ):\\n super(DDQNPursuitEvasion, self).__init__(CONFIG)\\n\\n self.mode = mode # only supports 'RA'\\n self.terminalType = terminalType\\n\\n # == ENV PARAM ==\\n self.numJoinAction = int(numActionList[0] * numActionList[1])\\n self.numActionList = numActionList\\n\\n # == Build neural network for (D)DQN ==\\n assert dimList is not None, \\\"Define the architecture\\\"\\n assert dimList[-1] == self.numJoinAction, (\\n \\\"We expect the dim of the last layer to be \\\"\\n + \\\"{:d}, but get {:d}\\\".format(self.numJoinAction, dimList[-1])\\n )\\n self.dimList = dimList\\n self.actType = CONFIG.ACTIVATION\\n self.build_network(dimList, self.actType, verbose)\\n print(\\n \\\"DDQN: mode-{}; terminalType-{}\\\".format(self.mode, self.terminalType)\\n )\",\n \"def get_config():\\n config = dict(\\n name=\\\"defaults\\\",\\n # Either use geometric, zipf, or uniform i.e., data variable\\n # can take one of \\\"geometric\\\", \\\"zipf\\\", \\\"uniform\\\".\\n distribution=\\\"zipf\\\",\\n lbd_geometric=0.8,\\n degree_zipf=1.0,\\n # Flags to indicate which methods to compare.\\n run_approx_miracle=False,\\n run_miracle=False,\\n run_modified_miracle=True,\\n run_ss=True,\\n run_rhr=True,\\n encoding_type=\\\"fast\\\", # Can take either fast or normal\\n # Common parameters.\\n num_itr=1,\\n coding_cost=14,\\n coding_cost_multiplier=1,\\n approx_coding_cost_multiplier=3,\\n approx_t=6,\\n # Specific parameters (leave them as they are for now).\\n delta=10**(-6),\\n alpha=1.0,\\n # Variation.\\n vary=\\\"eps\\\", # Can take one of \\\"cc\\\", \\\"k\\\", \\\"n\\\", \\\"eps\\\".\\n cc_space=[6, 8, 10, 12, 14],\\n k_space=[200, 400, 600, 800, 1000],\\n n_space=[2000, 4000, 6000, 8000, 10000],\\n eps_space=list(range(1, 9)),\\n # Defaults.\\n n=5000,\\n k=500,\\n t=3,\\n epsilon_target=6,\\n )\\n config = config_dict.ConfigDict(config)\\n config.lock() # Prevent addition of new fields.\\n return config\",\n \"def __init__(self, config, fe_space, scheme_type=SchemeType.EXPLICIT_ORDERONE, init_cond_type=InitialConditionType.NONE):\\n self.config = config\\n self.fe_space = fe_space\\n self.init_cond_type = init_cond_type\\n self.scheme_type = scheme_type\\n\\n self.u0 = None\\n self.u1 = None\\n self.u2 = None\\n self.ustar = None\\n self.operator1 = None\\n self.operator2 = None\\n self.rhs_operator = None\\n self.inv_operator = None\\n self.timestep = None\\n self.time = 0.0\",\n \"def __init__(self, classCount):\\n self.NUM_CLASSES = 1+classCount\\n self.STEPS_PER_EPOCH = self.STEPS_PER_EPOCH / self.IMAGES_PER_GPU\\n self.VALIDATION_STEPS = self.VALIDATION_STEPS / self.IMAGES_PER_GPU\\n super(ModelConfig, self).__init__()\",\n \"def create_configuration(self):\\r\\n self.current_config = np.zeros((self.n, self.d))\\r\\n for point_charge in range(self.n):\\r\\n angle = 2 * PI * np.random.rand()\\r\\n radius = self.r * np.random.rand()\\r\\n x = radius * np.cos(angle)\\r\\n y = radius * np.sin(angle)\\r\\n self.current_config[point_charge][0] = x\\r\\n self.current_config[point_charge][1] = y\\r\\n\\r\\n self.E1 = calculate_total_energy(self.current_config)\",\n \"def __init__(self, cfg):\\r\\n\\r\\n\\t\\tself.image_size = cfg.MODEL.INPUT.IMAGE_SIZE\\r\\n\\t\\tanchor_config = cfg.MODEL.ANCHORS\\r\\n\\t\\tself.feature_maps = anchor_config.FEATURE_MAPS\\r\\n\\t\\tself.min_sizes = anchor_config.MIN_SIZES\\r\\n\\t\\tself.max_sizes = anchor_config.MAX_SIZES \\r\\n\\t\\tself.aspect_ratios = anchor_config.ASPECT_RATIOS\\r\\n\\t\\tself.clip = anchor_config.CLIP\",\n \"def __init__(self, config={}):\\n\\n _, fileprefix = mkstemp()\\n idx = fileprefix.rfind('/')\\n fileprefix = fileprefix[idx + 1:]\\n self.network = NetworkCompression(prefix = fileprefix)\\n self.num_actions = self.network.get_action_space()\\n self.input_shape = self.network.get_observation_space()\\n self.reward = self.network.get_reward()\\n self.done = False\\n\\n self.action_space = Box(low=0, high=1, shape=(self.num_actions,), dtype=np.uint8)\\n self.observation_space = Box(low=0, high=10000,\\n shape=(self.input_shape[0]*self.input_shape[1],),\\n dtype=np.uint8)\\n\\n self.cur_pos = self._get_current_pos_in_1d()\\n\\n return\",\n \"def createCfg_project(self, jobOptions):\\n last_line = '%s %s %s %s' % (jobOptions['projection_module'], self.era, jobOptions['histName'], jobOptions['outputFile'])\\n if self.projection_module != 'puHist':\\n last_line += ' %.6e' % jobOptions['ref_genWeight']\\n lines = jobOptions['inputFiles'] + [ '', last_line ]\\n assert(len(lines) >= 3)\\n createFile(jobOptions['cfgFile_path'], lines, nofNewLines = 1)\",\n \"def ncwrt_retrieval_config( retr_setup, outname=None ):\\n\\n #-- set name of file to be generated\\n act_outname = outname if outname!=None else 'retrconfig.nc'\\n msg = \\\"Start writing configuration file ***{}***...\\\".format(act_outname)\\n FileLogger.info(msg)\\n\\n\\n #-- compression settings\\n zlev = retr_setup.zlev\\n use_zlib = retr_setup.use_zlib\\n\\n #--\\n schedule_dct = retr_setup.schedule_dct\\n statevector = retr_setup.prstate\\n #-- turn list into array\\n sim_typ = np.array(schedule_dct['sim_typ'], dtype=np.int32)\\n timepts = schedule_dct['date_utc']\\n nstvar,npts = statevector.shape\\n #-- overpass geometries SZA,SAA,VZA,VAA\\n ivgeom = np.empty((npts,4), dtype=np.float64)\\n ivgeom[:,0] = schedule_dct['sza']\\n ivgeom[:,1] = schedule_dct['saa']\\n ivgeom[:,2] = schedule_dct['vza']\\n ivgeom[:,3] = schedule_dct['vaa']\\n\\n #-- temporal settings, create time-values (time-since)\\n time_start, time_end = datelst_get_month_aligned_bounds(timepts)\\n time_coverage_start = time_start.strftime('%Y-%m-%dT%H:%M:%S')\\n time_coverage_end = time_end.strftime('%Y-%m-%dT%H:%M:%S')\\n ref_time = dt.datetime(timepts[0].year,1,1) #January1st in year of first point in time\\n time_unit = 'seconds since {}'.format(ref_time.strftime('%Y-%m-%dT%H:%M:%S'))\\n time_values = nc4.date2num(timepts, time_unit)\\n\\n #-- ensure directory exists\\n mkdirp_smart(os.path.dirname(act_outname))\\n\\n #-- open file pointer\\n ncfp = nc4.Dataset(act_outname, 'w')\\n #-- add dimensions\\n d1 = ncfp.createDimension('npoints',npts)\\n d2 = ncfp.createDimension('ngeo',4)\\n\\n #-- time-value\\n ncvar = ncfp.createVariable( 'time', statevector.dtype, ('npoints',),\\n zlib=use_zlib, complevel=zlev )\\n ncvar.setncattr('standard_name','time')\\n ncvar.setncattr('long_name','time')\\n ncvar.setncattr('units', time_unit)\\n ncvar[:] = time_values[:]\\n\\n #-- simulation type\\n ncvar = ncfp.createVariable( 'sim_typ', sim_typ.dtype, ('npoints',),\\n zlib=use_zlib, complevel=zlev )\\n ncvar[:] = sim_typ[:]\\n ncvar.setncattr('long_name','simulation_type')\\n ncvar.setncattr('comment', 'integer value which is to be bit-interpreted')\\n ncvar.setncattr('nobits_set', 'time-point with other state')\\n ncvar.setncattr('bit0_is_set', 'time-point for S1 simulation')\\n ncvar.setncattr('bit1_is_set', 'time-point for S2 simulation')\\n ncvar.setncattr('bit2_is_set', 'time-point for S1A simulation')\\n ncvar.setncattr('bit3_is_set', 'time-point for S1B simulation')\\n ncvar.setncattr('bit4_is_set', 'time-point for S2A simulation')\\n ncvar.setncattr('bit5_is_set', 'time-point for S2B simulation')\\n \\n #-- illumination-view geometry\\n ncvar = ncfp.createVariable( 'ivgeom', ivgeom.dtype, ('npoints','ngeo'),\\n zlib=use_zlib, complevel=zlev )\\n ncvar.setncattr('sza','igeo: 0')\\n ncvar.setncattr('saa','igeo: 1')\\n ncvar.setncattr('vza','igeo: 2')\\n ncvar.setncattr('vaa','igeo: 3')\\n ncvar[:,:] = ivgeom[:,:]\\n \\n #-- global attributes\\n ncfp.setncattr('creator_name',\\\"The Inversion Lab, Hamburg, Germany\\\")\\n ncfp.setncattr('creator_email', \\\"Michael.Vossbeck(at)Inversion-Lab.com\\\")\\n ncfp.setncattr('netcdf_libversion',\\\"{}\\\".format(nc4.__netcdf4libversion__))\\n ncfp.setncattr('date_created',\\\"{}\\\".format(dt.datetime.utcnow().isoformat()))\\n ncfp.setncattr('time_coverage_start',time_coverage_start)\\n ncfp.setncattr('time_coverage_end',time_coverage_end)\\n\\n #-- close file pointer\\n ncfp.close()\\n\\n # logging\\n msg = \\\"...writing ***{}*** DONE\\\".format(act_outname)\\n FileLogger.info(msg)\",\n \"def createCfg_prep_dcard(self, jobOptions):\\n category_output = self.channel\\n if jobOptions['label']:\\n category_output += \\\"_%s\\\" % jobOptions['label']\\n lines = []\\n lines.append(\\\"process.fwliteInput.fileNames = cms.vstring('%s')\\\" % jobOptions['inputFile'])\\n lines.append(\\\"process.fwliteOutput.fileName = cms.string('%s')\\\" % jobOptions['datacardFile'])\\n lines.append(\\\"process.prepareDatacards.processesToCopy = cms.vstring(%s)\\\" % self.prep_dcard_processesToCopy)\\n lines.append(\\\"process.prepareDatacards.signals = cms.vstring(%s)\\\" % self.prep_dcard_signals)\\n lines.append(\\\"process.prepareDatacards.makeSubDir = cms.bool(True)\\\")\\n lines.append(\\\"process.prepareDatacards.categories = cms.VPSet(\\\")\\n for charge in [\\\"OS\\\", \\\"SS\\\"]:\\n for ptEtaBin in [\\n \\\"BB_LL\\\", \\\"BB_ML\\\", \\\"BB_MM\\\", \\\"BB_HL\\\", \\\"BB_HM\\\", \\\"BB_HH\\\",\\n \\\"EE_LL\\\", \\\"EE_ML\\\", \\\"EE_MM\\\", \\\"EE_HL\\\", \\\"EE_HM\\\", \\\"EE_HH\\\",\\n \\\"BE_LL\\\", \\\"BE_ML\\\", \\\"EB_ML\\\",\\\"BE_MM\\\", \\\"BE_HL\\\", \\\"EB_HL\\\",\\n \\\"BE_HM\\\", \\\"EB_HM\\\", \\\"BE_HH\\\", \\\"total\\\",\\n ]:\\n lines.append(\\\" cms.PSet(\\\")\\n lines.append(\\\" input = cms.string('%s/%s'),\\\" % (charge, ptEtaBin))\\n lines.append(\\\" output = cms.string('ttH_%s_%s_%s')\\\" % (self.channel, charge, ptEtaBin))\\n lines.append(\\\" ),\\\")\\n lines.append(\\\")\\\")\\n lines.append(\\\"process.prepareDatacards.histogramToFit = cms.string('%s')\\\" % jobOptions['histogramToFit'])\\n lines.append(\\\"process.prepareDatacards.sysShifts = cms.vstring(%s)\\\" % systematics.muon_E)\\n create_cfg(self.cfgFile_prep_dcard, jobOptions['cfgFile_modified'], lines)\",\n \"def configure(self):\\n self.data_batch_file = self.get_value_from_config('data_batch_file')\\n self.batch_meta_file = self.get_value_from_config('batch_meta_file')\\n self.has_background = self.get_value_from_config('has_background')\\n self.num_classes = self.get_value_from_config('num_classes')\\n self.converted_images_dir = self.get_value_from_config('converted_images_dir')\\n if not self.converted_images_dir:\\n self.converted_images_dir = self.data_batch_file.parent / 'converted_images'\\n self.convert_images = self.get_value_from_config('convert_images')\\n # create directory for storing images if it is necessary\\n if self.convert_images and not self.converted_images_dir.exists():\\n self.converted_images_dir.mkdir(parents=True)\\n self.dataset_meta = self.get_value_from_config('dataset_meta_file')\",\n \"def __init__(self, **config):\\n super(CNN, self).__init__()\\n in_channel = [26] + config['cnn_target_filters']\\n kernels = config['cnn_target_kernels']\\n self.layer_size = len(config['cnn_target_filters'])\\n self.visual_attention=config['visual_attention']\\n self.concatenation=config['concatenation']\\n self.convs = nn.ModuleList([nn.Conv1d(in_channels=in_channel[i],\\n out_channels=in_channel[i + 1],\\n kernel_size=kernels[i]) for i in range(self.layer_size)])\\n self.convs = self.convs.float()\\n self.attention = config['attention']\\n protein_size = self.simulate_output((26, 1000))\\n self.fc = nn.Linear(protein_size, config['hidden_dim_protein'])\\n self.Attention=Attention(**config)\",\n \"def __init__(self, model_config):\\n # Training Parameters\\n self.__learning_rate = model_config[\\\"cnnLearningRate\\\"]\\n\\n # Network Parameters\\n self.__num_classes = model_config[\\\"numClasses\\\"]\\n self.__weight_decay = 1e-4\\n self.__num_gpus = model_config[\\\"numGpus\\\"]\\n self.__use_csnn = model_config[\\\"useCsnn\\\"]\\n\\n self.__csnn = Csnn(model_config)\",\n \"def __init__(self, config, osc=None):\\n super(BasicConsolidation, self).__init__(config, osc)\\n\\n # set default value for the number of released nodes\\n self.number_of_released_nodes = 0\\n # set default value for the number of migrations\\n self.number_of_migrations = 0\\n # set default value for number of allowed migration attempts\\n self.migration_attempts = 0\\n\\n # set default value for the efficacy\\n self.efficacy = 100\\n\\n self._ceilometer = None\\n\\n # TODO(jed): improve threshold overbooking?\\n self.threshold_mem = 1\\n self.threshold_disk = 1\\n self.threshold_cores = 1\\n\\n # TODO(jed): target efficacy\\n self.target_efficacy = 60\\n\\n # TODO(jed): weight\\n self.weight_cpu = 1\\n self.weight_mem = 1\\n self.weight_disk = 1\\n\\n # TODO(jed): bound migration attempts (80 %)\\n self.bound_migration = 0.80\",\n \"def reproduct_config(config):\\r\\n\\r\\n config.dim_emb = 256\\r\\n config.dim_phone = 128\\r\\n config.dim_phone_emb = 128\\r\\n config.dim_pre = 512\\r\\n config.use_drop = True\\r\\n config.num_emo_classes = 4\\r\\n \\r\\n config.len_crop = 96\\r\\n config.num_mels = 80\\r\\n config.wav2vec_feat_len = 1024\\r\\n\\r\\n config.batch_size = 2\\r\\n config.num_iters = 1000000\\r\\n config.checkpoint_step = 1000001 # do not save checkpoints\\r\\n\\r\\n ## save checkpoints every 50k iterations\\r\\n # config.num_iters = 500000\\r\\n # config.checkpoint_step = 50000\\r\\n \\r\\n config.speech_input = \\\"wav2vec\\\"\\r\\n\\r\\n if config.reproduct_mode == \\\"small\\\":\\r\\n config.dim_neck = 8\\r\\n config.freq = 48\\r\\n elif config.reproduct_mode == \\\"large\\\":\\r\\n config.dim_neck = 128\\r\\n config.freq = 2\\r\\n elif config.reproduct_mode == \\\"spec\\\":\\r\\n config.speech_input = \\\"spec\\\"\\r\\n config.dim_neck = 8\\r\\n config.freq = 48\\r\\n\\r\\n return config\",\n \"def create_nncf_network(\\n model: torch.nn.Module,\\n config: NNCFConfig,\\n dummy_forward_fn: Callable[[Module], Any] = None,\\n wrap_inputs_fn: Callable = None,\\n wrap_outputs_fn: Callable = None,\\n) -> NNCFNetwork:\\n\\n if dummy_forward_fn is not None and wrap_inputs_fn is None:\\n raise ValueError(\\n \\\"A custom dummy forward function was specified, but the corresponding input wrapping function \\\"\\n \\\"was not. In case a custom dummy forward function is specified for purposes of NNCF graph \\\"\\n \\\"building, then the wrap_inputs_fn parameter MUST also be specified and be consistent with \\\"\\n \\\"the input wrapping done in dummy_forward_fn.\\\"\\n )\\n\\n # Preserve `.training`/`.requires_grad` state since we will be building NNCFNetwork in `.eval` mode\\n with training_mode_switcher(model, is_training=False):\\n # Compress model that will be deployed for the inference on target device. No need to compress parts of the\\n # model that are used on training stage only (e.g. AuxLogits of Inception-v3 model) or unused modules with\\n # weights. As a consequence, no need to care about spoiling BN statistics, as they're disabled in eval mode.\\n\\n input_info_list = create_input_infos(config)\\n scopes_without_shape_matching = config.get(\\\"scopes_without_shape_matching\\\", [])\\n ignored_scopes = config.get(\\\"ignored_scopes\\\")\\n target_scopes = config.get(\\\"target_scopes\\\")\\n\\n nncf_network = NNCFNetwork(\\n model,\\n input_infos=input_info_list,\\n dummy_forward_fn=dummy_forward_fn,\\n wrap_inputs_fn=wrap_inputs_fn,\\n wrap_outputs_fn=wrap_outputs_fn,\\n ignored_scopes=ignored_scopes,\\n target_scopes=target_scopes,\\n scopes_without_shape_matching=scopes_without_shape_matching,\\n )\\n\\n nncf_network.nncf.get_tracing_context().disable_trace_dynamic_graph()\\n\\n synchronize_all_processes_in_distributed_mode()\\n return nncf_network\",\n \"def setup(args):\\n cfg = get_cfg()\\n\\n cfg.merge_from_file(model_zoo.get_config_file(\\\"COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\\\"))\\n cfg.merge_from_list(args.opts)\\n\\n # configs for training\\n if args.small_vidor: # cfg.DATASETS.VIDOR.SIZE == 'small':\\n cfg.DATASETS.TRAIN = (\\\"vidor_small_train\\\",)\\n elif args.small_vidor_10imgs: # cfg.DATASETS.VIDOR.SIZE == 'small-10imgs':\\n cfg.DATASETS.TRAIN = (\\\"vidor_small_10imgs_train\\\",)\\n else:\\n cfg.DATASETS.TRAIN = (\\\"vidor_large_train\\\",)\\n # cfg.DATALOADER.NUM_WORKERS = 2\\n if not args.eval_only:\\n cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(\\\"COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\\\") # Let training initialize from model zoo\\n factor = 4\\n cfg.SOLVER.IMS_PER_BATCH = 16 * factor\\n cfg.SOLVER.BASE_LR = 0.0001 * factor # finetune using 10x smaller base_lr\\n cfg.SOLVER.MAX_ITER = 270000 // factor \\n cfg.SOLVER.STEPS = [210000 // factor, 250000 // factor]\\n # cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128 # default: 512\\n cfg.MODEL.ROI_HEADS.NUM_CLASSES = 78\\n\\n # configs for testing\\n # cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, \\\"model_final.pth\\\")\\n if args.small_vidor: # cfg.DATASETS.VIDOR.SIZE == 'small':\\n cfg.DATASETS.TEST = (\\\"vidor_small_val\\\",)\\n elif args.small_vidor_10imgs: # cfg.DATASETS.VIDOR.SIZE == 'small-10imgs':\\n cfg.DATASETS.TEST = (\\\"vidor_small_10imgs_val\\\",)\\n else:\\n cfg.DATASETS.TEST = (\\\"vidor_large_val\\\",)\\n # cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5\\n\\n # cfg.OUTPUT_DIR = './output/train_vidor_with_pseudo_labels'\\n \\n \\n if not args.eval_only:\\n os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)\\n cfg.freeze()\\n default_setup(cfg, args)\\n return cfg\",\n \"def __init__(self,\\n env_spec,\\n conv_filters,\\n conv_filter_sizes,\\n conv_strides,\\n conv_pads,\\n hidden_sizes=(32, 32),\\n pool_size=2,\\n name=\\\"ContinuousConvQFunc\\\",\\n hidden_nonlinearity=tf.nn.relu,\\n output_nonlinearity=None,\\n action_merge_layer=-2,\\n input_include_goal=False,\\n weight_normalization=False,\\n pooling=False,\\n bn=False):\\n Serializable.quick_init(self, locals())\\n\\n self.name = name\\n self._env_spec = env_spec\\n if input_include_goal:\\n self._obs_dim = env_spec.observation_space.flat_dim_with_keys(\\n [\\\"observation\\\", \\\"desired_goal\\\"])\\n else:\\n self._obs_dim = env_spec.observation_space.flat_dim\\n self._action_dim = env_spec.action_space.flat_dim\\n self._conv_filters = conv_filters\\n self._conv_filter_sizes = conv_filter_sizes\\n self._conv_strides = conv_strides\\n self._conv_pads = conv_pads\\n self._hidden_sizes = hidden_sizes\\n self._hidden_nonlinearity = hidden_nonlinearity\\n self._action_merge_layer = action_merge_layer\\n self._output_nonlinearity = output_nonlinearity\\n self._batch_norm = bn\\n self._weight_normalization = weight_normalization\\n self._pooling = pooling\\n self._pool_size = pool_size\\n self._f_qval, self._output_layer, self._obs_layer, self._action_layer = self.build_net( # noqa: E501\\n name=self.name)\\n LayersPowered.__init__(self, [self._output_layer])\",\n \"def get_config(self) -> Dict[str, Any]:\\n return {\\n 'num_classes': self.num_classes,\\n 'name': self.name,\\n 'dtype': self.dtype,\\n 'sparse_y_true': self.sparse_y_true,\\n 'sparse_y_pred': self.sparse_y_pred,\\n 'axis': self.axis,\\n }\",\n \"def configure(self, cnf=None, **kw):\\n return self.config(cnf, **kw)\",\n \"def fpp_config(koi, **kwargs):\\n folder = os.path.join(KOI_FPPDIR, ku.koiname(koi))\\n if not os.path.exists(folder):\\n os.makedirs(folder)\\n config = ConfigObj(os.path.join(folder,'fpp.ini'))\\n\\n koi = ku.koiname(koi)\\n\\n rowefit = jrowe_fit(koi)\\n\\n config['name'] = koi\\n ra,dec = ku.radec(koi)\\n config['ra'] = ra\\n config['dec'] = dec\\n config['rprs'] = rowefit.ix['RD1','val']\\n config['period'] = rowefit.ix['PE1', 'val']\\n\\n config['starfield'] = kepler_starfield_file(koi)\\n\\n for kw,val in kwargs.items():\\n config[kw] = val\\n\\n config['constraints'] = {}\\n config['constraints']['maxrad'] = default_r_exclusion(koi)\\n try:\\n config['constraints']['secthresh'] = pipeline_weaksec(koi)\\n except NoWeakSecondaryError:\\n pass\\n\\n return config\",\n \"def configure_nccl():\\n os.environ[\\\"NCCL_DEBUG\\\"] = \\\"INFO\\\"\\n os.environ[\\\"NCCL_SOCKET_IFNAME\\\"] = \\\"ib0\\\"\\n os.environ[\\\"NCCL_IB_DISABLE\\\"] = \\\"1\\\"\\n\\n os.environ[\\\"NCCL_LAUNCH_MODE\\\"] = \\\"PARALLEL\\\"\\n os.environ[\\\"NCCL_IB_HCA\\\"] = subprocess.getoutput(\\n \\\"cd /sys/class/infiniband/ > /dev/null; for i in mlx5_*; \\\"\\n \\\"do cat $i/ports/1/gid_attrs/types/* 2>/dev/null \\\"\\n \\\"| grep v >/dev/null && echo $i ; done; > /dev/null\\\"\\n )\\n os.environ[\\\"NCCL_IB_GID_INDEX\\\"] = \\\"3\\\"\\n os.environ[\\\"NCCL_IB_TC\\\"] = \\\"106\\\"\",\n \"def __init__(self, n, **kwargs):\\n super(Quay, self).__init__(QC)\\n\\n for i in range(n):\\n super(Quay, self).append(QC())\\n\\n for p in QC.PROPERTY:\\n if p in kwargs.keys():\\n if isinstance(kwargs[p], (float, int)):\\n for q in self.qcs:\\n setattr(q, p, kwargs[p])\\n elif len(kwargs[p]) == self.size:\\n for i, q in enumerate(self.qcs):\\n setattr(q, p, kwargs[p][i])\\n else:\\n raise QCSPGenException(\\\"- {0}=n or =[{0}_1,...{0}_n] where n is the number of QCs!\\\".format(p))\",\n \"def get_default_config(self):\\n \\n config = {}\\n \\n # default z_0_hat, zeros, flexible\\n config['z_0_hat_option'] = 'flexible'\\n config['initial_z_0_hat'] = np.zeros(self.dimension)\\n \\n # default P_0_hat, identity times a small scalar, flexible\\n config['P_0_hat_option'] = 'flexible'\\n config['initial_P_0_hat'] = 0.1 * np.eye(self.dimension)\\n \\n # default A, identity, flexible\\n config['AB_option'] = 'flexible'\\n config['initial_A'] = np.eye(self.dimension)\\n config['initial_B'] = np.zeros((self.dimension, self.control_dimension))\\n \\n # default Q, identity times a small scalar, flexible\\n config['Q_option'] = 'flexible'\\n config['initial_Q'] = 0.1 * np.eye(self.dimension)\\n \\n # default R, identity times a small scalar, flexible\\n config['R_option'] = 'flexible'\\n config['initial_R'] = 0.1 * np.eye(self.dimension)\\n \\n # default stopping criteria, threshold 1e-5, num_iterations 1000\\n # stop whenever either of the two critieria is reached\\n config['threshold'] = 1e-5\\n config['num_iterations'] = 1000\\n\\n return config\",\n \"def configure(config_file):\\n Config.config_file = config_file\\n config = ConfigParser()\\n config.optionxform = str\\n config.read(config_file)\\n dct = {}\\n for item in list(config.sections()):\\n dct[item] = dict(config.items(item))\\n\\n for item in dct.keys():\\n if item == 'config':\\n for key in dct[item].keys():\\n dct[item][key] = tf(dct[item][key])\\n\\n if item == 'continuum':\\n for key, val in dct[item].items():\\n val = val.split(',')\\n if len(val) == 2:\\n dct[item][key] = {'xlim': float(val[0]), 'ylim': float(val[1])}\\n dct[item][key] = {'ylim': float(val[0])}\\n else:\\n for k in [\\\"N\\\", \\\"b\\\", \\\"z\\\"]:\\n try:\\n dct[item][k] = dct[item][k].replace(\\\" \\\", \\\"\\\")\\n dct[item][k] = list(map(float, dct[item][k].strip().split(',')))\\n except KeyError:\\n pass\\n\\n cond = []\\n if \\\"N\\\" in dct[item].keys():\\n cond += [dct[item][\\\"N\\\"][0] < 10., dct[item][\\\"N\\\"][-1] > 23.,\\n dct[item][\\\"N\\\"][-1] < dct[item][\\\"N\\\"][0]]\\n\\n if \\\"b\\\" in dct[item].keys():\\n cond += [dct[item][\\\"b\\\"][0] < 0., dct[item][\\\"b\\\"][-1] < dct[item][\\\"b\\\"][0]]\\n\\n if \\\"z\\\" in dct[item].keys():\\n cond += [dct[item][\\\"z\\\"][-1] < dct[item][\\\"z\\\"][0]]\\n\\n if any(cond):\\n raise Exception(\\\"check your random sampling inputs\\\")\\n\\n set_config_defaults(dct)\\n _set_values(dct)\",\n \"def __init__(self):\\n parameters_list = []\\n self.config_dict = self.open_config(parameters_list)\\n\\n # Define defaults\\n self.disc_gt = 0.0\\n self.disc_out = 0.0\",\n \"def __init__(\\n self,\\n f,\\n lb,\\n ub,\\n n_init,\\n max_evals,\\n batch_size=1,\\n jitter=1e-4,\\n n_discrete_points=5000,\\n max_preconditioner_size=50,\\n num_ciq_samples=15,\\n max_minres_iterations=200,\\n minres_tolerance=1e-3,\\n use_ciq=True,\\n use_keops=False,\\n dtype=None,\\n device=None,\\n verbose=False,\\n ):\\n self.f = f\\n self.lb = lb.to(dtype=dtype, device=device)\\n self.ub = ub.to(dtype=dtype, device=device)\\n self.n_init = n_init\\n self.max_evals = max_evals\\n self.batch_size = batch_size\\n self.jitter = jitter\\n self.n_discrete_points = n_discrete_points\\n self.max_preconditioner_size = max_preconditioner_size\\n self.num_ciq_samples = num_ciq_samples\\n self.max_minres_iterations = max_minres_iterations\\n self.minres_tolerance = minres_tolerance\\n self.use_ciq = use_ciq\\n self.use_keops = use_keops\\n self.dtype = dtype\\n self.device = device\\n self.verbose = verbose\\n\\n self.n_evals = 0\\n self.dim = len(lb)\",\n \"def createCfg_comp_jetToTauFakeRate(self, jobOptions):\\n lines = []\\n lines.append(\\\"process.fwliteInput.fileNames = cms.vstring('%s')\\\" % jobOptions['inputFile'])\\n lines.append(\\\"process.fwliteOutput.fileName = cms.string('%s')\\\" % os.path.basename(jobOptions['outputFile']))\\n lines.append(\\\"process.comp_jetToTauFakeRate.looseRegion = cms.string('%s')\\\" % jobOptions['looseRegion'])\\n lines.append(\\\"process.comp_jetToTauFakeRate.tightRegion = cms.string('%s')\\\" % jobOptions['tightRegion'])\\n lines.append(\\\"process.comp_jetToTauFakeRate.processData = cms.string('data_obs')\\\")\\n lines.append(\\\"process.comp_jetToTauFakeRate.processesToSubtract = cms.vstring(\\\")\\n for process in self.processes_to_comp:\\n lines.append(\\\" '{process}t', '{process}l',\\\".format(process = process))\\n lines.append(\\\")\\\")\\n if self.event_selection == \\\"TTemu\\\":\\n lines.append(\\\"process.comp_jetToTauFakeRate.processMC = cms.string('TTj')\\\")\\n elif self.event_selection == \\\"DYmumu\\\":\\n lines.append(\\\"process.comp_jetToTauFakeRate.processMC = cms.string('EWKj')\\\")\\n else:\\n raise ValueError(\\\"Invalid event selection: %s\\\" % self.event_selection)\\n lines.append(\\\"process.comp_jetToTauFakeRate.absEtaBins = cms.vdouble(%s)\\\" % jobOptions['absEtaBins'])\\n lines.append(\\\"process.comp_jetToTauFakeRate.ptBins = cms.vdouble(%s)\\\" % jobOptions['ptBins'])\\n lines.append(\\\"process.comp_jetToTauFakeRate.decayModes = cms.vint32(%s)\\\" % jobOptions['decayModes'])\\n lines.append(\\\"process.comp_jetToTauFakeRate.hadTauSelections = cms.vstring(%s)\\\" % jobOptions['hadTauSelections'])\\n lines.append(\\\"process.comp_jetToTauFakeRate.trigMatchingOption = cms.string('%s')\\\" % jobOptions['trigMatchingOption']) \\n lines.append(\\\"process.comp_jetToTauFakeRate.outputFileName = cms.string('%s')\\\" % jobOptions['plots_outputFileName'])\\n create_cfg(self.cfgFile_comp_jetToTauFakeRate, jobOptions['cfgFile_modified'], lines)\",\n \"def cook_config(ext_config_filename):\\n mc = base_model_config()\\n with open(ext_config_filename, \\\"r\\\") as fp:\\n ext_mc = edict(json.load(fp, encoding=\\\"utf8\\\"))\\n for s in ext_mc.keys():\\n mc[s] = ext_mc[s]\\n # mc.ANCHOR_BOX = set_anchors(mc)\\n # print(np.max(np.square(np.array(set_anchors_testing(mc)) - np.array(set_anchors(mc)))))\\n # mc.ANCHORS = len(mc.ANCHOR_BOX)\\n # H, W, C = _get_output_shape(mc)\\n # mc.MODEL_OUTPUT_SHAPE = [H, W, mc.ANCHOR_PER_GRID]\\n return mc\",\n \"def add_conl_config(cfg):\\n # We retry random cropping until no single category in semantic segmentation GT occupies more\\n cfg.MODEL.CONL = CN()\\n\\n cfg.MODEL.CONL.STAGES=['res4']\\n cfg.MODEL.CONL.BLOCKS=[[-1,],]\\n\\n cfg.MODEL.CONL.RATIO = 1.0/4.0\\n cfg.MODEL.CONL.DOWNSAMPLE=True\\n cfg.MODEL.CONL.USE_GN=False\\n cfg.MODEL.CONL.LR_MULT=0\\n cfg.MODEL.CONL.USE_OUT=False\\n cfg.MODEL.CONL.OUT_BN=False\\n cfg.MODEL.CONL.WHITEN_TYPE=['channel']\\n cfg.MODEL.CONL.TEMP = 1.0\\n cfg.MODEL.CONL.WITH_GC=False\\n cfg.MODEL.CONL.WITH_2FC=False\\n cfg.MODEL.CONL.DOUBLE_CONV=False\\n\\n cfg.MODEL.CONL.WITH_STATE=False\\n cfg.MODEL.CONL.NCLS=32\",\n \"def configure_nccl():\\n os.environ[\\\"NCCL_SOCKET_IFNAME\\\"] = \\\"ib0\\\"\\n os.environ[\\\"NCCL_IB_DISABLE\\\"] = \\\"1\\\"\\n\\n os.environ[\\\"NCCL_LAUNCH_MODE\\\"] = \\\"PARALLEL\\\"\\n os.environ[\\\"NCCL_IB_HCA\\\"] = subprocess.getoutput(\\n \\\"cd /sys/class/infiniband/ > /dev/null; for i in mlx5_*; \\\"\\n \\\"do cat $i/ports/1/gid_attrs/types/* 2>/dev/null \\\"\\n \\\"| grep v >/dev/null && echo $i ; done; > /dev/null\\\"\\n )\\n os.environ[\\\"NCCL_IB_GID_INDEX\\\"] = \\\"3\\\"\\n os.environ[\\\"NCCL_IB_TC\\\"] = \\\"106\\\"\",\n \"def get_configspace():\\n configspace = cs.ConfigurationSpace()\\n\\n memory = cs.hyperparameters.UniformIntegerHyperparameter(name='memory', lower=2, upper=25)\\n configspace.add_hyperparameter(hyperparameter=memory)\\n\\n batch_size = cs.hyperparameters.UniformIntegerHyperparameter(\\n name='batch_size', lower=32, upper=8192, log=True\\n )\\n configspace.add_hyperparameter(hyperparameter=batch_size)\\n\\n frequency = cs.hyperparameters.UniformFloatHyperparameter(\\n name='frequency', lower=3e-2, upper=1.0, log=True\\n )\\n configspace.add_hyperparameter(hyperparameter=frequency)\\n\\n learning_rate = cs.hyperparameters.UniformFloatHyperparameter(\\n name='learning_rate', lower=1e-5, upper=3e-2, log=True\\n )\\n configspace.add_hyperparameter(hyperparameter=learning_rate)\\n\\n horizon = cs.hyperparameters.UniformIntegerHyperparameter(\\n name='horizon', lower=1, upper=50\\n )\\n configspace.add_hyperparameter(hyperparameter=horizon)\\n\\n discount = cs.hyperparameters.UniformFloatHyperparameter(\\n name='discount', lower=0.8, upper=1.0, log=True\\n )\\n configspace.add_hyperparameter(hyperparameter=discount)\\n\\n ratio_based = cs.hyperparameters.CategoricalHyperparameter(\\n name='ratio_based', choices=('no', 'yes')\\n )\\n configspace.add_hyperparameter(hyperparameter=ratio_based)\\n\\n clipping_value = cs.hyperparameters.UniformFloatHyperparameter(\\n name='clipping_value', lower=0.05, upper=0.5\\n )\\n configspace.add_hyperparameter(hyperparameter=clipping_value)\\n\\n baseline = cs.hyperparameters.CategoricalHyperparameter(\\n name='baseline',\\n choices=('no', 'auto', 'same-network', 'same-policy', 'same-policy-noopt')\\n )\\n configspace.add_hyperparameter(hyperparameter=baseline)\\n\\n baseline_learning_rate = cs.hyperparameters.UniformFloatHyperparameter(\\n name='baseline_learning_rate', lower=1e-5, upper=3e-2, log=True\\n )\\n configspace.add_hyperparameter(hyperparameter=baseline_learning_rate)\\n\\n estimate_advantage = cs.hyperparameters.CategoricalHyperparameter(\\n name='estimate_advantage', choices=('no', 'yes')\\n )\\n configspace.add_hyperparameter(hyperparameter=estimate_advantage)\\n\\n entropy_regularization = cs.hyperparameters.UniformFloatHyperparameter(\\n name='entropy_regularization', lower=1e-5, upper=1.0, log=True\\n )\\n configspace.add_hyperparameter(hyperparameter=entropy_regularization)\\n\\n configspace.add_condition(\\n condition=cs.EqualsCondition(child=clipping_value, parent=ratio_based, value='yes')\\n )\\n\\n configspace.add_condition(\\n condition=cs.NotEqualsCondition(\\n child=baseline_learning_rate, parent=baseline, value='no'\\n )\\n )\\n\\n configspace.add_condition(\\n condition=cs.NotEqualsCondition(\\n child=estimate_advantage, parent=baseline, value='no'\\n )\\n )\\n\\n return configspace\",\n \"def get_default_config(self):\\n config = super(SignalfxHandler, self).get_default_config()\\n\\n config.update({\\n 'url': 'https://ingest.signalfx.com/v2/datapoint',\\n 'batch': 300,\\n # Don't wait more than 10 sec between pushes\\n 'batch_max_interval': 10,\\n 'auth_token': '',\\n })\\n\\n return config\",\n \"def setup(self, num_qubit, fusion_enable, use_cu1):\",\n \"def get_config():\\n config = ml_collections.ConfigDict()\\n\\n # Which model to use -- see ./models.py\\n config.model_name = 'ViT-B_32'\\n # Where to store training logs.\\n config.log_dir = '.'\\n\\n # Number of steps to measure.\\n config.steps = 30\\n # Number of steps before measuring.\\n config.initial_steps = 10\\n\\n # Batch size\\n config.batch = 0\\n # Number of output classes.\\n config.num_classes = 0\\n # Image size (width=height).\\n config.image_size = 0\\n\\n config.train = 'inference_time'\\n\\n return config\",\n \"def __init__(self, config):\\n super().__init__(config)\\n\\n # For NaMaster you need to pass the masks\\n self.mask_files = self.config[\\\"tjpcov\\\"].get(\\\"mask_file\\\")\\n self.mask_names = self.config[\\\"tjpcov\\\"].get(\\\"mask_names\\\")\\n\\n # Binning info is only needed if workspaces are not passed\\n self.binning_info = self.config[\\\"tjpcov\\\"].get(\\\"binning_info\\\", None)\\n\\n # nside is needed if mask_files is a hdf5 file\\n self.nside = self.config[\\\"tjpcov\\\"].get(\\\"nside\\\", None)\\n\\n # Read NaMaster specific options\\n self.nmt_conf = self.config.get(\\\"NaMaster\\\", {})\\n for k in [\\\"f\\\", \\\"w\\\", \\\"cw\\\"]:\\n if k not in self.nmt_conf:\\n self.nmt_conf[k] = {}\\n\\n # Read cache from input file. It will update the cache passed as an\\n # argument of the different methods\\n self.cache = self.config.get(\\\"cache\\\", {})\",\n \"def config():\\n\\n compared_algorithms_type: AlgorithmsType = AlgorithmsType.LinearRegression\\n compared_methods: List = [] # Leave empty for using all solvers.\\n numpy_distribution: NumpyDistribution = NumpyDistribution.IntelDistribution\\n used_database: DatabaseType = DatabaseType.Synthetic\\n experiment_type: ExperimentType = ExperimentType.RunTimeExperiment\\n cross_validation_folds: int = 1\\n n_alphas: int = 100\\n reduction_factor: int = 1\\n\\n run_time_experiments_config: Dict[str, range] = {\\n \\\"run_time_compared_data_sizes\\\": range(int(5000 / reduction_factor), int(15000 / reduction_factor),\\n int(5000 / reduction_factor)),\\n \\\"calc_transpose_dot_residuals\\\": compared_algorithms_type == AlgorithmsType.LinearRegression\\n }\\n number_of_alphas_experiments_config: Dict[str, range] = {\\n \\\"alphas_range\\\": range(1, 221, 20)\\n }\\n\\n synthetic_data_config: Dict[str, int] = {\\n \\\"data_size\\\": int(15000 / reduction_factor),\\n \\\"features_num\\\": 7\\n }\\n\\n sketch_preconditioned_config: Dict[str, float] = {\\n \\\"sampled_rows\\\": 0.005,\\n \\\"switch_sign_probability\\\": 0.5,\\n \\\"min_sampled_rows\\\": 100.0\\n }\\n resources_path: str = r'Resources'\\n results_path: str = r'Results'\\n clusters_count: int = _choose_clusters_num(used_database, synthetic_data_config[\\\"features_num\\\"])\\n elastic_net_factor: float = 0.5 # Rho factor in Elastic-Net regularization.\\n is_positive_definite: bool = True\",\n \"def from_config(cls, config):\\n if 'np_initval' in config:\\n config['np_initval'] = {\\n rel: NeuralQueryContext._dict_to_sparse(matrix_dict)\\n for (rel, matrix_dict) in config['np_initval'].items()\\n }\\n if 'symtab' in config:\\n symtab = dict()\\n for (k, v) in config['symtab']:\\n symtab[k] = symbol.create_from_dict(v)\\n config['symtab'] = symtab\\n return cls(**config)\",\n \"def get_config_object() -> \\\"BaseConfig\\\":\\n assert (\\n len(G_CONFIG_OBJECT) == 1\\n ), \\\"Have you created quantize config object before calling `quantize_model`?\\\"\\n if G_CONFIG_OBJECT:\\n return G_CONFIG_OBJECT[0]\",\n \"def configure(self, config_json):\\n log.info(\\\"Configuring EDD backend for processing\\\")\\n log.debug(\\\"Configuration string: '{}'\\\".format(config_json))\\n\\n yield self.set(config_json)\\n\\n cfs = json.dumps(self._config, indent=4)\\n log.info(\\\"Final configuration:\\\\n\\\" + cfs)\\n\\n\\n\\n self.__numa_node_pool = []\\n # remove numa nodes with missing capabilities\\n for node in numa.getInfo():\\n if len(numa.getInfo()[node]['gpus']) < 1:\\n log.debug(\\\"Not enough gpus on numa node {} - removing from pool.\\\".format(node))\\n continue\\n elif len(numa.getInfo()[node]['net_devices']) < 1:\\n log.debug(\\\"Not enough nics on numa node {} - removing from pool.\\\".format(node))\\n continue\\n else:\\n self.__numa_node_pool.append(node)\\n\\n log.debug(\\\"{} numa nodes remaining in pool after cosntraints.\\\".format(len(self.__numa_node_pool)))\\n\\n if len(self._config['input_data_streams']) > len(self.__numa_node_pool):\\n raise FailReply(\\\"Not enough numa nodes to process {} polarizations!\\\".format(len(self._config['input_data_streams'])))\\n\\n self._subprocessMonitor = SubprocessMonitor()\\n #ToDo: Check that all input data streams have the same format, or allow different formats\\n for i, streamid in enumerate(self._config['input_data_streams']):\\n # calculate input buffer parameters\\n stream_description = self._config['input_data_streams'][streamid]\\n stream_description[\\\"dada_key\\\"] = DADABUFFERS[i]\\n self.add_input_stream_sensor(streamid)\\n self.input_heapSize = stream_description[\\\"samples_per_heap\\\"] * stream_description['bit_depth'] / 8\\n\\n nHeaps = self._config[\\\"samples_per_block\\\"] / stream_description[\\\"samples_per_heap\\\"]\\n input_bufferSize = nHeaps * (self.input_heapSize)\\n log.info('Input dada parameters created from configuration:\\\\n\\\\\\n heap size: {} byte\\\\n\\\\\\n heaps per block: {}\\\\n\\\\\\n buffer size: {} byte'.format(self.input_heapSize, nHeaps, input_bufferSize))\\n\\n\\n final_payloads, final_fpss, final_framens = EDD_VDIF_Frame_Size(stream_description['sample_rate'])\\n\\n if self._config['payload_size'] == 'auto':\\n payload_size = final_payloads[-1]\\n else:\\n payload_size = int(self._config['payload_size'])\\n\\n log.info('Possible frame payload sizes (add 32 for framesize):')\\n for k in range(final_payloads.size):\\n if payload_size == final_payloads[k]:\\n M = \\\"*\\\"\\n else:\\n M = \\\" \\\"\\n log.info(' {}{:5.0f} byte {:8.0f} frames per sec {:6.3f} nsec/frame'.format(M, final_payloads[k], final_fpss[k], final_framens[k]))\\n\\n if payload_size not in final_payloads:\\n log.warning(\\\"Payload size {} possibly not conform with VDIF format!\\\".format(payload_size))\\n\\n # calculate output buffer parameters\\n size_of_samples = ceil(1. * self._config[\\\"samples_per_block\\\"] * 2 / 8.) # byte for two bit mode\\n number_of_packages = ceil(size_of_samples / float(payload_size))\\n\\n output_buffer_size = number_of_packages * (payload_size + self._config['vdif_header_size'])\\n\\n integration_time = self._config[\\\"samples_per_block\\\"] / float(stream_description[\\\"sample_rate\\\"])\\n self._integration_time_status.set_value(integration_time)\\n\\n rate = output_buffer_size/ integration_time # in spead documentation BYTE per second and not bit!\\n rate *= self._config[\\\"output_rate_factor\\\"] # set rate to (100+X)% of expected rate\\n self._output_rate_status.set_value(rate / 1E9)\\n\\n log.info('Output parameters calculated from configuration:\\\\n\\\\\\n total size of data samples: {} byte\\\\n\\\\\\n number_of_packages: {}\\\\n\\\\\\n size of output buffer: {} byte\\\\n\\\\\\n rate ({:.0f}%): {} Gbps'.format(size_of_samples,\\n number_of_packages, output_buffer_size,\\n self._config[\\\"output_rate_factor\\\"]*100, rate / 1E9))\\n\\n numa_node = self.__numa_node_pool[i]\\n log.debug(\\\"Associating {} with numa node {}\\\".format(streamid, numa_node))\\n\\n # configure dada buffer\\n bufferName = stream_description['dada_key']\\n yield self._create_ring_buffer(input_bufferSize, 64, bufferName, numa_node)\\n\\n ofname = bufferName[::-1]\\n # we write nSlice blocks on each go\\n yield self._create_ring_buffer(output_buffer_size, 8, ofname, numa_node)\\n\\n # Configure + launch \\n physcpu = numa.getInfo()[numa_node]['cores'][0]\\n thread_id = self._config['thread_id'][streamid]\\n station_id = self._config['thread_id'][streamid]\\n cmd = \\\"taskset -c {physcpu} VLBI --input_key={dada_key} --speadheap_size={heapSize} --thread_id={thread_id} --station_id={station_id} --payload_size={payload_size} --sample_rate={sample_rate} --nbits={bit_depth} -o {ofname} --log_level={log_level} --output_type=dada\\\".format(ofname=ofname, heapSize=self.input_heapSize, numa_node=numa_node, physcpu=physcpu, thread_id=thread_id, station_id=station_id, payload_size=payload_size, log_level=self._config['log_level'], **stream_description)\\n log.debug(\\\"Command to run: {}\\\".format(cmd))\\n\\n cudaDevice = numa.getInfo()[numa_node]['gpus'][0]\\n cli = ManagedProcess(cmd, env={\\\"CUDA_VISIBLE_DEVICES\\\": cudaDevice})\\n self._subprocessMonitor.add(cli, self._subprocess_error)\\n self._subprocesses.append(cli)\\n\\n cfg = self._config.copy()\\n cfg.update(stream_description)\\n\\n ip_range = []\\n port = set()\\n for key in self._config[\\\"output_data_streams\\\"]:\\n if streamid in key:\\n ip_range.append(self._config[\\\"output_data_streams\\\"][key]['ip'])\\n port.add(self._config[\\\"output_data_streams\\\"][key]['port'])\\n if len(port)!=1:\\n raise FailReply(\\\"Output data for one plarization has to be on the same port! \\\")\\n\\n if self._config[\\\"output_type\\\"] == 'network':\\n physcpu = \\\",\\\".join(numa.getInfo()[numa_node]['cores'][1:2])\\n fastest_nic, nic_params = numa.getFastestNic(numa_node)\\n log.info(\\\"Sending data for {} on NIC {} [ {} ] @ {} Mbit/s\\\".format(streamid, fastest_nic, nic_params['ip'], nic_params['speed']))\\n\\n cmd = \\\"taskset -c {physcpu} vdif_send --input_key {ofname} --if_ip {ibv_if} --dest_ip {mcast_dest} --port {port_tx} --max_rate {rate}\\\".format(ofname=ofname, \\n physcpu=physcpu, ibv_if=nic_params['ip'], mcast_dest=\\\" \\\".join(ip_range), port_tx=port.pop(), rate=rate)\\n log.debug(\\\"Command to run: {}\\\".format(cmd))\\n\\n elif self._config[\\\"output_type\\\"] == 'disk':\\n ofpath = os.path.join(cfg[\\\"output_directory\\\"], ofname)\\n log.debug(\\\"Writing output to {}\\\".format(ofpath))\\n if not os.path.isdir(ofpath):\\n os.makedirs(ofpath)\\n cmd = \\\"dada_dbdisk -k {ofname} -D {ofpath} -W\\\".format(ofname=ofname, ofpath=ofpath, **cfg)\\n else:\\n log.warning(\\\"Selected null output. Not sending data!\\\")\\n cmd = \\\"dada_dbnull -z -k {}\\\".format(ofname)\\n\\n log.debug(\\\"Command to run: {}\\\".format(cmd))\\n mks = ManagedProcess(cmd, env={\\\"CUDA_VISIBLE_DEVICES\\\": cudaDevice})\\n self._subprocessMonitor.add(mks, self._subprocess_error)\\n self._subprocesses.append(mks)\\n\\n self._subprocessMonitor.start()\",\n \"def __init__(self, \\n n_neurons = \\\"micro\\\", # else: \\\"brunel\\\" or arrays\\n C_ab = \\\"micro\\\", # else: \\\"brunel\\\" or arrays\\n area = net.area, # simulation size\\n neuron_model = net.neuron_model, # \\\"iaf_psc_delta\\\" or \\\"iaf_psc_exp\\\"\\n connection_rule = net.connection_rule, # \\\"fixed_total_number\\\" or \\\"fixed_indegree\\\"\\n j02 = net.j02, \\n weight_rel_sd = net.weight_rel_sd, \\n delay_rel_sd = net.delay_rel_sd, \\n g = net.g, \\n rate_ext = net.rate_ext):\\n ###################################################\\n ### \\tNetwork parameters\\t\\t### \\n ###################################################\\n\\n # area of network in mm^2; scales numbers of neurons\\n # use 1 for the full-size network (77,169 neurons)\\n self.area = area\\n \\n self.layers = net.layers #np.array([\\\"L23\\\", \\\"L4\\\", \\\"L5\\\", \\\"L6\\\"])\\n self.types = net.types #np.array([\\\"e\\\", \\\"i\\\"]) \\n self.populations = np.array([layer + typus for layer in self.layers for typus in self.types])\\n self.n_populations = len(self.populations)\\n self.n_layers = len(self.layers)\\n self.n_types = len(self.types)\\n \\n # Neuron numbers\\n if n_neurons == \\\"micro\\\":\\n self.n_neurons = np.int_(net.full_scale_n_neurons * self.area)\\n elif n_neurons == \\\"brunel\\\":\\n # Provide an array of equal number of neurons in each exc./inh. population\\n gamma = 0.25\\n inh_factor = 1. / (gamma + 1.)\\n exc_factor = 1. - inh_factor \\n n_total_micro = np.sum(net.full_scale_n_neurons * self.area)\\n N_exc = n_total_micro/self.n_populations * exc_factor\\n N_inh = n_total_micro/self.n_populations * inh_factor\\n self.n_neurons = np.tile([N_exc, N_inh], self.n_layers).astype(int)\\n else:\\n if type(n_neurons) == np.ndarray:\\n if n_neurons.shape == (self.n_populations, ):\\n self.n_neurons = np.int_(n_neurons)\\n else:\\n raise Exception(\\\"'n_neurons' has wrong shape. \\\"+\\n \\\"Expects (%i,)\\\"%self.n_populations)\\n else: \\n raise Exception(\\\"'n_neurons' expects either numpy.ndarray or string \\\"+\\n \\\"in {'micro', 'brunel'}\\\")\\n self.n_total = np.sum(self.n_neurons)\\n\\n \\n # Synapse numbers\\n # Connection probabilities: conn_probs[post, pre] = conn_probs[target, source]\\n conn_probs = net.conn_probs\\n # Scale synapse numbers of the C_ab\\n if net.scale_C_linearly:\\n n_outer_full = np.outer(net.full_scale_n_neurons, net.full_scale_n_neurons)\\n C_full_scale = np.log(1. - conn_probs) / np.log(1. - 1. / n_outer_full)\\n C_scaled = np.int_(C_full_scale * self.area)\\n else:\\n n_outer = np.outer(self.n_neurons, self.n_neurons)\\n C_scaled = np.int_(np.log(1. - conn_probs) / np.log(1. - 1. / n_outer))\\n\\n self.connection_rule = connection_rule\\n if self.connection_rule == \\\"fixed_total_number\\\":\\n C_ab_micro = C_scaled # total number, do not divide! \\n elif self.connection_rule == \\\"fixed_indegree\\\":\\n C_ab_micro = (C_scaled.T / (net.full_scale_n_neurons * self.area)).T\\n else:\\n raise Exception(\\\"Unexpected connection type. Use 'fixed_total_number' for microcircuit \\\" + \\n \\\"model or 'fixed_indegree' for Brunel's model!\\\")\\n\\n if C_ab == \\\"micro\\\":\\n self.C_ab = C_ab_micro # shall not be integer at this point!\\n elif C_ab == \\\"brunel\\\":\\n C_e = np.mean(C_ab_micro) # mean for microcircuit (= 501 in full scale)\\n C_i = gamma * C_e\\n self.C_ab = np.tile([C_e, C_i], (self.n_populations, self.n_layers)).astype(int) \\n else:\\n if type(C_ab) == np.ndarray:\\n if C_ab.shape == (self.n_populations, self.n_populations):\\n self.C_ab = np.int_(C_ab)\\n else:\\n raise Exception(\\\"'C_ab' has wrong shape. \\\"+\\n \\\"Expects (%i, %i)\\\"%(self.n_populations, self.n_populations))\\n else: \\n raise Exception(\\\"'C_ab' expects either numpy.ndarray or string \\\"+\\n \\\"in {'micro', 'brunel'}\\\")\\n\\n\\n ###################################################\\n ### Single-neuron parameters\\t\\t### \\n ###################################################\\n self.neuron_model = neuron_model\\n self.Vm0_mean = net.Vm0_mean # mean of initial membrane potential (mV)\\n self.Vm0_std = net.Vm0_std # std of initial membrane potential (mV)\\n self.model_params = net.model_params\\n if not self.neuron_model==\\\"iaf_psc_delta\\\":\\n self.model_params[\\\"tau_syn_ex\\\"] = net.tau_syn_ex # excitatory synaptic time constant (ms)\\n self.model_params[\\\"tau_syn_in\\\"] = net.tau_syn_in # inhibitory synaptic time constant (ms)\\n self.tau_syn_ex = net.tau_syn_ex # ms\\n self.tau_syn_in = net.tau_syn_in # ms\\n self.tau_syn = np.tile([self.tau_syn_ex, self.tau_syn_in], (self.n_populations, self.n_layers))\\n # Rescaling for model calculations: these values are not used in the simulation!\\n self.tau_m = self.model_params[\\\"tau_m\\\"] # ms\\n self.t_ref = self.model_params[\\\"t_ref\\\"] # ms\\n self.E_L = self.model_params[\\\"E_L\\\"] # mV\\n self.V_r = self.model_params[\\\"V_reset\\\"] - self.E_L # mV\\n self.theta = self.model_params[\\\"V_th\\\"] - self.E_L # mV\\n self.C_m = self.model_params[\\\"C_m\\\"] # pF\\n\\n\\n ######################################################\\n # Synaptic weights. Depend on neuron_model! ##\\n ######################################################\\n self.g = g\\n self.j02 = j02\\n\\n g_all = np.tile([1., -self.g], (self.n_populations, self.n_layers))\\n L23e_index = np.where(self.populations == \\\"L23e\\\")[0][0]\\n L4e_index = np.where(self.populations == \\\"L4e\\\")[0][0]\\n g_all[L23e_index, L4e_index] *= self.j02\\n \\n self.J = net.PSP_e # mv; mean PSP, used as reference PSP\\n self.J_ab = self.J * g_all\\n self.weight_rel_sd = weight_rel_sd # Standard deviation of weight relative to mean weight\\n # Transformation from peak PSP to PSC\\n delta_tau = self.tau_syn - self.tau_m\\n ratio_tau = self.tau_m / self.tau_syn\\n PSC_over_PSP = self.C_m * delta_tau / (self.tau_m * self.tau_syn * \\\\\\n (ratio_tau**(self.tau_m / delta_tau) - ratio_tau**(self.tau_syn / delta_tau))) \\n # Actual weights have to be adapted: from peak PSP to PSC (and back...)\\n if self.neuron_model==\\\"iaf_psc_exp\\\": # PSCs calculated from PSP amplitudes\\n self.weights = self.J_ab * PSC_over_PSP # neuron populations\\n elif self.neuron_model==\\\"iaf_psc_delta\\\":\\n self.weights = self.J_ab * PSC_over_PSP * (self.tau_syn_ex) / self.C_m\\n # This might be an overkill / doing things twice...\\n elif self.neuron_model==\\\"iaf_psc_alpha\\\": # PSCs calculated from PSP amplitudes\\n self.weights = self.J_ab * np.exp(1) / (self.tau_syn_ex) / self.C_m\\n else:\\n raise Exception(\\\"Neuron model should be iaf_psc_ - {delta, exp, alpha}!\\\")\\n\\n\\n ###################################################\\n ### Delays and dicts ### \\n ###################################################\\n # mean dendritic delays for excitatory and inhibitory transmission (ms)\\n self.delay_e = net.delay_e # ms, excitatory synapses\\n self.delay_i = net.delay_i # ms, inhibitory synapses\\n\\n self.delays = np.tile([self.delay_e, self.delay_i], (self.n_populations, self.n_layers)) # adapt...\\n self.delay_rel_sd = delay_rel_sd \\n \\n # Synapse dictionaries\\n # default connection dictionary\\n self.conn_dict = {\\\"rule\\\": connection_rule}\\n # weight distribution of connections between populations\\n self.weight_dict_exc = net.weight_dict_exc\\n self.weight_dict_inh = net.weight_dict_inh\\n # delay distribution of connections between populations\\n self.delay_dict = net.delay_dict\\n # default synapse dictionary\\n self.syn_dict = net.syn_dict\\n \\n \\n ###################################################\\n ### External stimuli ## \\n ###################################################\\n # rate of background Poisson input at each external input synapse (spikes/s) \\n self.rate_ext = rate_ext # Hz \\n self.J_ext = net.PSP_ext # external synaptic weight\\n self.delay_ext = self.delay_e # ms; mean delay of external input\\n self.dc_amplitude = net.dc_amplitude # constant bg amplitude\\n self.C_aext = net.C_aext # in-degrees for background input\\n # Adapt weights\\n if self.neuron_model==\\\"iaf_psc_exp\\\": # PSCs calculated from PSP amplitudes\\n self.weight_ext = self.J_ext * PSC_over_PSP[0, 0] \\n elif self.neuron_model==\\\"iaf_psc_delta\\\":\\n self.weight_ext = self.J_ext * PSC_over_PSP[0, 0] * self.tau_syn_ex / self.C_m\\n elif self.neuron_model==\\\"iaf_psc_alpha\\\": # PSCs calculated from PSP amplitudes\\n self.weight_ext = self.J_ext * np.exp(1) / self.tau_syn_ex / self.C_m\\n\\n # optional additional thalamic input (Poisson)\\n self.n_th = net.n_th # size of thalamic population\\n self.th_start = net.th_start # onset of thalamic input (ms)\\n self.th_duration = net.th_duration # duration of thalamic input (ms)\\n self.th_rate = net.th_rate # rate of thalamic neurons (spikes/s)\\n self.J_th = net.PSP_th # mean EPSP amplitude (mV) for thalamic input\\n # Adapt weights\\n if self.neuron_model==\\\"iaf_psc_exp\\\": # PSCs calculated from PSP amplitudes\\n self.weight_th = self.J_th * PSC_over_PSP[0, 0] \\n elif self.neuron_model==\\\"iaf_psc_delta\\\":\\n self.weight_th = self.J_th * PSC_over_PSP[0, 0] * self.tau_syn_ex / self.C_m\\n elif self.neuron_model==\\\"iaf_psc_alpha\\\": # PSCs calculated from PSP amplitudes\\n self.weight_th = self.J_th * np.exp(1) / self.tau_syn_ex / self.C_m\\n\\n \\n # connection probabilities for thalamic input\\n conn_probs_th = net.conn_probs_th\\n if net.scale_C_linearly:\\n if not self.n_th == 0:\\n C_th_full_scale = np.log(1. - conn_probs_th) / \\\\\\n np.log(1. - 1. / (self.n_th * net.full_scale_n_neurons))\\n self.C_th_scaled = np.int_(C_th_full_scale * self.area)\\n else:\\n if not self.n_th == 0:\\n self.C_th_scaled = np.int_(np.log(1. - conn_probs_th) / \\\\\\n np.log(1. - 1. / (self.n_th * self.n_neurons_micro)))\\n if self.n_th == 0:\\n self.C_th_scaled = None\\n \\n # mean delay of thalamic input (ms)\\n self.delay_th = net.delay_th\\n # standard deviation relative to mean delay of thalamic input\\n self.delay_th_rel_sd = net.delay_th_rel_sd\\n\\n\\n ######################################################\\n # Predefine matrices for mean field ##\\n ######################################################\\n if self.neuron_model==\\\"iaf_psc_delta\\\":\\n self.J_mu = self.weights\\n self.J_sd = self.weights\\n self.J_mu_ext = self.weight_ext \\n self.J_sd_ext = self.weight_ext\\n elif self.neuron_model==\\\"iaf_psc_exp\\\":\\n self.J_mu = self.weights * self.tau_syn / self.C_m\\n self.J_sd = self.weights * np.sqrt(self.tau_syn / 2.) / self.C_m\\n self.J_mu_ext = self.weight_ext * self.tau_syn_ex / self.C_m\\n self.J_sd_ext = self.weight_ext * np.sqrt(self.tau_syn_ex / 2.) / self.C_m\\n elif self.neuron_model==\\\"iaf_psc_alpha\\\":\\n self.J_mu = self.weights * self.tau_syn**2 / self.C_m\\n self.J_sd = self.weights * self.tau_syn**(3./2.) / (self.C_m * 2.)\\n self.J_mu_ext = self.weight_ext * self.tau_syn_ex**2 / self.C_m\\n self.J_sd_ext = self.weight_ext * self.tau_syn_ex**(3./2.) / (self.C_m * 2.)\\n self.mat_mu = self.tau_m * 1e-3 * self.J_mu * self.C_ab\\n self.mu_ext = self.tau_m * 1e-3 * self.J_mu_ext * self.C_aext * self.rate_ext\\n self.mat_var = self.tau_m * 1e-3 * (1 + self.weight_rel_sd ** 2) * self.J_sd**2 * self.C_ab\\n self.var_ext = self.tau_m * 1e-3 * (1 + self.weight_rel_sd ** 2) * self.J_sd_ext**2 * self.C_aext * self.rate_ext\",\n \"def make_config():\\n # find date of data obtained\\n current_pathname = os.path.basename(os.getcwd())\\n guess_date = extract_date(current_pathname)\\n\\n while(True):\\n if guess_date is None:\\n prompt = 'YYYYMMDD'\\n else:\\n prompt = guess_date\\n\\n string = input('Date of observation [{}]: '.format(prompt))\\n input_date = extract_date(string)\\n if input_date is None:\\n if guess_date is None:\\n continue\\n else:\\n input_date = guess_date\\n break\\n else:\\n break\\n \\n input_datetime = datetime.datetime.strptime(input_date, '%Y-%m-%d')\\n\\n # create config object\\n config = configparser.ConfigParser()\\n\\n config.add_section('data')\\n\\n config.set('data', 'telescope', 'Keck-I')\\n config.set('data', 'instrument', 'HIRES')\\n config.set('data', 'rawpath', 'rawdata')\\n #config.set('data', 'statime_key', statime_key)\\n #config.set('data', 'exptime_key', exptime_key)\\n\\n config.add_section('reduce')\\n config.set('reduce', 'midpath', 'midproc')\\n config.set('reduce', 'figpath', 'images')\\n config.set('reduce', 'odspath', 'onedspec')\\n config.set('reduce', 'mode', 'normal')\\n config.set('reduce', 'oned_suffix', 'ods')\\n config.set('reduce', 'fig_format', 'png')\\n \\n config.add_section('reduce.bias')\\n config.set('reduce.bias', 'bias_file', '${reduce:midpath}/bias.fits')\\n config.set('reduce.bias', 'cosmic_clip', str(10))\\n config.set('reduce.bias', 'maxiter', str(5))\\n config.set('reduce.bias', 'smooth', 'yes')\\n config.set('reduce.bias', 'smooth_method', 'gaussian')\\n config.set('reduce.bias', 'smooth_sigma', str(3))\\n config.set('reduce.bias', 'smooth_mode', 'nearest')\\n\\n config.add_section('reduce.trace')\\n config.set('reduce.trace', 'minimum', str(1e-3))\\n config.set('reduce.trace', 'scan_step', str(100))\\n config.set('reduce.trace', 'separation', '100:84, 1500:45, 3000:14')\\n config.set('reduce.trace', 'filling', str(0.2))\\n config.set('reduce.trace', 'align_deg', str(2))\\n config.set('reduce.trace', 'display', 'no')\\n config.set('reduce.trace', 'degree', str(4))\\n config.set('reduce.trace', 'file', '${reduce:midpath}/trace.fits')\\n\\n config.add_section('reduce.flat')\\n config.set('reduce.flat', 'file', '${reduce:midpath}/flat.fits')\\n\\n # write to config file\\n filename = 'HIRES.{}.cfg'.format(input_date)\\n outfile = open(filename, 'w')\\n for section in config.sections():\\n maxkeylen = max([len(key) for key in config[section].keys()])\\n outfile.write('[{}]'.format(section)+os.linesep)\\n fmt = '{{:{}s}} = {{}}'.format(maxkeylen)\\n for key, value in config[section].items():\\n outfile.write(fmt.format(key, value)+os.linesep)\\n outfile.write(os.linesep)\\n outfile.close()\\n\\n print('Config file written to {}'.format(filename))\",\n \"def __init__(self):\\n self.label = \\\"Partition NNInput Files\\\"\\n self.description = \\\"Partitions Neural Network class.dta of more than 200,000 records into files of 200,000 or less.\\\"\\n self.canRunInBackground = False\\n self.category = \\\"Neural network\\\"\",\n \"def __init__(self,\\n cfg: CfgNode,\\n max_iters: int = 10,\\n ftol: float = 1e-9,\\n gtol: float = 1e-9,\\n device: torch.device = torch.device('cuda')):\\n\\n # Store options\\n self.cfg = cfg\\n self.max_iters = max_iters\\n self.ftol = ftol\\n self.gtol = gtol\\n self.device = device\\n\\n # Load SMPL model\\n smpl_cfg = {k.lower(): v for k,v in dict(cfg.SMPL).items()}\\n self.smpl = SMPL(**smpl_cfg).to(device)\",\n \"def __init__(self, workdir, encut, struct_path, name=\\\"relax_bwmn\\\"): \\n potcar_path = \\\"../pseudos/BWO_Mn_POTCAR\\\" \\n kgrid = [2, 2, 2] \\n input_param = DefaultOptimizationParameters(encut) \\n relax_calc = SCFCalculation(workdir, pseudo_par=None, kgrid=kgrid, name=\\\"BWO_Mn_relax\\\", encut=encut, input_parameters=input_param) \\n relax_calc.make_calculation(struct_path, potcar_path=potcar_path)\",\n \"def build_resnet_test_config(self, test_id, test_args, batch_size=32, gpus=1,\\n total_batches=300, repeat=3):\\n config = {}\\n config['total_batches'] = total_batches\\n # Relative path in the repo to the test folder.\\n config['cmd_path'] = 'imagenet'\\n\\n # PyTorch Automatically uses all GPUs it can see.\\n gpu_list = ','.join(str(x) for x in range(gpus))\\n visible_devices = 'CUDA_VISIBLE_DEVICES={}'.format(gpu_list)\\n if gpus > 1:\\n multi_gpu = (\\\" --dist-url 'tcp://127.0.0.1:6001' --dist-backend 'nccl'\\\"\\n \\\"--multiprocessing-distributed --world-size 1 --rank 0 \\\")\\n config['pycmd'] = 'python3 main.py {} {} {}'.format('{}',\\n multi_gpu,\\n self.imagenet_dir)\\n else:\\n config['pycmd'] = '{} python3 main.py {} {}'.format(visible_devices,\\n '{}',\\n self.imagenet_dir)\\n config['test_id'] = test_id\\n config['repeat'] = self.auto_test_config.get('repeat', repeat)\\n # Normalized name of model being tested\\n config['model'] = 'resnet50_v1.5'\\n config['gpus'] = gpus\\n config['batch_size'] = batch_size\\n args = {}\\n config['args'] = args\\n # print stats every epoch.\\n args['print-freq'] = self.auto_test_config.get('print_freq', 1)\\n args['epochs'] = 1\\n args['arch'] = 'resnet50'\\n args['batch-size'] = batch_size * gpus\\n args['workers'] = self.auto_test_config.get('input_threads', 5)\\n\\n # Override any args with the tests args\\n args.update(test_args)\\n\\n return config\",\n \"def config(self, cnf=None, **kw):\\n if cnf:\\n kw.update(cnf)\\n for k, v in kw.items():\\n if k in self.ttlabel.keys():\\n self.ttlabel.config(**{k: v})\\n elif k in self.custom_defs:\\n setattr(self, k, v)\\n else:\\n raise Exception(\\n \\\"Unexpected Key/Val Pair {k}:{v}\\\".format(k=k, v=v)\\n )\\n if not cnf and not kw:\\n return _flesh_config(\\n self,\\n self.ttlabel.config(),\\n defaults=self.defaults_d,\\n base=(\\\"\\\", self.base),\\n delay=(500, self.delay),\\n ipadx=(2, self.ipadx),\\n ipady=(1, self.ipady),\\n offsetx=(2, self.offsetx),\\n offsety=(2, self.offsety),\\n )\\n return None\",\n \"def subconfig(self) -> \\\"Config\\\":\\n config = Config()\\n config.func = self.func\\n config.targets = self.targets.copy()\\n config.blacklisted_targets = self.blacklisted_targets.copy()\\n config.variables = self.variables.copy()\\n config.unit_iterator = self.unit_iterator.copy()\\n config.ran_units = self.ran_units.copy()\\n config.active_units = self.active_units.copy()\\n config.entry_point = self.unit_iterator.lst[-1]\\n config.signals = self.signals.copy()\\n return config\",\n \"def __init__(self, fsmt_layer, config):\\n super().__init__(config)\\n self.in_proj_weight = nn.Parameter(torch.cat([fsmt_layer.self_attn.q_proj.weight, fsmt_layer.self_attn.k_proj.weight, fsmt_layer.self_attn.v_proj.weight]))\\n self.in_proj_bias = nn.Parameter(torch.cat([fsmt_layer.self_attn.q_proj.bias, fsmt_layer.self_attn.k_proj.bias, fsmt_layer.self_attn.v_proj.bias]))\\n self.out_proj_weight = fsmt_layer.self_attn.out_proj.weight\\n self.out_proj_bias = fsmt_layer.self_attn.out_proj.bias\\n self.linear1_weight = fsmt_layer.fc1.weight\\n self.linear1_bias = fsmt_layer.fc1.bias\\n self.linear2_weight = fsmt_layer.fc2.weight\\n self.linear2_bias = fsmt_layer.fc2.bias\\n self.norm1_eps = fsmt_layer.self_attn_layer_norm.eps\\n self.norm1_weight = fsmt_layer.self_attn_layer_norm.weight\\n self.norm1_bias = fsmt_layer.self_attn_layer_norm.bias\\n self.norm2_eps = fsmt_layer.final_layer_norm.eps\\n self.norm2_weight = fsmt_layer.final_layer_norm.weight\\n self.norm2_bias = fsmt_layer.final_layer_norm.bias\\n self.num_heads = fsmt_layer.self_attn.num_heads\\n self.embed_dim = fsmt_layer.self_attn.embed_dim\\n self.is_last_layer = False\\n self.validate_bettertransformer()\",\n \"def get_default_config():\\r\\n config = {}\\r\\n\\r\\n config[\\\"kl_coeff\\\"] = 1.0\\r\\n config[\\\"_num_workers_tf\\\"] = 4\\r\\n config[\\\"use_gae\\\"] = True\\r\\n config[\\\"num_gpus\\\"] = 0\\r\\n\\r\\n config[\\\"_env_name_rllib\\\"] = \\\"multicomp\\\"\\r\\n config[\\\"_env_fcn\\\"] = create_env\\r\\n config['_policies'] = [None, \\\"from_scratch\\\", \\\"pretrained\\\"]\\r\\n config[\\\"_env\\\"] = {'with_video': False,\\r\\n \\\"SingleAgentToMultiAgent\\\": False,\\r\\n \\\"env_name\\\": \\\"multicomp/YouShallNotPassHumans-v0\\\"}\\r\\n config['framework'] = 'tfe'\\r\\n\\r\\n config['_train_policies'] = ['player_1']\\r\\n config['_call'] = {}\\r\\n config['_trainer'] = \\\"PPO\\\"\\r\\n config['_policy'] = \\\"PPO\\\"\\r\\n config['_call']['checkpoint_freq'] = 0\\r\\n config['_train_steps'] = 99999999\\r\\n config['_update_config'] = None\\r\\n config['_run_inline'] = False\\r\\n config['_postprocess'] = None\\r\\n\\r\\n config['num_envs_per_worker'] = 4\\r\\n config['_log_error'] = True\\r\\n config['_model_params'] = {\\r\\n \\\"use_lstm\\\": False,\\r\\n \\\"fcnet_hiddens\\\": [64, 64],\\r\\n # \\\"custom_action_dist\\\": \\\"DiagGaussian\\\",\\r\\n \\\"fcnet_activation\\\": \\\"tanh\\\",\\r\\n \\\"free_log_std\\\": True,\\r\\n }\\r\\n\\r\\n config['_select_policy'] = select_policy_default\\r\\n config['_get_policies'] = get_policies_default\\r\\n config['_do_not_train_policies'] = []\\r\\n config['_update_withpolicies'] = None\\r\\n config['callbacks'] = InfoCallbacks\\r\\n\\r\\n return config\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6211365","0.5947636","0.5926075","0.5917099","0.58897614","0.584447","0.584447","0.56702006","0.5627676","0.55820346","0.5560999","0.55283594","0.54756486","0.54677653","0.5465148","0.54618865","0.5459208","0.54586923","0.54444957","0.54440576","0.5437182","0.5412364","0.54110235","0.5406182","0.5401891","0.539887","0.5388667","0.53857255","0.5381153","0.53794265","0.53671765","0.53660214","0.53644407","0.53613544","0.5359351","0.5342886","0.5339787","0.5338481","0.53353596","0.5332928","0.5329549","0.53258365","0.53169453","0.52966887","0.5296622","0.5288054","0.528353","0.5283243","0.52742887","0.5274182","0.5270683","0.52696353","0.5267786","0.52657145","0.52472836","0.5246372","0.5245727","0.5212938","0.52117723","0.5199961","0.51905817","0.5157758","0.51552707","0.5150389","0.5150208","0.51461786","0.5144735","0.5133444","0.512696","0.5124734","0.5120608","0.51196","0.51095134","0.51042783","0.5103837","0.5095214","0.50908893","0.50750375","0.50746393","0.5063856","0.50633025","0.50610197","0.50560284","0.5056025","0.5046571","0.50457877","0.5031844","0.5023371","0.50219643","0.5020381","0.5019792","0.5019","0.50188696","0.50176585","0.5016423","0.5014474","0.50137174","0.5010782","0.5010635","0.5009944"],"string":"[\n \"0.6211365\",\n \"0.5947636\",\n \"0.5926075\",\n \"0.5917099\",\n \"0.58897614\",\n \"0.584447\",\n \"0.584447\",\n \"0.56702006\",\n \"0.5627676\",\n \"0.55820346\",\n \"0.5560999\",\n \"0.55283594\",\n \"0.54756486\",\n \"0.54677653\",\n \"0.5465148\",\n \"0.54618865\",\n \"0.5459208\",\n \"0.54586923\",\n \"0.54444957\",\n \"0.54440576\",\n \"0.5437182\",\n \"0.5412364\",\n \"0.54110235\",\n \"0.5406182\",\n \"0.5401891\",\n \"0.539887\",\n \"0.5388667\",\n \"0.53857255\",\n \"0.5381153\",\n \"0.53794265\",\n \"0.53671765\",\n \"0.53660214\",\n \"0.53644407\",\n \"0.53613544\",\n \"0.5359351\",\n \"0.5342886\",\n \"0.5339787\",\n \"0.5338481\",\n \"0.53353596\",\n \"0.5332928\",\n \"0.5329549\",\n \"0.53258365\",\n \"0.53169453\",\n \"0.52966887\",\n \"0.5296622\",\n \"0.5288054\",\n \"0.528353\",\n \"0.5283243\",\n \"0.52742887\",\n \"0.5274182\",\n \"0.5270683\",\n \"0.52696353\",\n \"0.5267786\",\n \"0.52657145\",\n \"0.52472836\",\n \"0.5246372\",\n \"0.5245727\",\n \"0.5212938\",\n \"0.52117723\",\n \"0.5199961\",\n \"0.51905817\",\n \"0.5157758\",\n \"0.51552707\",\n \"0.5150389\",\n \"0.5150208\",\n \"0.51461786\",\n \"0.5144735\",\n \"0.5133444\",\n \"0.512696\",\n \"0.5124734\",\n \"0.5120608\",\n \"0.51196\",\n \"0.51095134\",\n \"0.51042783\",\n \"0.5103837\",\n \"0.5095214\",\n \"0.50908893\",\n \"0.50750375\",\n \"0.50746393\",\n \"0.5063856\",\n \"0.50633025\",\n \"0.50610197\",\n \"0.50560284\",\n \"0.5056025\",\n \"0.5046571\",\n \"0.50457877\",\n \"0.5031844\",\n \"0.5023371\",\n \"0.50219643\",\n \"0.5020381\",\n \"0.5019792\",\n \"0.5019\",\n \"0.50188696\",\n \"0.50176585\",\n \"0.5016423\",\n \"0.5014474\",\n \"0.50137174\",\n \"0.5010782\",\n \"0.5010635\",\n \"0.5009944\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7439748"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":20089,"cells":{"query":{"kind":"string","value":"Implementation of the `quantize()` method for the PyTorch backend."},"document":{"kind":"string","value":"def quantize_impl(\n model: torch.nn.Module,\n calibration_dataset: Dataset,\n preset: QuantizationPreset,\n target_device: TargetDevice,\n subset_size: int,\n fast_bias_correction: bool,\n model_type: Optional[ModelType] = None,\n ignored_scope: Optional[IgnoredScope] = None,\n advanced_parameters: Optional[AdvancedQuantizationParameters] = None,\n) -> torch.nn.Module:\n if fast_bias_correction is False:\n raise ValueError(f\"fast_bias_correction={fast_bias_correction} is not \" \"supported\")\n if ignored_scope is not None and ignored_scope.types:\n raise RuntimeError(\n \"Quantization algorithm from the PyTorch backend \"\n \"does not support operation types in the ignored \"\n \"scopes yet\"\n )\n if target_device == TargetDevice.CPU_SPR:\n raise RuntimeError(\"target_device == CPU_SPR is not supported\")\n\n nncf_config = _create_nncf_config(\n preset, target_device, subset_size, model_type, ignored_scope, advanced_parameters\n )\n\n calibration_data_loader = CalibrationDataLoader(calibration_dataset)\n nncf_config.register_extra_structs(\n [\n QuantizationRangeInitArgs(data_loader=calibration_data_loader),\n BNAdaptationInitArgs(data_loader=calibration_data_loader),\n ]\n )\n\n def wrap_inputs(args, kwargs):\n return wrap_nncf_model_inputs_with_objwalk(args, kwargs)\n\n def wrap_outputs(retval):\n return wrap_nncf_model_outputs_with_objwalk(retval)\n\n def create_dummy_forward_fn(data_loader, device):\n def dummy_forward(model):\n with no_nncf_trace():\n data_item = next(iter(data_loader))\n args, kwargs = data_loader.get_inputs(data_item)\n\n def send_to_device(tensor):\n return tensor.to(device)\n\n args = objwalk(args, is_tensor, send_to_device)\n kwargs = objwalk(kwargs, is_tensor, send_to_device)\n\n args, kwargs = wrap_inputs(args, kwargs)\n retval = model(*args, **kwargs)\n retval = replicate_same_tensors(retval)\n return wrap_outputs(retval)\n\n return dummy_forward\n\n dummy_forward_fn = create_dummy_forward_fn(calibration_data_loader, get_model_device(model))\n\n clone_model = deepcopy(model)\n compression_ctrl, compressed_model = create_compressed_model(\n model=clone_model,\n config=nncf_config,\n dummy_forward_fn=dummy_forward_fn,\n wrap_inputs_fn=wrap_inputs,\n wrap_outputs_fn=wrap_outputs,\n )\n compression_ctrl.prepare_for_export()\n compressed_model.nncf.disable_dynamic_graph_building()\n\n return compressed_model"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _quantize_activation(self, tensor_quantizer: Union[StaticGridPerTensorQuantizer, LearnedGridTensorQuantizer],\n tensors_to_quantize: Union[List[torch.Tensor], torch.Tensor]) -> \\\n Union[List[torch.Tensor], torch.Tensor]:\n\n if not tensor_quantizer.enabled:\n return tensors_to_quantize\n\n outputs = []\n if not isinstance(tensors_to_quantize, list):\n tensors_to_quantize = [tensors_to_quantize]\n for input_tensor in tensors_to_quantize:\n\n if self._mode is QcQuantizeOpMode.ANALYSIS:\n\n if isinstance(input_tensor, tuple):\n for tensor in input_tensor:\n tensor_quantizer.update_encoding_stats(tensor)\n else:\n tensor_quantizer.update_encoding_stats(input_tensor)\n output = input_tensor\n\n elif self._mode is QcQuantizeOpMode.ACTIVE:\n # if we are not in training, then only nearest rounding should be used\n if self.training:\n round_mode = tensor_quantizer.round_mode\n else:\n round_mode = libpymo.RoundingMode.ROUND_NEAREST\n output = tensor_quantizer.quantize_dequantize(input_tensor, round_mode)\n\n elif self._mode is QcQuantizeOpMode.LEARN_ENCODINGS:\n encoding_min = getattr(self, tensor_quantizer.name + '_encoding_min')\n encoding_max = getattr(self, tensor_quantizer.name + '_encoding_max')\n output = tensor_quantizer.quantize_dequantize(input_tensor, encoding_min, encoding_max)\n\n else:\n output = input_tensor\n\n outputs.append(output)\n\n # Flatten if there is only one output\n return outputs[0] if len(outputs) == 1 else tuple(outputs)","def _quantize(self):\n self._quantized_subsamples = [0] * self._number_of_subsamples\n for index, value in enumerate(self._subsamples):\n self._quantized_subsamples[index] = self._quantize_one_value(value)\n logging.debug('quantized down-samples: %r', self._quantized_subsamples)","def _quantize(self, model: nn.Module) -> nn.Module:\n if self.mask:\n model_utils.remove_pruning_reparameterization(self.params_all)\n\n # check the accuracy after each epoch\n quantized_model = torch.quantization.convert(model.eval(), inplace=False)\n quantized_model.eval()\n\n # set masks again\n if self.mask:\n self._load_masks()\n\n return quantized_model","def _quantize_one_value(self, value):\n add = 0.5 if value > 0 else -0.5\n return int(value / self._quantization_factor + add)","def quantize(im_orig, n_quant, n_iter):\n shape_len = len(im_orig.shape)\n if shape_len == 2: # grayscale\n return quantization_helper(im_orig, n_quant, n_iter)\n\n elif shape_len == 3: # rgb\n im_yiq = rgb2yiq(im_orig)\n y = im_yiq[:, :, 0]\n y_quant, error = quantization_helper(y, n_quant, n_iter)\n y_quant = y_quant/ 255\n im_yiq[:, :, 0] = y_quant\n im_quants = yiq2rgb(im_yiq)\n return im_quants, error","def _compute_quantized_subsamples(self):\n self._down_sample()\n self._quantize()","def _quantize(self) :\n self.A[self.A <= self.thr] = -1\n self.A[self.A > self.thr] = 1\n self.A = self.A.astype(\"int\")","def quantize(self, tune_cfg, model, data_loader, q_func=None):\n assert q_func is None, \"quantization aware training mode is not support on tensorflow\"\n logger.info('Start to run model quantization...')\n quantized_model = os.path.join(os.getcwd(), \"tf_quantized.pb\")\n self.tuning_cfg_to_fw(tune_cfg)\n logger.debug('Dump quantization configurations:')\n logger.debug(self.quantize_config)\n from .tf_utils.graph_converter import GraphConverter\n converter = GraphConverter(self.pre_optimized_graph if self.pre_optimized_graph else model,\n quantized_model,\n inputs=self.inputs,\n outputs=self.outputs,\n qt_config=self.quantize_config,\n fp32_ops=self.fp32_ops,\n bf16_ops=self.bf16_ops,\n data_loader=data_loader)\n return converter.convert()","def quantize(coords):\n assert isinstance(coords, np.ndarray) or isinstance(\n coords, torch.Tensor\n ), \"Invalid coords type\"\n if isinstance(coords, np.ndarray):\n assert (\n coords.dtype == np.int32\n ), f\"Invalid coords type {coords.dtype} != np.int32\"\n return MEB.quantize_np(coords.astype(np.int32))\n else:\n # Type check done inside\n return MEB.quantize_th(coords.int())","def quantize(im_orig, n_quant, n_iter):\n if im_orig.ndim == RGB_DIM:\n # Quantize only Y channel.\n im_yiq = rgb2yiq(im_orig)\n result = __gray_quantize(im_yiq[:, :, Y_CHANNEL], n_quant, n_iter)\n im_yiq[:, :, Y_CHANNEL] = result[IMAGE_LOCATION]\n\n # Convert back to RGB space.\n result[IMAGE_LOCATION] = yiq2rgb(im_yiq)\n return result\n # Otherwise, just quantize.\n return __gray_quantize(im_orig, n_quant, n_iter)","def _quantize_dequantize_params(self, inputs=None) -> Dict[str, torch.Tensor]:\n quantized_params = dict()\n shadow_params = {name: param.detach().clone() for name, param in self.named_parameters(recurse=False)}\n\n if self._mode != QcQuantizeOpMode.LEARN_ENCODINGS:\n quantizer_param_map = defaultdict(list)\n for name, param in self.named_parameters(recurse=False):\n quantizer_param_map[self._param_quantizers[name]].append((name, param))\n\n for param_quantizer, param_list in quantizer_param_map.items():\n # If we are in training mode with quant-sim nodes,\n # then we want to calculate encodings for the parameters in every pass\n if self.training or param_quantizer.encoding is None:\n param_quantizer.reset_encoding_stats()\n for _, param in param_list:\n param_quantizer.update_encoding_stats(param.data)\n param_quantizer.compute_encoding()\n\n for name, param in param_list:\n quantized_params[name] = param.data = self._param_quantize_dequantize(param.clone(), param_quantizer)\n else:\n encoding_list_for_params = []\n for name, _ in self.get_named_parameters():\n # Create a list of encoding parameters for params\n quantizer = self.param_quantizers[name]\n if quantizer.enabled:\n # if param uses a group quantizer remap to group quantizer min/max encoding params.\n if quantizer in self._grouped_quantizers.values():\n name, *_ = [n for n, q in self._grouped_quantizers.items() if q == quantizer]\n encoding_list_for_params.append(getattr(self, name + '_encoding_min'))\n encoding_list_for_params.append(getattr(self, name + '_encoding_max'))\n\n # Quantize the parameters\n inputs = ParameterQuantizer.apply(inputs, self, *encoding_list_for_params)\n\n # clone() the outputs of Custom function to avoid incorrect gradient calculation for in-place modification\n # of view (view is created since Custom function's forward return input as-is)\n inputs = inputs.clone()\n quantized_params = {name: param.clone() for name, param in self.named_parameters(recurse=False)\n if '_encoding_' not in name}\n\n yield quantized_params, inputs\n\n for name, param in self.named_parameters(recurse=False):\n if name in shadow_params:\n param.data.copy_(shadow_params[name].data)","def post_training_quantize(\n model: onnx.ModelProto,\n dataset: List[Dict[str, np.ndarray]],\n per_channel: bool = True,\n) -> onnx.ModelProto:\n model = optimize_model(model)\n ranges = calibrate(model, dataset)\n return quantize(model, per_channel, True, quantizer.QuantizationMode.dfg, ranges)","def quantize(self, some_tensor, bits=32) -> dict:\n raise NotImplementedError(\"To be Overidden by derived class\")","def jpeg_quantize(block: np.ndarray, option: model.QTables):\n t = table[option]\n return deadzone_quantize(block, t)","def _compute_quantization_factor(self):\n self._quantization_factor = (float(self._full_value_range) /\n self._number_of_intervals)\n logging.debug(\n 'full value range: %r, number of intervals: %r',\n self._full_value_range, self._number_of_intervals)\n logging.debug('quantization factor: %r', self._quantization_factor)","def quantize(self, df):\n if len(self.dict_scalers) == 0:\n raise Exception(\"[ERROR] quantize method called prior to\"\n \"normalization transform method \")\n\n quant_df = pd.DataFrame()\n if 'OneForAll' in self.dict_scalers:\n # quantization is applied on all features\n min_fp = float(np.amin(df))\n max_fp = float(np.amax(df))\n scale = (max_fp - min_fp) / (127 - (-127))\n zero_point = 127 - (max_fp / scale)\n quant_df = df / scale + zero_point\n else:\n # quantization is applied independently for each feature/column\n lbl_list = df.columns.values\n for lbl in lbl_list:\n min_fp = float(np.amin(df[lbl]))\n max_fp = float(np.amax(df[lbl]))\n scale = (max_fp - min_fp) / (127 - (-127))\n zero_point = 127 - (max_fp / scale)\n quant_df[lbl] = df[lbl] / scale + zero_point\n return quant_df.astype(np.int8)","def _param_quantize_dequantize(self, data: torch.Tensor, param_quantizer: StaticGridPerTensorQuantizer) -> \\\n torch.Tensor:\n # if we are not in training, then only nearest rounding should be used\n # else we should use whatever the user desires (i.e.. stochastic rounding is a valid option)\n if self.training:\n round_mode = param_quantizer.round_mode\n else:\n round_mode = libpymo.RoundingMode.ROUND_NEAREST\n return param_quantizer.quantize_dequantize(data, round_mode)","def quantize(model: nn.Module,\n precision: str = 'int8',\n accelerator: str = None,\n use_ipex: bool = False,\n calib_dataloader: DataLoader = None,\n metric: Metric = None,\n accuracy_criterion: dict = None,\n approach: str = 'static',\n method: str = None,\n conf: str = None,\n tuning_strategy: str = None,\n timeout: int = None,\n max_trials: int = None,\n input_sample=None,\n thread_num: int = None,\n onnxruntime_session_options=None,\n logging: bool = True,\n **export_kwargs):\n return InferenceOptimizer.quantize(model=model,\n precision=precision,\n accelerator=accelerator,\n use_ipex=use_ipex,\n calib_dataloader=calib_dataloader,\n metric=metric,\n accuracy_criterion=accuracy_criterion,\n approach=approach,\n method=method,\n conf=conf,\n tuning_strategy=tuning_strategy,\n timeout=timeout,\n max_trials=max_trials,\n input_sample=input_sample,\n thread_num=thread_num,\n onnxruntime_session_options=onnxruntime_session_options,\n logging=logging,\n **export_kwargs)","def quantizer(forward_number=None, backward_number=None,\n forward_rounding=\"stochastic\", backward_rounding=\"stochastic\",\n clamping_grad_zero=False, backward_hooks=[]):\n if forward_number is not None:\n if forward_number.exp == -1 or forward_number.man == -1:\n forward_number = None\n if backward_number is not None:\n if backward_number.exp == -1 or backward_number.man == -1:\n backward_number = None\n\n\n for rounding in [forward_rounding, backward_rounding]:\n assert rounding in [\"stochastic\", \"nearest\"], \"invalid rounding type {:s}\".format(rounding)\n for num in [forward_number, backward_number]:\n if num != None: assert isinstance(num, Number)\n\n \n # forward and backward quantisation functions\n tensor_type = \"w\" if backward_number is None else \"x\"\n forward_quant = lambda x, num, rd, tt: block_minifloat_quantize(x, num, rd, tt)\n backward_quant = lambda x, num, rd, tt: block_minifloat_quantize(x, num, rd, tt) \n\n\n class Rounding(torch.autograd.Function):\n @staticmethod\n def forward(self, x):\n if forward_number==None: return x\n\n out = forward_quant(x.contiguous(), forward_number, forward_rounding, tensor_type)\n\n return out.clone()\n\n @staticmethod\n def backward(self, grad_output):\n if self.needs_input_grad[0]:\n if backward_number == None:\n grad_input = grad_output\n else:\n grad_input = backward_quant(grad_output.contiguous(), backward_number, \n backward_rounding, tensor_type)\n else:\n grad_input = None\n\n return grad_input.clone()\n\n return Rounding.apply","def quantize_float(f, q):\n return int(round(f / q) * q)","def quantize(im_orig, n_quant, n_iter):\n rgb, y, im_yiq = check_rgb(im_orig)\n hist_orig, bin_edges = np.histogram(y*(BITS - 1), BITS, (0, BITS - 1))\n z = np.concatenate([[0], initial_z(hist_orig, n_quant), [(BITS-1)]])\n q = find_q(z, hist_orig)\n error = [calc_err(q, z, hist_orig)]\n for i in range(n_iter - 1): # first iteration was already done\n z_new = find_z(q)\n if np.array_equal(z_new, z): # stop if the z and q vectors are already optimal\n break\n q = find_q(z_new, hist_orig)\n z = z_new\n error.append(calc_err(q, z, hist_orig))\n lut = im_lut(q, z)\n im_quant = lut[(y*(BITS - 1)).astype(np.uint8)].astype(np.uint8) # calculate quantized image.\n im_quant = gray2rgb(rgb, im_quant, im_yiq) # if the original image was RGB then convert back to RGB\n return im_quant, error","def quant_image(self, image=None, dst=None, ndeep=None, ndigits=None, override=True, delete=True):\n # Check Whether Pngquant Exist\n if not self.file_exists(self.quant_file):\n raise ValueError(self.err_pngquant)\n\n # Check Whether Image Exist\n if not self.file_exists(image):\n raise ValueError(self.err_image)\n\n # Compress Image By Call Function quant_data\n # Dst Should Pass Image When Override Origin Image\n return self.quant_data(self.open_file(image), dst=dst or (override and image), ndeep=ndeep, ndigits=ndigits, delete=delete)","def TorchRound():\n class identity_quant(torch.autograd.Function):\n @staticmethod\n def forward(ctx, input):\n out = torch.round(input)\n return out\n\n @staticmethod\n def backward(ctx, grad_output):\n return grad_output\n\n return identity_quant().apply","def quantize(self, exp, rounding=None, context=None):\r\n self._real.quantize(exp, rounding, context)\r\n self._imag.quantize(exp, rounding, context)","def quantize(im_orig, n_quant, n_iter):\n img = get_gray_channel(im_orig)\n img = float2int(img)\n\n hist, bins = np.histogram(img, bins=np.arange(MAX_VALUE + 1))\n hist_times_color = hist * np.arange(MAX_VALUE)\n z = guess_first_z(n_quant, hist)\n error, q = [], []\n for i in range(0, n_iter):\n q = calculate_q(z, hist, hist_times_color)\n\n new_z = [0]\n for j in range(1, n_quant):\n new_z.append((q[j - 1] + q[j]) // 2)\n new_z.append(MAX_VALUE - 1)\n\n error.append(calculate_error(hist, new_z, q))\n\n if np.array_equal(z, new_z):\n break\n z = new_z\n\n lut = np.zeros(MAX_VALUE)\n for i in range(0, len(q)):\n lut[z[i]:z[i + 1]] = q[i]\n lut[MAX_VALUE - 1] = q[len(q) - 1]\n \n im_quant = lut[img.astype(int)]\n im_quant = int2float(im_quant)\n im_quant = update_gray_channel(im_orig, im_quant)\n \n return im_quant, error","def native_quantize_impl(\n model: ov.Model,\n calibration_dataset: Dataset,\n preset: QuantizationPreset = QuantizationPreset.PERFORMANCE,\n target_device: TargetDevice = TargetDevice.ANY,\n subset_size: int = 300,\n fast_bias_correction: bool = True,\n model_type: Optional[ModelType] = None,\n ignored_scope: Optional[IgnoredScope] = None,\n advanced_parameters: Optional[AdvancedQuantizationParameters] = None,\n) -> ov.Model:\n quantization_algorithm = PostTrainingQuantization(\n preset=preset,\n target_device=target_device,\n subset_size=subset_size,\n fast_bias_correction=fast_bias_correction,\n model_type=model_type,\n ignored_scope=ignored_scope,\n advanced_parameters=advanced_parameters,\n )\n\n graph = GraphConverter.create_nncf_graph(model)\n quantized_model = quantization_algorithm.apply(model, graph, dataset=calibration_dataset)\n\n if is_weight_compression_needed(advanced_parameters):\n compress_quantize_weights_transformation(quantized_model)\n\n dump_parameters(\n quantized_model,\n {\n \"preset\": preset.value,\n \"target_device\": target_device.value,\n \"subset_size\": subset_size,\n \"fast_bias_correction\": fast_bias_correction,\n \"model_type\": model_type,\n \"ignored_scope\": ignored_scope,\n \"advanced_parameters\": convert_to_dict_recursively(advanced_parameters),\n },\n )\n return quantized_model","def test_qc_quantize_op_gradient_computation(self):\n\n graph = tf.Graph()\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\n sess = tf.compat.v1.Session(graph=graph, config=config)\n\n with graph.as_default():\n # place holder for the input\n with tf.device(\"/device:CPU:0\"):\n inp = tf.compat.v1.placeholder(tf.float32, shape=[2, 2], name='input')\n tensor_quantizer = libpymo.TensorQuantizer(libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\n libpymo.RoundingMode.ROUND_NEAREST)\n tensor_quantizer_val = libpymo.PtrToInt64(tensor_quantizer)\n tensor_quant_ref = tf.Variable(initial_value=tensor_quantizer_val, trainable=False, dtype=tf.int64)\n\n mode_var = tf.Variable(initial_value=int(libpymo.TensorQuantizerOpMode.oneShotQuantizeDequantize),\n trainable=False, dtype=tf.int32)\n\n # fix min max and bitwidth to be used\n encoding_min = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\n encoding_max = tf.Variable(initial_value=5.0, trainable=True, dtype=tf.double)\n bit_width = tf.Variable(initial_value=8, trainable=False, dtype=tf.int8)\n use_symmetric_encoding = tf.Variable(initial_value=False, trainable=False, dtype=tf.bool)\n is_int_data_type = tf.Variable(initial_value=True, trainable=False, dtype=tf.bool)\n\n sess.run([mode_var.initializer, tensor_quant_ref.initializer, encoding_min.initializer,\n encoding_max.initializer, bit_width.initializer, use_symmetric_encoding.initializer,\n is_int_data_type.initializer])\n\n with graph.gradient_override_map(\n {\"QcQuantize\": \"QcQuantizeRangeLearningCustomGradient\"}):\n\n pass_through_op_output = zero_out_module.qc_quantize(name='quant_op', in_tensor=inp,\n op_mode=mode_var,\n tensor_quantizer_reference=tensor_quant_ref,\n encoding_min=encoding_min,\n encoding_max=encoding_max,\n bit_width=bit_width,\n use_symmetric_encoding=use_symmetric_encoding,\n is_int_data_type=is_int_data_type)\n\n pass_through_op = graph.get_operation_by_name('quant_op')\n\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\n # fixed input data used\n inp_data = [[0.4581, 0.4829], [0.3125, 0.6150]]\n\n # get the output data @todo match these\n tensor_quantizer.isEncodingValid = True\n mode_var.load(int(libpymo.TensorQuantizerOpMode.quantizeDequantize), sess)\n\n # for matching with golden output, truncate to 4\n tf_output_data = np.around(sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data}), 4)\n exp_output = [[0.4510, 0.4902], [0.3137, 0.6078]]\n\n # dummy loss function to match with Pytorch\n def custom_loss(y_actual, y_pred):\n return tf.reduce_sum(tf.subtract(y_pred, y_actual-y_actual))\n\n with graph.as_default():\n var_list = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES)\n labels_placeholder = tf.compat.v1.placeholder(tf.float32, [2, 2], name='labels')\n\n # output tensor\n logits = sess.graph.get_tensor_by_name('quant_op:0')\n\n # dummy loss function is set to sum(output)\n current_loss = custom_loss(labels_placeholder, logits)\n labels = np.ones((2), dtype=int) # np.random.randint(2, size=batches)\n one_hot_labels = np.eye(2)[labels]\n\n update_ops = []\n global_step = tf.compat.v1.train.create_global_step()\n # Stochastic GD in tf with momentum param\n optimizer = tf.compat.v1.train.MomentumOptimizer(learning_rate=0.05, momentum=0.5)\n gradients = optimizer.compute_gradients(current_loss, var_list)\n\n grad_updates = optimizer.apply_gradients(gradients, global_step=global_step)\n init_global = tf.compat.v1.global_variables_initializer()\n init_local = tf.compat.v1.local_variables_initializer()\n init = tf.group(init_global, init_local)\n sess.run(init)\n update_ops.append(grad_updates)\n update_op = tf.group(*update_ops)\n\n with tf.control_dependencies([update_op]):\n train_op = tf.identity(current_loss, name='train_op')\n\n # enable this to check current loss value used\n _ = sess.run(current_loss, feed_dict={inp_tensor: inp_data, labels_placeholder: one_hot_labels})\n # start training\n _ = sess.run(train_op, feed_dict={inp_tensor: inp_data, labels_placeholder: one_hot_labels})\n tf_enc_min_after_train = sess.run(pass_through_op.inputs[QuantizeOpIndices.encoding_min])\n tf_enc_max_after_train = sess.run(pass_through_op.inputs[QuantizeOpIndices.encoding_max])\n\n # match outputs\n assert np.allclose(exp_output, tf_output_data)\n\n # compare min and max after update with expected values (Pytorch values)\n expected_enc_min_after_train = -5.7160621508955956e-05\n expected_enc_max_after_train = 5.000057220458984\n assert np.allclose(tf_enc_min_after_train, expected_enc_min_after_train, atol=1e-6)\n assert np.allclose(tf_enc_max_after_train, expected_enc_max_after_train, atol=1e-6)\n\n sess.close()","def test_quantize_conv_transpose_u8u8(self):\n\n np.random.seed(1)\n model_fp32_path = \"conv_transpose_fp32.onnx\"\n self.construct_model(model_fp32_path)\n data_reader = self.input_feeds(1, {\"input\": [1, 1, 7, 7]})\n\n self.static_quant_test_qdq(\n model_fp32_path,\n data_reader,\n activation_type=QuantType.QUInt8,\n weight_type=QuantType.QUInt8,\n )","def quantize(self, tune_cfg, model, data_loader, q_func=None):\n assert q_func is None, \"quantization aware training has not been supported on ONNXRUNTIME\"\n model = self.pre_optimized_model if self.pre_optimized_model else model\n ort_version = StrictVersion(ort.__version__)\n if ort_version < ONNXRT152_VERSION: # pragma: no cover\n logger.warning(\"Quantize input needs onnxruntime 1.5.2 or newer.\")\n return model\n if model.model.opset_import[0].version < 11: # pragma: no cover\n logger.warning(\"Quantize input needs model opset 11 or newer.\")\n from neural_compressor.adaptor.ox_utils.onnx_quantizer import ONNXQuantizer\n from onnxruntime.quantization.quant_utils import QuantizationMode\n backend = QuantizationMode.QLinearOps if self.backend == \\\n \"qlinearops\" else QuantizationMode.IntegerOps\n\n self.quantizable_ops = self._query_quantizable_ops(model.model)\n tmp_model = copy.deepcopy(model)\n \n quantize_config = self._cfg_to_quantize_config(tune_cfg)\n iterations = tune_cfg.get('calib_iteration', 1)\n if self.static:\n quantize_params = self._get_quantize_params(tmp_model.model, data_loader, \\\n quantize_config, iterations)\n else:\n quantize_params = None\n self.quantize_params = quantize_params\n quantizer = ONNXQuantizer(tmp_model.model,\n quantize_config,\n backend,\n self.static,\n quantize_params,\n self.quantizable_op_types)\n quantizer.quantize_model()\n tmp_model.q_config = self._generate_qconfig(model.model, tune_cfg, quantize_params)\n tmp_model.model = quantizer.model.model\n self.quantize_config = quantize_config # update so other methods can know current configs\n \n self._dump_model_op_stastics(tmp_model)\n return tmp_model","def apply(self, model, layer_quantize_map, quantize_registry, mode):\n\n transforms = [\n vitis_8bit_quantize_transforms.InputLayerQuantize(\n quantize_registry.get_input_quantizer(), mode),\n vitis_8bit_quantize_transforms.ConvActivationQuantize(),\n vitis_8bit_quantize_transforms.AddActivationQuantize(),\n ]\n return model_transformer.ModelTransformer(model, transforms,\n set(layer_quantize_map.keys()),\n layer_quantize_map).transform()","def __quantize_layer_operands(self, params_type, var_type, accum_bits):\n # define how much bits should we keep unused in operands\n w_shape = self.__weights.get_shape()\n macs_per_out = int(np.prod(w_shape[1:])) + 1 if w_shape is not None else 1\n extra_bits_required = int(np.ceil(np.log2(macs_per_out)))\n acc_extra_bits = accum_bits - 1 - (np.iinfo(params_type).bits - 1 + np.iinfo(var_type).bits - 1)\n not_enough_bits = max(0, extra_bits_required - acc_extra_bits)\n weights_extra_bits = not_enough_bits // 2 if not_enough_bits != 0 else 0\n input_extra_bits = not_enough_bits - weights_extra_bits\n\n # Info preparation: Defining Qmn format of operands\n return_dict = {key: None for key in ['weights', 'bias', 'input', 'output']}\n return_dict['weights'] = {'int_bits': self.__weights.int_bits() + weights_extra_bits,\n 'frac_bits': self.__weights.frac_bits(params_type) - weights_extra_bits,\n 'min': self.__weights.min(), 'max': self.__weights.max()}\n return_dict['input'] = {'int_bits': self.input.int_bits() + input_extra_bits,\n 'frac_bits': self.input.frac_bits(var_type) - input_extra_bits,\n 'min': self.input.min(), 'max': self.input.max()}\n return_dict['bias'] = {'int_bits': self.__bias.int_bits(), 'frac_bits': self.__bias.frac_bits(params_type),\n 'min': self.__bias.min(), 'max': self.__bias.max()}\n return_dict['output'] = {'int_bits': self.output.int_bits(), 'frac_bits': self.output.frac_bits(var_type),\n 'min': self.output.min(), 'max': self.output.max()}\n\n # bias frac_bits must be less or equal to sum of weights and input frac_bits\n mul_frac_bits = return_dict['weights']['frac_bits'] + return_dict['input']['frac_bits']\n return_dict['bias']['frac_bits'] = min(mul_frac_bits, return_dict['bias']['frac_bits'])\n\n # Quantize data according to defined Qmn format\n return_dict['bias']['data'] = self.__bias.get_data_quantazed(params_type, return_dict['bias']['frac_bits'])\n return_dict['input']['data'] = self.input.get_data_quantazed(var_type, return_dict['input']['frac_bits'])\n return_dict['output']['data'] = self.output.get_data_quantazed(var_type, return_dict['output']['frac_bits'])\n return_dict['weights']['data'] = self.__weights.get_data_quantazed(params_type,\n return_dict['weights']['frac_bits'])\n return return_dict","def quant_data(self, data=None, dst=None, ndeep=None, ndigits=None, delete=True):\n # Check Whether Pngquant Exist\n if not self.file_exists(self.quant_file):\n raise ValueError(self.err_pngquant)\n\n # Check Whether Data Exist\n if not data:\n raise ValueError(self.err_data)\n\n # Save Data As TMP File\n self.save_tmp_file(data)\n\n # Calculate Length Of Data & Assign Value Ndeep As Pass or Default\n data_len, ndeep = len(data), ndeep or self.ndeep\n\n # Compress Image Using Pngquant\n # If Not Compressed\n # Then Using Pillow.Save's Optimize Option\n compressed_data, compressed_len = self.quant_compress(data, ndeep)\n if compressed_len >= data_len:\n compressed_data, compressed_len = self.pillow_compress(data, ndeep)\n\n # Copy And Delete TMP File\n if compressed_len < data_len:\n self.copy_tmp_file(dst)\n self.delete_tmp_file(delete)\n\n return (self.compression_ratio(data_len, compressed_len, ndigits), compressed_data) if compressed_len < data_len else (0, data)","def quantize_impl(\n model: ov.Model,\n calibration_dataset: Dataset,\n preset: QuantizationPreset = QuantizationPreset.PERFORMANCE,\n target_device: TargetDevice = TargetDevice.ANY,\n subset_size: int = 300,\n fast_bias_correction: bool = True,\n model_type: Optional[ModelType] = None,\n ignored_scope: Optional[IgnoredScope] = None,\n advanced_parameters: Optional[AdvancedQuantizationParameters] = None,\n) -> ov.Model:\n if should_use_pot(advanced_parameters):\n from nncf.openvino.pot.quantization.quantize_model import quantize_impl as pot_quantize_impl\n\n quantize_fn = pot_quantize_impl\n else:\n quantize_fn = native_quantize_impl\n\n return quantize_fn(\n model,\n calibration_dataset,\n preset,\n target_device,\n subset_size,\n fast_bias_correction,\n model_type,\n ignored_scope,\n advanced_parameters,\n )","def test_qc_quantize_op_cpu_fp16_quantize_dequantize(self):\n graph = tf.Graph()\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\n sess = tf.compat.v1.Session(graph=graph, config=config)\n bitwidth = 8\n use_symm_encoding = True\n\n with graph.as_default():\n # place holder for the input\n with tf.device(\"/device:CPU:0\"):\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\n tensor_quantizer = libpymo.TensorQuantizer(libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\n libpymo.RoundingMode.ROUND_NEAREST)\n tensor_quantizer_val = libpymo.PtrToInt64(tensor_quantizer)\n tensor_quant_ref = tf.Variable(initial_value=tensor_quantizer_val, trainable=False, dtype=tf.int64)\n\n encoding_min = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\n encoding_max = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\n bit_width = tf.Variable(initial_value=bitwidth, trainable=False, dtype=tf.int8)\n use_symmetric_encoding = tf.Variable(initial_value=use_symm_encoding, trainable=False, dtype=tf.bool)\n is_int_data_type = tf.Variable(initial_value=False, trainable=False, dtype=tf.bool)\n mode_var = tf.Variable(initial_value=int(libpymo.TensorQuantizerOpMode.quantizeDequantize),\n trainable=False, dtype=tf.int32)\n\n sess.run([mode_var.initializer, tensor_quant_ref.initializer, encoding_min.initializer,\n encoding_max.initializer, bit_width.initializer, use_symmetric_encoding.initializer,\n is_int_data_type.initializer])\n\n pass_through_op_output = zero_out_module.qc_quantize(name='quant_op', in_tensor=inp,\n op_mode=mode_var,\n tensor_quantizer_reference=tensor_quant_ref,\n encoding_min=encoding_min,\n encoding_max=encoding_max,\n bit_width=bit_width,\n use_symmetric_encoding=use_symmetric_encoding,\n is_int_data_type=is_int_data_type)\n\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\n\n inp_data = np.array([0.78027299, 0.44164284, 0.6942797, 0.69774088, 0.55863863, 0.29553034, 0.219199,\n 0.09483732, 0.55075674, 0.6348504], dtype=np.float32)\n\n out_exp = np.array([0.78027344, 0.4416504, 0.69433594, 0.6977539, 0.55859375, 0.29541016, 0.21923828,\n 0.09484863, 0.55078125, 0.6347656], dtype=np.float32)\n\n # get the output\n print(\"inp_data\", inp_data)\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\n print(\"out_data\", out_data)\n\n # compare qc_quantize op's output with expected output\n assert np.allclose(out_data, out_exp)\n\n sess.close()","def get_quantized_representation(\n tensor: torch.Tensor,\n quantize_params: QuantizeAffineParams2,\n) -> torch.Tensor:\n _validate_tensor(tensor)\n\n scale = quantize_params.scale\n zero_point = quantize_params.zero_point\n num_bits = quantize_params.num_bits\n if scale == 0:\n # Special case, all elements are zeros.\n if zero_point != 0:\n raise ValueError(\n \"The given QuantizeAffineParams (={}) has a non-zero zero point\"\n \" with a scale of 0.\".format(quantize_params)\n )\n quantized_tensor = torch.zeros_like(tensor)\n tag_with_metadata(quantized_tensor, quantize_params)\n return quantized_tensor\n\n qmin, qmax = get_qmin_qmax(num_bits)\n reciprocal = 1 / scale\n quantized_tensor = ((tensor * reciprocal).round_() + zero_point).clamp_(\n qmin, qmax\n )\n\n tag_with_metadata(quantized_tensor, quantize_params)\n return quantized_tensor","def quantize(\n arr: np.ndarray, quantize_params: QuantizeAffineParams2\n) -> np.ndarray:\n scale = quantize_params.scale\n zero_point = quantize_params.zero_point\n num_bits = quantize_params.num_bits\n if scale == 0:\n # Special case, all elements are zeros.\n if zero_point != 0:\n raise ValueError(\n \"The given QuantizeAffineParams (={}) has a non-zero zero point\"\n \" with a scale of 0.\".format(quantize_params)\n )\n return np.zeros_like(arr, dtype=np.int32)\n\n qmin, qmax = get_qmin_qmax(num_bits)\n reciprocal = 1 / scale\n quantized_values = (arr * reciprocal).round() + zero_point\n quantized_values = quantized_values.clip(qmin, qmax)\n return quantized_values","def test_qc_quantize_op_cpu(self):\n graph = tf.Graph()\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\n sess = tf.compat.v1.Session(graph=graph, config=config)\n bitwidth = 8\n use_symm_encoding = True\n\n with graph.as_default():\n # place holder for the input\n with tf.device(\"/device:CPU:0\"):\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\n tensor_quantizer = libpymo.TensorQuantizer(libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\n libpymo.RoundingMode.ROUND_NEAREST)\n tensor_quantizer_val = libpymo.PtrToInt64(tensor_quantizer)\n tensor_quant_ref = tf.Variable(initial_value=tensor_quantizer_val, trainable=False, dtype=tf.int64)\n\n encoding_min = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\n encoding_max = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\n bit_width = tf.Variable(initial_value=bitwidth, trainable=False, dtype=tf.int8)\n use_symmetric_encoding = tf.Variable(initial_value=use_symm_encoding, trainable=False, dtype=tf.bool)\n is_int_data_type = tf.Variable(initial_value=True, trainable=False, dtype=tf.bool)\n\n mode_var = tf.Variable(initial_value=int(libpymo.TensorQuantizerOpMode.updateStats),\n trainable=False, dtype=tf.int32)\n\n sess.run([mode_var.initializer, tensor_quant_ref.initializer, encoding_min.initializer,\n encoding_max.initializer, bit_width.initializer, use_symmetric_encoding.initializer,\n is_int_data_type.initializer])\n\n pass_through_op_output = zero_out_module.qc_quantize(name='quant_op', in_tensor=inp,\n op_mode=mode_var,\n tensor_quantizer_reference=tensor_quant_ref,\n encoding_min=encoding_min,\n encoding_max=encoding_max,\n bit_width=bit_width,\n use_symmetric_encoding=use_symmetric_encoding,\n is_int_data_type=is_int_data_type)\n\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\n inp_data = np.random.rand(10)\n\n # get the output\n print(\"inp_data\", inp_data)\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\n print(\"out_data\", out_data)\n\n # compare qc_quantize op's output with input\n assert np.allclose(out_data, inp_data)\n\n # compute encodings\n assert not tensor_quantizer.isEncodingValid\n encoding = tensor_quantizer.computeEncoding(bitwidth, use_symm_encoding)\n assert tensor_quantizer.isEncodingValid\n print('min=', encoding.min, ', max=', encoding.max)\n\n # get the output\n inp_data = np.random.rand(10) * 2\n print(inp_data)\n mode_var.load(int(libpymo.TensorQuantizerOpMode.quantizeDequantize), sess)\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\n print(out_data)\n\n # compare qc_quantize op's output with input\n assert not np.allclose(out_data, inp_data)\n sess.close()","def mulaw_quantize(x, quantization_channels=256):\n mu = quantization_channels - 1\n if isinstance(x, np.ndarray):\n x_mu = np.sign(x) * np.log1p(mu * np.abs(x)) / np.log1p(mu)\n x_mu = ((x_mu + 1) / 2 * mu + 0.5).astype(int)\n elif isinstance(x, (torch.Tensor, torch.LongTensor)):\n\n if isinstance(x, torch.LongTensor):\n x = x.float()\n mu = torch.FloatTensor([mu])\n x_mu = torch.sign(x) * torch.log1p(mu * torch.abs(x)) / torch.log1p(mu)\n x_mu = ((x_mu + 1) / 2 * mu + 0.5).long()\n return x_mu","def dequantize(\n q_arr: np.ndarray,\n quantize_params: QuantizeAffineParams2,\n) -> np.ndarray:\n zero_point = quantize_params.zero_point\n scale = quantize_params.scale\n return (q_arr - zero_point) * scale","def _quant(self, input):\n quant_input = self._input_quantizer(input)\n quant_weight = self._weight_quantizer(self.weight)\n\n return (quant_input, quant_weight)","def _quant(self, input):\n quant_input = self._input_quantizer(input)\n quant_weight = self._weight_quantizer(self.weight)\n\n return (quant_input, quant_weight)","def quantise(images, q_levels):\n return (np.digitize(images, np.arange(q_levels) / q_levels) - 1).astype('float32')","def quantise(images, q_levels):\n return (np.digitize(images, np.arange(q_levels) / q_levels) - 1).astype('float32')","def quantise(images, q_levels):\n return (np.digitize(images, np.arange(q_levels) / q_levels) - 1).astype('float32')","def is_quantized_softmax(self, call):\n if not self.call_contains_op(call, \"qnn.quantize\"):\n return False\n softmax_call = call.args[0]\n if not self.call_contains_op(softmax_call, \"nn.softmax\"):\n return False\n dequantize_call = softmax_call.args[0]\n if not self.call_contains_op(dequantize_call, \"qnn.dequantize\"):\n return False\n self.scale = dequantize_call.args[1].data.numpy().item(0)\n return True","def test_qc_quantize_op_straight_through_gradient_computation(self):\n graph = tf.Graph()\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\n sess = tf.compat.v1.Session(graph=graph, config=config)\n with graph.as_default():\n inp = tf.compat.v1.placeholder(tf.float32, shape=[2, 2], name='input')\n tensor_quantizer = libpymo.TensorQuantizer(libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\n libpymo.RoundingMode.ROUND_NEAREST)\n tensor_quantizer_val = libpymo.PtrToInt64(tensor_quantizer)\n tensor_quant_ref = tf.Variable(initial_value=tensor_quantizer_val, trainable=False, dtype=tf.int64)\n\n mode_var = tf.Variable(initial_value=int(libpymo.TensorQuantizerOpMode.oneShotQuantizeDequantize),\n trainable=False, dtype=tf.int32)\n\n # fix min max and bitwidth to be used\n encoding_min = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\n encoding_max = tf.Variable(initial_value=5.0, trainable=True, dtype=tf.double)\n bit_width = tf.Variable(initial_value=8, trainable=False, dtype=tf.int8)\n use_symmetric_encoding = tf.Variable(initial_value=False, trainable=False, dtype=tf.bool)\n is_int_data_type = tf.Variable(initial_value=True, trainable=False, dtype=tf.bool)\n\n sess.run([mode_var.initializer, tensor_quant_ref.initializer, encoding_min.initializer,\n encoding_max.initializer, bit_width.initializer, use_symmetric_encoding.initializer,\n is_int_data_type.initializer])\n\n # use default gradient\n pass_through_op_output = zero_out_module.qc_quantize(name='quant_op', in_tensor=inp,\n op_mode=mode_var,\n tensor_quantizer_reference=tensor_quant_ref,\n encoding_min=encoding_min,\n encoding_max=encoding_max,\n bit_width=bit_width,\n use_symmetric_encoding=use_symmetric_encoding,\n is_int_data_type=is_int_data_type)\n\n # pass_through_op = graph.get_operation_by_name('quant_op')\n\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\n\n # set the encodings\n tensor_quantizer.isEncodingValid = True\n mode_var.load(int(libpymo.TensorQuantizerOpMode.quantizeDequantize), sess)\n\n # compute default gradient\n grads = tf.gradients(pass_through_op_output, [inp_tensor])\n dlossbydx = grads\n\n # send input, note the last value sent here is > 5.0 ,\n # we set encodings earlier to be min = 0.0 , max = 5.0\n # input has data > p\n inp_data = [[1.4581, 0.4829], [0.3125, 5.6150]]\n # check the gradient returned is a gated version, in this case should be [[1.0, 1.0],[1.0, 0.0]]\n with graph.as_default():\n input_gradient = sess.run([dlossbydx], feed_dict={inp_tensor: inp_data})[0]\n\n # validate valid clamping in gradient computation\n assert input_gradient[0][0][0] == 1.0\n assert input_gradient[0][0][1] == 1.0\n assert input_gradient[0][1][0] == 1.0\n assert input_gradient[0][1][1] == 0.0\n\n # pass input in correct range\n inp_data = [[1.4581, 0.4829], [0.3125, 1.6150]]\n # check the gradient returned is a gated version, in this case should be [[1.0, 1.0],[1.0, 0.0]]\n with graph.as_default():\n input_gradient = sess.run([dlossbydx], feed_dict={inp_tensor: inp_data})[0]\n\n # validate no clamping case in gradient computation\n assert input_gradient[0][0][0] == 1.0\n assert input_gradient[0][0][1] == 1.0\n assert input_gradient[0][1][0] == 1.0\n assert input_gradient[0][1][1] == 1.0\n\n # pass input with data < n , first value here is -0.5\n inp_data = [[-0.5, 0.4829], [0.3125, 1.6150]]\n # check the gradient returned is a gated version, in this case should be [[1.0, 1.0],[1.0, 0.0]]\n with graph.as_default():\n input_gradient = sess.run([dlossbydx], feed_dict={inp_tensor: inp_data})[0]\n\n # validate valid clamping case in gradient computation\n assert input_gradient[0][0][0] == 0.0\n assert input_gradient[0][0][1] == 1.0\n assert input_gradient[0][1][0] == 1.0\n assert input_gradient[0][1][1] == 1.0","def test_qc_quantize_op_gpu_fp16(self):\n graph = tf.Graph()\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\n sess = tf.compat.v1.Session(graph=graph, config=config)\n bitwidth = 8\n use_symm_encoding = False\n with graph.as_default():\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\n tensor_quantizer = libpymo.TensorQuantizer(libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\n libpymo.RoundingMode.ROUND_NEAREST)\n tensor_quantizer_val = libpymo.PtrToInt64(tensor_quantizer)\n tensor_quant_ref = tf.Variable(initial_value=tensor_quantizer_val, trainable=False, dtype=tf.int64)\n\n mode_var = tf.Variable(initial_value=int(libpymo.TensorQuantizerOpMode.quantizeDequantize),\n trainable=False, dtype=tf.int32)\n\n encoding_min = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\n encoding_max = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\n bit_width = tf.Variable(initial_value=bitwidth, trainable=False, dtype=tf.int8)\n use_symmetric_encoding = tf.Variable(initial_value=use_symm_encoding, trainable=False, dtype=tf.bool)\n is_int_data_type = tf.Variable(initial_value=False, trainable=False, dtype=tf.bool)\n\n sess.run([mode_var.initializer, tensor_quant_ref.initializer, encoding_min.initializer,\n encoding_max.initializer, bit_width.initializer, use_symmetric_encoding.initializer,\n is_int_data_type.initializer])\n\n # place holder for the input\n with tf.device(\"/device:GPU:0\"):\n pass_through_op_output = zero_out_module.qc_quantize(name='quant_op', in_tensor=inp,\n op_mode=mode_var,\n tensor_quantizer_reference=tensor_quant_ref,\n encoding_min=encoding_min,\n encoding_max=encoding_max,\n bit_width=bit_width,\n use_symmetric_encoding=use_symmetric_encoding,\n is_int_data_type=is_int_data_type)\n\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\n inp_data = np.array([0.78027299, 0.44164284, 0.6942797, 0.69774088, 0.55863863, 0.29553034, 0.219199,\n 0.09483732, 0.55075674, 0.6348504], dtype=np.float32)\n\n out_exp = np.array([0.78027344, 0.4416504, 0.69433594, 0.6977539, 0.55859375, 0.29541016, 0.21923828,\n 0.09484863, 0.55078125, 0.6347656], dtype=np.float32)\n\n print(\"inp_data\", inp_data)\n with tf.device(\"/device:GPU:0\"):\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\n print(\"out_data\", out_data)\n assert np.allclose(out_data, out_exp)\n sess.close()","def forward(self, input, quant):\n self.save_for_backward(input)\n c = (input.clamp(min=-1, max =1)+1)/2.0 * quant\n c = 2 * (c.round()/quant) - 1\n return c","def dequantize(input=None, min_range=None, max_range=None, out_type=_Null, out=None, name=None, **kwargs):\n return (0,)","def on_compression_begin(self, context):\n\n super(MKLDNNPostTrainingQuantStrategy,\n self).on_compression_begin(context)\n _logger.info('InferQuantStrategy::on_compression_begin')\n\n # Prepare the Analysis Config\n infer_config = core.AnalysisConfig(\"AnalysisConfig\")\n infer_config.switch_ir_optim(True)\n infer_config.disable_gpu()\n infer_config.set_model(self.fp32_model_path)\n infer_config.enable_mkldnn()\n infer_config.set_cpu_math_library_num_threads(\n self.cpu_math_library_num_threads)\n\n # Prepare the data for calculating the quantization scales\n warmup_reader = context.eval_reader()\n if six.PY2:\n data = warmup_reader.next()\n\n if six.PY3:\n data = warmup_reader.__next__()\n\n num_images = len(data)\n image_data = [img.tolist() for (img, _) in data]\n image_data = np.array(image_data).astype(\"float32\").reshape(\n [num_images, ] + list(data[0][0].shape))\n image_data = image_data.ravel()\n images = core.PaddleTensor(image_data, \"x\")\n images.shape = [num_images, ] + list(data[0][0].shape)\n\n label_data = [label for (_, label) in data]\n labels = core.PaddleTensor(\n np.array(label_data).astype(\"int64\").reshape([num_images, 1]), \"y\")\n\n warmup_data = [images, labels]\n\n # Enable the INT8 Quantization\n infer_config.enable_quantizer()\n infer_config.quantizer_config().set_quant_data(warmup_data)\n infer_config.quantizer_config().set_quant_batch_size(num_images)\n\n # Run INT8 MKL-DNN Quantization\n predictor = core.create_paddle_predictor(infer_config)\n if self.int8_model_save_path:\n if not os.path.exists(self.int8_model_save_path):\n os.makedirs(self.int8_model_save_path)\n predictor.SaveOptimModel(self.int8_model_save_path)\n\n _logger.info(\n 'Finish MKLDNNPostTrainingQuantStrategy::on_compresseion_begin')","def QuantizeEmulate(to_quantize,\n num_bits,\n narrow_range=True,\n symmetric=True,\n **kwargs):\n\n def _QuantizeList(layers, **params):\n \"\"\"Apply QuantizeEmulate wrapper to a list of layers.\n\n Args:\n layers: List of keras layers to apply QuantizeEmulate.\n **params: QuantizationParams for the entire list.\n\n Returns:\n List of layers wrapped with QuantizeEmulate.\n \"\"\"\n wrapped_layers = []\n\n for layer in layers:\n # Already quantized. Simply use and return. This supports usage such as\n # model = QuantizeEmulate([\n # Dense(),\n # QuantizeEmulate(Dense(), layer_params)\n # Dense()\n # ], model_params)\n if isinstance(layer, QuantizeEmulateWrapper):\n wrapped_layers.append(layer)\n continue\n\n wrapped_layers.append(QuantizeEmulate(layer, **params))\n\n return wrapped_layers\n\n params = {\n 'num_bits': num_bits,\n 'narrow_range': narrow_range,\n 'symmetric': symmetric\n }\n params.update(kwargs)\n\n if isinstance(to_quantize, list):\n return _QuantizeList(to_quantize, **params)\n elif isinstance(to_quantize, keras.Sequential):\n return keras.models.Sequential(_QuantizeList(to_quantize.layers, **params))\n elif isinstance(to_quantize, keras.layers.Layer):\n return QuantizeEmulateWrapper(to_quantize, **params)","def inverse_scaled_quantization(x, scale):\n return x / tf.cast(scale, x.dtype)","def quantize_scope(*args):\n quantization_objects = {\n 'QuantizeAwareActivation':\n vitis_quantize_aware_activation.QuantizeAwareActivation,\n 'NoQuantizeActivation':\n vitis_quantize_aware_activation.NoQuantizeActivation,\n 'QuantizeWrapper':\n vitis_quantize_wrapper.QuantizeWrapper,\n 'CustomOpWrapper':\n vitis_custom_wrapper.CustomOpWrapper,\n }\n quantization_objects.update(vitis_quantizers._types_dict())\n quantization_objects.update(vitis_quantize_configs._types_dict())\n quantization_objects.update(vitis_quantize_layer._types_dict())\n quantization_objects.update(vitis_activation._types_dict())\n quantization_objects.update(vitis_pooling._types_dict())\n\n return tf.keras.utils.custom_object_scope(*(args + (quantization_objects,)))","def quantization_helper(im, n_quant, n_iter):\n im *= (255 / im.max())\n hist, bins = np.histogram(im, bins=256, range=[0, 256])\n cumulative_hist = np.cumsum(hist)\n # initial division such that each segment will contain approximately the same number of pixels.\n num_of_pixels = cumulative_hist.max() / n_quant\n z = np.zeros(shape=n_quant + 1, dtype='int')\n for i in range(0, len(z) - 1):\n z[i] = np.argmin(np.absolute(cumulative_hist - num_of_pixels * (i)))\n\n z[len(z) - 1] = 255 # The first and last elements are 0 and 255 respectively.\n q = np.zeros(shape=n_quant, dtype='float64')\n\n z, q, error = perform_quantization_loop(z, q, n_iter, hist, bins)\n lookup_table = np.array([0]*256,dtype='float64')\n\n for i in range(n_quant):\n lookup_table[z[i]:z[i+1]] = q[i]\n\n im_quant = lookup_table[im.astype(int)]\n return im_quant, error","def test_qc_quantize_op_cpu_fp16_pass_through(self):\n graph = tf.Graph()\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\n sess = tf.compat.v1.Session(graph=graph, config=config)\n bitwidth = 8\n use_symm_encoding = True\n\n with graph.as_default():\n # place holder for the input\n with tf.device(\"/device:CPU:0\"):\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\n tensor_quantizer = libpymo.TensorQuantizer(libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\n libpymo.RoundingMode.ROUND_NEAREST)\n tensor_quantizer_val = libpymo.PtrToInt64(tensor_quantizer)\n tensor_quant_ref = tf.Variable(initial_value=tensor_quantizer_val, trainable=False, dtype=tf.int64)\n\n encoding_min = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\n encoding_max = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\n bit_width = tf.Variable(initial_value=bitwidth, trainable=False, dtype=tf.int8)\n use_symmetric_encoding = tf.Variable(initial_value=use_symm_encoding, trainable=False, dtype=tf.bool)\n is_int_data_type = tf.Variable(initial_value=False, trainable=False, dtype=tf.bool)\n mode_var = tf.Variable(initial_value=int(libpymo.TensorQuantizerOpMode.passThrough),\n trainable=False, dtype=tf.int32)\n\n sess.run([mode_var.initializer, tensor_quant_ref.initializer, encoding_min.initializer,\n encoding_max.initializer, bit_width.initializer, use_symmetric_encoding.initializer,\n is_int_data_type.initializer])\n\n pass_through_op_output = zero_out_module.qc_quantize(name='quant_op', in_tensor=inp,\n op_mode=mode_var,\n tensor_quantizer_reference=tensor_quant_ref,\n encoding_min=encoding_min,\n encoding_max=encoding_max,\n bit_width=bit_width,\n use_symmetric_encoding=use_symmetric_encoding,\n is_int_data_type=is_int_data_type)\n\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\n\n inp_data = np.array([0.78027299, 0.44164284, 0.6942797, 0.69774088, 0.55863863, 0.29553034, 0.219199,\n 0.09483732, 0.55075674, 0.6348504], dtype=np.float32)\n\n # get the output\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\n\n # compare qc_quantize op's output with expected output\n assert np.allclose(out_data, inp_data)\n\n sess.close()","def quantizer(self, nbits: int):\n raise NotImplementedError","def quantize_affine_given_quant_params(\n input: torch.Tensor,\n quantize_params: QuantizeAffineParams2,\n) -> torch.Tensor:\n return QuantizeAffineFunction.apply(input, quantize_params)","def test_quantize_conv_transpose_s8s8(self):\n\n np.random.seed(1)\n model_fp32_path = \"conv_transpose_fp32.onnx\"\n self.construct_model(model_fp32_path)\n data_reader = self.input_feeds(1, {\"input\": [1, 1, 7, 7]})\n\n self.static_quant_test_qdq(\n model_fp32_path,\n data_reader,\n activation_type=QuantType.QInt8,\n weight_type=QuantType.QInt8,\n extra_options={\"ActivationSymmetric\": True},\n )","def Dequantize(scale, mantissa, nScaleBits=3, nMantBits=5):\n\n aNum = 0.0 # REMOVE THIS LINE WHEN YOUR FUNCTION IS DONE\n\n ### YOUR CODE STARTS HERE ###\n R = 2**nScaleBits - 1 + nMantBits\n aNum = 0\n s = mantissa & (1 << (nMantBits - 1))\n if s > 0:\n s = 1 << (R - 1)\n code = mantissa & (2**(nMantBits - 1) - 1)\n aNum += s\n aNum += code << max(R - scale - nMantBits, 0)\n\n if scale < (2**nScaleBits - 1):\n if code > 0:\n shift = R - scale - nMantBits - 1\n aNum += 1 << shift\n aNum = DequantizeUniform(aNum, R)\n ### YOUR CODE ENDS HERE ###\n\n return aNum","def test_quantize_training():\n config = EasyDict()\n\n config.NETWORK_CLASS = FlowNetSV3Quantized\n config.DATASET_CLASS = FlyingChairs\n\n config.IS_DEBUG = False\n config.IMAGE_SIZE = [384, 512]\n config.BATCH_SIZE = 8\n config.TEST_STEPS = 200\n config.MAX_STEPS = 5000\n config.SAVE_CHECKPOINT_STEPS = 100\n config.KEEP_CHECKPOINT_MAX = 5\n config.SUMMARISE_STEPS = 100\n config.IS_PRETRAIN = False\n config.IS_DISTRIBUTION = False\n config.TASK = Tasks.OPTICAL_FLOW_ESTIMATION\n\n # network model config\n config.NETWORK = EasyDict()\n config.NETWORK.OPTIMIZER_CLASS = tf.train.AdamOptimizer\n config.NETWORK.OPTIMIZER_KWARGS = {\"beta1\": 0.9, \"beta2\": 0.999}\n config.NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant\n config.NETWORK.LEARNING_RATE_KWARGS = {\n \"values\": [0.0000125, 0.00005],\n \"boundaries\": [5000]\n }\n\n config.NETWORK.IMAGE_SIZE = config.IMAGE_SIZE\n config.NETWORK.BATCH_SIZE = config.BATCH_SIZE\n config.NETWORK.DATA_FORMAT = \"NHWC\"\n config.NETWORK.ACTIVATION_QUANTIZER = linear_mid_tread_half_quantizer\n config.NETWORK.ACTIVATION_QUANTIZER_KWARGS = {\n 'bit': 2,\n 'max_value': 2.0\n }\n config.NETWORK.WEIGHT_QUANTIZER = binary_channel_wise_mean_scaling_quantizer\n config.NETWORK.WEIGHT_QUANTIZER_KWARGS = {}\n\n # dataset config\n config.DATASET = EasyDict()\n config.DATASET.PRE_PROCESSOR = None\n config.DATASET.BATCH_SIZE = config.BATCH_SIZE\n config.DATASET.DATA_FORMAT = \"NHWC\"\n config.DATASET.VALIDATION_RATE = 0.2\n config.DATASET.VALIDATION_SEED = 2019\n config.DATASET.AUGMENTOR = Sequence([\n # Geometric transformation\n FlipLeftRight(0.5),\n FlipTopBottom(0.5),\n Translate(-0.2, 0.2),\n Rotate(-17, +17),\n Scale(1.0, 2.0),\n # Pixel-wise augmentation\n Brightness(0.8, 1.2),\n Contrast(0.2, 1.4),\n Color(0.5, 2.0),\n Gamma(0.7, 1.5),\n # Hue(-128.0, 128.0),\n GaussianNoise(0.0, 10.0)\n ])\n config.DATASET.PRE_PROCESSOR = Sequence([\n DevideBy255(),\n ])\n environment.init(\"test_flownet_s_v3_quantize\")\n prepare_dirs(recreate=True)\n start_training(config)","def test_qc_quantize_op_gpu(self):\n graph = tf.Graph()\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\n sess = tf.compat.v1.Session(graph=graph, config=config)\n bitwidth = 8\n use_symm_encoding = False\n with graph.as_default():\n\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\n tensor_quantizer = libpymo.TensorQuantizer(libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\n libpymo.RoundingMode.ROUND_NEAREST)\n tensor_quantizer_val = libpymo.PtrToInt64(tensor_quantizer)\n tensor_quant_ref = tf.Variable(initial_value=tensor_quantizer_val, trainable=False, dtype=tf.int64)\n\n mode_var = tf.Variable(initial_value=int(libpymo.TensorQuantizerOpMode.updateStats),\n trainable=False, dtype=tf.int32)\n\n encoding_min = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\n encoding_max = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\n bit_width = tf.Variable(initial_value=bitwidth, trainable=False, dtype=tf.int8)\n use_symmetric_encoding = tf.Variable(initial_value=use_symm_encoding, trainable=False, dtype=tf.bool)\n is_int_data_type = tf.Variable(initial_value=True, trainable=False, dtype=tf.bool)\n\n sess.run([mode_var.initializer, tensor_quant_ref.initializer, encoding_min.initializer,\n encoding_max.initializer, bit_width.initializer, use_symmetric_encoding.initializer,\n is_int_data_type.initializer])\n\n # place holder for the input\n with tf.device(\"/device:GPU:0\"):\n\n pass_through_op_output = zero_out_module.qc_quantize(name='quant_op', in_tensor=inp,\n op_mode=mode_var,\n tensor_quantizer_reference=tensor_quant_ref,\n encoding_min=encoding_min,\n encoding_max=encoding_max,\n bit_width=bit_width,\n use_symmetric_encoding=use_symmetric_encoding,\n is_int_data_type=is_int_data_type)\n\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\n inp_data = np.random.rand(10)\n\n # get the output\n\n print(\"inp_data\", inp_data)\n with tf.device(\"/device:GPU:0\"):\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\n print(\"out_data\", out_data)\n\n # compare qc_quantize op's output with input\n assert np.allclose(out_data, inp_data)\n\n # compute encodings\n assert not tensor_quantizer.isEncodingValid\n encoding = tensor_quantizer.computeEncoding(bitwidth, use_symm_encoding)\n assert tensor_quantizer.isEncodingValid\n print('min=', encoding.min, ', max=', encoding.max)\n\n # get the output\n inp_data = np.random.rand(10) * 2\n print(\"inp_data\", inp_data)\n mode_var.load(int(libpymo.TensorQuantizerOpMode.quantizeDequantize), sess)\n with tf.device(\"/device:GPU:0\"):\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\n print(\"out_data\", out_data)\n\n # compare qc_quantize op's output with input\n assert not np.allclose(out_data, inp_data)\n\n sess.close()","def _quant_unimplemented(self, *input: Any) -> None:\n raise NotImplementedError","def update_params(self):\n for layer in range(self.num_layers):\n for direction in range(self.num_directions):\n for param_name in self._get_param_names(direction, layer):\n param = getattr(self.module_to_quantize, param_name)\n param.data = getattr(self, param_name).data\n\n _logger.debug('Updated params for QcQuantizeRNN')","def test_qc_quantize_op_oneshot_cpu(self):\n graph = tf.Graph()\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\n sess = tf.compat.v1.Session(graph=graph, config=config)\n bitwidth = 8\n use_symm_encoding = False\n with graph.as_default():\n # place holder for the input\n with tf.device(\"/device:CPU:0\"):\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\n tensor_quantizer = libpymo.TensorQuantizer(libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\n libpymo.RoundingMode.ROUND_NEAREST)\n tensor_quantizer_val = libpymo.PtrToInt64(tensor_quantizer)\n tensor_quant_ref = tf.Variable(initial_value=tensor_quantizer_val, trainable=False, dtype=tf.int64)\n\n mode_var = tf.Variable(initial_value=int(libpymo.TensorQuantizerOpMode.oneShotQuantizeDequantize),\n trainable=False, dtype=tf.int32)\n\n encoding_min = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\n encoding_max = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\n bit_width = tf.Variable(initial_value=bitwidth, trainable=False, dtype=tf.int8)\n use_symmetric_encoding = tf.Variable(initial_value=use_symm_encoding, trainable=False, dtype=tf.bool)\n is_int_data_type = tf.Variable(initial_value=True, trainable=False, dtype=tf.bool)\n\n sess.run([mode_var.initializer, tensor_quant_ref.initializer, encoding_min.initializer,\n encoding_max.initializer, bit_width.initializer, use_symmetric_encoding.initializer,\n is_int_data_type.initializer])\n\n pass_through_op_output = zero_out_module.qc_quantize(name='quant_op', in_tensor=inp,\n op_mode=mode_var,\n tensor_quantizer_reference=tensor_quant_ref,\n encoding_min=encoding_min,\n encoding_max=encoding_max,\n bit_width=bit_width,\n use_symmetric_encoding=use_symmetric_encoding,\n is_int_data_type=is_int_data_type)\n\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\n inp_data = np.random.rand(10) * 256\n\n # get the output\n print(inp_data)\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\n print(out_data)\n\n assert tensor_quantizer.isEncodingValid\n encoding = tensor_quantizer.computeEncoding(bitwidth, use_symm_encoding)\n\n print('min=', encoding.min, ', max=', encoding.max)\n\n # compare qc_quantize op's output with input\n assert not np.allclose(out_data, inp_data)\n\n sess.close()","def __float__(self):\n return float(self.encoded) / (1 << self.frac_bits)","def normalization_factor(self) -> _VectorisedFloat:\n raise NotImplementedError(\"Subclass must implement\")","def _scalar_conversion(self, p_input:float) -> float:\n return (p_input/self.units[self._unit_in]*self.units[self._unit_out])","def quantifyValues( values, scale):\r\n\r\n\tfor v in range(len(values)):\r\n\t\tvalues[v] = st.quantify( values[v], scale)\r\n\treturn values","def _quantize_hidden_cell_state(self, layer_index: int, cell_hx: Union[torch.Tensor, Tuple[torch.Tensor]]) -> \\\n Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:\n if isinstance(cell_hx, tuple):\n quantized_cell_hx = (self._quantize_activation(self._output_quantizers['h_l{}'.format(layer_index)], cell_hx[0]),\n self._quantize_activation(self._output_quantizers['c_l{}'.format(layer_index)], cell_hx[1]))\n else:\n quantized_cell_hx = self._quantize_activation(self._output_quantizers['h_l{}'.format(layer_index)], cell_hx)\n return quantized_cell_hx","def quantizer(iterable):\n return (int(32767.0 * sample) for sample in iterable)","def quantize(input=None, min_range=None, max_range=None, out_type=_Null, out=None, name=None, **kwargs):\n return (0,)","def quantify ( value, scale ):\r\n\r\n\r\n\tif value < scale[0] or value > scale[-1]: return value\r\n\tfor i in range (1, len(scale)):\r\n\t\tif value <= scale[i]:\r\n\t\t\tif scale[i] - value > value - scale[i-1]: return scale[i-1]\r\n\t\t\telse: return scale[i]","def test_qc_quantize_static_op_cpu(self):\n graph = tf.Graph()\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\n sess = tf.compat.v1.Session(graph=graph, config=config)\n\n with graph.as_default():\n # place holder for the input\n with tf.device(\"/device:CPU:0\"):\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\n\n pass_through_op_output = zero_out_module.qc_quantize_static(name='quant_op', in_tensor=inp,\n encoding_min=-1.0,\n encoding_max=1.0,\n bitwidth=8,\n quant_scheme=libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\n op_mode=libpymo.TensorQuantizerOpMode.passThrough,\n is_symmetric=False)\n\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\n inp_data = np.random.rand(10).astype(np.float32)\n\n # get the output\n print(inp_data)\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\n print(out_data)\n\n # compare qc_quantize op's output with input\n assert np.allclose(out_data, inp_data, atol=1e-6)\n sess.close()\n\n graph = tf.Graph()\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\n sess = tf.compat.v1.Session(graph=graph, config=config)\n\n with graph.as_default():\n # place holder for the input\n with tf.device(\"/device:CPU:0\"):\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\n\n pass_through_op_output = zero_out_module.qc_quantize_static(name='quant_op', in_tensor=inp,\n encoding_min=-1.0,\n encoding_max=0.5,\n bitwidth=8,\n quant_scheme=libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\n op_mode=libpymo.TensorQuantizerOpMode.quantizeDequantize,\n is_symmetric=False)\n\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\n inp_data = np.random.rand(10).astype(np.float32)\n\n # get the output\n print(inp_data)\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\n print(out_data)\n\n # compare qc_quantize op's output with input\n assert not np.allclose(out_data, inp_data, atol=1e-1)\n sess.close()\n\n graph = tf.Graph()\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\n sess = tf.compat.v1.Session(graph=graph, config=config)\n\n with graph.as_default():\n # place holder for the input\n with tf.device(\"/device:CPU:0\"):\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\n\n pass_through_op_output = zero_out_module.qc_quantize_static(name='quant_op', in_tensor=inp,\n encoding_min=-1.0,\n encoding_max=1.0,\n bitwidth=8,\n quant_scheme=libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\n op_mode=libpymo.TensorQuantizerOpMode.oneShotQuantizeDequantize,\n is_symmetric=False)\n\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\n inp_data = np.random.rand(10).astype(np.float32)\n\n # get the output\n print(inp_data)\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\n print(out_data)\n\n # compare qc_quantize op's output with input\n assert not np.allclose(out_data, inp_data, atol=1e-3)\n\n sess.close()","def scale(self):","def quantization_error(self, in_tensor, bits=32):\n raise NotImplementedError('To be Overidden for derived class')","def graph_mode_quantize(self, inputs, data_loader, calibration_num_batches=64):\n if (\n isinstance(self.right_encoder, RoBERTaEncoder)\n and self.right_encoder.use_linformer_encoder\n and isinstance(self.left_encoder, RoBERTaEncoder)\n and self.left_encoder.use_linformer_encoder\n ):\n trace = self.trace(inputs)\n qconfig = get_default_qconfig(\"fbgemm\")\n qconfig_dict = {\"\": qconfig}\n prepare_m = prepare_jit(trace, qconfig_dict, inplace=False)\n prepare_m.eval()\n with torch.no_grad():\n for i, (_, batch) in enumerate(data_loader):\n print(\"Running calibration with batch {}\".format(i))\n input_data = self.onnx_trace_input(batch)\n prepare_m(*input_data)\n if i == calibration_num_batches - 1:\n break\n trace = convert_jit(prepare_m, inplace=True)\n else:\n super().quantize()\n trace = self.trace(inputs)\n\n return trace","def __call__(self, tensor, mode=0):\n\n return tensor\n data_cuda_array = cuda.as_cuda_array(tensor.data.detach())\n data_p = data_cuda_array.device_ctypes_pointer\n self._param.mode = mode\n ret = self._libquant.HI_GFPQ_QuantAndDeQuant_GPU_PY(\n data_p, data_cuda_array.size, self._bit_width,\n ctypes.byref(self._param), self._stream.handle,\n self._cublas_handle)\n assert ret == 0, \"HI_GFPQ_QuantAndDeQuant failed(%d)\\n\" % (ret)\n return tensor","def increment_quality(self, increment_unit):\n if self.quality > self.min_quality and self.quality < self.max_quality:\n self.quality = self.quality + increment_unit\n return self.quality","def compress(self, tensor, *args, **kwargs):\n pass","def quantize_whole_model(net, bits=8):\n cluster_centers = []\n assert isinstance(net, nn.Module)\n layer_ind = 0\n for n, m in net.named_modules():\n if isinstance(m, PrunedConv):\n \"\"\"\n Apply quantization for the PrunedConv layer.\n --------------Your Code---------------------\n \"\"\"\n\n # Cluster the Weights\n num_centroid = pow(2, bits)\n\n all_weight = m.conv.weight.data.cpu().detach().numpy()\n weight_shape = all_weight.shape\n \n all_weight = all_weight.reshape(-1,1)\n k_init = np.linspace(all_weight.min(), all_weight.max(), num_centroid)\n\n kmeans = KMeans(n_clusters=num_centroid, init=k_init.reshape(-1, 1), n_init=1).fit(all_weight)\n\n # Generate Code Book\n cluster_center = kmeans.cluster_centers_.flatten()\n\n # Quantize\n indexs = kmeans.predict(all_weight)\n indexs = indexs.reshape(weight_shape)\n \n vmap = np.vectorize(lambda x:cluster_center[x])\n m.conv.weight.data = torch.from_numpy(vmap(indexs)).to(device)\n \n _cluster_center = [ \"{0:b}\".format(x).zfill(bits) for x in range(len(cluster_center)) ]\n cluster_centers.append(_cluster_center)\n\n layer_ind += 1\n print(\"Complete %d layers quantization...\" %layer_ind)\n \n elif isinstance(m, PruneLinear):\n \"\"\"\n Apply quantization for the PrunedLinear layer.\n --------------Your Code---------------------\n \"\"\"\n \n # Cluster the Weights\n num_centroid = pow(2, bits)\n \n all_weight = m.linear.weight.data.cpu().detach().numpy()\n weight_shape = all_weight.shape\n\n all_weight = all_weight.reshape(-1,1)\n k_init = np.linspace(all_weight.min(), all_weight.max(), num_centroid)\n \n kmeans = KMeans(n_clusters=num_centroid, init=k_init.reshape(-1, 1), n_init=1).fit(all_weight)\n \n # Generate Code Book\n cluster_center = kmeans.cluster_centers_.flatten()\n\n # Quantize\n indexs = kmeans.predict(all_weight)\n indexs = indexs.reshape(weight_shape)\n\n vmap = np.vectorize(lambda x:cluster_center[x])\n m.linear.weight.data = torch.from_numpy(vmap(indexs)).to(device)\n \n _cluster_center = [ \"{0:b}\".format(x).zfill(bits) for x in range(len(cluster_center)) ]\n cluster_centers.append(_cluster_center)\n \n layer_ind += 1\n print(\"Complete %d layers quantization...\" %layer_ind)\n \n return np.array(cluster_centers)","def set_quantize_strategy(self,\n new_quantize_strategy='quantize_strategy.json'):\n if self._model_format == 'pb':\n logger.warning('This function does not support pb format model.')\n return None\n\n if isinstance(new_quantize_strategy, str):\n new_quantize_strategy = common_utils.load_json(new_quantize_strategy)\n elif not isinstance(new_quantize_strategy, dict):\n logger.error(\n 'new_quantize_strategy should be filepath or dict, but found {}'\n .format(type(new_quantize_strategy)))\n\n self._quantize_strategy.update(new_quantize_strategy)","def DequantizeFP(scale, mantissa, nScaleBits=3, nMantBits=5):\n\n ### YOUR CODE STARTS HERE ###\n R = 2**nScaleBits - 1 + nMantBits\n aNum = 0\n s = mantissa & (1 << (nMantBits - 1))\n if s > 0:\n s = 1 << (R - 1)\n code = mantissa & (2**(nMantBits - 1) - 1)\n aNum += s\n aNum += code << max(R - scale - nMantBits - 1, 0)\n if scale != (2**nScaleBits - 1):\n aNum += 1 << (R - scale - 2)\n shift = R - scale - nMantBits - 2\n if shift > 0:\n aNum += 1 << shift\n aNum = DequantizeUniform(aNum, R)\n ### YOUR CODE ENDS HERE ###\n\n return aNum","def quantify(self, samples=None):\n from scipy import stats\n\n if samples:\n ratios = [q['ratio'] for s,q in self.quantification.items() if s in samples]\n else:\n ratios = [q['ratio'] for q in self.quantification.values()]\n\n # Filter out 0 and NaN\n ratios = np.array([r for r in ratios if r != 0])\n ratios = ratios[~np.isnan(ratios)]\n\n log_ratios = np.log(ratios)\n\n t, p = stats.ttest_1samp(log_ratios, 0)\n\n self.avg_ratio = np.mean(ratios)\n self.p_value = p","def sinkhorn(self, Q: torch.Tensor, nmb_iters: int) ->torch.Tensor:\n with torch.no_grad():\n sum_Q = torch.sum(Q)\n Q /= sum_Q\n K, B = Q.shape\n if self.gpus > 0:\n u = torch.zeros(K)\n r = torch.ones(K) / K\n c = torch.ones(B) / B\n else:\n u = torch.zeros(K)\n r = torch.ones(K) / K\n c = torch.ones(B) / B\n for _ in range(nmb_iters):\n u = torch.sum(Q, dim=1)\n Q *= (r / u).unsqueeze(1)\n Q *= (c / torch.sum(Q, dim=0)).unsqueeze(0)\n return (Q / torch.sum(Q, dim=0, keepdim=True)).t().float()","def encode(self, x):\n self.eval()\n x = torch.as_tensor(x).unsqueeze(0)\n if self.do_mt:\n enc_output, _ = self.encoder_mt(x, None)\n else:\n enc_output, _ = self.encoder(x, None)\n return enc_output.squeeze(0)","def quantize_affine(\n input: torch.Tensor,\n min_value: Optional[numbers.Real] = None,\n max_value: Optional[numbers.Real] = None,\n num_bits: int = None,\n) -> torch.Tensor:\n if num_bits is None:\n raise ValueError(\"num_bits must be supplied\")\n\n if min_value is None:\n # Force include 0 in our calculation of min_value.\n min_value = min(input.min().item(), 0.0)\n if max_value is None:\n # Force include 0 in our calculation of max_value.\n max_value = max(input.max().item(), 0.0)\n\n quantize_params = get_quantization_params(min_value, max_value, num_bits)\n return QuantizeAffineFunction.apply(input, quantize_params)","def _create_qat_model(self, dataset):\n if not self._optimized_model:\n logger.error('Should call `optimize_model()` before `_create_qat_model`.')\n self._qat_model, self._layer_metadata = self._quantizer.create_quantize_model(\n self._optimized_model,\n candidate_layers=self._candidate_layers,\n layer_metadata=self._layer_metadata,\n quantize_strategy=self._quantize_strategy,\n mode='QAT',\n target=self._target,\n dataset=dataset)","def quantile_concrete(self, u):\n y = torch.sigmoid((torch.log(u) - torch.log(1.0 - u) + self.qz_log_alpha) / self.beta)\n return y * (self.zeta - self.gamma) + self.gamma","def reduce_unit(self, guide_unit=None):\n key = (self.unit, guide_unit)\n if key in Quantity._reduce_cache:\n (unit, value_factor) = Quantity._reduce_cache[key]\n else:\n value_factor = 1.0\n canonical_units = {} # dict of dimensionTuple: (Base/ScaledUnit, exponent)\n # Bias result toward guide units\n if guide_unit != None:\n for u, exponent in guide_unit.iter_base_or_scaled_units():\n d = u.get_dimension_tuple()\n if d not in canonical_units:\n canonical_units[d] = [u, 0]\n for u, exponent in self.unit.iter_base_or_scaled_units():\n d = u.get_dimension_tuple()\n # Take first unit found in a dimension as canonical\n if d not in canonical_units:\n canonical_units[d] = [u, exponent]\n else:\n value_factor *= (u.conversion_factor_to(canonical_units[d][0])**exponent)\n canonical_units[d][1] += exponent\n new_base_units = {}\n for d in canonical_units:\n u, exponent = canonical_units[d]\n if exponent != 0:\n assert u not in new_base_units\n new_base_units[u] = exponent\n # Create new unit\n if len(new_base_units) == 0:\n unit = dimensionless\n else:\n unit = Unit(new_base_units)\n # There might be a factor due to unit conversion, even though unit is dimensionless\n # e.g. suppose unit is meter/centimeter\n if unit.is_dimensionless():\n unit_factor = unit.conversion_factor_to(dimensionless)\n if unit_factor != 1.0:\n value_factor *= unit_factor\n # print \"value_factor = %s\" % value_factor\n unit = dimensionless\n Quantity._reduce_cache[key] = (unit, value_factor)\n # Create Quantity, then scale (in case value is a container)\n # That's why we don't just scale the value.\n result = Quantity(self._value, unit)\n if value_factor != 1.0:\n # __mul__ strips off dimensionless, if appropriate\n result = result * value_factor\n if unit.is_dimensionless():\n assert unit is dimensionless # should have been set earlier in this method\n if is_quantity(result):\n result = result._value\n return result","def test_quantization_does_not_violate_bounds():\n dim = Integer(\"yo\", \"uniform\", 3, 7)\n transformers = [Reverse(Quantize())]\n tdim = TransformedDimension(Compose(transformers, dim.type), dim)\n assert 11 not in dim\n assert 10 in dim\n # but be careful, because upper bound is inclusive\n assert 11.5 not in tdim\n # rounded to 11\n assert 10.6 not in tdim\n # rounded to 10\n assert 10.4 in tdim\n assert tdim.reverse(9.6) in dim\n assert tdim.reverse(9.6) == 10\n assert tdim.reverse(9.4) == 9","def compress(self, tensor):","def test_qc_quantize_static_op_gpu(self):\n graph = tf.Graph()\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\n sess = tf.compat.v1.Session(graph=graph, config=config)\n\n with graph.as_default():\n # place holder for the input\n with tf.device(\"/device:GPU:0\"):\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\n\n pass_through_op_output = zero_out_module.qc_quantize_static(name='quant_op', in_tensor=inp,\n encoding_min=-1.0,\n encoding_max=1.0,\n bitwidth=8,\n quant_scheme=libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\n op_mode=libpymo.TensorQuantizerOpMode.passThrough,\n is_symmetric=False)\n\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\n inp_data = np.random.rand(10).astype(np.float32)\n\n # get the output\n print(inp_data)\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\n print(out_data)\n\n # compare qc_quantize op's output with input\n assert np.allclose(out_data, inp_data, atol=1e-6)\n sess.close()\n\n graph = tf.Graph()\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\n sess = tf.compat.v1.Session(graph=graph, config=config)\n\n with graph.as_default():\n # place holder for the input\n with tf.device(\"/device:GPU:0\"):\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\n\n pass_through_op_output = zero_out_module.qc_quantize_static(name='quant_op', in_tensor=inp,\n encoding_min=-1.0,\n encoding_max=0.5,\n bitwidth=8,\n quant_scheme=libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\n op_mode=libpymo.TensorQuantizerOpMode.quantizeDequantize,\n is_symmetric=False)\n\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\n inp_data = np.random.rand(10).astype(np.float32)\n\n # get the output\n print(inp_data)\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\n print(out_data)\n\n # compare qc_quantize op's output with input\n assert not np.allclose(out_data, inp_data, atol=1e-1)\n sess.close()\n\n graph = tf.Graph()\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\n sess = tf.compat.v1.Session(graph=graph, config=config)\n\n with graph.as_default():\n # place holder for the input\n with tf.device(\"/device:GPU:0\"):\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\n\n pass_through_op_output = zero_out_module.qc_quantize_static(name='quant_op', in_tensor=inp,\n encoding_min=-1.0,\n encoding_max=1.0,\n bitwidth=8,\n quant_scheme=libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\n op_mode=libpymo.TensorQuantizerOpMode.oneShotQuantizeDequantize,\n is_symmetric=False)\n\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\n inp_data = np.random.rand(10).astype(np.float32)\n\n # get the output\n print(inp_data)\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\n print(out_data)\n\n # compare qc_quantize op's output with input\n assert not np.allclose(out_data, inp_data, atol=1e-3)\n\n sess.close()","def quantized(values, steps, input_min=0, input_max=1):\n values = _normalize(values)\n if steps < 1:\n raise ValueError(\"steps must be 1 or larger\")\n if input_min >= input_max:\n raise ValueError('input_min must be smaller than input_max')\n input_size = input_max - input_min\n for v in scaled(values, 0, 1, input_min, input_max):\n yield ((int(v * steps) / steps) * input_size) + input_min","def _freeze_quantize_info(self, quantize_info):\n if not self._qcb_model:\n logger.error('No qcb_model found.')\n\n if not self._qcbev_model:\n logger.error('No qcbev_model found.')\n\n # Freeze the quantize info into the quantized model\n model_utils.set_quantize_info(self._qcb_model, quantize_info)\n model_utils.set_quantize_info(self._qcbev_model, quantize_info)","def convert(self, value):\n\n\t\tif self.converter is not None:\n\t\t\treturn self.converter(value)\n\t\telif self.units is not None:\n\t\t\tq = Quantity(value)\n\t\t\tq.assert_dimensions(self.units)\n\n\t\t\treturn q\n\t\telse:\n\t\t\treturn value","def gradfactor(self, f):\r\n raise NotImplementedError","def quantile(self, hypercube):\n raise NotImplementedError()","def quadTo(self, *__args): # real signature unknown; restored from __doc__ with multiple overloads\r\n pass","def test_qc_quantize_recurrent_param_op(self):\n graph = tf.Graph()\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\n sess = tf.compat.v1.Session(graph=graph, config=config)\n bitwidth = 8\n use_symm_encoding = True\n\n with graph.as_default():\n # place holder for the input\n with tf.device(\"/device:CPU:0\"):\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\n tensor_quantizer = libpymo.TensorQuantizer(libpymo.QuantizationMode.QUANTIZATION_TF,\n libpymo.RoundingMode.ROUND_NEAREST)\n tensor_quantizer_val = libpymo.PtrToInt64(tensor_quantizer)\n tensor_quant_ref = tf.Variable(initial_value=tensor_quantizer_val, trainable=False, dtype=tf.int64)\n\n time_step_tensor = tf.constant(1, dtype=tf.int32)\n\n encoding_min = tf.Variable(initial_value=-0.5, trainable=True, dtype=tf.double)\n encoding_max = tf.Variable(initial_value=0.5, trainable=True, dtype=tf.double)\n bit_width = tf.Variable(initial_value=bitwidth, trainable=False, dtype=tf.int8)\n use_symmetric_encoding = tf.Variable(initial_value=use_symm_encoding, trainable=False,\n dtype=tf.bool)\n\n mode_var = tf.Variable(initial_value=int(libpymo.TensorQuantizerOpMode.oneShotQuantizeDequantize),\n trainable=False, dtype=tf.int32)\n\n sess.run([mode_var.initializer, tensor_quant_ref.initializer, encoding_min.initializer,\n encoding_max.initializer, bit_width.initializer, use_symmetric_encoding.initializer])\n\n pass_through_op_output = zero_out_module.qc_quantize_recurrent_param(name='quant_op', in_tensor=inp,\n op_mode=mode_var,\n tensor_quantizer_reference=tensor_quant_ref,\n encoding_min=encoding_min,\n encoding_max=encoding_max,\n bit_width=bit_width,\n use_symmetric_encoding=use_symmetric_encoding,\n time_steps=time_step_tensor)\n\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\n # inp_data = np.random.rand(10).astype(np.float32)\n np.random.seed(18)\n inp_data = np.random.randint(low=-1, high=2, size=10).astype(np.float32)\n\n # get the output\n print(inp_data)\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\n print(out_data)\n\n # compare qc_quantize op's output with input\n # encodings being set to -0.5 and 0.5 should not have a bearing on this quantized output\n # we should not observe truncation if op's encoding min/max input values are used instead of cached values\n assert np.allclose(out_data, inp_data, atol=1e-6)\n sess.close()","def q(self, element):\n \n if self.is_empty():\n return 0\n\n if element.is_empty():\n return self._sum()\n\n if not element.is_compatible(next(iter(self.focals))):\n return 0\n\n result = 0\n for focal, value in self.items():\n if element.is_subset(focal):\n result += value\n return round(result, 6)"],"string":"[\n \"def _quantize_activation(self, tensor_quantizer: Union[StaticGridPerTensorQuantizer, LearnedGridTensorQuantizer],\\n tensors_to_quantize: Union[List[torch.Tensor], torch.Tensor]) -> \\\\\\n Union[List[torch.Tensor], torch.Tensor]:\\n\\n if not tensor_quantizer.enabled:\\n return tensors_to_quantize\\n\\n outputs = []\\n if not isinstance(tensors_to_quantize, list):\\n tensors_to_quantize = [tensors_to_quantize]\\n for input_tensor in tensors_to_quantize:\\n\\n if self._mode is QcQuantizeOpMode.ANALYSIS:\\n\\n if isinstance(input_tensor, tuple):\\n for tensor in input_tensor:\\n tensor_quantizer.update_encoding_stats(tensor)\\n else:\\n tensor_quantizer.update_encoding_stats(input_tensor)\\n output = input_tensor\\n\\n elif self._mode is QcQuantizeOpMode.ACTIVE:\\n # if we are not in training, then only nearest rounding should be used\\n if self.training:\\n round_mode = tensor_quantizer.round_mode\\n else:\\n round_mode = libpymo.RoundingMode.ROUND_NEAREST\\n output = tensor_quantizer.quantize_dequantize(input_tensor, round_mode)\\n\\n elif self._mode is QcQuantizeOpMode.LEARN_ENCODINGS:\\n encoding_min = getattr(self, tensor_quantizer.name + '_encoding_min')\\n encoding_max = getattr(self, tensor_quantizer.name + '_encoding_max')\\n output = tensor_quantizer.quantize_dequantize(input_tensor, encoding_min, encoding_max)\\n\\n else:\\n output = input_tensor\\n\\n outputs.append(output)\\n\\n # Flatten if there is only one output\\n return outputs[0] if len(outputs) == 1 else tuple(outputs)\",\n \"def _quantize(self):\\n self._quantized_subsamples = [0] * self._number_of_subsamples\\n for index, value in enumerate(self._subsamples):\\n self._quantized_subsamples[index] = self._quantize_one_value(value)\\n logging.debug('quantized down-samples: %r', self._quantized_subsamples)\",\n \"def _quantize(self, model: nn.Module) -> nn.Module:\\n if self.mask:\\n model_utils.remove_pruning_reparameterization(self.params_all)\\n\\n # check the accuracy after each epoch\\n quantized_model = torch.quantization.convert(model.eval(), inplace=False)\\n quantized_model.eval()\\n\\n # set masks again\\n if self.mask:\\n self._load_masks()\\n\\n return quantized_model\",\n \"def _quantize_one_value(self, value):\\n add = 0.5 if value > 0 else -0.5\\n return int(value / self._quantization_factor + add)\",\n \"def quantize(im_orig, n_quant, n_iter):\\n shape_len = len(im_orig.shape)\\n if shape_len == 2: # grayscale\\n return quantization_helper(im_orig, n_quant, n_iter)\\n\\n elif shape_len == 3: # rgb\\n im_yiq = rgb2yiq(im_orig)\\n y = im_yiq[:, :, 0]\\n y_quant, error = quantization_helper(y, n_quant, n_iter)\\n y_quant = y_quant/ 255\\n im_yiq[:, :, 0] = y_quant\\n im_quants = yiq2rgb(im_yiq)\\n return im_quants, error\",\n \"def _compute_quantized_subsamples(self):\\n self._down_sample()\\n self._quantize()\",\n \"def _quantize(self) :\\n self.A[self.A <= self.thr] = -1\\n self.A[self.A > self.thr] = 1\\n self.A = self.A.astype(\\\"int\\\")\",\n \"def quantize(self, tune_cfg, model, data_loader, q_func=None):\\n assert q_func is None, \\\"quantization aware training mode is not support on tensorflow\\\"\\n logger.info('Start to run model quantization...')\\n quantized_model = os.path.join(os.getcwd(), \\\"tf_quantized.pb\\\")\\n self.tuning_cfg_to_fw(tune_cfg)\\n logger.debug('Dump quantization configurations:')\\n logger.debug(self.quantize_config)\\n from .tf_utils.graph_converter import GraphConverter\\n converter = GraphConverter(self.pre_optimized_graph if self.pre_optimized_graph else model,\\n quantized_model,\\n inputs=self.inputs,\\n outputs=self.outputs,\\n qt_config=self.quantize_config,\\n fp32_ops=self.fp32_ops,\\n bf16_ops=self.bf16_ops,\\n data_loader=data_loader)\\n return converter.convert()\",\n \"def quantize(coords):\\n assert isinstance(coords, np.ndarray) or isinstance(\\n coords, torch.Tensor\\n ), \\\"Invalid coords type\\\"\\n if isinstance(coords, np.ndarray):\\n assert (\\n coords.dtype == np.int32\\n ), f\\\"Invalid coords type {coords.dtype} != np.int32\\\"\\n return MEB.quantize_np(coords.astype(np.int32))\\n else:\\n # Type check done inside\\n return MEB.quantize_th(coords.int())\",\n \"def quantize(im_orig, n_quant, n_iter):\\n if im_orig.ndim == RGB_DIM:\\n # Quantize only Y channel.\\n im_yiq = rgb2yiq(im_orig)\\n result = __gray_quantize(im_yiq[:, :, Y_CHANNEL], n_quant, n_iter)\\n im_yiq[:, :, Y_CHANNEL] = result[IMAGE_LOCATION]\\n\\n # Convert back to RGB space.\\n result[IMAGE_LOCATION] = yiq2rgb(im_yiq)\\n return result\\n # Otherwise, just quantize.\\n return __gray_quantize(im_orig, n_quant, n_iter)\",\n \"def _quantize_dequantize_params(self, inputs=None) -> Dict[str, torch.Tensor]:\\n quantized_params = dict()\\n shadow_params = {name: param.detach().clone() for name, param in self.named_parameters(recurse=False)}\\n\\n if self._mode != QcQuantizeOpMode.LEARN_ENCODINGS:\\n quantizer_param_map = defaultdict(list)\\n for name, param in self.named_parameters(recurse=False):\\n quantizer_param_map[self._param_quantizers[name]].append((name, param))\\n\\n for param_quantizer, param_list in quantizer_param_map.items():\\n # If we are in training mode with quant-sim nodes,\\n # then we want to calculate encodings for the parameters in every pass\\n if self.training or param_quantizer.encoding is None:\\n param_quantizer.reset_encoding_stats()\\n for _, param in param_list:\\n param_quantizer.update_encoding_stats(param.data)\\n param_quantizer.compute_encoding()\\n\\n for name, param in param_list:\\n quantized_params[name] = param.data = self._param_quantize_dequantize(param.clone(), param_quantizer)\\n else:\\n encoding_list_for_params = []\\n for name, _ in self.get_named_parameters():\\n # Create a list of encoding parameters for params\\n quantizer = self.param_quantizers[name]\\n if quantizer.enabled:\\n # if param uses a group quantizer remap to group quantizer min/max encoding params.\\n if quantizer in self._grouped_quantizers.values():\\n name, *_ = [n for n, q in self._grouped_quantizers.items() if q == quantizer]\\n encoding_list_for_params.append(getattr(self, name + '_encoding_min'))\\n encoding_list_for_params.append(getattr(self, name + '_encoding_max'))\\n\\n # Quantize the parameters\\n inputs = ParameterQuantizer.apply(inputs, self, *encoding_list_for_params)\\n\\n # clone() the outputs of Custom function to avoid incorrect gradient calculation for in-place modification\\n # of view (view is created since Custom function's forward return input as-is)\\n inputs = inputs.clone()\\n quantized_params = {name: param.clone() for name, param in self.named_parameters(recurse=False)\\n if '_encoding_' not in name}\\n\\n yield quantized_params, inputs\\n\\n for name, param in self.named_parameters(recurse=False):\\n if name in shadow_params:\\n param.data.copy_(shadow_params[name].data)\",\n \"def post_training_quantize(\\n model: onnx.ModelProto,\\n dataset: List[Dict[str, np.ndarray]],\\n per_channel: bool = True,\\n) -> onnx.ModelProto:\\n model = optimize_model(model)\\n ranges = calibrate(model, dataset)\\n return quantize(model, per_channel, True, quantizer.QuantizationMode.dfg, ranges)\",\n \"def quantize(self, some_tensor, bits=32) -> dict:\\n raise NotImplementedError(\\\"To be Overidden by derived class\\\")\",\n \"def jpeg_quantize(block: np.ndarray, option: model.QTables):\\n t = table[option]\\n return deadzone_quantize(block, t)\",\n \"def _compute_quantization_factor(self):\\n self._quantization_factor = (float(self._full_value_range) /\\n self._number_of_intervals)\\n logging.debug(\\n 'full value range: %r, number of intervals: %r',\\n self._full_value_range, self._number_of_intervals)\\n logging.debug('quantization factor: %r', self._quantization_factor)\",\n \"def quantize(self, df):\\n if len(self.dict_scalers) == 0:\\n raise Exception(\\\"[ERROR] quantize method called prior to\\\"\\n \\\"normalization transform method \\\")\\n\\n quant_df = pd.DataFrame()\\n if 'OneForAll' in self.dict_scalers:\\n # quantization is applied on all features\\n min_fp = float(np.amin(df))\\n max_fp = float(np.amax(df))\\n scale = (max_fp - min_fp) / (127 - (-127))\\n zero_point = 127 - (max_fp / scale)\\n quant_df = df / scale + zero_point\\n else:\\n # quantization is applied independently for each feature/column\\n lbl_list = df.columns.values\\n for lbl in lbl_list:\\n min_fp = float(np.amin(df[lbl]))\\n max_fp = float(np.amax(df[lbl]))\\n scale = (max_fp - min_fp) / (127 - (-127))\\n zero_point = 127 - (max_fp / scale)\\n quant_df[lbl] = df[lbl] / scale + zero_point\\n return quant_df.astype(np.int8)\",\n \"def _param_quantize_dequantize(self, data: torch.Tensor, param_quantizer: StaticGridPerTensorQuantizer) -> \\\\\\n torch.Tensor:\\n # if we are not in training, then only nearest rounding should be used\\n # else we should use whatever the user desires (i.e.. stochastic rounding is a valid option)\\n if self.training:\\n round_mode = param_quantizer.round_mode\\n else:\\n round_mode = libpymo.RoundingMode.ROUND_NEAREST\\n return param_quantizer.quantize_dequantize(data, round_mode)\",\n \"def quantize(model: nn.Module,\\n precision: str = 'int8',\\n accelerator: str = None,\\n use_ipex: bool = False,\\n calib_dataloader: DataLoader = None,\\n metric: Metric = None,\\n accuracy_criterion: dict = None,\\n approach: str = 'static',\\n method: str = None,\\n conf: str = None,\\n tuning_strategy: str = None,\\n timeout: int = None,\\n max_trials: int = None,\\n input_sample=None,\\n thread_num: int = None,\\n onnxruntime_session_options=None,\\n logging: bool = True,\\n **export_kwargs):\\n return InferenceOptimizer.quantize(model=model,\\n precision=precision,\\n accelerator=accelerator,\\n use_ipex=use_ipex,\\n calib_dataloader=calib_dataloader,\\n metric=metric,\\n accuracy_criterion=accuracy_criterion,\\n approach=approach,\\n method=method,\\n conf=conf,\\n tuning_strategy=tuning_strategy,\\n timeout=timeout,\\n max_trials=max_trials,\\n input_sample=input_sample,\\n thread_num=thread_num,\\n onnxruntime_session_options=onnxruntime_session_options,\\n logging=logging,\\n **export_kwargs)\",\n \"def quantizer(forward_number=None, backward_number=None,\\n forward_rounding=\\\"stochastic\\\", backward_rounding=\\\"stochastic\\\",\\n clamping_grad_zero=False, backward_hooks=[]):\\n if forward_number is not None:\\n if forward_number.exp == -1 or forward_number.man == -1:\\n forward_number = None\\n if backward_number is not None:\\n if backward_number.exp == -1 or backward_number.man == -1:\\n backward_number = None\\n\\n\\n for rounding in [forward_rounding, backward_rounding]:\\n assert rounding in [\\\"stochastic\\\", \\\"nearest\\\"], \\\"invalid rounding type {:s}\\\".format(rounding)\\n for num in [forward_number, backward_number]:\\n if num != None: assert isinstance(num, Number)\\n\\n \\n # forward and backward quantisation functions\\n tensor_type = \\\"w\\\" if backward_number is None else \\\"x\\\"\\n forward_quant = lambda x, num, rd, tt: block_minifloat_quantize(x, num, rd, tt)\\n backward_quant = lambda x, num, rd, tt: block_minifloat_quantize(x, num, rd, tt) \\n\\n\\n class Rounding(torch.autograd.Function):\\n @staticmethod\\n def forward(self, x):\\n if forward_number==None: return x\\n\\n out = forward_quant(x.contiguous(), forward_number, forward_rounding, tensor_type)\\n\\n return out.clone()\\n\\n @staticmethod\\n def backward(self, grad_output):\\n if self.needs_input_grad[0]:\\n if backward_number == None:\\n grad_input = grad_output\\n else:\\n grad_input = backward_quant(grad_output.contiguous(), backward_number, \\n backward_rounding, tensor_type)\\n else:\\n grad_input = None\\n\\n return grad_input.clone()\\n\\n return Rounding.apply\",\n \"def quantize_float(f, q):\\n return int(round(f / q) * q)\",\n \"def quantize(im_orig, n_quant, n_iter):\\n rgb, y, im_yiq = check_rgb(im_orig)\\n hist_orig, bin_edges = np.histogram(y*(BITS - 1), BITS, (0, BITS - 1))\\n z = np.concatenate([[0], initial_z(hist_orig, n_quant), [(BITS-1)]])\\n q = find_q(z, hist_orig)\\n error = [calc_err(q, z, hist_orig)]\\n for i in range(n_iter - 1): # first iteration was already done\\n z_new = find_z(q)\\n if np.array_equal(z_new, z): # stop if the z and q vectors are already optimal\\n break\\n q = find_q(z_new, hist_orig)\\n z = z_new\\n error.append(calc_err(q, z, hist_orig))\\n lut = im_lut(q, z)\\n im_quant = lut[(y*(BITS - 1)).astype(np.uint8)].astype(np.uint8) # calculate quantized image.\\n im_quant = gray2rgb(rgb, im_quant, im_yiq) # if the original image was RGB then convert back to RGB\\n return im_quant, error\",\n \"def quant_image(self, image=None, dst=None, ndeep=None, ndigits=None, override=True, delete=True):\\n # Check Whether Pngquant Exist\\n if not self.file_exists(self.quant_file):\\n raise ValueError(self.err_pngquant)\\n\\n # Check Whether Image Exist\\n if not self.file_exists(image):\\n raise ValueError(self.err_image)\\n\\n # Compress Image By Call Function quant_data\\n # Dst Should Pass Image When Override Origin Image\\n return self.quant_data(self.open_file(image), dst=dst or (override and image), ndeep=ndeep, ndigits=ndigits, delete=delete)\",\n \"def TorchRound():\\n class identity_quant(torch.autograd.Function):\\n @staticmethod\\n def forward(ctx, input):\\n out = torch.round(input)\\n return out\\n\\n @staticmethod\\n def backward(ctx, grad_output):\\n return grad_output\\n\\n return identity_quant().apply\",\n \"def quantize(self, exp, rounding=None, context=None):\\r\\n self._real.quantize(exp, rounding, context)\\r\\n self._imag.quantize(exp, rounding, context)\",\n \"def quantize(im_orig, n_quant, n_iter):\\n img = get_gray_channel(im_orig)\\n img = float2int(img)\\n\\n hist, bins = np.histogram(img, bins=np.arange(MAX_VALUE + 1))\\n hist_times_color = hist * np.arange(MAX_VALUE)\\n z = guess_first_z(n_quant, hist)\\n error, q = [], []\\n for i in range(0, n_iter):\\n q = calculate_q(z, hist, hist_times_color)\\n\\n new_z = [0]\\n for j in range(1, n_quant):\\n new_z.append((q[j - 1] + q[j]) // 2)\\n new_z.append(MAX_VALUE - 1)\\n\\n error.append(calculate_error(hist, new_z, q))\\n\\n if np.array_equal(z, new_z):\\n break\\n z = new_z\\n\\n lut = np.zeros(MAX_VALUE)\\n for i in range(0, len(q)):\\n lut[z[i]:z[i + 1]] = q[i]\\n lut[MAX_VALUE - 1] = q[len(q) - 1]\\n \\n im_quant = lut[img.astype(int)]\\n im_quant = int2float(im_quant)\\n im_quant = update_gray_channel(im_orig, im_quant)\\n \\n return im_quant, error\",\n \"def native_quantize_impl(\\n model: ov.Model,\\n calibration_dataset: Dataset,\\n preset: QuantizationPreset = QuantizationPreset.PERFORMANCE,\\n target_device: TargetDevice = TargetDevice.ANY,\\n subset_size: int = 300,\\n fast_bias_correction: bool = True,\\n model_type: Optional[ModelType] = None,\\n ignored_scope: Optional[IgnoredScope] = None,\\n advanced_parameters: Optional[AdvancedQuantizationParameters] = None,\\n) -> ov.Model:\\n quantization_algorithm = PostTrainingQuantization(\\n preset=preset,\\n target_device=target_device,\\n subset_size=subset_size,\\n fast_bias_correction=fast_bias_correction,\\n model_type=model_type,\\n ignored_scope=ignored_scope,\\n advanced_parameters=advanced_parameters,\\n )\\n\\n graph = GraphConverter.create_nncf_graph(model)\\n quantized_model = quantization_algorithm.apply(model, graph, dataset=calibration_dataset)\\n\\n if is_weight_compression_needed(advanced_parameters):\\n compress_quantize_weights_transformation(quantized_model)\\n\\n dump_parameters(\\n quantized_model,\\n {\\n \\\"preset\\\": preset.value,\\n \\\"target_device\\\": target_device.value,\\n \\\"subset_size\\\": subset_size,\\n \\\"fast_bias_correction\\\": fast_bias_correction,\\n \\\"model_type\\\": model_type,\\n \\\"ignored_scope\\\": ignored_scope,\\n \\\"advanced_parameters\\\": convert_to_dict_recursively(advanced_parameters),\\n },\\n )\\n return quantized_model\",\n \"def test_qc_quantize_op_gradient_computation(self):\\n\\n graph = tf.Graph()\\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\\n sess = tf.compat.v1.Session(graph=graph, config=config)\\n\\n with graph.as_default():\\n # place holder for the input\\n with tf.device(\\\"/device:CPU:0\\\"):\\n inp = tf.compat.v1.placeholder(tf.float32, shape=[2, 2], name='input')\\n tensor_quantizer = libpymo.TensorQuantizer(libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\\n libpymo.RoundingMode.ROUND_NEAREST)\\n tensor_quantizer_val = libpymo.PtrToInt64(tensor_quantizer)\\n tensor_quant_ref = tf.Variable(initial_value=tensor_quantizer_val, trainable=False, dtype=tf.int64)\\n\\n mode_var = tf.Variable(initial_value=int(libpymo.TensorQuantizerOpMode.oneShotQuantizeDequantize),\\n trainable=False, dtype=tf.int32)\\n\\n # fix min max and bitwidth to be used\\n encoding_min = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\\n encoding_max = tf.Variable(initial_value=5.0, trainable=True, dtype=tf.double)\\n bit_width = tf.Variable(initial_value=8, trainable=False, dtype=tf.int8)\\n use_symmetric_encoding = tf.Variable(initial_value=False, trainable=False, dtype=tf.bool)\\n is_int_data_type = tf.Variable(initial_value=True, trainable=False, dtype=tf.bool)\\n\\n sess.run([mode_var.initializer, tensor_quant_ref.initializer, encoding_min.initializer,\\n encoding_max.initializer, bit_width.initializer, use_symmetric_encoding.initializer,\\n is_int_data_type.initializer])\\n\\n with graph.gradient_override_map(\\n {\\\"QcQuantize\\\": \\\"QcQuantizeRangeLearningCustomGradient\\\"}):\\n\\n pass_through_op_output = zero_out_module.qc_quantize(name='quant_op', in_tensor=inp,\\n op_mode=mode_var,\\n tensor_quantizer_reference=tensor_quant_ref,\\n encoding_min=encoding_min,\\n encoding_max=encoding_max,\\n bit_width=bit_width,\\n use_symmetric_encoding=use_symmetric_encoding,\\n is_int_data_type=is_int_data_type)\\n\\n pass_through_op = graph.get_operation_by_name('quant_op')\\n\\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\\n # fixed input data used\\n inp_data = [[0.4581, 0.4829], [0.3125, 0.6150]]\\n\\n # get the output data @todo match these\\n tensor_quantizer.isEncodingValid = True\\n mode_var.load(int(libpymo.TensorQuantizerOpMode.quantizeDequantize), sess)\\n\\n # for matching with golden output, truncate to 4\\n tf_output_data = np.around(sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data}), 4)\\n exp_output = [[0.4510, 0.4902], [0.3137, 0.6078]]\\n\\n # dummy loss function to match with Pytorch\\n def custom_loss(y_actual, y_pred):\\n return tf.reduce_sum(tf.subtract(y_pred, y_actual-y_actual))\\n\\n with graph.as_default():\\n var_list = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES)\\n labels_placeholder = tf.compat.v1.placeholder(tf.float32, [2, 2], name='labels')\\n\\n # output tensor\\n logits = sess.graph.get_tensor_by_name('quant_op:0')\\n\\n # dummy loss function is set to sum(output)\\n current_loss = custom_loss(labels_placeholder, logits)\\n labels = np.ones((2), dtype=int) # np.random.randint(2, size=batches)\\n one_hot_labels = np.eye(2)[labels]\\n\\n update_ops = []\\n global_step = tf.compat.v1.train.create_global_step()\\n # Stochastic GD in tf with momentum param\\n optimizer = tf.compat.v1.train.MomentumOptimizer(learning_rate=0.05, momentum=0.5)\\n gradients = optimizer.compute_gradients(current_loss, var_list)\\n\\n grad_updates = optimizer.apply_gradients(gradients, global_step=global_step)\\n init_global = tf.compat.v1.global_variables_initializer()\\n init_local = tf.compat.v1.local_variables_initializer()\\n init = tf.group(init_global, init_local)\\n sess.run(init)\\n update_ops.append(grad_updates)\\n update_op = tf.group(*update_ops)\\n\\n with tf.control_dependencies([update_op]):\\n train_op = tf.identity(current_loss, name='train_op')\\n\\n # enable this to check current loss value used\\n _ = sess.run(current_loss, feed_dict={inp_tensor: inp_data, labels_placeholder: one_hot_labels})\\n # start training\\n _ = sess.run(train_op, feed_dict={inp_tensor: inp_data, labels_placeholder: one_hot_labels})\\n tf_enc_min_after_train = sess.run(pass_through_op.inputs[QuantizeOpIndices.encoding_min])\\n tf_enc_max_after_train = sess.run(pass_through_op.inputs[QuantizeOpIndices.encoding_max])\\n\\n # match outputs\\n assert np.allclose(exp_output, tf_output_data)\\n\\n # compare min and max after update with expected values (Pytorch values)\\n expected_enc_min_after_train = -5.7160621508955956e-05\\n expected_enc_max_after_train = 5.000057220458984\\n assert np.allclose(tf_enc_min_after_train, expected_enc_min_after_train, atol=1e-6)\\n assert np.allclose(tf_enc_max_after_train, expected_enc_max_after_train, atol=1e-6)\\n\\n sess.close()\",\n \"def test_quantize_conv_transpose_u8u8(self):\\n\\n np.random.seed(1)\\n model_fp32_path = \\\"conv_transpose_fp32.onnx\\\"\\n self.construct_model(model_fp32_path)\\n data_reader = self.input_feeds(1, {\\\"input\\\": [1, 1, 7, 7]})\\n\\n self.static_quant_test_qdq(\\n model_fp32_path,\\n data_reader,\\n activation_type=QuantType.QUInt8,\\n weight_type=QuantType.QUInt8,\\n )\",\n \"def quantize(self, tune_cfg, model, data_loader, q_func=None):\\n assert q_func is None, \\\"quantization aware training has not been supported on ONNXRUNTIME\\\"\\n model = self.pre_optimized_model if self.pre_optimized_model else model\\n ort_version = StrictVersion(ort.__version__)\\n if ort_version < ONNXRT152_VERSION: # pragma: no cover\\n logger.warning(\\\"Quantize input needs onnxruntime 1.5.2 or newer.\\\")\\n return model\\n if model.model.opset_import[0].version < 11: # pragma: no cover\\n logger.warning(\\\"Quantize input needs model opset 11 or newer.\\\")\\n from neural_compressor.adaptor.ox_utils.onnx_quantizer import ONNXQuantizer\\n from onnxruntime.quantization.quant_utils import QuantizationMode\\n backend = QuantizationMode.QLinearOps if self.backend == \\\\\\n \\\"qlinearops\\\" else QuantizationMode.IntegerOps\\n\\n self.quantizable_ops = self._query_quantizable_ops(model.model)\\n tmp_model = copy.deepcopy(model)\\n \\n quantize_config = self._cfg_to_quantize_config(tune_cfg)\\n iterations = tune_cfg.get('calib_iteration', 1)\\n if self.static:\\n quantize_params = self._get_quantize_params(tmp_model.model, data_loader, \\\\\\n quantize_config, iterations)\\n else:\\n quantize_params = None\\n self.quantize_params = quantize_params\\n quantizer = ONNXQuantizer(tmp_model.model,\\n quantize_config,\\n backend,\\n self.static,\\n quantize_params,\\n self.quantizable_op_types)\\n quantizer.quantize_model()\\n tmp_model.q_config = self._generate_qconfig(model.model, tune_cfg, quantize_params)\\n tmp_model.model = quantizer.model.model\\n self.quantize_config = quantize_config # update so other methods can know current configs\\n \\n self._dump_model_op_stastics(tmp_model)\\n return tmp_model\",\n \"def apply(self, model, layer_quantize_map, quantize_registry, mode):\\n\\n transforms = [\\n vitis_8bit_quantize_transforms.InputLayerQuantize(\\n quantize_registry.get_input_quantizer(), mode),\\n vitis_8bit_quantize_transforms.ConvActivationQuantize(),\\n vitis_8bit_quantize_transforms.AddActivationQuantize(),\\n ]\\n return model_transformer.ModelTransformer(model, transforms,\\n set(layer_quantize_map.keys()),\\n layer_quantize_map).transform()\",\n \"def __quantize_layer_operands(self, params_type, var_type, accum_bits):\\n # define how much bits should we keep unused in operands\\n w_shape = self.__weights.get_shape()\\n macs_per_out = int(np.prod(w_shape[1:])) + 1 if w_shape is not None else 1\\n extra_bits_required = int(np.ceil(np.log2(macs_per_out)))\\n acc_extra_bits = accum_bits - 1 - (np.iinfo(params_type).bits - 1 + np.iinfo(var_type).bits - 1)\\n not_enough_bits = max(0, extra_bits_required - acc_extra_bits)\\n weights_extra_bits = not_enough_bits // 2 if not_enough_bits != 0 else 0\\n input_extra_bits = not_enough_bits - weights_extra_bits\\n\\n # Info preparation: Defining Qmn format of operands\\n return_dict = {key: None for key in ['weights', 'bias', 'input', 'output']}\\n return_dict['weights'] = {'int_bits': self.__weights.int_bits() + weights_extra_bits,\\n 'frac_bits': self.__weights.frac_bits(params_type) - weights_extra_bits,\\n 'min': self.__weights.min(), 'max': self.__weights.max()}\\n return_dict['input'] = {'int_bits': self.input.int_bits() + input_extra_bits,\\n 'frac_bits': self.input.frac_bits(var_type) - input_extra_bits,\\n 'min': self.input.min(), 'max': self.input.max()}\\n return_dict['bias'] = {'int_bits': self.__bias.int_bits(), 'frac_bits': self.__bias.frac_bits(params_type),\\n 'min': self.__bias.min(), 'max': self.__bias.max()}\\n return_dict['output'] = {'int_bits': self.output.int_bits(), 'frac_bits': self.output.frac_bits(var_type),\\n 'min': self.output.min(), 'max': self.output.max()}\\n\\n # bias frac_bits must be less or equal to sum of weights and input frac_bits\\n mul_frac_bits = return_dict['weights']['frac_bits'] + return_dict['input']['frac_bits']\\n return_dict['bias']['frac_bits'] = min(mul_frac_bits, return_dict['bias']['frac_bits'])\\n\\n # Quantize data according to defined Qmn format\\n return_dict['bias']['data'] = self.__bias.get_data_quantazed(params_type, return_dict['bias']['frac_bits'])\\n return_dict['input']['data'] = self.input.get_data_quantazed(var_type, return_dict['input']['frac_bits'])\\n return_dict['output']['data'] = self.output.get_data_quantazed(var_type, return_dict['output']['frac_bits'])\\n return_dict['weights']['data'] = self.__weights.get_data_quantazed(params_type,\\n return_dict['weights']['frac_bits'])\\n return return_dict\",\n \"def quant_data(self, data=None, dst=None, ndeep=None, ndigits=None, delete=True):\\n # Check Whether Pngquant Exist\\n if not self.file_exists(self.quant_file):\\n raise ValueError(self.err_pngquant)\\n\\n # Check Whether Data Exist\\n if not data:\\n raise ValueError(self.err_data)\\n\\n # Save Data As TMP File\\n self.save_tmp_file(data)\\n\\n # Calculate Length Of Data & Assign Value Ndeep As Pass or Default\\n data_len, ndeep = len(data), ndeep or self.ndeep\\n\\n # Compress Image Using Pngquant\\n # If Not Compressed\\n # Then Using Pillow.Save's Optimize Option\\n compressed_data, compressed_len = self.quant_compress(data, ndeep)\\n if compressed_len >= data_len:\\n compressed_data, compressed_len = self.pillow_compress(data, ndeep)\\n\\n # Copy And Delete TMP File\\n if compressed_len < data_len:\\n self.copy_tmp_file(dst)\\n self.delete_tmp_file(delete)\\n\\n return (self.compression_ratio(data_len, compressed_len, ndigits), compressed_data) if compressed_len < data_len else (0, data)\",\n \"def quantize_impl(\\n model: ov.Model,\\n calibration_dataset: Dataset,\\n preset: QuantizationPreset = QuantizationPreset.PERFORMANCE,\\n target_device: TargetDevice = TargetDevice.ANY,\\n subset_size: int = 300,\\n fast_bias_correction: bool = True,\\n model_type: Optional[ModelType] = None,\\n ignored_scope: Optional[IgnoredScope] = None,\\n advanced_parameters: Optional[AdvancedQuantizationParameters] = None,\\n) -> ov.Model:\\n if should_use_pot(advanced_parameters):\\n from nncf.openvino.pot.quantization.quantize_model import quantize_impl as pot_quantize_impl\\n\\n quantize_fn = pot_quantize_impl\\n else:\\n quantize_fn = native_quantize_impl\\n\\n return quantize_fn(\\n model,\\n calibration_dataset,\\n preset,\\n target_device,\\n subset_size,\\n fast_bias_correction,\\n model_type,\\n ignored_scope,\\n advanced_parameters,\\n )\",\n \"def test_qc_quantize_op_cpu_fp16_quantize_dequantize(self):\\n graph = tf.Graph()\\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\\n sess = tf.compat.v1.Session(graph=graph, config=config)\\n bitwidth = 8\\n use_symm_encoding = True\\n\\n with graph.as_default():\\n # place holder for the input\\n with tf.device(\\\"/device:CPU:0\\\"):\\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\\n tensor_quantizer = libpymo.TensorQuantizer(libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\\n libpymo.RoundingMode.ROUND_NEAREST)\\n tensor_quantizer_val = libpymo.PtrToInt64(tensor_quantizer)\\n tensor_quant_ref = tf.Variable(initial_value=tensor_quantizer_val, trainable=False, dtype=tf.int64)\\n\\n encoding_min = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\\n encoding_max = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\\n bit_width = tf.Variable(initial_value=bitwidth, trainable=False, dtype=tf.int8)\\n use_symmetric_encoding = tf.Variable(initial_value=use_symm_encoding, trainable=False, dtype=tf.bool)\\n is_int_data_type = tf.Variable(initial_value=False, trainable=False, dtype=tf.bool)\\n mode_var = tf.Variable(initial_value=int(libpymo.TensorQuantizerOpMode.quantizeDequantize),\\n trainable=False, dtype=tf.int32)\\n\\n sess.run([mode_var.initializer, tensor_quant_ref.initializer, encoding_min.initializer,\\n encoding_max.initializer, bit_width.initializer, use_symmetric_encoding.initializer,\\n is_int_data_type.initializer])\\n\\n pass_through_op_output = zero_out_module.qc_quantize(name='quant_op', in_tensor=inp,\\n op_mode=mode_var,\\n tensor_quantizer_reference=tensor_quant_ref,\\n encoding_min=encoding_min,\\n encoding_max=encoding_max,\\n bit_width=bit_width,\\n use_symmetric_encoding=use_symmetric_encoding,\\n is_int_data_type=is_int_data_type)\\n\\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\\n\\n inp_data = np.array([0.78027299, 0.44164284, 0.6942797, 0.69774088, 0.55863863, 0.29553034, 0.219199,\\n 0.09483732, 0.55075674, 0.6348504], dtype=np.float32)\\n\\n out_exp = np.array([0.78027344, 0.4416504, 0.69433594, 0.6977539, 0.55859375, 0.29541016, 0.21923828,\\n 0.09484863, 0.55078125, 0.6347656], dtype=np.float32)\\n\\n # get the output\\n print(\\\"inp_data\\\", inp_data)\\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\\n print(\\\"out_data\\\", out_data)\\n\\n # compare qc_quantize op's output with expected output\\n assert np.allclose(out_data, out_exp)\\n\\n sess.close()\",\n \"def get_quantized_representation(\\n tensor: torch.Tensor,\\n quantize_params: QuantizeAffineParams2,\\n) -> torch.Tensor:\\n _validate_tensor(tensor)\\n\\n scale = quantize_params.scale\\n zero_point = quantize_params.zero_point\\n num_bits = quantize_params.num_bits\\n if scale == 0:\\n # Special case, all elements are zeros.\\n if zero_point != 0:\\n raise ValueError(\\n \\\"The given QuantizeAffineParams (={}) has a non-zero zero point\\\"\\n \\\" with a scale of 0.\\\".format(quantize_params)\\n )\\n quantized_tensor = torch.zeros_like(tensor)\\n tag_with_metadata(quantized_tensor, quantize_params)\\n return quantized_tensor\\n\\n qmin, qmax = get_qmin_qmax(num_bits)\\n reciprocal = 1 / scale\\n quantized_tensor = ((tensor * reciprocal).round_() + zero_point).clamp_(\\n qmin, qmax\\n )\\n\\n tag_with_metadata(quantized_tensor, quantize_params)\\n return quantized_tensor\",\n \"def quantize(\\n arr: np.ndarray, quantize_params: QuantizeAffineParams2\\n) -> np.ndarray:\\n scale = quantize_params.scale\\n zero_point = quantize_params.zero_point\\n num_bits = quantize_params.num_bits\\n if scale == 0:\\n # Special case, all elements are zeros.\\n if zero_point != 0:\\n raise ValueError(\\n \\\"The given QuantizeAffineParams (={}) has a non-zero zero point\\\"\\n \\\" with a scale of 0.\\\".format(quantize_params)\\n )\\n return np.zeros_like(arr, dtype=np.int32)\\n\\n qmin, qmax = get_qmin_qmax(num_bits)\\n reciprocal = 1 / scale\\n quantized_values = (arr * reciprocal).round() + zero_point\\n quantized_values = quantized_values.clip(qmin, qmax)\\n return quantized_values\",\n \"def test_qc_quantize_op_cpu(self):\\n graph = tf.Graph()\\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\\n sess = tf.compat.v1.Session(graph=graph, config=config)\\n bitwidth = 8\\n use_symm_encoding = True\\n\\n with graph.as_default():\\n # place holder for the input\\n with tf.device(\\\"/device:CPU:0\\\"):\\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\\n tensor_quantizer = libpymo.TensorQuantizer(libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\\n libpymo.RoundingMode.ROUND_NEAREST)\\n tensor_quantizer_val = libpymo.PtrToInt64(tensor_quantizer)\\n tensor_quant_ref = tf.Variable(initial_value=tensor_quantizer_val, trainable=False, dtype=tf.int64)\\n\\n encoding_min = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\\n encoding_max = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\\n bit_width = tf.Variable(initial_value=bitwidth, trainable=False, dtype=tf.int8)\\n use_symmetric_encoding = tf.Variable(initial_value=use_symm_encoding, trainable=False, dtype=tf.bool)\\n is_int_data_type = tf.Variable(initial_value=True, trainable=False, dtype=tf.bool)\\n\\n mode_var = tf.Variable(initial_value=int(libpymo.TensorQuantizerOpMode.updateStats),\\n trainable=False, dtype=tf.int32)\\n\\n sess.run([mode_var.initializer, tensor_quant_ref.initializer, encoding_min.initializer,\\n encoding_max.initializer, bit_width.initializer, use_symmetric_encoding.initializer,\\n is_int_data_type.initializer])\\n\\n pass_through_op_output = zero_out_module.qc_quantize(name='quant_op', in_tensor=inp,\\n op_mode=mode_var,\\n tensor_quantizer_reference=tensor_quant_ref,\\n encoding_min=encoding_min,\\n encoding_max=encoding_max,\\n bit_width=bit_width,\\n use_symmetric_encoding=use_symmetric_encoding,\\n is_int_data_type=is_int_data_type)\\n\\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\\n inp_data = np.random.rand(10)\\n\\n # get the output\\n print(\\\"inp_data\\\", inp_data)\\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\\n print(\\\"out_data\\\", out_data)\\n\\n # compare qc_quantize op's output with input\\n assert np.allclose(out_data, inp_data)\\n\\n # compute encodings\\n assert not tensor_quantizer.isEncodingValid\\n encoding = tensor_quantizer.computeEncoding(bitwidth, use_symm_encoding)\\n assert tensor_quantizer.isEncodingValid\\n print('min=', encoding.min, ', max=', encoding.max)\\n\\n # get the output\\n inp_data = np.random.rand(10) * 2\\n print(inp_data)\\n mode_var.load(int(libpymo.TensorQuantizerOpMode.quantizeDequantize), sess)\\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\\n print(out_data)\\n\\n # compare qc_quantize op's output with input\\n assert not np.allclose(out_data, inp_data)\\n sess.close()\",\n \"def mulaw_quantize(x, quantization_channels=256):\\n mu = quantization_channels - 1\\n if isinstance(x, np.ndarray):\\n x_mu = np.sign(x) * np.log1p(mu * np.abs(x)) / np.log1p(mu)\\n x_mu = ((x_mu + 1) / 2 * mu + 0.5).astype(int)\\n elif isinstance(x, (torch.Tensor, torch.LongTensor)):\\n\\n if isinstance(x, torch.LongTensor):\\n x = x.float()\\n mu = torch.FloatTensor([mu])\\n x_mu = torch.sign(x) * torch.log1p(mu * torch.abs(x)) / torch.log1p(mu)\\n x_mu = ((x_mu + 1) / 2 * mu + 0.5).long()\\n return x_mu\",\n \"def dequantize(\\n q_arr: np.ndarray,\\n quantize_params: QuantizeAffineParams2,\\n) -> np.ndarray:\\n zero_point = quantize_params.zero_point\\n scale = quantize_params.scale\\n return (q_arr - zero_point) * scale\",\n \"def _quant(self, input):\\n quant_input = self._input_quantizer(input)\\n quant_weight = self._weight_quantizer(self.weight)\\n\\n return (quant_input, quant_weight)\",\n \"def _quant(self, input):\\n quant_input = self._input_quantizer(input)\\n quant_weight = self._weight_quantizer(self.weight)\\n\\n return (quant_input, quant_weight)\",\n \"def quantise(images, q_levels):\\n return (np.digitize(images, np.arange(q_levels) / q_levels) - 1).astype('float32')\",\n \"def quantise(images, q_levels):\\n return (np.digitize(images, np.arange(q_levels) / q_levels) - 1).astype('float32')\",\n \"def quantise(images, q_levels):\\n return (np.digitize(images, np.arange(q_levels) / q_levels) - 1).astype('float32')\",\n \"def is_quantized_softmax(self, call):\\n if not self.call_contains_op(call, \\\"qnn.quantize\\\"):\\n return False\\n softmax_call = call.args[0]\\n if not self.call_contains_op(softmax_call, \\\"nn.softmax\\\"):\\n return False\\n dequantize_call = softmax_call.args[0]\\n if not self.call_contains_op(dequantize_call, \\\"qnn.dequantize\\\"):\\n return False\\n self.scale = dequantize_call.args[1].data.numpy().item(0)\\n return True\",\n \"def test_qc_quantize_op_straight_through_gradient_computation(self):\\n graph = tf.Graph()\\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\\n sess = tf.compat.v1.Session(graph=graph, config=config)\\n with graph.as_default():\\n inp = tf.compat.v1.placeholder(tf.float32, shape=[2, 2], name='input')\\n tensor_quantizer = libpymo.TensorQuantizer(libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\\n libpymo.RoundingMode.ROUND_NEAREST)\\n tensor_quantizer_val = libpymo.PtrToInt64(tensor_quantizer)\\n tensor_quant_ref = tf.Variable(initial_value=tensor_quantizer_val, trainable=False, dtype=tf.int64)\\n\\n mode_var = tf.Variable(initial_value=int(libpymo.TensorQuantizerOpMode.oneShotQuantizeDequantize),\\n trainable=False, dtype=tf.int32)\\n\\n # fix min max and bitwidth to be used\\n encoding_min = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\\n encoding_max = tf.Variable(initial_value=5.0, trainable=True, dtype=tf.double)\\n bit_width = tf.Variable(initial_value=8, trainable=False, dtype=tf.int8)\\n use_symmetric_encoding = tf.Variable(initial_value=False, trainable=False, dtype=tf.bool)\\n is_int_data_type = tf.Variable(initial_value=True, trainable=False, dtype=tf.bool)\\n\\n sess.run([mode_var.initializer, tensor_quant_ref.initializer, encoding_min.initializer,\\n encoding_max.initializer, bit_width.initializer, use_symmetric_encoding.initializer,\\n is_int_data_type.initializer])\\n\\n # use default gradient\\n pass_through_op_output = zero_out_module.qc_quantize(name='quant_op', in_tensor=inp,\\n op_mode=mode_var,\\n tensor_quantizer_reference=tensor_quant_ref,\\n encoding_min=encoding_min,\\n encoding_max=encoding_max,\\n bit_width=bit_width,\\n use_symmetric_encoding=use_symmetric_encoding,\\n is_int_data_type=is_int_data_type)\\n\\n # pass_through_op = graph.get_operation_by_name('quant_op')\\n\\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\\n\\n # set the encodings\\n tensor_quantizer.isEncodingValid = True\\n mode_var.load(int(libpymo.TensorQuantizerOpMode.quantizeDequantize), sess)\\n\\n # compute default gradient\\n grads = tf.gradients(pass_through_op_output, [inp_tensor])\\n dlossbydx = grads\\n\\n # send input, note the last value sent here is > 5.0 ,\\n # we set encodings earlier to be min = 0.0 , max = 5.0\\n # input has data > p\\n inp_data = [[1.4581, 0.4829], [0.3125, 5.6150]]\\n # check the gradient returned is a gated version, in this case should be [[1.0, 1.0],[1.0, 0.0]]\\n with graph.as_default():\\n input_gradient = sess.run([dlossbydx], feed_dict={inp_tensor: inp_data})[0]\\n\\n # validate valid clamping in gradient computation\\n assert input_gradient[0][0][0] == 1.0\\n assert input_gradient[0][0][1] == 1.0\\n assert input_gradient[0][1][0] == 1.0\\n assert input_gradient[0][1][1] == 0.0\\n\\n # pass input in correct range\\n inp_data = [[1.4581, 0.4829], [0.3125, 1.6150]]\\n # check the gradient returned is a gated version, in this case should be [[1.0, 1.0],[1.0, 0.0]]\\n with graph.as_default():\\n input_gradient = sess.run([dlossbydx], feed_dict={inp_tensor: inp_data})[0]\\n\\n # validate no clamping case in gradient computation\\n assert input_gradient[0][0][0] == 1.0\\n assert input_gradient[0][0][1] == 1.0\\n assert input_gradient[0][1][0] == 1.0\\n assert input_gradient[0][1][1] == 1.0\\n\\n # pass input with data < n , first value here is -0.5\\n inp_data = [[-0.5, 0.4829], [0.3125, 1.6150]]\\n # check the gradient returned is a gated version, in this case should be [[1.0, 1.0],[1.0, 0.0]]\\n with graph.as_default():\\n input_gradient = sess.run([dlossbydx], feed_dict={inp_tensor: inp_data})[0]\\n\\n # validate valid clamping case in gradient computation\\n assert input_gradient[0][0][0] == 0.0\\n assert input_gradient[0][0][1] == 1.0\\n assert input_gradient[0][1][0] == 1.0\\n assert input_gradient[0][1][1] == 1.0\",\n \"def test_qc_quantize_op_gpu_fp16(self):\\n graph = tf.Graph()\\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\\n sess = tf.compat.v1.Session(graph=graph, config=config)\\n bitwidth = 8\\n use_symm_encoding = False\\n with graph.as_default():\\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\\n tensor_quantizer = libpymo.TensorQuantizer(libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\\n libpymo.RoundingMode.ROUND_NEAREST)\\n tensor_quantizer_val = libpymo.PtrToInt64(tensor_quantizer)\\n tensor_quant_ref = tf.Variable(initial_value=tensor_quantizer_val, trainable=False, dtype=tf.int64)\\n\\n mode_var = tf.Variable(initial_value=int(libpymo.TensorQuantizerOpMode.quantizeDequantize),\\n trainable=False, dtype=tf.int32)\\n\\n encoding_min = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\\n encoding_max = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\\n bit_width = tf.Variable(initial_value=bitwidth, trainable=False, dtype=tf.int8)\\n use_symmetric_encoding = tf.Variable(initial_value=use_symm_encoding, trainable=False, dtype=tf.bool)\\n is_int_data_type = tf.Variable(initial_value=False, trainable=False, dtype=tf.bool)\\n\\n sess.run([mode_var.initializer, tensor_quant_ref.initializer, encoding_min.initializer,\\n encoding_max.initializer, bit_width.initializer, use_symmetric_encoding.initializer,\\n is_int_data_type.initializer])\\n\\n # place holder for the input\\n with tf.device(\\\"/device:GPU:0\\\"):\\n pass_through_op_output = zero_out_module.qc_quantize(name='quant_op', in_tensor=inp,\\n op_mode=mode_var,\\n tensor_quantizer_reference=tensor_quant_ref,\\n encoding_min=encoding_min,\\n encoding_max=encoding_max,\\n bit_width=bit_width,\\n use_symmetric_encoding=use_symmetric_encoding,\\n is_int_data_type=is_int_data_type)\\n\\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\\n inp_data = np.array([0.78027299, 0.44164284, 0.6942797, 0.69774088, 0.55863863, 0.29553034, 0.219199,\\n 0.09483732, 0.55075674, 0.6348504], dtype=np.float32)\\n\\n out_exp = np.array([0.78027344, 0.4416504, 0.69433594, 0.6977539, 0.55859375, 0.29541016, 0.21923828,\\n 0.09484863, 0.55078125, 0.6347656], dtype=np.float32)\\n\\n print(\\\"inp_data\\\", inp_data)\\n with tf.device(\\\"/device:GPU:0\\\"):\\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\\n print(\\\"out_data\\\", out_data)\\n assert np.allclose(out_data, out_exp)\\n sess.close()\",\n \"def forward(self, input, quant):\\n self.save_for_backward(input)\\n c = (input.clamp(min=-1, max =1)+1)/2.0 * quant\\n c = 2 * (c.round()/quant) - 1\\n return c\",\n \"def dequantize(input=None, min_range=None, max_range=None, out_type=_Null, out=None, name=None, **kwargs):\\n return (0,)\",\n \"def on_compression_begin(self, context):\\n\\n super(MKLDNNPostTrainingQuantStrategy,\\n self).on_compression_begin(context)\\n _logger.info('InferQuantStrategy::on_compression_begin')\\n\\n # Prepare the Analysis Config\\n infer_config = core.AnalysisConfig(\\\"AnalysisConfig\\\")\\n infer_config.switch_ir_optim(True)\\n infer_config.disable_gpu()\\n infer_config.set_model(self.fp32_model_path)\\n infer_config.enable_mkldnn()\\n infer_config.set_cpu_math_library_num_threads(\\n self.cpu_math_library_num_threads)\\n\\n # Prepare the data for calculating the quantization scales\\n warmup_reader = context.eval_reader()\\n if six.PY2:\\n data = warmup_reader.next()\\n\\n if six.PY3:\\n data = warmup_reader.__next__()\\n\\n num_images = len(data)\\n image_data = [img.tolist() for (img, _) in data]\\n image_data = np.array(image_data).astype(\\\"float32\\\").reshape(\\n [num_images, ] + list(data[0][0].shape))\\n image_data = image_data.ravel()\\n images = core.PaddleTensor(image_data, \\\"x\\\")\\n images.shape = [num_images, ] + list(data[0][0].shape)\\n\\n label_data = [label for (_, label) in data]\\n labels = core.PaddleTensor(\\n np.array(label_data).astype(\\\"int64\\\").reshape([num_images, 1]), \\\"y\\\")\\n\\n warmup_data = [images, labels]\\n\\n # Enable the INT8 Quantization\\n infer_config.enable_quantizer()\\n infer_config.quantizer_config().set_quant_data(warmup_data)\\n infer_config.quantizer_config().set_quant_batch_size(num_images)\\n\\n # Run INT8 MKL-DNN Quantization\\n predictor = core.create_paddle_predictor(infer_config)\\n if self.int8_model_save_path:\\n if not os.path.exists(self.int8_model_save_path):\\n os.makedirs(self.int8_model_save_path)\\n predictor.SaveOptimModel(self.int8_model_save_path)\\n\\n _logger.info(\\n 'Finish MKLDNNPostTrainingQuantStrategy::on_compresseion_begin')\",\n \"def QuantizeEmulate(to_quantize,\\n num_bits,\\n narrow_range=True,\\n symmetric=True,\\n **kwargs):\\n\\n def _QuantizeList(layers, **params):\\n \\\"\\\"\\\"Apply QuantizeEmulate wrapper to a list of layers.\\n\\n Args:\\n layers: List of keras layers to apply QuantizeEmulate.\\n **params: QuantizationParams for the entire list.\\n\\n Returns:\\n List of layers wrapped with QuantizeEmulate.\\n \\\"\\\"\\\"\\n wrapped_layers = []\\n\\n for layer in layers:\\n # Already quantized. Simply use and return. This supports usage such as\\n # model = QuantizeEmulate([\\n # Dense(),\\n # QuantizeEmulate(Dense(), layer_params)\\n # Dense()\\n # ], model_params)\\n if isinstance(layer, QuantizeEmulateWrapper):\\n wrapped_layers.append(layer)\\n continue\\n\\n wrapped_layers.append(QuantizeEmulate(layer, **params))\\n\\n return wrapped_layers\\n\\n params = {\\n 'num_bits': num_bits,\\n 'narrow_range': narrow_range,\\n 'symmetric': symmetric\\n }\\n params.update(kwargs)\\n\\n if isinstance(to_quantize, list):\\n return _QuantizeList(to_quantize, **params)\\n elif isinstance(to_quantize, keras.Sequential):\\n return keras.models.Sequential(_QuantizeList(to_quantize.layers, **params))\\n elif isinstance(to_quantize, keras.layers.Layer):\\n return QuantizeEmulateWrapper(to_quantize, **params)\",\n \"def inverse_scaled_quantization(x, scale):\\n return x / tf.cast(scale, x.dtype)\",\n \"def quantize_scope(*args):\\n quantization_objects = {\\n 'QuantizeAwareActivation':\\n vitis_quantize_aware_activation.QuantizeAwareActivation,\\n 'NoQuantizeActivation':\\n vitis_quantize_aware_activation.NoQuantizeActivation,\\n 'QuantizeWrapper':\\n vitis_quantize_wrapper.QuantizeWrapper,\\n 'CustomOpWrapper':\\n vitis_custom_wrapper.CustomOpWrapper,\\n }\\n quantization_objects.update(vitis_quantizers._types_dict())\\n quantization_objects.update(vitis_quantize_configs._types_dict())\\n quantization_objects.update(vitis_quantize_layer._types_dict())\\n quantization_objects.update(vitis_activation._types_dict())\\n quantization_objects.update(vitis_pooling._types_dict())\\n\\n return tf.keras.utils.custom_object_scope(*(args + (quantization_objects,)))\",\n \"def quantization_helper(im, n_quant, n_iter):\\n im *= (255 / im.max())\\n hist, bins = np.histogram(im, bins=256, range=[0, 256])\\n cumulative_hist = np.cumsum(hist)\\n # initial division such that each segment will contain approximately the same number of pixels.\\n num_of_pixels = cumulative_hist.max() / n_quant\\n z = np.zeros(shape=n_quant + 1, dtype='int')\\n for i in range(0, len(z) - 1):\\n z[i] = np.argmin(np.absolute(cumulative_hist - num_of_pixels * (i)))\\n\\n z[len(z) - 1] = 255 # The first and last elements are 0 and 255 respectively.\\n q = np.zeros(shape=n_quant, dtype='float64')\\n\\n z, q, error = perform_quantization_loop(z, q, n_iter, hist, bins)\\n lookup_table = np.array([0]*256,dtype='float64')\\n\\n for i in range(n_quant):\\n lookup_table[z[i]:z[i+1]] = q[i]\\n\\n im_quant = lookup_table[im.astype(int)]\\n return im_quant, error\",\n \"def test_qc_quantize_op_cpu_fp16_pass_through(self):\\n graph = tf.Graph()\\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\\n sess = tf.compat.v1.Session(graph=graph, config=config)\\n bitwidth = 8\\n use_symm_encoding = True\\n\\n with graph.as_default():\\n # place holder for the input\\n with tf.device(\\\"/device:CPU:0\\\"):\\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\\n tensor_quantizer = libpymo.TensorQuantizer(libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\\n libpymo.RoundingMode.ROUND_NEAREST)\\n tensor_quantizer_val = libpymo.PtrToInt64(tensor_quantizer)\\n tensor_quant_ref = tf.Variable(initial_value=tensor_quantizer_val, trainable=False, dtype=tf.int64)\\n\\n encoding_min = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\\n encoding_max = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\\n bit_width = tf.Variable(initial_value=bitwidth, trainable=False, dtype=tf.int8)\\n use_symmetric_encoding = tf.Variable(initial_value=use_symm_encoding, trainable=False, dtype=tf.bool)\\n is_int_data_type = tf.Variable(initial_value=False, trainable=False, dtype=tf.bool)\\n mode_var = tf.Variable(initial_value=int(libpymo.TensorQuantizerOpMode.passThrough),\\n trainable=False, dtype=tf.int32)\\n\\n sess.run([mode_var.initializer, tensor_quant_ref.initializer, encoding_min.initializer,\\n encoding_max.initializer, bit_width.initializer, use_symmetric_encoding.initializer,\\n is_int_data_type.initializer])\\n\\n pass_through_op_output = zero_out_module.qc_quantize(name='quant_op', in_tensor=inp,\\n op_mode=mode_var,\\n tensor_quantizer_reference=tensor_quant_ref,\\n encoding_min=encoding_min,\\n encoding_max=encoding_max,\\n bit_width=bit_width,\\n use_symmetric_encoding=use_symmetric_encoding,\\n is_int_data_type=is_int_data_type)\\n\\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\\n\\n inp_data = np.array([0.78027299, 0.44164284, 0.6942797, 0.69774088, 0.55863863, 0.29553034, 0.219199,\\n 0.09483732, 0.55075674, 0.6348504], dtype=np.float32)\\n\\n # get the output\\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\\n\\n # compare qc_quantize op's output with expected output\\n assert np.allclose(out_data, inp_data)\\n\\n sess.close()\",\n \"def quantizer(self, nbits: int):\\n raise NotImplementedError\",\n \"def quantize_affine_given_quant_params(\\n input: torch.Tensor,\\n quantize_params: QuantizeAffineParams2,\\n) -> torch.Tensor:\\n return QuantizeAffineFunction.apply(input, quantize_params)\",\n \"def test_quantize_conv_transpose_s8s8(self):\\n\\n np.random.seed(1)\\n model_fp32_path = \\\"conv_transpose_fp32.onnx\\\"\\n self.construct_model(model_fp32_path)\\n data_reader = self.input_feeds(1, {\\\"input\\\": [1, 1, 7, 7]})\\n\\n self.static_quant_test_qdq(\\n model_fp32_path,\\n data_reader,\\n activation_type=QuantType.QInt8,\\n weight_type=QuantType.QInt8,\\n extra_options={\\\"ActivationSymmetric\\\": True},\\n )\",\n \"def Dequantize(scale, mantissa, nScaleBits=3, nMantBits=5):\\n\\n aNum = 0.0 # REMOVE THIS LINE WHEN YOUR FUNCTION IS DONE\\n\\n ### YOUR CODE STARTS HERE ###\\n R = 2**nScaleBits - 1 + nMantBits\\n aNum = 0\\n s = mantissa & (1 << (nMantBits - 1))\\n if s > 0:\\n s = 1 << (R - 1)\\n code = mantissa & (2**(nMantBits - 1) - 1)\\n aNum += s\\n aNum += code << max(R - scale - nMantBits, 0)\\n\\n if scale < (2**nScaleBits - 1):\\n if code > 0:\\n shift = R - scale - nMantBits - 1\\n aNum += 1 << shift\\n aNum = DequantizeUniform(aNum, R)\\n ### YOUR CODE ENDS HERE ###\\n\\n return aNum\",\n \"def test_quantize_training():\\n config = EasyDict()\\n\\n config.NETWORK_CLASS = FlowNetSV3Quantized\\n config.DATASET_CLASS = FlyingChairs\\n\\n config.IS_DEBUG = False\\n config.IMAGE_SIZE = [384, 512]\\n config.BATCH_SIZE = 8\\n config.TEST_STEPS = 200\\n config.MAX_STEPS = 5000\\n config.SAVE_CHECKPOINT_STEPS = 100\\n config.KEEP_CHECKPOINT_MAX = 5\\n config.SUMMARISE_STEPS = 100\\n config.IS_PRETRAIN = False\\n config.IS_DISTRIBUTION = False\\n config.TASK = Tasks.OPTICAL_FLOW_ESTIMATION\\n\\n # network model config\\n config.NETWORK = EasyDict()\\n config.NETWORK.OPTIMIZER_CLASS = tf.train.AdamOptimizer\\n config.NETWORK.OPTIMIZER_KWARGS = {\\\"beta1\\\": 0.9, \\\"beta2\\\": 0.999}\\n config.NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant\\n config.NETWORK.LEARNING_RATE_KWARGS = {\\n \\\"values\\\": [0.0000125, 0.00005],\\n \\\"boundaries\\\": [5000]\\n }\\n\\n config.NETWORK.IMAGE_SIZE = config.IMAGE_SIZE\\n config.NETWORK.BATCH_SIZE = config.BATCH_SIZE\\n config.NETWORK.DATA_FORMAT = \\\"NHWC\\\"\\n config.NETWORK.ACTIVATION_QUANTIZER = linear_mid_tread_half_quantizer\\n config.NETWORK.ACTIVATION_QUANTIZER_KWARGS = {\\n 'bit': 2,\\n 'max_value': 2.0\\n }\\n config.NETWORK.WEIGHT_QUANTIZER = binary_channel_wise_mean_scaling_quantizer\\n config.NETWORK.WEIGHT_QUANTIZER_KWARGS = {}\\n\\n # dataset config\\n config.DATASET = EasyDict()\\n config.DATASET.PRE_PROCESSOR = None\\n config.DATASET.BATCH_SIZE = config.BATCH_SIZE\\n config.DATASET.DATA_FORMAT = \\\"NHWC\\\"\\n config.DATASET.VALIDATION_RATE = 0.2\\n config.DATASET.VALIDATION_SEED = 2019\\n config.DATASET.AUGMENTOR = Sequence([\\n # Geometric transformation\\n FlipLeftRight(0.5),\\n FlipTopBottom(0.5),\\n Translate(-0.2, 0.2),\\n Rotate(-17, +17),\\n Scale(1.0, 2.0),\\n # Pixel-wise augmentation\\n Brightness(0.8, 1.2),\\n Contrast(0.2, 1.4),\\n Color(0.5, 2.0),\\n Gamma(0.7, 1.5),\\n # Hue(-128.0, 128.0),\\n GaussianNoise(0.0, 10.0)\\n ])\\n config.DATASET.PRE_PROCESSOR = Sequence([\\n DevideBy255(),\\n ])\\n environment.init(\\\"test_flownet_s_v3_quantize\\\")\\n prepare_dirs(recreate=True)\\n start_training(config)\",\n \"def test_qc_quantize_op_gpu(self):\\n graph = tf.Graph()\\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\\n sess = tf.compat.v1.Session(graph=graph, config=config)\\n bitwidth = 8\\n use_symm_encoding = False\\n with graph.as_default():\\n\\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\\n tensor_quantizer = libpymo.TensorQuantizer(libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\\n libpymo.RoundingMode.ROUND_NEAREST)\\n tensor_quantizer_val = libpymo.PtrToInt64(tensor_quantizer)\\n tensor_quant_ref = tf.Variable(initial_value=tensor_quantizer_val, trainable=False, dtype=tf.int64)\\n\\n mode_var = tf.Variable(initial_value=int(libpymo.TensorQuantizerOpMode.updateStats),\\n trainable=False, dtype=tf.int32)\\n\\n encoding_min = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\\n encoding_max = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\\n bit_width = tf.Variable(initial_value=bitwidth, trainable=False, dtype=tf.int8)\\n use_symmetric_encoding = tf.Variable(initial_value=use_symm_encoding, trainable=False, dtype=tf.bool)\\n is_int_data_type = tf.Variable(initial_value=True, trainable=False, dtype=tf.bool)\\n\\n sess.run([mode_var.initializer, tensor_quant_ref.initializer, encoding_min.initializer,\\n encoding_max.initializer, bit_width.initializer, use_symmetric_encoding.initializer,\\n is_int_data_type.initializer])\\n\\n # place holder for the input\\n with tf.device(\\\"/device:GPU:0\\\"):\\n\\n pass_through_op_output = zero_out_module.qc_quantize(name='quant_op', in_tensor=inp,\\n op_mode=mode_var,\\n tensor_quantizer_reference=tensor_quant_ref,\\n encoding_min=encoding_min,\\n encoding_max=encoding_max,\\n bit_width=bit_width,\\n use_symmetric_encoding=use_symmetric_encoding,\\n is_int_data_type=is_int_data_type)\\n\\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\\n inp_data = np.random.rand(10)\\n\\n # get the output\\n\\n print(\\\"inp_data\\\", inp_data)\\n with tf.device(\\\"/device:GPU:0\\\"):\\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\\n print(\\\"out_data\\\", out_data)\\n\\n # compare qc_quantize op's output with input\\n assert np.allclose(out_data, inp_data)\\n\\n # compute encodings\\n assert not tensor_quantizer.isEncodingValid\\n encoding = tensor_quantizer.computeEncoding(bitwidth, use_symm_encoding)\\n assert tensor_quantizer.isEncodingValid\\n print('min=', encoding.min, ', max=', encoding.max)\\n\\n # get the output\\n inp_data = np.random.rand(10) * 2\\n print(\\\"inp_data\\\", inp_data)\\n mode_var.load(int(libpymo.TensorQuantizerOpMode.quantizeDequantize), sess)\\n with tf.device(\\\"/device:GPU:0\\\"):\\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\\n print(\\\"out_data\\\", out_data)\\n\\n # compare qc_quantize op's output with input\\n assert not np.allclose(out_data, inp_data)\\n\\n sess.close()\",\n \"def _quant_unimplemented(self, *input: Any) -> None:\\n raise NotImplementedError\",\n \"def update_params(self):\\n for layer in range(self.num_layers):\\n for direction in range(self.num_directions):\\n for param_name in self._get_param_names(direction, layer):\\n param = getattr(self.module_to_quantize, param_name)\\n param.data = getattr(self, param_name).data\\n\\n _logger.debug('Updated params for QcQuantizeRNN')\",\n \"def test_qc_quantize_op_oneshot_cpu(self):\\n graph = tf.Graph()\\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\\n sess = tf.compat.v1.Session(graph=graph, config=config)\\n bitwidth = 8\\n use_symm_encoding = False\\n with graph.as_default():\\n # place holder for the input\\n with tf.device(\\\"/device:CPU:0\\\"):\\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\\n tensor_quantizer = libpymo.TensorQuantizer(libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\\n libpymo.RoundingMode.ROUND_NEAREST)\\n tensor_quantizer_val = libpymo.PtrToInt64(tensor_quantizer)\\n tensor_quant_ref = tf.Variable(initial_value=tensor_quantizer_val, trainable=False, dtype=tf.int64)\\n\\n mode_var = tf.Variable(initial_value=int(libpymo.TensorQuantizerOpMode.oneShotQuantizeDequantize),\\n trainable=False, dtype=tf.int32)\\n\\n encoding_min = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\\n encoding_max = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\\n bit_width = tf.Variable(initial_value=bitwidth, trainable=False, dtype=tf.int8)\\n use_symmetric_encoding = tf.Variable(initial_value=use_symm_encoding, trainable=False, dtype=tf.bool)\\n is_int_data_type = tf.Variable(initial_value=True, trainable=False, dtype=tf.bool)\\n\\n sess.run([mode_var.initializer, tensor_quant_ref.initializer, encoding_min.initializer,\\n encoding_max.initializer, bit_width.initializer, use_symmetric_encoding.initializer,\\n is_int_data_type.initializer])\\n\\n pass_through_op_output = zero_out_module.qc_quantize(name='quant_op', in_tensor=inp,\\n op_mode=mode_var,\\n tensor_quantizer_reference=tensor_quant_ref,\\n encoding_min=encoding_min,\\n encoding_max=encoding_max,\\n bit_width=bit_width,\\n use_symmetric_encoding=use_symmetric_encoding,\\n is_int_data_type=is_int_data_type)\\n\\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\\n inp_data = np.random.rand(10) * 256\\n\\n # get the output\\n print(inp_data)\\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\\n print(out_data)\\n\\n assert tensor_quantizer.isEncodingValid\\n encoding = tensor_quantizer.computeEncoding(bitwidth, use_symm_encoding)\\n\\n print('min=', encoding.min, ', max=', encoding.max)\\n\\n # compare qc_quantize op's output with input\\n assert not np.allclose(out_data, inp_data)\\n\\n sess.close()\",\n \"def __float__(self):\\n return float(self.encoded) / (1 << self.frac_bits)\",\n \"def normalization_factor(self) -> _VectorisedFloat:\\n raise NotImplementedError(\\\"Subclass must implement\\\")\",\n \"def _scalar_conversion(self, p_input:float) -> float:\\n return (p_input/self.units[self._unit_in]*self.units[self._unit_out])\",\n \"def quantifyValues( values, scale):\\r\\n\\r\\n\\tfor v in range(len(values)):\\r\\n\\t\\tvalues[v] = st.quantify( values[v], scale)\\r\\n\\treturn values\",\n \"def _quantize_hidden_cell_state(self, layer_index: int, cell_hx: Union[torch.Tensor, Tuple[torch.Tensor]]) -> \\\\\\n Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:\\n if isinstance(cell_hx, tuple):\\n quantized_cell_hx = (self._quantize_activation(self._output_quantizers['h_l{}'.format(layer_index)], cell_hx[0]),\\n self._quantize_activation(self._output_quantizers['c_l{}'.format(layer_index)], cell_hx[1]))\\n else:\\n quantized_cell_hx = self._quantize_activation(self._output_quantizers['h_l{}'.format(layer_index)], cell_hx)\\n return quantized_cell_hx\",\n \"def quantizer(iterable):\\n return (int(32767.0 * sample) for sample in iterable)\",\n \"def quantize(input=None, min_range=None, max_range=None, out_type=_Null, out=None, name=None, **kwargs):\\n return (0,)\",\n \"def quantify ( value, scale ):\\r\\n\\r\\n\\r\\n\\tif value < scale[0] or value > scale[-1]: return value\\r\\n\\tfor i in range (1, len(scale)):\\r\\n\\t\\tif value <= scale[i]:\\r\\n\\t\\t\\tif scale[i] - value > value - scale[i-1]: return scale[i-1]\\r\\n\\t\\t\\telse: return scale[i]\",\n \"def test_qc_quantize_static_op_cpu(self):\\n graph = tf.Graph()\\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\\n sess = tf.compat.v1.Session(graph=graph, config=config)\\n\\n with graph.as_default():\\n # place holder for the input\\n with tf.device(\\\"/device:CPU:0\\\"):\\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\\n\\n pass_through_op_output = zero_out_module.qc_quantize_static(name='quant_op', in_tensor=inp,\\n encoding_min=-1.0,\\n encoding_max=1.0,\\n bitwidth=8,\\n quant_scheme=libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\\n op_mode=libpymo.TensorQuantizerOpMode.passThrough,\\n is_symmetric=False)\\n\\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\\n inp_data = np.random.rand(10).astype(np.float32)\\n\\n # get the output\\n print(inp_data)\\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\\n print(out_data)\\n\\n # compare qc_quantize op's output with input\\n assert np.allclose(out_data, inp_data, atol=1e-6)\\n sess.close()\\n\\n graph = tf.Graph()\\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\\n sess = tf.compat.v1.Session(graph=graph, config=config)\\n\\n with graph.as_default():\\n # place holder for the input\\n with tf.device(\\\"/device:CPU:0\\\"):\\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\\n\\n pass_through_op_output = zero_out_module.qc_quantize_static(name='quant_op', in_tensor=inp,\\n encoding_min=-1.0,\\n encoding_max=0.5,\\n bitwidth=8,\\n quant_scheme=libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\\n op_mode=libpymo.TensorQuantizerOpMode.quantizeDequantize,\\n is_symmetric=False)\\n\\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\\n inp_data = np.random.rand(10).astype(np.float32)\\n\\n # get the output\\n print(inp_data)\\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\\n print(out_data)\\n\\n # compare qc_quantize op's output with input\\n assert not np.allclose(out_data, inp_data, atol=1e-1)\\n sess.close()\\n\\n graph = tf.Graph()\\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\\n sess = tf.compat.v1.Session(graph=graph, config=config)\\n\\n with graph.as_default():\\n # place holder for the input\\n with tf.device(\\\"/device:CPU:0\\\"):\\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\\n\\n pass_through_op_output = zero_out_module.qc_quantize_static(name='quant_op', in_tensor=inp,\\n encoding_min=-1.0,\\n encoding_max=1.0,\\n bitwidth=8,\\n quant_scheme=libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\\n op_mode=libpymo.TensorQuantizerOpMode.oneShotQuantizeDequantize,\\n is_symmetric=False)\\n\\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\\n inp_data = np.random.rand(10).astype(np.float32)\\n\\n # get the output\\n print(inp_data)\\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\\n print(out_data)\\n\\n # compare qc_quantize op's output with input\\n assert not np.allclose(out_data, inp_data, atol=1e-3)\\n\\n sess.close()\",\n \"def scale(self):\",\n \"def quantization_error(self, in_tensor, bits=32):\\n raise NotImplementedError('To be Overidden for derived class')\",\n \"def graph_mode_quantize(self, inputs, data_loader, calibration_num_batches=64):\\n if (\\n isinstance(self.right_encoder, RoBERTaEncoder)\\n and self.right_encoder.use_linformer_encoder\\n and isinstance(self.left_encoder, RoBERTaEncoder)\\n and self.left_encoder.use_linformer_encoder\\n ):\\n trace = self.trace(inputs)\\n qconfig = get_default_qconfig(\\\"fbgemm\\\")\\n qconfig_dict = {\\\"\\\": qconfig}\\n prepare_m = prepare_jit(trace, qconfig_dict, inplace=False)\\n prepare_m.eval()\\n with torch.no_grad():\\n for i, (_, batch) in enumerate(data_loader):\\n print(\\\"Running calibration with batch {}\\\".format(i))\\n input_data = self.onnx_trace_input(batch)\\n prepare_m(*input_data)\\n if i == calibration_num_batches - 1:\\n break\\n trace = convert_jit(prepare_m, inplace=True)\\n else:\\n super().quantize()\\n trace = self.trace(inputs)\\n\\n return trace\",\n \"def __call__(self, tensor, mode=0):\\n\\n return tensor\\n data_cuda_array = cuda.as_cuda_array(tensor.data.detach())\\n data_p = data_cuda_array.device_ctypes_pointer\\n self._param.mode = mode\\n ret = self._libquant.HI_GFPQ_QuantAndDeQuant_GPU_PY(\\n data_p, data_cuda_array.size, self._bit_width,\\n ctypes.byref(self._param), self._stream.handle,\\n self._cublas_handle)\\n assert ret == 0, \\\"HI_GFPQ_QuantAndDeQuant failed(%d)\\\\n\\\" % (ret)\\n return tensor\",\n \"def increment_quality(self, increment_unit):\\n if self.quality > self.min_quality and self.quality < self.max_quality:\\n self.quality = self.quality + increment_unit\\n return self.quality\",\n \"def compress(self, tensor, *args, **kwargs):\\n pass\",\n \"def quantize_whole_model(net, bits=8):\\n cluster_centers = []\\n assert isinstance(net, nn.Module)\\n layer_ind = 0\\n for n, m in net.named_modules():\\n if isinstance(m, PrunedConv):\\n \\\"\\\"\\\"\\n Apply quantization for the PrunedConv layer.\\n --------------Your Code---------------------\\n \\\"\\\"\\\"\\n\\n # Cluster the Weights\\n num_centroid = pow(2, bits)\\n\\n all_weight = m.conv.weight.data.cpu().detach().numpy()\\n weight_shape = all_weight.shape\\n \\n all_weight = all_weight.reshape(-1,1)\\n k_init = np.linspace(all_weight.min(), all_weight.max(), num_centroid)\\n\\n kmeans = KMeans(n_clusters=num_centroid, init=k_init.reshape(-1, 1), n_init=1).fit(all_weight)\\n\\n # Generate Code Book\\n cluster_center = kmeans.cluster_centers_.flatten()\\n\\n # Quantize\\n indexs = kmeans.predict(all_weight)\\n indexs = indexs.reshape(weight_shape)\\n \\n vmap = np.vectorize(lambda x:cluster_center[x])\\n m.conv.weight.data = torch.from_numpy(vmap(indexs)).to(device)\\n \\n _cluster_center = [ \\\"{0:b}\\\".format(x).zfill(bits) for x in range(len(cluster_center)) ]\\n cluster_centers.append(_cluster_center)\\n\\n layer_ind += 1\\n print(\\\"Complete %d layers quantization...\\\" %layer_ind)\\n \\n elif isinstance(m, PruneLinear):\\n \\\"\\\"\\\"\\n Apply quantization for the PrunedLinear layer.\\n --------------Your Code---------------------\\n \\\"\\\"\\\"\\n \\n # Cluster the Weights\\n num_centroid = pow(2, bits)\\n \\n all_weight = m.linear.weight.data.cpu().detach().numpy()\\n weight_shape = all_weight.shape\\n\\n all_weight = all_weight.reshape(-1,1)\\n k_init = np.linspace(all_weight.min(), all_weight.max(), num_centroid)\\n \\n kmeans = KMeans(n_clusters=num_centroid, init=k_init.reshape(-1, 1), n_init=1).fit(all_weight)\\n \\n # Generate Code Book\\n cluster_center = kmeans.cluster_centers_.flatten()\\n\\n # Quantize\\n indexs = kmeans.predict(all_weight)\\n indexs = indexs.reshape(weight_shape)\\n\\n vmap = np.vectorize(lambda x:cluster_center[x])\\n m.linear.weight.data = torch.from_numpy(vmap(indexs)).to(device)\\n \\n _cluster_center = [ \\\"{0:b}\\\".format(x).zfill(bits) for x in range(len(cluster_center)) ]\\n cluster_centers.append(_cluster_center)\\n \\n layer_ind += 1\\n print(\\\"Complete %d layers quantization...\\\" %layer_ind)\\n \\n return np.array(cluster_centers)\",\n \"def set_quantize_strategy(self,\\n new_quantize_strategy='quantize_strategy.json'):\\n if self._model_format == 'pb':\\n logger.warning('This function does not support pb format model.')\\n return None\\n\\n if isinstance(new_quantize_strategy, str):\\n new_quantize_strategy = common_utils.load_json(new_quantize_strategy)\\n elif not isinstance(new_quantize_strategy, dict):\\n logger.error(\\n 'new_quantize_strategy should be filepath or dict, but found {}'\\n .format(type(new_quantize_strategy)))\\n\\n self._quantize_strategy.update(new_quantize_strategy)\",\n \"def DequantizeFP(scale, mantissa, nScaleBits=3, nMantBits=5):\\n\\n ### YOUR CODE STARTS HERE ###\\n R = 2**nScaleBits - 1 + nMantBits\\n aNum = 0\\n s = mantissa & (1 << (nMantBits - 1))\\n if s > 0:\\n s = 1 << (R - 1)\\n code = mantissa & (2**(nMantBits - 1) - 1)\\n aNum += s\\n aNum += code << max(R - scale - nMantBits - 1, 0)\\n if scale != (2**nScaleBits - 1):\\n aNum += 1 << (R - scale - 2)\\n shift = R - scale - nMantBits - 2\\n if shift > 0:\\n aNum += 1 << shift\\n aNum = DequantizeUniform(aNum, R)\\n ### YOUR CODE ENDS HERE ###\\n\\n return aNum\",\n \"def quantify(self, samples=None):\\n from scipy import stats\\n\\n if samples:\\n ratios = [q['ratio'] for s,q in self.quantification.items() if s in samples]\\n else:\\n ratios = [q['ratio'] for q in self.quantification.values()]\\n\\n # Filter out 0 and NaN\\n ratios = np.array([r for r in ratios if r != 0])\\n ratios = ratios[~np.isnan(ratios)]\\n\\n log_ratios = np.log(ratios)\\n\\n t, p = stats.ttest_1samp(log_ratios, 0)\\n\\n self.avg_ratio = np.mean(ratios)\\n self.p_value = p\",\n \"def sinkhorn(self, Q: torch.Tensor, nmb_iters: int) ->torch.Tensor:\\n with torch.no_grad():\\n sum_Q = torch.sum(Q)\\n Q /= sum_Q\\n K, B = Q.shape\\n if self.gpus > 0:\\n u = torch.zeros(K)\\n r = torch.ones(K) / K\\n c = torch.ones(B) / B\\n else:\\n u = torch.zeros(K)\\n r = torch.ones(K) / K\\n c = torch.ones(B) / B\\n for _ in range(nmb_iters):\\n u = torch.sum(Q, dim=1)\\n Q *= (r / u).unsqueeze(1)\\n Q *= (c / torch.sum(Q, dim=0)).unsqueeze(0)\\n return (Q / torch.sum(Q, dim=0, keepdim=True)).t().float()\",\n \"def encode(self, x):\\n self.eval()\\n x = torch.as_tensor(x).unsqueeze(0)\\n if self.do_mt:\\n enc_output, _ = self.encoder_mt(x, None)\\n else:\\n enc_output, _ = self.encoder(x, None)\\n return enc_output.squeeze(0)\",\n \"def quantize_affine(\\n input: torch.Tensor,\\n min_value: Optional[numbers.Real] = None,\\n max_value: Optional[numbers.Real] = None,\\n num_bits: int = None,\\n) -> torch.Tensor:\\n if num_bits is None:\\n raise ValueError(\\\"num_bits must be supplied\\\")\\n\\n if min_value is None:\\n # Force include 0 in our calculation of min_value.\\n min_value = min(input.min().item(), 0.0)\\n if max_value is None:\\n # Force include 0 in our calculation of max_value.\\n max_value = max(input.max().item(), 0.0)\\n\\n quantize_params = get_quantization_params(min_value, max_value, num_bits)\\n return QuantizeAffineFunction.apply(input, quantize_params)\",\n \"def _create_qat_model(self, dataset):\\n if not self._optimized_model:\\n logger.error('Should call `optimize_model()` before `_create_qat_model`.')\\n self._qat_model, self._layer_metadata = self._quantizer.create_quantize_model(\\n self._optimized_model,\\n candidate_layers=self._candidate_layers,\\n layer_metadata=self._layer_metadata,\\n quantize_strategy=self._quantize_strategy,\\n mode='QAT',\\n target=self._target,\\n dataset=dataset)\",\n \"def quantile_concrete(self, u):\\n y = torch.sigmoid((torch.log(u) - torch.log(1.0 - u) + self.qz_log_alpha) / self.beta)\\n return y * (self.zeta - self.gamma) + self.gamma\",\n \"def reduce_unit(self, guide_unit=None):\\n key = (self.unit, guide_unit)\\n if key in Quantity._reduce_cache:\\n (unit, value_factor) = Quantity._reduce_cache[key]\\n else:\\n value_factor = 1.0\\n canonical_units = {} # dict of dimensionTuple: (Base/ScaledUnit, exponent)\\n # Bias result toward guide units\\n if guide_unit != None:\\n for u, exponent in guide_unit.iter_base_or_scaled_units():\\n d = u.get_dimension_tuple()\\n if d not in canonical_units:\\n canonical_units[d] = [u, 0]\\n for u, exponent in self.unit.iter_base_or_scaled_units():\\n d = u.get_dimension_tuple()\\n # Take first unit found in a dimension as canonical\\n if d not in canonical_units:\\n canonical_units[d] = [u, exponent]\\n else:\\n value_factor *= (u.conversion_factor_to(canonical_units[d][0])**exponent)\\n canonical_units[d][1] += exponent\\n new_base_units = {}\\n for d in canonical_units:\\n u, exponent = canonical_units[d]\\n if exponent != 0:\\n assert u not in new_base_units\\n new_base_units[u] = exponent\\n # Create new unit\\n if len(new_base_units) == 0:\\n unit = dimensionless\\n else:\\n unit = Unit(new_base_units)\\n # There might be a factor due to unit conversion, even though unit is dimensionless\\n # e.g. suppose unit is meter/centimeter\\n if unit.is_dimensionless():\\n unit_factor = unit.conversion_factor_to(dimensionless)\\n if unit_factor != 1.0:\\n value_factor *= unit_factor\\n # print \\\"value_factor = %s\\\" % value_factor\\n unit = dimensionless\\n Quantity._reduce_cache[key] = (unit, value_factor)\\n # Create Quantity, then scale (in case value is a container)\\n # That's why we don't just scale the value.\\n result = Quantity(self._value, unit)\\n if value_factor != 1.0:\\n # __mul__ strips off dimensionless, if appropriate\\n result = result * value_factor\\n if unit.is_dimensionless():\\n assert unit is dimensionless # should have been set earlier in this method\\n if is_quantity(result):\\n result = result._value\\n return result\",\n \"def test_quantization_does_not_violate_bounds():\\n dim = Integer(\\\"yo\\\", \\\"uniform\\\", 3, 7)\\n transformers = [Reverse(Quantize())]\\n tdim = TransformedDimension(Compose(transformers, dim.type), dim)\\n assert 11 not in dim\\n assert 10 in dim\\n # but be careful, because upper bound is inclusive\\n assert 11.5 not in tdim\\n # rounded to 11\\n assert 10.6 not in tdim\\n # rounded to 10\\n assert 10.4 in tdim\\n assert tdim.reverse(9.6) in dim\\n assert tdim.reverse(9.6) == 10\\n assert tdim.reverse(9.4) == 9\",\n \"def compress(self, tensor):\",\n \"def test_qc_quantize_static_op_gpu(self):\\n graph = tf.Graph()\\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\\n sess = tf.compat.v1.Session(graph=graph, config=config)\\n\\n with graph.as_default():\\n # place holder for the input\\n with tf.device(\\\"/device:GPU:0\\\"):\\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\\n\\n pass_through_op_output = zero_out_module.qc_quantize_static(name='quant_op', in_tensor=inp,\\n encoding_min=-1.0,\\n encoding_max=1.0,\\n bitwidth=8,\\n quant_scheme=libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\\n op_mode=libpymo.TensorQuantizerOpMode.passThrough,\\n is_symmetric=False)\\n\\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\\n inp_data = np.random.rand(10).astype(np.float32)\\n\\n # get the output\\n print(inp_data)\\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\\n print(out_data)\\n\\n # compare qc_quantize op's output with input\\n assert np.allclose(out_data, inp_data, atol=1e-6)\\n sess.close()\\n\\n graph = tf.Graph()\\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\\n sess = tf.compat.v1.Session(graph=graph, config=config)\\n\\n with graph.as_default():\\n # place holder for the input\\n with tf.device(\\\"/device:GPU:0\\\"):\\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\\n\\n pass_through_op_output = zero_out_module.qc_quantize_static(name='quant_op', in_tensor=inp,\\n encoding_min=-1.0,\\n encoding_max=0.5,\\n bitwidth=8,\\n quant_scheme=libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\\n op_mode=libpymo.TensorQuantizerOpMode.quantizeDequantize,\\n is_symmetric=False)\\n\\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\\n inp_data = np.random.rand(10).astype(np.float32)\\n\\n # get the output\\n print(inp_data)\\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\\n print(out_data)\\n\\n # compare qc_quantize op's output with input\\n assert not np.allclose(out_data, inp_data, atol=1e-1)\\n sess.close()\\n\\n graph = tf.Graph()\\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\\n sess = tf.compat.v1.Session(graph=graph, config=config)\\n\\n with graph.as_default():\\n # place holder for the input\\n with tf.device(\\\"/device:GPU:0\\\"):\\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\\n\\n pass_through_op_output = zero_out_module.qc_quantize_static(name='quant_op', in_tensor=inp,\\n encoding_min=-1.0,\\n encoding_max=1.0,\\n bitwidth=8,\\n quant_scheme=libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\\n op_mode=libpymo.TensorQuantizerOpMode.oneShotQuantizeDequantize,\\n is_symmetric=False)\\n\\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\\n inp_data = np.random.rand(10).astype(np.float32)\\n\\n # get the output\\n print(inp_data)\\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\\n print(out_data)\\n\\n # compare qc_quantize op's output with input\\n assert not np.allclose(out_data, inp_data, atol=1e-3)\\n\\n sess.close()\",\n \"def quantized(values, steps, input_min=0, input_max=1):\\n values = _normalize(values)\\n if steps < 1:\\n raise ValueError(\\\"steps must be 1 or larger\\\")\\n if input_min >= input_max:\\n raise ValueError('input_min must be smaller than input_max')\\n input_size = input_max - input_min\\n for v in scaled(values, 0, 1, input_min, input_max):\\n yield ((int(v * steps) / steps) * input_size) + input_min\",\n \"def _freeze_quantize_info(self, quantize_info):\\n if not self._qcb_model:\\n logger.error('No qcb_model found.')\\n\\n if not self._qcbev_model:\\n logger.error('No qcbev_model found.')\\n\\n # Freeze the quantize info into the quantized model\\n model_utils.set_quantize_info(self._qcb_model, quantize_info)\\n model_utils.set_quantize_info(self._qcbev_model, quantize_info)\",\n \"def convert(self, value):\\n\\n\\t\\tif self.converter is not None:\\n\\t\\t\\treturn self.converter(value)\\n\\t\\telif self.units is not None:\\n\\t\\t\\tq = Quantity(value)\\n\\t\\t\\tq.assert_dimensions(self.units)\\n\\n\\t\\t\\treturn q\\n\\t\\telse:\\n\\t\\t\\treturn value\",\n \"def gradfactor(self, f):\\r\\n raise NotImplementedError\",\n \"def quantile(self, hypercube):\\n raise NotImplementedError()\",\n \"def quadTo(self, *__args): # real signature unknown; restored from __doc__ with multiple overloads\\r\\n pass\",\n \"def test_qc_quantize_recurrent_param_op(self):\\n graph = tf.Graph()\\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\\n sess = tf.compat.v1.Session(graph=graph, config=config)\\n bitwidth = 8\\n use_symm_encoding = True\\n\\n with graph.as_default():\\n # place holder for the input\\n with tf.device(\\\"/device:CPU:0\\\"):\\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\\n tensor_quantizer = libpymo.TensorQuantizer(libpymo.QuantizationMode.QUANTIZATION_TF,\\n libpymo.RoundingMode.ROUND_NEAREST)\\n tensor_quantizer_val = libpymo.PtrToInt64(tensor_quantizer)\\n tensor_quant_ref = tf.Variable(initial_value=tensor_quantizer_val, trainable=False, dtype=tf.int64)\\n\\n time_step_tensor = tf.constant(1, dtype=tf.int32)\\n\\n encoding_min = tf.Variable(initial_value=-0.5, trainable=True, dtype=tf.double)\\n encoding_max = tf.Variable(initial_value=0.5, trainable=True, dtype=tf.double)\\n bit_width = tf.Variable(initial_value=bitwidth, trainable=False, dtype=tf.int8)\\n use_symmetric_encoding = tf.Variable(initial_value=use_symm_encoding, trainable=False,\\n dtype=tf.bool)\\n\\n mode_var = tf.Variable(initial_value=int(libpymo.TensorQuantizerOpMode.oneShotQuantizeDequantize),\\n trainable=False, dtype=tf.int32)\\n\\n sess.run([mode_var.initializer, tensor_quant_ref.initializer, encoding_min.initializer,\\n encoding_max.initializer, bit_width.initializer, use_symmetric_encoding.initializer])\\n\\n pass_through_op_output = zero_out_module.qc_quantize_recurrent_param(name='quant_op', in_tensor=inp,\\n op_mode=mode_var,\\n tensor_quantizer_reference=tensor_quant_ref,\\n encoding_min=encoding_min,\\n encoding_max=encoding_max,\\n bit_width=bit_width,\\n use_symmetric_encoding=use_symmetric_encoding,\\n time_steps=time_step_tensor)\\n\\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\\n # inp_data = np.random.rand(10).astype(np.float32)\\n np.random.seed(18)\\n inp_data = np.random.randint(low=-1, high=2, size=10).astype(np.float32)\\n\\n # get the output\\n print(inp_data)\\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\\n print(out_data)\\n\\n # compare qc_quantize op's output with input\\n # encodings being set to -0.5 and 0.5 should not have a bearing on this quantized output\\n # we should not observe truncation if op's encoding min/max input values are used instead of cached values\\n assert np.allclose(out_data, inp_data, atol=1e-6)\\n sess.close()\",\n \"def q(self, element):\\n \\n if self.is_empty():\\n return 0\\n\\n if element.is_empty():\\n return self._sum()\\n\\n if not element.is_compatible(next(iter(self.focals))):\\n return 0\\n\\n result = 0\\n for focal, value in self.items():\\n if element.is_subset(focal):\\n result += value\\n return round(result, 6)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66118073","0.6562525","0.6480266","0.6165468","0.6131837","0.59161913","0.59054255","0.5854331","0.58431715","0.58389634","0.580356","0.578608","0.5622065","0.5571132","0.5530895","0.5513868","0.5501805","0.54813254","0.54429746","0.5433842","0.5411219","0.53472346","0.5332422","0.5331159","0.5312934","0.5280252","0.5271287","0.5264658","0.5245002","0.52416193","0.5219758","0.5202947","0.51709414","0.51505476","0.5133206","0.5124794","0.5094165","0.5093455","0.5078805","0.5056698","0.5056698","0.5031407","0.5031407","0.5031407","0.5029948","0.5020144","0.5013087","0.50062937","0.49830908","0.49603865","0.4910138","0.49073878","0.4894246","0.48887157","0.48813325","0.48793265","0.48777193","0.48775363","0.4861896","0.48565972","0.48449394","0.48218286","0.47854373","0.4768313","0.4754529","0.47227544","0.4722006","0.47168788","0.46736595","0.46733022","0.46461964","0.46413842","0.4617969","0.46151668","0.46144557","0.46074384","0.4597581","0.45742404","0.457131","0.45595154","0.45583555","0.45547998","0.45037487","0.45023438","0.44950944","0.44895604","0.44856057","0.44806904","0.44750294","0.44745302","0.44741973","0.4472128","0.44713268","0.44679317","0.44677338","0.44598374","0.44551814","0.44544357","0.4437436","0.44229046"],"string":"[\n \"0.66118073\",\n \"0.6562525\",\n \"0.6480266\",\n \"0.6165468\",\n \"0.6131837\",\n \"0.59161913\",\n \"0.59054255\",\n \"0.5854331\",\n \"0.58431715\",\n \"0.58389634\",\n \"0.580356\",\n \"0.578608\",\n \"0.5622065\",\n \"0.5571132\",\n \"0.5530895\",\n \"0.5513868\",\n \"0.5501805\",\n \"0.54813254\",\n \"0.54429746\",\n \"0.5433842\",\n \"0.5411219\",\n \"0.53472346\",\n \"0.5332422\",\n \"0.5331159\",\n \"0.5312934\",\n \"0.5280252\",\n \"0.5271287\",\n \"0.5264658\",\n \"0.5245002\",\n \"0.52416193\",\n \"0.5219758\",\n \"0.5202947\",\n \"0.51709414\",\n \"0.51505476\",\n \"0.5133206\",\n \"0.5124794\",\n \"0.5094165\",\n \"0.5093455\",\n \"0.5078805\",\n \"0.5056698\",\n \"0.5056698\",\n \"0.5031407\",\n \"0.5031407\",\n \"0.5031407\",\n \"0.5029948\",\n \"0.5020144\",\n \"0.5013087\",\n \"0.50062937\",\n \"0.49830908\",\n \"0.49603865\",\n \"0.4910138\",\n \"0.49073878\",\n \"0.4894246\",\n \"0.48887157\",\n \"0.48813325\",\n \"0.48793265\",\n \"0.48777193\",\n \"0.48775363\",\n \"0.4861896\",\n \"0.48565972\",\n \"0.48449394\",\n \"0.48218286\",\n \"0.47854373\",\n \"0.4768313\",\n \"0.4754529\",\n \"0.47227544\",\n \"0.4722006\",\n \"0.47168788\",\n \"0.46736595\",\n \"0.46733022\",\n \"0.46461964\",\n \"0.46413842\",\n \"0.4617969\",\n \"0.46151668\",\n \"0.46144557\",\n \"0.46074384\",\n \"0.4597581\",\n \"0.45742404\",\n \"0.457131\",\n \"0.45595154\",\n \"0.45583555\",\n \"0.45547998\",\n \"0.45037487\",\n \"0.45023438\",\n \"0.44950944\",\n \"0.44895604\",\n \"0.44856057\",\n \"0.44806904\",\n \"0.44750294\",\n \"0.44745302\",\n \"0.44741973\",\n \"0.4472128\",\n \"0.44713268\",\n \"0.44679317\",\n \"0.44677338\",\n \"0.44598374\",\n \"0.44551814\",\n \"0.44544357\",\n \"0.4437436\",\n \"0.44229046\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.46610415"},"document_rank":{"kind":"string","value":"70"}}},{"rowIdx":20090,"cells":{"query":{"kind":"string","value":"Implementation of the `compress_weights()` method for the PyTorch backend."},"document":{"kind":"string","value":"def compress_weights(model: torch.nn.Module, use_fake_quantize: bool = False) -> torch.nn.Module:\n compressed_model, _ = replace_modules_by_nncf_modules(model)\n insert_pre_compression_operations(model, use_fake_quantize)\n\n return compressed_model"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def compress(self, tensor):","def compress(self, tensor, *args, **kwargs):\n pass","def weight_compression(weights, bits, axis=0, quantizer=None):\n assert bits <= 8\n n = 2**bits\n index_table = []\n codebook_table = np.zeros((weights.shape[axis], n))\n km_models = [None] * weights.shape[axis]\n\n for i, w in tqdm(enumerate(np.split(weights, weights.shape[axis], axis))):\n original_shape = w.shape\n w = w.ravel()\n km = KMeans(n)\n km.fit(w.reshape(-1, 1))\n if quantizer:\n km.cluster_centers_ = quantizer(km.cluster_centers_).numpy()\n km.cluster_centers_.sort(axis=0)\n\n km_models[i] = km\n codebook_table[i, :] = km.cluster_centers_.flatten()\n preds = km.predict(w.reshape(-1, 1))\n index_table.append(preds.reshape(original_shape))\n\n index_table = np.concatenate(index_table, axis)\n return index_table, codebook_table","def compress(self, tensor, *args, **kwargs):\n return self.compressor.compress(tensor)","def compress(self, tensor, *args, **kwargs):\n return self.compressor.compress(tensor)","def apply_compressed_sensing(self, inputs, rng):\n print('using compressed sensing!')\n train_path = os.path.join(\n self.data_dir, 'assist{0}-{1}'.format(self.which_year, 'train'))\n\n if self.which_set == 'test':\n loaded = np.load(train_path + '-compression-matrix.npz')\n self.compress_matrix = loaded['compress_matrix']\n self.compress_dim = self.compress_matrix.shape[1]\n elif self.which_set == 'train':\n self.compress_matrix = self.make_compression_matrix(train_path, rng)\n\n inputs = self.compress_inputs(inputs)\n return inputs","def mask_weights(self, mask, weights):\n new_weights = list()\n for idx, layer in enumerate(self.model.layers):\n if len(layer.get_weights())>0:\n new_weights.append(weights[idx]*mask[idx])\n new_weights.append(layer.get_weights()[1])\n else:\n continue\n return new_weights","def init_weights_(self):\n raise NotImplementedError","def compress(emb):\n if params.sum_word_vecs:\n return np.sum(emb, axis=0)\n if params.max_pool_word_vecs:\n return np.amax(emb, axis=0)\n if params.concat_word_vecs:\n return concat_word_vecs(emb, params.max_transcript_len)\n if params.avg_word_vecs:\n return np.mean(emb, axis=0)","def init_weights(self):\n for m in self.modules():\n if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):\n xavier_uniform_(m.weight)\n if m.bias is not None:\n zeros_(m.bias)","def compress(self, tensor, *args, **kwargs):\n tensor_compressed = tensor\n if 'float' in str(tensor.dtype):\n # Only allow compression from other floating point types\n tensor_compressed = tensor.astype('float16', copy=False)\n return tensor_compressed, tensor.dtype","def normalizeWeights(self):\n for wt in self.weights:\n wt[wt>1] = 1\n wt[wt<-1] = -1\n for bs in self.bias:\n bs[bs>1] = 1\n bs[bs<-1] = -1","def _set_weights(self, weights):\r\n self.weights = weights.reshape(self.output_size, self.input_size+1)","def compress(self, tensor, *args, **kwargs):\n return tensor, None","def init_weights(self):\n with torch.no_grad():\n self._init_weights()","def _init_weights(self):\n nn.init.xavier_normal_(self.out.weight)","def _create_weights(self):\n gate_size = self._hidden_size * self._num_gates\n # Compute the shape of weight and bias.\n matrix_shapes, bias_shapes = [], []\n for layer in range(self._num_layers):\n for direction in range(self._num_directions):\n layer_input_size = self._input_size if layer == 0 \\\n else self._hidden_size * self._num_directions\n w_ih_shape = [gate_size, layer_input_size]\n w_hh_shape = [gate_size, self._hidden_size]\n b_ih_shape, b_hh_shape = [gate_size], [gate_size]\n matrix_shapes.extend([w_ih_shape, w_hh_shape])\n bias_shapes.extend([b_ih_shape, b_hh_shape])\n # Create single float32 weights.\n weights_count = 0\n self._weights_shapes = matrix_shapes + bias_shapes\n for shape in self._weights_shapes:\n weights_count += math_util.prod(shape)\n self._weights = Tensor([weights_count])\n self._weights.requires_grad = True","def update_weights(self):\n self._weights = self._weights + self.update_weights_value\n self.weights_clipping()","def sparsify_weights(self, threshold = 1e-6):\n weights = self.list_cnn[-1].get_weights()\n sparsified_weights = []\n for w in weights:\n bool_mask = (abs(w) > threshold).astype(int)\n sparsified_weights.append(w * bool_mask)\n self.list_cnn[-1].set_weights(sparsified_weights)","def update_weights(self):\n\t\tpass","def init_weights(self):\n # Initialize weights\n self.apply(self._init_weights)\n # Tie weights if needed\n self.tie_weights()","def decompress(self, tensor, ctx, *args, **kwargs):\n if \"x\" not in kwargs:\n raise ValueError(\"x is missing\")\n\n x = kwargs[\"x\"].astype(tensor.dtype, copy=False) \n \n if not self.inited:\n self.cache = nd.zeros_like(tensor)\n if size(tensor.shape) >= self.threshold:\n self.mom = nd.zeros_like(tensor)\n self.wdmom = True\n self.inited = True\n \n # weight decay\n nd._internal._mul_scalar(x, self.wd, out=self.cache)\n\n # weight decay momentum\n if self.wdmom:\n self.mom += self.cache\n nd._internal._mul_scalar(self.mom, self.mu, out=self.mom)\n tensor += self.mom\n\n tensor += self.cache\n return self.compressor.decompress(tensor, ctx, *args, **kwargs)","def compress(self, samples):\n rms = np.sqrt(np.dot(samples, samples) / window)\n power = self.power * (1.0 - self.smooth) + rms * self.smooth\n self.power = power\n if power <= 1e-40:\n samples *= 0\n return\n db_in = 10.0 * math.log10(power)\n if db_in <= self.limit:\n samples *= 0\n return\n db_out = self.cf(db_in)\n db_gain = db_out - db_in + self.postgain\n gain = 10**(0.1 * db_gain)\n samples *= gain","def init_weights(self):\n # Initialize weights\n self.apply(self._init_weights)","def make_conv_weight_image(all_weights, limit=144):\n import vtool as vt\n # Try to infer if use_color should be shown\n num, channels, height, width = all_weights.shape\n # Try to infer if use_color should be shown\n use_color = (channels == 3)\n # non-use_color features need to be flattened\n if not use_color:\n all_weights_ = all_weights.reshape(num * channels, height, width, 1)\n else:\n # convert from theano to cv2 BGR\n all_weights_ = utils.convert_theano_images_to_cv2_images(all_weights)\n # convert from BGR to RGB\n all_weights_ = all_weights_[..., ::-1]\n #cv2.cvtColor(all_weights_[-1], cv2.COLOR_BGR2RGB)\n\n # Limit all_weights_\n #num = all_weights_.shape[0]\n num, height, width, channels = all_weights_.shape\n if limit is not None and num > limit:\n all_weights_ = all_weights_[:limit]\n num = all_weights_.shape[0]\n\n # Convert weight values to image values\n normalize_individually = False\n if normalize_individually:\n # Normalize each feature individually\n all_max = vt.multiaxis_reduce(np.amax, all_weights_, startaxis=1)\n all_min = vt.multiaxis_reduce(np.amin, all_weights_, startaxis=1)\n all_domain = all_max - all_min\n extra_dims = (None,) * (len(all_weights_.shape) - 1)\n broadcaster = (slice(None),) + extra_dims\n all_features = ((all_weights_ - all_min[broadcaster]) *\n (255.0 / all_domain[broadcaster])).astype(np.uint8)\n else:\n # Normalize jointly across all filters\n _max = all_weights_.max()\n _min = all_weights_.min()\n _domain = _max - _min\n all_features = ((all_weights_ - _min) * (255.0 / _domain)).astype(np.uint8)\n\n #import scipy.misc\n # resize feature, give them a border, and stack them together\n new_height, new_width = max(32, height), max(32, width)\n nbp_ = 1 # num border pixels\n _resized_features = np.array([\n cv2.resize(img, (new_width, new_height),\n interpolation=cv2.INTER_NEAREST)\n for img in all_features\n ])\n resized_features = _resized_features.reshape(\n num, new_height, new_width, channels)\n border_shape = (num, new_height + (nbp_ * 2),\n new_width + (nbp_ * 2), channels)\n bordered_features = np.zeros(border_shape, dtype=resized_features.dtype)\n bordered_features[:, nbp_:-nbp_, nbp_:-nbp_, :] = resized_features\n #img_list = bordered_features\n stacked_img = vt.stack_square_images(bordered_features)\n return stacked_img","def make_compression_matrix(self, train_path, rng):\n self.compress_dim = 100 # value used in original DKT paper\n if rng:\n compress_matrix = rng.randn(self.encoding_dim, self.compress_dim)\n else:\n compress_matrix = np.random.randn(self.encoding_dim, self.compress_dim)\n\n np.savez(train_path + '-compression-matrix', compress_matrix=compress_matrix)\n return compress_matrix","def _init_weights(self):\n for m in self.modules():\n if type(m) in {\n nn.Linear,\n nn.Conv3d,\n nn.Conv2d,\n nn.ConvTranspose2d,\n nn.ConvTranspose3d\n }:\n nn.init.kaiming_normal_(\n m.weight.data, a=0, mode='fan_out', nonlinearity='relu',\n )\n if m.bias is not None:\n fan_in, fan_out = \\\n nn.init._calculate_fan_in_and_fan_out(m.weight.data)\n bound = 1 / math.sqrt(fan_out)\n nn.init.normal_(m.bias, -bound, bound)","def put_weights(self, content: ndarray, var_id: int, batch_no: int, block_id: int) -> None:\n pass","def get_weights_tensor(self):\n return [self.hidden_layers[i].get_weight_tensor() for i in range(self.depth)] + \\\n [self.output_weight.clone()]","def __create_conv_weights(self, conv_weights):\n\n conv_xform_weights = []\n curr_n = 32\n k = 5\n for idx, conv_w in enumerate(conv_weights):\n\n curr_n = self.n_values[idx]\n W = self.__create_W_matrix(curr_n, conv_w)\n conv_xform_weights.append(W)\n\n return conv_xform_weights","def get_weights(self):\n # First part is iterating over hidden weights. Then append the output weight.\n return [self.hidden_layers[i].get_weight() for i in range(self.depth)] + \\\n [self.output_weight.cpu().detach().numpy()]","def _init_weights(self):\n for layer in self.modules():\n if isinstance(layer, (nn.Conv1d, nn.Linear)):\n nn.init.xavier_uniform_(layer.weight)\n if layer.bias is not None:\n nn.init.constant_(layer.bias, 0)\n elif isinstance(layer, nn.BatchNorm1d):\n nn.init.constant_(layer.weight, 1)\n nn.init.constant_(layer.bias, 0)","def _init_weights(self):\n for layer in self.modules():\n if isinstance(layer, (nn.Conv1d, nn.Linear)):\n nn.init.xavier_uniform_(layer.weight)\n if layer.bias is not None:\n nn.init.constant_(layer.bias, 0)\n elif isinstance(layer, nn.BatchNorm1d):\n nn.init.constant_(layer.weight, 1)\n nn.init.constant_(layer.bias, 0)","def _init_weights(self):\n for layer in self.modules():\n if isinstance(layer, (nn.Conv1d, nn.Linear)):\n nn.init.xavier_uniform_(layer.weight)\n if layer.bias is not None:\n nn.init.constant_(layer.bias, 0)\n elif isinstance(layer, nn.BatchNorm1d):\n nn.init.constant_(layer.weight, 1)\n nn.init.constant_(layer.bias, 0)","def get_weights(self):","def get_weights(self):\n # First part is iterating over hidden weights. Then append the output weight.\n return [self.hidden_layers[i].get_weight().cpu().detach().numpy() for i in range(self.depth)] + \\\n [self.output_weight.cpu().detach().numpy()]","def initialize_weights(self):\n weights_initializer.WeightsInitializer.initialize_layer_or_model(\n self._batch)","def _set_weights(self):\n self.Wc = torch.mm(self.TP.T, self.W).mm(self.TP)","def copy_cluster_weights(shape, weight_file, method=\"bilinear\"):\n\n # gets the temporary folder path\n temp_path = get_temp_folder()\n short_name = get_prefix_less_name(shape)\n\n for node in weight_file:\n if not weight_file[node]:\n continue\n cmds.deformerWeights(weight_file[node], im=True, shape=short_name,\n deformer=node, path=temp_path, method=method,\n vertexConnections=True)","def set_weights(self, W):\r\n if self.weights.shape!=W.shape:\r\n return -1\r\n self.weights = W","def init_weights(self):\n self.transformer.init_weights()\n if self.loss_cls.use_sigmoid:\n bias_init = bias_init_with_prob(0.01)\n for m in self.cls_branches:\n nn.init.constant_(m[-1].bias, bias_init)","def normalize_weights(self, labels, weights):\n if self._ragged:\n labels, _, weights, _ = utils.ragged_to_dense(labels, None, weights)\n return self._normalize_weights_impl(labels, weights)","def get_weights(self):\n \n w = torch.exp(self._weight) * self.mask_d + self._weight * self.mask_o\n\n w_squared_norm = (w ** 2).sum(-1, keepdim=True)\n \n w = self._diag_weight.exp() * w / w_squared_norm.sqrt()\n \n wpl = self._diag_weight + self._weight - 0.5 * torch.log(w_squared_norm) \n\n return w.t(), wpl.t()[self.mask_d.bool().t()].view(\n self.dim, self.in_features // self.dim, self.out_features // self.dim)","def init_weights(self):\n\n for ch in self.children():\n if issubclass(ch.__class__, torch.nn.Module) and not issubclass(ch.__class__, PreTrainedModel):\n ch.apply(lambda module: self.transformer.__class__._init_weights(self.transformer, module))","def _load_weights(self):\n self.npz_weights = np.load(self._weight_file)\n self._load_byte_embedding()\n self._load_cnn_weights()\n self._load_highway()\n self._load_projection()","def init_weight(self):\r\n xavier_init(self.output_proj, distribution='uniform', bias=0.)","def encode_weights(self):\n weights = []\n for param in self.global_policy.parameters():\n shape = list(param.shape)\n param_list = torch.flatten(param.data).tolist()\n weights.append(Tensor(shape, param_list))\n\n return weights","def compress_inputs(self, inputs):\n num_students = inputs.shape[0]\n inputs = inputs.toarray()\n inputs = np.dot(inputs.reshape(-1, self.encoding_dim), self.compress_matrix)\n self.encoding_dim = self.compress_dim\n\n return sp.csr_matrix(inputs.reshape(num_students, -1))","def __init__(\n self,\n compressed_model: NNCFNetwork,\n weight_quantizers: Dict[WeightQuantizerId, WeightQuantizerInfo],\n non_weight_quantizers: Dict[NonWeightQuantizerId, NonWeightQuantizerInfo],\n ):\n self._compressed_model = compressed_model\n self._weight_quantizers = weight_quantizers\n self._non_weight_quantizers = non_weight_quantizers","def tie_weights(self):\n if hasattr(self, \"get_output_embeddings\") and hasattr(\n self, \"get_input_embeddings\"):\n output_embeddings = self.get_output_embeddings()\n if output_embeddings is not None:\n self._tie_or_clone_weights(output_embeddings,\n self.get_input_embeddings())","def _weight_initializer(self):\n for m in self.modules():\n if isinstance(m, nn.ConvTranspose2d):\n nn.init.kaiming_normal_(m.weight)\n elif isinstance(m, nn.BatchNorm2d):\n nn.init.constant_(m.weight, 1.0)\n nn.init.constant_(m.bias, 0.0)","def _initialize_weights(self):\n pass","def _compress_mask(self, mask: Tensor) -> Tensor:\n if self.dim is None or len(mask.size()) == 1:\n mask = mask.clone()\n else:\n mask_dim = list(range(len(mask.size())))\n for dim in self.dim:\n mask_dim.remove(dim)\n mask = torch.sum(mask, dim=mask_dim)\n\n if self.block_sparse_size is not None:\n # operation like pooling\n lower_case_letters = 'abcdefghijklmnopqrstuvwxyz'\n ein_expression = ''\n for i, step in enumerate(self.block_sparse_size):\n mask = mask.unfold(i, step, step)\n ein_expression += lower_case_letters[i]\n ein_expression = '...{},{}'.format(ein_expression, ein_expression)\n mask = torch.einsum(ein_expression, mask, torch.ones(self.block_sparse_size).to(mask.device))\n\n return (mask != 0).type_as(mask)","def init_weights(self):\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n xavier_init(m)","def _load_cnn_weights(self):\n convolutions = []\n for i, (num_filters, kernel_size) in enumerate(self.convolutions_params):\n conv = torch.nn.Conv1d(in_channels=self.char_embed_dim, out_channels=num_filters, kernel_size=kernel_size, padding=kernel_size, bias=True)\n weight = self.npz_weights['W_cnn_{}'.format(i)]\n bias = self.npz_weights['b_cnn_{}'.format(i)]\n w_reshaped = np.transpose(weight.squeeze(axis=0), axes=(2, 1, 0))\n if w_reshaped.shape != tuple(conv.weight.data.shape):\n raise ValueError('Invalid weight file')\n conv.weight.data.copy_(torch.div(torch.FloatTensor(w_reshaped), kernel_size * 1.0))\n conv.bias.data.copy_(torch.div(torch.FloatTensor(bias), kernel_size * 1.0))\n conv.weight.requires_grad = self._finetune_pretrained_weights\n conv.bias.requires_grad = self._finetune_pretrained_weights\n convolutions.append(nn.Sequential(conv))\n self.convolutions = nn.ModuleList(convolutions)","def set_weights(self, W):\n if(self.weights.shape != W.shape):\n return -1\n self.weights = W","def __transition_block(ip, nb_filter, compression=1.0, weight_decay=1e-4):\n concat_axis = 1 if K.image_data_format() == 'channels_first' else -1\n\n x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5, fused=fuse)(ip)\n x = Activation('relu')(x)\n x = Conv2D(int(nb_filter * compression), (1, 1),\n kernel_initializer='he_normal', padding='same',\n use_bias=False, kernel_regularizer=l2(weight_decay))(x)\n x = AveragePooling2D((2, 2), strides=(2, 2))(x)\n\n return x","def adjust_weights(dim, weight_bool, weights):\n if weight_bool:\n drop_dims = [i for i in weights.dims if i not in dim]\n drop_dims = {k: 0 for k in drop_dims}\n return weights.isel(drop_dims)\n else:\n return None","def normalize_weights(self):\n \n # Set negative weights to zero\n # Normalize to sum to one.\n \n\n\n self.new_weight=[]\n for i in self._weights:\n if any(i < 0 for i in self._weights):\n self.new_weight = [0,1]\n\n elif all(i == 0 for i in self._weights):\n i = 1/len(self._weights)\n self.new_weight.append(i)\n else:\n i = i/sum(self._weights)\n self.new_weight.append(i)\n\n # If the weights are all zeros, set weights equal to 1/k, where k is the number\n # of components.\n self._weights = self.new_weight\n self._weights = np.round(self._weights,3)","def compress_weights(W, l):\n\n # numpy doesn't seem to have a fast truncated SVD algorithm...\n # this could be faster\n U, s, V = np.linalg.svd(W, full_matrices=False)\n\n Ul = U[:, :l]\n sl = s[:l]\n Vl = V[:l, :]\n\n L = np.dot(np.diag(sl), Vl)\n return Ul, L","def init_weights(self, leveledinit: bool, kernel_size: int, bias: bool) -> None:\n if leveledinit:\n nn.init.normal_(self.conv1d.weight, std=1e-3)\n nn.init.normal_(self.conv1d.bias, std=1e-6)\n with torch.no_grad():\n self.conv1d.weight[:, 0, :] += 1.0 / kernel_size\n else:\n nn.init.xavier_uniform_(self.conv1d.weight)\n\n if self.embed in (\"pre\", \"post\"):\n nn.init.xavier_uniform_(self.embedding.weight)","def initialize_weights(m):\n if isinstance(m, nn.Linear) or isinstance(m, nn.ConvTranspose2d):\n init.xavier_uniform(m.weight.data)","def weights_clipping(self):\n max_weigth = np.amax(np.abs(self._weights))\n self._weights = self._clipping*self._weights/max_weigth","def copy_conv_weights_from(self, source: \"Encoder\") -> None:\n pass","def initialise_weights(self): \n \n def initialise_process(param):\n \n \"\"\"\n Initialises weights of a given parameter following either Xavier or Kaiming uniform or normal processes.\n \n : param (torch.Tensor):\n \n \"\"\"\n \n if self._initialisation_process == 'xavier_uniform':\n tnni.xavier_uniform_(param.data)\n elif self._initialisation_process == 'xavier_normal':\n tnni.xavier_normal_(param.data)\n elif self._initialisation_process == 'kaiming_uniform':\n tnni.kaiming_uniform_(param.data)\n elif self._initialisation_process == 'kaiming_normal':\n tnni.kaiming_normal_(param.data)\n \n if self._initialisation_process is not None:\n for m in self.modules():\n # Embedding\n if type(m) is nn.Embedding:\n tnni.normal_(self.embedding.weight)\n # RNN\n elif type(m) in [nn.GRU, nn.LSTM, nn.RNN]: \n for name, param in m.named_parameters():\n if 'weight_ih' in name:\n initialise_process(param)\n #torch.nn.init.kaiming_normal_(param.data)\n elif 'weight_hh' in name:\n tnni.orthogonal_(param.data)\n elif 'bias' in name:\n # Bias initialised with zero will get the bias from\n # the forget gate\n param.data.fill_(0.0)\n param.data[self._hidden_size:self.directions*self._hidden_size].fill_(1.0)\n # Attention linear layer\n elif type(m) is nn.Linear:\n for name, param in m.named_parameters():\n if 'weight' in name:\n initialise_process(param.data)\n elif 'bias' in name:\n param.data.normal_()","def set_weights(self, weights):\r\n self.weights = weights","def compress(self, *args):\n return _osgAnimation.Vec3Packed_compress(self, *args)","def _generate_weights_file(self, graph):\n\n ops_to_ignore = ['Reshape', 'Mul']\n\n buffers_written = []\n\n packed_file = list(bytes())\n\n tupac = bytes(\"FD\\n\", \"ascii\")\n packed_file.append(struct.pack('{}s'.format(len(tupac)), tupac))\n packed_file.append(struct.pack('{}s'.format(len(self.model_name)+1), bytes(self.model_name+\"\\n\", \"ascii\")))\n\n num_layers = 0\n\n for node in graph.nodes:\n if len(node.input_tensors) > 0 and node.op_type not in ops_to_ignore:\n num_layers += 1\n\n packed_file.append(struct.pack('i', num_layers))\n\n weights_packed = list(bytes())\n\n for node in graph.nodes:\n if len(node.input_tensors) > 0 and node.op_type not in ops_to_ignore:\n layer_name = bytes(node.name + \"\\n\", \"ascii\")\n weights_packed.append(struct.pack('{}s'.format(len(layer_name)), layer_name))\n layer_type = bytes(node.op_type + \"\\n\", \"ascii\")\n weights_packed.append(struct.pack('{}s'.format(len(layer_type)), layer_type))\n else:\n continue\n\n for num, input in enumerate(node.input_tensors):\n\n if input in buffers_written:\n write_buffer = False\n else:\n buffers_written.append(input)\n write_buffer = True\n\n data = node.input_tensors[input]\n\n # if node.op_type == \"MatMul\":\n # data = data.transpose()\n\n if len(data.shape) == 4:\n\n if write_buffer:\n num_output_channels = data.shape[0]\n num_input_channels = data.shape[1]\n height = data.shape[2] # height\n width = data.shape[3] # width\n else:\n num_output_channels = 0\n num_input_channels = 0\n height = 0 # height\n width = 0 # width\n\n weights_packed.append(struct.pack('i', num_output_channels))\n weights_packed.append(struct.pack('i', num_input_channels))\n weights_packed.append(struct.pack('i', height))\n weights_packed.append(struct.pack('i', width))\n\n if write_buffer:\n for channel in data:\n for kernel in channel:\n for row in kernel:\n weights_packed.append(struct.pack('f'*len(row), *row))\n\n elif len(data.shape) == 3:\n\n if write_buffer:\n num_output_channels = data.shape[0]\n num_input_channels = data.shape[1]\n height = 1\n width = data.shape[2]\n else:\n num_output_channels = 0\n num_input_channels = 0\n height = 0\n width = 0\n\n weights_packed.append(struct.pack('i', num_output_channels))\n weights_packed.append(struct.pack('i', num_input_channels))\n weights_packed.append(struct.pack('i', height))\n weights_packed.append(struct.pack('i', width))\n\n if write_buffer:\n for channel in data:\n for kernel in channel:\n weights_packed.append(struct.pack('f'*len(kernel), *kernel))\n\n elif len(data.shape) == 2:\n\n if write_buffer:\n num_data = 1 # num_kernels\n height = data.shape[0] # height\n width = data.shape[1] # width\n else:\n num_data = 0 # num_kernels\n height = 0 # height\n width = 0 # width\n\n weights_packed.append(struct.pack('i', num_data))\n weights_packed.append(struct.pack('i', height))\n weights_packed.append(struct.pack('i', width))\n\n if write_buffer:\n for row in data:\n weights_packed.append(struct.pack('f'*len(row), *row))\n\n elif len(data.shape) == 1:\n\n if write_buffer:\n num_data = data.shape[0] # num_biases\n else:\n num_data = 0\n\n weights_packed.append(struct.pack('i', num_data))\n\n if write_buffer:\n weights_packed.append(struct.pack('f'*len(data), *data))\n\n else:\n print(\"ERROR: Unknown weights/biases/etc. tensor shape!\")\n exit(1)\n\n # This handles the case that no bias values are available in the onnx file.\n # So we need to add num_biases = 0 into the binary file.\n if len(node.input_tensors) == 1 and node.op_type != \"Add\":\n # print(\"No biases in onnx file.\")\n weights_packed.append(struct.pack('i', 0))\n\n packed_file += weights_packed\n\n tupac = bytes(\"end\\n\", \"ascii\")\n packed_file.append(struct.pack('{}s'.format(len(tupac)), tupac))\n\n self.packed_file = packed_file","def load_weights(self, weights):\n\n i = 0\n for l in range(1, self.num_layers()):\n for n in range(self.get_layer(l).num_nodes):\n for w in range(len(self.get_node_with_layer(l, n).weights)):\n self.get_node_with_layer(l, n).weights[w] = weights[i]\n i += 1","def apply_weight_norm(self):\n\n def _apply_weight_norm(m):\n if isinstance(m, torch.nn.Conv1d) or isinstance(\n m, torch.nn.ConvTranspose1d\n ):\n torch.nn.utils.weight_norm(m)\n logging.debug(f\"Weight norm is applied to {m}.\")\n\n self.apply(_apply_weight_norm)","def apply_weight_norm(self):\n\n def _apply_weight_norm(m):\n if isinstance(m, torch.nn.Conv1d) or isinstance(\n m, torch.nn.ConvTranspose1d\n ):\n torch.nn.utils.weight_norm(m)\n logging.debug(f\"Weight norm is applied to {m}.\")\n\n self.apply(_apply_weight_norm)","def apply_weight_norm(self):\n\n def _apply_weight_norm(m):\n if isinstance(m, torch.nn.Conv1d) or isinstance(\n m, torch.nn.ConvTranspose1d\n ):\n torch.nn.utils.weight_norm(m)\n logging.debug(f\"Weight norm is applied to {m}.\")\n\n self.apply(_apply_weight_norm)","def apply_weights(self):\n w0_array = np.ones(self.N)*self.w0\n return w0_array + self.X.dot(self.w)","def forward(ctx, inX, in_weight, in_bias=None, convparam=None):\n # note: for demo purpose, assume dilation=1 and padding_mode='zeros',\n # also assume the padding and stride is the same for ROWS and COLS, respectively\n\n if convparam is not None:\n padding, stride = convparam\n else:\n padding, stride = 0, 1\n\n nOutCh, nInCh, nKnRows, nKnCols = in_weight.shape\n nImgSamples, nInCh, nInImgRows, nInImgCols = inX.shape\n\n # determine the output shape\n nOutRows = (nInImgRows + 2 * padding - nKnRows) // stride + 1\n nOutCols = (nInImgCols + 2 * padding - nKnCols) // stride + 1\n\n ''' \n using torch.nn.functional.unfold to extract nL blocks of size of inChannels x nKnRows x nKnCols elements\n Each block can be used to do multiplication with the kernels\n Input shape: (nImgSamples, nInCh, ∗)\n Output shape: (nImgSamples, nB = nInCh X ∏(kernel_sizes), nL = nOutRows X nOutCols)\n '''\n inX_nSamp_nB_nL = torch.nn.functional.unfold(inX, (nKnRows, nKnCols), padding=padding, stride=stride)\n inX_nSamp_nL_nB = inX_nSamp_nB_nL.transpose(1, 2)\n # \"view\" won't work if some part of the tensor is not contiguous, for example, \n # when coming from torch.flip() of the original one. \n # Therefore, \"view\" is changed to \"reshape\"\n # kn_nOutCh_nB = in_weight.view(nOutCh, -1) \n kn_nOutCh_nB = in_weight.reshape(nOutCh, -1)\n kn_nB_nOutCh = kn_nOutCh_nB.t()\n out_nSamp_nL_nOutCh = inX_nSamp_nL_nB.matmul(kn_nB_nOutCh)\n out_nSamp_nOutCh_nL = out_nSamp_nL_nOutCh.transpose(1, 2)\n out = out_nSamp_nOutCh_nL.reshape(nImgSamples, nOutCh, nOutRows, nOutCols)\n\n if in_bias is not None:\n out += in_bias.view(1, -1, 1, 1)\n\n # cache these objects for use in the backward pass\n ctx.InImgSize = (nInImgRows, nInImgCols)\n ctx.out_nSamp_nOutCh_nL_shape = out_nSamp_nOutCh_nL.shape\n ctx.inX_nSamp_nL_nB = inX_nSamp_nL_nB\n ctx.kn_nB_nOutCh = kn_nB_nOutCh\n ctx.parameters = (nOutCh, nInCh, nKnRows, nKnCols, padding, stride)\n # ctx.save_for_backward(inX_nSamp_nL_nB, kn_nB_nOutCh)\n\n return out","def compress_wrapper(args: Namespace) -> None:\n directory_path = os.path.join(DATASETS_DIR, args.directory)\n compress_datasets(directory_path, args.holdout)","def _extract_weights(self,W):\n wl1_size = self._D*self._hidden_layer_size\n bl1_size = self._hidden_layer_size\n \n wl2_size = self._hidden_layer_size*self._output_size\n bl2_size = self._output_size\n\n \n weights_L1 = W[0:wl1_size].reshape((self._D,self._hidden_layer_size))\n bias_L1 = W[wl1_size:wl1_size+bl1_size]\n \n start_l2 = wl1_size+bl1_size\n\n weights_L2 = W[start_l2: start_l2 + wl2_size].reshape((self._hidden_layer_size,self._output_size))\n bias_L2 = W[start_l2 + wl2_size : start_l2 + wl2_size + bl2_size]\n \n \n \n return weights_L1,bias_L1,weights_L2,bias_L2","def set_output_weights(self, weight_matrix):\n pass","def get_weights_topo(self):\n if (not isinstance(self.input_space.components[0], Conv2DSpace) or\n not isinstance(self.input_space.components[1], Conv2DSpace)):\n raise NotImplementedError()\n wxf = self.wxf.get_value(borrow=False).T\n wyf = self.wyf.get_value(borrow=False).T\n convx = self.input_space.components[0]\n convy = self.input_space.components[1]\n vecx = VectorSpace(self.nvisx)\n vecy = VectorSpace(self.nvisy)\n wxf_view = vecx.np_format_as(wxf,\n Conv2DSpace(\n convx.shape,\n num_channels=convx.num_channels,\n axes=('b', 0, 1, 'c')))\n wyf_view = vecy.np_format_as(wyf,\n Conv2DSpace(\n convy.shape,\n num_channels=convy.num_channels,\n axes=('b', 0, 1, 'c')))\n h = int(numpy.ceil(numpy.sqrt(self.nfac)))\n new_weights = numpy.zeros((wxf_view.shape[0] * 2,\n wxf_view.shape[1],\n wxf_view.shape[2],\n wxf_view.shape[3]), dtype=wxf_view.dtype)\n t = 0\n while t < (self.nfac // h):\n filter_pair = numpy.concatenate(\n (\n wxf_view[h * t:h * (t + 1), ...],\n wyf_view[h * t:h * (t + 1), ...]\n ), 0)\n new_weights[h * 2 * t:h * 2 * (t + 1), ...] = filter_pair\n t += 1\n return new_weights","def decompress(self, tensors):","def copy_weights(self):\n return copy.deepcopy(self.weights_ih), copy.deepcopy(self.weights_ho)","def init_weights(self):\n\n params = torch.load(self.resnet_weight)\n\n self.fc1.weight.data = params['state_dict']['module.fc.weight'].clone()\n self.fc1.bias.data = params['state_dict']['module.fc.bias'].clone()\n\n\n r = np.sqrt(1.) / np.sqrt(self.fc3.in_features +\n self.fc3.out_features)\n self.fc3.weight.data.uniform_(-r, r)\n self.fc3.bias.data.fill_(0)\n r = np.sqrt(1.) / np.sqrt(self.fc2.in_features +\n self.fc2.out_features)\n self.fc2.weight.data.uniform_(-r, r)\n self.fc2.bias.data.fill_(0)\n r = np.sqrt(1.) / np.sqrt(self.fc4.in_features +\n self.fc4.out_features)\n self.fc4.weight.data.uniform_(-r, r)\n self.fc4.bias.data.fill_(0)","def set_weights(self, weights):\n tuples = []\n for layer in self.layers:\n num_param = len(layer.weights)\n layer_weights = weights[:num_param]\n for sw, w in zip(layer.weights, layer_weights):\n tuples.append((sw, w))\n weights = weights[num_param:]\n K.batch_set_value(tuples)","def _weight_initializer(self):\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n nn.init.kaiming_normal_(m.weight)\n elif isinstance(m, nn.BatchNorm2d):\n nn.init.constant_(m.weight, 1.0)\n nn.init.constant_(m.bias, 0.0)","def initialize_weights_xavier(self):\n\t\tself.weights = [np.random.uniform(-1/sqrt(size1), 1/sqrt(size1)) for size1, size2 in zip(self.sizes[:-1], self.sizes[1:])]\n\t\tself.biases = [np.zeros([size, ]) for size in self.sizes[1:]]","def build(self, weight = 0.5):\n \n self.weight = weight\n \n # Defining weighting matrixes\n self.processing_fw = torch.randn((self._hidden_size, self._expansion_size), requires_grad = True).to(self.device)\n self.processing_bw = torch.randn((self._hidden_size, self._expansion_size), requires_grad = True).to(self.device)\n self.processing_last_ht = torch.randn((self._hidden_size*2, self._hidden_size*2), requires_grad = True).to(self.device)\n \n # These will only be applied to the intermediate hidden states\n self.linear_fw = nn.Linear(self._seqlen - 1, 1).to(self.device)\n self.linear_bw = nn.Linear(self._seqlen - 1, 1).to(self.device)\n \n self.compression = torch.randn((self._expansion_size*2, self._hidden_size*2), requires_grad = True).to(self.device)\n \n if self._activation_fn == 'tanh' or isinstance(self._activation_fn, torch.nn.modules.activation.Tanh):\n self._activation_fn = nn.Tanh()\n elif self._activation_fn == 'sigmoid' or isinstance(self._activation_fn, torch.nn.modules.activation.Sigmoid):\n self._activation_fn = nn.Sigmoid()\n elif self._activation_fn == 'leaky ReLU' or isinstance(self._activation_fn, torch.nn.modules.activation.LeakyReLU):\n self._activation_fn = nn.LeakyReLU()\n else:\n raise ValueError('Non-linear activation function must be \"tanh\", \"sigmoid\" or \"leaky ReLU\"')\n \n # Passing it onto the relevant device\n self._activation_fn = self._activation_fn.to(self.device)","def reset_weights(self):\r\n self._weights = deepcopy(self._tmp_weights)\r\n self._tmp_weights = None","def get_weights(self):\n return [self.w, self.b]","def u_weights(self):\n for i in range(self.n_inputs):\n self._q_neuron.cx(self._weights[i], self.inputs[i])","def _init_weights(layer):\n if isinstance(layer, (nn.Conv2d, nn.Linear)):\n torch.nn.init.xavier_uniform_(layer.weight)\n try:\n # Some layers may not have biases, so catch the exception and pass.\n layer.bias.data.fill_(0.0)\n except AttributeError:\n pass","def update_weights(self):\n self._weights = self._weights + self.update_weights_value","def init_weights(self) -> None:\n nn.init.kaiming_normal_(self._U)\n nn.init.kaiming_normal_(self._W)\n nn.init.kaiming_normal_(self._V)\n\n nn.init.normal_(self._b)","def weights(self, weights):\n\n self._weights = weights","def init_weights(model):\n ...","def targets_weights_fn(self):\n return common_layers.weights_all","def weight_wrtg(self, wrtg):\n # Clear caches because weights are going to change.\n # TODO: it might be possible to not clear the caches\n # if the weight doesn't change, and re-use previous decoding.\n wrtg.ClearCaches()\n for p in wrtg.P:\n rule = p.rhs.rule\n assert isinstance(rule.features, list)\n rule.weight = self.weight_rule(rule)","def my_assign_weights(context, data):\n pass","def get_weights(self):\n return self.__weights","def initialize_weights(self, weights_initializer, bias_initializer):\n wshapes = [\n [self.input_size, self.hidden_size[0]],\n [self.hidden_size[0], self.hidden_size[1]],\n [self.hidden_size[1], self.output_size]\n ]\n\n bshapes = [\n [1, self.hidden_size[0]],\n [1, self.hidden_size[1]],\n [1, self.output_size]\n ]\n\n self.weights = [init_weights(s, weights_initializer) for s in wshapes]\n self.biases = [init_weights(s, bias_initializer) for s in bshapes]\n\n self.trainable_variables = self.weights + self.biases","def write_weights_images(self):\n for weight_name, weight in self._weights.items():\n self._write_weight_image_to_tensorboard(\n name=f\"{self._Sections.WEIGHTS}/{weight_name}\",\n weight=weight,\n step=self._epochs,\n )","def _initialize_weights(self):\r\n for m in self.modules():\r\n if isinstance(m, nn.Conv2d):\r\n m.weight.data.normal_(0, 0.05)\r\n if m.bias is not None:\r\n m.bias.data.zero_()"],"string":"[\n \"def compress(self, tensor):\",\n \"def compress(self, tensor, *args, **kwargs):\\n pass\",\n \"def weight_compression(weights, bits, axis=0, quantizer=None):\\n assert bits <= 8\\n n = 2**bits\\n index_table = []\\n codebook_table = np.zeros((weights.shape[axis], n))\\n km_models = [None] * weights.shape[axis]\\n\\n for i, w in tqdm(enumerate(np.split(weights, weights.shape[axis], axis))):\\n original_shape = w.shape\\n w = w.ravel()\\n km = KMeans(n)\\n km.fit(w.reshape(-1, 1))\\n if quantizer:\\n km.cluster_centers_ = quantizer(km.cluster_centers_).numpy()\\n km.cluster_centers_.sort(axis=0)\\n\\n km_models[i] = km\\n codebook_table[i, :] = km.cluster_centers_.flatten()\\n preds = km.predict(w.reshape(-1, 1))\\n index_table.append(preds.reshape(original_shape))\\n\\n index_table = np.concatenate(index_table, axis)\\n return index_table, codebook_table\",\n \"def compress(self, tensor, *args, **kwargs):\\n return self.compressor.compress(tensor)\",\n \"def compress(self, tensor, *args, **kwargs):\\n return self.compressor.compress(tensor)\",\n \"def apply_compressed_sensing(self, inputs, rng):\\n print('using compressed sensing!')\\n train_path = os.path.join(\\n self.data_dir, 'assist{0}-{1}'.format(self.which_year, 'train'))\\n\\n if self.which_set == 'test':\\n loaded = np.load(train_path + '-compression-matrix.npz')\\n self.compress_matrix = loaded['compress_matrix']\\n self.compress_dim = self.compress_matrix.shape[1]\\n elif self.which_set == 'train':\\n self.compress_matrix = self.make_compression_matrix(train_path, rng)\\n\\n inputs = self.compress_inputs(inputs)\\n return inputs\",\n \"def mask_weights(self, mask, weights):\\n new_weights = list()\\n for idx, layer in enumerate(self.model.layers):\\n if len(layer.get_weights())>0:\\n new_weights.append(weights[idx]*mask[idx])\\n new_weights.append(layer.get_weights()[1])\\n else:\\n continue\\n return new_weights\",\n \"def init_weights_(self):\\n raise NotImplementedError\",\n \"def compress(emb):\\n if params.sum_word_vecs:\\n return np.sum(emb, axis=0)\\n if params.max_pool_word_vecs:\\n return np.amax(emb, axis=0)\\n if params.concat_word_vecs:\\n return concat_word_vecs(emb, params.max_transcript_len)\\n if params.avg_word_vecs:\\n return np.mean(emb, axis=0)\",\n \"def init_weights(self):\\n for m in self.modules():\\n if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):\\n xavier_uniform_(m.weight)\\n if m.bias is not None:\\n zeros_(m.bias)\",\n \"def compress(self, tensor, *args, **kwargs):\\n tensor_compressed = tensor\\n if 'float' in str(tensor.dtype):\\n # Only allow compression from other floating point types\\n tensor_compressed = tensor.astype('float16', copy=False)\\n return tensor_compressed, tensor.dtype\",\n \"def normalizeWeights(self):\\n for wt in self.weights:\\n wt[wt>1] = 1\\n wt[wt<-1] = -1\\n for bs in self.bias:\\n bs[bs>1] = 1\\n bs[bs<-1] = -1\",\n \"def _set_weights(self, weights):\\r\\n self.weights = weights.reshape(self.output_size, self.input_size+1)\",\n \"def compress(self, tensor, *args, **kwargs):\\n return tensor, None\",\n \"def init_weights(self):\\n with torch.no_grad():\\n self._init_weights()\",\n \"def _init_weights(self):\\n nn.init.xavier_normal_(self.out.weight)\",\n \"def _create_weights(self):\\n gate_size = self._hidden_size * self._num_gates\\n # Compute the shape of weight and bias.\\n matrix_shapes, bias_shapes = [], []\\n for layer in range(self._num_layers):\\n for direction in range(self._num_directions):\\n layer_input_size = self._input_size if layer == 0 \\\\\\n else self._hidden_size * self._num_directions\\n w_ih_shape = [gate_size, layer_input_size]\\n w_hh_shape = [gate_size, self._hidden_size]\\n b_ih_shape, b_hh_shape = [gate_size], [gate_size]\\n matrix_shapes.extend([w_ih_shape, w_hh_shape])\\n bias_shapes.extend([b_ih_shape, b_hh_shape])\\n # Create single float32 weights.\\n weights_count = 0\\n self._weights_shapes = matrix_shapes + bias_shapes\\n for shape in self._weights_shapes:\\n weights_count += math_util.prod(shape)\\n self._weights = Tensor([weights_count])\\n self._weights.requires_grad = True\",\n \"def update_weights(self):\\n self._weights = self._weights + self.update_weights_value\\n self.weights_clipping()\",\n \"def sparsify_weights(self, threshold = 1e-6):\\n weights = self.list_cnn[-1].get_weights()\\n sparsified_weights = []\\n for w in weights:\\n bool_mask = (abs(w) > threshold).astype(int)\\n sparsified_weights.append(w * bool_mask)\\n self.list_cnn[-1].set_weights(sparsified_weights)\",\n \"def update_weights(self):\\n\\t\\tpass\",\n \"def init_weights(self):\\n # Initialize weights\\n self.apply(self._init_weights)\\n # Tie weights if needed\\n self.tie_weights()\",\n \"def decompress(self, tensor, ctx, *args, **kwargs):\\n if \\\"x\\\" not in kwargs:\\n raise ValueError(\\\"x is missing\\\")\\n\\n x = kwargs[\\\"x\\\"].astype(tensor.dtype, copy=False) \\n \\n if not self.inited:\\n self.cache = nd.zeros_like(tensor)\\n if size(tensor.shape) >= self.threshold:\\n self.mom = nd.zeros_like(tensor)\\n self.wdmom = True\\n self.inited = True\\n \\n # weight decay\\n nd._internal._mul_scalar(x, self.wd, out=self.cache)\\n\\n # weight decay momentum\\n if self.wdmom:\\n self.mom += self.cache\\n nd._internal._mul_scalar(self.mom, self.mu, out=self.mom)\\n tensor += self.mom\\n\\n tensor += self.cache\\n return self.compressor.decompress(tensor, ctx, *args, **kwargs)\",\n \"def compress(self, samples):\\n rms = np.sqrt(np.dot(samples, samples) / window)\\n power = self.power * (1.0 - self.smooth) + rms * self.smooth\\n self.power = power\\n if power <= 1e-40:\\n samples *= 0\\n return\\n db_in = 10.0 * math.log10(power)\\n if db_in <= self.limit:\\n samples *= 0\\n return\\n db_out = self.cf(db_in)\\n db_gain = db_out - db_in + self.postgain\\n gain = 10**(0.1 * db_gain)\\n samples *= gain\",\n \"def init_weights(self):\\n # Initialize weights\\n self.apply(self._init_weights)\",\n \"def make_conv_weight_image(all_weights, limit=144):\\n import vtool as vt\\n # Try to infer if use_color should be shown\\n num, channels, height, width = all_weights.shape\\n # Try to infer if use_color should be shown\\n use_color = (channels == 3)\\n # non-use_color features need to be flattened\\n if not use_color:\\n all_weights_ = all_weights.reshape(num * channels, height, width, 1)\\n else:\\n # convert from theano to cv2 BGR\\n all_weights_ = utils.convert_theano_images_to_cv2_images(all_weights)\\n # convert from BGR to RGB\\n all_weights_ = all_weights_[..., ::-1]\\n #cv2.cvtColor(all_weights_[-1], cv2.COLOR_BGR2RGB)\\n\\n # Limit all_weights_\\n #num = all_weights_.shape[0]\\n num, height, width, channels = all_weights_.shape\\n if limit is not None and num > limit:\\n all_weights_ = all_weights_[:limit]\\n num = all_weights_.shape[0]\\n\\n # Convert weight values to image values\\n normalize_individually = False\\n if normalize_individually:\\n # Normalize each feature individually\\n all_max = vt.multiaxis_reduce(np.amax, all_weights_, startaxis=1)\\n all_min = vt.multiaxis_reduce(np.amin, all_weights_, startaxis=1)\\n all_domain = all_max - all_min\\n extra_dims = (None,) * (len(all_weights_.shape) - 1)\\n broadcaster = (slice(None),) + extra_dims\\n all_features = ((all_weights_ - all_min[broadcaster]) *\\n (255.0 / all_domain[broadcaster])).astype(np.uint8)\\n else:\\n # Normalize jointly across all filters\\n _max = all_weights_.max()\\n _min = all_weights_.min()\\n _domain = _max - _min\\n all_features = ((all_weights_ - _min) * (255.0 / _domain)).astype(np.uint8)\\n\\n #import scipy.misc\\n # resize feature, give them a border, and stack them together\\n new_height, new_width = max(32, height), max(32, width)\\n nbp_ = 1 # num border pixels\\n _resized_features = np.array([\\n cv2.resize(img, (new_width, new_height),\\n interpolation=cv2.INTER_NEAREST)\\n for img in all_features\\n ])\\n resized_features = _resized_features.reshape(\\n num, new_height, new_width, channels)\\n border_shape = (num, new_height + (nbp_ * 2),\\n new_width + (nbp_ * 2), channels)\\n bordered_features = np.zeros(border_shape, dtype=resized_features.dtype)\\n bordered_features[:, nbp_:-nbp_, nbp_:-nbp_, :] = resized_features\\n #img_list = bordered_features\\n stacked_img = vt.stack_square_images(bordered_features)\\n return stacked_img\",\n \"def make_compression_matrix(self, train_path, rng):\\n self.compress_dim = 100 # value used in original DKT paper\\n if rng:\\n compress_matrix = rng.randn(self.encoding_dim, self.compress_dim)\\n else:\\n compress_matrix = np.random.randn(self.encoding_dim, self.compress_dim)\\n\\n np.savez(train_path + '-compression-matrix', compress_matrix=compress_matrix)\\n return compress_matrix\",\n \"def _init_weights(self):\\n for m in self.modules():\\n if type(m) in {\\n nn.Linear,\\n nn.Conv3d,\\n nn.Conv2d,\\n nn.ConvTranspose2d,\\n nn.ConvTranspose3d\\n }:\\n nn.init.kaiming_normal_(\\n m.weight.data, a=0, mode='fan_out', nonlinearity='relu',\\n )\\n if m.bias is not None:\\n fan_in, fan_out = \\\\\\n nn.init._calculate_fan_in_and_fan_out(m.weight.data)\\n bound = 1 / math.sqrt(fan_out)\\n nn.init.normal_(m.bias, -bound, bound)\",\n \"def put_weights(self, content: ndarray, var_id: int, batch_no: int, block_id: int) -> None:\\n pass\",\n \"def get_weights_tensor(self):\\n return [self.hidden_layers[i].get_weight_tensor() for i in range(self.depth)] + \\\\\\n [self.output_weight.clone()]\",\n \"def __create_conv_weights(self, conv_weights):\\n\\n conv_xform_weights = []\\n curr_n = 32\\n k = 5\\n for idx, conv_w in enumerate(conv_weights):\\n\\n curr_n = self.n_values[idx]\\n W = self.__create_W_matrix(curr_n, conv_w)\\n conv_xform_weights.append(W)\\n\\n return conv_xform_weights\",\n \"def get_weights(self):\\n # First part is iterating over hidden weights. Then append the output weight.\\n return [self.hidden_layers[i].get_weight() for i in range(self.depth)] + \\\\\\n [self.output_weight.cpu().detach().numpy()]\",\n \"def _init_weights(self):\\n for layer in self.modules():\\n if isinstance(layer, (nn.Conv1d, nn.Linear)):\\n nn.init.xavier_uniform_(layer.weight)\\n if layer.bias is not None:\\n nn.init.constant_(layer.bias, 0)\\n elif isinstance(layer, nn.BatchNorm1d):\\n nn.init.constant_(layer.weight, 1)\\n nn.init.constant_(layer.bias, 0)\",\n \"def _init_weights(self):\\n for layer in self.modules():\\n if isinstance(layer, (nn.Conv1d, nn.Linear)):\\n nn.init.xavier_uniform_(layer.weight)\\n if layer.bias is not None:\\n nn.init.constant_(layer.bias, 0)\\n elif isinstance(layer, nn.BatchNorm1d):\\n nn.init.constant_(layer.weight, 1)\\n nn.init.constant_(layer.bias, 0)\",\n \"def _init_weights(self):\\n for layer in self.modules():\\n if isinstance(layer, (nn.Conv1d, nn.Linear)):\\n nn.init.xavier_uniform_(layer.weight)\\n if layer.bias is not None:\\n nn.init.constant_(layer.bias, 0)\\n elif isinstance(layer, nn.BatchNorm1d):\\n nn.init.constant_(layer.weight, 1)\\n nn.init.constant_(layer.bias, 0)\",\n \"def get_weights(self):\",\n \"def get_weights(self):\\n # First part is iterating over hidden weights. Then append the output weight.\\n return [self.hidden_layers[i].get_weight().cpu().detach().numpy() for i in range(self.depth)] + \\\\\\n [self.output_weight.cpu().detach().numpy()]\",\n \"def initialize_weights(self):\\n weights_initializer.WeightsInitializer.initialize_layer_or_model(\\n self._batch)\",\n \"def _set_weights(self):\\n self.Wc = torch.mm(self.TP.T, self.W).mm(self.TP)\",\n \"def copy_cluster_weights(shape, weight_file, method=\\\"bilinear\\\"):\\n\\n # gets the temporary folder path\\n temp_path = get_temp_folder()\\n short_name = get_prefix_less_name(shape)\\n\\n for node in weight_file:\\n if not weight_file[node]:\\n continue\\n cmds.deformerWeights(weight_file[node], im=True, shape=short_name,\\n deformer=node, path=temp_path, method=method,\\n vertexConnections=True)\",\n \"def set_weights(self, W):\\r\\n if self.weights.shape!=W.shape:\\r\\n return -1\\r\\n self.weights = W\",\n \"def init_weights(self):\\n self.transformer.init_weights()\\n if self.loss_cls.use_sigmoid:\\n bias_init = bias_init_with_prob(0.01)\\n for m in self.cls_branches:\\n nn.init.constant_(m[-1].bias, bias_init)\",\n \"def normalize_weights(self, labels, weights):\\n if self._ragged:\\n labels, _, weights, _ = utils.ragged_to_dense(labels, None, weights)\\n return self._normalize_weights_impl(labels, weights)\",\n \"def get_weights(self):\\n \\n w = torch.exp(self._weight) * self.mask_d + self._weight * self.mask_o\\n\\n w_squared_norm = (w ** 2).sum(-1, keepdim=True)\\n \\n w = self._diag_weight.exp() * w / w_squared_norm.sqrt()\\n \\n wpl = self._diag_weight + self._weight - 0.5 * torch.log(w_squared_norm) \\n\\n return w.t(), wpl.t()[self.mask_d.bool().t()].view(\\n self.dim, self.in_features // self.dim, self.out_features // self.dim)\",\n \"def init_weights(self):\\n\\n for ch in self.children():\\n if issubclass(ch.__class__, torch.nn.Module) and not issubclass(ch.__class__, PreTrainedModel):\\n ch.apply(lambda module: self.transformer.__class__._init_weights(self.transformer, module))\",\n \"def _load_weights(self):\\n self.npz_weights = np.load(self._weight_file)\\n self._load_byte_embedding()\\n self._load_cnn_weights()\\n self._load_highway()\\n self._load_projection()\",\n \"def init_weight(self):\\r\\n xavier_init(self.output_proj, distribution='uniform', bias=0.)\",\n \"def encode_weights(self):\\n weights = []\\n for param in self.global_policy.parameters():\\n shape = list(param.shape)\\n param_list = torch.flatten(param.data).tolist()\\n weights.append(Tensor(shape, param_list))\\n\\n return weights\",\n \"def compress_inputs(self, inputs):\\n num_students = inputs.shape[0]\\n inputs = inputs.toarray()\\n inputs = np.dot(inputs.reshape(-1, self.encoding_dim), self.compress_matrix)\\n self.encoding_dim = self.compress_dim\\n\\n return sp.csr_matrix(inputs.reshape(num_students, -1))\",\n \"def __init__(\\n self,\\n compressed_model: NNCFNetwork,\\n weight_quantizers: Dict[WeightQuantizerId, WeightQuantizerInfo],\\n non_weight_quantizers: Dict[NonWeightQuantizerId, NonWeightQuantizerInfo],\\n ):\\n self._compressed_model = compressed_model\\n self._weight_quantizers = weight_quantizers\\n self._non_weight_quantizers = non_weight_quantizers\",\n \"def tie_weights(self):\\n if hasattr(self, \\\"get_output_embeddings\\\") and hasattr(\\n self, \\\"get_input_embeddings\\\"):\\n output_embeddings = self.get_output_embeddings()\\n if output_embeddings is not None:\\n self._tie_or_clone_weights(output_embeddings,\\n self.get_input_embeddings())\",\n \"def _weight_initializer(self):\\n for m in self.modules():\\n if isinstance(m, nn.ConvTranspose2d):\\n nn.init.kaiming_normal_(m.weight)\\n elif isinstance(m, nn.BatchNorm2d):\\n nn.init.constant_(m.weight, 1.0)\\n nn.init.constant_(m.bias, 0.0)\",\n \"def _initialize_weights(self):\\n pass\",\n \"def _compress_mask(self, mask: Tensor) -> Tensor:\\n if self.dim is None or len(mask.size()) == 1:\\n mask = mask.clone()\\n else:\\n mask_dim = list(range(len(mask.size())))\\n for dim in self.dim:\\n mask_dim.remove(dim)\\n mask = torch.sum(mask, dim=mask_dim)\\n\\n if self.block_sparse_size is not None:\\n # operation like pooling\\n lower_case_letters = 'abcdefghijklmnopqrstuvwxyz'\\n ein_expression = ''\\n for i, step in enumerate(self.block_sparse_size):\\n mask = mask.unfold(i, step, step)\\n ein_expression += lower_case_letters[i]\\n ein_expression = '...{},{}'.format(ein_expression, ein_expression)\\n mask = torch.einsum(ein_expression, mask, torch.ones(self.block_sparse_size).to(mask.device))\\n\\n return (mask != 0).type_as(mask)\",\n \"def init_weights(self):\\n for m in self.modules():\\n if isinstance(m, nn.Conv2d):\\n xavier_init(m)\",\n \"def _load_cnn_weights(self):\\n convolutions = []\\n for i, (num_filters, kernel_size) in enumerate(self.convolutions_params):\\n conv = torch.nn.Conv1d(in_channels=self.char_embed_dim, out_channels=num_filters, kernel_size=kernel_size, padding=kernel_size, bias=True)\\n weight = self.npz_weights['W_cnn_{}'.format(i)]\\n bias = self.npz_weights['b_cnn_{}'.format(i)]\\n w_reshaped = np.transpose(weight.squeeze(axis=0), axes=(2, 1, 0))\\n if w_reshaped.shape != tuple(conv.weight.data.shape):\\n raise ValueError('Invalid weight file')\\n conv.weight.data.copy_(torch.div(torch.FloatTensor(w_reshaped), kernel_size * 1.0))\\n conv.bias.data.copy_(torch.div(torch.FloatTensor(bias), kernel_size * 1.0))\\n conv.weight.requires_grad = self._finetune_pretrained_weights\\n conv.bias.requires_grad = self._finetune_pretrained_weights\\n convolutions.append(nn.Sequential(conv))\\n self.convolutions = nn.ModuleList(convolutions)\",\n \"def set_weights(self, W):\\n if(self.weights.shape != W.shape):\\n return -1\\n self.weights = W\",\n \"def __transition_block(ip, nb_filter, compression=1.0, weight_decay=1e-4):\\n concat_axis = 1 if K.image_data_format() == 'channels_first' else -1\\n\\n x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5, fused=fuse)(ip)\\n x = Activation('relu')(x)\\n x = Conv2D(int(nb_filter * compression), (1, 1),\\n kernel_initializer='he_normal', padding='same',\\n use_bias=False, kernel_regularizer=l2(weight_decay))(x)\\n x = AveragePooling2D((2, 2), strides=(2, 2))(x)\\n\\n return x\",\n \"def adjust_weights(dim, weight_bool, weights):\\n if weight_bool:\\n drop_dims = [i for i in weights.dims if i not in dim]\\n drop_dims = {k: 0 for k in drop_dims}\\n return weights.isel(drop_dims)\\n else:\\n return None\",\n \"def normalize_weights(self):\\n \\n # Set negative weights to zero\\n # Normalize to sum to one.\\n \\n\\n\\n self.new_weight=[]\\n for i in self._weights:\\n if any(i < 0 for i in self._weights):\\n self.new_weight = [0,1]\\n\\n elif all(i == 0 for i in self._weights):\\n i = 1/len(self._weights)\\n self.new_weight.append(i)\\n else:\\n i = i/sum(self._weights)\\n self.new_weight.append(i)\\n\\n # If the weights are all zeros, set weights equal to 1/k, where k is the number\\n # of components.\\n self._weights = self.new_weight\\n self._weights = np.round(self._weights,3)\",\n \"def compress_weights(W, l):\\n\\n # numpy doesn't seem to have a fast truncated SVD algorithm...\\n # this could be faster\\n U, s, V = np.linalg.svd(W, full_matrices=False)\\n\\n Ul = U[:, :l]\\n sl = s[:l]\\n Vl = V[:l, :]\\n\\n L = np.dot(np.diag(sl), Vl)\\n return Ul, L\",\n \"def init_weights(self, leveledinit: bool, kernel_size: int, bias: bool) -> None:\\n if leveledinit:\\n nn.init.normal_(self.conv1d.weight, std=1e-3)\\n nn.init.normal_(self.conv1d.bias, std=1e-6)\\n with torch.no_grad():\\n self.conv1d.weight[:, 0, :] += 1.0 / kernel_size\\n else:\\n nn.init.xavier_uniform_(self.conv1d.weight)\\n\\n if self.embed in (\\\"pre\\\", \\\"post\\\"):\\n nn.init.xavier_uniform_(self.embedding.weight)\",\n \"def initialize_weights(m):\\n if isinstance(m, nn.Linear) or isinstance(m, nn.ConvTranspose2d):\\n init.xavier_uniform(m.weight.data)\",\n \"def weights_clipping(self):\\n max_weigth = np.amax(np.abs(self._weights))\\n self._weights = self._clipping*self._weights/max_weigth\",\n \"def copy_conv_weights_from(self, source: \\\"Encoder\\\") -> None:\\n pass\",\n \"def initialise_weights(self): \\n \\n def initialise_process(param):\\n \\n \\\"\\\"\\\"\\n Initialises weights of a given parameter following either Xavier or Kaiming uniform or normal processes.\\n \\n : param (torch.Tensor):\\n \\n \\\"\\\"\\\"\\n \\n if self._initialisation_process == 'xavier_uniform':\\n tnni.xavier_uniform_(param.data)\\n elif self._initialisation_process == 'xavier_normal':\\n tnni.xavier_normal_(param.data)\\n elif self._initialisation_process == 'kaiming_uniform':\\n tnni.kaiming_uniform_(param.data)\\n elif self._initialisation_process == 'kaiming_normal':\\n tnni.kaiming_normal_(param.data)\\n \\n if self._initialisation_process is not None:\\n for m in self.modules():\\n # Embedding\\n if type(m) is nn.Embedding:\\n tnni.normal_(self.embedding.weight)\\n # RNN\\n elif type(m) in [nn.GRU, nn.LSTM, nn.RNN]: \\n for name, param in m.named_parameters():\\n if 'weight_ih' in name:\\n initialise_process(param)\\n #torch.nn.init.kaiming_normal_(param.data)\\n elif 'weight_hh' in name:\\n tnni.orthogonal_(param.data)\\n elif 'bias' in name:\\n # Bias initialised with zero will get the bias from\\n # the forget gate\\n param.data.fill_(0.0)\\n param.data[self._hidden_size:self.directions*self._hidden_size].fill_(1.0)\\n # Attention linear layer\\n elif type(m) is nn.Linear:\\n for name, param in m.named_parameters():\\n if 'weight' in name:\\n initialise_process(param.data)\\n elif 'bias' in name:\\n param.data.normal_()\",\n \"def set_weights(self, weights):\\r\\n self.weights = weights\",\n \"def compress(self, *args):\\n return _osgAnimation.Vec3Packed_compress(self, *args)\",\n \"def _generate_weights_file(self, graph):\\n\\n ops_to_ignore = ['Reshape', 'Mul']\\n\\n buffers_written = []\\n\\n packed_file = list(bytes())\\n\\n tupac = bytes(\\\"FD\\\\n\\\", \\\"ascii\\\")\\n packed_file.append(struct.pack('{}s'.format(len(tupac)), tupac))\\n packed_file.append(struct.pack('{}s'.format(len(self.model_name)+1), bytes(self.model_name+\\\"\\\\n\\\", \\\"ascii\\\")))\\n\\n num_layers = 0\\n\\n for node in graph.nodes:\\n if len(node.input_tensors) > 0 and node.op_type not in ops_to_ignore:\\n num_layers += 1\\n\\n packed_file.append(struct.pack('i', num_layers))\\n\\n weights_packed = list(bytes())\\n\\n for node in graph.nodes:\\n if len(node.input_tensors) > 0 and node.op_type not in ops_to_ignore:\\n layer_name = bytes(node.name + \\\"\\\\n\\\", \\\"ascii\\\")\\n weights_packed.append(struct.pack('{}s'.format(len(layer_name)), layer_name))\\n layer_type = bytes(node.op_type + \\\"\\\\n\\\", \\\"ascii\\\")\\n weights_packed.append(struct.pack('{}s'.format(len(layer_type)), layer_type))\\n else:\\n continue\\n\\n for num, input in enumerate(node.input_tensors):\\n\\n if input in buffers_written:\\n write_buffer = False\\n else:\\n buffers_written.append(input)\\n write_buffer = True\\n\\n data = node.input_tensors[input]\\n\\n # if node.op_type == \\\"MatMul\\\":\\n # data = data.transpose()\\n\\n if len(data.shape) == 4:\\n\\n if write_buffer:\\n num_output_channels = data.shape[0]\\n num_input_channels = data.shape[1]\\n height = data.shape[2] # height\\n width = data.shape[3] # width\\n else:\\n num_output_channels = 0\\n num_input_channels = 0\\n height = 0 # height\\n width = 0 # width\\n\\n weights_packed.append(struct.pack('i', num_output_channels))\\n weights_packed.append(struct.pack('i', num_input_channels))\\n weights_packed.append(struct.pack('i', height))\\n weights_packed.append(struct.pack('i', width))\\n\\n if write_buffer:\\n for channel in data:\\n for kernel in channel:\\n for row in kernel:\\n weights_packed.append(struct.pack('f'*len(row), *row))\\n\\n elif len(data.shape) == 3:\\n\\n if write_buffer:\\n num_output_channels = data.shape[0]\\n num_input_channels = data.shape[1]\\n height = 1\\n width = data.shape[2]\\n else:\\n num_output_channels = 0\\n num_input_channels = 0\\n height = 0\\n width = 0\\n\\n weights_packed.append(struct.pack('i', num_output_channels))\\n weights_packed.append(struct.pack('i', num_input_channels))\\n weights_packed.append(struct.pack('i', height))\\n weights_packed.append(struct.pack('i', width))\\n\\n if write_buffer:\\n for channel in data:\\n for kernel in channel:\\n weights_packed.append(struct.pack('f'*len(kernel), *kernel))\\n\\n elif len(data.shape) == 2:\\n\\n if write_buffer:\\n num_data = 1 # num_kernels\\n height = data.shape[0] # height\\n width = data.shape[1] # width\\n else:\\n num_data = 0 # num_kernels\\n height = 0 # height\\n width = 0 # width\\n\\n weights_packed.append(struct.pack('i', num_data))\\n weights_packed.append(struct.pack('i', height))\\n weights_packed.append(struct.pack('i', width))\\n\\n if write_buffer:\\n for row in data:\\n weights_packed.append(struct.pack('f'*len(row), *row))\\n\\n elif len(data.shape) == 1:\\n\\n if write_buffer:\\n num_data = data.shape[0] # num_biases\\n else:\\n num_data = 0\\n\\n weights_packed.append(struct.pack('i', num_data))\\n\\n if write_buffer:\\n weights_packed.append(struct.pack('f'*len(data), *data))\\n\\n else:\\n print(\\\"ERROR: Unknown weights/biases/etc. tensor shape!\\\")\\n exit(1)\\n\\n # This handles the case that no bias values are available in the onnx file.\\n # So we need to add num_biases = 0 into the binary file.\\n if len(node.input_tensors) == 1 and node.op_type != \\\"Add\\\":\\n # print(\\\"No biases in onnx file.\\\")\\n weights_packed.append(struct.pack('i', 0))\\n\\n packed_file += weights_packed\\n\\n tupac = bytes(\\\"end\\\\n\\\", \\\"ascii\\\")\\n packed_file.append(struct.pack('{}s'.format(len(tupac)), tupac))\\n\\n self.packed_file = packed_file\",\n \"def load_weights(self, weights):\\n\\n i = 0\\n for l in range(1, self.num_layers()):\\n for n in range(self.get_layer(l).num_nodes):\\n for w in range(len(self.get_node_with_layer(l, n).weights)):\\n self.get_node_with_layer(l, n).weights[w] = weights[i]\\n i += 1\",\n \"def apply_weight_norm(self):\\n\\n def _apply_weight_norm(m):\\n if isinstance(m, torch.nn.Conv1d) or isinstance(\\n m, torch.nn.ConvTranspose1d\\n ):\\n torch.nn.utils.weight_norm(m)\\n logging.debug(f\\\"Weight norm is applied to {m}.\\\")\\n\\n self.apply(_apply_weight_norm)\",\n \"def apply_weight_norm(self):\\n\\n def _apply_weight_norm(m):\\n if isinstance(m, torch.nn.Conv1d) or isinstance(\\n m, torch.nn.ConvTranspose1d\\n ):\\n torch.nn.utils.weight_norm(m)\\n logging.debug(f\\\"Weight norm is applied to {m}.\\\")\\n\\n self.apply(_apply_weight_norm)\",\n \"def apply_weight_norm(self):\\n\\n def _apply_weight_norm(m):\\n if isinstance(m, torch.nn.Conv1d) or isinstance(\\n m, torch.nn.ConvTranspose1d\\n ):\\n torch.nn.utils.weight_norm(m)\\n logging.debug(f\\\"Weight norm is applied to {m}.\\\")\\n\\n self.apply(_apply_weight_norm)\",\n \"def apply_weights(self):\\n w0_array = np.ones(self.N)*self.w0\\n return w0_array + self.X.dot(self.w)\",\n \"def forward(ctx, inX, in_weight, in_bias=None, convparam=None):\\n # note: for demo purpose, assume dilation=1 and padding_mode='zeros',\\n # also assume the padding and stride is the same for ROWS and COLS, respectively\\n\\n if convparam is not None:\\n padding, stride = convparam\\n else:\\n padding, stride = 0, 1\\n\\n nOutCh, nInCh, nKnRows, nKnCols = in_weight.shape\\n nImgSamples, nInCh, nInImgRows, nInImgCols = inX.shape\\n\\n # determine the output shape\\n nOutRows = (nInImgRows + 2 * padding - nKnRows) // stride + 1\\n nOutCols = (nInImgCols + 2 * padding - nKnCols) // stride + 1\\n\\n ''' \\n using torch.nn.functional.unfold to extract nL blocks of size of inChannels x nKnRows x nKnCols elements\\n Each block can be used to do multiplication with the kernels\\n Input shape: (nImgSamples, nInCh, ∗)\\n Output shape: (nImgSamples, nB = nInCh X ∏(kernel_sizes), nL = nOutRows X nOutCols)\\n '''\\n inX_nSamp_nB_nL = torch.nn.functional.unfold(inX, (nKnRows, nKnCols), padding=padding, stride=stride)\\n inX_nSamp_nL_nB = inX_nSamp_nB_nL.transpose(1, 2)\\n # \\\"view\\\" won't work if some part of the tensor is not contiguous, for example, \\n # when coming from torch.flip() of the original one. \\n # Therefore, \\\"view\\\" is changed to \\\"reshape\\\"\\n # kn_nOutCh_nB = in_weight.view(nOutCh, -1) \\n kn_nOutCh_nB = in_weight.reshape(nOutCh, -1)\\n kn_nB_nOutCh = kn_nOutCh_nB.t()\\n out_nSamp_nL_nOutCh = inX_nSamp_nL_nB.matmul(kn_nB_nOutCh)\\n out_nSamp_nOutCh_nL = out_nSamp_nL_nOutCh.transpose(1, 2)\\n out = out_nSamp_nOutCh_nL.reshape(nImgSamples, nOutCh, nOutRows, nOutCols)\\n\\n if in_bias is not None:\\n out += in_bias.view(1, -1, 1, 1)\\n\\n # cache these objects for use in the backward pass\\n ctx.InImgSize = (nInImgRows, nInImgCols)\\n ctx.out_nSamp_nOutCh_nL_shape = out_nSamp_nOutCh_nL.shape\\n ctx.inX_nSamp_nL_nB = inX_nSamp_nL_nB\\n ctx.kn_nB_nOutCh = kn_nB_nOutCh\\n ctx.parameters = (nOutCh, nInCh, nKnRows, nKnCols, padding, stride)\\n # ctx.save_for_backward(inX_nSamp_nL_nB, kn_nB_nOutCh)\\n\\n return out\",\n \"def compress_wrapper(args: Namespace) -> None:\\n directory_path = os.path.join(DATASETS_DIR, args.directory)\\n compress_datasets(directory_path, args.holdout)\",\n \"def _extract_weights(self,W):\\n wl1_size = self._D*self._hidden_layer_size\\n bl1_size = self._hidden_layer_size\\n \\n wl2_size = self._hidden_layer_size*self._output_size\\n bl2_size = self._output_size\\n\\n \\n weights_L1 = W[0:wl1_size].reshape((self._D,self._hidden_layer_size))\\n bias_L1 = W[wl1_size:wl1_size+bl1_size]\\n \\n start_l2 = wl1_size+bl1_size\\n\\n weights_L2 = W[start_l2: start_l2 + wl2_size].reshape((self._hidden_layer_size,self._output_size))\\n bias_L2 = W[start_l2 + wl2_size : start_l2 + wl2_size + bl2_size]\\n \\n \\n \\n return weights_L1,bias_L1,weights_L2,bias_L2\",\n \"def set_output_weights(self, weight_matrix):\\n pass\",\n \"def get_weights_topo(self):\\n if (not isinstance(self.input_space.components[0], Conv2DSpace) or\\n not isinstance(self.input_space.components[1], Conv2DSpace)):\\n raise NotImplementedError()\\n wxf = self.wxf.get_value(borrow=False).T\\n wyf = self.wyf.get_value(borrow=False).T\\n convx = self.input_space.components[0]\\n convy = self.input_space.components[1]\\n vecx = VectorSpace(self.nvisx)\\n vecy = VectorSpace(self.nvisy)\\n wxf_view = vecx.np_format_as(wxf,\\n Conv2DSpace(\\n convx.shape,\\n num_channels=convx.num_channels,\\n axes=('b', 0, 1, 'c')))\\n wyf_view = vecy.np_format_as(wyf,\\n Conv2DSpace(\\n convy.shape,\\n num_channels=convy.num_channels,\\n axes=('b', 0, 1, 'c')))\\n h = int(numpy.ceil(numpy.sqrt(self.nfac)))\\n new_weights = numpy.zeros((wxf_view.shape[0] * 2,\\n wxf_view.shape[1],\\n wxf_view.shape[2],\\n wxf_view.shape[3]), dtype=wxf_view.dtype)\\n t = 0\\n while t < (self.nfac // h):\\n filter_pair = numpy.concatenate(\\n (\\n wxf_view[h * t:h * (t + 1), ...],\\n wyf_view[h * t:h * (t + 1), ...]\\n ), 0)\\n new_weights[h * 2 * t:h * 2 * (t + 1), ...] = filter_pair\\n t += 1\\n return new_weights\",\n \"def decompress(self, tensors):\",\n \"def copy_weights(self):\\n return copy.deepcopy(self.weights_ih), copy.deepcopy(self.weights_ho)\",\n \"def init_weights(self):\\n\\n params = torch.load(self.resnet_weight)\\n\\n self.fc1.weight.data = params['state_dict']['module.fc.weight'].clone()\\n self.fc1.bias.data = params['state_dict']['module.fc.bias'].clone()\\n\\n\\n r = np.sqrt(1.) / np.sqrt(self.fc3.in_features +\\n self.fc3.out_features)\\n self.fc3.weight.data.uniform_(-r, r)\\n self.fc3.bias.data.fill_(0)\\n r = np.sqrt(1.) / np.sqrt(self.fc2.in_features +\\n self.fc2.out_features)\\n self.fc2.weight.data.uniform_(-r, r)\\n self.fc2.bias.data.fill_(0)\\n r = np.sqrt(1.) / np.sqrt(self.fc4.in_features +\\n self.fc4.out_features)\\n self.fc4.weight.data.uniform_(-r, r)\\n self.fc4.bias.data.fill_(0)\",\n \"def set_weights(self, weights):\\n tuples = []\\n for layer in self.layers:\\n num_param = len(layer.weights)\\n layer_weights = weights[:num_param]\\n for sw, w in zip(layer.weights, layer_weights):\\n tuples.append((sw, w))\\n weights = weights[num_param:]\\n K.batch_set_value(tuples)\",\n \"def _weight_initializer(self):\\n for m in self.modules():\\n if isinstance(m, nn.Conv2d):\\n nn.init.kaiming_normal_(m.weight)\\n elif isinstance(m, nn.BatchNorm2d):\\n nn.init.constant_(m.weight, 1.0)\\n nn.init.constant_(m.bias, 0.0)\",\n \"def initialize_weights_xavier(self):\\n\\t\\tself.weights = [np.random.uniform(-1/sqrt(size1), 1/sqrt(size1)) for size1, size2 in zip(self.sizes[:-1], self.sizes[1:])]\\n\\t\\tself.biases = [np.zeros([size, ]) for size in self.sizes[1:]]\",\n \"def build(self, weight = 0.5):\\n \\n self.weight = weight\\n \\n # Defining weighting matrixes\\n self.processing_fw = torch.randn((self._hidden_size, self._expansion_size), requires_grad = True).to(self.device)\\n self.processing_bw = torch.randn((self._hidden_size, self._expansion_size), requires_grad = True).to(self.device)\\n self.processing_last_ht = torch.randn((self._hidden_size*2, self._hidden_size*2), requires_grad = True).to(self.device)\\n \\n # These will only be applied to the intermediate hidden states\\n self.linear_fw = nn.Linear(self._seqlen - 1, 1).to(self.device)\\n self.linear_bw = nn.Linear(self._seqlen - 1, 1).to(self.device)\\n \\n self.compression = torch.randn((self._expansion_size*2, self._hidden_size*2), requires_grad = True).to(self.device)\\n \\n if self._activation_fn == 'tanh' or isinstance(self._activation_fn, torch.nn.modules.activation.Tanh):\\n self._activation_fn = nn.Tanh()\\n elif self._activation_fn == 'sigmoid' or isinstance(self._activation_fn, torch.nn.modules.activation.Sigmoid):\\n self._activation_fn = nn.Sigmoid()\\n elif self._activation_fn == 'leaky ReLU' or isinstance(self._activation_fn, torch.nn.modules.activation.LeakyReLU):\\n self._activation_fn = nn.LeakyReLU()\\n else:\\n raise ValueError('Non-linear activation function must be \\\"tanh\\\", \\\"sigmoid\\\" or \\\"leaky ReLU\\\"')\\n \\n # Passing it onto the relevant device\\n self._activation_fn = self._activation_fn.to(self.device)\",\n \"def reset_weights(self):\\r\\n self._weights = deepcopy(self._tmp_weights)\\r\\n self._tmp_weights = None\",\n \"def get_weights(self):\\n return [self.w, self.b]\",\n \"def u_weights(self):\\n for i in range(self.n_inputs):\\n self._q_neuron.cx(self._weights[i], self.inputs[i])\",\n \"def _init_weights(layer):\\n if isinstance(layer, (nn.Conv2d, nn.Linear)):\\n torch.nn.init.xavier_uniform_(layer.weight)\\n try:\\n # Some layers may not have biases, so catch the exception and pass.\\n layer.bias.data.fill_(0.0)\\n except AttributeError:\\n pass\",\n \"def update_weights(self):\\n self._weights = self._weights + self.update_weights_value\",\n \"def init_weights(self) -> None:\\n nn.init.kaiming_normal_(self._U)\\n nn.init.kaiming_normal_(self._W)\\n nn.init.kaiming_normal_(self._V)\\n\\n nn.init.normal_(self._b)\",\n \"def weights(self, weights):\\n\\n self._weights = weights\",\n \"def init_weights(model):\\n ...\",\n \"def targets_weights_fn(self):\\n return common_layers.weights_all\",\n \"def weight_wrtg(self, wrtg):\\n # Clear caches because weights are going to change.\\n # TODO: it might be possible to not clear the caches\\n # if the weight doesn't change, and re-use previous decoding.\\n wrtg.ClearCaches()\\n for p in wrtg.P:\\n rule = p.rhs.rule\\n assert isinstance(rule.features, list)\\n rule.weight = self.weight_rule(rule)\",\n \"def my_assign_weights(context, data):\\n pass\",\n \"def get_weights(self):\\n return self.__weights\",\n \"def initialize_weights(self, weights_initializer, bias_initializer):\\n wshapes = [\\n [self.input_size, self.hidden_size[0]],\\n [self.hidden_size[0], self.hidden_size[1]],\\n [self.hidden_size[1], self.output_size]\\n ]\\n\\n bshapes = [\\n [1, self.hidden_size[0]],\\n [1, self.hidden_size[1]],\\n [1, self.output_size]\\n ]\\n\\n self.weights = [init_weights(s, weights_initializer) for s in wshapes]\\n self.biases = [init_weights(s, bias_initializer) for s in bshapes]\\n\\n self.trainable_variables = self.weights + self.biases\",\n \"def write_weights_images(self):\\n for weight_name, weight in self._weights.items():\\n self._write_weight_image_to_tensorboard(\\n name=f\\\"{self._Sections.WEIGHTS}/{weight_name}\\\",\\n weight=weight,\\n step=self._epochs,\\n )\",\n \"def _initialize_weights(self):\\r\\n for m in self.modules():\\r\\n if isinstance(m, nn.Conv2d):\\r\\n m.weight.data.normal_(0, 0.05)\\r\\n if m.bias is not None:\\r\\n m.bias.data.zero_()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67688566","0.6305428","0.62857914","0.59490013","0.59490013","0.57656217","0.5713356","0.57009125","0.56955546","0.56836677","0.55329573","0.5532722","0.5511265","0.5505167","0.5443281","0.54317117","0.5425128","0.54003054","0.53999305","0.5396085","0.5390862","0.53767896","0.53749967","0.53343743","0.5334274","0.5331206","0.5327853","0.5327203","0.5318852","0.5310714","0.5308704","0.53043735","0.53043735","0.53043735","0.52943766","0.52932745","0.526328","0.5260141","0.52502435","0.52458084","0.52357537","0.52242607","0.52237934","0.5219452","0.52155906","0.52121973","0.5210707","0.5192433","0.51881856","0.5184881","0.51816803","0.51768994","0.51758754","0.5166955","0.51622933","0.51574856","0.5155051","0.5148961","0.5145253","0.5140591","0.51402783","0.51304007","0.51218873","0.5119787","0.51131415","0.51115954","0.51107967","0.5088776","0.5087805","0.50794256","0.50794256","0.50794256","0.5071767","0.507038","0.5065676","0.50644827","0.50620806","0.5051882","0.50515234","0.50324005","0.50312054","0.502996","0.50253445","0.5025125","0.50198317","0.5014188","0.5012714","0.50125724","0.50117207","0.5008512","0.5007773","0.50055873","0.50041825","0.50026935","0.49999636","0.49977404","0.49940082","0.49930814","0.49920252","0.49884155"],"string":"[\n \"0.67688566\",\n \"0.6305428\",\n \"0.62857914\",\n \"0.59490013\",\n \"0.59490013\",\n \"0.57656217\",\n \"0.5713356\",\n \"0.57009125\",\n \"0.56955546\",\n \"0.56836677\",\n \"0.55329573\",\n \"0.5532722\",\n \"0.5511265\",\n \"0.5505167\",\n \"0.5443281\",\n \"0.54317117\",\n \"0.5425128\",\n \"0.54003054\",\n \"0.53999305\",\n \"0.5396085\",\n \"0.5390862\",\n \"0.53767896\",\n \"0.53749967\",\n \"0.53343743\",\n \"0.5334274\",\n \"0.5331206\",\n \"0.5327853\",\n \"0.5327203\",\n \"0.5318852\",\n \"0.5310714\",\n \"0.5308704\",\n \"0.53043735\",\n \"0.53043735\",\n \"0.53043735\",\n \"0.52943766\",\n \"0.52932745\",\n \"0.526328\",\n \"0.5260141\",\n \"0.52502435\",\n \"0.52458084\",\n \"0.52357537\",\n \"0.52242607\",\n \"0.52237934\",\n \"0.5219452\",\n \"0.52155906\",\n \"0.52121973\",\n \"0.5210707\",\n \"0.5192433\",\n \"0.51881856\",\n \"0.5184881\",\n \"0.51816803\",\n \"0.51768994\",\n \"0.51758754\",\n \"0.5166955\",\n \"0.51622933\",\n \"0.51574856\",\n \"0.5155051\",\n \"0.5148961\",\n \"0.5145253\",\n \"0.5140591\",\n \"0.51402783\",\n \"0.51304007\",\n \"0.51218873\",\n \"0.5119787\",\n \"0.51131415\",\n \"0.51115954\",\n \"0.51107967\",\n \"0.5088776\",\n \"0.5087805\",\n \"0.50794256\",\n \"0.50794256\",\n \"0.50794256\",\n \"0.5071767\",\n \"0.507038\",\n \"0.5065676\",\n \"0.50644827\",\n \"0.50620806\",\n \"0.5051882\",\n \"0.50515234\",\n \"0.50324005\",\n \"0.50312054\",\n \"0.502996\",\n \"0.50253445\",\n \"0.5025125\",\n \"0.50198317\",\n \"0.5014188\",\n \"0.5012714\",\n \"0.50125724\",\n \"0.50117207\",\n \"0.5008512\",\n \"0.5007773\",\n \"0.50055873\",\n \"0.50041825\",\n \"0.50026935\",\n \"0.49999636\",\n \"0.49977404\",\n \"0.49940082\",\n \"0.49930814\",\n \"0.49920252\",\n \"0.49884155\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.67650056"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":20091,"cells":{"query":{"kind":"string","value":"Reset the list of document's modified items."},"document":{"kind":"string","value":"def clear_modified(self):\n self._data.clear_modified()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def reset_original(self):\n self._original = [] # Empty out self._originals","def reset_modified(self):\n self.modified_fields = set()\n\n # compensate for us not having knowledge of certain fields changing\n for field_name, field in self.schema.normal_fields.items():\n if isinstance(field, ObjectField):\n self.modified_fields.add(field_name)","def reset(cls):\n cls.POS = 0\n cls.ITEMS = MOCKED_SEARCH_ITEMS.copy()","def removeOldItems(self):\n pass","def reset(self):\n resetdata = {\n \"words\" : {},\n \"cur_index\" : -1,\n }\n wordlist_path = self.get_wordlist_path()\n with open(wordlist_path, 'w') as f:\n json.dump(resetdata, f, indent = 4)\n print(f\"[{self.pos}] wordlist has been reset.\")","def _reset_changes(self):\r\n self._original = {}\r\n if self.last_updated is not None:\r\n self._original['last_updated'] = self.last_updated","def resetModifiedFlags(self):\n return _libsbml.Date_resetModifiedFlags(self)","def resetModifiedFlags(self):\n return _libsbml.ModelHistory_resetModifiedFlags(self)","def _reset(self, *args):\n self.dirty = True","def resetModifiedFlags(self):\n return _libsbml.ModelCreator_resetModifiedFlags(self)","def reset(self):\n # FIXME: this state does not make sense\n self.reset_creation_info()\n self.reset_document()\n self.reset_package()\n self.reset_file_stat()\n self.reset_reviews()\n self.reset_annotations()\n self.reset_extr_lics()\n self.reset_snippet()","def reset_document(self):\n # FIXME: this state does not make sense\n self.doc_version_set = False\n self.doc_comment_set = False\n self.doc_namespace_set = False\n self.doc_data_lics_set = False\n self.doc_name_set = False\n self.doc_spdx_id_set = False","def reset(self):\n self.vna.write(edit_list(self.model))\n self.vna.write(clear_list(self.model))","def clear(self):\n self[:] = []\n self.emit('cleared')\n self.emit('modified')","def reset(self):\n # FIXME: this state does not make sense\n self.reset_creation_info()\n self.reset_document()\n self.reset_package()\n self.reset_file_stat()\n self.reset_reviews()\n self.reset_annotations()\n self.reset_extr_lics()","def resetItemsFreqDist(self):\n from models import PostItem\n\n results = PostItem.most_common.delete_everything()\n #results = PostItem.objects.all().delete()\n print \"Resetting %s items...\" % results.count()\n return\n updated = 0\n for res in results:\n if res.numeric != 0:\n print \"Resetting: %s # %s\" % (res.word, updated)\n res.numeric = 0\n res.save()\n updated += 1\n return updated","def reset(self):\n self._data = []","def clear(self):\n self._items = []","def OldItems(self) -> _n_1_t_7:","def reset(self):\n self.elements = [0] * len(self)","def reset(self):\n for i in range(self.k):\n self.list[i] = self.dic[i]\n return self.list","def reset(self):\n self.footnotes = OrderedDict()\n self.unique_prefix += 1","def reset(self):\n self.ship_list = self.backup_list","def reset(self):\n for item in TextChannelFilterItem.objects(channel_filter=self):\n item.delete()\n self.reset_counters()\n self.retrain()","def reset(self):\n self.liidx = 0\n self.clidx = 0","def reset(self):\n self._idx = 0","def reset(self):\n self.items = np.arange(self.ratings.shape[1])","def _update_items(self):\n\n self._item_list = []\n for f in os.listdir(self._folder):\n # Skip text files\n # -> It is important that we don't delete the list file if the user puts it here!\n ext = os.path.splitext(f)[1]\n if ext not in ['.csv', 'txt']:\n self._item_list.append(f)","def reset(self) -> List[int]:\n pass","def reset(self):\r\n\t\tself.index = 0","def _reset_collection(self):\r\n\r\n self._meta.queryset._document._collection = None\r\n self._meta.queryset._collection_obj = self._meta.queryset._document._get_collection()\r\n if hasattr(self._meta.queryset, '_reset_already_indexed'):\r\n self._meta.queryset._reset_already_indexed()","def clear(self):\n self.changeHistory = []","def reset(self):\n self._current_index = 0","def reset(self):\n\t\tself.keywords = []\n\t\tself.locations = []\n\t\tself.usernames = []\n\n\t\tself._reset_buffer()","def reset(self):\n\n self.__styles = list();","def clear(self):\n self.__list = []","def reset(self):\n debug('resetting')\n self.marked = False","def modified(self):\n raise NotImplementedError","def reset(self):\n for c in self.children:\n c.reset()\n self.marked = False","def reset(self):\n self.__sets = []\n self._computed = False","def reset(self) -> List[int]:","def clear_changes(self):\n possible_changes = [Constants.ADDED_USER,\n Constants.DELETED_USER,\n Constants.DELETED_ATTRIBUTE,\n Constants.ADDED_ATTRIBUTE,\n Constants.CHANGED_ATTRIBUTE]\n\n for change in possible_changes:\n if not self.changes.get(change):\n self.changes.pop(change)","def clear(self):\n self.versions = {}\n self.clearItems()","def modified(self, modified):\n\n self._modified = modified","def reset(self, i):\n self.img_collection[i] = []","def recache_updates(self):\n ks = ['BooksUpdated', '%s'%self.key().id()]\n decaches(ks)","def reset(self):\n self.clear()\n self.addItem(self.default_entry)\n self.default_present = True","def reset(self) -> List[int]:\n self.array = deepcopy(self.original)\n return self.array","def _items_updated(self, change):\n if self.root:\n # The whole list changed.\n if change['type'] == 'update':\n added = set(change['value']) - set(change['oldvalue'])\n removed = set(change['oldvalue']) - set(change['value'])\n for item in removed:\n self._item_removed(item)\n for item in added:\n self._item_added(item)\n\n # An operation has been performed on the list.\n elif change['type'] == 'container':\n op = change['operation']\n\n # itemren have been added\n if op in ('__iadd__', 'append', 'extend', 'insert'):\n if 'item' in change:\n self._item_added(change['item'])\n if 'items' in change:\n for item in change['items']:\n self._item_added(item)\n\n # itemren have been removed.\n elif op in ('__delitem__', 'remove', 'pop'):\n if 'item' in change:\n self._item_removed(change['item'])\n if 'items' in change:\n for item in change['items']:\n self._item_removed(item)\n\n # One item was replaced.\n elif op in ('__setitem__'):\n old = change['olditem']\n if isinstance(old, list):\n for item in old:\n self._item_removed(item)\n else:\n self._item_removed(old)\n\n new = change['newitem']\n if isinstance(new, list):\n for item in new:\n self._item_added(item)\n else:\n self._item_added(new)\n\n self._recompute_indexes()","def setitems(self, items):\n self.clear()\n # FIXME: this allows you to pass in an OrderedDict as well :-)\n self.update(items)","def reset(self):\n self.source_data = self.get_dict_from_range(None, None)\n self.selection_bounds = None\n self.selection = []\n for c in self.callbacks[\"reset_data\"]:\n c()\n if self.context is not None:\n self.context.doc.add_next_tick_callback(self.update_source)","def reset(self):\n\t\tself.offsets = self.start_off.copy()","def reset(self):\n self.fscore_history = []","def reset_data(self):\n self.data = []","def resetModifiedFlags(self):\n return _libsbml.CVTerm_resetModifiedFlags(self)","def __clearBookmarked(self):\n self.bookmarked = []","def reset(self) -> List[int]:\n return self.orignial #Return the original copy.","def clear(self) -> None:\n self._items = []\n self._size = 0","def reset(self):\n for k,v in self.events.items():\n self.events[k] = None","def reset(self):\n self.selection_bounds = None\n self.selection = []\n for c in self.callbacks[\"reset_data\"]:\n c()\n if self.context is not None and self.context.doc is not None:\n self.context.doc.add_next_tick_callback(self.update_source)","def Clear(self):\r\n\r\n self._items = []\r\n self._sizer_element_count = 0","def _reset_stored(self):\n ## Main information\n self.idxs = None\n self.sp_relative_pos = None\n self._setted = False\n self.ks = None\n self.iss = [0]","def mark_no_changes(self):","def clear_removed_items(self, doctype, items):\n\t\tif items:\n\t\t\tfrappe.db.delete(doctype, dict(parent=self.doc_type, custom=1, name=(\"not in\", items)))\n\t\telse:\n\t\t\tfrappe.db.delete(doctype, dict(parent=self.doc_type, custom=1))","def reset_loc_changes(self) -> None:\n raise NotImplementedError('not implemented')","def reset_changed(self):\n self._changed = False","def reset_iterators(self):\r\n\r\n self.default_dict['iterators'] = []\r\n self.default_dict['iterator_names'] = {}\r\n self.dd_changed = True","def SetOldItem(self, item):\r\n \r\n self._itemOld = item","def reset(self):\n self.dict_lock.acquire()\n self.list_lock.acquire()\n\n self.beginResetModel()\n self.levels_dict = {}\n self.view_list = []\n self.endResetModel()\n \n self.list_lock.release()\n self.dict_lock.release()","def reset(self, silent=False):\n if not silent:\n ActionHistory().add_action(\n \"javatar.core.snippets_manager.reset\", \"Reset all snippets\"\n )\n self.snippets = None","def reset(self) -> None:","def reset(self) -> None:","def reset(self) -> None:","def clear_lists(self): \n self.fp_config_files = []\n self.txt_files = []\n self.fr_config_files = []","def clear(self):\n self._items.clear()\n self._first = None\n self._last = None","def reset(self):\n for k in self.data_keys:\n setattr(self, k, [])\n self.size = 0","def reset_to_empty(self):\n url = DeckhandClient.get_path(DeckhandPaths.REVISION_LIST)\n response = self._delete_request(url)\n self._handle_bad_response(response)","def _clear(self):\n self._items = []\n self.key_listbox.delete(0, tk.END)\n self.value_listbox.delete(0, tk.END)","def clean_all_documents(cls):\n for index, text in enumerate(cls.documents):\n text_processed = cls.clean_document(text)\n cls.processed_documents.append(text_processed)","def resetConflictList(self):\n return\n #self.conflict_list = PersistentMapping()","def reset(self) -> None:\n self.val = None\n self.notes = []\n self.blocked = False\n self.forbidden = False","def _reset(self) -> None:\n self.images = []\n self.activations = []\n self.labels = []\n self.preds = []\n self.n_found = 0","def reset(self) -> None:\n ...","def reset(self) -> None:\n ...","def reset(self) -> None:\n ...","def reset(self) -> None:\n ...","def reset(self) -> None:\n ...","def reset(self) -> None:\n ...","def reset(self) -> None:\n ...","def reset(self) -> None:\n ...","def reset(self):\n self._last_item = None\n self._connected_items = []\n\n self._title_label.deleteLater()\n\n for item in self._items:\n item.deleteLater()\n\n for i in range(self._column_span):\n self._grid.setColumnStretch(self._column_id + i, 0)\n\n self._items = []\n self._row_index = 0","def reset(self):\n ...","def reset(self):\n ...","def reset(self):\n\t\tpass","def reset(self, ):\n self.clear()\n self.create_all_menus()","def clean(self):\n self.change_events = []","def reset(self):","def reset(self):","def reset(self):","def reset(self):"],"string":"[\n \"def reset_original(self):\\n self._original = [] # Empty out self._originals\",\n \"def reset_modified(self):\\n self.modified_fields = set()\\n\\n # compensate for us not having knowledge of certain fields changing\\n for field_name, field in self.schema.normal_fields.items():\\n if isinstance(field, ObjectField):\\n self.modified_fields.add(field_name)\",\n \"def reset(cls):\\n cls.POS = 0\\n cls.ITEMS = MOCKED_SEARCH_ITEMS.copy()\",\n \"def removeOldItems(self):\\n pass\",\n \"def reset(self):\\n resetdata = {\\n \\\"words\\\" : {},\\n \\\"cur_index\\\" : -1,\\n }\\n wordlist_path = self.get_wordlist_path()\\n with open(wordlist_path, 'w') as f:\\n json.dump(resetdata, f, indent = 4)\\n print(f\\\"[{self.pos}] wordlist has been reset.\\\")\",\n \"def _reset_changes(self):\\r\\n self._original = {}\\r\\n if self.last_updated is not None:\\r\\n self._original['last_updated'] = self.last_updated\",\n \"def resetModifiedFlags(self):\\n return _libsbml.Date_resetModifiedFlags(self)\",\n \"def resetModifiedFlags(self):\\n return _libsbml.ModelHistory_resetModifiedFlags(self)\",\n \"def _reset(self, *args):\\n self.dirty = True\",\n \"def resetModifiedFlags(self):\\n return _libsbml.ModelCreator_resetModifiedFlags(self)\",\n \"def reset(self):\\n # FIXME: this state does not make sense\\n self.reset_creation_info()\\n self.reset_document()\\n self.reset_package()\\n self.reset_file_stat()\\n self.reset_reviews()\\n self.reset_annotations()\\n self.reset_extr_lics()\\n self.reset_snippet()\",\n \"def reset_document(self):\\n # FIXME: this state does not make sense\\n self.doc_version_set = False\\n self.doc_comment_set = False\\n self.doc_namespace_set = False\\n self.doc_data_lics_set = False\\n self.doc_name_set = False\\n self.doc_spdx_id_set = False\",\n \"def reset(self):\\n self.vna.write(edit_list(self.model))\\n self.vna.write(clear_list(self.model))\",\n \"def clear(self):\\n self[:] = []\\n self.emit('cleared')\\n self.emit('modified')\",\n \"def reset(self):\\n # FIXME: this state does not make sense\\n self.reset_creation_info()\\n self.reset_document()\\n self.reset_package()\\n self.reset_file_stat()\\n self.reset_reviews()\\n self.reset_annotations()\\n self.reset_extr_lics()\",\n \"def resetItemsFreqDist(self):\\n from models import PostItem\\n\\n results = PostItem.most_common.delete_everything()\\n #results = PostItem.objects.all().delete()\\n print \\\"Resetting %s items...\\\" % results.count()\\n return\\n updated = 0\\n for res in results:\\n if res.numeric != 0:\\n print \\\"Resetting: %s # %s\\\" % (res.word, updated)\\n res.numeric = 0\\n res.save()\\n updated += 1\\n return updated\",\n \"def reset(self):\\n self._data = []\",\n \"def clear(self):\\n self._items = []\",\n \"def OldItems(self) -> _n_1_t_7:\",\n \"def reset(self):\\n self.elements = [0] * len(self)\",\n \"def reset(self):\\n for i in range(self.k):\\n self.list[i] = self.dic[i]\\n return self.list\",\n \"def reset(self):\\n self.footnotes = OrderedDict()\\n self.unique_prefix += 1\",\n \"def reset(self):\\n self.ship_list = self.backup_list\",\n \"def reset(self):\\n for item in TextChannelFilterItem.objects(channel_filter=self):\\n item.delete()\\n self.reset_counters()\\n self.retrain()\",\n \"def reset(self):\\n self.liidx = 0\\n self.clidx = 0\",\n \"def reset(self):\\n self._idx = 0\",\n \"def reset(self):\\n self.items = np.arange(self.ratings.shape[1])\",\n \"def _update_items(self):\\n\\n self._item_list = []\\n for f in os.listdir(self._folder):\\n # Skip text files\\n # -> It is important that we don't delete the list file if the user puts it here!\\n ext = os.path.splitext(f)[1]\\n if ext not in ['.csv', 'txt']:\\n self._item_list.append(f)\",\n \"def reset(self) -> List[int]:\\n pass\",\n \"def reset(self):\\r\\n\\t\\tself.index = 0\",\n \"def _reset_collection(self):\\r\\n\\r\\n self._meta.queryset._document._collection = None\\r\\n self._meta.queryset._collection_obj = self._meta.queryset._document._get_collection()\\r\\n if hasattr(self._meta.queryset, '_reset_already_indexed'):\\r\\n self._meta.queryset._reset_already_indexed()\",\n \"def clear(self):\\n self.changeHistory = []\",\n \"def reset(self):\\n self._current_index = 0\",\n \"def reset(self):\\n\\t\\tself.keywords = []\\n\\t\\tself.locations = []\\n\\t\\tself.usernames = []\\n\\n\\t\\tself._reset_buffer()\",\n \"def reset(self):\\n\\n self.__styles = list();\",\n \"def clear(self):\\n self.__list = []\",\n \"def reset(self):\\n debug('resetting')\\n self.marked = False\",\n \"def modified(self):\\n raise NotImplementedError\",\n \"def reset(self):\\n for c in self.children:\\n c.reset()\\n self.marked = False\",\n \"def reset(self):\\n self.__sets = []\\n self._computed = False\",\n \"def reset(self) -> List[int]:\",\n \"def clear_changes(self):\\n possible_changes = [Constants.ADDED_USER,\\n Constants.DELETED_USER,\\n Constants.DELETED_ATTRIBUTE,\\n Constants.ADDED_ATTRIBUTE,\\n Constants.CHANGED_ATTRIBUTE]\\n\\n for change in possible_changes:\\n if not self.changes.get(change):\\n self.changes.pop(change)\",\n \"def clear(self):\\n self.versions = {}\\n self.clearItems()\",\n \"def modified(self, modified):\\n\\n self._modified = modified\",\n \"def reset(self, i):\\n self.img_collection[i] = []\",\n \"def recache_updates(self):\\n ks = ['BooksUpdated', '%s'%self.key().id()]\\n decaches(ks)\",\n \"def reset(self):\\n self.clear()\\n self.addItem(self.default_entry)\\n self.default_present = True\",\n \"def reset(self) -> List[int]:\\n self.array = deepcopy(self.original)\\n return self.array\",\n \"def _items_updated(self, change):\\n if self.root:\\n # The whole list changed.\\n if change['type'] == 'update':\\n added = set(change['value']) - set(change['oldvalue'])\\n removed = set(change['oldvalue']) - set(change['value'])\\n for item in removed:\\n self._item_removed(item)\\n for item in added:\\n self._item_added(item)\\n\\n # An operation has been performed on the list.\\n elif change['type'] == 'container':\\n op = change['operation']\\n\\n # itemren have been added\\n if op in ('__iadd__', 'append', 'extend', 'insert'):\\n if 'item' in change:\\n self._item_added(change['item'])\\n if 'items' in change:\\n for item in change['items']:\\n self._item_added(item)\\n\\n # itemren have been removed.\\n elif op in ('__delitem__', 'remove', 'pop'):\\n if 'item' in change:\\n self._item_removed(change['item'])\\n if 'items' in change:\\n for item in change['items']:\\n self._item_removed(item)\\n\\n # One item was replaced.\\n elif op in ('__setitem__'):\\n old = change['olditem']\\n if isinstance(old, list):\\n for item in old:\\n self._item_removed(item)\\n else:\\n self._item_removed(old)\\n\\n new = change['newitem']\\n if isinstance(new, list):\\n for item in new:\\n self._item_added(item)\\n else:\\n self._item_added(new)\\n\\n self._recompute_indexes()\",\n \"def setitems(self, items):\\n self.clear()\\n # FIXME: this allows you to pass in an OrderedDict as well :-)\\n self.update(items)\",\n \"def reset(self):\\n self.source_data = self.get_dict_from_range(None, None)\\n self.selection_bounds = None\\n self.selection = []\\n for c in self.callbacks[\\\"reset_data\\\"]:\\n c()\\n if self.context is not None:\\n self.context.doc.add_next_tick_callback(self.update_source)\",\n \"def reset(self):\\n\\t\\tself.offsets = self.start_off.copy()\",\n \"def reset(self):\\n self.fscore_history = []\",\n \"def reset_data(self):\\n self.data = []\",\n \"def resetModifiedFlags(self):\\n return _libsbml.CVTerm_resetModifiedFlags(self)\",\n \"def __clearBookmarked(self):\\n self.bookmarked = []\",\n \"def reset(self) -> List[int]:\\n return self.orignial #Return the original copy.\",\n \"def clear(self) -> None:\\n self._items = []\\n self._size = 0\",\n \"def reset(self):\\n for k,v in self.events.items():\\n self.events[k] = None\",\n \"def reset(self):\\n self.selection_bounds = None\\n self.selection = []\\n for c in self.callbacks[\\\"reset_data\\\"]:\\n c()\\n if self.context is not None and self.context.doc is not None:\\n self.context.doc.add_next_tick_callback(self.update_source)\",\n \"def Clear(self):\\r\\n\\r\\n self._items = []\\r\\n self._sizer_element_count = 0\",\n \"def _reset_stored(self):\\n ## Main information\\n self.idxs = None\\n self.sp_relative_pos = None\\n self._setted = False\\n self.ks = None\\n self.iss = [0]\",\n \"def mark_no_changes(self):\",\n \"def clear_removed_items(self, doctype, items):\\n\\t\\tif items:\\n\\t\\t\\tfrappe.db.delete(doctype, dict(parent=self.doc_type, custom=1, name=(\\\"not in\\\", items)))\\n\\t\\telse:\\n\\t\\t\\tfrappe.db.delete(doctype, dict(parent=self.doc_type, custom=1))\",\n \"def reset_loc_changes(self) -> None:\\n raise NotImplementedError('not implemented')\",\n \"def reset_changed(self):\\n self._changed = False\",\n \"def reset_iterators(self):\\r\\n\\r\\n self.default_dict['iterators'] = []\\r\\n self.default_dict['iterator_names'] = {}\\r\\n self.dd_changed = True\",\n \"def SetOldItem(self, item):\\r\\n \\r\\n self._itemOld = item\",\n \"def reset(self):\\n self.dict_lock.acquire()\\n self.list_lock.acquire()\\n\\n self.beginResetModel()\\n self.levels_dict = {}\\n self.view_list = []\\n self.endResetModel()\\n \\n self.list_lock.release()\\n self.dict_lock.release()\",\n \"def reset(self, silent=False):\\n if not silent:\\n ActionHistory().add_action(\\n \\\"javatar.core.snippets_manager.reset\\\", \\\"Reset all snippets\\\"\\n )\\n self.snippets = None\",\n \"def reset(self) -> None:\",\n \"def reset(self) -> None:\",\n \"def reset(self) -> None:\",\n \"def clear_lists(self): \\n self.fp_config_files = []\\n self.txt_files = []\\n self.fr_config_files = []\",\n \"def clear(self):\\n self._items.clear()\\n self._first = None\\n self._last = None\",\n \"def reset(self):\\n for k in self.data_keys:\\n setattr(self, k, [])\\n self.size = 0\",\n \"def reset_to_empty(self):\\n url = DeckhandClient.get_path(DeckhandPaths.REVISION_LIST)\\n response = self._delete_request(url)\\n self._handle_bad_response(response)\",\n \"def _clear(self):\\n self._items = []\\n self.key_listbox.delete(0, tk.END)\\n self.value_listbox.delete(0, tk.END)\",\n \"def clean_all_documents(cls):\\n for index, text in enumerate(cls.documents):\\n text_processed = cls.clean_document(text)\\n cls.processed_documents.append(text_processed)\",\n \"def resetConflictList(self):\\n return\\n #self.conflict_list = PersistentMapping()\",\n \"def reset(self) -> None:\\n self.val = None\\n self.notes = []\\n self.blocked = False\\n self.forbidden = False\",\n \"def _reset(self) -> None:\\n self.images = []\\n self.activations = []\\n self.labels = []\\n self.preds = []\\n self.n_found = 0\",\n \"def reset(self) -> None:\\n ...\",\n \"def reset(self) -> None:\\n ...\",\n \"def reset(self) -> None:\\n ...\",\n \"def reset(self) -> None:\\n ...\",\n \"def reset(self) -> None:\\n ...\",\n \"def reset(self) -> None:\\n ...\",\n \"def reset(self) -> None:\\n ...\",\n \"def reset(self) -> None:\\n ...\",\n \"def reset(self):\\n self._last_item = None\\n self._connected_items = []\\n\\n self._title_label.deleteLater()\\n\\n for item in self._items:\\n item.deleteLater()\\n\\n for i in range(self._column_span):\\n self._grid.setColumnStretch(self._column_id + i, 0)\\n\\n self._items = []\\n self._row_index = 0\",\n \"def reset(self):\\n ...\",\n \"def reset(self):\\n ...\",\n \"def reset(self):\\n\\t\\tpass\",\n \"def reset(self, ):\\n self.clear()\\n self.create_all_menus()\",\n \"def clean(self):\\n self.change_events = []\",\n \"def reset(self):\",\n \"def reset(self):\",\n \"def reset(self):\",\n \"def reset(self):\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6702367","0.66882014","0.6433367","0.6393692","0.6267809","0.62651587","0.6241749","0.6123967","0.61093926","0.60061425","0.60025436","0.5998225","0.5958811","0.5949711","0.59314656","0.58863574","0.5874103","0.5873604","0.5864233","0.583386","0.5812609","0.58000416","0.57767606","0.577604","0.5772078","0.5770612","0.57253605","0.57228804","0.5719648","0.5717724","0.5707356","0.5671778","0.5632917","0.5623007","0.55939287","0.55865395","0.55856913","0.5583227","0.55700076","0.55679023","0.5552405","0.55491376","0.55483127","0.5539558","0.5535738","0.55329686","0.55238914","0.55175644","0.5505156","0.55018204","0.54932696","0.5489473","0.54878074","0.5487387","0.5483601","0.5481848","0.54802513","0.5475002","0.5469599","0.5466581","0.5464234","0.5459404","0.54470724","0.544433","0.54402953","0.54268914","0.5426545","0.54264766","0.5425213","0.54069966","0.54051274","0.54051274","0.54051274","0.54042435","0.5400808","0.53977114","0.5394296","0.53881115","0.5387658","0.53853136","0.5383178","0.53800297","0.53797966","0.53797966","0.53797966","0.53797966","0.53797966","0.53797966","0.53797966","0.53797966","0.53762054","0.5372791","0.5372791","0.53620166","0.5360954","0.5351696","0.5349342","0.5349342","0.5349342","0.5349342"],"string":"[\n \"0.6702367\",\n \"0.66882014\",\n \"0.6433367\",\n \"0.6393692\",\n \"0.6267809\",\n \"0.62651587\",\n \"0.6241749\",\n \"0.6123967\",\n \"0.61093926\",\n \"0.60061425\",\n \"0.60025436\",\n \"0.5998225\",\n \"0.5958811\",\n \"0.5949711\",\n \"0.59314656\",\n \"0.58863574\",\n \"0.5874103\",\n \"0.5873604\",\n \"0.5864233\",\n \"0.583386\",\n \"0.5812609\",\n \"0.58000416\",\n \"0.57767606\",\n \"0.577604\",\n \"0.5772078\",\n \"0.5770612\",\n \"0.57253605\",\n \"0.57228804\",\n \"0.5719648\",\n \"0.5717724\",\n \"0.5707356\",\n \"0.5671778\",\n \"0.5632917\",\n \"0.5623007\",\n \"0.55939287\",\n \"0.55865395\",\n \"0.55856913\",\n \"0.5583227\",\n \"0.55700076\",\n \"0.55679023\",\n \"0.5552405\",\n \"0.55491376\",\n \"0.55483127\",\n \"0.5539558\",\n \"0.5535738\",\n \"0.55329686\",\n \"0.55238914\",\n \"0.55175644\",\n \"0.5505156\",\n \"0.55018204\",\n \"0.54932696\",\n \"0.5489473\",\n \"0.54878074\",\n \"0.5487387\",\n \"0.5483601\",\n \"0.5481848\",\n \"0.54802513\",\n \"0.5475002\",\n \"0.5469599\",\n \"0.5466581\",\n \"0.5464234\",\n \"0.5459404\",\n \"0.54470724\",\n \"0.544433\",\n \"0.54402953\",\n \"0.54268914\",\n \"0.5426545\",\n \"0.54264766\",\n \"0.5425213\",\n \"0.54069966\",\n \"0.54051274\",\n \"0.54051274\",\n \"0.54051274\",\n \"0.54042435\",\n \"0.5400808\",\n \"0.53977114\",\n \"0.5394296\",\n \"0.53881115\",\n \"0.5387658\",\n \"0.53853136\",\n \"0.5383178\",\n \"0.53800297\",\n \"0.53797966\",\n \"0.53797966\",\n \"0.53797966\",\n \"0.53797966\",\n \"0.53797966\",\n \"0.53797966\",\n \"0.53797966\",\n \"0.53797966\",\n \"0.53762054\",\n \"0.5372791\",\n \"0.5372791\",\n \"0.53620166\",\n \"0.5360954\",\n \"0.5351696\",\n \"0.5349342\",\n \"0.5349342\",\n \"0.5349342\",\n \"0.5349342\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6440544"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":20092,"cells":{"query":{"kind":"string","value":"Create an embedded document instance from MongoDB data"},"document":{"kind":"string","value":"def build_from_mongo(cls, data, use_cls=True):\n # If a _cls is specified, we have to use this document class\n if use_cls and '_cls' in data:\n cls = cls.opts.instance.retrieve_embedded_document(data['_cls'])\n doc = cls()\n doc.from_mongo(data)\n return doc"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def from_mongo(data):\n if not data:\n return None\n\n data['id'] = str(data['_id'])\n return data","def from_mongo(cls, data: dict) -> Union[\"MongoModel\", Dict]:\n if not data:\n return data\n id = data.pop('_id', None)\n return cls(**dict(data, id=id))","def create(cls, collection, data, schema=None):\n validated = cls.validate(data, schema=schema)\n result = collection.insert_one(validated)\n return collection.find_one({\"_id\": result.inserted_id})","def _to_document(self, document):\n obj = self.document()\n obj._set_from_db(document)\n return obj","def _doc_create(type, data):\n doc = dict(data)\n doc.update({'model_type': type})\n return doc","def init_doc(self, obj, update_dict=True):\n try:\n obj.essentials\n except AttributeError:\n raise AttributeError(\"An object to be saved in db is supposed to have the essentials attribute\")\n\n if obj.essentials is None:\n raise AttributeError(\"An object to be saved in db should not have NoneType as its essentials\")\n\n print(\"Saving this object into db: {}\".format(type(obj)))\n\n start = datetime.now()\n essen = self.mongo_doc_generator(obj.essentials)\n document = {\"essentials\": essen, 'datetime': start, 'filepaths': obj.filepaths}\n\n db_location = obj.db\n element = obj.decide_element()\n host = db_location[\"host\"]\n project = db_location[\"project\"]\n\n target_db = connect_collection(host, project, element)\n doc_created = target_db.insert_one(document)\n inserted_id = doc_created.inserted_id\n\n return inserted_id","def create_document(document: DocumentIn, db: Session = Depends(get_db)):\n return add_document(db, document)","def createDocument(self, document):\n data = self.createDocumentAll([document])\n try:\n return data[0]\n except: pass","def new_document(klass, name=None, author=None):\n doc = Factory.new_document(klass, author)\n doc.name = name\n doc._osl.id = uuid.uuid4()\n return doc","def create_document(self, data):\n command = CreateDocumentFromOneOffixxTemplateCommand(self.context, data['title'], data['template'])\n return command.execute()","def subdocument(db_type):\n if db_type != \"ephemeraldb\":\n pytest.skip(\"ephemeraldb test only\")\n yield EphemeralDocument(\n {\"_id\": 1, \"hello\": \"there\", \"mighty\": \"duck\", \"and\": {\"the\": \"drake\"}}\n )","def create_from_dict(new_info: dict):\n doc = Db2Document(mhr_number=new_info.get('mhrNumber'),\n document_type=new_info.get('documentType'),\n document_reg_id=new_info.get('documentRegistrationId'),\n interimed=new_info.get('interimed', ''),\n owner_cross_reference=new_info.get('ownerCrossReference', ''),\n interest_denominator=new_info.get('interestDenominator', 0),\n declared_value=new_info.get('declaredValue', 0),\n own_land=new_info.get('ownLand', ''),\n routing_slip_number=new_info.get('routingSlipNumber', ''))\n doc.last_service = new_info.get('lastService', '')\n doc.bcol_account = new_info.get('bcolAccount', '')\n doc.dat_number = new_info.get('datNumber', '')\n doc.examiner_id = new_info.get('examinerId', '')\n doc.update_id = new_info.get('updateId', '')\n doc.phone_number = new_info.get('phoneNumber', '')\n doc.attention_reference = new_info.get('attentionReference', '')\n doc.name = new_info.get('name', '')\n doc.legacy_address = new_info.get('legacyAddress', '')\n doc.number_of_pages = new_info.get('numberOfPages', 0)\n doc.consideration_value = new_info.get('considerationValue', '')\n doc.affirm_by_name = new_info.get('affirmByName', '')\n doc.liens_with_consent = new_info.get('liensWithConsent', '')\n doc.client_reference_id = new_info.get('clientReferenceId', '')\n if new_info.get('createDateTime', None):\n doc.registration_ts = model_utils.ts_from_iso_format(new_info.get('createDateTime'))\n if new_info.get('draftDateTime', None):\n doc.draft_ts = model_utils.ts_from_iso_format(new_info.get('draftDateTime'))\n if new_info.get('transferExecutionDate', None):\n date_val: str = str(new_info.get('transferExecutionDate'))[0:10]\n doc.transfer_execution_date = model_utils.date_from_iso_format(date_val)\n return doc","def from_db(data):\n \n return Collection(\n dbid = data['id'],\n title = data['title'],\n query = data['query'],\n priority = data['priority'],\n export = data['export'])","def test_find_embedded(self):\n\n class User(EmbeddedDocument):\n name = StringField()\n\n class BlogPost(Document):\n content = StringField()\n author = EmbeddedDocumentField(User)\n\n BlogPost.drop_collection()\n\n user = User(name=\"Test User\")\n BlogPost.objects.create(author=user, content=\"Had a good coffee today...\")\n\n result = BlogPost.objects.first()\n assert isinstance(result.author, User)\n assert result.author.name == \"Test User\"\n\n result = BlogPost.objects.get(author__name=user.name)\n assert isinstance(result.author, User)\n assert result.author.name == \"Test User\"\n\n result = BlogPost.objects.get(author={\"name\": user.name})\n assert isinstance(result.author, User)\n assert result.author.name == \"Test User\"\n\n # Fails, since the string is not a type that is able to represent the\n # author's document structure (should be dict)\n with pytest.raises(InvalidQueryError):\n BlogPost.objects.get(author=user.name)","def test_as_pymongo(self):\n data = []\n self.maxDiff = None\n for item in self.docs[0:2]:\n el = item.to_dict(oid_as_str=False, call_child_to_dict=False)\n el[\"_id\"] = el.pop(\"id\")\n data.append(el)\n actual = self.doc_cls.objects[0:2].as_pymongo()\n self.assertEqual(data, list(actual))","def create(init_document: 'Document') -> 'DocumentArray':","def create_document(content: Union[str, bytes]) -> Document:\n r = requests.post(\"https://pastecord.com/documents\", data=content)\n r.raise_for_status()\n \n return Document(r.json()['key'])","def create_mongodb(config):\n\n \n mongo_url = \"mongodb://\"\n mongo_url += \",\".join(map(lambda srv: srv['host'] + \":\" + str(srv['port']), config['data']['mongoServers']))\n \n if 'replica' in config['data']:\n mongo_url += \"/?replicaSet={0}\".format(config['data']['replica'])\n\n client = MongoClient(mongo_url)\n\n return client","def _resolve_embedded_documents(document, resource, embedded_fields):\n schema = config.DOMAIN[resource]['schema']\n for field in embedded_fields:\n data_relation = schema[field]['data_relation']\n # Retrieve and serialize the requested document\n if 'version' in data_relation and data_relation['version'] is True:\n # support late versioning\n if document[field][config.VERSION] == 0:\n # there is a chance this document hasn't been saved\n # since versioning was turned on\n embedded_doc = missing_version_field(\n data_relation, document[field])\n\n if embedded_doc is None:\n # this document has been saved since the data_relation was\n # made - we basically do not have the copy of the document\n # that existed when the data relation was made, but we'll\n # try the next best thing - the first version\n document[field][config.VERSION] = 1\n embedded_doc = get_data_version_relation_document(\n data_relation, document[field])\n\n latest_embedded_doc = embedded_doc\n else:\n # grab the specific version\n embedded_doc = get_data_version_relation_document(\n data_relation, document[field])\n\n # grab the latest version\n latest_embedded_doc = get_data_version_relation_document(\n data_relation, document[field], latest=True)\n\n # make sure we got the documents\n if embedded_doc is None or latest_embedded_doc is None:\n # your database is not consistent!!! that is bad\n abort(404, description=debug_error_message(\n \"Unable to locate embedded documents for '%s'\" %\n field\n ))\n\n # build the response document\n _build_response_document(\n embedded_doc, data_relation['resource'],\n [], latest_embedded_doc)\n else:\n embedded_doc = app.data.find_one(\n data_relation['resource'], None,\n **{config.ID_FIELD: document[field]}\n )\n if embedded_doc:\n document[field] = embedded_doc","def create_document(self):\n # set single values\n if len(self.field_values) > 0:\n self._set_field_values()\n\n # set multi values\n if len(self.block_field_values) > 0:\n self._set_multi_field_values()\n\n self.field_values = {}\n self.block_field_values = {}\n\n self.client.service.CreateDocument()","def test_query_generic_embedded_document(self):\n\n class A(EmbeddedDocument):\n a_name = StringField()\n\n class B(EmbeddedDocument):\n b_name = StringField()\n\n class Doc(Document):\n document = GenericEmbeddedDocumentField(choices=(A, B))\n\n Doc.drop_collection()\n Doc(document=A(a_name=\"A doc\")).save()\n Doc(document=B(b_name=\"B doc\")).save()\n\n # Using raw in filter working fine\n assert Doc.objects(__raw__={\"document.a_name\": \"A doc\"}).count() == 1\n assert Doc.objects(__raw__={\"document.b_name\": \"B doc\"}).count() == 1\n assert Doc.objects(document__a_name=\"A doc\").count() == 1\n assert Doc.objects(document__b_name=\"B doc\").count() == 1","def test_find_empty_embedded(self):\n\n class User(EmbeddedDocument):\n name = StringField()\n\n class BlogPost(Document):\n content = StringField()\n author = EmbeddedDocumentField(User)\n\n BlogPost.drop_collection()\n\n BlogPost.objects.create(content=\"Anonymous post...\")\n\n result = BlogPost.objects.get(author=None)\n assert result.author is None","def transform_bson(self, value: Dict) -> Entity:\n try:\n return from_dict(\n data_class=self.python_type,\n config=self._config,\n data=value\n )\n except MissingValueError as e:\n raise e\n except DefaultValueNotFoundError as e:\n raise e","def test_mongodb_origin_simple_with_BSONBinary(sdc_builder, sdc_executor, mongodb):\n\n ORIG_BINARY_DOCS = [\n {'data': binary.Binary(b'Binary Data Flute')},\n {'data': binary.Binary(b'Binary Data Oboe')},\n {'data': binary.Binary(b'Binary Data Violin')}\n ]\n\n pipeline_builder = sdc_builder.get_pipeline_builder()\n pipeline_builder.add_error_stage('Discard')\n\n mongodb_origin = pipeline_builder.add_stage('MongoDB', type='origin')\n mongodb_origin.set_attributes(capped_collection=False,\n database=get_random_string(ascii_letters, 5),\n collection=get_random_string(ascii_letters, 10))\n\n trash = pipeline_builder.add_stage('Trash')\n mongodb_origin >> trash\n pipeline = pipeline_builder.build().configure_for_environment(mongodb)\n\n try:\n # MongoDB and PyMongo add '_id' to the dictionary entries e.g. docs_in_database\n # when used for inserting in collection. Hence the deep copy.\n docs_in_database = copy.deepcopy(ORIG_BINARY_DOCS)\n\n # Create documents in MongoDB using PyMongo.\n # First a database is created. Then a collection is created inside that database.\n # Then documents are created in that collection.\n logger.info('Adding documents into %s collection using PyMongo...', mongodb_origin.collection)\n mongodb_database = mongodb.engine[mongodb_origin.database]\n mongodb_collection = mongodb_database[mongodb_origin.collection]\n insert_list = [mongodb_collection.insert_one(doc) for doc in docs_in_database]\n assert len(insert_list) == len(docs_in_database)\n\n # Start pipeline and verify the documents using snaphot.\n sdc_executor.add_pipeline(pipeline)\n snapshot = sdc_executor.capture_snapshot(pipeline=pipeline, start_pipeline=True).snapshot\n sdc_executor.stop_pipeline(pipeline)\n rows_from_snapshot = [{'data': str(record.value2['data'])} for record in snapshot[mongodb_origin].output]\n\n assert rows_from_snapshot == [{'data': str(record.get('data'))} for record in ORIG_BINARY_DOCS]\n\n finally:\n logger.info('Dropping %s database...', mongodb_origin.database)\n mongodb.engine.drop_database(mongodb_origin.database)","def document(self):\n query = {\"_id\": ObjectId(self.document_id)}\n return Document(get_collection(\"documents\").find_one(query))","def create_from_document(doc: Dict[str, Any]) -> 'Publication':\n return Publication(\n name=doc['doc_name'],\n title=doc['doc_title'][:100],\n type=doc['doc_type'],\n number=doc['doc_num'],\n is_ignored=False,\n is_revoked=False\n )","def test_as_pymongo(self):\n data = []\n for item in self.users:\n el = item.to_dict(oid_as_str=False, call_child_to_dict=False)\n el[\"_id\"] = el.pop(\"id\")\n data.append(el)\n actual = User.objects.as_pymongo()\n self.assertEqual(data, list(actual))","def test_convert_hwpc_report_from_mongodb_work(hwpc_model, json_input):\n hwpc_model.from_mongodb(json_input)\n assert True","def create_instance(*, db_session, instance_in: WorkflowInstanceCreate) -> WorkflowInstance:\n instance = WorkflowInstance(\n **instance_in.dict(exclude={\"incident\", \"workflow\", \"creator\", \"artifacts\"})\n )\n\n incident = incident_service.get(db_session=db_session, incident_id=instance_in.incident[\"id\"])\n instance.incident = incident\n\n workflow = get(db_session=db_session, workflow_id=instance_in.workflow[\"id\"])\n instance.workflow = workflow\n\n creator = participant_service.get_by_incident_id_and_email(\n db_session=db_session, incident_id=incident.id, email=instance_in.creator[\"email\"]\n )\n instance.creator = creator\n\n for a in instance_in.artifacts:\n artifact_document = document_service.create(\n db_session=db_session, document_in=DocumentCreate(**a)\n )\n instance.artifacts.append(artifact_document)\n\n db_session.add(instance)\n db_session.commit()\n\n return instance","def test_find_dict_item(self):\n\n class BlogPost(Document):\n info = DictField()\n\n BlogPost.drop_collection()\n\n post = BlogPost(info={\"title\": \"test\"})\n post.save()\n\n post_obj = BlogPost.objects(info__title=\"test\").first()\n assert post_obj.id == post.id\n\n BlogPost.drop_collection()","def create_collection(collection):\n return db[collection]","def documents(required_fields=None,\n optional_fields=None,\n random_fields=json.values()):\n return json.objects(required_fields=required_fields,\n optional_fields=optional_fields,\n elements=random_fields)","def _build_response_document(\n document, resource, embedded_fields, latest_doc=None):\n # need to update the document field since the etag must be computed on the\n # same document representation that might have been used in the collection\n # 'get' method\n document[config.DATE_CREATED] = date_created(document)\n document[config.LAST_UPDATED] = last_updated(document)\n # TODO: last_update should include consideration for embedded documents\n\n # generate ETag\n if config.IF_MATCH:\n document[config.ETAG] = document_etag(document)\n\n # hateoas links\n if config.DOMAIN[resource]['hateoas']:\n _lookup_field = config.DOMAIN[resource]['item_lookup_field']\n document[config.LINKS] = {'self':\n document_link(resource,\n document[_lookup_field])}\n\n # add version numbers\n resolve_document_version(document, resource, 'GET', latest_doc)\n\n # media and embedded documents\n _resolve_media_files(document, resource)\n _resolve_embedded_documents(document, resource, embedded_fields)","def make_instance(self, data, **kwargs):\n instance = self.instance or self.get_instance(data)\n if instance is not None:\n for key, value in iteritems(data):\n setattr(instance, key, value)\n return instance\n kwargs, association_attrs = self._split_model_kwargs_association(data)\n instance = self.opts.model(**kwargs)\n for attr, value in iteritems(association_attrs):\n setattr(instance, attr, value)\n return instance","def init_mongo_db():\n try:\n app.mongo.cx.server_info()\n app.mongo.db = app.mongo.cx[\"kamistudio\"]\n if \"kami_corpora\" not in app.mongo.db.collection_names():\n app.mongo.db.create_collection(\"kami_corpora\")\n app.mongo.db.kami_corpora.create_index(\"id\", unique=True)\n\n if \"kami_models\" not in app.mongo.db.collection_names():\n app.mongo.db.create_collection(\"kami_models\")\n app.mongo.db.kami_models.create_index(\"id\", unique=True)\n\n if \"kami_definitions\" not in app.mongo.db.collection_names():\n app.mongo.db.create_collection(\"kami_definitions\")\n app.mongo.db.kami_definitions.create_index(\"id\", unique=True)\n\n if \"kami_new_definitions\" not in app.mongo.db.collection_names():\n app.mongo.db.create_collection(\"kami_new_definitions\")\n\n except ServerSelectionTimeoutError as e:\n app.mongo.db = None","def create_doc(c, d):\n try:\n c.insert_one(d)\n return True\n except:\n return False","def create(cls, data):\n doc = {\n \"type\": cls.type,\n **data\n }\n db_utils.set_created_at(doc)\n LOG.debug(\"Adding score {} with data: {}\".format(cls.type, data))\n return DB.save(doc)","def make_object(self, data, **kwargs):\n if not data:\n return None\n return Note(\n title=data['title'],\n content=data['content'],\n )","def __build_schema(meta_data):\n \n # Builds the dictionary that represents the schema.\n temporary_dictionary = {'$schema': None, '$id': None, 'title': None, 'type': None, 'properties': []}\n for x in meta_data:\n temporary_dictionary['properties'].append({\n 'name': x,\n 'type': None,\n 'description': None})\n # Creates a new instance of the schema and inserts the dictionary as a json into the field and returns it.\n returned_schema = Schema()\n returned_schema.data = json.dumps(temporary_dictionary)\n return returned_schema","def _create_document(result_dict):\n document = Document(\n name=result_dict['docname'],\n original_id=result_dict['itemid'],\n doctype=result_dict['doctype'],\n language=result_dict['languageisocode'],\n conclusion=result_dict['conclusion'],\n originatingbody=result_dict['originatingbody'],\n application=result_dict['application'],\n )\n return document","def create_document(obj):\n index = obj.get_index_name()\n doc_type = obj.get_document_type()\n body = obj.get_document_body()\n exists = ES.exists(index=index, doc_type=doc_type, id=obj.pk)\n\n if not exists:\n ES.create(index=index, doc_type=doc_type, body=body, id=obj.pk)\n return None\n\n return \"Conflict: document already exists for {0} with id {1}.\".format(\n obj.__class__.__name__, obj.pk)","def test_add_one_document_object(self):\n user_id = get_rand_string()\n data = get_rand_string()\n id = get_rand_string()\n \n doc = Document()\n doc[\"user_id\"] = user_id\n doc[\"data\"] = data\n doc[\"id\"] = id\n \n #raise Exception, doc.as_xml\n self.conn.add(doc)\n self.conn.commit()\n results = self.conn.query(\"id:\" + id).results\n \n self.assertEquals(len(results), 1,\n \"Could not find expected data (id:%s)\" % id)","def mongodb_init():\n # Load environment variables\n dotenv_path = find_dotenv()\n load_dotenv(dotenv_path)\n\n # Connect to the db\n # DB will be created if it doesn't already exist\n client = pymongo.MongoClient(os.environ.get(\"DATABASE_URL\"), 27017)\n # 'tweetbase' is the database name\n db = client.tweetbase\n\n #Sets both the tweet ID and user ID strings as unique indexes\n db.tweets.create_index([(\"id_str\", 1),\n (\"user.id_str\", 1)],\n unique=True)\n\n # The db is only actually created when something is inserted\n # So this inserts a test document and immediately deletes it...\n # AND EVERYTHING ELSE\n # DO NOT RUN THIS ON A DB YOU WANT TO KEEP. SERIOUSLY.\n db.tweets.insert_one({\"id_str\": 1, \"user\": {\"id_str\": 5}})\n db.tweets.remove()","def obj_get(self, request=None, **kwargs):\n return Document(self.get_collection(request).find_one({\n \"_id\": ObjectId(kwargs.get(\"pk\"))\n }))","def store_eeg_in_mongodb(eeg_data):\n con, eeg = connect_to_eeg_db()\n for eeg_record in eeg_data:\n eeg.insert(eeg_record)\n con.close()","def add_a_record(self, record):\n '''\n doc = { \"P/N\": record,#record.get_PN(),\n \"supplier\": \"\",\n \"inventory\": \"\",\n \"specification\": \"\",\n \"description\": \"\",\n \"OEM\": \"\",\n \"tags\": [\"mongodb\", \"python\", \"pymongo\"],\n \"date\": datetime.datetime.utcnow()}'''\n self.collection.insert(record)","def CreateDocument(name, product_id=None):\n nameFields = [search.TextField(name='name', value=name)]\n\n if product_id:\n # Specify using the product_id we want\n return search.Document(\n doc_id=product_id,\n fields=nameFields)\n else:\n # Let the search service supply the document id, for testing only\n return search.Document(fields=nameFields)","def test_add_one_document(self):\n user_id = get_rand_string()\n data = get_rand_string()\n id = get_rand_string()\n\n doc = {}\n doc[\"user_id\"] = user_id\n doc[\"data\"] = data\n doc[\"id\"] = id\n\n self.conn.add(**doc)\n self.conn.commit()\n results = self.conn.query(\"id:\" + id).results\n\n self.assertEquals(len(results), 1,\n \"Could not find expected data (id:%s)\" % id)\n\n doc = results[0]\n self.assertEquals(doc[\"user_id\"], user_id)\n self.assertEquals(doc[\"data\"], data)","def get_instance_from_words(data):\n inst = Dataset.get_instance_template()\n inst[\"words\"] = data\n return inst","def encode(\n cls: Type[\"DataDocument\"], encoding: str, data: D, **kwargs: Any\n ) -> \"DataDocument[D]\":\n # Dispatch encoding\n blob = lookup_serializer(encoding).dumps(data, **kwargs)\n\n inst = cls(blob=blob, encoding=encoding)\n inst._cache_data(data)\n return inst","def to_mongo(self, value, use_db_field=True, fields=None):\n Document = _import_class(\"Document\")\n EmbeddedDocument = _import_class(\"EmbeddedDocument\")\n GenericReferenceField = _import_class(\"GenericReferenceField\")\n\n if isinstance(value, str):\n return value\n\n if hasattr(value, \"to_mongo\"):\n if isinstance(value, Document):\n return GenericReferenceField().to_mongo(value)\n cls = value.__class__\n val = value.to_mongo(use_db_field, fields)\n # If it's a document that is not inherited add _cls\n if isinstance(value, EmbeddedDocument):\n val[\"_cls\"] = cls.__name__\n return val\n\n is_list = False\n if not hasattr(value, \"items\"):\n try:\n is_list = True\n value = {k: v for k, v in enumerate(value)}\n except TypeError: # Not iterable return the value\n return value\n\n if self.field:\n value_dict = {\n key: self.field._to_mongo_safe_call(item, use_db_field, fields)\n for key, item in value.items()\n }\n else:\n value_dict = {}\n for k, v in value.items():\n if isinstance(v, Document):\n # We need the id from the saved object to create the DBRef\n if v.pk is None:\n self.error(\n \"You can only reference documents once they\"\n \" have been saved to the database\"\n )\n\n # If it's a document that is not inheritable it won't have\n # any _cls data so make it a generic reference allows\n # us to dereference\n meta = getattr(v, \"_meta\", {})\n allow_inheritance = meta.get(\"allow_inheritance\")\n if not allow_inheritance:\n value_dict[k] = GenericReferenceField().to_mongo(v)\n else:\n collection = v._get_collection_name()\n value_dict[k] = DBRef(collection, v.pk)\n elif hasattr(v, \"to_mongo\"):\n cls = v.__class__\n val = v.to_mongo(use_db_field, fields)\n # If it's a document that is not inherited add _cls\n if isinstance(v, (Document, EmbeddedDocument)):\n val[\"_cls\"] = cls.__name__\n value_dict[k] = val\n else:\n value_dict[k] = self.to_mongo(v, use_db_field, fields)\n\n if is_list: # Convert back to a list\n return [\n v for _, v in sorted(value_dict.items(), key=operator.itemgetter(0))\n ]\n return value_dict","def insert(self, data):\n return self.collection.insert(data)","def build_document(self, labels_from_json):\n if not len(self.raw_labels):\n self.get_labels(labels_from_json)\n raw_text = self.instance_input_file.read()\n document = self.DOCUMENT_CLASS(self.identifier, title=self.identifier)\n document.build_from_text(raw_text, start_index=0)\n for start_index, end_index in self.raw_labels:\n document.add_label_for_position(\n 'claim', int(start_index), int(end_index))\n return document","async def _save(self, document, alias=None):\n doc = document.to_son()\n\n if document._id is not None:\n try:\n await self.coll(alias).update({\"_id\": document._id}, doc)\n except DuplicateKeyError as e:\n raise UniqueKeyViolationError.from_pymongo(str(e), self.__klass__)\n else:\n try:\n doc_id = await self.coll(alias).insert(doc)\n except DuplicateKeyError as e:\n raise UniqueKeyViolationError.from_pymongo(str(e), self.__klass__)\n document._id = doc_id\n\n return document","def extract_document(cls, obj_id, obj=None):\n if obj is None:\n obj = cls.get_model().get(id=obj_id)\n return {\n 'id': unicode(obj.id),\n 'init_user': {\n 'email': obj.init_user.email\n },\n 'text': obj.text,\n 'url': obj.get_absolute_url(),\n '_parent': unicode(obj.content_object.pk) # todo use only experiments pk\n }","def prep_data(data: list):\n book = {\n 'title': data['title'],\n 'authors': [],\n 'categories': []\n }\n try:\n for author in data['authors']:\n author_obj, created = Author.objects.get_or_create(name=author)\n book['authors'].append(author_obj.id)\n except KeyError:\n pass\n try:\n for category in data['categories']:\n category_obj, created = Category.objects.get_or_create(name=category)\n book['categories'].append(category_obj.id)\n except KeyError:\n pass\n book['published_date'] = data.get('publishedDate', None)\n book['average_rating'] = data.get('averageRating', None)\n book['ratings_count'] = data.get('ratingsCount', None)\n try:\n book['thumbnail'] = data['imageLinks']['thumbnail']\n except KeyError:\n book['thumbnail'] = None\n return book","def construct_schema_pymongo(\n collection: pymongo.collection.Collection,\n delimiter: str,\n use_random_sampling: bool,\n sample_size: Optional[int] = None,\n) -> Dict[Tuple[str, ...], SchemaDescription]:\n\n if sample_size:\n if use_random_sampling:\n # get sample documents in collection\n documents = collection.aggregate(\n [{\"$sample\": {\"size\": sample_size}}], allowDiskUse=True\n )\n else:\n documents = collection.aggregate(\n [{\"$limit\": sample_size}], allowDiskUse=True\n )\n else:\n # if sample_size is not provided, just take all items in the collection\n documents = collection.find({})\n\n return construct_schema(list(documents), delimiter)","def create(self, validated_data):\n # Create data\n instance = Data(\n template=validated_data['template'],\n title=validated_data['title'],\n user_id=str(validated_data['user'].id),\n )\n # Set xml content\n instance.xml_content = validated_data['xml_content']\n # Save the data\n data_api.upsert(instance, validated_data['user'])\n # Encode the response body\n instance.xml_content = validated_data['xml_content'].encode('utf-8')\n\n return instance","def __init__(\r\n self, db, collection, host, port=27017, tz_aware=True, user=None, password=None, **kwargs\r\n ):\r\n self.database = pymongo.database.Database(\r\n pymongo.MongoClient(\r\n host=host,\r\n port=port,\r\n tz_aware=tz_aware,\r\n document_class=son.SON,\r\n **kwargs\r\n ),\r\n db\r\n )\r\n\r\n if user is not None and password is not None:\r\n self.database.authenticate(user, password)\r\n\r\n self.course_index = self.database[collection + '.active_versions']\r\n self.structures = self.database[collection + '.structures']\r\n self.definitions = self.database[collection + '.definitions']\r\n\r\n # every app has write access to the db (v having a flag to indicate r/o v write)\r\n # Force mongo to report errors, at the expense of performance\r\n # pymongo docs suck but explanation:\r\n # http://api.mongodb.org/java/2.10.1/com/mongodb/WriteConcern.html\r\n self.course_index.write_concern = {'w': 1}\r\n self.structures.write_concern = {'w': 1}\r\n self.definitions.write_concern = {'w': 1}","def _construct_course_son(org, course, run):\r\n return bson.son.SON([\r\n ('org', org),\r\n ('course', course),\r\n ('name', run)\r\n ])","def post(self, data):\n self.validate(data)\n\n mongo['readable-api'].foo.insert_one(data)\n\n return self.request.resource_url(self.make_child(data['foo']))","def document_new():\n\n t = request.form['type']\n if t == 'book':\n doc = Book(\n title=request.form['title'],\n price=request.form['price'],\n keywords=comma_to_list(request.form['keywords']),\n authors=comma_to_list(request.form['authors']),\n edition=request.form['edition'],\n publisher=request.form['publisher'],\n publishment_year=request.form['publishment_year'],\n bestseller='bestseller' in request.form,\n reference='reference' in request.form\n )\n elif t == 'av':\n doc = AVMaterial(\n title=request.form['title'],\n price=request.form['price'],\n keywords=comma_to_list(request.form['keywords']),\n authors=comma_to_list(request.form['authors'])\n )\n elif t == 'article':\n doc = JournalArticle(\n title=request.form['title'],\n price=request.form['price'],\n keywords=comma_to_list(request.form['keywords']),\n authors=comma_to_list(request.form['authors']),\n issue_editor=request.form['issue_editor'],\n issue_publication_date=request.form['issue_publication_date'],\n journal=request.form['journal']\n )\n\n for i in range(int(request.form['copies'])):\n dc = DocumentCopy(document=doc)\n\n db.session.add(doc)\n db.session.commit()\n\n log(session['login'], 'created', 'document {}'.format(doc.id))\n\n # TODO\n return redirect('/admin/documents')","def instance_create(values):\n metadata = values.get('metadata')\n metadata_refs = []\n if metadata:\n for k, v in metadata.iteritems():\n metadata_ref = models.InstanceMetadata()\n metadata_ref['key'] = k\n metadata_ref['value'] = v\n metadata_refs.append(metadata_ref)\n values['metadata'] = metadata_refs\n\n instance_ref = models.Instance()\n instance_ref.update(values)\n\n session = get_session()\n with session.begin():\n instance_ref.save(session=session)\n return instance_ref","async def save(\n self, item: T, *,\n _id=None,\n mode: Literal['save', 'insert', 'update'] = 'save',\n inject_default_id: bool = False,\n inject_created: bool = False\n\n ) -> T:\n document = item.dict(by_alias=True)\n\n if self.identity != '_id':\n assert '_id' not in document, \"Should not have _id in the instance if collection's identity is non default.\"\n\n if _id is not None:\n assert self.identity == '_id', \"_id parameter can be provided only if collection's identity is default.\"\n assert '_id' not in document, \"_id should be provided ether in instance or by _id param.\"\n document['_id'] = ObjectId(_id)\n\n if mode == 'save':\n if document.get(self.identity) is None: # === New document.\n if self.identity == '_id':\n if '_id' in document: del document['_id']\n else:\n document[self.identity] = self.generate_id()\n\n result: InsertOneResult = await self.collection.insert_one(document) # will fail if exists due to index=unique violation for identity\n document['_id'] = result.inserted_id # will be removed while back-parsing if not necessary\n document['__created__'] = True\n\n else: # == Possibly an existing document that needs to be updated.\n result: UpdateResult = await self.collection.update_one(\n {self.identity: document[self.identity]},\n {'$set': document}, upsert=True\n )\n if result.upserted_id is not None:\n document['_id'] = result.upserted_id\n document['__created__'] = True\n\n elif mode == 'insert':\n assert document.get(self.identity) is not None, f\"Need identity ({self.identity}) for insert mode, use save mode to insert document without identity.\"\n _result: InsertOneResult = await self.collection.insert_one(document)\n document['__created__'] = True\n\n elif mode == 'update':\n assert document.get(self.identity) is not None, f\"Need identity ({self.identity}) for update mode.\"\n mongo_query = {self.identity: document[self.identity]}\n result: UpdateResult = await self.collection.update_one(\n mongo_query,\n {'$set': document}\n )\n if not result.matched_count: raise NotFound(mongo_query)\n\n else:\n assert False, f\"Mode {mode} is not supported.\"\n\n return self.parse_document(document, inject_default_id=inject_default_id, inject_created=inject_created)","def create_from_document(\n doc: Dict[str, Any],\n doc_location: str,\n filename: str,\n batch_timestamp: dt.datetime,\n pub: Publication) -> 'VersionedDoc':\n return VersionedDoc(\n publication=pub,\n name=doc['doc_name'],\n type=doc['doc_type'],\n number=doc['doc_num'],\n # TODO: Pass actual filename using ProcessedDoc instead of Doc\n # TODO: Tweak for clones?\n filename=filename,\n doc_location=doc_location,\n batch_timestamp=batch_timestamp,\n publication_date=parse_timestamp(doc['publication_date']),\n json_metadata=doc,\n version_hash=doc['version_hash'],\n md5_hash=\"\",\n is_ignored=False\n )","def __init__(self, api: MongoAPI):\n self.__api = api","def create_fhir_document(self, data, analysis):\n # Modify the data structure so that it is easier to use\n path_attributes_map = {attr.path: attr for attr in analysis.attributes}\n\n # Build path value map\n fhir_object = build_fhir_object(data, path_attributes_map)\n\n # Identify the fhir object\n fhir_object[\"id\"] = str(uuid4())\n fhir_object[\"resourceType\"] = analysis.definition[\"type\"]\n fhir_object[\"meta\"] = build_metadata(analysis)\n\n # Remove duplicates in fhir object\n fhir_object = clean_fhir_object(fhir_object)\n\n return fhir_object","def insert_school(mongo_collection, **kwargs):\n return mongo_collection.insert(kwargs)","def create(data):\n \n return Part(\n part_id = data['part_num'],\n category_id = data['part_cat_id'],\n external_ids = data.get('external_ids', {}),\n name = data['name'],\n year_from = data.get('year_from', None),\n year_to = data.get('year_to', None),\n url = data.get('part_url', None),\n img_url = data.get('part_img_url', None),\n print_of = data.get('print_of', None),\n prints = data.get('prints', []),\n molds = data.get('molds', []),\n alternates = data.get('alternates', []))","def _collection_from_response(data):\n return Collection(uuid=UUID(data['uuid']), title=data['title'])","def add_document(collection: str, document: dict) -> None:\n validate_arguments({'collection': [collection, str],\n 'document': [document, dict]})\n DB[collection].insert_one(document)","def __init__(self):\n self.client = MongoClient('localhost', 27017)#27017\n self.db = self.client.greency_db\n self.collection = self.db.inventory","def get_mongo_db():\n\n if not hasattr(g, 'mongodb'):\n g.mongodb = connect_mongo_db()\n\n g.mongodb.register([Testimonial])\n g.mongodb.register([Town])\n\n return g.mongodb","def open_doc(self, docid, **params):\n wrapper = None\n if \"wrapper\" in params:\n wrapper = params.pop(\"wrapper\")\n elif \"schema\" in params:\n schema = params.pop(\"schema\")\n if not hasattr(schema, \"wrap\"):\n raise TypeError(\"invalid schema\")\n wrapper = schema.wrap\n attachments = params.get('attachments', False)\n\n if six.PY2 and isinstance(docid, six.text_type):\n docid = docid.encode('utf-8')\n if six.PY3 and isinstance(docid, bytes):\n docid = docid.decode('utf-8')\n doc = Document(self.cloudant_database, docid)\n try:\n doc.fetch()\n except HTTPError as e:\n if e.response.status_code == 404:\n raise ResourceNotFound(json.loads(e.response.content.decode('utf-8'))['reason'])\n raise\n doc_dict = dict(doc)\n\n if attachments and '_attachments' in doc_dict:\n for attachment_name in doc_dict['_attachments']:\n attachment_data = doc.get_attachment(attachment_name, attachment_type='binary')\n doc_dict['_attachments'][attachment_name]['data'] = base64.b64encode(attachment_data)\n del doc_dict['_attachments'][attachment_name]['stub']\n del doc_dict['_attachments'][attachment_name]['length']\n\n if wrapper is not None:\n if not callable(wrapper):\n raise TypeError(\"wrapper isn't a callable\")\n\n return wrapper(doc_dict)\n\n return doc_dict","def __init__(self, dst_mongodb_uri, dst_database, dst_collection, dry_run):\n self.client = pymongo.MongoClient(dst_mongodb_uri)\n self.dst_mongodb_uri = dst_mongodb_uri\n self.lookup_col = self.client[dst_database][dst_collection]\n self.dry_run = dry_run","def generate_db_data(data):\n # pipeline database default is mongodb://localhost:27017/example_db\n db = hybrid.db.init(dbtype, database=MONGODB_URI, view_files=[], push_views=False, create=True,\n delete_existing=True)\n\n # training database for classifier default is mongodb://localhost:27017/example_training_db\n train_db = hybrid.db.init(dbtype, database=MONGODB_TRAINING_URI, view_files=[], push_views=False, create=True,\n delete_existing=True)\n\n # start inserting data\n try:\n if arguments.add_training_data:\n train = simple_generator_function(data)\n for _ in range(TRAINING_SIZE):\n train_data = train.next()\n print \"%s\\n inserting training data into \\t::::: example_training_db :::::\" % train_data\n db.storeDataBlobArray([data_blob.dict2blob(train_data)])\n\n for iris in simple_generator_function(data):\n print \"%s\\n inserting data into \\t::::: example_db :::::\" % iris\n train_db.storeDataBlobArray([data_blob.dict2blob(iris)])\n time.sleep(TIME_BETWEEN_GUESSES)\n\n except (KeyboardInterrupt, SystemExit):\n print \"\\n\"\n print \"closing db connection to example_db and example_training_db\"\n db.close()\n train_db.close()\n print \"closed\"\n except:\n raise","def save(self, data, model_type=None):\n print 'data is {}'.format(data)\n collection = self._db[model_type] if model_type else self._collection\n if type(data) == list:\n result = collection.insert_many(obj).inserted_ids\n elif type(data) != dict:\n obj = data.serialize()\n else:\n obj = data\n #if not hasattr(obj, '_id') or not obj._id:\n if not \"_id\" in obj or not obj[\"_id\"]:\n print 'obj does not exist in db: {}'.format(obj)\n # this is necessary for mongodb to auto-generate the _id field\n if \"_id\" in obj:\n obj.pop('_id')\n # TODO(cc) is there anyway to have insert_one() return the doc?\n inserted_id = collection.insert_one(obj).inserted_id\n print 'inserted_id is {}'.format(inserted_id)\n result = self.get(inserted_id, model_type)\n else:\n print 'obj exists: {}'.format(obj)\n updated = collection.find_and_modify({'_id': ObjectId(obj._id)}, obj)\n result = updated\n print 'update result is {}'.format(result)\n return result","def __init__(self, doc):\n\n self.doc = doc\n if self.doc.doi:\n self._populate()\n self.populated = True\n else:\n self.populated = False","def create_from_registration(registration, reg_json, doc_type: str, local_ts):\n doc_id = reg_json.get('documentId', '')\n doc = Db2Document(id=doc_id,\n mhr_number=registration.mhr_number,\n document_type=doc_type,\n document_reg_id=registration.doc_reg_number,\n registration_ts=local_ts,\n draft_ts=local_ts,\n interimed='',\n owner_cross_reference='',\n interest_denominator=0,\n declared_value=reg_json.get('declaredValue', 0),\n routing_slip_number='')\n doc.last_service = ''\n doc.bcol_account = ''\n doc.dat_number = ''\n doc.examiner_id = ''\n doc.update_id = ''\n doc.number_of_pages = 0\n doc.consideration_value = reg_json.get('consideration', '')\n doc.affirm_by_name = ''\n if reg_json.get('affirmByName'):\n doc.affirm_by_name = str(reg_json.get('affirmByName')).upper()[0:40]\n doc.liens_with_consent = ''\n if reg_json.get('submittingParty'):\n submitting = reg_json.get('submittingParty')\n if submitting.get('phoneNumber'):\n doc.phone_number = str(submitting.get('phoneNumber'))[0:10]\n else:\n doc.phone_number = ''\n doc.name = Db2Document.to_db2_submitting_name(submitting)\n doc.legacy_address = address_utils.to_db2_address(submitting.get('address'))\n else:\n doc.phone_number = ''\n doc.name = ''\n doc.legacy_address = ''\n if reg_json.get('attentionReference'):\n doc.attention_reference = str(reg_json['attentionReference'])[0:40]\n else:\n doc.attention_reference = ''\n if registration.client_reference_id:\n doc.client_reference_id = registration.client_reference_id[0:30]\n else:\n doc.client_reference_id = ''\n if doc_type in (Db2Document.DocumentTypes.TRANS,\n Db2Document.DocumentTypes.TRAND,\n Db2Document.DocumentTypes.TRANS_ADMIN,\n Db2Document.DocumentTypes.TRANS_AFFIDAVIT,\n Db2Document.DocumentTypes.TRANS_WILL):\n if reg_json.get('transferDate'):\n doc.transfer_execution_date = model_utils.date_from_iso_format(str(reg_json['transferDate'])[0:10])\n else:\n doc.transfer_execution_date = local_ts.date()\n else:\n doc.transfer_execution_date = model_utils.date_from_iso_format('0001-01-01')\n if reg_json.get('ownLand'):\n doc.own_land = 'Y'\n else:\n doc.own_land = 'N'\n return doc","def new(self):\n\n if not hasattr(self, 'required_attribs'):\n self.required_attribs = []\n\n # sanity check\n for req_var in self.required_attribs:\n if req_var not in self.kwargs:\n err = \"The '%s' kwarg is required when creating new %s!\"\n msg = err % (req_var, self.collection)\n self.logger.error(msg)\n self.logger.error('Incoming kwargs dict: %s' % self.kwargs)\n raise ValueError(msg)\n\n # do it\n self.logger.warn('Creating new %s record!' % self.collection)\n\n for req_var in self.required_attribs:\n setattr(self, req_var, self.kwargs[req_var])\n\n self.created_on = datetime.now()\n self.updated_on = datetime.now()\n self.created_by = flask_login.current_user._id\n self._id = self.mdb.insert({})\n\n try:\n self.save()\n except pymongo.errors.DuplicateKeyError as e:\n self.mdb.remove({'_id': self._id})\n self.logger.error(e)\n self.logger.error('Cannot create asset: %s' % self)\n raise ValueError('Duplicate key error prevented asset creation!')","def _build_db_data(self):\n self.logger.debug('Bulding task db document.')\n db_data = {}\n db_data.update(self.query)\n db_data['condition'] = False\n db_data['records'] = []\n self.db_collection.insert_one(db_data)\n return db_data","def __init__(self, cfg_mongo):\n # Connect to mongodb\n self.logger = logging.getLogger(\"DB\")\n self.cfg_mongo = cfg_mongo\n\n # Parse location for binary data storage\n assert cfg_mongo[\"datastore\"] in [\"gridfs\", \"numpy\", \"adios2\"]\n self.datastore = cfg_mongo[\"datastore\"]","def test_document_creation(self):\n\t\timgfile = StringIO.StringIO('GIF87a\\x01\\x00\\x01\\x00\\x80\\x01\\x00\\x00\\x00\\x00ccc,\\x00'\n\t '\\x00\\x00\\x00\\x01\\x00\\x01\\x00\\x00\\x02\\x02D\\x01\\x00;')\n\t\tf = SimpleUploadedFile('test_img_file.gif', imgfile.read(), 'image/gif')\n\t\tm, __ = Map.objects.get_or_create(id=1, title='foo', projection='4326', zoom=2, center_x=0, center_y=0,\n\t owner=User.objects.get_or_create(username='foo')[0])\n\t\tm.set_default_permissions()\n\t\td,created = Document.objects.get_or_create(id=1, file=f)\n\t\td.maps.add(m)\n\t\tself.assertTrue(created)","def make_collection(data_ids):\n idlist = []\n count = 0\n for c in range(0, len(data_ids)):\n data_id = data_ids[c]\n idlist.append({'src': \"hda\", 'id': data_id, 'name': str(count)})\n count += 1\n collection = {'collection_type': 'list', 'element_identifiers': idlist, 'name': 'collection'}\n return collection","def morpho_doc2(doc_id):\n db.doc_apply(doc_id, morpho_doc)","async def make_object(model, conn: Database, data: dict or List[dict]):\n if isinstance(data, dict):\n data = [data]\n\n query = insert(model).values(*data)\n return await conn.execute(query=query)","def add_document(self, doc):\n assert isinstance(doc, pylastica.document.Document), \"doc must be of type Document: %r\" % doc\n path = urllib.quote_plus(str(doc.doc_id))\n request_type = pylastica.request.Request.PUT\n if path is None or path == '':\n #no doc id has been given; use post so that an id is automatically created\n request_type = pylastica.request.Request.POST\n options = doc.get_options([\n 'version',\n 'version_type',\n 'routing',\n 'percolate',\n 'parent',\n 'ttl',\n 'timestamp',\n 'op_type',\n 'consistency',\n 'replication',\n 'refresh',\n 'timeout'\n ])\n response = self.request(path, request_type, doc.data, options)\n data = response.data\n if (doc.auto_populate or self.index.client.get_config_value(['document', 'autoPopulate'], False)) and response.is_ok():\n if doc.has_id():\n if '_id' in data:\n doc.doc_id = data['_id']\n if '_version' in data:\n doc.version = data['_version']\n return response","def _get_object(data, position, obj_end, opts, dummy):\n obj_size = bson._UNPACK_INT(data[position:position + 4])[0]\n end = position + obj_size - 1\n if data[end:position + obj_size] != b\"\\x00\":\n raise bson.InvalidBSON(\"bad eoo\")\n if end >= obj_end:\n raise bson.InvalidBSON(\"invalid object length\")\n if _raw_document_class(opts.document_class):\n return (opts.document_class(data[position:end + 1], opts),\n position + obj_size)\n\n obj = _elements_to_dict(data, position + 4, end, opts, subdocument=True)\n position += obj_size\n if \"$ref\" in obj:\n return (bson.DBRef(obj.pop(\"$ref\"), obj.pop(\"$id\", None),\n obj.pop(\"$db\", None), obj), position)\n return obj, position","def insert_player(document):\n players_col.insert_one(document)","def create(self, validated_data):\n return Speaker.objects.create(**validated_data)","def __init__(self, db_name):\n uri = re.sub('<username>', mongoUser, connection_uri)\n uri = re.sub('<password>', mongoPassword, uri)\n self.client = pymongo.MongoClient(uri)\n # Create your database\n self.db = self.client[db_name]","def test_bulk_insert(self):\n\n class Comment(EmbeddedDocument):\n name = StringField()\n\n class Post(EmbeddedDocument):\n comments = ListField(EmbeddedDocumentField(Comment))\n\n class Blog(Document):\n title = StringField(unique=True)\n tags = ListField(StringField())\n posts = ListField(EmbeddedDocumentField(Post))\n\n Blog.drop_collection()\n\n # Recreates the collection\n assert 0 == Blog.objects.count()\n\n comment1 = Comment(name=\"testa\")\n comment2 = Comment(name=\"testb\")\n post1 = Post(comments=[comment1, comment2])\n post2 = Post(comments=[comment2, comment2])\n\n # Check bulk insert using load_bulk=False\n blogs = [Blog(title=\"%s\" % i, posts=[post1, post2]) for i in range(99)]\n with query_counter() as q:\n assert q == 0\n Blog.objects.insert(blogs, load_bulk=False)\n assert q == 1 # 1 entry containing the list of inserts\n\n assert Blog.objects.count() == len(blogs)\n\n Blog.drop_collection()\n Blog.ensure_indexes()\n\n # Check bulk insert using load_bulk=True\n blogs = [Blog(title=\"%s\" % i, posts=[post1, post2]) for i in range(99)]\n with query_counter() as q:\n assert q == 0\n Blog.objects.insert(blogs)\n assert q == 2 # 1 for insert 1 for fetch\n\n Blog.drop_collection()\n\n comment1 = Comment(name=\"testa\")\n comment2 = Comment(name=\"testb\")\n post1 = Post(comments=[comment1, comment2])\n post2 = Post(comments=[comment2, comment2])\n blog1 = Blog(title=\"code\", posts=[post1, post2])\n blog2 = Blog(title=\"mongodb\", posts=[post2, post1])\n blog1, blog2 = Blog.objects.insert([blog1, blog2])\n assert blog1.title == \"code\"\n assert blog2.title == \"mongodb\"\n\n assert Blog.objects.count() == 2\n\n # test inserting an existing document (shouldn't be allowed)\n with pytest.raises(OperationError) as exc_info:\n blog = Blog.objects.first()\n Blog.objects.insert(blog)\n assert (\n str(exc_info.value)\n == \"Some documents have ObjectIds, use doc.update() instead\"\n )\n\n # test inserting a query set\n with pytest.raises(OperationError) as exc_info:\n blogs_qs = Blog.objects\n Blog.objects.insert(blogs_qs)\n assert (\n str(exc_info.value)\n == \"Some documents have ObjectIds, use doc.update() instead\"\n )\n\n # insert 1 new doc\n new_post = Blog(title=\"code123\", id=ObjectId())\n Blog.objects.insert(new_post)\n\n Blog.drop_collection()\n\n blog1 = Blog(title=\"code\", posts=[post1, post2])\n blog1 = Blog.objects.insert(blog1)\n assert blog1.title == \"code\"\n assert Blog.objects.count() == 1\n\n Blog.drop_collection()\n blog1 = Blog(title=\"code\", posts=[post1, post2])\n obj_id = Blog.objects.insert(blog1, load_bulk=False)\n assert isinstance(obj_id, ObjectId)\n\n Blog.drop_collection()\n post3 = Post(comments=[comment1, comment1])\n blog1 = Blog(title=\"foo\", posts=[post1, post2])\n blog2 = Blog(title=\"bar\", posts=[post2, post3])\n Blog.objects.insert([blog1, blog2])\n\n with pytest.raises(NotUniqueError):\n Blog.objects.insert(Blog(title=blog2.title))\n\n assert Blog.objects.count() == 2","def write_data_to_mongo(self, db_name, collection_name, list_of_dicts):\n self.db_client.db_name = db_name\n self.db = self.db_client.affirm_client()\n collection = self.__write(collection_name, list_of_dicts)\n return collection","def create(cls, data=None):\n # allow create() calls with no input\n if not data:\n data = {}\n\n return cls(**data)","def __init__(self, instance=None):\n self.instance = instance\n self.schema = None\n if self.instance:\n self.schema = surveys.SurveySchema(self.instance.survey)","def _get_schema_from_object(self, data):\n if \"items\" in data:\n return self._get_schema_from_object(data[\"items\"])\n\n url_key = None\n\n if '$id' in data:\n url_key = '$id'\n\n if 'id' in data:\n url_key = 'id'\n\n if url_key:\n url = data[url_key]\n schema = Schema().build()\n schema.domain_entity = self.get_domain_entity_from_url(url)\n schema.high_level_entity = self.get_high_level_entity_from_url(url)\n schema.module = self.get_module_from_url(url)\n schema.url = url\n return schema\n\n return None","def create(self, request, *args, **kwargs):\n # Deserialize and validate the data from the user.\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n\n # Execute the document and annotation creation\n self.perform_create(serializer)\n\n # Get the headers and return a response\n headers = self.get_success_headers(serializer.data)\n return Response(serializer.data, status=status.HTTP_201_CREATED, headers=headers)","def make_example_data():\n\n #create users\n charlie = User(firstname=\"Charlie\", lastname=\"Dog\",\n email=\"charlie@dogsaregreat.com\", password=\"marshmallow\")\n maisey = User(firstname=\"Maisey\", lastname=\"Puppy\",\n email=\"maisey@squirrelchasers.com\", password=\"monkey\")\n\n #create a chart that they share\n chart = Chart(title=\"StarChart\")\n chart.users = [charlie, maisey]\n\n #add some stars to the chart\n star1 = Star(giver=charlie, receiver=maisey, reason=\"For licking my nose\",\n timestamp=datetime(2016, 12, 31), chart=chart)\n star2 = Star(giver=maisey, receiver=charlie,\n reason=\"For being a great big sister\",\n timestamp=datetime(2016, 11, 21), chart=chart)\n\n #put the whole collection in the database; everything hangs off of the\n #stars so we can just add them\n db.session.add_all([star1, star2])\n db.session.commit()\n\n # import pdb; pdb.set_trace()","def __init__(self, dbname='cards'):\n\n self._client = MongoClient()\n self._db = self._client[dbname]\n self._collection = self._db[dbname]\n self._keys = ['set', 'color', 'text', 'creator']","def find_or_create(cls, data, **kwargs):\n\t\tinstance = cls.query.filter_by(**kwargs).first()\n\t\tif not instance:\n\t\t\tinstance = cls(**data).save()\n\t\treturn instance"],"string":"[\n \"def from_mongo(data):\\n if not data:\\n return None\\n\\n data['id'] = str(data['_id'])\\n return data\",\n \"def from_mongo(cls, data: dict) -> Union[\\\"MongoModel\\\", Dict]:\\n if not data:\\n return data\\n id = data.pop('_id', None)\\n return cls(**dict(data, id=id))\",\n \"def create(cls, collection, data, schema=None):\\n validated = cls.validate(data, schema=schema)\\n result = collection.insert_one(validated)\\n return collection.find_one({\\\"_id\\\": result.inserted_id})\",\n \"def _to_document(self, document):\\n obj = self.document()\\n obj._set_from_db(document)\\n return obj\",\n \"def _doc_create(type, data):\\n doc = dict(data)\\n doc.update({'model_type': type})\\n return doc\",\n \"def init_doc(self, obj, update_dict=True):\\n try:\\n obj.essentials\\n except AttributeError:\\n raise AttributeError(\\\"An object to be saved in db is supposed to have the essentials attribute\\\")\\n\\n if obj.essentials is None:\\n raise AttributeError(\\\"An object to be saved in db should not have NoneType as its essentials\\\")\\n\\n print(\\\"Saving this object into db: {}\\\".format(type(obj)))\\n\\n start = datetime.now()\\n essen = self.mongo_doc_generator(obj.essentials)\\n document = {\\\"essentials\\\": essen, 'datetime': start, 'filepaths': obj.filepaths}\\n\\n db_location = obj.db\\n element = obj.decide_element()\\n host = db_location[\\\"host\\\"]\\n project = db_location[\\\"project\\\"]\\n\\n target_db = connect_collection(host, project, element)\\n doc_created = target_db.insert_one(document)\\n inserted_id = doc_created.inserted_id\\n\\n return inserted_id\",\n \"def create_document(document: DocumentIn, db: Session = Depends(get_db)):\\n return add_document(db, document)\",\n \"def createDocument(self, document):\\n data = self.createDocumentAll([document])\\n try:\\n return data[0]\\n except: pass\",\n \"def new_document(klass, name=None, author=None):\\n doc = Factory.new_document(klass, author)\\n doc.name = name\\n doc._osl.id = uuid.uuid4()\\n return doc\",\n \"def create_document(self, data):\\n command = CreateDocumentFromOneOffixxTemplateCommand(self.context, data['title'], data['template'])\\n return command.execute()\",\n \"def subdocument(db_type):\\n if db_type != \\\"ephemeraldb\\\":\\n pytest.skip(\\\"ephemeraldb test only\\\")\\n yield EphemeralDocument(\\n {\\\"_id\\\": 1, \\\"hello\\\": \\\"there\\\", \\\"mighty\\\": \\\"duck\\\", \\\"and\\\": {\\\"the\\\": \\\"drake\\\"}}\\n )\",\n \"def create_from_dict(new_info: dict):\\n doc = Db2Document(mhr_number=new_info.get('mhrNumber'),\\n document_type=new_info.get('documentType'),\\n document_reg_id=new_info.get('documentRegistrationId'),\\n interimed=new_info.get('interimed', ''),\\n owner_cross_reference=new_info.get('ownerCrossReference', ''),\\n interest_denominator=new_info.get('interestDenominator', 0),\\n declared_value=new_info.get('declaredValue', 0),\\n own_land=new_info.get('ownLand', ''),\\n routing_slip_number=new_info.get('routingSlipNumber', ''))\\n doc.last_service = new_info.get('lastService', '')\\n doc.bcol_account = new_info.get('bcolAccount', '')\\n doc.dat_number = new_info.get('datNumber', '')\\n doc.examiner_id = new_info.get('examinerId', '')\\n doc.update_id = new_info.get('updateId', '')\\n doc.phone_number = new_info.get('phoneNumber', '')\\n doc.attention_reference = new_info.get('attentionReference', '')\\n doc.name = new_info.get('name', '')\\n doc.legacy_address = new_info.get('legacyAddress', '')\\n doc.number_of_pages = new_info.get('numberOfPages', 0)\\n doc.consideration_value = new_info.get('considerationValue', '')\\n doc.affirm_by_name = new_info.get('affirmByName', '')\\n doc.liens_with_consent = new_info.get('liensWithConsent', '')\\n doc.client_reference_id = new_info.get('clientReferenceId', '')\\n if new_info.get('createDateTime', None):\\n doc.registration_ts = model_utils.ts_from_iso_format(new_info.get('createDateTime'))\\n if new_info.get('draftDateTime', None):\\n doc.draft_ts = model_utils.ts_from_iso_format(new_info.get('draftDateTime'))\\n if new_info.get('transferExecutionDate', None):\\n date_val: str = str(new_info.get('transferExecutionDate'))[0:10]\\n doc.transfer_execution_date = model_utils.date_from_iso_format(date_val)\\n return doc\",\n \"def from_db(data):\\n \\n return Collection(\\n dbid = data['id'],\\n title = data['title'],\\n query = data['query'],\\n priority = data['priority'],\\n export = data['export'])\",\n \"def test_find_embedded(self):\\n\\n class User(EmbeddedDocument):\\n name = StringField()\\n\\n class BlogPost(Document):\\n content = StringField()\\n author = EmbeddedDocumentField(User)\\n\\n BlogPost.drop_collection()\\n\\n user = User(name=\\\"Test User\\\")\\n BlogPost.objects.create(author=user, content=\\\"Had a good coffee today...\\\")\\n\\n result = BlogPost.objects.first()\\n assert isinstance(result.author, User)\\n assert result.author.name == \\\"Test User\\\"\\n\\n result = BlogPost.objects.get(author__name=user.name)\\n assert isinstance(result.author, User)\\n assert result.author.name == \\\"Test User\\\"\\n\\n result = BlogPost.objects.get(author={\\\"name\\\": user.name})\\n assert isinstance(result.author, User)\\n assert result.author.name == \\\"Test User\\\"\\n\\n # Fails, since the string is not a type that is able to represent the\\n # author's document structure (should be dict)\\n with pytest.raises(InvalidQueryError):\\n BlogPost.objects.get(author=user.name)\",\n \"def test_as_pymongo(self):\\n data = []\\n self.maxDiff = None\\n for item in self.docs[0:2]:\\n el = item.to_dict(oid_as_str=False, call_child_to_dict=False)\\n el[\\\"_id\\\"] = el.pop(\\\"id\\\")\\n data.append(el)\\n actual = self.doc_cls.objects[0:2].as_pymongo()\\n self.assertEqual(data, list(actual))\",\n \"def create(init_document: 'Document') -> 'DocumentArray':\",\n \"def create_document(content: Union[str, bytes]) -> Document:\\n r = requests.post(\\\"https://pastecord.com/documents\\\", data=content)\\n r.raise_for_status()\\n \\n return Document(r.json()['key'])\",\n \"def create_mongodb(config):\\n\\n \\n mongo_url = \\\"mongodb://\\\"\\n mongo_url += \\\",\\\".join(map(lambda srv: srv['host'] + \\\":\\\" + str(srv['port']), config['data']['mongoServers']))\\n \\n if 'replica' in config['data']:\\n mongo_url += \\\"/?replicaSet={0}\\\".format(config['data']['replica'])\\n\\n client = MongoClient(mongo_url)\\n\\n return client\",\n \"def _resolve_embedded_documents(document, resource, embedded_fields):\\n schema = config.DOMAIN[resource]['schema']\\n for field in embedded_fields:\\n data_relation = schema[field]['data_relation']\\n # Retrieve and serialize the requested document\\n if 'version' in data_relation and data_relation['version'] is True:\\n # support late versioning\\n if document[field][config.VERSION] == 0:\\n # there is a chance this document hasn't been saved\\n # since versioning was turned on\\n embedded_doc = missing_version_field(\\n data_relation, document[field])\\n\\n if embedded_doc is None:\\n # this document has been saved since the data_relation was\\n # made - we basically do not have the copy of the document\\n # that existed when the data relation was made, but we'll\\n # try the next best thing - the first version\\n document[field][config.VERSION] = 1\\n embedded_doc = get_data_version_relation_document(\\n data_relation, document[field])\\n\\n latest_embedded_doc = embedded_doc\\n else:\\n # grab the specific version\\n embedded_doc = get_data_version_relation_document(\\n data_relation, document[field])\\n\\n # grab the latest version\\n latest_embedded_doc = get_data_version_relation_document(\\n data_relation, document[field], latest=True)\\n\\n # make sure we got the documents\\n if embedded_doc is None or latest_embedded_doc is None:\\n # your database is not consistent!!! that is bad\\n abort(404, description=debug_error_message(\\n \\\"Unable to locate embedded documents for '%s'\\\" %\\n field\\n ))\\n\\n # build the response document\\n _build_response_document(\\n embedded_doc, data_relation['resource'],\\n [], latest_embedded_doc)\\n else:\\n embedded_doc = app.data.find_one(\\n data_relation['resource'], None,\\n **{config.ID_FIELD: document[field]}\\n )\\n if embedded_doc:\\n document[field] = embedded_doc\",\n \"def create_document(self):\\n # set single values\\n if len(self.field_values) > 0:\\n self._set_field_values()\\n\\n # set multi values\\n if len(self.block_field_values) > 0:\\n self._set_multi_field_values()\\n\\n self.field_values = {}\\n self.block_field_values = {}\\n\\n self.client.service.CreateDocument()\",\n \"def test_query_generic_embedded_document(self):\\n\\n class A(EmbeddedDocument):\\n a_name = StringField()\\n\\n class B(EmbeddedDocument):\\n b_name = StringField()\\n\\n class Doc(Document):\\n document = GenericEmbeddedDocumentField(choices=(A, B))\\n\\n Doc.drop_collection()\\n Doc(document=A(a_name=\\\"A doc\\\")).save()\\n Doc(document=B(b_name=\\\"B doc\\\")).save()\\n\\n # Using raw in filter working fine\\n assert Doc.objects(__raw__={\\\"document.a_name\\\": \\\"A doc\\\"}).count() == 1\\n assert Doc.objects(__raw__={\\\"document.b_name\\\": \\\"B doc\\\"}).count() == 1\\n assert Doc.objects(document__a_name=\\\"A doc\\\").count() == 1\\n assert Doc.objects(document__b_name=\\\"B doc\\\").count() == 1\",\n \"def test_find_empty_embedded(self):\\n\\n class User(EmbeddedDocument):\\n name = StringField()\\n\\n class BlogPost(Document):\\n content = StringField()\\n author = EmbeddedDocumentField(User)\\n\\n BlogPost.drop_collection()\\n\\n BlogPost.objects.create(content=\\\"Anonymous post...\\\")\\n\\n result = BlogPost.objects.get(author=None)\\n assert result.author is None\",\n \"def transform_bson(self, value: Dict) -> Entity:\\n try:\\n return from_dict(\\n data_class=self.python_type,\\n config=self._config,\\n data=value\\n )\\n except MissingValueError as e:\\n raise e\\n except DefaultValueNotFoundError as e:\\n raise e\",\n \"def test_mongodb_origin_simple_with_BSONBinary(sdc_builder, sdc_executor, mongodb):\\n\\n ORIG_BINARY_DOCS = [\\n {'data': binary.Binary(b'Binary Data Flute')},\\n {'data': binary.Binary(b'Binary Data Oboe')},\\n {'data': binary.Binary(b'Binary Data Violin')}\\n ]\\n\\n pipeline_builder = sdc_builder.get_pipeline_builder()\\n pipeline_builder.add_error_stage('Discard')\\n\\n mongodb_origin = pipeline_builder.add_stage('MongoDB', type='origin')\\n mongodb_origin.set_attributes(capped_collection=False,\\n database=get_random_string(ascii_letters, 5),\\n collection=get_random_string(ascii_letters, 10))\\n\\n trash = pipeline_builder.add_stage('Trash')\\n mongodb_origin >> trash\\n pipeline = pipeline_builder.build().configure_for_environment(mongodb)\\n\\n try:\\n # MongoDB and PyMongo add '_id' to the dictionary entries e.g. docs_in_database\\n # when used for inserting in collection. Hence the deep copy.\\n docs_in_database = copy.deepcopy(ORIG_BINARY_DOCS)\\n\\n # Create documents in MongoDB using PyMongo.\\n # First a database is created. Then a collection is created inside that database.\\n # Then documents are created in that collection.\\n logger.info('Adding documents into %s collection using PyMongo...', mongodb_origin.collection)\\n mongodb_database = mongodb.engine[mongodb_origin.database]\\n mongodb_collection = mongodb_database[mongodb_origin.collection]\\n insert_list = [mongodb_collection.insert_one(doc) for doc in docs_in_database]\\n assert len(insert_list) == len(docs_in_database)\\n\\n # Start pipeline and verify the documents using snaphot.\\n sdc_executor.add_pipeline(pipeline)\\n snapshot = sdc_executor.capture_snapshot(pipeline=pipeline, start_pipeline=True).snapshot\\n sdc_executor.stop_pipeline(pipeline)\\n rows_from_snapshot = [{'data': str(record.value2['data'])} for record in snapshot[mongodb_origin].output]\\n\\n assert rows_from_snapshot == [{'data': str(record.get('data'))} for record in ORIG_BINARY_DOCS]\\n\\n finally:\\n logger.info('Dropping %s database...', mongodb_origin.database)\\n mongodb.engine.drop_database(mongodb_origin.database)\",\n \"def document(self):\\n query = {\\\"_id\\\": ObjectId(self.document_id)}\\n return Document(get_collection(\\\"documents\\\").find_one(query))\",\n \"def create_from_document(doc: Dict[str, Any]) -> 'Publication':\\n return Publication(\\n name=doc['doc_name'],\\n title=doc['doc_title'][:100],\\n type=doc['doc_type'],\\n number=doc['doc_num'],\\n is_ignored=False,\\n is_revoked=False\\n )\",\n \"def test_as_pymongo(self):\\n data = []\\n for item in self.users:\\n el = item.to_dict(oid_as_str=False, call_child_to_dict=False)\\n el[\\\"_id\\\"] = el.pop(\\\"id\\\")\\n data.append(el)\\n actual = User.objects.as_pymongo()\\n self.assertEqual(data, list(actual))\",\n \"def test_convert_hwpc_report_from_mongodb_work(hwpc_model, json_input):\\n hwpc_model.from_mongodb(json_input)\\n assert True\",\n \"def create_instance(*, db_session, instance_in: WorkflowInstanceCreate) -> WorkflowInstance:\\n instance = WorkflowInstance(\\n **instance_in.dict(exclude={\\\"incident\\\", \\\"workflow\\\", \\\"creator\\\", \\\"artifacts\\\"})\\n )\\n\\n incident = incident_service.get(db_session=db_session, incident_id=instance_in.incident[\\\"id\\\"])\\n instance.incident = incident\\n\\n workflow = get(db_session=db_session, workflow_id=instance_in.workflow[\\\"id\\\"])\\n instance.workflow = workflow\\n\\n creator = participant_service.get_by_incident_id_and_email(\\n db_session=db_session, incident_id=incident.id, email=instance_in.creator[\\\"email\\\"]\\n )\\n instance.creator = creator\\n\\n for a in instance_in.artifacts:\\n artifact_document = document_service.create(\\n db_session=db_session, document_in=DocumentCreate(**a)\\n )\\n instance.artifacts.append(artifact_document)\\n\\n db_session.add(instance)\\n db_session.commit()\\n\\n return instance\",\n \"def test_find_dict_item(self):\\n\\n class BlogPost(Document):\\n info = DictField()\\n\\n BlogPost.drop_collection()\\n\\n post = BlogPost(info={\\\"title\\\": \\\"test\\\"})\\n post.save()\\n\\n post_obj = BlogPost.objects(info__title=\\\"test\\\").first()\\n assert post_obj.id == post.id\\n\\n BlogPost.drop_collection()\",\n \"def create_collection(collection):\\n return db[collection]\",\n \"def documents(required_fields=None,\\n optional_fields=None,\\n random_fields=json.values()):\\n return json.objects(required_fields=required_fields,\\n optional_fields=optional_fields,\\n elements=random_fields)\",\n \"def _build_response_document(\\n document, resource, embedded_fields, latest_doc=None):\\n # need to update the document field since the etag must be computed on the\\n # same document representation that might have been used in the collection\\n # 'get' method\\n document[config.DATE_CREATED] = date_created(document)\\n document[config.LAST_UPDATED] = last_updated(document)\\n # TODO: last_update should include consideration for embedded documents\\n\\n # generate ETag\\n if config.IF_MATCH:\\n document[config.ETAG] = document_etag(document)\\n\\n # hateoas links\\n if config.DOMAIN[resource]['hateoas']:\\n _lookup_field = config.DOMAIN[resource]['item_lookup_field']\\n document[config.LINKS] = {'self':\\n document_link(resource,\\n document[_lookup_field])}\\n\\n # add version numbers\\n resolve_document_version(document, resource, 'GET', latest_doc)\\n\\n # media and embedded documents\\n _resolve_media_files(document, resource)\\n _resolve_embedded_documents(document, resource, embedded_fields)\",\n \"def make_instance(self, data, **kwargs):\\n instance = self.instance or self.get_instance(data)\\n if instance is not None:\\n for key, value in iteritems(data):\\n setattr(instance, key, value)\\n return instance\\n kwargs, association_attrs = self._split_model_kwargs_association(data)\\n instance = self.opts.model(**kwargs)\\n for attr, value in iteritems(association_attrs):\\n setattr(instance, attr, value)\\n return instance\",\n \"def init_mongo_db():\\n try:\\n app.mongo.cx.server_info()\\n app.mongo.db = app.mongo.cx[\\\"kamistudio\\\"]\\n if \\\"kami_corpora\\\" not in app.mongo.db.collection_names():\\n app.mongo.db.create_collection(\\\"kami_corpora\\\")\\n app.mongo.db.kami_corpora.create_index(\\\"id\\\", unique=True)\\n\\n if \\\"kami_models\\\" not in app.mongo.db.collection_names():\\n app.mongo.db.create_collection(\\\"kami_models\\\")\\n app.mongo.db.kami_models.create_index(\\\"id\\\", unique=True)\\n\\n if \\\"kami_definitions\\\" not in app.mongo.db.collection_names():\\n app.mongo.db.create_collection(\\\"kami_definitions\\\")\\n app.mongo.db.kami_definitions.create_index(\\\"id\\\", unique=True)\\n\\n if \\\"kami_new_definitions\\\" not in app.mongo.db.collection_names():\\n app.mongo.db.create_collection(\\\"kami_new_definitions\\\")\\n\\n except ServerSelectionTimeoutError as e:\\n app.mongo.db = None\",\n \"def create_doc(c, d):\\n try:\\n c.insert_one(d)\\n return True\\n except:\\n return False\",\n \"def create(cls, data):\\n doc = {\\n \\\"type\\\": cls.type,\\n **data\\n }\\n db_utils.set_created_at(doc)\\n LOG.debug(\\\"Adding score {} with data: {}\\\".format(cls.type, data))\\n return DB.save(doc)\",\n \"def make_object(self, data, **kwargs):\\n if not data:\\n return None\\n return Note(\\n title=data['title'],\\n content=data['content'],\\n )\",\n \"def __build_schema(meta_data):\\n \\n # Builds the dictionary that represents the schema.\\n temporary_dictionary = {'$schema': None, '$id': None, 'title': None, 'type': None, 'properties': []}\\n for x in meta_data:\\n temporary_dictionary['properties'].append({\\n 'name': x,\\n 'type': None,\\n 'description': None})\\n # Creates a new instance of the schema and inserts the dictionary as a json into the field and returns it.\\n returned_schema = Schema()\\n returned_schema.data = json.dumps(temporary_dictionary)\\n return returned_schema\",\n \"def _create_document(result_dict):\\n document = Document(\\n name=result_dict['docname'],\\n original_id=result_dict['itemid'],\\n doctype=result_dict['doctype'],\\n language=result_dict['languageisocode'],\\n conclusion=result_dict['conclusion'],\\n originatingbody=result_dict['originatingbody'],\\n application=result_dict['application'],\\n )\\n return document\",\n \"def create_document(obj):\\n index = obj.get_index_name()\\n doc_type = obj.get_document_type()\\n body = obj.get_document_body()\\n exists = ES.exists(index=index, doc_type=doc_type, id=obj.pk)\\n\\n if not exists:\\n ES.create(index=index, doc_type=doc_type, body=body, id=obj.pk)\\n return None\\n\\n return \\\"Conflict: document already exists for {0} with id {1}.\\\".format(\\n obj.__class__.__name__, obj.pk)\",\n \"def test_add_one_document_object(self):\\n user_id = get_rand_string()\\n data = get_rand_string()\\n id = get_rand_string()\\n \\n doc = Document()\\n doc[\\\"user_id\\\"] = user_id\\n doc[\\\"data\\\"] = data\\n doc[\\\"id\\\"] = id\\n \\n #raise Exception, doc.as_xml\\n self.conn.add(doc)\\n self.conn.commit()\\n results = self.conn.query(\\\"id:\\\" + id).results\\n \\n self.assertEquals(len(results), 1,\\n \\\"Could not find expected data (id:%s)\\\" % id)\",\n \"def mongodb_init():\\n # Load environment variables\\n dotenv_path = find_dotenv()\\n load_dotenv(dotenv_path)\\n\\n # Connect to the db\\n # DB will be created if it doesn't already exist\\n client = pymongo.MongoClient(os.environ.get(\\\"DATABASE_URL\\\"), 27017)\\n # 'tweetbase' is the database name\\n db = client.tweetbase\\n\\n #Sets both the tweet ID and user ID strings as unique indexes\\n db.tweets.create_index([(\\\"id_str\\\", 1),\\n (\\\"user.id_str\\\", 1)],\\n unique=True)\\n\\n # The db is only actually created when something is inserted\\n # So this inserts a test document and immediately deletes it...\\n # AND EVERYTHING ELSE\\n # DO NOT RUN THIS ON A DB YOU WANT TO KEEP. SERIOUSLY.\\n db.tweets.insert_one({\\\"id_str\\\": 1, \\\"user\\\": {\\\"id_str\\\": 5}})\\n db.tweets.remove()\",\n \"def obj_get(self, request=None, **kwargs):\\n return Document(self.get_collection(request).find_one({\\n \\\"_id\\\": ObjectId(kwargs.get(\\\"pk\\\"))\\n }))\",\n \"def store_eeg_in_mongodb(eeg_data):\\n con, eeg = connect_to_eeg_db()\\n for eeg_record in eeg_data:\\n eeg.insert(eeg_record)\\n con.close()\",\n \"def add_a_record(self, record):\\n '''\\n doc = { \\\"P/N\\\": record,#record.get_PN(),\\n \\\"supplier\\\": \\\"\\\",\\n \\\"inventory\\\": \\\"\\\",\\n \\\"specification\\\": \\\"\\\",\\n \\\"description\\\": \\\"\\\",\\n \\\"OEM\\\": \\\"\\\",\\n \\\"tags\\\": [\\\"mongodb\\\", \\\"python\\\", \\\"pymongo\\\"],\\n \\\"date\\\": datetime.datetime.utcnow()}'''\\n self.collection.insert(record)\",\n \"def CreateDocument(name, product_id=None):\\n nameFields = [search.TextField(name='name', value=name)]\\n\\n if product_id:\\n # Specify using the product_id we want\\n return search.Document(\\n doc_id=product_id,\\n fields=nameFields)\\n else:\\n # Let the search service supply the document id, for testing only\\n return search.Document(fields=nameFields)\",\n \"def test_add_one_document(self):\\n user_id = get_rand_string()\\n data = get_rand_string()\\n id = get_rand_string()\\n\\n doc = {}\\n doc[\\\"user_id\\\"] = user_id\\n doc[\\\"data\\\"] = data\\n doc[\\\"id\\\"] = id\\n\\n self.conn.add(**doc)\\n self.conn.commit()\\n results = self.conn.query(\\\"id:\\\" + id).results\\n\\n self.assertEquals(len(results), 1,\\n \\\"Could not find expected data (id:%s)\\\" % id)\\n\\n doc = results[0]\\n self.assertEquals(doc[\\\"user_id\\\"], user_id)\\n self.assertEquals(doc[\\\"data\\\"], data)\",\n \"def get_instance_from_words(data):\\n inst = Dataset.get_instance_template()\\n inst[\\\"words\\\"] = data\\n return inst\",\n \"def encode(\\n cls: Type[\\\"DataDocument\\\"], encoding: str, data: D, **kwargs: Any\\n ) -> \\\"DataDocument[D]\\\":\\n # Dispatch encoding\\n blob = lookup_serializer(encoding).dumps(data, **kwargs)\\n\\n inst = cls(blob=blob, encoding=encoding)\\n inst._cache_data(data)\\n return inst\",\n \"def to_mongo(self, value, use_db_field=True, fields=None):\\n Document = _import_class(\\\"Document\\\")\\n EmbeddedDocument = _import_class(\\\"EmbeddedDocument\\\")\\n GenericReferenceField = _import_class(\\\"GenericReferenceField\\\")\\n\\n if isinstance(value, str):\\n return value\\n\\n if hasattr(value, \\\"to_mongo\\\"):\\n if isinstance(value, Document):\\n return GenericReferenceField().to_mongo(value)\\n cls = value.__class__\\n val = value.to_mongo(use_db_field, fields)\\n # If it's a document that is not inherited add _cls\\n if isinstance(value, EmbeddedDocument):\\n val[\\\"_cls\\\"] = cls.__name__\\n return val\\n\\n is_list = False\\n if not hasattr(value, \\\"items\\\"):\\n try:\\n is_list = True\\n value = {k: v for k, v in enumerate(value)}\\n except TypeError: # Not iterable return the value\\n return value\\n\\n if self.field:\\n value_dict = {\\n key: self.field._to_mongo_safe_call(item, use_db_field, fields)\\n for key, item in value.items()\\n }\\n else:\\n value_dict = {}\\n for k, v in value.items():\\n if isinstance(v, Document):\\n # We need the id from the saved object to create the DBRef\\n if v.pk is None:\\n self.error(\\n \\\"You can only reference documents once they\\\"\\n \\\" have been saved to the database\\\"\\n )\\n\\n # If it's a document that is not inheritable it won't have\\n # any _cls data so make it a generic reference allows\\n # us to dereference\\n meta = getattr(v, \\\"_meta\\\", {})\\n allow_inheritance = meta.get(\\\"allow_inheritance\\\")\\n if not allow_inheritance:\\n value_dict[k] = GenericReferenceField().to_mongo(v)\\n else:\\n collection = v._get_collection_name()\\n value_dict[k] = DBRef(collection, v.pk)\\n elif hasattr(v, \\\"to_mongo\\\"):\\n cls = v.__class__\\n val = v.to_mongo(use_db_field, fields)\\n # If it's a document that is not inherited add _cls\\n if isinstance(v, (Document, EmbeddedDocument)):\\n val[\\\"_cls\\\"] = cls.__name__\\n value_dict[k] = val\\n else:\\n value_dict[k] = self.to_mongo(v, use_db_field, fields)\\n\\n if is_list: # Convert back to a list\\n return [\\n v for _, v in sorted(value_dict.items(), key=operator.itemgetter(0))\\n ]\\n return value_dict\",\n \"def insert(self, data):\\n return self.collection.insert(data)\",\n \"def build_document(self, labels_from_json):\\n if not len(self.raw_labels):\\n self.get_labels(labels_from_json)\\n raw_text = self.instance_input_file.read()\\n document = self.DOCUMENT_CLASS(self.identifier, title=self.identifier)\\n document.build_from_text(raw_text, start_index=0)\\n for start_index, end_index in self.raw_labels:\\n document.add_label_for_position(\\n 'claim', int(start_index), int(end_index))\\n return document\",\n \"async def _save(self, document, alias=None):\\n doc = document.to_son()\\n\\n if document._id is not None:\\n try:\\n await self.coll(alias).update({\\\"_id\\\": document._id}, doc)\\n except DuplicateKeyError as e:\\n raise UniqueKeyViolationError.from_pymongo(str(e), self.__klass__)\\n else:\\n try:\\n doc_id = await self.coll(alias).insert(doc)\\n except DuplicateKeyError as e:\\n raise UniqueKeyViolationError.from_pymongo(str(e), self.__klass__)\\n document._id = doc_id\\n\\n return document\",\n \"def extract_document(cls, obj_id, obj=None):\\n if obj is None:\\n obj = cls.get_model().get(id=obj_id)\\n return {\\n 'id': unicode(obj.id),\\n 'init_user': {\\n 'email': obj.init_user.email\\n },\\n 'text': obj.text,\\n 'url': obj.get_absolute_url(),\\n '_parent': unicode(obj.content_object.pk) # todo use only experiments pk\\n }\",\n \"def prep_data(data: list):\\n book = {\\n 'title': data['title'],\\n 'authors': [],\\n 'categories': []\\n }\\n try:\\n for author in data['authors']:\\n author_obj, created = Author.objects.get_or_create(name=author)\\n book['authors'].append(author_obj.id)\\n except KeyError:\\n pass\\n try:\\n for category in data['categories']:\\n category_obj, created = Category.objects.get_or_create(name=category)\\n book['categories'].append(category_obj.id)\\n except KeyError:\\n pass\\n book['published_date'] = data.get('publishedDate', None)\\n book['average_rating'] = data.get('averageRating', None)\\n book['ratings_count'] = data.get('ratingsCount', None)\\n try:\\n book['thumbnail'] = data['imageLinks']['thumbnail']\\n except KeyError:\\n book['thumbnail'] = None\\n return book\",\n \"def construct_schema_pymongo(\\n collection: pymongo.collection.Collection,\\n delimiter: str,\\n use_random_sampling: bool,\\n sample_size: Optional[int] = None,\\n) -> Dict[Tuple[str, ...], SchemaDescription]:\\n\\n if sample_size:\\n if use_random_sampling:\\n # get sample documents in collection\\n documents = collection.aggregate(\\n [{\\\"$sample\\\": {\\\"size\\\": sample_size}}], allowDiskUse=True\\n )\\n else:\\n documents = collection.aggregate(\\n [{\\\"$limit\\\": sample_size}], allowDiskUse=True\\n )\\n else:\\n # if sample_size is not provided, just take all items in the collection\\n documents = collection.find({})\\n\\n return construct_schema(list(documents), delimiter)\",\n \"def create(self, validated_data):\\n # Create data\\n instance = Data(\\n template=validated_data['template'],\\n title=validated_data['title'],\\n user_id=str(validated_data['user'].id),\\n )\\n # Set xml content\\n instance.xml_content = validated_data['xml_content']\\n # Save the data\\n data_api.upsert(instance, validated_data['user'])\\n # Encode the response body\\n instance.xml_content = validated_data['xml_content'].encode('utf-8')\\n\\n return instance\",\n \"def __init__(\\r\\n self, db, collection, host, port=27017, tz_aware=True, user=None, password=None, **kwargs\\r\\n ):\\r\\n self.database = pymongo.database.Database(\\r\\n pymongo.MongoClient(\\r\\n host=host,\\r\\n port=port,\\r\\n tz_aware=tz_aware,\\r\\n document_class=son.SON,\\r\\n **kwargs\\r\\n ),\\r\\n db\\r\\n )\\r\\n\\r\\n if user is not None and password is not None:\\r\\n self.database.authenticate(user, password)\\r\\n\\r\\n self.course_index = self.database[collection + '.active_versions']\\r\\n self.structures = self.database[collection + '.structures']\\r\\n self.definitions = self.database[collection + '.definitions']\\r\\n\\r\\n # every app has write access to the db (v having a flag to indicate r/o v write)\\r\\n # Force mongo to report errors, at the expense of performance\\r\\n # pymongo docs suck but explanation:\\r\\n # http://api.mongodb.org/java/2.10.1/com/mongodb/WriteConcern.html\\r\\n self.course_index.write_concern = {'w': 1}\\r\\n self.structures.write_concern = {'w': 1}\\r\\n self.definitions.write_concern = {'w': 1}\",\n \"def _construct_course_son(org, course, run):\\r\\n return bson.son.SON([\\r\\n ('org', org),\\r\\n ('course', course),\\r\\n ('name', run)\\r\\n ])\",\n \"def post(self, data):\\n self.validate(data)\\n\\n mongo['readable-api'].foo.insert_one(data)\\n\\n return self.request.resource_url(self.make_child(data['foo']))\",\n \"def document_new():\\n\\n t = request.form['type']\\n if t == 'book':\\n doc = Book(\\n title=request.form['title'],\\n price=request.form['price'],\\n keywords=comma_to_list(request.form['keywords']),\\n authors=comma_to_list(request.form['authors']),\\n edition=request.form['edition'],\\n publisher=request.form['publisher'],\\n publishment_year=request.form['publishment_year'],\\n bestseller='bestseller' in request.form,\\n reference='reference' in request.form\\n )\\n elif t == 'av':\\n doc = AVMaterial(\\n title=request.form['title'],\\n price=request.form['price'],\\n keywords=comma_to_list(request.form['keywords']),\\n authors=comma_to_list(request.form['authors'])\\n )\\n elif t == 'article':\\n doc = JournalArticle(\\n title=request.form['title'],\\n price=request.form['price'],\\n keywords=comma_to_list(request.form['keywords']),\\n authors=comma_to_list(request.form['authors']),\\n issue_editor=request.form['issue_editor'],\\n issue_publication_date=request.form['issue_publication_date'],\\n journal=request.form['journal']\\n )\\n\\n for i in range(int(request.form['copies'])):\\n dc = DocumentCopy(document=doc)\\n\\n db.session.add(doc)\\n db.session.commit()\\n\\n log(session['login'], 'created', 'document {}'.format(doc.id))\\n\\n # TODO\\n return redirect('/admin/documents')\",\n \"def instance_create(values):\\n metadata = values.get('metadata')\\n metadata_refs = []\\n if metadata:\\n for k, v in metadata.iteritems():\\n metadata_ref = models.InstanceMetadata()\\n metadata_ref['key'] = k\\n metadata_ref['value'] = v\\n metadata_refs.append(metadata_ref)\\n values['metadata'] = metadata_refs\\n\\n instance_ref = models.Instance()\\n instance_ref.update(values)\\n\\n session = get_session()\\n with session.begin():\\n instance_ref.save(session=session)\\n return instance_ref\",\n \"async def save(\\n self, item: T, *,\\n _id=None,\\n mode: Literal['save', 'insert', 'update'] = 'save',\\n inject_default_id: bool = False,\\n inject_created: bool = False\\n\\n ) -> T:\\n document = item.dict(by_alias=True)\\n\\n if self.identity != '_id':\\n assert '_id' not in document, \\\"Should not have _id in the instance if collection's identity is non default.\\\"\\n\\n if _id is not None:\\n assert self.identity == '_id', \\\"_id parameter can be provided only if collection's identity is default.\\\"\\n assert '_id' not in document, \\\"_id should be provided ether in instance or by _id param.\\\"\\n document['_id'] = ObjectId(_id)\\n\\n if mode == 'save':\\n if document.get(self.identity) is None: # === New document.\\n if self.identity == '_id':\\n if '_id' in document: del document['_id']\\n else:\\n document[self.identity] = self.generate_id()\\n\\n result: InsertOneResult = await self.collection.insert_one(document) # will fail if exists due to index=unique violation for identity\\n document['_id'] = result.inserted_id # will be removed while back-parsing if not necessary\\n document['__created__'] = True\\n\\n else: # == Possibly an existing document that needs to be updated.\\n result: UpdateResult = await self.collection.update_one(\\n {self.identity: document[self.identity]},\\n {'$set': document}, upsert=True\\n )\\n if result.upserted_id is not None:\\n document['_id'] = result.upserted_id\\n document['__created__'] = True\\n\\n elif mode == 'insert':\\n assert document.get(self.identity) is not None, f\\\"Need identity ({self.identity}) for insert mode, use save mode to insert document without identity.\\\"\\n _result: InsertOneResult = await self.collection.insert_one(document)\\n document['__created__'] = True\\n\\n elif mode == 'update':\\n assert document.get(self.identity) is not None, f\\\"Need identity ({self.identity}) for update mode.\\\"\\n mongo_query = {self.identity: document[self.identity]}\\n result: UpdateResult = await self.collection.update_one(\\n mongo_query,\\n {'$set': document}\\n )\\n if not result.matched_count: raise NotFound(mongo_query)\\n\\n else:\\n assert False, f\\\"Mode {mode} is not supported.\\\"\\n\\n return self.parse_document(document, inject_default_id=inject_default_id, inject_created=inject_created)\",\n \"def create_from_document(\\n doc: Dict[str, Any],\\n doc_location: str,\\n filename: str,\\n batch_timestamp: dt.datetime,\\n pub: Publication) -> 'VersionedDoc':\\n return VersionedDoc(\\n publication=pub,\\n name=doc['doc_name'],\\n type=doc['doc_type'],\\n number=doc['doc_num'],\\n # TODO: Pass actual filename using ProcessedDoc instead of Doc\\n # TODO: Tweak for clones?\\n filename=filename,\\n doc_location=doc_location,\\n batch_timestamp=batch_timestamp,\\n publication_date=parse_timestamp(doc['publication_date']),\\n json_metadata=doc,\\n version_hash=doc['version_hash'],\\n md5_hash=\\\"\\\",\\n is_ignored=False\\n )\",\n \"def __init__(self, api: MongoAPI):\\n self.__api = api\",\n \"def create_fhir_document(self, data, analysis):\\n # Modify the data structure so that it is easier to use\\n path_attributes_map = {attr.path: attr for attr in analysis.attributes}\\n\\n # Build path value map\\n fhir_object = build_fhir_object(data, path_attributes_map)\\n\\n # Identify the fhir object\\n fhir_object[\\\"id\\\"] = str(uuid4())\\n fhir_object[\\\"resourceType\\\"] = analysis.definition[\\\"type\\\"]\\n fhir_object[\\\"meta\\\"] = build_metadata(analysis)\\n\\n # Remove duplicates in fhir object\\n fhir_object = clean_fhir_object(fhir_object)\\n\\n return fhir_object\",\n \"def insert_school(mongo_collection, **kwargs):\\n return mongo_collection.insert(kwargs)\",\n \"def create(data):\\n \\n return Part(\\n part_id = data['part_num'],\\n category_id = data['part_cat_id'],\\n external_ids = data.get('external_ids', {}),\\n name = data['name'],\\n year_from = data.get('year_from', None),\\n year_to = data.get('year_to', None),\\n url = data.get('part_url', None),\\n img_url = data.get('part_img_url', None),\\n print_of = data.get('print_of', None),\\n prints = data.get('prints', []),\\n molds = data.get('molds', []),\\n alternates = data.get('alternates', []))\",\n \"def _collection_from_response(data):\\n return Collection(uuid=UUID(data['uuid']), title=data['title'])\",\n \"def add_document(collection: str, document: dict) -> None:\\n validate_arguments({'collection': [collection, str],\\n 'document': [document, dict]})\\n DB[collection].insert_one(document)\",\n \"def __init__(self):\\n self.client = MongoClient('localhost', 27017)#27017\\n self.db = self.client.greency_db\\n self.collection = self.db.inventory\",\n \"def get_mongo_db():\\n\\n if not hasattr(g, 'mongodb'):\\n g.mongodb = connect_mongo_db()\\n\\n g.mongodb.register([Testimonial])\\n g.mongodb.register([Town])\\n\\n return g.mongodb\",\n \"def open_doc(self, docid, **params):\\n wrapper = None\\n if \\\"wrapper\\\" in params:\\n wrapper = params.pop(\\\"wrapper\\\")\\n elif \\\"schema\\\" in params:\\n schema = params.pop(\\\"schema\\\")\\n if not hasattr(schema, \\\"wrap\\\"):\\n raise TypeError(\\\"invalid schema\\\")\\n wrapper = schema.wrap\\n attachments = params.get('attachments', False)\\n\\n if six.PY2 and isinstance(docid, six.text_type):\\n docid = docid.encode('utf-8')\\n if six.PY3 and isinstance(docid, bytes):\\n docid = docid.decode('utf-8')\\n doc = Document(self.cloudant_database, docid)\\n try:\\n doc.fetch()\\n except HTTPError as e:\\n if e.response.status_code == 404:\\n raise ResourceNotFound(json.loads(e.response.content.decode('utf-8'))['reason'])\\n raise\\n doc_dict = dict(doc)\\n\\n if attachments and '_attachments' in doc_dict:\\n for attachment_name in doc_dict['_attachments']:\\n attachment_data = doc.get_attachment(attachment_name, attachment_type='binary')\\n doc_dict['_attachments'][attachment_name]['data'] = base64.b64encode(attachment_data)\\n del doc_dict['_attachments'][attachment_name]['stub']\\n del doc_dict['_attachments'][attachment_name]['length']\\n\\n if wrapper is not None:\\n if not callable(wrapper):\\n raise TypeError(\\\"wrapper isn't a callable\\\")\\n\\n return wrapper(doc_dict)\\n\\n return doc_dict\",\n \"def __init__(self, dst_mongodb_uri, dst_database, dst_collection, dry_run):\\n self.client = pymongo.MongoClient(dst_mongodb_uri)\\n self.dst_mongodb_uri = dst_mongodb_uri\\n self.lookup_col = self.client[dst_database][dst_collection]\\n self.dry_run = dry_run\",\n \"def generate_db_data(data):\\n # pipeline database default is mongodb://localhost:27017/example_db\\n db = hybrid.db.init(dbtype, database=MONGODB_URI, view_files=[], push_views=False, create=True,\\n delete_existing=True)\\n\\n # training database for classifier default is mongodb://localhost:27017/example_training_db\\n train_db = hybrid.db.init(dbtype, database=MONGODB_TRAINING_URI, view_files=[], push_views=False, create=True,\\n delete_existing=True)\\n\\n # start inserting data\\n try:\\n if arguments.add_training_data:\\n train = simple_generator_function(data)\\n for _ in range(TRAINING_SIZE):\\n train_data = train.next()\\n print \\\"%s\\\\n inserting training data into \\\\t::::: example_training_db :::::\\\" % train_data\\n db.storeDataBlobArray([data_blob.dict2blob(train_data)])\\n\\n for iris in simple_generator_function(data):\\n print \\\"%s\\\\n inserting data into \\\\t::::: example_db :::::\\\" % iris\\n train_db.storeDataBlobArray([data_blob.dict2blob(iris)])\\n time.sleep(TIME_BETWEEN_GUESSES)\\n\\n except (KeyboardInterrupt, SystemExit):\\n print \\\"\\\\n\\\"\\n print \\\"closing db connection to example_db and example_training_db\\\"\\n db.close()\\n train_db.close()\\n print \\\"closed\\\"\\n except:\\n raise\",\n \"def save(self, data, model_type=None):\\n print 'data is {}'.format(data)\\n collection = self._db[model_type] if model_type else self._collection\\n if type(data) == list:\\n result = collection.insert_many(obj).inserted_ids\\n elif type(data) != dict:\\n obj = data.serialize()\\n else:\\n obj = data\\n #if not hasattr(obj, '_id') or not obj._id:\\n if not \\\"_id\\\" in obj or not obj[\\\"_id\\\"]:\\n print 'obj does not exist in db: {}'.format(obj)\\n # this is necessary for mongodb to auto-generate the _id field\\n if \\\"_id\\\" in obj:\\n obj.pop('_id')\\n # TODO(cc) is there anyway to have insert_one() return the doc?\\n inserted_id = collection.insert_one(obj).inserted_id\\n print 'inserted_id is {}'.format(inserted_id)\\n result = self.get(inserted_id, model_type)\\n else:\\n print 'obj exists: {}'.format(obj)\\n updated = collection.find_and_modify({'_id': ObjectId(obj._id)}, obj)\\n result = updated\\n print 'update result is {}'.format(result)\\n return result\",\n \"def __init__(self, doc):\\n\\n self.doc = doc\\n if self.doc.doi:\\n self._populate()\\n self.populated = True\\n else:\\n self.populated = False\",\n \"def create_from_registration(registration, reg_json, doc_type: str, local_ts):\\n doc_id = reg_json.get('documentId', '')\\n doc = Db2Document(id=doc_id,\\n mhr_number=registration.mhr_number,\\n document_type=doc_type,\\n document_reg_id=registration.doc_reg_number,\\n registration_ts=local_ts,\\n draft_ts=local_ts,\\n interimed='',\\n owner_cross_reference='',\\n interest_denominator=0,\\n declared_value=reg_json.get('declaredValue', 0),\\n routing_slip_number='')\\n doc.last_service = ''\\n doc.bcol_account = ''\\n doc.dat_number = ''\\n doc.examiner_id = ''\\n doc.update_id = ''\\n doc.number_of_pages = 0\\n doc.consideration_value = reg_json.get('consideration', '')\\n doc.affirm_by_name = ''\\n if reg_json.get('affirmByName'):\\n doc.affirm_by_name = str(reg_json.get('affirmByName')).upper()[0:40]\\n doc.liens_with_consent = ''\\n if reg_json.get('submittingParty'):\\n submitting = reg_json.get('submittingParty')\\n if submitting.get('phoneNumber'):\\n doc.phone_number = str(submitting.get('phoneNumber'))[0:10]\\n else:\\n doc.phone_number = ''\\n doc.name = Db2Document.to_db2_submitting_name(submitting)\\n doc.legacy_address = address_utils.to_db2_address(submitting.get('address'))\\n else:\\n doc.phone_number = ''\\n doc.name = ''\\n doc.legacy_address = ''\\n if reg_json.get('attentionReference'):\\n doc.attention_reference = str(reg_json['attentionReference'])[0:40]\\n else:\\n doc.attention_reference = ''\\n if registration.client_reference_id:\\n doc.client_reference_id = registration.client_reference_id[0:30]\\n else:\\n doc.client_reference_id = ''\\n if doc_type in (Db2Document.DocumentTypes.TRANS,\\n Db2Document.DocumentTypes.TRAND,\\n Db2Document.DocumentTypes.TRANS_ADMIN,\\n Db2Document.DocumentTypes.TRANS_AFFIDAVIT,\\n Db2Document.DocumentTypes.TRANS_WILL):\\n if reg_json.get('transferDate'):\\n doc.transfer_execution_date = model_utils.date_from_iso_format(str(reg_json['transferDate'])[0:10])\\n else:\\n doc.transfer_execution_date = local_ts.date()\\n else:\\n doc.transfer_execution_date = model_utils.date_from_iso_format('0001-01-01')\\n if reg_json.get('ownLand'):\\n doc.own_land = 'Y'\\n else:\\n doc.own_land = 'N'\\n return doc\",\n \"def new(self):\\n\\n if not hasattr(self, 'required_attribs'):\\n self.required_attribs = []\\n\\n # sanity check\\n for req_var in self.required_attribs:\\n if req_var not in self.kwargs:\\n err = \\\"The '%s' kwarg is required when creating new %s!\\\"\\n msg = err % (req_var, self.collection)\\n self.logger.error(msg)\\n self.logger.error('Incoming kwargs dict: %s' % self.kwargs)\\n raise ValueError(msg)\\n\\n # do it\\n self.logger.warn('Creating new %s record!' % self.collection)\\n\\n for req_var in self.required_attribs:\\n setattr(self, req_var, self.kwargs[req_var])\\n\\n self.created_on = datetime.now()\\n self.updated_on = datetime.now()\\n self.created_by = flask_login.current_user._id\\n self._id = self.mdb.insert({})\\n\\n try:\\n self.save()\\n except pymongo.errors.DuplicateKeyError as e:\\n self.mdb.remove({'_id': self._id})\\n self.logger.error(e)\\n self.logger.error('Cannot create asset: %s' % self)\\n raise ValueError('Duplicate key error prevented asset creation!')\",\n \"def _build_db_data(self):\\n self.logger.debug('Bulding task db document.')\\n db_data = {}\\n db_data.update(self.query)\\n db_data['condition'] = False\\n db_data['records'] = []\\n self.db_collection.insert_one(db_data)\\n return db_data\",\n \"def __init__(self, cfg_mongo):\\n # Connect to mongodb\\n self.logger = logging.getLogger(\\\"DB\\\")\\n self.cfg_mongo = cfg_mongo\\n\\n # Parse location for binary data storage\\n assert cfg_mongo[\\\"datastore\\\"] in [\\\"gridfs\\\", \\\"numpy\\\", \\\"adios2\\\"]\\n self.datastore = cfg_mongo[\\\"datastore\\\"]\",\n \"def test_document_creation(self):\\n\\t\\timgfile = StringIO.StringIO('GIF87a\\\\x01\\\\x00\\\\x01\\\\x00\\\\x80\\\\x01\\\\x00\\\\x00\\\\x00\\\\x00ccc,\\\\x00'\\n\\t '\\\\x00\\\\x00\\\\x00\\\\x01\\\\x00\\\\x01\\\\x00\\\\x00\\\\x02\\\\x02D\\\\x01\\\\x00;')\\n\\t\\tf = SimpleUploadedFile('test_img_file.gif', imgfile.read(), 'image/gif')\\n\\t\\tm, __ = Map.objects.get_or_create(id=1, title='foo', projection='4326', zoom=2, center_x=0, center_y=0,\\n\\t owner=User.objects.get_or_create(username='foo')[0])\\n\\t\\tm.set_default_permissions()\\n\\t\\td,created = Document.objects.get_or_create(id=1, file=f)\\n\\t\\td.maps.add(m)\\n\\t\\tself.assertTrue(created)\",\n \"def make_collection(data_ids):\\n idlist = []\\n count = 0\\n for c in range(0, len(data_ids)):\\n data_id = data_ids[c]\\n idlist.append({'src': \\\"hda\\\", 'id': data_id, 'name': str(count)})\\n count += 1\\n collection = {'collection_type': 'list', 'element_identifiers': idlist, 'name': 'collection'}\\n return collection\",\n \"def morpho_doc2(doc_id):\\n db.doc_apply(doc_id, morpho_doc)\",\n \"async def make_object(model, conn: Database, data: dict or List[dict]):\\n if isinstance(data, dict):\\n data = [data]\\n\\n query = insert(model).values(*data)\\n return await conn.execute(query=query)\",\n \"def add_document(self, doc):\\n assert isinstance(doc, pylastica.document.Document), \\\"doc must be of type Document: %r\\\" % doc\\n path = urllib.quote_plus(str(doc.doc_id))\\n request_type = pylastica.request.Request.PUT\\n if path is None or path == '':\\n #no doc id has been given; use post so that an id is automatically created\\n request_type = pylastica.request.Request.POST\\n options = doc.get_options([\\n 'version',\\n 'version_type',\\n 'routing',\\n 'percolate',\\n 'parent',\\n 'ttl',\\n 'timestamp',\\n 'op_type',\\n 'consistency',\\n 'replication',\\n 'refresh',\\n 'timeout'\\n ])\\n response = self.request(path, request_type, doc.data, options)\\n data = response.data\\n if (doc.auto_populate or self.index.client.get_config_value(['document', 'autoPopulate'], False)) and response.is_ok():\\n if doc.has_id():\\n if '_id' in data:\\n doc.doc_id = data['_id']\\n if '_version' in data:\\n doc.version = data['_version']\\n return response\",\n \"def _get_object(data, position, obj_end, opts, dummy):\\n obj_size = bson._UNPACK_INT(data[position:position + 4])[0]\\n end = position + obj_size - 1\\n if data[end:position + obj_size] != b\\\"\\\\x00\\\":\\n raise bson.InvalidBSON(\\\"bad eoo\\\")\\n if end >= obj_end:\\n raise bson.InvalidBSON(\\\"invalid object length\\\")\\n if _raw_document_class(opts.document_class):\\n return (opts.document_class(data[position:end + 1], opts),\\n position + obj_size)\\n\\n obj = _elements_to_dict(data, position + 4, end, opts, subdocument=True)\\n position += obj_size\\n if \\\"$ref\\\" in obj:\\n return (bson.DBRef(obj.pop(\\\"$ref\\\"), obj.pop(\\\"$id\\\", None),\\n obj.pop(\\\"$db\\\", None), obj), position)\\n return obj, position\",\n \"def insert_player(document):\\n players_col.insert_one(document)\",\n \"def create(self, validated_data):\\n return Speaker.objects.create(**validated_data)\",\n \"def __init__(self, db_name):\\n uri = re.sub('<username>', mongoUser, connection_uri)\\n uri = re.sub('<password>', mongoPassword, uri)\\n self.client = pymongo.MongoClient(uri)\\n # Create your database\\n self.db = self.client[db_name]\",\n \"def test_bulk_insert(self):\\n\\n class Comment(EmbeddedDocument):\\n name = StringField()\\n\\n class Post(EmbeddedDocument):\\n comments = ListField(EmbeddedDocumentField(Comment))\\n\\n class Blog(Document):\\n title = StringField(unique=True)\\n tags = ListField(StringField())\\n posts = ListField(EmbeddedDocumentField(Post))\\n\\n Blog.drop_collection()\\n\\n # Recreates the collection\\n assert 0 == Blog.objects.count()\\n\\n comment1 = Comment(name=\\\"testa\\\")\\n comment2 = Comment(name=\\\"testb\\\")\\n post1 = Post(comments=[comment1, comment2])\\n post2 = Post(comments=[comment2, comment2])\\n\\n # Check bulk insert using load_bulk=False\\n blogs = [Blog(title=\\\"%s\\\" % i, posts=[post1, post2]) for i in range(99)]\\n with query_counter() as q:\\n assert q == 0\\n Blog.objects.insert(blogs, load_bulk=False)\\n assert q == 1 # 1 entry containing the list of inserts\\n\\n assert Blog.objects.count() == len(blogs)\\n\\n Blog.drop_collection()\\n Blog.ensure_indexes()\\n\\n # Check bulk insert using load_bulk=True\\n blogs = [Blog(title=\\\"%s\\\" % i, posts=[post1, post2]) for i in range(99)]\\n with query_counter() as q:\\n assert q == 0\\n Blog.objects.insert(blogs)\\n assert q == 2 # 1 for insert 1 for fetch\\n\\n Blog.drop_collection()\\n\\n comment1 = Comment(name=\\\"testa\\\")\\n comment2 = Comment(name=\\\"testb\\\")\\n post1 = Post(comments=[comment1, comment2])\\n post2 = Post(comments=[comment2, comment2])\\n blog1 = Blog(title=\\\"code\\\", posts=[post1, post2])\\n blog2 = Blog(title=\\\"mongodb\\\", posts=[post2, post1])\\n blog1, blog2 = Blog.objects.insert([blog1, blog2])\\n assert blog1.title == \\\"code\\\"\\n assert blog2.title == \\\"mongodb\\\"\\n\\n assert Blog.objects.count() == 2\\n\\n # test inserting an existing document (shouldn't be allowed)\\n with pytest.raises(OperationError) as exc_info:\\n blog = Blog.objects.first()\\n Blog.objects.insert(blog)\\n assert (\\n str(exc_info.value)\\n == \\\"Some documents have ObjectIds, use doc.update() instead\\\"\\n )\\n\\n # test inserting a query set\\n with pytest.raises(OperationError) as exc_info:\\n blogs_qs = Blog.objects\\n Blog.objects.insert(blogs_qs)\\n assert (\\n str(exc_info.value)\\n == \\\"Some documents have ObjectIds, use doc.update() instead\\\"\\n )\\n\\n # insert 1 new doc\\n new_post = Blog(title=\\\"code123\\\", id=ObjectId())\\n Blog.objects.insert(new_post)\\n\\n Blog.drop_collection()\\n\\n blog1 = Blog(title=\\\"code\\\", posts=[post1, post2])\\n blog1 = Blog.objects.insert(blog1)\\n assert blog1.title == \\\"code\\\"\\n assert Blog.objects.count() == 1\\n\\n Blog.drop_collection()\\n blog1 = Blog(title=\\\"code\\\", posts=[post1, post2])\\n obj_id = Blog.objects.insert(blog1, load_bulk=False)\\n assert isinstance(obj_id, ObjectId)\\n\\n Blog.drop_collection()\\n post3 = Post(comments=[comment1, comment1])\\n blog1 = Blog(title=\\\"foo\\\", posts=[post1, post2])\\n blog2 = Blog(title=\\\"bar\\\", posts=[post2, post3])\\n Blog.objects.insert([blog1, blog2])\\n\\n with pytest.raises(NotUniqueError):\\n Blog.objects.insert(Blog(title=blog2.title))\\n\\n assert Blog.objects.count() == 2\",\n \"def write_data_to_mongo(self, db_name, collection_name, list_of_dicts):\\n self.db_client.db_name = db_name\\n self.db = self.db_client.affirm_client()\\n collection = self.__write(collection_name, list_of_dicts)\\n return collection\",\n \"def create(cls, data=None):\\n # allow create() calls with no input\\n if not data:\\n data = {}\\n\\n return cls(**data)\",\n \"def __init__(self, instance=None):\\n self.instance = instance\\n self.schema = None\\n if self.instance:\\n self.schema = surveys.SurveySchema(self.instance.survey)\",\n \"def _get_schema_from_object(self, data):\\n if \\\"items\\\" in data:\\n return self._get_schema_from_object(data[\\\"items\\\"])\\n\\n url_key = None\\n\\n if '$id' in data:\\n url_key = '$id'\\n\\n if 'id' in data:\\n url_key = 'id'\\n\\n if url_key:\\n url = data[url_key]\\n schema = Schema().build()\\n schema.domain_entity = self.get_domain_entity_from_url(url)\\n schema.high_level_entity = self.get_high_level_entity_from_url(url)\\n schema.module = self.get_module_from_url(url)\\n schema.url = url\\n return schema\\n\\n return None\",\n \"def create(self, request, *args, **kwargs):\\n # Deserialize and validate the data from the user.\\n serializer = self.get_serializer(data=request.data)\\n serializer.is_valid(raise_exception=True)\\n\\n # Execute the document and annotation creation\\n self.perform_create(serializer)\\n\\n # Get the headers and return a response\\n headers = self.get_success_headers(serializer.data)\\n return Response(serializer.data, status=status.HTTP_201_CREATED, headers=headers)\",\n \"def make_example_data():\\n\\n #create users\\n charlie = User(firstname=\\\"Charlie\\\", lastname=\\\"Dog\\\",\\n email=\\\"charlie@dogsaregreat.com\\\", password=\\\"marshmallow\\\")\\n maisey = User(firstname=\\\"Maisey\\\", lastname=\\\"Puppy\\\",\\n email=\\\"maisey@squirrelchasers.com\\\", password=\\\"monkey\\\")\\n\\n #create a chart that they share\\n chart = Chart(title=\\\"StarChart\\\")\\n chart.users = [charlie, maisey]\\n\\n #add some stars to the chart\\n star1 = Star(giver=charlie, receiver=maisey, reason=\\\"For licking my nose\\\",\\n timestamp=datetime(2016, 12, 31), chart=chart)\\n star2 = Star(giver=maisey, receiver=charlie,\\n reason=\\\"For being a great big sister\\\",\\n timestamp=datetime(2016, 11, 21), chart=chart)\\n\\n #put the whole collection in the database; everything hangs off of the\\n #stars so we can just add them\\n db.session.add_all([star1, star2])\\n db.session.commit()\\n\\n # import pdb; pdb.set_trace()\",\n \"def __init__(self, dbname='cards'):\\n\\n self._client = MongoClient()\\n self._db = self._client[dbname]\\n self._collection = self._db[dbname]\\n self._keys = ['set', 'color', 'text', 'creator']\",\n \"def find_or_create(cls, data, **kwargs):\\n\\t\\tinstance = cls.query.filter_by(**kwargs).first()\\n\\t\\tif not instance:\\n\\t\\t\\tinstance = cls(**data).save()\\n\\t\\treturn instance\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7066903","0.6612481","0.63551545","0.6348332","0.6091033","0.6033079","0.5778242","0.5773537","0.5751162","0.5727225","0.5722528","0.5691701","0.56832474","0.56717724","0.55828565","0.55790466","0.5539406","0.5517863","0.5484895","0.54323846","0.5430872","0.54172623","0.5416529","0.54002947","0.5398329","0.53900117","0.5369912","0.5361611","0.5354257","0.53513414","0.53297466","0.53286356","0.53247577","0.5314616","0.5305657","0.5305246","0.52851456","0.5281838","0.52817386","0.52797073","0.5278691","0.52622795","0.52601707","0.5257541","0.5257325","0.52572846","0.52085364","0.52070045","0.52067083","0.5206526","0.5201036","0.51997477","0.51647526","0.51628","0.5159955","0.51562834","0.5148661","0.51365906","0.5127444","0.5123964","0.51198655","0.5116976","0.51085806","0.50901645","0.5075417","0.5073743","0.5065419","0.5063219","0.5034542","0.50313586","0.5030387","0.5023571","0.50149184","0.5013868","0.50101453","0.500188","0.49953634","0.49945906","0.49933696","0.4993283","0.4989647","0.49886292","0.49857768","0.49783158","0.4976185","0.4973539","0.49651006","0.49590656","0.4954239","0.49449718","0.4927874","0.49118954","0.49078375","0.4896854","0.48953435","0.4891228","0.4890558","0.4888093","0.48852262","0.48758107"],"string":"[\n \"0.7066903\",\n \"0.6612481\",\n \"0.63551545\",\n \"0.6348332\",\n \"0.6091033\",\n \"0.6033079\",\n \"0.5778242\",\n \"0.5773537\",\n \"0.5751162\",\n \"0.5727225\",\n \"0.5722528\",\n \"0.5691701\",\n \"0.56832474\",\n \"0.56717724\",\n \"0.55828565\",\n \"0.55790466\",\n \"0.5539406\",\n \"0.5517863\",\n \"0.5484895\",\n \"0.54323846\",\n \"0.5430872\",\n \"0.54172623\",\n \"0.5416529\",\n \"0.54002947\",\n \"0.5398329\",\n \"0.53900117\",\n \"0.5369912\",\n \"0.5361611\",\n \"0.5354257\",\n \"0.53513414\",\n \"0.53297466\",\n \"0.53286356\",\n \"0.53247577\",\n \"0.5314616\",\n \"0.5305657\",\n \"0.5305246\",\n \"0.52851456\",\n \"0.5281838\",\n \"0.52817386\",\n \"0.52797073\",\n \"0.5278691\",\n \"0.52622795\",\n \"0.52601707\",\n \"0.5257541\",\n \"0.5257325\",\n \"0.52572846\",\n \"0.52085364\",\n \"0.52070045\",\n \"0.52067083\",\n \"0.5206526\",\n \"0.5201036\",\n \"0.51997477\",\n \"0.51647526\",\n \"0.51628\",\n \"0.5159955\",\n \"0.51562834\",\n \"0.5148661\",\n \"0.51365906\",\n \"0.5127444\",\n \"0.5123964\",\n \"0.51198655\",\n \"0.5116976\",\n \"0.51085806\",\n \"0.50901645\",\n \"0.5075417\",\n \"0.5073743\",\n \"0.5065419\",\n \"0.5063219\",\n \"0.5034542\",\n \"0.50313586\",\n \"0.5030387\",\n \"0.5023571\",\n \"0.50149184\",\n \"0.5013868\",\n \"0.50101453\",\n \"0.500188\",\n \"0.49953634\",\n \"0.49945906\",\n \"0.49933696\",\n \"0.4993283\",\n \"0.4989647\",\n \"0.49886292\",\n \"0.49857768\",\n \"0.49783158\",\n \"0.4976185\",\n \"0.4973539\",\n \"0.49651006\",\n \"0.49590656\",\n \"0.4954239\",\n \"0.49449718\",\n \"0.4927874\",\n \"0.49118954\",\n \"0.49078375\",\n \"0.4896854\",\n \"0.48953435\",\n \"0.4891228\",\n \"0.4890558\",\n \"0.4888093\",\n \"0.48852262\",\n \"0.48758107\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73655903"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":20093,"cells":{"query":{"kind":"string","value":"Update the embedded document with the given data."},"document":{"kind":"string","value":"def update(self, data):\n return self._data.update(data)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def update_document(self, data):\n if not isinstance(data, pylastica.document.Document) and not isinstance(data, pylastica.script.Script):\n raise TypeError(\"data must be an instance of Document or Script: %r\" % data)\n if not data.has_id():\n raise pylastica.exception.InvalidException(\"Document id is not set.\")\n return self.index.client.update_document(data.doc_id, data, self.index.name, self.name)","def update_document(self):\n pass","def put(self, data):\n self.validate(data)\n\n mongo['readable-api'].foo.update(\n {\"foo\": self.data[\"foo\"]},\n data,\n )","def update(self, data, id_obj=None, query_data=None):\n if id_obj:\n return self.collection.update({'_id': id_obj}, {\"$set\": data})\n return self.collection.update(query_data, {\"$set\": data})","def update_document(collection: str, query: dict, data: dict) -> None:\n validate_arguments({'collection': [collection, str],\n 'query': [query, dict],\n 'data': [data, dict]})\n new_document = find_document(collection, query=query)\n if new_document is None:\n raise Exception('Didnt find a document to update')\n DB[collection].delete_one(query)\n for key in data:\n new_document[key] = data[key]\n add_document(collection, new_document)","def update_document(self, type: Type, id: str,\n new_data: Dict[str, Any]) -> None:\n if collection := self.client.collection(f'{type}'):\n if document_ref := collection.document(document_id=id):\n if document_ref.get().exists:\n document_ref.update(new_data)\n else:\n document_ref.create(new_data)","def update(self, data):\n pass","def update(self, data):\n pass","def update(self, data):\n pass","def update(self, data):\n pass","def update(self, data):\n self.data.update(data)","def Update(self, data):\n self.Write(data)","def update_document(obj):\n index = obj.get_index_name()\n doc_type = obj.get_document_type()\n body = dict(doc=obj.get_document_body())\n try:\n ES.update(index=index, doc_type=doc_type, body=body, id=obj.pk)\n except NotFoundError:\n raise DocumentNotFound(obj.get_index_name(), obj.pk)","def update(self, identifier, data):\n self.client.request_with_method(Methods.UPDATE % (self.name, identifier,),\n data=data)","def update_item(self, data_id, json, *args, **kwargs):\n result = {\"doc\": JsonHelper.to_dict(json), \"doc_as_upsert\": \"true\"}\n json_result = JsonHelper.to_json(result)\n self.connection.update(index=Properties.Elasticsearch.PARSER_INDEX,\n doc_type=Properties.Elasticsearch.PARSER_DOC_TYPE,\n id=data_id,\n body=json_result,\n *args,\n **kwargs)","def update(self, data):\n self.content = data","def update(self, data):\n self.data = data\n # extract data\n subject = data.identifier\n self.id = _uri_to_oai(subject)\n self.modified = datetime.utcnow()\n self.deleted = False\n\n itemtype, subtype = _get_itemtype(data, subject)\n\n self.metadata = {}\n\n # fixed fields:\n self.metadata['rif_key'] = _uri_to_key(subject)\n self.metadata['rif_group'] = self.provider.groupDescription\n self.metadata['rif_originatingSource'] = self.provider.originatingSource\n self.metadata['rif_object'] = {'value': itemtype,\n 'type': subtype,\n #'dateModified': '',\n }\n\n if itemtype == 'collection':\n self.updateCollection(data, subject)\n elif itemtype == 'party':\n if subtype == 'person':\n self.updatePartyPerson(data, subject)\n else:\n self.updateParty(data, subject)\n elif itemtype == 'activity':\n self.updateActivity(data, subject)\n elif itemtype == 'service':\n self.updateService(data, subject)","def update(self, instance, validated_data):\n instance.title = validated_data.get('title', instance.title)\n instance.xml_content = validated_data.get('xml_content', instance.xml_content)\n return data_api.upsert(instance, validated_data['user'])","def update_document(\n self,\n index: str,\n doc_id: str,\n document: Dict[str, Any],\n partial_update: bool = False,\n ):\n if partial_update:\n self.__client__.update(index=index, id=doc_id, body={\"doc\": document})\n self.__client__.index(index=index, id=doc_id, body=document)","def update(self, spec, document, upsert=False, multi=False):\n return self.database.connection.request.update_documents(\n self.database.name, self.name, spec, document, upsert, multi)","def update_document(self, portal_name, content_id, document):\n if isinstance(document, dict):\n document = json.dumps(document)\n r = requests.put('/'.join([self.base_url,\n self.DOCUMENTS_ENDPOINT,\n portal_name,\n str(content_id)]),\n data=document,\n headers={'Content-Type': 'application/json'})\n return r.json()","def update_document(self, database, collection, _id, document):\n r = self.__get_response(settings.UPD_DOC,\n {\"db\": database, \"col\": collection, \"id\": str(_id)},\n data=document)\n if r[\"status\"] == 200:\n return r[\"result\"]\n raise Exception(r[\"result\"][\"message\"])","def update(self, data):\n return data","def SetDataDocument(self, name, data):\n self.__context.builder.WaveletSetDataDoc(self.GetWaveId(), self.GetId(),\n name, data)\n self._data.data_documents[name] = data","def _update(self, data: Dict[str, Any], fields_to_modify: List[str]):\n pass","def update(account, data):\n stripe_account = stripe.Account.retrieve(id=account.stripe_id)\n\n if data.get(\"dob\"):\n stripe_account.legal_entity.dob = data[\"dob\"]\n\n if data.get(\"first_name\"):\n stripe_account.legal_entity.first_name = data[\"first_name\"]\n\n if data.get(\"last_name\"):\n stripe_account.legal_entity.last_name = data[\"last_name\"]\n\n if data.get(\"personal_id_number\"):\n stripe_account.legal_entity.personal_id_number = data[\"personal_id_number\"]\n\n if data.get(\"document\"):\n response = stripe.FileUpload.create(\n purpose=\"identity_document\",\n file=data[\"document\"],\n stripe_account=stripe_account.id\n )\n stripe_account.legal_entity.verification.document = response[\"id\"]\n\n stripe_account.save()\n return sync_account_from_stripe_data(stripe_account)","def update(self, doc):\n if app.config.get(\"READ_ONLY_MODE\", False) and app.config.get(\"SCRIPTS_READ_ONLY_MODE\", False):\n app.logger.warn(\"System is in READ-ONLY mode, update command cannot run\")\n return\n\n return requests.post(self.target() + self.id + \"/_update\", data=json.dumps({\"doc\": doc}))","def updateDocument(self, document):\n data = self.updateDocumentAll([document])\n try:\n return data[0]\n except: pass","def update(self, data):\n if self.service is not None:\n self.service.update_response(data)","def update_data():\n pass","def update(data):\n username, message_id, message_text, room = data['username'], int(data['message_id']), data['message'], data['room']\n message = Message.query.filter_by(id=message_id, username=session['username']).first()\n if message:\n message.text = data['message']\n db.session.add(message)\n db.session.commit()\n emit('update', {'username': username, 'message': {'id': message.id, 'text': message.text}}, room=room)","def update(self, instance, validated_data):\n instance.body = validated_data[\"body\"]\n instance.save()\n return instance","def update(self, instance, validated_data):\n instance.title = validated_data.get('title', instance.title)\n instance.owner = validated_data.get('owner', instance.owner)\n instance.language = validated_data.get('language', instance.language)\n instance.classes = validated_data.get('classes', instance.classes)\n instance.methods = validated_data.get('methods', instance.methods)\n instance.style = validated_data.get('style', instance.style)\n instance.example = validated_data.get('example', instance.example)\n instance.save()\n return instance","def update(self, index, id, **kwargs):\n url = f'{self.host}{index}/_doc/{id}/_update'\n data = {'doc': {**kwargs}}\n requests.post(url, json=data)\n self.flush(index)\n return self.get(index, id)","def update(self, instance, validated_data):\n pass","def test_update(self):\n doc_fields = document_fields.DocumentFields({\n 'foo@': 'bar',\n })\n self.assertEquals('bar', doc_fields['foo'])\n doc_fields.update({\n 'foo@': 'bbq',\n })\n self.assertEquals('bbq', doc_fields['foo'])","def update(self, identity, data=None, record=None, **kwargs):\n record.custom_fields = data.get(\"custom_fields\", {})","def update(self, instance, validated_data):\n instance.title = validated_data.get('title', instance.title)\n instance.inspection_tag = validated_data.get('inspection_tag', instance.code)\n instance.content = validated_data.get('content', instance.language)\n instance.status = validated_data.get('status', instance.style)\n instance.save()\n return instance","def update(self, instance, validated_data):\n instance.title = validated_data.get('title', instance.title)\n instance.inspection_tag = validated_data.get('inspection_tag', instance.code)\n instance.content = validated_data.get('content', instance.language)\n instance.status = validated_data.get('status', instance.style)\n instance.save()\n return instance","def update(self, docs: DocumentArray, *args, **kwargs):\n cursor = self.connection.cursor()\n psycopg2.extras.execute_batch(\n cursor,\n f'UPDATE {self.table} SET DOC = %s WHERE ID = %s',\n [\n (\n doc.SerializeToString(),\n doc.id,\n )\n for doc in docs\n ],\n )\n self.connection.commit()","def update(_id, new_doc, **metadata):\n # NOTE\n # Internally, the update is performed by\n # Revalidating and replacing the original document.\n\n new_doc = ensure_document(new_doc)\n dataset = ESDataset.get(id=_id, ignore=404)\n\n if not dataset:\n raise NoEntityError(f\"dataset {_id} does not exist.\")\n\n # Cannot change the identifier field, because it would result\n # in changing the document _id. Delete and add again instead.\n if new_doc.get(\"identifier\") != dataset.identifier:\n raise ConflictError(\"cannot change identifier field.\")\n\n # NOTE **important**\n # Patch the original document metadata with the partial update.\n _meta = dataset[\"_meta\"].to_dict()\n _meta.update(_clean(new_doc.pop(\"_meta\", {})))\n _meta.update(_clean(metadata))\n _meta = _clean(_meta)\n\n new_doc = validate(new_doc, _meta[\"schema\"])\n\n dataset = _index(\n new_doc,\n _meta,\n _addon={\n # Carry over our internal metadata like\n # N3C ticket info and creation timestamp.\n \"date_created\": dataset._ts.date_created,\n \"n3c_url\": dataset._n3c.url,\n \"n3c_status\": dataset._n3c.status,\n \"n3c_timestamp\": dataset._n3c.timestamp,\n },\n )\n\n return dataset.meta.version","def update(self, data: Union[QueryWithResponse, List[QueryWithResponse]], **kwargs):\n raise NotImplementedError","def document_update(index_name, doc_type, doc_id, doc=None, new=None):\n if doc:\n resp = es.index(index=index_name, doc_type=doc_type,\n id=doc_id, body=doc)\n print(resp)\n else:\n resp = es.update(index=index_name, doc_type=doc_type,\n id=doc_id, body={\"doc\": new})","def update(self, instance, validated_data):\n instance.title = validated_data.get('title', instance.title)\n instance.code = validated_data.get('code', instance.code)\n instance.linenos = validated_data.get('linenos', instance.linenos)\n instance.language = validated_data.get('language', instance.language)\n instance.style = validated_data.get('style', instance.style)\n instance.save()\n return instance","def _update(self, data: Dict[str, Any], fields_to_modify: List[str]):\n\n to_set = [\n \"{0} = %({0})s\".format(f) for f in fields_to_modify\n if f in self.editable_fields\n ]\n if len(to_set) == 0:\n print('Warning: No data to set', data)\n return\n\n query = \"UPDATE {} SET {} WHERE {}\".format(\n self._NAME,\n ', '.join(to_set),\n ' AND '.join(\"{0} = %({0})s\".format(f) for f in self.primary_fields),\n )\n self._execute(query, data)","def update(self, instance, validated_data):","def update(cls, doc_id, document, specs=None, updater=raw_updater, *args, **kwargs):\n document = updater(document)\n ret = cls._make_call('update', cls._make_specs(doc_id, specs), document, *args, **kwargs)\n if ret:\n return ret['n']","def update(cls, collection, uid, data):\n validated = cls.validate(data)\n validated.pop(\"_id\", None) # remove field \"_id\" if set\n object_uid = cls.object_id(uid)\n collection.update_one({\"_id\": object_uid}, {\"$set\": validated}, upsert=True)\n return collection.find_one({\"_id\": object_uid})","def update(self, resource, data, target=None, verb='update', **kwargs):\n return self._modify_resource(resource, data, target, verb, **kwargs)","def update(self, data: Mapping[str, np.ndarray]) -> Self:\n\n raise NotImplementedError","def put(self, _id=None, **data):\n new_data, numpy_array = Database._put_prep(data)\n doc = self._put_assume_new(_id, **new_data)\n if numpy_array is not None:\n temp_file = TemporaryFile()\n np.save(temp_file, numpy_array)\n temp_file.seek(0)\n # TODO: check attachment success\n doc.put_attachment(\n attachment='value', content_type='application/octet-stream',\n data=temp_file)\n return doc","def put(self):\n document = upload_file(self.request)\n self.request.validated[\"qualification\"].documents.append(document)\n if save_tender(self.request):\n self.LOGGER.info(\n \"Updated tender qualification document {}\".format(self.request.context.id),\n extra=context_unpack(self.request, {\"MESSAGE_ID\": \"tender_qualification_document_put\"}),\n )\n return {\"data\": document.serialize(\"view\")}","def _update_object(self, data_dict):\r\n pass","def update_doc(c, i, d, u=False):\n try:\n c.update_one({'_id': i}, {'$set': d}, upsert = u)\n return True\n except:\n return False","def update(self, document_id, update_spec, namespace, timestamp):\n\n index, doc_type = self._index_and_mapping(namespace)\n document = self.BulkBuffer.get_from_sources(index,doc_type,u(document_id))\n if document:\n updated = self.apply_update(document, update_spec)\n # _id is immutable in MongoDB, so won't have changed in update\n updated['_id'] = document_id\n self.upsert(updated, namespace, timestamp)\n else:\n updated = {\"_id\": document_id}\n self.upsert(updated, namespace, timestamp, update_spec)\n # upsert() strips metadata, so only _id + fields in _source still here\n return updated","def update(self, **kwargs):\n return self._update_data(self.put(None, data=kwargs))","def update_documents(self, database, collection, spec, doc_or_docs, upsert,\n multi):\n validators.check_document_to_update(doc_or_docs)\n r = self.__get_response(settings.UPD_DOCS,\n {\"db\": database, \"col\": collection},\n data=doc_or_docs, q=spec, m=multi, u=upsert)\n if r[\"status\"] == 200:\n if r[\"result\"][\"error\"]:\n raise Exception(r[\"result\"][\"error\"])\n return r[\"result\"][\"n\"]\n raise Exception(r[\"result\"][\"message\"])","def update(self, resource, id, **data):\n self.request('/' + resource + '/' + str(id), 'PUT', body=urllib.urlencode(data))\n return True","def update_data(self, extra_data):\n self._data.update(extra_data)\n return self","def update(self, instance, validated_data):\n instance.href = validated_data.get('href', instance.href)\n instance.title = validated_data.get('title', instance.title)\n instance.datetime = validated_data.get('datetime', instance.datetime)\n instance.content = validated_data.get('content', instance.content)\n instance.coverimg = validated_data.get('coverimg', instance.title)\n instance.save()\n return instance","def updateDoc(self, path):\n self.db.setDb(self.db_file)\n \n if not self.authd:\n self._authorize()\n \n db_row = self.db.getRowFromPath(path)\n if not db_row:\n return False\n \n resource_id = db_row[0]\n etag = db_row[1]\n title = db_row[2]\n \n ms = gdata.data.MediaSource(file_path=path, content_type=MIMETYPES['ODT'])\n doc = self.client.GetDoc(resource_id.replace(':', '%3A'))\n new_version = self.client.Update(doc, media_source=ms)\n print 'Document pushed:', new_version.GetAlternateLink().href\n \n self.db.resetEtag(new_version)","def Update(WaveName,Data):\n assert WaveName in self\n self.UpdateData(WaveName,Data)","def update_draft(self, identity, data=None, record=None, **kwargs):\n record.metadata = data.get('metadata', {})","def upsert(self, doc, namespace, timestamp, update_spec=None):\n index, doc_type = self._index_and_mapping(namespace)\n # No need to duplicate '_id' in source document\n doc_id = u(doc.pop(\"_id\"))\n metadata = {\n 'ns': namespace,\n '_ts': timestamp\n }\n \n # Index the source document, using lowercase namespace as index name.\n action = {\n '_op_type': 'index',\n '_index': index,\n '_type': doc_type,\n '_id': doc_id,\n '_source': self._formatter.format_document(doc)\n }\n # Index document metadata with original namespace (mixed upper/lower).\n meta_action = {\n '_op_type': 'index',\n '_index': self.meta_index_name,\n '_type': self.meta_type,\n '_id': doc_id,\n '_source': bson.json_util.dumps(metadata)\n }\n \n self.index(action,meta_action,doc,update_spec)\n \n # Leave _id, since it's part of the original document\n doc['_id'] = doc_id","def _update_from_rest_data(self) -> None:","async def update_one(self, where, data):\n\n pass","def update(self):\n data = self.serialize()\n\n self.validate(data)\n\n saved_data = DATABASE_CONNECTION.update(self.__class__.__name__, data['id'], data)\n\n self.__dict__.update(saved_data)","def update(self, title=None, description = None):\n jsonData = self.metaData.jsonObj\n header = self._baseHeader.copy()\n\n header['Content-type'] = \"application/vnd.huddle.data+json\"\n url = self.metaData.getLink(\"edit\")\n assert url is not None\n\n if title is not None: jsonData['title'] = title\n if description is not None: jsonData['description'] = description\n\n response = self._adapter.putRequest(url, header, json.dumps(jsonData))\n\n return Document(self._client, self._client.getUrlFromHeaderLink(response['Headers']['link']))","def update(self, docid, doc):\n self.delete(docid)\n wrapper = RedisWrapper(self.dbprefix, self.client, docid)\n self._store_doc(doc, wrapper)","def update_data(self):\n self._model.update()\n self.__refresh()","def _update(self, data=None, _validate=False):\n if data is None:\n # If no data is passed, take no action.\n pass\n elif _mapping_resolver.get_type(data) == \"MAPPING\":\n with self._suspend_sync:\n for key, new_value in data.items():\n try:\n # The most common usage of SyncedCollections is with a\n # single object referencing an underlying resource at a\n # time, so we should almost always find that elements\n # of data are already contained in self._data, so EAFP\n # is the best choice for performance.\n existing = self._data[key]\n except KeyError:\n # If the item wasn't present at all, we can simply\n # assign it.\n if not _validate:\n self._validate({key: new_value})\n self._data[key] = self._from_base(new_value, parent=self)\n else:\n if new_value == existing:\n continue\n if _sc_resolver.get_type(existing) == \"SYNCEDCOLLECTION\":\n try:\n existing._update(new_value)\n continue\n except ValueError:\n pass\n\n # Fall through if the new value is not identical to the\n # existing value and\n # 1) The existing value is not a SyncedCollection\n # (in which case we would have tried to update it), OR\n # 2) The existing value is a SyncedCollection, but\n # the new value is not a compatible type for _update.\n if not _validate:\n self._validate({key: new_value})\n self._data[key] = self._from_base(new_value, parent=self)\n\n to_remove = [key for key in self._data if key not in data]\n for key in to_remove:\n del self._data[key]\n else:\n raise ValueError(\n \"Unsupported type: {}. The data must be a mapping or None.\".format(\n type(data)\n )\n )","def do_PUT(self):\n note_details = NoteDetails\n if self.path == '/note/api/update':\n response_data=note_details.update_data(self)\n Response(self).jsonResponse(status=200, data=response_data)","def update(self, new_data):\n if type(new_data) is not dict:\n raise TypeError(\"Input parameter must be a dict\")\n # Update parameters\n self._type = new_data.get(\"_type\", self._type)\n self.time_units = new_data.get(\"time_units\", self.time_units)\n self.len_units = new_data.get(\"len_units\", self.len_units)\n self.pump_units = new_data.get(\"pump_units\", self.pump_units)\n self.parameters = new_data.get(\"parameters\", self.parameters)\n # Update pumping rate\n self.pumprate.update(new_data.get(\"pumprate\", self.pumprate.to_dict()))\n # Update data\n if \"wells\" in new_data:\n n = len(new_data[\"wells\"])\n if n > 1:\n self.delete_all_wells()\n for i in range(n):\n self.add_well(0, 0, new_data[\"wells\"][i][\"_type\"] - 2)\n self.wells[i].update(new_data[\"wells\"][i])\n # End Function","def update_player(ident, document):\n players_col.update_one({\"_id\": ident}, {\"$set\": document})","def put(self):\n if not self.validate_complaint_document(\"update\"):\n return\n document = upload_file(self.request)\n document.author = self.request.authenticated_role\n self.request.validated[\"complaint\"].documents.append(document)\n if save_tender(self.request):\n self.LOGGER.info(\n \"Updated tender award complaint document {}\".format(self.request.context.id),\n extra=context_unpack(self.request, {\"MESSAGE_ID\": \"tender_award_complaint_document_put\"}),\n )\n return {\"data\": document.serialize(\"view\")}","def update(cls, project_id, resource_type, resource_id, data):\n\n for key, value in data.items():\n cls.edit(\n resource_type=resource_type,\n resource_id=resource_id,\n project_id=project_id,\n key=key,\n value=value\n )","def update(self, new_data):\n if type(new_data) is not dict:\n raise TypeError(\"Input parameter must be a dict\")\n # Update parameters\n self._type = new_data.get(\"_type\", self._type)\n self.time_units = new_data.get(\"time_units\", self.time_units)\n self.len_units = new_data.get(\"len_units\", self.len_units)\n self.parameters = new_data.get(\"parameters\", self.parameters)\n # Update drawdown\n self.drawdown.update(new_data.get(\"drawdown\", self.drawdown.to_dict()))\n # Update data\n if \"data\" in new_data:\n n = len(new_data[\"data\"])\n if n > 0:\n self.reset_data()\n for i in range(n):\n self.add_data(0, 0)\n self.data[i].update(new_data[\"data\"][i])\n # End Function","def update_in_place(self, request, original_bundle, new_data):\r\n\r\n # TODO: Is this the place to use MongoDB atomic operations to update the document?\r\n\r\n from tastypie.utils import dict_strip_unicode_keys\r\n original_bundle.data.update(**dict_strip_unicode_keys(new_data))\r\n\r\n # Now we've got a bundle with the new data sitting in it and we're\r\n # we're basically in the same spot as a PUT request. So the rest of this\r\n # function is cribbed from put_detail.\r\n self.alter_deserialized_detail_data(request, original_bundle.data)\r\n\r\n # Removed request from kwargs, breaking obj_get filter, currently present\r\n # in tastypie. See https://github.com/toastdriven/django-tastypie/issues/824.\r\n kwargs = {\r\n self._meta.detail_uri_name: self.get_bundle_detail_data(original_bundle),\r\n }\r\n return self.obj_update(bundle=original_bundle, **kwargs)","def update(self, instance, validated_data):\n instance.user = validated_data.get('user', instance.user)\n instance.book_id = validated_data.get('book_id', instance.book_id)\n instance.save()\n return instance","def update(self, data: bytes):\n self.send(data)","def test_update_using_positional_operator_embedded_document(self):\n\n class Vote(EmbeddedDocument):\n score = IntField()\n\n class Comment(EmbeddedDocument):\n by = StringField()\n votes = EmbeddedDocumentField(Vote)\n\n class BlogPost(Document):\n title = StringField()\n comments = ListField(EmbeddedDocumentField(Comment))\n\n BlogPost.drop_collection()\n\n c1 = Comment(by=\"joe\", votes=Vote(score=3))\n c2 = Comment(by=\"jane\", votes=Vote(score=7))\n\n BlogPost(title=\"ABC\", comments=[c1, c2]).save()\n\n BlogPost.objects(comments__by=\"joe\").update(\n set__comments__S__votes=Vote(score=4)\n )\n\n post = BlogPost.objects.first()\n assert post.comments[0].by == \"joe\"\n assert post.comments[0].votes.score == 4","def update(self, instance, validated_data):\n instance.title = validated_data.get('title', instance.title)\n instance.url = validated_data.get('url', instance.url)\n instance.star = validated_data.get('star', instance.star)\n instance.save()\n return instance","def testUpdate(self):\n response = self.runPut(self.root, sequencer=self.hiseq2000.sodar_uuid, data=self.post_data)\n self.response_200(response)\n data = json.loads(response.content.decode(\"utf-8\"))\n self.assertEqual(data[\"vendor_id\"], self.post_data[\"vendor_id\"])","def update(self, obj, data):\n self.get(obj[self.model.pk_field.name])\n self.validate_fields(data)\n\n fields = []\n values = []\n\n for k, v in data.iteritems():\n if k in self.model.get_fields_name():\n fields.append(k)\n values.append(v)\n\n conn = self.get_connector()\n cursor = conn.cursor()\n update = \" ,\".join([\"{0}='{1}'\".format(f, v) for f, v in zip(fields,\n values)])\n query = \"update {0} set {1} WHERE {2}={3}\".format(\n self.ressource_config[\"table\"],\n update,\n self.model.pk_field.name,\n obj[self.model.pk_field.name]\n )\n\n cursor.execute(query)\n conn.commit()\n conn.close()\n\n return self.get(obj[self.model.pk_field.name])","def _update_data(self, data, update_original=False):\n self._data.update(dict((key, self._deserialize(key, value))\n for key, value in data.items()))\n\n if update_original:\n self._original_data = copy.deepcopy(self._data)","def update_document(self, collection, query, mongo_id):\n\n try:\n self.client[self.db][collection].update_one(\n {'_id': mongo_id},\n query)\n except errors.PyMongoError as e:\n print \"Exception\", e","def update_documents(self, engine_name, documents):\n endpoint = \"engines/{}/documents\".format(engine_name)\n data = json.dumps(documents)\n\n return self.swiftype_session.request('patch', endpoint, data=data)","def update_post(prev_data, data, db_conn):\n\n schema = get_post_schema(data)\n post_kind = prev_data['kind']\n if post_kind is 'post' or post_kind is 'proposal':\n data = pick(data, ('body',))\n elif post_kind is 'vote':\n data = pick(data, ('body', 'response',))\n data, errors = update_document(schema, prev_data, data, db_conn)\n if not errors:\n add_post_to_es(data, db_conn)\n return data, errors","def updateData(self,d):\n for f in self.fields:\n n = f.name()\n if n in d:\n f.setValue(d[n])","def WaveletSetDataDoc(self, wave_id, wavelet_id, name, data):\n op = Operation(WAVELET_DATADOC_SET, wave_id, wavelet_id,\n blip_id=name, prop=data)\n self.__context.AddOperation(op)","def put(self, doc_id):\n\t\t\n\t\t### DEBUGGING\n\t\tprint()\n\t\tprint(\"-+- \"*40)\n\t\tlog.debug( \"ROUTE class : %s\", self.__class__.__name__ )\n\n\t\t### DEBUG check\n\t\tlog.debug (\"payload : \\n{}\".format(pformat(ns.payload)))\n\n\t\t### check client identity and claims\n\t\tclaims \t\t\t= get_jwt_claims() \n\t\tlog.debug(\"claims : \\n %s\", pformat(claims) )\n\n\t\t### update doc in DB\n\t\tupdated_doc, response_code\t= Query_db_update (\n\t\t\tns, \n\t\t\tmodels,\n\t\t\tdocument_type,\n\t\t\tdoc_id,\n\t\t\tclaims,\n\t\t\troles_for_complete = [\"admin\"],\n\t\t\tpayload = ns.payload\n\t\t)\n\n\t\tlog.debug(\"updated_doc : \\n%s \", pformat(updated_doc) )\n\n\t\t### return updated document\n\t\t# return {\n\t\t# \t\"msg\" : \"updating doc....\"\n\t\t# }, 200\n\t\treturn updated_doc, response_code","def update(self, instance, validated_data):\n instance.description = validated_data.get('description', instance.description)\n instance.tender_number = validated_data.get('tender_number', instance.tender_number)\n instance.start_date = validated_data.get('start_date', instance.start_date)\n instance.end_date = validated_data.get('end_date', instance.end_date)\n instance.save()\n return instance","def patch(self):\n if not self.validate_complaint_document(\"update\"):\n return\n if apply_patch(self.request, src=self.request.context.serialize()):\n update_file_content_type(self.request)\n self.LOGGER.info(\n \"Updated tender award complaint document {}\".format(self.request.context.id),\n extra=context_unpack(self.request, {\"MESSAGE_ID\": \"tender_award_complaint_document_patch\"}),\n )\n return {\"data\": self.request.context.serialize(\"view\")}","def update(self, instance, validated_data):\n # instance.id = validated_data.get('id', instance.id)\n instance.teeth = validated_data.get('teeth', instance.teeth)\n instance.species = validated_data.get('species', instance.species)\n instance.save()\n return instance","def update(self, data: dict):\n for key in data:\n model_att = getattr(self.__class__, key, None)\n value = data.get(key)\n\n setattr(self, key, type(model_att.type.python_type())(value))\n\n self.commit()\n return self","def put(self, request, code, suggested_format=None):\n context = {'request': request}\n conten_type = request.content_type\n uploaded_obj = None\n if 'multipart/form-data' in conten_type:\n # We have a file upload encoded with multipart/form-data\n file_content = StringIO(request.body)\n parser = MultiPartParser()\n dnf = parser.parse(file_content, media_type=conten_type, parser_context=context)\n extra = dnf.data\n if 'file' in dnf.files:\n uploaded_obj = dnf.files['file']\n else:\n extra = request.QUERY_PARAMS\n # TODO: do we require SQL tagging logic in this API call?\n sql_tag_string = extra.get('tag_string', None)\n sql_remove_tag_string = extra.get('remove_tag_string', None)\n new_name = extra.get('new_name', None)\n new_type = extra.get('new_type', None)\n index_data = extra.get('indexing_data', None)\n if index_data:\n index_data = json.loads(index_data)\n processor = DocumentProcessor()\n options = {\n 'tag_string': sql_tag_string,\n 'remove_tag_string': sql_remove_tag_string,\n 'extension': suggested_format,\n 'new_name': new_name,\n 'new_type': new_type,\n 'new_indexes': index_data,\n 'update_file': uploaded_obj,\n 'user': request.user,\n }\n document = processor.update(code, options)\n if len(processor.errors) > 0:\n log.error('FileHandler.update manager errors %s' % processor.errors)\n if settings.DEBUG:\n print processor.errors\n raise Exception('FileHandler.update manager errors')\n else:\n return Response(status=status.HTTP_400_BAD_REQUEST)\n log.info('FileHandler.update request fulfilled for code: %s, extension: %s'\n % (code, suggested_format))\n resp = DMSOBjectRevisionsData(document).data\n return Response(resp, status=status.HTTP_200_OK)","def update(self, instance, validated_data):\n for attr, value in list(validated_data.items()):\n if attr == 'part_details':\n for a, v in list(value.items()):\n setattr(instance.part_details, attr, value)\n else:\n setattr(instance, attr, value)\n instance.save()\n return instance","async def put(self, _id: str, doc: dict, *,\n rev: Optional[str] = None,\n batch: Optional[bool] = None,\n new_edits: Optional[bool] = None) -> dict:\n\n params = dict(\n rev=rev,\n batch=\"ok\" if batch else None,\n new_edits=new_edits\n )\n\n return await self.__connection.query('PUT', self._get_path(_id), params=params, data=doc)","def update(self, request, *args, **kwargs):\n response = super(NoteViewSet, self).update(request, *args, **kwargs)\n obj = self.get_object()\n instance = obj.instance\n # update mongo data\n instance.parsed_instance.save()\n return response","async def update_one(\n self,\n where: t.Mapping[str, t.Any],\n data: t.Mapping[str, t.Any],\n ) -> t.Optional[t.Type[Model]]:\n\n data = await self.collection.find_one_and_update(\n filter=where,\n update={'$set': data},\n return_document=ReturnDocument.AFTER,\n )\n return self.model_class(**data) if data else None"],"string":"[\n \"def update_document(self, data):\\n if not isinstance(data, pylastica.document.Document) and not isinstance(data, pylastica.script.Script):\\n raise TypeError(\\\"data must be an instance of Document or Script: %r\\\" % data)\\n if not data.has_id():\\n raise pylastica.exception.InvalidException(\\\"Document id is not set.\\\")\\n return self.index.client.update_document(data.doc_id, data, self.index.name, self.name)\",\n \"def update_document(self):\\n pass\",\n \"def put(self, data):\\n self.validate(data)\\n\\n mongo['readable-api'].foo.update(\\n {\\\"foo\\\": self.data[\\\"foo\\\"]},\\n data,\\n )\",\n \"def update(self, data, id_obj=None, query_data=None):\\n if id_obj:\\n return self.collection.update({'_id': id_obj}, {\\\"$set\\\": data})\\n return self.collection.update(query_data, {\\\"$set\\\": data})\",\n \"def update_document(collection: str, query: dict, data: dict) -> None:\\n validate_arguments({'collection': [collection, str],\\n 'query': [query, dict],\\n 'data': [data, dict]})\\n new_document = find_document(collection, query=query)\\n if new_document is None:\\n raise Exception('Didnt find a document to update')\\n DB[collection].delete_one(query)\\n for key in data:\\n new_document[key] = data[key]\\n add_document(collection, new_document)\",\n \"def update_document(self, type: Type, id: str,\\n new_data: Dict[str, Any]) -> None:\\n if collection := self.client.collection(f'{type}'):\\n if document_ref := collection.document(document_id=id):\\n if document_ref.get().exists:\\n document_ref.update(new_data)\\n else:\\n document_ref.create(new_data)\",\n \"def update(self, data):\\n pass\",\n \"def update(self, data):\\n pass\",\n \"def update(self, data):\\n pass\",\n \"def update(self, data):\\n pass\",\n \"def update(self, data):\\n self.data.update(data)\",\n \"def Update(self, data):\\n self.Write(data)\",\n \"def update_document(obj):\\n index = obj.get_index_name()\\n doc_type = obj.get_document_type()\\n body = dict(doc=obj.get_document_body())\\n try:\\n ES.update(index=index, doc_type=doc_type, body=body, id=obj.pk)\\n except NotFoundError:\\n raise DocumentNotFound(obj.get_index_name(), obj.pk)\",\n \"def update(self, identifier, data):\\n self.client.request_with_method(Methods.UPDATE % (self.name, identifier,),\\n data=data)\",\n \"def update_item(self, data_id, json, *args, **kwargs):\\n result = {\\\"doc\\\": JsonHelper.to_dict(json), \\\"doc_as_upsert\\\": \\\"true\\\"}\\n json_result = JsonHelper.to_json(result)\\n self.connection.update(index=Properties.Elasticsearch.PARSER_INDEX,\\n doc_type=Properties.Elasticsearch.PARSER_DOC_TYPE,\\n id=data_id,\\n body=json_result,\\n *args,\\n **kwargs)\",\n \"def update(self, data):\\n self.content = data\",\n \"def update(self, data):\\n self.data = data\\n # extract data\\n subject = data.identifier\\n self.id = _uri_to_oai(subject)\\n self.modified = datetime.utcnow()\\n self.deleted = False\\n\\n itemtype, subtype = _get_itemtype(data, subject)\\n\\n self.metadata = {}\\n\\n # fixed fields:\\n self.metadata['rif_key'] = _uri_to_key(subject)\\n self.metadata['rif_group'] = self.provider.groupDescription\\n self.metadata['rif_originatingSource'] = self.provider.originatingSource\\n self.metadata['rif_object'] = {'value': itemtype,\\n 'type': subtype,\\n #'dateModified': '',\\n }\\n\\n if itemtype == 'collection':\\n self.updateCollection(data, subject)\\n elif itemtype == 'party':\\n if subtype == 'person':\\n self.updatePartyPerson(data, subject)\\n else:\\n self.updateParty(data, subject)\\n elif itemtype == 'activity':\\n self.updateActivity(data, subject)\\n elif itemtype == 'service':\\n self.updateService(data, subject)\",\n \"def update(self, instance, validated_data):\\n instance.title = validated_data.get('title', instance.title)\\n instance.xml_content = validated_data.get('xml_content', instance.xml_content)\\n return data_api.upsert(instance, validated_data['user'])\",\n \"def update_document(\\n self,\\n index: str,\\n doc_id: str,\\n document: Dict[str, Any],\\n partial_update: bool = False,\\n ):\\n if partial_update:\\n self.__client__.update(index=index, id=doc_id, body={\\\"doc\\\": document})\\n self.__client__.index(index=index, id=doc_id, body=document)\",\n \"def update(self, spec, document, upsert=False, multi=False):\\n return self.database.connection.request.update_documents(\\n self.database.name, self.name, spec, document, upsert, multi)\",\n \"def update_document(self, portal_name, content_id, document):\\n if isinstance(document, dict):\\n document = json.dumps(document)\\n r = requests.put('/'.join([self.base_url,\\n self.DOCUMENTS_ENDPOINT,\\n portal_name,\\n str(content_id)]),\\n data=document,\\n headers={'Content-Type': 'application/json'})\\n return r.json()\",\n \"def update_document(self, database, collection, _id, document):\\n r = self.__get_response(settings.UPD_DOC,\\n {\\\"db\\\": database, \\\"col\\\": collection, \\\"id\\\": str(_id)},\\n data=document)\\n if r[\\\"status\\\"] == 200:\\n return r[\\\"result\\\"]\\n raise Exception(r[\\\"result\\\"][\\\"message\\\"])\",\n \"def update(self, data):\\n return data\",\n \"def SetDataDocument(self, name, data):\\n self.__context.builder.WaveletSetDataDoc(self.GetWaveId(), self.GetId(),\\n name, data)\\n self._data.data_documents[name] = data\",\n \"def _update(self, data: Dict[str, Any], fields_to_modify: List[str]):\\n pass\",\n \"def update(account, data):\\n stripe_account = stripe.Account.retrieve(id=account.stripe_id)\\n\\n if data.get(\\\"dob\\\"):\\n stripe_account.legal_entity.dob = data[\\\"dob\\\"]\\n\\n if data.get(\\\"first_name\\\"):\\n stripe_account.legal_entity.first_name = data[\\\"first_name\\\"]\\n\\n if data.get(\\\"last_name\\\"):\\n stripe_account.legal_entity.last_name = data[\\\"last_name\\\"]\\n\\n if data.get(\\\"personal_id_number\\\"):\\n stripe_account.legal_entity.personal_id_number = data[\\\"personal_id_number\\\"]\\n\\n if data.get(\\\"document\\\"):\\n response = stripe.FileUpload.create(\\n purpose=\\\"identity_document\\\",\\n file=data[\\\"document\\\"],\\n stripe_account=stripe_account.id\\n )\\n stripe_account.legal_entity.verification.document = response[\\\"id\\\"]\\n\\n stripe_account.save()\\n return sync_account_from_stripe_data(stripe_account)\",\n \"def update(self, doc):\\n if app.config.get(\\\"READ_ONLY_MODE\\\", False) and app.config.get(\\\"SCRIPTS_READ_ONLY_MODE\\\", False):\\n app.logger.warn(\\\"System is in READ-ONLY mode, update command cannot run\\\")\\n return\\n\\n return requests.post(self.target() + self.id + \\\"/_update\\\", data=json.dumps({\\\"doc\\\": doc}))\",\n \"def updateDocument(self, document):\\n data = self.updateDocumentAll([document])\\n try:\\n return data[0]\\n except: pass\",\n \"def update(self, data):\\n if self.service is not None:\\n self.service.update_response(data)\",\n \"def update_data():\\n pass\",\n \"def update(data):\\n username, message_id, message_text, room = data['username'], int(data['message_id']), data['message'], data['room']\\n message = Message.query.filter_by(id=message_id, username=session['username']).first()\\n if message:\\n message.text = data['message']\\n db.session.add(message)\\n db.session.commit()\\n emit('update', {'username': username, 'message': {'id': message.id, 'text': message.text}}, room=room)\",\n \"def update(self, instance, validated_data):\\n instance.body = validated_data[\\\"body\\\"]\\n instance.save()\\n return instance\",\n \"def update(self, instance, validated_data):\\n instance.title = validated_data.get('title', instance.title)\\n instance.owner = validated_data.get('owner', instance.owner)\\n instance.language = validated_data.get('language', instance.language)\\n instance.classes = validated_data.get('classes', instance.classes)\\n instance.methods = validated_data.get('methods', instance.methods)\\n instance.style = validated_data.get('style', instance.style)\\n instance.example = validated_data.get('example', instance.example)\\n instance.save()\\n return instance\",\n \"def update(self, index, id, **kwargs):\\n url = f'{self.host}{index}/_doc/{id}/_update'\\n data = {'doc': {**kwargs}}\\n requests.post(url, json=data)\\n self.flush(index)\\n return self.get(index, id)\",\n \"def update(self, instance, validated_data):\\n pass\",\n \"def test_update(self):\\n doc_fields = document_fields.DocumentFields({\\n 'foo@': 'bar',\\n })\\n self.assertEquals('bar', doc_fields['foo'])\\n doc_fields.update({\\n 'foo@': 'bbq',\\n })\\n self.assertEquals('bbq', doc_fields['foo'])\",\n \"def update(self, identity, data=None, record=None, **kwargs):\\n record.custom_fields = data.get(\\\"custom_fields\\\", {})\",\n \"def update(self, instance, validated_data):\\n instance.title = validated_data.get('title', instance.title)\\n instance.inspection_tag = validated_data.get('inspection_tag', instance.code)\\n instance.content = validated_data.get('content', instance.language)\\n instance.status = validated_data.get('status', instance.style)\\n instance.save()\\n return instance\",\n \"def update(self, instance, validated_data):\\n instance.title = validated_data.get('title', instance.title)\\n instance.inspection_tag = validated_data.get('inspection_tag', instance.code)\\n instance.content = validated_data.get('content', instance.language)\\n instance.status = validated_data.get('status', instance.style)\\n instance.save()\\n return instance\",\n \"def update(self, docs: DocumentArray, *args, **kwargs):\\n cursor = self.connection.cursor()\\n psycopg2.extras.execute_batch(\\n cursor,\\n f'UPDATE {self.table} SET DOC = %s WHERE ID = %s',\\n [\\n (\\n doc.SerializeToString(),\\n doc.id,\\n )\\n for doc in docs\\n ],\\n )\\n self.connection.commit()\",\n \"def update(_id, new_doc, **metadata):\\n # NOTE\\n # Internally, the update is performed by\\n # Revalidating and replacing the original document.\\n\\n new_doc = ensure_document(new_doc)\\n dataset = ESDataset.get(id=_id, ignore=404)\\n\\n if not dataset:\\n raise NoEntityError(f\\\"dataset {_id} does not exist.\\\")\\n\\n # Cannot change the identifier field, because it would result\\n # in changing the document _id. Delete and add again instead.\\n if new_doc.get(\\\"identifier\\\") != dataset.identifier:\\n raise ConflictError(\\\"cannot change identifier field.\\\")\\n\\n # NOTE **important**\\n # Patch the original document metadata with the partial update.\\n _meta = dataset[\\\"_meta\\\"].to_dict()\\n _meta.update(_clean(new_doc.pop(\\\"_meta\\\", {})))\\n _meta.update(_clean(metadata))\\n _meta = _clean(_meta)\\n\\n new_doc = validate(new_doc, _meta[\\\"schema\\\"])\\n\\n dataset = _index(\\n new_doc,\\n _meta,\\n _addon={\\n # Carry over our internal metadata like\\n # N3C ticket info and creation timestamp.\\n \\\"date_created\\\": dataset._ts.date_created,\\n \\\"n3c_url\\\": dataset._n3c.url,\\n \\\"n3c_status\\\": dataset._n3c.status,\\n \\\"n3c_timestamp\\\": dataset._n3c.timestamp,\\n },\\n )\\n\\n return dataset.meta.version\",\n \"def update(self, data: Union[QueryWithResponse, List[QueryWithResponse]], **kwargs):\\n raise NotImplementedError\",\n \"def document_update(index_name, doc_type, doc_id, doc=None, new=None):\\n if doc:\\n resp = es.index(index=index_name, doc_type=doc_type,\\n id=doc_id, body=doc)\\n print(resp)\\n else:\\n resp = es.update(index=index_name, doc_type=doc_type,\\n id=doc_id, body={\\\"doc\\\": new})\",\n \"def update(self, instance, validated_data):\\n instance.title = validated_data.get('title', instance.title)\\n instance.code = validated_data.get('code', instance.code)\\n instance.linenos = validated_data.get('linenos', instance.linenos)\\n instance.language = validated_data.get('language', instance.language)\\n instance.style = validated_data.get('style', instance.style)\\n instance.save()\\n return instance\",\n \"def _update(self, data: Dict[str, Any], fields_to_modify: List[str]):\\n\\n to_set = [\\n \\\"{0} = %({0})s\\\".format(f) for f in fields_to_modify\\n if f in self.editable_fields\\n ]\\n if len(to_set) == 0:\\n print('Warning: No data to set', data)\\n return\\n\\n query = \\\"UPDATE {} SET {} WHERE {}\\\".format(\\n self._NAME,\\n ', '.join(to_set),\\n ' AND '.join(\\\"{0} = %({0})s\\\".format(f) for f in self.primary_fields),\\n )\\n self._execute(query, data)\",\n \"def update(self, instance, validated_data):\",\n \"def update(cls, doc_id, document, specs=None, updater=raw_updater, *args, **kwargs):\\n document = updater(document)\\n ret = cls._make_call('update', cls._make_specs(doc_id, specs), document, *args, **kwargs)\\n if ret:\\n return ret['n']\",\n \"def update(cls, collection, uid, data):\\n validated = cls.validate(data)\\n validated.pop(\\\"_id\\\", None) # remove field \\\"_id\\\" if set\\n object_uid = cls.object_id(uid)\\n collection.update_one({\\\"_id\\\": object_uid}, {\\\"$set\\\": validated}, upsert=True)\\n return collection.find_one({\\\"_id\\\": object_uid})\",\n \"def update(self, resource, data, target=None, verb='update', **kwargs):\\n return self._modify_resource(resource, data, target, verb, **kwargs)\",\n \"def update(self, data: Mapping[str, np.ndarray]) -> Self:\\n\\n raise NotImplementedError\",\n \"def put(self, _id=None, **data):\\n new_data, numpy_array = Database._put_prep(data)\\n doc = self._put_assume_new(_id, **new_data)\\n if numpy_array is not None:\\n temp_file = TemporaryFile()\\n np.save(temp_file, numpy_array)\\n temp_file.seek(0)\\n # TODO: check attachment success\\n doc.put_attachment(\\n attachment='value', content_type='application/octet-stream',\\n data=temp_file)\\n return doc\",\n \"def put(self):\\n document = upload_file(self.request)\\n self.request.validated[\\\"qualification\\\"].documents.append(document)\\n if save_tender(self.request):\\n self.LOGGER.info(\\n \\\"Updated tender qualification document {}\\\".format(self.request.context.id),\\n extra=context_unpack(self.request, {\\\"MESSAGE_ID\\\": \\\"tender_qualification_document_put\\\"}),\\n )\\n return {\\\"data\\\": document.serialize(\\\"view\\\")}\",\n \"def _update_object(self, data_dict):\\r\\n pass\",\n \"def update_doc(c, i, d, u=False):\\n try:\\n c.update_one({'_id': i}, {'$set': d}, upsert = u)\\n return True\\n except:\\n return False\",\n \"def update(self, document_id, update_spec, namespace, timestamp):\\n\\n index, doc_type = self._index_and_mapping(namespace)\\n document = self.BulkBuffer.get_from_sources(index,doc_type,u(document_id))\\n if document:\\n updated = self.apply_update(document, update_spec)\\n # _id is immutable in MongoDB, so won't have changed in update\\n updated['_id'] = document_id\\n self.upsert(updated, namespace, timestamp)\\n else:\\n updated = {\\\"_id\\\": document_id}\\n self.upsert(updated, namespace, timestamp, update_spec)\\n # upsert() strips metadata, so only _id + fields in _source still here\\n return updated\",\n \"def update(self, **kwargs):\\n return self._update_data(self.put(None, data=kwargs))\",\n \"def update_documents(self, database, collection, spec, doc_or_docs, upsert,\\n multi):\\n validators.check_document_to_update(doc_or_docs)\\n r = self.__get_response(settings.UPD_DOCS,\\n {\\\"db\\\": database, \\\"col\\\": collection},\\n data=doc_or_docs, q=spec, m=multi, u=upsert)\\n if r[\\\"status\\\"] == 200:\\n if r[\\\"result\\\"][\\\"error\\\"]:\\n raise Exception(r[\\\"result\\\"][\\\"error\\\"])\\n return r[\\\"result\\\"][\\\"n\\\"]\\n raise Exception(r[\\\"result\\\"][\\\"message\\\"])\",\n \"def update(self, resource, id, **data):\\n self.request('/' + resource + '/' + str(id), 'PUT', body=urllib.urlencode(data))\\n return True\",\n \"def update_data(self, extra_data):\\n self._data.update(extra_data)\\n return self\",\n \"def update(self, instance, validated_data):\\n instance.href = validated_data.get('href', instance.href)\\n instance.title = validated_data.get('title', instance.title)\\n instance.datetime = validated_data.get('datetime', instance.datetime)\\n instance.content = validated_data.get('content', instance.content)\\n instance.coverimg = validated_data.get('coverimg', instance.title)\\n instance.save()\\n return instance\",\n \"def updateDoc(self, path):\\n self.db.setDb(self.db_file)\\n \\n if not self.authd:\\n self._authorize()\\n \\n db_row = self.db.getRowFromPath(path)\\n if not db_row:\\n return False\\n \\n resource_id = db_row[0]\\n etag = db_row[1]\\n title = db_row[2]\\n \\n ms = gdata.data.MediaSource(file_path=path, content_type=MIMETYPES['ODT'])\\n doc = self.client.GetDoc(resource_id.replace(':', '%3A'))\\n new_version = self.client.Update(doc, media_source=ms)\\n print 'Document pushed:', new_version.GetAlternateLink().href\\n \\n self.db.resetEtag(new_version)\",\n \"def Update(WaveName,Data):\\n assert WaveName in self\\n self.UpdateData(WaveName,Data)\",\n \"def update_draft(self, identity, data=None, record=None, **kwargs):\\n record.metadata = data.get('metadata', {})\",\n \"def upsert(self, doc, namespace, timestamp, update_spec=None):\\n index, doc_type = self._index_and_mapping(namespace)\\n # No need to duplicate '_id' in source document\\n doc_id = u(doc.pop(\\\"_id\\\"))\\n metadata = {\\n 'ns': namespace,\\n '_ts': timestamp\\n }\\n \\n # Index the source document, using lowercase namespace as index name.\\n action = {\\n '_op_type': 'index',\\n '_index': index,\\n '_type': doc_type,\\n '_id': doc_id,\\n '_source': self._formatter.format_document(doc)\\n }\\n # Index document metadata with original namespace (mixed upper/lower).\\n meta_action = {\\n '_op_type': 'index',\\n '_index': self.meta_index_name,\\n '_type': self.meta_type,\\n '_id': doc_id,\\n '_source': bson.json_util.dumps(metadata)\\n }\\n \\n self.index(action,meta_action,doc,update_spec)\\n \\n # Leave _id, since it's part of the original document\\n doc['_id'] = doc_id\",\n \"def _update_from_rest_data(self) -> None:\",\n \"async def update_one(self, where, data):\\n\\n pass\",\n \"def update(self):\\n data = self.serialize()\\n\\n self.validate(data)\\n\\n saved_data = DATABASE_CONNECTION.update(self.__class__.__name__, data['id'], data)\\n\\n self.__dict__.update(saved_data)\",\n \"def update(self, title=None, description = None):\\n jsonData = self.metaData.jsonObj\\n header = self._baseHeader.copy()\\n\\n header['Content-type'] = \\\"application/vnd.huddle.data+json\\\"\\n url = self.metaData.getLink(\\\"edit\\\")\\n assert url is not None\\n\\n if title is not None: jsonData['title'] = title\\n if description is not None: jsonData['description'] = description\\n\\n response = self._adapter.putRequest(url, header, json.dumps(jsonData))\\n\\n return Document(self._client, self._client.getUrlFromHeaderLink(response['Headers']['link']))\",\n \"def update(self, docid, doc):\\n self.delete(docid)\\n wrapper = RedisWrapper(self.dbprefix, self.client, docid)\\n self._store_doc(doc, wrapper)\",\n \"def update_data(self):\\n self._model.update()\\n self.__refresh()\",\n \"def _update(self, data=None, _validate=False):\\n if data is None:\\n # If no data is passed, take no action.\\n pass\\n elif _mapping_resolver.get_type(data) == \\\"MAPPING\\\":\\n with self._suspend_sync:\\n for key, new_value in data.items():\\n try:\\n # The most common usage of SyncedCollections is with a\\n # single object referencing an underlying resource at a\\n # time, so we should almost always find that elements\\n # of data are already contained in self._data, so EAFP\\n # is the best choice for performance.\\n existing = self._data[key]\\n except KeyError:\\n # If the item wasn't present at all, we can simply\\n # assign it.\\n if not _validate:\\n self._validate({key: new_value})\\n self._data[key] = self._from_base(new_value, parent=self)\\n else:\\n if new_value == existing:\\n continue\\n if _sc_resolver.get_type(existing) == \\\"SYNCEDCOLLECTION\\\":\\n try:\\n existing._update(new_value)\\n continue\\n except ValueError:\\n pass\\n\\n # Fall through if the new value is not identical to the\\n # existing value and\\n # 1) The existing value is not a SyncedCollection\\n # (in which case we would have tried to update it), OR\\n # 2) The existing value is a SyncedCollection, but\\n # the new value is not a compatible type for _update.\\n if not _validate:\\n self._validate({key: new_value})\\n self._data[key] = self._from_base(new_value, parent=self)\\n\\n to_remove = [key for key in self._data if key not in data]\\n for key in to_remove:\\n del self._data[key]\\n else:\\n raise ValueError(\\n \\\"Unsupported type: {}. The data must be a mapping or None.\\\".format(\\n type(data)\\n )\\n )\",\n \"def do_PUT(self):\\n note_details = NoteDetails\\n if self.path == '/note/api/update':\\n response_data=note_details.update_data(self)\\n Response(self).jsonResponse(status=200, data=response_data)\",\n \"def update(self, new_data):\\n if type(new_data) is not dict:\\n raise TypeError(\\\"Input parameter must be a dict\\\")\\n # Update parameters\\n self._type = new_data.get(\\\"_type\\\", self._type)\\n self.time_units = new_data.get(\\\"time_units\\\", self.time_units)\\n self.len_units = new_data.get(\\\"len_units\\\", self.len_units)\\n self.pump_units = new_data.get(\\\"pump_units\\\", self.pump_units)\\n self.parameters = new_data.get(\\\"parameters\\\", self.parameters)\\n # Update pumping rate\\n self.pumprate.update(new_data.get(\\\"pumprate\\\", self.pumprate.to_dict()))\\n # Update data\\n if \\\"wells\\\" in new_data:\\n n = len(new_data[\\\"wells\\\"])\\n if n > 1:\\n self.delete_all_wells()\\n for i in range(n):\\n self.add_well(0, 0, new_data[\\\"wells\\\"][i][\\\"_type\\\"] - 2)\\n self.wells[i].update(new_data[\\\"wells\\\"][i])\\n # End Function\",\n \"def update_player(ident, document):\\n players_col.update_one({\\\"_id\\\": ident}, {\\\"$set\\\": document})\",\n \"def put(self):\\n if not self.validate_complaint_document(\\\"update\\\"):\\n return\\n document = upload_file(self.request)\\n document.author = self.request.authenticated_role\\n self.request.validated[\\\"complaint\\\"].documents.append(document)\\n if save_tender(self.request):\\n self.LOGGER.info(\\n \\\"Updated tender award complaint document {}\\\".format(self.request.context.id),\\n extra=context_unpack(self.request, {\\\"MESSAGE_ID\\\": \\\"tender_award_complaint_document_put\\\"}),\\n )\\n return {\\\"data\\\": document.serialize(\\\"view\\\")}\",\n \"def update(cls, project_id, resource_type, resource_id, data):\\n\\n for key, value in data.items():\\n cls.edit(\\n resource_type=resource_type,\\n resource_id=resource_id,\\n project_id=project_id,\\n key=key,\\n value=value\\n )\",\n \"def update(self, new_data):\\n if type(new_data) is not dict:\\n raise TypeError(\\\"Input parameter must be a dict\\\")\\n # Update parameters\\n self._type = new_data.get(\\\"_type\\\", self._type)\\n self.time_units = new_data.get(\\\"time_units\\\", self.time_units)\\n self.len_units = new_data.get(\\\"len_units\\\", self.len_units)\\n self.parameters = new_data.get(\\\"parameters\\\", self.parameters)\\n # Update drawdown\\n self.drawdown.update(new_data.get(\\\"drawdown\\\", self.drawdown.to_dict()))\\n # Update data\\n if \\\"data\\\" in new_data:\\n n = len(new_data[\\\"data\\\"])\\n if n > 0:\\n self.reset_data()\\n for i in range(n):\\n self.add_data(0, 0)\\n self.data[i].update(new_data[\\\"data\\\"][i])\\n # End Function\",\n \"def update_in_place(self, request, original_bundle, new_data):\\r\\n\\r\\n # TODO: Is this the place to use MongoDB atomic operations to update the document?\\r\\n\\r\\n from tastypie.utils import dict_strip_unicode_keys\\r\\n original_bundle.data.update(**dict_strip_unicode_keys(new_data))\\r\\n\\r\\n # Now we've got a bundle with the new data sitting in it and we're\\r\\n # we're basically in the same spot as a PUT request. So the rest of this\\r\\n # function is cribbed from put_detail.\\r\\n self.alter_deserialized_detail_data(request, original_bundle.data)\\r\\n\\r\\n # Removed request from kwargs, breaking obj_get filter, currently present\\r\\n # in tastypie. See https://github.com/toastdriven/django-tastypie/issues/824.\\r\\n kwargs = {\\r\\n self._meta.detail_uri_name: self.get_bundle_detail_data(original_bundle),\\r\\n }\\r\\n return self.obj_update(bundle=original_bundle, **kwargs)\",\n \"def update(self, instance, validated_data):\\n instance.user = validated_data.get('user', instance.user)\\n instance.book_id = validated_data.get('book_id', instance.book_id)\\n instance.save()\\n return instance\",\n \"def update(self, data: bytes):\\n self.send(data)\",\n \"def test_update_using_positional_operator_embedded_document(self):\\n\\n class Vote(EmbeddedDocument):\\n score = IntField()\\n\\n class Comment(EmbeddedDocument):\\n by = StringField()\\n votes = EmbeddedDocumentField(Vote)\\n\\n class BlogPost(Document):\\n title = StringField()\\n comments = ListField(EmbeddedDocumentField(Comment))\\n\\n BlogPost.drop_collection()\\n\\n c1 = Comment(by=\\\"joe\\\", votes=Vote(score=3))\\n c2 = Comment(by=\\\"jane\\\", votes=Vote(score=7))\\n\\n BlogPost(title=\\\"ABC\\\", comments=[c1, c2]).save()\\n\\n BlogPost.objects(comments__by=\\\"joe\\\").update(\\n set__comments__S__votes=Vote(score=4)\\n )\\n\\n post = BlogPost.objects.first()\\n assert post.comments[0].by == \\\"joe\\\"\\n assert post.comments[0].votes.score == 4\",\n \"def update(self, instance, validated_data):\\n instance.title = validated_data.get('title', instance.title)\\n instance.url = validated_data.get('url', instance.url)\\n instance.star = validated_data.get('star', instance.star)\\n instance.save()\\n return instance\",\n \"def testUpdate(self):\\n response = self.runPut(self.root, sequencer=self.hiseq2000.sodar_uuid, data=self.post_data)\\n self.response_200(response)\\n data = json.loads(response.content.decode(\\\"utf-8\\\"))\\n self.assertEqual(data[\\\"vendor_id\\\"], self.post_data[\\\"vendor_id\\\"])\",\n \"def update(self, obj, data):\\n self.get(obj[self.model.pk_field.name])\\n self.validate_fields(data)\\n\\n fields = []\\n values = []\\n\\n for k, v in data.iteritems():\\n if k in self.model.get_fields_name():\\n fields.append(k)\\n values.append(v)\\n\\n conn = self.get_connector()\\n cursor = conn.cursor()\\n update = \\\" ,\\\".join([\\\"{0}='{1}'\\\".format(f, v) for f, v in zip(fields,\\n values)])\\n query = \\\"update {0} set {1} WHERE {2}={3}\\\".format(\\n self.ressource_config[\\\"table\\\"],\\n update,\\n self.model.pk_field.name,\\n obj[self.model.pk_field.name]\\n )\\n\\n cursor.execute(query)\\n conn.commit()\\n conn.close()\\n\\n return self.get(obj[self.model.pk_field.name])\",\n \"def _update_data(self, data, update_original=False):\\n self._data.update(dict((key, self._deserialize(key, value))\\n for key, value in data.items()))\\n\\n if update_original:\\n self._original_data = copy.deepcopy(self._data)\",\n \"def update_document(self, collection, query, mongo_id):\\n\\n try:\\n self.client[self.db][collection].update_one(\\n {'_id': mongo_id},\\n query)\\n except errors.PyMongoError as e:\\n print \\\"Exception\\\", e\",\n \"def update_documents(self, engine_name, documents):\\n endpoint = \\\"engines/{}/documents\\\".format(engine_name)\\n data = json.dumps(documents)\\n\\n return self.swiftype_session.request('patch', endpoint, data=data)\",\n \"def update_post(prev_data, data, db_conn):\\n\\n schema = get_post_schema(data)\\n post_kind = prev_data['kind']\\n if post_kind is 'post' or post_kind is 'proposal':\\n data = pick(data, ('body',))\\n elif post_kind is 'vote':\\n data = pick(data, ('body', 'response',))\\n data, errors = update_document(schema, prev_data, data, db_conn)\\n if not errors:\\n add_post_to_es(data, db_conn)\\n return data, errors\",\n \"def updateData(self,d):\\n for f in self.fields:\\n n = f.name()\\n if n in d:\\n f.setValue(d[n])\",\n \"def WaveletSetDataDoc(self, wave_id, wavelet_id, name, data):\\n op = Operation(WAVELET_DATADOC_SET, wave_id, wavelet_id,\\n blip_id=name, prop=data)\\n self.__context.AddOperation(op)\",\n \"def put(self, doc_id):\\n\\t\\t\\n\\t\\t### DEBUGGING\\n\\t\\tprint()\\n\\t\\tprint(\\\"-+- \\\"*40)\\n\\t\\tlog.debug( \\\"ROUTE class : %s\\\", self.__class__.__name__ )\\n\\n\\t\\t### DEBUG check\\n\\t\\tlog.debug (\\\"payload : \\\\n{}\\\".format(pformat(ns.payload)))\\n\\n\\t\\t### check client identity and claims\\n\\t\\tclaims \\t\\t\\t= get_jwt_claims() \\n\\t\\tlog.debug(\\\"claims : \\\\n %s\\\", pformat(claims) )\\n\\n\\t\\t### update doc in DB\\n\\t\\tupdated_doc, response_code\\t= Query_db_update (\\n\\t\\t\\tns, \\n\\t\\t\\tmodels,\\n\\t\\t\\tdocument_type,\\n\\t\\t\\tdoc_id,\\n\\t\\t\\tclaims,\\n\\t\\t\\troles_for_complete = [\\\"admin\\\"],\\n\\t\\t\\tpayload = ns.payload\\n\\t\\t)\\n\\n\\t\\tlog.debug(\\\"updated_doc : \\\\n%s \\\", pformat(updated_doc) )\\n\\n\\t\\t### return updated document\\n\\t\\t# return {\\n\\t\\t# \\t\\\"msg\\\" : \\\"updating doc....\\\"\\n\\t\\t# }, 200\\n\\t\\treturn updated_doc, response_code\",\n \"def update(self, instance, validated_data):\\n instance.description = validated_data.get('description', instance.description)\\n instance.tender_number = validated_data.get('tender_number', instance.tender_number)\\n instance.start_date = validated_data.get('start_date', instance.start_date)\\n instance.end_date = validated_data.get('end_date', instance.end_date)\\n instance.save()\\n return instance\",\n \"def patch(self):\\n if not self.validate_complaint_document(\\\"update\\\"):\\n return\\n if apply_patch(self.request, src=self.request.context.serialize()):\\n update_file_content_type(self.request)\\n self.LOGGER.info(\\n \\\"Updated tender award complaint document {}\\\".format(self.request.context.id),\\n extra=context_unpack(self.request, {\\\"MESSAGE_ID\\\": \\\"tender_award_complaint_document_patch\\\"}),\\n )\\n return {\\\"data\\\": self.request.context.serialize(\\\"view\\\")}\",\n \"def update(self, instance, validated_data):\\n # instance.id = validated_data.get('id', instance.id)\\n instance.teeth = validated_data.get('teeth', instance.teeth)\\n instance.species = validated_data.get('species', instance.species)\\n instance.save()\\n return instance\",\n \"def update(self, data: dict):\\n for key in data:\\n model_att = getattr(self.__class__, key, None)\\n value = data.get(key)\\n\\n setattr(self, key, type(model_att.type.python_type())(value))\\n\\n self.commit()\\n return self\",\n \"def put(self, request, code, suggested_format=None):\\n context = {'request': request}\\n conten_type = request.content_type\\n uploaded_obj = None\\n if 'multipart/form-data' in conten_type:\\n # We have a file upload encoded with multipart/form-data\\n file_content = StringIO(request.body)\\n parser = MultiPartParser()\\n dnf = parser.parse(file_content, media_type=conten_type, parser_context=context)\\n extra = dnf.data\\n if 'file' in dnf.files:\\n uploaded_obj = dnf.files['file']\\n else:\\n extra = request.QUERY_PARAMS\\n # TODO: do we require SQL tagging logic in this API call?\\n sql_tag_string = extra.get('tag_string', None)\\n sql_remove_tag_string = extra.get('remove_tag_string', None)\\n new_name = extra.get('new_name', None)\\n new_type = extra.get('new_type', None)\\n index_data = extra.get('indexing_data', None)\\n if index_data:\\n index_data = json.loads(index_data)\\n processor = DocumentProcessor()\\n options = {\\n 'tag_string': sql_tag_string,\\n 'remove_tag_string': sql_remove_tag_string,\\n 'extension': suggested_format,\\n 'new_name': new_name,\\n 'new_type': new_type,\\n 'new_indexes': index_data,\\n 'update_file': uploaded_obj,\\n 'user': request.user,\\n }\\n document = processor.update(code, options)\\n if len(processor.errors) > 0:\\n log.error('FileHandler.update manager errors %s' % processor.errors)\\n if settings.DEBUG:\\n print processor.errors\\n raise Exception('FileHandler.update manager errors')\\n else:\\n return Response(status=status.HTTP_400_BAD_REQUEST)\\n log.info('FileHandler.update request fulfilled for code: %s, extension: %s'\\n % (code, suggested_format))\\n resp = DMSOBjectRevisionsData(document).data\\n return Response(resp, status=status.HTTP_200_OK)\",\n \"def update(self, instance, validated_data):\\n for attr, value in list(validated_data.items()):\\n if attr == 'part_details':\\n for a, v in list(value.items()):\\n setattr(instance.part_details, attr, value)\\n else:\\n setattr(instance, attr, value)\\n instance.save()\\n return instance\",\n \"async def put(self, _id: str, doc: dict, *,\\n rev: Optional[str] = None,\\n batch: Optional[bool] = None,\\n new_edits: Optional[bool] = None) -> dict:\\n\\n params = dict(\\n rev=rev,\\n batch=\\\"ok\\\" if batch else None,\\n new_edits=new_edits\\n )\\n\\n return await self.__connection.query('PUT', self._get_path(_id), params=params, data=doc)\",\n \"def update(self, request, *args, **kwargs):\\n response = super(NoteViewSet, self).update(request, *args, **kwargs)\\n obj = self.get_object()\\n instance = obj.instance\\n # update mongo data\\n instance.parsed_instance.save()\\n return response\",\n \"async def update_one(\\n self,\\n where: t.Mapping[str, t.Any],\\n data: t.Mapping[str, t.Any],\\n ) -> t.Optional[t.Type[Model]]:\\n\\n data = await self.collection.find_one_and_update(\\n filter=where,\\n update={'$set': data},\\n return_document=ReturnDocument.AFTER,\\n )\\n return self.model_class(**data) if data else None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7426288","0.72898763","0.712248","0.7063579","0.6942613","0.6895699","0.67748654","0.67748654","0.67748654","0.67748654","0.6651414","0.6568333","0.6562047","0.6497421","0.6475764","0.6431798","0.6416607","0.63776666","0.6309925","0.62621003","0.62315226","0.6228649","0.6181212","0.6170161","0.6167463","0.6166069","0.6138759","0.6120709","0.6116092","0.60725194","0.60522974","0.604894","0.60488105","0.60243356","0.601213","0.6009004","0.5983987","0.5980239","0.5980239","0.597002","0.59698457","0.5956837","0.5948315","0.593965","0.5926694","0.5925635","0.59226656","0.5922316","0.592137","0.59162927","0.59027624","0.58977103","0.5881881","0.5850796","0.5844372","0.5843668","0.58417755","0.5836657","0.58272165","0.58232504","0.58225405","0.58180916","0.58163685","0.58085674","0.5808068","0.580212","0.5798234","0.5781126","0.57476085","0.57411087","0.5735738","0.57304317","0.5726547","0.5719296","0.57136333","0.57064843","0.5702126","0.5701355","0.56811196","0.5678596","0.56683725","0.56670386","0.5660356","0.5653764","0.56453246","0.5644696","0.5642049","0.5640967","0.5615544","0.56087184","0.5606482","0.5590663","0.5545079","0.5543062","0.554208","0.55314523","0.5529741","0.55292434","0.5527005","0.55153483"],"string":"[\n \"0.7426288\",\n \"0.72898763\",\n \"0.712248\",\n \"0.7063579\",\n \"0.6942613\",\n \"0.6895699\",\n \"0.67748654\",\n \"0.67748654\",\n \"0.67748654\",\n \"0.67748654\",\n \"0.6651414\",\n \"0.6568333\",\n \"0.6562047\",\n \"0.6497421\",\n \"0.6475764\",\n \"0.6431798\",\n \"0.6416607\",\n \"0.63776666\",\n \"0.6309925\",\n \"0.62621003\",\n \"0.62315226\",\n \"0.6228649\",\n \"0.6181212\",\n \"0.6170161\",\n \"0.6167463\",\n \"0.6166069\",\n \"0.6138759\",\n \"0.6120709\",\n \"0.6116092\",\n \"0.60725194\",\n \"0.60522974\",\n \"0.604894\",\n \"0.60488105\",\n \"0.60243356\",\n \"0.601213\",\n \"0.6009004\",\n \"0.5983987\",\n \"0.5980239\",\n \"0.5980239\",\n \"0.597002\",\n \"0.59698457\",\n \"0.5956837\",\n \"0.5948315\",\n \"0.593965\",\n \"0.5926694\",\n \"0.5925635\",\n \"0.59226656\",\n \"0.5922316\",\n \"0.592137\",\n \"0.59162927\",\n \"0.59027624\",\n \"0.58977103\",\n \"0.5881881\",\n \"0.5850796\",\n \"0.5844372\",\n \"0.5843668\",\n \"0.58417755\",\n \"0.5836657\",\n \"0.58272165\",\n \"0.58232504\",\n \"0.58225405\",\n \"0.58180916\",\n \"0.58163685\",\n \"0.58085674\",\n \"0.5808068\",\n \"0.580212\",\n \"0.5798234\",\n \"0.5781126\",\n \"0.57476085\",\n \"0.57411087\",\n \"0.5735738\",\n \"0.57304317\",\n \"0.5726547\",\n \"0.5719296\",\n \"0.57136333\",\n \"0.57064843\",\n \"0.5702126\",\n \"0.5701355\",\n \"0.56811196\",\n \"0.5678596\",\n \"0.56683725\",\n \"0.56670386\",\n \"0.5660356\",\n \"0.5653764\",\n \"0.56453246\",\n \"0.5644696\",\n \"0.5642049\",\n \"0.5640967\",\n \"0.5615544\",\n \"0.56087184\",\n \"0.5606482\",\n \"0.5590663\",\n \"0.5545079\",\n \"0.5543062\",\n \"0.554208\",\n \"0.55314523\",\n \"0.5529741\",\n \"0.55292434\",\n \"0.5527005\",\n \"0.55153483\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.66224176"},"document_rank":{"kind":"string","value":"11"}}},{"rowIdx":20094,"cells":{"query":{"kind":"string","value":"Dump the embedded document."},"document":{"kind":"string","value":"def dump(self):\n return self._data.dump()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def dump(self, f):\n ret = libxml2mod.xmlDocDump(f, self._o)\n return ret","def debugDumpDocument(self, output):\n libxml2mod.xmlDebugDumpDocument(output, self._o)","def dump(self):\n if self.__root is None:\n return\n\n elist = self.__root.getElements()\n if len(elist) > 0:\n for e in elist:\n print(\"Element: %s\" % e.getName())\n print(\"Data: %s\" % e.getData())\n print(\"Attr:\")\n for attr in list(e.getAttr().keys()):\n print(\" {} = {}\".format(attr, e.getAttr(attr)))\n child = e.getElements()\n self.dump2(child)","def dump(self) -> None:\n ...","def dump(self):\n return","def htmlDocContentDumpOutput(self, cur, encoding):\n if cur is None: cur__o = None\n else: cur__o = cur._o\n libxml2mod.htmlDocContentDumpOutput(self._o, cur__o, encoding)","def dump(self):\n return self.dump_internal(0)","def formatDump(self, f, format):\n ret = libxml2mod.xmlDocFormatDump(f, self._o, format)\n return ret","def document(self):\n ...","def htmlDocContentDumpOutput(self, buf, encoding):\n if buf is None: buf__o = None\n else: buf__o = buf._o\n libxml2mod.htmlDocContentDumpOutput(buf__o, self._o, encoding)","def htmlDocDump(self, f):\n ret = libxml2mod.htmlDocDump(f, self._o)\n return ret","def dump(self, indent=0):\n print(indent * \" \",\n \"ID=%r\" % hex(self.ID.value),\n \"size=%r\" % self.get_size())\n for variable in self.variables:\n variable.dump(indent + 1)\n for subchunk in self.subchunks:\n subchunk.dump(indent + 1)","def dump(self):\n return self.ast.dump()","def htmlDocContentDumpFormatOutput(self, buf, encoding, format):\n if buf is None: buf__o = None\n else: buf__o = buf._o\n libxml2mod.htmlDocContentDumpFormatOutput(buf__o, self._o, encoding, format)","def dump_content(self):\n raise NotImplementedError()","def dump(self):\n # dump self.data\n pickle.dump(self.data, open(self.data_dir + DATA_PATH, 'wb+'))\n # dump self.code2desc\n pickle.dump(self.code2desc, open(self.data_dir + CODE2DESC_PATH, 'wb+'))\n # dump self.family2tf\n pickle.dump(self.family2tf, open(self.data_dir + FAMILY2TF_PATH, 'wb+'))\n # dump self.word2tf\n pickle.dump(self.word2tf, open(self.data_dir + WORD2TF_PATH, 'wb+'))\n # dump self.word2df\n pickle.dump(self.word2df, open(self.data_dir + WORD2DF_PATH, 'wb+'))\n return None","def htmlDocContentDumpFormatOutput(self, cur, encoding, format):\n if cur is None: cur__o = None\n else: cur__o = cur._o\n libxml2mod.htmlDocContentDumpFormatOutput(self._o, cur__o, encoding, format)","def savedoc():\r\n document.save('QSDoc_{0}_{1}_{2}_{3}.docx'.format(args.server, year, month, day))","def write(doc: Document, encoding='utf8', xml_declaration=True, pretty_print=False) -> bytes:\n tree = Parser.to_element_tree(doc)\n return etree.tostring(tree, encoding=encoding, xml_declaration=xml_declaration, pretty_print=pretty_print)","def store(self, out, includeDocs=True):\n self._write(out, dict(prettyPrint=False), includeDocs=includeDocs)","def dump(self, indent=0):\n for variable in self.variables:\n variable.dump(indent + 1)\n for subchunk in self.subchunks:\n subchunk.dump(indent + 1)","def generate_document(self):\n if not self.soup:\n self.soup = BeautifulSoup(self.text)\n body = self.soup.find('body')\n with open('document.txt', 'wb') as f1:\n for tag in body.children:\n text = (str(tag)\n if isinstance(tag, NavigableString)\n else tag.get_text())\n if not text.endswith('\\n'):\n text += '\\n'\n f1.write(text.encode())","def _write_docx(self):\n with ZipFile(self.out_file, 'w') as f:\n self._write_content_types(f)\n self._write_app(f)\n self._write_core(f)\n self._write_rels(f)\n self._write_document(f)\n self._write_fonts(f)\n self._write_document_rels(f)\n self._write_settings(f)\n self._write_styles(f)","def dump(self):\n return self.output","def save(self, filename=None):\n if filename:\n self.filename = filename\n content = \"\\n\".join([x.dump() for x in self.paragraphs])\n control_file = open(self.filename, \"wb\")\n control_file.write(content.encode(\"utf-8\"))\n control_file.close()","def htmlNodeDumpOutput(self, doc, cur, encoding):\n if doc is None: doc__o = None\n else: doc__o = doc._o\n if cur is None: cur__o = None\n else: cur__o = cur._o\n libxml2mod.htmlNodeDumpOutput(self._o, doc__o, cur__o, encoding)","def save(self):\n if not self._fully_loaded:\n self._load()\n with open(self._path, \"w+\", encoding=self._encoding) as h:\n h.write(\"<!-- | \")\n h.write(json.dumps(self._pack()))\n h.write(\" | -->\")\n h.write(NEWLINE)\n h.write(\"<!-- [[FRONT]] -->\")\n h.write(NEWLINE)\n h.write(self._front)\n h.write(NEWLINE)\n h.write(\"<!-- [[BACK]] -->\")\n h.write(NEWLINE)\n h.write(self._back)\n h.write(NEWLINE)","def convert(self):\n self._convert()\n self._write_docx()","def dump_context(self, context, filename):\n\n envelope = {\n \"body\": context[\"body\"],\n \"title\": context[\"title\"],\n \"layout_key\": context[\"deconst_layout_key\"]\n }\n\n super().dump_context(envelope, filename)","def write(self):\n self.f.write(yaml.safe_dump(self.data, default_flow_style=False, indent=4))","def build_document(self):\n pass","def dump(self): \n self.contents[:] = []","def dump(self):\n self._dump(self._head, [])","def serialize(self):\n self.printer('\\n ...Serializing database content...\\n')\n self.release()\n\n # self.strip()\n import cPickle\n\n f = open('database.pkl', 'wb')\n cPickle.dump(self, f)","def dump_internal(self, indent):\n XML_INDENT = 4 # Number if chars to indent children.\n text = ' ' * indent + self.__repr__() + '\\n'\n\n for el in self.children:\n text += el.dump_internal(indent + XML_INDENT)\n\n text += ' ' * indent + '</%s>\\n' % self.tag\n\n return text","def debug_dump(prs:Presentation):\n print(\"Presentation has\", len(prs.slides), \"slides\")\n\n # Print summary of all slides, plus text\n n = 0\n for slide in prs.slides:\n n += 1\n print(\"========== slide {} ========== [{}]\".format(n, slide.slide_layout.name))\n for shape in slide.shapes:\n if not shape.has_text_frame:\n continue\n print(shape.name)\n for paragraph in shape.text_frame.paragraphs:\n for run in paragraph.runs:\n print(\" \" + run.text)","def dump_parts(self, io):\n\n # XXX refactor with Tempita\n title = \"Parts created by the docutils writer '%s'\" % self.strategy.name\n io.say(title + os.linesep)\n io.say(len(title) * '-')\n io.say(2 * os.linesep)\n io.say('Part keys: ' + 2 * os.linesep)\n\n parts = self.publish_parts(io)\n io.say(os.linesep.join(sorted(parts.keys())))\n io.say(2 * os.linesep)\n for part in parts:\n io.say(\"Value of part '%s':%s\" % (part, os.linesep))\n io.say(parts[part].encode('utf-8') + os.linesep)\n io.say(80*'-'+os.linesep)\n io.say(os.linesep)","def save(self, pretty=True):\n self.endInstance()\n if pretty:\n _indent(self.root, whitespace=self._whiteSpace)\n tree = ET.ElementTree(self.root)\n tree.write(self.path, encoding=\"utf-8\", method='xml', xml_declaration=True)\n if self.logger:\n self.logger.info(\"Writing %s\", self.path)","def dump_data(self,filename,dump_id):\n import pickle\n from Auxiliary import tdc_Filenames\n data = [ d.get_pure_data_copy() for d in self.plotter.data ]\n dump_dict={}\n dump_dict['fft_data'] = data\n dump_dict['fitting_type'] = self.fft_fit.type \n dump_dict['nk_plot'] = self.fft_fit.nk_plot\n # full file name of the file with manipulator dump\n filename=tdc_Filenames.get_full_vis_filename(dump_id, filename+'.pickle')\n pickle.dump( dump_dict, open(filename,'w') )\n print '\\nContent dumped in \"%s\" \\n' % filename","def includeInDump(self):\n pass","def save(self):\n with self.open(self.filename, 'wt') as fd:\n for node in self.elements:\n fd.write(node.text)","def dumpme(self) :\n fileName = \"./data/oP4_ModelBuilder.dump\"\n with open(fileName,\"wb\") as dumpedFile:\n oPickler = pickle.Pickler(dumpedFile)\n oPickler.dump(self)","def pretty_print_content(self):\n\n return lxml.etree.tostring(self.get_content(),\n pretty_print = True,\n encoding = self.encoding,\n xml_declaration = True)","def dump_data(self,filename,dump_id):\n # get pure data copy\n data = [ d.get_pure_data_copy() for d in self.plotter.data ]\n # full file name of the file with manipulator dump\n filename=tdc_Filenames.get_full_vis_filename(dump_id, filename+'.pickle')\n pickle.dump( data, open(filename,'w') )\n print '\\nContent dumped in \"%s\" \\n' % filename","def htmlNodeDumpFormatOutput(self, doc, cur, encoding, format):\n if doc is None: doc__o = None\n else: doc__o = doc._o\n if cur is None: cur__o = None\n else: cur__o = cur._o\n libxml2mod.htmlNodeDumpFormatOutput(self._o, doc__o, cur__o, encoding, format)","def dumps(self) -> str:\n ...","def dump(self, contents=False):\n print(self)\n for region in self.regions:\n print(region)\n hexdump(region.data, address=region.address)","def dump(self, obj, context=None):\n return self.schema_class(context=context).dump(obj).data","def dump_model(self):","def dump_designs(self):\n param_dict = {'startkey': '\"_design/\"',\n 'endkey': '\"_design0\"',\n 'include_docs': 'true'} \n r = requests.get('/'.join([self.url, self.db, \"_all_docs\"]), params=param_dict)\n # Write out the JSON document itself\n for row in r.json()['rows']:\n design_str = row['id'].replace('/', '%2F')\n json.dump(row, open(design_str + '.json', 'w'), indent=4)\n # Also create separate files for the map/reduce javascript\n for view in row[\"doc\"][\"views\"]:\n view_str = '%2F'.join(['_view', view])\n f = open('%2F'.join([design_str, view_str + \".js\"]), 'w')\n for func in row[\"doc\"][\"views\"][view]:\n f.write(func + \" = \" + row[\"doc\"][\"views\"][view][func] + \"\\n\")\n f.close()","def dump(self, obj):\r\n return self.localpath.dump(obj)","def dump(self, indent=None):\n indent = indent if indent else '.'\n\n print('-------------------------------------------------------------------------------------------------------')\n print('id =', id(self), '\\nnodes =', self)\n if self.nodes:\n def walk(_cfg, count):\n count += 1\n for key, value in _cfg.items():\n if isinstance(value, dict):\n item = '' if value else '{}'\n print(indent * count, key, item)\n walk(value, count)\n else:\n if isinstance(value, str):\n value = f'\"{value}\"'\n print(indent * count, key, f'value={value}')\n walk(self.nodes, 0)\n else:\n print(' (No Data)')\n\n print('-------------------------------------------------------------------------------------------------------')","def dump(self):\n print(\"Total number of documents/queries processed: \"+str(self.num_documents))\n print(\"Total number of unique expressions per document involved: \"+str(self.num_expressions))\n print(\"Total number of expression instances involved: \" + str(self.global_expressions))\n print(\"Total number of keywords involved: \"+str(self.num_keywords))\n print(\"Total non-unique expressions containing error nodes: \" + str(self.expressions_with_e))\n if len(self.missing_tags) == 0:\n print(\"No unrecognized tags found in expressions\")\n else:\n print(\"Unrecognized tags found in expressions:\")\n for key,value in self.missing_tags.items():\n print(\" \",key,\": \",value)\n if len(self.problem_files) == 0:\n print(\"All files/queries parsed successfully\")\n else:\n print(\"Problem files/queries:\")\n for key,value in self.problem_files.items():\n print(\" \",key,\": \",value)","def serialize(self, root):","def dumpData(self,out):\n #--Get sizes\n for record in self.records:\n #--Text\n if record.name == 'NAME':\n #--Truncate text?\n if len(self.text) > 511:\n self.text = self.text[:511]\n record.data = self.text\n record.size = len(self.text)\n #--Speaker\n elif record.name == 'ONAM':\n record.data = self.spId+'\\x00'\n record.size = len(self.spId) + 1\n record.getSize()\n record.dump(out)","def dump(self):\n # omit output if there's no content.\n if not self.has_content():\n return\n\n guard = self.get_guard()\n\n print self.GUARD_FORMAT.format(\"BEGIN \" + guard)\n self.dump_content()\n print self.GUARD_FORMAT.format(\"END \" + guard)","def dump(self, out):\n libxml2mod.xmlACatalogDump(self._o, out)","def test_dump_collection(self):\n\n test_oplog, primary_conn, search_ts = self.get_oplog_thread()\n solr = DocManager()\n test_oplog.doc_manager = solr\n\n #with documents\n primary_conn['test']['test'].insert({'name': 'paulie'})\n search_ts = test_oplog.get_last_oplog_timestamp()\n test_oplog.dump_collection()\n\n test_oplog.doc_manager.commit()\n solr_results = solr._search()\n self.assertEqual(len(solr_results), 1)\n solr_doc = solr_results[0]\n self.assertEqual(long_to_bson_ts(solr_doc['_ts']), search_ts)\n self.assertEqual(solr_doc['name'], 'paulie')\n self.assertEqual(solr_doc['ns'], 'test.test')","def dump(self):\n self.logger.debug(self)","def write(self):\n temp_string = minidom.parseString(ET.tostring(self.root)).toprettyxml(encoding=\"UTF-8\")\n with open(self.xml_file, 'w') as f:\n f.write(temp_string)\n # f = open(self.xml_file, \"w\")\n # f.write(temp_string)\n # f.close()","def dumpDoc(modulename, directory=None):\n docco = getObjectsDefinedIn(modulename, directory)\n print('codegrab.py - ReportLab Documentation Utility')\n print('documenting', modulename + '.py')\n print('-------------------------------------------------------')\n print()\n if docco.functions == []:\n print('No functions found')\n else:\n print('Functions:')\n for f in docco.functions:\n print(f.proto)\n print(' ' + f.doc)\n\n if docco.classes == []:\n print('No classes found')\n else:\n print('Classes:')\n for c in docco.classes:\n print(c.name)\n print(' ' + c.doc)\n for m in c.methods:\n print(m.proto) # it is already indented in the file!\n print(' ' + m.doc)\n print()","def dumps(self):\n pass","def dump(self, output_stream):\n raise NotImplementedError","def schemaDump(self, output):\n libxml2mod.xmlSchemaDump(output, self._o)","def dumps(self):\n buff = io.StringIO()\n self.dump(buff)\n buff.seek(0)\n return buff.read()","def _DumpSchema(root, out, path):\n if isinstance(root, type):\n root = root()\n elif hasattr(root, '__call__') and not hasattr(root, 'export_params'):\n root = root()\n for i in root.export_params:\n name = i.replace('-', '_')\n out.append('.'.join(path + [name]))\n for i in root.export_objects:\n name = i.replace('-', '_')\n out.append('.'.join(path + [name, '']))\n _DumpSchema(getattr(root, name), out, path + [name])\n for i in root.export_object_lists:\n name = i.replace('-', '_')\n out.append('.'.join(path + [name, '']))\n out.append('.'.join(path + [name, '{i}']))\n _DumpSchema(getattr(root, name), out, path + [name, '{i}'])","def document(self):\n return self.parent.document()","def elemDump(self, f, cur):\n if cur is None: cur__o = None\n else: cur__o = cur._o\n libxml2mod.xmlElemDump(f, self._o, cur__o)","def help_dump(self):\n print(DUMP)","def save_object_as_xml_doc(data_object, doc_filepath):\n root = convert_object_to_element(data_object)\n xml_tree = etree.ElementTree(root)\n xml_tree.write(doc_filepath, pretty_print=True)","def repr_fx(self):\n return yaml.dump(self)","def DumpShape(*args):\n return _XCAFDoc.XCAFDoc_ShapeTool_DumpShape(*args)","def dumpData(self,out):\n out.packSub0('NAME',self.id)\n if getattr(self,'isDeleted',False):\n out.packSub('DELE','i',0)\n return\n out.packSub0('MODL',self.model)\n if self.title: out.packSub0('FNAM',self.title)\n out.packSub('BKDT','f4i',\n self.weight, self.value, self.isScroll, self.teaches, self.enchantPoints)\n if self.script: out.packSub0('SCRI',self.script)\n if self.icon: out.packSub0('ITEX',self.icon)\n if self.text: out.packSub0('TEXT',self.text)\n if self.enchant: out.packSub0('TEXT',self.enchant)","def dumpo(self):\n return self.do_all()","def get_string(self):\n return json.dumps(self.document, indent=4)","def save(self, filename: str):\n dump(self, filename)","def dump_objects():\n pass","def dump_topic(dump_args):\n topic_records = db[dump_args.topic].find({}) # '{}' pulls all the records\n records = [record for record in topic_records]\n with open(os.path.join(os.getcwd(), dump_args.topic), 'w') as f:\n dump(loads(dumps(records)), f, indent=4)","def dump(self):\n print PccUtString.trimString(self.dumpBuf(), \"\\n\")\n return self","def dump_element(element):\n return _schema_lookup[element.__class__]().dump(element)","def handle_doc (collection, mode, doc) :\n\n name = doc['_id']\n\n if mode == 'list' :\n\n for key in doc :\n print \" | | | +-- %s\" % (key)\n\n elif mode == 'tree' :\n print \" | | +-- doc %s\" % (name)\n for key in doc :\n print \" | | | +-- %s\" % (key)\n\n elif mode == 'dump' :\n print \" | | +-- doc %s\" % (name)\n for key in doc :\n txt_in = pprint.pformat (doc[key])\n txt_out = \"\"\n lnum = 1\n for line in txt_in.split ('\\n') :\n if lnum != 1 :\n txt_out += ' | | | | '\n txt_out += line\n txt_out += '\\n'\n lnum += 1\n\n print \" | | | +-- %-10s : %s\" % (key, txt_out[:-1]) # remove last \\n","def dump(self):\n output = self._serialize(self._value)\n output = XS_SCHEMA(output) # validate on the way out too\n return output","def debugDumpDocumentHead(self, output):\n libxml2mod.xmlDebugDumpDocumentHead(output, self._o)","def dump(self, dest_folder):\n path.ensure(dest_folder)\n plat_file = os.path.join(dest_folder, \"definition.json\")\n self.dump_json(plat_file)","def save(self, fp):\n fp.write(self.dump())","def dump_blob(self, blob):\n raise NotImplementedError()","def _print_doc(self, name):\r\n print(self._get_doc(name)) # noqa\r","def Write(self):\n f = open(self.project_path, 'wt')\n self.doc.writexml(f,\n encoding='Windows-1252',\n addindent=' ',\n newl='\\n')\n f.close()","def dump(self, file_name=None, sort=None):\n if file_name:\n dump_file = open(file_name, 'w')\n results = [\"Schema: %s\\n\" % self.name]\n objects = list(self.schema.keys())\n if sort:\n objects.sort()\n for schema_object in objects:\n results.append(self.schema[schema_object].dump())\n if file_name:\n dump_file.write('\\n'.join(results))\n dump_file.close()\n else:\n return '\\n'.join(results)","def nodeDumpOutput(self, doc, cur, level, format, encoding):\n if doc is None: doc__o = None\n else: doc__o = doc._o\n if cur is None: cur__o = None\n else: cur__o = cur._o\n libxml2mod.xmlNodeDumpOutput(self._o, doc__o, cur__o, level, format, encoding)","def save(self, filename:str):\n dump(self, filename=filename)","def dump(self, filename):\n\n utils.save(filename, {'model': self}, zipped=True)","def save(self):\n html_file = '{}/{}.html'.format(self.web_dir, self.title)\n f = open(html_file, 'wt')\n f.write(self.doc.render())\n f.close()","def emit_document(self, document):\n raise NotImplementedError()","def document(self):\n return self._modelPart.document()","def print_schemas(self):\n self.indent_depth += 1\n for i in self.definitions:\n def_name = i.split('/')[-1]\n self.write(f'.. _{def_name}:')\n self.write('')\n self.write(f'{def_name} Schema')\n self.write(f'{\"`\" * (len(def_name) + 7)}')\n self.write('')\n self.write('.. code-block:: json', self.indent_depth)\n self.indent_depth += 1\n self.write('')\n self.definition_rst(def_name)\n self.indent_depth -= 1\n self.write('')\n self.write('')\n self.indent_depth -= 1","def save_soup_to_file(self, filename='soup.html', prettify=True):\n with open(filename, 'w', encoding='utf-8') as fd_div:\n if prettify:\n fd_div.write(self.soup.prettify())\n fd_div.write('\\n')\n else:\n # for item in self.soup:\n # #fd_div.write(item)\n fd_div.write(str(self.soup))\n fd_div.write('\\n')","def _get_raw_doc(self):\n self.logger.debug(\"In _get_raw_doc.\")\n\n doc = {\n 'acl': {\n 'read': ['all'],\n 'write': [HostSeqPrep.namespace]\n },\n 'linkage': self._links,\n 'ns': HostSeqPrep.namespace,\n 'node_type': 'host_seq_prep',\n 'meta': {\n 'comment': self._comment,\n 'lib_layout': self._lib_layout,\n 'lib_selection': self._lib_selection,\n 'ncbi_taxon_id': self._ncbi_taxon_id,\n 'prep_id': self._prep_id,\n 'sequencing_center': self._sequencing_center,\n 'sequencing_contact': self._sequencing_contact,\n 'storage_duration': self._storage_duration,\n 'subtype': \"host\",\n 'tags': self._tags\n }\n }\n\n if self._id is not None:\n self.logger.debug(\"%s object has the OSDF id set.\", __name__)\n doc['id'] = self._id\n\n if self._version is not None:\n self.logger.debug(\"%s object has the OSDF version set.\", __name__)\n doc['ver'] = self._version\n\n if self._adapters is not None:\n self.logger.debug(\"%s object has adapters set.\", __name__)\n doc['meta']['adapters'] = self._adapters\n\n if self._experimental_factor is not None:\n self.logger.debug(\"%s object has experimental_factor set.\", __name__)\n doc['meta']['experimental_factor'] = self._experimental_factor\n\n if self._findex is not None:\n self.logger.debug(\"%s object has findex set.\", __name__)\n doc['meta']['findex'] = self._findex\n\n if self._frag_size is not None:\n self.logger.debug(\"%s object has frag_size set.\", __name__)\n doc['meta']['frag_size'] = self._frag_size\n\n if self._lib_const_meth is not None:\n self.logger.debug(\"%s object has lib_const_meth set.\", __name__)\n doc['meta']['lib_const_meth'] = self._lib_const_meth\n\n if self._lib_screen is not None:\n self.logger.debug(\"%s object has lib_screen set.\", __name__)\n doc['meta']['lib_screen'] = self._lib_screen\n\n if self._lib_size is not None:\n self.logger.debug(\"%s object has lib_size set.\", __name__)\n doc['meta']['lib_size'] = self._lib_size\n\n if self._lib_vector is not None:\n self.logger.debug(\"%s object has lib_vector set.\", __name__)\n doc['meta']['lib_vector'] = self._lib_vector\n\n if self._mims is not None:\n self.logger.debug(\"%s object has mims set.\", __name__)\n doc['meta']['mims'] = self._mims\n\n if self._nucl_acid_amp is not None:\n self.logger.debug(\"%s object has nucl_acid_amp set.\", __name__)\n doc['meta']['nucl_acid_amp'] = self._nucl_acid_amp\n\n if self._nucl_acid_ext is not None:\n self.logger.debug(\"%s object has nucl_acid_ext set.\", __name__)\n doc['meta']['nucl_acid_ext'] = self._nucl_acid_ext\n\n if self._rindex is not None:\n self.logger.debug(\"%s object has rindex set.\", __name__)\n doc['meta']['rindex'] = self._rindex\n\n if self._srs_id is not None:\n self.logger.debug(\"%s object has srs_id set.\", __name__)\n doc['meta']['srs_id'] = self._srs_id\n\n return doc","def write(self, file=sys.stdout):\n d = self.to_dict()\n if d:\n yaml.dump([d], file, default_flow_style=False)","def dump(self):\n self.dumpInit.emit()\n\n worker = DumpThread()\n thread = QtCore.QThread(self)\n self.__thread_maps['dump'] = [thread, worker]\n worker.moveToThread(thread)\n\n worker.dumpSig.connect(self.dumpSig)\n worker.dumpDone.connect(self.dumpDone)\n thread.started.connect(worker.dump)\n\n thread.start()"],"string":"[\n \"def dump(self, f):\\n ret = libxml2mod.xmlDocDump(f, self._o)\\n return ret\",\n \"def debugDumpDocument(self, output):\\n libxml2mod.xmlDebugDumpDocument(output, self._o)\",\n \"def dump(self):\\n if self.__root is None:\\n return\\n\\n elist = self.__root.getElements()\\n if len(elist) > 0:\\n for e in elist:\\n print(\\\"Element: %s\\\" % e.getName())\\n print(\\\"Data: %s\\\" % e.getData())\\n print(\\\"Attr:\\\")\\n for attr in list(e.getAttr().keys()):\\n print(\\\" {} = {}\\\".format(attr, e.getAttr(attr)))\\n child = e.getElements()\\n self.dump2(child)\",\n \"def dump(self) -> None:\\n ...\",\n \"def dump(self):\\n return\",\n \"def htmlDocContentDumpOutput(self, cur, encoding):\\n if cur is None: cur__o = None\\n else: cur__o = cur._o\\n libxml2mod.htmlDocContentDumpOutput(self._o, cur__o, encoding)\",\n \"def dump(self):\\n return self.dump_internal(0)\",\n \"def formatDump(self, f, format):\\n ret = libxml2mod.xmlDocFormatDump(f, self._o, format)\\n return ret\",\n \"def document(self):\\n ...\",\n \"def htmlDocContentDumpOutput(self, buf, encoding):\\n if buf is None: buf__o = None\\n else: buf__o = buf._o\\n libxml2mod.htmlDocContentDumpOutput(buf__o, self._o, encoding)\",\n \"def htmlDocDump(self, f):\\n ret = libxml2mod.htmlDocDump(f, self._o)\\n return ret\",\n \"def dump(self, indent=0):\\n print(indent * \\\" \\\",\\n \\\"ID=%r\\\" % hex(self.ID.value),\\n \\\"size=%r\\\" % self.get_size())\\n for variable in self.variables:\\n variable.dump(indent + 1)\\n for subchunk in self.subchunks:\\n subchunk.dump(indent + 1)\",\n \"def dump(self):\\n return self.ast.dump()\",\n \"def htmlDocContentDumpFormatOutput(self, buf, encoding, format):\\n if buf is None: buf__o = None\\n else: buf__o = buf._o\\n libxml2mod.htmlDocContentDumpFormatOutput(buf__o, self._o, encoding, format)\",\n \"def dump_content(self):\\n raise NotImplementedError()\",\n \"def dump(self):\\n # dump self.data\\n pickle.dump(self.data, open(self.data_dir + DATA_PATH, 'wb+'))\\n # dump self.code2desc\\n pickle.dump(self.code2desc, open(self.data_dir + CODE2DESC_PATH, 'wb+'))\\n # dump self.family2tf\\n pickle.dump(self.family2tf, open(self.data_dir + FAMILY2TF_PATH, 'wb+'))\\n # dump self.word2tf\\n pickle.dump(self.word2tf, open(self.data_dir + WORD2TF_PATH, 'wb+'))\\n # dump self.word2df\\n pickle.dump(self.word2df, open(self.data_dir + WORD2DF_PATH, 'wb+'))\\n return None\",\n \"def htmlDocContentDumpFormatOutput(self, cur, encoding, format):\\n if cur is None: cur__o = None\\n else: cur__o = cur._o\\n libxml2mod.htmlDocContentDumpFormatOutput(self._o, cur__o, encoding, format)\",\n \"def savedoc():\\r\\n document.save('QSDoc_{0}_{1}_{2}_{3}.docx'.format(args.server, year, month, day))\",\n \"def write(doc: Document, encoding='utf8', xml_declaration=True, pretty_print=False) -> bytes:\\n tree = Parser.to_element_tree(doc)\\n return etree.tostring(tree, encoding=encoding, xml_declaration=xml_declaration, pretty_print=pretty_print)\",\n \"def store(self, out, includeDocs=True):\\n self._write(out, dict(prettyPrint=False), includeDocs=includeDocs)\",\n \"def dump(self, indent=0):\\n for variable in self.variables:\\n variable.dump(indent + 1)\\n for subchunk in self.subchunks:\\n subchunk.dump(indent + 1)\",\n \"def generate_document(self):\\n if not self.soup:\\n self.soup = BeautifulSoup(self.text)\\n body = self.soup.find('body')\\n with open('document.txt', 'wb') as f1:\\n for tag in body.children:\\n text = (str(tag)\\n if isinstance(tag, NavigableString)\\n else tag.get_text())\\n if not text.endswith('\\\\n'):\\n text += '\\\\n'\\n f1.write(text.encode())\",\n \"def _write_docx(self):\\n with ZipFile(self.out_file, 'w') as f:\\n self._write_content_types(f)\\n self._write_app(f)\\n self._write_core(f)\\n self._write_rels(f)\\n self._write_document(f)\\n self._write_fonts(f)\\n self._write_document_rels(f)\\n self._write_settings(f)\\n self._write_styles(f)\",\n \"def dump(self):\\n return self.output\",\n \"def save(self, filename=None):\\n if filename:\\n self.filename = filename\\n content = \\\"\\\\n\\\".join([x.dump() for x in self.paragraphs])\\n control_file = open(self.filename, \\\"wb\\\")\\n control_file.write(content.encode(\\\"utf-8\\\"))\\n control_file.close()\",\n \"def htmlNodeDumpOutput(self, doc, cur, encoding):\\n if doc is None: doc__o = None\\n else: doc__o = doc._o\\n if cur is None: cur__o = None\\n else: cur__o = cur._o\\n libxml2mod.htmlNodeDumpOutput(self._o, doc__o, cur__o, encoding)\",\n \"def save(self):\\n if not self._fully_loaded:\\n self._load()\\n with open(self._path, \\\"w+\\\", encoding=self._encoding) as h:\\n h.write(\\\"<!-- | \\\")\\n h.write(json.dumps(self._pack()))\\n h.write(\\\" | -->\\\")\\n h.write(NEWLINE)\\n h.write(\\\"<!-- [[FRONT]] -->\\\")\\n h.write(NEWLINE)\\n h.write(self._front)\\n h.write(NEWLINE)\\n h.write(\\\"<!-- [[BACK]] -->\\\")\\n h.write(NEWLINE)\\n h.write(self._back)\\n h.write(NEWLINE)\",\n \"def convert(self):\\n self._convert()\\n self._write_docx()\",\n \"def dump_context(self, context, filename):\\n\\n envelope = {\\n \\\"body\\\": context[\\\"body\\\"],\\n \\\"title\\\": context[\\\"title\\\"],\\n \\\"layout_key\\\": context[\\\"deconst_layout_key\\\"]\\n }\\n\\n super().dump_context(envelope, filename)\",\n \"def write(self):\\n self.f.write(yaml.safe_dump(self.data, default_flow_style=False, indent=4))\",\n \"def build_document(self):\\n pass\",\n \"def dump(self): \\n self.contents[:] = []\",\n \"def dump(self):\\n self._dump(self._head, [])\",\n \"def serialize(self):\\n self.printer('\\\\n ...Serializing database content...\\\\n')\\n self.release()\\n\\n # self.strip()\\n import cPickle\\n\\n f = open('database.pkl', 'wb')\\n cPickle.dump(self, f)\",\n \"def dump_internal(self, indent):\\n XML_INDENT = 4 # Number if chars to indent children.\\n text = ' ' * indent + self.__repr__() + '\\\\n'\\n\\n for el in self.children:\\n text += el.dump_internal(indent + XML_INDENT)\\n\\n text += ' ' * indent + '</%s>\\\\n' % self.tag\\n\\n return text\",\n \"def debug_dump(prs:Presentation):\\n print(\\\"Presentation has\\\", len(prs.slides), \\\"slides\\\")\\n\\n # Print summary of all slides, plus text\\n n = 0\\n for slide in prs.slides:\\n n += 1\\n print(\\\"========== slide {} ========== [{}]\\\".format(n, slide.slide_layout.name))\\n for shape in slide.shapes:\\n if not shape.has_text_frame:\\n continue\\n print(shape.name)\\n for paragraph in shape.text_frame.paragraphs:\\n for run in paragraph.runs:\\n print(\\\" \\\" + run.text)\",\n \"def dump_parts(self, io):\\n\\n # XXX refactor with Tempita\\n title = \\\"Parts created by the docutils writer '%s'\\\" % self.strategy.name\\n io.say(title + os.linesep)\\n io.say(len(title) * '-')\\n io.say(2 * os.linesep)\\n io.say('Part keys: ' + 2 * os.linesep)\\n\\n parts = self.publish_parts(io)\\n io.say(os.linesep.join(sorted(parts.keys())))\\n io.say(2 * os.linesep)\\n for part in parts:\\n io.say(\\\"Value of part '%s':%s\\\" % (part, os.linesep))\\n io.say(parts[part].encode('utf-8') + os.linesep)\\n io.say(80*'-'+os.linesep)\\n io.say(os.linesep)\",\n \"def save(self, pretty=True):\\n self.endInstance()\\n if pretty:\\n _indent(self.root, whitespace=self._whiteSpace)\\n tree = ET.ElementTree(self.root)\\n tree.write(self.path, encoding=\\\"utf-8\\\", method='xml', xml_declaration=True)\\n if self.logger:\\n self.logger.info(\\\"Writing %s\\\", self.path)\",\n \"def dump_data(self,filename,dump_id):\\n import pickle\\n from Auxiliary import tdc_Filenames\\n data = [ d.get_pure_data_copy() for d in self.plotter.data ]\\n dump_dict={}\\n dump_dict['fft_data'] = data\\n dump_dict['fitting_type'] = self.fft_fit.type \\n dump_dict['nk_plot'] = self.fft_fit.nk_plot\\n # full file name of the file with manipulator dump\\n filename=tdc_Filenames.get_full_vis_filename(dump_id, filename+'.pickle')\\n pickle.dump( dump_dict, open(filename,'w') )\\n print '\\\\nContent dumped in \\\"%s\\\" \\\\n' % filename\",\n \"def includeInDump(self):\\n pass\",\n \"def save(self):\\n with self.open(self.filename, 'wt') as fd:\\n for node in self.elements:\\n fd.write(node.text)\",\n \"def dumpme(self) :\\n fileName = \\\"./data/oP4_ModelBuilder.dump\\\"\\n with open(fileName,\\\"wb\\\") as dumpedFile:\\n oPickler = pickle.Pickler(dumpedFile)\\n oPickler.dump(self)\",\n \"def pretty_print_content(self):\\n\\n return lxml.etree.tostring(self.get_content(),\\n pretty_print = True,\\n encoding = self.encoding,\\n xml_declaration = True)\",\n \"def dump_data(self,filename,dump_id):\\n # get pure data copy\\n data = [ d.get_pure_data_copy() for d in self.plotter.data ]\\n # full file name of the file with manipulator dump\\n filename=tdc_Filenames.get_full_vis_filename(dump_id, filename+'.pickle')\\n pickle.dump( data, open(filename,'w') )\\n print '\\\\nContent dumped in \\\"%s\\\" \\\\n' % filename\",\n \"def htmlNodeDumpFormatOutput(self, doc, cur, encoding, format):\\n if doc is None: doc__o = None\\n else: doc__o = doc._o\\n if cur is None: cur__o = None\\n else: cur__o = cur._o\\n libxml2mod.htmlNodeDumpFormatOutput(self._o, doc__o, cur__o, encoding, format)\",\n \"def dumps(self) -> str:\\n ...\",\n \"def dump(self, contents=False):\\n print(self)\\n for region in self.regions:\\n print(region)\\n hexdump(region.data, address=region.address)\",\n \"def dump(self, obj, context=None):\\n return self.schema_class(context=context).dump(obj).data\",\n \"def dump_model(self):\",\n \"def dump_designs(self):\\n param_dict = {'startkey': '\\\"_design/\\\"',\\n 'endkey': '\\\"_design0\\\"',\\n 'include_docs': 'true'} \\n r = requests.get('/'.join([self.url, self.db, \\\"_all_docs\\\"]), params=param_dict)\\n # Write out the JSON document itself\\n for row in r.json()['rows']:\\n design_str = row['id'].replace('/', '%2F')\\n json.dump(row, open(design_str + '.json', 'w'), indent=4)\\n # Also create separate files for the map/reduce javascript\\n for view in row[\\\"doc\\\"][\\\"views\\\"]:\\n view_str = '%2F'.join(['_view', view])\\n f = open('%2F'.join([design_str, view_str + \\\".js\\\"]), 'w')\\n for func in row[\\\"doc\\\"][\\\"views\\\"][view]:\\n f.write(func + \\\" = \\\" + row[\\\"doc\\\"][\\\"views\\\"][view][func] + \\\"\\\\n\\\")\\n f.close()\",\n \"def dump(self, obj):\\r\\n return self.localpath.dump(obj)\",\n \"def dump(self, indent=None):\\n indent = indent if indent else '.'\\n\\n print('-------------------------------------------------------------------------------------------------------')\\n print('id =', id(self), '\\\\nnodes =', self)\\n if self.nodes:\\n def walk(_cfg, count):\\n count += 1\\n for key, value in _cfg.items():\\n if isinstance(value, dict):\\n item = '' if value else '{}'\\n print(indent * count, key, item)\\n walk(value, count)\\n else:\\n if isinstance(value, str):\\n value = f'\\\"{value}\\\"'\\n print(indent * count, key, f'value={value}')\\n walk(self.nodes, 0)\\n else:\\n print(' (No Data)')\\n\\n print('-------------------------------------------------------------------------------------------------------')\",\n \"def dump(self):\\n print(\\\"Total number of documents/queries processed: \\\"+str(self.num_documents))\\n print(\\\"Total number of unique expressions per document involved: \\\"+str(self.num_expressions))\\n print(\\\"Total number of expression instances involved: \\\" + str(self.global_expressions))\\n print(\\\"Total number of keywords involved: \\\"+str(self.num_keywords))\\n print(\\\"Total non-unique expressions containing error nodes: \\\" + str(self.expressions_with_e))\\n if len(self.missing_tags) == 0:\\n print(\\\"No unrecognized tags found in expressions\\\")\\n else:\\n print(\\\"Unrecognized tags found in expressions:\\\")\\n for key,value in self.missing_tags.items():\\n print(\\\" \\\",key,\\\": \\\",value)\\n if len(self.problem_files) == 0:\\n print(\\\"All files/queries parsed successfully\\\")\\n else:\\n print(\\\"Problem files/queries:\\\")\\n for key,value in self.problem_files.items():\\n print(\\\" \\\",key,\\\": \\\",value)\",\n \"def serialize(self, root):\",\n \"def dumpData(self,out):\\n #--Get sizes\\n for record in self.records:\\n #--Text\\n if record.name == 'NAME':\\n #--Truncate text?\\n if len(self.text) > 511:\\n self.text = self.text[:511]\\n record.data = self.text\\n record.size = len(self.text)\\n #--Speaker\\n elif record.name == 'ONAM':\\n record.data = self.spId+'\\\\x00'\\n record.size = len(self.spId) + 1\\n record.getSize()\\n record.dump(out)\",\n \"def dump(self):\\n # omit output if there's no content.\\n if not self.has_content():\\n return\\n\\n guard = self.get_guard()\\n\\n print self.GUARD_FORMAT.format(\\\"BEGIN \\\" + guard)\\n self.dump_content()\\n print self.GUARD_FORMAT.format(\\\"END \\\" + guard)\",\n \"def dump(self, out):\\n libxml2mod.xmlACatalogDump(self._o, out)\",\n \"def test_dump_collection(self):\\n\\n test_oplog, primary_conn, search_ts = self.get_oplog_thread()\\n solr = DocManager()\\n test_oplog.doc_manager = solr\\n\\n #with documents\\n primary_conn['test']['test'].insert({'name': 'paulie'})\\n search_ts = test_oplog.get_last_oplog_timestamp()\\n test_oplog.dump_collection()\\n\\n test_oplog.doc_manager.commit()\\n solr_results = solr._search()\\n self.assertEqual(len(solr_results), 1)\\n solr_doc = solr_results[0]\\n self.assertEqual(long_to_bson_ts(solr_doc['_ts']), search_ts)\\n self.assertEqual(solr_doc['name'], 'paulie')\\n self.assertEqual(solr_doc['ns'], 'test.test')\",\n \"def dump(self):\\n self.logger.debug(self)\",\n \"def write(self):\\n temp_string = minidom.parseString(ET.tostring(self.root)).toprettyxml(encoding=\\\"UTF-8\\\")\\n with open(self.xml_file, 'w') as f:\\n f.write(temp_string)\\n # f = open(self.xml_file, \\\"w\\\")\\n # f.write(temp_string)\\n # f.close()\",\n \"def dumpDoc(modulename, directory=None):\\n docco = getObjectsDefinedIn(modulename, directory)\\n print('codegrab.py - ReportLab Documentation Utility')\\n print('documenting', modulename + '.py')\\n print('-------------------------------------------------------')\\n print()\\n if docco.functions == []:\\n print('No functions found')\\n else:\\n print('Functions:')\\n for f in docco.functions:\\n print(f.proto)\\n print(' ' + f.doc)\\n\\n if docco.classes == []:\\n print('No classes found')\\n else:\\n print('Classes:')\\n for c in docco.classes:\\n print(c.name)\\n print(' ' + c.doc)\\n for m in c.methods:\\n print(m.proto) # it is already indented in the file!\\n print(' ' + m.doc)\\n print()\",\n \"def dumps(self):\\n pass\",\n \"def dump(self, output_stream):\\n raise NotImplementedError\",\n \"def schemaDump(self, output):\\n libxml2mod.xmlSchemaDump(output, self._o)\",\n \"def dumps(self):\\n buff = io.StringIO()\\n self.dump(buff)\\n buff.seek(0)\\n return buff.read()\",\n \"def _DumpSchema(root, out, path):\\n if isinstance(root, type):\\n root = root()\\n elif hasattr(root, '__call__') and not hasattr(root, 'export_params'):\\n root = root()\\n for i in root.export_params:\\n name = i.replace('-', '_')\\n out.append('.'.join(path + [name]))\\n for i in root.export_objects:\\n name = i.replace('-', '_')\\n out.append('.'.join(path + [name, '']))\\n _DumpSchema(getattr(root, name), out, path + [name])\\n for i in root.export_object_lists:\\n name = i.replace('-', '_')\\n out.append('.'.join(path + [name, '']))\\n out.append('.'.join(path + [name, '{i}']))\\n _DumpSchema(getattr(root, name), out, path + [name, '{i}'])\",\n \"def document(self):\\n return self.parent.document()\",\n \"def elemDump(self, f, cur):\\n if cur is None: cur__o = None\\n else: cur__o = cur._o\\n libxml2mod.xmlElemDump(f, self._o, cur__o)\",\n \"def help_dump(self):\\n print(DUMP)\",\n \"def save_object_as_xml_doc(data_object, doc_filepath):\\n root = convert_object_to_element(data_object)\\n xml_tree = etree.ElementTree(root)\\n xml_tree.write(doc_filepath, pretty_print=True)\",\n \"def repr_fx(self):\\n return yaml.dump(self)\",\n \"def DumpShape(*args):\\n return _XCAFDoc.XCAFDoc_ShapeTool_DumpShape(*args)\",\n \"def dumpData(self,out):\\n out.packSub0('NAME',self.id)\\n if getattr(self,'isDeleted',False):\\n out.packSub('DELE','i',0)\\n return\\n out.packSub0('MODL',self.model)\\n if self.title: out.packSub0('FNAM',self.title)\\n out.packSub('BKDT','f4i',\\n self.weight, self.value, self.isScroll, self.teaches, self.enchantPoints)\\n if self.script: out.packSub0('SCRI',self.script)\\n if self.icon: out.packSub0('ITEX',self.icon)\\n if self.text: out.packSub0('TEXT',self.text)\\n if self.enchant: out.packSub0('TEXT',self.enchant)\",\n \"def dumpo(self):\\n return self.do_all()\",\n \"def get_string(self):\\n return json.dumps(self.document, indent=4)\",\n \"def save(self, filename: str):\\n dump(self, filename)\",\n \"def dump_objects():\\n pass\",\n \"def dump_topic(dump_args):\\n topic_records = db[dump_args.topic].find({}) # '{}' pulls all the records\\n records = [record for record in topic_records]\\n with open(os.path.join(os.getcwd(), dump_args.topic), 'w') as f:\\n dump(loads(dumps(records)), f, indent=4)\",\n \"def dump(self):\\n print PccUtString.trimString(self.dumpBuf(), \\\"\\\\n\\\")\\n return self\",\n \"def dump_element(element):\\n return _schema_lookup[element.__class__]().dump(element)\",\n \"def handle_doc (collection, mode, doc) :\\n\\n name = doc['_id']\\n\\n if mode == 'list' :\\n\\n for key in doc :\\n print \\\" | | | +-- %s\\\" % (key)\\n\\n elif mode == 'tree' :\\n print \\\" | | +-- doc %s\\\" % (name)\\n for key in doc :\\n print \\\" | | | +-- %s\\\" % (key)\\n\\n elif mode == 'dump' :\\n print \\\" | | +-- doc %s\\\" % (name)\\n for key in doc :\\n txt_in = pprint.pformat (doc[key])\\n txt_out = \\\"\\\"\\n lnum = 1\\n for line in txt_in.split ('\\\\n') :\\n if lnum != 1 :\\n txt_out += ' | | | | '\\n txt_out += line\\n txt_out += '\\\\n'\\n lnum += 1\\n\\n print \\\" | | | +-- %-10s : %s\\\" % (key, txt_out[:-1]) # remove last \\\\n\",\n \"def dump(self):\\n output = self._serialize(self._value)\\n output = XS_SCHEMA(output) # validate on the way out too\\n return output\",\n \"def debugDumpDocumentHead(self, output):\\n libxml2mod.xmlDebugDumpDocumentHead(output, self._o)\",\n \"def dump(self, dest_folder):\\n path.ensure(dest_folder)\\n plat_file = os.path.join(dest_folder, \\\"definition.json\\\")\\n self.dump_json(plat_file)\",\n \"def save(self, fp):\\n fp.write(self.dump())\",\n \"def dump_blob(self, blob):\\n raise NotImplementedError()\",\n \"def _print_doc(self, name):\\r\\n print(self._get_doc(name)) # noqa\\r\",\n \"def Write(self):\\n f = open(self.project_path, 'wt')\\n self.doc.writexml(f,\\n encoding='Windows-1252',\\n addindent=' ',\\n newl='\\\\n')\\n f.close()\",\n \"def dump(self, file_name=None, sort=None):\\n if file_name:\\n dump_file = open(file_name, 'w')\\n results = [\\\"Schema: %s\\\\n\\\" % self.name]\\n objects = list(self.schema.keys())\\n if sort:\\n objects.sort()\\n for schema_object in objects:\\n results.append(self.schema[schema_object].dump())\\n if file_name:\\n dump_file.write('\\\\n'.join(results))\\n dump_file.close()\\n else:\\n return '\\\\n'.join(results)\",\n \"def nodeDumpOutput(self, doc, cur, level, format, encoding):\\n if doc is None: doc__o = None\\n else: doc__o = doc._o\\n if cur is None: cur__o = None\\n else: cur__o = cur._o\\n libxml2mod.xmlNodeDumpOutput(self._o, doc__o, cur__o, level, format, encoding)\",\n \"def save(self, filename:str):\\n dump(self, filename=filename)\",\n \"def dump(self, filename):\\n\\n utils.save(filename, {'model': self}, zipped=True)\",\n \"def save(self):\\n html_file = '{}/{}.html'.format(self.web_dir, self.title)\\n f = open(html_file, 'wt')\\n f.write(self.doc.render())\\n f.close()\",\n \"def emit_document(self, document):\\n raise NotImplementedError()\",\n \"def document(self):\\n return self._modelPart.document()\",\n \"def print_schemas(self):\\n self.indent_depth += 1\\n for i in self.definitions:\\n def_name = i.split('/')[-1]\\n self.write(f'.. _{def_name}:')\\n self.write('')\\n self.write(f'{def_name} Schema')\\n self.write(f'{\\\"`\\\" * (len(def_name) + 7)}')\\n self.write('')\\n self.write('.. code-block:: json', self.indent_depth)\\n self.indent_depth += 1\\n self.write('')\\n self.definition_rst(def_name)\\n self.indent_depth -= 1\\n self.write('')\\n self.write('')\\n self.indent_depth -= 1\",\n \"def save_soup_to_file(self, filename='soup.html', prettify=True):\\n with open(filename, 'w', encoding='utf-8') as fd_div:\\n if prettify:\\n fd_div.write(self.soup.prettify())\\n fd_div.write('\\\\n')\\n else:\\n # for item in self.soup:\\n # #fd_div.write(item)\\n fd_div.write(str(self.soup))\\n fd_div.write('\\\\n')\",\n \"def _get_raw_doc(self):\\n self.logger.debug(\\\"In _get_raw_doc.\\\")\\n\\n doc = {\\n 'acl': {\\n 'read': ['all'],\\n 'write': [HostSeqPrep.namespace]\\n },\\n 'linkage': self._links,\\n 'ns': HostSeqPrep.namespace,\\n 'node_type': 'host_seq_prep',\\n 'meta': {\\n 'comment': self._comment,\\n 'lib_layout': self._lib_layout,\\n 'lib_selection': self._lib_selection,\\n 'ncbi_taxon_id': self._ncbi_taxon_id,\\n 'prep_id': self._prep_id,\\n 'sequencing_center': self._sequencing_center,\\n 'sequencing_contact': self._sequencing_contact,\\n 'storage_duration': self._storage_duration,\\n 'subtype': \\\"host\\\",\\n 'tags': self._tags\\n }\\n }\\n\\n if self._id is not None:\\n self.logger.debug(\\\"%s object has the OSDF id set.\\\", __name__)\\n doc['id'] = self._id\\n\\n if self._version is not None:\\n self.logger.debug(\\\"%s object has the OSDF version set.\\\", __name__)\\n doc['ver'] = self._version\\n\\n if self._adapters is not None:\\n self.logger.debug(\\\"%s object has adapters set.\\\", __name__)\\n doc['meta']['adapters'] = self._adapters\\n\\n if self._experimental_factor is not None:\\n self.logger.debug(\\\"%s object has experimental_factor set.\\\", __name__)\\n doc['meta']['experimental_factor'] = self._experimental_factor\\n\\n if self._findex is not None:\\n self.logger.debug(\\\"%s object has findex set.\\\", __name__)\\n doc['meta']['findex'] = self._findex\\n\\n if self._frag_size is not None:\\n self.logger.debug(\\\"%s object has frag_size set.\\\", __name__)\\n doc['meta']['frag_size'] = self._frag_size\\n\\n if self._lib_const_meth is not None:\\n self.logger.debug(\\\"%s object has lib_const_meth set.\\\", __name__)\\n doc['meta']['lib_const_meth'] = self._lib_const_meth\\n\\n if self._lib_screen is not None:\\n self.logger.debug(\\\"%s object has lib_screen set.\\\", __name__)\\n doc['meta']['lib_screen'] = self._lib_screen\\n\\n if self._lib_size is not None:\\n self.logger.debug(\\\"%s object has lib_size set.\\\", __name__)\\n doc['meta']['lib_size'] = self._lib_size\\n\\n if self._lib_vector is not None:\\n self.logger.debug(\\\"%s object has lib_vector set.\\\", __name__)\\n doc['meta']['lib_vector'] = self._lib_vector\\n\\n if self._mims is not None:\\n self.logger.debug(\\\"%s object has mims set.\\\", __name__)\\n doc['meta']['mims'] = self._mims\\n\\n if self._nucl_acid_amp is not None:\\n self.logger.debug(\\\"%s object has nucl_acid_amp set.\\\", __name__)\\n doc['meta']['nucl_acid_amp'] = self._nucl_acid_amp\\n\\n if self._nucl_acid_ext is not None:\\n self.logger.debug(\\\"%s object has nucl_acid_ext set.\\\", __name__)\\n doc['meta']['nucl_acid_ext'] = self._nucl_acid_ext\\n\\n if self._rindex is not None:\\n self.logger.debug(\\\"%s object has rindex set.\\\", __name__)\\n doc['meta']['rindex'] = self._rindex\\n\\n if self._srs_id is not None:\\n self.logger.debug(\\\"%s object has srs_id set.\\\", __name__)\\n doc['meta']['srs_id'] = self._srs_id\\n\\n return doc\",\n \"def write(self, file=sys.stdout):\\n d = self.to_dict()\\n if d:\\n yaml.dump([d], file, default_flow_style=False)\",\n \"def dump(self):\\n self.dumpInit.emit()\\n\\n worker = DumpThread()\\n thread = QtCore.QThread(self)\\n self.__thread_maps['dump'] = [thread, worker]\\n worker.moveToThread(thread)\\n\\n worker.dumpSig.connect(self.dumpSig)\\n worker.dumpDone.connect(self.dumpDone)\\n thread.started.connect(worker.dump)\\n\\n thread.start()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64431685","0.6325379","0.62365735","0.6215594","0.5980638","0.5913904","0.5842076","0.5824335","0.58150154","0.577763","0.57628286","0.57517964","0.57165134","0.5693397","0.5673982","0.5672529","0.5656097","0.56052685","0.5553852","0.5550597","0.55452025","0.5531472","0.55278206","0.55220926","0.550407","0.5471236","0.5453836","0.54522","0.54402477","0.543877","0.54299945","0.54223156","0.5401027","0.5397138","0.5395293","0.53882396","0.53826386","0.53678423","0.5351718","0.53353494","0.533065","0.5314858","0.53105956","0.5286999","0.52831614","0.52652955","0.5264277","0.5259131","0.52510154","0.5248513","0.524718","0.52443147","0.5240002","0.52396595","0.52332246","0.5231294","0.5220025","0.52087444","0.5198106","0.5197318","0.51966417","0.518883","0.51888293","0.5179692","0.51789474","0.5176041","0.5173868","0.51736873","0.5167846","0.5167316","0.51488596","0.5138582","0.5138247","0.5130438","0.51267993","0.5118154","0.51156807","0.5114899","0.510592","0.51051444","0.5089771","0.5082516","0.5077711","0.5077047","0.506867","0.5062193","0.50526506","0.5049973","0.504763","0.50436276","0.5026545","0.50249606","0.5023037","0.50142777","0.50108856","0.5003777","0.50005686","0.49895883","0.49842775","0.49797997"],"string":"[\n \"0.64431685\",\n \"0.6325379\",\n \"0.62365735\",\n \"0.6215594\",\n \"0.5980638\",\n \"0.5913904\",\n \"0.5842076\",\n \"0.5824335\",\n \"0.58150154\",\n \"0.577763\",\n \"0.57628286\",\n \"0.57517964\",\n \"0.57165134\",\n \"0.5693397\",\n \"0.5673982\",\n \"0.5672529\",\n \"0.5656097\",\n \"0.56052685\",\n \"0.5553852\",\n \"0.5550597\",\n \"0.55452025\",\n \"0.5531472\",\n \"0.55278206\",\n \"0.55220926\",\n \"0.550407\",\n \"0.5471236\",\n \"0.5453836\",\n \"0.54522\",\n \"0.54402477\",\n \"0.543877\",\n \"0.54299945\",\n \"0.54223156\",\n \"0.5401027\",\n \"0.5397138\",\n \"0.5395293\",\n \"0.53882396\",\n \"0.53826386\",\n \"0.53678423\",\n \"0.5351718\",\n \"0.53353494\",\n \"0.533065\",\n \"0.5314858\",\n \"0.53105956\",\n \"0.5286999\",\n \"0.52831614\",\n \"0.52652955\",\n \"0.5264277\",\n \"0.5259131\",\n \"0.52510154\",\n \"0.5248513\",\n \"0.524718\",\n \"0.52443147\",\n \"0.5240002\",\n \"0.52396595\",\n \"0.52332246\",\n \"0.5231294\",\n \"0.5220025\",\n \"0.52087444\",\n \"0.5198106\",\n \"0.5197318\",\n \"0.51966417\",\n \"0.518883\",\n \"0.51888293\",\n \"0.5179692\",\n \"0.51789474\",\n \"0.5176041\",\n \"0.5173868\",\n \"0.51736873\",\n \"0.5167846\",\n \"0.5167316\",\n \"0.51488596\",\n \"0.5138582\",\n \"0.5138247\",\n \"0.5130438\",\n \"0.51267993\",\n \"0.5118154\",\n \"0.51156807\",\n \"0.5114899\",\n \"0.510592\",\n \"0.51051444\",\n \"0.5089771\",\n \"0.5082516\",\n \"0.5077711\",\n \"0.5077047\",\n \"0.506867\",\n \"0.5062193\",\n \"0.50526506\",\n \"0.5049973\",\n \"0.504763\",\n \"0.50436276\",\n \"0.5026545\",\n \"0.50249606\",\n \"0.5023037\",\n \"0.50142777\",\n \"0.50108856\",\n \"0.5003777\",\n \"0.50005686\",\n \"0.49895883\",\n \"0.49842775\",\n \"0.49797997\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5563799"},"document_rank":{"kind":"string","value":"18"}}},{"rowIdx":20095,"cells":{"query":{"kind":"string","value":"Multidimensional Gaussian fourier filter. The array is multiplied with the fourier transform of a Gaussian kernel."},"document":{"kind":"string","value":"def fourier_gaussian(input, sigma, n=-1, axis=-1, output=None):\n input = numpy.asarray(input)\n output = _get_output_fourier(output, input)\n axis = normalize_axis_index(axis, input.ndim)\n sigmas = _ni_support._normalize_sequence(sigma, input.ndim)\n sigmas = numpy.asarray(sigmas, dtype=numpy.float64)\n if not sigmas.flags.contiguous:\n sigmas = sigmas.copy()\n\n _nd_image.fourier_filter(input, sigmas, n, axis, output, 0)\n return output"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def fmgf(array, sigma):\n x, y = np.arange(len(array)), array.copy()\n yg = ndimage.filters.gaussian_filter(y, sigma)\n y -= yg\n\n # digitizing\n m = 101\n dy = 6.0 * mad(y) / m\n ybin = np.arange(np.min(y) - 5 * dy, np.max(y) + 5 * dy + dy, dy)\n z = np.zeros([len(ybin), len(x)])\n z[np.digitize(y, ybin), x] = 1.0\n\n # filtering\n g = partial(ndimage.filters.gaussian_filter, sigma=(0, sigma))\n c = partial(ndimage.filters.convolve1d, weights=np.ones(m), axis=0)\n zf = c(c(c(g(z))))\n\n # estimates\n ym1, y0, yp1 = [ybin[np.argmax(zf, 0) + i] for i in (-1, 0, 1)]\n zm1, z0, zp1 = [zf[np.argmax(zf, 0) + i, x] for i in (-1, 0, 1)]\n t = (zm1 - z0) / (zm1 - 2 * z0 + zp1)\n\n filtered = yg + ((1 - t) ** 2) * ym1 + (2 * t * (1 - t)) * y0 + (t**2) * yp1\n return filtered","def gaussian2d(filter_size=5, sig=1.0):\n ax = np.arange(-filter_size // 2 + 1., filter_size // 2 + 1.)\n xx, yy = np.meshgrid(ax, ax)\n kernel = np.exp(-0.5 * (np.square(xx) + np.square(yy)) / np.square(sig))\n return kernel / np.sum(kernel)","def blur_fourier(im, kernel_size):\n im = im.astype(np.float64)\n # build the kernel with zero padding\n kernel_base = gaussian_kernel_factory(kernel_size)\n window = np.zeros_like(im).astype(np.float64)\n M, N = im.shape\n dx, dy = kernel_base.shape\n x_middle, y_middle = N//2, M//2\n\n window[(y_middle-dy//2):(y_middle+dy//2+1), (x_middle-dx//2):(x_middle+dx//2+1)] = kernel_base\n\n # multiply in the freq domain\n return IDFT2(DFT2(im) * DFT2(np.fft.ifftshift(window))).real","def fourier(img):\n return fourierCV(img)","def blur_fourier(im, kernel_size):\n kernel = gaus_kernel_calc(kernel_size)\n\n zeros = np.zeros(im.shape)\n x_mid = np.math.floor(im.shape[1] / 2)\n y_mid = np.math.floor(im.shape[0] / 2)\n distance = np.math.floor(kernel_size / 2)\n zeros[x_mid - distance: x_mid + distance + 1, y_mid - distance: y_mid + distance + 1] = kernel\n\n fourier_kernel = DFT2(np.fft.ifftshift(zeros))\n fourier_img = DFT2(im)\n fourier_blured = fourier_kernel * fourier_img\n\n return IDFT2(fourier_blured).real.astype(np.float64)","def gaussian_filter(x):\n return _gaussian_filter(x, 3)","def Gauss_filter(data, sigma=(0,2,2), mode='wrap'): \n import scipy.ndimage.filters as flt\n return flt.gaussian_filter(data, sigma=sigma, mode=mode)","def gaussian_filter(stddev, array):\n\n return astropy.convolution.convolve(\n array, astropy.convolution.Gaussian2DKernel(stddev))","def apply_gaussian(X, sigma):\n return np.array([ndimage.gaussian_filter(x, sigma) for x in X])","def _get_fourier_filter(self):\n size = max(64, int(2 ** np.ceil(np.log2(2 * self.m[-1].item()))))\n\n pi = torch.acos(torch.zeros(1)).item() * 2.0\n n = torch.cat(\n [\n torch.arange(1, size // 2 + 1, 2, device=self.n.device),\n torch.arange(size // 2 - 1, 0, -2, device=self.n.device),\n ]\n )\n f = torch.zeros(size, device=self.n.device)\n f[0] = 0.25\n if self.flat:\n f[1::2] = -1 / (pi * n).pow(2)\n else:\n f[1::2] = -self.s_detect.abs().pow(2) / (\n pi\n * (self.d_source + self._d_detect())\n * torch.sin(\n n\n * self.s_detect.abs()\n / (self.d_source + self._d_detect())\n )\n ).pow(2)\n f = torch.stack(\n [f, torch.zeros(f.shape, device=self.n.device)], dim=-1\n )\n f = fftshift(f, dim=(-2,))\n\n filt = fft1(f)[..., 0]\n\n if self.filter_type == \"hamming\":\n # hamming filter\n fac = torch.tensor(\n np.hamming(size).astype(np.float32), device=f.device\n )\n elif self.filter_type == \"hann\":\n # hann filter\n fac = torch.tensor(\n np.hanning(size).astype(np.float32), device=f.device\n )\n elif self.filter_type == \"cosine\":\n # cosine filter\n fac = torch.sin(\n torch.linspace(0, pi, size + 1, device=f.device)[:-1]\n )\n else:\n # ramp / ram-lak filter\n fac = 1.0\n\n return fac * filt","def _irfft2d(f_x) :","def gaussian_filter(size,sigma=-1):\n\n if sigma == -1:\n sigma = np.sqrt(size)\n\n filter = np.zeros((size,size))\n\n for i,j in it.product(range(size),range(size)):\n x = j-size//2\n y = i-size//2\n filter[i,j] = 1/(2*np.pi*sigma**2) * np.exp(-(x**2+y**2)/(2*sigma**2))\n\n filter = filter/filter[0,0]\n filter = filter/filter.sum()\n\n return filter","def filterf(self):\n from scipy.ndimage.filters import gaussian_filter as gf\n self._obj['u'] = xr.DataArray(gf(self._obj['u'],1),dims=('x','y'))\n self._obj['v'] = xr.DataArray(gf(self._obj['v'],1),dims=('x','y'))\n return self._obj","def gauss_convolution_fft(im_array, n_fwhm, fwhm) :\n \n sigma = fwhm / (2.*math.sqrt(2.*math.log(2.)))\n\t\n im_kernel_array = gauss_kernel(n_fwhm, sigma)\n fftconv_image = signal.fftconvolve(im_array,im_kernel_array,mode = 'same')\n\n return (fftconv_image)","def gs_blur(self,k,img):\n SIG = self.sigma\n sig = [SIG,k*SIG,k*k*SIG,k*k*k*SIG,k*k*k*k*SIG]\n gsArray = [0,1,2,3,4]\n scaleImages = [0,1,2,3,4]\n \n for i in range(5):\n gsArray[i] = scipy.ndimage.filters.gaussian_filter(img,sig[i])\n\n return gsArray","def calc_psf_fwhm_inpix_gaussian(arr):\n\tmodel = fit_gaussian(arr)\n\n\tsigma = max(model.y_stddev, model.x_stddev)\n\tfwhm = 2.355 * sigma\n\n\treturn fwhm","def smooth_spectra(xarr, farr, sigma=3, nkern=20):\n xkern = np.arange(nkern)\n kern = np.exp(-(xkern - 0.5 * nkern) ** 2 / (sigma) ** 2)\n\n return gaussian_filter1d(farr, sigma)","def conv_gaus(array, sigma = 1.0):\r\n arrayout = fft2(array + 0.0J)\r\n arrayout = ifft2(arrayout * gaus_Fourier(array.shape[0], sigma))\r\n arrayout = np.array(arrayout, dtype=array.dtype)\r\n return arrayout","def fourier_transform2d(self):\n\n zerofill = np.zeros(1024 * np.array([1,1])) #so it will always be square\n zerofill[:len(self.windowed), :len(self.windowed)] = self.windowed\n transform = np.fft.fft2(zerofill)\n transform = np.fft.fftshift(transform) # shift center to zero\n transformed = np.absolute(transform)\n tmax = transformed.max()\n zdata = (transformed)/(tmax) # normalize to maximum value\n\n return zdata","def gauss_convolution(im_array, n_fwhm, fwhm) :\n \n sigma = fwhm / (2.*math.sqrt(2.*math.log(2.)))\n\t\n im_kernel_array = gauss_kernel(n_fwhm, sigma)\n conv_image = signal.convolve(im_array,im_kernel_array,mode = 'same')\n\n return (conv_image)","def kernel_gaussiano(image: np.ndarray, sigma: float, kind: str = 'low') -> np.ndarray:\n U, V = fourier_meshgrid(image)\n D = fourier_distance(U, V)\n H = np.exp( (-1.0 * D) / (2.0 * sigma**2) )\n \n if kind == 'high' or kind == 'highpass':\n H = 1.0 - H\n \n return H","def gaussian(dims: Tuple[int, int], cutoff_freq: float) -> np.ndarray:\n # create grid\n m, n = [(dim - 1) / 2 for dim in dims]\n yy, xx = np.ogrid[-m : m + 1, -n : n + 1]\n\n # compute transfer function\n tf = np.exp(-(np.power(xx, 2) + np.power(yy, 2)) / (2 * np.power(cutoff_freq, 2)))\n\n # normalize and return transfer func\n return (tf - np.max(tf)) / (np.max(tf) - np.min(tf))","def create_gaussian_filter(size, sigma):\n h = size[0] #height of the template\n w = size[1] #width of the template \n if h % 2 == 0: h += 1 #add 1 if dimensions are even\n if w % 2 == 0: w += 1\n x = math.floor(h/2)\n y = math.floor(w/2) \n sum = 0\n #create our template\n template = np.zeros((h,w))\n #fill the template in with the numbers from Gaussian distribution\n for i in range(h):\n for j in range(w):\n template[i,j] = math.exp(-((((j-x)**2)+((i-y)**2))/(2*(sigma**2))))\n sum = sum + template[i,j]\n #normalise the numbers\n gaussian_filter = template/sum\n return gaussian_filter","def gaussian_filter(shape=(3,3),sigma=0.5):\n m,n = [(ss-1.)/2. for ss in shape]\n y,x = np.ogrid[-m:m+1,-n:n+1]\n h = np.exp( -(x*x + y*y) / (2.*sigma*sigma))\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\n sumh = h.sum()\n if sumh != 0:\n h /= sumh\n return h","def test_gaussian_filter():\n\n def rgb2gray(rgb):\n r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]\n gray = 0.2989 * r + 0.5870 * g + 0.1140 * b\n\n return gray\n\n img = rgb2gray(np.array(Image.open('data/graf.png')))\n gx, x = gauss_module.gauss(4)\n gx = gx.reshape(1, gx.shape[0])\n gy = gx.reshape(gx.shape[1], gx.shape[0])\n smooth_img = conv2(img, gx * np.array(gy))\n\n test_smooth_img = gauss_module.gaussianfilter(img, 4)\n\n assert np.all(smooth_img.round(5) == test_smooth_img.round(5))","def fftkernel(x, w):\n L = len(x)\n Lmax = L + 3 * w\n n = nextpow2(Lmax)\n X = np.fft.fft(x, n)\n f = np.arange(0, n, 1.0) / n\n f = np.concatenate((-f[:int(n / 2)], f[int(n / 2):0:-1]))\n K = np.exp(-0.5 * (w * 2 * np.pi * f) ** 2)\n y = np.fft.ifft(X * K, n)\n y = y[:L].copy()\n return y","def _FWHMGauss(sigma, pixel=12):\n return sigma*2*np.sqrt(2*np.log(2))*pixel","def flatten(img,sigma=20.) :\n\n for i in range(img.shape[0]) :\n img[i] /= np.median(img[i])\n for i in range(img.shape[1]) :\n img[:,i] /= np.median(img[:,i])\n\n hw=int(3*sigma)\n u=np.linspace(-hw,hw,2*hw+1)\n x=np.tile(u,(2*hw+1,1))\n y=x.T\n k=np.exp(-x**2/2/sigma**2-y**2/2/sigma**2)\n k /= np.sum(k)\n smooth=convolve2d(img,k,weight=None)\n img /= smooth\n\n return img","def apply_gaussian_filter(mat, sigma_x, sigma_y, pad=None, mode=None):\n if mode is None:\n # Default for a sinogram image.\n mode1 = \"edge\"\n mode2 = \"mean\"\n else:\n if isinstance(mode, list) or isinstance(mode, tuple):\n mode1 = mode[0]\n mode2 = mode[1]\n else:\n mode1 = mode2 = mode\n if pad is None:\n pad = min(150, int(0.1 * min(mat.shape)))\n mat_pad = np.pad(mat, ((0, 0), (pad, pad)), mode=mode1)\n mat_pad = np.pad(mat_pad, ((pad, pad), (0, 0)), mode=mode2)\n (nrow, ncol) = mat_pad.shape\n window = make_2d_gaussian_window(nrow, ncol, sigma_x, sigma_y)\n xlist = np.arange(0, ncol)\n ylist = np.arange(0, nrow)\n x, y = np.meshgrid(xlist, ylist)\n mat_sign = np.power(-1.0, x + y)\n mat_filt = np.real(\n fft.ifft2(fft.fft2(mat_pad * mat_sign) * window) * mat_sign)\n return mat_filt[pad:nrow - pad, pad:ncol - pad]","def tomography_filter(arr, axis=2, filter_type='hamming', backend='autograd'):\n func = getattr(scipy.signal.windows, filter_type)\n filter = func(arr.shape[axis])\n if axis != len(arr.shape) - 1:\n arr = swap_axes(arr, [axis, len(arr.shape) - 1])\n if backend == 'pytorch':\n args = {'device': arr.device}\n else:\n args = {}\n arr_r, arr_i = fft(arr, zeros_like(arr, requires_grad=False, **args))\n arr_r = arr_r * filter\n arr_i = arr_i * filter\n arr, _ = ifft(arr_r, arr_i)\n if axis != len(arr.shape) - 1:\n arr = swap_axes(arr, [axis, len(arr.shape) - 1])\n return arr","def build_filter(n, sigma):\n filter_ = np.zeros((n,n))\n\n begin = n//2\n\n for i in range(n):\n for j in range(n):\n val = ((i-begin)**2 + (j-begin)**2)**0.5\n filter_[i][j] = gaussian(val, sigma)\n\n return filter_","def gaussian(amp, fwhm, mean):\n return lambda x: amp * np.exp(-4. * np.log(2) * (x-mean)**2 / fwhm**2)","def dfda(x: np.array) -> np.array:\n return x**2","def fourier_der(im):\n ft_img = DFT2(im)\n ft_img = np.fft.fftshift(ft_img)\n\n n_x = im.shape[1]\n coeff_x = (2 * np.pi * 1j)/n_x\n u_freq = np.array([n if n < int(n_x/2) else (n-n_x) for n in range(n_x)]) * 1j\n u_freq = np.array([np.fft.fftshift(u_freq)]*im.shape[0]).transpose()\n dx_ft = coeff_x * IDFT2(np.fft.ifftshift(u_freq.transpose() * ft_img))\n\n m_y = im.shape[0]\n coeff_y = (2 * np.pi * 1j)/m_y\n v_freq = np.array([m if m < int(m_y/2) else (m-m_y) for m in range(m_y)]) * 1j\n v_freq = np.array([np.fft.fftshift(v_freq)] * im.shape[1]).transpose()\n tr = IDFT2(np.fft.ifftshift(v_freq * ft_img))\n dy_ft = coeff_y * tr\n\n magnitude = np.sqrt(np.abs(dx_ft)**2 + np.abs(dy_ft)**2)\n return magnitude.real.astype(np.float64)","def _filter_gauss(image: np.array,\n axis: str,\n krnsize: int,\n krnsigma: float):\n assert(axis == 'x' or axis == 'y')\n krn = cv.getGaussianKernel(krnsize, krnsigma)\n krn = krn * krn.T\n krny, krnx = np.gradient(krn)\n\n if axis == 'x':\n return _filter_custom(image, krnx)\n elif axis == 'y':\n return _filter_custom(image, krny)","def gauss_convolve(array, sigma):\r\n\t##remove singleton dimesions and make sure values are floats\r\n\tarray = array.squeeze().astype(float)\r\n\t##allocate memory for result\r\n\tresult = np.zeros(array.shape)\r\n\t##if the array is 2-D, handle each trial separately\r\n\ttry:\r\n\t\tfor trial in range(array.shape[1]):\r\n\t\t\tresult[:,trial] = gaussian_filter(array[:, trial], sigma = sigma, order = 0, mode = \"constant\", cval = 0.0)\r\n\t##if it's 1-D:\r\n\texcept IndexError:\r\n\t\tif array.shape[0] == array.size:\r\n\t\t\tresult = gaussian_filter(array, sigma = sigma, order = 0, mode = \"constant\", cval = 0.0)\r\n\t\telse:\r\n\t\t\tprint \"Check your array dimenszions!\"\r\n\treturn result","def gaussianFilter(gain,BT,spSym,nTaps):\n\n a = np.sqrt(np.log(2)/2)/BT\n t = np.linspace(-.5*nTaps,.5*nTaps-1,nTaps)/spSym\n\n ft = np.sqrt(np.pi)/a *np.exp(-(np.pi**2*(t)**2)/a**2)\n ft /= np.sum(ft) * gain # normalize filter\n\n return ft","def fourier_der(im):\n im = im.astype(np.float64)\n # constants\n M, N = im.shape\n u = np.meshgrid(np.arange(N), np.arange(M))[0] - N//2\n v = np.meshgrid(np.arange(N), np.arange(M))[1] - M//2\n u_der, v_der = (2 * np.pi * 1j / N), (2 * np.pi * 1j / M)\n\n # calculate dx, dy\n dx = u_der * IDFT2(np.fft.fftshift(u) * DFT2(im))\n dy = v_der * IDFT2(np.fft.fftshift(v) * DFT2(im))\n\n return np.sqrt(np.abs(dx)**2 + np.abs(dy)**2) # = magnitude","def create_gaussian_array(self):\n\n # Fill array of size l x w with Gaussian Noise.\n terrain_length = int(ceil(self.length/self.resolution))\n terrain_width = int(ceil(self.width/self.resolution))\n gaussian_array = np.random.normal(self.mu, self.sigma, (terrain_length,terrain_width))\n\n # Filter the array to smoothen the variation of the noise\n gaussian_array = gaussian_filter(gaussian_array, self.sigma_filter)\n\n return gaussian_array","def dwt(image_array, quantization_Array):\n # Create the high pass and low pass filters\n # both filters are non-causal\n # symmetric\n # [-2, -1, 0, 1, 2]\n LPF = [-0.125, 0.25, 0.75, 0.25, -0.125]\n LPF_center = 2\n\n # [ -2,-1, 0]\n HPF = [-0.5, 1, -0.5]\n HPF_center = 2\n\n nrow, ncol = image_array.shape\n\n # create an array that will contain the 4 different subbands of the image\n LL = np.zeros((nrow, ncol))\n LH = np.zeros((nrow, ncol))\n HL = np.zeros((nrow, ncol))\n HH = np.zeros((nrow, ncol))\n filtered_image = [LL, LH, HL, HH]\n\n # filtering the rows using a low pass and high pass filters\n LowPass_rows = np.zeros((nrow, ncol))\n HighPass_rows = np.zeros((nrow, ncol))\n for i in range(0, nrow):\n LowPass_rows[i, :] = lfilter(LPF, image_array[i, :], LPF_center)\n HighPass_rows[i, :] = lfilter(HPF, image_array[i, :], HPF_center)\n\n # down sample rows.\n # which means we will have half the number of columns\n for i in range(0, len(filtered_image)):\n filtered_image[i] = filtered_image[i][:, ::2]\n\n # apply filters accross columns\n for i in range(0, ncol):\n LL[:, i] = lfilter(LPF, LowPass_rows[:, i], LPF_center)\n LH[:, i] = lfilter(HPF, LowPass_rows[:, i], HPF_center)\n HL[:, i] = lfilter(LPF, HighPass_rows[:, i], LPF_center)\n HH[:, i] = lfilter(HPF, HighPass_rows[:, i], HPF_center)\n\n # down sample columns and quantize\n for i in range(0, len(filtered_image)):\n filtered_image[i] = filtered_image[i][::2, :]\n filtered_image[i] = np.round(\n filtered_image[i]/quantization_Array[i]).astype(int)\n\n return filtered_image","def _imfilter(x_data, f_data):\n return pipe(f_data, ifftshift, fftn, lambda x: x * fftn(x_data), ifftn).real","def pseudo_flatfield(img_plane, sigma=5):\n filtered_img = gaussian_filter(img_plane, sigma)\n return img_plane / (filtered_img + 1)","def gaussian(amp, fwhm, mean, x):\n return amp * np.exp(-4. * np.log(2) * (x-mean)**2 / fwhm**2)","def all_features(sigma=0.1):\n # to fill in f, array of features w shape (863, 863)\n f = np.zeros((X.shape[0], X.shape[0]))\n # iterate over every example twice to make every possible comparison\n for i in range(m):\n for j in range(m):\n f[i, j] = gaussian_kernel(X[i], X[j], sigma=sigma)\n return f","def fourierCV(img):\n\tgray = grayscale(img)\n\tdft = cv2.dft(np.float32(gray), flags = cv2.DFT_COMPLEX_OUTPUT)\n\tdft_shift = np.fft.fftshift(dft)\n\tmagnitude_spectrum = 20*np.log(cv2.magnitude(dft_shift[:,:,0], dft_shift[:,:,1]))\n return magnitude_spectrum","def makeGaussianKernel(sigma: float) -> np.ndarray:\n\n # Your code here.\n kernel_size = 8*sigma+1\n kernel = np.zeros([kernel_size,kernel_size], dtype=float)\n center = kernel_size//2\n \n \n s = 2*(sigma**2)\n sum_val = 0\n for i in range(0,kernel_size):\n for j in range(0,kernel_size):\n x = i-center\n y = j-center\n kernel[i,j] = np.exp(-(x**2+y**2) / s)\n sum_val += kernel[i,j]\n #/(np.pi * s)\n sum_val = 1/sum_val\n print(\"here is the kernel\", kernel*sum_val)\n return kernel*sum_val","def gaussian_filter(self, input, win, size):\n\n N, C, H, W = input.shape\n f = F.conv2d(input, win, stride=1, groups=C)\n f = F.conv2d(f, win.transpose(2, 3), stride=1, groups=C)\n\n out = torch.zeros(input.shape)\n out[:, :, self.win_size // 2:self.win_size // 2 * -1, self.win_size // 2:self.win_size // 2 * -1] = f.unsqueeze(\n 0).unsqueeze(0)\n return out\n\n # N, C, H, W = input.shape\n # out = F.conv2d(input, win, stride=1, padding=(0, self.win_size//2), groups=C)\n # out = F.conv2d(out, win.transpose(2, 3), stride=1, padding=(self.win_size//2, 0), groups=C)\n # return out","def denoise_frequency_domain(ut: np.ndarray, \n filter_3d: np.ndarray) -> np.ndarray:\n\n num_rows = ut.shape[0]\n ut_denoised = np.empty(ut.shape, dtype=complex)\n for row in range(num_rows):\n ut_slice_cube = np.reshape(ut[row, :], filter_3d.shape)\n ut_slice_cube_denoised = ut_slice_cube*filter_3d\n ut_denoised[row, :] = ut_slice_cube_denoised.flatten()\n\n return ut_denoised","def fourier_transform(hamiltonian, grid, spinless):\n return _fourier_transform_helper(hamiltonian=hamiltonian,\n grid=grid,\n spinless=spinless,\n phase_factor=+1,\n vec_func_1=grid.momentum_vector,\n vec_func_2=grid.position_vector)","def smooth(img, sigma):\n if sigma < 0:\n raise ValueError('smoothing kernel size is negative')\n elif sigma == 0:\n return img.get_data()\n else:\n sigma_vox = sigma / np.sqrt(np.sum(img.get_affine()[0:3, 0:3] ** 2, 0))\n return nd.gaussian_filter(img.get_data(), sigma_vox)","def filtered_fourier(self):\r\n\r\n freqs = tsu.get_freqs(self.sampling_rate, self.data.shape[-1])\r\n\r\n if self.ub is None:\r\n self.ub = freqs[-1]\r\n\r\n power = fftpack.fft(self.data)\r\n idx_0 = np.hstack([np.where(freqs < self.lb)[0],\r\n np.where(freqs > self.ub)[0]])\r\n\r\n #Make sure that you keep the DC component:\r\n keep_dc = np.copy(power[..., 0])\r\n power[..., idx_0] = 0\r\n power[..., -1 * idx_0] = 0 # Take care of the negative frequencies\r\n power[..., 0] = keep_dc # And put the DC back in when you're done:\r\n\r\n data_out = fftpack.ifft(power)\r\n\r\n data_out = np.real(data_out) # In order to make sure that you are not\r\n # left with float-precision residual\r\n # complex parts\r\n\r\n return ts.TimeSeries(data=data_out,\r\n sampling_rate=self.sampling_rate,\r\n time_unit=self.time_unit)","def DFT2(image):\n image = image.astype(np.float64)\n M, N = image.shape\n\n # build the dft2_matrix transform\n omega_y = np.exp(-2 * np.pi * 1j / M)\n u, v = np.meshgrid(np.arange(M), np.arange(M))\n dft2_matrix = np.power(omega_y, u*v)\n\n # calculate the 2D fourier transform\n fourier_image = np.dot(dft2_matrix, DFT(image))\n\n return fourier_image","def gaussian_kernel(values, \n bandwidth):\n # Compute the kernel value for the given values\n temp_1 = np.multiply(np.pi, np.square(bandwidth))\n temp_2 = np.divide(1, np.sqrt(temp_1))\n temp_3 = np.divide(values, np.square(bandwidth))\n kernel_value = np.exp(np.multiply(np.negative(0.5), temp_3))\n # Return the computed kernel value\n return kernel_value","def _gaussian_distribution(self, x: ndarray, mu: float, sigma: float) -> ndarray:\n return 1 / (np.sqrt(2 * np.pi) * sigma) * np.exp(\n -np.power(\n (x - mu) / sigma, 2) / 2)","def features_sigma(img,\n sigma,\n intensity=True,\n edges=True,\n texture=True):\n\n features = []\n\n gx,gy = np.meshgrid(np.arange(img.shape[1]), np.arange(img.shape[0]))\n # print(gx.shape)\n #features.append(gx)\n gx = filters.gaussian(gx, sigma)\n gy = filters.gaussian(gy, sigma)\n\n features.append(np.sqrt(gx**2 + gy**2)) #use polar radius of pixel locations as cartesian coordinates\n\n del gx, gy\n\n logging.info(datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\"))\n logging.info('Location features extracted using sigma= %f' % (sigma))\n\n img_blur = filters.gaussian(img, sigma)\n\n if intensity:\n features.append(img_blur)\n\n logging.info(datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\"))\n logging.info('Intensity features extracted using sigma= %f' % (sigma))\n\n if edges:\n features.append(filters.sobel(img_blur))\n\n logging.info(datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\"))\n logging.info('Edge features extracted using sigma= %f' % (sigma))\n\n if texture:\n H_elems = [\n np.gradient(np.gradient(img_blur)[ax0], axis=ax1)\n for ax0, ax1 in itertools.combinations_with_replacement(range(img.ndim), 2)\n ]\n\n eigvals = feature.hessian_matrix_eigvals(H_elems)\n del H_elems\n\n for eigval_mat in eigvals:\n features.append(eigval_mat)\n del eigval_mat\n\n logging.info(datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\"))\n logging.info('Texture features extracted using sigma= %f' % (sigma))\n\n logging.info(datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\"))\n logging.info('Image features extracted using sigma= %f' % (sigma))\n\n return features","def dfdsig(self, x: np.array) -> np.array:\n return (((x - self.mu)**2)/self.sig**2 - 1/self.sig)*self.f(x)","def dft(X): \n N = len(X)\n x = np.zeros(N, 'complex')\n K = np.arange(0, N, 1)\n for n in range(0, N, 1):\n x[n] = np.dot(X, np.exp(1j * 2 * np.pi * K * n / N))\n return x","def fake_gaussian(img, vertical_horizontal_sigma, iter=3):\n sigma_vertical, sigma_horizontal = vertical_horizontal_sigma\n h_blured = box_filter1d(img, sigma_horizontal, horizontal=True, iter=iter)\n blured = box_filter1d(h_blured, sigma_vertical, horizontal=False, iter=iter)\n return blured","def gauss_kernel(n_fwhm,sigma):\n\n x_length = int(n_fwhm * sigma + 0.5) #Add 0.5 to approximate to nearest integer\n y_length = x_length\n \n \n x, y = mgrid[-x_length:x_length+1, -y_length:y_length+1]\n g = numpy.exp(-(x**2/(2*(float(sigma)**2))+y**2/(2*(float(sigma)**2))))\n return g / g.sum()","def process( fids, ndim=2 ):\n\timg = np.empty_like( fids )\n\tax = -1*(np.array( range(ndim) )+1)\n\t\n\timg = np.fft.fftshift( np.fft.fftn( fids, axes=ax, ).astype( np.complex64), axes=ax )\n\t\n\treturn np.squeeze(img)","def fit_gaussian(array):\n\n shape = array.shape\n xmean, ymean = numpy.array(shape) / 2.\n\n xx, yy = numpy.mgrid[:shape[0], :shape[1]]\n\n g_init = astropy.modeling.models.Gaussian2D(amplitude=1., x_mean=xmean, y_mean=ymean,\n x_stddev=1., y_stddev=1.)\n\n f2 = astropy.modeling.fitting.LevMarLSQFitter()\n\n gg = f2(g_init, xx, yy, array)\n\n return gg","def _get_fir_filter(passband, fs, order=183, weights=[5.75, 1., 5.75], mask=[0, 1, 0]):\n # return remez(order, passband, mask, weights, Hz=fs), 1.\n return remez(order, passband, mask, Hz=fs), 1.","def computebandfftfilter_gauss(signal0,dt,fc,sigma0,lTime): \n signal1=np.concatenate((signal0[0]*np.linspace(1,1,100), signal0, signal0[-1]*np.linspace(1,1,100)))\n fc=fc*2*np.pi\n sigma=sigma0*2*np.pi\n\n NFFT = 2**nextpow2(len(signal1))\n fourier=scipy.fft(signal1,NFFT) \n \n# %dt=.5;\n f = 2*np.pi*(1/dt)*(np.linspace(0,NFFT-1,NFFT))/NFFT #CORRECT! \n fil = np.exp((-(f-fc)**2)/(2*sigma**2))\n signfiltf = np.multiply(fil,fourier)\n\n #new ifft \n points = int(NFFT/2)\n fas = signfiltf[range(points)]\n \n # Using only first half, rebuild full FAS by taking conjugate\n fas_eqsig = np.zeros(len(signfiltf), dtype=complex)\n #! Do not start from fas[0]\n fas_eqsig[1:NFFT // 2] = fas[1:]\n fas_eqsig[NFFT // 2 + 1:] = np.flip(np.conj(fas[1:]), axis=0) \n # Inverse the rebuilt FAS to obtain the time series\n sft = np.fft.ifft(fas_eqsig, n=NFFT) \n \n# %signal=interp1(0:length(sft)-1,sft,time);\n stfinal=sft[100:len(signal1)-100] \n \n return stfinal","def filtering(self):\n from numpy import fft\n import numpy as np\n\n _image_dft = fft.fft2(self.image)\n _image_dft = fft.fftshift(_image_dft)\n # dft = DFT.DFT()\n # plt.figure(1) \n # plt.imshow(self.image)\n # plt.figure(2)\n # plt.imshow(20*np.log10(abs(_image_dft))) \n # print(_image_dft)\n # print(abs(_image_dft))\n # plt.show()\n filter = self.filter(self.image.shape, self.cutoff, self.order) \\\n if self.filter_name.startswith('butterworth') \\\n else self.filter(self.image.shape, self.cutoff)\n \n _image_dft_filtered = _image_dft * filter\n _image_filtered = abs(fft.ifft2(_image_dft_filtered))\n \n return [ self.post_process_image(_image_filtered), \\\n self.post_process_image(20*np.log10(abs(_image_dft)+.00001)), \\\n self.post_process_image(20*np.log10(abs(_image_dft_filtered)+.00001)) ]","def random_signal_fir_filtering(ds, num_coefs=10, flag=None):\n logger.info(\"Applying random FIR filters of size %d on signals\", num_coefs)\n\n num_coefs = tf.constant(num_coefs, tf.int32)\n\n def _apply_random_filter(x):\n if flag and not x[flag]:\n return x\n return dict(x, signal=audio_features.random_gaussian_fir_filter(x[\"signal\"], num_coefs))\n\n return ds.map(_apply_random_filter, num_parallel_calls=TF_AUTOTUNE)","def dct(n_filters, n_input):\n\n basis = np.empty((n_filters, n_input))\n basis[0, :] = 1.0 / np.sqrt(n_input)\n\n samples = np.arange(1, 2*n_input, 2) * np.pi / (2.0 * n_input)\n\n for i in range(1, n_filters):\n basis[i, :] = np.cos(i*samples) * np.sqrt(2.0/n_input)\n\n return basis","def f(self, x: np.array) -> np.array:\n return (1/np.sqrt(2*np.pi*self.sig**2))*np.exp(-1*((x - self.mu)**2/(2*self.sig**2)))","def blurImage1(in_image: np.ndarray, kernel_size: np.ndarray) -> np.ndarray:\r\n size = kernel_size[0]\r\n sigma = 1\r\n x, y = np.mgrid[-size:size + 1, -size:size + 1]\r\n normal = 1 / (2.0 * np.pi * sigma ** 2)\r\n g = np.exp(-((x ** 2 + y ** 2) / (2.0 * sigma ** 2))) * normal\r\n in_image = cv2.filter2D(in_image, -1, g)\r\n return in_image","def dfdm(x: np.array) -> np.array:\n return x","def scipy_smooth(img, sigma=5):\n return ndimage.gaussian_filter(img, sigma=sigma)","def run_gaussian_smoothing(image, kernel_size=5):\n return cv2.GaussianBlur(image, (kernel_size, kernel_size), 0)","def idft(X):\n N = len(X)\n x = np.zeros(N, 'complex')\n \n K = np.arange(0, N, 1)\n for n in range(0, N, 1):\n x[n] = np.dot(X, np.exp(-1j * 2 * np.pi * K * n / N))\n return x / N","def filtermed(f, medfiltsize=9): # medfiltfunc='ndimage'\n # # Median filter to smooth out single-pixel deviations\n # if medfiltfunc == 'ndimage': f_medfilt = median_filter(f, size=medfiltsize)\n # elif medfiltfunc == 'signal': f_medfilt = medfilt(f, kernel_size=medfiltsize)\n f_medfilt = median_filter(f, size=medfiltsize)\n return f_medfilt","def gaussian_weights(self, pad, feather):\n weights=[]\n for dim, xy in zip([0, 1], [self.x, self.y]):\n xy0 = np.mean(xy)\n W = xy[-1]-xy[0]\n dist = np.abs(xy-xy0)\n wt = np.zeros_like(dist)\n i_feather = (dist >= W/2 - pad - feather) & ( dist <= W/2 -pad )\n wt_feather = np.exp(-((xy[i_feather]-xy0)/(feather/2.))**2)\n wt[ i_feather ] = wt_feather\n wt[ dist <= W/2 - pad - feather ] = 1\n wt[ dist >= W/2 - pad] = 0\n weights += [wt]\n self.weight *= weights[0][:,None].dot(weights[1][None,:])","def subsample_fourier(x, k):\n return subsamplefourier(x,k)","def build_gaussian_pyramid(im, max_levels, filter_size):\n\n filter_vec = gaus_1d(filter_size).reshape(1,filter_size)\n pyr = []\n pyr.append(im)\n for i in range(max_levels - 1):\n if(im.shape[0] <= 16 or im.shape[1] <= 16):\n break\n\n im = ndimage.filters.convolve(im, filter_vec.T, mode='mirror')\n im = ndimage.filters.convolve(im, filter_vec, mode='mirror')\n\n im = im[::2, ::2]\n pyr.append(im)\n\n return [pyr,filter_vec]","def blur2D(image2D,sigmaFrac=10):\n #NOTE: sigma may not be number of pixels\n ftimage = np.fft.fftshift(np.fft.fft2(image2D))\n ncols, nrows = image2D.shape\n cy, cx = nrows/2, ncols/2\n sigmax,sigmay=ncols/sigmaFrac,nrows/sigmaFrac\n x = np.linspace(0, nrows, nrows)\n y = np.linspace(0, ncols, ncols)\n X, Y = np.meshgrid(x, y)\n gmask = np.exp(-(((X-cx)/sigmax)**2 + ((Y-cy)/sigmay)**2))\n return np.abs(np.fft.ifft2(ftimage * gmask))","def gkern2(kernlen=21, nsig=3):\n # create nxn zeros\n inp = np.zeros((kernlen, kernlen))\n # set element at the middle to one, a dirac delta\n inp[kernlen//2, kernlen//2] = 1\n # gaussian-smooth the dirac, resulting in a gaussian filter mask\n kernel = scipy.ndimage.filters.gaussian_filter(inp, nsig)\n\n return kernel","def median_filter(self):\n print \"Median-Filtering...\"\n D = self.D\n x = np.median(np.median(D,axis=1),axis=1)\n for i in xrange(len(x)):\n D[i,:,:] -= x[i]\n self.D = D\n print \"done.\"","def fourier_uniform(input, size, n=-1, axis=-1, output=None):\n input = numpy.asarray(input)\n output = _get_output_fourier(output, input)\n axis = normalize_axis_index(axis, input.ndim)\n sizes = _ni_support._normalize_sequence(size, input.ndim)\n sizes = numpy.asarray(sizes, dtype=numpy.float64)\n if not sizes.flags.contiguous:\n sizes = sizes.copy()\n _nd_image.fourier_filter(input, sizes, n, axis, output, 1)\n return output","def gaussian_filter(img,f=5,K=1,var=1):\n i_x, i_y = np.shape(img) # image size\n radi = f//2 # window radius\n\n # create gaussian kernel\n def gaussian_kernel(f,K,var):\n \n # create coordinate information \n if f//2 == 0:\n x = np.linspace(-radi,radi,f+1)\n y = np.linspace(-radi,radi,f+1)\n x = np.delete(x, radi)\n y = np.delete(y, radi)\n else:\n x = np.linspace(-radi,radi,f)\n y = np.linspace(-radi,radi,f)\n\n m_x, m_y = np.meshgrid(x,y) # create coordinate\n r_gauss = m_x**2 + m_y**2 # distance to origin\n gauss = K*(np.exp(-r_gauss/(2*(var**2)))) # create kernel\n return gauss/gauss.sum()\n \n #mirror padding\n def mir_padding(img,f):\n img_p = np.zeros((i_x+2*radi,i_y+2*radi)) #create padding image\n img_p[radi:i_x+radi,radi:i_y+radi] = img #throw original image to padding image\n img_p[0:radi,radi:i_y+radi] = img[radi-1::-1,:] # padding top rows\n img_p[-radi::1,radi:i_y+radi] = img[-1:-radi-1:-1,:] # padding bottom rows\n img_p[radi:i_x+radi,0:radi] = img[:,radi-1::-1] # padding left column\n img_p[radi:i_x+radi,-radi::1] = img[:,-1:-radi-1:-1] # padding right column\n for i in range(f):\n img_p[0:radi,i] = img[radi-1-i,radi-1::-1] # padding upper-left corner\n img_p[0:radi,-i] = img[radi-1-i,-radi::1] # padding upper-righ corner\n img_p[-1:-radi-1:-1,i] = img[-radi+i,radi-1::-1] # padding lower-left corner\n img_p[-1:-radi-1:-1,-i] = img[-radi+i,-radi::1] # padding lower-right corner\n return img_p\n\n img_p = mir_padding(img,f) # create padding image\n g_kernel = gaussian_kernel(f,K,var) # create gaussian kernel\n\n #seperate kernel\n E = g_kernel[0,0]\n c = g_kernel[:,0]\n wT = np.reshape(g_kernel[0,:]/E,(f,1))\n\n gauss_image = np.zeros([i_x,i_y]) # create gauss image\n temp_image = np.zeros([i_x,i_y]) # create temp image for two 1D convolution\n old_c_sum = c.sum() # calculate sum of c before modification\n\n # if elements of kernel are located within area of padding, substitute value with 0\n # calculate new value base on ratio between sum before and after modification\n for j in range(i_y):\n y_bound = i_y - j\n mod_c = c.copy()\n if j < radi:\n mod_c[0:radi-j] = 0 \n new_c_sum = mod_c.sum()\n mod_c = mod_c*old_c_sum/new_c_sum \n if j > i_y - radi - 1:\n mod_c[-1:-radi+y_bound-1:-1] = 0 \n new_c_sum = mod_c.sum()\n mod_c = mod_c*old_c_sum/new_c_sum \n for i in range(i_x):\n temp_image[i,j] = np.sum(img_p[i+radi,j:j+f]*mod_c)\n\n temp_image = mir_padding(temp_image,f) # create padding temp image for next 1D convolution\n old_wT_sum = wT.sum() # calculate sum of wT before modification\n\n # if elements of kernel are located within area of padding, substitute value with 0\n # calculate new value base on ratio between sum before and after modification\n for i in range(i_x):\n x_bound = i_x - i\n mod_wT = wT.copy()\n if i < radi:\n mod_wT[0:radi-i] = 0 \n new_wT_sum = mod_wT.sum()\n mod_wT = mod_wT*old_wT_sum/new_wT_sum \n if i > i_x - radi - 1:\n mod_wT[-1:-radi+x_bound-1:-1] = 0 \n new_wT_sum = mod_wT.sum()\n mod_wT = mod_wT*old_wT_sum/new_wT_sum \n for j in range(i_y):\n gauss_image[i,j] = np.sum(temp_image[i:i+f,j+radi]*mod_wT.T)\n\n return gauss_image","def convolve(img, fourier_kernel):\n return np.fft.ifftshift(np.fft.irfft2(np.fft.rfft2(img) * fourier_kernel))","def __gaussian_blur(self, img, kernel_size=3):\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)","def gaussianFilter3D(size, points):\n \n matrix = numpy.zeros((size, size, size))\n \n for point in points:\n x0 = point[0]\n y0 = point[1]\n z0 = point[2]\n x2SigmaSquared = pow(point[3] * size/4, 2) * 2\n y2SigmaSquared = pow(point[4] * size/4, 2) * 2\n z2SigmaSquared = pow(point[5] * size/4, 2) * 2\n tempMatrix = numpy.zeros((size, size, size))\n for x in range(0, size):\n for y in range(0, size):\n for z in range(0, size):\n tempMatrix[y, x, z] = math.exp(-1 * \\\n (math.pow(x-x0, 2)/x2SigmaSquared +\\\n math.pow(y-y0, 2)/y2SigmaSquared +\\\n math.pow(z-z0, 2)/z2SigmaSquared))\n \n matrix = numpy.add(matrix, tempMatrix)\n \n matrix = matrixfix.flatten(matrix, 0, 1)\n \n return matrix","def smoothfield( d , boxsize, Rth ):\n\n ng = N.shape(d)[0]\n dk = N.fft.rfftn(d)\n kgrid = getkgrid(boxsize,ng)\n dk = dk * N.exp( -(kgrid * Rth) ** 2. / 2. )\n d = N.fft.irfftn(dk)\n\n return d","def convolve_im(im: np.array,\n kernel: np.array,\n verbose=True):\n ### START YOUR CODE HERE ### (You can change anything inside this block)\n \"\"\"\n\tcompared to the 4a solution this just adds padding to the filter if its smaller than the image\n\tthis is done by using the second parameter in fft.fft2 \n\t\n\tfirst it applies fourier transforms on the kernel and the image\n\tthen it sets the image to be the pointwise multiplication of the transforms\n\n the image is inverse fourier transformed and filtered for real values\n the domain image is shifted and taken the absolute value of\n the fourier transform of the image and kernel are also shifted and set to be the absolute value\n\tlastly everything is displayed in the subplots\n \"\"\"\n conv_result = im \n \n if verbose:\n fftKernel=np.fft.fft2(kernel,im.shape)\n fftImage=np.fft.fft2(conv_result)\n\t\t\n\t\t\n\t\t\n conv_result=np.multiply(fftImage,fftKernel)\n fftImageTransformed=conv_result\n\t\t\n \n conv_result=np.fft.ifft2(conv_result)\n \n conv_result=np.real(conv_result)\n\n fftImageTransformed=np.fft.fftshift(fftImageTransformed)\n fftImage=np.fft.fftshift(fftImage)\n fftKernel=np.fft.fftshift(fftKernel)\n\n fftImageTransformed=np.absolute(fftImageTransformed)\n fftImage=np.absolute(fftImage)\n fftKernel=np.absolute(fftKernel)\n\t\t\n\t\t\n # Use plt.subplot to place two or more images beside eachother\n plt.figure(figsize=(20, 4))\n # plt.subplot(num_rows, num_cols, position (1-indexed))\n plt.subplot(1, 5, 1)\n plt.imshow(im, cmap=\"gray\")\n plt.subplot(1, 5, 2)\n plt.imshow(fftImage, cmap=\"gray\")\n plt.subplot(1, 5, 3)\n plt.imshow(fftKernel, cmap=\"gray\")\n plt.subplot(1, 5, 4)\n plt.imshow(fftImageTransformed, cmap=\"gray\")\n plt.subplot(1, 5, 5)\n plt.imshow(conv_result, cmap=\"gray\")\n ### END YOUR CODE HERE ###\n return conv_result","def filter2D(img, kernel = (5,5)):\n\ttmp = img.copy()\n\tk = np.ones((kernel[0], kernel[1]), np.float32) / (kernel[0]*kernel[1])\n\tdst = cv2.filter2D(tmp, -1, k)\n\treturn dst","def gaussian_blur(img: np.ndarray, kernel_size: int = 3):\n imgtype = img.dtype\n h,w,c = img.shape\n\n #Get a valid kernel size\n kernel_size = valid_kernel(h,w,kernel_size)\n \n #Gaussian Filter Blur\n blurred = cv2.GaussianBlur(img,(kernel_size,kernel_size),0)\n\n return blurred.astype(imgtype)","def gaussian(N, dt, width):\n df = 1/(N*dt) # frequency step\n f = np.arange(0, round(0.5*N), 1, dtype=float)*df # frequency array\n w = 2*np.pi*f # angular frequency\n\n G = np.array([0]*N, dtype=float)\n G[0:round(N/2)] = np.exp(-w**2/(4*width**2))/dt\n G_lr = np.flip(G)\n G[round(N/2)+1:] = G_lr[-len(G[round(N/2)+1:]):]\n return G","def expgaussian(mu, wid, timeconstant, x): \n # Gaussian signal broadened by an exponetial signal\n g = gaussian(mu, wid, x)\n \n hly = np.round( len(g) / 2.0 )\n ey = np.r_[np.zeros(hly),g,np.zeros(hly)]\n fy = np.fft.fft(ey)\n a = np.exp(-(np.arange(len(fy))) / timeconstant )\n fa = np.fft.fft(a)\n fy1 = fy * fa\n ybz = np.real(np.fft.ifft(fy1)) / np.sum(a)\n yb = ybz[hly:len(ybz)-hly]\n \n return yb","def gauss_mask(shape, low):\n h, w = shape\n outw=w/2+1 # if reduced\n # we have full heightfreqs:\n irow = np.fft.fftfreq(h).reshape(h,1)\n # cols are halfed\n icol = np.fft.fftfreq(w)[:outw].reshape(1,outw)\n r = np.exp(-(icol*icol+irow*irow)/(low*low))\n return r","def gauss(self, X, xm, amp, w):\n return amp * np.exp(-((X - xm) / w) ** 2)","def gaussian_2d(df:pd.DataFrame, sigmas:tuple, **kwargs) -> pd.DataFrame:\n df = pd.DataFrame(\n gaussian_filter(df, sigmas, **kwargs),\n index=df.index,\n columns=df.columns)\n return df","def gaussian_filter(self, sigma):\n\n mask = self.get_weighted_mask()\n mask_f = ni.gaussian_filter(mask, sigma=sigma)\n\n return SpatialReceptiveField(mask_f, self.altPos, self.aziPos, sign=self.sign,\n temporalWindow=self.temporalWindow, pixelSizeUnit=self.pixelSizeUnit,\n dataType=self.dataType, thr=self.thr, filter_sigma=sigma,\n interpolate_rate=self.interpolate_rate)","def conv_gauss_custom(x, y, fwhm, dwindow=2):\n # fwhm = sigma * 2 * np.sqrt(2 * np.log(2))\n\n # Check if fwhm is a number or a list\n if isinstance(fwhm, (int, float)):\n # If fwhm is a number, make an array with fwhm in each entry\n fwhm = np.ones_like(x) * fwhm\n else:\n # Check fwhm has same dimensions as x\n if len(fwhm) != len(x):\n sys.exit('Array `fwhm` has different length than `x`: len(fwhm)={}, len(x)={}'.format(len(fwhm), len(x)))\n\n # Number of total datapoints\n nx = len(x)\n\n # -----------------------\n\n # For each datapoint define a \"bin\" or \"pixel\"\n # E.g. for the datapoint x_3:\n # - Bin center: value of the datapoint: x_3\n # - Bin left edge: half the distance between the current datapoint and the previous one: x_3 - (x_3 - x_2) * 0.5\n # - Bin right edge: half the distance between the current datapoint and the next one: x_3 + (x_4 - x_3) * 0.5\n\n # Distances between center of each bin\n bin_distance = x[1:] - x[:-1] # length = len(x) - 1\n # Define left/right edge of each bin as half the distance to the bin previous/next to it\n bin_edgesmiddle = x[:-1] + 0.5 * bin_distance # middle points\n bin_edgesfirst = x[0] - 0.5 * bin_distance[0] # first point\n bin_edgeslast = x[-1] + 0.5 * bin_distance[-1] # last point\n edges = np.concatenate(([bin_edgesfirst], bin_edgesmiddle, [bin_edgeslast]), axis=0) # length = len(x) + 1\n\n # Width of each bin\n # If the input array x is equally spaced, `bin_width` will be equal to `bin_distance`\n bin_width = edges[1:] - edges[:-1] # length = len(x)\n\n # -----------------------\n\n # Convert FWHM from wavelength units to bins -> Number of bins per FWHM\n fwhm_bin = fwhm / bin_width\n # Round number of bins per FWHM\n nbins = np.ceil(fwhm_bin) #npixels\n\n ## Convert sigma from wavelength units to bins -> Number of bins per sigma\n #sigma_bin = sigma / bin_width\n ## Round number of bins per sigma\n #nbins = np.ceil(sigma_bin) #npixels\n\n # -----------------------\n\n yconv = np.zeros_like(x)\n for i, x_i in enumerate(x):\n\n # Slow method -> THIS IS WHAT MAKES THE OTHER FUNCTION SLOW!\n # # Select kernel window\n # dwindow = 2 * fwhm #2 * fwhm\n # x1 = (np.argmin(np.abs(x - (x_i - dwindow))))\n # x2 = (np.argmin(np.abs(x - (x_i + dwindow))))\n # irang = slice(x1, x2+1)\n\n # Number of pixels at each side of x_i:\n dx = dwindow * nbins[i] * 0.5\n i1 = int(max(0, i - dx))\n i2 = int(min(nx, i + dx + 1))\n irang = slice(i1, i2 + 1)\n\n # Gaussian kernel\n kernel = 1./(np.sqrt(2*np.pi)*fwhm[i]) * np.exp(- ((x[irang] - x_i)**2) / (2 * fwhm[i]**2))\n kernel = kernel / np.sum(kernel)\n\n # Convolve\n yconv[i] = np.sum(y[irang] * kernel)\n\n return yconv","def fourier_spectra(x, axis=1, flip=True, duplicate=True, **kwargs):\n\n x = forward_fourier(x, axis=axis, duplicate=duplicate)\n ns = x.shape[axis]\n nw = np.int32(np.floor(ns/2) + 1)\n slc = [slice(None)] * len(x.shape)\n slc[axis] = slice(0, nw)\n if flip:\n x = np.flip(x[tuple(slc)], axis=axis)\n return x","def spectrum_fourier(self):\r\n\r\n data = self.input.data\r\n sampling_rate = self.input.sampling_rate\r\n\r\n fft = fftpack.fft\r\n if np.any(np.iscomplex(data)):\r\n # Get negative frequencies, as well as positive:\r\n f = np.linspace(-sampling_rate/2., sampling_rate/2., data.shape[-1])\r\n spectrum_fourier = np.fft.fftshift(fft(data))\r\n else:\r\n f = tsu.get_freqs(sampling_rate, data.shape[-1])\r\n spectrum_fourier = fft(data)[..., :f.shape[0]]\r\n \r\n return f, spectrum_fourier","def Gaussian(x, mu=0, sigma=26.4, A=1, y0=0):\r\n #width = sigma*(2*np.sqrt(2*np.log(2)))\r\n b = 1/(sigma*np.sqrt(2*np.pi))\r\n f = b*np.power(np.e, -(((x-mu)**2)/(2*sigma**2)))\r\n return A*f + y0","def uni_gaussian(X, mu, sigma2):\n p = (1 / sqrt(2 * pi * sigma2))\n p = p * exp(-power(X - mu, 2) / (2 * sigma2))\n\n def prod(x, y):\n return x * y\n p = array([[reduce(prod, el)] for el in p])\n\n return p","def gaussianBlur(img,ksize=(5,5),sigma=10):\n #kernel = cv2.getGaussianKernel(ksize,sigma)\n dst = np.zeros_like(img)\n cv2.GaussianBlur(src=img,dst=dst,ksize=ksize,sigmaX=0)\n return dst"],"string":"[\n \"def fmgf(array, sigma):\\n x, y = np.arange(len(array)), array.copy()\\n yg = ndimage.filters.gaussian_filter(y, sigma)\\n y -= yg\\n\\n # digitizing\\n m = 101\\n dy = 6.0 * mad(y) / m\\n ybin = np.arange(np.min(y) - 5 * dy, np.max(y) + 5 * dy + dy, dy)\\n z = np.zeros([len(ybin), len(x)])\\n z[np.digitize(y, ybin), x] = 1.0\\n\\n # filtering\\n g = partial(ndimage.filters.gaussian_filter, sigma=(0, sigma))\\n c = partial(ndimage.filters.convolve1d, weights=np.ones(m), axis=0)\\n zf = c(c(c(g(z))))\\n\\n # estimates\\n ym1, y0, yp1 = [ybin[np.argmax(zf, 0) + i] for i in (-1, 0, 1)]\\n zm1, z0, zp1 = [zf[np.argmax(zf, 0) + i, x] for i in (-1, 0, 1)]\\n t = (zm1 - z0) / (zm1 - 2 * z0 + zp1)\\n\\n filtered = yg + ((1 - t) ** 2) * ym1 + (2 * t * (1 - t)) * y0 + (t**2) * yp1\\n return filtered\",\n \"def gaussian2d(filter_size=5, sig=1.0):\\n ax = np.arange(-filter_size // 2 + 1., filter_size // 2 + 1.)\\n xx, yy = np.meshgrid(ax, ax)\\n kernel = np.exp(-0.5 * (np.square(xx) + np.square(yy)) / np.square(sig))\\n return kernel / np.sum(kernel)\",\n \"def blur_fourier(im, kernel_size):\\n im = im.astype(np.float64)\\n # build the kernel with zero padding\\n kernel_base = gaussian_kernel_factory(kernel_size)\\n window = np.zeros_like(im).astype(np.float64)\\n M, N = im.shape\\n dx, dy = kernel_base.shape\\n x_middle, y_middle = N//2, M//2\\n\\n window[(y_middle-dy//2):(y_middle+dy//2+1), (x_middle-dx//2):(x_middle+dx//2+1)] = kernel_base\\n\\n # multiply in the freq domain\\n return IDFT2(DFT2(im) * DFT2(np.fft.ifftshift(window))).real\",\n \"def fourier(img):\\n return fourierCV(img)\",\n \"def blur_fourier(im, kernel_size):\\n kernel = gaus_kernel_calc(kernel_size)\\n\\n zeros = np.zeros(im.shape)\\n x_mid = np.math.floor(im.shape[1] / 2)\\n y_mid = np.math.floor(im.shape[0] / 2)\\n distance = np.math.floor(kernel_size / 2)\\n zeros[x_mid - distance: x_mid + distance + 1, y_mid - distance: y_mid + distance + 1] = kernel\\n\\n fourier_kernel = DFT2(np.fft.ifftshift(zeros))\\n fourier_img = DFT2(im)\\n fourier_blured = fourier_kernel * fourier_img\\n\\n return IDFT2(fourier_blured).real.astype(np.float64)\",\n \"def gaussian_filter(x):\\n return _gaussian_filter(x, 3)\",\n \"def Gauss_filter(data, sigma=(0,2,2), mode='wrap'): \\n import scipy.ndimage.filters as flt\\n return flt.gaussian_filter(data, sigma=sigma, mode=mode)\",\n \"def gaussian_filter(stddev, array):\\n\\n return astropy.convolution.convolve(\\n array, astropy.convolution.Gaussian2DKernel(stddev))\",\n \"def apply_gaussian(X, sigma):\\n return np.array([ndimage.gaussian_filter(x, sigma) for x in X])\",\n \"def _get_fourier_filter(self):\\n size = max(64, int(2 ** np.ceil(np.log2(2 * self.m[-1].item()))))\\n\\n pi = torch.acos(torch.zeros(1)).item() * 2.0\\n n = torch.cat(\\n [\\n torch.arange(1, size // 2 + 1, 2, device=self.n.device),\\n torch.arange(size // 2 - 1, 0, -2, device=self.n.device),\\n ]\\n )\\n f = torch.zeros(size, device=self.n.device)\\n f[0] = 0.25\\n if self.flat:\\n f[1::2] = -1 / (pi * n).pow(2)\\n else:\\n f[1::2] = -self.s_detect.abs().pow(2) / (\\n pi\\n * (self.d_source + self._d_detect())\\n * torch.sin(\\n n\\n * self.s_detect.abs()\\n / (self.d_source + self._d_detect())\\n )\\n ).pow(2)\\n f = torch.stack(\\n [f, torch.zeros(f.shape, device=self.n.device)], dim=-1\\n )\\n f = fftshift(f, dim=(-2,))\\n\\n filt = fft1(f)[..., 0]\\n\\n if self.filter_type == \\\"hamming\\\":\\n # hamming filter\\n fac = torch.tensor(\\n np.hamming(size).astype(np.float32), device=f.device\\n )\\n elif self.filter_type == \\\"hann\\\":\\n # hann filter\\n fac = torch.tensor(\\n np.hanning(size).astype(np.float32), device=f.device\\n )\\n elif self.filter_type == \\\"cosine\\\":\\n # cosine filter\\n fac = torch.sin(\\n torch.linspace(0, pi, size + 1, device=f.device)[:-1]\\n )\\n else:\\n # ramp / ram-lak filter\\n fac = 1.0\\n\\n return fac * filt\",\n \"def _irfft2d(f_x) :\",\n \"def gaussian_filter(size,sigma=-1):\\n\\n if sigma == -1:\\n sigma = np.sqrt(size)\\n\\n filter = np.zeros((size,size))\\n\\n for i,j in it.product(range(size),range(size)):\\n x = j-size//2\\n y = i-size//2\\n filter[i,j] = 1/(2*np.pi*sigma**2) * np.exp(-(x**2+y**2)/(2*sigma**2))\\n\\n filter = filter/filter[0,0]\\n filter = filter/filter.sum()\\n\\n return filter\",\n \"def filterf(self):\\n from scipy.ndimage.filters import gaussian_filter as gf\\n self._obj['u'] = xr.DataArray(gf(self._obj['u'],1),dims=('x','y'))\\n self._obj['v'] = xr.DataArray(gf(self._obj['v'],1),dims=('x','y'))\\n return self._obj\",\n \"def gauss_convolution_fft(im_array, n_fwhm, fwhm) :\\n \\n sigma = fwhm / (2.*math.sqrt(2.*math.log(2.)))\\n\\t\\n im_kernel_array = gauss_kernel(n_fwhm, sigma)\\n fftconv_image = signal.fftconvolve(im_array,im_kernel_array,mode = 'same')\\n\\n return (fftconv_image)\",\n \"def gs_blur(self,k,img):\\n SIG = self.sigma\\n sig = [SIG,k*SIG,k*k*SIG,k*k*k*SIG,k*k*k*k*SIG]\\n gsArray = [0,1,2,3,4]\\n scaleImages = [0,1,2,3,4]\\n \\n for i in range(5):\\n gsArray[i] = scipy.ndimage.filters.gaussian_filter(img,sig[i])\\n\\n return gsArray\",\n \"def calc_psf_fwhm_inpix_gaussian(arr):\\n\\tmodel = fit_gaussian(arr)\\n\\n\\tsigma = max(model.y_stddev, model.x_stddev)\\n\\tfwhm = 2.355 * sigma\\n\\n\\treturn fwhm\",\n \"def smooth_spectra(xarr, farr, sigma=3, nkern=20):\\n xkern = np.arange(nkern)\\n kern = np.exp(-(xkern - 0.5 * nkern) ** 2 / (sigma) ** 2)\\n\\n return gaussian_filter1d(farr, sigma)\",\n \"def conv_gaus(array, sigma = 1.0):\\r\\n arrayout = fft2(array + 0.0J)\\r\\n arrayout = ifft2(arrayout * gaus_Fourier(array.shape[0], sigma))\\r\\n arrayout = np.array(arrayout, dtype=array.dtype)\\r\\n return arrayout\",\n \"def fourier_transform2d(self):\\n\\n zerofill = np.zeros(1024 * np.array([1,1])) #so it will always be square\\n zerofill[:len(self.windowed), :len(self.windowed)] = self.windowed\\n transform = np.fft.fft2(zerofill)\\n transform = np.fft.fftshift(transform) # shift center to zero\\n transformed = np.absolute(transform)\\n tmax = transformed.max()\\n zdata = (transformed)/(tmax) # normalize to maximum value\\n\\n return zdata\",\n \"def gauss_convolution(im_array, n_fwhm, fwhm) :\\n \\n sigma = fwhm / (2.*math.sqrt(2.*math.log(2.)))\\n\\t\\n im_kernel_array = gauss_kernel(n_fwhm, sigma)\\n conv_image = signal.convolve(im_array,im_kernel_array,mode = 'same')\\n\\n return (conv_image)\",\n \"def kernel_gaussiano(image: np.ndarray, sigma: float, kind: str = 'low') -> np.ndarray:\\n U, V = fourier_meshgrid(image)\\n D = fourier_distance(U, V)\\n H = np.exp( (-1.0 * D) / (2.0 * sigma**2) )\\n \\n if kind == 'high' or kind == 'highpass':\\n H = 1.0 - H\\n \\n return H\",\n \"def gaussian(dims: Tuple[int, int], cutoff_freq: float) -> np.ndarray:\\n # create grid\\n m, n = [(dim - 1) / 2 for dim in dims]\\n yy, xx = np.ogrid[-m : m + 1, -n : n + 1]\\n\\n # compute transfer function\\n tf = np.exp(-(np.power(xx, 2) + np.power(yy, 2)) / (2 * np.power(cutoff_freq, 2)))\\n\\n # normalize and return transfer func\\n return (tf - np.max(tf)) / (np.max(tf) - np.min(tf))\",\n \"def create_gaussian_filter(size, sigma):\\n h = size[0] #height of the template\\n w = size[1] #width of the template \\n if h % 2 == 0: h += 1 #add 1 if dimensions are even\\n if w % 2 == 0: w += 1\\n x = math.floor(h/2)\\n y = math.floor(w/2) \\n sum = 0\\n #create our template\\n template = np.zeros((h,w))\\n #fill the template in with the numbers from Gaussian distribution\\n for i in range(h):\\n for j in range(w):\\n template[i,j] = math.exp(-((((j-x)**2)+((i-y)**2))/(2*(sigma**2))))\\n sum = sum + template[i,j]\\n #normalise the numbers\\n gaussian_filter = template/sum\\n return gaussian_filter\",\n \"def gaussian_filter(shape=(3,3),sigma=0.5):\\n m,n = [(ss-1.)/2. for ss in shape]\\n y,x = np.ogrid[-m:m+1,-n:n+1]\\n h = np.exp( -(x*x + y*y) / (2.*sigma*sigma))\\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\\n sumh = h.sum()\\n if sumh != 0:\\n h /= sumh\\n return h\",\n \"def test_gaussian_filter():\\n\\n def rgb2gray(rgb):\\n r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]\\n gray = 0.2989 * r + 0.5870 * g + 0.1140 * b\\n\\n return gray\\n\\n img = rgb2gray(np.array(Image.open('data/graf.png')))\\n gx, x = gauss_module.gauss(4)\\n gx = gx.reshape(1, gx.shape[0])\\n gy = gx.reshape(gx.shape[1], gx.shape[0])\\n smooth_img = conv2(img, gx * np.array(gy))\\n\\n test_smooth_img = gauss_module.gaussianfilter(img, 4)\\n\\n assert np.all(smooth_img.round(5) == test_smooth_img.round(5))\",\n \"def fftkernel(x, w):\\n L = len(x)\\n Lmax = L + 3 * w\\n n = nextpow2(Lmax)\\n X = np.fft.fft(x, n)\\n f = np.arange(0, n, 1.0) / n\\n f = np.concatenate((-f[:int(n / 2)], f[int(n / 2):0:-1]))\\n K = np.exp(-0.5 * (w * 2 * np.pi * f) ** 2)\\n y = np.fft.ifft(X * K, n)\\n y = y[:L].copy()\\n return y\",\n \"def _FWHMGauss(sigma, pixel=12):\\n return sigma*2*np.sqrt(2*np.log(2))*pixel\",\n \"def flatten(img,sigma=20.) :\\n\\n for i in range(img.shape[0]) :\\n img[i] /= np.median(img[i])\\n for i in range(img.shape[1]) :\\n img[:,i] /= np.median(img[:,i])\\n\\n hw=int(3*sigma)\\n u=np.linspace(-hw,hw,2*hw+1)\\n x=np.tile(u,(2*hw+1,1))\\n y=x.T\\n k=np.exp(-x**2/2/sigma**2-y**2/2/sigma**2)\\n k /= np.sum(k)\\n smooth=convolve2d(img,k,weight=None)\\n img /= smooth\\n\\n return img\",\n \"def apply_gaussian_filter(mat, sigma_x, sigma_y, pad=None, mode=None):\\n if mode is None:\\n # Default for a sinogram image.\\n mode1 = \\\"edge\\\"\\n mode2 = \\\"mean\\\"\\n else:\\n if isinstance(mode, list) or isinstance(mode, tuple):\\n mode1 = mode[0]\\n mode2 = mode[1]\\n else:\\n mode1 = mode2 = mode\\n if pad is None:\\n pad = min(150, int(0.1 * min(mat.shape)))\\n mat_pad = np.pad(mat, ((0, 0), (pad, pad)), mode=mode1)\\n mat_pad = np.pad(mat_pad, ((pad, pad), (0, 0)), mode=mode2)\\n (nrow, ncol) = mat_pad.shape\\n window = make_2d_gaussian_window(nrow, ncol, sigma_x, sigma_y)\\n xlist = np.arange(0, ncol)\\n ylist = np.arange(0, nrow)\\n x, y = np.meshgrid(xlist, ylist)\\n mat_sign = np.power(-1.0, x + y)\\n mat_filt = np.real(\\n fft.ifft2(fft.fft2(mat_pad * mat_sign) * window) * mat_sign)\\n return mat_filt[pad:nrow - pad, pad:ncol - pad]\",\n \"def tomography_filter(arr, axis=2, filter_type='hamming', backend='autograd'):\\n func = getattr(scipy.signal.windows, filter_type)\\n filter = func(arr.shape[axis])\\n if axis != len(arr.shape) - 1:\\n arr = swap_axes(arr, [axis, len(arr.shape) - 1])\\n if backend == 'pytorch':\\n args = {'device': arr.device}\\n else:\\n args = {}\\n arr_r, arr_i = fft(arr, zeros_like(arr, requires_grad=False, **args))\\n arr_r = arr_r * filter\\n arr_i = arr_i * filter\\n arr, _ = ifft(arr_r, arr_i)\\n if axis != len(arr.shape) - 1:\\n arr = swap_axes(arr, [axis, len(arr.shape) - 1])\\n return arr\",\n \"def build_filter(n, sigma):\\n filter_ = np.zeros((n,n))\\n\\n begin = n//2\\n\\n for i in range(n):\\n for j in range(n):\\n val = ((i-begin)**2 + (j-begin)**2)**0.5\\n filter_[i][j] = gaussian(val, sigma)\\n\\n return filter_\",\n \"def gaussian(amp, fwhm, mean):\\n return lambda x: amp * np.exp(-4. * np.log(2) * (x-mean)**2 / fwhm**2)\",\n \"def dfda(x: np.array) -> np.array:\\n return x**2\",\n \"def fourier_der(im):\\n ft_img = DFT2(im)\\n ft_img = np.fft.fftshift(ft_img)\\n\\n n_x = im.shape[1]\\n coeff_x = (2 * np.pi * 1j)/n_x\\n u_freq = np.array([n if n < int(n_x/2) else (n-n_x) for n in range(n_x)]) * 1j\\n u_freq = np.array([np.fft.fftshift(u_freq)]*im.shape[0]).transpose()\\n dx_ft = coeff_x * IDFT2(np.fft.ifftshift(u_freq.transpose() * ft_img))\\n\\n m_y = im.shape[0]\\n coeff_y = (2 * np.pi * 1j)/m_y\\n v_freq = np.array([m if m < int(m_y/2) else (m-m_y) for m in range(m_y)]) * 1j\\n v_freq = np.array([np.fft.fftshift(v_freq)] * im.shape[1]).transpose()\\n tr = IDFT2(np.fft.ifftshift(v_freq * ft_img))\\n dy_ft = coeff_y * tr\\n\\n magnitude = np.sqrt(np.abs(dx_ft)**2 + np.abs(dy_ft)**2)\\n return magnitude.real.astype(np.float64)\",\n \"def _filter_gauss(image: np.array,\\n axis: str,\\n krnsize: int,\\n krnsigma: float):\\n assert(axis == 'x' or axis == 'y')\\n krn = cv.getGaussianKernel(krnsize, krnsigma)\\n krn = krn * krn.T\\n krny, krnx = np.gradient(krn)\\n\\n if axis == 'x':\\n return _filter_custom(image, krnx)\\n elif axis == 'y':\\n return _filter_custom(image, krny)\",\n \"def gauss_convolve(array, sigma):\\r\\n\\t##remove singleton dimesions and make sure values are floats\\r\\n\\tarray = array.squeeze().astype(float)\\r\\n\\t##allocate memory for result\\r\\n\\tresult = np.zeros(array.shape)\\r\\n\\t##if the array is 2-D, handle each trial separately\\r\\n\\ttry:\\r\\n\\t\\tfor trial in range(array.shape[1]):\\r\\n\\t\\t\\tresult[:,trial] = gaussian_filter(array[:, trial], sigma = sigma, order = 0, mode = \\\"constant\\\", cval = 0.0)\\r\\n\\t##if it's 1-D:\\r\\n\\texcept IndexError:\\r\\n\\t\\tif array.shape[0] == array.size:\\r\\n\\t\\t\\tresult = gaussian_filter(array, sigma = sigma, order = 0, mode = \\\"constant\\\", cval = 0.0)\\r\\n\\t\\telse:\\r\\n\\t\\t\\tprint \\\"Check your array dimenszions!\\\"\\r\\n\\treturn result\",\n \"def gaussianFilter(gain,BT,spSym,nTaps):\\n\\n a = np.sqrt(np.log(2)/2)/BT\\n t = np.linspace(-.5*nTaps,.5*nTaps-1,nTaps)/spSym\\n\\n ft = np.sqrt(np.pi)/a *np.exp(-(np.pi**2*(t)**2)/a**2)\\n ft /= np.sum(ft) * gain # normalize filter\\n\\n return ft\",\n \"def fourier_der(im):\\n im = im.astype(np.float64)\\n # constants\\n M, N = im.shape\\n u = np.meshgrid(np.arange(N), np.arange(M))[0] - N//2\\n v = np.meshgrid(np.arange(N), np.arange(M))[1] - M//2\\n u_der, v_der = (2 * np.pi * 1j / N), (2 * np.pi * 1j / M)\\n\\n # calculate dx, dy\\n dx = u_der * IDFT2(np.fft.fftshift(u) * DFT2(im))\\n dy = v_der * IDFT2(np.fft.fftshift(v) * DFT2(im))\\n\\n return np.sqrt(np.abs(dx)**2 + np.abs(dy)**2) # = magnitude\",\n \"def create_gaussian_array(self):\\n\\n # Fill array of size l x w with Gaussian Noise.\\n terrain_length = int(ceil(self.length/self.resolution))\\n terrain_width = int(ceil(self.width/self.resolution))\\n gaussian_array = np.random.normal(self.mu, self.sigma, (terrain_length,terrain_width))\\n\\n # Filter the array to smoothen the variation of the noise\\n gaussian_array = gaussian_filter(gaussian_array, self.sigma_filter)\\n\\n return gaussian_array\",\n \"def dwt(image_array, quantization_Array):\\n # Create the high pass and low pass filters\\n # both filters are non-causal\\n # symmetric\\n # [-2, -1, 0, 1, 2]\\n LPF = [-0.125, 0.25, 0.75, 0.25, -0.125]\\n LPF_center = 2\\n\\n # [ -2,-1, 0]\\n HPF = [-0.5, 1, -0.5]\\n HPF_center = 2\\n\\n nrow, ncol = image_array.shape\\n\\n # create an array that will contain the 4 different subbands of the image\\n LL = np.zeros((nrow, ncol))\\n LH = np.zeros((nrow, ncol))\\n HL = np.zeros((nrow, ncol))\\n HH = np.zeros((nrow, ncol))\\n filtered_image = [LL, LH, HL, HH]\\n\\n # filtering the rows using a low pass and high pass filters\\n LowPass_rows = np.zeros((nrow, ncol))\\n HighPass_rows = np.zeros((nrow, ncol))\\n for i in range(0, nrow):\\n LowPass_rows[i, :] = lfilter(LPF, image_array[i, :], LPF_center)\\n HighPass_rows[i, :] = lfilter(HPF, image_array[i, :], HPF_center)\\n\\n # down sample rows.\\n # which means we will have half the number of columns\\n for i in range(0, len(filtered_image)):\\n filtered_image[i] = filtered_image[i][:, ::2]\\n\\n # apply filters accross columns\\n for i in range(0, ncol):\\n LL[:, i] = lfilter(LPF, LowPass_rows[:, i], LPF_center)\\n LH[:, i] = lfilter(HPF, LowPass_rows[:, i], HPF_center)\\n HL[:, i] = lfilter(LPF, HighPass_rows[:, i], LPF_center)\\n HH[:, i] = lfilter(HPF, HighPass_rows[:, i], HPF_center)\\n\\n # down sample columns and quantize\\n for i in range(0, len(filtered_image)):\\n filtered_image[i] = filtered_image[i][::2, :]\\n filtered_image[i] = np.round(\\n filtered_image[i]/quantization_Array[i]).astype(int)\\n\\n return filtered_image\",\n \"def _imfilter(x_data, f_data):\\n return pipe(f_data, ifftshift, fftn, lambda x: x * fftn(x_data), ifftn).real\",\n \"def pseudo_flatfield(img_plane, sigma=5):\\n filtered_img = gaussian_filter(img_plane, sigma)\\n return img_plane / (filtered_img + 1)\",\n \"def gaussian(amp, fwhm, mean, x):\\n return amp * np.exp(-4. * np.log(2) * (x-mean)**2 / fwhm**2)\",\n \"def all_features(sigma=0.1):\\n # to fill in f, array of features w shape (863, 863)\\n f = np.zeros((X.shape[0], X.shape[0]))\\n # iterate over every example twice to make every possible comparison\\n for i in range(m):\\n for j in range(m):\\n f[i, j] = gaussian_kernel(X[i], X[j], sigma=sigma)\\n return f\",\n \"def fourierCV(img):\\n\\tgray = grayscale(img)\\n\\tdft = cv2.dft(np.float32(gray), flags = cv2.DFT_COMPLEX_OUTPUT)\\n\\tdft_shift = np.fft.fftshift(dft)\\n\\tmagnitude_spectrum = 20*np.log(cv2.magnitude(dft_shift[:,:,0], dft_shift[:,:,1]))\\n return magnitude_spectrum\",\n \"def makeGaussianKernel(sigma: float) -> np.ndarray:\\n\\n # Your code here.\\n kernel_size = 8*sigma+1\\n kernel = np.zeros([kernel_size,kernel_size], dtype=float)\\n center = kernel_size//2\\n \\n \\n s = 2*(sigma**2)\\n sum_val = 0\\n for i in range(0,kernel_size):\\n for j in range(0,kernel_size):\\n x = i-center\\n y = j-center\\n kernel[i,j] = np.exp(-(x**2+y**2) / s)\\n sum_val += kernel[i,j]\\n #/(np.pi * s)\\n sum_val = 1/sum_val\\n print(\\\"here is the kernel\\\", kernel*sum_val)\\n return kernel*sum_val\",\n \"def gaussian_filter(self, input, win, size):\\n\\n N, C, H, W = input.shape\\n f = F.conv2d(input, win, stride=1, groups=C)\\n f = F.conv2d(f, win.transpose(2, 3), stride=1, groups=C)\\n\\n out = torch.zeros(input.shape)\\n out[:, :, self.win_size // 2:self.win_size // 2 * -1, self.win_size // 2:self.win_size // 2 * -1] = f.unsqueeze(\\n 0).unsqueeze(0)\\n return out\\n\\n # N, C, H, W = input.shape\\n # out = F.conv2d(input, win, stride=1, padding=(0, self.win_size//2), groups=C)\\n # out = F.conv2d(out, win.transpose(2, 3), stride=1, padding=(self.win_size//2, 0), groups=C)\\n # return out\",\n \"def denoise_frequency_domain(ut: np.ndarray, \\n filter_3d: np.ndarray) -> np.ndarray:\\n\\n num_rows = ut.shape[0]\\n ut_denoised = np.empty(ut.shape, dtype=complex)\\n for row in range(num_rows):\\n ut_slice_cube = np.reshape(ut[row, :], filter_3d.shape)\\n ut_slice_cube_denoised = ut_slice_cube*filter_3d\\n ut_denoised[row, :] = ut_slice_cube_denoised.flatten()\\n\\n return ut_denoised\",\n \"def fourier_transform(hamiltonian, grid, spinless):\\n return _fourier_transform_helper(hamiltonian=hamiltonian,\\n grid=grid,\\n spinless=spinless,\\n phase_factor=+1,\\n vec_func_1=grid.momentum_vector,\\n vec_func_2=grid.position_vector)\",\n \"def smooth(img, sigma):\\n if sigma < 0:\\n raise ValueError('smoothing kernel size is negative')\\n elif sigma == 0:\\n return img.get_data()\\n else:\\n sigma_vox = sigma / np.sqrt(np.sum(img.get_affine()[0:3, 0:3] ** 2, 0))\\n return nd.gaussian_filter(img.get_data(), sigma_vox)\",\n \"def filtered_fourier(self):\\r\\n\\r\\n freqs = tsu.get_freqs(self.sampling_rate, self.data.shape[-1])\\r\\n\\r\\n if self.ub is None:\\r\\n self.ub = freqs[-1]\\r\\n\\r\\n power = fftpack.fft(self.data)\\r\\n idx_0 = np.hstack([np.where(freqs < self.lb)[0],\\r\\n np.where(freqs > self.ub)[0]])\\r\\n\\r\\n #Make sure that you keep the DC component:\\r\\n keep_dc = np.copy(power[..., 0])\\r\\n power[..., idx_0] = 0\\r\\n power[..., -1 * idx_0] = 0 # Take care of the negative frequencies\\r\\n power[..., 0] = keep_dc # And put the DC back in when you're done:\\r\\n\\r\\n data_out = fftpack.ifft(power)\\r\\n\\r\\n data_out = np.real(data_out) # In order to make sure that you are not\\r\\n # left with float-precision residual\\r\\n # complex parts\\r\\n\\r\\n return ts.TimeSeries(data=data_out,\\r\\n sampling_rate=self.sampling_rate,\\r\\n time_unit=self.time_unit)\",\n \"def DFT2(image):\\n image = image.astype(np.float64)\\n M, N = image.shape\\n\\n # build the dft2_matrix transform\\n omega_y = np.exp(-2 * np.pi * 1j / M)\\n u, v = np.meshgrid(np.arange(M), np.arange(M))\\n dft2_matrix = np.power(omega_y, u*v)\\n\\n # calculate the 2D fourier transform\\n fourier_image = np.dot(dft2_matrix, DFT(image))\\n\\n return fourier_image\",\n \"def gaussian_kernel(values, \\n bandwidth):\\n # Compute the kernel value for the given values\\n temp_1 = np.multiply(np.pi, np.square(bandwidth))\\n temp_2 = np.divide(1, np.sqrt(temp_1))\\n temp_3 = np.divide(values, np.square(bandwidth))\\n kernel_value = np.exp(np.multiply(np.negative(0.5), temp_3))\\n # Return the computed kernel value\\n return kernel_value\",\n \"def _gaussian_distribution(self, x: ndarray, mu: float, sigma: float) -> ndarray:\\n return 1 / (np.sqrt(2 * np.pi) * sigma) * np.exp(\\n -np.power(\\n (x - mu) / sigma, 2) / 2)\",\n \"def features_sigma(img,\\n sigma,\\n intensity=True,\\n edges=True,\\n texture=True):\\n\\n features = []\\n\\n gx,gy = np.meshgrid(np.arange(img.shape[1]), np.arange(img.shape[0]))\\n # print(gx.shape)\\n #features.append(gx)\\n gx = filters.gaussian(gx, sigma)\\n gy = filters.gaussian(gy, sigma)\\n\\n features.append(np.sqrt(gx**2 + gy**2)) #use polar radius of pixel locations as cartesian coordinates\\n\\n del gx, gy\\n\\n logging.info(datetime.now().strftime(\\\"%Y-%m-%d-%H-%M-%S\\\"))\\n logging.info('Location features extracted using sigma= %f' % (sigma))\\n\\n img_blur = filters.gaussian(img, sigma)\\n\\n if intensity:\\n features.append(img_blur)\\n\\n logging.info(datetime.now().strftime(\\\"%Y-%m-%d-%H-%M-%S\\\"))\\n logging.info('Intensity features extracted using sigma= %f' % (sigma))\\n\\n if edges:\\n features.append(filters.sobel(img_blur))\\n\\n logging.info(datetime.now().strftime(\\\"%Y-%m-%d-%H-%M-%S\\\"))\\n logging.info('Edge features extracted using sigma= %f' % (sigma))\\n\\n if texture:\\n H_elems = [\\n np.gradient(np.gradient(img_blur)[ax0], axis=ax1)\\n for ax0, ax1 in itertools.combinations_with_replacement(range(img.ndim), 2)\\n ]\\n\\n eigvals = feature.hessian_matrix_eigvals(H_elems)\\n del H_elems\\n\\n for eigval_mat in eigvals:\\n features.append(eigval_mat)\\n del eigval_mat\\n\\n logging.info(datetime.now().strftime(\\\"%Y-%m-%d-%H-%M-%S\\\"))\\n logging.info('Texture features extracted using sigma= %f' % (sigma))\\n\\n logging.info(datetime.now().strftime(\\\"%Y-%m-%d-%H-%M-%S\\\"))\\n logging.info('Image features extracted using sigma= %f' % (sigma))\\n\\n return features\",\n \"def dfdsig(self, x: np.array) -> np.array:\\n return (((x - self.mu)**2)/self.sig**2 - 1/self.sig)*self.f(x)\",\n \"def dft(X): \\n N = len(X)\\n x = np.zeros(N, 'complex')\\n K = np.arange(0, N, 1)\\n for n in range(0, N, 1):\\n x[n] = np.dot(X, np.exp(1j * 2 * np.pi * K * n / N))\\n return x\",\n \"def fake_gaussian(img, vertical_horizontal_sigma, iter=3):\\n sigma_vertical, sigma_horizontal = vertical_horizontal_sigma\\n h_blured = box_filter1d(img, sigma_horizontal, horizontal=True, iter=iter)\\n blured = box_filter1d(h_blured, sigma_vertical, horizontal=False, iter=iter)\\n return blured\",\n \"def gauss_kernel(n_fwhm,sigma):\\n\\n x_length = int(n_fwhm * sigma + 0.5) #Add 0.5 to approximate to nearest integer\\n y_length = x_length\\n \\n \\n x, y = mgrid[-x_length:x_length+1, -y_length:y_length+1]\\n g = numpy.exp(-(x**2/(2*(float(sigma)**2))+y**2/(2*(float(sigma)**2))))\\n return g / g.sum()\",\n \"def process( fids, ndim=2 ):\\n\\timg = np.empty_like( fids )\\n\\tax = -1*(np.array( range(ndim) )+1)\\n\\t\\n\\timg = np.fft.fftshift( np.fft.fftn( fids, axes=ax, ).astype( np.complex64), axes=ax )\\n\\t\\n\\treturn np.squeeze(img)\",\n \"def fit_gaussian(array):\\n\\n shape = array.shape\\n xmean, ymean = numpy.array(shape) / 2.\\n\\n xx, yy = numpy.mgrid[:shape[0], :shape[1]]\\n\\n g_init = astropy.modeling.models.Gaussian2D(amplitude=1., x_mean=xmean, y_mean=ymean,\\n x_stddev=1., y_stddev=1.)\\n\\n f2 = astropy.modeling.fitting.LevMarLSQFitter()\\n\\n gg = f2(g_init, xx, yy, array)\\n\\n return gg\",\n \"def _get_fir_filter(passband, fs, order=183, weights=[5.75, 1., 5.75], mask=[0, 1, 0]):\\n # return remez(order, passband, mask, weights, Hz=fs), 1.\\n return remez(order, passband, mask, Hz=fs), 1.\",\n \"def computebandfftfilter_gauss(signal0,dt,fc,sigma0,lTime): \\n signal1=np.concatenate((signal0[0]*np.linspace(1,1,100), signal0, signal0[-1]*np.linspace(1,1,100)))\\n fc=fc*2*np.pi\\n sigma=sigma0*2*np.pi\\n\\n NFFT = 2**nextpow2(len(signal1))\\n fourier=scipy.fft(signal1,NFFT) \\n \\n# %dt=.5;\\n f = 2*np.pi*(1/dt)*(np.linspace(0,NFFT-1,NFFT))/NFFT #CORRECT! \\n fil = np.exp((-(f-fc)**2)/(2*sigma**2))\\n signfiltf = np.multiply(fil,fourier)\\n\\n #new ifft \\n points = int(NFFT/2)\\n fas = signfiltf[range(points)]\\n \\n # Using only first half, rebuild full FAS by taking conjugate\\n fas_eqsig = np.zeros(len(signfiltf), dtype=complex)\\n #! Do not start from fas[0]\\n fas_eqsig[1:NFFT // 2] = fas[1:]\\n fas_eqsig[NFFT // 2 + 1:] = np.flip(np.conj(fas[1:]), axis=0) \\n # Inverse the rebuilt FAS to obtain the time series\\n sft = np.fft.ifft(fas_eqsig, n=NFFT) \\n \\n# %signal=interp1(0:length(sft)-1,sft,time);\\n stfinal=sft[100:len(signal1)-100] \\n \\n return stfinal\",\n \"def filtering(self):\\n from numpy import fft\\n import numpy as np\\n\\n _image_dft = fft.fft2(self.image)\\n _image_dft = fft.fftshift(_image_dft)\\n # dft = DFT.DFT()\\n # plt.figure(1) \\n # plt.imshow(self.image)\\n # plt.figure(2)\\n # plt.imshow(20*np.log10(abs(_image_dft))) \\n # print(_image_dft)\\n # print(abs(_image_dft))\\n # plt.show()\\n filter = self.filter(self.image.shape, self.cutoff, self.order) \\\\\\n if self.filter_name.startswith('butterworth') \\\\\\n else self.filter(self.image.shape, self.cutoff)\\n \\n _image_dft_filtered = _image_dft * filter\\n _image_filtered = abs(fft.ifft2(_image_dft_filtered))\\n \\n return [ self.post_process_image(_image_filtered), \\\\\\n self.post_process_image(20*np.log10(abs(_image_dft)+.00001)), \\\\\\n self.post_process_image(20*np.log10(abs(_image_dft_filtered)+.00001)) ]\",\n \"def random_signal_fir_filtering(ds, num_coefs=10, flag=None):\\n logger.info(\\\"Applying random FIR filters of size %d on signals\\\", num_coefs)\\n\\n num_coefs = tf.constant(num_coefs, tf.int32)\\n\\n def _apply_random_filter(x):\\n if flag and not x[flag]:\\n return x\\n return dict(x, signal=audio_features.random_gaussian_fir_filter(x[\\\"signal\\\"], num_coefs))\\n\\n return ds.map(_apply_random_filter, num_parallel_calls=TF_AUTOTUNE)\",\n \"def dct(n_filters, n_input):\\n\\n basis = np.empty((n_filters, n_input))\\n basis[0, :] = 1.0 / np.sqrt(n_input)\\n\\n samples = np.arange(1, 2*n_input, 2) * np.pi / (2.0 * n_input)\\n\\n for i in range(1, n_filters):\\n basis[i, :] = np.cos(i*samples) * np.sqrt(2.0/n_input)\\n\\n return basis\",\n \"def f(self, x: np.array) -> np.array:\\n return (1/np.sqrt(2*np.pi*self.sig**2))*np.exp(-1*((x - self.mu)**2/(2*self.sig**2)))\",\n \"def blurImage1(in_image: np.ndarray, kernel_size: np.ndarray) -> np.ndarray:\\r\\n size = kernel_size[0]\\r\\n sigma = 1\\r\\n x, y = np.mgrid[-size:size + 1, -size:size + 1]\\r\\n normal = 1 / (2.0 * np.pi * sigma ** 2)\\r\\n g = np.exp(-((x ** 2 + y ** 2) / (2.0 * sigma ** 2))) * normal\\r\\n in_image = cv2.filter2D(in_image, -1, g)\\r\\n return in_image\",\n \"def dfdm(x: np.array) -> np.array:\\n return x\",\n \"def scipy_smooth(img, sigma=5):\\n return ndimage.gaussian_filter(img, sigma=sigma)\",\n \"def run_gaussian_smoothing(image, kernel_size=5):\\n return cv2.GaussianBlur(image, (kernel_size, kernel_size), 0)\",\n \"def idft(X):\\n N = len(X)\\n x = np.zeros(N, 'complex')\\n \\n K = np.arange(0, N, 1)\\n for n in range(0, N, 1):\\n x[n] = np.dot(X, np.exp(-1j * 2 * np.pi * K * n / N))\\n return x / N\",\n \"def filtermed(f, medfiltsize=9): # medfiltfunc='ndimage'\\n # # Median filter to smooth out single-pixel deviations\\n # if medfiltfunc == 'ndimage': f_medfilt = median_filter(f, size=medfiltsize)\\n # elif medfiltfunc == 'signal': f_medfilt = medfilt(f, kernel_size=medfiltsize)\\n f_medfilt = median_filter(f, size=medfiltsize)\\n return f_medfilt\",\n \"def gaussian_weights(self, pad, feather):\\n weights=[]\\n for dim, xy in zip([0, 1], [self.x, self.y]):\\n xy0 = np.mean(xy)\\n W = xy[-1]-xy[0]\\n dist = np.abs(xy-xy0)\\n wt = np.zeros_like(dist)\\n i_feather = (dist >= W/2 - pad - feather) & ( dist <= W/2 -pad )\\n wt_feather = np.exp(-((xy[i_feather]-xy0)/(feather/2.))**2)\\n wt[ i_feather ] = wt_feather\\n wt[ dist <= W/2 - pad - feather ] = 1\\n wt[ dist >= W/2 - pad] = 0\\n weights += [wt]\\n self.weight *= weights[0][:,None].dot(weights[1][None,:])\",\n \"def subsample_fourier(x, k):\\n return subsamplefourier(x,k)\",\n \"def build_gaussian_pyramid(im, max_levels, filter_size):\\n\\n filter_vec = gaus_1d(filter_size).reshape(1,filter_size)\\n pyr = []\\n pyr.append(im)\\n for i in range(max_levels - 1):\\n if(im.shape[0] <= 16 or im.shape[1] <= 16):\\n break\\n\\n im = ndimage.filters.convolve(im, filter_vec.T, mode='mirror')\\n im = ndimage.filters.convolve(im, filter_vec, mode='mirror')\\n\\n im = im[::2, ::2]\\n pyr.append(im)\\n\\n return [pyr,filter_vec]\",\n \"def blur2D(image2D,sigmaFrac=10):\\n #NOTE: sigma may not be number of pixels\\n ftimage = np.fft.fftshift(np.fft.fft2(image2D))\\n ncols, nrows = image2D.shape\\n cy, cx = nrows/2, ncols/2\\n sigmax,sigmay=ncols/sigmaFrac,nrows/sigmaFrac\\n x = np.linspace(0, nrows, nrows)\\n y = np.linspace(0, ncols, ncols)\\n X, Y = np.meshgrid(x, y)\\n gmask = np.exp(-(((X-cx)/sigmax)**2 + ((Y-cy)/sigmay)**2))\\n return np.abs(np.fft.ifft2(ftimage * gmask))\",\n \"def gkern2(kernlen=21, nsig=3):\\n # create nxn zeros\\n inp = np.zeros((kernlen, kernlen))\\n # set element at the middle to one, a dirac delta\\n inp[kernlen//2, kernlen//2] = 1\\n # gaussian-smooth the dirac, resulting in a gaussian filter mask\\n kernel = scipy.ndimage.filters.gaussian_filter(inp, nsig)\\n\\n return kernel\",\n \"def median_filter(self):\\n print \\\"Median-Filtering...\\\"\\n D = self.D\\n x = np.median(np.median(D,axis=1),axis=1)\\n for i in xrange(len(x)):\\n D[i,:,:] -= x[i]\\n self.D = D\\n print \\\"done.\\\"\",\n \"def fourier_uniform(input, size, n=-1, axis=-1, output=None):\\n input = numpy.asarray(input)\\n output = _get_output_fourier(output, input)\\n axis = normalize_axis_index(axis, input.ndim)\\n sizes = _ni_support._normalize_sequence(size, input.ndim)\\n sizes = numpy.asarray(sizes, dtype=numpy.float64)\\n if not sizes.flags.contiguous:\\n sizes = sizes.copy()\\n _nd_image.fourier_filter(input, sizes, n, axis, output, 1)\\n return output\",\n \"def gaussian_filter(img,f=5,K=1,var=1):\\n i_x, i_y = np.shape(img) # image size\\n radi = f//2 # window radius\\n\\n # create gaussian kernel\\n def gaussian_kernel(f,K,var):\\n \\n # create coordinate information \\n if f//2 == 0:\\n x = np.linspace(-radi,radi,f+1)\\n y = np.linspace(-radi,radi,f+1)\\n x = np.delete(x, radi)\\n y = np.delete(y, radi)\\n else:\\n x = np.linspace(-radi,radi,f)\\n y = np.linspace(-radi,radi,f)\\n\\n m_x, m_y = np.meshgrid(x,y) # create coordinate\\n r_gauss = m_x**2 + m_y**2 # distance to origin\\n gauss = K*(np.exp(-r_gauss/(2*(var**2)))) # create kernel\\n return gauss/gauss.sum()\\n \\n #mirror padding\\n def mir_padding(img,f):\\n img_p = np.zeros((i_x+2*radi,i_y+2*radi)) #create padding image\\n img_p[radi:i_x+radi,radi:i_y+radi] = img #throw original image to padding image\\n img_p[0:radi,radi:i_y+radi] = img[radi-1::-1,:] # padding top rows\\n img_p[-radi::1,radi:i_y+radi] = img[-1:-radi-1:-1,:] # padding bottom rows\\n img_p[radi:i_x+radi,0:radi] = img[:,radi-1::-1] # padding left column\\n img_p[radi:i_x+radi,-radi::1] = img[:,-1:-radi-1:-1] # padding right column\\n for i in range(f):\\n img_p[0:radi,i] = img[radi-1-i,radi-1::-1] # padding upper-left corner\\n img_p[0:radi,-i] = img[radi-1-i,-radi::1] # padding upper-righ corner\\n img_p[-1:-radi-1:-1,i] = img[-radi+i,radi-1::-1] # padding lower-left corner\\n img_p[-1:-radi-1:-1,-i] = img[-radi+i,-radi::1] # padding lower-right corner\\n return img_p\\n\\n img_p = mir_padding(img,f) # create padding image\\n g_kernel = gaussian_kernel(f,K,var) # create gaussian kernel\\n\\n #seperate kernel\\n E = g_kernel[0,0]\\n c = g_kernel[:,0]\\n wT = np.reshape(g_kernel[0,:]/E,(f,1))\\n\\n gauss_image = np.zeros([i_x,i_y]) # create gauss image\\n temp_image = np.zeros([i_x,i_y]) # create temp image for two 1D convolution\\n old_c_sum = c.sum() # calculate sum of c before modification\\n\\n # if elements of kernel are located within area of padding, substitute value with 0\\n # calculate new value base on ratio between sum before and after modification\\n for j in range(i_y):\\n y_bound = i_y - j\\n mod_c = c.copy()\\n if j < radi:\\n mod_c[0:radi-j] = 0 \\n new_c_sum = mod_c.sum()\\n mod_c = mod_c*old_c_sum/new_c_sum \\n if j > i_y - radi - 1:\\n mod_c[-1:-radi+y_bound-1:-1] = 0 \\n new_c_sum = mod_c.sum()\\n mod_c = mod_c*old_c_sum/new_c_sum \\n for i in range(i_x):\\n temp_image[i,j] = np.sum(img_p[i+radi,j:j+f]*mod_c)\\n\\n temp_image = mir_padding(temp_image,f) # create padding temp image for next 1D convolution\\n old_wT_sum = wT.sum() # calculate sum of wT before modification\\n\\n # if elements of kernel are located within area of padding, substitute value with 0\\n # calculate new value base on ratio between sum before and after modification\\n for i in range(i_x):\\n x_bound = i_x - i\\n mod_wT = wT.copy()\\n if i < radi:\\n mod_wT[0:radi-i] = 0 \\n new_wT_sum = mod_wT.sum()\\n mod_wT = mod_wT*old_wT_sum/new_wT_sum \\n if i > i_x - radi - 1:\\n mod_wT[-1:-radi+x_bound-1:-1] = 0 \\n new_wT_sum = mod_wT.sum()\\n mod_wT = mod_wT*old_wT_sum/new_wT_sum \\n for j in range(i_y):\\n gauss_image[i,j] = np.sum(temp_image[i:i+f,j+radi]*mod_wT.T)\\n\\n return gauss_image\",\n \"def convolve(img, fourier_kernel):\\n return np.fft.ifftshift(np.fft.irfft2(np.fft.rfft2(img) * fourier_kernel))\",\n \"def __gaussian_blur(self, img, kernel_size=3):\\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)\",\n \"def gaussianFilter3D(size, points):\\n \\n matrix = numpy.zeros((size, size, size))\\n \\n for point in points:\\n x0 = point[0]\\n y0 = point[1]\\n z0 = point[2]\\n x2SigmaSquared = pow(point[3] * size/4, 2) * 2\\n y2SigmaSquared = pow(point[4] * size/4, 2) * 2\\n z2SigmaSquared = pow(point[5] * size/4, 2) * 2\\n tempMatrix = numpy.zeros((size, size, size))\\n for x in range(0, size):\\n for y in range(0, size):\\n for z in range(0, size):\\n tempMatrix[y, x, z] = math.exp(-1 * \\\\\\n (math.pow(x-x0, 2)/x2SigmaSquared +\\\\\\n math.pow(y-y0, 2)/y2SigmaSquared +\\\\\\n math.pow(z-z0, 2)/z2SigmaSquared))\\n \\n matrix = numpy.add(matrix, tempMatrix)\\n \\n matrix = matrixfix.flatten(matrix, 0, 1)\\n \\n return matrix\",\n \"def smoothfield( d , boxsize, Rth ):\\n\\n ng = N.shape(d)[0]\\n dk = N.fft.rfftn(d)\\n kgrid = getkgrid(boxsize,ng)\\n dk = dk * N.exp( -(kgrid * Rth) ** 2. / 2. )\\n d = N.fft.irfftn(dk)\\n\\n return d\",\n \"def convolve_im(im: np.array,\\n kernel: np.array,\\n verbose=True):\\n ### START YOUR CODE HERE ### (You can change anything inside this block)\\n \\\"\\\"\\\"\\n\\tcompared to the 4a solution this just adds padding to the filter if its smaller than the image\\n\\tthis is done by using the second parameter in fft.fft2 \\n\\t\\n\\tfirst it applies fourier transforms on the kernel and the image\\n\\tthen it sets the image to be the pointwise multiplication of the transforms\\n\\n the image is inverse fourier transformed and filtered for real values\\n the domain image is shifted and taken the absolute value of\\n the fourier transform of the image and kernel are also shifted and set to be the absolute value\\n\\tlastly everything is displayed in the subplots\\n \\\"\\\"\\\"\\n conv_result = im \\n \\n if verbose:\\n fftKernel=np.fft.fft2(kernel,im.shape)\\n fftImage=np.fft.fft2(conv_result)\\n\\t\\t\\n\\t\\t\\n\\t\\t\\n conv_result=np.multiply(fftImage,fftKernel)\\n fftImageTransformed=conv_result\\n\\t\\t\\n \\n conv_result=np.fft.ifft2(conv_result)\\n \\n conv_result=np.real(conv_result)\\n\\n fftImageTransformed=np.fft.fftshift(fftImageTransformed)\\n fftImage=np.fft.fftshift(fftImage)\\n fftKernel=np.fft.fftshift(fftKernel)\\n\\n fftImageTransformed=np.absolute(fftImageTransformed)\\n fftImage=np.absolute(fftImage)\\n fftKernel=np.absolute(fftKernel)\\n\\t\\t\\n\\t\\t\\n # Use plt.subplot to place two or more images beside eachother\\n plt.figure(figsize=(20, 4))\\n # plt.subplot(num_rows, num_cols, position (1-indexed))\\n plt.subplot(1, 5, 1)\\n plt.imshow(im, cmap=\\\"gray\\\")\\n plt.subplot(1, 5, 2)\\n plt.imshow(fftImage, cmap=\\\"gray\\\")\\n plt.subplot(1, 5, 3)\\n plt.imshow(fftKernel, cmap=\\\"gray\\\")\\n plt.subplot(1, 5, 4)\\n plt.imshow(fftImageTransformed, cmap=\\\"gray\\\")\\n plt.subplot(1, 5, 5)\\n plt.imshow(conv_result, cmap=\\\"gray\\\")\\n ### END YOUR CODE HERE ###\\n return conv_result\",\n \"def filter2D(img, kernel = (5,5)):\\n\\ttmp = img.copy()\\n\\tk = np.ones((kernel[0], kernel[1]), np.float32) / (kernel[0]*kernel[1])\\n\\tdst = cv2.filter2D(tmp, -1, k)\\n\\treturn dst\",\n \"def gaussian_blur(img: np.ndarray, kernel_size: int = 3):\\n imgtype = img.dtype\\n h,w,c = img.shape\\n\\n #Get a valid kernel size\\n kernel_size = valid_kernel(h,w,kernel_size)\\n \\n #Gaussian Filter Blur\\n blurred = cv2.GaussianBlur(img,(kernel_size,kernel_size),0)\\n\\n return blurred.astype(imgtype)\",\n \"def gaussian(N, dt, width):\\n df = 1/(N*dt) # frequency step\\n f = np.arange(0, round(0.5*N), 1, dtype=float)*df # frequency array\\n w = 2*np.pi*f # angular frequency\\n\\n G = np.array([0]*N, dtype=float)\\n G[0:round(N/2)] = np.exp(-w**2/(4*width**2))/dt\\n G_lr = np.flip(G)\\n G[round(N/2)+1:] = G_lr[-len(G[round(N/2)+1:]):]\\n return G\",\n \"def expgaussian(mu, wid, timeconstant, x): \\n # Gaussian signal broadened by an exponetial signal\\n g = gaussian(mu, wid, x)\\n \\n hly = np.round( len(g) / 2.0 )\\n ey = np.r_[np.zeros(hly),g,np.zeros(hly)]\\n fy = np.fft.fft(ey)\\n a = np.exp(-(np.arange(len(fy))) / timeconstant )\\n fa = np.fft.fft(a)\\n fy1 = fy * fa\\n ybz = np.real(np.fft.ifft(fy1)) / np.sum(a)\\n yb = ybz[hly:len(ybz)-hly]\\n \\n return yb\",\n \"def gauss_mask(shape, low):\\n h, w = shape\\n outw=w/2+1 # if reduced\\n # we have full heightfreqs:\\n irow = np.fft.fftfreq(h).reshape(h,1)\\n # cols are halfed\\n icol = np.fft.fftfreq(w)[:outw].reshape(1,outw)\\n r = np.exp(-(icol*icol+irow*irow)/(low*low))\\n return r\",\n \"def gauss(self, X, xm, amp, w):\\n return amp * np.exp(-((X - xm) / w) ** 2)\",\n \"def gaussian_2d(df:pd.DataFrame, sigmas:tuple, **kwargs) -> pd.DataFrame:\\n df = pd.DataFrame(\\n gaussian_filter(df, sigmas, **kwargs),\\n index=df.index,\\n columns=df.columns)\\n return df\",\n \"def gaussian_filter(self, sigma):\\n\\n mask = self.get_weighted_mask()\\n mask_f = ni.gaussian_filter(mask, sigma=sigma)\\n\\n return SpatialReceptiveField(mask_f, self.altPos, self.aziPos, sign=self.sign,\\n temporalWindow=self.temporalWindow, pixelSizeUnit=self.pixelSizeUnit,\\n dataType=self.dataType, thr=self.thr, filter_sigma=sigma,\\n interpolate_rate=self.interpolate_rate)\",\n \"def conv_gauss_custom(x, y, fwhm, dwindow=2):\\n # fwhm = sigma * 2 * np.sqrt(2 * np.log(2))\\n\\n # Check if fwhm is a number or a list\\n if isinstance(fwhm, (int, float)):\\n # If fwhm is a number, make an array with fwhm in each entry\\n fwhm = np.ones_like(x) * fwhm\\n else:\\n # Check fwhm has same dimensions as x\\n if len(fwhm) != len(x):\\n sys.exit('Array `fwhm` has different length than `x`: len(fwhm)={}, len(x)={}'.format(len(fwhm), len(x)))\\n\\n # Number of total datapoints\\n nx = len(x)\\n\\n # -----------------------\\n\\n # For each datapoint define a \\\"bin\\\" or \\\"pixel\\\"\\n # E.g. for the datapoint x_3:\\n # - Bin center: value of the datapoint: x_3\\n # - Bin left edge: half the distance between the current datapoint and the previous one: x_3 - (x_3 - x_2) * 0.5\\n # - Bin right edge: half the distance between the current datapoint and the next one: x_3 + (x_4 - x_3) * 0.5\\n\\n # Distances between center of each bin\\n bin_distance = x[1:] - x[:-1] # length = len(x) - 1\\n # Define left/right edge of each bin as half the distance to the bin previous/next to it\\n bin_edgesmiddle = x[:-1] + 0.5 * bin_distance # middle points\\n bin_edgesfirst = x[0] - 0.5 * bin_distance[0] # first point\\n bin_edgeslast = x[-1] + 0.5 * bin_distance[-1] # last point\\n edges = np.concatenate(([bin_edgesfirst], bin_edgesmiddle, [bin_edgeslast]), axis=0) # length = len(x) + 1\\n\\n # Width of each bin\\n # If the input array x is equally spaced, `bin_width` will be equal to `bin_distance`\\n bin_width = edges[1:] - edges[:-1] # length = len(x)\\n\\n # -----------------------\\n\\n # Convert FWHM from wavelength units to bins -> Number of bins per FWHM\\n fwhm_bin = fwhm / bin_width\\n # Round number of bins per FWHM\\n nbins = np.ceil(fwhm_bin) #npixels\\n\\n ## Convert sigma from wavelength units to bins -> Number of bins per sigma\\n #sigma_bin = sigma / bin_width\\n ## Round number of bins per sigma\\n #nbins = np.ceil(sigma_bin) #npixels\\n\\n # -----------------------\\n\\n yconv = np.zeros_like(x)\\n for i, x_i in enumerate(x):\\n\\n # Slow method -> THIS IS WHAT MAKES THE OTHER FUNCTION SLOW!\\n # # Select kernel window\\n # dwindow = 2 * fwhm #2 * fwhm\\n # x1 = (np.argmin(np.abs(x - (x_i - dwindow))))\\n # x2 = (np.argmin(np.abs(x - (x_i + dwindow))))\\n # irang = slice(x1, x2+1)\\n\\n # Number of pixels at each side of x_i:\\n dx = dwindow * nbins[i] * 0.5\\n i1 = int(max(0, i - dx))\\n i2 = int(min(nx, i + dx + 1))\\n irang = slice(i1, i2 + 1)\\n\\n # Gaussian kernel\\n kernel = 1./(np.sqrt(2*np.pi)*fwhm[i]) * np.exp(- ((x[irang] - x_i)**2) / (2 * fwhm[i]**2))\\n kernel = kernel / np.sum(kernel)\\n\\n # Convolve\\n yconv[i] = np.sum(y[irang] * kernel)\\n\\n return yconv\",\n \"def fourier_spectra(x, axis=1, flip=True, duplicate=True, **kwargs):\\n\\n x = forward_fourier(x, axis=axis, duplicate=duplicate)\\n ns = x.shape[axis]\\n nw = np.int32(np.floor(ns/2) + 1)\\n slc = [slice(None)] * len(x.shape)\\n slc[axis] = slice(0, nw)\\n if flip:\\n x = np.flip(x[tuple(slc)], axis=axis)\\n return x\",\n \"def spectrum_fourier(self):\\r\\n\\r\\n data = self.input.data\\r\\n sampling_rate = self.input.sampling_rate\\r\\n\\r\\n fft = fftpack.fft\\r\\n if np.any(np.iscomplex(data)):\\r\\n # Get negative frequencies, as well as positive:\\r\\n f = np.linspace(-sampling_rate/2., sampling_rate/2., data.shape[-1])\\r\\n spectrum_fourier = np.fft.fftshift(fft(data))\\r\\n else:\\r\\n f = tsu.get_freqs(sampling_rate, data.shape[-1])\\r\\n spectrum_fourier = fft(data)[..., :f.shape[0]]\\r\\n \\r\\n return f, spectrum_fourier\",\n \"def Gaussian(x, mu=0, sigma=26.4, A=1, y0=0):\\r\\n #width = sigma*(2*np.sqrt(2*np.log(2)))\\r\\n b = 1/(sigma*np.sqrt(2*np.pi))\\r\\n f = b*np.power(np.e, -(((x-mu)**2)/(2*sigma**2)))\\r\\n return A*f + y0\",\n \"def uni_gaussian(X, mu, sigma2):\\n p = (1 / sqrt(2 * pi * sigma2))\\n p = p * exp(-power(X - mu, 2) / (2 * sigma2))\\n\\n def prod(x, y):\\n return x * y\\n p = array([[reduce(prod, el)] for el in p])\\n\\n return p\",\n \"def gaussianBlur(img,ksize=(5,5),sigma=10):\\n #kernel = cv2.getGaussianKernel(ksize,sigma)\\n dst = np.zeros_like(img)\\n cv2.GaussianBlur(src=img,dst=dst,ksize=ksize,sigmaX=0)\\n return dst\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6724297","0.6515853","0.64436597","0.64298147","0.6300525","0.62142223","0.6133565","0.61210185","0.60772467","0.6005786","0.59797704","0.58723","0.58492655","0.5830647","0.575321","0.56844056","0.5630864","0.56108207","0.55966944","0.5580227","0.55787235","0.55725807","0.55296147","0.551112","0.550536","0.54819894","0.5481008","0.54772764","0.546164","0.54606855","0.5453436","0.5393654","0.53866506","0.5383842","0.5369849","0.5363525","0.5353278","0.5352939","0.53366727","0.53348374","0.5333904","0.5304051","0.53007513","0.52854997","0.5280885","0.52779776","0.527374","0.5253105","0.52522695","0.52511275","0.52470165","0.5245335","0.5243128","0.52378017","0.52261406","0.52210534","0.52200156","0.5218165","0.5215092","0.52121556","0.5194859","0.5191786","0.5185242","0.51838213","0.5181583","0.51797885","0.51792413","0.5165181","0.5161683","0.5161411","0.51497847","0.51472807","0.51460534","0.51248163","0.51209813","0.51208454","0.51192015","0.5114635","0.5111289","0.511114","0.51013684","0.5096981","0.50951076","0.5092931","0.5088636","0.50842315","0.5075782","0.5074295","0.5070594","0.50549984","0.50512534","0.5046672","0.50418395","0.50394654","0.5037283","0.5036264","0.5036079","0.5015097","0.50095415","0.50089616"],"string":"[\n \"0.6724297\",\n \"0.6515853\",\n \"0.64436597\",\n \"0.64298147\",\n \"0.6300525\",\n \"0.62142223\",\n \"0.6133565\",\n \"0.61210185\",\n \"0.60772467\",\n \"0.6005786\",\n \"0.59797704\",\n \"0.58723\",\n \"0.58492655\",\n \"0.5830647\",\n \"0.575321\",\n \"0.56844056\",\n \"0.5630864\",\n \"0.56108207\",\n \"0.55966944\",\n \"0.5580227\",\n \"0.55787235\",\n \"0.55725807\",\n \"0.55296147\",\n \"0.551112\",\n \"0.550536\",\n \"0.54819894\",\n \"0.5481008\",\n \"0.54772764\",\n \"0.546164\",\n \"0.54606855\",\n \"0.5453436\",\n \"0.5393654\",\n \"0.53866506\",\n \"0.5383842\",\n \"0.5369849\",\n \"0.5363525\",\n \"0.5353278\",\n \"0.5352939\",\n \"0.53366727\",\n \"0.53348374\",\n \"0.5333904\",\n \"0.5304051\",\n \"0.53007513\",\n \"0.52854997\",\n \"0.5280885\",\n \"0.52779776\",\n \"0.527374\",\n \"0.5253105\",\n \"0.52522695\",\n \"0.52511275\",\n \"0.52470165\",\n \"0.5245335\",\n \"0.5243128\",\n \"0.52378017\",\n \"0.52261406\",\n \"0.52210534\",\n \"0.52200156\",\n \"0.5218165\",\n \"0.5215092\",\n \"0.52121556\",\n \"0.5194859\",\n \"0.5191786\",\n \"0.5185242\",\n \"0.51838213\",\n \"0.5181583\",\n \"0.51797885\",\n \"0.51792413\",\n \"0.5165181\",\n \"0.5161683\",\n \"0.5161411\",\n \"0.51497847\",\n \"0.51472807\",\n \"0.51460534\",\n \"0.51248163\",\n \"0.51209813\",\n \"0.51208454\",\n \"0.51192015\",\n \"0.5114635\",\n \"0.5111289\",\n \"0.511114\",\n \"0.51013684\",\n \"0.5096981\",\n \"0.50951076\",\n \"0.5092931\",\n \"0.5088636\",\n \"0.50842315\",\n \"0.5075782\",\n \"0.5074295\",\n \"0.5070594\",\n \"0.50549984\",\n \"0.50512534\",\n \"0.5046672\",\n \"0.50418395\",\n \"0.50394654\",\n \"0.5037283\",\n \"0.5036264\",\n \"0.5036079\",\n \"0.5015097\",\n \"0.50095415\",\n \"0.50089616\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.66539884"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":20096,"cells":{"query":{"kind":"string","value":"Multidimensional uniform fourier filter. The array is multiplied with the Fourier transform of a box of given size."},"document":{"kind":"string","value":"def fourier_uniform(input, size, n=-1, axis=-1, output=None):\n input = numpy.asarray(input)\n output = _get_output_fourier(output, input)\n axis = normalize_axis_index(axis, input.ndim)\n sizes = _ni_support._normalize_sequence(size, input.ndim)\n sizes = numpy.asarray(sizes, dtype=numpy.float64)\n if not sizes.flags.contiguous:\n sizes = sizes.copy()\n _nd_image.fourier_filter(input, sizes, n, axis, output, 1)\n return output"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _get_fourier_filter(self):\n size = max(64, int(2 ** np.ceil(np.log2(2 * self.m[-1].item()))))\n\n pi = torch.acos(torch.zeros(1)).item() * 2.0\n n = torch.cat(\n [\n torch.arange(1, size // 2 + 1, 2, device=self.n.device),\n torch.arange(size // 2 - 1, 0, -2, device=self.n.device),\n ]\n )\n f = torch.zeros(size, device=self.n.device)\n f[0] = 0.25\n if self.flat:\n f[1::2] = -1 / (pi * n).pow(2)\n else:\n f[1::2] = -self.s_detect.abs().pow(2) / (\n pi\n * (self.d_source + self._d_detect())\n * torch.sin(\n n\n * self.s_detect.abs()\n / (self.d_source + self._d_detect())\n )\n ).pow(2)\n f = torch.stack(\n [f, torch.zeros(f.shape, device=self.n.device)], dim=-1\n )\n f = fftshift(f, dim=(-2,))\n\n filt = fft1(f)[..., 0]\n\n if self.filter_type == \"hamming\":\n # hamming filter\n fac = torch.tensor(\n np.hamming(size).astype(np.float32), device=f.device\n )\n elif self.filter_type == \"hann\":\n # hann filter\n fac = torch.tensor(\n np.hanning(size).astype(np.float32), device=f.device\n )\n elif self.filter_type == \"cosine\":\n # cosine filter\n fac = torch.sin(\n torch.linspace(0, pi, size + 1, device=f.device)[:-1]\n )\n else:\n # ramp / ram-lak filter\n fac = 1.0\n\n return fac * filt","def _irfft2d(f_x) :","def smoothfield( d , boxsize, Rth ):\n\n ng = N.shape(d)[0]\n dk = N.fft.rfftn(d)\n kgrid = getkgrid(boxsize,ng)\n dk = dk * N.exp( -(kgrid * Rth) ** 2. / 2. )\n d = N.fft.irfftn(dk)\n\n return d","def blur_fourier(im, kernel_size):\n im = im.astype(np.float64)\n # build the kernel with zero padding\n kernel_base = gaussian_kernel_factory(kernel_size)\n window = np.zeros_like(im).astype(np.float64)\n M, N = im.shape\n dx, dy = kernel_base.shape\n x_middle, y_middle = N//2, M//2\n\n window[(y_middle-dy//2):(y_middle+dy//2+1), (x_middle-dx//2):(x_middle+dx//2+1)] = kernel_base\n\n # multiply in the freq domain\n return IDFT2(DFT2(im) * DFT2(np.fft.ifftshift(window))).real","def fourier_ellipsoid(input, size, n=-1, axis=-1, output=None):\n input = numpy.asarray(input)\n if input.ndim > 3:\n raise NotImplementedError(\"Only 1d, 2d and 3d inputs are supported\")\n output = _get_output_fourier(output, input)\n axis = normalize_axis_index(axis, input.ndim)\n sizes = _ni_support._normalize_sequence(size, input.ndim)\n sizes = numpy.asarray(sizes, dtype=numpy.float64)\n if not sizes.flags.contiguous:\n sizes = sizes.copy()\n _nd_image.fourier_filter(input, sizes, n, axis, output, 2)\n return output","def blur_fourier(im, kernel_size):\n kernel = gaus_kernel_calc(kernel_size)\n\n zeros = np.zeros(im.shape)\n x_mid = np.math.floor(im.shape[1] / 2)\n y_mid = np.math.floor(im.shape[0] / 2)\n distance = np.math.floor(kernel_size / 2)\n zeros[x_mid - distance: x_mid + distance + 1, y_mid - distance: y_mid + distance + 1] = kernel\n\n fourier_kernel = DFT2(np.fft.ifftshift(zeros))\n fourier_img = DFT2(im)\n fourier_blured = fourier_kernel * fourier_img\n\n return IDFT2(fourier_blured).real.astype(np.float64)","def fourier(img):\n return fourierCV(img)","def fourier_transform2d(self):\n\n zerofill = np.zeros(1024 * np.array([1,1])) #so it will always be square\n zerofill[:len(self.windowed), :len(self.windowed)] = self.windowed\n transform = np.fft.fft2(zerofill)\n transform = np.fft.fftshift(transform) # shift center to zero\n transformed = np.absolute(transform)\n tmax = transformed.max()\n zdata = (transformed)/(tmax) # normalize to maximum value\n\n return zdata","def process( fids, ndim=2 ):\n\timg = np.empty_like( fids )\n\tax = -1*(np.array( range(ndim) )+1)\n\t\n\timg = np.fft.fftshift( np.fft.fftn( fids, axes=ax, ).astype( np.complex64), axes=ax )\n\t\n\treturn np.squeeze(img)","def fourier_spectra(x, axis=1, flip=True, duplicate=True, **kwargs):\n\n x = forward_fourier(x, axis=axis, duplicate=duplicate)\n ns = x.shape[axis]\n nw = np.int32(np.floor(ns/2) + 1)\n slc = [slice(None)] * len(x.shape)\n slc[axis] = slice(0, nw)\n if flip:\n x = np.flip(x[tuple(slc)], axis=axis)\n return x","def urfft2(inarray):\n return urfftn(inarray, 2)","def dwt(image_array, quantization_Array):\n # Create the high pass and low pass filters\n # both filters are non-causal\n # symmetric\n # [-2, -1, 0, 1, 2]\n LPF = [-0.125, 0.25, 0.75, 0.25, -0.125]\n LPF_center = 2\n\n # [ -2,-1, 0]\n HPF = [-0.5, 1, -0.5]\n HPF_center = 2\n\n nrow, ncol = image_array.shape\n\n # create an array that will contain the 4 different subbands of the image\n LL = np.zeros((nrow, ncol))\n LH = np.zeros((nrow, ncol))\n HL = np.zeros((nrow, ncol))\n HH = np.zeros((nrow, ncol))\n filtered_image = [LL, LH, HL, HH]\n\n # filtering the rows using a low pass and high pass filters\n LowPass_rows = np.zeros((nrow, ncol))\n HighPass_rows = np.zeros((nrow, ncol))\n for i in range(0, nrow):\n LowPass_rows[i, :] = lfilter(LPF, image_array[i, :], LPF_center)\n HighPass_rows[i, :] = lfilter(HPF, image_array[i, :], HPF_center)\n\n # down sample rows.\n # which means we will have half the number of columns\n for i in range(0, len(filtered_image)):\n filtered_image[i] = filtered_image[i][:, ::2]\n\n # apply filters accross columns\n for i in range(0, ncol):\n LL[:, i] = lfilter(LPF, LowPass_rows[:, i], LPF_center)\n LH[:, i] = lfilter(HPF, LowPass_rows[:, i], HPF_center)\n HL[:, i] = lfilter(LPF, HighPass_rows[:, i], LPF_center)\n HH[:, i] = lfilter(HPF, HighPass_rows[:, i], HPF_center)\n\n # down sample columns and quantize\n for i in range(0, len(filtered_image)):\n filtered_image[i] = filtered_image[i][::2, :]\n filtered_image[i] = np.round(\n filtered_image[i]/quantization_Array[i]).astype(int)\n\n return filtered_image","def IDFT2D_slow(x):\r\n x = np.asarray(x, dtype=complex)\r\n \r\n M = len(x)\r\n N = len(x[0])\r\n mat = np.zeros((M, N), np.complex)\r\n \r\n for row in range(M):\r\n mat[row] = IDFT_slow(x[row])\r\n\r\n for col in range(N):\r\n mat[:,col] = IDFT_slow(mat[:,col])\r\n\r\n return mat","def fftn_1d(a):\r\n b = np.fft.fftpack.fftn(a, axes=(len(a.shape)-1, )) \r\n return np.divide(b, np.sqrt(a.shape[-1]))","def ufft2(inarray):\n return ufftn(inarray, 2)","def SpectrumGen(data, bin_size):\n nfft = bin_size\n mask = np.isfinite(data)\n data = data-np.nanmean(data)\n \n\n spectrum = scipy.fftpack.fft(data[mask], n=nfft)\n \n\n return spectrum","def medianfilter(data, filtersize, threshold=None,verbose=False):\n # 2006/02/01 IJC at the Jet Propulsion Laboratory\n # 2010-02-18 13:52 IJC: Converted to python\n from numpy import zeros, median, abs, std\n\n print \"Just use scipy.signal.medfilt !!!\"\n print \"Just use scipy.signal.medfilt !!!\"\n print \"Just use scipy.signal.medfilt !!!\"\n\n if len(filtersize)<1:\n print 'medianfilter2 requires that filtersize be a 1- or 2-element vector'\n return -1\n elif len(filtersize)==1:\n filtersize = [filtersize[0], filtersize[0]]\n else:\n filtersize = filtersize[0:2]\n\n npix = data.shape[0]\n npiy = data.shape[1]\n bigsize = npix+2*(filtersize[0]-1)\n bigdata = zeros((bigsize,bigsize),float)\n ind = filtersize[0]-1\n if ind==0:\n bigdata = data\n else:\n bigdata[ind:(bigsize-ind), ind:(bigsize-ind)] = data\n\n\n # FOR NOW, WE ASSUME FILTERSIZE IS ODD!!\n # AND THAT DATA IS SQUARE!\n niter_x = npix + (filtersize[0]-1)\n niter_y = npiy + (filtersize[1]-1)\n filt = zeros((niter_x,niter_y), float)\n\n for ii in range(niter_x):\n for jj in range(niter_y):\n if verbose>1:\n print \"ii,jj>>\",ii,jj\n if filtersize[0]==1:\n indi = 1\n else:\n indi = filtersize[0]-1\n if filtersize[1]==1:\n indj = 1\n else:\n indj = filtersize[1]-1\n select = bigdata[ii:(ii+indi),jj:(jj+indj)].ravel()\n select = select[isfinite(select)]\n #residualSelection = abs(select - median(select))\n\n if verbose: \n print \"select.shape>>\",select.shape\n print \"threshold>>\",threshold\n\n if threshold is not None:\n if threshold >= 0: # raw threshold\n doFilter = abs(bigdata[ii,jj]-median(select))/std(select)>=threshold\n elif threshold<0: # remove outliers before applying threshold\n npts_init = len(select)\n select = removeoutliers(select, abs(threshold), center='median')\n npts_final = len(select)\n if verbose>1:\n print \"threshold=\",threshold,\", removed %i points\" % (npts_init-npts_final)\n \n doFilter = abs(bigdata[ii,jj]-median(select))/std(select)>=threshold \n else: # filter everything; threshold not set.\n doFilter = True\n\n if verbose:\n print \"doFilter?>>\",doFilter\n if verbose>1:\n print \"select>>\",select\n\n if doFilter: \n newval = median( select )\n else:\n newval = bigdata[ii,jj]\n\n if verbose>1:\n print \"newval>>\",newval\n\n filt[ii,jj] = newval\n\n print filt.shape, [(filtersize[0]-1)/2,niter_x-(filtersize[0]-1)/2,(filtersize[0]-1)/2,niter_y-(filtersize[0]-1)/2]\n return filt[(filtersize[0]-1)/2:niter_x-(filtersize[0]-1)/2,(filtersize[0]-1)/2:niter_y-(filtersize[0]-1)/2]","def test_fft_complex_2d():\n\ta, b, c = np.meshgrid([0, 1, 0, 0], [0, 1j, 1j], [0, 1, 1, 1])\n\tdummy_array = xr.DataArray(a * b * c, dims=['x', 'y', 'z'])\n\tchunked_array = dummy_array.chunk(chunks={'x': 2, 'y': 2, 'z': 2})\n\tspectrum_array, spectrum_coords, spectrum_dims = \\\n\t\txfft._fft(chunked_array, nfft={'y': 6, 'z': 8}, dim=['y', 'z'],\n\t\t dx={'y': 0.01, 'z': 0.02})\n\tassert np.allclose(spectrum_array.compute(),\n\t np.fft.fftn(a * b * c, s=(8, 6), axes=(2, 1)))\n\tassert np.array_equal(spectrum_coords['f_y'], np.fft.fftfreq(6, d=0.01))\n\tassert np.array_equal(spectrum_coords['f_z'], np.fft.fftfreq(8, d=0.02))\n\tassert ('x', 'f_y', 'f_z') == spectrum_dims","def get_fft(u: np.ndarray, n: int) -> np.ndarray:\n\n # We get the fft of each time slice.\n num_slices = u.shape[0]\n ut = np.empty(u.shape, dtype=complex) # shape (20, 262144)\n for s in range(num_slices):\n # We reshape each slice into a 3D cube.\n u_slice = np.reshape(u[s,:], (n, n, n)) # shape (64, 64, 64)\n # We then take the fft of the 3D cube and add it to ut.\n ut_slice = np.fft.fftshift(np.fft.fftn(u_slice)) # shape (64, 64, 64)\n ut[s, :] = ut_slice.flatten()\n\n return ut","def forward_fft(self, array_in):\r\n # Find side length, as real array may or may not be doubled for\r\n # aliasing control\r\n side = array_in.shape[0]\r\n div_side = 1.0/side**2\r\n\r\n out = np.fft.fft2(self.sign_mat[0:side, 0:side]*array_in)*div_side\r\n return out","def denoise_frequency_domain(ut: np.ndarray, \n filter_3d: np.ndarray) -> np.ndarray:\n\n num_rows = ut.shape[0]\n ut_denoised = np.empty(ut.shape, dtype=complex)\n for row in range(num_rows):\n ut_slice_cube = np.reshape(ut[row, :], filter_3d.shape)\n ut_slice_cube_denoised = ut_slice_cube*filter_3d\n ut_denoised[row, :] = ut_slice_cube_denoised.flatten()\n\n return ut_denoised","def DFT2D_slow(x):\r\n x = np.asarray(x, dtype=complex)\r\n \r\n M = len(x)\r\n N = len(x[0])\r\n mat = np.zeros((M, N), np.complex)\r\n \r\n for row in range(M):\r\n mat[row] = DFT_slow(x[row])\r\n\r\n for col in range(N):\r\n mat[:,col] = DFT_slow(mat[:,col])\r\n\r\n return mat","def stdfilt2d(data, filtersize, threshold=None,verbose=False):\n # 2012-08-07 13:42 IJMC: Created from medianfilter\n from numpy import zeros, median, abs, std, isfinite\n\n if not hasattr(filtersize, '__iter__'):\n filtersize = [filtersize]\n\n if len(filtersize)<1:\n print 'medianfilter2 requires that filtersize be a 1- or 2-element vector'\n return -1\n elif len(filtersize)==1:\n filtersize = [filtersize[0], filtersize[0]]\n else:\n filtersize = filtersize[0:2]\n\n npix = data.shape[0]\n npiy = data.shape[1]\n bigsize_x = npix+2*(filtersize[0]-1)\n bigsize_y = npiy+2*(filtersize[1]-1)\n bigdata = zeros((bigsize_x,bigsize_y),float)\n ind = filtersize[0]-1\n if ind==0:\n bigdata = data\n else:\n bigdata[ind:(bigsize_x-ind), ind:(bigsize_y-ind)] = data\n\n\n # FOR NOW, WE ASSUME FILTERSIZE IS ODD!!\n # AND THAT DATA IS SQUARE!\n niter_x = npix + (filtersize[0]-1)\n niter_y = npiy + (filtersize[1]-1)\n filt = zeros((niter_x,niter_y), float)\n\n for ii in range(niter_x):\n for jj in range(niter_y):\n if verbose>1:\n print \"ii,jj>>\",ii,jj\n if filtersize[0]==1:\n indi = 1\n else:\n indi = filtersize[0]-1\n if filtersize[1]==1:\n indj = 1\n else:\n indj = filtersize[1]-1\n select = bigdata[ii:(ii+indi),jj:(jj+indj)].ravel()\n #select = select[isfinite(select)]\n #residualSelection = abs(select - median(select))\n\n doFilter = True\n\n if verbose:\n print \"doFilter?>>\",doFilter\n if verbose>1:\n print \"select>>\",select\n\n if doFilter: \n newval = ( select ).std()\n else:\n newval = bigdata[ii,jj]\n\n if verbose>1:\n print \"newval>>\",newval\n\n filt[ii,jj] = newval\n\n #print filt.shape, [(filtersize[0]-1)/2,niter_x-(filtersize[0]-1)/2,(filtersize[0]-1)/2,niter_y-(filtersize[0]-1)/2]\n return filt[(filtersize[0]-1)/2:niter_x-(filtersize[0]-1)/2,(filtersize[0]-1)/2:niter_y-(filtersize[0]-1)/2]","def fourier_coefficient(t, freq, n=1, axis=-1):\n t, freq, n = map(np.asanyarray, (t, freq, n))\n return np.exp(1j * 2 * np.pi * freq * n * t).sum(axis=axis)","def test_fft_complex_1d():\n\ta = np.exp(2j * np.pi * np.arange(8) / 8)\n\tdummy_array = xr.DataArray(a, dims=['x'])\n\tchunked_array = dummy_array.chunk(chunks={'x': 2})\n\tspectrum_array, spectrum_coords, spectrum_dims = \\\n\t\txfft._fft(chunked_array, nfft={'x': 16}, dim=['x'], dx={'x': 0.5})\n\tassert np.array_equal(spectrum_array.compute(), np.fft.fft(a, n=16))\n\tassert np.array_equal(spectrum_coords['f_x'], np.fft.fftfreq(16, d=0.5))\n\tassert 'f_x' in spectrum_dims","def stft(x, fft_size, hopsamp):\n w = np.hamming(fft_size)\n return np.array([np.fft.rfft(w*x[i:i+fft_size]) \n for i in range(0, len(x)-fft_size, hopsamp)])","def FFT(x):\n x = np.asarray(x, dtype=float)\n N = x.shape[0]\n \n if N % 2 > 0:\n raise ValueError(\"size of x must be a power of 2\")\n elif N <= 32: # this cutoff should be optimized\n return DFT(x)\n else:\n X_even = FFT(x[::2])\n X_odd = FFT(x[1::2])\n factor = np.exp(-2j * np.pi * np.arange(N) / N)\n return np.concatenate([X_even + factor[:int(N / 2)] * X_odd,\n X_even + factor[int(N / 2):] * X_odd])","def tomography_filter(arr, axis=2, filter_type='hamming', backend='autograd'):\n func = getattr(scipy.signal.windows, filter_type)\n filter = func(arr.shape[axis])\n if axis != len(arr.shape) - 1:\n arr = swap_axes(arr, [axis, len(arr.shape) - 1])\n if backend == 'pytorch':\n args = {'device': arr.device}\n else:\n args = {}\n arr_r, arr_i = fft(arr, zeros_like(arr, requires_grad=False, **args))\n arr_r = arr_r * filter\n arr_i = arr_i * filter\n arr, _ = ifft(arr_r, arr_i)\n if axis != len(arr.shape) - 1:\n arr = swap_axes(arr, [axis, len(arr.shape) - 1])\n return arr","def test_fft_real_2d():\n\ta = np.mgrid[:5, :5, :5][0]\n\tdummy_array = xr.DataArray(a, dims=['x', 'y', 'z'])\n\tchunked_array = dummy_array.chunk(chunks={'x': 2, 'y': 2, 'z': 2})\n\tspectrum_array, spectrum_coords, spectrum_dims = \\\n\t\txfft._fft(chunked_array, nfft={'y': 14, 'z': 18}, dim=['y', 'z'],\n\t\t dx={'y': 0.01, 'z': 0.02}, sym=False)\n\tassert np.allclose(spectrum_array.compute(),\n\t np.fft.rfftn(a, s=(18, 14), axes=(2, 1)))\n\tassert np.array_equal(spectrum_coords['f_y'], np.fft.rfftfreq(14, d=0.01))\n\tassert np.array_equal(spectrum_coords['f_z'], np.fft.fftfreq(18, d=0.02))\n\tassert ('x', 'f_y', 'f_z') == spectrum_dims","def IDFT2(fourier_image):\n return IDFT(IDFT(fourier_image).transpose()).transpose()","def fourier(array, zero_in_middle = False, first_correction = False, zeropad_to = None, window_function = \"none\", window_length = 0, flag_plot = False):\n # shift time = 0 to first element\n if zero_in_middle == True:\n array = numpy.fft.ifftshift(array)\n \n \n \n # half the first element\n if first_correction == True: \n dim = len(numpy.shape(array))\n if dim == 1:\n array[0] /= 2\n elif dim == 2:\n array[0,:] /= 2\n array[:,0] /= 2\n elif dim > 2:\n print(\"WARNING (fourier.fourier.py): correction of the first element is not done!\")\n \n \n # window function\n if window_function != \"none\": \n array = window_functions(array, window_function, window_length, flag_plot = flag_plot)\n \n \n \n # the fft\n array = numpy.fft.fft(array, n = zeropad_to)\n \n # move the array back if it was shifted\n if zero_in_middle == True:\n array = numpy.fft.fftshift(array)\n \n return array","def uirfft2(inarray, shape=None):\n return uirfftn(inarray, 2, shape=shape)","def ifftn_1d(a):\r\n b = np.fft.fftpack.ifftn(a, axes=(len(a.shape)-1, )) \r\n return np.multiply(b, np.sqrt(a.shape[-1]))","def fftn(a):\r\n b = np.fft.fftpack.fftn(a, axes=(len(a.shape)-2,len(a.shape)-1)) \r\n return np.divide(b, np.sqrt(a.shape[-1] * a.shape[-2]))","def uifftn(inarray, dim=None):\n if dim is None:\n dim = inarray.ndim\n outarray = np.fft.ifftn(inarray, axes=range(-dim, 0))\n return outarray * np.sqrt(np.prod(inarray.shape[-dim:]))","def upsample_filt(size):\r\n factor = (size + 1) // 2\r\n if size % 2 == 1:\r\n center = factor - 1\r\n else:\r\n center = factor - 0.5\r\n og = np.ogrid[:size, :size]\r\n return (1 - abs(og[0] - center) / factor) * \\\r\n (1 - abs(og[1] - center) / factor)","def dct(n_filters, n_input):\n\n basis = np.empty((n_filters, n_input))\n basis[0, :] = 1.0 / np.sqrt(n_input)\n\n samples = np.arange(1, 2*n_input, 2) * np.pi / (2.0 * n_input)\n\n for i in range(1, n_filters):\n basis[i, :] = np.cos(i*samples) * np.sqrt(2.0/n_input)\n\n return basis","def filtered_fourier(self):\r\n\r\n freqs = tsu.get_freqs(self.sampling_rate, self.data.shape[-1])\r\n\r\n if self.ub is None:\r\n self.ub = freqs[-1]\r\n\r\n power = fftpack.fft(self.data)\r\n idx_0 = np.hstack([np.where(freqs < self.lb)[0],\r\n np.where(freqs > self.ub)[0]])\r\n\r\n #Make sure that you keep the DC component:\r\n keep_dc = np.copy(power[..., 0])\r\n power[..., idx_0] = 0\r\n power[..., -1 * idx_0] = 0 # Take care of the negative frequencies\r\n power[..., 0] = keep_dc # And put the DC back in when you're done:\r\n\r\n data_out = fftpack.ifft(power)\r\n\r\n data_out = np.real(data_out) # In order to make sure that you are not\r\n # left with float-precision residual\r\n # complex parts\r\n\r\n return ts.TimeSeries(data=data_out,\r\n sampling_rate=self.sampling_rate,\r\n time_unit=self.time_unit)","def FourierTransform(data, nPoints):\r\n tdf = np.fft.fft(data, nPoints)\r\n return tdf","def test_fft_complex_3d():\n\ta, b, c = np.meshgrid([0, 1, 0, 0], [0, 1j, 1j], [0, 1, 1, 1])\n\tdummy_array = xr.DataArray(a * b * c, dims=['x', 'y', 'z'])\n\tchunked_array = dummy_array.chunk(chunks={'x': 2, 'y': 2, 'z': 2})\n\tspectrum_array, spectrum_coords, spectrum_dims = \\\n\t\txfft._fft(chunked_array, nfft={'x': 8, 'y': 6, 'z': 8},\n\t\t dim=['x', 'y', 'z'], dx={'x':12, 'y': 0.01, 'z': 0.02})\n\tassert np.allclose(spectrum_array.compute(),\n\t np.fft.fftn(a * b * c, s=(8, 6, 8)))\n\tassert np.array_equal(spectrum_coords['f_x'], np.fft.fftfreq(8, d=12))\n\tassert np.array_equal(spectrum_coords['f_y'], np.fft.fftfreq(6, d=0.01))\n\tassert np.array_equal(spectrum_coords['f_z'], np.fft.fftfreq(8, d=0.02))\n\tassert ('f_x', 'f_y', 'f_z') == spectrum_dims","def T_fourier(shape, T, is_fft_shifted = True):\n # make i, j, k for each pixel\n i = np.fft.fftfreq(shape[0]) \n j = np.fft.fftfreq(shape[1])\n k = np.fft.fftfreq(shape[2])\n i, j, k = np.meshgrid(i, j, k, indexing='ij')\n\n if is_fft_shifted is False :\n i = np.fft.ifftshift(i)\n j = np.fft.ifftshift(j)\n k = np.fft.ifftshift(k)\n\n phase_ramp = np.exp(- 2J * np.pi * (i * T[0] + j * T[1] + k * T[2]))\n return phase_ramp","def fft(a, n=None, axes=-1, norm=None):\n\n return image.image(np.fft.fft(a, n, axes, norm), pixelsize=a.pixelsize)","def fourier_gaussian(input, sigma, n=-1, axis=-1, output=None):\n input = numpy.asarray(input)\n output = _get_output_fourier(output, input)\n axis = normalize_axis_index(axis, input.ndim)\n sigmas = _ni_support._normalize_sequence(sigma, input.ndim)\n sigmas = numpy.asarray(sigmas, dtype=numpy.float64)\n if not sigmas.flags.contiguous:\n sigmas = sigmas.copy()\n\n _nd_image.fourier_filter(input, sigmas, n, axis, output, 0)\n return output","def _imfilter(x_data, f_data):\n return pipe(f_data, ifftshift, fftn, lambda x: x * fftn(x_data), ifftn).real","def spectrum_fourier(self):\r\n\r\n data = self.input.data\r\n sampling_rate = self.input.sampling_rate\r\n\r\n fft = fftpack.fft\r\n if np.any(np.iscomplex(data)):\r\n # Get negative frequencies, as well as positive:\r\n f = np.linspace(-sampling_rate/2., sampling_rate/2., data.shape[-1])\r\n spectrum_fourier = np.fft.fftshift(fft(data))\r\n else:\r\n f = tsu.get_freqs(sampling_rate, data.shape[-1])\r\n spectrum_fourier = fft(data)[..., :f.shape[0]]\r\n \r\n return f, spectrum_fourier","def test_fft_real_3d():\n\ta = np.mgrid[:7, :5, :5][0]\n\tdummy_array = xr.DataArray(a, dims=['x', 'y', 'z'])\n\tchunked_array = dummy_array.chunk(chunks={'x': 7, 'y': 5, 'z': 5})\n\tspectrum_array, spectrum_coords, spectrum_dims = \\\n\t\txfft._fft(chunked_array, nfft={'x': 11, 'y': 14, 'z': 18},\n\t\t dim=['x', 'y', 'z'], dx={'x':12, 'y': 0.01, 'z': 0.02},\n\t\t sym=False)\n\tassert np.allclose(spectrum_array.compute(),\n\t np.fft.rfftn(a.T, s=(18, 14, 11)).T)\n\tassert np.array_equal(spectrum_coords['f_x'], np.fft.rfftfreq(11, d=12))\n\tassert np.array_equal(spectrum_coords['f_y'], np.fft.fftfreq(14, d=0.01))\n\tassert np.array_equal(spectrum_coords['f_z'], np.fft.fftfreq(18, d=0.02))\n\tassert ('f_x', 'f_y', 'f_z') == spectrum_dims","def irfft(a, n=None, axes=-1, norm=None):\n return image.image(np.fft.irfft(a, n, axes, norm), pixelsize=image.getPixelsize(a))","def U_QFT(n):\n dim = 2**n # Hilbert space dimensionality\n Gate= [[np.exp(2 * np.pi * 1j * x * y / dim) for x in range(dim)] for y in range(dim)]\n Gate = np.array(Gate)/np.sqrt(dim)\n return Gate","def median_filter(self):\n print \"Median-Filtering...\"\n D = self.D\n x = np.median(np.median(D,axis=1),axis=1)\n for i in xrange(len(x)):\n D[i,:,:] -= x[i]\n self.D = D\n print \"done.\"","def fftfilter1d(vec, bandwidth, retfilter=False):\n # 2011-03-11 14:15 IJC: Created\n\n from numpy import real, fft, floor, ceil\n\n vec = array(vec, copy=True)\n\n # Errorchecking\n if len(vec.shape)<>1:\n print \"Input array must be 1D -- try using .ravel()\"\n return -1\n\n\n npts = vec.size\n\n filter = concatenate((zeros(floor(npts/2.) - bandwidth), \n ones(bandwidth * 2 + 1),\n zeros(ceil(npts/2.) - bandwidth - 1)))\n\n ret = real(fft.ifft(fft.ifftshift( fft.fftshift(fft.fft(vec)) * filter )))\n\n if retfilter==True:\n ret = [ret, filter]\n\n return ret","def uifft2(inarray):\n return uifftn(inarray, 2)","def fft(signal):\r\n if signal.size == 1:\r\n return signal\r\n\r\n even_part = fft(signal[::2]) # Only grab even elements\r\n odd_part = fft(signal[1::2]) # Only grab odd elements\r\n\r\n factor = np.exp(-2j * np.pi * np.arange(signal.size) / signal.size)\r\n return np.concatenate([even_part + factor[:int(signal.size / 2)] * odd_part,\r\n even_part + factor[int(signal.size / 2):] * odd_part])","def ft(x):\n y = np.fft.rfft(x)\n\n phi = 2 * np.pi * np.random.random(len(y))\n\n phi[0] = 0.0\n if len(x) % 2 == 0:\n phi[-1] = 0.0\n\n y = y * np.exp(1j * phi)\n return np.fft.irfft(y, n=len(x))","def IDFT(fourier_signal):\n n = fourier_signal.shape[0]\n omega = np.exp((((2 * np.pi)*1j) / n))\n\n e_items = np.vander(omega**np.arange(n), n, True)\n org_signal = np.dot(e_items, fourier_signal)/n\n\n return org_signal","def __normalize_after_fft(arr):\n\n n1, n2 = arr.shape[0], arr.shape[1]\n for i in range(n1):\n for j in range(n2):\n arr[i, j] *= n1 * n2\n\n return arr","def SIDFT(X,D):\n N=len(X)\n x=np.zeros(N,'complex')\n for n in range(0,N,1):\n for k in range(0,N,1):\n x[n]=x[n]+np.exp(-1j*2*np.pi*k*D/N)*X[k]*np.exp(1j*2*np.pi*k*n/N)\n return x/N","def frft2d(matrix, a=[0.7, 0.7]):\n temp = numpy.zeros((matrix.shape[0], matrix.shape[1]), dtype=numpy.complex)\n for k in range(0, matrix.shape[0]):\n temp[k, :] = frft(matrix[k, :], a[0])\n out = numpy.zeros((temp.shape[0], temp.shape[1]), dtype=numpy.complex)\n for m in range(0, temp.shape[1]):\n out[:, m] = frft(temp[:, m], a[1])\n\n return out","def downsample(x, height=494):\n m,n = x.shape[-2:]\n # factor = width/n\n factor = m/height\n width = round(n/factor/2)*2\n F = np.fft.rfft2(x)\n A = F[...,0:height//2,0:width//2+1]\n B = F[...,-height//2:,0:width//2+1]\n F = np.concatenate([A,B], axis=0)\n # S = round(2*factor)\n # A = F[...,0:m//S,0:n//S+2]\n # B = F[...,-m//S+1:,0:n//S+2]\n # F = np.concatenate([A,B], axis=-2)\n f = np.fft.irfft2(F, s=(height, width))\n return f","def subsample_fourier(x, k):\n return subsamplefourier(x,k)","def fourier(self, k, index = None):\n if self.dims == 1 and index is None:\n index = 0\n\n if not index is None:\n\n new_data = {}\n\n for key, value in self.__m__.items():\n\n key2 = tuple(key[:index] + key[index+1:])\n matrix = value*numpy.exp(2j*numpy.pi*key[index]*k)\n\n if key2 in new_data:\n new_data[key2] += matrix\n\n else:\n new_data[key2] = matrix\n\n return TightBinding(new_data)\n\n else:\n\n result = self\n for i in k:\n result = result.fourier(i, index = 0)\n return result","def ifft(a, n=None, axes=-1, norm=None):\n return image.image(np.fft.ifft(a, n, axes, norm), pixelsize=image.getPixelsize(a))","def ManualFourier(x):\n x = np.asarray(x, dtype=float)\n N = x.shape[0]\n\n if np.log2(N) % 1 > 0:\n raise ValueError(\"size of x must be a power of 2\")\n\n # N_min here is equivalent to the stopping condition above,\n # and should be a power of 2\n N_min = min(N, 32)\n\n # Perform an O[N^2] DFT on all length-N_min sub-problems at once\n n = np.arange(N_min)\n k = n[:, None]\n M = np.exp(-2j * np.pi * n * k / N_min)\n X = np.dot(M, x.reshape((N_min, -1)))\n\n # build-up each level of the recursive calculation all at once\n while X.shape[0] < N:\n X_even = X[:, :int(X.shape[1] / 2)]\n X_odd = X[:, int(X.shape[1] / 2):]\n factor = np.exp(-1j * np.pi * np.arange(X.shape[0])\n / X.shape[0])[:, None]\n X = np.vstack([X_even + factor * X_odd,\n X_even - factor * X_odd])\n return X.ravel()","def FourierSeries(input,N=None):\n \n N=len(input);\n\n w=2*cmath.pi/N;\n input=input[0:N];\n n=numpy.arange(0,N); \n r=cexp(-1j*w*n);\n\n output = [complex(0)] * N \n for k in range(N): \n r=input*cexp(-1j*w*n*k) \n output[k]=np.sum(r);\n \n \n return output;","def _inv_fft(cls, n_extp, n, idx, a):\n import numpy as np\n ts = []\n for i in range(0, n_extp):\n # Sinusoid for the ith frequency\n s_array = cls._signals(idx, i, n)\n\n # Computing the inverse Fouries transformation term for the significant coefficients obtained from the\n # spectral density\n ts.append(np.sum(a * s_array) // n)\n return np.array(ts)","def ifftn(a):\r\n b = np.fft.fftpack.ifftn(a, axes=(len(a.shape)-2,len(a.shape)-1)) \r\n return np.multiply(b, np.sqrt(a.shape[-1] * a.shape[-2]))","def _frz(a):\n if a.ndim == 0:\n a.shape = (1,)\n return a","def scales_from_fourier(f, wf, w0):\n\n f_arr = np.asarray(f)\n\n if wf == 'morlet':\n return (f_arr * (w0 + np.sqrt(2 + w0**2))) / (4 * np.pi)\n else:\n raise ValueError('wavelet function not available')","def frft(f, a):\n ret = numpy.zeros_like(f, dtype=numpy.complex)\n f = f.copy().astype(numpy.complex)\n N = len(f)\n shft = numpy.fmod(numpy.arange(N) + numpy.fix(N / 2), N).astype(int)\n sN = numpy.sqrt(N)\n a = numpy.remainder(a, 4.0)\n\n # Special cases\n if a == 0.0:\n return f\n if a == 2.0:\n return numpy.flipud(f)\n if a == 1.0:\n ret[shft] = numpy.fft.fft(f[shft]) / sN\n return ret\n if a == 3.0:\n ret[shft] = numpy.fft.ifft(f[shft]) * sN\n return ret\n\n # reduce to interval 0.5 < a < 1.5\n if a > 2.0:\n a = a - 2.0\n f = numpy.flipud(f)\n if a > 1.5:\n a = a - 1\n f[shft] = numpy.fft.fft(f[shft]) / sN\n if a < 0.5:\n a = a + 1\n f[shft] = numpy.fft.ifft(f[shft]) * sN\n\n # the general case for 0.5 < a < 1.5\n alpha = a * numpy.pi / 2\n tana2 = numpy.tan(alpha / 2)\n sina = numpy.sin(alpha)\n f = numpy.hstack((numpy.zeros(N - 1), sincinterp(f), numpy.zeros(N - 1))).T\n\n # chirp premultiplication\n chrp = numpy.exp(-1j * numpy.pi / N * tana2 / 4 *\n numpy.arange(-2 * N + 2, 2 * N - 1).T ** 2)\n f = chrp * f\n\n # chirp convolution\n c = numpy.pi / N / sina / 4\n tmp = numpy.exp(1j * c * numpy.arange(-(4 * N - 4), 4 * N - 3).T ** 2)\n ret = scipy.signal.fftconvolve(\n numpy.exp(1j * c * numpy.arange(-(4 * N - 4), 4 * N - 3).T ** 2),\n f\n )\n ret = ret[4 * N - 4:8 * N - 7] * numpy.sqrt(c / numpy.pi)\n\n # chirp post multiplication\n ret = chrp * ret\n\n # normalizing constant\n ret = numpy.exp(-1j * (1 - a) * numpy.pi / 4) * ret[N - 1:-N + 1:2]\n\n return ret","def DFT(signal):\n n = signal.shape[0]\n omega = np.exp(((((-2) * np.pi)*1j) / n))\n\n e_items = np.vander(omega**np.arange(n), n, True)\n fourier_signal = np.dot(e_items, signal)\n\n return fourier_signal.astype(np.complex128)","def fourier_der(im):\n ft_img = DFT2(im)\n ft_img = np.fft.fftshift(ft_img)\n\n n_x = im.shape[1]\n coeff_x = (2 * np.pi * 1j)/n_x\n u_freq = np.array([n if n < int(n_x/2) else (n-n_x) for n in range(n_x)]) * 1j\n u_freq = np.array([np.fft.fftshift(u_freq)]*im.shape[0]).transpose()\n dx_ft = coeff_x * IDFT2(np.fft.ifftshift(u_freq.transpose() * ft_img))\n\n m_y = im.shape[0]\n coeff_y = (2 * np.pi * 1j)/m_y\n v_freq = np.array([m if m < int(m_y/2) else (m-m_y) for m in range(m_y)]) * 1j\n v_freq = np.array([np.fft.fftshift(v_freq)] * im.shape[1]).transpose()\n tr = IDFT2(np.fft.ifftshift(v_freq * ft_img))\n dy_ft = coeff_y * tr\n\n magnitude = np.sqrt(np.abs(dx_ft)**2 + np.abs(dy_ft)**2)\n return magnitude.real.astype(np.float64)","def FourierTransform2D(xdata, zdata, nPoints):\r\n freq = FourierFrequency(xdata, nPoints)\r\n tdf = np.zeros_like(zdata, dtype=complex)\r\n for u, i in enumerate(zdata):\r\n tdf[u] = FourierTransform(xdata, i, nPoints)\r\n return freq, tdf","def test_fourier_dim_2():\n expected_res = np.array([[1 / np.sqrt(2), 1 / np.sqrt(2)], [1 / np.sqrt(2), -1 / np.sqrt(2)]])\n\n res = fourier(2)\n\n bool_mat = np.isclose(res, expected_res)\n np.testing.assert_equal(np.all(bool_mat), True)","def fftn(a, s=None, axes=None, norm=None):\n return image.image(np.fft.fftn(a, s, axes, norm), pixelsize=image.getPixelsize(a))","def IDFT2(fourier_image):\n fourier_image = fourier_image.astype(np.complex128)\n M, N = fourier_image.shape\n # build the idft2_matrix transform\n omega_y = np.exp(2 * np.pi * 1j / M)\n u, v = np.meshgrid(np.arange(M), np.arange(M))\n idft2_matrix = np.power(omega_y, u*v)\n\n # calculate the 2D inverse fourier transform\n return 1/M * np.dot(idft2_matrix, IDFT(fourier_image))","def boxfilter(y, N):\n n = np.clip(np.arange(len(y)), 0, N-1) + 1\n return np.array([ y[i+1-ni:i+1].sum() / ni for i,ni in zip(range(len(y)), n) ])","def IDFT(fourier_signal):\n fourier_signal = fourier_signal.astype(np.complex128)\n # find the length of the signal\n N = fourier_signal.shape[0]\n if fourier_signal.ndim == 2:\n M, N = fourier_signal.shape\n\n # calculate IDFT matrix\n u, v = np.meshgrid(np.arange(N), np.arange(N))\n omega = np.exp(2 * np.pi * 1j / N)\n idft_matrix = np.power(omega, u*v)\n\n # if it is a matrix of fourier signals\n if fourier_signal.ndim == 2:\n # calculate the Fourier Transform\n signal = np.dot(idft_matrix, fourier_signal.transpose())\n return 1/N * signal.transpose()\n\n # calculate the inverse Fourier Transform\n signal = np.dot(idft_matrix, fourier_signal)\n return 1/N * signal","def apply_1d_median_filter(n, timage):\n image_shape = timage.shape\n ovrlay = int(n / 2)\n res_matrix = np.copy(timage)\n for i in np.arange(image_shape[0])[1:-1]:\n local_matrix = timage[i - ovrlay:i + ovrlay + 1] \n median = np.median(local_matrix)\n res_matrix[i] = median\n return res_matrix","def samp_from_freq(n_samples):\n datapath = os.path.normpath(os.getcwd()+os.sep+os.pardir+os.sep+\"Dataset\"+os.sep+\"All_channels_500hz.npy\")\n data = np.load(datapath)\n fourier = np.fft.rfft(data,axis=0)\n fourier_mean = np.mean(fourier,axis=1)\n print(fourier.shape)\n print(fourier_mean.shape)\n signal = np.fft.irfft(fourier_mean)[1:]\n plt.plot(fourier_mean[1:])\n plt.show()\n plt.plot(signal)\n plt.show()\n #Generating frequency spectrum\n \"\"\"\n x = np.linspace(0,100,251)\n x2 = np.linspace(0,5,251)\n spectrum = 50*np.exp(-(x-30)**2/2)\n spectrum += 60*np.sin(np.random.randn(251)*2*np.pi)*np.exp(-x2)\n #spectrum += np.random.randn(251)\n plt.plot(spectrum)\n plt.show()\n signal = np.fft.irfft(spectrum)\n plt.plot(signal)\n plt.show()\n \"\"\"\n signal = None\n return signal","def apply_fourier_transform(chunked_audio):\n pass","def vvc_filters_2d(kernel_size):\n vvc_filters = []\n half_kernel = (kernel_size - 8) // 2\n for frac_pos in frac_positions():\n filter_x = filter_coefficients(int(frac_pos.split(\",\")[0]))\n filter_y = filter_coefficients(int(frac_pos.split(\",\")[1]))\n\n filter_vvc = np.tile(filter_x, 8).reshape((8, 8))\n for index in range(len(filter_y)):\n filter_vvc[index, :] *= filter_y[index]\n filter_vvc = filter_vvc / (64 * 64)\n\n vvc_filters.append(np.pad(filter_vvc, ((half_kernel + 1, half_kernel), (half_kernel + 1, half_kernel)),\n 'constant', constant_values=0))\n return vvc_filters","def ufftn(inarray, dim=None):\n if dim is None:\n dim = inarray.ndim\n outarray = np.fft.fftn(inarray, axes=range(-dim, 0))\n return outarray / np.sqrt(np.prod(inarray.shape[-dim:]))","def padded_ft_op(space, padded_size):\n padded_op = ResizingOperator(\n space, ran_shp=[padded_size for _ in range(space.ndim)])\n shifts = [not s % 2 for s in space.shape]\n ft_op = FourierTransform(\n padded_op.range, halfcomplex=False, shift=shifts)\n \n return ft_op * padded_op","def filtermed(f, medfiltsize=9): # medfiltfunc='ndimage'\n # # Median filter to smooth out single-pixel deviations\n # if medfiltfunc == 'ndimage': f_medfilt = median_filter(f, size=medfiltsize)\n # elif medfiltfunc == 'signal': f_medfilt = medfilt(f, kernel_size=medfiltsize)\n f_medfilt = median_filter(f, size=medfiltsize)\n return f_medfilt","def apply3filter(array, filter_):\n s = int(len(filter_)/2)\n width = len(array[0])\n height = len(array)\n new_array = np.array(np.zeros((height,width)))\n for row in range(s, (height-s)):\n for col in range(s, (width-s)):\n new_array[row,col] = np.sum(filter_ * array[(row-s):(row+s+1),(col-s):(col+s+1)])\n return new_array","def stretch(snd_array, factor, window_size=2**13, h=2**11):\n phase = np.zeros(window_size)\n hanning_window = np.hanning(window_size)\n result = np.zeros( len(snd_array) /factor + window_size)\n\n for i in np.arange(0, len(snd_array)-(window_size+h), h*factor):\n\n # two potentially overlapping subarrays\n a1 = snd_array[i: i + window_size]\n a2 = snd_array[i + h: i + window_size + h]\n\n # the spectra of these arrays\n s1 = np.fft.fft(hanning_window * a1)\n s2 = np.fft.fft(hanning_window * a2)\n\n # rephase all frequencies\n phase = (phase + np.angle(s2/s1)) % 2*np.pi\n\n a2_rephased = np.fft.ifft(np.abs(s2)*np.exp(1j*phase))\n i2 = int(i/factor)\n result[i2 : i2 + window_size] += hanning_window*a2_rephased\n\n result = ((2**(16-4)) * result/result.max()) # normalize (16bit)\n\n return result.astype('int16')","def rfft(a, n=None, axes=-1, norm=None):\n return image.image(np.fft.rfft(a, n, axes, norm), pixelsize=image.getPixelsize(a))","def get_fft(self):\n\t\t# Get the \"ideal\" evenly spaced times\n\t\teven_times = numpy.linspace(self.buf[0][0], self.buf[-1][0], len(self.buf))\n\t\t\n\t\t# Interpolate the data to generate evenly temporally spaced samples\n\t\tinterpolated = numpy.interp(even_times, *zip(*self.buf))\n\t\t\n\t\t# Perform the FFT\n\t\tfft = numpy.fft.rfft(interpolated)\n\t\treturn zip(numpy.abs(fft), numpy.angle(fft))","def _get_split_spectrum(T,WK):\n\n n_samples, n_frames, n_frequencies = T.shape\n U = np.zeros([n_samples, n_samples, n_frames, n_frequencies], dtype=np.complex64)\n \n for l in range(n_frequencies):\n for n in range(n_samples):\n _T = np.zeros([n_samples, n_frames], dtype=np.complex64)\n _T[n,:] = T[n,:,l]\n inv_WK = np.linalg.inv(WK[:,:,l])\n U[n,:,:,l] = np.dot(inv_WK, _T)\n \n return U","def filters(array, sample_frequency):\n strain = TimeSeries(array, sample_rate=int(sample_frequency))\n white_data = strain.whiten(fftlength=4, fduration=4)\n bp_data = white_data.bandpass(50, 250)\n return bp_data.value","def ifft2(self, array):\n self._ifft2.input_array[:] = array\n return self._ifft2()","def istft(X, hop_size):\n\n # make an empty signal of the appropriate length\n window_size,num_hops = X.shape\n signal_length = (num_hops-1)*hop_size + window_size\n signal = np.zeros(signal_length,dtype='complex');\n\n #fill the signal\n for n in range(num_hops):\n start = n * hop_size\n end = start + window_size\n signal[start:end] = signal[start:end] + ifft(X[:,n])\n return signal","def fourier_der(im):\n im = im.astype(np.float64)\n # constants\n M, N = im.shape\n u = np.meshgrid(np.arange(N), np.arange(M))[0] - N//2\n v = np.meshgrid(np.arange(N), np.arange(M))[1] - M//2\n u_der, v_der = (2 * np.pi * 1j / N), (2 * np.pi * 1j / M)\n\n # calculate dx, dy\n dx = u_der * IDFT2(np.fft.fftshift(u) * DFT2(im))\n dy = v_der * IDFT2(np.fft.fftshift(v) * DFT2(im))\n\n return np.sqrt(np.abs(dx)**2 + np.abs(dy)**2) # = magnitude","def filled_space_prop(e: np.ndarray, arg: np.ndarray) -> np.ndarray:\n\n freq = np.exp(1j*arg)\n out = np.fft.ifft2(np.fft.fft2(e)*freq)\n\n return out","def urfftn(inarray, dim=None):\n if dim is None:\n dim = inarray.ndim\n outarray = np.fft.rfftn(inarray, axes=range(-dim, 0))\n return outarray / np.sqrt(np.prod(inarray.shape[-dim:]))","def fourier(data, temp_freq, axis, output = 'amplitude'):\n\t\t\n\t\n\t# take largest possible multiple of F1 from PSTH.\n\t# Generate freq and fft\n\t# generate amplitude\n\t# return amplitude, F0, F1 and F2 values","def _discrete_fourier_transform(\n series,\n dft_length,\n norm,\n inverse_sqrt_win_size,\n lower_bounding,\n apply_normalising_factor=True,\n cut_start_if_norm=True,\n):\n start = 2 if norm else 0\n output_length = start + dft_length\n\n if cut_start_if_norm:\n c = int(start / 2)\n else:\n c = 0\n start = 0\n\n dft = np.zeros(output_length - start)\n for i in range(c, int(output_length / 2)):\n for n in range(len(series)):\n dft[(i - c) * 2] += series[n] * math.cos(2 * math.pi * n * i / len(series))\n dft[(i - c) * 2 + 1] += -series[n] * math.sin(\n 2 * math.pi * n * i / len(series)\n )\n\n if apply_normalising_factor:\n if lower_bounding:\n dft[1::2] = dft[1::2] * -1 # lower bounding\n\n std = np.std(series)\n if std == 0:\n std = 1\n dft *= inverse_sqrt_win_size / std\n\n return dft","def inverse_fft(self, array_in):\r\n # Find side length, as spectrum may or may not have been doubled\r\n # for aliasing control\r\n side = array_in.shape[0]\r\n\r\n out = side**2*self.sign_mat[0:side, 0:side]*np.fft.ifft2(array_in)\r\n return out","def fftkernel(x, w):\n L = len(x)\n Lmax = L + 3 * w\n n = nextpow2(Lmax)\n X = np.fft.fft(x, n)\n f = np.arange(0, n, 1.0) / n\n f = np.concatenate((-f[:int(n / 2)], f[int(n / 2):0:-1]))\n K = np.exp(-0.5 * (w * 2 * np.pi * f) ** 2)\n y = np.fft.ifft(X * K, n)\n y = y[:L].copy()\n return y","def apply_1d_filter(bfilter, timage):\n image_length = len(timage)\n ovrlay = int(bfilter.shape[0] / 2)\n tmp_array = np.zeros(image_length + 2 * ovrlay)\n tmp_array[ovrlay:-ovrlay] = timage\n res_array = np.zeros(image_length )\n for i in np.arange(image_length) + ovrlay:\n local_matrix = tmp_array[i - ovrlay:i + ovrlay + 1]\n res_array[i - ovrlay] = sum(local_matrix * bfilter)\n return res_array","def __qft_input_state(self, size) -> None:\n\n for i in range(size):\n self.h(i)\n self.u1(-math.pi / float(2 ** i), i)"],"string":"[\n \"def _get_fourier_filter(self):\\n size = max(64, int(2 ** np.ceil(np.log2(2 * self.m[-1].item()))))\\n\\n pi = torch.acos(torch.zeros(1)).item() * 2.0\\n n = torch.cat(\\n [\\n torch.arange(1, size // 2 + 1, 2, device=self.n.device),\\n torch.arange(size // 2 - 1, 0, -2, device=self.n.device),\\n ]\\n )\\n f = torch.zeros(size, device=self.n.device)\\n f[0] = 0.25\\n if self.flat:\\n f[1::2] = -1 / (pi * n).pow(2)\\n else:\\n f[1::2] = -self.s_detect.abs().pow(2) / (\\n pi\\n * (self.d_source + self._d_detect())\\n * torch.sin(\\n n\\n * self.s_detect.abs()\\n / (self.d_source + self._d_detect())\\n )\\n ).pow(2)\\n f = torch.stack(\\n [f, torch.zeros(f.shape, device=self.n.device)], dim=-1\\n )\\n f = fftshift(f, dim=(-2,))\\n\\n filt = fft1(f)[..., 0]\\n\\n if self.filter_type == \\\"hamming\\\":\\n # hamming filter\\n fac = torch.tensor(\\n np.hamming(size).astype(np.float32), device=f.device\\n )\\n elif self.filter_type == \\\"hann\\\":\\n # hann filter\\n fac = torch.tensor(\\n np.hanning(size).astype(np.float32), device=f.device\\n )\\n elif self.filter_type == \\\"cosine\\\":\\n # cosine filter\\n fac = torch.sin(\\n torch.linspace(0, pi, size + 1, device=f.device)[:-1]\\n )\\n else:\\n # ramp / ram-lak filter\\n fac = 1.0\\n\\n return fac * filt\",\n \"def _irfft2d(f_x) :\",\n \"def smoothfield( d , boxsize, Rth ):\\n\\n ng = N.shape(d)[0]\\n dk = N.fft.rfftn(d)\\n kgrid = getkgrid(boxsize,ng)\\n dk = dk * N.exp( -(kgrid * Rth) ** 2. / 2. )\\n d = N.fft.irfftn(dk)\\n\\n return d\",\n \"def blur_fourier(im, kernel_size):\\n im = im.astype(np.float64)\\n # build the kernel with zero padding\\n kernel_base = gaussian_kernel_factory(kernel_size)\\n window = np.zeros_like(im).astype(np.float64)\\n M, N = im.shape\\n dx, dy = kernel_base.shape\\n x_middle, y_middle = N//2, M//2\\n\\n window[(y_middle-dy//2):(y_middle+dy//2+1), (x_middle-dx//2):(x_middle+dx//2+1)] = kernel_base\\n\\n # multiply in the freq domain\\n return IDFT2(DFT2(im) * DFT2(np.fft.ifftshift(window))).real\",\n \"def fourier_ellipsoid(input, size, n=-1, axis=-1, output=None):\\n input = numpy.asarray(input)\\n if input.ndim > 3:\\n raise NotImplementedError(\\\"Only 1d, 2d and 3d inputs are supported\\\")\\n output = _get_output_fourier(output, input)\\n axis = normalize_axis_index(axis, input.ndim)\\n sizes = _ni_support._normalize_sequence(size, input.ndim)\\n sizes = numpy.asarray(sizes, dtype=numpy.float64)\\n if not sizes.flags.contiguous:\\n sizes = sizes.copy()\\n _nd_image.fourier_filter(input, sizes, n, axis, output, 2)\\n return output\",\n \"def blur_fourier(im, kernel_size):\\n kernel = gaus_kernel_calc(kernel_size)\\n\\n zeros = np.zeros(im.shape)\\n x_mid = np.math.floor(im.shape[1] / 2)\\n y_mid = np.math.floor(im.shape[0] / 2)\\n distance = np.math.floor(kernel_size / 2)\\n zeros[x_mid - distance: x_mid + distance + 1, y_mid - distance: y_mid + distance + 1] = kernel\\n\\n fourier_kernel = DFT2(np.fft.ifftshift(zeros))\\n fourier_img = DFT2(im)\\n fourier_blured = fourier_kernel * fourier_img\\n\\n return IDFT2(fourier_blured).real.astype(np.float64)\",\n \"def fourier(img):\\n return fourierCV(img)\",\n \"def fourier_transform2d(self):\\n\\n zerofill = np.zeros(1024 * np.array([1,1])) #so it will always be square\\n zerofill[:len(self.windowed), :len(self.windowed)] = self.windowed\\n transform = np.fft.fft2(zerofill)\\n transform = np.fft.fftshift(transform) # shift center to zero\\n transformed = np.absolute(transform)\\n tmax = transformed.max()\\n zdata = (transformed)/(tmax) # normalize to maximum value\\n\\n return zdata\",\n \"def process( fids, ndim=2 ):\\n\\timg = np.empty_like( fids )\\n\\tax = -1*(np.array( range(ndim) )+1)\\n\\t\\n\\timg = np.fft.fftshift( np.fft.fftn( fids, axes=ax, ).astype( np.complex64), axes=ax )\\n\\t\\n\\treturn np.squeeze(img)\",\n \"def fourier_spectra(x, axis=1, flip=True, duplicate=True, **kwargs):\\n\\n x = forward_fourier(x, axis=axis, duplicate=duplicate)\\n ns = x.shape[axis]\\n nw = np.int32(np.floor(ns/2) + 1)\\n slc = [slice(None)] * len(x.shape)\\n slc[axis] = slice(0, nw)\\n if flip:\\n x = np.flip(x[tuple(slc)], axis=axis)\\n return x\",\n \"def urfft2(inarray):\\n return urfftn(inarray, 2)\",\n \"def dwt(image_array, quantization_Array):\\n # Create the high pass and low pass filters\\n # both filters are non-causal\\n # symmetric\\n # [-2, -1, 0, 1, 2]\\n LPF = [-0.125, 0.25, 0.75, 0.25, -0.125]\\n LPF_center = 2\\n\\n # [ -2,-1, 0]\\n HPF = [-0.5, 1, -0.5]\\n HPF_center = 2\\n\\n nrow, ncol = image_array.shape\\n\\n # create an array that will contain the 4 different subbands of the image\\n LL = np.zeros((nrow, ncol))\\n LH = np.zeros((nrow, ncol))\\n HL = np.zeros((nrow, ncol))\\n HH = np.zeros((nrow, ncol))\\n filtered_image = [LL, LH, HL, HH]\\n\\n # filtering the rows using a low pass and high pass filters\\n LowPass_rows = np.zeros((nrow, ncol))\\n HighPass_rows = np.zeros((nrow, ncol))\\n for i in range(0, nrow):\\n LowPass_rows[i, :] = lfilter(LPF, image_array[i, :], LPF_center)\\n HighPass_rows[i, :] = lfilter(HPF, image_array[i, :], HPF_center)\\n\\n # down sample rows.\\n # which means we will have half the number of columns\\n for i in range(0, len(filtered_image)):\\n filtered_image[i] = filtered_image[i][:, ::2]\\n\\n # apply filters accross columns\\n for i in range(0, ncol):\\n LL[:, i] = lfilter(LPF, LowPass_rows[:, i], LPF_center)\\n LH[:, i] = lfilter(HPF, LowPass_rows[:, i], HPF_center)\\n HL[:, i] = lfilter(LPF, HighPass_rows[:, i], LPF_center)\\n HH[:, i] = lfilter(HPF, HighPass_rows[:, i], HPF_center)\\n\\n # down sample columns and quantize\\n for i in range(0, len(filtered_image)):\\n filtered_image[i] = filtered_image[i][::2, :]\\n filtered_image[i] = np.round(\\n filtered_image[i]/quantization_Array[i]).astype(int)\\n\\n return filtered_image\",\n \"def IDFT2D_slow(x):\\r\\n x = np.asarray(x, dtype=complex)\\r\\n \\r\\n M = len(x)\\r\\n N = len(x[0])\\r\\n mat = np.zeros((M, N), np.complex)\\r\\n \\r\\n for row in range(M):\\r\\n mat[row] = IDFT_slow(x[row])\\r\\n\\r\\n for col in range(N):\\r\\n mat[:,col] = IDFT_slow(mat[:,col])\\r\\n\\r\\n return mat\",\n \"def fftn_1d(a):\\r\\n b = np.fft.fftpack.fftn(a, axes=(len(a.shape)-1, )) \\r\\n return np.divide(b, np.sqrt(a.shape[-1]))\",\n \"def ufft2(inarray):\\n return ufftn(inarray, 2)\",\n \"def SpectrumGen(data, bin_size):\\n nfft = bin_size\\n mask = np.isfinite(data)\\n data = data-np.nanmean(data)\\n \\n\\n spectrum = scipy.fftpack.fft(data[mask], n=nfft)\\n \\n\\n return spectrum\",\n \"def medianfilter(data, filtersize, threshold=None,verbose=False):\\n # 2006/02/01 IJC at the Jet Propulsion Laboratory\\n # 2010-02-18 13:52 IJC: Converted to python\\n from numpy import zeros, median, abs, std\\n\\n print \\\"Just use scipy.signal.medfilt !!!\\\"\\n print \\\"Just use scipy.signal.medfilt !!!\\\"\\n print \\\"Just use scipy.signal.medfilt !!!\\\"\\n\\n if len(filtersize)<1:\\n print 'medianfilter2 requires that filtersize be a 1- or 2-element vector'\\n return -1\\n elif len(filtersize)==1:\\n filtersize = [filtersize[0], filtersize[0]]\\n else:\\n filtersize = filtersize[0:2]\\n\\n npix = data.shape[0]\\n npiy = data.shape[1]\\n bigsize = npix+2*(filtersize[0]-1)\\n bigdata = zeros((bigsize,bigsize),float)\\n ind = filtersize[0]-1\\n if ind==0:\\n bigdata = data\\n else:\\n bigdata[ind:(bigsize-ind), ind:(bigsize-ind)] = data\\n\\n\\n # FOR NOW, WE ASSUME FILTERSIZE IS ODD!!\\n # AND THAT DATA IS SQUARE!\\n niter_x = npix + (filtersize[0]-1)\\n niter_y = npiy + (filtersize[1]-1)\\n filt = zeros((niter_x,niter_y), float)\\n\\n for ii in range(niter_x):\\n for jj in range(niter_y):\\n if verbose>1:\\n print \\\"ii,jj>>\\\",ii,jj\\n if filtersize[0]==1:\\n indi = 1\\n else:\\n indi = filtersize[0]-1\\n if filtersize[1]==1:\\n indj = 1\\n else:\\n indj = filtersize[1]-1\\n select = bigdata[ii:(ii+indi),jj:(jj+indj)].ravel()\\n select = select[isfinite(select)]\\n #residualSelection = abs(select - median(select))\\n\\n if verbose: \\n print \\\"select.shape>>\\\",select.shape\\n print \\\"threshold>>\\\",threshold\\n\\n if threshold is not None:\\n if threshold >= 0: # raw threshold\\n doFilter = abs(bigdata[ii,jj]-median(select))/std(select)>=threshold\\n elif threshold<0: # remove outliers before applying threshold\\n npts_init = len(select)\\n select = removeoutliers(select, abs(threshold), center='median')\\n npts_final = len(select)\\n if verbose>1:\\n print \\\"threshold=\\\",threshold,\\\", removed %i points\\\" % (npts_init-npts_final)\\n \\n doFilter = abs(bigdata[ii,jj]-median(select))/std(select)>=threshold \\n else: # filter everything; threshold not set.\\n doFilter = True\\n\\n if verbose:\\n print \\\"doFilter?>>\\\",doFilter\\n if verbose>1:\\n print \\\"select>>\\\",select\\n\\n if doFilter: \\n newval = median( select )\\n else:\\n newval = bigdata[ii,jj]\\n\\n if verbose>1:\\n print \\\"newval>>\\\",newval\\n\\n filt[ii,jj] = newval\\n\\n print filt.shape, [(filtersize[0]-1)/2,niter_x-(filtersize[0]-1)/2,(filtersize[0]-1)/2,niter_y-(filtersize[0]-1)/2]\\n return filt[(filtersize[0]-1)/2:niter_x-(filtersize[0]-1)/2,(filtersize[0]-1)/2:niter_y-(filtersize[0]-1)/2]\",\n \"def test_fft_complex_2d():\\n\\ta, b, c = np.meshgrid([0, 1, 0, 0], [0, 1j, 1j], [0, 1, 1, 1])\\n\\tdummy_array = xr.DataArray(a * b * c, dims=['x', 'y', 'z'])\\n\\tchunked_array = dummy_array.chunk(chunks={'x': 2, 'y': 2, 'z': 2})\\n\\tspectrum_array, spectrum_coords, spectrum_dims = \\\\\\n\\t\\txfft._fft(chunked_array, nfft={'y': 6, 'z': 8}, dim=['y', 'z'],\\n\\t\\t dx={'y': 0.01, 'z': 0.02})\\n\\tassert np.allclose(spectrum_array.compute(),\\n\\t np.fft.fftn(a * b * c, s=(8, 6), axes=(2, 1)))\\n\\tassert np.array_equal(spectrum_coords['f_y'], np.fft.fftfreq(6, d=0.01))\\n\\tassert np.array_equal(spectrum_coords['f_z'], np.fft.fftfreq(8, d=0.02))\\n\\tassert ('x', 'f_y', 'f_z') == spectrum_dims\",\n \"def get_fft(u: np.ndarray, n: int) -> np.ndarray:\\n\\n # We get the fft of each time slice.\\n num_slices = u.shape[0]\\n ut = np.empty(u.shape, dtype=complex) # shape (20, 262144)\\n for s in range(num_slices):\\n # We reshape each slice into a 3D cube.\\n u_slice = np.reshape(u[s,:], (n, n, n)) # shape (64, 64, 64)\\n # We then take the fft of the 3D cube and add it to ut.\\n ut_slice = np.fft.fftshift(np.fft.fftn(u_slice)) # shape (64, 64, 64)\\n ut[s, :] = ut_slice.flatten()\\n\\n return ut\",\n \"def forward_fft(self, array_in):\\r\\n # Find side length, as real array may or may not be doubled for\\r\\n # aliasing control\\r\\n side = array_in.shape[0]\\r\\n div_side = 1.0/side**2\\r\\n\\r\\n out = np.fft.fft2(self.sign_mat[0:side, 0:side]*array_in)*div_side\\r\\n return out\",\n \"def denoise_frequency_domain(ut: np.ndarray, \\n filter_3d: np.ndarray) -> np.ndarray:\\n\\n num_rows = ut.shape[0]\\n ut_denoised = np.empty(ut.shape, dtype=complex)\\n for row in range(num_rows):\\n ut_slice_cube = np.reshape(ut[row, :], filter_3d.shape)\\n ut_slice_cube_denoised = ut_slice_cube*filter_3d\\n ut_denoised[row, :] = ut_slice_cube_denoised.flatten()\\n\\n return ut_denoised\",\n \"def DFT2D_slow(x):\\r\\n x = np.asarray(x, dtype=complex)\\r\\n \\r\\n M = len(x)\\r\\n N = len(x[0])\\r\\n mat = np.zeros((M, N), np.complex)\\r\\n \\r\\n for row in range(M):\\r\\n mat[row] = DFT_slow(x[row])\\r\\n\\r\\n for col in range(N):\\r\\n mat[:,col] = DFT_slow(mat[:,col])\\r\\n\\r\\n return mat\",\n \"def stdfilt2d(data, filtersize, threshold=None,verbose=False):\\n # 2012-08-07 13:42 IJMC: Created from medianfilter\\n from numpy import zeros, median, abs, std, isfinite\\n\\n if not hasattr(filtersize, '__iter__'):\\n filtersize = [filtersize]\\n\\n if len(filtersize)<1:\\n print 'medianfilter2 requires that filtersize be a 1- or 2-element vector'\\n return -1\\n elif len(filtersize)==1:\\n filtersize = [filtersize[0], filtersize[0]]\\n else:\\n filtersize = filtersize[0:2]\\n\\n npix = data.shape[0]\\n npiy = data.shape[1]\\n bigsize_x = npix+2*(filtersize[0]-1)\\n bigsize_y = npiy+2*(filtersize[1]-1)\\n bigdata = zeros((bigsize_x,bigsize_y),float)\\n ind = filtersize[0]-1\\n if ind==0:\\n bigdata = data\\n else:\\n bigdata[ind:(bigsize_x-ind), ind:(bigsize_y-ind)] = data\\n\\n\\n # FOR NOW, WE ASSUME FILTERSIZE IS ODD!!\\n # AND THAT DATA IS SQUARE!\\n niter_x = npix + (filtersize[0]-1)\\n niter_y = npiy + (filtersize[1]-1)\\n filt = zeros((niter_x,niter_y), float)\\n\\n for ii in range(niter_x):\\n for jj in range(niter_y):\\n if verbose>1:\\n print \\\"ii,jj>>\\\",ii,jj\\n if filtersize[0]==1:\\n indi = 1\\n else:\\n indi = filtersize[0]-1\\n if filtersize[1]==1:\\n indj = 1\\n else:\\n indj = filtersize[1]-1\\n select = bigdata[ii:(ii+indi),jj:(jj+indj)].ravel()\\n #select = select[isfinite(select)]\\n #residualSelection = abs(select - median(select))\\n\\n doFilter = True\\n\\n if verbose:\\n print \\\"doFilter?>>\\\",doFilter\\n if verbose>1:\\n print \\\"select>>\\\",select\\n\\n if doFilter: \\n newval = ( select ).std()\\n else:\\n newval = bigdata[ii,jj]\\n\\n if verbose>1:\\n print \\\"newval>>\\\",newval\\n\\n filt[ii,jj] = newval\\n\\n #print filt.shape, [(filtersize[0]-1)/2,niter_x-(filtersize[0]-1)/2,(filtersize[0]-1)/2,niter_y-(filtersize[0]-1)/2]\\n return filt[(filtersize[0]-1)/2:niter_x-(filtersize[0]-1)/2,(filtersize[0]-1)/2:niter_y-(filtersize[0]-1)/2]\",\n \"def fourier_coefficient(t, freq, n=1, axis=-1):\\n t, freq, n = map(np.asanyarray, (t, freq, n))\\n return np.exp(1j * 2 * np.pi * freq * n * t).sum(axis=axis)\",\n \"def test_fft_complex_1d():\\n\\ta = np.exp(2j * np.pi * np.arange(8) / 8)\\n\\tdummy_array = xr.DataArray(a, dims=['x'])\\n\\tchunked_array = dummy_array.chunk(chunks={'x': 2})\\n\\tspectrum_array, spectrum_coords, spectrum_dims = \\\\\\n\\t\\txfft._fft(chunked_array, nfft={'x': 16}, dim=['x'], dx={'x': 0.5})\\n\\tassert np.array_equal(spectrum_array.compute(), np.fft.fft(a, n=16))\\n\\tassert np.array_equal(spectrum_coords['f_x'], np.fft.fftfreq(16, d=0.5))\\n\\tassert 'f_x' in spectrum_dims\",\n \"def stft(x, fft_size, hopsamp):\\n w = np.hamming(fft_size)\\n return np.array([np.fft.rfft(w*x[i:i+fft_size]) \\n for i in range(0, len(x)-fft_size, hopsamp)])\",\n \"def FFT(x):\\n x = np.asarray(x, dtype=float)\\n N = x.shape[0]\\n \\n if N % 2 > 0:\\n raise ValueError(\\\"size of x must be a power of 2\\\")\\n elif N <= 32: # this cutoff should be optimized\\n return DFT(x)\\n else:\\n X_even = FFT(x[::2])\\n X_odd = FFT(x[1::2])\\n factor = np.exp(-2j * np.pi * np.arange(N) / N)\\n return np.concatenate([X_even + factor[:int(N / 2)] * X_odd,\\n X_even + factor[int(N / 2):] * X_odd])\",\n \"def tomography_filter(arr, axis=2, filter_type='hamming', backend='autograd'):\\n func = getattr(scipy.signal.windows, filter_type)\\n filter = func(arr.shape[axis])\\n if axis != len(arr.shape) - 1:\\n arr = swap_axes(arr, [axis, len(arr.shape) - 1])\\n if backend == 'pytorch':\\n args = {'device': arr.device}\\n else:\\n args = {}\\n arr_r, arr_i = fft(arr, zeros_like(arr, requires_grad=False, **args))\\n arr_r = arr_r * filter\\n arr_i = arr_i * filter\\n arr, _ = ifft(arr_r, arr_i)\\n if axis != len(arr.shape) - 1:\\n arr = swap_axes(arr, [axis, len(arr.shape) - 1])\\n return arr\",\n \"def test_fft_real_2d():\\n\\ta = np.mgrid[:5, :5, :5][0]\\n\\tdummy_array = xr.DataArray(a, dims=['x', 'y', 'z'])\\n\\tchunked_array = dummy_array.chunk(chunks={'x': 2, 'y': 2, 'z': 2})\\n\\tspectrum_array, spectrum_coords, spectrum_dims = \\\\\\n\\t\\txfft._fft(chunked_array, nfft={'y': 14, 'z': 18}, dim=['y', 'z'],\\n\\t\\t dx={'y': 0.01, 'z': 0.02}, sym=False)\\n\\tassert np.allclose(spectrum_array.compute(),\\n\\t np.fft.rfftn(a, s=(18, 14), axes=(2, 1)))\\n\\tassert np.array_equal(spectrum_coords['f_y'], np.fft.rfftfreq(14, d=0.01))\\n\\tassert np.array_equal(spectrum_coords['f_z'], np.fft.fftfreq(18, d=0.02))\\n\\tassert ('x', 'f_y', 'f_z') == spectrum_dims\",\n \"def IDFT2(fourier_image):\\n return IDFT(IDFT(fourier_image).transpose()).transpose()\",\n \"def fourier(array, zero_in_middle = False, first_correction = False, zeropad_to = None, window_function = \\\"none\\\", window_length = 0, flag_plot = False):\\n # shift time = 0 to first element\\n if zero_in_middle == True:\\n array = numpy.fft.ifftshift(array)\\n \\n \\n \\n # half the first element\\n if first_correction == True: \\n dim = len(numpy.shape(array))\\n if dim == 1:\\n array[0] /= 2\\n elif dim == 2:\\n array[0,:] /= 2\\n array[:,0] /= 2\\n elif dim > 2:\\n print(\\\"WARNING (fourier.fourier.py): correction of the first element is not done!\\\")\\n \\n \\n # window function\\n if window_function != \\\"none\\\": \\n array = window_functions(array, window_function, window_length, flag_plot = flag_plot)\\n \\n \\n \\n # the fft\\n array = numpy.fft.fft(array, n = zeropad_to)\\n \\n # move the array back if it was shifted\\n if zero_in_middle == True:\\n array = numpy.fft.fftshift(array)\\n \\n return array\",\n \"def uirfft2(inarray, shape=None):\\n return uirfftn(inarray, 2, shape=shape)\",\n \"def ifftn_1d(a):\\r\\n b = np.fft.fftpack.ifftn(a, axes=(len(a.shape)-1, )) \\r\\n return np.multiply(b, np.sqrt(a.shape[-1]))\",\n \"def fftn(a):\\r\\n b = np.fft.fftpack.fftn(a, axes=(len(a.shape)-2,len(a.shape)-1)) \\r\\n return np.divide(b, np.sqrt(a.shape[-1] * a.shape[-2]))\",\n \"def uifftn(inarray, dim=None):\\n if dim is None:\\n dim = inarray.ndim\\n outarray = np.fft.ifftn(inarray, axes=range(-dim, 0))\\n return outarray * np.sqrt(np.prod(inarray.shape[-dim:]))\",\n \"def upsample_filt(size):\\r\\n factor = (size + 1) // 2\\r\\n if size % 2 == 1:\\r\\n center = factor - 1\\r\\n else:\\r\\n center = factor - 0.5\\r\\n og = np.ogrid[:size, :size]\\r\\n return (1 - abs(og[0] - center) / factor) * \\\\\\r\\n (1 - abs(og[1] - center) / factor)\",\n \"def dct(n_filters, n_input):\\n\\n basis = np.empty((n_filters, n_input))\\n basis[0, :] = 1.0 / np.sqrt(n_input)\\n\\n samples = np.arange(1, 2*n_input, 2) * np.pi / (2.0 * n_input)\\n\\n for i in range(1, n_filters):\\n basis[i, :] = np.cos(i*samples) * np.sqrt(2.0/n_input)\\n\\n return basis\",\n \"def filtered_fourier(self):\\r\\n\\r\\n freqs = tsu.get_freqs(self.sampling_rate, self.data.shape[-1])\\r\\n\\r\\n if self.ub is None:\\r\\n self.ub = freqs[-1]\\r\\n\\r\\n power = fftpack.fft(self.data)\\r\\n idx_0 = np.hstack([np.where(freqs < self.lb)[0],\\r\\n np.where(freqs > self.ub)[0]])\\r\\n\\r\\n #Make sure that you keep the DC component:\\r\\n keep_dc = np.copy(power[..., 0])\\r\\n power[..., idx_0] = 0\\r\\n power[..., -1 * idx_0] = 0 # Take care of the negative frequencies\\r\\n power[..., 0] = keep_dc # And put the DC back in when you're done:\\r\\n\\r\\n data_out = fftpack.ifft(power)\\r\\n\\r\\n data_out = np.real(data_out) # In order to make sure that you are not\\r\\n # left with float-precision residual\\r\\n # complex parts\\r\\n\\r\\n return ts.TimeSeries(data=data_out,\\r\\n sampling_rate=self.sampling_rate,\\r\\n time_unit=self.time_unit)\",\n \"def FourierTransform(data, nPoints):\\r\\n tdf = np.fft.fft(data, nPoints)\\r\\n return tdf\",\n \"def test_fft_complex_3d():\\n\\ta, b, c = np.meshgrid([0, 1, 0, 0], [0, 1j, 1j], [0, 1, 1, 1])\\n\\tdummy_array = xr.DataArray(a * b * c, dims=['x', 'y', 'z'])\\n\\tchunked_array = dummy_array.chunk(chunks={'x': 2, 'y': 2, 'z': 2})\\n\\tspectrum_array, spectrum_coords, spectrum_dims = \\\\\\n\\t\\txfft._fft(chunked_array, nfft={'x': 8, 'y': 6, 'z': 8},\\n\\t\\t dim=['x', 'y', 'z'], dx={'x':12, 'y': 0.01, 'z': 0.02})\\n\\tassert np.allclose(spectrum_array.compute(),\\n\\t np.fft.fftn(a * b * c, s=(8, 6, 8)))\\n\\tassert np.array_equal(spectrum_coords['f_x'], np.fft.fftfreq(8, d=12))\\n\\tassert np.array_equal(spectrum_coords['f_y'], np.fft.fftfreq(6, d=0.01))\\n\\tassert np.array_equal(spectrum_coords['f_z'], np.fft.fftfreq(8, d=0.02))\\n\\tassert ('f_x', 'f_y', 'f_z') == spectrum_dims\",\n \"def T_fourier(shape, T, is_fft_shifted = True):\\n # make i, j, k for each pixel\\n i = np.fft.fftfreq(shape[0]) \\n j = np.fft.fftfreq(shape[1])\\n k = np.fft.fftfreq(shape[2])\\n i, j, k = np.meshgrid(i, j, k, indexing='ij')\\n\\n if is_fft_shifted is False :\\n i = np.fft.ifftshift(i)\\n j = np.fft.ifftshift(j)\\n k = np.fft.ifftshift(k)\\n\\n phase_ramp = np.exp(- 2J * np.pi * (i * T[0] + j * T[1] + k * T[2]))\\n return phase_ramp\",\n \"def fft(a, n=None, axes=-1, norm=None):\\n\\n return image.image(np.fft.fft(a, n, axes, norm), pixelsize=a.pixelsize)\",\n \"def fourier_gaussian(input, sigma, n=-1, axis=-1, output=None):\\n input = numpy.asarray(input)\\n output = _get_output_fourier(output, input)\\n axis = normalize_axis_index(axis, input.ndim)\\n sigmas = _ni_support._normalize_sequence(sigma, input.ndim)\\n sigmas = numpy.asarray(sigmas, dtype=numpy.float64)\\n if not sigmas.flags.contiguous:\\n sigmas = sigmas.copy()\\n\\n _nd_image.fourier_filter(input, sigmas, n, axis, output, 0)\\n return output\",\n \"def _imfilter(x_data, f_data):\\n return pipe(f_data, ifftshift, fftn, lambda x: x * fftn(x_data), ifftn).real\",\n \"def spectrum_fourier(self):\\r\\n\\r\\n data = self.input.data\\r\\n sampling_rate = self.input.sampling_rate\\r\\n\\r\\n fft = fftpack.fft\\r\\n if np.any(np.iscomplex(data)):\\r\\n # Get negative frequencies, as well as positive:\\r\\n f = np.linspace(-sampling_rate/2., sampling_rate/2., data.shape[-1])\\r\\n spectrum_fourier = np.fft.fftshift(fft(data))\\r\\n else:\\r\\n f = tsu.get_freqs(sampling_rate, data.shape[-1])\\r\\n spectrum_fourier = fft(data)[..., :f.shape[0]]\\r\\n \\r\\n return f, spectrum_fourier\",\n \"def test_fft_real_3d():\\n\\ta = np.mgrid[:7, :5, :5][0]\\n\\tdummy_array = xr.DataArray(a, dims=['x', 'y', 'z'])\\n\\tchunked_array = dummy_array.chunk(chunks={'x': 7, 'y': 5, 'z': 5})\\n\\tspectrum_array, spectrum_coords, spectrum_dims = \\\\\\n\\t\\txfft._fft(chunked_array, nfft={'x': 11, 'y': 14, 'z': 18},\\n\\t\\t dim=['x', 'y', 'z'], dx={'x':12, 'y': 0.01, 'z': 0.02},\\n\\t\\t sym=False)\\n\\tassert np.allclose(spectrum_array.compute(),\\n\\t np.fft.rfftn(a.T, s=(18, 14, 11)).T)\\n\\tassert np.array_equal(spectrum_coords['f_x'], np.fft.rfftfreq(11, d=12))\\n\\tassert np.array_equal(spectrum_coords['f_y'], np.fft.fftfreq(14, d=0.01))\\n\\tassert np.array_equal(spectrum_coords['f_z'], np.fft.fftfreq(18, d=0.02))\\n\\tassert ('f_x', 'f_y', 'f_z') == spectrum_dims\",\n \"def irfft(a, n=None, axes=-1, norm=None):\\n return image.image(np.fft.irfft(a, n, axes, norm), pixelsize=image.getPixelsize(a))\",\n \"def U_QFT(n):\\n dim = 2**n # Hilbert space dimensionality\\n Gate= [[np.exp(2 * np.pi * 1j * x * y / dim) for x in range(dim)] for y in range(dim)]\\n Gate = np.array(Gate)/np.sqrt(dim)\\n return Gate\",\n \"def median_filter(self):\\n print \\\"Median-Filtering...\\\"\\n D = self.D\\n x = np.median(np.median(D,axis=1),axis=1)\\n for i in xrange(len(x)):\\n D[i,:,:] -= x[i]\\n self.D = D\\n print \\\"done.\\\"\",\n \"def fftfilter1d(vec, bandwidth, retfilter=False):\\n # 2011-03-11 14:15 IJC: Created\\n\\n from numpy import real, fft, floor, ceil\\n\\n vec = array(vec, copy=True)\\n\\n # Errorchecking\\n if len(vec.shape)<>1:\\n print \\\"Input array must be 1D -- try using .ravel()\\\"\\n return -1\\n\\n\\n npts = vec.size\\n\\n filter = concatenate((zeros(floor(npts/2.) - bandwidth), \\n ones(bandwidth * 2 + 1),\\n zeros(ceil(npts/2.) - bandwidth - 1)))\\n\\n ret = real(fft.ifft(fft.ifftshift( fft.fftshift(fft.fft(vec)) * filter )))\\n\\n if retfilter==True:\\n ret = [ret, filter]\\n\\n return ret\",\n \"def uifft2(inarray):\\n return uifftn(inarray, 2)\",\n \"def fft(signal):\\r\\n if signal.size == 1:\\r\\n return signal\\r\\n\\r\\n even_part = fft(signal[::2]) # Only grab even elements\\r\\n odd_part = fft(signal[1::2]) # Only grab odd elements\\r\\n\\r\\n factor = np.exp(-2j * np.pi * np.arange(signal.size) / signal.size)\\r\\n return np.concatenate([even_part + factor[:int(signal.size / 2)] * odd_part,\\r\\n even_part + factor[int(signal.size / 2):] * odd_part])\",\n \"def ft(x):\\n y = np.fft.rfft(x)\\n\\n phi = 2 * np.pi * np.random.random(len(y))\\n\\n phi[0] = 0.0\\n if len(x) % 2 == 0:\\n phi[-1] = 0.0\\n\\n y = y * np.exp(1j * phi)\\n return np.fft.irfft(y, n=len(x))\",\n \"def IDFT(fourier_signal):\\n n = fourier_signal.shape[0]\\n omega = np.exp((((2 * np.pi)*1j) / n))\\n\\n e_items = np.vander(omega**np.arange(n), n, True)\\n org_signal = np.dot(e_items, fourier_signal)/n\\n\\n return org_signal\",\n \"def __normalize_after_fft(arr):\\n\\n n1, n2 = arr.shape[0], arr.shape[1]\\n for i in range(n1):\\n for j in range(n2):\\n arr[i, j] *= n1 * n2\\n\\n return arr\",\n \"def SIDFT(X,D):\\n N=len(X)\\n x=np.zeros(N,'complex')\\n for n in range(0,N,1):\\n for k in range(0,N,1):\\n x[n]=x[n]+np.exp(-1j*2*np.pi*k*D/N)*X[k]*np.exp(1j*2*np.pi*k*n/N)\\n return x/N\",\n \"def frft2d(matrix, a=[0.7, 0.7]):\\n temp = numpy.zeros((matrix.shape[0], matrix.shape[1]), dtype=numpy.complex)\\n for k in range(0, matrix.shape[0]):\\n temp[k, :] = frft(matrix[k, :], a[0])\\n out = numpy.zeros((temp.shape[0], temp.shape[1]), dtype=numpy.complex)\\n for m in range(0, temp.shape[1]):\\n out[:, m] = frft(temp[:, m], a[1])\\n\\n return out\",\n \"def downsample(x, height=494):\\n m,n = x.shape[-2:]\\n # factor = width/n\\n factor = m/height\\n width = round(n/factor/2)*2\\n F = np.fft.rfft2(x)\\n A = F[...,0:height//2,0:width//2+1]\\n B = F[...,-height//2:,0:width//2+1]\\n F = np.concatenate([A,B], axis=0)\\n # S = round(2*factor)\\n # A = F[...,0:m//S,0:n//S+2]\\n # B = F[...,-m//S+1:,0:n//S+2]\\n # F = np.concatenate([A,B], axis=-2)\\n f = np.fft.irfft2(F, s=(height, width))\\n return f\",\n \"def subsample_fourier(x, k):\\n return subsamplefourier(x,k)\",\n \"def fourier(self, k, index = None):\\n if self.dims == 1 and index is None:\\n index = 0\\n\\n if not index is None:\\n\\n new_data = {}\\n\\n for key, value in self.__m__.items():\\n\\n key2 = tuple(key[:index] + key[index+1:])\\n matrix = value*numpy.exp(2j*numpy.pi*key[index]*k)\\n\\n if key2 in new_data:\\n new_data[key2] += matrix\\n\\n else:\\n new_data[key2] = matrix\\n\\n return TightBinding(new_data)\\n\\n else:\\n\\n result = self\\n for i in k:\\n result = result.fourier(i, index = 0)\\n return result\",\n \"def ifft(a, n=None, axes=-1, norm=None):\\n return image.image(np.fft.ifft(a, n, axes, norm), pixelsize=image.getPixelsize(a))\",\n \"def ManualFourier(x):\\n x = np.asarray(x, dtype=float)\\n N = x.shape[0]\\n\\n if np.log2(N) % 1 > 0:\\n raise ValueError(\\\"size of x must be a power of 2\\\")\\n\\n # N_min here is equivalent to the stopping condition above,\\n # and should be a power of 2\\n N_min = min(N, 32)\\n\\n # Perform an O[N^2] DFT on all length-N_min sub-problems at once\\n n = np.arange(N_min)\\n k = n[:, None]\\n M = np.exp(-2j * np.pi * n * k / N_min)\\n X = np.dot(M, x.reshape((N_min, -1)))\\n\\n # build-up each level of the recursive calculation all at once\\n while X.shape[0] < N:\\n X_even = X[:, :int(X.shape[1] / 2)]\\n X_odd = X[:, int(X.shape[1] / 2):]\\n factor = np.exp(-1j * np.pi * np.arange(X.shape[0])\\n / X.shape[0])[:, None]\\n X = np.vstack([X_even + factor * X_odd,\\n X_even - factor * X_odd])\\n return X.ravel()\",\n \"def FourierSeries(input,N=None):\\n \\n N=len(input);\\n\\n w=2*cmath.pi/N;\\n input=input[0:N];\\n n=numpy.arange(0,N); \\n r=cexp(-1j*w*n);\\n\\n output = [complex(0)] * N \\n for k in range(N): \\n r=input*cexp(-1j*w*n*k) \\n output[k]=np.sum(r);\\n \\n \\n return output;\",\n \"def _inv_fft(cls, n_extp, n, idx, a):\\n import numpy as np\\n ts = []\\n for i in range(0, n_extp):\\n # Sinusoid for the ith frequency\\n s_array = cls._signals(idx, i, n)\\n\\n # Computing the inverse Fouries transformation term for the significant coefficients obtained from the\\n # spectral density\\n ts.append(np.sum(a * s_array) // n)\\n return np.array(ts)\",\n \"def ifftn(a):\\r\\n b = np.fft.fftpack.ifftn(a, axes=(len(a.shape)-2,len(a.shape)-1)) \\r\\n return np.multiply(b, np.sqrt(a.shape[-1] * a.shape[-2]))\",\n \"def _frz(a):\\n if a.ndim == 0:\\n a.shape = (1,)\\n return a\",\n \"def scales_from_fourier(f, wf, w0):\\n\\n f_arr = np.asarray(f)\\n\\n if wf == 'morlet':\\n return (f_arr * (w0 + np.sqrt(2 + w0**2))) / (4 * np.pi)\\n else:\\n raise ValueError('wavelet function not available')\",\n \"def frft(f, a):\\n ret = numpy.zeros_like(f, dtype=numpy.complex)\\n f = f.copy().astype(numpy.complex)\\n N = len(f)\\n shft = numpy.fmod(numpy.arange(N) + numpy.fix(N / 2), N).astype(int)\\n sN = numpy.sqrt(N)\\n a = numpy.remainder(a, 4.0)\\n\\n # Special cases\\n if a == 0.0:\\n return f\\n if a == 2.0:\\n return numpy.flipud(f)\\n if a == 1.0:\\n ret[shft] = numpy.fft.fft(f[shft]) / sN\\n return ret\\n if a == 3.0:\\n ret[shft] = numpy.fft.ifft(f[shft]) * sN\\n return ret\\n\\n # reduce to interval 0.5 < a < 1.5\\n if a > 2.0:\\n a = a - 2.0\\n f = numpy.flipud(f)\\n if a > 1.5:\\n a = a - 1\\n f[shft] = numpy.fft.fft(f[shft]) / sN\\n if a < 0.5:\\n a = a + 1\\n f[shft] = numpy.fft.ifft(f[shft]) * sN\\n\\n # the general case for 0.5 < a < 1.5\\n alpha = a * numpy.pi / 2\\n tana2 = numpy.tan(alpha / 2)\\n sina = numpy.sin(alpha)\\n f = numpy.hstack((numpy.zeros(N - 1), sincinterp(f), numpy.zeros(N - 1))).T\\n\\n # chirp premultiplication\\n chrp = numpy.exp(-1j * numpy.pi / N * tana2 / 4 *\\n numpy.arange(-2 * N + 2, 2 * N - 1).T ** 2)\\n f = chrp * f\\n\\n # chirp convolution\\n c = numpy.pi / N / sina / 4\\n tmp = numpy.exp(1j * c * numpy.arange(-(4 * N - 4), 4 * N - 3).T ** 2)\\n ret = scipy.signal.fftconvolve(\\n numpy.exp(1j * c * numpy.arange(-(4 * N - 4), 4 * N - 3).T ** 2),\\n f\\n )\\n ret = ret[4 * N - 4:8 * N - 7] * numpy.sqrt(c / numpy.pi)\\n\\n # chirp post multiplication\\n ret = chrp * ret\\n\\n # normalizing constant\\n ret = numpy.exp(-1j * (1 - a) * numpy.pi / 4) * ret[N - 1:-N + 1:2]\\n\\n return ret\",\n \"def DFT(signal):\\n n = signal.shape[0]\\n omega = np.exp(((((-2) * np.pi)*1j) / n))\\n\\n e_items = np.vander(omega**np.arange(n), n, True)\\n fourier_signal = np.dot(e_items, signal)\\n\\n return fourier_signal.astype(np.complex128)\",\n \"def fourier_der(im):\\n ft_img = DFT2(im)\\n ft_img = np.fft.fftshift(ft_img)\\n\\n n_x = im.shape[1]\\n coeff_x = (2 * np.pi * 1j)/n_x\\n u_freq = np.array([n if n < int(n_x/2) else (n-n_x) for n in range(n_x)]) * 1j\\n u_freq = np.array([np.fft.fftshift(u_freq)]*im.shape[0]).transpose()\\n dx_ft = coeff_x * IDFT2(np.fft.ifftshift(u_freq.transpose() * ft_img))\\n\\n m_y = im.shape[0]\\n coeff_y = (2 * np.pi * 1j)/m_y\\n v_freq = np.array([m if m < int(m_y/2) else (m-m_y) for m in range(m_y)]) * 1j\\n v_freq = np.array([np.fft.fftshift(v_freq)] * im.shape[1]).transpose()\\n tr = IDFT2(np.fft.ifftshift(v_freq * ft_img))\\n dy_ft = coeff_y * tr\\n\\n magnitude = np.sqrt(np.abs(dx_ft)**2 + np.abs(dy_ft)**2)\\n return magnitude.real.astype(np.float64)\",\n \"def FourierTransform2D(xdata, zdata, nPoints):\\r\\n freq = FourierFrequency(xdata, nPoints)\\r\\n tdf = np.zeros_like(zdata, dtype=complex)\\r\\n for u, i in enumerate(zdata):\\r\\n tdf[u] = FourierTransform(xdata, i, nPoints)\\r\\n return freq, tdf\",\n \"def test_fourier_dim_2():\\n expected_res = np.array([[1 / np.sqrt(2), 1 / np.sqrt(2)], [1 / np.sqrt(2), -1 / np.sqrt(2)]])\\n\\n res = fourier(2)\\n\\n bool_mat = np.isclose(res, expected_res)\\n np.testing.assert_equal(np.all(bool_mat), True)\",\n \"def fftn(a, s=None, axes=None, norm=None):\\n return image.image(np.fft.fftn(a, s, axes, norm), pixelsize=image.getPixelsize(a))\",\n \"def IDFT2(fourier_image):\\n fourier_image = fourier_image.astype(np.complex128)\\n M, N = fourier_image.shape\\n # build the idft2_matrix transform\\n omega_y = np.exp(2 * np.pi * 1j / M)\\n u, v = np.meshgrid(np.arange(M), np.arange(M))\\n idft2_matrix = np.power(omega_y, u*v)\\n\\n # calculate the 2D inverse fourier transform\\n return 1/M * np.dot(idft2_matrix, IDFT(fourier_image))\",\n \"def boxfilter(y, N):\\n n = np.clip(np.arange(len(y)), 0, N-1) + 1\\n return np.array([ y[i+1-ni:i+1].sum() / ni for i,ni in zip(range(len(y)), n) ])\",\n \"def IDFT(fourier_signal):\\n fourier_signal = fourier_signal.astype(np.complex128)\\n # find the length of the signal\\n N = fourier_signal.shape[0]\\n if fourier_signal.ndim == 2:\\n M, N = fourier_signal.shape\\n\\n # calculate IDFT matrix\\n u, v = np.meshgrid(np.arange(N), np.arange(N))\\n omega = np.exp(2 * np.pi * 1j / N)\\n idft_matrix = np.power(omega, u*v)\\n\\n # if it is a matrix of fourier signals\\n if fourier_signal.ndim == 2:\\n # calculate the Fourier Transform\\n signal = np.dot(idft_matrix, fourier_signal.transpose())\\n return 1/N * signal.transpose()\\n\\n # calculate the inverse Fourier Transform\\n signal = np.dot(idft_matrix, fourier_signal)\\n return 1/N * signal\",\n \"def apply_1d_median_filter(n, timage):\\n image_shape = timage.shape\\n ovrlay = int(n / 2)\\n res_matrix = np.copy(timage)\\n for i in np.arange(image_shape[0])[1:-1]:\\n local_matrix = timage[i - ovrlay:i + ovrlay + 1] \\n median = np.median(local_matrix)\\n res_matrix[i] = median\\n return res_matrix\",\n \"def samp_from_freq(n_samples):\\n datapath = os.path.normpath(os.getcwd()+os.sep+os.pardir+os.sep+\\\"Dataset\\\"+os.sep+\\\"All_channels_500hz.npy\\\")\\n data = np.load(datapath)\\n fourier = np.fft.rfft(data,axis=0)\\n fourier_mean = np.mean(fourier,axis=1)\\n print(fourier.shape)\\n print(fourier_mean.shape)\\n signal = np.fft.irfft(fourier_mean)[1:]\\n plt.plot(fourier_mean[1:])\\n plt.show()\\n plt.plot(signal)\\n plt.show()\\n #Generating frequency spectrum\\n \\\"\\\"\\\"\\n x = np.linspace(0,100,251)\\n x2 = np.linspace(0,5,251)\\n spectrum = 50*np.exp(-(x-30)**2/2)\\n spectrum += 60*np.sin(np.random.randn(251)*2*np.pi)*np.exp(-x2)\\n #spectrum += np.random.randn(251)\\n plt.plot(spectrum)\\n plt.show()\\n signal = np.fft.irfft(spectrum)\\n plt.plot(signal)\\n plt.show()\\n \\\"\\\"\\\"\\n signal = None\\n return signal\",\n \"def apply_fourier_transform(chunked_audio):\\n pass\",\n \"def vvc_filters_2d(kernel_size):\\n vvc_filters = []\\n half_kernel = (kernel_size - 8) // 2\\n for frac_pos in frac_positions():\\n filter_x = filter_coefficients(int(frac_pos.split(\\\",\\\")[0]))\\n filter_y = filter_coefficients(int(frac_pos.split(\\\",\\\")[1]))\\n\\n filter_vvc = np.tile(filter_x, 8).reshape((8, 8))\\n for index in range(len(filter_y)):\\n filter_vvc[index, :] *= filter_y[index]\\n filter_vvc = filter_vvc / (64 * 64)\\n\\n vvc_filters.append(np.pad(filter_vvc, ((half_kernel + 1, half_kernel), (half_kernel + 1, half_kernel)),\\n 'constant', constant_values=0))\\n return vvc_filters\",\n \"def ufftn(inarray, dim=None):\\n if dim is None:\\n dim = inarray.ndim\\n outarray = np.fft.fftn(inarray, axes=range(-dim, 0))\\n return outarray / np.sqrt(np.prod(inarray.shape[-dim:]))\",\n \"def padded_ft_op(space, padded_size):\\n padded_op = ResizingOperator(\\n space, ran_shp=[padded_size for _ in range(space.ndim)])\\n shifts = [not s % 2 for s in space.shape]\\n ft_op = FourierTransform(\\n padded_op.range, halfcomplex=False, shift=shifts)\\n \\n return ft_op * padded_op\",\n \"def filtermed(f, medfiltsize=9): # medfiltfunc='ndimage'\\n # # Median filter to smooth out single-pixel deviations\\n # if medfiltfunc == 'ndimage': f_medfilt = median_filter(f, size=medfiltsize)\\n # elif medfiltfunc == 'signal': f_medfilt = medfilt(f, kernel_size=medfiltsize)\\n f_medfilt = median_filter(f, size=medfiltsize)\\n return f_medfilt\",\n \"def apply3filter(array, filter_):\\n s = int(len(filter_)/2)\\n width = len(array[0])\\n height = len(array)\\n new_array = np.array(np.zeros((height,width)))\\n for row in range(s, (height-s)):\\n for col in range(s, (width-s)):\\n new_array[row,col] = np.sum(filter_ * array[(row-s):(row+s+1),(col-s):(col+s+1)])\\n return new_array\",\n \"def stretch(snd_array, factor, window_size=2**13, h=2**11):\\n phase = np.zeros(window_size)\\n hanning_window = np.hanning(window_size)\\n result = np.zeros( len(snd_array) /factor + window_size)\\n\\n for i in np.arange(0, len(snd_array)-(window_size+h), h*factor):\\n\\n # two potentially overlapping subarrays\\n a1 = snd_array[i: i + window_size]\\n a2 = snd_array[i + h: i + window_size + h]\\n\\n # the spectra of these arrays\\n s1 = np.fft.fft(hanning_window * a1)\\n s2 = np.fft.fft(hanning_window * a2)\\n\\n # rephase all frequencies\\n phase = (phase + np.angle(s2/s1)) % 2*np.pi\\n\\n a2_rephased = np.fft.ifft(np.abs(s2)*np.exp(1j*phase))\\n i2 = int(i/factor)\\n result[i2 : i2 + window_size] += hanning_window*a2_rephased\\n\\n result = ((2**(16-4)) * result/result.max()) # normalize (16bit)\\n\\n return result.astype('int16')\",\n \"def rfft(a, n=None, axes=-1, norm=None):\\n return image.image(np.fft.rfft(a, n, axes, norm), pixelsize=image.getPixelsize(a))\",\n \"def get_fft(self):\\n\\t\\t# Get the \\\"ideal\\\" evenly spaced times\\n\\t\\teven_times = numpy.linspace(self.buf[0][0], self.buf[-1][0], len(self.buf))\\n\\t\\t\\n\\t\\t# Interpolate the data to generate evenly temporally spaced samples\\n\\t\\tinterpolated = numpy.interp(even_times, *zip(*self.buf))\\n\\t\\t\\n\\t\\t# Perform the FFT\\n\\t\\tfft = numpy.fft.rfft(interpolated)\\n\\t\\treturn zip(numpy.abs(fft), numpy.angle(fft))\",\n \"def _get_split_spectrum(T,WK):\\n\\n n_samples, n_frames, n_frequencies = T.shape\\n U = np.zeros([n_samples, n_samples, n_frames, n_frequencies], dtype=np.complex64)\\n \\n for l in range(n_frequencies):\\n for n in range(n_samples):\\n _T = np.zeros([n_samples, n_frames], dtype=np.complex64)\\n _T[n,:] = T[n,:,l]\\n inv_WK = np.linalg.inv(WK[:,:,l])\\n U[n,:,:,l] = np.dot(inv_WK, _T)\\n \\n return U\",\n \"def filters(array, sample_frequency):\\n strain = TimeSeries(array, sample_rate=int(sample_frequency))\\n white_data = strain.whiten(fftlength=4, fduration=4)\\n bp_data = white_data.bandpass(50, 250)\\n return bp_data.value\",\n \"def ifft2(self, array):\\n self._ifft2.input_array[:] = array\\n return self._ifft2()\",\n \"def istft(X, hop_size):\\n\\n # make an empty signal of the appropriate length\\n window_size,num_hops = X.shape\\n signal_length = (num_hops-1)*hop_size + window_size\\n signal = np.zeros(signal_length,dtype='complex');\\n\\n #fill the signal\\n for n in range(num_hops):\\n start = n * hop_size\\n end = start + window_size\\n signal[start:end] = signal[start:end] + ifft(X[:,n])\\n return signal\",\n \"def fourier_der(im):\\n im = im.astype(np.float64)\\n # constants\\n M, N = im.shape\\n u = np.meshgrid(np.arange(N), np.arange(M))[0] - N//2\\n v = np.meshgrid(np.arange(N), np.arange(M))[1] - M//2\\n u_der, v_der = (2 * np.pi * 1j / N), (2 * np.pi * 1j / M)\\n\\n # calculate dx, dy\\n dx = u_der * IDFT2(np.fft.fftshift(u) * DFT2(im))\\n dy = v_der * IDFT2(np.fft.fftshift(v) * DFT2(im))\\n\\n return np.sqrt(np.abs(dx)**2 + np.abs(dy)**2) # = magnitude\",\n \"def filled_space_prop(e: np.ndarray, arg: np.ndarray) -> np.ndarray:\\n\\n freq = np.exp(1j*arg)\\n out = np.fft.ifft2(np.fft.fft2(e)*freq)\\n\\n return out\",\n \"def urfftn(inarray, dim=None):\\n if dim is None:\\n dim = inarray.ndim\\n outarray = np.fft.rfftn(inarray, axes=range(-dim, 0))\\n return outarray / np.sqrt(np.prod(inarray.shape[-dim:]))\",\n \"def fourier(data, temp_freq, axis, output = 'amplitude'):\\n\\t\\t\\n\\t\\n\\t# take largest possible multiple of F1 from PSTH.\\n\\t# Generate freq and fft\\n\\t# generate amplitude\\n\\t# return amplitude, F0, F1 and F2 values\",\n \"def _discrete_fourier_transform(\\n series,\\n dft_length,\\n norm,\\n inverse_sqrt_win_size,\\n lower_bounding,\\n apply_normalising_factor=True,\\n cut_start_if_norm=True,\\n):\\n start = 2 if norm else 0\\n output_length = start + dft_length\\n\\n if cut_start_if_norm:\\n c = int(start / 2)\\n else:\\n c = 0\\n start = 0\\n\\n dft = np.zeros(output_length - start)\\n for i in range(c, int(output_length / 2)):\\n for n in range(len(series)):\\n dft[(i - c) * 2] += series[n] * math.cos(2 * math.pi * n * i / len(series))\\n dft[(i - c) * 2 + 1] += -series[n] * math.sin(\\n 2 * math.pi * n * i / len(series)\\n )\\n\\n if apply_normalising_factor:\\n if lower_bounding:\\n dft[1::2] = dft[1::2] * -1 # lower bounding\\n\\n std = np.std(series)\\n if std == 0:\\n std = 1\\n dft *= inverse_sqrt_win_size / std\\n\\n return dft\",\n \"def inverse_fft(self, array_in):\\r\\n # Find side length, as spectrum may or may not have been doubled\\r\\n # for aliasing control\\r\\n side = array_in.shape[0]\\r\\n\\r\\n out = side**2*self.sign_mat[0:side, 0:side]*np.fft.ifft2(array_in)\\r\\n return out\",\n \"def fftkernel(x, w):\\n L = len(x)\\n Lmax = L + 3 * w\\n n = nextpow2(Lmax)\\n X = np.fft.fft(x, n)\\n f = np.arange(0, n, 1.0) / n\\n f = np.concatenate((-f[:int(n / 2)], f[int(n / 2):0:-1]))\\n K = np.exp(-0.5 * (w * 2 * np.pi * f) ** 2)\\n y = np.fft.ifft(X * K, n)\\n y = y[:L].copy()\\n return y\",\n \"def apply_1d_filter(bfilter, timage):\\n image_length = len(timage)\\n ovrlay = int(bfilter.shape[0] / 2)\\n tmp_array = np.zeros(image_length + 2 * ovrlay)\\n tmp_array[ovrlay:-ovrlay] = timage\\n res_array = np.zeros(image_length )\\n for i in np.arange(image_length) + ovrlay:\\n local_matrix = tmp_array[i - ovrlay:i + ovrlay + 1]\\n res_array[i - ovrlay] = sum(local_matrix * bfilter)\\n return res_array\",\n \"def __qft_input_state(self, size) -> None:\\n\\n for i in range(size):\\n self.h(i)\\n self.u1(-math.pi / float(2 ** i), i)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6378481","0.6309765","0.62861556","0.62434644","0.61714876","0.60929567","0.6092913","0.6006388","0.59960955","0.59704673","0.57724774","0.57447904","0.5739366","0.5689734","0.5677718","0.5673864","0.56690097","0.5644021","0.56193554","0.56177664","0.56163305","0.5598021","0.5587617","0.5584242","0.55652815","0.5549149","0.5548605","0.55480033","0.55172896","0.5505413","0.55052626","0.5501446","0.54848313","0.54810643","0.54791","0.5471032","0.546981","0.5467672","0.5450758","0.5449411","0.54438484","0.5442451","0.54139847","0.54128975","0.5399385","0.5399077","0.53819436","0.5367141","0.5360452","0.5353703","0.5352995","0.5349632","0.53457814","0.534296","0.5308967","0.52900594","0.5286411","0.52855617","0.5283071","0.52821136","0.52767676","0.52706474","0.52659255","0.5261159","0.5258052","0.5257588","0.52432287","0.52371216","0.52262235","0.522476","0.5216915","0.52139604","0.5211845","0.520583","0.5201802","0.51966363","0.51962966","0.5188271","0.51879174","0.518241","0.51771903","0.5177083","0.5173122","0.5170519","0.5168802","0.51678735","0.5162113","0.5159968","0.5159189","0.51534337","0.51479715","0.51478595","0.5138733","0.5135874","0.5134379","0.5133407","0.5131214","0.51308453","0.5123905","0.5121765"],"string":"[\n \"0.6378481\",\n \"0.6309765\",\n \"0.62861556\",\n \"0.62434644\",\n \"0.61714876\",\n \"0.60929567\",\n \"0.6092913\",\n \"0.6006388\",\n \"0.59960955\",\n \"0.59704673\",\n \"0.57724774\",\n \"0.57447904\",\n \"0.5739366\",\n \"0.5689734\",\n \"0.5677718\",\n \"0.5673864\",\n \"0.56690097\",\n \"0.5644021\",\n \"0.56193554\",\n \"0.56177664\",\n \"0.56163305\",\n \"0.5598021\",\n \"0.5587617\",\n \"0.5584242\",\n \"0.55652815\",\n \"0.5549149\",\n \"0.5548605\",\n \"0.55480033\",\n \"0.55172896\",\n \"0.5505413\",\n \"0.55052626\",\n \"0.5501446\",\n \"0.54848313\",\n \"0.54810643\",\n \"0.54791\",\n \"0.5471032\",\n \"0.546981\",\n \"0.5467672\",\n \"0.5450758\",\n \"0.5449411\",\n \"0.54438484\",\n \"0.5442451\",\n \"0.54139847\",\n \"0.54128975\",\n \"0.5399385\",\n \"0.5399077\",\n \"0.53819436\",\n \"0.5367141\",\n \"0.5360452\",\n \"0.5353703\",\n \"0.5352995\",\n \"0.5349632\",\n \"0.53457814\",\n \"0.534296\",\n \"0.5308967\",\n \"0.52900594\",\n \"0.5286411\",\n \"0.52855617\",\n \"0.5283071\",\n \"0.52821136\",\n \"0.52767676\",\n \"0.52706474\",\n \"0.52659255\",\n \"0.5261159\",\n \"0.5258052\",\n \"0.5257588\",\n \"0.52432287\",\n \"0.52371216\",\n \"0.52262235\",\n \"0.522476\",\n \"0.5216915\",\n \"0.52139604\",\n \"0.5211845\",\n \"0.520583\",\n \"0.5201802\",\n \"0.51966363\",\n \"0.51962966\",\n \"0.5188271\",\n \"0.51879174\",\n \"0.518241\",\n \"0.51771903\",\n \"0.5177083\",\n \"0.5173122\",\n \"0.5170519\",\n \"0.5168802\",\n \"0.51678735\",\n \"0.5162113\",\n \"0.5159968\",\n \"0.5159189\",\n \"0.51534337\",\n \"0.51479715\",\n \"0.51478595\",\n \"0.5138733\",\n \"0.5135874\",\n \"0.5134379\",\n \"0.5133407\",\n \"0.5131214\",\n \"0.51308453\",\n \"0.5123905\",\n \"0.5121765\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.672973"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":20097,"cells":{"query":{"kind":"string","value":"Multidimensional ellipsoid Fourier filter. The array is multiplied with the fourier transform of a ellipsoid of given sizes."},"document":{"kind":"string","value":"def fourier_ellipsoid(input, size, n=-1, axis=-1, output=None):\n input = numpy.asarray(input)\n if input.ndim > 3:\n raise NotImplementedError(\"Only 1d, 2d and 3d inputs are supported\")\n output = _get_output_fourier(output, input)\n axis = normalize_axis_index(axis, input.ndim)\n sizes = _ni_support._normalize_sequence(size, input.ndim)\n sizes = numpy.asarray(sizes, dtype=numpy.float64)\n if not sizes.flags.contiguous:\n sizes = sizes.copy()\n _nd_image.fourier_filter(input, sizes, n, axis, output, 2)\n return output"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _irfft2d(f_x) :","def process( fids, ndim=2 ):\n\timg = np.empty_like( fids )\n\tax = -1*(np.array( range(ndim) )+1)\n\t\n\timg = np.fft.fftshift( np.fft.fftn( fids, axes=ax, ).astype( np.complex64), axes=ax )\n\t\n\treturn np.squeeze(img)","def _get_fourier_filter(self):\n size = max(64, int(2 ** np.ceil(np.log2(2 * self.m[-1].item()))))\n\n pi = torch.acos(torch.zeros(1)).item() * 2.0\n n = torch.cat(\n [\n torch.arange(1, size // 2 + 1, 2, device=self.n.device),\n torch.arange(size // 2 - 1, 0, -2, device=self.n.device),\n ]\n )\n f = torch.zeros(size, device=self.n.device)\n f[0] = 0.25\n if self.flat:\n f[1::2] = -1 / (pi * n).pow(2)\n else:\n f[1::2] = -self.s_detect.abs().pow(2) / (\n pi\n * (self.d_source + self._d_detect())\n * torch.sin(\n n\n * self.s_detect.abs()\n / (self.d_source + self._d_detect())\n )\n ).pow(2)\n f = torch.stack(\n [f, torch.zeros(f.shape, device=self.n.device)], dim=-1\n )\n f = fftshift(f, dim=(-2,))\n\n filt = fft1(f)[..., 0]\n\n if self.filter_type == \"hamming\":\n # hamming filter\n fac = torch.tensor(\n np.hamming(size).astype(np.float32), device=f.device\n )\n elif self.filter_type == \"hann\":\n # hann filter\n fac = torch.tensor(\n np.hanning(size).astype(np.float32), device=f.device\n )\n elif self.filter_type == \"cosine\":\n # cosine filter\n fac = torch.sin(\n torch.linspace(0, pi, size + 1, device=f.device)[:-1]\n )\n else:\n # ramp / ram-lak filter\n fac = 1.0\n\n return fac * filt","def blur_fourier(im, kernel_size):\n im = im.astype(np.float64)\n # build the kernel with zero padding\n kernel_base = gaussian_kernel_factory(kernel_size)\n window = np.zeros_like(im).astype(np.float64)\n M, N = im.shape\n dx, dy = kernel_base.shape\n x_middle, y_middle = N//2, M//2\n\n window[(y_middle-dy//2):(y_middle+dy//2+1), (x_middle-dx//2):(x_middle+dx//2+1)] = kernel_base\n\n # multiply in the freq domain\n return IDFT2(DFT2(im) * DFT2(np.fft.ifftshift(window))).real","def make_quad_frs_imag(dims,numlevels,numorientations,bandwidth):\n \n freq_resps_imag = make_steer_frs(dims,numlevels,numorientations,bandwidth)\n freq_resps_imag[0] = np.zeros(dims)\n freq_resps_imag[2] = np.zeros(dims)\n return freq_resps_imag","def make_ellipsoid_faces(center, dims, nsubdivs=2):\n t = (1+sqrt(5))/2.0\n s = sqrt(1+t**2)\n \n vertices = [(t/s,1/s,0), (-t/s,1/s,0), (t/s,-1/s,0),\\\n (-t/s,-1/s,0), (1/s,0,t/s), (1/s,0,-t/s), (-1/s,0,t/s),(-1/s,0,-t/s),\\\n (0,t/s,1/s), (0,-t/s,1/s), (0,t/s,-1/s), (0,-t/s,-1/s)]\n\n v = vertices\n faces = [(v[0],v[8],v[4]),(v[1],v[10],v[7]),(v[2],v[9],v[11]),(v[7],v[3],v[1]),(v[0],v[5],v[10]),(v[3],v[9],v[6]),\\\n (v[3],v[11],v[9]),(v[8],v[6],v[4]),(v[2],v[4],v[9]),(v[3],v[7],v[11]),(v[4],v[2],v[0]),\\\n (v[9],v[4],v[6]),(v[2],v[11],v[5]),(v[0],v[10],v[8]),(v[5],v[0],v[2]),(v[10],v[5],v[7]),(v[1],v[6],v[8]),\\\n (v[1],v[8],v[10]),(v[6],v[1],v[3]),(v[11],v[7],v[5])]\n \n #subdivide each of the faces into 9 faces\n for i in xrange(nsubdivs):\n new_faces = []\n for face in faces:\n new_faces.extend(subdivide(face[0], face[1], face[2]))\n faces = new_faces\n faces = scale_faces(dims[0], dims[1], dims[2], faces)\n faces = translate_faces(center, faces)\n return faces","def smoothfield( d , boxsize, Rth ):\n\n ng = N.shape(d)[0]\n dk = N.fft.rfftn(d)\n kgrid = getkgrid(boxsize,ng)\n dk = dk * N.exp( -(kgrid * Rth) ** 2. / 2. )\n d = N.fft.irfftn(dk)\n\n return d","def blur_fourier(im, kernel_size):\n kernel = gaus_kernel_calc(kernel_size)\n\n zeros = np.zeros(im.shape)\n x_mid = np.math.floor(im.shape[1] / 2)\n y_mid = np.math.floor(im.shape[0] / 2)\n distance = np.math.floor(kernel_size / 2)\n zeros[x_mid - distance: x_mid + distance + 1, y_mid - distance: y_mid + distance + 1] = kernel\n\n fourier_kernel = DFT2(np.fft.ifftshift(zeros))\n fourier_img = DFT2(im)\n fourier_blured = fourier_kernel * fourier_img\n\n return IDFT2(fourier_blured).real.astype(np.float64)","def make_steer_frs(dims, numlevels, numorientations, bandwidth):\n \n result = []\n bands=[]\n p = numorientations-1\n const = math.sqrt(float(math.pow(2,(2*p))*math.pow(math.factorial(p),2)) / float(math.factorial(2*p)*(p+1)))\n f1 = freqspace(dims[0])\n f2 = freqspace(dims[1])\n wx, wy = np.meshgrid(f1, f2)\n size = wx.shape\n r = np.sqrt(wx**2 + wy**2)\n theta = np.arctan2(wy, wx) \n \n bands = np.full((numlevels, numorientations, dims[0], dims[1]), const*1j)\n for level in range(numlevels):\n for orientation in range(numorientations):\n theta_offset = orientation * np.pi / numorientations\n ctrfreq = pi / math.pow(2, (level+1)*bandwidth)\n band = np.cos(theta - theta_offset)**p * log_raised_cos(r, ctrfreq, bandwidth)\n bands[level,orientation,:,:] *= band\n \n hi = log_raised_coshi(r, pi / math.pow(2, bandwidth), bandwidth)\n\n lo = log_raised_coslo(r, pi / math.pow(2, bandwidth * numlevels), bandwidth)\n \n result.append(hi)\n result.append(bands)\n result.append(lo)\n return result","def vvc_filters_2d(kernel_size):\n vvc_filters = []\n half_kernel = (kernel_size - 8) // 2\n for frac_pos in frac_positions():\n filter_x = filter_coefficients(int(frac_pos.split(\",\")[0]))\n filter_y = filter_coefficients(int(frac_pos.split(\",\")[1]))\n\n filter_vvc = np.tile(filter_x, 8).reshape((8, 8))\n for index in range(len(filter_y)):\n filter_vvc[index, :] *= filter_y[index]\n filter_vvc = filter_vvc / (64 * 64)\n\n vvc_filters.append(np.pad(filter_vvc, ((half_kernel + 1, half_kernel), (half_kernel + 1, half_kernel)),\n 'constant', constant_values=0))\n return vvc_filters","def IDFT2D_slow(x):\r\n x = np.asarray(x, dtype=complex)\r\n \r\n M = len(x)\r\n N = len(x[0])\r\n mat = np.zeros((M, N), np.complex)\r\n \r\n for row in range(M):\r\n mat[row] = IDFT_slow(x[row])\r\n\r\n for col in range(N):\r\n mat[:,col] = IDFT_slow(mat[:,col])\r\n\r\n return mat","def test_fft_complex_2d():\n\ta, b, c = np.meshgrid([0, 1, 0, 0], [0, 1j, 1j], [0, 1, 1, 1])\n\tdummy_array = xr.DataArray(a * b * c, dims=['x', 'y', 'z'])\n\tchunked_array = dummy_array.chunk(chunks={'x': 2, 'y': 2, 'z': 2})\n\tspectrum_array, spectrum_coords, spectrum_dims = \\\n\t\txfft._fft(chunked_array, nfft={'y': 6, 'z': 8}, dim=['y', 'z'],\n\t\t dx={'y': 0.01, 'z': 0.02})\n\tassert np.allclose(spectrum_array.compute(),\n\t np.fft.fftn(a * b * c, s=(8, 6), axes=(2, 1)))\n\tassert np.array_equal(spectrum_coords['f_y'], np.fft.fftfreq(6, d=0.01))\n\tassert np.array_equal(spectrum_coords['f_z'], np.fft.fftfreq(8, d=0.02))\n\tassert ('x', 'f_y', 'f_z') == spectrum_dims","def _fourier_extp(cls, series=None, max_trun=None, forecast_period=None):\n import numpy as np\n import copy\n n = len(series)\n\n smoothing_loc = np.where((series < np.mean(series) - 3 * np.std(series)) | (series > np.mean(series)\n + 3 * np.std(series)))\n smoothed_series = copy.deepcopy(series)\n if len(smoothing_loc[0]) > 0:\n for idx in smoothing_loc[0]:\n smoothed_series[idx] = np.mean(smoothed_series[max(0, idx - 6): max(0, idx - 1)]) if idx > 5 \\\n else smoothed_series[idx]\n\n iyf = []\n\n # Generating the indices based on odd and event number of terms in the time series\n if int(n) % 2 != 0:\n all_idx = np.arange(1, n // 2 + 1)\n else:\n all_idx = np.arange(1, n // 2)\n\n # Performing Fourier transformation\n yf = np.fft.rfft(smoothed_series)\n\n # Spectral density for the fourier transformation (to identify the significant frequencies)\n psd = abs(yf[all_idx]) ** 2 + abs(yf[-all_idx]) ** 2\n psd_sorted = np.copy(psd)\n psd_sorted[::-1].sort()\n\n max_trun = min(max_trun, max(len(psd_sorted) - 1, 0))\n\n # Computing inverse Fourier transformation by appending next two significant frequencies up to (2 * max_trun)\n # frequencies\n\n idx = all_idx[np.where(psd > psd_sorted[max_trun])[0]]\n idx = np.concatenate((np.array([0]), idx), axis=0)\n a = yf[idx]\n\n # Storing the inverse Fourier transformations with (2 * trun) many frequencies\n iyf.append(cls._inv_fft(n + forecast_period, n, idx, a))\n\n return np.array(iyf)","def idft(X):\n N = len(X)\n x = np.zeros(N, 'complex')\n \n K = np.arange(0, N, 1)\n for n in range(0, N, 1):\n x[n] = np.dot(X, np.exp(-1j * 2 * np.pi * K * n / N))\n return x / N","def fourier_transform2d(self):\n\n zerofill = np.zeros(1024 * np.array([1,1])) #so it will always be square\n zerofill[:len(self.windowed), :len(self.windowed)] = self.windowed\n transform = np.fft.fft2(zerofill)\n transform = np.fft.fftshift(transform) # shift center to zero\n transformed = np.absolute(transform)\n tmax = transformed.max()\n zdata = (transformed)/(tmax) # normalize to maximum value\n\n return zdata","def test_fft_complex_1d():\n\ta = np.exp(2j * np.pi * np.arange(8) / 8)\n\tdummy_array = xr.DataArray(a, dims=['x'])\n\tchunked_array = dummy_array.chunk(chunks={'x': 2})\n\tspectrum_array, spectrum_coords, spectrum_dims = \\\n\t\txfft._fft(chunked_array, nfft={'x': 16}, dim=['x'], dx={'x': 0.5})\n\tassert np.array_equal(spectrum_array.compute(), np.fft.fft(a, n=16))\n\tassert np.array_equal(spectrum_coords['f_x'], np.fft.fftfreq(16, d=0.5))\n\tassert 'f_x' in spectrum_dims","def fourier(img):\n return fourierCV(img)","def test_fft_complex_3d():\n\ta, b, c = np.meshgrid([0, 1, 0, 0], [0, 1j, 1j], [0, 1, 1, 1])\n\tdummy_array = xr.DataArray(a * b * c, dims=['x', 'y', 'z'])\n\tchunked_array = dummy_array.chunk(chunks={'x': 2, 'y': 2, 'z': 2})\n\tspectrum_array, spectrum_coords, spectrum_dims = \\\n\t\txfft._fft(chunked_array, nfft={'x': 8, 'y': 6, 'z': 8},\n\t\t dim=['x', 'y', 'z'], dx={'x':12, 'y': 0.01, 'z': 0.02})\n\tassert np.allclose(spectrum_array.compute(),\n\t np.fft.fftn(a * b * c, s=(8, 6, 8)))\n\tassert np.array_equal(spectrum_coords['f_x'], np.fft.fftfreq(8, d=12))\n\tassert np.array_equal(spectrum_coords['f_y'], np.fft.fftfreq(6, d=0.01))\n\tassert np.array_equal(spectrum_coords['f_z'], np.fft.fftfreq(8, d=0.02))\n\tassert ('f_x', 'f_y', 'f_z') == spectrum_dims","def fourier_der(im):\n ft_img = DFT2(im)\n ft_img = np.fft.fftshift(ft_img)\n\n n_x = im.shape[1]\n coeff_x = (2 * np.pi * 1j)/n_x\n u_freq = np.array([n if n < int(n_x/2) else (n-n_x) for n in range(n_x)]) * 1j\n u_freq = np.array([np.fft.fftshift(u_freq)]*im.shape[0]).transpose()\n dx_ft = coeff_x * IDFT2(np.fft.ifftshift(u_freq.transpose() * ft_img))\n\n m_y = im.shape[0]\n coeff_y = (2 * np.pi * 1j)/m_y\n v_freq = np.array([m if m < int(m_y/2) else (m-m_y) for m in range(m_y)]) * 1j\n v_freq = np.array([np.fft.fftshift(v_freq)] * im.shape[1]).transpose()\n tr = IDFT2(np.fft.ifftshift(v_freq * ft_img))\n dy_ft = coeff_y * tr\n\n magnitude = np.sqrt(np.abs(dx_ft)**2 + np.abs(dy_ft)**2)\n return magnitude.real.astype(np.float64)","def fft_wrapping_illustration(ez=10, en=20, sev_clause='', small2=0, cmap='plasma'):\n fig, axs = plt.subplots(1, 3, figsize=(3 * FIG_W, FIG_H + 0.3), constrained_layout=True)\n ax0, ax1, ax2 = axs.flat\n\n if ez == 0 or sev_clause != '':\n if sev_clause == '':\n raise ValueError('Must input one of ez>0 or a valid DecL sev_clause')\n sev = build(f'agg Junk 1 claim {sev_clause} fixed')\n ez = sev.sev_m\n q1 = sev.q(1)\n if small2 == 0:\n small2 = int(np.ceil(np.log2(q1)))\n xs = np.hstack((-np.inf, np.arange(1 << small2) + 0.5))\n z = np.diff(sev.sev.cdf(xs))\n # enough space for aggregate\n big2 = int(np.ceil(np.log2(q1 * en)))\n elif ez == 1:\n assert small2, 'Need to input small2 in Poisson mode'\n z = np.zeros(1 << small2)\n z[1] = 1\n # full space\n sigma = np.sqrt(en)\n big2 = int(np.ceil(np.log2(en + 5 * sigma)))\n else:\n # enough space for severity and make sev\n if small2 == 0:\n small2 = int(np.ceil(np.log2(2 * ez)))\n z = np.zeros(1 << small2)\n z[:ez*2] = 1 / ez / 2\n # enough space for aggregate\n big2 = int(np.ceil(np.log2(2 * ez * en)))\n\n if big2 <= 8:\n ds = 'steps-post'\n else:\n ds = 'default'\n if ez == 1:\n wrapped = irfft( np.exp(en * (rfft(z) - 1)))\n full = irfft( np.exp(en * (rfft(z, 1 << big2) - 1)))\n else:\n wrapped = irfft( rfft(z) ** en )\n full = irfft( rfft(z, 1 << big2) ** en )\n\n ax0.plot(wrapped, c='C0', drawstyle=ds)\n ax0.xaxis.set_major_locator(mpl.ticker.MultipleLocator(4))\n ax0.set(title=f'Wrapped distribution\\nlog2={small2}')\n lm = ax0.get_ylim()\n lm = (-lm[1] / 20, lm[1]* 1.1)\n ax0.set(ylim=lm)\n\n norm = mpl.colors.Normalize(0, 1, clip=True)\n cmappable = mpl.cm.ScalarMappable(norm=norm, cmap=cmap)\n mapper = cmappable.to_rgba\n cc = list(map(mapper, np.linspace(0, 1, 1 << big2-small2)))\n ax1.plot(full, label='Full computation', c='w', alpha=1, lw=3, drawstyle=ds)\n ax1.xaxis.set_major_locator(mpl.ticker.MultipleLocator(32))\n for n, (s, c) in enumerate(zip(full.reshape((1<<big2-small2, 1<<small2)), cc)):\n ax1.plot(s, c=c, label=f'Part {n}', drawstyle=ds)\n ax1.plot(np.arange((1<<small2) * n, (1<<small2) * (n+1)), s, c=c, lw=2,\n drawstyle=ds, label=None)\n for n in range(1 << big2-small2):\n ax1.axvline(n * (1 << small2), lw=.25, c='C7')\n for n in range(1 << big2-small2):\n ax1.axvline(n * (1 << small2), lw=.25, c='C7')\n if big2 - small2 <= 3:\n ax1.legend(loc='center right')\n ax1.set(title=f'Full distribution\\nlog2={big2}, {1<<big2-small2} components')\n\n wrapped_from_full = full.reshape((1<<big2-small2, 1<<small2))\n ax2.plot(wrapped_from_full.T, label=None, c='C7', lw=.5, drawstyle=ds)\n ax2.plot(wrapped_from_full.sum(0), lw=3\n , drawstyle=ds, label='Wrapped from full', c='C1')\n ax2.plot(wrapped, lw=1, label='Wrapped', c='C0', drawstyle=ds)\n ax2.xaxis.set_major_locator(mpl.ticker.MultipleLocator(4))\n ax2.set(title='Wrapping components (grey)\\nSums (blue, organge as left)')\n # ax2.legend(loc)\n ax2.set(ylim=lm)\n\n assert np.allclose(wrapped_from_full.sum(0), wrapped)","def numpyInverseFourierTransform(self,coefficients,**kwargs):\n return [(z.real,z.imag) for z in np.fft.ifft(coefficients,**kwargs)]","def elliptic_mesh(F):\n # draw sphere\n u, v = np.mgrid[0:2*np.pi:40j, 0:np.pi:20j]\n x = np.cos(u)*np.sin(v)\n y = np.sin(u)*np.sin(v)\n z = np.cos(v)\n # put into array\n XYZ = np.vstack([x.flatten(),y.flatten(),z.flatten()])\n # deform according to F\n XYZ = np.dot(F.Fij,XYZ)\n # put back into meshgrid\n a, b, c = np.vsplit(XYZ,3)\n x = np.reshape(a,x.shape)\n y = np.reshape(b,y.shape)\n z = np.reshape(c,z.shape)\n return x, y, z","def IDFT(fourier_signal):\n fourier_signal = fourier_signal.astype(np.complex128)\n # find the length of the signal\n N = fourier_signal.shape[0]\n if fourier_signal.ndim == 2:\n M, N = fourier_signal.shape\n\n # calculate IDFT matrix\n u, v = np.meshgrid(np.arange(N), np.arange(N))\n omega = np.exp(2 * np.pi * 1j / N)\n idft_matrix = np.power(omega, u*v)\n\n # if it is a matrix of fourier signals\n if fourier_signal.ndim == 2:\n # calculate the Fourier Transform\n signal = np.dot(idft_matrix, fourier_signal.transpose())\n return 1/N * signal.transpose()\n\n # calculate the inverse Fourier Transform\n signal = np.dot(idft_matrix, fourier_signal)\n return 1/N * signal","def fourier_der(im):\n im = im.astype(np.float64)\n # constants\n M, N = im.shape\n u = np.meshgrid(np.arange(N), np.arange(M))[0] - N//2\n v = np.meshgrid(np.arange(N), np.arange(M))[1] - M//2\n u_der, v_der = (2 * np.pi * 1j / N), (2 * np.pi * 1j / M)\n\n # calculate dx, dy\n dx = u_der * IDFT2(np.fft.fftshift(u) * DFT2(im))\n dy = v_der * IDFT2(np.fft.fftshift(v) * DFT2(im))\n\n return np.sqrt(np.abs(dx)**2 + np.abs(dy)**2) # = magnitude","def wave_vectors_2D(nx, ny, d=1):\n\n return 2*np.pi*vector_vector_grid(\n np.fft.fftfreq(nx, d=d),\n np.fft.fftfreq(ny, d=d))","def spherefcn(x: np.ndarray) -> np.ndarray:\n if x.ndim == 1:\n x = x.reshape(-1, len(x))\n f = np.sum(x**2, axis=1)\n return f.reshape(-1, 1)","def half_hermitian_to_real_inverse_fft_image_filter(*args, **kwargs):\n import itk\n instance = itk.HalfHermitianToRealInverseFFTImageFilter.New(*args, **kwargs)\n return instance.__internal_call__()","def fourier_uniform(input, size, n=-1, axis=-1, output=None):\n input = numpy.asarray(input)\n output = _get_output_fourier(output, input)\n axis = normalize_axis_index(axis, input.ndim)\n sizes = _ni_support._normalize_sequence(size, input.ndim)\n sizes = numpy.asarray(sizes, dtype=numpy.float64)\n if not sizes.flags.contiguous:\n sizes = sizes.copy()\n _nd_image.fourier_filter(input, sizes, n, axis, output, 1)\n return output","def IDFT(fourier_signal):\n n = fourier_signal.shape[0]\n omega = np.exp((((2 * np.pi)*1j) / n))\n\n e_items = np.vander(omega**np.arange(n), n, True)\n org_signal = np.dot(e_items, fourier_signal)/n\n\n return org_signal","def _inv_fft(cls, n_extp, n, idx, a):\n import numpy as np\n ts = []\n for i in range(0, n_extp):\n # Sinusoid for the ith frequency\n s_array = cls._signals(idx, i, n)\n\n # Computing the inverse Fouries transformation term for the significant coefficients obtained from the\n # spectral density\n ts.append(np.sum(a * s_array) // n)\n return np.array(ts)","def _iczt_two_dimension(fd_data, ini_t, fin_t, n_time_pts, ini_f, fin_f,\r\n axis=0):\r\n\r\n # Assert that this is only for 2D arrays\r\n assert axis in [0, 1], 'Axis error: axis must be in [0, 1]'\r\n\r\n # Find the number of frequencies use\r\n n_freqs = np.size(fd_data, axis=0)\r\n\r\n # Find the conversion factor to convert from time-of-response to\r\n # angle around the unit circle\r\n time_to_angle = (2 * np.pi) / np.max(get_scan_times(ini_f, fin_f, n_freqs))\r\n\r\n # Find the parameters for computing the ICZT over the specified time\r\n # window\r\n theta_naught = ini_t * time_to_angle\r\n phi_naught = (fin_t - ini_t) * time_to_angle / (n_time_pts - 1)\r\n\r\n # Compute the exponential values only once\r\n exp_theta_naught = np.exp(1j * theta_naught)\r\n exp_phi_naught = np.exp(1j * phi_naught)\r\n\r\n # Make a dummy vector to facilitate vectorized computation\r\n dummy_vec = np.arange(n_freqs)\r\n\r\n time_pts = np.arange(n_time_pts) # Get time-index vector\r\n\r\n # Find the z-value matrix, to facilitate vectorized computation\r\n z_vals = exp_theta_naught * exp_phi_naught**time_pts\r\n zs_power = np.power(z_vals[None, :], dummy_vec[:, None])\r\n\r\n # If wanting to compute the transform along the 0th axis\r\n if axis == 0:\r\n\r\n # Init return arr\r\n iczt_data = np.zeros([n_time_pts, np.size(fd_data, axis=1)],\r\n dtype=np.complex64)\r\n\r\n # For every point along the other dimension\r\n for ii in range(np.size(fd_data, axis=1)):\r\n\r\n # Compute the 1D ICZT, converting this frequency domain data\r\n # to the time domain\r\n iczt_data[:, ii] = _iczt_one_dimension(fd_data[:, ii], zs_power,\r\n n_freqs)\r\n\r\n else: # If wanting to compute along the 1st axis\r\n iczt_data = np.zeros([np.size(fd_data, axis=0), n_time_pts],\r\n dtype=np.complex64)\r\n\r\n # For every point along the other dimension\r\n for ii in range(np.size(fd_data, axis=0)):\r\n\r\n # Compute the 1D ICZT, converting this frequency domain data\r\n # to the time domain\r\n iczt_data[ii, :] = _iczt_one_dimension(fd_data[ii, :], zs_power,\r\n n_freqs)\r\n\r\n return iczt_data","def test_fourier(self):\n m = np.array([-1, 0, 1])\n t = np.linspace(0, 2 * np.pi, 8, endpoint=False) # theta coordinates\n\n correct_vals = np.array([np.sin(t), np.ones_like(t), np.cos(t)]).T\n correct_ders = np.array([np.cos(t), np.zeros_like(t), -np.sin(t)]).T\n\n values = fourier(t[:, np.newaxis], m, dt=0)\n derivs = fourier(t[:, np.newaxis], m, dt=1)\n\n np.testing.assert_allclose(values, correct_vals, atol=1e-8)\n np.testing.assert_allclose(derivs, correct_ders, atol=1e-8)","def numpyInverseFourierTransform2D(self,coefficients,**kwargs):\n return np.fft.ifft2(coefficients,**kwargs)","def fourier_spectra(x, axis=1, flip=True, duplicate=True, **kwargs):\n\n x = forward_fourier(x, axis=axis, duplicate=duplicate)\n ns = x.shape[axis]\n nw = np.int32(np.floor(ns/2) + 1)\n slc = [slice(None)] * len(x.shape)\n slc[axis] = slice(0, nw)\n if flip:\n x = np.flip(x[tuple(slc)], axis=axis)\n return x","def IDFT2(fourier_image):\n fourier_image = fourier_image.astype(np.complex128)\n M, N = fourier_image.shape\n # build the idft2_matrix transform\n omega_y = np.exp(2 * np.pi * 1j / M)\n u, v = np.meshgrid(np.arange(M), np.arange(M))\n idft2_matrix = np.power(omega_y, u*v)\n\n # calculate the 2D inverse fourier transform\n return 1/M * np.dot(idft2_matrix, IDFT(fourier_image))","def test_fft_real_3d():\n\ta = np.mgrid[:7, :5, :5][0]\n\tdummy_array = xr.DataArray(a, dims=['x', 'y', 'z'])\n\tchunked_array = dummy_array.chunk(chunks={'x': 7, 'y': 5, 'z': 5})\n\tspectrum_array, spectrum_coords, spectrum_dims = \\\n\t\txfft._fft(chunked_array, nfft={'x': 11, 'y': 14, 'z': 18},\n\t\t dim=['x', 'y', 'z'], dx={'x':12, 'y': 0.01, 'z': 0.02},\n\t\t sym=False)\n\tassert np.allclose(spectrum_array.compute(),\n\t np.fft.rfftn(a.T, s=(18, 14, 11)).T)\n\tassert np.array_equal(spectrum_coords['f_x'], np.fft.rfftfreq(11, d=12))\n\tassert np.array_equal(spectrum_coords['f_y'], np.fft.fftfreq(14, d=0.01))\n\tassert np.array_equal(spectrum_coords['f_z'], np.fft.fftfreq(18, d=0.02))\n\tassert ('f_x', 'f_y', 'f_z') == spectrum_dims","def SIDFT(X,D):\n N=len(X)\n x=np.zeros(N,'complex')\n for n in range(0,N,1):\n for k in range(0,N,1):\n x[n]=x[n]+np.exp(-1j*2*np.pi*k*D/N)*X[k]*np.exp(1j*2*np.pi*k*n/N)\n return x/N","def inverse(self, image, nlamD, npix, offset=None):\n self._validate_args(nlamD, npix, offset)\n _log.debug(\n \"Inverse MatrixFourierTransform: array shape {}, \"\n \"centering style {}, \"\n \"output region size {} in lambda / D units, \"\n \"output array size {} pixels, \"\n \"offset {}\".format(image.shape, self.centering, nlamD, npix,\n offset)\n )\n return matrix_idft(image, nlamD, npix,\n centering=self.centering, offset=offset)","def irfft(a, n=None, axes=-1, norm=None):\n return image.image(np.fft.irfft(a, n, axes, norm), pixelsize=image.getPixelsize(a))","def DFT2D_slow(x):\r\n x = np.asarray(x, dtype=complex)\r\n \r\n M = len(x)\r\n N = len(x[0])\r\n mat = np.zeros((M, N), np.complex)\r\n \r\n for row in range(M):\r\n mat[row] = DFT_slow(x[row])\r\n\r\n for col in range(N):\r\n mat[:,col] = DFT_slow(mat[:,col])\r\n\r\n return mat","def gaussianFilter3D(size, points):\n \n matrix = numpy.zeros((size, size, size))\n \n for point in points:\n x0 = point[0]\n y0 = point[1]\n z0 = point[2]\n x2SigmaSquared = pow(point[3] * size/4, 2) * 2\n y2SigmaSquared = pow(point[4] * size/4, 2) * 2\n z2SigmaSquared = pow(point[5] * size/4, 2) * 2\n tempMatrix = numpy.zeros((size, size, size))\n for x in range(0, size):\n for y in range(0, size):\n for z in range(0, size):\n tempMatrix[y, x, z] = math.exp(-1 * \\\n (math.pow(x-x0, 2)/x2SigmaSquared +\\\n math.pow(y-y0, 2)/y2SigmaSquared +\\\n math.pow(z-z0, 2)/z2SigmaSquared))\n \n matrix = numpy.add(matrix, tempMatrix)\n \n matrix = matrixfix.flatten(matrix, 0, 1)\n \n return matrix","def analysis_fourier_map(self, target=1, mode=0):\r\n\r\n \r\n\r\n print('Starting fourier analysis:')\r\n\r\n self.print_image_info()\r\n\r\n # get the average image and the average of the whole image over time\r\n\r\n avgimg = np.mean(self.imageData, axis=0) # get mean image for reference later: average across all time\r\n\r\n self.meanimagevalue = np.mean(np.mean(avgimg, axis=1), axis=0)\r\n\r\n self.stdimg = np.std(self.imageData, axis= 0) # and standard deviation\r\n\r\n\r\n\r\n width = int(self.period*self.framerate*2)\r\n\r\n print( \" Detrending:\")\r\n\r\n print( ' Median filter width: ', width)\r\n\r\n # footprint = np.ones((width, 1, 1))\r\n\r\n # self.imageData = self.imageData - scipy.ndimage.median_filter(self.imageData, footprint=footprint)\r\n\r\n print( \" Done detrending\")\r\n\r\n\r\n\r\n self.n_times = self.timebase\r\n\r\n\r\n\r\n # calculate FFT and get amplitude and phase\r\n\r\n self.DF = np.fft.fft(self.imageData, axis = 0)\r\n self.freqs = np.fft.fftfreq(self.DF.shape[0], d=1./self.framerate)\r\n\r\n # self.freqs = np.fft.fftfreq(self.DF.shape[0], d=1./self.framerate)\r\n\r\n print (' df shape: ', self.DF.shape)\r\n\r\n print (' 1/framerate: ', 1./self.framerate)\r\n\r\n self.freq_point = np.argmin(np.abs(self.freqs - 1./self.period))\r\n print ('period:', self.period)\r\n print ('frequency: ', 1./self.period)\r\n print ('freq_point: ', self.freq_point)\r\n print ('frequency value: ',self.freqs[self.freq_point])\r\n steps = np.arange(1,6,dtype=np.float)\r\n steps = (steps)+1.\r\n self.assigned_freqs=2.*np.pi*1./1.6*steps\r\n print ('assigned freqs', self.assigned_freqs)\r\n\r\n #j = j + 2 # just looking at FFT leakage...`\r\n\r\n print (' closest index/freq, period: ', self.freq_point, self.freqs[self.freq_point], 1./self.period)\r\n\r\n self.print_image_info()\r\n\r\n ampimg = np.absolute(self.DF[self.freq_point,:,:])\r\n\r\n phaseimg = np.angle(self.DF[self.freq_point,:,:])\r\n\r\n \r\n # ampimg = np.absolute(self.DF[self.freq_point,:,:])\r\n\r\n\r\n # phaseimg = np.angle(self.DF[self.freq_point,:,:])\r\n\r\n if target == 1:\r\n\r\n f = open('img_phase1.dat', 'w')\r\n\r\n pickle.dump(phaseimg, f)\r\n\r\n f.close()\r\n\r\n f = open('img_amplitude1.dat', 'w')\r\n\r\n pickle.dump(ampimg, f)\r\n\r\n f.close()\r\n\r\n self.amplitudeImage1 = ampimg\r\n\r\n self.phaseImage1 = phaseimg\r\n\r\n if target == 2:\r\n\r\n f = open('img_phase2.dat', 'w')\r\n\r\n pickle.dump(phaseimg, f)\r\n\r\n f.close()\r\n\r\n f = open('img_amplitude2.dat', 'w')\r\n\r\n pickle.dump(ampimg, f)\r\n\r\n f.close()\r\n\r\n self.amplitudeImage2 = ampimg\r\n\r\n self.phaseImage2 = phaseimg\r\n\r\n print (\" FFT calculated, data saved.\\n\")\r\n\r\n # save most recent calculation to disk\r","def ifft(a, n=None, axes=-1, norm=None):\n return image.image(np.fft.ifft(a, n, axes, norm), pixelsize=image.getPixelsize(a))","def phantom_ellipses(n_points,E):\n \n #Rescaling according to image size \n E[:,0] = E[:,0]*n_points/2 #semiaxis a\n E[:,1] = E[:,1]*n_points/2 #semiaxis b\n E[:,2] = E[:,2]*n_points/2 #x\n E[:,3] = E[:,3]*n_points/2 #y\n E[:,4] = E[:,4]*math.pi/180\n \n x,y = np.meshgrid(np.arange(0,n_points)-n_points//2 ,np.arange(0,n_points)-n_points//2 )\n nrow,ncol = E.shape\n phantom1 = np.zeros((y.shape[0], y.shape[1], nrow))\n\n for k in range(nrow): #itero sulle ellissi\n x_new = x - E[k,2]\n y_new = y - E[k,3]\n\n #find(( (x.*cosp + y.*sinp).^2)./asq + ((y.*cosp - x.*sinp).^2)./bsq <= 1); \n cosp = math.cos(E[k,4])\n sinp = math.sin(E[k,4])\n cond = np.square( x_new * cosp + y_new * sinp )*1/(E[k,0]*E[k,0]) + \\\n np.square(y_new * cosp - x_new * sinp)*1/(E[k,1]*E[k,1]) - 1\n\n for i in range(x.shape[0]):\n for j in range(x.shape[1]):\n if (cond[i,j] <= 0.0):\n phantom1[i,j,k] = E[k,5]; # gray scale\n else:\n phantom1[i,j,k] = 0.0;\n #endif\n #endfor\n #endfor\n #endfor\n phantom1 = phantom1.sum(axis=2)\n phantom = np.flipud(phantom1)\n return phantom","def imwofz_vectorized(x, y):\n ncut=27\n xy=x*y\n xyp=2.0*xy/jnp.pi\n exx=jnp.exp(-x*x)\n f=-exx*erfcx(y)*jnp.sin(2.0*xy)+x/jnp.pi*exx*jnp.sinc(xyp)\n n=jnp.arange(1,ncut+1)\n n2=n*n\n x2=x*x\n y2=y*y\n vec0=0.5*n/(0.25*n2+y2)\n vec1=jnp.exp(-(0.25*n2[None,:]+x2[:,None]))\n vec4=jnp.exp(-(0.5*n[None,:]+x[:,None])*(0.5*n[None,:]+x[:,None]))\n vec5=jnp.exp(-(0.5*n[None,:]-x[:,None])*(0.5*n[None,:]-x[:,None]))\n Sigma1=jnp.sum(vec0*vec1,axis=1)\n Sigma4=jnp.sum(vec0*vec4,axis=1)\n Sigma5=jnp.sum(vec0*vec5,axis=1)\n f = f + 1.0/jnp.pi*(y*jnp.sin(2.0*xy)*Sigma1 + 0.5*(Sigma5-Sigma4))\n\n return f","def test_fft_real_2d():\n\ta = np.mgrid[:5, :5, :5][0]\n\tdummy_array = xr.DataArray(a, dims=['x', 'y', 'z'])\n\tchunked_array = dummy_array.chunk(chunks={'x': 2, 'y': 2, 'z': 2})\n\tspectrum_array, spectrum_coords, spectrum_dims = \\\n\t\txfft._fft(chunked_array, nfft={'y': 14, 'z': 18}, dim=['y', 'z'],\n\t\t dx={'y': 0.01, 'z': 0.02}, sym=False)\n\tassert np.allclose(spectrum_array.compute(),\n\t np.fft.rfftn(a, s=(18, 14), axes=(2, 1)))\n\tassert np.array_equal(spectrum_coords['f_y'], np.fft.rfftfreq(14, d=0.01))\n\tassert np.array_equal(spectrum_coords['f_z'], np.fft.fftfreq(18, d=0.02))\n\tassert ('x', 'f_y', 'f_z') == spectrum_dims","def dct(n_filters, n_input):\n\n basis = np.empty((n_filters, n_input))\n basis[0, :] = 1.0 / np.sqrt(n_input)\n\n samples = np.arange(1, 2*n_input, 2) * np.pi / (2.0 * n_input)\n\n for i in range(1, n_filters):\n basis[i, :] = np.cos(i*samples) * np.sqrt(2.0/n_input)\n\n return basis","def filled_space_prop(e: np.ndarray, arg: np.ndarray) -> np.ndarray:\n\n freq = np.exp(1j*arg)\n out = np.fft.ifft2(np.fft.fft2(e)*freq)\n\n return out","def fourier_coefficient(t, freq, n=1, axis=-1):\n t, freq, n = map(np.asanyarray, (t, freq, n))\n return np.exp(1j * 2 * np.pi * freq * n * t).sum(axis=axis)","def scales_from_fourier(f, wf, w0):\n\n f_arr = np.asarray(f)\n\n if wf == 'morlet':\n return (f_arr * (w0 + np.sqrt(2 + w0**2))) / (4 * np.pi)\n else:\n raise ValueError('wavelet function not available')","def dft(X): \n N = len(X)\n x = np.zeros(N, 'complex')\n K = np.arange(0, N, 1)\n for n in range(0, N, 1):\n x[n] = np.dot(X, np.exp(1j * 2 * np.pi * K * n / N))\n return x","def denoise_frequency_domain(ut: np.ndarray, \n filter_3d: np.ndarray) -> np.ndarray:\n\n num_rows = ut.shape[0]\n ut_denoised = np.empty(ut.shape, dtype=complex)\n for row in range(num_rows):\n ut_slice_cube = np.reshape(ut[row, :], filter_3d.shape)\n ut_slice_cube_denoised = ut_slice_cube*filter_3d\n ut_denoised[row, :] = ut_slice_cube_denoised.flatten()\n\n return ut_denoised","def FourierTransform(data, nPoints):\r\n tdf = np.fft.fft(data, nPoints)\r\n return tdf","def calc_ds_3d(slip_x, slip_y, dx, dy, mu, poisson, expand = 0):\n\n newlenx = len(slip_x)+2*expand\n newleny = len(slip_x[0])+2*expand\n\n newslipx = np.zeros((newlenx, newleny))\n newslipy = np.zeros((newlenx, newleny))\n newslipx[expand:expand+len(slip_x),expand:expand+len(slip_x[0])] = np.copy(slip_x)\n newslipy[expand:expand+len(slip_x),expand:expand+len(slip_x[0])] = np.copy(slip_y)\n \n k = np.fft.fftfreq(newlenx, dx)\n m = np.fft.fftfreq(newleny, dy)\n\n kxy, mxy = np.meshgrid(k, m, indexing='ij')\n\n kmag = np.sqrt(kxy**2+mxy**2)\n kmag[0,0] = 1.\n\n fx = np.fft.fft2(newslipx)\n fy = np.fft.fft2(newslipy)\n\n sx = -mu/2./kmag*(1./(1.-poisson)*(kxy**2*fx+mxy*kxy*fy)+(mxy**2*fx-mxy*kxy*fy))\n sy = -mu/2./kmag*(1./(1.-poisson)*(mxy**2*fy+mxy*kxy*fx)+(kxy**2*fy-mxy*kxy*fx))\n\n return (np.real(np.fft.ifft2(sx))[expand:expand+len(slip_x),expand:expand+len(slip_x[0])],\n np.real(np.fft.ifft2(sy))[expand:expand+len(slip_x),expand:expand+len(slip_x[0])])","def IDFT2(fourier_image):\n return IDFT(IDFT(fourier_image).transpose()).transpose()","def perform(self, pupil, nlamD, npix, offset=None):\n self._validate_args(nlamD, npix, offset)\n _log.debug(\n \"Forward MatrixFourierTransform: array shape {}, \"\n \"centering style {}, \"\n \"output region size {} in lambda / D units, \"\n \"output array size {} pixels, \"\n \"offset {}\".format(pupil.shape, self.centering, nlamD, npix,\n offset)\n\n )\n return matrix_dft(pupil, nlamD, npix,\n centering=self.centering, offset=offset)","def _imfilter(x_data, f_data):\n return pipe(f_data, ifftshift, fftn, lambda x: x * fftn(x_data), ifftn).real","def dwt(image_array, quantization_Array):\n # Create the high pass and low pass filters\n # both filters are non-causal\n # symmetric\n # [-2, -1, 0, 1, 2]\n LPF = [-0.125, 0.25, 0.75, 0.25, -0.125]\n LPF_center = 2\n\n # [ -2,-1, 0]\n HPF = [-0.5, 1, -0.5]\n HPF_center = 2\n\n nrow, ncol = image_array.shape\n\n # create an array that will contain the 4 different subbands of the image\n LL = np.zeros((nrow, ncol))\n LH = np.zeros((nrow, ncol))\n HL = np.zeros((nrow, ncol))\n HH = np.zeros((nrow, ncol))\n filtered_image = [LL, LH, HL, HH]\n\n # filtering the rows using a low pass and high pass filters\n LowPass_rows = np.zeros((nrow, ncol))\n HighPass_rows = np.zeros((nrow, ncol))\n for i in range(0, nrow):\n LowPass_rows[i, :] = lfilter(LPF, image_array[i, :], LPF_center)\n HighPass_rows[i, :] = lfilter(HPF, image_array[i, :], HPF_center)\n\n # down sample rows.\n # which means we will have half the number of columns\n for i in range(0, len(filtered_image)):\n filtered_image[i] = filtered_image[i][:, ::2]\n\n # apply filters accross columns\n for i in range(0, ncol):\n LL[:, i] = lfilter(LPF, LowPass_rows[:, i], LPF_center)\n LH[:, i] = lfilter(HPF, LowPass_rows[:, i], HPF_center)\n HL[:, i] = lfilter(LPF, HighPass_rows[:, i], LPF_center)\n HH[:, i] = lfilter(HPF, HighPass_rows[:, i], HPF_center)\n\n # down sample columns and quantize\n for i in range(0, len(filtered_image)):\n filtered_image[i] = filtered_image[i][::2, :]\n filtered_image[i] = np.round(\n filtered_image[i]/quantization_Array[i]).astype(int)\n\n return filtered_image","def fft(a, n=None, axes=-1, norm=None):\n\n return image.image(np.fft.fft(a, n, axes, norm), pixelsize=a.pixelsize)","def app_complex(data_pupil,data_phase,oversize=4):\n#phase colors\n # cdict = {'red': ((0.0, 1.0, 1.0),(0.25, 0.0, 0.0),(0.5, 0.0, 0.0),(0.75, 1.0, 1.0),(1.00, 1.0, 1.0)),'green': ((0.0, 0.0, 0.0),(0.25, 1.0, 1.0),(0.5, 0.0, 0.0),(0.75, 1.0, 1.0),(1.0, 0.0, 0.0)),'blue': ((0.0, 0.0, 0.0),(0.25, 0.0, 0.0),(0.5, 1.0, 1.0),(0.75, 0.0, 0.0),(1.0, 0.0, 0.0))}\n #my_cmap = matplotlib.colors.LinearSegmentedColormap('my_colormap',cdict,256)\n\n size=data_pupil.shape[0]\n#make empty oversized array\n\n expand_phase=zeros([oversize*size,oversize*size])\n expand_amp=zeros([oversize*size,oversize*size])\n\n#copy fits into lower left corner\n\n expand_amp[0:size,0:size]=data_pupil[0:size,0:size]\n expand_phase[0:size,0:size]=data_phase[0:size,0:size]\n\n#move to corners\n\n expand_phase=roll(expand_phase,-size/2,0)\n expand_phase=roll(expand_phase,-size/2,1)\n\n expand_amp=roll(expand_amp,-size/2,0)\n expand_amp=roll(expand_amp,-size/2,1)\n\n# recalculate real and imaginary part\n\n #xr=expand_amp*cos(expand_phase)\n #yr=expand_amp*sin(expand_phase)\n\n# make complex array\n\n complexr=expand_amp*numpy.exp(1j*expand_phase)\n\n# apply 2d-fft\n\n complexr=numpy.fft.fftpack.fft2(complexr)\n return fftshift(complexr)","def DFT(signal):\n n = signal.shape[0]\n omega = np.exp(((((-2) * np.pi)*1j) / n))\n\n e_items = np.vander(omega**np.arange(n), n, True)\n fourier_signal = np.dot(e_items, signal)\n\n return fourier_signal.astype(np.complex128)","def fourier_from_scales(scales, wf, w0):\n\n scales_arr = np.asarray(scales)\n\n if wf == 'morlet':\n return (4 * np.pi * scales_arr) / (w0 + np.sqrt(2 + w0**2))\n else:\n raise ValueError('wavelet function not available')","def medianfilter(data, filtersize, threshold=None,verbose=False):\n # 2006/02/01 IJC at the Jet Propulsion Laboratory\n # 2010-02-18 13:52 IJC: Converted to python\n from numpy import zeros, median, abs, std\n\n print \"Just use scipy.signal.medfilt !!!\"\n print \"Just use scipy.signal.medfilt !!!\"\n print \"Just use scipy.signal.medfilt !!!\"\n\n if len(filtersize)<1:\n print 'medianfilter2 requires that filtersize be a 1- or 2-element vector'\n return -1\n elif len(filtersize)==1:\n filtersize = [filtersize[0], filtersize[0]]\n else:\n filtersize = filtersize[0:2]\n\n npix = data.shape[0]\n npiy = data.shape[1]\n bigsize = npix+2*(filtersize[0]-1)\n bigdata = zeros((bigsize,bigsize),float)\n ind = filtersize[0]-1\n if ind==0:\n bigdata = data\n else:\n bigdata[ind:(bigsize-ind), ind:(bigsize-ind)] = data\n\n\n # FOR NOW, WE ASSUME FILTERSIZE IS ODD!!\n # AND THAT DATA IS SQUARE!\n niter_x = npix + (filtersize[0]-1)\n niter_y = npiy + (filtersize[1]-1)\n filt = zeros((niter_x,niter_y), float)\n\n for ii in range(niter_x):\n for jj in range(niter_y):\n if verbose>1:\n print \"ii,jj>>\",ii,jj\n if filtersize[0]==1:\n indi = 1\n else:\n indi = filtersize[0]-1\n if filtersize[1]==1:\n indj = 1\n else:\n indj = filtersize[1]-1\n select = bigdata[ii:(ii+indi),jj:(jj+indj)].ravel()\n select = select[isfinite(select)]\n #residualSelection = abs(select - median(select))\n\n if verbose: \n print \"select.shape>>\",select.shape\n print \"threshold>>\",threshold\n\n if threshold is not None:\n if threshold >= 0: # raw threshold\n doFilter = abs(bigdata[ii,jj]-median(select))/std(select)>=threshold\n elif threshold<0: # remove outliers before applying threshold\n npts_init = len(select)\n select = removeoutliers(select, abs(threshold), center='median')\n npts_final = len(select)\n if verbose>1:\n print \"threshold=\",threshold,\", removed %i points\" % (npts_init-npts_final)\n \n doFilter = abs(bigdata[ii,jj]-median(select))/std(select)>=threshold \n else: # filter everything; threshold not set.\n doFilter = True\n\n if verbose:\n print \"doFilter?>>\",doFilter\n if verbose>1:\n print \"select>>\",select\n\n if doFilter: \n newval = median( select )\n else:\n newval = bigdata[ii,jj]\n\n if verbose>1:\n print \"newval>>\",newval\n\n filt[ii,jj] = newval\n\n print filt.shape, [(filtersize[0]-1)/2,niter_x-(filtersize[0]-1)/2,(filtersize[0]-1)/2,niter_y-(filtersize[0]-1)/2]\n return filt[(filtersize[0]-1)/2:niter_x-(filtersize[0]-1)/2,(filtersize[0]-1)/2:niter_y-(filtersize[0]-1)/2]","def forward_fft(self, array_in):\r\n # Find side length, as real array may or may not be doubled for\r\n # aliasing control\r\n side = array_in.shape[0]\r\n div_side = 1.0/side**2\r\n\r\n out = np.fft.fft2(self.sign_mat[0:side, 0:side]*array_in)*div_side\r\n return out","def IDFTdirect(X):\n \n N = len(X)\n x = []\n for n in range(N):\n x_n = Complex(0,0)\n for k in range(N):\n a = 2*math.pi*k*n/N\n x_n += X[k]*Complex(math.cos(a), math.sin(a))\n x.append((1/N)*x_n)\n return x","def fourier_decomposition(times, magnitudes, order):\n times = times[:,0]\n magnitudes = magnitudes[:,0]\n\n num_examples = times.shape[0]\n num_coef = order * 2 + 1\n\n if num_coef > num_examples:\n raise Exception(\"Too few examples for the specified order. Number of examples must be at least order * 2 + 1. Required: %d, Actual: %d\" % (num_coef, num_examples))\n\n initial_coef = np.ones(num_coef)\n\n cost_function = partial(fourier_series_cost, times, magnitudes, order)\n\n fitted_coef, success = leastsq(cost_function, initial_coef)\n\n final_coef = correct_coef(fitted_coef, order)\n\n return final_coef","def ManualFourier(x):\n x = np.asarray(x, dtype=float)\n N = x.shape[0]\n\n if np.log2(N) % 1 > 0:\n raise ValueError(\"size of x must be a power of 2\")\n\n # N_min here is equivalent to the stopping condition above,\n # and should be a power of 2\n N_min = min(N, 32)\n\n # Perform an O[N^2] DFT on all length-N_min sub-problems at once\n n = np.arange(N_min)\n k = n[:, None]\n M = np.exp(-2j * np.pi * n * k / N_min)\n X = np.dot(M, x.reshape((N_min, -1)))\n\n # build-up each level of the recursive calculation all at once\n while X.shape[0] < N:\n X_even = X[:, :int(X.shape[1] / 2)]\n X_odd = X[:, int(X.shape[1] / 2):]\n factor = np.exp(-1j * np.pi * np.arange(X.shape[0])\n / X.shape[0])[:, None]\n X = np.vstack([X_even + factor * X_odd,\n X_even - factor * X_odd])\n return X.ravel()","def freqdomain(self):\n \n\n #self.df = self.f[1] - self.f[0]\n #frequency vector\n #fv = fftshift(fftfreq(len(eta),1./fs))\n #fv = fv[len(fv)/2:]\n \n #spectral analysis\n self.sn1 = self.espec1(self.n1)\n self.sn2 = self.espec1(self.n2)\n self.sn3 = self.espec1(self.n3)\n self.sn12 = self.espec2(self.n1,self.n2)\n self.sn13 = self.espec2(self.n1,self.n3)\n self.sn23 = self.espec2(self.n2,self.n3)\n \n #delta freq\n self.df = self.f[3] - self.f[2]\n\n #calculo do numero de onda\n #self.wavenumber()\n #k = numeronda(h,f,len(f))\n #k = np.array(k)\n\n #calculo dos coeficientes de fourier - NDBC 96_01 e Steele (1992)\n c = self.sn2[:,1] + self.sn3[:,1]\n cc = np.sqrt(self.sn1[:,1] * (c))\n \n self.a1 = self.sn12[:,3] / cc\n self.b1 = self.sn13[:,3] / cc\n \n self.a2 = (self.sn2[:,1] - self.sn3[:,1]) / c\n self.b2 = 2 * self.sn12[:,2] / c\n \n #calcula direcao de onda\n #mean direction\n self.dire1 = np.array([np.angle(np.complex(self.b1[i],self.a1[i]),deg=True) for i in range(len(self.a1))])\n \n #principal direction\n self.dire2 = 0.5 * np.array([np.angle(np.complex(self.b2[i],self.a2[i]),deg=True) for i in range(len(self.a2))])\n \n #condicao para valores maiores que 360 e menores que 0\n self.dire1[np.where(self.dire1 < 0)] = self.dire1[np.where(self.dire1 < 0)] + 360\n self.dire1[np.where(self.dire1 > 360)] = self.dire1[np.where(self.dire1 > 360)] - 360\n self.dire2[np.where(self.dire2 < 0)] = self.dire2[np.where(self.dire2 < 0)] + 360\n self.dire2[np.where(self.dire2 > 360)] = self.dire2[np.where(self.dire2 > 360)] - 360\n \n #acha o indice da frequencia de pico\n ind = np.where(self.sn1[:,1] == np.max(self.sn1[:,1]))[0]\n \n #periodo de pico\n self.tp = (1. / self.f[ind])[0]\n \n #momento espectral de ordem zero total - m0\n self.m0 = np.sum(self.sn1[:,1]) * self.df\n \n #calculo da altura significativa\n self.hm0 = 4.01 * np.sqrt(self.m0)\n \n #direcao do periodo de pico\n self.dp = self.dire1[ind][0]\n \n #Espalhamento direcional\n #Formula do sigma1 do livro Tucker&Pitt(2001) \"Waves in Ocean Engineering\" pags 196-198\n c1 = np.sqrt(self.a1 ** 2 + self.b1 ** 2)\n c2 = np.sqrt(self.a2 ** 2 + self.b2 ** 2)\n \n s1 = c1 / (1-c1)\n s2 = (1 + 3 * c2 + np.sqrt(1 + 14 * c2 + c2 ** 2)) / (2 * (1 - c2))\n \n self.sigma1 = np.sqrt(2 - 2 * c1) * 180 / np.pi\n self.sigma2 = np.sqrt((1 - c2) / 2) * 180 / np.pi\n \n self.sigma1p = np.real(self.sigma1[ind])[0]\n self.sigma2p = np.real(self.sigma2[ind])[0]\n \n # pondaf = np.array([hm0, tp, dp, sigma1p, sigma2p])\n \n #hm0, tp, dp, sigma1, sigma2, sigma1p, sigma2p, f, df, k, sn, snx, sny, snn, snnx, snny, snxny, snxnx, snyny, a1, b1, a2, b2, dire1, dire2\n #return hm0, tp, dp, sigma1, sigma2, sigma1p, sigma2p, f, df, k, sn, snx, sny, snn, snnx, snny, snxny, snxnx, snyny, a1, b1, a2, b2, dire1, dire2","def harmonics(y, n, /, axis=-1):\n # Get fourier transform\n y = np.moveaxis(y, axis, -1)\n fft = np.fft.fft(y, axis=-1)\n\n # Remove frequencies outside range. The FFT will have some error and give\n # non-zero imaginary components, but we can get magnitude or naively cast to real\n fft[..., 0] = 0\n fft[..., n + 1:-n] = 0\n yf = np.real(np.fft.ifft(fft, axis=-1))\n # yf = np.abs(np.fft.ifft(fft, axis=-1))\n return np.moveaxis(yf, -1, axis)","def filtering(self):\r\n # 1 ###########################################################################################################\r\n fft_image = np.fft.fft2(self.image)\r\n # 2 ###########################################################################################################\r\n fft_shift_image = np.fft.fftshift(fft_image)\r\n\r\n ###\r\n mag_dft = np.log(np.abs(fft_shift_image))\r\n mag_dft = (255 * (mag_dft / np.max(mag_dft))).astype(dtype='uint8')\r\n ###\r\n\r\n # 3 ###########################################################################################################\r\n if self.filter_name == 'butterworth_l' or self.filter_name == 'butterworth_h':\r\n mask = self.filter(fft_shift_image.shape, self.cutoff, self.order)\r\n else:\r\n mask = self.filter(fft_shift_image.shape, self.cutoff)\r\n # 4 ###########################################################################################################\r\n # multiply the dft (fft shift image) by the mask\r\n filtered_image = fft_shift_image * mask\r\n\r\n ###\r\n mag_filtered_image = mag_dft * mask\r\n ###\r\n\r\n # 5 ###########################################################################################################\r\n inverse_fft_shift_image = np.fft.ifftshift(filtered_image)\r\n # 6 ###########################################################################################################\r\n inverse_fft_image = np.fft.ifft2(inverse_fft_shift_image)\r\n # 7 ###########################################################################################################\r\n mag_image = np.zeros(inverse_fft_image.shape, dtype=complex)\r\n for i in range(inverse_fft_image.shape[0]):\r\n for j in range(inverse_fft_image.shape[1]):\r\n if inverse_fft_image[i][j] < 0:\r\n mag_image[i][j] = -1 * inverse_fft_image[i][j]\r\n else:\r\n mag_image[i][j] = inverse_fft_image[i][j]\r\n # magnitude of inverse fft is complete\r\n # 8 ###########################################################################################################\r\n full_contrast_image = self.post_process_image(mag_image)\r\n\r\n return [mag_dft, mag_filtered_image, full_contrast_image]","def _field_Fresnel(z, field, dx, lam, dtype, usepyFFTW):\n \n \"\"\" *************************************************************\n Major differences to Cpp based LP version:\n - dx =siz/N instead of dx=siz/(N-1), more consistent with physics \n and rest of LP package\n - fftw DLL uses no normalization, numpy uses 1/N on ifft -> omitted\n factor of 1/(2*N)**2 in final calc before return\n - bug in Cpp version: did not touch top row/col, now we extract one\n more row/col to fill entire field. No errors noticed with the new\n method so far\n ************************************************************* \"\"\"\n _using_pyfftw = False # determined if loading is successful \n if usepyFFTW or _USE_PYFFTW:\n try:\n import pyfftw as _pyfftw\n from pyfftw.interfaces.numpy_fft import fft2 as _fft2\n from pyfftw.interfaces.numpy_fft import ifft2 as _ifft2\n _fftargs = {'planner_effort': 'FFTW_ESTIMATE',\n 'overwrite_input': True,\n 'threads': -1} #<0 means use multiprocessing.cpu_count()\n _using_pyfftw = True \n except ImportError:\n #import warnings\n #warnings.warn(_WARNING)\n _WARNING = '\\n**************************** WARNING ***********************\\n'\\\n +'In the Fresnel command you required FFT with the pyFFTW package.\\n'\\\n +'or _USE_PYFFTW = True in your config.py file.\\n'\\\n +'However LightPipes cannot import pyFFTW because it is not installed.\\n'\\\n +'Falling back to numpy.fft.\\n'\\\n +'(Try to) install pyFFTW on your computer for faster performance.\\n'\\\n +'Enter at a terminal prompt: python -m pip install pyfftw.\\n'\\\n +'Or reinstall LightPipes with the option pyfftw\\n'\\\n +'Enter: python -m pip install lightpipes[pyfftw]\\n\\n'\\\n +'*************************************************************'\n print(_WARNING)\n if not _using_pyfftw:\n from numpy.fft import fft2 as _fft2\n from numpy.fft import ifft2 as _ifft2\n _fftargs = {}\n tictoc.tic()\n N = field.shape[0] #assert square\n \n legacy = True #switch on to numerically compare oldLP/new results\n if legacy:\n kz = 2.*3.141592654/lam * z\n siz = N*dx\n dx = siz/(N-1) #like old Cpp code, even though unlogical\n else:\n kz = 2*_np.pi/lam*z\n \n \n cokz = _np.cos(kz)\n sikz = _np.sin(kz)\n \n No2 = int(N/2) #\"N over 2\"\n \"\"\"The following section contains a lot of uses which boil down to\n 2*No2. For even N, this is N. For odd N, this is NOT redundant:\n 2*No2 is N-1 for odd N, therefore sampling an even subset of the\n field instead of the whole field. Necessary for symmetry of first\n step involving Fresnel integral calc.\n \"\"\"\n if _using_pyfftw:\n in_outF = _pyfftw.zeros_aligned((2*N, 2*N),dtype=dtype)\n in_outK = _pyfftw.zeros_aligned((2*N, 2*N),dtype=dtype)\n else:\n in_outF = _np.zeros((2*N, 2*N),dtype=dtype)\n in_outK = _np.zeros((2*N, 2*N),dtype=dtype)\n \n \"\"\"Our grid is zero-centered, i.e. the 0 coordiante (beam axis) is\n not at field[0,0], but field[No2, No2]. The FFT however is implemented\n such that the frequency 0 will be the first element of the output array,\n and it also expects the input to have the 0 in the corner.\n For the correct handling, an fftshift is necessary before *and* after\n the FFT/IFFT:\n X = fftshift(fft(ifftshift(x))) # correct magnitude and phase\n x = fftshift(ifft(ifftshift(X))) # correct magnitude and phase\n X = fftshift(fft(x)) # correct magnitude but wrong phase !\n x = fftshift(ifft(X)) # correct magnitude but wrong phase !\n A numerically faster way to achieve the same result is by multiplying\n with an alternating phase factor as done below.\n Speed for N=2000 was ~0.4s for a double fftshift and ~0.1s for a double\n phase multiplication -> use the phase factor approach (iiij).\n \"\"\"\n # Create the sign-flip pattern for largest use case and \n # reference smaller grids with a view to the same data for\n # memory saving.\n ii2N = _np.ones((2*N),dtype=float)\n ii2N[1::2] = -1 #alternating pattern +,-,+,-,+,-,...\n iiij2N = _np.outer(ii2N, ii2N)\n iiij2No2 = iiij2N[:2*No2,:2*No2] #slice to size used below\n iiijN = iiij2N[:N, :N]\n\n RR = _np.sqrt(1/(2*lam*z))*dx*2\n io = _np.arange(0, (2*No2)+1) #add one extra to stride fresnel integrals\n R1 = RR*(io - No2)\n fs, fc = _fresnel(R1)\n fss = _np.outer(fs, fs) # out[i, j] = a[i] * b[j]\n fsc = _np.outer(fs, fc)\n fcs = _np.outer(fc, fs)\n fcc = _np.outer(fc, fc)\n \n \"\"\"Old notation (0.26-0.33s):\n temp_re = (a + b + c - d + ...)\n # numpy func add takes 2 operands A, B only\n # -> each operation needs to create a new temporary array, i.e.\n # ((((a+b)+c)+d)+...)\n # since python does not optimize to += here (at least is seems)\n New notation (0.14-0.16s):\n temp_re = (a + b) #operation with 2 operands\n temp_re += c\n temp_re -= d\n ...\n Wrong notation:\n temp_re = a #copy reference to array a\n temp_re += b\n ...\n # changing `a` in-place, re-using `a` will give corrupted\n # result\n \"\"\"\n temp_re = (fsc[1:, 1:] #s[i+1]c[j+1]\n + fcs[1:, 1:]) #c[+1]s[+1]\n temp_re -= fsc[:-1, 1:] #-scp [p=+1, without letter =+0]\n temp_re -= fcs[:-1, 1:] #-csp\n temp_re -= fsc[1:, :-1] #-spc\n temp_re -= fcs[1:, :-1] #-cps\n temp_re += fsc[:-1, :-1] #sc\n temp_re += fcs[:-1, :-1] #cs\n \n temp_im = (-fcc[1:, 1:] #-cpcp\n + fss[1:, 1:]) # +spsp\n temp_im += fcc[:-1, 1:] # +ccp\n temp_im -= fss[:-1, 1:] # -ssp\n temp_im += fcc[1:, :-1] # +cpc\n temp_im -= fss[1:, :-1] # -sps\n temp_im -= fcc[:-1, :-1] # -cc\n temp_im += fss[:-1, :-1]# +ss\n \n temp_K = 1j * temp_im # a * b creates copy and casts to complex\n temp_K += temp_re\n temp_K *= iiij2No2\n temp_K *= 0.5\n in_outK[(N-No2):(N+No2), (N-No2):(N+No2)] = temp_K\n \n in_outF[(N-No2):(N+No2), (N-No2):(N+No2)] \\\n = field[(N-2*No2):N,(N-2*No2):N] #cutting off field if N odd (!)\n in_outF[(N-No2):(N+No2), (N-No2):(N+No2)] *= iiij2No2\n \n tictoc.tic()\n in_outK = _fft2(in_outK, **_fftargs)\n in_outF = _fft2(in_outF, **_fftargs)\n t_fft1 = tictoc.toc()\n \n in_outF *= in_outK\n \n in_outF *= iiij2N\n tictoc.tic()\n in_outF = _ifft2(in_outF, **_fftargs)\n t_fft2 = tictoc.toc()\n #TODO check normalization if USE_PYFFTW\n \n Ftemp = (in_outF[No2:N+No2, No2:N+No2]\n - in_outF[No2-1:N+No2-1, No2:N+No2])\n Ftemp += in_outF[No2-1:N+No2-1, No2-1:N+No2-1]\n Ftemp -= in_outF[No2:N+No2, No2-1:N+No2-1]\n comp = complex(cokz, sikz)\n Ftemp *= 0.25 * comp\n Ftemp *= iiijN\n field = Ftemp #reassign without data copy\n ttotal = tictoc.toc()\n t_fft = t_fft1 + t_fft2\n t_outside = ttotal - t_fft\n debug_time = False\n if debug_time:\n print('Time total = fft + rest: {:.2f}={:.2f}+{:.2f}'.format(\n ttotal, t_fft, t_outside))\n return field","def FourierTransform2D(xdata, zdata, nPoints):\r\n freq = FourierFrequency(xdata, nPoints)\r\n tdf = np.zeros_like(zdata, dtype=complex)\r\n for u, i in enumerate(zdata):\r\n tdf[u] = FourierTransform(xdata, i, nPoints)\r\n return freq, tdf","def DFT2(image):\n image = image.astype(np.float64)\n M, N = image.shape\n\n # build the dft2_matrix transform\n omega_y = np.exp(-2 * np.pi * 1j / M)\n u, v = np.meshgrid(np.arange(M), np.arange(M))\n dft2_matrix = np.power(omega_y, u*v)\n\n # calculate the 2D fourier transform\n fourier_image = np.dot(dft2_matrix, DFT(image))\n\n return fourier_image","def Fourier_cheb_transform(vec, x_points, y_points) :\n rVec = zeros((numYs, numXs),dtype='complex')\n for xIndx in range(numXs):\n for yIndx in range(numYs):\n for n in range(2*N+1):\n for m in range(M):\n x = x_points[xIndx]\n y = y_points[yIndx]\n term = vec[n*M + m] * exp(1.j*(n-N)*kx*x) * cos(m*arccos(y))\n rVec[yIndx, xIndx] += term\n del x,y,n,m\n\n return real(rVec)","def defineCoords(dimensions, steps):\n ### NOT CURRENTLY USED\n \n print(\"1D\")\n xCoords = np.arange(-X/2, X/2+dx, dx) # 1D \n fxCoords = np.arange(0, X/dx+dx)\n fxCoords = fxCoords - fxCoords[-1]/2 # Shift everything over so the center of array is at f = 0\n realCoords = xCoords\n fourierCoords = fxCoords\n realSpace = np.zeros(Nx+1)\n xRealSpace = np.zeros(Nx)\n #realSpace = np.zeros(Nx) #1D\n \n fourierSpace = np.zeros_like(realSpace, complex)\n\n return realCoords, fourierCoords, realSpace, fourierSpace","def toeplitz_inverse_multiplication(u, x_0, phi, psi, D_phi, D_psi, Lambda_1, Lambda_2, Lambda_3, Lambda_4):\n\n y = fft(D_phi*u)\n a = Lambda_1*fft(D_psi*(1/D_phi)*ifft(Lambda_2*y))\n b = Lambda_3*fft(D_psi*(1/D_phi)*ifft(Lambda_4*y))\n y = (1/D_psi)*real(ifft(a-b))/(x_0*(phi-psi))\n \n return y","def spectrum_fourier(self):\r\n\r\n data = self.input.data\r\n sampling_rate = self.input.sampling_rate\r\n\r\n fft = fftpack.fft\r\n if np.any(np.iscomplex(data)):\r\n # Get negative frequencies, as well as positive:\r\n f = np.linspace(-sampling_rate/2., sampling_rate/2., data.shape[-1])\r\n spectrum_fourier = np.fft.fftshift(fft(data))\r\n else:\r\n f = tsu.get_freqs(sampling_rate, data.shape[-1])\r\n spectrum_fourier = fft(data)[..., :f.shape[0]]\r\n \r\n return f, spectrum_fourier","def mce_filter(freq, f_raw, params):\n\tz = np.exp(-2j*np.pi*freq/f_raw)\n\tb11, b12, b21, b22 = np.array(params[:4])*0.5**14\n\tH = (1+z)**4 / (1-b11*z+b12*z**2) / (1-b21*z+b22*z**2)\n\tH /= 2**4 / (1-b11+b12) / (1-b21+b22)\n\treturn H","def createEllipsoid( position=(0,0,0), radius=(1,1,1), colour=(0.6,0.6,0.6), samplesY = 20, samplesXZ = 20, exponentBottom = 2, exponentTop = 2, exponentSide = 2 ):\r\n \r\n if exponentBottom < 2.0 or exponentTop < 2.0 or exponentSide < 2.0 :\r\n raise ValueError( 'Exponents for ellipsoid must all be under 2.0!' )\r\n \r\n position = PyUtils.toPoint3d(position)\r\n vertices = []\r\n for i in range(1,samplesY):\r\n thetaI = i*math.pi/float(samplesY)\r\n if i < samplesY / 2 : \r\n n = exponentTop\r\n else:\r\n n = exponentBottom\r\n cos = math.cos(thetaI) \r\n y = cos * radius[1]\r\n scaleXZ = math.pow( 1-math.pow(math.fabs(cos),n), 1.0/float(n) )\r\n for j in range(0,samplesXZ):\r\n thetaJ = j*2.0*math.pi/float(samplesXZ)\r\n n = exponentSide\r\n cos = math.cos(thetaJ)\r\n x = cos * scaleXZ * radius[0]\r\n z = math.pow( 1-math.pow(math.fabs(cos),n), 1.0/float(n) ) * math.copysign(1, math.sin(thetaJ)) * scaleXZ * radius[2]\r\n vertices.append( position + Vector3d(x,y,z) )\r\n vertices.append( position + Vector3d(0,radius[1],0) )\r\n vertices.append( position + Vector3d(0,-radius[1],0) ) \r\n\r\n faces = []\r\n for i in range(0,(samplesY-2)*samplesXZ,samplesXZ) :\r\n for j in range(0,samplesXZ) :\r\n faces.append( (i+j, i+(j+1)%samplesXZ, i+samplesXZ+(j+1)%samplesXZ, i+samplesXZ+j) ) \r\n\r\n for i in range(0,samplesXZ) :\r\n base = (samplesY-2)*samplesXZ\r\n faces.append( ((i+1)%samplesXZ, i, (samplesY-1)*samplesXZ) ) \r\n faces.append( (base+i, base+(i+1)%samplesXZ, (samplesY-1)*samplesXZ+1) ) \r\n\r\n \r\n return create( vertices, faces, colour )","def DFT_slow(x):\r\n x = np.asarray(x, dtype=complex)\r\n N = len(x)\r\n n = np.arange(N)\r\n k = n.reshape((N, 1))\r\n M = np.exp(-2j * np.pi * k * n / N)\r\n return np.dot(M, x)","def ft(x):\n y = np.fft.rfft(x)\n\n phi = 2 * np.pi * np.random.random(len(y))\n\n phi[0] = 0.0\n if len(x) % 2 == 0:\n phi[-1] = 0.0\n\n y = y * np.exp(1j * phi)\n return np.fft.irfft(y, n=len(x))","def _adj_filt(self, ndims):\n\n # inner filter, that is 3x3x...\n filt_inner = np.zeros([3] * ndims)\n for j in range(ndims):\n o = [[1]] * ndims\n o[j] = [0, 2]\n filt_inner[np.ix_(*o)] = 1\n\n # full filter, that makes sure the inner filter is applied \n # ith feature to ith feature\n filt = np.zeros([3] * ndims + [ndims, ndims])\n for i in range(ndims):\n filt[..., i, i] = filt_inner\n \n return filt","def fRes(self, mag, size, ps):\n x = 2 * np.pi * mag / (size[0] * ps[0])\n y = 2 * np.pi * mag / (size[1] * ps[1])\n return [x, y]","def apply_fourier_transform(chunked_audio):\n pass","def makeSpectrum(E, dx, dy, upsample=10):\n zeropadded = np.array(E.shape) * upsample\n F = fft.fftshift(fft.fft2(E, zeropadded)) / E.size\n xf = fft.fftshift(fft.fftfreq(zeropadded[1], d=dx))\n yf = fft.fftshift(fft.fftfreq(zeropadded[0], d=dy))\n return (F, xf, yf)","def fft(self, *args, **kwargs):\n return _image.image_fft(self, *args, **kwargs)","def fourier_transform(hamiltonian, grid, spinless):\n return _fourier_transform_helper(hamiltonian=hamiltonian,\n grid=grid,\n spinless=spinless,\n phase_factor=+1,\n vec_func_1=grid.momentum_vector,\n vec_func_2=grid.position_vector)","def fft(self, z, out=None):\n z = np.asfarray(z)\n out = np.multiply(z, z, out)\n out *= -0.5\n np.exp(out, out)\n return out","def __qft_input_state(self, size) -> None:\n\n for i in range(size):\n self.h(i)\n self.u1(-math.pi / float(2 ** i), i)","def FFT(x):\n x = np.asarray(x, dtype=float)\n N = x.shape[0]\n \n if N % 2 > 0:\n raise ValueError(\"size of x must be a power of 2\")\n elif N <= 32: # this cutoff should be optimized\n return DFT(x)\n else:\n X_even = FFT(x[::2])\n X_odd = FFT(x[1::2])\n factor = np.exp(-2j * np.pi * np.arange(N) / N)\n return np.concatenate([X_even + factor[:int(N / 2)] * X_odd,\n X_even + factor[int(N / 2):] * X_odd])","def DFTdirect(x):\n \n N = len(x)\n X = []\n for k in range(N):\n X_k = Complex(0,0)\n for n in range(N):\n a = 2*math.pi*k*n/N\n X_k += x[n]*Complex(math.cos(a), -math.sin(a))\n X.append(X_k)\n return X","def IDFT_slow(x):\r\n x = np.asarray(x, dtype=complex)\r\n N = len(x)\r\n n = np.arange(N)\r\n k = n.reshape((N, 1))\r\n M = np.exp(2j * np.pi * k * n / N)\r\n return 1.0/N * np.dot(M, x)","def fir_design_matrix(events, len_hrf):\r\n event_types = np.unique(events)[np.unique(events) != 0]\r\n fir_matrix = np.zeros((events.shape[0], len_hrf * event_types.shape[0]))\r\n\r\n for t in event_types:\r\n idx_h_a = np.where(event_types == t)[0] * len_hrf\r\n idx_h_b = idx_h_a + len_hrf\r\n idx_v = np.where(events == t)[0]\r\n for idx_v_a in idx_v:\r\n idx_v_b = idx_v_a + len_hrf\r\n fir_matrix[idx_v_a:idx_v_b, idx_h_a:idx_h_b] += (np.eye(len_hrf) *\r\n np.sign(t))\r\n\r\n return fir_matrix","def getFileAsFiltFloatMatrix(dirPath, filt, columns, delim=\",\"):\n\tmat = list()\n\tfor rec in fileFiltSelFieldsRecGen(dirPath, filt, columns, delim):\n\t\tmat.append(asFloatList(rec))\n\treturn mat","def dft_map(input_array, *, u, v, center=(0.0, 0.0) * apu.arcsec, pixel_size=(1.0, 1.0) * apu.arcsec):\n m, n = input_array.shape\n\n y = generate_xy(m, center[1], pixel_size[1])\n x = generate_xy(n, center[0], pixel_size[0])\n\n x, y = np.meshgrid(x, y)\n uv = np.vstack([u, v])\n # Check units are correct for exp need to be dimensionless and then remove units for speed\n if (uv[0, :] * x[0, 0]).unit == apu.dimensionless_unscaled and \\\n (uv[1, :] * y[0, 0]).unit == apu.dimensionless_unscaled:\n\n uv = uv.value\n x = x.value\n y = y.value\n\n vis = np.sum(input_array[..., np.newaxis] * np.exp(-2j * np.pi * (\n x[..., np.newaxis] * uv[np.newaxis, 0, :] + y[..., np.newaxis] * uv[np.newaxis, 1, :])),\n axis=(0, 1))\n\n return vis\n else:\n raise UnitsError(\"Incompatible units on uv {uv.unit} should cancel with xy \"\n \"to leave a dimensionless quantity\")","def _apply_array_spatial1234(self, h1e: 'Nparray', h2e: 'Nparray',\n h3e: 'Nparray', h4e: 'Nparray') -> 'Nparray':\n norb = self.norb()\n assert h4e.shape == (norb, norb, norb, norb, norb, norb, norb, norb)\n lena = self.lena()\n lenb = self.lenb()\n\n nh1e = numpy.copy(h1e)\n nh2e = numpy.copy(h2e)\n nh3e = numpy.copy(h3e)\n\n for i in range(norb):\n for j in range(norb):\n for k in range(norb):\n nh1e[:, :] -= h4e[:, j, i, k, j, i, k, :]\n for l in range(norb):\n nh2e[i, j, :, :] += (h4e[j, l, i, k, l, k, :, :] +\n h4e[i, j, l, k, l, k, :, :] +\n h4e[i, l, k, j, l, k, :, :] +\n h4e[j, i, k, l, l, k, :, :] +\n h4e[i, k, j, l, k, :, l, :] +\n h4e[j, i, k, l, k, :, l, :] +\n h4e[i, j, k, l, :, k, l, :])\n nh3e[i, j, k, :, :, :] += (h4e[k, i, j, l, l, :, :, :] +\n h4e[j, i, l, k, l, :, :, :] +\n h4e[i, l, j, k, l, :, :, :] +\n h4e[i, k, j, l, :, l, :, :] +\n h4e[i, j, l, k, :, l, :, :] +\n h4e[i, j, k, l, :, :, l, :])\n\n dvec = self.calculate_dvec_spatial()\n evec = numpy.zeros((norb, norb, norb, norb, lena, lenb),\n dtype=self._dtype)\n\n for i in range(norb):\n for j in range(norb):\n tmp = dvec[i, j, :, :]\n tmp2 = self._calculate_dvec_spatial_with_coeff(tmp)\n evec[:, :, i, j, :, :] = tmp2[:, :, :, :]\n\n out = self._apply_array_spatial123(nh1e, nh2e, nh3e, dvec, evec)\n\n evec = numpy.transpose(numpy.tensordot(h4e,\n evec,\n axes=((2, 6, 3, 7), (0, 1, 2,\n 3))),\n axes=[0, 2, 1, 3, 4, 5])\n\n dvec2 = numpy.zeros(dvec.shape, dtype=self._dtype)\n for i in range(norb):\n for j in range(norb):\n dvec[:, :, :, :] = evec[i, j, :, :, :, :]\n cvec = self._calculate_coeff_spatial_with_dvec(dvec)\n dvec2[i, j, :, :] += cvec[:, :]\n\n out += self._calculate_coeff_spatial_with_dvec(dvec2)\n return out","def filtering(self):\n from numpy import fft\n import numpy as np\n\n _image_dft = fft.fft2(self.image)\n _image_dft = fft.fftshift(_image_dft)\n # dft = DFT.DFT()\n # plt.figure(1) \n # plt.imshow(self.image)\n # plt.figure(2)\n # plt.imshow(20*np.log10(abs(_image_dft))) \n # print(_image_dft)\n # print(abs(_image_dft))\n # plt.show()\n filter = self.filter(self.image.shape, self.cutoff, self.order) \\\n if self.filter_name.startswith('butterworth') \\\n else self.filter(self.image.shape, self.cutoff)\n \n _image_dft_filtered = _image_dft * filter\n _image_filtered = abs(fft.ifft2(_image_dft_filtered))\n \n return [ self.post_process_image(_image_filtered), \\\n self.post_process_image(20*np.log10(abs(_image_dft)+.00001)), \\\n self.post_process_image(20*np.log10(abs(_image_dft_filtered)+.00001)) ]","def ifftn_1d(a):\r\n b = np.fft.fftpack.ifftn(a, axes=(len(a.shape)-1, )) \r\n return np.multiply(b, np.sqrt(a.shape[-1]))","def DARP2016_MicArray():\n\n M = 36 # number of mics\n array_height = -0.49 # [m] (ref. to airfoil height at z=0)\n\n # mic coordinates (corrected for DARP2016 configuration)\n XYZ_array = np.array([[0., 0.025, 0.08477, 0.12044, 0.18311, 0.19394,\n 0.01559, 0.08549, 0.16173, 0.19659, 0.24426, -0.00556,\n 0.02184, 0.08124, 0.06203, 0.11065, -0.02252, -0.05825,\n -0.06043, -0.11924, -0.10628, -0.02252, -0.09449, -0.15659,\n -0.21072, -0.24318, -0.00556, -0.05957, -0.13484, -0.14352,\n -0.19696, 0.01559, 0.02021, -0.01155, 0.03174, -0.00242],\n [-0., -0., 0.04175, 0.11082, 0.10542, 0.15776,\n -0.01955, -0.04024, -0.02507, -0.07743, -0.05327, -0.02437,\n -0.09193, -0.14208, -0.20198, -0.22418, -0.01085, -0.0744,\n -0.1521, -0.17443, -0.22628, 0.01085, -0.00084, -0.04759,\n -0.01553, -0.05799, 0.02437, 0.07335, 0.09276, 0.15506,\n 0.15397, 0.01955, 0.09231, 0.16326, 0.20889, 0.24999],\n array_height*np.ones(M)])\n\n # calibration factors\n array_cal = np.array([73.92182641429085, 96.84446743391487, 85.48777846463159,\n 85.24410968090712, 83.63917149322562, 68.94090765134432,\n 79.2385037527723, 112.77357210746612, 84.8483307868491,\n 87.18956628936178, 97.75046920293282, 89.2829545690508,\n 79.51644155562396, 90.39403884030057, 80.71754629014218,\n 89.4418210091059, 98.33634233056068, 79.2212022850229,\n 91.25543447201031, 89.55040012572815, 85.77495667666254,\n 82.74418222820202, 84.63061055646973, 77.01568014644964,\n 95.52764533324982, 92.16734812591154, 95.27123074600838,\n 87.93335310521428, 96.65066131188675, 93.58564782091074,\n 78.1446818728945, 101.3047738767648, 83.68569643491034,\n 84.7981031520437, 94.40796508430756, 83.52266614867919])\n\n return XYZ_array, array_cal","def _deform(self, volumes: Union[np.ndarray, Sequence[np.ndarray]]) -> \\\n List[np.ndarray]:\n # Make sure xy shape (last two axes) are the same for all volumes\n xy_shapes = [v.shape[-2:] for v in volumes]\n if xy_shapes.count(xy_shapes[0]) != len(xy_shapes):\n # True when xy shapes don't all match\n raise ValueError('Volumes passed to DataHandler._deform() must '\n 'all have the same shape.')\n\n # Build a new pixel index deformation map\n # Assumed to be the same\n xy_shape = xy_shapes[0]\n deform_map = self._deformation_map(xy_shape)\n\n deformed_volumes = []\n\n for volume in volumes:\n shape = volume.shape\n ndim = volume.ndim\n new_vol = np.zeros_like(volume)\n if ndim == 4:\n # 3D multichannel data. Apply 2D deformations to each z slice\n # in each channel of the volume\n for c in range(shape[0]):\n for z in range(shape[1]):\n new_vol[c, z, ...] = \\\n map_coordinates(volume[c, z, ...],\n deform_map,\n order=0).reshape(xy_shape)\n elif ndim == 3:\n # 3D single channel data. Apply 2D deformations to each z slice\n # of the volume\n for z in range(shape[0]):\n new_vol[z, ...] = map_coordinates(volume[z, ...],\n deform_map,\n order=0).reshape(xy_shape)\n elif ndim == 2:\n # Volume is 2D, deform the whole thing at once\n new_vol = map_coordinates(volume,\n deform_map,\n order=0).reshape(xy_shape)\n else:\n raise ValueError(f'Cannot deform volume with ndim {ndim}')\n\n deformed_volumes.append(new_vol)\n\n return deformed_volumes"],"string":"[\n \"def _irfft2d(f_x) :\",\n \"def process( fids, ndim=2 ):\\n\\timg = np.empty_like( fids )\\n\\tax = -1*(np.array( range(ndim) )+1)\\n\\t\\n\\timg = np.fft.fftshift( np.fft.fftn( fids, axes=ax, ).astype( np.complex64), axes=ax )\\n\\t\\n\\treturn np.squeeze(img)\",\n \"def _get_fourier_filter(self):\\n size = max(64, int(2 ** np.ceil(np.log2(2 * self.m[-1].item()))))\\n\\n pi = torch.acos(torch.zeros(1)).item() * 2.0\\n n = torch.cat(\\n [\\n torch.arange(1, size // 2 + 1, 2, device=self.n.device),\\n torch.arange(size // 2 - 1, 0, -2, device=self.n.device),\\n ]\\n )\\n f = torch.zeros(size, device=self.n.device)\\n f[0] = 0.25\\n if self.flat:\\n f[1::2] = -1 / (pi * n).pow(2)\\n else:\\n f[1::2] = -self.s_detect.abs().pow(2) / (\\n pi\\n * (self.d_source + self._d_detect())\\n * torch.sin(\\n n\\n * self.s_detect.abs()\\n / (self.d_source + self._d_detect())\\n )\\n ).pow(2)\\n f = torch.stack(\\n [f, torch.zeros(f.shape, device=self.n.device)], dim=-1\\n )\\n f = fftshift(f, dim=(-2,))\\n\\n filt = fft1(f)[..., 0]\\n\\n if self.filter_type == \\\"hamming\\\":\\n # hamming filter\\n fac = torch.tensor(\\n np.hamming(size).astype(np.float32), device=f.device\\n )\\n elif self.filter_type == \\\"hann\\\":\\n # hann filter\\n fac = torch.tensor(\\n np.hanning(size).astype(np.float32), device=f.device\\n )\\n elif self.filter_type == \\\"cosine\\\":\\n # cosine filter\\n fac = torch.sin(\\n torch.linspace(0, pi, size + 1, device=f.device)[:-1]\\n )\\n else:\\n # ramp / ram-lak filter\\n fac = 1.0\\n\\n return fac * filt\",\n \"def blur_fourier(im, kernel_size):\\n im = im.astype(np.float64)\\n # build the kernel with zero padding\\n kernel_base = gaussian_kernel_factory(kernel_size)\\n window = np.zeros_like(im).astype(np.float64)\\n M, N = im.shape\\n dx, dy = kernel_base.shape\\n x_middle, y_middle = N//2, M//2\\n\\n window[(y_middle-dy//2):(y_middle+dy//2+1), (x_middle-dx//2):(x_middle+dx//2+1)] = kernel_base\\n\\n # multiply in the freq domain\\n return IDFT2(DFT2(im) * DFT2(np.fft.ifftshift(window))).real\",\n \"def make_quad_frs_imag(dims,numlevels,numorientations,bandwidth):\\n \\n freq_resps_imag = make_steer_frs(dims,numlevels,numorientations,bandwidth)\\n freq_resps_imag[0] = np.zeros(dims)\\n freq_resps_imag[2] = np.zeros(dims)\\n return freq_resps_imag\",\n \"def make_ellipsoid_faces(center, dims, nsubdivs=2):\\n t = (1+sqrt(5))/2.0\\n s = sqrt(1+t**2)\\n \\n vertices = [(t/s,1/s,0), (-t/s,1/s,0), (t/s,-1/s,0),\\\\\\n (-t/s,-1/s,0), (1/s,0,t/s), (1/s,0,-t/s), (-1/s,0,t/s),(-1/s,0,-t/s),\\\\\\n (0,t/s,1/s), (0,-t/s,1/s), (0,t/s,-1/s), (0,-t/s,-1/s)]\\n\\n v = vertices\\n faces = [(v[0],v[8],v[4]),(v[1],v[10],v[7]),(v[2],v[9],v[11]),(v[7],v[3],v[1]),(v[0],v[5],v[10]),(v[3],v[9],v[6]),\\\\\\n (v[3],v[11],v[9]),(v[8],v[6],v[4]),(v[2],v[4],v[9]),(v[3],v[7],v[11]),(v[4],v[2],v[0]),\\\\\\n (v[9],v[4],v[6]),(v[2],v[11],v[5]),(v[0],v[10],v[8]),(v[5],v[0],v[2]),(v[10],v[5],v[7]),(v[1],v[6],v[8]),\\\\\\n (v[1],v[8],v[10]),(v[6],v[1],v[3]),(v[11],v[7],v[5])]\\n \\n #subdivide each of the faces into 9 faces\\n for i in xrange(nsubdivs):\\n new_faces = []\\n for face in faces:\\n new_faces.extend(subdivide(face[0], face[1], face[2]))\\n faces = new_faces\\n faces = scale_faces(dims[0], dims[1], dims[2], faces)\\n faces = translate_faces(center, faces)\\n return faces\",\n \"def smoothfield( d , boxsize, Rth ):\\n\\n ng = N.shape(d)[0]\\n dk = N.fft.rfftn(d)\\n kgrid = getkgrid(boxsize,ng)\\n dk = dk * N.exp( -(kgrid * Rth) ** 2. / 2. )\\n d = N.fft.irfftn(dk)\\n\\n return d\",\n \"def blur_fourier(im, kernel_size):\\n kernel = gaus_kernel_calc(kernel_size)\\n\\n zeros = np.zeros(im.shape)\\n x_mid = np.math.floor(im.shape[1] / 2)\\n y_mid = np.math.floor(im.shape[0] / 2)\\n distance = np.math.floor(kernel_size / 2)\\n zeros[x_mid - distance: x_mid + distance + 1, y_mid - distance: y_mid + distance + 1] = kernel\\n\\n fourier_kernel = DFT2(np.fft.ifftshift(zeros))\\n fourier_img = DFT2(im)\\n fourier_blured = fourier_kernel * fourier_img\\n\\n return IDFT2(fourier_blured).real.astype(np.float64)\",\n \"def make_steer_frs(dims, numlevels, numorientations, bandwidth):\\n \\n result = []\\n bands=[]\\n p = numorientations-1\\n const = math.sqrt(float(math.pow(2,(2*p))*math.pow(math.factorial(p),2)) / float(math.factorial(2*p)*(p+1)))\\n f1 = freqspace(dims[0])\\n f2 = freqspace(dims[1])\\n wx, wy = np.meshgrid(f1, f2)\\n size = wx.shape\\n r = np.sqrt(wx**2 + wy**2)\\n theta = np.arctan2(wy, wx) \\n \\n bands = np.full((numlevels, numorientations, dims[0], dims[1]), const*1j)\\n for level in range(numlevels):\\n for orientation in range(numorientations):\\n theta_offset = orientation * np.pi / numorientations\\n ctrfreq = pi / math.pow(2, (level+1)*bandwidth)\\n band = np.cos(theta - theta_offset)**p * log_raised_cos(r, ctrfreq, bandwidth)\\n bands[level,orientation,:,:] *= band\\n \\n hi = log_raised_coshi(r, pi / math.pow(2, bandwidth), bandwidth)\\n\\n lo = log_raised_coslo(r, pi / math.pow(2, bandwidth * numlevels), bandwidth)\\n \\n result.append(hi)\\n result.append(bands)\\n result.append(lo)\\n return result\",\n \"def vvc_filters_2d(kernel_size):\\n vvc_filters = []\\n half_kernel = (kernel_size - 8) // 2\\n for frac_pos in frac_positions():\\n filter_x = filter_coefficients(int(frac_pos.split(\\\",\\\")[0]))\\n filter_y = filter_coefficients(int(frac_pos.split(\\\",\\\")[1]))\\n\\n filter_vvc = np.tile(filter_x, 8).reshape((8, 8))\\n for index in range(len(filter_y)):\\n filter_vvc[index, :] *= filter_y[index]\\n filter_vvc = filter_vvc / (64 * 64)\\n\\n vvc_filters.append(np.pad(filter_vvc, ((half_kernel + 1, half_kernel), (half_kernel + 1, half_kernel)),\\n 'constant', constant_values=0))\\n return vvc_filters\",\n \"def IDFT2D_slow(x):\\r\\n x = np.asarray(x, dtype=complex)\\r\\n \\r\\n M = len(x)\\r\\n N = len(x[0])\\r\\n mat = np.zeros((M, N), np.complex)\\r\\n \\r\\n for row in range(M):\\r\\n mat[row] = IDFT_slow(x[row])\\r\\n\\r\\n for col in range(N):\\r\\n mat[:,col] = IDFT_slow(mat[:,col])\\r\\n\\r\\n return mat\",\n \"def test_fft_complex_2d():\\n\\ta, b, c = np.meshgrid([0, 1, 0, 0], [0, 1j, 1j], [0, 1, 1, 1])\\n\\tdummy_array = xr.DataArray(a * b * c, dims=['x', 'y', 'z'])\\n\\tchunked_array = dummy_array.chunk(chunks={'x': 2, 'y': 2, 'z': 2})\\n\\tspectrum_array, spectrum_coords, spectrum_dims = \\\\\\n\\t\\txfft._fft(chunked_array, nfft={'y': 6, 'z': 8}, dim=['y', 'z'],\\n\\t\\t dx={'y': 0.01, 'z': 0.02})\\n\\tassert np.allclose(spectrum_array.compute(),\\n\\t np.fft.fftn(a * b * c, s=(8, 6), axes=(2, 1)))\\n\\tassert np.array_equal(spectrum_coords['f_y'], np.fft.fftfreq(6, d=0.01))\\n\\tassert np.array_equal(spectrum_coords['f_z'], np.fft.fftfreq(8, d=0.02))\\n\\tassert ('x', 'f_y', 'f_z') == spectrum_dims\",\n \"def _fourier_extp(cls, series=None, max_trun=None, forecast_period=None):\\n import numpy as np\\n import copy\\n n = len(series)\\n\\n smoothing_loc = np.where((series < np.mean(series) - 3 * np.std(series)) | (series > np.mean(series)\\n + 3 * np.std(series)))\\n smoothed_series = copy.deepcopy(series)\\n if len(smoothing_loc[0]) > 0:\\n for idx in smoothing_loc[0]:\\n smoothed_series[idx] = np.mean(smoothed_series[max(0, idx - 6): max(0, idx - 1)]) if idx > 5 \\\\\\n else smoothed_series[idx]\\n\\n iyf = []\\n\\n # Generating the indices based on odd and event number of terms in the time series\\n if int(n) % 2 != 0:\\n all_idx = np.arange(1, n // 2 + 1)\\n else:\\n all_idx = np.arange(1, n // 2)\\n\\n # Performing Fourier transformation\\n yf = np.fft.rfft(smoothed_series)\\n\\n # Spectral density for the fourier transformation (to identify the significant frequencies)\\n psd = abs(yf[all_idx]) ** 2 + abs(yf[-all_idx]) ** 2\\n psd_sorted = np.copy(psd)\\n psd_sorted[::-1].sort()\\n\\n max_trun = min(max_trun, max(len(psd_sorted) - 1, 0))\\n\\n # Computing inverse Fourier transformation by appending next two significant frequencies up to (2 * max_trun)\\n # frequencies\\n\\n idx = all_idx[np.where(psd > psd_sorted[max_trun])[0]]\\n idx = np.concatenate((np.array([0]), idx), axis=0)\\n a = yf[idx]\\n\\n # Storing the inverse Fourier transformations with (2 * trun) many frequencies\\n iyf.append(cls._inv_fft(n + forecast_period, n, idx, a))\\n\\n return np.array(iyf)\",\n \"def idft(X):\\n N = len(X)\\n x = np.zeros(N, 'complex')\\n \\n K = np.arange(0, N, 1)\\n for n in range(0, N, 1):\\n x[n] = np.dot(X, np.exp(-1j * 2 * np.pi * K * n / N))\\n return x / N\",\n \"def fourier_transform2d(self):\\n\\n zerofill = np.zeros(1024 * np.array([1,1])) #so it will always be square\\n zerofill[:len(self.windowed), :len(self.windowed)] = self.windowed\\n transform = np.fft.fft2(zerofill)\\n transform = np.fft.fftshift(transform) # shift center to zero\\n transformed = np.absolute(transform)\\n tmax = transformed.max()\\n zdata = (transformed)/(tmax) # normalize to maximum value\\n\\n return zdata\",\n \"def test_fft_complex_1d():\\n\\ta = np.exp(2j * np.pi * np.arange(8) / 8)\\n\\tdummy_array = xr.DataArray(a, dims=['x'])\\n\\tchunked_array = dummy_array.chunk(chunks={'x': 2})\\n\\tspectrum_array, spectrum_coords, spectrum_dims = \\\\\\n\\t\\txfft._fft(chunked_array, nfft={'x': 16}, dim=['x'], dx={'x': 0.5})\\n\\tassert np.array_equal(spectrum_array.compute(), np.fft.fft(a, n=16))\\n\\tassert np.array_equal(spectrum_coords['f_x'], np.fft.fftfreq(16, d=0.5))\\n\\tassert 'f_x' in spectrum_dims\",\n \"def fourier(img):\\n return fourierCV(img)\",\n \"def test_fft_complex_3d():\\n\\ta, b, c = np.meshgrid([0, 1, 0, 0], [0, 1j, 1j], [0, 1, 1, 1])\\n\\tdummy_array = xr.DataArray(a * b * c, dims=['x', 'y', 'z'])\\n\\tchunked_array = dummy_array.chunk(chunks={'x': 2, 'y': 2, 'z': 2})\\n\\tspectrum_array, spectrum_coords, spectrum_dims = \\\\\\n\\t\\txfft._fft(chunked_array, nfft={'x': 8, 'y': 6, 'z': 8},\\n\\t\\t dim=['x', 'y', 'z'], dx={'x':12, 'y': 0.01, 'z': 0.02})\\n\\tassert np.allclose(spectrum_array.compute(),\\n\\t np.fft.fftn(a * b * c, s=(8, 6, 8)))\\n\\tassert np.array_equal(spectrum_coords['f_x'], np.fft.fftfreq(8, d=12))\\n\\tassert np.array_equal(spectrum_coords['f_y'], np.fft.fftfreq(6, d=0.01))\\n\\tassert np.array_equal(spectrum_coords['f_z'], np.fft.fftfreq(8, d=0.02))\\n\\tassert ('f_x', 'f_y', 'f_z') == spectrum_dims\",\n \"def fourier_der(im):\\n ft_img = DFT2(im)\\n ft_img = np.fft.fftshift(ft_img)\\n\\n n_x = im.shape[1]\\n coeff_x = (2 * np.pi * 1j)/n_x\\n u_freq = np.array([n if n < int(n_x/2) else (n-n_x) for n in range(n_x)]) * 1j\\n u_freq = np.array([np.fft.fftshift(u_freq)]*im.shape[0]).transpose()\\n dx_ft = coeff_x * IDFT2(np.fft.ifftshift(u_freq.transpose() * ft_img))\\n\\n m_y = im.shape[0]\\n coeff_y = (2 * np.pi * 1j)/m_y\\n v_freq = np.array([m if m < int(m_y/2) else (m-m_y) for m in range(m_y)]) * 1j\\n v_freq = np.array([np.fft.fftshift(v_freq)] * im.shape[1]).transpose()\\n tr = IDFT2(np.fft.ifftshift(v_freq * ft_img))\\n dy_ft = coeff_y * tr\\n\\n magnitude = np.sqrt(np.abs(dx_ft)**2 + np.abs(dy_ft)**2)\\n return magnitude.real.astype(np.float64)\",\n \"def fft_wrapping_illustration(ez=10, en=20, sev_clause='', small2=0, cmap='plasma'):\\n fig, axs = plt.subplots(1, 3, figsize=(3 * FIG_W, FIG_H + 0.3), constrained_layout=True)\\n ax0, ax1, ax2 = axs.flat\\n\\n if ez == 0 or sev_clause != '':\\n if sev_clause == '':\\n raise ValueError('Must input one of ez>0 or a valid DecL sev_clause')\\n sev = build(f'agg Junk 1 claim {sev_clause} fixed')\\n ez = sev.sev_m\\n q1 = sev.q(1)\\n if small2 == 0:\\n small2 = int(np.ceil(np.log2(q1)))\\n xs = np.hstack((-np.inf, np.arange(1 << small2) + 0.5))\\n z = np.diff(sev.sev.cdf(xs))\\n # enough space for aggregate\\n big2 = int(np.ceil(np.log2(q1 * en)))\\n elif ez == 1:\\n assert small2, 'Need to input small2 in Poisson mode'\\n z = np.zeros(1 << small2)\\n z[1] = 1\\n # full space\\n sigma = np.sqrt(en)\\n big2 = int(np.ceil(np.log2(en + 5 * sigma)))\\n else:\\n # enough space for severity and make sev\\n if small2 == 0:\\n small2 = int(np.ceil(np.log2(2 * ez)))\\n z = np.zeros(1 << small2)\\n z[:ez*2] = 1 / ez / 2\\n # enough space for aggregate\\n big2 = int(np.ceil(np.log2(2 * ez * en)))\\n\\n if big2 <= 8:\\n ds = 'steps-post'\\n else:\\n ds = 'default'\\n if ez == 1:\\n wrapped = irfft( np.exp(en * (rfft(z) - 1)))\\n full = irfft( np.exp(en * (rfft(z, 1 << big2) - 1)))\\n else:\\n wrapped = irfft( rfft(z) ** en )\\n full = irfft( rfft(z, 1 << big2) ** en )\\n\\n ax0.plot(wrapped, c='C0', drawstyle=ds)\\n ax0.xaxis.set_major_locator(mpl.ticker.MultipleLocator(4))\\n ax0.set(title=f'Wrapped distribution\\\\nlog2={small2}')\\n lm = ax0.get_ylim()\\n lm = (-lm[1] / 20, lm[1]* 1.1)\\n ax0.set(ylim=lm)\\n\\n norm = mpl.colors.Normalize(0, 1, clip=True)\\n cmappable = mpl.cm.ScalarMappable(norm=norm, cmap=cmap)\\n mapper = cmappable.to_rgba\\n cc = list(map(mapper, np.linspace(0, 1, 1 << big2-small2)))\\n ax1.plot(full, label='Full computation', c='w', alpha=1, lw=3, drawstyle=ds)\\n ax1.xaxis.set_major_locator(mpl.ticker.MultipleLocator(32))\\n for n, (s, c) in enumerate(zip(full.reshape((1<<big2-small2, 1<<small2)), cc)):\\n ax1.plot(s, c=c, label=f'Part {n}', drawstyle=ds)\\n ax1.plot(np.arange((1<<small2) * n, (1<<small2) * (n+1)), s, c=c, lw=2,\\n drawstyle=ds, label=None)\\n for n in range(1 << big2-small2):\\n ax1.axvline(n * (1 << small2), lw=.25, c='C7')\\n for n in range(1 << big2-small2):\\n ax1.axvline(n * (1 << small2), lw=.25, c='C7')\\n if big2 - small2 <= 3:\\n ax1.legend(loc='center right')\\n ax1.set(title=f'Full distribution\\\\nlog2={big2}, {1<<big2-small2} components')\\n\\n wrapped_from_full = full.reshape((1<<big2-small2, 1<<small2))\\n ax2.plot(wrapped_from_full.T, label=None, c='C7', lw=.5, drawstyle=ds)\\n ax2.plot(wrapped_from_full.sum(0), lw=3\\n , drawstyle=ds, label='Wrapped from full', c='C1')\\n ax2.plot(wrapped, lw=1, label='Wrapped', c='C0', drawstyle=ds)\\n ax2.xaxis.set_major_locator(mpl.ticker.MultipleLocator(4))\\n ax2.set(title='Wrapping components (grey)\\\\nSums (blue, organge as left)')\\n # ax2.legend(loc)\\n ax2.set(ylim=lm)\\n\\n assert np.allclose(wrapped_from_full.sum(0), wrapped)\",\n \"def numpyInverseFourierTransform(self,coefficients,**kwargs):\\n return [(z.real,z.imag) for z in np.fft.ifft(coefficients,**kwargs)]\",\n \"def elliptic_mesh(F):\\n # draw sphere\\n u, v = np.mgrid[0:2*np.pi:40j, 0:np.pi:20j]\\n x = np.cos(u)*np.sin(v)\\n y = np.sin(u)*np.sin(v)\\n z = np.cos(v)\\n # put into array\\n XYZ = np.vstack([x.flatten(),y.flatten(),z.flatten()])\\n # deform according to F\\n XYZ = np.dot(F.Fij,XYZ)\\n # put back into meshgrid\\n a, b, c = np.vsplit(XYZ,3)\\n x = np.reshape(a,x.shape)\\n y = np.reshape(b,y.shape)\\n z = np.reshape(c,z.shape)\\n return x, y, z\",\n \"def IDFT(fourier_signal):\\n fourier_signal = fourier_signal.astype(np.complex128)\\n # find the length of the signal\\n N = fourier_signal.shape[0]\\n if fourier_signal.ndim == 2:\\n M, N = fourier_signal.shape\\n\\n # calculate IDFT matrix\\n u, v = np.meshgrid(np.arange(N), np.arange(N))\\n omega = np.exp(2 * np.pi * 1j / N)\\n idft_matrix = np.power(omega, u*v)\\n\\n # if it is a matrix of fourier signals\\n if fourier_signal.ndim == 2:\\n # calculate the Fourier Transform\\n signal = np.dot(idft_matrix, fourier_signal.transpose())\\n return 1/N * signal.transpose()\\n\\n # calculate the inverse Fourier Transform\\n signal = np.dot(idft_matrix, fourier_signal)\\n return 1/N * signal\",\n \"def fourier_der(im):\\n im = im.astype(np.float64)\\n # constants\\n M, N = im.shape\\n u = np.meshgrid(np.arange(N), np.arange(M))[0] - N//2\\n v = np.meshgrid(np.arange(N), np.arange(M))[1] - M//2\\n u_der, v_der = (2 * np.pi * 1j / N), (2 * np.pi * 1j / M)\\n\\n # calculate dx, dy\\n dx = u_der * IDFT2(np.fft.fftshift(u) * DFT2(im))\\n dy = v_der * IDFT2(np.fft.fftshift(v) * DFT2(im))\\n\\n return np.sqrt(np.abs(dx)**2 + np.abs(dy)**2) # = magnitude\",\n \"def wave_vectors_2D(nx, ny, d=1):\\n\\n return 2*np.pi*vector_vector_grid(\\n np.fft.fftfreq(nx, d=d),\\n np.fft.fftfreq(ny, d=d))\",\n \"def spherefcn(x: np.ndarray) -> np.ndarray:\\n if x.ndim == 1:\\n x = x.reshape(-1, len(x))\\n f = np.sum(x**2, axis=1)\\n return f.reshape(-1, 1)\",\n \"def half_hermitian_to_real_inverse_fft_image_filter(*args, **kwargs):\\n import itk\\n instance = itk.HalfHermitianToRealInverseFFTImageFilter.New(*args, **kwargs)\\n return instance.__internal_call__()\",\n \"def fourier_uniform(input, size, n=-1, axis=-1, output=None):\\n input = numpy.asarray(input)\\n output = _get_output_fourier(output, input)\\n axis = normalize_axis_index(axis, input.ndim)\\n sizes = _ni_support._normalize_sequence(size, input.ndim)\\n sizes = numpy.asarray(sizes, dtype=numpy.float64)\\n if not sizes.flags.contiguous:\\n sizes = sizes.copy()\\n _nd_image.fourier_filter(input, sizes, n, axis, output, 1)\\n return output\",\n \"def IDFT(fourier_signal):\\n n = fourier_signal.shape[0]\\n omega = np.exp((((2 * np.pi)*1j) / n))\\n\\n e_items = np.vander(omega**np.arange(n), n, True)\\n org_signal = np.dot(e_items, fourier_signal)/n\\n\\n return org_signal\",\n \"def _inv_fft(cls, n_extp, n, idx, a):\\n import numpy as np\\n ts = []\\n for i in range(0, n_extp):\\n # Sinusoid for the ith frequency\\n s_array = cls._signals(idx, i, n)\\n\\n # Computing the inverse Fouries transformation term for the significant coefficients obtained from the\\n # spectral density\\n ts.append(np.sum(a * s_array) // n)\\n return np.array(ts)\",\n \"def _iczt_two_dimension(fd_data, ini_t, fin_t, n_time_pts, ini_f, fin_f,\\r\\n axis=0):\\r\\n\\r\\n # Assert that this is only for 2D arrays\\r\\n assert axis in [0, 1], 'Axis error: axis must be in [0, 1]'\\r\\n\\r\\n # Find the number of frequencies use\\r\\n n_freqs = np.size(fd_data, axis=0)\\r\\n\\r\\n # Find the conversion factor to convert from time-of-response to\\r\\n # angle around the unit circle\\r\\n time_to_angle = (2 * np.pi) / np.max(get_scan_times(ini_f, fin_f, n_freqs))\\r\\n\\r\\n # Find the parameters for computing the ICZT over the specified time\\r\\n # window\\r\\n theta_naught = ini_t * time_to_angle\\r\\n phi_naught = (fin_t - ini_t) * time_to_angle / (n_time_pts - 1)\\r\\n\\r\\n # Compute the exponential values only once\\r\\n exp_theta_naught = np.exp(1j * theta_naught)\\r\\n exp_phi_naught = np.exp(1j * phi_naught)\\r\\n\\r\\n # Make a dummy vector to facilitate vectorized computation\\r\\n dummy_vec = np.arange(n_freqs)\\r\\n\\r\\n time_pts = np.arange(n_time_pts) # Get time-index vector\\r\\n\\r\\n # Find the z-value matrix, to facilitate vectorized computation\\r\\n z_vals = exp_theta_naught * exp_phi_naught**time_pts\\r\\n zs_power = np.power(z_vals[None, :], dummy_vec[:, None])\\r\\n\\r\\n # If wanting to compute the transform along the 0th axis\\r\\n if axis == 0:\\r\\n\\r\\n # Init return arr\\r\\n iczt_data = np.zeros([n_time_pts, np.size(fd_data, axis=1)],\\r\\n dtype=np.complex64)\\r\\n\\r\\n # For every point along the other dimension\\r\\n for ii in range(np.size(fd_data, axis=1)):\\r\\n\\r\\n # Compute the 1D ICZT, converting this frequency domain data\\r\\n # to the time domain\\r\\n iczt_data[:, ii] = _iczt_one_dimension(fd_data[:, ii], zs_power,\\r\\n n_freqs)\\r\\n\\r\\n else: # If wanting to compute along the 1st axis\\r\\n iczt_data = np.zeros([np.size(fd_data, axis=0), n_time_pts],\\r\\n dtype=np.complex64)\\r\\n\\r\\n # For every point along the other dimension\\r\\n for ii in range(np.size(fd_data, axis=0)):\\r\\n\\r\\n # Compute the 1D ICZT, converting this frequency domain data\\r\\n # to the time domain\\r\\n iczt_data[ii, :] = _iczt_one_dimension(fd_data[ii, :], zs_power,\\r\\n n_freqs)\\r\\n\\r\\n return iczt_data\",\n \"def test_fourier(self):\\n m = np.array([-1, 0, 1])\\n t = np.linspace(0, 2 * np.pi, 8, endpoint=False) # theta coordinates\\n\\n correct_vals = np.array([np.sin(t), np.ones_like(t), np.cos(t)]).T\\n correct_ders = np.array([np.cos(t), np.zeros_like(t), -np.sin(t)]).T\\n\\n values = fourier(t[:, np.newaxis], m, dt=0)\\n derivs = fourier(t[:, np.newaxis], m, dt=1)\\n\\n np.testing.assert_allclose(values, correct_vals, atol=1e-8)\\n np.testing.assert_allclose(derivs, correct_ders, atol=1e-8)\",\n \"def numpyInverseFourierTransform2D(self,coefficients,**kwargs):\\n return np.fft.ifft2(coefficients,**kwargs)\",\n \"def fourier_spectra(x, axis=1, flip=True, duplicate=True, **kwargs):\\n\\n x = forward_fourier(x, axis=axis, duplicate=duplicate)\\n ns = x.shape[axis]\\n nw = np.int32(np.floor(ns/2) + 1)\\n slc = [slice(None)] * len(x.shape)\\n slc[axis] = slice(0, nw)\\n if flip:\\n x = np.flip(x[tuple(slc)], axis=axis)\\n return x\",\n \"def IDFT2(fourier_image):\\n fourier_image = fourier_image.astype(np.complex128)\\n M, N = fourier_image.shape\\n # build the idft2_matrix transform\\n omega_y = np.exp(2 * np.pi * 1j / M)\\n u, v = np.meshgrid(np.arange(M), np.arange(M))\\n idft2_matrix = np.power(omega_y, u*v)\\n\\n # calculate the 2D inverse fourier transform\\n return 1/M * np.dot(idft2_matrix, IDFT(fourier_image))\",\n \"def test_fft_real_3d():\\n\\ta = np.mgrid[:7, :5, :5][0]\\n\\tdummy_array = xr.DataArray(a, dims=['x', 'y', 'z'])\\n\\tchunked_array = dummy_array.chunk(chunks={'x': 7, 'y': 5, 'z': 5})\\n\\tspectrum_array, spectrum_coords, spectrum_dims = \\\\\\n\\t\\txfft._fft(chunked_array, nfft={'x': 11, 'y': 14, 'z': 18},\\n\\t\\t dim=['x', 'y', 'z'], dx={'x':12, 'y': 0.01, 'z': 0.02},\\n\\t\\t sym=False)\\n\\tassert np.allclose(spectrum_array.compute(),\\n\\t np.fft.rfftn(a.T, s=(18, 14, 11)).T)\\n\\tassert np.array_equal(spectrum_coords['f_x'], np.fft.rfftfreq(11, d=12))\\n\\tassert np.array_equal(spectrum_coords['f_y'], np.fft.fftfreq(14, d=0.01))\\n\\tassert np.array_equal(spectrum_coords['f_z'], np.fft.fftfreq(18, d=0.02))\\n\\tassert ('f_x', 'f_y', 'f_z') == spectrum_dims\",\n \"def SIDFT(X,D):\\n N=len(X)\\n x=np.zeros(N,'complex')\\n for n in range(0,N,1):\\n for k in range(0,N,1):\\n x[n]=x[n]+np.exp(-1j*2*np.pi*k*D/N)*X[k]*np.exp(1j*2*np.pi*k*n/N)\\n return x/N\",\n \"def inverse(self, image, nlamD, npix, offset=None):\\n self._validate_args(nlamD, npix, offset)\\n _log.debug(\\n \\\"Inverse MatrixFourierTransform: array shape {}, \\\"\\n \\\"centering style {}, \\\"\\n \\\"output region size {} in lambda / D units, \\\"\\n \\\"output array size {} pixels, \\\"\\n \\\"offset {}\\\".format(image.shape, self.centering, nlamD, npix,\\n offset)\\n )\\n return matrix_idft(image, nlamD, npix,\\n centering=self.centering, offset=offset)\",\n \"def irfft(a, n=None, axes=-1, norm=None):\\n return image.image(np.fft.irfft(a, n, axes, norm), pixelsize=image.getPixelsize(a))\",\n \"def DFT2D_slow(x):\\r\\n x = np.asarray(x, dtype=complex)\\r\\n \\r\\n M = len(x)\\r\\n N = len(x[0])\\r\\n mat = np.zeros((M, N), np.complex)\\r\\n \\r\\n for row in range(M):\\r\\n mat[row] = DFT_slow(x[row])\\r\\n\\r\\n for col in range(N):\\r\\n mat[:,col] = DFT_slow(mat[:,col])\\r\\n\\r\\n return mat\",\n \"def gaussianFilter3D(size, points):\\n \\n matrix = numpy.zeros((size, size, size))\\n \\n for point in points:\\n x0 = point[0]\\n y0 = point[1]\\n z0 = point[2]\\n x2SigmaSquared = pow(point[3] * size/4, 2) * 2\\n y2SigmaSquared = pow(point[4] * size/4, 2) * 2\\n z2SigmaSquared = pow(point[5] * size/4, 2) * 2\\n tempMatrix = numpy.zeros((size, size, size))\\n for x in range(0, size):\\n for y in range(0, size):\\n for z in range(0, size):\\n tempMatrix[y, x, z] = math.exp(-1 * \\\\\\n (math.pow(x-x0, 2)/x2SigmaSquared +\\\\\\n math.pow(y-y0, 2)/y2SigmaSquared +\\\\\\n math.pow(z-z0, 2)/z2SigmaSquared))\\n \\n matrix = numpy.add(matrix, tempMatrix)\\n \\n matrix = matrixfix.flatten(matrix, 0, 1)\\n \\n return matrix\",\n \"def analysis_fourier_map(self, target=1, mode=0):\\r\\n\\r\\n \\r\\n\\r\\n print('Starting fourier analysis:')\\r\\n\\r\\n self.print_image_info()\\r\\n\\r\\n # get the average image and the average of the whole image over time\\r\\n\\r\\n avgimg = np.mean(self.imageData, axis=0) # get mean image for reference later: average across all time\\r\\n\\r\\n self.meanimagevalue = np.mean(np.mean(avgimg, axis=1), axis=0)\\r\\n\\r\\n self.stdimg = np.std(self.imageData, axis= 0) # and standard deviation\\r\\n\\r\\n\\r\\n\\r\\n width = int(self.period*self.framerate*2)\\r\\n\\r\\n print( \\\" Detrending:\\\")\\r\\n\\r\\n print( ' Median filter width: ', width)\\r\\n\\r\\n # footprint = np.ones((width, 1, 1))\\r\\n\\r\\n # self.imageData = self.imageData - scipy.ndimage.median_filter(self.imageData, footprint=footprint)\\r\\n\\r\\n print( \\\" Done detrending\\\")\\r\\n\\r\\n\\r\\n\\r\\n self.n_times = self.timebase\\r\\n\\r\\n\\r\\n\\r\\n # calculate FFT and get amplitude and phase\\r\\n\\r\\n self.DF = np.fft.fft(self.imageData, axis = 0)\\r\\n self.freqs = np.fft.fftfreq(self.DF.shape[0], d=1./self.framerate)\\r\\n\\r\\n # self.freqs = np.fft.fftfreq(self.DF.shape[0], d=1./self.framerate)\\r\\n\\r\\n print (' df shape: ', self.DF.shape)\\r\\n\\r\\n print (' 1/framerate: ', 1./self.framerate)\\r\\n\\r\\n self.freq_point = np.argmin(np.abs(self.freqs - 1./self.period))\\r\\n print ('period:', self.period)\\r\\n print ('frequency: ', 1./self.period)\\r\\n print ('freq_point: ', self.freq_point)\\r\\n print ('frequency value: ',self.freqs[self.freq_point])\\r\\n steps = np.arange(1,6,dtype=np.float)\\r\\n steps = (steps)+1.\\r\\n self.assigned_freqs=2.*np.pi*1./1.6*steps\\r\\n print ('assigned freqs', self.assigned_freqs)\\r\\n\\r\\n #j = j + 2 # just looking at FFT leakage...`\\r\\n\\r\\n print (' closest index/freq, period: ', self.freq_point, self.freqs[self.freq_point], 1./self.period)\\r\\n\\r\\n self.print_image_info()\\r\\n\\r\\n ampimg = np.absolute(self.DF[self.freq_point,:,:])\\r\\n\\r\\n phaseimg = np.angle(self.DF[self.freq_point,:,:])\\r\\n\\r\\n \\r\\n # ampimg = np.absolute(self.DF[self.freq_point,:,:])\\r\\n\\r\\n\\r\\n # phaseimg = np.angle(self.DF[self.freq_point,:,:])\\r\\n\\r\\n if target == 1:\\r\\n\\r\\n f = open('img_phase1.dat', 'w')\\r\\n\\r\\n pickle.dump(phaseimg, f)\\r\\n\\r\\n f.close()\\r\\n\\r\\n f = open('img_amplitude1.dat', 'w')\\r\\n\\r\\n pickle.dump(ampimg, f)\\r\\n\\r\\n f.close()\\r\\n\\r\\n self.amplitudeImage1 = ampimg\\r\\n\\r\\n self.phaseImage1 = phaseimg\\r\\n\\r\\n if target == 2:\\r\\n\\r\\n f = open('img_phase2.dat', 'w')\\r\\n\\r\\n pickle.dump(phaseimg, f)\\r\\n\\r\\n f.close()\\r\\n\\r\\n f = open('img_amplitude2.dat', 'w')\\r\\n\\r\\n pickle.dump(ampimg, f)\\r\\n\\r\\n f.close()\\r\\n\\r\\n self.amplitudeImage2 = ampimg\\r\\n\\r\\n self.phaseImage2 = phaseimg\\r\\n\\r\\n print (\\\" FFT calculated, data saved.\\\\n\\\")\\r\\n\\r\\n # save most recent calculation to disk\\r\",\n \"def ifft(a, n=None, axes=-1, norm=None):\\n return image.image(np.fft.ifft(a, n, axes, norm), pixelsize=image.getPixelsize(a))\",\n \"def phantom_ellipses(n_points,E):\\n \\n #Rescaling according to image size \\n E[:,0] = E[:,0]*n_points/2 #semiaxis a\\n E[:,1] = E[:,1]*n_points/2 #semiaxis b\\n E[:,2] = E[:,2]*n_points/2 #x\\n E[:,3] = E[:,3]*n_points/2 #y\\n E[:,4] = E[:,4]*math.pi/180\\n \\n x,y = np.meshgrid(np.arange(0,n_points)-n_points//2 ,np.arange(0,n_points)-n_points//2 )\\n nrow,ncol = E.shape\\n phantom1 = np.zeros((y.shape[0], y.shape[1], nrow))\\n\\n for k in range(nrow): #itero sulle ellissi\\n x_new = x - E[k,2]\\n y_new = y - E[k,3]\\n\\n #find(( (x.*cosp + y.*sinp).^2)./asq + ((y.*cosp - x.*sinp).^2)./bsq <= 1); \\n cosp = math.cos(E[k,4])\\n sinp = math.sin(E[k,4])\\n cond = np.square( x_new * cosp + y_new * sinp )*1/(E[k,0]*E[k,0]) + \\\\\\n np.square(y_new * cosp - x_new * sinp)*1/(E[k,1]*E[k,1]) - 1\\n\\n for i in range(x.shape[0]):\\n for j in range(x.shape[1]):\\n if (cond[i,j] <= 0.0):\\n phantom1[i,j,k] = E[k,5]; # gray scale\\n else:\\n phantom1[i,j,k] = 0.0;\\n #endif\\n #endfor\\n #endfor\\n #endfor\\n phantom1 = phantom1.sum(axis=2)\\n phantom = np.flipud(phantom1)\\n return phantom\",\n \"def imwofz_vectorized(x, y):\\n ncut=27\\n xy=x*y\\n xyp=2.0*xy/jnp.pi\\n exx=jnp.exp(-x*x)\\n f=-exx*erfcx(y)*jnp.sin(2.0*xy)+x/jnp.pi*exx*jnp.sinc(xyp)\\n n=jnp.arange(1,ncut+1)\\n n2=n*n\\n x2=x*x\\n y2=y*y\\n vec0=0.5*n/(0.25*n2+y2)\\n vec1=jnp.exp(-(0.25*n2[None,:]+x2[:,None]))\\n vec4=jnp.exp(-(0.5*n[None,:]+x[:,None])*(0.5*n[None,:]+x[:,None]))\\n vec5=jnp.exp(-(0.5*n[None,:]-x[:,None])*(0.5*n[None,:]-x[:,None]))\\n Sigma1=jnp.sum(vec0*vec1,axis=1)\\n Sigma4=jnp.sum(vec0*vec4,axis=1)\\n Sigma5=jnp.sum(vec0*vec5,axis=1)\\n f = f + 1.0/jnp.pi*(y*jnp.sin(2.0*xy)*Sigma1 + 0.5*(Sigma5-Sigma4))\\n\\n return f\",\n \"def test_fft_real_2d():\\n\\ta = np.mgrid[:5, :5, :5][0]\\n\\tdummy_array = xr.DataArray(a, dims=['x', 'y', 'z'])\\n\\tchunked_array = dummy_array.chunk(chunks={'x': 2, 'y': 2, 'z': 2})\\n\\tspectrum_array, spectrum_coords, spectrum_dims = \\\\\\n\\t\\txfft._fft(chunked_array, nfft={'y': 14, 'z': 18}, dim=['y', 'z'],\\n\\t\\t dx={'y': 0.01, 'z': 0.02}, sym=False)\\n\\tassert np.allclose(spectrum_array.compute(),\\n\\t np.fft.rfftn(a, s=(18, 14), axes=(2, 1)))\\n\\tassert np.array_equal(spectrum_coords['f_y'], np.fft.rfftfreq(14, d=0.01))\\n\\tassert np.array_equal(spectrum_coords['f_z'], np.fft.fftfreq(18, d=0.02))\\n\\tassert ('x', 'f_y', 'f_z') == spectrum_dims\",\n \"def dct(n_filters, n_input):\\n\\n basis = np.empty((n_filters, n_input))\\n basis[0, :] = 1.0 / np.sqrt(n_input)\\n\\n samples = np.arange(1, 2*n_input, 2) * np.pi / (2.0 * n_input)\\n\\n for i in range(1, n_filters):\\n basis[i, :] = np.cos(i*samples) * np.sqrt(2.0/n_input)\\n\\n return basis\",\n \"def filled_space_prop(e: np.ndarray, arg: np.ndarray) -> np.ndarray:\\n\\n freq = np.exp(1j*arg)\\n out = np.fft.ifft2(np.fft.fft2(e)*freq)\\n\\n return out\",\n \"def fourier_coefficient(t, freq, n=1, axis=-1):\\n t, freq, n = map(np.asanyarray, (t, freq, n))\\n return np.exp(1j * 2 * np.pi * freq * n * t).sum(axis=axis)\",\n \"def scales_from_fourier(f, wf, w0):\\n\\n f_arr = np.asarray(f)\\n\\n if wf == 'morlet':\\n return (f_arr * (w0 + np.sqrt(2 + w0**2))) / (4 * np.pi)\\n else:\\n raise ValueError('wavelet function not available')\",\n \"def dft(X): \\n N = len(X)\\n x = np.zeros(N, 'complex')\\n K = np.arange(0, N, 1)\\n for n in range(0, N, 1):\\n x[n] = np.dot(X, np.exp(1j * 2 * np.pi * K * n / N))\\n return x\",\n \"def denoise_frequency_domain(ut: np.ndarray, \\n filter_3d: np.ndarray) -> np.ndarray:\\n\\n num_rows = ut.shape[0]\\n ut_denoised = np.empty(ut.shape, dtype=complex)\\n for row in range(num_rows):\\n ut_slice_cube = np.reshape(ut[row, :], filter_3d.shape)\\n ut_slice_cube_denoised = ut_slice_cube*filter_3d\\n ut_denoised[row, :] = ut_slice_cube_denoised.flatten()\\n\\n return ut_denoised\",\n \"def FourierTransform(data, nPoints):\\r\\n tdf = np.fft.fft(data, nPoints)\\r\\n return tdf\",\n \"def calc_ds_3d(slip_x, slip_y, dx, dy, mu, poisson, expand = 0):\\n\\n newlenx = len(slip_x)+2*expand\\n newleny = len(slip_x[0])+2*expand\\n\\n newslipx = np.zeros((newlenx, newleny))\\n newslipy = np.zeros((newlenx, newleny))\\n newslipx[expand:expand+len(slip_x),expand:expand+len(slip_x[0])] = np.copy(slip_x)\\n newslipy[expand:expand+len(slip_x),expand:expand+len(slip_x[0])] = np.copy(slip_y)\\n \\n k = np.fft.fftfreq(newlenx, dx)\\n m = np.fft.fftfreq(newleny, dy)\\n\\n kxy, mxy = np.meshgrid(k, m, indexing='ij')\\n\\n kmag = np.sqrt(kxy**2+mxy**2)\\n kmag[0,0] = 1.\\n\\n fx = np.fft.fft2(newslipx)\\n fy = np.fft.fft2(newslipy)\\n\\n sx = -mu/2./kmag*(1./(1.-poisson)*(kxy**2*fx+mxy*kxy*fy)+(mxy**2*fx-mxy*kxy*fy))\\n sy = -mu/2./kmag*(1./(1.-poisson)*(mxy**2*fy+mxy*kxy*fx)+(kxy**2*fy-mxy*kxy*fx))\\n\\n return (np.real(np.fft.ifft2(sx))[expand:expand+len(slip_x),expand:expand+len(slip_x[0])],\\n np.real(np.fft.ifft2(sy))[expand:expand+len(slip_x),expand:expand+len(slip_x[0])])\",\n \"def IDFT2(fourier_image):\\n return IDFT(IDFT(fourier_image).transpose()).transpose()\",\n \"def perform(self, pupil, nlamD, npix, offset=None):\\n self._validate_args(nlamD, npix, offset)\\n _log.debug(\\n \\\"Forward MatrixFourierTransform: array shape {}, \\\"\\n \\\"centering style {}, \\\"\\n \\\"output region size {} in lambda / D units, \\\"\\n \\\"output array size {} pixels, \\\"\\n \\\"offset {}\\\".format(pupil.shape, self.centering, nlamD, npix,\\n offset)\\n\\n )\\n return matrix_dft(pupil, nlamD, npix,\\n centering=self.centering, offset=offset)\",\n \"def _imfilter(x_data, f_data):\\n return pipe(f_data, ifftshift, fftn, lambda x: x * fftn(x_data), ifftn).real\",\n \"def dwt(image_array, quantization_Array):\\n # Create the high pass and low pass filters\\n # both filters are non-causal\\n # symmetric\\n # [-2, -1, 0, 1, 2]\\n LPF = [-0.125, 0.25, 0.75, 0.25, -0.125]\\n LPF_center = 2\\n\\n # [ -2,-1, 0]\\n HPF = [-0.5, 1, -0.5]\\n HPF_center = 2\\n\\n nrow, ncol = image_array.shape\\n\\n # create an array that will contain the 4 different subbands of the image\\n LL = np.zeros((nrow, ncol))\\n LH = np.zeros((nrow, ncol))\\n HL = np.zeros((nrow, ncol))\\n HH = np.zeros((nrow, ncol))\\n filtered_image = [LL, LH, HL, HH]\\n\\n # filtering the rows using a low pass and high pass filters\\n LowPass_rows = np.zeros((nrow, ncol))\\n HighPass_rows = np.zeros((nrow, ncol))\\n for i in range(0, nrow):\\n LowPass_rows[i, :] = lfilter(LPF, image_array[i, :], LPF_center)\\n HighPass_rows[i, :] = lfilter(HPF, image_array[i, :], HPF_center)\\n\\n # down sample rows.\\n # which means we will have half the number of columns\\n for i in range(0, len(filtered_image)):\\n filtered_image[i] = filtered_image[i][:, ::2]\\n\\n # apply filters accross columns\\n for i in range(0, ncol):\\n LL[:, i] = lfilter(LPF, LowPass_rows[:, i], LPF_center)\\n LH[:, i] = lfilter(HPF, LowPass_rows[:, i], HPF_center)\\n HL[:, i] = lfilter(LPF, HighPass_rows[:, i], LPF_center)\\n HH[:, i] = lfilter(HPF, HighPass_rows[:, i], HPF_center)\\n\\n # down sample columns and quantize\\n for i in range(0, len(filtered_image)):\\n filtered_image[i] = filtered_image[i][::2, :]\\n filtered_image[i] = np.round(\\n filtered_image[i]/quantization_Array[i]).astype(int)\\n\\n return filtered_image\",\n \"def fft(a, n=None, axes=-1, norm=None):\\n\\n return image.image(np.fft.fft(a, n, axes, norm), pixelsize=a.pixelsize)\",\n \"def app_complex(data_pupil,data_phase,oversize=4):\\n#phase colors\\n # cdict = {'red': ((0.0, 1.0, 1.0),(0.25, 0.0, 0.0),(0.5, 0.0, 0.0),(0.75, 1.0, 1.0),(1.00, 1.0, 1.0)),'green': ((0.0, 0.0, 0.0),(0.25, 1.0, 1.0),(0.5, 0.0, 0.0),(0.75, 1.0, 1.0),(1.0, 0.0, 0.0)),'blue': ((0.0, 0.0, 0.0),(0.25, 0.0, 0.0),(0.5, 1.0, 1.0),(0.75, 0.0, 0.0),(1.0, 0.0, 0.0))}\\n #my_cmap = matplotlib.colors.LinearSegmentedColormap('my_colormap',cdict,256)\\n\\n size=data_pupil.shape[0]\\n#make empty oversized array\\n\\n expand_phase=zeros([oversize*size,oversize*size])\\n expand_amp=zeros([oversize*size,oversize*size])\\n\\n#copy fits into lower left corner\\n\\n expand_amp[0:size,0:size]=data_pupil[0:size,0:size]\\n expand_phase[0:size,0:size]=data_phase[0:size,0:size]\\n\\n#move to corners\\n\\n expand_phase=roll(expand_phase,-size/2,0)\\n expand_phase=roll(expand_phase,-size/2,1)\\n\\n expand_amp=roll(expand_amp,-size/2,0)\\n expand_amp=roll(expand_amp,-size/2,1)\\n\\n# recalculate real and imaginary part\\n\\n #xr=expand_amp*cos(expand_phase)\\n #yr=expand_amp*sin(expand_phase)\\n\\n# make complex array\\n\\n complexr=expand_amp*numpy.exp(1j*expand_phase)\\n\\n# apply 2d-fft\\n\\n complexr=numpy.fft.fftpack.fft2(complexr)\\n return fftshift(complexr)\",\n \"def DFT(signal):\\n n = signal.shape[0]\\n omega = np.exp(((((-2) * np.pi)*1j) / n))\\n\\n e_items = np.vander(omega**np.arange(n), n, True)\\n fourier_signal = np.dot(e_items, signal)\\n\\n return fourier_signal.astype(np.complex128)\",\n \"def fourier_from_scales(scales, wf, w0):\\n\\n scales_arr = np.asarray(scales)\\n\\n if wf == 'morlet':\\n return (4 * np.pi * scales_arr) / (w0 + np.sqrt(2 + w0**2))\\n else:\\n raise ValueError('wavelet function not available')\",\n \"def medianfilter(data, filtersize, threshold=None,verbose=False):\\n # 2006/02/01 IJC at the Jet Propulsion Laboratory\\n # 2010-02-18 13:52 IJC: Converted to python\\n from numpy import zeros, median, abs, std\\n\\n print \\\"Just use scipy.signal.medfilt !!!\\\"\\n print \\\"Just use scipy.signal.medfilt !!!\\\"\\n print \\\"Just use scipy.signal.medfilt !!!\\\"\\n\\n if len(filtersize)<1:\\n print 'medianfilter2 requires that filtersize be a 1- or 2-element vector'\\n return -1\\n elif len(filtersize)==1:\\n filtersize = [filtersize[0], filtersize[0]]\\n else:\\n filtersize = filtersize[0:2]\\n\\n npix = data.shape[0]\\n npiy = data.shape[1]\\n bigsize = npix+2*(filtersize[0]-1)\\n bigdata = zeros((bigsize,bigsize),float)\\n ind = filtersize[0]-1\\n if ind==0:\\n bigdata = data\\n else:\\n bigdata[ind:(bigsize-ind), ind:(bigsize-ind)] = data\\n\\n\\n # FOR NOW, WE ASSUME FILTERSIZE IS ODD!!\\n # AND THAT DATA IS SQUARE!\\n niter_x = npix + (filtersize[0]-1)\\n niter_y = npiy + (filtersize[1]-1)\\n filt = zeros((niter_x,niter_y), float)\\n\\n for ii in range(niter_x):\\n for jj in range(niter_y):\\n if verbose>1:\\n print \\\"ii,jj>>\\\",ii,jj\\n if filtersize[0]==1:\\n indi = 1\\n else:\\n indi = filtersize[0]-1\\n if filtersize[1]==1:\\n indj = 1\\n else:\\n indj = filtersize[1]-1\\n select = bigdata[ii:(ii+indi),jj:(jj+indj)].ravel()\\n select = select[isfinite(select)]\\n #residualSelection = abs(select - median(select))\\n\\n if verbose: \\n print \\\"select.shape>>\\\",select.shape\\n print \\\"threshold>>\\\",threshold\\n\\n if threshold is not None:\\n if threshold >= 0: # raw threshold\\n doFilter = abs(bigdata[ii,jj]-median(select))/std(select)>=threshold\\n elif threshold<0: # remove outliers before applying threshold\\n npts_init = len(select)\\n select = removeoutliers(select, abs(threshold), center='median')\\n npts_final = len(select)\\n if verbose>1:\\n print \\\"threshold=\\\",threshold,\\\", removed %i points\\\" % (npts_init-npts_final)\\n \\n doFilter = abs(bigdata[ii,jj]-median(select))/std(select)>=threshold \\n else: # filter everything; threshold not set.\\n doFilter = True\\n\\n if verbose:\\n print \\\"doFilter?>>\\\",doFilter\\n if verbose>1:\\n print \\\"select>>\\\",select\\n\\n if doFilter: \\n newval = median( select )\\n else:\\n newval = bigdata[ii,jj]\\n\\n if verbose>1:\\n print \\\"newval>>\\\",newval\\n\\n filt[ii,jj] = newval\\n\\n print filt.shape, [(filtersize[0]-1)/2,niter_x-(filtersize[0]-1)/2,(filtersize[0]-1)/2,niter_y-(filtersize[0]-1)/2]\\n return filt[(filtersize[0]-1)/2:niter_x-(filtersize[0]-1)/2,(filtersize[0]-1)/2:niter_y-(filtersize[0]-1)/2]\",\n \"def forward_fft(self, array_in):\\r\\n # Find side length, as real array may or may not be doubled for\\r\\n # aliasing control\\r\\n side = array_in.shape[0]\\r\\n div_side = 1.0/side**2\\r\\n\\r\\n out = np.fft.fft2(self.sign_mat[0:side, 0:side]*array_in)*div_side\\r\\n return out\",\n \"def IDFTdirect(X):\\n \\n N = len(X)\\n x = []\\n for n in range(N):\\n x_n = Complex(0,0)\\n for k in range(N):\\n a = 2*math.pi*k*n/N\\n x_n += X[k]*Complex(math.cos(a), math.sin(a))\\n x.append((1/N)*x_n)\\n return x\",\n \"def fourier_decomposition(times, magnitudes, order):\\n times = times[:,0]\\n magnitudes = magnitudes[:,0]\\n\\n num_examples = times.shape[0]\\n num_coef = order * 2 + 1\\n\\n if num_coef > num_examples:\\n raise Exception(\\\"Too few examples for the specified order. Number of examples must be at least order * 2 + 1. Required: %d, Actual: %d\\\" % (num_coef, num_examples))\\n\\n initial_coef = np.ones(num_coef)\\n\\n cost_function = partial(fourier_series_cost, times, magnitudes, order)\\n\\n fitted_coef, success = leastsq(cost_function, initial_coef)\\n\\n final_coef = correct_coef(fitted_coef, order)\\n\\n return final_coef\",\n \"def ManualFourier(x):\\n x = np.asarray(x, dtype=float)\\n N = x.shape[0]\\n\\n if np.log2(N) % 1 > 0:\\n raise ValueError(\\\"size of x must be a power of 2\\\")\\n\\n # N_min here is equivalent to the stopping condition above,\\n # and should be a power of 2\\n N_min = min(N, 32)\\n\\n # Perform an O[N^2] DFT on all length-N_min sub-problems at once\\n n = np.arange(N_min)\\n k = n[:, None]\\n M = np.exp(-2j * np.pi * n * k / N_min)\\n X = np.dot(M, x.reshape((N_min, -1)))\\n\\n # build-up each level of the recursive calculation all at once\\n while X.shape[0] < N:\\n X_even = X[:, :int(X.shape[1] / 2)]\\n X_odd = X[:, int(X.shape[1] / 2):]\\n factor = np.exp(-1j * np.pi * np.arange(X.shape[0])\\n / X.shape[0])[:, None]\\n X = np.vstack([X_even + factor * X_odd,\\n X_even - factor * X_odd])\\n return X.ravel()\",\n \"def freqdomain(self):\\n \\n\\n #self.df = self.f[1] - self.f[0]\\n #frequency vector\\n #fv = fftshift(fftfreq(len(eta),1./fs))\\n #fv = fv[len(fv)/2:]\\n \\n #spectral analysis\\n self.sn1 = self.espec1(self.n1)\\n self.sn2 = self.espec1(self.n2)\\n self.sn3 = self.espec1(self.n3)\\n self.sn12 = self.espec2(self.n1,self.n2)\\n self.sn13 = self.espec2(self.n1,self.n3)\\n self.sn23 = self.espec2(self.n2,self.n3)\\n \\n #delta freq\\n self.df = self.f[3] - self.f[2]\\n\\n #calculo do numero de onda\\n #self.wavenumber()\\n #k = numeronda(h,f,len(f))\\n #k = np.array(k)\\n\\n #calculo dos coeficientes de fourier - NDBC 96_01 e Steele (1992)\\n c = self.sn2[:,1] + self.sn3[:,1]\\n cc = np.sqrt(self.sn1[:,1] * (c))\\n \\n self.a1 = self.sn12[:,3] / cc\\n self.b1 = self.sn13[:,3] / cc\\n \\n self.a2 = (self.sn2[:,1] - self.sn3[:,1]) / c\\n self.b2 = 2 * self.sn12[:,2] / c\\n \\n #calcula direcao de onda\\n #mean direction\\n self.dire1 = np.array([np.angle(np.complex(self.b1[i],self.a1[i]),deg=True) for i in range(len(self.a1))])\\n \\n #principal direction\\n self.dire2 = 0.5 * np.array([np.angle(np.complex(self.b2[i],self.a2[i]),deg=True) for i in range(len(self.a2))])\\n \\n #condicao para valores maiores que 360 e menores que 0\\n self.dire1[np.where(self.dire1 < 0)] = self.dire1[np.where(self.dire1 < 0)] + 360\\n self.dire1[np.where(self.dire1 > 360)] = self.dire1[np.where(self.dire1 > 360)] - 360\\n self.dire2[np.where(self.dire2 < 0)] = self.dire2[np.where(self.dire2 < 0)] + 360\\n self.dire2[np.where(self.dire2 > 360)] = self.dire2[np.where(self.dire2 > 360)] - 360\\n \\n #acha o indice da frequencia de pico\\n ind = np.where(self.sn1[:,1] == np.max(self.sn1[:,1]))[0]\\n \\n #periodo de pico\\n self.tp = (1. / self.f[ind])[0]\\n \\n #momento espectral de ordem zero total - m0\\n self.m0 = np.sum(self.sn1[:,1]) * self.df\\n \\n #calculo da altura significativa\\n self.hm0 = 4.01 * np.sqrt(self.m0)\\n \\n #direcao do periodo de pico\\n self.dp = self.dire1[ind][0]\\n \\n #Espalhamento direcional\\n #Formula do sigma1 do livro Tucker&Pitt(2001) \\\"Waves in Ocean Engineering\\\" pags 196-198\\n c1 = np.sqrt(self.a1 ** 2 + self.b1 ** 2)\\n c2 = np.sqrt(self.a2 ** 2 + self.b2 ** 2)\\n \\n s1 = c1 / (1-c1)\\n s2 = (1 + 3 * c2 + np.sqrt(1 + 14 * c2 + c2 ** 2)) / (2 * (1 - c2))\\n \\n self.sigma1 = np.sqrt(2 - 2 * c1) * 180 / np.pi\\n self.sigma2 = np.sqrt((1 - c2) / 2) * 180 / np.pi\\n \\n self.sigma1p = np.real(self.sigma1[ind])[0]\\n self.sigma2p = np.real(self.sigma2[ind])[0]\\n \\n # pondaf = np.array([hm0, tp, dp, sigma1p, sigma2p])\\n \\n #hm0, tp, dp, sigma1, sigma2, sigma1p, sigma2p, f, df, k, sn, snx, sny, snn, snnx, snny, snxny, snxnx, snyny, a1, b1, a2, b2, dire1, dire2\\n #return hm0, tp, dp, sigma1, sigma2, sigma1p, sigma2p, f, df, k, sn, snx, sny, snn, snnx, snny, snxny, snxnx, snyny, a1, b1, a2, b2, dire1, dire2\",\n \"def harmonics(y, n, /, axis=-1):\\n # Get fourier transform\\n y = np.moveaxis(y, axis, -1)\\n fft = np.fft.fft(y, axis=-1)\\n\\n # Remove frequencies outside range. The FFT will have some error and give\\n # non-zero imaginary components, but we can get magnitude or naively cast to real\\n fft[..., 0] = 0\\n fft[..., n + 1:-n] = 0\\n yf = np.real(np.fft.ifft(fft, axis=-1))\\n # yf = np.abs(np.fft.ifft(fft, axis=-1))\\n return np.moveaxis(yf, -1, axis)\",\n \"def filtering(self):\\r\\n # 1 ###########################################################################################################\\r\\n fft_image = np.fft.fft2(self.image)\\r\\n # 2 ###########################################################################################################\\r\\n fft_shift_image = np.fft.fftshift(fft_image)\\r\\n\\r\\n ###\\r\\n mag_dft = np.log(np.abs(fft_shift_image))\\r\\n mag_dft = (255 * (mag_dft / np.max(mag_dft))).astype(dtype='uint8')\\r\\n ###\\r\\n\\r\\n # 3 ###########################################################################################################\\r\\n if self.filter_name == 'butterworth_l' or self.filter_name == 'butterworth_h':\\r\\n mask = self.filter(fft_shift_image.shape, self.cutoff, self.order)\\r\\n else:\\r\\n mask = self.filter(fft_shift_image.shape, self.cutoff)\\r\\n # 4 ###########################################################################################################\\r\\n # multiply the dft (fft shift image) by the mask\\r\\n filtered_image = fft_shift_image * mask\\r\\n\\r\\n ###\\r\\n mag_filtered_image = mag_dft * mask\\r\\n ###\\r\\n\\r\\n # 5 ###########################################################################################################\\r\\n inverse_fft_shift_image = np.fft.ifftshift(filtered_image)\\r\\n # 6 ###########################################################################################################\\r\\n inverse_fft_image = np.fft.ifft2(inverse_fft_shift_image)\\r\\n # 7 ###########################################################################################################\\r\\n mag_image = np.zeros(inverse_fft_image.shape, dtype=complex)\\r\\n for i in range(inverse_fft_image.shape[0]):\\r\\n for j in range(inverse_fft_image.shape[1]):\\r\\n if inverse_fft_image[i][j] < 0:\\r\\n mag_image[i][j] = -1 * inverse_fft_image[i][j]\\r\\n else:\\r\\n mag_image[i][j] = inverse_fft_image[i][j]\\r\\n # magnitude of inverse fft is complete\\r\\n # 8 ###########################################################################################################\\r\\n full_contrast_image = self.post_process_image(mag_image)\\r\\n\\r\\n return [mag_dft, mag_filtered_image, full_contrast_image]\",\n \"def _field_Fresnel(z, field, dx, lam, dtype, usepyFFTW):\\n \\n \\\"\\\"\\\" *************************************************************\\n Major differences to Cpp based LP version:\\n - dx =siz/N instead of dx=siz/(N-1), more consistent with physics \\n and rest of LP package\\n - fftw DLL uses no normalization, numpy uses 1/N on ifft -> omitted\\n factor of 1/(2*N)**2 in final calc before return\\n - bug in Cpp version: did not touch top row/col, now we extract one\\n more row/col to fill entire field. No errors noticed with the new\\n method so far\\n ************************************************************* \\\"\\\"\\\"\\n _using_pyfftw = False # determined if loading is successful \\n if usepyFFTW or _USE_PYFFTW:\\n try:\\n import pyfftw as _pyfftw\\n from pyfftw.interfaces.numpy_fft import fft2 as _fft2\\n from pyfftw.interfaces.numpy_fft import ifft2 as _ifft2\\n _fftargs = {'planner_effort': 'FFTW_ESTIMATE',\\n 'overwrite_input': True,\\n 'threads': -1} #<0 means use multiprocessing.cpu_count()\\n _using_pyfftw = True \\n except ImportError:\\n #import warnings\\n #warnings.warn(_WARNING)\\n _WARNING = '\\\\n**************************** WARNING ***********************\\\\n'\\\\\\n +'In the Fresnel command you required FFT with the pyFFTW package.\\\\n'\\\\\\n +'or _USE_PYFFTW = True in your config.py file.\\\\n'\\\\\\n +'However LightPipes cannot import pyFFTW because it is not installed.\\\\n'\\\\\\n +'Falling back to numpy.fft.\\\\n'\\\\\\n +'(Try to) install pyFFTW on your computer for faster performance.\\\\n'\\\\\\n +'Enter at a terminal prompt: python -m pip install pyfftw.\\\\n'\\\\\\n +'Or reinstall LightPipes with the option pyfftw\\\\n'\\\\\\n +'Enter: python -m pip install lightpipes[pyfftw]\\\\n\\\\n'\\\\\\n +'*************************************************************'\\n print(_WARNING)\\n if not _using_pyfftw:\\n from numpy.fft import fft2 as _fft2\\n from numpy.fft import ifft2 as _ifft2\\n _fftargs = {}\\n tictoc.tic()\\n N = field.shape[0] #assert square\\n \\n legacy = True #switch on to numerically compare oldLP/new results\\n if legacy:\\n kz = 2.*3.141592654/lam * z\\n siz = N*dx\\n dx = siz/(N-1) #like old Cpp code, even though unlogical\\n else:\\n kz = 2*_np.pi/lam*z\\n \\n \\n cokz = _np.cos(kz)\\n sikz = _np.sin(kz)\\n \\n No2 = int(N/2) #\\\"N over 2\\\"\\n \\\"\\\"\\\"The following section contains a lot of uses which boil down to\\n 2*No2. For even N, this is N. For odd N, this is NOT redundant:\\n 2*No2 is N-1 for odd N, therefore sampling an even subset of the\\n field instead of the whole field. Necessary for symmetry of first\\n step involving Fresnel integral calc.\\n \\\"\\\"\\\"\\n if _using_pyfftw:\\n in_outF = _pyfftw.zeros_aligned((2*N, 2*N),dtype=dtype)\\n in_outK = _pyfftw.zeros_aligned((2*N, 2*N),dtype=dtype)\\n else:\\n in_outF = _np.zeros((2*N, 2*N),dtype=dtype)\\n in_outK = _np.zeros((2*N, 2*N),dtype=dtype)\\n \\n \\\"\\\"\\\"Our grid is zero-centered, i.e. the 0 coordiante (beam axis) is\\n not at field[0,0], but field[No2, No2]. The FFT however is implemented\\n such that the frequency 0 will be the first element of the output array,\\n and it also expects the input to have the 0 in the corner.\\n For the correct handling, an fftshift is necessary before *and* after\\n the FFT/IFFT:\\n X = fftshift(fft(ifftshift(x))) # correct magnitude and phase\\n x = fftshift(ifft(ifftshift(X))) # correct magnitude and phase\\n X = fftshift(fft(x)) # correct magnitude but wrong phase !\\n x = fftshift(ifft(X)) # correct magnitude but wrong phase !\\n A numerically faster way to achieve the same result is by multiplying\\n with an alternating phase factor as done below.\\n Speed for N=2000 was ~0.4s for a double fftshift and ~0.1s for a double\\n phase multiplication -> use the phase factor approach (iiij).\\n \\\"\\\"\\\"\\n # Create the sign-flip pattern for largest use case and \\n # reference smaller grids with a view to the same data for\\n # memory saving.\\n ii2N = _np.ones((2*N),dtype=float)\\n ii2N[1::2] = -1 #alternating pattern +,-,+,-,+,-,...\\n iiij2N = _np.outer(ii2N, ii2N)\\n iiij2No2 = iiij2N[:2*No2,:2*No2] #slice to size used below\\n iiijN = iiij2N[:N, :N]\\n\\n RR = _np.sqrt(1/(2*lam*z))*dx*2\\n io = _np.arange(0, (2*No2)+1) #add one extra to stride fresnel integrals\\n R1 = RR*(io - No2)\\n fs, fc = _fresnel(R1)\\n fss = _np.outer(fs, fs) # out[i, j] = a[i] * b[j]\\n fsc = _np.outer(fs, fc)\\n fcs = _np.outer(fc, fs)\\n fcc = _np.outer(fc, fc)\\n \\n \\\"\\\"\\\"Old notation (0.26-0.33s):\\n temp_re = (a + b + c - d + ...)\\n # numpy func add takes 2 operands A, B only\\n # -> each operation needs to create a new temporary array, i.e.\\n # ((((a+b)+c)+d)+...)\\n # since python does not optimize to += here (at least is seems)\\n New notation (0.14-0.16s):\\n temp_re = (a + b) #operation with 2 operands\\n temp_re += c\\n temp_re -= d\\n ...\\n Wrong notation:\\n temp_re = a #copy reference to array a\\n temp_re += b\\n ...\\n # changing `a` in-place, re-using `a` will give corrupted\\n # result\\n \\\"\\\"\\\"\\n temp_re = (fsc[1:, 1:] #s[i+1]c[j+1]\\n + fcs[1:, 1:]) #c[+1]s[+1]\\n temp_re -= fsc[:-1, 1:] #-scp [p=+1, without letter =+0]\\n temp_re -= fcs[:-1, 1:] #-csp\\n temp_re -= fsc[1:, :-1] #-spc\\n temp_re -= fcs[1:, :-1] #-cps\\n temp_re += fsc[:-1, :-1] #sc\\n temp_re += fcs[:-1, :-1] #cs\\n \\n temp_im = (-fcc[1:, 1:] #-cpcp\\n + fss[1:, 1:]) # +spsp\\n temp_im += fcc[:-1, 1:] # +ccp\\n temp_im -= fss[:-1, 1:] # -ssp\\n temp_im += fcc[1:, :-1] # +cpc\\n temp_im -= fss[1:, :-1] # -sps\\n temp_im -= fcc[:-1, :-1] # -cc\\n temp_im += fss[:-1, :-1]# +ss\\n \\n temp_K = 1j * temp_im # a * b creates copy and casts to complex\\n temp_K += temp_re\\n temp_K *= iiij2No2\\n temp_K *= 0.5\\n in_outK[(N-No2):(N+No2), (N-No2):(N+No2)] = temp_K\\n \\n in_outF[(N-No2):(N+No2), (N-No2):(N+No2)] \\\\\\n = field[(N-2*No2):N,(N-2*No2):N] #cutting off field if N odd (!)\\n in_outF[(N-No2):(N+No2), (N-No2):(N+No2)] *= iiij2No2\\n \\n tictoc.tic()\\n in_outK = _fft2(in_outK, **_fftargs)\\n in_outF = _fft2(in_outF, **_fftargs)\\n t_fft1 = tictoc.toc()\\n \\n in_outF *= in_outK\\n \\n in_outF *= iiij2N\\n tictoc.tic()\\n in_outF = _ifft2(in_outF, **_fftargs)\\n t_fft2 = tictoc.toc()\\n #TODO check normalization if USE_PYFFTW\\n \\n Ftemp = (in_outF[No2:N+No2, No2:N+No2]\\n - in_outF[No2-1:N+No2-1, No2:N+No2])\\n Ftemp += in_outF[No2-1:N+No2-1, No2-1:N+No2-1]\\n Ftemp -= in_outF[No2:N+No2, No2-1:N+No2-1]\\n comp = complex(cokz, sikz)\\n Ftemp *= 0.25 * comp\\n Ftemp *= iiijN\\n field = Ftemp #reassign without data copy\\n ttotal = tictoc.toc()\\n t_fft = t_fft1 + t_fft2\\n t_outside = ttotal - t_fft\\n debug_time = False\\n if debug_time:\\n print('Time total = fft + rest: {:.2f}={:.2f}+{:.2f}'.format(\\n ttotal, t_fft, t_outside))\\n return field\",\n \"def FourierTransform2D(xdata, zdata, nPoints):\\r\\n freq = FourierFrequency(xdata, nPoints)\\r\\n tdf = np.zeros_like(zdata, dtype=complex)\\r\\n for u, i in enumerate(zdata):\\r\\n tdf[u] = FourierTransform(xdata, i, nPoints)\\r\\n return freq, tdf\",\n \"def DFT2(image):\\n image = image.astype(np.float64)\\n M, N = image.shape\\n\\n # build the dft2_matrix transform\\n omega_y = np.exp(-2 * np.pi * 1j / M)\\n u, v = np.meshgrid(np.arange(M), np.arange(M))\\n dft2_matrix = np.power(omega_y, u*v)\\n\\n # calculate the 2D fourier transform\\n fourier_image = np.dot(dft2_matrix, DFT(image))\\n\\n return fourier_image\",\n \"def Fourier_cheb_transform(vec, x_points, y_points) :\\n rVec = zeros((numYs, numXs),dtype='complex')\\n for xIndx in range(numXs):\\n for yIndx in range(numYs):\\n for n in range(2*N+1):\\n for m in range(M):\\n x = x_points[xIndx]\\n y = y_points[yIndx]\\n term = vec[n*M + m] * exp(1.j*(n-N)*kx*x) * cos(m*arccos(y))\\n rVec[yIndx, xIndx] += term\\n del x,y,n,m\\n\\n return real(rVec)\",\n \"def defineCoords(dimensions, steps):\\n ### NOT CURRENTLY USED\\n \\n print(\\\"1D\\\")\\n xCoords = np.arange(-X/2, X/2+dx, dx) # 1D \\n fxCoords = np.arange(0, X/dx+dx)\\n fxCoords = fxCoords - fxCoords[-1]/2 # Shift everything over so the center of array is at f = 0\\n realCoords = xCoords\\n fourierCoords = fxCoords\\n realSpace = np.zeros(Nx+1)\\n xRealSpace = np.zeros(Nx)\\n #realSpace = np.zeros(Nx) #1D\\n \\n fourierSpace = np.zeros_like(realSpace, complex)\\n\\n return realCoords, fourierCoords, realSpace, fourierSpace\",\n \"def toeplitz_inverse_multiplication(u, x_0, phi, psi, D_phi, D_psi, Lambda_1, Lambda_2, Lambda_3, Lambda_4):\\n\\n y = fft(D_phi*u)\\n a = Lambda_1*fft(D_psi*(1/D_phi)*ifft(Lambda_2*y))\\n b = Lambda_3*fft(D_psi*(1/D_phi)*ifft(Lambda_4*y))\\n y = (1/D_psi)*real(ifft(a-b))/(x_0*(phi-psi))\\n \\n return y\",\n \"def spectrum_fourier(self):\\r\\n\\r\\n data = self.input.data\\r\\n sampling_rate = self.input.sampling_rate\\r\\n\\r\\n fft = fftpack.fft\\r\\n if np.any(np.iscomplex(data)):\\r\\n # Get negative frequencies, as well as positive:\\r\\n f = np.linspace(-sampling_rate/2., sampling_rate/2., data.shape[-1])\\r\\n spectrum_fourier = np.fft.fftshift(fft(data))\\r\\n else:\\r\\n f = tsu.get_freqs(sampling_rate, data.shape[-1])\\r\\n spectrum_fourier = fft(data)[..., :f.shape[0]]\\r\\n \\r\\n return f, spectrum_fourier\",\n \"def mce_filter(freq, f_raw, params):\\n\\tz = np.exp(-2j*np.pi*freq/f_raw)\\n\\tb11, b12, b21, b22 = np.array(params[:4])*0.5**14\\n\\tH = (1+z)**4 / (1-b11*z+b12*z**2) / (1-b21*z+b22*z**2)\\n\\tH /= 2**4 / (1-b11+b12) / (1-b21+b22)\\n\\treturn H\",\n \"def createEllipsoid( position=(0,0,0), radius=(1,1,1), colour=(0.6,0.6,0.6), samplesY = 20, samplesXZ = 20, exponentBottom = 2, exponentTop = 2, exponentSide = 2 ):\\r\\n \\r\\n if exponentBottom < 2.0 or exponentTop < 2.0 or exponentSide < 2.0 :\\r\\n raise ValueError( 'Exponents for ellipsoid must all be under 2.0!' )\\r\\n \\r\\n position = PyUtils.toPoint3d(position)\\r\\n vertices = []\\r\\n for i in range(1,samplesY):\\r\\n thetaI = i*math.pi/float(samplesY)\\r\\n if i < samplesY / 2 : \\r\\n n = exponentTop\\r\\n else:\\r\\n n = exponentBottom\\r\\n cos = math.cos(thetaI) \\r\\n y = cos * radius[1]\\r\\n scaleXZ = math.pow( 1-math.pow(math.fabs(cos),n), 1.0/float(n) )\\r\\n for j in range(0,samplesXZ):\\r\\n thetaJ = j*2.0*math.pi/float(samplesXZ)\\r\\n n = exponentSide\\r\\n cos = math.cos(thetaJ)\\r\\n x = cos * scaleXZ * radius[0]\\r\\n z = math.pow( 1-math.pow(math.fabs(cos),n), 1.0/float(n) ) * math.copysign(1, math.sin(thetaJ)) * scaleXZ * radius[2]\\r\\n vertices.append( position + Vector3d(x,y,z) )\\r\\n vertices.append( position + Vector3d(0,radius[1],0) )\\r\\n vertices.append( position + Vector3d(0,-radius[1],0) ) \\r\\n\\r\\n faces = []\\r\\n for i in range(0,(samplesY-2)*samplesXZ,samplesXZ) :\\r\\n for j in range(0,samplesXZ) :\\r\\n faces.append( (i+j, i+(j+1)%samplesXZ, i+samplesXZ+(j+1)%samplesXZ, i+samplesXZ+j) ) \\r\\n\\r\\n for i in range(0,samplesXZ) :\\r\\n base = (samplesY-2)*samplesXZ\\r\\n faces.append( ((i+1)%samplesXZ, i, (samplesY-1)*samplesXZ) ) \\r\\n faces.append( (base+i, base+(i+1)%samplesXZ, (samplesY-1)*samplesXZ+1) ) \\r\\n\\r\\n \\r\\n return create( vertices, faces, colour )\",\n \"def DFT_slow(x):\\r\\n x = np.asarray(x, dtype=complex)\\r\\n N = len(x)\\r\\n n = np.arange(N)\\r\\n k = n.reshape((N, 1))\\r\\n M = np.exp(-2j * np.pi * k * n / N)\\r\\n return np.dot(M, x)\",\n \"def ft(x):\\n y = np.fft.rfft(x)\\n\\n phi = 2 * np.pi * np.random.random(len(y))\\n\\n phi[0] = 0.0\\n if len(x) % 2 == 0:\\n phi[-1] = 0.0\\n\\n y = y * np.exp(1j * phi)\\n return np.fft.irfft(y, n=len(x))\",\n \"def _adj_filt(self, ndims):\\n\\n # inner filter, that is 3x3x...\\n filt_inner = np.zeros([3] * ndims)\\n for j in range(ndims):\\n o = [[1]] * ndims\\n o[j] = [0, 2]\\n filt_inner[np.ix_(*o)] = 1\\n\\n # full filter, that makes sure the inner filter is applied \\n # ith feature to ith feature\\n filt = np.zeros([3] * ndims + [ndims, ndims])\\n for i in range(ndims):\\n filt[..., i, i] = filt_inner\\n \\n return filt\",\n \"def fRes(self, mag, size, ps):\\n x = 2 * np.pi * mag / (size[0] * ps[0])\\n y = 2 * np.pi * mag / (size[1] * ps[1])\\n return [x, y]\",\n \"def apply_fourier_transform(chunked_audio):\\n pass\",\n \"def makeSpectrum(E, dx, dy, upsample=10):\\n zeropadded = np.array(E.shape) * upsample\\n F = fft.fftshift(fft.fft2(E, zeropadded)) / E.size\\n xf = fft.fftshift(fft.fftfreq(zeropadded[1], d=dx))\\n yf = fft.fftshift(fft.fftfreq(zeropadded[0], d=dy))\\n return (F, xf, yf)\",\n \"def fft(self, *args, **kwargs):\\n return _image.image_fft(self, *args, **kwargs)\",\n \"def fourier_transform(hamiltonian, grid, spinless):\\n return _fourier_transform_helper(hamiltonian=hamiltonian,\\n grid=grid,\\n spinless=spinless,\\n phase_factor=+1,\\n vec_func_1=grid.momentum_vector,\\n vec_func_2=grid.position_vector)\",\n \"def fft(self, z, out=None):\\n z = np.asfarray(z)\\n out = np.multiply(z, z, out)\\n out *= -0.5\\n np.exp(out, out)\\n return out\",\n \"def __qft_input_state(self, size) -> None:\\n\\n for i in range(size):\\n self.h(i)\\n self.u1(-math.pi / float(2 ** i), i)\",\n \"def FFT(x):\\n x = np.asarray(x, dtype=float)\\n N = x.shape[0]\\n \\n if N % 2 > 0:\\n raise ValueError(\\\"size of x must be a power of 2\\\")\\n elif N <= 32: # this cutoff should be optimized\\n return DFT(x)\\n else:\\n X_even = FFT(x[::2])\\n X_odd = FFT(x[1::2])\\n factor = np.exp(-2j * np.pi * np.arange(N) / N)\\n return np.concatenate([X_even + factor[:int(N / 2)] * X_odd,\\n X_even + factor[int(N / 2):] * X_odd])\",\n \"def DFTdirect(x):\\n \\n N = len(x)\\n X = []\\n for k in range(N):\\n X_k = Complex(0,0)\\n for n in range(N):\\n a = 2*math.pi*k*n/N\\n X_k += x[n]*Complex(math.cos(a), -math.sin(a))\\n X.append(X_k)\\n return X\",\n \"def IDFT_slow(x):\\r\\n x = np.asarray(x, dtype=complex)\\r\\n N = len(x)\\r\\n n = np.arange(N)\\r\\n k = n.reshape((N, 1))\\r\\n M = np.exp(2j * np.pi * k * n / N)\\r\\n return 1.0/N * np.dot(M, x)\",\n \"def fir_design_matrix(events, len_hrf):\\r\\n event_types = np.unique(events)[np.unique(events) != 0]\\r\\n fir_matrix = np.zeros((events.shape[0], len_hrf * event_types.shape[0]))\\r\\n\\r\\n for t in event_types:\\r\\n idx_h_a = np.where(event_types == t)[0] * len_hrf\\r\\n idx_h_b = idx_h_a + len_hrf\\r\\n idx_v = np.where(events == t)[0]\\r\\n for idx_v_a in idx_v:\\r\\n idx_v_b = idx_v_a + len_hrf\\r\\n fir_matrix[idx_v_a:idx_v_b, idx_h_a:idx_h_b] += (np.eye(len_hrf) *\\r\\n np.sign(t))\\r\\n\\r\\n return fir_matrix\",\n \"def getFileAsFiltFloatMatrix(dirPath, filt, columns, delim=\\\",\\\"):\\n\\tmat = list()\\n\\tfor rec in fileFiltSelFieldsRecGen(dirPath, filt, columns, delim):\\n\\t\\tmat.append(asFloatList(rec))\\n\\treturn mat\",\n \"def dft_map(input_array, *, u, v, center=(0.0, 0.0) * apu.arcsec, pixel_size=(1.0, 1.0) * apu.arcsec):\\n m, n = input_array.shape\\n\\n y = generate_xy(m, center[1], pixel_size[1])\\n x = generate_xy(n, center[0], pixel_size[0])\\n\\n x, y = np.meshgrid(x, y)\\n uv = np.vstack([u, v])\\n # Check units are correct for exp need to be dimensionless and then remove units for speed\\n if (uv[0, :] * x[0, 0]).unit == apu.dimensionless_unscaled and \\\\\\n (uv[1, :] * y[0, 0]).unit == apu.dimensionless_unscaled:\\n\\n uv = uv.value\\n x = x.value\\n y = y.value\\n\\n vis = np.sum(input_array[..., np.newaxis] * np.exp(-2j * np.pi * (\\n x[..., np.newaxis] * uv[np.newaxis, 0, :] + y[..., np.newaxis] * uv[np.newaxis, 1, :])),\\n axis=(0, 1))\\n\\n return vis\\n else:\\n raise UnitsError(\\\"Incompatible units on uv {uv.unit} should cancel with xy \\\"\\n \\\"to leave a dimensionless quantity\\\")\",\n \"def _apply_array_spatial1234(self, h1e: 'Nparray', h2e: 'Nparray',\\n h3e: 'Nparray', h4e: 'Nparray') -> 'Nparray':\\n norb = self.norb()\\n assert h4e.shape == (norb, norb, norb, norb, norb, norb, norb, norb)\\n lena = self.lena()\\n lenb = self.lenb()\\n\\n nh1e = numpy.copy(h1e)\\n nh2e = numpy.copy(h2e)\\n nh3e = numpy.copy(h3e)\\n\\n for i in range(norb):\\n for j in range(norb):\\n for k in range(norb):\\n nh1e[:, :] -= h4e[:, j, i, k, j, i, k, :]\\n for l in range(norb):\\n nh2e[i, j, :, :] += (h4e[j, l, i, k, l, k, :, :] +\\n h4e[i, j, l, k, l, k, :, :] +\\n h4e[i, l, k, j, l, k, :, :] +\\n h4e[j, i, k, l, l, k, :, :] +\\n h4e[i, k, j, l, k, :, l, :] +\\n h4e[j, i, k, l, k, :, l, :] +\\n h4e[i, j, k, l, :, k, l, :])\\n nh3e[i, j, k, :, :, :] += (h4e[k, i, j, l, l, :, :, :] +\\n h4e[j, i, l, k, l, :, :, :] +\\n h4e[i, l, j, k, l, :, :, :] +\\n h4e[i, k, j, l, :, l, :, :] +\\n h4e[i, j, l, k, :, l, :, :] +\\n h4e[i, j, k, l, :, :, l, :])\\n\\n dvec = self.calculate_dvec_spatial()\\n evec = numpy.zeros((norb, norb, norb, norb, lena, lenb),\\n dtype=self._dtype)\\n\\n for i in range(norb):\\n for j in range(norb):\\n tmp = dvec[i, j, :, :]\\n tmp2 = self._calculate_dvec_spatial_with_coeff(tmp)\\n evec[:, :, i, j, :, :] = tmp2[:, :, :, :]\\n\\n out = self._apply_array_spatial123(nh1e, nh2e, nh3e, dvec, evec)\\n\\n evec = numpy.transpose(numpy.tensordot(h4e,\\n evec,\\n axes=((2, 6, 3, 7), (0, 1, 2,\\n 3))),\\n axes=[0, 2, 1, 3, 4, 5])\\n\\n dvec2 = numpy.zeros(dvec.shape, dtype=self._dtype)\\n for i in range(norb):\\n for j in range(norb):\\n dvec[:, :, :, :] = evec[i, j, :, :, :, :]\\n cvec = self._calculate_coeff_spatial_with_dvec(dvec)\\n dvec2[i, j, :, :] += cvec[:, :]\\n\\n out += self._calculate_coeff_spatial_with_dvec(dvec2)\\n return out\",\n \"def filtering(self):\\n from numpy import fft\\n import numpy as np\\n\\n _image_dft = fft.fft2(self.image)\\n _image_dft = fft.fftshift(_image_dft)\\n # dft = DFT.DFT()\\n # plt.figure(1) \\n # plt.imshow(self.image)\\n # plt.figure(2)\\n # plt.imshow(20*np.log10(abs(_image_dft))) \\n # print(_image_dft)\\n # print(abs(_image_dft))\\n # plt.show()\\n filter = self.filter(self.image.shape, self.cutoff, self.order) \\\\\\n if self.filter_name.startswith('butterworth') \\\\\\n else self.filter(self.image.shape, self.cutoff)\\n \\n _image_dft_filtered = _image_dft * filter\\n _image_filtered = abs(fft.ifft2(_image_dft_filtered))\\n \\n return [ self.post_process_image(_image_filtered), \\\\\\n self.post_process_image(20*np.log10(abs(_image_dft)+.00001)), \\\\\\n self.post_process_image(20*np.log10(abs(_image_dft_filtered)+.00001)) ]\",\n \"def ifftn_1d(a):\\r\\n b = np.fft.fftpack.ifftn(a, axes=(len(a.shape)-1, )) \\r\\n return np.multiply(b, np.sqrt(a.shape[-1]))\",\n \"def DARP2016_MicArray():\\n\\n M = 36 # number of mics\\n array_height = -0.49 # [m] (ref. to airfoil height at z=0)\\n\\n # mic coordinates (corrected for DARP2016 configuration)\\n XYZ_array = np.array([[0., 0.025, 0.08477, 0.12044, 0.18311, 0.19394,\\n 0.01559, 0.08549, 0.16173, 0.19659, 0.24426, -0.00556,\\n 0.02184, 0.08124, 0.06203, 0.11065, -0.02252, -0.05825,\\n -0.06043, -0.11924, -0.10628, -0.02252, -0.09449, -0.15659,\\n -0.21072, -0.24318, -0.00556, -0.05957, -0.13484, -0.14352,\\n -0.19696, 0.01559, 0.02021, -0.01155, 0.03174, -0.00242],\\n [-0., -0., 0.04175, 0.11082, 0.10542, 0.15776,\\n -0.01955, -0.04024, -0.02507, -0.07743, -0.05327, -0.02437,\\n -0.09193, -0.14208, -0.20198, -0.22418, -0.01085, -0.0744,\\n -0.1521, -0.17443, -0.22628, 0.01085, -0.00084, -0.04759,\\n -0.01553, -0.05799, 0.02437, 0.07335, 0.09276, 0.15506,\\n 0.15397, 0.01955, 0.09231, 0.16326, 0.20889, 0.24999],\\n array_height*np.ones(M)])\\n\\n # calibration factors\\n array_cal = np.array([73.92182641429085, 96.84446743391487, 85.48777846463159,\\n 85.24410968090712, 83.63917149322562, 68.94090765134432,\\n 79.2385037527723, 112.77357210746612, 84.8483307868491,\\n 87.18956628936178, 97.75046920293282, 89.2829545690508,\\n 79.51644155562396, 90.39403884030057, 80.71754629014218,\\n 89.4418210091059, 98.33634233056068, 79.2212022850229,\\n 91.25543447201031, 89.55040012572815, 85.77495667666254,\\n 82.74418222820202, 84.63061055646973, 77.01568014644964,\\n 95.52764533324982, 92.16734812591154, 95.27123074600838,\\n 87.93335310521428, 96.65066131188675, 93.58564782091074,\\n 78.1446818728945, 101.3047738767648, 83.68569643491034,\\n 84.7981031520437, 94.40796508430756, 83.52266614867919])\\n\\n return XYZ_array, array_cal\",\n \"def _deform(self, volumes: Union[np.ndarray, Sequence[np.ndarray]]) -> \\\\\\n List[np.ndarray]:\\n # Make sure xy shape (last two axes) are the same for all volumes\\n xy_shapes = [v.shape[-2:] for v in volumes]\\n if xy_shapes.count(xy_shapes[0]) != len(xy_shapes):\\n # True when xy shapes don't all match\\n raise ValueError('Volumes passed to DataHandler._deform() must '\\n 'all have the same shape.')\\n\\n # Build a new pixel index deformation map\\n # Assumed to be the same\\n xy_shape = xy_shapes[0]\\n deform_map = self._deformation_map(xy_shape)\\n\\n deformed_volumes = []\\n\\n for volume in volumes:\\n shape = volume.shape\\n ndim = volume.ndim\\n new_vol = np.zeros_like(volume)\\n if ndim == 4:\\n # 3D multichannel data. Apply 2D deformations to each z slice\\n # in each channel of the volume\\n for c in range(shape[0]):\\n for z in range(shape[1]):\\n new_vol[c, z, ...] = \\\\\\n map_coordinates(volume[c, z, ...],\\n deform_map,\\n order=0).reshape(xy_shape)\\n elif ndim == 3:\\n # 3D single channel data. Apply 2D deformations to each z slice\\n # of the volume\\n for z in range(shape[0]):\\n new_vol[z, ...] = map_coordinates(volume[z, ...],\\n deform_map,\\n order=0).reshape(xy_shape)\\n elif ndim == 2:\\n # Volume is 2D, deform the whole thing at once\\n new_vol = map_coordinates(volume,\\n deform_map,\\n order=0).reshape(xy_shape)\\n else:\\n raise ValueError(f'Cannot deform volume with ndim {ndim}')\\n\\n deformed_volumes.append(new_vol)\\n\\n return deformed_volumes\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5919297","0.5817323","0.5366147","0.5307928","0.52779627","0.5270385","0.5188855","0.5180645","0.51758784","0.5060478","0.5059644","0.5033383","0.50284445","0.5026053","0.5017994","0.50149393","0.5008967","0.4998893","0.4998793","0.49833018","0.49821383","0.49507245","0.4931019","0.49096128","0.4901749","0.48875242","0.48857862","0.48810938","0.4865674","0.48550248","0.4853197","0.48415035","0.48344097","0.48088518","0.48037645","0.4799745","0.47952077","0.4791444","0.47826955","0.47781828","0.4768471","0.4764471","0.47556686","0.47485766","0.474112","0.47381648","0.47373018","0.47316897","0.4722811","0.47138366","0.47138157","0.47024035","0.47012496","0.4683176","0.46798772","0.46708444","0.4669822","0.4650707","0.4649874","0.464714","0.46468523","0.46441588","0.46438667","0.46424136","0.4635153","0.4630707","0.4624183","0.46227926","0.4620424","0.46202973","0.46200812","0.460793","0.46069282","0.46056333","0.46046078","0.46008903","0.46005854","0.45821014","0.4576848","0.45705676","0.45681745","0.45673946","0.45672306","0.45641008","0.45587867","0.45474133","0.45452332","0.45411497","0.45406908","0.4537331","0.45368642","0.4535827","0.45338732","0.45321608","0.45244628","0.45182917","0.45171407","0.45126736","0.45101005","0.45086542"],"string":"[\n \"0.5919297\",\n \"0.5817323\",\n \"0.5366147\",\n \"0.5307928\",\n \"0.52779627\",\n \"0.5270385\",\n \"0.5188855\",\n \"0.5180645\",\n \"0.51758784\",\n \"0.5060478\",\n \"0.5059644\",\n \"0.5033383\",\n \"0.50284445\",\n \"0.5026053\",\n \"0.5017994\",\n \"0.50149393\",\n \"0.5008967\",\n \"0.4998893\",\n \"0.4998793\",\n \"0.49833018\",\n \"0.49821383\",\n \"0.49507245\",\n \"0.4931019\",\n \"0.49096128\",\n \"0.4901749\",\n \"0.48875242\",\n \"0.48857862\",\n \"0.48810938\",\n \"0.4865674\",\n \"0.48550248\",\n \"0.4853197\",\n \"0.48415035\",\n \"0.48344097\",\n \"0.48088518\",\n \"0.48037645\",\n \"0.4799745\",\n \"0.47952077\",\n \"0.4791444\",\n \"0.47826955\",\n \"0.47781828\",\n \"0.4768471\",\n \"0.4764471\",\n \"0.47556686\",\n \"0.47485766\",\n \"0.474112\",\n \"0.47381648\",\n \"0.47373018\",\n \"0.47316897\",\n \"0.4722811\",\n \"0.47138366\",\n \"0.47138157\",\n \"0.47024035\",\n \"0.47012496\",\n \"0.4683176\",\n \"0.46798772\",\n \"0.46708444\",\n \"0.4669822\",\n \"0.4650707\",\n \"0.4649874\",\n \"0.464714\",\n \"0.46468523\",\n \"0.46441588\",\n \"0.46438667\",\n \"0.46424136\",\n \"0.4635153\",\n \"0.4630707\",\n \"0.4624183\",\n \"0.46227926\",\n \"0.4620424\",\n \"0.46202973\",\n \"0.46200812\",\n \"0.460793\",\n \"0.46069282\",\n \"0.46056333\",\n \"0.46046078\",\n \"0.46008903\",\n \"0.46005854\",\n \"0.45821014\",\n \"0.4576848\",\n \"0.45705676\",\n \"0.45681745\",\n \"0.45673946\",\n \"0.45672306\",\n \"0.45641008\",\n \"0.45587867\",\n \"0.45474133\",\n \"0.45452332\",\n \"0.45411497\",\n \"0.45406908\",\n \"0.4537331\",\n \"0.45368642\",\n \"0.4535827\",\n \"0.45338732\",\n \"0.45321608\",\n \"0.45244628\",\n \"0.45182917\",\n \"0.45171407\",\n \"0.45126736\",\n \"0.45101005\",\n \"0.45086542\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6894624"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":20098,"cells":{"query":{"kind":"string","value":"Multidimensional Fourier shift filter. The array is multiplied with the Fourier transform of a shift operation."},"document":{"kind":"string","value":"def fourier_shift(input, shift, n=-1, axis=-1, output=None):\n input = numpy.asarray(input)\n output = _get_output_fourier_complex(output, input)\n axis = normalize_axis_index(axis, input.ndim)\n shifts = _ni_support._normalize_sequence(shift, input.ndim)\n shifts = numpy.asarray(shifts, dtype=numpy.float64)\n if not shifts.flags.contiguous:\n shifts = shifts.copy()\n _nd_image.fourier_shift(input, shifts, n, axis, output)\n return output"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def fftshift(X):\r\n # return scipy.fftpack.fftshift(X)\r\n return np.fft.fftshift(X)","def ifftshift(a, axes=None):\n return image.image(np.fft.ifftshift(a, axes), pixelsize = image.getPixelsize(a))","def fftshift(a, axes=None):\n return image.image(np.fft.fftshift(a, axes), pixelsize = image.getPixelsize(a))","def roll(arrayin, shift = (0, 0), silent = True):\r\n arrayout = arrayin.copy()\r\n # if shift is integer valued then use np.roll\r\n if (type(shift[0]) == int) or (type(shift[0]) == np.int) or (type(shift[0]) == np.int32) or (type(shift[0]) == np.int64):\r\n if shift[-1] != 0 :\r\n if silent == False :\r\n print 'arrayout = np.roll(arrayout, shift[-1], -1)'\r\n arrayout = np.roll(arrayout, shift[-1], -1)\r\n # if shift is 1d then don't roll the other dim (if it even exists)\r\n if len(arrayout.shape) >= 2 :\r\n if shift[-2] != 0 :\r\n if silent == False :\r\n print 'arrayout = np.roll(arrayout, shift[-2], -2)'\r\n arrayout = np.roll(arrayout, shift[-2], -2)\r\n # if shift is float valued then use the Fourier shift theorem\r\n elif (type(shift[0]) == float) or (type(shift[0]) == np.float32) or (type(shift[0]) == np.float64):\r\n # if shift is 1d\r\n if len(shift) == 1 :\r\n if silent == False :\r\n print 'arrayout = fftn_1d(arrayout)'\r\n print 'arrayout = arrayout * phase_ramp(arrayout.shape, shift, origin = (0, 0))'\r\n print 'arrayout = ifftn_1d(arrayout)'\r\n arrayout = fftn_1d(arrayout)\r\n arrayout = arrayout * phase_ramp(arrayout.shape, shift, origin = (0, 0))\r\n arrayout = ifftn_1d(arrayout)\r\n elif len(shift) == 2 :\r\n if silent == False :\r\n print 'arrayout = fftn(arrayout)'\r\n print 'arrayout = arrayout * phase_ramp(arrayout.shape, shift, origin = (0, 0))'\r\n print 'arrayout = ifftn(arrayout)'\r\n arrayout = fftn(arrayout)\r\n arrayout = arrayout * phase_ramp(arrayout.shape, shift, origin = (0, 0))\r\n arrayout = ifftn(arrayout)\r\n return arrayout","def array_shift(arr, shift):\r\n return pd.DataFrame(arr).shift(shift).values","def fftshift(x, dim=None):\n if dim is None:\n dim = tuple(range(x.dim()))\n shift = [dim // 2 for dim in x.shape]\n elif isinstance(dim, int):\n shift = x.shape[dim] // 2\n else:\n shift = [x.shape[i] // 2 for i in dim]\n return roll(x, shift, dim)","def fftshift(x, dim=None):\n if dim is None:\n dim = tuple(range(x.dim()))\n shift = [dim // 2 for dim in x.shape]\n elif isinstance(dim, int):\n shift = x.shape[dim] // 2\n else:\n shift = [x.shape[i] // 2 for i in dim]\n return roll(x, shift, dim)","def fftshift(x, dim=None):\n if dim is None:\n dim = tuple(range(x.dim()))\n shift = [dim // 2 for dim in x.shape]\n elif isinstance(dim, int):\n shift = x.shape[dim] // 2\n else:\n shift = [x.shape[i] // 2 for i in dim]\n return roll(x, shift, dim)","def fftshift(x, dim=None):\n if dim is None:\n dim = tuple(range(x.dim()))\n shift = [xdim // 2 for xdim in x.shape]\n elif isinstance(dim, int):\n shift = x.shape[dim] // 2\n else:\n shift = [x.shape[i] // 2 for i in dim]\n return roll(x, shift, dim)","def SHIFTFT(X,D):\n N=len(X)\n for k in range(N):\n X[k]=np.exp(-1j*2*np.pi*k*D/N)*X[k]\n return X","def irfft2(a, shift_in=False, shift_out=False):\n s = a.shape\n if len(s) != 2:\n raise GalSimValueError(\"Input array must be 2D.\",s)\n M,No2 = s\n No2 -= 1 # s is (M,No2+1)\n Mo2 = M // 2\n\n if M != Mo2*2:\n raise GalSimValueError(\"Input array must have even sizes.\",s)\n\n a = a.astype(np.complex128, copy=False)\n kim = ImageCD(a, xmin = 0, ymin = -Mo2)\n xim = ImageD(BoundsI(-No2,No2+1,-Mo2,Mo2-1))\n with convert_cpp_errors():\n _galsim.irfft(kim._image, xim._image, shift_in, shift_out)\n xim = xim.subImage(BoundsI(-No2,No2-1,-Mo2,Mo2-1))\n return xim.array","def fft2(a, shift_in=False, shift_out=False):\n s = a.shape\n if len(s) != 2:\n raise GalSimValueError(\"Input array must be 2D.\",s)\n M, N = s\n Mo2 = M // 2\n No2 = N // 2\n\n if M != Mo2*2 or N != No2*2:\n raise GalSimValueError(\"Input array must have even sizes.\",s)\n\n if a.dtype.kind == 'c':\n a = a.astype(np.complex128, copy=False)\n xim = ImageCD(a, xmin = -No2, ymin = -Mo2)\n kim = ImageCD(BoundsI(-No2,No2-1,-Mo2,Mo2-1))\n with convert_cpp_errors():\n _galsim.cfft(xim._image, kim._image, False, shift_in, shift_out)\n kar = kim.array\n else:\n a = a.astype(np.float64, copy=False)\n xim = ImageD(a, xmin = -No2, ymin = -Mo2)\n\n # This works, but it's a bit slower.\n #kim = ImageCD(BoundsI(-No2,No2-1,-Mo2,Mo2-1))\n #_galsim.cfft(xim._image, kim._image, False, shift_in, shift_out)\n #kar = kim.array\n\n # Faster to start with rfft2 version\n rkim = ImageCD(BoundsI(0,No2,-Mo2,Mo2-1))\n with convert_cpp_errors():\n _galsim.rfft(xim._image, rkim._image, shift_in, shift_out)\n # This only returns kx >= 0. Fill out the full image.\n kar = np.empty( (M,N), dtype=np.complex128)\n rkar = rkim.array\n if shift_out:\n kar[:,No2:N] = rkar[:,0:No2]\n kar[0,0:No2] = rkar[0,No2:0:-1].conjugate()\n kar[1:Mo2,0:No2] = rkar[M-1:Mo2:-1,No2:0:-1].conjugate()\n kar[Mo2:M,0:No2] = rkar[Mo2:0:-1,No2:0:-1].conjugate()\n else:\n kar[:,0:No2] = rkar[:,0:No2]\n kar[0,No2:N] = rkar[0,No2:0:-1].conjugate()\n kar[1:M,No2:N] = rkar[M-1:0:-1,No2:0:-1].conjugate()\n return kar","def ifft2(a, shift_in=False, shift_out=False):\n s = a.shape\n if len(s) != 2:\n raise GalSimValueError(\"Input array must be 2D.\",s)\n M,N = s\n Mo2 = M // 2\n No2 = N // 2\n\n if M != Mo2*2 or N != No2*2:\n raise GalSimValueError(\"Input array must have even sizes.\",s)\n\n if a.dtype.kind == 'c':\n a = a.astype(np.complex128, copy=False)\n kim = ImageCD(a, xmin = -No2, ymin = -Mo2)\n else:\n a = a.astype(np.float64, copy=False)\n kim = ImageD(a, xmin = -No2, ymin = -Mo2)\n xim = ImageCD(BoundsI(-No2,No2-1,-Mo2,Mo2-1))\n with convert_cpp_errors():\n _galsim.cfft(kim._image, xim._image, True, shift_in, shift_out)\n return xim.array","def process( fids, ndim=2 ):\n\timg = np.empty_like( fids )\n\tax = -1*(np.array( range(ndim) )+1)\n\t\n\timg = np.fft.fftshift( np.fft.fftn( fids, axes=ax, ).astype( np.complex64), axes=ax )\n\t\n\treturn np.squeeze(img)","def fftshift(x, additional_shift=None, axes=None):\n tmp = np.asarray(x)\n ndim = len(tmp.shape)\n if axes is None:\n axes = list(range(ndim))\n elif isinstance(axes, integer_types):\n axes = (axes,)\n\n # If no additional shift is supplied, reproduce `numpy.fft.fftshift` result\n if additional_shift is None:\n additional_shift = [0, 0]\n\n y = tmp\n for k, extra_shift in zip(axes, additional_shift):\n n = tmp.shape[k]\n if (n+1)//2 - extra_shift < n:\n p2 = (n+1)//2 - extra_shift\n else:\n p2 = abs(extra_shift) - (n+1)//2\n mylist = np.concatenate((np.arange(p2, n), np.arange(0, p2)))\n y = np.take(y, mylist, k)\n return y","def shift_spect_array_cpu( field_array, shift_factor, n_move ):\n Nz, Nr = field_array.shape\n\n # Loop over the 2D array (in parallel over z if threading is enabled)\n for iz in prange( Nz ):\n power_shift = 1. + 0.j\n # Calculate the shift factor (raising to the power n_move ;\n # for negative n_move, we take the complex conjugate, since\n # shift_factor is of the form e^{i k dz})\n for i in range( abs(n_move) ):\n power_shift *= shift_factor[iz]\n if n_move < 0:\n power_shift = power_shift.conjugate()\n # Shift the fields\n for ir in range( Nr ):\n field_array[iz, ir] *= power_shift","def ifftshift(x, dim=None):\n if dim is None:\n dim = tuple(range(x.dim()))\n shift = [(dim + 1) // 2 for dim in x.shape]\n elif isinstance(dim, int):\n shift = (x.shape[dim] + 1) // 2\n else:\n shift = [(x.shape[i] + 1) // 2 for i in dim]\n return roll(x, shift, dim)","def ifftshift(x, dim=None):\n if dim is None:\n dim = tuple(range(x.dim()))\n shift = [(dim + 1) // 2 for dim in x.shape]\n elif isinstance(dim, int):\n shift = (x.shape[dim] + 1) // 2\n else:\n shift = [(x.shape[i] + 1) // 2 for i in dim]\n return roll(x, shift, dim)","def ifftshift(x, dim=None):\n if dim is None:\n dim = tuple(range(x.dim()))\n shift = [(dim + 1) // 2 for dim in x.shape]\n elif isinstance(dim, int):\n shift = (x.shape[dim] + 1) // 2\n else:\n shift = [(x.shape[i] + 1) // 2 for i in dim]\n return roll(x, shift, dim)","def fft_shift(fft_mat):\n if type(fft_mat) is np.ndarray:\n rs = np.ceil(fft_mat.shape[0]/2).astype('int')\n cs = np.ceil(fft_mat.shape[1]/2).astype('int')\n quad1 = fft_mat[:rs, :cs]\n quad2 = fft_mat[:rs, cs:]\n quad3 = fft_mat[rs:, cs:]\n quad4 = fft_mat[rs:, :cs]\n centered_t = np.concatenate((quad4, quad1), axis=0)\n centered_b = np.concatenate((quad3, quad2), axis=0)\n centered = np.concatenate((centered_b, centered_t), axis=1)\n # Thus centered is formed by shifting the entries of fft_mat\n # up/left by [rs, cs] indices, or equivalently down/right by\n # (fft_mat.shape - [rs, cs]) indices, with edges wrapping. \n return centered\n else:\n print('input to fft_shift() should be a matrix')\n return","def fftshift(x, dim=-1):\n dim = fix_dim(dim, x.dim())\n return torch.roll(x, x.shape[dim] // 2, dim)","def ifftshift(x, dim=None):\n if dim is None:\n dim = tuple(range(x.dim()))\n shift = [(xdim + 1) // 2 for xdim in x.shape]\n elif isinstance(dim, int):\n shift = (x.shape[dim] + 1) // 2\n else:\n shift = [(x.shape[i] + 1) // 2 for i in dim]\n return roll(x, shift, dim)","def _irfft2d(f_x) :","def rfft2(a, shift_in=False, shift_out=False):\n s = a.shape\n if len(s) != 2:\n raise GalSimValueError(\"Input array must be 2D.\",s)\n M,N = s\n Mo2 = M // 2\n No2 = N // 2\n\n if M != Mo2*2 or N != No2*2:\n raise GalSimValueError(\"Input array must have even sizes.\",s)\n\n a = a.astype(np.float64, copy=False)\n xim = ImageD(a, xmin = -No2, ymin = -Mo2)\n kim = ImageCD(BoundsI(0,No2,-Mo2,Mo2-1))\n with convert_cpp_errors():\n _galsim.rfft(xim._image, kim._image, shift_in, shift_out)\n return kim.array","def fft2(data):\n assert data.size(-1) == 2\n data = ifftshift(data, dim=(-3, -2))\n data = torch.fft(data, 2, normalized=False)\n data = fftshift(data, dim=(-3, -2))\n return data","def fft2(data):\n assert data.size(-1) == 2\n data = ifftshift(data, dim=(-3, -2))\n data = torch.fft(data, 2, normalized=True)\n data = fftshift(data, dim=(-3, -2))\n return data","def _imfilter(x_data, f_data):\n return pipe(f_data, ifftshift, fftn, lambda x: x * fftn(x_data), ifftn).real","def forward_fft(self, array_in):\r\n # Find side length, as real array may or may not be doubled for\r\n # aliasing control\r\n side = array_in.shape[0]\r\n div_side = 1.0/side**2\r\n\r\n out = np.fft.fft2(self.sign_mat[0:side, 0:side]*array_in)*div_side\r\n return out","def realign_image(arr, shift, angle=0):\n # if both shifts are integers, do circular shift; otherwise perform Fourier shift.\n if np.count_nonzero(np.abs(np.array(shift) - np.round(shift)) < 0.01) == 2:\n temp = np.roll(arr, int(shift[0]), axis=0)\n temp = np.roll(temp, int(shift[1]), axis=1)\n temp = temp.astype('float32')\n else:\n temp = fourier_shift(np.fft.fftn(arr), shift)\n temp = np.fft.ifftn(temp)\n temp = np.abs(temp).astype('float32')\n return temp","def shift(image,shift_x,shift_y):\n return np.roll(np.roll(image,shift_y,axis=0),shift_x,axis=1)","def FD_shift(self, signal, FD_params):\n #freq in MHz, delays in milliseconds\n freq_array = signal._dat_freq\n # define the reference frequency\n ref_freq = make_quant(1000.0, 'MHz')\n # calculate the delay added in for the parameters\n time_delays = make_quant(np.zeros(len(freq_array)), 'ms') # will be in seconds\n for ii in range(len(FD_params)):\n time_delays += np.double(make_quant(FD_params[ii], 's').to('ms') * \\\n np.power(np.log(freq_array/ref_freq),ii+1)) # will be in seconds\n\n if signal.delay==None:\n signal._delay=time_delays\n else:\n signal._delay += time_delays\n # get time shift based on the sample rate\n shift_dt = (1/signal._samprate).to('ms')\n shift_start = time.time()\n # check if there are less than 20 frequency channels\n if signal.Nchan <= 20:\n div_fac = 1\n else:\n div_fac = 20\n\n for ii, freq in enumerate(freq_array):\n signal._data[ii,:] = shift_t(signal._data[ii,:],\n time_delays[ii].value,\n dt=shift_dt.value)\n if (ii+1) % int(signal.Nchan//div_fac) ==0:\n shift_check = time.time()\n percent = round((ii + 1)*100/signal.Nchan)\n elapsed = shift_check-shift_start\n chk_str = '\\r{0:2.0f}% shifted'.format(percent)\n chk_str += ' in {0:4.3f} seconds.'.format(elapsed)\n\n try:\n print(chk_str , end='', flush=True)\n #This is the Python 2 version\n #__future__ does not have 'flush' kwarg.\n except:\n print(chk_str , end='')\n sys.stdout.flush()\n\n # May need to add tihs parameter to signal\n signal._FDshifted = True","def fft1(x):\n assert x.size(-1) == 2\n x = ifftshift(x, dim=(-2,))\n x = torch.fft(x, 1, normalized=True)\n x = fftshift(x, dim=(-2,))\n return x","def ifft2(data):\n assert data.size(-1) == 2\n data = ifftshift(data, dim=(-3, -2))\n data = torch.ifft(data, 2, normalized=False)\n data = fftshift(data, dim=(-3, -2))\n return data","def shift(self, da, dim, shift):\n # TODO: generalize rolling function, allow custom shifts, handle\n # boundary conditions, etc.\n return da.roll(**{dim: shift})","def ifft2(data):\n assert data.size(-1) == 2\n data = ifftshift(data, dim=(-3, -2))\n data = torch.ifft(data, 2, normalized=True)\n data = fftshift(data, dim=(-3, -2))\n return data","def shift_spect_array_gpu( field_array, shift_factor, n_move ):\n # Get a 2D CUDA grid\n iz, ir = cuda.grid(2)\n\n # Only access values that are actually in the array\n if ir < field_array.shape[1] and iz < field_array.shape[0]:\n power_shift = 1. + 0.j\n # Calculate the shift factor (raising to the power n_move ;\n # for negative n_move, we take the complex conjugate, since\n # shift_factor is of the form e^{i k dz})\n for i in range( abs(n_move) ):\n power_shift *= shift_factor[iz]\n if n_move < 0:\n power_shift = power_shift.conjugate()\n # Shift fields\n field_array[iz, ir] *= power_shift","def fft2(data):\n if not data.shape[-1] == 2:\n raise ValueError(\"Tensor does not have separate complex dim.\")\n\n data = ifftshift(data, dim=[-3, -2])\n data = torch.view_as_real(\n torch.fft.fftn( # type: ignore\n torch.view_as_complex(data), dim=(-2, -1), norm=\"ortho\"\n )\n )\n data = fftshift(data, dim=[-3, -2])\n\n return data","def ifft1(x):\n assert x.size(-1) == 2\n x = ifftshift(x, dim=(-2,))\n x = torch.ifft(x, 1, normalized=True)\n x = fftshift(x, dim=(-2,))\n return x","def fft2(a, origin='centre'):\r\n if origin == 'centre' :\r\n b = quadshift(a)\r\n else :\r\n b = a.copy()\r\n if len(b.shape) == 1 :\r\n b = np.fft.fftpack.fft(b) \r\n elif len(b.shape) == 2 :\r\n b = np.fft.fftpack.fft2(b) \r\n elif len(b.shape) > 2 :\r\n b = fftn(b)\r\n if origin == 'centre' :\r\n b = iquadshift(b)\r\n return b\r\n if origin == 'centre' :\r\n b = iquadshift(b)\r\n return np.divide(b, np.sqrt(b.size))","def sshift(s, N2, dt, shift):\n S = np.fft.fft(s, n=N2)\n\n k = round(shift/dt) # discrete shift\n # p = 2*np.pi*np.arange(0, N2, 1, dtype=float)*k/N2 # phase shift\n p = 2*np.pi*np.arange(1, N2+1, 1, dtype=float)*k/N2 # phase shift\n S = S*(np.cos(p) - 1j*np.sin(p))\n\n s_out = np.real(np.fft.ifft(S, N2))/np.cos(2*np.pi*k/N2) # correct scaling\n return s_out","def A(x,mask,S):\n xS = x*S\n y_full = fft.fftshift(fft.fft2(fft.ifftshift(xS)))\n y = y_full[:,mask]\n \n return y","def fft_viz(image: np.ndarray, shift: bool = True) -> None:\n plt.imshow(img_fft(image, shift=shift), cmap='gray')","def filtering(self):\r\n # 1 ###########################################################################################################\r\n fft_image = np.fft.fft2(self.image)\r\n # 2 ###########################################################################################################\r\n fft_shift_image = np.fft.fftshift(fft_image)\r\n\r\n ###\r\n mag_dft = np.log(np.abs(fft_shift_image))\r\n mag_dft = (255 * (mag_dft / np.max(mag_dft))).astype(dtype='uint8')\r\n ###\r\n\r\n # 3 ###########################################################################################################\r\n if self.filter_name == 'butterworth_l' or self.filter_name == 'butterworth_h':\r\n mask = self.filter(fft_shift_image.shape, self.cutoff, self.order)\r\n else:\r\n mask = self.filter(fft_shift_image.shape, self.cutoff)\r\n # 4 ###########################################################################################################\r\n # multiply the dft (fft shift image) by the mask\r\n filtered_image = fft_shift_image * mask\r\n\r\n ###\r\n mag_filtered_image = mag_dft * mask\r\n ###\r\n\r\n # 5 ###########################################################################################################\r\n inverse_fft_shift_image = np.fft.ifftshift(filtered_image)\r\n # 6 ###########################################################################################################\r\n inverse_fft_image = np.fft.ifft2(inverse_fft_shift_image)\r\n # 7 ###########################################################################################################\r\n mag_image = np.zeros(inverse_fft_image.shape, dtype=complex)\r\n for i in range(inverse_fft_image.shape[0]):\r\n for j in range(inverse_fft_image.shape[1]):\r\n if inverse_fft_image[i][j] < 0:\r\n mag_image[i][j] = -1 * inverse_fft_image[i][j]\r\n else:\r\n mag_image[i][j] = inverse_fft_image[i][j]\r\n # magnitude of inverse fft is complete\r\n # 8 ###########################################################################################################\r\n full_contrast_image = self.post_process_image(mag_image)\r\n\r\n return [mag_dft, mag_filtered_image, full_contrast_image]","def filter(f,fcutoff=10.,w=10.0,dt=.001):\r\n\r\n tshift=float(w)/2.\r\n \r\n fpad=padzeros(f)\r\n Fpad=np.fft.fft(fpad)\r\n fc=fcutoff\r\n \r\n t=np.arange(start=-tshift,stop=tshift,step=dt)\r\n filt=np.zeros(len(fpad))\r\n fs=2*fc*np.sinc(2*t*fc)\r\n norm=sum(fs)\r\n filt[0:len(t)]=fs/norm\r\n Filt=np.fft.fft(filt)\r\n \r\n Filtfunc=Fpad*Filt\r\n filtfunc=np.fft.ifft(Filtfunc)\r\n filtfunc=filtfunc[len(t)/2:len(f)+len(t)/2]\r\n \r\n return filtfunc","def ifft2(self, array):\n self._ifft2.input_array[:] = array\n return self._ifft2()","def shift_axes(self, shift, axes=None):\n axes = self._get_axes_numbers(axes)\n shift = self._cook_args(shift, axes, [0.0, 0.0])\n return Signal2D(self.values, index=self.index-shift[0], columns=self.columns-shift[1])","def shift_by(self, xshift):\n return Waveform(self.xvec + xshift, self.yvec, self.xtol, order=self.order, ext=self.ext)","def fourier(array, zero_in_middle = False, first_correction = False, zeropad_to = None, window_function = \"none\", window_length = 0, flag_plot = False):\n # shift time = 0 to first element\n if zero_in_middle == True:\n array = numpy.fft.ifftshift(array)\n \n \n \n # half the first element\n if first_correction == True: \n dim = len(numpy.shape(array))\n if dim == 1:\n array[0] /= 2\n elif dim == 2:\n array[0,:] /= 2\n array[:,0] /= 2\n elif dim > 2:\n print(\"WARNING (fourier.fourier.py): correction of the first element is not done!\")\n \n \n # window function\n if window_function != \"none\": \n array = window_functions(array, window_function, window_length, flag_plot = flag_plot)\n \n \n \n # the fft\n array = numpy.fft.fft(array, n = zeropad_to)\n \n # move the array back if it was shifted\n if zero_in_middle == True:\n array = numpy.fft.fftshift(array)\n \n return array","def shift(self, origin=None):\n assert(not self.Fourier)\n\n if origin==self.origin:\n return self\n elif origin is None:\n if self.origin in [0]:\n self.val = np.fft.fftshift(self.val, self.axes)\n self.origin='c'\n elif self.origin in ['c']:\n self.val = np.fft.ifftshift(self.val, self.axes)\n self.origin=0\n return self\n else:\n raise ValueError()","def shift(self, arr, shift_amt, pre_context, post_context):\n result = arr[pre_context - shift_amt:arr.shape[0] - post_context -\n shift_amt, :]\n return result","def ifft2(a, origin='centre'):\r\n if origin == 'centre' :\r\n b = quadshift(a)\r\n else :\r\n b = a.copy()\r\n if len(b.shape) == 1 :\r\n b = np.fft.fftpack.ifft(b) \r\n elif len(b.shape) == 2 :\r\n b = np.fft.fftpack.ifft2(b) \r\n elif len(b.shape) > 2 :\r\n b = ifftn(b)\r\n if origin == 'centre' :\r\n b = iquadshift(b)\r\n return b\r\n if origin == 'centre' :\r\n b = iquadshift(b)\r\n return np.multiply(b, np.sqrt(b.size))","def numpyFourierTransform(self,graph):\n z=[complex(*graph[i]) for i in range(len(graph))]\n return np.fft.fft(z)","def multiroll(x, shift, axis=None):\n x = np.asarray(x)\n if axis is None:\n if len(shift) != x.ndim:\n raise ValueError(\"The array has %d axes, but len(shift) is only \"\n \"%d. When 'axis' is not given, a shift must be \"\n \"provided for all axes.\" % (x.ndim, len(shift)))\n axis = range(x.ndim)\n else:\n # axis does not have to contain all the axes. Here we append the\n # missing axes to axis, and for each missing axis, append 0 to shift.\n missing_axes = set(range(x.ndim)) - set(axis)\n num_missing = len(missing_axes)\n axis = tuple(axis) + tuple(missing_axes)\n shift = tuple(shift) + (0,)*num_missing\n\n # Use mod to convert all shifts to be values between 0 and the length\n # of the corresponding axis.\n shift = [s % x.shape[ax] for s, ax in zip(shift, axis)]\n\n # Reorder the values in shift to correspond to axes 0, 1, ..., x.ndim-1.\n shift = np.take(shift, np.argsort(axis))\n\n # Create the output array, and copy the shifted blocks from x to y.\n y = np.empty_like(x)\n src_slices = [(slice(n-shft, n), slice(0, n-shft))\n for shft, n in zip(shift, x.shape)]\n dst_slices = [(slice(0, shft), slice(shft, n))\n for shft, n in zip(shift, x.shape)]\n src_blks = product(*src_slices)\n dst_blks = product(*dst_slices)\n for src_blk, dst_blk in zip(src_blks, dst_blks):\n y[dst_blk] = x[src_blk]\n\n return y","def shift_image(img, shft_int = 1):\n no_cols = img[0].shape[1]\n lst_col = no_cols - 1\n col_sty = no_cols - shft_int \n col_idx = torch.cat([torch.zeros(col_sty, dtype = torch.bool),\n torch.ones(shft_int, dtype = torch.bool)])\n cols = torch.reshape(img[0][0,:,col_idx], (no_cols,shft_int))\n cols_sum = torch.sum(cols)\n inval_shft = torch.is_nonzero(cols_sum)\n\n if inval_shft:\n col_idx = torch.cat([torch.ones(shft_int, dtype = torch.bool),\n torch.zeros(col_sty, dtype = torch.bool)])\n cols = torch.reshape(img[0][0,:,col_idx], (no_cols,shft_int))\n cols_sum = torch.sum(cols)\n inval_shft = torch.is_nonzero(cols_sum)\n if inval_shft:\n raise ValueError('Consider shifting along another axis.')\n mod_img = torch.cat([img[0][0,:,~col_idx],cols], dim = 1)\n mod_img = torch.reshape(mod_img, (1,mod_img.shape[0], mod_img.shape[1]))\n mod_img = (mod_img,img[1])\n return mod_img\n \n mod_img = torch.cat([cols,img[0][0,:,~col_idx]], dim = 1)\n mod_img = torch.reshape(mod_img, (1,mod_img.shape[0], mod_img.shape[1]))\n mod_img = (mod_img,img[1])\n return mod_img","def shift_list(array, s):\n # Calculate acutal shift amount (e.g. 11 --> 1 if lenght of the array is 5)\n s %= len(array)\n\n # reverse the shift direction to be more intuitive\n s *= -1\n\n #shift arry with slicing\n shifted_array = array[s:] + array[:s]\n\n return shifted_array","def ft(im, shift_after=True, shift_before=True, ret='complex', axes=None, s=None, norm='ortho'):\n # create axes list\n axes = __checkAxes__(axes, im)\n\n if shift_before == True:\n im = ifftshift(im, axes=axes) # mid to corner\n if (not s is None) and (not axes is None) and len(axes) < len(s):\n s = s[-len(axes):] # it will automatically deal with the other axes\n im = fftn(im, axes=axes, s=s, norm=norm)\n if shift_after == True:\n im = fftshift(im, axes=axes) # corner freq to mid freq\n return __ret_val__(im, ret)","def shift_f(f, tau):\n n, T = f.shape\n fn = zeros((n, T))\n fn[:, 0:(T - 1)] = roll(f[:, 0:(T - 1)], tau, axis=1)\n fn[:, T - 1] = fn[:, 0]\n\n return (fn)","def __call__(self, t, u):\n \n # Rename to skip the self.\n f_rate = self.f; w_hat = self.w_hat;\n Ux = self.Ux; nx = self.nx;\n\n _u = u.reshape((nx,nx)) # To Matrix\n\n # FFT of firing rate at u\n f_u = f_rate(_u)\n f_hat = fft2(f_u) # 2D fft of matrix\n\n p_prod = np.real( ifft2(f_hat*w_hat) ) # Pointwise product of matrices\n shift = ((2*Ux/nx)**2)*ifftshift(p_prod) # Shift result\n F = - _u + shift\n return F.flatten()","def tomography_filter(arr, axis=2, filter_type='hamming', backend='autograd'):\n func = getattr(scipy.signal.windows, filter_type)\n filter = func(arr.shape[axis])\n if axis != len(arr.shape) - 1:\n arr = swap_axes(arr, [axis, len(arr.shape) - 1])\n if backend == 'pytorch':\n args = {'device': arr.device}\n else:\n args = {}\n arr_r, arr_i = fft(arr, zeros_like(arr, requires_grad=False, **args))\n arr_r = arr_r * filter\n arr_i = arr_i * filter\n arr, _ = ifft(arr_r, arr_i)\n if axis != len(arr.shape) - 1:\n arr = swap_axes(arr, [axis, len(arr.shape) - 1])\n return arr","def fourier_transform2d(self):\n\n zerofill = np.zeros(1024 * np.array([1,1])) #so it will always be square\n zerofill[:len(self.windowed), :len(self.windowed)] = self.windowed\n transform = np.fft.fft2(zerofill)\n transform = np.fft.fftshift(transform) # shift center to zero\n transformed = np.absolute(transform)\n tmax = transformed.max()\n zdata = (transformed)/(tmax) # normalize to maximum value\n\n return zdata","def modified_dft(arr, fs, nfft, window, axis, detrend, scaling):\n\n nsamples = arr.shape[axis]\n\n if nfft < nsamples:\n # crop arr before detrending & windowing; see rfft crop\n arr = slice_along_axis(arr, 0, nfft, axis=-1)\n\n # detrend the array\n arr = sps.detrend(arr, axis=axis, type=detrend)\n\n # fetch and apply window\n coeffs = sps.get_window(window, arr.shape[axis])\n arr = multiply_along_axis(arr, coeffs, axis=axis)\n\n # compute real DFT. Zeropad for nfft > nsamples is automatic\n # rfft uses 'backward' norm default which is no norm on rfft\n arr = np.fft.rfft(arr, nfft, axis=axis)\n freqs = np.fft.rfftfreq(nfft, d=1/fs)\n\n # scale using weighted mean of window values\n if scaling == 'spectrum':\n norm = 1 / np.sum(coeffs)**2\n\n elif scaling == 'density':\n #process loss Shiavi Eqn 7.54\n norm = 1 / (fs * np.sum(coeffs**2))\n \n else:\n msg = 'Unknown scaling: {}'\n raise ValueError(msg.format(scaling))\n \n # before conjugate multiplication unlike scipy\n # see _spectral_helper lines 1808 an 1842.\n arr *= np.sqrt(norm)\n\n return freqs, arr","def convolution_as_maultiplication(I, F, print_ir=False):\n # number of columns and rows of the input \n I_row_num, I_col_num = I.shape \n\n # number of columns and rows of the filter\n F_row_num, F_col_num = F.shape\n\n # calculate the output dimensions\n output_row_num = I_row_num + F_row_num - 1\n output_col_num = I_col_num + F_col_num - 1\n if print_ir: print('output dimension:', output_row_num, output_col_num)\n\n # zero pad the filter\n F_zero_padded = np.pad(F, ((output_row_num - F_row_num, 0),\n (0, output_col_num - F_col_num)),\n 'constant', constant_values=0)\n if print_ir: print('F_zero_padded: ', F_zero_padded)\n\n # use each row of the zero-padded F to creat a toeplitz matrix. \n # Number of columns in this matrices are same as numbe of columns of input signal\n toeplitz_list = []\n for i in range(F_zero_padded.shape[0]-1, -1, -1): # iterate from last row to the first row\n c = F_zero_padded[i, :] # i th row of the F \n r = np.r_[c[0], np.zeros(I_col_num-1)] # first row for the toeplitz fuction should be defined otherwise\n # the result is wrong\n toeplitz_m = toeplitz(c,r) # this function is in scipy.linalg library\n toeplitz_list.append(toeplitz_m)\n if print_ir: print('F '+ str(i)+'\\n', toeplitz_m)\n\n # doubly blocked toeplitz indices: \n # this matrix defines which toeplitz matrix from toeplitz_list goes to which part of the doubly blocked\n c = range(1, F_zero_padded.shape[0]+1)\n r = np.r_[c[0], np.zeros(I_row_num-1, dtype=int)]\n doubly_indices = toeplitz(c, r)\n if print_ir: print('doubly indices \\n', doubly_indices)\n\n ## creat doubly blocked matrix with zero values\n toeplitz_shape = toeplitz_list[0].shape # shape of one toeplitz matrix\n h = toeplitz_shape[0]*doubly_indices.shape[0]\n w = toeplitz_shape[1]*doubly_indices.shape[1]\n doubly_blocked_shape = [h, w]\n doubly_blocked = np.zeros(doubly_blocked_shape)\n\n # tile toeplitz matrices for each row in the doubly blocked matrix\n b_h, b_w = toeplitz_shape # hight and withs of each block\n for i in range(doubly_indices.shape[0]):\n for j in range(doubly_indices.shape[1]):\n start_i = i * b_h\n start_j = j * b_w\n end_i = start_i + b_h\n end_j = start_j + b_w\n doubly_blocked[start_i: end_i, start_j:end_j] = toeplitz_list[doubly_indices[i,j]-1]\n\n if print_ir: print('doubly_blocked: ', doubly_blocked)\n\n # convert I to a vector\n vectorized_I = matrix_to_vector(I)\n if print_ir: print('vectorized_I: ', vectorized_I)\n \n # get result of the convolution by matrix mupltiplication\n result_vector = np.matmul(doubly_blocked, vectorized_I)\n if print_ir: print('result_vector: ', result_vector)\n\n # reshape the raw rsult to desired matrix form\n out_shape = [output_row_num, output_col_num]\n output = vector_to_matrix(result_vector, out_shape)\n if print_ir: print('Result of implemented method: \\n', output)\n \n return output","def FIR(self):\r\n\r\n #Make a list to put the outputs in:\r\n h = [0] * self._len_h\r\n\r\n for i in range(self._len_h):\r\n #XXX Check that the offset makes sense (there can't be an event\r\n #happening within one offset duration of the beginning of the\r\n #time-series:\r\n\r\n #Get the design matrix (roll by the offset, in order to get the\r\n #right thing):\r\n\r\n roll_events = np.roll(self.events[i], self.offset)\r\n design = tsu.fir_design_matrix(roll_events, self.len_et)\r\n #Compute the fir estimate, in linear form:\r\n this_h = tsa.fir(self.data[i], design)\r\n #Reshape the linear fir estimate into a event_types*hrf_len array\r\n u = np.unique(self.events[i])\r\n event_types = u[np.unique(self.events[i]) != 0]\r\n h[i] = np.reshape(this_h, (event_types.shape[0], self.len_et))\r\n\r\n h = np.array(h).squeeze()\r\n\r\n return ts.TimeSeries(data=h,\r\n sampling_rate=self.sampling_rate,\r\n t0=self.offset * self.sampling_interval,\r\n time_unit=self.time_unit)","def stft(x, fs, framesz, hop):\n framesamp = int(framesz*fs)\n hopsamp = int(hop*fs)\n w = scipy.hamming(framesamp)\n X = scipy.array([scipy.fft(w*x[i:i+framesamp],256)\n for i in range(0, len(x)-framesamp, hopsamp)])\n X=X[:,0:128]\n return X","def ifft2(data):\n if not data.shape[-1] == 2:\n raise ValueError(\"Tensor does not have separate complex dim.\")\n\n data = ifftshift(data, dim=[-3, -2])\n data = torch.view_as_real(\n torch.fft.ifftn( # type: ignore\n torch.view_as_complex(data), dim=(-2, -1), norm=\"ortho\"\n )\n )\n data = fftshift(data, dim=[-3, -2])\n\n return data","def filtering(self):\n from numpy import fft\n import numpy as np\n\n _image_dft = fft.fft2(self.image)\n _image_dft = fft.fftshift(_image_dft)\n # dft = DFT.DFT()\n # plt.figure(1) \n # plt.imshow(self.image)\n # plt.figure(2)\n # plt.imshow(20*np.log10(abs(_image_dft))) \n # print(_image_dft)\n # print(abs(_image_dft))\n # plt.show()\n filter = self.filter(self.image.shape, self.cutoff, self.order) \\\n if self.filter_name.startswith('butterworth') \\\n else self.filter(self.image.shape, self.cutoff)\n \n _image_dft_filtered = _image_dft * filter\n _image_filtered = abs(fft.ifft2(_image_dft_filtered))\n \n return [ self.post_process_image(_image_filtered), \\\n self.post_process_image(20*np.log10(abs(_image_dft)+.00001)), \\\n self.post_process_image(20*np.log10(abs(_image_dft_filtered)+.00001)) ]","def sf01(arr):\n s = arr.shape\n return arr.swapaxes(0, 1).reshape(s[0] * s[1], *s[2:])","def sf01(arr):\n s = arr.shape\n return arr.swapaxes(0, 1).reshape(s[0] * s[1], *s[2:])","def sf01(arr):\n s = arr.shape\n return arr.swapaxes(0, 1).reshape(s[0] * s[1], *s[2:])","def sf01(arr):\n s = arr.shape\n return arr.swapaxes(0, 1).reshape(s[0] * s[1], *s[2:])","def FT(x, ufunc=np.real, real=False):\n assert isinstance(x, np.ndarray)\n if len(x.shape) == 1:\n if real:\n F = np.fft.rfft(x)\n else:\n F = np.fft.fft(x)\n elif len(x.shape) == 2:\n if real:\n F = np.fft.rfft2(x)\n else:\n F = np.fft.fft2(x)\n else:\n raise TypeError(\"The array should be 1D or 2D\")\n return ufunc(np.fft.fftshift(F))","def stft(x, fs, framesz, hop):\n framesamp = int(framesz*fs)\n hopsamp = int(hop*fs)\n w = scipy.hamming(framesamp)\n X = scipy.array([scipy.fft(w*x[i:i+framesamp]) \n for i in range(0, len(x)-framesamp, hopsamp)])\n return X","def perform_fft(self, use_flat=True):\n for scan_dir in range(len(self.ff)):\n if use_flat and 'data_flat' in self.ff[scan_dir].__dict__.keys():\n self.ff[scan_dir].data_fft = np.fft.fftshift(np.fft.fft2(self.ff[scan_dir].data_flat))\n else:\n self.ff[scan_dir].data_fft = np.fft.fftshift(np.fft.fft2(self.ff[scan_dir].data))","def DFTpower2(time, signal, freqs):\n \n powerSpectrum = np.zeros(len(freqs))\n\n for i, freq in enumerate(freqs):\n arg = 2.0 * np.pi * freq * time\n powerSpectrum[i] = np.sum(signal * np.cos(arg))**2 + np.sum(signal * np.sin(arg))**2\n\n powerSpectrum = powerSpectrum * 4.0 / len(time)**2\n return(powerSpectrum)","def rotate(array, shift):\n shift %= len(array)\n return array[-shift:] + array[:-shift]","def T_fourier(shape, T, is_fft_shifted = True):\n # make i, j, k for each pixel\n i = np.fft.fftfreq(shape[0]) \n j = np.fft.fftfreq(shape[1])\n k = np.fft.fftfreq(shape[2])\n i, j, k = np.meshgrid(i, j, k, indexing='ij')\n\n if is_fft_shifted is False :\n i = np.fft.ifftshift(i)\n j = np.fft.ifftshift(j)\n k = np.fft.ifftshift(k)\n\n phase_ramp = np.exp(- 2J * np.pi * (i * T[0] + j * T[1] + k * T[2]))\n return phase_ramp","def _get_fourier_filter(self):\n size = max(64, int(2 ** np.ceil(np.log2(2 * self.m[-1].item()))))\n\n pi = torch.acos(torch.zeros(1)).item() * 2.0\n n = torch.cat(\n [\n torch.arange(1, size // 2 + 1, 2, device=self.n.device),\n torch.arange(size // 2 - 1, 0, -2, device=self.n.device),\n ]\n )\n f = torch.zeros(size, device=self.n.device)\n f[0] = 0.25\n if self.flat:\n f[1::2] = -1 / (pi * n).pow(2)\n else:\n f[1::2] = -self.s_detect.abs().pow(2) / (\n pi\n * (self.d_source + self._d_detect())\n * torch.sin(\n n\n * self.s_detect.abs()\n / (self.d_source + self._d_detect())\n )\n ).pow(2)\n f = torch.stack(\n [f, torch.zeros(f.shape, device=self.n.device)], dim=-1\n )\n f = fftshift(f, dim=(-2,))\n\n filt = fft1(f)[..., 0]\n\n if self.filter_type == \"hamming\":\n # hamming filter\n fac = torch.tensor(\n np.hamming(size).astype(np.float32), device=f.device\n )\n elif self.filter_type == \"hann\":\n # hann filter\n fac = torch.tensor(\n np.hanning(size).astype(np.float32), device=f.device\n )\n elif self.filter_type == \"cosine\":\n # cosine filter\n fac = torch.sin(\n torch.linspace(0, pi, size + 1, device=f.device)[:-1]\n )\n else:\n # ramp / ram-lak filter\n fac = 1.0\n\n return fac * filt","def stft(x, fft_size, hopsamp):\n w = np.hamming(fft_size)\n return np.array([np.fft.rfft(w*x[i:i+fft_size]) \n for i in range(0, len(x)-fft_size, hopsamp)])","def highPassFilter(img, window=30):\n gray = grayscale(img)\n\tf = np.fft.fft2(gray)\n\tfshift = np.fft.fftshift(f)\n\trows, cols = gray.shape\n\tcrow, ccol = rows/2, cols/2\n\tfshift[crow-window:crow+window, ccol-window:ccol+window] = 0\n\tf_ishift = np.fft.ifftshift(fshift)\n\timg_back = np.fft.ifft2(f_ishift)\n\timg_back = np.abs(img_back)\n\treturn img_back","def signalTransform(dat):\n return numpy.convolve(dat, slopWindow, \"same\")","def shift(options):\n signal = audio.read(options.ipath)\n result = op.shift(signal.data, int(options.factor))\n audio.write(options.opath, result, signal.rate, sampwidth=1)\n if options.plot:\n plotter.plot(**{'Input: '+options.ipath: signal.data,\n 'Output: '+options.opath: result})","def iquadshift(a):\r\n if len(a.shape) == 1 :\r\n b = np.roll(a, +(a.shape[-1]/2-1), -1)\r\n else :\r\n b = np.roll(a, +(a.shape[-2]/2-1), -2)\r\n b = np.roll(b, +(b.shape[-1]/2-1), -1)\r\n return b","def _morletT(x, f0s, w=3, fs=1000, s=1):\n if w <= 0:\n raise ValueError(\n 'Number of cycles in a filter must be a positive number.')\n\n T = len(x)\n F = len(f0s)\n mwt = np.zeros([F, T], dtype=complex)\n for f in range(F):\n mwt[f] = morletf(x, f0s[f], fs=fs, w=w, s=s)\n\n return mwt","def filter(s, F, dt, nf):\n S = np.fft.fft(s, n=nf)\n S_f = np.multiply(S, F)*dt\n s_f = np.real(np.fft.ifft(S_f, n=nf))\n return s_f","def shift_augmentation():\n shift = np.random.randint(-200, 201, size=2)\n return lambda image: shift_with_extension(image, shift)","def ShiftFrame(Frame, PixShift):\n \n import numpy as np\n \n F, R, C = Frame.shape\n \n if F > 1:\n msg = f\"'Frame' must be a 2D frame with shape (1, R, C) but has shape\"\\\n + f\" ({F}, {R}, {C}).\"\n \n raise Exception(msg)\n \n # Initialise ShiftedFrame:\n ShiftedFrame = np.zeros((1, R, C), dtype='uint')\n #ShiftedFrame = np.empty_like(Frame, dtype='uint') # this creates 42,932\n # unique values for some reason!\n \n #unique = UniqueItems(Nda=Frame, NonZero=False)\n #print(f'\\n---> There are {len(unique)} unique items in Frame')\n #unique = UniqueItems(Nda=ShiftedFrame, NonZero=False)\n #print(f'\\n---> There are {len(unique)} unique items in the initialised',\n # f'ShiftedFrame: {unique[:11]}...')\n \n di, dj, dk = PixShift\n \n ##ShiftedFrame[0, dj:, di:] = Frame[0, :-(1+dj), :-(1+di)]\n ##ShiftedFrame[0, :-(1+dj), :-(1+di)] = Frame[0, dj:, di:]\n #ShiftedFrame[0, :R-dj, :C-di] = Frame[0, dj:, di:]\n \n if di > 0 and dj > 0:\n ShiftedFrame[0, dj:, di:] = Frame[0, :-dj, :-di]\n \n elif di < 0 and dj < 0:\n ShiftedFrame[0, :dj, :di] = Frame[0, -dj:, -di:]\n \n elif di > 0 and dj < 0:\n ShiftedFrame[0, :dj, di:] = Frame[0, -dj:, :-di]\n \n elif di < 0 and dj > 0:\n ShiftedFrame[0, dj:, :di] = Frame[0, :-dj, -di:]\n \n elif di == 0 and dj > 0:\n ShiftedFrame[0, dj:, :] = Frame[0, :-dj, :]\n \n elif di == 0 and dj < 0:\n ShiftedFrame[0, :dj, :] = Frame[0, -dj:, :]\n \n elif di > 0 and dj == 0:\n ShiftedFrame[0, :, di:] = Frame[0, :, :-di]\n \n elif di < 0 and dj == 0:\n ShiftedFrame[0, :, :di] = Frame[0, :, -di:]\n \n elif di == 0 and dj == 0:\n ShiftedFrame[0] = Frame[0]\n \n #unique = UniqueItems(Nda=ShiftedFrame, NonZero=False)\n #print(f'\\n---> There are {len(unique)} unique items in the ShiftedFrame',\n # 'after shifting.')\n \n return ShiftedFrame","def womyshift(hop):\n import matplotlib.pyplot as plt\n import logging\n from tmath.wombat.inputter import inputter\n from tmath.wombat.wshow import wshow\n plt.cla()\n plt.plot(hop[0].wave,hop[0].flux,drawstyle='steps-mid')\n plt.xlabel('Wavelength')\n plt.ylabel('Flux')\n plt.title(hop[0].obname)\n wshow()\n\n print('Routine to linearly shift flux scale\\n')\n\n shift=inputter('Enter flux shift: ','float',False)\n\n hop[0].flux=hop[0].flux+shift\n\n plt.plot(hop[0].wave,hop[0].flux,drawstyle='steps-mid')\n\n logging.debug('File {} flux scale shifted by {} A'.format\\\n (hop[0].obname,shift))\n\n #FIX header\n return hop","def resampz(x, m_type, shift=1):\n sx = np.array(x.shape)\n\n if m_type == 0 or m_type == 1:\n y = np.zeros((sx[0] + np.abs(shift * (sx[1] - 1)), sx[1]))\n\n if m_type == 0:\n shift1 = np.arange(0, sx[1]) * (- shift)\n else:\n shift1 = np.arange(0, sx[1]) * shift\n\n if shift1[-1] < 0:\n shift1 = shift1 - shift1[-1]\n\n for n in range(sx[1]):\n y[shift1[n] + np.arange(0, sx[0]), n] = x[:, n]\n\n # Remove extra rows\n start = 0\n finish = y.shape[0]\n\n while np.linalg.norm(y[start, :], 2) == 0:\n start += 1\n\n while np.linalg.norm(y[finish-1, :], 2) == 0:\n finish -= 1\n\n y = y[start:finish, :]\n\n elif m_type == 2 or m_type == 3:\n y = np.zeros((sx[0], sx[1] + np.abs(shift * (sx[0] - 1))))\n\n if m_type == 2:\n shift2 = np.arange(0, sx[0]) * (- shift)\n else:\n shift2 = np.arange(0, sx[0]) * shift\n\n if shift2[-1] < 0:\n shift2 = shift2 - shift2[-1]\n\n for m in range(sx[0]):\n y[m, shift2[m] + np.arange(0, sx[1])] = x[m, :]\n\n # Remove extra rows\n start = 0\n finish = y.shape[1]\n\n while np.linalg.norm(y[:, start], 2) == 0:\n start += 1\n\n while np.linalg.norm(y[:, finish-1], 2) == 0:\n finish -= 1\n\n y = y[:, start:finish]\n\n else:\n print('Error: type not valid.')\n y = 0\n\n return y","def fft_phase_shift(self, im0):\n shape = im0.shape\n f0 = fft2(im0)\n f1 = fft2(self.initial_frame)\n ir = abs(ifft2((f0 * f1.conjugate()) / (abs(f0) * abs(f1))))\n t0, t1, c = np.unravel_index(np.argmax(ir), shape)\n if t0 > shape[0] // 2:\n t0 -= shape[0]\n if t1 > shape[1] // 2:\n t1 -= shape[1]\n return [t0, t1]","def calculate_shift(self, y, x, t, axis):\n return DeformationModel.calculate_shifts_from_coeffs(y, x, t,\n self.coeffs[axis])","def get_fft(self):\n\t\t# Get the \"ideal\" evenly spaced times\n\t\teven_times = numpy.linspace(self.buf[0][0], self.buf[-1][0], len(self.buf))\n\t\t\n\t\t# Interpolate the data to generate evenly temporally spaced samples\n\t\tinterpolated = numpy.interp(even_times, *zip(*self.buf))\n\t\t\n\t\t# Perform the FFT\n\t\tfft = numpy.fft.rfft(interpolated)\n\t\treturn zip(numpy.abs(fft), numpy.angle(fft))","def combine_components(self):\n\n # ensure is array with correct shape\n if self.separated_components_ft.shape[1] != 3:\n raise ValueError(\"Expected shifted_components_ft to have shape (nangles, 3, ny, nx), where components are\"\n \"O(f)*otf(f), O(f-fo)*otf(f), O(f+fo)*otf(f). But size of second dimension was not 3.\")\n\n # upsample image before shifting\n f_upsample = 2\n otf_us = tools.expand_fourier_sp(self.otf, mx=f_upsample, my=f_upsample, centered=True)\n\n # upsampled frequency data\n fx_us = tools.get_fft_frqs(f_upsample * self.nx, self.dx / f_upsample)\n dfx_us = fx_us[1] - fx_us[0]\n fy_us = tools.get_fft_frqs(f_upsample * self.ny, self.dx / f_upsample)\n dfy_us = fy_us[1] - fy_us[0]\n\n def ff_shift(f): return np.sqrt((self.fx[None, :] - f[0]) ** 2 + (self.fy[:, None] - f[1]) ** 2)\n def ff_shift_upsample(f): return np.sqrt((fx_us[None, :] - f[0]) ** 2 + (fy_us[:, None] - f[1]) ** 2)\n\n # wiener filter deconvolution to divide by H(k)\n # components_deconvolved_ft = np.zeros((self.nangles, 3, self.ny, self.nx), dtype=np.complex)\n components_deconvolved_ft = np.zeros((self.nangles, 3, self.ny * f_upsample, self.nx * f_upsample), dtype=np.complex)\n snr = np.zeros((self.nangles, 3, self.ny, self.nx))\n # shift to correct place in frq space\n components_shifted_ft = np.zeros((self.nangles, 3, self.ny * f_upsample, self.nx * f_upsample), dtype=np.complex)\n snr_shifted = np.zeros(components_shifted_ft.shape)\n # weight and average\n components_weighted = np.zeros(components_shifted_ft.shape, dtype=np.complex)\n weights = np.zeros(components_weighted.shape)\n\n # shift and filter components\n for ii in range(self.nangles):\n # loop over components, O(f)H(f), m*O(f - f_o)H(f), m*O(f + f_o)H(f)\n for jj, eps in enumerate([0, 1, -1]):\n params = list(self.power_spectrum_params[ii, jj, :-1]) + [0]\n snr[ii, jj] = power_spectrum_fn(params, ff_shift(self.frqs[ii] * eps), 1) / self.noise_power[ii, jj]\n\n # get shifted SNR\n otf_shifted, _, _ = tools.translate_pix(otf_us, eps*self.frqs[ii], dx=dfx_us, dy=dfy_us, mode='no-wrap')\n snr_shifted[ii, jj] = power_spectrum_fn(params, ff_shift_upsample((0, 0)), 1) / self.noise_power[ii, jj]\n\n # weights\n weights[ii, jj] = get_snr_weight(otf_shifted, snr_shifted[ii, jj])\n\n if True:\n # deconvolve, then shift\n\n # deconvolve\n deconv_temp, _ = \\\n wiener_deconvolution(self.separated_components_ft[ii, jj] / self.mod_depths[ii, jj], self.otf,\n snr[ii, jj])\n\n components_deconvolved_ft[ii, jj] = tools.expand_fourier_sp(deconv_temp, mx=f_upsample, my=f_upsample, centered=True)\n\n # shift and expand\n components_shifted_ft[ii, jj] = tools.translate_ft(components_deconvolved_ft[ii, jj], eps * self.frqs[ii], self.dx / f_upsample)\n else:\n # shift then deconvolve\n\n # shift and expand\n components_shifted_ft[ii, jj] = tools.translate_ft(\n tools.expand_fourier_sp(self.separated_components_ft[ii, jj], mx=f_upsample, my=f_upsample, centered=True),\n eps * self.frqs[ii], self.dc / f_upsample)\n\n # deconvolved\n components_shifted_ft[ii, jj], _ = \\\n wiener_deconvolution(components_shifted_ft[ii, jj] / self.mod_depths[ii, jj], otf_shifted,\n snr_shifted[ii, jj])\n\n # optionally remove frequency data around modulation frequency, to avoid artifacts\n if self.size_near_fo_to_remove != 0:\n to_remove = np.abs(ff_shift_upsample(-self.frqs[ii])) < self.size_near_fo_to_remove * np.linalg.norm(self.frqs[ii])\n components_shifted_ft[ii, jj][to_remove] = 0\n\n to_remove = np.abs(ff_shift_upsample(self.frqs[ii])) < self.size_near_fo_to_remove * np.linalg.norm(self.frqs[ii])\n components_shifted_ft[ii, jj][to_remove] = 0\n\n # correct for wrong global phases (on shifted components before weighting,\n # but then apply to weighted components)\n if self.global_phase_correction:\n self.phase_corrections = global_phase_correction(components_shifted_ft)\n else:\n self.phase_corrections = np.zeros(self.nangles)\n\n # combine components\n components_weighted = components_shifted_ft * weights\n for ii in range(self.nangles):\n components_weighted[ii, 1] = np.exp(1j * self.phase_corrections[ii]) * components_weighted[ii, 1]\n components_weighted[ii, 2] = np.exp(-1j * self.phase_corrections[ii]) * components_weighted[ii, 2]\n\n # final averaging\n weight_norm = np.sum(weights, axis=(0, 1)) + self.wiener_parameter\n sim_sr_ft = np.nansum(components_weighted, axis=(0, 1)) / weight_norm\n\n # Fourier transform back to get real-space reconstructed image\n apod = scipy.signal.windows.tukey(sim_sr_ft.shape[1], alpha=0.1)[None, :] * \\\n scipy.signal.windows.tukey(sim_sr_ft.shape[0], alpha=0.1)[:, None]\n\n sim_sr = fft.fftshift(fft.ifft2(fft.ifftshift(sim_sr_ft * apod))).real\n\n return sim_sr, sim_sr_ft, components_deconvolved_ft, components_shifted_ft,\\\n weights, weight_norm, snr, snr_shifted","def transform(self, X): # noqa: E501\n X = np.atleast_2d(X)\n\n if X.ndim > 3:\n raise ValueError(\n \"Array must be of at max 3 dimensions instead \"\n \"got %s dimensional matrix\" % (X.ndim)\n )\n\n shape = X.shape\n X = X.reshape(-1, shape[-1])\n X = filter_data(\n X,\n self.sfreq,\n self.l_freq,\n self.h_freq,\n filter_length=self.filter_length,\n l_trans_bandwidth=self.l_trans_bandwidth,\n h_trans_bandwidth=self.h_trans_bandwidth,\n n_jobs=self.n_jobs,\n method=self.method,\n iir_params=self.iir_params,\n copy=False,\n fir_window=self.fir_window,\n fir_design=self.fir_design,\n )\n return X.reshape(shape)","def fixed_point_multiply(data, multiplier, shift):\n return _make.fixed_point_multiply(data, multiplier, shift)","def pitch_shift(\n spectrogram: tf.Tensor,\n semitone_shift: float = 0.0,\n method: tf.image.ResizeMethod = tf.image.ResizeMethod.BILINEAR,\n) -> tf.Tensor:\n factor = 2 ** (semitone_shift / 12.0)\n T = tf.shape(spectrogram)[0]\n F = tf.shape(spectrogram)[1]\n F_ps = tf.cast(tf.cast(F, tf.float32) * factor, tf.int32)[0]\n ps_spec = tf.image.resize_images(\n spectrogram, [T, F_ps], method=method, align_corners=True\n )\n paddings = [[0, 0], [0, tf.maximum(0, F - F_ps)], [0, 0]]\n return tf.pad(ps_spec[:, :F, :], paddings, \"CONSTANT\")","def _width_shift_(self, x: np.array, m: np.array) -> (np.array, np.array):\n # get a random sign for the shifting direction\n sign = np.random.randint(0, 2)\n shift_pix = np.random.randint(0, self.shift)\n x = shift(x, [0, sign*shift_pix])\n m = shift(m, [0, sign*shift_pix, 0], mode='nearest')\n return x,m","def FourierSeries(input,N=None):\n \n N=len(input);\n\n w=2*cmath.pi/N;\n input=input[0:N];\n n=numpy.arange(0,N); \n r=cexp(-1j*w*n);\n\n output = [complex(0)] * N \n for k in range(N): \n r=input*cexp(-1j*w*n*k) \n output[k]=np.sum(r);\n \n \n return output;","def fwd_conv2d_fft(input_mat: np.ndarray,\n filter_mat: np.ndarray) -> np.ndarray:\n flipped_mat = filter_mat[::-1, ::-1]\n return fftconvolve(input_mat, flipped_mat, mode='full')","def one_hot_add(inputs, shift):\n inputs = torch.stack((inputs, torch.zeros_like(inputs)), dim = -1)\n shift = torch.stack((shift, torch.zeros_like(shift)), dim = -1)\n inputs_fft = torch.fft(inputs, 1) #ignore last and first dimension to do batched fft\n shift_fft = torch.fft(shift, 1)\n result_fft_real = inputs_fft[...,0]*shift_fft[...,0] - inputs_fft[...,1]*shift_fft[...,1]\n result_fft_imag = inputs_fft[...,0]*shift_fft[...,1] + inputs_fft[...,1]*shift_fft[...,0]\n result_fft = torch.stack((result_fft_real,result_fft_imag), dim = -1)\n return torch.ifft(result_fft, 1)[...,0], result_fft, inputs_fft, shift_fft #return only the real part","def matrixMultiplication(imagei, filterf):\n n1, n2 = imagei.shape\n m1, m2 = filterf.shape\n\n p = n1 - m1 + 1 #column num\n q = n2 - m2 + 1 #row num\n output = np.zeros((p, q))\n\n for i in range(p):\n for j in range(q):\n img = imagei[i:i + m1, j:j + m2]\n out = np.multiply(img, filterf)\n output[i][j] = np.sum(out)\n\n return output"],"string":"[\n \"def fftshift(X):\\r\\n # return scipy.fftpack.fftshift(X)\\r\\n return np.fft.fftshift(X)\",\n \"def ifftshift(a, axes=None):\\n return image.image(np.fft.ifftshift(a, axes), pixelsize = image.getPixelsize(a))\",\n \"def fftshift(a, axes=None):\\n return image.image(np.fft.fftshift(a, axes), pixelsize = image.getPixelsize(a))\",\n \"def roll(arrayin, shift = (0, 0), silent = True):\\r\\n arrayout = arrayin.copy()\\r\\n # if shift is integer valued then use np.roll\\r\\n if (type(shift[0]) == int) or (type(shift[0]) == np.int) or (type(shift[0]) == np.int32) or (type(shift[0]) == np.int64):\\r\\n if shift[-1] != 0 :\\r\\n if silent == False :\\r\\n print 'arrayout = np.roll(arrayout, shift[-1], -1)'\\r\\n arrayout = np.roll(arrayout, shift[-1], -1)\\r\\n # if shift is 1d then don't roll the other dim (if it even exists)\\r\\n if len(arrayout.shape) >= 2 :\\r\\n if shift[-2] != 0 :\\r\\n if silent == False :\\r\\n print 'arrayout = np.roll(arrayout, shift[-2], -2)'\\r\\n arrayout = np.roll(arrayout, shift[-2], -2)\\r\\n # if shift is float valued then use the Fourier shift theorem\\r\\n elif (type(shift[0]) == float) or (type(shift[0]) == np.float32) or (type(shift[0]) == np.float64):\\r\\n # if shift is 1d\\r\\n if len(shift) == 1 :\\r\\n if silent == False :\\r\\n print 'arrayout = fftn_1d(arrayout)'\\r\\n print 'arrayout = arrayout * phase_ramp(arrayout.shape, shift, origin = (0, 0))'\\r\\n print 'arrayout = ifftn_1d(arrayout)'\\r\\n arrayout = fftn_1d(arrayout)\\r\\n arrayout = arrayout * phase_ramp(arrayout.shape, shift, origin = (0, 0))\\r\\n arrayout = ifftn_1d(arrayout)\\r\\n elif len(shift) == 2 :\\r\\n if silent == False :\\r\\n print 'arrayout = fftn(arrayout)'\\r\\n print 'arrayout = arrayout * phase_ramp(arrayout.shape, shift, origin = (0, 0))'\\r\\n print 'arrayout = ifftn(arrayout)'\\r\\n arrayout = fftn(arrayout)\\r\\n arrayout = arrayout * phase_ramp(arrayout.shape, shift, origin = (0, 0))\\r\\n arrayout = ifftn(arrayout)\\r\\n return arrayout\",\n \"def array_shift(arr, shift):\\r\\n return pd.DataFrame(arr).shift(shift).values\",\n \"def fftshift(x, dim=None):\\n if dim is None:\\n dim = tuple(range(x.dim()))\\n shift = [dim // 2 for dim in x.shape]\\n elif isinstance(dim, int):\\n shift = x.shape[dim] // 2\\n else:\\n shift = [x.shape[i] // 2 for i in dim]\\n return roll(x, shift, dim)\",\n \"def fftshift(x, dim=None):\\n if dim is None:\\n dim = tuple(range(x.dim()))\\n shift = [dim // 2 for dim in x.shape]\\n elif isinstance(dim, int):\\n shift = x.shape[dim] // 2\\n else:\\n shift = [x.shape[i] // 2 for i in dim]\\n return roll(x, shift, dim)\",\n \"def fftshift(x, dim=None):\\n if dim is None:\\n dim = tuple(range(x.dim()))\\n shift = [dim // 2 for dim in x.shape]\\n elif isinstance(dim, int):\\n shift = x.shape[dim] // 2\\n else:\\n shift = [x.shape[i] // 2 for i in dim]\\n return roll(x, shift, dim)\",\n \"def fftshift(x, dim=None):\\n if dim is None:\\n dim = tuple(range(x.dim()))\\n shift = [xdim // 2 for xdim in x.shape]\\n elif isinstance(dim, int):\\n shift = x.shape[dim] // 2\\n else:\\n shift = [x.shape[i] // 2 for i in dim]\\n return roll(x, shift, dim)\",\n \"def SHIFTFT(X,D):\\n N=len(X)\\n for k in range(N):\\n X[k]=np.exp(-1j*2*np.pi*k*D/N)*X[k]\\n return X\",\n \"def irfft2(a, shift_in=False, shift_out=False):\\n s = a.shape\\n if len(s) != 2:\\n raise GalSimValueError(\\\"Input array must be 2D.\\\",s)\\n M,No2 = s\\n No2 -= 1 # s is (M,No2+1)\\n Mo2 = M // 2\\n\\n if M != Mo2*2:\\n raise GalSimValueError(\\\"Input array must have even sizes.\\\",s)\\n\\n a = a.astype(np.complex128, copy=False)\\n kim = ImageCD(a, xmin = 0, ymin = -Mo2)\\n xim = ImageD(BoundsI(-No2,No2+1,-Mo2,Mo2-1))\\n with convert_cpp_errors():\\n _galsim.irfft(kim._image, xim._image, shift_in, shift_out)\\n xim = xim.subImage(BoundsI(-No2,No2-1,-Mo2,Mo2-1))\\n return xim.array\",\n \"def fft2(a, shift_in=False, shift_out=False):\\n s = a.shape\\n if len(s) != 2:\\n raise GalSimValueError(\\\"Input array must be 2D.\\\",s)\\n M, N = s\\n Mo2 = M // 2\\n No2 = N // 2\\n\\n if M != Mo2*2 or N != No2*2:\\n raise GalSimValueError(\\\"Input array must have even sizes.\\\",s)\\n\\n if a.dtype.kind == 'c':\\n a = a.astype(np.complex128, copy=False)\\n xim = ImageCD(a, xmin = -No2, ymin = -Mo2)\\n kim = ImageCD(BoundsI(-No2,No2-1,-Mo2,Mo2-1))\\n with convert_cpp_errors():\\n _galsim.cfft(xim._image, kim._image, False, shift_in, shift_out)\\n kar = kim.array\\n else:\\n a = a.astype(np.float64, copy=False)\\n xim = ImageD(a, xmin = -No2, ymin = -Mo2)\\n\\n # This works, but it's a bit slower.\\n #kim = ImageCD(BoundsI(-No2,No2-1,-Mo2,Mo2-1))\\n #_galsim.cfft(xim._image, kim._image, False, shift_in, shift_out)\\n #kar = kim.array\\n\\n # Faster to start with rfft2 version\\n rkim = ImageCD(BoundsI(0,No2,-Mo2,Mo2-1))\\n with convert_cpp_errors():\\n _galsim.rfft(xim._image, rkim._image, shift_in, shift_out)\\n # This only returns kx >= 0. Fill out the full image.\\n kar = np.empty( (M,N), dtype=np.complex128)\\n rkar = rkim.array\\n if shift_out:\\n kar[:,No2:N] = rkar[:,0:No2]\\n kar[0,0:No2] = rkar[0,No2:0:-1].conjugate()\\n kar[1:Mo2,0:No2] = rkar[M-1:Mo2:-1,No2:0:-1].conjugate()\\n kar[Mo2:M,0:No2] = rkar[Mo2:0:-1,No2:0:-1].conjugate()\\n else:\\n kar[:,0:No2] = rkar[:,0:No2]\\n kar[0,No2:N] = rkar[0,No2:0:-1].conjugate()\\n kar[1:M,No2:N] = rkar[M-1:0:-1,No2:0:-1].conjugate()\\n return kar\",\n \"def ifft2(a, shift_in=False, shift_out=False):\\n s = a.shape\\n if len(s) != 2:\\n raise GalSimValueError(\\\"Input array must be 2D.\\\",s)\\n M,N = s\\n Mo2 = M // 2\\n No2 = N // 2\\n\\n if M != Mo2*2 or N != No2*2:\\n raise GalSimValueError(\\\"Input array must have even sizes.\\\",s)\\n\\n if a.dtype.kind == 'c':\\n a = a.astype(np.complex128, copy=False)\\n kim = ImageCD(a, xmin = -No2, ymin = -Mo2)\\n else:\\n a = a.astype(np.float64, copy=False)\\n kim = ImageD(a, xmin = -No2, ymin = -Mo2)\\n xim = ImageCD(BoundsI(-No2,No2-1,-Mo2,Mo2-1))\\n with convert_cpp_errors():\\n _galsim.cfft(kim._image, xim._image, True, shift_in, shift_out)\\n return xim.array\",\n \"def process( fids, ndim=2 ):\\n\\timg = np.empty_like( fids )\\n\\tax = -1*(np.array( range(ndim) )+1)\\n\\t\\n\\timg = np.fft.fftshift( np.fft.fftn( fids, axes=ax, ).astype( np.complex64), axes=ax )\\n\\t\\n\\treturn np.squeeze(img)\",\n \"def fftshift(x, additional_shift=None, axes=None):\\n tmp = np.asarray(x)\\n ndim = len(tmp.shape)\\n if axes is None:\\n axes = list(range(ndim))\\n elif isinstance(axes, integer_types):\\n axes = (axes,)\\n\\n # If no additional shift is supplied, reproduce `numpy.fft.fftshift` result\\n if additional_shift is None:\\n additional_shift = [0, 0]\\n\\n y = tmp\\n for k, extra_shift in zip(axes, additional_shift):\\n n = tmp.shape[k]\\n if (n+1)//2 - extra_shift < n:\\n p2 = (n+1)//2 - extra_shift\\n else:\\n p2 = abs(extra_shift) - (n+1)//2\\n mylist = np.concatenate((np.arange(p2, n), np.arange(0, p2)))\\n y = np.take(y, mylist, k)\\n return y\",\n \"def shift_spect_array_cpu( field_array, shift_factor, n_move ):\\n Nz, Nr = field_array.shape\\n\\n # Loop over the 2D array (in parallel over z if threading is enabled)\\n for iz in prange( Nz ):\\n power_shift = 1. + 0.j\\n # Calculate the shift factor (raising to the power n_move ;\\n # for negative n_move, we take the complex conjugate, since\\n # shift_factor is of the form e^{i k dz})\\n for i in range( abs(n_move) ):\\n power_shift *= shift_factor[iz]\\n if n_move < 0:\\n power_shift = power_shift.conjugate()\\n # Shift the fields\\n for ir in range( Nr ):\\n field_array[iz, ir] *= power_shift\",\n \"def ifftshift(x, dim=None):\\n if dim is None:\\n dim = tuple(range(x.dim()))\\n shift = [(dim + 1) // 2 for dim in x.shape]\\n elif isinstance(dim, int):\\n shift = (x.shape[dim] + 1) // 2\\n else:\\n shift = [(x.shape[i] + 1) // 2 for i in dim]\\n return roll(x, shift, dim)\",\n \"def ifftshift(x, dim=None):\\n if dim is None:\\n dim = tuple(range(x.dim()))\\n shift = [(dim + 1) // 2 for dim in x.shape]\\n elif isinstance(dim, int):\\n shift = (x.shape[dim] + 1) // 2\\n else:\\n shift = [(x.shape[i] + 1) // 2 for i in dim]\\n return roll(x, shift, dim)\",\n \"def ifftshift(x, dim=None):\\n if dim is None:\\n dim = tuple(range(x.dim()))\\n shift = [(dim + 1) // 2 for dim in x.shape]\\n elif isinstance(dim, int):\\n shift = (x.shape[dim] + 1) // 2\\n else:\\n shift = [(x.shape[i] + 1) // 2 for i in dim]\\n return roll(x, shift, dim)\",\n \"def fft_shift(fft_mat):\\n if type(fft_mat) is np.ndarray:\\n rs = np.ceil(fft_mat.shape[0]/2).astype('int')\\n cs = np.ceil(fft_mat.shape[1]/2).astype('int')\\n quad1 = fft_mat[:rs, :cs]\\n quad2 = fft_mat[:rs, cs:]\\n quad3 = fft_mat[rs:, cs:]\\n quad4 = fft_mat[rs:, :cs]\\n centered_t = np.concatenate((quad4, quad1), axis=0)\\n centered_b = np.concatenate((quad3, quad2), axis=0)\\n centered = np.concatenate((centered_b, centered_t), axis=1)\\n # Thus centered is formed by shifting the entries of fft_mat\\n # up/left by [rs, cs] indices, or equivalently down/right by\\n # (fft_mat.shape - [rs, cs]) indices, with edges wrapping. \\n return centered\\n else:\\n print('input to fft_shift() should be a matrix')\\n return\",\n \"def fftshift(x, dim=-1):\\n dim = fix_dim(dim, x.dim())\\n return torch.roll(x, x.shape[dim] // 2, dim)\",\n \"def ifftshift(x, dim=None):\\n if dim is None:\\n dim = tuple(range(x.dim()))\\n shift = [(xdim + 1) // 2 for xdim in x.shape]\\n elif isinstance(dim, int):\\n shift = (x.shape[dim] + 1) // 2\\n else:\\n shift = [(x.shape[i] + 1) // 2 for i in dim]\\n return roll(x, shift, dim)\",\n \"def _irfft2d(f_x) :\",\n \"def rfft2(a, shift_in=False, shift_out=False):\\n s = a.shape\\n if len(s) != 2:\\n raise GalSimValueError(\\\"Input array must be 2D.\\\",s)\\n M,N = s\\n Mo2 = M // 2\\n No2 = N // 2\\n\\n if M != Mo2*2 or N != No2*2:\\n raise GalSimValueError(\\\"Input array must have even sizes.\\\",s)\\n\\n a = a.astype(np.float64, copy=False)\\n xim = ImageD(a, xmin = -No2, ymin = -Mo2)\\n kim = ImageCD(BoundsI(0,No2,-Mo2,Mo2-1))\\n with convert_cpp_errors():\\n _galsim.rfft(xim._image, kim._image, shift_in, shift_out)\\n return kim.array\",\n \"def fft2(data):\\n assert data.size(-1) == 2\\n data = ifftshift(data, dim=(-3, -2))\\n data = torch.fft(data, 2, normalized=False)\\n data = fftshift(data, dim=(-3, -2))\\n return data\",\n \"def fft2(data):\\n assert data.size(-1) == 2\\n data = ifftshift(data, dim=(-3, -2))\\n data = torch.fft(data, 2, normalized=True)\\n data = fftshift(data, dim=(-3, -2))\\n return data\",\n \"def _imfilter(x_data, f_data):\\n return pipe(f_data, ifftshift, fftn, lambda x: x * fftn(x_data), ifftn).real\",\n \"def forward_fft(self, array_in):\\r\\n # Find side length, as real array may or may not be doubled for\\r\\n # aliasing control\\r\\n side = array_in.shape[0]\\r\\n div_side = 1.0/side**2\\r\\n\\r\\n out = np.fft.fft2(self.sign_mat[0:side, 0:side]*array_in)*div_side\\r\\n return out\",\n \"def realign_image(arr, shift, angle=0):\\n # if both shifts are integers, do circular shift; otherwise perform Fourier shift.\\n if np.count_nonzero(np.abs(np.array(shift) - np.round(shift)) < 0.01) == 2:\\n temp = np.roll(arr, int(shift[0]), axis=0)\\n temp = np.roll(temp, int(shift[1]), axis=1)\\n temp = temp.astype('float32')\\n else:\\n temp = fourier_shift(np.fft.fftn(arr), shift)\\n temp = np.fft.ifftn(temp)\\n temp = np.abs(temp).astype('float32')\\n return temp\",\n \"def shift(image,shift_x,shift_y):\\n return np.roll(np.roll(image,shift_y,axis=0),shift_x,axis=1)\",\n \"def FD_shift(self, signal, FD_params):\\n #freq in MHz, delays in milliseconds\\n freq_array = signal._dat_freq\\n # define the reference frequency\\n ref_freq = make_quant(1000.0, 'MHz')\\n # calculate the delay added in for the parameters\\n time_delays = make_quant(np.zeros(len(freq_array)), 'ms') # will be in seconds\\n for ii in range(len(FD_params)):\\n time_delays += np.double(make_quant(FD_params[ii], 's').to('ms') * \\\\\\n np.power(np.log(freq_array/ref_freq),ii+1)) # will be in seconds\\n\\n if signal.delay==None:\\n signal._delay=time_delays\\n else:\\n signal._delay += time_delays\\n # get time shift based on the sample rate\\n shift_dt = (1/signal._samprate).to('ms')\\n shift_start = time.time()\\n # check if there are less than 20 frequency channels\\n if signal.Nchan <= 20:\\n div_fac = 1\\n else:\\n div_fac = 20\\n\\n for ii, freq in enumerate(freq_array):\\n signal._data[ii,:] = shift_t(signal._data[ii,:],\\n time_delays[ii].value,\\n dt=shift_dt.value)\\n if (ii+1) % int(signal.Nchan//div_fac) ==0:\\n shift_check = time.time()\\n percent = round((ii + 1)*100/signal.Nchan)\\n elapsed = shift_check-shift_start\\n chk_str = '\\\\r{0:2.0f}% shifted'.format(percent)\\n chk_str += ' in {0:4.3f} seconds.'.format(elapsed)\\n\\n try:\\n print(chk_str , end='', flush=True)\\n #This is the Python 2 version\\n #__future__ does not have 'flush' kwarg.\\n except:\\n print(chk_str , end='')\\n sys.stdout.flush()\\n\\n # May need to add tihs parameter to signal\\n signal._FDshifted = True\",\n \"def fft1(x):\\n assert x.size(-1) == 2\\n x = ifftshift(x, dim=(-2,))\\n x = torch.fft(x, 1, normalized=True)\\n x = fftshift(x, dim=(-2,))\\n return x\",\n \"def ifft2(data):\\n assert data.size(-1) == 2\\n data = ifftshift(data, dim=(-3, -2))\\n data = torch.ifft(data, 2, normalized=False)\\n data = fftshift(data, dim=(-3, -2))\\n return data\",\n \"def shift(self, da, dim, shift):\\n # TODO: generalize rolling function, allow custom shifts, handle\\n # boundary conditions, etc.\\n return da.roll(**{dim: shift})\",\n \"def ifft2(data):\\n assert data.size(-1) == 2\\n data = ifftshift(data, dim=(-3, -2))\\n data = torch.ifft(data, 2, normalized=True)\\n data = fftshift(data, dim=(-3, -2))\\n return data\",\n \"def shift_spect_array_gpu( field_array, shift_factor, n_move ):\\n # Get a 2D CUDA grid\\n iz, ir = cuda.grid(2)\\n\\n # Only access values that are actually in the array\\n if ir < field_array.shape[1] and iz < field_array.shape[0]:\\n power_shift = 1. + 0.j\\n # Calculate the shift factor (raising to the power n_move ;\\n # for negative n_move, we take the complex conjugate, since\\n # shift_factor is of the form e^{i k dz})\\n for i in range( abs(n_move) ):\\n power_shift *= shift_factor[iz]\\n if n_move < 0:\\n power_shift = power_shift.conjugate()\\n # Shift fields\\n field_array[iz, ir] *= power_shift\",\n \"def fft2(data):\\n if not data.shape[-1] == 2:\\n raise ValueError(\\\"Tensor does not have separate complex dim.\\\")\\n\\n data = ifftshift(data, dim=[-3, -2])\\n data = torch.view_as_real(\\n torch.fft.fftn( # type: ignore\\n torch.view_as_complex(data), dim=(-2, -1), norm=\\\"ortho\\\"\\n )\\n )\\n data = fftshift(data, dim=[-3, -2])\\n\\n return data\",\n \"def ifft1(x):\\n assert x.size(-1) == 2\\n x = ifftshift(x, dim=(-2,))\\n x = torch.ifft(x, 1, normalized=True)\\n x = fftshift(x, dim=(-2,))\\n return x\",\n \"def fft2(a, origin='centre'):\\r\\n if origin == 'centre' :\\r\\n b = quadshift(a)\\r\\n else :\\r\\n b = a.copy()\\r\\n if len(b.shape) == 1 :\\r\\n b = np.fft.fftpack.fft(b) \\r\\n elif len(b.shape) == 2 :\\r\\n b = np.fft.fftpack.fft2(b) \\r\\n elif len(b.shape) > 2 :\\r\\n b = fftn(b)\\r\\n if origin == 'centre' :\\r\\n b = iquadshift(b)\\r\\n return b\\r\\n if origin == 'centre' :\\r\\n b = iquadshift(b)\\r\\n return np.divide(b, np.sqrt(b.size))\",\n \"def sshift(s, N2, dt, shift):\\n S = np.fft.fft(s, n=N2)\\n\\n k = round(shift/dt) # discrete shift\\n # p = 2*np.pi*np.arange(0, N2, 1, dtype=float)*k/N2 # phase shift\\n p = 2*np.pi*np.arange(1, N2+1, 1, dtype=float)*k/N2 # phase shift\\n S = S*(np.cos(p) - 1j*np.sin(p))\\n\\n s_out = np.real(np.fft.ifft(S, N2))/np.cos(2*np.pi*k/N2) # correct scaling\\n return s_out\",\n \"def A(x,mask,S):\\n xS = x*S\\n y_full = fft.fftshift(fft.fft2(fft.ifftshift(xS)))\\n y = y_full[:,mask]\\n \\n return y\",\n \"def fft_viz(image: np.ndarray, shift: bool = True) -> None:\\n plt.imshow(img_fft(image, shift=shift), cmap='gray')\",\n \"def filtering(self):\\r\\n # 1 ###########################################################################################################\\r\\n fft_image = np.fft.fft2(self.image)\\r\\n # 2 ###########################################################################################################\\r\\n fft_shift_image = np.fft.fftshift(fft_image)\\r\\n\\r\\n ###\\r\\n mag_dft = np.log(np.abs(fft_shift_image))\\r\\n mag_dft = (255 * (mag_dft / np.max(mag_dft))).astype(dtype='uint8')\\r\\n ###\\r\\n\\r\\n # 3 ###########################################################################################################\\r\\n if self.filter_name == 'butterworth_l' or self.filter_name == 'butterworth_h':\\r\\n mask = self.filter(fft_shift_image.shape, self.cutoff, self.order)\\r\\n else:\\r\\n mask = self.filter(fft_shift_image.shape, self.cutoff)\\r\\n # 4 ###########################################################################################################\\r\\n # multiply the dft (fft shift image) by the mask\\r\\n filtered_image = fft_shift_image * mask\\r\\n\\r\\n ###\\r\\n mag_filtered_image = mag_dft * mask\\r\\n ###\\r\\n\\r\\n # 5 ###########################################################################################################\\r\\n inverse_fft_shift_image = np.fft.ifftshift(filtered_image)\\r\\n # 6 ###########################################################################################################\\r\\n inverse_fft_image = np.fft.ifft2(inverse_fft_shift_image)\\r\\n # 7 ###########################################################################################################\\r\\n mag_image = np.zeros(inverse_fft_image.shape, dtype=complex)\\r\\n for i in range(inverse_fft_image.shape[0]):\\r\\n for j in range(inverse_fft_image.shape[1]):\\r\\n if inverse_fft_image[i][j] < 0:\\r\\n mag_image[i][j] = -1 * inverse_fft_image[i][j]\\r\\n else:\\r\\n mag_image[i][j] = inverse_fft_image[i][j]\\r\\n # magnitude of inverse fft is complete\\r\\n # 8 ###########################################################################################################\\r\\n full_contrast_image = self.post_process_image(mag_image)\\r\\n\\r\\n return [mag_dft, mag_filtered_image, full_contrast_image]\",\n \"def filter(f,fcutoff=10.,w=10.0,dt=.001):\\r\\n\\r\\n tshift=float(w)/2.\\r\\n \\r\\n fpad=padzeros(f)\\r\\n Fpad=np.fft.fft(fpad)\\r\\n fc=fcutoff\\r\\n \\r\\n t=np.arange(start=-tshift,stop=tshift,step=dt)\\r\\n filt=np.zeros(len(fpad))\\r\\n fs=2*fc*np.sinc(2*t*fc)\\r\\n norm=sum(fs)\\r\\n filt[0:len(t)]=fs/norm\\r\\n Filt=np.fft.fft(filt)\\r\\n \\r\\n Filtfunc=Fpad*Filt\\r\\n filtfunc=np.fft.ifft(Filtfunc)\\r\\n filtfunc=filtfunc[len(t)/2:len(f)+len(t)/2]\\r\\n \\r\\n return filtfunc\",\n \"def ifft2(self, array):\\n self._ifft2.input_array[:] = array\\n return self._ifft2()\",\n \"def shift_axes(self, shift, axes=None):\\n axes = self._get_axes_numbers(axes)\\n shift = self._cook_args(shift, axes, [0.0, 0.0])\\n return Signal2D(self.values, index=self.index-shift[0], columns=self.columns-shift[1])\",\n \"def shift_by(self, xshift):\\n return Waveform(self.xvec + xshift, self.yvec, self.xtol, order=self.order, ext=self.ext)\",\n \"def fourier(array, zero_in_middle = False, first_correction = False, zeropad_to = None, window_function = \\\"none\\\", window_length = 0, flag_plot = False):\\n # shift time = 0 to first element\\n if zero_in_middle == True:\\n array = numpy.fft.ifftshift(array)\\n \\n \\n \\n # half the first element\\n if first_correction == True: \\n dim = len(numpy.shape(array))\\n if dim == 1:\\n array[0] /= 2\\n elif dim == 2:\\n array[0,:] /= 2\\n array[:,0] /= 2\\n elif dim > 2:\\n print(\\\"WARNING (fourier.fourier.py): correction of the first element is not done!\\\")\\n \\n \\n # window function\\n if window_function != \\\"none\\\": \\n array = window_functions(array, window_function, window_length, flag_plot = flag_plot)\\n \\n \\n \\n # the fft\\n array = numpy.fft.fft(array, n = zeropad_to)\\n \\n # move the array back if it was shifted\\n if zero_in_middle == True:\\n array = numpy.fft.fftshift(array)\\n \\n return array\",\n \"def shift(self, origin=None):\\n assert(not self.Fourier)\\n\\n if origin==self.origin:\\n return self\\n elif origin is None:\\n if self.origin in [0]:\\n self.val = np.fft.fftshift(self.val, self.axes)\\n self.origin='c'\\n elif self.origin in ['c']:\\n self.val = np.fft.ifftshift(self.val, self.axes)\\n self.origin=0\\n return self\\n else:\\n raise ValueError()\",\n \"def shift(self, arr, shift_amt, pre_context, post_context):\\n result = arr[pre_context - shift_amt:arr.shape[0] - post_context -\\n shift_amt, :]\\n return result\",\n \"def ifft2(a, origin='centre'):\\r\\n if origin == 'centre' :\\r\\n b = quadshift(a)\\r\\n else :\\r\\n b = a.copy()\\r\\n if len(b.shape) == 1 :\\r\\n b = np.fft.fftpack.ifft(b) \\r\\n elif len(b.shape) == 2 :\\r\\n b = np.fft.fftpack.ifft2(b) \\r\\n elif len(b.shape) > 2 :\\r\\n b = ifftn(b)\\r\\n if origin == 'centre' :\\r\\n b = iquadshift(b)\\r\\n return b\\r\\n if origin == 'centre' :\\r\\n b = iquadshift(b)\\r\\n return np.multiply(b, np.sqrt(b.size))\",\n \"def numpyFourierTransform(self,graph):\\n z=[complex(*graph[i]) for i in range(len(graph))]\\n return np.fft.fft(z)\",\n \"def multiroll(x, shift, axis=None):\\n x = np.asarray(x)\\n if axis is None:\\n if len(shift) != x.ndim:\\n raise ValueError(\\\"The array has %d axes, but len(shift) is only \\\"\\n \\\"%d. When 'axis' is not given, a shift must be \\\"\\n \\\"provided for all axes.\\\" % (x.ndim, len(shift)))\\n axis = range(x.ndim)\\n else:\\n # axis does not have to contain all the axes. Here we append the\\n # missing axes to axis, and for each missing axis, append 0 to shift.\\n missing_axes = set(range(x.ndim)) - set(axis)\\n num_missing = len(missing_axes)\\n axis = tuple(axis) + tuple(missing_axes)\\n shift = tuple(shift) + (0,)*num_missing\\n\\n # Use mod to convert all shifts to be values between 0 and the length\\n # of the corresponding axis.\\n shift = [s % x.shape[ax] for s, ax in zip(shift, axis)]\\n\\n # Reorder the values in shift to correspond to axes 0, 1, ..., x.ndim-1.\\n shift = np.take(shift, np.argsort(axis))\\n\\n # Create the output array, and copy the shifted blocks from x to y.\\n y = np.empty_like(x)\\n src_slices = [(slice(n-shft, n), slice(0, n-shft))\\n for shft, n in zip(shift, x.shape)]\\n dst_slices = [(slice(0, shft), slice(shft, n))\\n for shft, n in zip(shift, x.shape)]\\n src_blks = product(*src_slices)\\n dst_blks = product(*dst_slices)\\n for src_blk, dst_blk in zip(src_blks, dst_blks):\\n y[dst_blk] = x[src_blk]\\n\\n return y\",\n \"def shift_image(img, shft_int = 1):\\n no_cols = img[0].shape[1]\\n lst_col = no_cols - 1\\n col_sty = no_cols - shft_int \\n col_idx = torch.cat([torch.zeros(col_sty, dtype = torch.bool),\\n torch.ones(shft_int, dtype = torch.bool)])\\n cols = torch.reshape(img[0][0,:,col_idx], (no_cols,shft_int))\\n cols_sum = torch.sum(cols)\\n inval_shft = torch.is_nonzero(cols_sum)\\n\\n if inval_shft:\\n col_idx = torch.cat([torch.ones(shft_int, dtype = torch.bool),\\n torch.zeros(col_sty, dtype = torch.bool)])\\n cols = torch.reshape(img[0][0,:,col_idx], (no_cols,shft_int))\\n cols_sum = torch.sum(cols)\\n inval_shft = torch.is_nonzero(cols_sum)\\n if inval_shft:\\n raise ValueError('Consider shifting along another axis.')\\n mod_img = torch.cat([img[0][0,:,~col_idx],cols], dim = 1)\\n mod_img = torch.reshape(mod_img, (1,mod_img.shape[0], mod_img.shape[1]))\\n mod_img = (mod_img,img[1])\\n return mod_img\\n \\n mod_img = torch.cat([cols,img[0][0,:,~col_idx]], dim = 1)\\n mod_img = torch.reshape(mod_img, (1,mod_img.shape[0], mod_img.shape[1]))\\n mod_img = (mod_img,img[1])\\n return mod_img\",\n \"def shift_list(array, s):\\n # Calculate acutal shift amount (e.g. 11 --> 1 if lenght of the array is 5)\\n s %= len(array)\\n\\n # reverse the shift direction to be more intuitive\\n s *= -1\\n\\n #shift arry with slicing\\n shifted_array = array[s:] + array[:s]\\n\\n return shifted_array\",\n \"def ft(im, shift_after=True, shift_before=True, ret='complex', axes=None, s=None, norm='ortho'):\\n # create axes list\\n axes = __checkAxes__(axes, im)\\n\\n if shift_before == True:\\n im = ifftshift(im, axes=axes) # mid to corner\\n if (not s is None) and (not axes is None) and len(axes) < len(s):\\n s = s[-len(axes):] # it will automatically deal with the other axes\\n im = fftn(im, axes=axes, s=s, norm=norm)\\n if shift_after == True:\\n im = fftshift(im, axes=axes) # corner freq to mid freq\\n return __ret_val__(im, ret)\",\n \"def shift_f(f, tau):\\n n, T = f.shape\\n fn = zeros((n, T))\\n fn[:, 0:(T - 1)] = roll(f[:, 0:(T - 1)], tau, axis=1)\\n fn[:, T - 1] = fn[:, 0]\\n\\n return (fn)\",\n \"def __call__(self, t, u):\\n \\n # Rename to skip the self.\\n f_rate = self.f; w_hat = self.w_hat;\\n Ux = self.Ux; nx = self.nx;\\n\\n _u = u.reshape((nx,nx)) # To Matrix\\n\\n # FFT of firing rate at u\\n f_u = f_rate(_u)\\n f_hat = fft2(f_u) # 2D fft of matrix\\n\\n p_prod = np.real( ifft2(f_hat*w_hat) ) # Pointwise product of matrices\\n shift = ((2*Ux/nx)**2)*ifftshift(p_prod) # Shift result\\n F = - _u + shift\\n return F.flatten()\",\n \"def tomography_filter(arr, axis=2, filter_type='hamming', backend='autograd'):\\n func = getattr(scipy.signal.windows, filter_type)\\n filter = func(arr.shape[axis])\\n if axis != len(arr.shape) - 1:\\n arr = swap_axes(arr, [axis, len(arr.shape) - 1])\\n if backend == 'pytorch':\\n args = {'device': arr.device}\\n else:\\n args = {}\\n arr_r, arr_i = fft(arr, zeros_like(arr, requires_grad=False, **args))\\n arr_r = arr_r * filter\\n arr_i = arr_i * filter\\n arr, _ = ifft(arr_r, arr_i)\\n if axis != len(arr.shape) - 1:\\n arr = swap_axes(arr, [axis, len(arr.shape) - 1])\\n return arr\",\n \"def fourier_transform2d(self):\\n\\n zerofill = np.zeros(1024 * np.array([1,1])) #so it will always be square\\n zerofill[:len(self.windowed), :len(self.windowed)] = self.windowed\\n transform = np.fft.fft2(zerofill)\\n transform = np.fft.fftshift(transform) # shift center to zero\\n transformed = np.absolute(transform)\\n tmax = transformed.max()\\n zdata = (transformed)/(tmax) # normalize to maximum value\\n\\n return zdata\",\n \"def modified_dft(arr, fs, nfft, window, axis, detrend, scaling):\\n\\n nsamples = arr.shape[axis]\\n\\n if nfft < nsamples:\\n # crop arr before detrending & windowing; see rfft crop\\n arr = slice_along_axis(arr, 0, nfft, axis=-1)\\n\\n # detrend the array\\n arr = sps.detrend(arr, axis=axis, type=detrend)\\n\\n # fetch and apply window\\n coeffs = sps.get_window(window, arr.shape[axis])\\n arr = multiply_along_axis(arr, coeffs, axis=axis)\\n\\n # compute real DFT. Zeropad for nfft > nsamples is automatic\\n # rfft uses 'backward' norm default which is no norm on rfft\\n arr = np.fft.rfft(arr, nfft, axis=axis)\\n freqs = np.fft.rfftfreq(nfft, d=1/fs)\\n\\n # scale using weighted mean of window values\\n if scaling == 'spectrum':\\n norm = 1 / np.sum(coeffs)**2\\n\\n elif scaling == 'density':\\n #process loss Shiavi Eqn 7.54\\n norm = 1 / (fs * np.sum(coeffs**2))\\n \\n else:\\n msg = 'Unknown scaling: {}'\\n raise ValueError(msg.format(scaling))\\n \\n # before conjugate multiplication unlike scipy\\n # see _spectral_helper lines 1808 an 1842.\\n arr *= np.sqrt(norm)\\n\\n return freqs, arr\",\n \"def convolution_as_maultiplication(I, F, print_ir=False):\\n # number of columns and rows of the input \\n I_row_num, I_col_num = I.shape \\n\\n # number of columns and rows of the filter\\n F_row_num, F_col_num = F.shape\\n\\n # calculate the output dimensions\\n output_row_num = I_row_num + F_row_num - 1\\n output_col_num = I_col_num + F_col_num - 1\\n if print_ir: print('output dimension:', output_row_num, output_col_num)\\n\\n # zero pad the filter\\n F_zero_padded = np.pad(F, ((output_row_num - F_row_num, 0),\\n (0, output_col_num - F_col_num)),\\n 'constant', constant_values=0)\\n if print_ir: print('F_zero_padded: ', F_zero_padded)\\n\\n # use each row of the zero-padded F to creat a toeplitz matrix. \\n # Number of columns in this matrices are same as numbe of columns of input signal\\n toeplitz_list = []\\n for i in range(F_zero_padded.shape[0]-1, -1, -1): # iterate from last row to the first row\\n c = F_zero_padded[i, :] # i th row of the F \\n r = np.r_[c[0], np.zeros(I_col_num-1)] # first row for the toeplitz fuction should be defined otherwise\\n # the result is wrong\\n toeplitz_m = toeplitz(c,r) # this function is in scipy.linalg library\\n toeplitz_list.append(toeplitz_m)\\n if print_ir: print('F '+ str(i)+'\\\\n', toeplitz_m)\\n\\n # doubly blocked toeplitz indices: \\n # this matrix defines which toeplitz matrix from toeplitz_list goes to which part of the doubly blocked\\n c = range(1, F_zero_padded.shape[0]+1)\\n r = np.r_[c[0], np.zeros(I_row_num-1, dtype=int)]\\n doubly_indices = toeplitz(c, r)\\n if print_ir: print('doubly indices \\\\n', doubly_indices)\\n\\n ## creat doubly blocked matrix with zero values\\n toeplitz_shape = toeplitz_list[0].shape # shape of one toeplitz matrix\\n h = toeplitz_shape[0]*doubly_indices.shape[0]\\n w = toeplitz_shape[1]*doubly_indices.shape[1]\\n doubly_blocked_shape = [h, w]\\n doubly_blocked = np.zeros(doubly_blocked_shape)\\n\\n # tile toeplitz matrices for each row in the doubly blocked matrix\\n b_h, b_w = toeplitz_shape # hight and withs of each block\\n for i in range(doubly_indices.shape[0]):\\n for j in range(doubly_indices.shape[1]):\\n start_i = i * b_h\\n start_j = j * b_w\\n end_i = start_i + b_h\\n end_j = start_j + b_w\\n doubly_blocked[start_i: end_i, start_j:end_j] = toeplitz_list[doubly_indices[i,j]-1]\\n\\n if print_ir: print('doubly_blocked: ', doubly_blocked)\\n\\n # convert I to a vector\\n vectorized_I = matrix_to_vector(I)\\n if print_ir: print('vectorized_I: ', vectorized_I)\\n \\n # get result of the convolution by matrix mupltiplication\\n result_vector = np.matmul(doubly_blocked, vectorized_I)\\n if print_ir: print('result_vector: ', result_vector)\\n\\n # reshape the raw rsult to desired matrix form\\n out_shape = [output_row_num, output_col_num]\\n output = vector_to_matrix(result_vector, out_shape)\\n if print_ir: print('Result of implemented method: \\\\n', output)\\n \\n return output\",\n \"def FIR(self):\\r\\n\\r\\n #Make a list to put the outputs in:\\r\\n h = [0] * self._len_h\\r\\n\\r\\n for i in range(self._len_h):\\r\\n #XXX Check that the offset makes sense (there can't be an event\\r\\n #happening within one offset duration of the beginning of the\\r\\n #time-series:\\r\\n\\r\\n #Get the design matrix (roll by the offset, in order to get the\\r\\n #right thing):\\r\\n\\r\\n roll_events = np.roll(self.events[i], self.offset)\\r\\n design = tsu.fir_design_matrix(roll_events, self.len_et)\\r\\n #Compute the fir estimate, in linear form:\\r\\n this_h = tsa.fir(self.data[i], design)\\r\\n #Reshape the linear fir estimate into a event_types*hrf_len array\\r\\n u = np.unique(self.events[i])\\r\\n event_types = u[np.unique(self.events[i]) != 0]\\r\\n h[i] = np.reshape(this_h, (event_types.shape[0], self.len_et))\\r\\n\\r\\n h = np.array(h).squeeze()\\r\\n\\r\\n return ts.TimeSeries(data=h,\\r\\n sampling_rate=self.sampling_rate,\\r\\n t0=self.offset * self.sampling_interval,\\r\\n time_unit=self.time_unit)\",\n \"def stft(x, fs, framesz, hop):\\n framesamp = int(framesz*fs)\\n hopsamp = int(hop*fs)\\n w = scipy.hamming(framesamp)\\n X = scipy.array([scipy.fft(w*x[i:i+framesamp],256)\\n for i in range(0, len(x)-framesamp, hopsamp)])\\n X=X[:,0:128]\\n return X\",\n \"def ifft2(data):\\n if not data.shape[-1] == 2:\\n raise ValueError(\\\"Tensor does not have separate complex dim.\\\")\\n\\n data = ifftshift(data, dim=[-3, -2])\\n data = torch.view_as_real(\\n torch.fft.ifftn( # type: ignore\\n torch.view_as_complex(data), dim=(-2, -1), norm=\\\"ortho\\\"\\n )\\n )\\n data = fftshift(data, dim=[-3, -2])\\n\\n return data\",\n \"def filtering(self):\\n from numpy import fft\\n import numpy as np\\n\\n _image_dft = fft.fft2(self.image)\\n _image_dft = fft.fftshift(_image_dft)\\n # dft = DFT.DFT()\\n # plt.figure(1) \\n # plt.imshow(self.image)\\n # plt.figure(2)\\n # plt.imshow(20*np.log10(abs(_image_dft))) \\n # print(_image_dft)\\n # print(abs(_image_dft))\\n # plt.show()\\n filter = self.filter(self.image.shape, self.cutoff, self.order) \\\\\\n if self.filter_name.startswith('butterworth') \\\\\\n else self.filter(self.image.shape, self.cutoff)\\n \\n _image_dft_filtered = _image_dft * filter\\n _image_filtered = abs(fft.ifft2(_image_dft_filtered))\\n \\n return [ self.post_process_image(_image_filtered), \\\\\\n self.post_process_image(20*np.log10(abs(_image_dft)+.00001)), \\\\\\n self.post_process_image(20*np.log10(abs(_image_dft_filtered)+.00001)) ]\",\n \"def sf01(arr):\\n s = arr.shape\\n return arr.swapaxes(0, 1).reshape(s[0] * s[1], *s[2:])\",\n \"def sf01(arr):\\n s = arr.shape\\n return arr.swapaxes(0, 1).reshape(s[0] * s[1], *s[2:])\",\n \"def sf01(arr):\\n s = arr.shape\\n return arr.swapaxes(0, 1).reshape(s[0] * s[1], *s[2:])\",\n \"def sf01(arr):\\n s = arr.shape\\n return arr.swapaxes(0, 1).reshape(s[0] * s[1], *s[2:])\",\n \"def FT(x, ufunc=np.real, real=False):\\n assert isinstance(x, np.ndarray)\\n if len(x.shape) == 1:\\n if real:\\n F = np.fft.rfft(x)\\n else:\\n F = np.fft.fft(x)\\n elif len(x.shape) == 2:\\n if real:\\n F = np.fft.rfft2(x)\\n else:\\n F = np.fft.fft2(x)\\n else:\\n raise TypeError(\\\"The array should be 1D or 2D\\\")\\n return ufunc(np.fft.fftshift(F))\",\n \"def stft(x, fs, framesz, hop):\\n framesamp = int(framesz*fs)\\n hopsamp = int(hop*fs)\\n w = scipy.hamming(framesamp)\\n X = scipy.array([scipy.fft(w*x[i:i+framesamp]) \\n for i in range(0, len(x)-framesamp, hopsamp)])\\n return X\",\n \"def perform_fft(self, use_flat=True):\\n for scan_dir in range(len(self.ff)):\\n if use_flat and 'data_flat' in self.ff[scan_dir].__dict__.keys():\\n self.ff[scan_dir].data_fft = np.fft.fftshift(np.fft.fft2(self.ff[scan_dir].data_flat))\\n else:\\n self.ff[scan_dir].data_fft = np.fft.fftshift(np.fft.fft2(self.ff[scan_dir].data))\",\n \"def DFTpower2(time, signal, freqs):\\n \\n powerSpectrum = np.zeros(len(freqs))\\n\\n for i, freq in enumerate(freqs):\\n arg = 2.0 * np.pi * freq * time\\n powerSpectrum[i] = np.sum(signal * np.cos(arg))**2 + np.sum(signal * np.sin(arg))**2\\n\\n powerSpectrum = powerSpectrum * 4.0 / len(time)**2\\n return(powerSpectrum)\",\n \"def rotate(array, shift):\\n shift %= len(array)\\n return array[-shift:] + array[:-shift]\",\n \"def T_fourier(shape, T, is_fft_shifted = True):\\n # make i, j, k for each pixel\\n i = np.fft.fftfreq(shape[0]) \\n j = np.fft.fftfreq(shape[1])\\n k = np.fft.fftfreq(shape[2])\\n i, j, k = np.meshgrid(i, j, k, indexing='ij')\\n\\n if is_fft_shifted is False :\\n i = np.fft.ifftshift(i)\\n j = np.fft.ifftshift(j)\\n k = np.fft.ifftshift(k)\\n\\n phase_ramp = np.exp(- 2J * np.pi * (i * T[0] + j * T[1] + k * T[2]))\\n return phase_ramp\",\n \"def _get_fourier_filter(self):\\n size = max(64, int(2 ** np.ceil(np.log2(2 * self.m[-1].item()))))\\n\\n pi = torch.acos(torch.zeros(1)).item() * 2.0\\n n = torch.cat(\\n [\\n torch.arange(1, size // 2 + 1, 2, device=self.n.device),\\n torch.arange(size // 2 - 1, 0, -2, device=self.n.device),\\n ]\\n )\\n f = torch.zeros(size, device=self.n.device)\\n f[0] = 0.25\\n if self.flat:\\n f[1::2] = -1 / (pi * n).pow(2)\\n else:\\n f[1::2] = -self.s_detect.abs().pow(2) / (\\n pi\\n * (self.d_source + self._d_detect())\\n * torch.sin(\\n n\\n * self.s_detect.abs()\\n / (self.d_source + self._d_detect())\\n )\\n ).pow(2)\\n f = torch.stack(\\n [f, torch.zeros(f.shape, device=self.n.device)], dim=-1\\n )\\n f = fftshift(f, dim=(-2,))\\n\\n filt = fft1(f)[..., 0]\\n\\n if self.filter_type == \\\"hamming\\\":\\n # hamming filter\\n fac = torch.tensor(\\n np.hamming(size).astype(np.float32), device=f.device\\n )\\n elif self.filter_type == \\\"hann\\\":\\n # hann filter\\n fac = torch.tensor(\\n np.hanning(size).astype(np.float32), device=f.device\\n )\\n elif self.filter_type == \\\"cosine\\\":\\n # cosine filter\\n fac = torch.sin(\\n torch.linspace(0, pi, size + 1, device=f.device)[:-1]\\n )\\n else:\\n # ramp / ram-lak filter\\n fac = 1.0\\n\\n return fac * filt\",\n \"def stft(x, fft_size, hopsamp):\\n w = np.hamming(fft_size)\\n return np.array([np.fft.rfft(w*x[i:i+fft_size]) \\n for i in range(0, len(x)-fft_size, hopsamp)])\",\n \"def highPassFilter(img, window=30):\\n gray = grayscale(img)\\n\\tf = np.fft.fft2(gray)\\n\\tfshift = np.fft.fftshift(f)\\n\\trows, cols = gray.shape\\n\\tcrow, ccol = rows/2, cols/2\\n\\tfshift[crow-window:crow+window, ccol-window:ccol+window] = 0\\n\\tf_ishift = np.fft.ifftshift(fshift)\\n\\timg_back = np.fft.ifft2(f_ishift)\\n\\timg_back = np.abs(img_back)\\n\\treturn img_back\",\n \"def signalTransform(dat):\\n return numpy.convolve(dat, slopWindow, \\\"same\\\")\",\n \"def shift(options):\\n signal = audio.read(options.ipath)\\n result = op.shift(signal.data, int(options.factor))\\n audio.write(options.opath, result, signal.rate, sampwidth=1)\\n if options.plot:\\n plotter.plot(**{'Input: '+options.ipath: signal.data,\\n 'Output: '+options.opath: result})\",\n \"def iquadshift(a):\\r\\n if len(a.shape) == 1 :\\r\\n b = np.roll(a, +(a.shape[-1]/2-1), -1)\\r\\n else :\\r\\n b = np.roll(a, +(a.shape[-2]/2-1), -2)\\r\\n b = np.roll(b, +(b.shape[-1]/2-1), -1)\\r\\n return b\",\n \"def _morletT(x, f0s, w=3, fs=1000, s=1):\\n if w <= 0:\\n raise ValueError(\\n 'Number of cycles in a filter must be a positive number.')\\n\\n T = len(x)\\n F = len(f0s)\\n mwt = np.zeros([F, T], dtype=complex)\\n for f in range(F):\\n mwt[f] = morletf(x, f0s[f], fs=fs, w=w, s=s)\\n\\n return mwt\",\n \"def filter(s, F, dt, nf):\\n S = np.fft.fft(s, n=nf)\\n S_f = np.multiply(S, F)*dt\\n s_f = np.real(np.fft.ifft(S_f, n=nf))\\n return s_f\",\n \"def shift_augmentation():\\n shift = np.random.randint(-200, 201, size=2)\\n return lambda image: shift_with_extension(image, shift)\",\n \"def ShiftFrame(Frame, PixShift):\\n \\n import numpy as np\\n \\n F, R, C = Frame.shape\\n \\n if F > 1:\\n msg = f\\\"'Frame' must be a 2D frame with shape (1, R, C) but has shape\\\"\\\\\\n + f\\\" ({F}, {R}, {C}).\\\"\\n \\n raise Exception(msg)\\n \\n # Initialise ShiftedFrame:\\n ShiftedFrame = np.zeros((1, R, C), dtype='uint')\\n #ShiftedFrame = np.empty_like(Frame, dtype='uint') # this creates 42,932\\n # unique values for some reason!\\n \\n #unique = UniqueItems(Nda=Frame, NonZero=False)\\n #print(f'\\\\n---> There are {len(unique)} unique items in Frame')\\n #unique = UniqueItems(Nda=ShiftedFrame, NonZero=False)\\n #print(f'\\\\n---> There are {len(unique)} unique items in the initialised',\\n # f'ShiftedFrame: {unique[:11]}...')\\n \\n di, dj, dk = PixShift\\n \\n ##ShiftedFrame[0, dj:, di:] = Frame[0, :-(1+dj), :-(1+di)]\\n ##ShiftedFrame[0, :-(1+dj), :-(1+di)] = Frame[0, dj:, di:]\\n #ShiftedFrame[0, :R-dj, :C-di] = Frame[0, dj:, di:]\\n \\n if di > 0 and dj > 0:\\n ShiftedFrame[0, dj:, di:] = Frame[0, :-dj, :-di]\\n \\n elif di < 0 and dj < 0:\\n ShiftedFrame[0, :dj, :di] = Frame[0, -dj:, -di:]\\n \\n elif di > 0 and dj < 0:\\n ShiftedFrame[0, :dj, di:] = Frame[0, -dj:, :-di]\\n \\n elif di < 0 and dj > 0:\\n ShiftedFrame[0, dj:, :di] = Frame[0, :-dj, -di:]\\n \\n elif di == 0 and dj > 0:\\n ShiftedFrame[0, dj:, :] = Frame[0, :-dj, :]\\n \\n elif di == 0 and dj < 0:\\n ShiftedFrame[0, :dj, :] = Frame[0, -dj:, :]\\n \\n elif di > 0 and dj == 0:\\n ShiftedFrame[0, :, di:] = Frame[0, :, :-di]\\n \\n elif di < 0 and dj == 0:\\n ShiftedFrame[0, :, :di] = Frame[0, :, -di:]\\n \\n elif di == 0 and dj == 0:\\n ShiftedFrame[0] = Frame[0]\\n \\n #unique = UniqueItems(Nda=ShiftedFrame, NonZero=False)\\n #print(f'\\\\n---> There are {len(unique)} unique items in the ShiftedFrame',\\n # 'after shifting.')\\n \\n return ShiftedFrame\",\n \"def womyshift(hop):\\n import matplotlib.pyplot as plt\\n import logging\\n from tmath.wombat.inputter import inputter\\n from tmath.wombat.wshow import wshow\\n plt.cla()\\n plt.plot(hop[0].wave,hop[0].flux,drawstyle='steps-mid')\\n plt.xlabel('Wavelength')\\n plt.ylabel('Flux')\\n plt.title(hop[0].obname)\\n wshow()\\n\\n print('Routine to linearly shift flux scale\\\\n')\\n\\n shift=inputter('Enter flux shift: ','float',False)\\n\\n hop[0].flux=hop[0].flux+shift\\n\\n plt.plot(hop[0].wave,hop[0].flux,drawstyle='steps-mid')\\n\\n logging.debug('File {} flux scale shifted by {} A'.format\\\\\\n (hop[0].obname,shift))\\n\\n #FIX header\\n return hop\",\n \"def resampz(x, m_type, shift=1):\\n sx = np.array(x.shape)\\n\\n if m_type == 0 or m_type == 1:\\n y = np.zeros((sx[0] + np.abs(shift * (sx[1] - 1)), sx[1]))\\n\\n if m_type == 0:\\n shift1 = np.arange(0, sx[1]) * (- shift)\\n else:\\n shift1 = np.arange(0, sx[1]) * shift\\n\\n if shift1[-1] < 0:\\n shift1 = shift1 - shift1[-1]\\n\\n for n in range(sx[1]):\\n y[shift1[n] + np.arange(0, sx[0]), n] = x[:, n]\\n\\n # Remove extra rows\\n start = 0\\n finish = y.shape[0]\\n\\n while np.linalg.norm(y[start, :], 2) == 0:\\n start += 1\\n\\n while np.linalg.norm(y[finish-1, :], 2) == 0:\\n finish -= 1\\n\\n y = y[start:finish, :]\\n\\n elif m_type == 2 or m_type == 3:\\n y = np.zeros((sx[0], sx[1] + np.abs(shift * (sx[0] - 1))))\\n\\n if m_type == 2:\\n shift2 = np.arange(0, sx[0]) * (- shift)\\n else:\\n shift2 = np.arange(0, sx[0]) * shift\\n\\n if shift2[-1] < 0:\\n shift2 = shift2 - shift2[-1]\\n\\n for m in range(sx[0]):\\n y[m, shift2[m] + np.arange(0, sx[1])] = x[m, :]\\n\\n # Remove extra rows\\n start = 0\\n finish = y.shape[1]\\n\\n while np.linalg.norm(y[:, start], 2) == 0:\\n start += 1\\n\\n while np.linalg.norm(y[:, finish-1], 2) == 0:\\n finish -= 1\\n\\n y = y[:, start:finish]\\n\\n else:\\n print('Error: type not valid.')\\n y = 0\\n\\n return y\",\n \"def fft_phase_shift(self, im0):\\n shape = im0.shape\\n f0 = fft2(im0)\\n f1 = fft2(self.initial_frame)\\n ir = abs(ifft2((f0 * f1.conjugate()) / (abs(f0) * abs(f1))))\\n t0, t1, c = np.unravel_index(np.argmax(ir), shape)\\n if t0 > shape[0] // 2:\\n t0 -= shape[0]\\n if t1 > shape[1] // 2:\\n t1 -= shape[1]\\n return [t0, t1]\",\n \"def calculate_shift(self, y, x, t, axis):\\n return DeformationModel.calculate_shifts_from_coeffs(y, x, t,\\n self.coeffs[axis])\",\n \"def get_fft(self):\\n\\t\\t# Get the \\\"ideal\\\" evenly spaced times\\n\\t\\teven_times = numpy.linspace(self.buf[0][0], self.buf[-1][0], len(self.buf))\\n\\t\\t\\n\\t\\t# Interpolate the data to generate evenly temporally spaced samples\\n\\t\\tinterpolated = numpy.interp(even_times, *zip(*self.buf))\\n\\t\\t\\n\\t\\t# Perform the FFT\\n\\t\\tfft = numpy.fft.rfft(interpolated)\\n\\t\\treturn zip(numpy.abs(fft), numpy.angle(fft))\",\n \"def combine_components(self):\\n\\n # ensure is array with correct shape\\n if self.separated_components_ft.shape[1] != 3:\\n raise ValueError(\\\"Expected shifted_components_ft to have shape (nangles, 3, ny, nx), where components are\\\"\\n \\\"O(f)*otf(f), O(f-fo)*otf(f), O(f+fo)*otf(f). But size of second dimension was not 3.\\\")\\n\\n # upsample image before shifting\\n f_upsample = 2\\n otf_us = tools.expand_fourier_sp(self.otf, mx=f_upsample, my=f_upsample, centered=True)\\n\\n # upsampled frequency data\\n fx_us = tools.get_fft_frqs(f_upsample * self.nx, self.dx / f_upsample)\\n dfx_us = fx_us[1] - fx_us[0]\\n fy_us = tools.get_fft_frqs(f_upsample * self.ny, self.dx / f_upsample)\\n dfy_us = fy_us[1] - fy_us[0]\\n\\n def ff_shift(f): return np.sqrt((self.fx[None, :] - f[0]) ** 2 + (self.fy[:, None] - f[1]) ** 2)\\n def ff_shift_upsample(f): return np.sqrt((fx_us[None, :] - f[0]) ** 2 + (fy_us[:, None] - f[1]) ** 2)\\n\\n # wiener filter deconvolution to divide by H(k)\\n # components_deconvolved_ft = np.zeros((self.nangles, 3, self.ny, self.nx), dtype=np.complex)\\n components_deconvolved_ft = np.zeros((self.nangles, 3, self.ny * f_upsample, self.nx * f_upsample), dtype=np.complex)\\n snr = np.zeros((self.nangles, 3, self.ny, self.nx))\\n # shift to correct place in frq space\\n components_shifted_ft = np.zeros((self.nangles, 3, self.ny * f_upsample, self.nx * f_upsample), dtype=np.complex)\\n snr_shifted = np.zeros(components_shifted_ft.shape)\\n # weight and average\\n components_weighted = np.zeros(components_shifted_ft.shape, dtype=np.complex)\\n weights = np.zeros(components_weighted.shape)\\n\\n # shift and filter components\\n for ii in range(self.nangles):\\n # loop over components, O(f)H(f), m*O(f - f_o)H(f), m*O(f + f_o)H(f)\\n for jj, eps in enumerate([0, 1, -1]):\\n params = list(self.power_spectrum_params[ii, jj, :-1]) + [0]\\n snr[ii, jj] = power_spectrum_fn(params, ff_shift(self.frqs[ii] * eps), 1) / self.noise_power[ii, jj]\\n\\n # get shifted SNR\\n otf_shifted, _, _ = tools.translate_pix(otf_us, eps*self.frqs[ii], dx=dfx_us, dy=dfy_us, mode='no-wrap')\\n snr_shifted[ii, jj] = power_spectrum_fn(params, ff_shift_upsample((0, 0)), 1) / self.noise_power[ii, jj]\\n\\n # weights\\n weights[ii, jj] = get_snr_weight(otf_shifted, snr_shifted[ii, jj])\\n\\n if True:\\n # deconvolve, then shift\\n\\n # deconvolve\\n deconv_temp, _ = \\\\\\n wiener_deconvolution(self.separated_components_ft[ii, jj] / self.mod_depths[ii, jj], self.otf,\\n snr[ii, jj])\\n\\n components_deconvolved_ft[ii, jj] = tools.expand_fourier_sp(deconv_temp, mx=f_upsample, my=f_upsample, centered=True)\\n\\n # shift and expand\\n components_shifted_ft[ii, jj] = tools.translate_ft(components_deconvolved_ft[ii, jj], eps * self.frqs[ii], self.dx / f_upsample)\\n else:\\n # shift then deconvolve\\n\\n # shift and expand\\n components_shifted_ft[ii, jj] = tools.translate_ft(\\n tools.expand_fourier_sp(self.separated_components_ft[ii, jj], mx=f_upsample, my=f_upsample, centered=True),\\n eps * self.frqs[ii], self.dc / f_upsample)\\n\\n # deconvolved\\n components_shifted_ft[ii, jj], _ = \\\\\\n wiener_deconvolution(components_shifted_ft[ii, jj] / self.mod_depths[ii, jj], otf_shifted,\\n snr_shifted[ii, jj])\\n\\n # optionally remove frequency data around modulation frequency, to avoid artifacts\\n if self.size_near_fo_to_remove != 0:\\n to_remove = np.abs(ff_shift_upsample(-self.frqs[ii])) < self.size_near_fo_to_remove * np.linalg.norm(self.frqs[ii])\\n components_shifted_ft[ii, jj][to_remove] = 0\\n\\n to_remove = np.abs(ff_shift_upsample(self.frqs[ii])) < self.size_near_fo_to_remove * np.linalg.norm(self.frqs[ii])\\n components_shifted_ft[ii, jj][to_remove] = 0\\n\\n # correct for wrong global phases (on shifted components before weighting,\\n # but then apply to weighted components)\\n if self.global_phase_correction:\\n self.phase_corrections = global_phase_correction(components_shifted_ft)\\n else:\\n self.phase_corrections = np.zeros(self.nangles)\\n\\n # combine components\\n components_weighted = components_shifted_ft * weights\\n for ii in range(self.nangles):\\n components_weighted[ii, 1] = np.exp(1j * self.phase_corrections[ii]) * components_weighted[ii, 1]\\n components_weighted[ii, 2] = np.exp(-1j * self.phase_corrections[ii]) * components_weighted[ii, 2]\\n\\n # final averaging\\n weight_norm = np.sum(weights, axis=(0, 1)) + self.wiener_parameter\\n sim_sr_ft = np.nansum(components_weighted, axis=(0, 1)) / weight_norm\\n\\n # Fourier transform back to get real-space reconstructed image\\n apod = scipy.signal.windows.tukey(sim_sr_ft.shape[1], alpha=0.1)[None, :] * \\\\\\n scipy.signal.windows.tukey(sim_sr_ft.shape[0], alpha=0.1)[:, None]\\n\\n sim_sr = fft.fftshift(fft.ifft2(fft.ifftshift(sim_sr_ft * apod))).real\\n\\n return sim_sr, sim_sr_ft, components_deconvolved_ft, components_shifted_ft,\\\\\\n weights, weight_norm, snr, snr_shifted\",\n \"def transform(self, X): # noqa: E501\\n X = np.atleast_2d(X)\\n\\n if X.ndim > 3:\\n raise ValueError(\\n \\\"Array must be of at max 3 dimensions instead \\\"\\n \\\"got %s dimensional matrix\\\" % (X.ndim)\\n )\\n\\n shape = X.shape\\n X = X.reshape(-1, shape[-1])\\n X = filter_data(\\n X,\\n self.sfreq,\\n self.l_freq,\\n self.h_freq,\\n filter_length=self.filter_length,\\n l_trans_bandwidth=self.l_trans_bandwidth,\\n h_trans_bandwidth=self.h_trans_bandwidth,\\n n_jobs=self.n_jobs,\\n method=self.method,\\n iir_params=self.iir_params,\\n copy=False,\\n fir_window=self.fir_window,\\n fir_design=self.fir_design,\\n )\\n return X.reshape(shape)\",\n \"def fixed_point_multiply(data, multiplier, shift):\\n return _make.fixed_point_multiply(data, multiplier, shift)\",\n \"def pitch_shift(\\n spectrogram: tf.Tensor,\\n semitone_shift: float = 0.0,\\n method: tf.image.ResizeMethod = tf.image.ResizeMethod.BILINEAR,\\n) -> tf.Tensor:\\n factor = 2 ** (semitone_shift / 12.0)\\n T = tf.shape(spectrogram)[0]\\n F = tf.shape(spectrogram)[1]\\n F_ps = tf.cast(tf.cast(F, tf.float32) * factor, tf.int32)[0]\\n ps_spec = tf.image.resize_images(\\n spectrogram, [T, F_ps], method=method, align_corners=True\\n )\\n paddings = [[0, 0], [0, tf.maximum(0, F - F_ps)], [0, 0]]\\n return tf.pad(ps_spec[:, :F, :], paddings, \\\"CONSTANT\\\")\",\n \"def _width_shift_(self, x: np.array, m: np.array) -> (np.array, np.array):\\n # get a random sign for the shifting direction\\n sign = np.random.randint(0, 2)\\n shift_pix = np.random.randint(0, self.shift)\\n x = shift(x, [0, sign*shift_pix])\\n m = shift(m, [0, sign*shift_pix, 0], mode='nearest')\\n return x,m\",\n \"def FourierSeries(input,N=None):\\n \\n N=len(input);\\n\\n w=2*cmath.pi/N;\\n input=input[0:N];\\n n=numpy.arange(0,N); \\n r=cexp(-1j*w*n);\\n\\n output = [complex(0)] * N \\n for k in range(N): \\n r=input*cexp(-1j*w*n*k) \\n output[k]=np.sum(r);\\n \\n \\n return output;\",\n \"def fwd_conv2d_fft(input_mat: np.ndarray,\\n filter_mat: np.ndarray) -> np.ndarray:\\n flipped_mat = filter_mat[::-1, ::-1]\\n return fftconvolve(input_mat, flipped_mat, mode='full')\",\n \"def one_hot_add(inputs, shift):\\n inputs = torch.stack((inputs, torch.zeros_like(inputs)), dim = -1)\\n shift = torch.stack((shift, torch.zeros_like(shift)), dim = -1)\\n inputs_fft = torch.fft(inputs, 1) #ignore last and first dimension to do batched fft\\n shift_fft = torch.fft(shift, 1)\\n result_fft_real = inputs_fft[...,0]*shift_fft[...,0] - inputs_fft[...,1]*shift_fft[...,1]\\n result_fft_imag = inputs_fft[...,0]*shift_fft[...,1] + inputs_fft[...,1]*shift_fft[...,0]\\n result_fft = torch.stack((result_fft_real,result_fft_imag), dim = -1)\\n return torch.ifft(result_fft, 1)[...,0], result_fft, inputs_fft, shift_fft #return only the real part\",\n \"def matrixMultiplication(imagei, filterf):\\n n1, n2 = imagei.shape\\n m1, m2 = filterf.shape\\n\\n p = n1 - m1 + 1 #column num\\n q = n2 - m2 + 1 #row num\\n output = np.zeros((p, q))\\n\\n for i in range(p):\\n for j in range(q):\\n img = imagei[i:i + m1, j:j + m2]\\n out = np.multiply(img, filterf)\\n output[i][j] = np.sum(out)\\n\\n return output\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67731035","0.6459094","0.6423767","0.6338069","0.60735834","0.6052101","0.6052101","0.6052101","0.6013318","0.6007814","0.599912","0.59832346","0.59766555","0.59196067","0.58802474","0.58419585","0.5822599","0.5822599","0.5822599","0.581095","0.580473","0.5778783","0.57602596","0.57489634","0.5737551","0.5717823","0.5675997","0.56674373","0.566398","0.56404924","0.56300306","0.56039834","0.5576278","0.55754715","0.5567257","0.5563203","0.5555692","0.55308497","0.5529372","0.55109245","0.54319614","0.54159105","0.5397125","0.5376059","0.5354349","0.535049","0.5349805","0.5349586","0.53445333","0.5343733","0.533944","0.5334595","0.5329136","0.5293661","0.5289477","0.52813804","0.5276873","0.5269663","0.526653","0.52635825","0.5240111","0.52340436","0.5226548","0.5225072","0.5218881","0.52172995","0.5214661","0.5214661","0.5214661","0.5214661","0.51988477","0.5180765","0.5167163","0.5166239","0.5157251","0.5156828","0.51455754","0.5138145","0.51330096","0.51212513","0.5116481","0.5107923","0.50868154","0.5084079","0.5077159","0.5068717","0.5068168","0.5067043","0.5063224","0.5047233","0.5045482","0.50273216","0.5024671","0.5015015","0.50143784","0.50142676","0.5009521","0.50072664","0.5003248","0.5000685"],"string":"[\n \"0.67731035\",\n \"0.6459094\",\n \"0.6423767\",\n \"0.6338069\",\n \"0.60735834\",\n \"0.6052101\",\n \"0.6052101\",\n \"0.6052101\",\n \"0.6013318\",\n \"0.6007814\",\n \"0.599912\",\n \"0.59832346\",\n \"0.59766555\",\n \"0.59196067\",\n \"0.58802474\",\n \"0.58419585\",\n \"0.5822599\",\n \"0.5822599\",\n \"0.5822599\",\n \"0.581095\",\n \"0.580473\",\n \"0.5778783\",\n \"0.57602596\",\n \"0.57489634\",\n \"0.5737551\",\n \"0.5717823\",\n \"0.5675997\",\n \"0.56674373\",\n \"0.566398\",\n \"0.56404924\",\n \"0.56300306\",\n \"0.56039834\",\n \"0.5576278\",\n \"0.55754715\",\n \"0.5567257\",\n \"0.5563203\",\n \"0.5555692\",\n \"0.55308497\",\n \"0.5529372\",\n \"0.55109245\",\n \"0.54319614\",\n \"0.54159105\",\n \"0.5397125\",\n \"0.5376059\",\n \"0.5354349\",\n \"0.535049\",\n \"0.5349805\",\n \"0.5349586\",\n \"0.53445333\",\n \"0.5343733\",\n \"0.533944\",\n \"0.5334595\",\n \"0.5329136\",\n \"0.5293661\",\n \"0.5289477\",\n \"0.52813804\",\n \"0.5276873\",\n \"0.5269663\",\n \"0.526653\",\n \"0.52635825\",\n \"0.5240111\",\n \"0.52340436\",\n \"0.5226548\",\n \"0.5225072\",\n \"0.5218881\",\n \"0.52172995\",\n \"0.5214661\",\n \"0.5214661\",\n \"0.5214661\",\n \"0.5214661\",\n \"0.51988477\",\n \"0.5180765\",\n \"0.5167163\",\n \"0.5166239\",\n \"0.5157251\",\n \"0.5156828\",\n \"0.51455754\",\n \"0.5138145\",\n \"0.51330096\",\n \"0.51212513\",\n \"0.5116481\",\n \"0.5107923\",\n \"0.50868154\",\n \"0.5084079\",\n \"0.5077159\",\n \"0.5068717\",\n \"0.5068168\",\n \"0.5067043\",\n \"0.5063224\",\n \"0.5047233\",\n \"0.5045482\",\n \"0.50273216\",\n \"0.5024671\",\n \"0.5015015\",\n \"0.50143784\",\n \"0.50142676\",\n \"0.5009521\",\n \"0.50072664\",\n \"0.5003248\",\n \"0.5000685\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.60626906"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":20099,"cells":{"query":{"kind":"string","value":"Given positive int n and array P representing probabilities corresponding to an allel frequency, returns array B representing the expected allele frequency of the next generation"},"document":{"kind":"string","value":"def ExpectedVal():\n input = f.LoadFile('\\\\rosalind_ebin.txt').splitlines()\n n = int(input[0])\n P = [float(x) for x in input[1].split()]\n \n B = [str(round(i*n,4)) for i in P]\n f.ExportToFile('rosalind_ebin_output.txt',' '.join(B))\n return"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def binomial(n: int, p: float) -> int:\n return sum(bernoulli_trial(p) for _ in range(n))","def bpmfln(k, n, p):\n bnm = np.empty_like(n, dtype=np.float64)\n logp = math.log(p)\n one_logp = math.log(1 - p)\n for i in range(len(k)):\n bnm[i] = math.exp(combinln(n[i], k[i]) + k[i] *\n logp + (n[i] - k[i]) * one_logp)\n return bnm","def bpmfln(k, n, p):\n return np.exp(combinln(n, k) + k * np.log(p) + (n - k) * np.log(1 - p))","def binomial(n, p):\n sum_ans = 0\n for k in range(n):\n sum_ans = sum_ans + bernoulli(p)\n return sum_ans","def probability(n, k, p):\n prob = 0\n power = expotentation_by_squaring((1-p), n)\n count_mult = math.log(n, 2)\n p_fraction = p/(1-p)\n count_mult += 1\n for i in range(0, k+1):\n element = newton(n, i)*power\n prob += element\n power *= p_fraction\n count_mult += 2\n return prob, count_mult","def chance(n, p):\n total = 0.0\n for k in range(n+1):\n total += comb(n, k, exact=False) * p**k * (1-p) ** (n-k)\n return total","def prob1(n):\n#raise NotImplementedError(\"Problem 1 Incomplete\")\n if n == 0 :\n raise ValueError(\"Sampling 0 points is not defined.\")\n total = 0\n for i in xrange(n) :\n if np.random.normal() > 3 :\n total += 1\n return float(total)/n","def perform_bernoulli_trials(n, p):\n # Initialize number of successes: n_success\n n_success = 0\n\n\n # Perform trials\n for i in range(n):\n # Choose random number between zero and one: random_number\n random_number = np.random.random()\n\n\n # If less than p, it's a success so add one to n_success\n if random_number < p:\n n_success += 1\n\n return n_success","def perform_bernoulli_trials(n, p):\n # Initialize number of successes: n_success\n n_success = 0\n\n\n # Perform trials\n for i in range(n):\n # Choose random number between zero and one: random_number\n random_number = np.random.random()\n\n # If less than p, it's a success so add one to n_success\n if random_number< p:\n n_success += 1\n\n return n_success","def perform_bernoulli_trials(n, p):\n # Initialize number of successes: n_success\n n_success = 0\n # Perform trials\n for i in range(n):\n # Choose random number between zero and one: random_number\n random_number = np.random.random()\n # If less than p, it's a success so add one to n_success\n if random_number < p:\n n_success += 1\n\n return n_success","def perform_bernoulli_trials(n, p):\n # Initialize number of successes: n_success\n n_success = 0\n\n # Perform trials\n for i in range(n):\n # Choose random number between zero and one: random_number\n random_number = np.random.random()\n\n # If less than p, it's a success so add one to n_success\n if random_number < p:\n n_success += 1\n\n return n_success","def b_n(n):\n if n <= 0.36: # MCH03\n ei = np.array([0, 1, 2, 3, 4])\n ai = np.array([0.01945, -0.8902, 10.95, -19.67, 13.43])\n else: # CB99\n ei = np.array([1, 0, -1, -2])\n ai = np.array([2, -1./3, 4./405, 46./25515])\n return np.sum(ai * np.power(float(n), ei))","def bpmf(k, n, p):\n # this does not work for large n\n return comb(n, k) * (p**k) * ((1 - p)**(n - k))","def erb2freq(n_erb):\n return 24.7 * 9.265 * (np.exp(n_erb / 9.265) - 1)","def beta_gen_posmnt(p):\n return np.array([0.0]*int(0.7*p) + [1.0]*(p-int(0.7*p)))","def probability_of_all_successes(p: float, r: int, n: int) -> float:\n\n if r == 1:\n return pow(p, n)\n elif n == 0:\n return 1\n else:\n result = 0\n for x in range(0, n+1):\n result += pow(p, x) * pow(1-p, n-x) * probability_of_all_successes(p, r-1, n-x)\n return result","def powBeta( n ):\n return (1-alphaval)*Fib(n) + Fib(n-1)\n #return Fib(n+1) - Fib(n) * alphaval","def bernoulli(p):\r\n if np.random.random() < p:\r\n return 0\r\n else:\r\n return 1","def base_binom_pro(pro,n0):\n res = stats.binom.pmf(range(n0+1), n0, 1/2.0)\n a = 0\n for i in range(n0+1):\n a = a + res[i]\n if a>=pro: \n return i","def generate_possible_freqL(pL,sL,er):\n h = sum(pL) # number of different haplotypes\n L = [ bin(x)[2:] for x in range(1,2**h-1) ] # range from 1 to 2**h-1 because we don't want 0% or 100% allele freq\n M = [ '0'*(len(L[-1])-len(x))+x for x in L ]\n p_freqL = []\n for i in range(len(pL)):\n p_freqL += [sL[i]/pL[i]]*pL[i]\n p_freqA = np.array(p_freqL)\n sA = np.array(sL)\n aL = []\n for g in M:\n aL.append(sum(np.array([ int(x) for x in list(g) ])*p_freqL))\n return sorted(list(set(aL+[er,1-er])))","def binom_by_normal(n, p, N=1):\n n = np.atleast_1d(n) # This is a bit annoying, but I don't see any simpler way to handle the possibility of scalar inputs\n small_n = n < (1 << 31)\n\n if len(n) == 1: # Single state in\n if small_n:\n return np.int64(binom.rvs(n, p, size=N))\n else:\n return np.int64(np.random.normal(n * p, np.sqrt(n * p * (1 - p)), size=N))\n else: # Multiple states in\n samples = np.zeros(N,dtype=np.int64)\n large_n = np.invert(small_n)\n num_small_n = sum(small_n)\n if np.asarray(p).ndim==0: # Single scalar variance\n samples[small_n] = np.int64(binom.rvs(n[small_n].astype(np.int32), p, size=num_small_n))\n samples[large_n] = np.int64(\n np.random.normal(n[large_n] * p, np.sqrt(n[large_n] * p * (1 - p)), size=N - num_small_n))\n else:\n samples[small_n] = np.int64(binom.rvs(n[small_n].astype(np.int32), p[small_n], size=num_small_n))\n samples[large_n] = np.int64(\n np.random.normal(n[large_n] * p[large_n], np.sqrt(n[large_n] * p[large_n] * (1 - p[large_n])), size=N - num_small_n))\n\n return samples","def sample(a, p):\n if (len(a) != len(p)):\n raise Exception('a != p')\n p = np.array(p)\n p = p / p.sum()\n r = random.random()\n n = len(a)\n total = 0 # range: [0,1]\n for i in xrange(n):\n total += p[i]\n if total > r:\n return a[i]\n return a[i]","def entropy_numba(p):\n return 2 ** np.sum(-p*np.log2(p+1e-10))","def test_bp_example():\n signal = np.array([4, 7, 9, 10, 6, 11, 3])\n\n pe = permutation_entropy(signal, 2)\n\n assert 0.91 < pe < 0.92 # Should be approx 0.918.\n\n pe = permutation_entropy(signal, 3)\n\n assert 1.52 < pe < 1.53 # Should be approx 1.522.","def _proba(y):\n N = len(y)\n _, counts = np.unique(y, return_counts=True)\n return counts / N","def bernoulli(p):\n bern = rn.binomial(1,p)\n return bern","def prob_t_N(genotype, base):\n cnter = Counter(genotype)\n return cnter.get(base, 0) * 1/len(genotype)","def semitone_diff_to_frequency(n: float) -> float:\n return A4 * A**n","def Pollard_pm1(n, primes, max_B=1000000):\n B = 10\n g = 1\n while B < max_B and g < n:\n a = randint(2, n - 2)\n g = gcd(a, n)\n if g != 1:\n return g\n for p in primes:\n if p >= B:\n break\n pd = 1 # p^d\n while pd * p <= B:\n pd *= p\n a = powmod(a, pd, n)\n g = gcd(a - 1, n)\n if g != 1 and g != n:\n return g\n B *= 2\n return 1","def uniform_but_one_dataset(n, p):\n elements = []\n for i in range(n):\n elements.append((i, 1))\n elements.append((1, (n**(1.0 / p)) - 1))\n return elements","def prob_m_of_n(m, n, T, l):\n PFD_one_unit = l*T\n m_of_n = binom(n, m) * (PFD_one_unit)**(n-m) * (1-PFD_one_unit)**m\n return m_of_n","def bic(self, p=1) -> float:\n assert p >= 0\n\n n = len(self.true)\n return float(n * np.log(self.sse() / n) + p * np.log(n))","def toep_gram(P: int, N: int) -> np.ndarray:\n weights = np.ones(shape=(N,)) * (P + 1)\n weights[:P] = np.arange(1, P + 1)\n weights[N - P:] = np.flip(np.arange(1, P + 1), axis=0)\n return weights","def random_walk(n, p):\n random_array = np.random.uniform(0, 1, n)\n left = random_array[random_array > p].size\n right = n - left\n \n return (right-left)","def payoff_n_p(p, n=3,\n MLB_contract=4158333, minor_contract=6600, thresh=1500000):\n distribution = []\n for n_makers in range(n + 1):\n if n_makers == 0:\n payoff_prob = [1 - prob for prob in p.values()]\n payoff_prob = np.prod(payoff_prob)\n distribution.append((minor_contract, payoff_prob))\n elif n_makers == n:\n payoff_prob = [prob for prob in p.values()]\n payoff_prob = np.prod(payoff_prob)\n distribution.append((MLB_contract, payoff_prob))\n else:\n makers = list(combinations(range(1, n + 1), n_makers))\n for maker_set in makers:\n if 1 in maker_set:\n payoff = MLB_contract - 0.1*(MLB_contract-thresh)\n payoff += (n_makers-1)*0.1*(MLB_contract-thresh)/(n-1)\n payoff_prob = [p[player] for player in maker_set]\n payoff_prob += [1-p[player] for player in p.keys() if player not in maker_set]\n payoff_prob = np.prod(payoff_prob)\n distribution.append((payoff, payoff_prob))\n else:\n payoff = minor_contract\n payoff += n_makers*0.1*(MLB_contract-thresh)/(n-1)\n payoff_prob = [p[player] for player in maker_set]\n payoff_prob += [1-p[player] for player in p.keys() if player not in maker_set]\n payoff_prob = np.prod(payoff_prob)\n distribution.append((payoff, payoff_prob))\n E_payoff = [a*b for (a, b) in distribution]\n E_payoff = sum(E_payoff)\n var_payoff = [((a-E_payoff)**2)*b for (a, b) in distribution]\n var_payoff = sum(var_payoff)\n return E_payoff, var_payoff**0.5","def prob_list(n, p):\n ...\n#pn is the probability of seeing a pair of aces two hands in a row in n hands.\n#qn = 1 - pn\n#thus qn is the probability of NOT seeing a par of aces two hands in a row in n hands.\n list = []\n qn_a = 1\n qn_b = 1\n list.append(qn_a)\n list.append(qn_b)\n\n for i in range(n-2):\n i += 0\n qn_next = (1-p)*qn_b + p*(1-p)*qn_a\n list.append(qn_next)\n qn_a = qn_b\n qn_b = qn_next\n return list\n\n\n # returns list of probs. [q0,... qn]","def normal_approximation_to_binomial(n, p):\r\n mu = p*n\r\n sigma = math.sqrt(p * (1 - p) * n)\r\n\r\n return mu, sigma","def powAlpha( n ):\n return (1-betaval)*Fib(n) + Fib(n-1)\n #return Fib(n+1) - Fib(n) * betaval","def get_probability(letters, n):\n return {l: c/n for l, c in letters.items()}","def uniform_but_one_dataset_no_weight(n, p):\n elements = []\n for i in range(n):\n elements.append(i)\n for i in range(int(n**(1.0 / p)) - 1):\n elements.append(1)\n return elements","def bernoulli_trial(p: float) -> int:\n return 1 if random.random() < p else 0","def beta_gen_lasso(p):\n cardi = 0.005\n return np.array([0]*int(p-int(cardi*p)) + [1]*int(cardi*p))","def binom_pdf(k, n,p,binom):\n return binom * p**k * (1-p)**(n-k)","def normal_aproximation_to_binomial(n: int, p: float) -> Tuple[float, float]:\n mu = p * n\n sigma = math.sqrt(p * (1 - p) * n)\n return mu, sigma","def beta_gen_mnt(p):\n return np.array([-1.0]*int(0.7*p) + [1.0]*(p-int(0.7*p)))","def prob1(n):\n\n # create a giant draw from a normal distribution\n random_draws = np.random.normal(loc= 0, scale = 1, size = n)\n\n # mask the values\n mask = random_draws > 3\n\n return np.sum(mask)/float(n)","def normal_approximation_to_binomial(n, p):\n mu = p * n\n sigma = math.sqrt(p * (1 - p) * n)\n return mu, sigma","def normal_approximation_to_binomial(n, p):\n mu = p * n\n sigma = math.sqrt(p * (1 - p) * n)\n return mu, sigma","def normal_approximation_to_binomial(n, p):\n mu = p * n\n sigma = math.sqrt(p * (1 - p) * n)\n return mu, sigma","def normal_approximation_to_binomial(n, p):\n mu = p * n\n sigma = math.sqrt(p * (1 - p) * n)\n return mu, sigma","def P(N,L) :\n accum = 0.0\n sign = 1.0\n for i in xrange(len(L)+1) :\n accum2 = 0.0\n for combin in combinations(L,i) :\n term = 1.0\n j = 0.0\n for Li in combin :\n term *= Li/(N-j)\n j += 1\n accum2 += term\n accum += sign*accum2\n sign *= -1.0\n return accum","def normal_approximation_to_binomial(n, p):\n mu = p * n\n sigma = math.sqrt(p * (1 - p ) * n)\n return mu, sigma","def prbs(m, n):\n return np.array(np.random.rand(m, n) > 0.5, dtype=np.int) - 0.5","def calculate_probability(k: int, m: int, n: int) -> float:\n population = [\"AA\" for _ in range(k)] + [\"Aa\" for _ in range(m)] + [\"aa\" for _ in range(n)]\n pairings = it.combinations(population, 2)\n probabilities = [PROBABILITIES[pairing] for pairing in pairings]\n output = sum(probabilities) / len(probabilities)\n\n return output","def bernoulli_logpmf(X, p):\n return -T.nnet.binary_crossentropy(p, X).sum(axis=-1)","def Bnum(n):\n n = int(n)\n a = {}\n for m in xrange(0, n+1):\n a[m] = 1/(m+1)\n for j in reversed(xrange(1, m+1)):\n a[j-1] = j*(a[j-1] - a[j])\n return a[0]","def sample_discrete(probs):\r\n q = np.random.rand()\r\n i = 0\r\n p_sum = 0.0\r\n while p_sum < q:\r\n p_sum += probs[i]\r\n i += 1\r\n return i - 1","def p_adjust_bh(p):\n p = np.asfarray(p)\n by_descend = p.argsort()[::-1]\n by_orig = by_descend.argsort()\n steps = float(len(p)) / np.arange(len(p), 0, -1)\n q = np.minimum(1, np.minimum.accumulate(steps * p[by_descend]))\n return q[by_orig]","def generate_bits(n, p):\n try:\n return (np.random.rand(*n) > p).astype(int)\n except TypeError:\n return (np.random.rand(n) > p).astype(int)","def calc_probs(log_p):\n\n N = log_p.shape[0]\n\n log_Z_per_N = np.zeros(shape=(N, 1))\n\n for i in range(N):\n\n log_Z_per_N[i] = log_norm(log_p[i])\n\n log_p_new = log_p - log_Z_per_N\n\n p = np.exp(log_p_new)\n\n # log_Z = log_norm(log_p)\n\n # p = np.exp(log_p - log_Z)\n\n return p","def binomVariance(n, p):\r\n variance_of_Y = p * (1- p)\r\n variance_of_X = n * variance_of_Y \r\n print(\"variance of X = \", variance_of_X)\r\n return variance_of_X","def sumFibPower(n, p, viaPowers = True):\n #Expand power, sum each element, then convert to fibonacci numbers\n s = 0\n for i in range(0, p+1):\n si = sumGeomAB( p-i, i, 0, n, viaPowers=viaPowers )\n s = s + binomial(p,i) * (-1)**(i) * si\n return s / (sqrts(5))**p","def dbinom(self, x, n, p):\n f = math.factorial\n C = Decimal(f(n) / (f(x) * f(n-x)))\n return C * p**x * (1-p)**(n-x)","def get_probs(Q_s, epsilon, nA):\n policy_s = np.ones(nA) * epsilon / nA\n best_a = np.argmax(Q_s)\n policy_s[best_a] = 1 - epsilon + (epsilon / nA)\n return policy_s","def _calc_cartwright_pf(n, a):\n args = numba.carray(a, n)\n x = args[0]\n num_extrema = args[1]\n bandwidth = args[2]\n return 1. - (1. - bandwidth * np.exp(-x * x)) ** num_extrema","def compute_prob_mle(X: np.ndarray, n: int) -> float:\n\n assert n > 1, \"for n = 1 use Bernoulli distribution.\"\n Binomial._check_input_data(X=X)\n Binomial._check_support(X=X, n=n)\n\n prob = X.mean() / n\n return prob","def get_prob_for_distributions(p):\n w1 = p[0]\n mu1 = p[1]\n sigma1 = p[2]\n w2 = p[3]\n mu2 = p[4]\n sigma2 = p[5]\n w3 = p[6]\n mu3 = p[7]\n sigma3 = p[8]\n dist_range = (0, 4.330310991999920844e+01)\n x = np.linspace(dist_range[0], dist_range[1], 1000)\n A1 = np.array(w1 * mlab.normpdf(x, mu1, sigma1)).sum()\n A2 = np.array(w2 * mlab.normpdf(x, mu2, sigma2)).sum()\n A3 = np.array(w3 * mlab.normpdf(x, mu3, sigma3)).sum()\n p1 = A1 / (A1 + A2 + A3)\n p2 = A2 / (A1 + A2 + A3)\n p3 = A3 / (A1 + A2 + A3)\n return p1, p2, p3","def random_pmf(nb_labels):\n random_numbers = np.random.random(nb_labels)\n return random_numbers / np.sum(random_numbers)","def a2p(a, N):\n return N + 2 + (a % N) + (a // N) * (N + 1)","def algorithm_2_21(p, t, x):\n\n t = np.array(t, dtype=np.float64)\n b = 1\n mu = index(x, t)\n\n for k in range(1, p + 1):\n t1 = t[mu - k + 1:mu + 1]\n t2 = t[mu + 1:mu + k + 1]\n omega = np.divide(\n (x - t1), (t2 - t1), out=np.zeros_like(t1), where=((t2 - t1) != 0))\n b = np.append((1 - omega) * b, 0) + np.insert((omega * b), 0, 0)\n\n return b","def find_probability(problist, listoffive):\n\tprobs = []\n\tfor i in listoffive:\n\t\tprobs.append(problist[i])\n\ttotprob = 1\n\tfor n in probs:\n\t\ttotprob = totprob * n\n\treturn totprob","def pt_bayescount(Pr, Nt):\n # all credit goes to panzeri-treves\n\n # dimension of space\n dim = Pr.size\n\n # non zero probs only\n PrNZ = Pr[Pr>np.finfo(np.float).eps]\n Rnaive = PrNZ.size\n\n R = Rnaive\n if Rnaive < dim:\n Rexpected = Rnaive - ((1.0-PrNZ)**Nt).sum()\n deltaR_prev = dim\n deltaR = np.abs(Rnaive - Rexpected)\n xtr = 0.0\n while (deltaR < deltaR_prev) and ((Rnaive+xtr)<dim):\n xtr = xtr+1.0\n Rexpected = 0.0\n # occupied bins\n gamma = xtr*(1.0 - ((Nt/(Nt+Rnaive))**(1.0/Nt)))\n Pbayes = ((1.0-gamma) / (Nt+Rnaive)) * (PrNZ*Nt+1.0)\n Rexpected = (1.0 - (1.0-Pbayes)**Nt).sum()\n # non-occupied bins\n Pbayes = gamma / xtr\n Rexpected = Rexpected + xtr*(1.0 - (1.0 - Pbayes)**Nt)\n deltaR_prev = deltaR\n deltaR = np.abs(Rnaive - Rexpected)\n Rnaive = Rnaive + xtr - 1.0\n if deltaR < deltaR_prev:\n Rnaive += 1.0\n return Rnaive","def categorical_sample(prob_n, np_random):\n prob_n = np.asarray(prob_n)\n csprob_n = np.cumsum(prob_n)\n return (csprob_n > np_random.rand()).argmax()","def categorical_sample(prob_n, np_random):\n prob_n = np.asarray(prob_n)\n csprob_n = np.cumsum(prob_n)\n return (csprob_n > np_random.rand()).argmax()","def prob_choice(p):\n \n return np.random.random_sample() < p","def discrete_sampling(N, probs, states=None):\n\n p = probs.squeeze()/np.sum(probs)\n\n bins = np.digitize(\n np.random.uniform(0., 1., (N, 1)), np.hstack((0, np.cumsum(p))))-1\n\n if states is None:\n x = bins\n else:\n assert(states.shape[0] == probs.shape[0])\n x = states[bins]\n\n return x.squeeze()","def _bernoulli_upper(self, p, n, delta):\n if p > 1 - 1e-6:\n return 1.\n else:\n upper = scipy.stats.beta.ppf(1. - delta / 2, p * n + 1, n - p * n)\n return upper","def bernoulli(n):\n\n x, res, s, c = Rat(0), Rat(0), Rat(0), Rat(-1)\n for k in range(1, n+2):\n c *= 1 - Rat(n + 2)/k\n s += x**n\n x += 1\n res += c*s/k\n return res","def at_most_n_ver(seq, n):\r\n num_AB = 0\r\n for item in seq:\r\n if np.array_equal(item, Quantifier.AB):\r\n if num_AB == n:\r\n return Quantifier.F\r\n else:\r\n num_AB += 1\r\n return Quantifier.T","def binomial_histogram(p: float, n: int, num_points: int) -> None:\n \n data = [binomial(n, p) for _ in range(num_points)]\n \n # use bar chart to show the actual binomial samples\n histogram = Counter(data)\n plt.bar([x - 0.4 for x in histogram.keys()], \n [v / num_points for v in histogram.values()],\n 0.8,\n color='0.75')\n \n mu = p * n\n sigma = math.sqrt(n * p * (1 - p))\n \n # use a line chart to show the normal approximation\n xs = range(min(data), max(data) + 1)\n ys = [normal_cdf(i + 0.5, mu, sigma) - normal_cdf(i - 0.5, mu, sigma)\n for i in xs]\n plt.plot(xs, ys)\n plt.title(\"Binomial distribution vs. Normal Approximation\")","def sdd(events,probs):\n \n import random\n nprobs=[x*1000 for x in probs] #so, here i multiply each float in 'probs' by 1000 and store the products in 'nprobs'\n newlist=[]\n for a in range(len(events)) : #then, in this loop, i create a list (newlist), in which each event appears 1000*its probability times\n b=nprobs[a]\n b=int(b)\n for c in range(b) :\n newlist.append(events[a]) \n return (random.choice(newlist)) #and finally, i ramdonly sample ","def sample_from_log_prob(A, n):\n A_cum = log_cum_sum(A)\n C_pos = [ -c for c in reversed(A_cum)]\n sel = np.log(np.random.random(n))\n pos = [len(A) - np.searchsorted(C_pos,-r) for r in sel]\n return pos","def multinomial_prob(counts, probs):\n return nCkarray(*counts.values) * (probs ** counts).prod()","def simple_genotype_matrix(n, p):\n genotypes = np.zeros(shape=(n, p))\n for item in range(0, p):\n genotypes[:, item] = np.random.binomial(1, np.random.uniform(0.1, 0.5, 1), n)\n\n return genotypes","def probability_array(self):\n q = self.apply_weights()\n return np.exp(q)/(1 + np.exp(q))","def freq_to_genotype(pL,sL,er):\n h = sum(pL) # number of different haplotypes\n L = [ bin(x)[2:] for x in range(1,2**h-1) ] # range from 1 to 2**h-1 because we don't want 0% or 100% allele freq\n M = [ '0'*(len(L[-1])-len(x))+x for x in L ]\n p_freqL = []\n for i in range(len(pL)):\n p_freqL += [sL[i]/pL[i]]*pL[i]\n p_freqA = np.array(p_freqL)\n sA = np.array(sL)\n aD = {} # dict where each key is an expected alternate allele frequency and each value is a list of genotypes consistent with this alternate allele frequency\n for g in M:\n alt_freq = sum(np.array([ int(x) for x in list(g) ])*p_freqL)\n if aD.has_key(alt_freq):\n aD[alt_freq].append(g)\n else:\n aD[alt_freq] = [g]\n aD[er] = ['0'*(len(L[-1])-1) + bin(0)[2:]] # add genotype for 0% alternate allele freq\n aD[1-er] = [bin(2**h-1)[2:]] # add genotype for 100% alternate allele freq\n return aD","def categorical_sample(prob_n, np_random = None):\n prob_n = np.asarray(prob_n)\n csprob_n = np.cumsum(prob_n)\n return (csprob_n > np.random.rand()).argmax()","def afprob (dfnum, dfden, F):\r\n if type(F) == N.ndarray:\r\n return abetai(0.5*dfden, 0.5*dfnum, dfden/(1.0*dfden+dfnum*F))\r\n else:\r\n return abetai(0.5*dfden, 0.5*dfnum, dfden/float(dfden+dfnum*F))","def sample_pagerank(corpus, damping_factor, n):\n probabilities = dict()\n samples = []\n\n # Random first sample\n page = random.choice(list(corpus.keys()))\n samples.append(page)\n \n # Remaining samples after first\n for i in range(n-1):\n p = transition_model(corpus, page, damping_factor)\n page = random.choices(list(p.keys()), weights=list(p.values()), k=1)[0]\n samples.append(page)\n\n # Count\n for p in corpus.keys():\n probabilities[p] = samples.count(p) / n\n\n return probabilities","def bin_dist(n, p, x):\n def factorial(x):\n if x >= 0:\n \n factorial = 1\n\n for i in range(1, x + 1):\n factorial = float(factorial * i)\n # print(f' The factorial of {x} is {factorial}') \n return factorial\n\n else:\n raise ValueError(\"Sorry x cannot be a negative number\")\n\n def combination(n, r):\n \"\"\"\n Given n total number of items,\n what is the number of possible ways\n to choose r items from it?\n\n :param n: total number of items (integer)\n :param r: number of items to arrange (int)\n :return: number of combinations\n :rtype: integer\n \"\"\"\n\n \n\n \n numerator = factorial(n)\n denominator = factorial(r)\n subtracted_answer = factorial(n-r)\n \n\n answer = numerator/(denominator * subtracted_answer)\n print(answer)\n return answer \n\n # from scipy.special import comb\n if x > n:\n raise ValueError(\"Error, x must be less than n\")\n else:\n\n\n prob_success = float((combination(n, x)) * ((p**x)*((1-p)**(n-x))))\n\n print(prob_success)\n return prob_success","def sample_from_probabilities(probabilities, topn=ALPHASIZE):\n p = np.squeeze(probabilities)\n p[np.argsort(p)[:-topn]] = 0\n p = p / np.sum(p)\n return np.random.choice(ALPHASIZE, 1, p=p)[0]","def get_probs(self, a):\n with torch.no_grad():\n probabilities = (np.array(self.priorities) ** a) / sum(np.array(self.priorities) ** a)\n return probabilities","def bin2freq(b, srate, N):\n return (b / float(N)) * float(srate)","def estimate_p(alignment, n=None):\n if n is None:\n n = alignment.shape[0]\n return np.sum(alignment, axis=0)/n","def pratt1(array):\n res = []\n max_size = len(array) // 2\n pow3 = 1 # start with q = 0\n while pow3 <= max_size:\n # At this point, pow3 = 3**q, so set p = 0\n pow2 = pow3\n while pow2 <= max_size:\n # At this point, pow2 = 2**p * 3**q\n res.append(pow2)\n pow2 = pow2 * 2 # this is like adding 1 to p\n # now that p overflowed the maximum size, add 1 to q and start over\n pow3 = pow3 * 3\n return list(reversed(sorted(res)))","def pratt1(array):\n res = []\n max_size = len(array) // 2\n pow3 = 1 # start with q = 0\n while pow3 <= max_size:\n # At this point, pow3 = 3**q, so set p = 0\n pow2 = pow3\n while pow2 <= max_size:\n # At this point, pow2 = 2**p * 3**q\n res.append(pow2)\n pow2 = pow2 * 2 # this is like adding 1 to p\n # now that p overflowed the maximum size, add 1 to q and start over\n pow3 = pow3 * 3\n return list(reversed(sorted(res)))","def p(self) -> Probability:\n ...","def at_least_n_ver(seq, n):\r\n num_AB = 0\r\n for item in seq:\r\n if np.array_equal(item, Quantifier.AB):\r\n if num_AB == n-1:\r\n return Quantifier.T\r\n else:\r\n num_AB += 1\r\n return Quantifier.F","def gen_a(cls, p):\n a = random.randint(1, p-1)\n while cls.gcd(a, p) != 1:\n a = random.randint(1, p-1)\n return a","def sample(probs):\n\n probs = probs / probs.sum()\n return np.random.choice(np.arange(len(probs)), p=probs.flatten())","def equation_of_state_p_N(N_balls_arr, temperature, volume):\r\n\tpressure_arr = np.array(list(map(lambda N: k_B*temperature/volume*N, N_balls_arr)))\r\n\treturn pressure_arr"],"string":"[\n \"def binomial(n: int, p: float) -> int:\\n return sum(bernoulli_trial(p) for _ in range(n))\",\n \"def bpmfln(k, n, p):\\n bnm = np.empty_like(n, dtype=np.float64)\\n logp = math.log(p)\\n one_logp = math.log(1 - p)\\n for i in range(len(k)):\\n bnm[i] = math.exp(combinln(n[i], k[i]) + k[i] *\\n logp + (n[i] - k[i]) * one_logp)\\n return bnm\",\n \"def bpmfln(k, n, p):\\n return np.exp(combinln(n, k) + k * np.log(p) + (n - k) * np.log(1 - p))\",\n \"def binomial(n, p):\\n sum_ans = 0\\n for k in range(n):\\n sum_ans = sum_ans + bernoulli(p)\\n return sum_ans\",\n \"def probability(n, k, p):\\n prob = 0\\n power = expotentation_by_squaring((1-p), n)\\n count_mult = math.log(n, 2)\\n p_fraction = p/(1-p)\\n count_mult += 1\\n for i in range(0, k+1):\\n element = newton(n, i)*power\\n prob += element\\n power *= p_fraction\\n count_mult += 2\\n return prob, count_mult\",\n \"def chance(n, p):\\n total = 0.0\\n for k in range(n+1):\\n total += comb(n, k, exact=False) * p**k * (1-p) ** (n-k)\\n return total\",\n \"def prob1(n):\\n#raise NotImplementedError(\\\"Problem 1 Incomplete\\\")\\n if n == 0 :\\n raise ValueError(\\\"Sampling 0 points is not defined.\\\")\\n total = 0\\n for i in xrange(n) :\\n if np.random.normal() > 3 :\\n total += 1\\n return float(total)/n\",\n \"def perform_bernoulli_trials(n, p):\\n # Initialize number of successes: n_success\\n n_success = 0\\n\\n\\n # Perform trials\\n for i in range(n):\\n # Choose random number between zero and one: random_number\\n random_number = np.random.random()\\n\\n\\n # If less than p, it's a success so add one to n_success\\n if random_number < p:\\n n_success += 1\\n\\n return n_success\",\n \"def perform_bernoulli_trials(n, p):\\n # Initialize number of successes: n_success\\n n_success = 0\\n\\n\\n # Perform trials\\n for i in range(n):\\n # Choose random number between zero and one: random_number\\n random_number = np.random.random()\\n\\n # If less than p, it's a success so add one to n_success\\n if random_number< p:\\n n_success += 1\\n\\n return n_success\",\n \"def perform_bernoulli_trials(n, p):\\n # Initialize number of successes: n_success\\n n_success = 0\\n # Perform trials\\n for i in range(n):\\n # Choose random number between zero and one: random_number\\n random_number = np.random.random()\\n # If less than p, it's a success so add one to n_success\\n if random_number < p:\\n n_success += 1\\n\\n return n_success\",\n \"def perform_bernoulli_trials(n, p):\\n # Initialize number of successes: n_success\\n n_success = 0\\n\\n # Perform trials\\n for i in range(n):\\n # Choose random number between zero and one: random_number\\n random_number = np.random.random()\\n\\n # If less than p, it's a success so add one to n_success\\n if random_number < p:\\n n_success += 1\\n\\n return n_success\",\n \"def b_n(n):\\n if n <= 0.36: # MCH03\\n ei = np.array([0, 1, 2, 3, 4])\\n ai = np.array([0.01945, -0.8902, 10.95, -19.67, 13.43])\\n else: # CB99\\n ei = np.array([1, 0, -1, -2])\\n ai = np.array([2, -1./3, 4./405, 46./25515])\\n return np.sum(ai * np.power(float(n), ei))\",\n \"def bpmf(k, n, p):\\n # this does not work for large n\\n return comb(n, k) * (p**k) * ((1 - p)**(n - k))\",\n \"def erb2freq(n_erb):\\n return 24.7 * 9.265 * (np.exp(n_erb / 9.265) - 1)\",\n \"def beta_gen_posmnt(p):\\n return np.array([0.0]*int(0.7*p) + [1.0]*(p-int(0.7*p)))\",\n \"def probability_of_all_successes(p: float, r: int, n: int) -> float:\\n\\n if r == 1:\\n return pow(p, n)\\n elif n == 0:\\n return 1\\n else:\\n result = 0\\n for x in range(0, n+1):\\n result += pow(p, x) * pow(1-p, n-x) * probability_of_all_successes(p, r-1, n-x)\\n return result\",\n \"def powBeta( n ):\\n return (1-alphaval)*Fib(n) + Fib(n-1)\\n #return Fib(n+1) - Fib(n) * alphaval\",\n \"def bernoulli(p):\\r\\n if np.random.random() < p:\\r\\n return 0\\r\\n else:\\r\\n return 1\",\n \"def base_binom_pro(pro,n0):\\n res = stats.binom.pmf(range(n0+1), n0, 1/2.0)\\n a = 0\\n for i in range(n0+1):\\n a = a + res[i]\\n if a>=pro: \\n return i\",\n \"def generate_possible_freqL(pL,sL,er):\\n h = sum(pL) # number of different haplotypes\\n L = [ bin(x)[2:] for x in range(1,2**h-1) ] # range from 1 to 2**h-1 because we don't want 0% or 100% allele freq\\n M = [ '0'*(len(L[-1])-len(x))+x for x in L ]\\n p_freqL = []\\n for i in range(len(pL)):\\n p_freqL += [sL[i]/pL[i]]*pL[i]\\n p_freqA = np.array(p_freqL)\\n sA = np.array(sL)\\n aL = []\\n for g in M:\\n aL.append(sum(np.array([ int(x) for x in list(g) ])*p_freqL))\\n return sorted(list(set(aL+[er,1-er])))\",\n \"def binom_by_normal(n, p, N=1):\\n n = np.atleast_1d(n) # This is a bit annoying, but I don't see any simpler way to handle the possibility of scalar inputs\\n small_n = n < (1 << 31)\\n\\n if len(n) == 1: # Single state in\\n if small_n:\\n return np.int64(binom.rvs(n, p, size=N))\\n else:\\n return np.int64(np.random.normal(n * p, np.sqrt(n * p * (1 - p)), size=N))\\n else: # Multiple states in\\n samples = np.zeros(N,dtype=np.int64)\\n large_n = np.invert(small_n)\\n num_small_n = sum(small_n)\\n if np.asarray(p).ndim==0: # Single scalar variance\\n samples[small_n] = np.int64(binom.rvs(n[small_n].astype(np.int32), p, size=num_small_n))\\n samples[large_n] = np.int64(\\n np.random.normal(n[large_n] * p, np.sqrt(n[large_n] * p * (1 - p)), size=N - num_small_n))\\n else:\\n samples[small_n] = np.int64(binom.rvs(n[small_n].astype(np.int32), p[small_n], size=num_small_n))\\n samples[large_n] = np.int64(\\n np.random.normal(n[large_n] * p[large_n], np.sqrt(n[large_n] * p[large_n] * (1 - p[large_n])), size=N - num_small_n))\\n\\n return samples\",\n \"def sample(a, p):\\n if (len(a) != len(p)):\\n raise Exception('a != p')\\n p = np.array(p)\\n p = p / p.sum()\\n r = random.random()\\n n = len(a)\\n total = 0 # range: [0,1]\\n for i in xrange(n):\\n total += p[i]\\n if total > r:\\n return a[i]\\n return a[i]\",\n \"def entropy_numba(p):\\n return 2 ** np.sum(-p*np.log2(p+1e-10))\",\n \"def test_bp_example():\\n signal = np.array([4, 7, 9, 10, 6, 11, 3])\\n\\n pe = permutation_entropy(signal, 2)\\n\\n assert 0.91 < pe < 0.92 # Should be approx 0.918.\\n\\n pe = permutation_entropy(signal, 3)\\n\\n assert 1.52 < pe < 1.53 # Should be approx 1.522.\",\n \"def _proba(y):\\n N = len(y)\\n _, counts = np.unique(y, return_counts=True)\\n return counts / N\",\n \"def bernoulli(p):\\n bern = rn.binomial(1,p)\\n return bern\",\n \"def prob_t_N(genotype, base):\\n cnter = Counter(genotype)\\n return cnter.get(base, 0) * 1/len(genotype)\",\n \"def semitone_diff_to_frequency(n: float) -> float:\\n return A4 * A**n\",\n \"def Pollard_pm1(n, primes, max_B=1000000):\\n B = 10\\n g = 1\\n while B < max_B and g < n:\\n a = randint(2, n - 2)\\n g = gcd(a, n)\\n if g != 1:\\n return g\\n for p in primes:\\n if p >= B:\\n break\\n pd = 1 # p^d\\n while pd * p <= B:\\n pd *= p\\n a = powmod(a, pd, n)\\n g = gcd(a - 1, n)\\n if g != 1 and g != n:\\n return g\\n B *= 2\\n return 1\",\n \"def uniform_but_one_dataset(n, p):\\n elements = []\\n for i in range(n):\\n elements.append((i, 1))\\n elements.append((1, (n**(1.0 / p)) - 1))\\n return elements\",\n \"def prob_m_of_n(m, n, T, l):\\n PFD_one_unit = l*T\\n m_of_n = binom(n, m) * (PFD_one_unit)**(n-m) * (1-PFD_one_unit)**m\\n return m_of_n\",\n \"def bic(self, p=1) -> float:\\n assert p >= 0\\n\\n n = len(self.true)\\n return float(n * np.log(self.sse() / n) + p * np.log(n))\",\n \"def toep_gram(P: int, N: int) -> np.ndarray:\\n weights = np.ones(shape=(N,)) * (P + 1)\\n weights[:P] = np.arange(1, P + 1)\\n weights[N - P:] = np.flip(np.arange(1, P + 1), axis=0)\\n return weights\",\n \"def random_walk(n, p):\\n random_array = np.random.uniform(0, 1, n)\\n left = random_array[random_array > p].size\\n right = n - left\\n \\n return (right-left)\",\n \"def payoff_n_p(p, n=3,\\n MLB_contract=4158333, minor_contract=6600, thresh=1500000):\\n distribution = []\\n for n_makers in range(n + 1):\\n if n_makers == 0:\\n payoff_prob = [1 - prob for prob in p.values()]\\n payoff_prob = np.prod(payoff_prob)\\n distribution.append((minor_contract, payoff_prob))\\n elif n_makers == n:\\n payoff_prob = [prob for prob in p.values()]\\n payoff_prob = np.prod(payoff_prob)\\n distribution.append((MLB_contract, payoff_prob))\\n else:\\n makers = list(combinations(range(1, n + 1), n_makers))\\n for maker_set in makers:\\n if 1 in maker_set:\\n payoff = MLB_contract - 0.1*(MLB_contract-thresh)\\n payoff += (n_makers-1)*0.1*(MLB_contract-thresh)/(n-1)\\n payoff_prob = [p[player] for player in maker_set]\\n payoff_prob += [1-p[player] for player in p.keys() if player not in maker_set]\\n payoff_prob = np.prod(payoff_prob)\\n distribution.append((payoff, payoff_prob))\\n else:\\n payoff = minor_contract\\n payoff += n_makers*0.1*(MLB_contract-thresh)/(n-1)\\n payoff_prob = [p[player] for player in maker_set]\\n payoff_prob += [1-p[player] for player in p.keys() if player not in maker_set]\\n payoff_prob = np.prod(payoff_prob)\\n distribution.append((payoff, payoff_prob))\\n E_payoff = [a*b for (a, b) in distribution]\\n E_payoff = sum(E_payoff)\\n var_payoff = [((a-E_payoff)**2)*b for (a, b) in distribution]\\n var_payoff = sum(var_payoff)\\n return E_payoff, var_payoff**0.5\",\n \"def prob_list(n, p):\\n ...\\n#pn is the probability of seeing a pair of aces two hands in a row in n hands.\\n#qn = 1 - pn\\n#thus qn is the probability of NOT seeing a par of aces two hands in a row in n hands.\\n list = []\\n qn_a = 1\\n qn_b = 1\\n list.append(qn_a)\\n list.append(qn_b)\\n\\n for i in range(n-2):\\n i += 0\\n qn_next = (1-p)*qn_b + p*(1-p)*qn_a\\n list.append(qn_next)\\n qn_a = qn_b\\n qn_b = qn_next\\n return list\\n\\n\\n # returns list of probs. [q0,... qn]\",\n \"def normal_approximation_to_binomial(n, p):\\r\\n mu = p*n\\r\\n sigma = math.sqrt(p * (1 - p) * n)\\r\\n\\r\\n return mu, sigma\",\n \"def powAlpha( n ):\\n return (1-betaval)*Fib(n) + Fib(n-1)\\n #return Fib(n+1) - Fib(n) * betaval\",\n \"def get_probability(letters, n):\\n return {l: c/n for l, c in letters.items()}\",\n \"def uniform_but_one_dataset_no_weight(n, p):\\n elements = []\\n for i in range(n):\\n elements.append(i)\\n for i in range(int(n**(1.0 / p)) - 1):\\n elements.append(1)\\n return elements\",\n \"def bernoulli_trial(p: float) -> int:\\n return 1 if random.random() < p else 0\",\n \"def beta_gen_lasso(p):\\n cardi = 0.005\\n return np.array([0]*int(p-int(cardi*p)) + [1]*int(cardi*p))\",\n \"def binom_pdf(k, n,p,binom):\\n return binom * p**k * (1-p)**(n-k)\",\n \"def normal_aproximation_to_binomial(n: int, p: float) -> Tuple[float, float]:\\n mu = p * n\\n sigma = math.sqrt(p * (1 - p) * n)\\n return mu, sigma\",\n \"def beta_gen_mnt(p):\\n return np.array([-1.0]*int(0.7*p) + [1.0]*(p-int(0.7*p)))\",\n \"def prob1(n):\\n\\n # create a giant draw from a normal distribution\\n random_draws = np.random.normal(loc= 0, scale = 1, size = n)\\n\\n # mask the values\\n mask = random_draws > 3\\n\\n return np.sum(mask)/float(n)\",\n \"def normal_approximation_to_binomial(n, p):\\n mu = p * n\\n sigma = math.sqrt(p * (1 - p) * n)\\n return mu, sigma\",\n \"def normal_approximation_to_binomial(n, p):\\n mu = p * n\\n sigma = math.sqrt(p * (1 - p) * n)\\n return mu, sigma\",\n \"def normal_approximation_to_binomial(n, p):\\n mu = p * n\\n sigma = math.sqrt(p * (1 - p) * n)\\n return mu, sigma\",\n \"def normal_approximation_to_binomial(n, p):\\n mu = p * n\\n sigma = math.sqrt(p * (1 - p) * n)\\n return mu, sigma\",\n \"def P(N,L) :\\n accum = 0.0\\n sign = 1.0\\n for i in xrange(len(L)+1) :\\n accum2 = 0.0\\n for combin in combinations(L,i) :\\n term = 1.0\\n j = 0.0\\n for Li in combin :\\n term *= Li/(N-j)\\n j += 1\\n accum2 += term\\n accum += sign*accum2\\n sign *= -1.0\\n return accum\",\n \"def normal_approximation_to_binomial(n, p):\\n mu = p * n\\n sigma = math.sqrt(p * (1 - p ) * n)\\n return mu, sigma\",\n \"def prbs(m, n):\\n return np.array(np.random.rand(m, n) > 0.5, dtype=np.int) - 0.5\",\n \"def calculate_probability(k: int, m: int, n: int) -> float:\\n population = [\\\"AA\\\" for _ in range(k)] + [\\\"Aa\\\" for _ in range(m)] + [\\\"aa\\\" for _ in range(n)]\\n pairings = it.combinations(population, 2)\\n probabilities = [PROBABILITIES[pairing] for pairing in pairings]\\n output = sum(probabilities) / len(probabilities)\\n\\n return output\",\n \"def bernoulli_logpmf(X, p):\\n return -T.nnet.binary_crossentropy(p, X).sum(axis=-1)\",\n \"def Bnum(n):\\n n = int(n)\\n a = {}\\n for m in xrange(0, n+1):\\n a[m] = 1/(m+1)\\n for j in reversed(xrange(1, m+1)):\\n a[j-1] = j*(a[j-1] - a[j])\\n return a[0]\",\n \"def sample_discrete(probs):\\r\\n q = np.random.rand()\\r\\n i = 0\\r\\n p_sum = 0.0\\r\\n while p_sum < q:\\r\\n p_sum += probs[i]\\r\\n i += 1\\r\\n return i - 1\",\n \"def p_adjust_bh(p):\\n p = np.asfarray(p)\\n by_descend = p.argsort()[::-1]\\n by_orig = by_descend.argsort()\\n steps = float(len(p)) / np.arange(len(p), 0, -1)\\n q = np.minimum(1, np.minimum.accumulate(steps * p[by_descend]))\\n return q[by_orig]\",\n \"def generate_bits(n, p):\\n try:\\n return (np.random.rand(*n) > p).astype(int)\\n except TypeError:\\n return (np.random.rand(n) > p).astype(int)\",\n \"def calc_probs(log_p):\\n\\n N = log_p.shape[0]\\n\\n log_Z_per_N = np.zeros(shape=(N, 1))\\n\\n for i in range(N):\\n\\n log_Z_per_N[i] = log_norm(log_p[i])\\n\\n log_p_new = log_p - log_Z_per_N\\n\\n p = np.exp(log_p_new)\\n\\n # log_Z = log_norm(log_p)\\n\\n # p = np.exp(log_p - log_Z)\\n\\n return p\",\n \"def binomVariance(n, p):\\r\\n variance_of_Y = p * (1- p)\\r\\n variance_of_X = n * variance_of_Y \\r\\n print(\\\"variance of X = \\\", variance_of_X)\\r\\n return variance_of_X\",\n \"def sumFibPower(n, p, viaPowers = True):\\n #Expand power, sum each element, then convert to fibonacci numbers\\n s = 0\\n for i in range(0, p+1):\\n si = sumGeomAB( p-i, i, 0, n, viaPowers=viaPowers )\\n s = s + binomial(p,i) * (-1)**(i) * si\\n return s / (sqrts(5))**p\",\n \"def dbinom(self, x, n, p):\\n f = math.factorial\\n C = Decimal(f(n) / (f(x) * f(n-x)))\\n return C * p**x * (1-p)**(n-x)\",\n \"def get_probs(Q_s, epsilon, nA):\\n policy_s = np.ones(nA) * epsilon / nA\\n best_a = np.argmax(Q_s)\\n policy_s[best_a] = 1 - epsilon + (epsilon / nA)\\n return policy_s\",\n \"def _calc_cartwright_pf(n, a):\\n args = numba.carray(a, n)\\n x = args[0]\\n num_extrema = args[1]\\n bandwidth = args[2]\\n return 1. - (1. - bandwidth * np.exp(-x * x)) ** num_extrema\",\n \"def compute_prob_mle(X: np.ndarray, n: int) -> float:\\n\\n assert n > 1, \\\"for n = 1 use Bernoulli distribution.\\\"\\n Binomial._check_input_data(X=X)\\n Binomial._check_support(X=X, n=n)\\n\\n prob = X.mean() / n\\n return prob\",\n \"def get_prob_for_distributions(p):\\n w1 = p[0]\\n mu1 = p[1]\\n sigma1 = p[2]\\n w2 = p[3]\\n mu2 = p[4]\\n sigma2 = p[5]\\n w3 = p[6]\\n mu3 = p[7]\\n sigma3 = p[8]\\n dist_range = (0, 4.330310991999920844e+01)\\n x = np.linspace(dist_range[0], dist_range[1], 1000)\\n A1 = np.array(w1 * mlab.normpdf(x, mu1, sigma1)).sum()\\n A2 = np.array(w2 * mlab.normpdf(x, mu2, sigma2)).sum()\\n A3 = np.array(w3 * mlab.normpdf(x, mu3, sigma3)).sum()\\n p1 = A1 / (A1 + A2 + A3)\\n p2 = A2 / (A1 + A2 + A3)\\n p3 = A3 / (A1 + A2 + A3)\\n return p1, p2, p3\",\n \"def random_pmf(nb_labels):\\n random_numbers = np.random.random(nb_labels)\\n return random_numbers / np.sum(random_numbers)\",\n \"def a2p(a, N):\\n return N + 2 + (a % N) + (a // N) * (N + 1)\",\n \"def algorithm_2_21(p, t, x):\\n\\n t = np.array(t, dtype=np.float64)\\n b = 1\\n mu = index(x, t)\\n\\n for k in range(1, p + 1):\\n t1 = t[mu - k + 1:mu + 1]\\n t2 = t[mu + 1:mu + k + 1]\\n omega = np.divide(\\n (x - t1), (t2 - t1), out=np.zeros_like(t1), where=((t2 - t1) != 0))\\n b = np.append((1 - omega) * b, 0) + np.insert((omega * b), 0, 0)\\n\\n return b\",\n \"def find_probability(problist, listoffive):\\n\\tprobs = []\\n\\tfor i in listoffive:\\n\\t\\tprobs.append(problist[i])\\n\\ttotprob = 1\\n\\tfor n in probs:\\n\\t\\ttotprob = totprob * n\\n\\treturn totprob\",\n \"def pt_bayescount(Pr, Nt):\\n # all credit goes to panzeri-treves\\n\\n # dimension of space\\n dim = Pr.size\\n\\n # non zero probs only\\n PrNZ = Pr[Pr>np.finfo(np.float).eps]\\n Rnaive = PrNZ.size\\n\\n R = Rnaive\\n if Rnaive < dim:\\n Rexpected = Rnaive - ((1.0-PrNZ)**Nt).sum()\\n deltaR_prev = dim\\n deltaR = np.abs(Rnaive - Rexpected)\\n xtr = 0.0\\n while (deltaR < deltaR_prev) and ((Rnaive+xtr)<dim):\\n xtr = xtr+1.0\\n Rexpected = 0.0\\n # occupied bins\\n gamma = xtr*(1.0 - ((Nt/(Nt+Rnaive))**(1.0/Nt)))\\n Pbayes = ((1.0-gamma) / (Nt+Rnaive)) * (PrNZ*Nt+1.0)\\n Rexpected = (1.0 - (1.0-Pbayes)**Nt).sum()\\n # non-occupied bins\\n Pbayes = gamma / xtr\\n Rexpected = Rexpected + xtr*(1.0 - (1.0 - Pbayes)**Nt)\\n deltaR_prev = deltaR\\n deltaR = np.abs(Rnaive - Rexpected)\\n Rnaive = Rnaive + xtr - 1.0\\n if deltaR < deltaR_prev:\\n Rnaive += 1.0\\n return Rnaive\",\n \"def categorical_sample(prob_n, np_random):\\n prob_n = np.asarray(prob_n)\\n csprob_n = np.cumsum(prob_n)\\n return (csprob_n > np_random.rand()).argmax()\",\n \"def categorical_sample(prob_n, np_random):\\n prob_n = np.asarray(prob_n)\\n csprob_n = np.cumsum(prob_n)\\n return (csprob_n > np_random.rand()).argmax()\",\n \"def prob_choice(p):\\n \\n return np.random.random_sample() < p\",\n \"def discrete_sampling(N, probs, states=None):\\n\\n p = probs.squeeze()/np.sum(probs)\\n\\n bins = np.digitize(\\n np.random.uniform(0., 1., (N, 1)), np.hstack((0, np.cumsum(p))))-1\\n\\n if states is None:\\n x = bins\\n else:\\n assert(states.shape[0] == probs.shape[0])\\n x = states[bins]\\n\\n return x.squeeze()\",\n \"def _bernoulli_upper(self, p, n, delta):\\n if p > 1 - 1e-6:\\n return 1.\\n else:\\n upper = scipy.stats.beta.ppf(1. - delta / 2, p * n + 1, n - p * n)\\n return upper\",\n \"def bernoulli(n):\\n\\n x, res, s, c = Rat(0), Rat(0), Rat(0), Rat(-1)\\n for k in range(1, n+2):\\n c *= 1 - Rat(n + 2)/k\\n s += x**n\\n x += 1\\n res += c*s/k\\n return res\",\n \"def at_most_n_ver(seq, n):\\r\\n num_AB = 0\\r\\n for item in seq:\\r\\n if np.array_equal(item, Quantifier.AB):\\r\\n if num_AB == n:\\r\\n return Quantifier.F\\r\\n else:\\r\\n num_AB += 1\\r\\n return Quantifier.T\",\n \"def binomial_histogram(p: float, n: int, num_points: int) -> None:\\n \\n data = [binomial(n, p) for _ in range(num_points)]\\n \\n # use bar chart to show the actual binomial samples\\n histogram = Counter(data)\\n plt.bar([x - 0.4 for x in histogram.keys()], \\n [v / num_points for v in histogram.values()],\\n 0.8,\\n color='0.75')\\n \\n mu = p * n\\n sigma = math.sqrt(n * p * (1 - p))\\n \\n # use a line chart to show the normal approximation\\n xs = range(min(data), max(data) + 1)\\n ys = [normal_cdf(i + 0.5, mu, sigma) - normal_cdf(i - 0.5, mu, sigma)\\n for i in xs]\\n plt.plot(xs, ys)\\n plt.title(\\\"Binomial distribution vs. Normal Approximation\\\")\",\n \"def sdd(events,probs):\\n \\n import random\\n nprobs=[x*1000 for x in probs] #so, here i multiply each float in 'probs' by 1000 and store the products in 'nprobs'\\n newlist=[]\\n for a in range(len(events)) : #then, in this loop, i create a list (newlist), in which each event appears 1000*its probability times\\n b=nprobs[a]\\n b=int(b)\\n for c in range(b) :\\n newlist.append(events[a]) \\n return (random.choice(newlist)) #and finally, i ramdonly sample \",\n \"def sample_from_log_prob(A, n):\\n A_cum = log_cum_sum(A)\\n C_pos = [ -c for c in reversed(A_cum)]\\n sel = np.log(np.random.random(n))\\n pos = [len(A) - np.searchsorted(C_pos,-r) for r in sel]\\n return pos\",\n \"def multinomial_prob(counts, probs):\\n return nCkarray(*counts.values) * (probs ** counts).prod()\",\n \"def simple_genotype_matrix(n, p):\\n genotypes = np.zeros(shape=(n, p))\\n for item in range(0, p):\\n genotypes[:, item] = np.random.binomial(1, np.random.uniform(0.1, 0.5, 1), n)\\n\\n return genotypes\",\n \"def probability_array(self):\\n q = self.apply_weights()\\n return np.exp(q)/(1 + np.exp(q))\",\n \"def freq_to_genotype(pL,sL,er):\\n h = sum(pL) # number of different haplotypes\\n L = [ bin(x)[2:] for x in range(1,2**h-1) ] # range from 1 to 2**h-1 because we don't want 0% or 100% allele freq\\n M = [ '0'*(len(L[-1])-len(x))+x for x in L ]\\n p_freqL = []\\n for i in range(len(pL)):\\n p_freqL += [sL[i]/pL[i]]*pL[i]\\n p_freqA = np.array(p_freqL)\\n sA = np.array(sL)\\n aD = {} # dict where each key is an expected alternate allele frequency and each value is a list of genotypes consistent with this alternate allele frequency\\n for g in M:\\n alt_freq = sum(np.array([ int(x) for x in list(g) ])*p_freqL)\\n if aD.has_key(alt_freq):\\n aD[alt_freq].append(g)\\n else:\\n aD[alt_freq] = [g]\\n aD[er] = ['0'*(len(L[-1])-1) + bin(0)[2:]] # add genotype for 0% alternate allele freq\\n aD[1-er] = [bin(2**h-1)[2:]] # add genotype for 100% alternate allele freq\\n return aD\",\n \"def categorical_sample(prob_n, np_random = None):\\n prob_n = np.asarray(prob_n)\\n csprob_n = np.cumsum(prob_n)\\n return (csprob_n > np.random.rand()).argmax()\",\n \"def afprob (dfnum, dfden, F):\\r\\n if type(F) == N.ndarray:\\r\\n return abetai(0.5*dfden, 0.5*dfnum, dfden/(1.0*dfden+dfnum*F))\\r\\n else:\\r\\n return abetai(0.5*dfden, 0.5*dfnum, dfden/float(dfden+dfnum*F))\",\n \"def sample_pagerank(corpus, damping_factor, n):\\n probabilities = dict()\\n samples = []\\n\\n # Random first sample\\n page = random.choice(list(corpus.keys()))\\n samples.append(page)\\n \\n # Remaining samples after first\\n for i in range(n-1):\\n p = transition_model(corpus, page, damping_factor)\\n page = random.choices(list(p.keys()), weights=list(p.values()), k=1)[0]\\n samples.append(page)\\n\\n # Count\\n for p in corpus.keys():\\n probabilities[p] = samples.count(p) / n\\n\\n return probabilities\",\n \"def bin_dist(n, p, x):\\n def factorial(x):\\n if x >= 0:\\n \\n factorial = 1\\n\\n for i in range(1, x + 1):\\n factorial = float(factorial * i)\\n # print(f' The factorial of {x} is {factorial}') \\n return factorial\\n\\n else:\\n raise ValueError(\\\"Sorry x cannot be a negative number\\\")\\n\\n def combination(n, r):\\n \\\"\\\"\\\"\\n Given n total number of items,\\n what is the number of possible ways\\n to choose r items from it?\\n\\n :param n: total number of items (integer)\\n :param r: number of items to arrange (int)\\n :return: number of combinations\\n :rtype: integer\\n \\\"\\\"\\\"\\n\\n \\n\\n \\n numerator = factorial(n)\\n denominator = factorial(r)\\n subtracted_answer = factorial(n-r)\\n \\n\\n answer = numerator/(denominator * subtracted_answer)\\n print(answer)\\n return answer \\n\\n # from scipy.special import comb\\n if x > n:\\n raise ValueError(\\\"Error, x must be less than n\\\")\\n else:\\n\\n\\n prob_success = float((combination(n, x)) * ((p**x)*((1-p)**(n-x))))\\n\\n print(prob_success)\\n return prob_success\",\n \"def sample_from_probabilities(probabilities, topn=ALPHASIZE):\\n p = np.squeeze(probabilities)\\n p[np.argsort(p)[:-topn]] = 0\\n p = p / np.sum(p)\\n return np.random.choice(ALPHASIZE, 1, p=p)[0]\",\n \"def get_probs(self, a):\\n with torch.no_grad():\\n probabilities = (np.array(self.priorities) ** a) / sum(np.array(self.priorities) ** a)\\n return probabilities\",\n \"def bin2freq(b, srate, N):\\n return (b / float(N)) * float(srate)\",\n \"def estimate_p(alignment, n=None):\\n if n is None:\\n n = alignment.shape[0]\\n return np.sum(alignment, axis=0)/n\",\n \"def pratt1(array):\\n res = []\\n max_size = len(array) // 2\\n pow3 = 1 # start with q = 0\\n while pow3 <= max_size:\\n # At this point, pow3 = 3**q, so set p = 0\\n pow2 = pow3\\n while pow2 <= max_size:\\n # At this point, pow2 = 2**p * 3**q\\n res.append(pow2)\\n pow2 = pow2 * 2 # this is like adding 1 to p\\n # now that p overflowed the maximum size, add 1 to q and start over\\n pow3 = pow3 * 3\\n return list(reversed(sorted(res)))\",\n \"def pratt1(array):\\n res = []\\n max_size = len(array) // 2\\n pow3 = 1 # start with q = 0\\n while pow3 <= max_size:\\n # At this point, pow3 = 3**q, so set p = 0\\n pow2 = pow3\\n while pow2 <= max_size:\\n # At this point, pow2 = 2**p * 3**q\\n res.append(pow2)\\n pow2 = pow2 * 2 # this is like adding 1 to p\\n # now that p overflowed the maximum size, add 1 to q and start over\\n pow3 = pow3 * 3\\n return list(reversed(sorted(res)))\",\n \"def p(self) -> Probability:\\n ...\",\n \"def at_least_n_ver(seq, n):\\r\\n num_AB = 0\\r\\n for item in seq:\\r\\n if np.array_equal(item, Quantifier.AB):\\r\\n if num_AB == n-1:\\r\\n return Quantifier.T\\r\\n else:\\r\\n num_AB += 1\\r\\n return Quantifier.F\",\n \"def gen_a(cls, p):\\n a = random.randint(1, p-1)\\n while cls.gcd(a, p) != 1:\\n a = random.randint(1, p-1)\\n return a\",\n \"def sample(probs):\\n\\n probs = probs / probs.sum()\\n return np.random.choice(np.arange(len(probs)), p=probs.flatten())\",\n \"def equation_of_state_p_N(N_balls_arr, temperature, volume):\\r\\n\\tpressure_arr = np.array(list(map(lambda N: k_B*temperature/volume*N, N_balls_arr)))\\r\\n\\treturn pressure_arr\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6789287","0.67728233","0.65926576","0.6576568","0.6520024","0.6460622","0.6324361","0.6279238","0.62723196","0.6234605","0.6230018","0.62065023","0.6202986","0.6180209","0.61650026","0.6157465","0.61489534","0.6131866","0.6128808","0.60880065","0.6084006","0.60727084","0.6059094","0.6046887","0.6040828","0.6020926","0.60138017","0.6002781","0.6001854","0.5978283","0.5974444","0.59680665","0.59665614","0.59543926","0.59514266","0.5949665","0.5946152","0.594179","0.5939038","0.593459","0.5929184","0.59251857","0.5923311","0.59123445","0.5905649","0.5891845","0.58820254","0.58820254","0.58820254","0.58820254","0.58784485","0.58758926","0.5874174","0.5870512","0.5869573","0.5865395","0.5840381","0.58398104","0.5833372","0.5822771","0.5822644","0.5821222","0.5817116","0.57906306","0.5781414","0.5780998","0.5776517","0.57662433","0.5757135","0.5753893","0.57526857","0.57493496","0.5745926","0.5745926","0.57432723","0.5721209","0.5718135","0.57156175","0.5703825","0.5702878","0.57024986","0.5698389","0.5691007","0.5686979","0.5686945","0.56818104","0.5679544","0.5678505","0.56777316","0.56746995","0.567316","0.56718373","0.56605643","0.56528455","0.56485903","0.56485903","0.5636411","0.56283236","0.5614729","0.5613321","0.56131506"],"string":"[\n \"0.6789287\",\n \"0.67728233\",\n \"0.65926576\",\n \"0.6576568\",\n \"0.6520024\",\n \"0.6460622\",\n \"0.6324361\",\n \"0.6279238\",\n \"0.62723196\",\n \"0.6234605\",\n \"0.6230018\",\n \"0.62065023\",\n \"0.6202986\",\n \"0.6180209\",\n \"0.61650026\",\n \"0.6157465\",\n \"0.61489534\",\n \"0.6131866\",\n \"0.6128808\",\n \"0.60880065\",\n \"0.6084006\",\n \"0.60727084\",\n \"0.6059094\",\n \"0.6046887\",\n \"0.6040828\",\n \"0.6020926\",\n \"0.60138017\",\n \"0.6002781\",\n \"0.6001854\",\n \"0.5978283\",\n \"0.5974444\",\n \"0.59680665\",\n \"0.59665614\",\n \"0.59543926\",\n \"0.59514266\",\n \"0.5949665\",\n \"0.5946152\",\n \"0.594179\",\n \"0.5939038\",\n \"0.593459\",\n \"0.5929184\",\n \"0.59251857\",\n \"0.5923311\",\n \"0.59123445\",\n \"0.5905649\",\n \"0.5891845\",\n \"0.58820254\",\n \"0.58820254\",\n \"0.58820254\",\n \"0.58820254\",\n \"0.58784485\",\n \"0.58758926\",\n \"0.5874174\",\n \"0.5870512\",\n \"0.5869573\",\n \"0.5865395\",\n \"0.5840381\",\n \"0.58398104\",\n \"0.5833372\",\n \"0.5822771\",\n \"0.5822644\",\n \"0.5821222\",\n \"0.5817116\",\n \"0.57906306\",\n \"0.5781414\",\n \"0.5780998\",\n \"0.5776517\",\n \"0.57662433\",\n \"0.5757135\",\n \"0.5753893\",\n \"0.57526857\",\n \"0.57493496\",\n \"0.5745926\",\n \"0.5745926\",\n \"0.57432723\",\n \"0.5721209\",\n \"0.5718135\",\n \"0.57156175\",\n \"0.5703825\",\n \"0.5702878\",\n \"0.57024986\",\n \"0.5698389\",\n \"0.5691007\",\n \"0.5686979\",\n \"0.5686945\",\n \"0.56818104\",\n \"0.5679544\",\n \"0.5678505\",\n \"0.56777316\",\n \"0.56746995\",\n \"0.567316\",\n \"0.56718373\",\n \"0.56605643\",\n \"0.56528455\",\n \"0.56485903\",\n \"0.56485903\",\n \"0.5636411\",\n \"0.56283236\",\n \"0.5614729\",\n \"0.5613321\",\n \"0.56131506\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":200,"numItemsPerPage":100,"numTotalItems":95772,"offset":20000,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjMzMzE5NywianRpIjoiMjM5ZDY1ZDEtYTg0ZC00MTRhLThlZTYtNGZhNTFiNGIxY2FjIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYzMzY3OTcsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.04Obc8Fo459mGt87JLjkwxE49dTV_ErHjV60fS-8Utwdd_VrvSRsxSqD9jmYhKFKAT0KBo-M5jnmfP97tBdpDw","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Execute the function task. | def run(self, *args, **kwargs):
if self.task_loader is None:
if 'task' not in kwargs:
if len(args) == 0 or not isinstance(args[0], self.flow_class.task_class):
raise FlowRuntimeError('Function {} should be called with task instance', self.name)
return ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _execute_task(task, function, config):\n logging.debug('<Task-%s> started.' % task.get_id())\n start_time = time.time()\n try:\n function(task.get_data())\n logging.debug('<Task-%s> finished in %2.2f seconds with result: %s' % (task.get_id(),\n ... | [
"0.73130786",
"0.727794",
"0.727794",
"0.7223112",
"0.7086795",
"0.7083772",
"0.70426476",
"0.69302607",
"0.6923544",
"0.6868409",
"0.6854366",
"0.67575186",
"0.67484105",
"0.67245996",
"0.66799116",
"0.66722405",
"0.663224",
"0.6616039",
"0.6605105",
"0.6604266",
"0.659991",... | 0.6494967 | 25 |
Summary for every series | def base_summary(series: pd.Series) -> dict:
summary = {
"frequencies": series.value_counts().to_dict(),
"n_records": series.shape[0],
"memory_size": series.memory_usage(index=True, deep=True),
"dtype": series.dtype,
"types": series.map(lambda x: type(x).__name__).value_count... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _summary(self, name=None):\n if name is None:\n if len(self._tracker_dict.keys()) > 1:\n dataframes = []\n for (_name, tracker) in self._tracker_dict.items():\n summary_df = tracker.series.summary()\n summary_df = summary_df.... | [
"0.7089863",
"0.65536255",
"0.653386",
"0.6407142",
"0.63080764",
"0.62465453",
"0.6217058",
"0.6183639",
"0.61406666",
"0.60740024",
"0.6058445",
"0.6054211",
"0.60406715",
"0.6035013",
"0.6030795",
"0.60064787",
"0.6001196",
"0.5956691",
"0.59167403",
"0.5907618",
"0.590478... | 0.7123433 | 0 |
Print Nodes in Top View of Binary Tree | def top_view(root):
if root is None:
return
# make an empty queue for BFS
q = deque()
# empty set
sets = set({})
# list to store top view keys
topview = []
# append root in the queue with horizontal distance as 0
q.append((root, 0))
while q:
# get the elemen... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_tree(self):\n\t\tprint(self.__print_tree('', True, ''))",
"def print_tree(self):\n\t\tself.root.print_recursive(0)",
"def print_bi_tree(self):\n\n to_print = [self]\n # current = None\n\n while to_print:\n current = to_print.pop(0)\n if current:\n ... | [
"0.7755528",
"0.76339066",
"0.76053905",
"0.760435",
"0.75482863",
"0.7485843",
"0.7462942",
"0.7462942",
"0.7416779",
"0.7387545",
"0.73497045",
"0.73221946",
"0.7304797",
"0.72974205",
"0.7279474",
"0.7267793",
"0.7253698",
"0.7226757",
"0.7221274",
"0.7214918",
"0.70488644... | 0.0 | -1 |
Creates a vector out of a string. Gets a string (e.g. Book), splits it into and returns a vector with all possible ngrams/features. | def create_vector(string):
vec = {}
words = string.split()
for word in words:
if len(word) <= NGRAM_SIZE:
add(vec, word)
else:
for i in range(len(word) - NGRAM_SIZE + 1):
add(vec, word[i : i + NGRAM_SIZE])
return vec | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_terms_from_string(s):\n u = s\n return u.split()",
"def ngramas(n, string):\n\n ngrams = []\n i = 0\n while i + n < len(string):\n ngrams.append(string[i:i + n])\n i += 1\n\n return ngrams",
"def from_string(string):\n return Sentence(string.split(\" \"))",
"de... | [
"0.6692081",
"0.620363",
"0.61938727",
"0.6170458",
"0.6045155",
"0.59495336",
"0.59380984",
"0.592855",
"0.587873",
"0.5878487",
"0.5867894",
"0.5849799",
"0.58483064",
"0.5833884",
"0.58311754",
"0.58128583",
"0.5803685",
"0.57831234",
"0.5774572",
"0.57539713",
"0.5753545"... | 0.7425697 | 0 |
Adds ngrams to the vector. Adds ngrams to our featurelistvector, if is not included yet (containing all possible ngrams/features). | def add(vector, ngram):
if ngram in vector:
vector[ngram] += 1
else:
vector[ngram] = 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_ngram(self, feature_vector, ngram):\n if ngram in self.ngrams:\n ngram_pos = self.ngrams[ngram]\n feature_vector[ngram_pos] = 1",
"def add_ngram(self, feature_vector, ngram):\n raise NotImplementedError('NgramExtractorBase:add_ngram() is not defined')",
"def _update_... | [
"0.78258324",
"0.7761324",
"0.6711486",
"0.6324257",
"0.6314163",
"0.62996507",
"0.62956494",
"0.6283911",
"0.62081075",
"0.61932635",
"0.61905193",
"0.61895627",
"0.6077353",
"0.6031984",
"0.5953315",
"0.5854282",
"0.582309",
"0.57579124",
"0.5724762",
"0.5719919",
"0.570135... | 0.6335167 | 3 |
Selects most frequent features. Selects the x most frequent ngrams/features (x=FEATURE_LENGTH) to avoid a (possibly) too big featurelist. | def select_features(vec):
return sorted(vec, key=vec.get, reverse=True)[
: min(len(vec), FEATURE_LENGTH)
] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_frequent_features(self):\n feature_terms = [sub_items for items in self.data['noun_and_np'].values for sub_items in items]\n C1 = apriori.createC1(feature_terms)\n D = map(set, feature_terms)\n L1, support_data = apriori.scanD(D,C1,0.01) # minimum support 0.01\n self.fre... | [
"0.6985161",
"0.6584252",
"0.64588726",
"0.64567083",
"0.6411913",
"0.6340169",
"0.6325521",
"0.63179886",
"0.63179886",
"0.6261018",
"0.620704",
"0.61992115",
"0.6151525",
"0.6142053",
"0.61385345",
"0.6105948",
"0.6049937",
"0.6019492",
"0.6014008",
"0.59916437",
"0.5987639... | 0.622837 | 10 |
Creates a feature map. Creates feature map that only saves the features that actually appear more frequently than 0. Thus, the featurelis tneeds less memory and can work faster. | def create_feature_map(string, features):
fmap = {}
vec = create_vector(string)
for ngram in features:
if ngram in vec:
fmap[ngram] = vec[ngram]
return fmap | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_feature_map():\n return {\n # 3 sparse feature with variable length. Use this if you have a\n # variable number or more than 1 feature value per example.\n \"feature_1\":\n tf.io.VarLenFeature(dtype=tf.int64),\n \"feature_2\":\n tf.io.VarL... | [
"0.6783414",
"0.6589923",
"0.6082466",
"0.5959431",
"0.5938566",
"0.5812138",
"0.5812138",
"0.5808928",
"0.57964915",
"0.5773302",
"0.57630265",
"0.5757909",
"0.575371",
"0.5715837",
"0.5711248",
"0.56986976",
"0.5684603",
"0.56828",
"0.5660406",
"0.5659658",
"0.56588423",
... | 0.5451646 | 31 |
Calculates the cosine similary of two vectors. Calculates cosine similarity of two vectors vec_x and vec_y. | def cosine_similarity(vec_x, vec_y):
sim_prod = 0.0
len_x = 0
len_y = 0
for ngram in vec_x:
len_x += vec_x[ngram] ** 2
for ngram in vec_y:
len_y += vec_y[ngram] ** 2
len_x = math.sqrt(len_x)
len_y = math.sqrt(len_y)
for ngram in vec_x:
if ngram in vec_y:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cosine_similarity(cls, vec_a, vec_b):\n return np.dot(vec_a, vec_b) / \\\n (np.linalg.norm(vec_a) * np.linalg.norm(vec_b))",
"def cosine_similarity(vector_x, vector_y):\n if(len(vector_x)!=len(vector_y)):\n raise Exception('Vectors must be the same dimensions')\n \n retu... | [
"0.89153063",
"0.8762753",
"0.85140526",
"0.83890986",
"0.83389425",
"0.82837796",
"0.8256471",
"0.82297283",
"0.8175448",
"0.8052344",
"0.8011905",
"0.7928554",
"0.78636724",
"0.7821731",
"0.77544606",
"0.77392095",
"0.773385",
"0.766548",
"0.76130956",
"0.7587458",
"0.75858... | 0.860079 | 2 |
Calculates the minmax similarity of two vectors. Calculates minmax similarity of two vectors vec_x and vec_y. | def minmax(vec_x, vec_y):
minsum = 0
maxsum = 0
for ngram in vec_x:
if ngram in vec_y:
# ngram is in both vectors
minsum += min(vec_x[ngram], vec_y[ngram])
maxsum += max(vec_x[ngram], vec_y[ngram])
else:
# ngram only in vec_x
maxsu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cosine_similarity(vector_x, vector_y):\n if(len(vector_x)!=len(vector_y)):\n raise Exception('Vectors must be the same dimensions')\n \n return 1-np.dot(vector_x,vector_y)/(np.linalg.norm(vector_x)*np.linalg.norm(vector_y))",
"def cosine_similarity(vec_x, vec_y):\n sim_prod = 0.0\n ... | [
"0.62996674",
"0.6249005",
"0.6130107",
"0.6097062",
"0.6059761",
"0.6027226",
"0.59534824",
"0.5932027",
"0.59160405",
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"0.5826938",
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"0.5791049",
"0.57742333",
"0.5760231",
"0.57585263",
"0.57527345",
"0.575017",
"0.5737195",... | 0.78629965 | 0 |
Returns a feature list of the vector from the string. Turns a given string into a ngram vector and returns its feature list. | def training(string):
print("Training...")
vec = create_vector(string)
print("Selecting features...")
feature_list = select_features(vec)
print("Done!")
return feature_list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_vector(string):\n vec = {}\n words = string.split()\n\n for word in words:\n if len(word) <= NGRAM_SIZE:\n add(vec, word)\n else:\n for i in range(len(word) - NGRAM_SIZE + 1):\n add(vec, word[i : i + NGRAM_SIZE])\n\n return vec",
"def crea... | [
"0.66118634",
"0.64273095",
"0.6327371",
"0.6292743",
"0.62504566",
"0.6237537",
"0.61925775",
"0.61103535",
"0.60202676",
"0.6020088",
"0.5987974",
"0.5959671",
"0.59375215",
"0.59162277",
"0.59138423",
"0.59009033",
"0.58965003",
"0.5834525",
"0.58342415",
"0.5805564",
"0.5... | 0.6722822 | 0 |
Returns the similarity value of two vectors. | def test_sim(vec_x, vec_y, feature_list, func):
feature_map_x = create_feature_map(vec_x, feature_list)
feature_map_y = create_feature_map(vec_y, feature_list)
if func == 0:
return cosine_similarity(feature_map_x, feature_map_y)
return minmax(feature_map_x, feature_map_y) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cosine_similarity(v1, v2):\n return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))",
"def cosine_similarity(v1: Vector, v2: Vector) -> float:\n return dot_product(v1, v2) / (vector_len(v1) * vector_len(v2))",
"def similarity(self, token1, token2):\n vec1 = self.get_vector(token1)\n ... | [
"0.79118186",
"0.7830324",
"0.77912253",
"0.7780094",
"0.76602757",
"0.765669",
"0.7611135",
"0.75025684",
"0.7484508",
"0.74626744",
"0.74408746",
"0.7354827",
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"0.7276158",
"0.7272085",
"0.7264649",
"0.725899",
"0.7200105",
"0.7190883",
"0.71873575"... | 0.0 | -1 |
Returns a random part of a string. Returns a random part of a string s that has a given length. | def get_random_string(string, length):
words = string.split()
random_part = random.randint(0, len(words) - length)
return "".join(words[random_part : random_part + length]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def my_random_string(string_length=17):\n random = str(uuid.uuid4())\n random = random.upper() \n random = random.replace(\"-\",\"\")\n return random[0:string_length]",
"def random_string(length=8, chars=string.ascii_letters + string.digits):\n return ''.join([chars[random.randint(0, len(chars) - ... | [
"0.72976446",
"0.7289028",
"0.72863644",
"0.7257486",
"0.7251793",
"0.7228094",
"0.7225105",
"0.72134733",
"0.72119886",
"0.72024626",
"0.72024626",
"0.72007173",
"0.71958107",
"0.7194601",
"0.7194079",
"0.7183809",
"0.7183112",
"0.7173273",
"0.7160337",
"0.7153696",
"0.71514... | 0.7963441 | 0 |
Initialize a sky dip model. The skydip model is used to fit elevation vs. data to determine the best fit parameters and error estimates. | def __init__(self):
self.configuration = None
self.initial_guess = self.default_initial_guess.copy()
self.bounds = self.default_bounds.copy()
self.fit_for = None
self.has_converged = False
self.data_unit = units.Unit("count")
self.use_points = 0
self.unifo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fit(self, skydip):\n parameter_order = ['tau', 'offset', 'kelvin', 'tsky']\n self.parameters = {}\n self.errors = {}\n self.p_opt = None\n self.p_cov = None\n self.fitted_values = None\n self.data = None\n self.sigma = None\n self.elevation = None\... | [
"0.688853",
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"0.48166892",
"0.48083803",
"0.4781016",
"0.47622022",
"0.47585717",
"0.475099... | 0.0 | -1 |
Return a copy of the skydip model. Returns SkyDipModel | def copy(self):
return deepcopy(self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sky_model(self, which=\"point\"):\n return SkyModel(\n spatial_model=self.spatial_model(which),\n spectral_model=self.spectral_model(which),\n name=self.name,\n )",
"def copy(self):\n new_model = Model(\n name=self.name,\n functions=... | [
"0.70133704",
"0.66789675",
"0.60469586",
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"0.5824889",
"0.576261",
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"0.53488886",
"0.53469974",
"0.53012586",
"0.5300153",
"0.525691",
"0.525562... | 0.514513 | 72 |
Set the sky dip model configuration | def set_configuration(self, configuration):
if not isinstance(configuration, Configuration):
raise ValueError(f"Configuration must be {Configuration} "
f"instance. Received {configuration}.")
self.configuration = configuration
if self.configuration.is_con... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_parameters(self, skydip):\n if self.configuration.is_configured('skydip.tsky'):\n self.initial_guess['tsky'] = self.configuration.get_float(\n 'skydip.tsky')\n elif skydip.tamb_weight > 0:\n temp = skydip.tamb\n if isinstance(temp, units.Quanti... | [
"0.6260431",
"0.59479874",
"0.59430236",
"0.57023",
"0.5684856",
"0.5664265",
"0.5639438",
"0.5633307",
"0.55982137",
"0.55243534",
"0.5451777",
"0.5441184",
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"0.5416396",
"0.5393091",
"0.53578675",
"0.53553176",
"0.53494585",
"0.5281941",
"0.5273201... | 0.63351405 | 0 |
Initialize the fitting parameters. | def init_parameters(self, skydip):
if self.configuration.is_configured('skydip.tsky'):
self.initial_guess['tsky'] = self.configuration.get_float(
'skydip.tsky')
elif skydip.tamb_weight > 0:
temp = skydip.tamb
if isinstance(temp, units.Quantity):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_fit_params(self):\n\n self.p0 = np.array([self.A_arr, self.T_a])\n # initial guess at A_arr and T_a\n\n self.popt, self.pcov = curve_fit(\n self.get_eta_fit, self.T_exp, self.eta_exp, p0=self.p0\n )\n\n self.A_arr = self.popt[0]\n self.T_a = self.popt[1]\n\n self.T_array = s... | [
"0.748414",
"0.73611045",
"0.72121745",
"0.71634656",
"0.70998245",
"0.7093689",
"0.7042686",
"0.70066947",
"0.69969213",
"0.69654155",
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"0.68209374",
"0.68203",
"0.67906654",
"0.6747684",
"0.6740722",
"0.67299... | 0.0 | -1 |
Fit the skydip model. | def fit(self, skydip):
parameter_order = ['tau', 'offset', 'kelvin', 'tsky']
self.parameters = {}
self.errors = {}
self.p_opt = None
self.p_cov = None
self.fitted_values = None
self.data = None
self.sigma = None
self.elevation = None
log.d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def skydip(scans):\n title = Path(scans[0]).name + \" \".join([Path(scan).name.split(\"_\")[4] for scan in scans[1:]])\n\n signal = []\n std = []\n elevation = []\n\n for scan in scans:\n kd = KissData(scan)\n kd.read_data(list_data=[\"A_masq\", \"I\", \"Q\", \"F_tone\", \"F_tl_Az\", \... | [
"0.71958387",
"0.6706675",
"0.66250366",
"0.6449008",
"0.62683755",
"0.6221359",
"0.61551917",
"0.6120607",
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"0.5972988",
"0.5968424",
"0.5968424",
"0.5968424",
"0.59320873",
"0.58962053",
"0.5896... | 0.7225869 | 0 |
Returns a fit to elevation with the model. | def fit_elevation(self, elevation):
if self.p_opt is None:
result = elevation * np.nan
else:
result = self.value_at(elevation, *self.p_opt)
if isinstance(result, units.Quantity):
result = result.value
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fit_sky(self):\n min_value = self.data.min()\n ring_model = models.Ring2D(\n min_value, self.x, self.y, self._box * 0.4, width=self._box * 0.4\n )\n ring_model.r_in.fixed = True\n ring_model.width.fixed = True\n ring_model.x_0.fixed = True\n ring_mode... | [
"0.6175527",
"0.5854759",
"0.57457453",
"0.5709625",
"0.5693753",
"0.5398284",
"0.53812927",
"0.53534794",
"0.53487504",
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"0.5186781",
"0.51401824",
"0.51340437",
"0.51340437",
"0.5090348",
"0.5071652",
"0.5067156... | 0.7041972 | 0 |
Return the result of the fitted value. | def value_at(elevation, tau, offset, kelvin, tsky):
with warnings.catch_warnings():
warnings.simplefilter('ignore', RuntimeWarning)
eps = -(np.exp(-tau / np.sin(elevation)) - 1)
t_obs = eps * tsky
return offset + (t_obs * kelvin) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fittedvalues(self):\n return self.model.predict(self.params)\n # TODO: GH#5255 is this necessarily equivalent to self.predict()?",
"def fit(self, x):\n pass",
"def get_estimate(self) -> np.ndarray:\n return self.fit_function(self.x, self.coefficients)",
"def fit():\n pa... | [
"0.72722286",
"0.67442375",
"0.67056286",
"0.6667116",
"0.6580389",
"0.65643084",
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"0.6303537",
"0.6298279",
"0.62980753",
"0.6270139",
"0.62555873",
"0.624... | 0.0 | -1 |
Return a string representation of a given parameter. | def get_parameter_string(self, parameter):
if not self.has_converged or self.parameters is None:
return None
if parameter not in self.parameters:
return None
fmt = self.get_parameter_format(parameter)
unit = self.get_parameter_unit(parameter)
value = fmt ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n\n return \"<ExoParameter>: {0}\".format(self.__dict__)",
"def __repr_parameter__(self, name: str, value: Any) -> str:\n return f\"{name}={value!r}\"",
"def format_parameter(param, required):\n\n param_string = check_param(flatten_param(param))\n if not required:\n ... | [
"0.7343561",
"0.71605086",
"0.710844",
"0.69979465",
"0.69655436",
"0.6828032",
"0.67813796",
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"0.63439494",
"0.6307336",
"0.62920564",
"0.628318",
"0.6257743",
"0.62439185... | 0.7855388 | 0 |
Return the string format for a given parameter. | def get_parameter_format(cls, parameter_name):
formats = {
'tau': '%.3f',
'tsky': '%.1f',
'kelvin': '%.3e'
}
return formats.get(parameter_name, '%.3e') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_parameter_string(self, parameter):\n if not self.has_converged or self.parameters is None:\n return None\n if parameter not in self.parameters:\n return None\n\n fmt = self.get_parameter_format(parameter)\n unit = self.get_parameter_unit(parameter)\n ... | [
"0.7667087",
"0.72878444",
"0.71903205",
"0.68664265",
"0.68105346",
"0.68105346",
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"0.6590343",
"0.6559082",
"0.6547835",
"0.6534224",
"0.6534224",
"0.645761... | 0.74919534 | 1 |
Return the parameter unit for the given parameter. | def get_parameter_unit(self, parameter_name):
parameter_units = {
'tsky': units.Unit("Kelvin"),
'kelvin': self.data_unit
}
return parameter_units.get(parameter_name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unit(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"unit\")",
"def get_unit(self):\n return self.unit",
"def unit(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"unit\")",
"def unit(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"un... | [
"0.73394704",
"0.7300321",
"0.7261834",
"0.7261834",
"0.7261834",
"0.7261834",
"0.7261834",
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"0.7261834",
"0.7261834",
"0.7261834",
"0.7261834",
"0.7261834",
... | 0.8258069 | 0 |
Return a string representation of the sky dip fit. Returns str | def __str__(self):
if not self.has_converged or self.parameters is None:
log.warning("The fit has not converged. Try again!")
return ''
result = []
for parameter in self.parameters.keys():
if parameter in self.fit_for:
parameter_string = self.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n #Get an ordered list of the elements strings so it outputs always the same\n #string given a mass function.\n elements = []\n for element in self.focals:\n elements.append((element, str(element)))\n sortedList = sorted(elements, key=lambda x:x[1])\... | [
"0.63235986",
"0.5925334",
"0.5918602",
"0.5917366",
"0.59062576",
"0.5836863",
"0.58009154",
"0.578009",
"0.5776318",
"0.57657",
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"0.574386",
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"0.569668",
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"0.5693888",
"0.56415474",
"0.56415474",
"0.5632635",
"0.5627902",
"0.5618648",
"... | 0.6171846 | 1 |
Report the HTTP server health. | def handle_health():
return flask.jsonify(status="up") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def health_check():\n app.logger.info(\"Health Check!\")\n return Response(\"All Good!\", status=200)",
"def health():\n global _is_healthy\n template = render_template('health.html', healthy=_is_healthy)\n return make_response(template, 200 if _is_healthy else 500)",
"def test_health(self) -> N... | [
"0.7330673",
"0.72990686",
"0.7261757",
"0.7057571",
"0.7043252",
"0.6982577",
"0.6782414",
"0.67748505",
"0.6683728",
"0.6658292",
"0.66400546",
"0.663814",
"0.66167766",
"0.6598805",
"0.6598805",
"0.6545743",
"0.64945024",
"0.6450732",
"0.6434992",
"0.6426685",
"0.6409966",... | 0.6197213 | 34 |
get probands sequenced in Iossifov et al., Neuron 2012 | def open_iossifov_neuron_cohort():
logging.info('getting Iossifov et al Neuron 2012 cohort')
s1 = pandas.read_excel(supp_s1_url, sheet_name='SNV.v4.1-normlized')
s2 = pandas.read_excel(supp_s2_url, sheet_name='suppLGKTable')
s3 = pandas.read_excel(supp_s3_url, sheet_name='ID.v4.1-normlized')
fa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def margprobssites(self) :\n sitemargprobs=[]\n import scipy\n pmatrix=scipy.linalg.expm(self.q*self.v)\n for i in range(self.nsites) :\n initial=self.starts[i]\n final=self.finals[i]\n iindex=self.staspa.index(initial)\n findex=self.staspa.in... | [
"0.6610351",
"0.65211",
"0.6002573",
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"0.58274126",
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"0.56781197",
"0.567707",
"0.5645529",
"0.5620201",
"0.560954",
"0.5567139",
"0.55655503",
"0.5520175",... | 0.0 | -1 |
Check preconditions of hparams. | def check_hparams(self, hparams):
error_messages = []
# Check global params.
feature_names = hparams.get_feature_names()
global_values, per_feature_values = hparams.get_global_and_feature_params(
['num_keypoints', 'missing_input_value',
'missing_output_value'], feature_names)
globa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_params(self):\n pass",
"def requires_hparams(self):\n return None",
"def checkParameters(self):\n self.DEBUG(\"EDPluginExecDatGnomv1_0.checkParameters\")\n self.checkMandatoryParameters(self.dataInput, \"Data Input is None\")\n self.checkMandatoryParameters(self.dataIn... | [
"0.7320391",
"0.7107705",
"0.68613213",
"0.6803819",
"0.6776897",
"0.6744108",
"0.67127216",
"0.6708308",
"0.67014897",
"0.66710943",
"0.66500753",
"0.66410565",
"0.6586223",
"0.65627277",
"0.6512611",
"0.6505922",
"0.6488907",
"0.645376",
"0.6422583",
"0.6355284",
"0.6300580... | 0.6701233 | 9 |
Calibrated linear classifier binary model. This model uses a piecewise linear calibration function on each of the real (as opposed to binary) inputs (parametrized) and then combines (sum up) the results. Optionally calibration can be made monotonic. It usually requires a preprocessing step on the data, to calculate the... | def calibrated_linear_classifier(feature_columns=None,
model_dir=None,
quantiles_dir=None,
keypoints_initializers_fn=None,
optimizer=None,
config=None,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def train_calibrate_predict(clf, X_t, y_t, X_v, y_v, params, jobs):\n\n # Indicate the classifier and the training set size\n print(\"Training a {} with None...\".format(clf.__class__.__name__))\n\n # Train the classifier\n clf = train_classifier(clf, X_t, y_t, params, jobs)\n\n # # Calibrate classi... | [
"0.60893214",
"0.60358334",
"0.5759546",
"0.5748178",
"0.57063353",
"0.56848466",
"0.56386817",
"0.5601485",
"0.5578915",
"0.5504322",
"0.55038977",
"0.549859",
"0.54438186",
"0.5441058",
"0.5316333",
"0.5284502",
"0.52768564",
"0.5275868",
"0.52657294",
"0.52634394",
"0.5245... | 0.6290439 | 0 |
Calibrated linear estimator (model) for regression. This model uses a piecewise linear calibration function on each of the inputs (parametrized) and then combine (sum up) the results. Optionally calibration can be made monotonic. It usually requires a preprocessing step on the data, to calculate the quantiles of each u... | def calibrated_linear_regressor(feature_columns=None,
model_dir=None,
quantiles_dir=None,
keypoints_initializers_fn=None,
optimizer=None,
config=None,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calibrated_linear_classifier(feature_columns=None,\n model_dir=None,\n quantiles_dir=None,\n keypoints_initializers_fn=None,\n optimizer=None,\n config... | [
"0.6054098",
"0.5928749",
"0.5801186",
"0.57329446",
"0.57322824",
"0.5710905",
"0.57070893",
"0.56966364",
"0.56741047",
"0.56055945",
"0.55519617",
"0.5540491",
"0.5518398",
"0.5488559",
"0.54868513",
"0.5474618",
"0.54654014",
"0.54624677",
"0.54408914",
"0.54407585",
"0.5... | 0.6264422 | 0 |
For a given WABBIT parameter file, check for the most common stupid errors | def check_parameters_for_stupid_errors( file ):
import os
# print('~~~~~~~~~~~~~~~~~~~~~ini-file~~~~~~~~~~~')
# # read jobfile
# with open(file) as f:
# # loop over all lines
# for line in f:
# line = line.lstrip()
# line = line.rstrip()
# if len(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def checkParamsError(self):\n # check if parameter combinations match with the simulation filename.\n for i, f in enumerate(self.yadeDataFiles):\n # get the file name fore the suffix\n f = f.split('.' + f.split('.')[-1])[0]\n # get parameters from the remaining string... | [
"0.6367045",
"0.63627934",
"0.626574",
"0.6190518",
"0.6190518",
"0.61401314",
"0.60877657",
"0.607748",
"0.6049413",
"0.59709966",
"0.59606194",
"0.5958436",
"0.5945568",
"0.58771724",
"0.5862091",
"0.5859376",
"0.5834433",
"0.58315766",
"0.5829629",
"0.5814439",
"0.5794336"... | 0.73345405 | 0 |
check if a given parameter in the ini file exists or not. can be used to detect deprecated entries somebody removed | def exists_ini_parameter( inifile, section, keyword ):
found_section = False
found_parameter = False
# read jobfile
with open(inifile) as f:
# loop over all lines
for line in f:
# once found, do not run to next section
if found_section and line[0] == "[":
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_config(cfg):",
"def exists_ini_section( inifile, section ):\n found_section = False\n\n # read jobfile\n with open(inifile) as f:\n # loop over all lines\n for line in f:\n # until we find the section\n if \"[\"+section+\"]\" in line and line[0]!=\";\" and l... | [
"0.6333489",
"0.63048834",
"0.61332923",
"0.60334665",
"0.6019133",
"0.59849584",
"0.58812857",
"0.5851556",
"0.58060586",
"0.57881004",
"0.5765527",
"0.5734839",
"0.5717389",
"0.56993365",
"0.568473",
"0.56755203",
"0.56699747",
"0.56562424",
"0.56547135",
"0.56511873",
"0.5... | 0.6938629 | 0 |
check if a given parameter in the ini file exists or not. can be used to detect deprecated entries somebody removed | def exists_ini_section( inifile, section ):
found_section = False
# read jobfile
with open(inifile) as f:
# loop over all lines
for line in f:
# until we find the section
if "["+section+"]" in line and line[0]!=";" and line[0]!="!" and line[0]!="#":
f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def exists_ini_parameter( inifile, section, keyword ):\n found_section = False\n found_parameter = False\n\n # read jobfile\n with open(inifile) as f:\n # loop over all lines\n for line in f:\n\n # once found, do not run to next section\n if found_section and line[0]... | [
"0.69377685",
"0.63323456",
"0.6133095",
"0.60329324",
"0.60197425",
"0.5983186",
"0.58808696",
"0.58505404",
"0.5804031",
"0.57878053",
"0.57635015",
"0.5735745",
"0.5716882",
"0.5698739",
"0.56842005",
"0.56746614",
"0.5669832",
"0.5655906",
"0.5654727",
"0.56496227",
"0.56... | 0.6303248 | 2 |
we look for the latest .h5 files to resume the simulation, and prepare the INI file accordingly. Some errors are caught. | def prepare_resuming_backup( inifile ):
import numpy as np
import os
import glob
import flusi_tools
# does the ini file exist?
if not os.path.isfile(inifile):
raise ValueError("Inifile not found!")
Tmax = get_ini_parameter(inifile, "Time", "time_max", float)
dim = get_ini_para... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def preprocess_phase(self):\r\n if not self.C.restart: # start preprocessing job from scratch\r\n if (\r\n os.path.exists(self.valid_h5_path)\r\n or os.path.exists(self.test_h5_path)\r\n or os.path.exists(self.train_h5_path)\r\n ):\r\n ... | [
"0.60261214",
"0.5696248",
"0.5661924",
"0.5481591",
"0.5385601",
"0.5378313",
"0.5364455",
"0.5312231",
"0.5307268",
"0.52681684",
"0.5235969",
"0.5219296",
"0.5212105",
"0.5211143",
"0.52081215",
"0.51801795",
"0.5176859",
"0.5160141",
"0.51429856",
"0.5139128",
"0.50798786... | 0.57191813 | 1 |
Read a 2D/3D wabbit file and return a list of how many blocks are at the different levels | def block_level_distribution_file( file ):
import h5py
import numpy as np
# open the h5 wabbit file
fid = h5py.File(file,'r')
# read treecode table
b = fid['block_treecode'][:]
treecode = np.array(b, dtype=float)
# close file
fid.close()
# number of blocks
Nb = treecode.s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def readGR3File(inputFilename):\n print 'Reading ' + inputFilename + ' ...'\n infile = open(inputFilename, 'r')\n description = infile.readline().strip() # remove leading/trailing whitespace\n tmpStr = infile.readline()\n nTriangles, nNodes = (int(s) for s in tmpStr.split())\n print ' nTriangle... | [
"0.62876856",
"0.62518936",
"0.60057634",
"0.5994908",
"0.5979423",
"0.5979423",
"0.59490186",
"0.5920892",
"0.58702004",
"0.5859132",
"0.5837403",
"0.5836277",
"0.5833768",
"0.58279437",
"0.5771492",
"0.5762479",
"0.57591116",
"0.5756959",
"0.5705301",
"0.56720674",
"0.56655... | 0.707528 | 0 |
Read a wabbittype HDF5 of blockstructured data. Return time, x0, dx, box, data, treecode. Get number of blocks and blocksize as N, Bs = data.shape[0], data.shape[1] | def read_wabbit_hdf5(file, verbose=True, return_iteration=False):
import h5py
import numpy as np
if verbose:
print("~~~~~~~~~~~~~~~~~~~~~~~~~")
print("Reading file %s" % (file) )
fid = h5py.File(file,'r')
b = fid['coords_origin'][:]
x0 = np.array(b, dtype=float)
b = fid['c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_wabbit_hdf5( file, time, x0, dx, box, data, treecode, iteration = 0, dtype=np.float64 ):\n import h5py\n import numpy as np\n\n\n Level = np.size(treecode,1)\n if len(data.shape)==4:\n # 3d data\n Bs = np.zeros([3,1])\n N, Bs[0], Bs[1], Bs[2] = data.shape\n Bs = B... | [
"0.7095553",
"0.684908",
"0.67961204",
"0.62631047",
"0.608267",
"0.6046284",
"0.6046284",
"0.5913353",
"0.585708",
"0.5756927",
"0.57358474",
"0.56931156",
"0.5675092",
"0.5658997",
"0.55911225",
"0.5576085",
"0.5573528",
"0.55517644",
"0.55503565",
"0.5536557",
"0.5531724",... | 0.74340147 | 0 |
Read a wabbittype HDF5 of blockstructured data. same as read_wabbit_hdf5, but reads ONLY the treecode array. | def read_treecode_hdf5(file):
import h5py
import numpy as np
fid = h5py.File(file,'r')
b = fid['block_treecode'][:]
treecode = np.array(b, dtype=float)
return treecode | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_wabbit_hdf5(file, verbose=True, return_iteration=False):\n import h5py\n import numpy as np\n\n if verbose:\n print(\"~~~~~~~~~~~~~~~~~~~~~~~~~\")\n print(\"Reading file %s\" % (file) )\n\n fid = h5py.File(file,'r')\n b = fid['coords_origin'][:]\n x0 = np.array(b, dtype=flo... | [
"0.7511299",
"0.68497235",
"0.6839176",
"0.6466713",
"0.6055406",
"0.6010037",
"0.6009357",
"0.5869854",
"0.5803738",
"0.57437724",
"0.57119524",
"0.5656739",
"0.5625802",
"0.5617597",
"0.5598882",
"0.55979973",
"0.55816346",
"0.5550571",
"0.5539923",
"0.5537304",
"0.548983",... | 0.7173856 | 1 |
Write data from wabbit to an HDF5 file | def write_wabbit_hdf5( file, time, x0, dx, box, data, treecode, iteration = 0, dtype=np.float64 ):
import h5py
import numpy as np
Level = np.size(treecode,1)
if len(data.shape)==4:
# 3d data
Bs = np.zeros([3,1])
N, Bs[0], Bs[1], Bs[2] = data.shape
Bs = Bs[::-1]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_as_hdf5(self, filename):",
"def write_hdf5(filename, data):\n \n if '.h5' in filename:\n fid = h5py.File(filename, 'w')\n else:\n filename = filename+'.h5'\n fid = h5py.File(filename, 'w')\n\n print('Writing %s...'%filename)\n\n write_hdf5_group(fid, data)\n\n fid.... | [
"0.739396",
"0.718801",
"0.7148199",
"0.69659054",
"0.68264276",
"0.6792738",
"0.6753575",
"0.66656506",
"0.66584826",
"0.6568733",
"0.65683955",
"0.6483694",
"0.64607877",
"0.63932073",
"0.63770145",
"0.632899",
"0.63053787",
"0.63048476",
"0.63000256",
"0.6252908",
"0.62099... | 0.7852353 | 0 |
Read all h5 files in directory dir. Return time, x0, dx, box, data, treecode. Use data["phi"][it] to reference quantity phi at iteration it | def read_wabbit_hdf5_dir(dir):
import numpy as np
import re
import ntpath
import os
it=0
data={'time': [],'x0':[],'dx':[],'treecode':[]}
# we loop over all files in the given directory
for file in os.listdir(dir):
# filter out the good ones (ending with .h5)
if file.ends... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_h5_file_arvind_format(folder, filen):\n \n ### file path\n \n fpath = folder + filen + '.h5'\n assert os.path.exists(fpath), \"The out.h5 file does NOT exist for \" + fpath\n fl = h5py.File(fpath, 'r')\n \n ### cell information\n \n xu = np.array(fl['/positions/xu'], dtype=np... | [
"0.66390574",
"0.65969133",
"0.65183514",
"0.64874375",
"0.64845943",
"0.6476374",
"0.6446031",
"0.6411749",
"0.63776684",
"0.63614887",
"0.63193595",
"0.62539995",
"0.6228941",
"0.6222934",
"0.61189467",
"0.61138064",
"0.60822386",
"0.6060295",
"0.60524553",
"0.60471475",
"0... | 0.7388911 | 0 |
This generic function adds the local convergence rate as nice labels between | def add_convergence_labels(dx, er):
import numpy as np
import matplotlib.pyplot as plt
for i in range(len(dx)-1):
x = 10**( 0.5 * ( np.log10(dx[i]) + np.log10(dx[i+1]) ) )
y = 10**( 0.5 * ( np.log10(er[i]) + np.log10(er[i+1]) ) )
order = "%2.1f" % ( convergence_order(dx[i:i+1+1],er[... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _show_learning_rate():\n fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(6.4 * 2, 4.8))\n\n # Visualize c_prime\n c_prime_list = np.linspace(1, 100, num=11)\n x_label = f\"c'\"\n y_label = \"Minimum Clusters Size\"\n title = \"\"\n\n ax = axes[0]\n x_list = c_prime_list\n\n # MNI... | [
"0.5802661",
"0.57484186",
"0.5591566",
"0.55610377",
"0.554035",
"0.5481976",
"0.541913",
"0.54128134",
"0.5400878",
"0.53928256",
"0.52825266",
"0.5259621",
"0.5238246",
"0.52341664",
"0.52058315",
"0.519785",
"0.5196426",
"0.5191651",
"0.5189148",
"0.51671124",
"0.5163327"... | 0.6630967 | 0 |
This is a small function that returns the convergence order, i.e. the least squares fit to the log of the two passed lists. | def convergence_order(N, err):
import numpy as np
if len(N) != len(err):
raise ValueError('Convergence order args do not have same length')
A = np.ones([len(err), 2])
B = np.ones([len(err), 1])
# ERR = A*N + B
for i in range( len(N) ) :
A[i,0] = np.log(N[i])
B[i] = np.l... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def logfit(N, err):\n import numpy as np\n\n if len(N) != len(err):\n raise ValueError('Convergence order args do not have same length')\n\n A = np.ones([len(err), 2])\n B = np.ones([len(err), 1])\n # ERR = A*N + B\n for i in range( len(N) ) :\n A[i,0] = np.log10(N[i])\n B[i]... | [
"0.6337905",
"0.62787294",
"0.6042211",
"0.5989017",
"0.5960942",
"0.5899967",
"0.58939946",
"0.58498496",
"0.5838031",
"0.5715212",
"0.56407213",
"0.56352633",
"0.5627826",
"0.56239766",
"0.5622977",
"0.55958897",
"0.5592456",
"0.55830264",
"0.55826944",
"0.55535156",
"0.555... | 0.67130005 | 0 |
This is a small function that returns the logfit, i.e. the least squares fit to the log of the two passed lists. | def logfit(N, err):
import numpy as np
if len(N) != len(err):
raise ValueError('Convergence order args do not have same length')
A = np.ones([len(err), 2])
B = np.ones([len(err), 1])
# ERR = A*N + B
for i in range( len(N) ) :
A[i,0] = np.log10(N[i])
B[i] = np.log10(err[... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fit_exp_data(x_vals, y_vals):\n log_vals = []\n for y in y_vals:\n log_vals.append(math.log(y, 2)) #get log base 2\n fit = np.polyfit(x_vals, log_vals, 1)\n return fit, 2",
"def logp(self, xs, ys, **kwargs):\n ind = np.isclose(self.predict(xs, **kwargs),ys)\n axis = tuple(ran... | [
"0.71505827",
"0.64535785",
"0.63110274",
"0.62836516",
"0.61950827",
"0.613543",
"0.6083297",
"0.60600305",
"0.6027912",
"0.6003056",
"0.5989711",
"0.59861887",
"0.59799457",
"0.5965218",
"0.59623635",
"0.5953407",
"0.5946081",
"0.59381616",
"0.59283286",
"0.59197503",
"0.59... | 0.6784488 | 1 |
This is a small function that returns the logfit, i.e. the least squares fit to the log of the two passed lists. | def linfit(N, err):
import numpy as np
if len(N) != len(err):
raise ValueError('Convergence order args do not have same length')
A = np.ones([len(err), 2])
B = np.ones([len(err), 1])
# ERR = A*N + B
for i in range( len(N) ) :
A[i,0] = N[i]
B[i] = err[i]
x, residual... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fit_exp_data(x_vals, y_vals):\n log_vals = []\n for y in y_vals:\n log_vals.append(math.log(y, 2)) #get log base 2\n fit = np.polyfit(x_vals, log_vals, 1)\n return fit, 2",
"def logfit(N, err):\n import numpy as np\n\n if len(N) != len(err):\n raise ValueError('Convergence ord... | [
"0.71505827",
"0.6784488",
"0.64535785",
"0.63110274",
"0.62836516",
"0.61950827",
"0.613543",
"0.6083297",
"0.60600305",
"0.6027912",
"0.6003056",
"0.5989711",
"0.59861887",
"0.59799457",
"0.5965218",
"0.59623635",
"0.5953407",
"0.5946081",
"0.59381616",
"0.59283286",
"0.591... | 0.0 | -1 |
Read and plot a 2D wabbit file. Not suitable for 3D data, use Paraview for that. | def plot_wabbit_file( file, savepng=False, savepdf=False, cmap='rainbow', caxis=None,
caxis_symmetric=False, title=True, mark_blocks=True, block_linewidth=1.0,
gridonly=False, contour=False, ax=None, fig=None, ticks=True,
colorbar=True, dpi=300, block_edge_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def readpil3d(self):\r\n\r\n # Read the data in as an array.\r\n res = np.loadtxt(self.name, delimiter=' ')\r\n\r\n # Split into useful chunks\r\n self.pos = res[:, 0:3] # Grid point locations\r\n self.Pn = res[:, 3:4] # Normal pressure [Pa]\r\n self.flux = res[... | [
"0.6419704",
"0.5728336",
"0.57164586",
"0.56596476",
"0.558751",
"0.5581649",
"0.55814993",
"0.55613047",
"0.5553209",
"0.5500591",
"0.54784054",
"0.5467062",
"0.5463366",
"0.54342484",
"0.5429662",
"0.53756815",
"0.53702176",
"0.536499",
"0.53567004",
"0.53496575",
"0.53309... | 0.6046627 | 1 |
Compute the error (in some norm) wrt a flusi field. Useful for example for the halfswirl test where no exact solution is available at midtime (the time of maximum distortion) | def wabbit_error_vs_flusi(fname_wabbit, fname_flusi, norm=2, dim=2):
import numpy as np
import insect_tools
import matplotlib.pyplot as plt
if dim==3:
print('I think due to fft2usapmle, this routine works only in 2D')
raise ValueError
# read in flusi's reference solution
time_r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def flusi_error_vs_flusi(fname_flusi1, fname_flusi2, norm=2, dim=2):\n import numpy as np\n import insect_tools\n\n # read in flusi's reference solution\n time_ref, box_ref, origin_ref, data_ref = insect_tools.read_flusi_HDF5( fname_flusi1 )\n\n time, box, origin, data_dense = insect_tools.read_flus... | [
"0.69146353",
"0.613854",
"0.6006402",
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"0.5956054",
"0.58681345",
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"0.5624339",
"0.5618825",
"0.5573806",
"0.5553906",
"0.55269307",
"0.551801... | 0.597056 | 4 |
compute error given two flusi fields | def flusi_error_vs_flusi(fname_flusi1, fname_flusi2, norm=2, dim=2):
import numpy as np
import insect_tools
# read in flusi's reference solution
time_ref, box_ref, origin_ref, data_ref = insect_tools.read_flusi_HDF5( fname_flusi1 )
time, box, origin, data_dense = insect_tools.read_flusi_HDF5( fnam... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def error_compute(self):\n self.tt_error = np.linalg.norm(self.rel_error)\n if self.global_rank==0:print('Overall error is::',self.tt_error)\n return {'NMF': self.rel_error, 'tt': self.tt_error}",
"def _compute_error(self,expected_out,actual_out,error_func):\n\n error = error_func(exp... | [
"0.6367832",
"0.62551594",
"0.61732644",
"0.6122448",
"0.60979503",
"0.60338694",
"0.60156256",
"0.6001351",
"0.59415543",
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"0.5922433",
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"0.5853913",
"0.5831379",
"0.58297896",
"0.5827409",
"0.5809093",
"0.57668793",
"0.5764... | 0.65988445 | 0 |
Read two wabbit files, which are supposed to have all blocks at the same level. Then, we rearrange the data in a dense matrix (wabbit_tools.dense_matrix) | def wabbit_error_vs_wabbit(fname_ref_list, fname_dat_list, norm=2, dim=2):
import numpy as np
import matplotlib.pyplot as plt
if not isinstance(fname_ref_list, list):
fname_ref_list = [fname_ref_list]
if not isinstance(fname_dat_list, list):
fname_dat_list = [fname_dat_list]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_matrices(self):\n self.wine_matrix = np.array(self.parse_file_into_array('winequality-red.csv', ';'))\n self.cancer_matrix = np.array(self.parse_file_into_array('breast-cancer-wisconsin.data', ','))",
"def to_dense_grid( fname_in, fname_out = None, dim=2 ):\n import numpy as np\n imp... | [
"0.5752716",
"0.574704",
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"0.5383064",
"0.53545874",
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"0.52273935",
"0.5201213",
"0.51901865",
"0.5185642",
"0.5175117",
"0.51649094"... | 0.49550754 | 43 |
Convert a WABBIT grid to a full dense grid in a single matrix. We asssume here that interpolation has already been performed, i.e. all blocks are on the same (finest) level. | def to_dense_grid( fname_in, fname_out = None, dim=2 ):
import numpy as np
import insect_tools
import matplotlib.pyplot as plt
# read data
time, x0, dx, box, data, treecode = read_wabbit_hdf5( fname_in )
# convert blocks to complete matrix
field, box = dense_matrix( x0, dx, data, treecode... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_sparse_matrix(self, grid, format=None):\n S = self.centered_stencil()\n # print(\"grid :\")\n\n grid = tuple(grid)\n # print(grid)\n if not (np.asarray(S.shape) % 2 == 1).all():\n raise ValueError('all stencil dimensions must be odd')\n\n assert_condition... | [
"0.5821582",
"0.5726809",
"0.5702125",
"0.5558751",
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"0.5539239",
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"0.5168702",
"0.5151044",
"0.51481164",
"0.51444805",
"0.51429015",
"0.513387",
... | 0.6513067 | 0 |
Compare two grids. The number returned is the % of blocks from treecode1 which have also been found in treecode2 | def compare_two_grids( treecode1, treecode2 ):
import numpy as np
common_blocks = 0
for i in range(treecode1.shape[0]):
# we look for this tree code in the second array
code1 = treecode1[i,:]
for j in range(treecode2.shape[0]):
code2 = treecode2[j,:]
if np.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def PDiffGrids(A, B):\n if (A.xllcorner,A.yllcorner) == (B.xllcorner,B.yllcorner) and (A.ncols,A.nrows)==(B.ncols,B.nrows):\n Bx = numpy.where(B.data != B.nodata, B.data, 1.0)\n Bx = numpy.where(B.data != 0., B.data, 1.0)\n C = 100. * (A.data-Bx)/Bx\n New = grid(C, A.xllcorner, A.yll... | [
"0.65731466",
"0.63465333",
"0.6300452",
"0.6264339",
"0.61934185",
"0.61712897",
"0.5996527",
"0.5989321",
"0.5979154",
"0.5934525",
"0.59334546",
"0.59026194",
"0.58587474",
"0.58309686",
"0.58261055",
"0.5805267",
"0.57949764",
"0.5784748",
"0.5769305",
"0.5745499",
"0.574... | 0.83875257 | 0 |
On all blocks of the data array, replace any function values by the level of the block | def overwrite_block_data_with_level(treecode, data):
if len(data.shape) == 4:
N = treecode.shape[0]
for i in range(N):
level = treecode_level(treecode[i,:])
data[i,:,:,:] = float( level )
elif len(data.shape) == 3:
N = treecode.shape[0]
for i in range(N... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def apply_block(self, block_id, func=..., edges=..., inplace=...): # -> None:\n ...",
"def postSI(self):\n # for cell in self.cells:\n # cell.resetTotOrdFlux()\n self.depth = 0",
"def replace(arr, fixers, data_tag='mydata', logger=None):\n # if logger not provided, create def... | [
"0.5757027",
"0.5234304",
"0.5134023",
"0.51197505",
"0.5062385",
"0.50325173",
"0.50212735",
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"0.4849501",
"0.48456857",
"0.47853938",
"0.47664374",
"0.4762566",
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"0.47483295",
"0.47385266",
"0.47344804",
"0.4733381",
"0.4728765",
"0.47240093",
"0.47... | 0.7074382 | 0 |
This routine performs a shell command on each .h5 file in a given directory! | def command_on_each_hdf5_file(directory, command):
import re
import os
import glob
if not os.path.exists(directory):
err("The given directory does not exist!")
files = glob.glob(directory+'/*.h5')
files.sort()
for file in files:
c = command % file
os.system(c) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_h5(walk_dir):\n\n file_list = []\n for root, subdirs, files in os.walk(walk_dir):\n\n for filename in files:\n file_path = os.path.join(root, filename)\n if file_path[-2:] == 'h5':\n file_list.append(file_path)\n\n return file_list",
"def h5ls(h5o, ma... | [
"0.69875836",
"0.6177355",
"0.59026027",
"0.57904077",
"0.5747277",
"0.57445157",
"0.5651457",
"0.5643685",
"0.56236434",
"0.5591664",
"0.55789",
"0.555372",
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"0.5523432",
"0.5506336",
"0.54694766",
"0.54544675",
"0.5440774",
"0.5433494",
"0.5421979",
"0.54104507"... | 0.8061157 | 0 |
Convert directory with flusi h5 files to wabbit h5 files | def flusi_to_wabbit_dir(dir_flusi, dir_wabbit , *args, **kwargs ):
import re
import os
import glob
if not os.path.exists(dir_wabbit):
os.makedirs(dir_wabbit)
if not os.path.exists(dir_flusi):
err("The given directory does not exist!")
files = glob.glob(dir_flusi+'/*.h5')
fi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def h5root():\n with h5py.File('dummy.nxs', mode='w', driver=\"core\", backing_store=False) as f:\n yield f",
"def read_wabbit_hdf5_dir(dir):\n import numpy as np\n import re\n import ntpath\n import os\n\n it=0\n data={'time': [],'x0':[],'dx':[],'treecode':[]}\n # we loop over all... | [
"0.63210124",
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"0.60205424",
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"0.5433757",
"0.54336375",
"0.54284495",
"0.53850937",
"0.5381837",
"0... | 0.74297935 | 0 |
Convert flusi data file to wabbit data file. | def flusi_to_wabbit(fname_flusi, fname_wabbit , level, dim=2, dtype=np.float64 ):
import numpy as np
import insect_tools
import matplotlib.pyplot as plt
# read in flusi's reference solution
time, box, origin, data_flusi = insect_tools.read_flusi_HDF5( fname_flusi, dtype=dtype )
box = box[1:]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def flusi_to_wabbit_dir(dir_flusi, dir_wabbit , *args, **kwargs ):\n import re\n import os\n import glob\n\n if not os.path.exists(dir_wabbit):\n os.makedirs(dir_wabbit)\n if not os.path.exists(dir_flusi):\n err(\"The given directory does not exist!\")\n\n files = glob.glob(dir_flus... | [
"0.6466376",
"0.5578025",
"0.5258005",
"0.52233136",
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"0.5156527",
"0.51492476",
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"0.50022244",
"0.48981017",
"0.4894581",
"0.48687062",
"0.48647705",
"0.48561457",
"0.484... | 0.6721087 | 0 |
This function creates a _.h5 file with the wabbit block structure from a given dense data matrix. Therefore the dense data is divided into equal blocks, similar as sparse_to_dense option in wabbitpost. | def dense_to_wabbit_hdf5(ddata, name , Bs, box_size = None, time = 0, iteration = 0, dtype=np.float64):
# concatenate filename in the same style as wabbit does
fname = name + "_%12.12d" % int(time*1e6) + ".h5"
Ndim = ddata.ndim
Nsize = np.asarray(ddata.shape)
level = 0
Bs = np.asarray(Bs)# make ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_dense_grid( fname_in, fname_out = None, dim=2 ):\n import numpy as np\n import insect_tools\n import matplotlib.pyplot as plt\n\n # read data\n time, x0, dx, box, data, treecode = read_wabbit_hdf5( fname_in )\n\n # convert blocks to complete matrix\n field, box = dense_matrix( x0, dx, ... | [
"0.7129957",
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"0.56526315",
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"0.5431243",
"0.5422526",
"0.54188895",
"0.5410818",
"0.5400482",
"0.53950... | 0.7183506 | 0 |
For a given shape of a dense field and maxtreelevel return the number of points per block wabbit uses | def field_shape_to_bs(Nshape, level):
n = np.asarray(Nshape)
for d in range(n.ndim):
# check if Block is devidable by Bs
if (np.remainder(n[d], 2**level) != 0):
err("Number of Grid points has to be a power of 2!")
# Note we have to flip n here because Bs = [Bs... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def levshape(self) -> Shape:\n return tuple(len(x) for x in self.levels)",
"def num_leaves(tree):\n return ((tree.n_node_samples > 0) & (tree.feature == INVALID_VALUE)).sum()",
"def look_for_biggest_structure(game, chunk, imgs, hmap, nmax, type_):\n for n in range(nmax,0,-1):\n i = 0\n ... | [
"0.5944209",
"0.5931977",
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"0.5644827",
"0.56374794",
"0.5623013",
"0.5621187",
"0.5620981",
"0.5608256"... | 0.5566646 | 24 |
Transform the data and write out as a TFRecord of Example protos. | def transform(train_data, test_data, working_dir):
options = PipelineOptions()
options.view_as(StandardOptions).runner = 'DirectRunner'
with beam.Pipeline(options=options) as pipeline:
_ = (pipeline | 'ReadTrainData' >> beam.Create(train_data) |
'EncodeTrainData' >> beam.Map(lambda data: to_example(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _convert_example(self, output_file, data_dict):\n print('Generating %s' % output_file)\n with tf.compat.v1.python_io.TFRecordWriter(output_file) as record_writer:\n data = data_dict['data'].astype(np.int8)\n labels = data_dict['label'].astype(np.int64)\n num_entries_in_batch = len(labels)\... | [
"0.7601864",
"0.7601864",
"0.73363864",
"0.7253738",
"0.704304",
"0.7021169",
"0.6914192",
"0.6816245",
"0.67292994",
"0.6721178",
"0.6699888",
"0.6662521",
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"0.65940547",
"0.65909016",
"0.6586768",
"0.6542893",
"0.6498155",
"0.6449436",
"0.6435666",... | 0.60572654 | 42 |
Semantic segmentation network definition | def inference():
print("setting up vgg initialized conv layers ...")
model_data = utils.get_model_data(FLAGS.model_dir, MODEL_URL)
mean = model_data['normalization'][0][0][0]
mean_pixel = np.mean(mean, axis=(0, 1))
weights = np.squeeze(model_data['layers'])
with tf.variable_scope("inference"... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def macro_network():\n # fmt: off\n tpm = np.array([\n [0.3, 0.3, 0.3, 0.3],\n [0.3, 0.3, 0.3, 0.3],\n [0.3, 0.3, 0.3, 0.3],\n [0.3, 0.3, 1.0, 1.0],\n [0.3, 0.3, 0.3, 0.3],\n [0.3, 0.3, 0.3, 0.3],\n [0.3, 0.3, 0.3, 0.3],\n [0.3, 0.3, 1.0, 1.0],\n ... | [
"0.60830534",
"0.60496855",
"0.59802425",
"0.595663",
"0.5847172",
"0.5820586",
"0.5727076",
"0.5702224",
"0.5695941",
"0.56869364",
"0.56733644",
"0.5624573",
"0.56224674",
"0.5618467",
"0.55787534",
"0.5577703",
"0.55772173",
"0.5574059",
"0.5552585",
"0.5543071",
"0.554214... | 0.0 | -1 |
convert hash_str to hash_dec | def hash2dec(hash_str: str) -> int:
length = len(hash_str)
bases = [32 ** i for i in range(length)][::-1]
dec = 0
for i, d in enumerate(hash_str):
dec += ch2int[d] * bases[i]
return dec | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hash_string_to_int(\r\n k: bytes,\r\n e: str,\r\n) -> int:\r\n return int.from_bytes(hash_string(k, e), 'big')",
"def strhash(s: str) -> int:\n h = hashlib.md5(s.encode('utf-8'))\n h = int(h.hexdigest(), base=16)\n return h",
"def dec2hash(hash_dec: int, pre: int) -> str:\n bas... | [
"0.676085",
"0.6701529",
"0.6644186",
"0.64678264",
"0.64379567",
"0.64147437",
"0.6400152",
"0.63800716",
"0.6376264",
"0.63746643",
"0.63039637",
"0.62607664",
"0.62387604",
"0.62307084",
"0.62161714",
"0.61618865",
"0.61301386",
"0.61214674",
"0.6107932",
"0.60902554",
"0.... | 0.81465983 | 0 |
convert hash_dec to hash_str | def dec2hash(hash_dec: int, pre: int) -> str:
bases = [32 ** i for i in range(pre)][::-1]
hash_str = ""
v = hash_dec
for b in bases:
a = v // b
v = v % b
hash_str += ch32[a]
return hash_str | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hash_str(c, hash_length):\n if isinstance(c, float):\n if numpy.isnan(c):\n return c\n raise ValueError(f\"numpy.nan expected, not {c}\")\n m = hashlib.sha256()\n m.update(c.encode(\"utf-8\"))\n r = m.hexdigest()\n if len(r) >= hash_length:\n return r[:hash_length... | [
"0.67123157",
"0.6692818",
"0.66889405",
"0.66310173",
"0.6559617",
"0.65580744",
"0.6501489",
"0.64891833",
"0.64672464",
"0.6397518",
"0.6363674",
"0.63632846",
"0.6330039",
"0.63063854",
"0.6293308",
"0.6279598",
"0.627666",
"0.6234947",
"0.62323284",
"0.62225",
"0.6185577... | 0.7153087 | 0 |
convert lat, lon coordinate to decimal geohash representation (pre=6) | def coords2geohash_dec(*, lat: float, lon: float, pre: int = 6) -> int:
return hash2dec(encoder(lat, lon, pre)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _decode(geohash):\n lat_val, lng_val, lat_err, lng_err = _decode_val_err(geohash)\r\n precision = _get_precision(lng_err)\n lat_val = \"%.*f\" % (precision, lat_val)\r\n lng_val = \"%.*f\" % (precision, lng_val)\r\n return lat_val, lng_val",
"def geohash_encode(latitude, longitude, precision=1... | [
"0.7197927",
"0.7073706",
"0.6947506",
"0.68074983",
"0.67318535",
"0.66749907",
"0.66701984",
"0.65034956",
"0.64117384",
"0.6199895",
"0.61259615",
"0.59396446",
"0.5933387",
"0.59319395",
"0.57969904",
"0.5766618",
"0.5754867",
"0.5753793",
"0.5708975",
"0.56883526",
"0.56... | 0.7521665 | 0 |
convert decimal geohash to lat, lon coordinate (we require pre=6) | def geohash_dec2coords(*, geohash_dec: int, pre: int = 6) -> Tuple[float, float]:
res = decoder(dec2hash(geohash_dec, pre=pre))
return round(sum(res[0]) / 2, max(3, pre - 3)), round(
sum(res[1]) / 2, max(3, pre - 3)
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _decode(geohash):\n lat_val, lng_val, lat_err, lng_err = _decode_val_err(geohash)\r\n precision = _get_precision(lng_err)\n lat_val = \"%.*f\" % (precision, lat_val)\r\n lng_val = \"%.*f\" % (precision, lng_val)\r\n return lat_val, lng_val",
"def coords2geohash_dec(*, lat: float, lon: float, p... | [
"0.7526719",
"0.7387216",
"0.7375263",
"0.63441426",
"0.6267863",
"0.624697",
"0.6224477",
"0.6146233",
"0.6016872",
"0.5977346",
"0.592548",
"0.5918737",
"0.5868537",
"0.5856564",
"0.5801432",
"0.5772634",
"0.57722",
"0.5735534",
"0.57101923",
"0.570122",
"0.5676153",
"0.5... | 0.7478197 | 1 |
Method to return a custom logger with the given name and level | def my_custom_logger(logger_name, level=logging.INFO):
logger = logging.getLogger(logger_name)
logger.setLevel(level)
format_string = ('%(asctime)s, %(levelname)s, %(filename)s, %(message)s')
log_format = logging.Formatter(format_string)
# Creating and adding the console handler
console_handler ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_logger(name: str, level: str = LOG_LEVEL) -> logging.Logger:\n logger = logging.getLogger(name)\n logger.propagate = False\n logger.setLevel(level)\n coloredlogs.install(\n level=level, logger=logger, fmt='%(asctime)s %(name)s: %(lineno)s %(levelname)s: %(message)s', field_styles=FIELD_S... | [
"0.81156564",
"0.80115414",
"0.79588497",
"0.774673",
"0.77105635",
"0.7634092",
"0.7624498",
"0.76038194",
"0.75276273",
"0.74171513",
"0.73107415",
"0.7245147",
"0.72039235",
"0.71859866",
"0.71816105",
"0.7040017",
"0.7033277",
"0.6987893",
"0.69791347",
"0.69768167",
"0.6... | 0.73898995 | 10 |
function that takes one argument, compares and returns results based on the argument supplied to the function | def data_type(value):
if type(value) == type(None):
return 'no value'
elif type(value) == list:
if len(value) >= 3:
return value[2]
else:
return None
elif type(value) == bool:
return value
elif type(value) == int:
if value < 100:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compare(a, b):\n return a - b",
"def compare(a, b):\n # Your function body should begin here.\n pass",
"def compare(a, b):\n if a > b:\n return a\n return b",
"def compareFn(impl1, impl2):\n for (v1, v2) in zip(\n [extractDigits(f.strip()) for f in impl1.split(\",\")],\n ... | [
"0.6585621",
"0.65484715",
"0.6427431",
"0.6168221",
"0.6134509",
"0.60055876",
"0.59661186",
"0.5960077",
"0.5960077",
"0.5960077",
"0.5941255",
"0.5940668",
"0.5891304",
"0.5846155",
"0.58251274",
"0.5801914",
"0.5783762",
"0.57126707",
"0.5700952",
"0.5664475",
"0.563011",... | 0.0 | -1 |
uploads file to Google Cloud storage | def _cloud_storage_upload(local_file, bucket, filename_on_bucket):
client = storage.Client()
bucket = client.get_bucket(bucket)
blob = bucket.blob(filename_on_bucket)
blob.upload_from_filename(local_file)
print('uploaded ', bucket, filename_on_bucket) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def upload_to_gcs():\n client = storage.Client(project=\"filmreccommendations\")\n bucket = client.get_bucket(\"filmreccommendations.appspot.com\")\n blob = bucket.blob(os.path.basename(PICKLE_FILENAME))\n blob.upload_from_filename(PICKLE_FILENAME)",
"def gcloud_upload_file(file):\n if not file:\n... | [
"0.7862477",
"0.7417298",
"0.73990583",
"0.7352191",
"0.7321791",
"0.7267003",
"0.6985354",
"0.69010127",
"0.6875503",
"0.68445647",
"0.68404883",
"0.6832378",
"0.6829256",
"0.67942363",
"0.67374986",
"0.67214787",
"0.67042154",
"0.66991466",
"0.6689875",
"0.6675743",
"0.6637... | 0.74267185 | 1 |
Returns a set with all nodes contained in the specified group. | def make_set(g, nodes):
s = Set()
names = nodes['names']
for ii,name in enumerate(names):
"""
We will assume node is entirely contained
in group if they have one atom in common
"""
atoms = mdn.dic2list(nodes[name]['atoms'])
atom0 = atoms[0]
if (atom0 in mdn.dic2list(g['atoms'])):
s.add(ii)
retu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def group_nodes(self, group, namespace=None):\n source = self._source(namespace)\n return self._list(source, 'map', group)",
"def get_nodeset(self):\n return set(self.nodeset) # return the nodeset",
"def get_nodes(self):\n return_set = set()\n for value in self._name:\n ... | [
"0.7057669",
"0.6412436",
"0.6143436",
"0.61363167",
"0.6067383",
"0.6000612",
"0.5960207",
"0.5856612",
"0.583617",
"0.57776505",
"0.5762336",
"0.5747678",
"0.56864786",
"0.56805146",
"0.56712276",
"0.5657101",
"0.5638051",
"0.5605475",
"0.5574436",
"0.55193394",
"0.5516074"... | 0.62811893 | 2 |
Lists all the catalystport bindings | def get_all_catalystport_bindings():
LOG.debug("get_all_catalystport_bindings() called")
session = db.get_session()
try:
bindings = session.query
(catalyst_models.CatalystPortBinding).all()
return bindings
except exc.NoResultFound:
return [] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bindings(self):\n return self.__bindings",
"def list_ports(state):\n\tstate.report()",
"def list_ports(self):\n return self.ironic_client.port.list()",
"def port_list(self):\n return self._port_list",
"def get_all_port(self, conf, dpid):\n\t\tpass",
"def getBindings(self):\n r... | [
"0.6451805",
"0.62612075",
"0.58983856",
"0.5897845",
"0.579027",
"0.5786138",
"0.5704574",
"0.56907016",
"0.5677487",
"0.56535304",
"0.56521446",
"0.56430465",
"0.5607622",
"0.56039107",
"0.5600516",
"0.55635554",
"0.55596274",
"0.5467314",
"0.5458655",
"0.5453633",
"0.54411... | 0.78534424 | 0 |
Lists catalyst port binding for particular vlan | def get_catalystport_binding(vland_id):
LOG.debug("get_catlystport_binding() called")
session = db.get_session()
try:
binding = (session.query(catalyst_models.CatalystPortBinding). \
filter_by(vland_id).all())
return binding
except exc.NoresultFound:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_ports(state):\n\tstate.report()",
"def display_port(self):\n ports=os.popen(\"sudo netstat -ntlp\").read().strip().splitlines()[2:]\n for port in ports:\n split=re.split('[\\s]+',port)\n self.portDic[\"Protcol\"]=split[0]\n self.portDic[\"Receive Q\"]=split... | [
"0.6723886",
"0.64618164",
"0.64385796",
"0.64146525",
"0.63710177",
"0.6335538",
"0.6304014",
"0.6206474",
"0.61226624",
"0.6054834",
"0.6002115",
"0.59913033",
"0.5865777",
"0.58342767",
"0.5811998",
"0.5795699",
"0.5788664",
"0.57422847",
"0.5704238",
"0.56896126",
"0.5671... | 0.63200647 | 6 |
Adds a catalystport binding | def add_catalystport_binding(port_id, vlan_id):
LOG.debug("add_catalystport_binding() called")
session = db.get_session()
binding = catalyst_models.CatalystPortBinding(port_id, vlan_id)
session.add(binding)
session.flush()
return binding | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_binding(ctx, binding_name, pool_name, acl_name, nat_type, twice_nat_id):\n\n entryFound = False\n table = 'NAT_BINDINGS'\n key = binding_name\n dataKey1 = 'access_list'\n dataKey2 = 'nat_pool'\n dataKey3 = 'nat_type'\n dataKey4 = 'twice_nat_id'\n\n if acl_name is None:\n acl_... | [
"0.6066342",
"0.6021122",
"0.5959869",
"0.5917959",
"0.5857227",
"0.5852962",
"0.5653113",
"0.56476676",
"0.55153096",
"0.5501996",
"0.5498605",
"0.5429331",
"0.5401681",
"0.53849334",
"0.5362603",
"0.53139096",
"0.52629244",
"0.5251845",
"0.5244616",
"0.5243897",
"0.52343386... | 0.7463989 | 0 |
Removes a catalystport binding | def remove_catalystport_binding(vlan_id):
LOG.debug("remove_catalystport_binding() called")
session = db.get_session()
try:
binding = (session.query(catalyst_models.CatalystPortBinding).
filter_by(vlan_id=vlan_id).all())
for bind in binding:
session.delete... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_binding(ctx, binding_name):\n\n entryFound = False\n table = 'NAT_BINDINGS'\n key = binding_name\n\n if len(binding_name) > 32:\n ctx.fail(\"Invalid binding name. Maximum allowed binding name is 32 characters !!\")\n\n config_db = ConfigDBConnector()\n config_db.connect()\n\n ... | [
"0.73616654",
"0.6799897",
"0.6344198",
"0.6337611",
"0.617905",
"0.6130057",
"0.61269933",
"0.6104561",
"0.5963844",
"0.5883558",
"0.58721596",
"0.5811271",
"0.5778574",
"0.5777863",
"0.57622343",
"0.5755362",
"0.5706742",
"0.5679512",
"0.5677332",
"0.5649031",
"0.5630688",
... | 0.7389213 | 0 |
Use encoder to get embedding vectors first. | def distances_from_obs(self, session, obs_first, obs_second, hashes_first=None, hashes_second=None, **kwargs):
obs_encoder = self.obs_encoder
if hashes_first is None:
hashes_first = [hash_observation(obs) for obs in obs_first]
if hashes_second is None:
hashes_second = [h... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_embeddings(encoder, data_batches):\n\n vectors = []\n for batch in iter(data_batches):\n X, Y = batch\n X_embedded = encoder(X)\n for vec in np.array(X_embedded):\n vectors.append(vec)\n vectors = np.array(vectors)\n\n return vectors",
"def set_embeddings(s... | [
"0.7020101",
"0.6637058",
"0.6564788",
"0.6535303",
"0.6484196",
"0.6471777",
"0.63643616",
"0.6341164",
"0.6330869",
"0.62297994",
"0.6209872",
"0.62074643",
"0.61839",
"0.61733645",
"0.61321247",
"0.6101453",
"0.6098147",
"0.6074956",
"0.607458",
"0.6064943",
"0.60478055",
... | 0.0 | -1 |
Returns True if the server is running | def is_running(self):
return self._running | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _ServerIsRunning( self ):\n return utils.ProcessIsRunning( self._gocode_handle )",
"def status(self):\n # process running ?\n pid = self.get_pidfile()\n \n running = True\n \n # process is not running\n if pid is None:\n running = False\n ... | [
"0.8809185",
"0.8009785",
"0.8002331",
"0.7958592",
"0.7890142",
"0.7879066",
"0.78770465",
"0.7851644",
"0.7850044",
"0.78008723",
"0.77929753",
"0.77749777",
"0.7774173",
"0.7757005",
"0.77514684",
"0.77514684",
"0.77514684",
"0.77452874",
"0.7717644",
"0.7717644",
"0.77032... | 0.7632167 | 26 |
Return interface being listened on | def interface(self):
return self._interface | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_interface(self):\n return self.__interface",
"def _get_interface(self):\n return self.__interface",
"def _get_interface(self):\n return self.__interface",
"def _get_interface(self):\n return self.__interface",
"def _get_interface(self):\n return self.__interface",
"def _get_interf... | [
"0.78353",
"0.78353",
"0.78353",
"0.78353",
"0.78353",
"0.78353",
"0.78353",
"0.78353",
"0.78353",
"0.78353",
"0.78353",
"0.78353",
"0.78353",
"0.78353",
"0.78353",
"0.7473333",
"0.7236905",
"0.7236905",
"0.7236905",
"0.7236905",
"0.7236905",
"0.7236905",
"0.70470303",
... | 0.7632904 | 15 |
Return interface port number listener is configured for | def port(self):
return self._port | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_port(self) -> int:\n return int(self.socket.getsockname()[1])",
"def get_port(self) -> int:\n return self._port",
"def port(self) -> int:",
"def get_port(self):\n return self.port",
"def port(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"port\")",
"def port(s... | [
"0.7530533",
"0.7471657",
"0.7419851",
"0.73832756",
"0.7280439",
"0.7280439",
"0.72383",
"0.7234966",
"0.7234966",
"0.7234966",
"0.7211949",
"0.7210165",
"0.71787095",
"0.7175771",
"0.71601677",
"0.71456337",
"0.7141916",
"0.7101398",
"0.7101398",
"0.7101398",
"0.7101398",
... | 0.68487716 | 44 |
Default access mechanism if API does not specify it | def default_access_control(self):
return self._default_access_control | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def request_access(self):\n pass",
"def access():",
"def api_access(self):\n return self._api_access",
"def maya_useNewAPI():\n\tpass",
"def maya_useNewAPI():\n\tpass",
"def maya_useNewAPI():\r\n\r\n pass",
"def api_get(self, name):\n try:\n r = self._get(['apis', nam... | [
"0.6620378",
"0.62082165",
"0.5927204",
"0.58691835",
"0.58691835",
"0.5855044",
"0.58468324",
"0.57812655",
"0.5763495",
"0.5763495",
"0.5763495",
"0.5763495",
"0.5763495",
"0.57499105",
"0.56996876",
"0.564842",
"0.5636248",
"0.56194305",
"0.5615304",
"0.558719",
"0.5577919... | 0.5198949 | 53 |
Get the current resource/API | def api(self):
return self._api | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_api(self):\n return self.api",
"def api(self):\r\n return self._api",
"def api(self):\n return self.__api",
"def getAPI(self):\n return self.api_url",
"def getResource(self):\n return self.serviceClass.app.resource()",
"def get_api(self):\n from geoffrey.... | [
"0.7800824",
"0.7528681",
"0.7499402",
"0.73490024",
"0.6924",
"0.69182336",
"0.68368024",
"0.68043464",
"0.66442335",
"0.66442335",
"0.66442335",
"0.66442335",
"0.66442335",
"0.66442335",
"0.66442335",
"0.65007555",
"0.6493793",
"0.6491067",
"0.6483726",
"0.6483726",
"0.6476... | 0.7439773 | 6 |
Set the API resources | def api(self, api):
if self._running:
raise ValueError('API cannot be modified while the server is running')
self._api = api | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def resources(self, resources):\n self._resources = resources",
"def resources(self, resources):\n\n self._resources = resources",
"def resources(self, resources):\n\n self._resources = resources",
"def resources(self, resources):\n\n self._resources = resources",
"def resources... | [
"0.74864537",
"0.742781",
"0.742781",
"0.742781",
"0.742781",
"0.69810915",
"0.6825962",
"0.6772192",
"0.66917735",
"0.66321164",
"0.64988405",
"0.64539236",
"0.6449976",
"0.6399484",
"0.63794327",
"0.6372078",
"0.63311076",
"0.62079686",
"0.6152585",
"0.6030097",
"0.6025588"... | 0.0 | -1 |
Start the server if it is not running | def start(self):
if not self._running:
try:
resource = self._default_access_control.secure_resource(self._api)
site = Site(resource=resource)
self._listener = reactor.listenTCP(self._port, # pylint: disable=no-member
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def start_server(self):\n if not self._server:",
"def start():\n\n start_server()",
"def local_webserver_start():\n if not _is_webserver_running():\n local(_webserver_command())",
"def run():\n server = current_server()\n server._auto_stop = True\n return start()",
"def... | [
"0.8494984",
"0.7766544",
"0.7548727",
"0.73513985",
"0.7320159",
"0.7309142",
"0.72342896",
"0.72342896",
"0.7074414",
"0.69020855",
"0.68957585",
"0.6856507",
"0.68551105",
"0.68490946",
"0.6826151",
"0.6805642",
"0.6805338",
"0.67718863",
"0.67642325",
"0.673737",
"0.67260... | 0.6707576 | 22 |
Test whether the numpy data type `dt` can be safely cast to an int. | def _safely_castable_to_int(dt):
int_size = np.dtype(int).itemsize
safe = (np.issubdtype(dt, np.signedinteger) and dt.itemsize <= int_size) or (
np.issubdtype(dt, np.unsignedinteger) and dt.itemsize < int_size
)
return safe | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _is_integer(x):\n return (not isinstance(x, (bool, np.bool))) and \\\n isinstance(x, (numbers.Integral, int, np.int, np.long, long)) # no long type in python 3",
"def is_int(x):\n # From sktime: BSD 3-Clause\n # boolean are subclasses of integers in Python, so explicitly exclude them\n re... | [
"0.68389726",
"0.67490387",
"0.6626902",
"0.6598004",
"0.6508628",
"0.64784265",
"0.64210194",
"0.6398246",
"0.63903487",
"0.6372374",
"0.6356024",
"0.6314418",
"0.63074124",
"0.6301813",
"0.6293644",
"0.62889963",
"0.6231258",
"0.62195265",
"0.6181786",
"0.6180847",
"0.61740... | 0.8405704 | 0 |
Calculate a percentile of the array values over labeled regions. | def percentile(data, qval, labels=None, index=None):
data = np.asanyarray(data)
def single_group(vals):
return np.percentile(vals, qval)
if labels is None:
return single_group(data)
# ensure input and labels match sizes
data, labels = np.broadcast_arrays(data, labels)
if inde... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _calc_perc(arr: np.array, p: Sequence[float] = None):\n if p is None:\n p = [50]\n\n nan_count = np.isnan(arr).sum(axis=-1)\n out = np.moveaxis(np.percentile(arr, p, axis=-1), 0, -1)\n nans = (nan_count > 0) & (nan_count < arr.shape[-1])\n if np.any(nans):\n out_mask = np.stack([na... | [
"0.70549434",
"0.6741164",
"0.6662035",
"0.66016424",
"0.6500401",
"0.6459244",
"0.6303143",
"0.6247118",
"0.6219375",
"0.61808074",
"0.6145661",
"0.6143983",
"0.6136194",
"0.61153173",
"0.60878253",
"0.60767406",
"0.60635877",
"0.6059681",
"0.5997567",
"0.59776396",
"0.59420... | 0.65258336 | 4 |
Initializes all the element of the GUI, supported by Tkinter | def __init__(self, tello):
self.tello = tello # videostream device
self.thread = None # thread of the Tkinter mainloop
self.stopEvent = None
# control variables
self.distance = 0.1 # default distance for 'move' cmd
self.degree = 30 # default degree for 'cw' o... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n self.window = Tk() # The main window\n self.__initialize_variables__() # Initialize the variables\n self.__initialize_menu__() # Initialize the Menu\n self.__initialize_status_bar__()\n self.__initialize_gui__() # Initialize the GUI widgets",... | [
"0.77829045",
"0.7319447",
"0.7121587",
"0.71154493",
"0.7044531",
"0.70197904",
"0.7015567",
"0.696209",
"0.68833053",
"0.6865509",
"0.6856705",
"0.6856659",
"0.68557364",
"0.6814217",
"0.6804366",
"0.67954916",
"0.6790265",
"0.67835164",
"0.67754626",
"0.6773747",
"0.676693... | 0.0 | -1 |
Starts a while loop that sends 'command' to tello every 5 second. | def _sendingCommand(self):
while True:
self.tello.send_command('command')
time.sleep(5) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Listen(self):\n while True:\n time.sleep(1)",
"def run(self):\n while True:\n time.sleep(RTM_READ_DELAY)\n for event in self._slack_client.rtm_read():\n self.handle_event(event)",
"def run():\n # 1 sec delay to allow DHT22 sensor to start as ... | [
"0.63576066",
"0.6166633",
"0.60652816",
"0.60415244",
"0.60114336",
"0.59595996",
"0.595747",
"0.5923307",
"0.5898725",
"0.58431983",
"0.58387035",
"0.58356047",
"0.5832241",
"0.56968737",
"0.5675272",
"0.56747895",
"0.56568396",
"0.56439304",
"0.562048",
"0.56196946",
"0.56... | 0.7657067 | 0 |
Set the variable as TRUE; it will stop computer waiting for response from tello. | def _setQuitWaitingFlag(self):
self.quit_waiting_flag = True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __bool__(self):\n return self.wait(0)",
"def _stop(self):\n return True",
"def kinbot(self):\n self.success = False",
"def stopCond(self):\n\t\treturn False",
"def stop(self):\n command = input(\"Enter anything to finish (or 'exit' to cancel)>>>\")\n return command !=... | [
"0.6232066",
"0.60531396",
"0.5993092",
"0.5933566",
"0.58755565",
"0.5861535",
"0.580857",
"0.5768193",
"0.57589114",
"0.57057345",
"0.5682822",
"0.5668132",
"0.56636673",
"0.5655856",
"0.56449413",
"0.56356215",
"0.5628359",
"0.5628359",
"0.56276935",
"0.56122077",
"0.56005... | 0.5679991 | 11 |
Open the cmd window and initial all the button and text. | def openCmdWindow(self):
panel = Toplevel(self.root)
panel.wm_title('Command Panel')
# create text input entry
text0 = tki.Label(panel,
text='This Controller map keyboard inputs to Tello control commands\n'
'Adjust the tra... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def open(self):\n self.state = True\n self.mainwindow.sendMessage('a')\n print(\"opening \" + self.name)",
"def build_initial() :\r\n titleframe = T.Frame(ROOT)\r\n TITLE = T.Label(titleframe, text = \"Welcome to Microgp!\")\r\n var = T.StringVar()\r\n INSTRUCTIONS = T.Message(ti... | [
"0.61820495",
"0.61781216",
"0.6119948",
"0.60934174",
"0.60702914",
"0.5959592",
"0.5948371",
"0.5943641",
"0.593772",
"0.5910269",
"0.59019053",
"0.5892788",
"0.58925205",
"0.5891495",
"0.5889927",
"0.58737874",
"0.5858974",
"0.5835644",
"0.5834646",
"0.58337766",
"0.582894... | 0.68487906 | 0 |
Open the flip window and initial all the button and text. | def openFlipWindow(self):
panel = Toplevel(self.root)
panel.wm_title('Gesture Recognition')
self.btn_flipl = tki.Button(
panel, text='Flip Left', relief='raised', command=self.telloFlip_l)
self.btn_flipl.pack(side='bottom', fill='both',
expand='ye... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def switch_state():\n\tDmg.OpenWindow()",
"def show(self):\r\n self.wf.Show()",
"def show(self, window):\r\n\r\n return",
"def finish_render():\n get_window().static_display = True\n get_window().flip_count = 0\n get_window().flip()",
"def cb_main_window(self, event):\n self.m... | [
"0.6244811",
"0.61130655",
"0.60361177",
"0.58716136",
"0.5835317",
"0.5802721",
"0.58015156",
"0.57977974",
"0.57581085",
"0.5736185",
"0.5727664",
"0.56942797",
"0.5663911",
"0.5637016",
"0.5626047",
"0.5598497",
"0.55893236",
"0.5587088",
"0.55717754",
"0.55463487",
"0.553... | 0.8400445 | 0 |
Sets the stop event, cleanup the camera, and allow the rest of the quit process to continue. | def on_close(self):
print('[INFO] closing...')
self.stopEvent.set()
del self.tello
self.root.quit() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stop(self):\n self.running = False\n self.cam.stop()\n self.amplifier.stop()\n pass",
"def stop():\n global running\n running = False\n messagebox.showinfo(\"Camera mode\",\"Stop image grab\")\n camera.stop_preview()",
"def stop(self):\n self.stop_aperture()",... | [
"0.7624942",
"0.76044846",
"0.7556506",
"0.744567",
"0.744567",
"0.7432174",
"0.7239621",
"0.72074336",
"0.72074336",
"0.72029865",
"0.71938837",
"0.71938837",
"0.7178905",
"0.7176315",
"0.71751827",
"0.71751827",
"0.71670294",
"0.7162203",
"0.7162203",
"0.7145708",
"0.713128... | 0.0 | -1 |
example action using the internationalization operator T and flash rendered by views/default/index.html or views/generic.html | def index():
return dict(message=T('Welcome to Audi Volkswagon Porsche')) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def index(request, *args, **kwargs):\n return Response({\"message\":\"Nobody expects the spanish inquisition!\"})",
"def index():\n return dict(message=T('Hello World'))",
"def index():\n response.flash = \"Welcome to Myapp!\"\n return dict(message=T('Hello World'))",
"def index_en(request):\n ... | [
"0.66694236",
"0.6106484",
"0.60288805",
"0.58857584",
"0.5849951",
"0.5769532",
"0.5752256",
"0.5746499",
"0.5689333",
"0.5651591",
"0.56413424",
"0.563141",
"0.5626008",
"0.5618876",
"0.5612004",
"0.5580761",
"0.55667937",
"0.55658025",
"0.5562467",
"0.55529743",
"0.5483404... | 0.53856117 | 44 |
allows downloading of uploaded files | def download():
return response.download(request, db) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def download_files(self):",
"def post_download(self, remote_files):\n pass",
"def download_file(self, parsed_event, input_dir_path):",
"def download(self,fn):\n\t\treturn False #TODO: implement meme download",
"def download(self):\n pass",
"def download(self):\n pass",
"def pre_dow... | [
"0.7924571",
"0.7199257",
"0.6978304",
"0.6841495",
"0.6764734",
"0.6764734",
"0.66671187",
"0.6631285",
"0.6588692",
"0.6555494",
"0.65398693",
"0.65012735",
"0.6458808",
"0.6451737",
"0.64442515",
"0.6402176",
"0.63864595",
"0.63758063",
"0.63659835",
"0.6345104",
"0.633138... | 0.0 | -1 |
View callable parameters are either context, request or just request. There is also request.context. In mako templates, request is accessible as request, context as _context. Values contained in the returned dictionary can be accessed within the template under variables named by the dictionary keys. | def home(context, request):
return {} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_context(self):\n return {\"request\": self.request, \"format\": self.format_kwarg, \"view\": self}",
"def get_renderer_context(self):\n # Note: Additionally 'response' will also be added to the context,\n # by the Response object.\n return {\n 'view': self,\n ... | [
"0.7701646",
"0.6560682",
"0.63035905",
"0.61721003",
"0.6156719",
"0.60182035",
"0.60104954",
"0.59970176",
"0.597518",
"0.5969505",
"0.5959369",
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"0.5925658",
"0.5925658",
"0.5907715",
"0.58984065",
"0.5887859",
"0.58788943",
"0.58776265",
"0.58464235",
"0.583259... | 0.0 | -1 |
Returns the quantization config for transformerbased models. | def _get_transformer_quantization_config(subset_size: int) -> Dict[str, Any]:
return {
"algorithm": "quantization",
"preset": "mixed",
"initializer": {
"range": {"num_init_samples": subset_size, "type": DEFAULT_RANGE_TYPE},
"batchnorm_adaptation": {"num_bn_adaptation_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_default_quantization_config(preset: QuantizationPreset, subset_size: int) -> Dict[str, Any]:\n return {\n \"algorithm\": \"quantization\",\n \"preset\": preset.value,\n \"initializer\": {\n \"range\": {\"num_init_samples\": subset_size, \"type\": DEFAULT_RANGE_TYPE},\n ... | [
"0.66286147",
"0.5942934",
"0.5907931",
"0.5872018",
"0.5804783",
"0.57093644",
"0.56935316",
"0.5644522",
"0.56273764",
"0.5596549",
"0.5582015",
"0.55781955",
"0.5568165",
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"0.5484123",
"0.54839206",
"0.54510504",
"0.5421256",
"0.54176253",
"0.5367854",
"0.53125... | 0.780377 | 0 |
Returns the default quantization config | def _get_default_quantization_config(preset: QuantizationPreset, subset_size: int) -> Dict[str, Any]:
return {
"algorithm": "quantization",
"preset": preset.value,
"initializer": {
"range": {"num_init_samples": subset_size, "type": DEFAULT_RANGE_TYPE},
"batchnorm_adap... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_transformer_quantization_config(subset_size: int) -> Dict[str, Any]:\n return {\n \"algorithm\": \"quantization\",\n \"preset\": \"mixed\",\n \"initializer\": {\n \"range\": {\"num_init_samples\": subset_size, \"type\": DEFAULT_RANGE_TYPE},\n \"batchnorm_adapt... | [
"0.7033103",
"0.6777367",
"0.6720709",
"0.65748835",
"0.6455274",
"0.642663",
"0.63270366",
"0.63183445",
"0.63130504",
"0.6306793",
"0.62361944",
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"0.6185362",
"0.6169694",
"0.6101184",
"0.6091094",
"0.6084804",
"0.60460657",
"0.59970856",
"0.5981526"... | 0.8306506 | 0 |
Creates the NNCFConfig for the quantization algorithm. | def _create_nncf_config(
preset: QuantizationPreset,
target_device: TargetDevice,
subset_size: int,
model_type: Optional[ModelType],
ignored_scope: Optional[IgnoredScope],
advanced_parameters: Optional[AdvancedQuantizationParameters],
) -> NNCFConfig:
if model_type is None:
compressi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, quantized_edges_in_cfg: int, total_edges_in_cfg: int):\n self.quantized_edges_in_cfg = quantized_edges_in_cfg\n self.total_edges_in_cfg = total_edges_in_cfg",
"def _add_fp_configs(CONFIG):\n CONFIG.declare(\n 'fp_cutoffdecr',\n ConfigValue(\n default=1... | [
"0.6211365",
"0.5947636",
"0.5926075",
"0.5917099",
"0.58897614",
"0.584447",
"0.584447",
"0.56702006",
"0.5627676",
"0.55820346",
"0.5560999",
"0.55283594",
"0.54756486",
"0.54677653",
"0.5465148",
"0.54618865",
"0.5459208",
"0.54586923",
"0.54444957",
"0.54440576",
"0.54371... | 0.7439748 | 0 |
Implementation of the `quantize()` method for the PyTorch backend. | def quantize_impl(
model: torch.nn.Module,
calibration_dataset: Dataset,
preset: QuantizationPreset,
target_device: TargetDevice,
subset_size: int,
fast_bias_correction: bool,
model_type: Optional[ModelType] = None,
ignored_scope: Optional[IgnoredScope] = None,
advanced_parameters: O... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _quantize_activation(self, tensor_quantizer: Union[StaticGridPerTensorQuantizer, LearnedGridTensorQuantizer],\n tensors_to_quantize: Union[List[torch.Tensor], torch.Tensor]) -> \\\n Union[List[torch.Tensor], torch.Tensor]:\n\n if not tensor_quantizer.enabled:\n ... | [
"0.66118073",
"0.6562525",
"0.6480266",
"0.6165468",
"0.6131837",
"0.59161913",
"0.59054255",
"0.5854331",
"0.58431715",
"0.58389634",
"0.580356",
"0.578608",
"0.5622065",
"0.5571132",
"0.5530895",
"0.5513868",
"0.5501805",
"0.54813254",
"0.54429746",
"0.5433842",
"0.5411219"... | 0.46610415 | 70 |
Implementation of the `compress_weights()` method for the PyTorch backend. | def compress_weights(model: torch.nn.Module, use_fake_quantize: bool = False) -> torch.nn.Module:
compressed_model, _ = replace_modules_by_nncf_modules(model)
insert_pre_compression_operations(model, use_fake_quantize)
return compressed_model | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compress(self, tensor):",
"def compress(self, tensor, *args, **kwargs):\n pass",
"def weight_compression(weights, bits, axis=0, quantizer=None):\n assert bits <= 8\n n = 2**bits\n index_table = []\n codebook_table = np.zeros((weights.shape[axis], n))\n km_models = [None] * weights.shape[axis]\n... | [
"0.67688566",
"0.6305428",
"0.62857914",
"0.59490013",
"0.59490013",
"0.57656217",
"0.5713356",
"0.57009125",
"0.56955546",
"0.56836677",
"0.55329573",
"0.5532722",
"0.5511265",
"0.5505167",
"0.5443281",
"0.54317117",
"0.5425128",
"0.54003054",
"0.53999305",
"0.5396085",
"0.5... | 0.67650056 | 1 |
Reset the list of document's modified items. | def clear_modified(self):
self._data.clear_modified() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reset_original(self):\n self._original = [] # Empty out self._originals",
"def reset_modified(self):\n self.modified_fields = set()\n\n # compensate for us not having knowledge of certain fields changing\n for field_name, field in self.schema.normal_fields.items():\n i... | [
"0.6702367",
"0.66882014",
"0.6433367",
"0.6393692",
"0.6267809",
"0.62651587",
"0.6241749",
"0.6123967",
"0.61093926",
"0.60061425",
"0.60025436",
"0.5998225",
"0.5958811",
"0.5949711",
"0.59314656",
"0.58863574",
"0.5874103",
"0.5873604",
"0.5864233",
"0.583386",
"0.5812609... | 0.6440544 | 2 |
Create an embedded document instance from MongoDB data | def build_from_mongo(cls, data, use_cls=True):
# If a _cls is specified, we have to use this document class
if use_cls and '_cls' in data:
cls = cls.opts.instance.retrieve_embedded_document(data['_cls'])
doc = cls()
doc.from_mongo(data)
return doc | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def from_mongo(data):\n if not data:\n return None\n\n data['id'] = str(data['_id'])\n return data",
"def from_mongo(cls, data: dict) -> Union[\"MongoModel\", Dict]:\n if not data:\n return data\n id = data.pop('_id', None)\n return cls(**dict(data, id=id))",
"def create... | [
"0.7066903",
"0.6612481",
"0.63551545",
"0.6348332",
"0.6091033",
"0.6033079",
"0.5778242",
"0.5773537",
"0.5751162",
"0.5727225",
"0.5722528",
"0.5691701",
"0.56832474",
"0.56717724",
"0.55828565",
"0.55790466",
"0.5539406",
"0.5517863",
"0.5484895",
"0.54323846",
"0.5430872... | 0.73655903 | 0 |
Update the embedded document with the given data. | def update(self, data):
return self._data.update(data) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_document(self, data):\n if not isinstance(data, pylastica.document.Document) and not isinstance(data, pylastica.script.Script):\n raise TypeError(\"data must be an instance of Document or Script: %r\" % data)\n if not data.has_id():\n raise pylastica.exception.Invalid... | [
"0.7426288",
"0.72898763",
"0.712248",
"0.7063579",
"0.6942613",
"0.6895699",
"0.67748654",
"0.67748654",
"0.67748654",
"0.67748654",
"0.6651414",
"0.6568333",
"0.6562047",
"0.6497421",
"0.6475764",
"0.6431798",
"0.6416607",
"0.63776666",
"0.6309925",
"0.62621003",
"0.6231522... | 0.66224176 | 11 |
Dump the embedded document. | def dump(self):
return self._data.dump() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dump(self, f):\n ret = libxml2mod.xmlDocDump(f, self._o)\n return ret",
"def debugDumpDocument(self, output):\n libxml2mod.xmlDebugDumpDocument(output, self._o)",
"def dump(self):\n if self.__root is None:\n return\n\n elist = self.__root.getElements()\n ... | [
"0.64431685",
"0.6325379",
"0.62365735",
"0.6215594",
"0.5980638",
"0.5913904",
"0.5842076",
"0.5824335",
"0.58150154",
"0.577763",
"0.57628286",
"0.57517964",
"0.57165134",
"0.5693397",
"0.5673982",
"0.5672529",
"0.5656097",
"0.56052685",
"0.5553852",
"0.5550597",
"0.5545202... | 0.5563799 | 18 |
Multidimensional Gaussian fourier filter. The array is multiplied with the fourier transform of a Gaussian kernel. | def fourier_gaussian(input, sigma, n=-1, axis=-1, output=None):
input = numpy.asarray(input)
output = _get_output_fourier(output, input)
axis = normalize_axis_index(axis, input.ndim)
sigmas = _ni_support._normalize_sequence(sigma, input.ndim)
sigmas = numpy.asarray(sigmas, dtype=numpy.float64)
i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fmgf(array, sigma):\n x, y = np.arange(len(array)), array.copy()\n yg = ndimage.filters.gaussian_filter(y, sigma)\n y -= yg\n\n # digitizing\n m = 101\n dy = 6.0 * mad(y) / m\n ybin = np.arange(np.min(y) - 5 * dy, np.max(y) + 5 * dy + dy, dy)\n z = np.zeros([len(ybin), len(x)])\n z[n... | [
"0.6724297",
"0.6515853",
"0.64436597",
"0.64298147",
"0.6300525",
"0.62142223",
"0.6133565",
"0.61210185",
"0.60772467",
"0.6005786",
"0.59797704",
"0.58723",
"0.58492655",
"0.5830647",
"0.575321",
"0.56844056",
"0.5630864",
"0.56108207",
"0.55966944",
"0.5580227",
"0.557872... | 0.66539884 | 1 |
Multidimensional uniform fourier filter. The array is multiplied with the Fourier transform of a box of given size. | def fourier_uniform(input, size, n=-1, axis=-1, output=None):
input = numpy.asarray(input)
output = _get_output_fourier(output, input)
axis = normalize_axis_index(axis, input.ndim)
sizes = _ni_support._normalize_sequence(size, input.ndim)
sizes = numpy.asarray(sizes, dtype=numpy.float64)
if not ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_fourier_filter(self):\n size = max(64, int(2 ** np.ceil(np.log2(2 * self.m[-1].item()))))\n\n pi = torch.acos(torch.zeros(1)).item() * 2.0\n n = torch.cat(\n [\n torch.arange(1, size // 2 + 1, 2, device=self.n.device),\n torch.arange(size // 2 ... | [
"0.6378481",
"0.6309765",
"0.62861556",
"0.62434644",
"0.61714876",
"0.60929567",
"0.6092913",
"0.6006388",
"0.59960955",
"0.59704673",
"0.57724774",
"0.57447904",
"0.5739366",
"0.5689734",
"0.5677718",
"0.5673864",
"0.56690097",
"0.5644021",
"0.56193554",
"0.56177664",
"0.56... | 0.672973 | 0 |
Multidimensional ellipsoid Fourier filter. The array is multiplied with the fourier transform of a ellipsoid of given sizes. | def fourier_ellipsoid(input, size, n=-1, axis=-1, output=None):
input = numpy.asarray(input)
if input.ndim > 3:
raise NotImplementedError("Only 1d, 2d and 3d inputs are supported")
output = _get_output_fourier(output, input)
axis = normalize_axis_index(axis, input.ndim)
sizes = _ni_support._... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _irfft2d(f_x) :",
"def process( fids, ndim=2 ):\n\timg = np.empty_like( fids )\n\tax = -1*(np.array( range(ndim) )+1)\n\t\n\timg = np.fft.fftshift( np.fft.fftn( fids, axes=ax, ).astype( np.complex64), axes=ax )\n\t\n\treturn np.squeeze(img)",
"def _get_fourier_filter(self):\n size = max(64, int(2 ... | [
"0.5919297",
"0.5817323",
"0.5366147",
"0.5307928",
"0.52779627",
"0.5270385",
"0.5188855",
"0.5180645",
"0.51758784",
"0.5060478",
"0.5059644",
"0.5033383",
"0.50284445",
"0.5026053",
"0.5017994",
"0.50149393",
"0.5008967",
"0.4998893",
"0.4998793",
"0.49833018",
"0.49821383... | 0.6894624 | 0 |
Multidimensional Fourier shift filter. The array is multiplied with the Fourier transform of a shift operation. | def fourier_shift(input, shift, n=-1, axis=-1, output=None):
input = numpy.asarray(input)
output = _get_output_fourier_complex(output, input)
axis = normalize_axis_index(axis, input.ndim)
shifts = _ni_support._normalize_sequence(shift, input.ndim)
shifts = numpy.asarray(shifts, dtype=numpy.float64)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fftshift(X):\r\n # return scipy.fftpack.fftshift(X)\r\n return np.fft.fftshift(X)",
"def ifftshift(a, axes=None):\n return image.image(np.fft.ifftshift(a, axes), pixelsize = image.getPixelsize(a))",
"def fftshift(a, axes=None):\n return image.image(np.fft.fftshift(a, axes), pixelsize = image.ge... | [
"0.67731035",
"0.6459094",
"0.6423767",
"0.6338069",
"0.60735834",
"0.6052101",
"0.6052101",
"0.6052101",
"0.6013318",
"0.6007814",
"0.599912",
"0.59832346",
"0.59766555",
"0.59196067",
"0.58802474",
"0.58419585",
"0.5822599",
"0.5822599",
"0.5822599",
"0.581095",
"0.580473",... | 0.60626906 | 5 |
Given positive int n and array P representing probabilities corresponding to an allel frequency, returns array B representing the expected allele frequency of the next generation | def ExpectedVal():
input = f.LoadFile('\\rosalind_ebin.txt').splitlines()
n = int(input[0])
P = [float(x) for x in input[1].split()]
B = [str(round(i*n,4)) for i in P]
f.ExportToFile('rosalind_ebin_output.txt',' '.join(B))
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def binomial(n: int, p: float) -> int:\n return sum(bernoulli_trial(p) for _ in range(n))",
"def bpmfln(k, n, p):\n bnm = np.empty_like(n, dtype=np.float64)\n logp = math.log(p)\n one_logp = math.log(1 - p)\n for i in range(len(k)):\n bnm[i] = math.exp(combinln(n[i], k[i... | [
"0.6789287",
"0.67728233",
"0.65926576",
"0.6576568",
"0.6520024",
"0.6460622",
"0.6324361",
"0.6279238",
"0.62723196",
"0.6234605",
"0.6230018",
"0.62065023",
"0.6202986",
"0.6180209",
"0.61650026",
"0.6157465",
"0.61489534",
"0.6131866",
"0.6128808",
"0.60880065",
"0.608400... | 0.0 | -1 |
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